A MULTI-CONFIGURATION EVALUATION OF THE SOIL AND …

A MULTI-CONFIGURATION EVALUATION OF THE SOIL AND WATER

ASSESSMENT TOOL (SWAT) IN A MIXED LAND USE WATERSHED IN THE

CENTRAL U.S.A.

_____________________________________________________

A Thesis presented to the

faculty of the Graduate

School at the University of

Missouri-Columbia

______________________________________________________________

In Partial Fulfillment of the

Requirements for the Degree

Master of Science

________________________________________________________

By

DANIEL P. SCOLLAN

Jason A. Hubbart, Thesis Advisor

May 2011

© Copyright by Daniel P. Scollan 2011

All Rights Reserved

The undersigned, appointed by the dean of the Graduate School, have examined the

thesis entitled



CENTRAL U.S.A.

presented by Daniel P. Scollan, a candidate for the degree of Master of Science, and

hereby certify that, in their opinion, it is worthy of acceptance.

Jason A. Hubbart, Ph. D., Thesis Advisor

Professor Stephen H. Anderson

Professor Richard P. Guyette

ii

ACKNOWLEDGMENTS

First and foremost acknowledgement is due to Dr. Jason A. Hubbart, Ph.D.,

Department of Forestry, and director of the Interdisciplinary Hydrology Laboratory

(IHL), who initiated and propelled this thesis. Also, thanks to current and former IHL

members for their tremendous assistance in field hydroclimatic data collection and post-

processing; without the assistance of these lab members, this thesis would not have been

possible. Second, but no less important, great appreciation is expressed to Mr. Gary L.

Wilson, U.S. Geological Survey (USGS) Hydrologist, at the Missouri Water Science

Center, Surface Water Unit, Rolla, Missouri, who not only helped provided essential

streamflow data for this study but did so per request on a schedule that was compatible

with the on-time completion of this work.

In addition, Missouri State Climatologist Dr. Patrick E. Guinan, Ph.D., provided

valuable assistance obtaining and selecting climate datasets from the University of

Missouri - Agriculture Experiment Station‟s Commercial Agriculture Automated

Weather Station Network. Thanks are extended to Dr. Aleksey Y. Sheshukov, Ph.D.,

Research Associate, Kansas State University, whose constructive and thought-provoking

criticism at the American Geophysical Union (AGU) Fall Meeting in 2009 improved this

thesis. Graduate tuition and stipend support was provided by the State of Missouri

Department of Natural Resources (MDNR) during the third year, and the University of

Missouri Division of Biological Sciences during the first and second years.

iii

Lastly, great thanks are due to each and every faculty member at the University of

Missouri who have provided assistance and teaching. They have aided the development

of this thesis in many ways both direct and indirect. A special appreciation is expressed to

both Dr. Christoph E. Geiss, Ph.D., undergraduate advisor at Trinity College, Hartford,

Connecticut, and Mr. Christopher K. Metcalf, M.S., Fish Biologist at the U.S. Fish and

Wildlife Service, Panama City, Florida, who provided critical guidance and direction.

Their contributions were invaluable.

iv

TABLE OF CONTENTS

ACKNOWLEDGMENTS .................................................................................... ii

TABLE OF CONTENTS .................................................................................... iv

LIST OF FIGURES ............................................................................................ vii

LIST OF TABLES ................................................................................................ x

ABSTRACT ......................................................................................................... xv

CHAPTER I Introduction .................................................................................... 1

Hydrologic / Water Quality Modeling ................................................................ 1

The H/WQ Modeling Process ......................................................................... 5

Description of the Soil and Water Assessment Tool .......................................... 5

A New Framework for Model Development and Evaluation ........................... 12

Study Objectives ............................................................................................... 17

CHAPTER II Methods ....................................................................................... 21

Study Site Description ...................................................................................... 21

Climate .......................................................................................................... 22

Land Use and Land Cover ............................................................................ 22

Soils and Vegetation ..................................................................................... 26

Topography ................................................................................................... 28

Water Quantity and Quality .......................................................................... 29

v

Data Collection ................................................................................................. 30

Nested Watershed Design ............................................................................. 30

Climate Data ................................................................................................. 31

Stage Data ..................................................................................................... 32

Streamflow Measurements............................................................................ 32

Rating Curve Development............................................................................... 39

Initial Rating Curve Development ................................................................ 39

Trial Rating Curve Testing and Selection of Final Rating Curves ............... 40

Computation of the Continuous Streamflow Record .................................... 41

Model Implementation ...................................................................................... 43

SWAT Model Configuration ........................................................................ 43

Evaluation of Uncalibrated Model Runs....................................................... 50

Test of the Built-In Automatic Calibration Method in SWAT ..................... 59

CHAPTER III Results ........................................................................................ 68

Observed Climate.............................................................................................. 68

Developed Stage-Discharge Rating Curves ...................................................... 76

Observed Streamflow........................................................................................ 78

Uncalibrated Model Configurations ................................................................. 83

Automatic Calibration Comparison .................................................................. 98

CHAPTER IV Discussion ................................................................................ 104

Analysis of Observed Climate Data ................................................................ 104

Analysis of Observed Streamflow Data .......................................................... 107

vi

Uncalibrated Model Performance ................................................................... 110

Effect of Watershed Subdivision and Input Dataset Selection on Model Fit . 111

Watershed Subdivision ............................................................................... 112

Soil Data Resolution ................................................................................... 114

Climate Data ............................................................................................... 119

Evaluation of the SWAT Automatic Calibration Procedure ........................... 121

Comparison of H/WQ Model Fit Evaluation Methods ................................... 125

CHAPTER V Conclusion ................................................................................. 129

Findings on Hydroclimate in the Hinkson Creek Watershed ......................... 129

Recommendations for H/WQ Model Configuration....................................... 130

Recommendations for H/WQ Model Calibration ........................................... 131

Recommendations for H/WQ Model Evaluation ............................................ 133

Recommendations for Future Research in H/WQ Modeling .......................... 134

Contributions to Science ................................................................................. 135

LITERATURE CITED .................................................................................... 137

APPENDIX A: Stage-Discharge Rating Curves ............................................ 144

APPENDIX B: Complete Modeling Results ................................................... 149

vii

LIST OF FIGURES

Figure Page

FIGURE 1. DIAGRAM OF STREAMFLOW SIMULATION IN THE SOIL AND WATER ASSESSMENT

TOOL (SWAT) MODEL, VERSION 2005. ....................................................................... 8

FIGURE 2. MAP OF GAUGING STATIONS AND ASSOCIATED SUB-BASINS WITH LAND USE /

LAND COVER CLASSIFICATIONS IN THE HINKSON CREEK WATERSHED, MISSOURI,

U.S.A......................................................................................................................... 25

FIGURE 3. HYDROLOGIC SOIL GROUPS BY SOIL TYPE IN THE HINKSON CREEK WATERSHED,

MISSOURI, U.S.A. SOIL DATA FROM THE SOIL SURVEY GEOGRAPHIC DATABASE

(SSURGO), 2011. ..................................................................................................... 27

FIGURE 4. HYPSOMETRIC CURVES SHOWING DISTRIBUTION OF ELEVATION FOR EACH SUB-

BASIN ASSOCIATED WITH FIVE NESTED GAUGING STATIONS AND THE OUTLET OF THE

HINKSON CREEK WATERSHED, MISSOURI, U.S.A. .................................................... 28

FIGURE 5. PHOTOGRAPH DATED MAY 16, 2009 SHOWING VARIABLE BACKWATER AFFECTED

STREAMFLOW AT THE SITE #5 GAUGING STATION, HINKSON CREEK WATERSHED,

MISSOURI, U.S.A. ...................................................................................................... 34

FIGURE 6. PHOTOGRAPH SHOWING WADING STREAMFLOW MEASUREMENT TECHNIQUE AT

SITE #2, HINKSON CREEK WATERSHED, MISSOURI, U.S.A. ....................................... 36

FIGURE 7. PHOTOGRAPH SHOWING OBLIQUE ANGLE BETWEEN HINKSON CREEK AND THE

TRANSPORTATION BRIDGE AT SITE #2, HINKSON CREEK WATERSHED, MISSOURI,

U.S.A......................................................................................................................... 38

viii

FIGURE 8. COMPARISON OF (1) LOW RESOLUTION SIX SUB-BASIN AND HIGH RESOLUTION 34

SUB-BASIN WATERSHED DISCRETIZATION SCHEMES AND (2) LOW RESOLUTION

STATSGO SOIL DATA AND HIGH RESOLUTION SSURGO SOIL USED IN SWAT

MODELING. ................................................................................................................. 47

FIGURE 9. PHOTOGRAPH OF SITE #1 CLIMATE STATION DATED FEBRUARY 2011, SHOWING

THE SURROUNDING WOODLAND, A POTENTIAL SOURCE OF PRECIPITATION

UNDERCATCH. HINKSON CREEK WATERSHED, MISSOURI, U.S.A. ............................. 49

FIGURE 10. DAILY PRECIPITATION AT EACH OF SEVEN CLIMATE STATIONS USED IN

MODELING OF THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. ....................... 70

FIGURE 11. DAILY MAXIMUM AIR TEMPERATURE AT EACH OF SEVEN CLIMATE STATIONS

USED IN MODELING OF THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. .......... 71

FIGURE 12. DAILY MINIMUM AIR TEMPERATURE AT EACH OF SEVEN CLIMATE STATIONS

USED IN MODELING OF THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. .......... 72

FIGURE 13. DAILY MEAN RELATIVE HUMIDITY AT EACH OF SEVEN CLIMATE STATIONS USED

IN MODELING OF THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. ................... 73

FIGURE 14. DAILY MEAN WIND SPEED AT EACH OF SEVEN CLIMATE STATIONS USED IN

MODELING OF THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. ....................... 74

FIGURE 15. DAILY TOTAL SOLAR RADIATION AT EACH OF SEVEN CLIMATE STATIONS USED

IN MODELING OF THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. ................... 75

FIGURE 16. PLOT OF FINAL RATING CURVES USED FOR CONTINUOUS STREAMFLOW

ESTIMATION AT ALL FIVE GAUGING STATIONS THAT ALSO SHOWS STREAMFLOW

MEASUREMENTS TAKEN AT EACH GAUGING STATION IN THE HINKSON CREEK

ix

WATERSHED, MISSOURI, U.S.A. FOR THE USGS-OPERATED GAUGING STATION, SITE

#4, ONLY AVAILABLE INFORMATION IS SHOWN. ......................................................... 77

FIGURE 17. FLOW DURATION CURVE FOR OBSERVED DAILY MEAN STREAMFLOW AT FIVE

GAUGING STATIONS IN THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. .......... 79

FIGURE 18. HYDROGRAPH FOR OBSERVED DAILY MEAN STREAMFLOW AT FIVE GAUGING

STATIONS IN THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. ......................... 80

FIGURE 19. FLOW DURATION CURVE FOR OBSERVED MONTHLY MEAN STREAMFLOW AT FIVE

GAUGING STATIONS IN THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. .......... 81

FIGURE 20. HYDROGRAPH FOR OBSERVED MONTHLY MEAN STREAMFLOW AT FIVE GAUGING


FIGURE 21. GRAPHICAL DIAGRAM SHOWING DIFFERENCES BETWEEN ABSOLUTE ERROR AND

SQUARED ERROR RELATIVE TO ACTUAL ERRORS BETWEEN MODELED AND OBSERVED

DATA. ....................................................................................................................... 128

FIGURE 22. DETAILED PLOT OF SITE #1 RATING CURVE AND FLOW MEASUREMENTS. ...... 145




x

LIST OF TABLES

Table Page

TABLE 1. TIMELINE OF SWAT DEVELOPMENT. ADAPTED FROM NEITSCH ET AL. (2005). .. 11

TABLE 2: TOTAL SUB-BASIN AREA (HA), LAND-USE AREA (%) FOR EACH OF FIVE GAUGE

SITES AND CUMULATIVE CONTRIBUTING AND LAND-USE AREA (ASSUMING 15 LAND-

USE DIVISIONS) IN THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A.

CONTRIBUTING AREAS AND LAND-USE COVER CLASSES WERE DETERMINED USING 10 M

DEM DATA AND 30 M LAND-USE COVER DATA. ......................................................... 24

TABLE 3. FIELD INSTRUMENTATION AND VARIABLES MEASURED AT FIVE HYDROCLIMATE


TABLE 4. LIST OF ALL 20 SWAT MODEL CONFIGURATIONS TESTED, THE ABBREVIATED

NAMES OF EACH MODEL, AND THE DEFINING CHARACTERISTICS OF EACH MODEL.

CONFIGURATIONS ARE SYSTEMATICALLY LISTED IN ORDER OF INCREASING INPUT

DATA RESOLUTION. .................................................................................................... 50

TABLE 5. DESCRIPTIVE TABLE SHOWING KEY CHARACTERISTICS OF GOODNESS-OF-FIT

INDICATORS USED FOR MODEL EVALUATION IN THIS STUDY. ...................................... 57

TABLE 6. SETTINGS USED IN ARCSWAT WHEN RUNNING THE BUILT-IN AUTOMATIC

CALIBRATION METHOD. .............................................................................................. 63

TABLE 7. THE SIX INPUT PARAMETERS IN SWAT SELECTED FOR OPTIMIZATION WITH THE

BUILT-IN AUTOMATIC CALIBRATION PROCEDURE, THEIR INITIAL VALUES, AND THE

VARIATION SETTINGS. ................................................................................................ 65

xi

TABLE 8. SUMMARY OF CLIMATE DATA DURING 2009-2010 FOR FIVE HYDROCLIMATE

STATIONS IN THE HINKSON CREEK WATERSHED, MISSOURI, U.S.A. AND THE MU

AGRICULTURAL EXPERIMENTAL STATION‟S SANBORN FIELD AND SOUTH FARMS

WEATHER STATIONS. VALUES FOR STANDARD DEVIATION ARE SHOWN IN

PARENTHESES............................................................................................................. 69

TABLE 9. DESCRIPTIVE STATISTICS FOR OBSERVED DAILY STREAMFLOW IN 2009-2010,


TABLE 10. DESCRIPTIVE STATISTICS FOR OBSERVED MONTHLY STREAMFLOW IN 2009-2010,


TABLE 11. GOODNESS-OF-FIT MODEL EVALUATION STATISTICS FOR ALL TWENTY

UNCALIBRATED MODEL CONFIGURATIONS. OPTIMAL GOODNESS-OF-FIT VALUES ARE IN

BOLD. CONFIGURATIONS ARE SYSTEMATICALLY LISTED IN ORDER OF INCREASING

INPUT DATA RESOLUTION. ERROR MEASURES GIVEN IN THE TABLE ARE THE MEAN OF

THE MEASURES FOR ALL FIVE GAUGING STATIONS. .................................................... 86

TABLE 12. GOODNESS-OF-FIT MODEL EVALUATION STATISTICS COMPARING MODEL RUNS

WITH 6 SUB-BASINS AND MODEL RUNS WITH 34 SUB-BASINS. ERROR MEASURES GIVEN

IN THE TABLE ARE THE MEAN OF THE MEASURES FOR ALL FIVE GAUGING STATIONS.

OPTIMAL GOODNESS-OF-FIT VALUES ARE IN BOLD. .................................................... 88

TABLE 13. GOODNESS-OF-FIT MODEL EVALUATION STATISTICS COMPARING MODELS USING

THE LOW RESOLUTION STATSGO SOIL DATASET AND MODELS USING THE SSURGO

SOIL DATASET. ERROR MEASURES SHOWN ARE THE MEAN OF THE MEASURES FOR ALL

FIVE GAUGING STATIONS. OPTIMAL GOODNESS-OF-FIT VALUES ARE IN BOLD. ........... 89

xii

TABLE 14. GOODNESS-OF-FIT MODEL EVALUATION STATISTICS COMPARING MODELS USING

THE SWAT WEATHER GENERATOR, THE SINGLE CLIMATE DATASET FROM URBAN-

LOCATED SANBORN FIELD, A SINGLE CLIMATE DATASET FROM RURAL-LOCATED

SOUTH FARMS, FOUR CLIMATE DATASETS FROM HCW SITES #2-5, AND FIVE CLIMATE

DATASETS FROM HCW SITES #1-5. ERROR MEASURES ARE THE MEAN OF THE

MEASURES FOR ALL FIVE GAUGING STATIONS. OPTIMAL GOODNESS-OF-FIT VALUES

ARE IN BOLD. .............................................................................................................. 91

TABLE 15. UNCALIBRATED MODEL RESULTS RANKED BY A MEASURE OF MASS BALANCE FIT:

DAILY PERCENT BIAS. THE PERCENT BIAS SHOWN IS THE AVERAGE OF THE ABSOLUTE

VALUES OF EACH PERCENT BIAS MEASURE AT ALL FIVE HCW GAUGING STATIONS. . 92

TABLE 16. UNCALIBRATED MODELS RANKED BY MEASURES OF HYDROGRAPH FIT: DAILY

NASH SUTCLIFFE EFFICIENCY AND MODIFIED NASH SUTCLIFFE EFFICIENCY. ........... 93

TABLE 17. UNCALIBRATED MODELS RANKED BY MEASURES OF FLOW DURATION FIT: DAILY

RANKED NASH SUTCLIFFE EFFICIENCY AND RANKED MODIFIED NASH SUTCLIFFE

EFFICIENCY. ............................................................................................................... 94

TABLE 18. DEFAULT UNCALIBRATED MODEL RUNS RANKED BY A MEASURE OF MASS

BALANCE FIT: MONTHLY PERCENT BIAS. PERCENT BIAS IS THE AVERAGE OF THE

ABSOLUTE VALUES OF EACH PERCENT BIAS MEASURE AT ALL FIVE HCW GAUGING

STATIONS. .................................................................................................................. 95

TABLE 19. DEFAULT UNCALIBRATED MODEL RUNS RANKED BY MEASURES OF HYDROGRAPH

FIT: MONTHLY NASH SUTCLIFFE EFFICIENCY AND MODIFIED NASH SUTCLIFFE

EFFICIENCY. ............................................................................................................... 96

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TABLE 20. DEFAULT UNCALIBRATED MODEL RUNS RANKED BY MEASURES OF FLOW

DURATION CURVE FIT: DAILY RANKED NASH SUTCLIFFE EFFICIENCY AND RANKED

MODIFIED NASH SUTCLIFFE EFFICIENCY. .................................................................. 97

TABLE 21. VALUES FOR THE BEST SET OF SIX INPUT PARAMETERS FOR THE SELECTED

MODEL CONFIGURATION OPTIMIZED FIRST BY MINIMIZING THE SUM OF SQUARED

ERROR IN DAILY STREAMFLOW AT SITE #4 AND SECOND BY MINIMIZING THE SUM OF

SQUARED ERROR AND THE SUM OF SQUARED ERROR AFTER RANKING USING THE BUILT-

IN AUTOMATIC CALIBRATION METHOD IN SWAT. .................................................... 100

TABLE 22. GOODNESS-OF-FIT MODEL EVALUATION STATISTICS FOR SITE #4 COMPARING

THE SELECTED UNCALIBRATED MODEL CONFIGURATION (34 SUB-BASIN, SSURGO

SOIL DATA, AND HCW-4 CLIMATE DATASET) WITH SINGLE OBJECTIVE (SSQ) AND

MULTIPLE OBJECTIVE (SSQ & SSQR) AUTOMATICALLY CALIBRATED RUNS OF THE

SELECTED MODEL. OPTIMAL GOODNESS-OF-FIT VALUES ARE IN BOLD. .................... 102

TABLE 23. GOODNESS-OF-FIT MODEL EVALUATION STATISTICS FOR SITES #1-3, AND 5

COMPARING THE SELECTED UNCALIBRATED MODEL CONFIGURATION (34 SUB-BASIN,

SSURGO SOIL DATA, AND HCW-4 CLIMATE DATASET) SINGLE OBJECTIVE (SSQ) AND

MULTIPLE OBJECTIVE (SSQ & SSQR) AUTOMATICALLY CALIBRATED RUNS OF THE

SELECTED MODEL. OPTIMAL GOODNESS-OF-FIT VALUES ARE IN BOLD. .................... 103

TABLE 24. GOODNESS-OF-FIT MODEL EVALUATION STATISTICS COMPARING SWAT MODEL

CONFIGURATIONS WITH VARYING LEVELS OF WATERSHED SUBDIVISION (KUMAR AND

MERWADE 1999). WATERSHED SUBDIVISIONS ARE DEFINED BY PERCENT

CONTRIBUTING SOURCE AREA (CSA). ERROR MEASURES SHOWN ARE THE MEAN OF

THE MEASURES FOR ALL 24 MODELS. ....................................................................... 114

xiv

TABLE 25. GOODNESS-OF-FIT MODEL EVALUATION STATISTICS COMPARING SWAT MODELS

CONFIGURED WITH LOW RESOLUTION SOIL DATA (STATSGO) AND HIGH RESOLUTION

SOIL DATA (SSURGO) (KUMAR AND MERWADE 2009). ERROR MEASURES SHOWN

ARE THE MEAN OF THE MEASURES FOR ALL 24 MODELS. .......................................... 117

TABLE 26. EQUATIONS FOR RATING CURVES APPLIED IN THE STUDY IN HINKSON CREEK

WATERSHED, MISSOURI, U.S.A. THE „Y‟ VARIABLE IS EQUIVALENT TO DISCHARGE IN

M3/S; THE „X‟ VARIABLE IS EQUIVALENT TO STAGE IN M........................................... 144

TABLE 27. FULL DAILY STREAMFLOW DESCRIPTIVE AND GOODNESS-OF-FIT STATISTICS FOR

ALL UNCALIBRATED SWAT MODEL CONFIGURATIONS. AVERAGE PERCENT BIAS IS

THE AVERAGE OF THE ABSOLUTE VALUES OF EACH PERCENT BIAS MEASURE. ......... 149

TABLE 28. FULL MONTHLY STREAMFLOW DESCRIPTIVE AND GOODNESS-OF-FIT STATISTICS

FOR ALL UNCALIBRATED SWAT MODEL CONFIGURATIONS. AVERAGE PERCENT BIAS

IS THE AVERAGE OF THE ABSOLUTE VALUES OF EACH PERCENT BIAS MEASURE. ..... 160

TABLE 29. FULL DAILY AND MONTHLY STREAMFLOW DESCRIPTIVE AND GOODNESS-OF-FIT

STATISTICS FOR THE SELECTED UNCALIBRATED SWAT MODEL CONFIGURATION, THE

SUM OF SQUARE ERROR (SSQ) OPTIMIZED MODEL, AND THE SUM OF SQUARED ERROR

(SSQ) AND SUM OF SQUARED ERROR AFTER RANKING (SSQR) OPTIMIZED MODEL.

AVERAGE PERCENT BIAS SHOWN IS THE AVERAGE OF THE ABSOLUTE VALUES OF EACH

PERCENT BIAS MEASURE. ........................................................................................ 171

xv



CENTRAL U.S.A.

Daniel P. Scollan

Jason A. Hubbart, Thesis Advisor

ABSTRACT

Distributed watershed hydrologic/water quality (H/WQ) models are ubiquitous tools for

watershed management. Despite advancements, there remain impediments for end-users.

This study presents a practical framework for use of the Soil and Water Assessment Tool

(SWAT). Results show variable accuracy across scales and evaluation methods using 20

model configurations based on two watershed subdivisions, two soil datasets, and five

climate datasets. Nine goodness-of-fit indicators were tested, including four new indices

(R-RMSE, R-MAE, R-NSE, and R-NSE1) designed to quantify model fit with flow

distribution. Sixteen of 20 configurations achieved satisfactory monthly streamflow fit

(NSE > 0.5, PBIAS < 25%) without calibration. Watershed and soil resolution had

negligible impact; climate input had considerable impact. Single climate station input is

best used for applications requiring monthly predictions; distributed climate station input

is needed for daily predictions. SWAT multi-objective auto-calibration better predicted

monthly flow (PBIAS=1%, NSE=0.8) than single-objective calibration (PBIAS=16%,

NSE=0.5). SWAT performs well in Central U.S. urbanizing watersheds. Accuracy can

improve with auto-calibration as presented and continued model development.

1

CHAPTER I

INTRODUCTION

HYDROLOGIC / WATER QUALITY MODELING

Fresh water is a critical and irreplaceable natural resource necessary for human

health, agriculture, industry, recreation, and ecosystem integrity (O‟Neill et al. 2006).

Increasingly, fresh water resources are threatened by human development activities

resulting in water quality problems that include sediment from terrestrial and aquatic

erosion processes, and contamination by chemicals from point and non-point sources

(Borah and Bera 2004). The EPA‟s Wade-able Streams Assessment, a biological

assessment of 1,392 randomly selected wade-able stream sites in the conterminous U.S.,

found that 42% of the nation‟s wade-able stream length is in poor biological condition

relative to reference site conditions (USEPA 2006).

To help address and manage pressing water resource problems, many

Hydrologic/Water Quality (H/WQ) models have been developed. H/WQ models are

generally conceptual (Nash and Sutcliffe 1970) mathematical models capable of

simulating hydrologic processes in the land phase of the hydrologic cycle (Borah and

Bera 2003; Migliaccio and Srivastava 2007; Srivastava et al. 2007). H/WQ models

attempt to represent physical processes (i.e. precipitation, evaporation, transpiration,

infiltration, runoff, streamflow) by means of discrete, analytical, and algebraic

mathematical expressions (Migliaccio and Srivastava 2007). The expressions may be

empirical, featuring a small number of parameters that are typically not physically

2

measureable and that are determined by calibration against a large number of observed

data (Merritt et al. 2003; Migliaccio and Srivastava 2007). H/WQ model expressions may

also be physically based, featuring a larger number of physically measurable parameters

arranged as a solution to fundamental physical laws such as the conservation of mass,

momentum, or energy (Merritt et al. 2003; Migliaccio and Srivastava 2007). The overall

hydrologic cycle is generally physically represented via the water (or mass) balance

equation (Srivastava et al. 2007). As presented by Dingman (2002), the mass balance for

a watershed may be written as:

P + Gin – (Q + ET + Gout) = S

where P is precipitation (rainfall, snow, occult precipitation), Gin is the inflow of

groundwater, Q is streamflow (discharge), ET is evapotranspiration (direct surface

evaporation and plant transpiration), Gout is the outflow of groundwater, and S is the

change in storage of water in the watershed.

In addition to simulating the movement of water through the hydrologic cycle, an

essential component to H/WQ models is their inclusion of mathematical simulations

quantifying processes critical to water quality, i.e. erosion, sediment transport, nutrient

transport, and pesticide transport (Borah and Bera 2003). H/WQ models are generally

designed to function at a particular spatial scale, including profile / horizon / pedon (point

scale), field or field scale, or watershed scale (Srivastava et al. 2007), and at a particular

temporal scale, ranging from single event to annual or decadal (Merritt et al. 2003).

Data that are generally required for input into watershed scale H/WQ models

include, but are not limited to, climate, topography, soil physical properties, and land

3

cover / land use (Engel et al. 2007). When models are run, simulated estimates of water

yield, sediment yield, and chemical (nutrient and pesticide) yields at the watershed outlet

are generated (USACE 1999). The estimates provided by H/WQ models have been

shown to be highly useful in water resource management applications including

predicting the effects of climate change (Mehta et al. 2011; Parajuli 2010; Stone et al.

2001), predicting the effects of land use change (Ghaffari et al. 2010), assessing the use

of Best Management Practices (BMPs) for water quality protection (Arabi et al. 2006;

O‟Donnell et al. 2008, Santhi et al. 2006), predicting soil nutrient (e.g. phosphorus) loss

in agricultural fields (White et al. 2009), and assessing bacterial pollutant (e.g.

Escherichia coli) loading in coastal estuaries (Bougeard et al. 2011).

A key component of H/WQ model applicability is their capability to extend

findings from localized monitoring studies for application in watersheds in other

environments and at broader scales. For example, H/WQ models may be used to evaluate

the effectiveness of Best Management Practices (BMPs). However, monitoring studies

conducted to evaluate Best Management Practices (BMPs) that protect water quantity and

quality in one area may not always be applicable to other areas. The lack of

transferability is normally due to the inherent variability of soil characteristics, vegetation

communities, climate and hydrologic regime, and land management practices across the

landscape (O‟Neill et al. 2006). Since it is impractical to monitor runoff, sediment and

other water quality variables in every watershed, modeled estimates of these variables

based on a set of inputs, including soil characteristics, slope, elevation, climate, and

management practices, may be used to evaluate BMPs in other ungauged watersheds and

develop BMP implementation strategies (O‟Neill et al. 2006). For instance, a Soil and

4

Water Assessment Tool (SWAT) model application by O‟Donnell et al. (2008) in the Le

Moine River Basin (3,494 km2) in Illinois, U.S.A., found that the current locations of

BMPs for sediment erosion prevention in the watershed were located in areas where they

had little effect on reducing sediment loads; the study therefore determined areas to target

future sediment BMPs. White et al. (2009) developed a simplified management tool

based on the SWAT model to combine field-scale quantitative management of soil

phosphorus loss with basin-scale estimation of downstream phosphorus water quality.

These examples illustrate the ability of H/WQ models to apply localized assessment of

management activities on a broad scale incorporating varying physical environmental

characteristics.

Hydrologic/Water Quality models may also be used to develop Total Maximum

Daily Loads (TMDLs) for impaired water bodies (Radcliffe et al. 2009; O‟Neill et al.

2006; Borah and Bera 2003). TMDLs are calculations of the maximum amount of a

pollutant that a water body may receive while meeting established water quality

standards; states are required by the Clean Water Act to develop TMDLs for impaired

water bodies (USEPA 2008). H/WQ models may also be used to provide

recommendations for the type and placement of specific BMPs, and for the prediction of

hydrologic and water quality changes in response to changes in land use, land

management, and climate (Borah and Bera 2004; O‟Neill et al. 2006; USACE 1999).

5

The H/WQ Modeling Process

To provide clarity in the coming text, the following terms, supplied in sequential

outline form, are presented describing the main steps for the end-user in H/WQ modeling

implementation and application process:

1) Implementation

a. Configuration: Selection and preparation of input datasets.

i. Goodness-of-fit evaluation of an uncalibrated model.

b. Calibration: Optimization of input parameters to reduce error between

modeled output and corresponding set of observed data. May be

preceded by a sensitivity analysis to identify parameters with the

greatest effect on model output.

c. Validation: A method of assessing calibrated values for the model

input parameters by the evaluation of the modeled output against an

independent set of observed data.

2) Application

a. Application of the model for predictions in ungauged basins or sub-

basins.

b. Alteration of model input datasets or input parameters to make a

modeled prediction of change due to a management scenario.

DESCRIPTION OF THE SOIL AND WATER ASSESSMENT TOOL

The Soil and Water Assessment Tool (SWAT) is a conceptual, physically-based,

semi-distributed parameter, long-term, daily-time-step Hydrologic/Water Quality

6

(H/WQ) model; SWAT was developed by the U.S. Department of Agriculture –

Agricultural Research Service (USDA-ARS) primarily at the Grassland, Soil, and Water

Research Laboratory in Temple, Texas (Gassman et al. 2007). The model was designed

for basin-scale applications, and has been previously applied to the conterminous U.S.A

(Srinivasan et al. 1998) and the entire Missouri River Basin (Stone et al. 2001; Mehta et

al. 2011) with mixed success. SWAT is currently being used as part of the U.S.

Department of Agriculture (USDA) Conservation Effects Assessment Program (CEAP),

which is intended to quantify the cumulative environmental benefits of the USDA‟s

conservation programs on cultivated, range, and irrigated lands in the U.S.A. (Van Liew

et al. 2007). SWAT is capable of predicting water, sediment, and chemical yields in

ungauged basins (Gassman et al. 2007). Major routines of the SWAT model include

weather generation, hydrology, sediment, crop growth, nutrients, and pesticides (Neitsch

et al. 2005).

The SWAT model, first released in the early 1990s, incorporates components

from several models developed over the last 30 years at USDA-ARS, including the

Chemical, Runoff, and Erosion from Agricultural Management Systems (CREAMS)

model, the Groundwater Loading Effects on Agricultural Management Systems

(GLEAMS) model, the Environmental Productivity Impact Calculator (EPIC) model, the

Simulator for Water Resources in Rural Basins (SWRRB) model, the Routing Outputs to

Outlets (ROTO) model, and the QUAL2E in-stream model (Gassman et al. 2007). The

latest version of the SWAT model is SWAT 2009; because the 2009 version had not yet

been released at the time of this study and because documentation for the 2009 version is

7

yet to be released, this study evaluates the next-most recent version, SWAT 2005. A

timeline of SWAT development milestones is presented in Table 1.

SWAT is capable of dividing the watershed into multiple sub-basins, which then

may be divided into multiple non-spatially-explicit hydrologic response units (HRUs)

that are populated with a lumped (single) set of input parameters that define homogenous

soil, management, and land use characteristics (Gassman et al. 2007) (Figure 1). HRUs

are defined by selecting a minimum, or threshold percentage of the sub-basin area that is

composed of a unique combination of a land use, soil type, and/or topographic slope

(Santhi et al. 2005). A geographic information system (GIS) extension, ArcSWAT, is

available to simplify the development of model input files (Di Luzio et al. 2004).

SWAT features routines to model the entire water balance (Neitsch et al. 2005)

(Figure 1). Surface runoff and infiltration are calculated using the empirical Soil

Conservation Service (SCS) Curve Number method by default, or, optionally, the semi-

theoretical Green-Ampt-Mein-Larson infiltration method. Canopy interception is implicit

in the Curve Number method. Potential evapotranspiration (PET) is calculated by the

semi-theoretical Penman-Monteith equation by default, or, optionally, the Priestly-Taylor

or Hargreaves-Samani method. Soil water redistribution is modeled using a storage

routing method (Neitsch et al. 2005; Gassman et al. 2007).

8

Figure 1. Diagram of streamflow simulation in the Soil and Water Assessment Tool (SWAT) model, version 2005.

Over 100 hydrologic calibration and/or validation studies of SWAT were

published in the literature (Gassman et al. 2007). Judged by the criteria set forth by

Moriasi et al. (2007), the majority of the studies reviewed by Gassman et al. (2007) were

considered to adequately simulate streamflow on a monthly basis. This judgment was

based on the Nash Sutcliffe Efficiency (NSE) (Nash and Sutcliffe 1970), a statistical

goodness-of-fit indicator that ranges from -∞ to 1, with 1 being the optimal value.

Gassman et al. (2007) found that most models obtained satisfactory results (NSE > 0.5),

however, daily estimates of streamflow were generally less accurate as judged by the

NSE. Factors weakening model performance included inadequate representation of

9

rainfall input, lack of model calibration, and relatively short (2 years or less) calibration

and validation periods (Gassman et al. 2007).

A validation study by Ahl et al. (2008) compared uncalibrated and calibrated

model runs of SWAT in a 2,251 ha snow-dominated, mountainous watershed in

Montana, U.S.A. The study used four years of observation data for calibration and

validation. The uncalibrated model produced acceptable results (NSE >0.5) for annual

water yield only. A manually calibrated model produced acceptable results (NSE >0.5)

for annual, monthly, and daily water yield for the snowmelt-runoff season, but not during

the baseflow season. NSE values of 0.90 and 0.76 were obtained for monthly and daily

water yield after calibration and independent comparison to a validation period. Negative

NSE values were obtained when assessing the baseflow season alone.

White and Chaubey (2005) conducted a validation study of the SWAT model in

the 362 km2

Beaver Reservoir watershed in Arkansas, U.S.A. During the two-year

calibration period of the study, the calibrated model obtained a NSE value of 0.89 for

monthly water yield. During the two-year study validation period, the calibrated model

obtained a NSE of 0.85.

Du et al. (2009) achieved satisfactory model results (daily NSE > 0.5) in a

calibration and validation study of SWAT in the 276 km2 Upper Oyster Creek watershed

in Texas, U.S.A., using a much more limited observed dataset. Their observed data

consisted of discontinuous, instantaneous measurements of streamflow at five main-stem

stream sites collected over a 24-month period to calibrate and validate the model. Du et

al. (2009) pointed out that many watersheds lack long-term continuous records of

streamflow. Comparison of modeled and measured flow data yielded an NSE of 0.66

10

during the calibration period (41 days during year 2004) and an NSE of 0.56 during the

validation period (70 days during years 2002-2003) (Du et al. 2009).

SWAT was applied previously in the state of Missouri, U.S.A. For example,

Stone et al. (2001) used a regional climate model in conjunction with SWAT to estimate

the effect of a doubling of current atmospheric carbon dioxide levels on water yield in the

Missouri River basin, which encompasses 310 eight-digit hydrologic unit code (HUC)

watersheds. The simulation indicated large increases in water yield in eight-digit

watersheds in the north and northwestern portions of the basin, some greater than 70

percent, and considerable overall decreases in water yield by generally less than 20

percent (Stone et al. 2001). However, the overall change in water yield for the entire

basin was estimated to be a decrease of 10 to 20 percent (Stone et al. 2001). Benham et

al. (2006) performed a validation study of SWAT in the 367 km2 Shoal Creek watershed

in Missouri, U.S.A. The calibrated model obtained monthly and daily NSE values of 0.21

and 0.63, respectively, for water yield during the study calibration period, and values of

0.54 and 0.66, respectively, during the validation period.

The SWAT model is not confined to use in the U.SA. The SWAT model was

applied recently in many countries, including not only developed countries, but also

many developing nations including South Korea (Bae et al. 2011), China (Li 2010),

Ethiopia (Setegn et al. 2010), and Iran (Ghaffari et al. 2010). In all cases, satisfactory

modeling results for streamflow were achieved (daily and/or monthly NSE > 0.5).

11

Table 1. Timeline of SWAT development. Adapted from Neitsch et al. (2005).

Release Year;

Model Version Major Changes Made to the SWAT Model

2005;

SWAT2005

This update includes improvements to the model's bacteria transport routines, the

added ability to input weather forecast data, and a new generator for sub-daily

precipitation data. In addition, the daily curve number calculations are altered to

allow the retention parameter to be a function of soil water content or plant

evapotranspiration.

2000:

SWAT2000

Additions to the model include bacteria transport routines, the Green-Ampt

infiltration method, and the Muskingum stream routing method. Improvements are

made to the built-in stochastic weather generator. Values for daily solar radiation,

relative humidity, and wind speed are allowed to be inputted or generated. Potential

evapotranspiration values are allowed to be inputted or generated by the model.

Elevation band processes for weather inputs are improved. In addition, the model is

modified to enable simulation of an unlimited number of reservoirs and the model's

dormancy calculations are altered for appropriate simulation in tropical

environments.

1999;

SWAT99.2

Several improvements are made to the model's nutrient cycling routines and

rice/wetland routines. Modeling of settling processes in reservoirs, ponds, and

wetlands are added. In addition, the update adds routines to model stream bank

water storage, metal routing in streams, and incorporates urban build up/wash off

equations from the SWMM model and USGS regression equations for modeling of

urban pollutant loading.

1998;

SWAT98.1

Model update includes improvements to snow melt routines, in-stream water quality

modeling, and nutrient cycling routines. New management options added include

grazing, manure applications, and tile flow drainage. The model is also modified for

used in the Southern hemisphere.

1996;

SWAT96.2

Update includes new management options for auto-fertilization and auto-irrigation.

C02 is incorporated into the crop growth model for modeling of climate change. The

Penman-Monteith equation for potential evapotranspiration is added. Subsurface

lateral flow of water is incorporated using a kinematic storage model. In addition,

in-stream nutrient equations from the QUAL2E model and in-stream pesticide

routing equations are added.

1994:

SWAT94.2

Ability to incorporate multiple hydrologic response units (HRUs) in each sub-

watershed is added.

12

A NEW FRAMEWORK FOR MODEL DEVELOPMENT AND EVALUATION

H/WQ model end-users face a number of questions and limitations when

developing a model such as SWAT for streamflow prediction. First, model end-users

must determine how to configure the model, including making a decision about how

finely to subdivide a given watershed to achieve accurate results. With a greater number

of sub-basins included in the modeled watershed; greater computational time is required.

When high-resolution datasets (i.e. climate, soil, and land use / land cover) are available,

model users must decide whether the additional computational time and input data

preparation (e.g. pre-processing climate datasets or high-resolution soil data for model

input) provides measurable improvement in the predictive accuracy of the model. For

many watersheds however, the only publically available land use / land cover (LULC)

datasets may be considerably outdated; that is, the LULC datasets do not accurately

represent the land use in the watershed for the period of interest (Engel et al. 2007).

An inadequate number of climate stations that include the necessary climate input

parameters (precipitation, solar radiation, relative humidity, maximum and minimum air

temperature, and wind speed) may often generate additional uncertainty in model

predictions (Engel et al. 2007), as spatial heterogeneity in rainfall has been shown to

introduce considerable error in modeled streamflow estimates when a uniform rainfall

distribution is assumed (Van Werkhoven et al. 2008). For highly physically-based H/WQ

models, collecting data for physical parameters that are often directly measured in

research applications, such as soil effective hydraulic conductivity (Rachman et al. 2008),

may be prohibitive due to high cost and labor requirements (Engel et al. 2007).

13

If the model is to be calibrated and validated, users must decide what method, if

any, is to be used to calibrate the model. Traditional methods for H/WQ model

calibration require time-consuming manual effort, in-depth experience in fine-tuning

numerous model parameters, and expertise in hydroclimatic, soil physical, and

biophysical processes (Van Liew et al. 2005; van Griensven et al. 2002, van Griensven

and Bauwens 2003). Van Liew et al. (2005) reported that approximately four to six weeks

of labor was required for calibrating streamflow for a single watershed used in their

study. It is notable that van Griensven et al. (2002) indicated that manual calibration

efforts are fundamentally flawed because they must consider one set of parameter

changes at a time; thus only part of the available information is being used at one time.

They point out that the manual approach risks an accumulation of errors. Furthermore,

model calibration requires a large amount of monitored streamflow data; however,

monitoring data for many watersheds is often unavailable (Borah and Bera 2004; Engel et

al. 2007). It was recommended that three to five years of monitoring data including wet,

dry, and average years for model calibration be used, however, long-term continuous

time-series of monitoring data are often unavailable (Engel et al. 2007).

A promising alternative to traditional, labor-intensive, and subjective manual

calibration methods has recently emerged (Nash and Sutcliffe 1970; Duan et al. (1994);

Eckhardt and Arnold 2001; van Griensven and Bauwens 2003). Automatic calibration

methods are often viewed as expensive in terms of time and computing resources. Kumar

and Merwade (2009) reported that two weeks of computing time would be required using

an automatic calibration method built into SWAT if they used a single desktop computer

alone. Conversely, Van Liew et al. (2005) reported only one day of runtime.

14

Automatic calibration methods use computer algorithms to determine the optimal

set of input parameters based on a single or multiple set of objective error functions. Nash

and Sutcliffe (1970) recommended automatic calibration to remove subjectivity in the

fitting of a H/WQ model to observed data. An automatic multi-objective and multi-site

capable calibration method is included in SWAT and is accessible via the ArcSWAT

graphical user interface. The SWAT automatic calibration tool is not capable of reducing

error on multiple time-scales simultaneously (e.g. daily and monthly). Nor is it capable of

simultaneous calibration of baseflow and surface flow as was shown by Zhang et al.

(2011). Recent scientific literature evaluating the SWAT calibration tool has not

investigated the SWAT multi-objective model optimization capabilities (Van Liew et al.

2005; Van Liew et al. 2007; Kumar and Merwade 2009; Setegn et al. 2010). At the time

of this writing, only one researcher in the scientific literature has evaluated SWAT‟s

multi-site automatic calibration capabilities (Zhang et al. 2008). Only the original

developers of SWAT‟s automatic calibration method have taken advantage of its multiple

streamflow objective functions (Van Griensven and Bauwens 2003). It is on this basis

that, in the following study, a single objective function and a multiple objective function

automatic calibration method for streamflow were compared.

Finally, when evaluating the model (calibration and validation), model users face

a confusing array of statistical and graphical options to consider for use as indicators of

model fit. Several publications offer a variety of alternatives (Moriasi et al. 2007; Legates

and McCabe 1999; Willmott 1981; Willmott et al. 1984, 1985; Nash and Sutcliffe 1970),

however, no clear consensus has emerged regarding the optimal technique(s). The

15

problem of properly assessing the predictions obtained from H/WQ models was

eloquently described by Nash and Sutcliffe (1970), with emphasis added:

“The results obtained [from conceptual hydrologic models] are not

always presented in a manner which makes possible a judgment of the

relative efficiency of these models, nor does there appear to be any

general agreement on the method of developing and testing a model for a

given catchment or group of catchments. It is intended to set out in this

paper, tentatively, as a basis for discussion and amendment, a systematic

approach towards developing, testing and modifying a model for a set of

catchments with the development of a forecasting technique for an

ungauged member of the set as a long term objective. These preliminary

ideas will be modified by experience …. It is hoped to encourage a

discussion of the general principles by which the conceptual model

technique may be put to best use in this difficult but intriguing problem.”

The Nash Sutcliffe Efficiency (NSE), the statistical goodness-of-fit indicator first

introduced by Nash and Sutcliffe in 1970, remains today, 41 years later; the most widely

used measure of H/WQ model fit and often is the sole measure considered (e.g.

Bouegeard 2011). The efficiency introduced by Nash and Sutcliffe (1970) corrects for the

unsuitability of the classic coefficient of determination, which represents the square of

the Pearson‟s product-moment correlation coefficient. Curiously, Nash and Sutcliffe used

the mathematical notation R2 when defining their statistical measure, a notation now

predominantly used to represent the classic coefficient of determination (Mehta et al.

2011; O‟Donnell et al. 2008). As Legates and McCabe (1999) clarified and further

explicated, the coefficient of determination (R2 or r

2) is poorly suited to measuring

goodness-of-fit due to its basis in linear correlation. The coefficient of determination may

give high values when model fit is low due to its misrepresentation of additive and

proportional errors. Only a 1:1 line on a scatter plot between observed and modeled

16

values should be viewed as a perfect fit. However, the coefficient of determination may

give high values for models which exhibit a slope not equal to one (proportional error) or

a y-intercept not equal to zero (additive error) (Legates and McCabe 1999).

Unlike the coefficient of determination, however, the NSE lacks a well-

understood error probability distribution (Legates and McCabe 1999). Thus, unlike the

coefficient of determination, statistical significance testing for the NSE requires complex,

time-consuming bootstrapping methods (Efron and Gong 1983) to establish a probability

distribution (Legates and McCabe 1999; Willmott et al. 1985) for use in statistical

significance testing.

Willmott (1984; 1985) and Legates and McCabe (1999) have tried somewhat

unsuccessfully to encourage scientific adoption of absolute value error functions over the

more common squared error functions, like the Nash-Sutcliffe Efficiency. They have

introduced several goodness-of-fit statistical indicators which were not widely reported.

Notably, the scientific and practical value of absolute value based error measures remains

to be fully understood.

Van Griensven (2002) eloquently summarized the previously stated practical

approach to H/WQ modeling. In Chapter XII: Conclusions and perspectives, van

Griensven (2002) states:

“A large amount of money and efforts are put in the development and

application of water quality models all over the world and this is even still

increasing. All these developments aim at the development of a tool that is

useful for decision making in water basin management. As there exist very

few examples of such applications, models seem rather to be a game for

engineers or scientists than useful tools that can help to improve the river

water quality. However, it is shown that river basin management based on

simple emission based standards for point sources failed and that, more

and more, diffuse pollution is responsible for bad water quality. As these

17

problems are characterised by a high temporal and spatial variability,

understanding and solving them requires dynamic models to point out the

important causes of pollution or to predict the effects of pollution

abatement programmes”.

As van Griensven (2002) affirmed, the intention of the study presented here was to

evaluate the SWAT model as a management tool. Furthermore, in the same chapter, van

Griensven eloquently describes the ideal H/WQ model:

“The ideal tool for river basin water quality management incorporates all

relevant process descriptions, to enable the simulation of the output

variables needed by decision makers of any kind, using readily available

information of the basin (such as GIS layers and climate data) without

requiring calibration. Unfortunately, tests of models without calibrations

are only published when there is a reasonable fit to the observations. It is

a very idealistic thought that models will be able to be applied on

ungauged basins.”

STUDY OBJECTIVES

Given the constraints on model development that have been described, for this

study, rather than develop a single model for a watershed, multiple configurations of the

SWAT model were developed and then assessed according to several different goodness-

of-fit statistical indicators. The overarching goal of the study was therefore to provide

answers to SWAT users who face the aforementioned questions and practical limitations.

These limitations include a lack of accurate data for model configuration and calibration,

high time and labor requirements for model calibration, and a lack of consensus on

appropriate model evaluation methods. However, the study addresses specific questions

which have wider implications for all H/WQ modelers.

18

To determine the effect of watershed discretization resolution, soil data resolution,

and the quantity and quality of climate station input, a set of twenty different model

configurations, were developed. The 20 model configurations represent all the possible

combinations when using (A) two different watershed discretization schemes, one with a

high number of sub-basins and hydrologic response units (HRUs) and one with a small

number, (B) two soil datasets, one with low spatial resolution, the State Soil Geographic

Database (STATSGO) and one with high resolution, the Soil Survey Geographic

Database (SSURGO), and (C) five different combinations of automatically generated,

single station, and multiple climate station data.

The 20 uncalibrated configurations were then ranked according to five different

goodness-of-fit statistical indicators, at both a daily and monthly scale. In addition, in

order to test the built-in automatic calibration method that is included in SWAT, one of

the 20 configurations was selected for parameter optimization (calibration). An attempt

was made to run the automatic calibration in a reasonable and practical amount of time

(less than 24 hours). When running the automatic calibration, the control parameters were

set to optimize the model‟s flow parameters based on two different sets of objective

functions: (1) the sum of the squared error (SSQ) for daily streamflow, and (2) both the

sum of the squared error (SSQ) and the sum of the squared error after ranking (SSQR) for

daily flow.

The basic questions addressed in this study may be summarized as follows:

(1) What is the effect of input data resolution (watershed discretization, soil, and

climate) on modeled streamflow predictions?

19

a. Null Hypothesis: If input data resolution is increased, the accuracy of

modeled streamflow predictions will not change.

b. Alternative Hypothesis: If input data resolution is increased, the

accuracy of modeled streamflow predictions will change.

(2) How well does the model predict streamflow without calibration of the input

parameters on both a daily and monthly scale?

a. Null Hypothesis: To meet published standards for model accuracy

(Moriasi et al. 2007), the SWAT input parameters do not need to be

calibrated.

b. Alternative Hypothesis: To meet published standards for model

accuracy (Moriasi et al. 2007), the SWAT input parameters must be

calibrated.

(3) Which goodness-of-fit statistical indicators should be used for evaluating the

model during the calibration and validation process? How are they different?

a. Null Hypothesis: The choice of goodness-of-fit statistical indicators

does not affect the selection of the most accurate model.

b. Alternative Hypothesis: The choice of goodness-of-fit statistical

indicators does affect the selection of the most accurate model.

(4) Can the automatic calibration method built-in to SWAT be run successfully on

a single desktop computer in a reasonable amount of time (less than 24

hours)?

a. Null Hypothesis: The built-in automatic calibration method in SWAT

cannot be run successfully in less than 24 hours.

20

b. Alternative Hypothesis: The built-in automatic calibration method in

SWAT can be run successfully in less than 24 hours.

(5) How do automatically calibrated model outputs compare when optimizing for

a single objective function versus a set of two objective functions?

a. Null Hypothesis: The accuracy of the modeled streamflow will be the

same when optimized for a single objective functions as when it is

optimized for a set of two different objective functions.

b. Alternative Hypothesis: The accuracy of the modeled streamflow will

be different when optimized for a single objective function as when it is

optimized for a set of two different objective functions.

21

CHAPTER II

METHODS

STUDY SITE DESCRIPTION

The SWAT model was tested using data collected in an urbanizing watershed, the

Hinkson Creek Watershed (HCW) in Boone County, central Missouri, U.S.A. The HCW

features high land cover spatial heterogeneity, rapid population growth, increasing spatial

change in land use and land cover (i.e. urbanization), and ongoing community, political,

and legal debate over watershed management. A recently developed Total Maximum

Daily Load (TMDL) for the watershed used urban stormwater runoff as a surrogate for

unidentified pollutants suspected to be impairing the biological diversity in the stream;

the TMDL process continues to generate political controversy and questions about

appropriate implementation (USEPA 2011).

The Hinkson Creek Watershed (HCW) is located in the Lower Missouri-Moreau

River Basin (LMMRB, HUC 10300102) in central Missouri, U.S.A. Comprising

approximately 231 km2, Hinkson Creek originates northeast of Hallsville, Boone County,

Missouri, and flows approximately 42 kilometers in a southwesterly direction to its

mouth at Perche Creek. Hinkson Creek is classified as a Missouri Ozark border stream

located in the transitional zone between Glaciated Plains and Ozark Natural Divisions

(Thom and Wilson 1980). Streams generally originate on level uplands underlain by

shale and descend into hilly terrain underlain by limestone (Pfleiger 1989).

22

Climate

The transitional climate of Missouri includes influences from winter dominant

continental polar air masses, and summer prevalent maritime and continental tropical air

masses. This translates to broad fluctuations in temperature (12.8 ºC yearly average) and

precipitation (1016 mm/year). The heaviest rainfall typically arrives in late spring and

early summer with 70% of the total precipitation falling in the period from April through

August. The driest period is from November through March. Annual snowfall is

approximately 508 mm (Nigh and Shroeder 2002).

Land Use and Land Cover

The HCW encompasses the city of Columbia and the surrounding urban-rural

interface (Hubbart et al. 2010; Hubbart and Freeman 2010; Hubbart and Gebo 2010),

allowing a distinct opportunity to study the dynamics of land use diversity and change.

The city of Columbia has experienced rapid population growth of 28.4% between 2000

and 2010 (current population, 108,500, U.S. Census 2010). The HCW is fully contained

within Boone County, Missouri, which has had population growth of 20.4% during the

same period (U.S. Census 2010).

Land use in the upper portion of the watershed consists of rural pastureland and

wooded areas, whereas the lower portion of the watershed is within the urbanized section

of the city of Columbia, Missouri (MDNR 2006). Sub-basin areas and land-use cover

classes were determined in ESRI© ArcGIS 9.3 software using 10 m resolution digital

elevation model (DEM) data from the National Elevation Dataset (NED) and 30 m land-

use cover data from the 2001 National Land Cover Dataset (NLCD). Total sub-basin

23

areas in hectares, land-use area in percent, and cumulative contributing and land-use area

for each of the five gauge sites located in the HCW are presented in Table 2 and Figure 2.

Site #1 has a sub-basin area of 7742.3 ha and the dominant land-use class is

pasture/hay (44.9%). Site #2 has a contributing area of 2358.5 ha and the dominant land-

use class is also pasture/hay (36.7%). Site #3 has a contributing area of 1327.4 ha and the

dominant land-use class is deciduous forest (29.6%). Site #4 has a contributing area of

6526.8 ha and the dominant land-use class is pasture/hay (28.0%). Site #5 has a

contributing area of 2630.1 ha and the dominant land-use class is developed, low

intensity (31.2%). The cumulative contributing area for all five gauge sites is 20,585.1 ha

and the dominant land-use class is deciduous forest (32.4%).

24

Table 2: Total sub-basin area (ha), land-use area (%) for each of five gauge sites and cumulative contributing and land-use area (assuming 15 land-use divisions) in the Hinkson Creek Watershed, Missouri, U.S.A. Contributing areas and land-use cover classes were determined using 10 m DEM data and 30 m land-use cover data.

Contributing Areas Open Water Developed,

Open Space

Developed,

Low

Intensity

Developed,

Med.

Intensity

Developed,

High Intensity

Site Area

(ha) Area (%) Area (%) Area (%) Area (%) Area (%)

1 7742.3 0.5 4.2 0.5 0.0 0.0

2 2358.5 0.6 5.0 1.8 1.2 0.4

3 1327.4 0.4 14.4 19.0 13.8 4.8

4 6526.8 0.7 8.1 9.6 5.5 1.6

5 2630.1 1.0 18.7 31.2 12.2 4.8

Cumulative 20585.1 0.6 8.1 8.7 4.3 1.5

Contributing Areas Barren Land Deciduous

Forest

Evergreen

Forest

Mixed

Forest Shrub/Scrub

1 7742.3 0.0 34.3 0.6 1.2 0.5

2 2358.5 1.4 34.7 0.8 2.2 1.1

3 1327.4 0.1 29.6 0.4 1.9 0.6

4 6526.8 0.4 33.9 1.0 1.6 0.3

5 2630.1 0.1 22.4 0.1 0.6 0.2

Cumulative 20585.1 0.3 32.4 0.7 1.4 0.5

Contributing Areas Grassland/

Herbaceous

Pasture/

Hay

Cultivated

Crops

Woody

Wetlands

Emergent

Herbaceous

Wetlands

1 7742.3 1.1 44.9 10.3 1.9 0.1

2 2358.5 0.7 36.7 12.2 1.3 0.0

3 1327.4 0.1 10.2 4.3 0.4 0.0

4 6526.8 0.6 28.0 7.6 0.9 0.0

5 2630.1 0.4 5.3 1.0 2.1 0.0

Cumulative 20585.1 0.8 31.3 8.1 1.4 0.0

25

Figure 2. Map of gauging stations and associated sub-basins with land use / land cover classifications in the Hinkson Creek Watershed, Missouri, U.S.A.

26

Soils and Vegetation

The Lower Missouri-Moreau River Basin (LMMRB) which contains the HCW is

largely comprised of prairie-forest transitional soils. Soils are poor to well-drained but are

easily erodible in part due to steep slopes (Perkins 1995). A map showing the distribution

of hydrologic soil groups using a high resolution soil dataset (SSURGO) is shown in

Figure 3. The HCW is dominated by the high runoff and moderately high runoff

hydrologic soil groups C and D. The soil type within the upper segments of Hinkson

Creek is characterized as loamy till with a well-developed claypan (Chapman et al.

2002). The soil types within the lower segments of HCW are characterized as thin cherty

clay and silty to sandy clay. Vegetation is loosely characterized as a mixed deciduous oak

forest (Rickett 1931). Riparian zones contain a diverse array of willows (Salix spp.),

birches (Betula spp.), cottonwoods (Populus deltoides), and sycamores (Platanus

occidentalis). Alluvial fans are covered with elms (Ulmus spp.), soft maples (Acer spp.),

basswoods (Tilia spp.), and woody shrubs (MDNR 2006).

27

Figure 3. Hydrologic soil groups by soil type in the Hinkson Creek Watershed, Missouri, U.S.A. Soil data from the Soil Survey Geographic Database (SSURGO), 2011.

28

Topography

Elevation ranges from 170 meters at the confluence with Perche Creek to 287

meters above sea level in the headwaters, as indicated by analysis of digital elevation

model data from the National Elevation Dataset. Hypsometric curves (Figure 4) showing

the areal distribution of elevation were developed for the entire HCW including each of

the sub-basin areas corresponding to the five gauging stations located in the watershed

and a sixth subbasin located at the outlet of the watershed.

Figure 4. Hypsometric curves showing distribution of elevation for each sub-basin associated with five nested gauging stations and the outlet of the Hinkson Creek Watershed, Missouri, U.S.A.

29

Water Quantity and Quality

A U.S. Geological Survey gauging station (#06910230) is located on Hinkson

Creek 122 m downstream of Providence Road in the city of Columbia, Missouri. The

gauging station has a total contributing drainage area of approximately 179.5 km2.

Average discharge measured at the intermittently-operated gauging station from October

1966 to September 1981, October 1986 to September 1991, and April 2007 to December

2008 was approximately 1.42 m3/s. Average annual discharge ranged from a low of 0.38

m3/s in 1980 to a high of 3.14 m

3/s in 1973. Average monthly discharge measured from

1967 to 1991 ranged from a low of 0.48 m3/s in August to a high of 2.66 m

3/s in May.

Since 2001, the Missouri Department of Natural Resources (MDNR) conducted

water quality and aquatic biota monitoring on main-stem Hinkson Creek and related

storm drainages. MDNR results documented that the aquatic community was impaired.

Toxicity tests showed that approximately 20% of stormwater discharges were polluted.

Pollution source procedures implicated a wide variety of urban-associated chemical

constituents. Visual sediment surveys documented increased sediment in the impaired

segment of Hinkson Creek. Analyses in 2005-2006 were contrary to early work and did

not indicate toxicity or measure organic chemical constituents above laboratory detection

levels. Thus, results to date have been confounding and attributable to any number of

environmental origins including irregular and poorly defined stormwater inputs,

snowmelt, in-stream processes, and other natural and/or anthropogenic factors (MDNR

2006; USEPA 2011).

30

DATA COLLECTION

Nested Watershed Design

Hydroclimatic data were collected at five fully-equipped co-located streamflow

gauging and climate monitoring stations located at five sites along the main-stem of

Hinkson Creek. Hydroclimate stations were installed in the winter of 2008-2009. Site #1

is located at the bridge at Rogers Road (39° 01.418‟ N, 92° 14.761‟ W) in Columbia,

Missouri; Site #2 at the bridge at Mexico Gravel Road, 6.8 km downstream of Site #1

(39° 58.964‟ N, 92° 16.758‟ W); Site #3 at the bridge at Broadway, 5.5 km downstream

of Site #2 (38° 56.891‟ N, 92° 18.321‟ W); Site #4 at the current U.S. Geological Survey

(USGS) gauge location at the bridge at Old Route K Road, 7.7 km downstream of Site #3

(38° 55.670‟ N, 92° 20.391‟ W), and Site #5 at the Scott Boulevard / Highway TT bridge,

9.6 km downstream of Site #4 (38° 54.847‟ N, 92° 24.011‟ W).

In addition to the hydroclimate stations, two additional publically available

climate stations from the University of Missouri Agriculture Experiment Station‟s

Commercial Agriculture Automated Weather Station Network were used in

implementing the SWAT model. One of these climate stations, Sanborn Field

(38.942471° N, 92.320468° W) is located 2.3 km northeast of Site #4 and 1.4 km

southeast of Site #3. The other additional climate station, South Farms (38.904675° N,

92.273542° W), is located 5.5 km southeast of Site #3, 0.5 km beyond the boundary of

the HCW. Figure 2 shows the locations of each gauging station in the Hinkson Creek

Watershed; also pictured are the sub-basin areas corresponding to each site, and the land

use / land cover (LULC) composition of each sub-basin.

31

Climate Data

A complete climate station, installed at each site, measured rainfall, minimum and

maximum daily air temperature, relative humidity, solar radiation, and wind speed. A

description of all measurement instruments installed at each site is presented in Table 3.

Five-minute data were logged on-site. Post-processing of the data involved averaging or

summing (i.e. reducing) five-minute time-series data to daily values. To correct for

occasional data gaps, linear regression models (R2 ≥ 0.92) were developed to correlate

the climate data at the Sanborn Field climate station (located within HCW sub-basin #4)

with the all other HCW climate stations. The regression model was then applied to the

data gap periods to simulate specific climate values at the other HCW sites.

32

Table 3. Field instrumentation and variables measured at five hydroclimate stations in the Hinkson Creek Watershed, Missouri, U.S.A.

Instrument Measurement

Accubar® Constant Flow Bubble Gauge/Recorder 56-0133 Water stage in mm

Campbell Scientific, Inc. Met One 034B Windset anemometer Horizontal wind speed in m/s and

direction

Campbell Scientific, Inc. Model 107 Temperature Probe Soil temperature in °C

Campbell Scientific, Inc. CS616 Water Content Reflectometer Soil volumetric water content in %

Campbell Scientific, Inc. SR50A Snow Depth Sensor Snow depth in cm

Campbell Scientific, Inc. Model HMP45C Temperature and

Relative Humidity Probe with radiation shield

Air temperature in °C and relative

humidity in %

Campbell Scientific, Inc. LI200X Pyranometer Incoming solar radiation (400 to

1100 nm) in W/m2

Campbell Scientific, Inc. TE525 Tipping Bucket Rain Gauge Precipitation in mm

Stage Data

At each streamflow gauging station (Sites #1-3, and #5) water stage in mm was

monitored at 5 minute intervals using an Accubar® Constant Flow Bubble Gauge and

Recorder. The USGS also measures stage at Site #4 using an Accubar® bubble gauge,

but at 15 minute intervals. Continuous data records of stage were logged using Campbell

Scientific, Inc. CR1000 data loggers remotely powered with 12-volt batteries charged by

solar panels.

Streamflow Measurements

Streamflow measurements (i.e. stream cross-sections) were conducted at all four

sites not operated by the USGS (#1-3, and #5) on a biweekly basis for a full one-year

33

period (2009). In addition, streamflow measurements were taken during storm events to

measure streamflow at medium and high (peak) stages. In 2010, the focus of streamflow

measurements shifted exclusively to measuring medium and high flow events as well as

measuring the discharge during periods of acute variable-backwater-affected streamflow

at Site #5.

Variable backwater, a phenomenon observed at Site #5, occurs when the energy

slope in a stream reach is variable for a given stage, resulting in unsteady flow (USGS

1982) (Figure 5). In most cases, variable backwater is caused by variable stage at a

downstream confluence for a given discharge at the stream gauge site or may be caused

by the operation of gates at a downstream dam (USGS 1982).

When variable backwater is present at a stream gauging site, the discharge is not

simply a function of stage; it is also a function of the energy slope in the reach (USGS

1982). Variable energy slopes in a stream channel may not only be a function of a

downstream variable backwater source, but also a function of changing discharge in a

stream reach (USGS 1982). Variable slopes caused by changing discharge occur when

the slope of the stream is nearly flat and the change in discharge is very fast (USGS

1982).

The variable backwater phenomena observed at Site #5 was primarily observed

during periods of historical record spring flooding on the Missouri River, indicating that

the Missouri River, downstream of Hinkson Creek, may serve as a source of the variable

energy slope at Site #5; variable backwater was also observed as a lagging response in

the recession limb of the hydrograph following large storm events in the HCW, which

indicates the possible presence of variable slope caused by changing discharge.

34

Figure 5. Photograph dated May 16, 2009 showing variable backwater affected streamflow at the Site #5 gauging station, Hinkson Creek Watershed, Missouri, U.S.A.

Streamflow was measured using the hydrologic standard velocity-area method

(USGS 1982). During low flows, water velocity was measured by wading the stream

while using a velocity sensor mounted to a top-setting wading rod (Figure 6). Velocity

measurements were made at the mid-points of 25 equal-width sampling intervals along

the wetted cross-section that is perpendicular to the downstream direction of flow. For

each interval, velocity was measured at the 0.6, 0.2, and 0.8 depths of the water column.

35

During high flows, streamflow was measured from atop the co-located

transportation bridges using a USGS Type A sounding reel. The sounding reel was

mounted on a USGS standard bridge board with either a 15 or 30 pound USGS

Columbus-Type sounding weight attached. The sounding weight was attached to the

hangar at the end of the sounding line, a USGS standard 0.25 cm thick stainless steel

Ellsworth cable. The Marsh-McBirney velocity sensor was mounted to the hangar bar

that is attached to the sounding weight. Velocity measurements were collected at the

downstream side of the bridge.

To reduce the time taken to measure the full stream channel cross-section during

high flows, where stage rapidly changes and therefore contributes considerable error to

streamflow determination (USGS 1982), the number of sampling intervals was reduced to

15 and the velocity measurements were only taken at the 0.6 depth, as recommended in

published USGS guidelines (USGS 1982).

36

Figure 6. Photograph showing wading streamflow measurement technique at Site #2, Hinkson Creek Watershed, Missouri, U.S.A.

To measure flow velocity, a Marsh-McBirney® Flo-Mate™ Model 2000 was

used (Marsh-McBirney, Inc. 1990). The Flo-Mate sensor was mounted to either the top-

setting wading rod or the hangar of the sounding weight. The Marsh-McBirney Flo-Mate

is a non-mechanical electromagnetic flow sensor that measures flow velocity (accuracy,

±2% of reading +0.015 m/s) by measuring changes in the voltage amplitude that is

created by a magnetic field (Marsh-McBirney, Inc. 1990). The Flo-Mate was set to

calculate a 30-second-averaged stream velocity in m3/s. Steps were taken to avoid high

tension on the fragile wiring within the sensor cable, as well as entanglement of the Flo-

37

Mate sensor cable with debris; when the cable was suspended from transportation

bridges, the sensor cable was affixed to the hangar bar and the Ellsworth cable (which

suspends the sounding weight from the sounding reel) with plastic tie-wraps at multiple

points. This method was recommended by Hach Flow® staff (Darby, pers. comm., 2010)

after and only after consultation. To measure discharge from bridges with pedestrian

safety fences greater in height than is usable with the bridge board alone (Sites #2 and

#3), a moveable hydraulic lift was used to raise the sounding reel operator and the bridge

board to a sufficient height.

The streamflow through each of the 25 and/or15 individual intervals was

estimated by the three-point method (USGS 1982) in which the average velocity at the

mean of the 0.2 and 0.8 depths is averaged with the velocity at 0.6 depth. For individual

intervals in which velocity was not recorded at all three depths due to extremely shallow

water depths as well as during bridge measurements, the six-tenths-depth method (USGS

1982) was used. For this method, only the velocity at the 0.6 depth is multiplied by the

cross-sectional area of the interval to estimate streamflow at each interval (USGS 1982).

In rare cases where only the 0.2 depth was possible to record due to shallow water depths,

the streamflow was measured using the two-tenths-depth method (USGS 1982). The 0.2

depth velocity was multiplied by a coefficient, 0.87, taken from an average vertical-

velocity index that was empirically calculated for numerous streams by the USGS (USGS

1982).

At Sites #2 and #3, high flows were measured at transportation bridges (co-

located with the hydroclimate stations) that travel at an oblique angle to the downstream

direction of streamflow (Figure 7). To account for resulting streamflow measurement

38

error, the initial cross-sectional width that was measured along the bridge road surface

was multiplied by a coefficient (0.82 for Site #2 and 0.87 for Site #3). The coefficient

was determined by calculating the tangent of the planform angle between the bridge and

the cross-section that lies perpendicular to the downstream direction of flow. The

planform angles were measured by determining the compass bearing for both the bridge

and the cross-section and then calculating the difference.

Figure 7. Photograph showing oblique angle between Hinkson Creek and the transportation bridge at Site #2, Hinkson Creek Watershed, Missouri, U.S.A.

39

RATING CURVE DEVELOPMENT

Initial Rating Curve Development

There are two general approaches in the literature for rating curve development.

The first approach involves the simple fitting of a smooth monotonic curve that is

typically logarithmic over most of its range, as is predicted by hydraulic theory (e.g.

Chezy formula). The fitted curve may also be a 2nd

or 3rd

order polynomial relationship;

polynomial curves may more closely fit measured stage-discharge values compared to the

logarithmic curves (Sivapragasam and Muttil 2005). The logarithmic approach has the

advantage of maintaining a monotonic trend through its entire range, while the

polynomial approach has the advantage of more closely fitting the measured data, and

vice-versa. Curves are fitted to a set of paired measurements of streamflow and stage

(water level) (USGS 1982), where stage is the level (i.e. elevation) of the water surface

above an arbitrary yet stationary reference point (USGS 1982).

The second approach, advocated by the USGS, creates rating curves that are

adjusted using judgments based on hydraulic principles and/or are adjusted based on

changing stream channel conditions. Changing stream channel conditions may occur

cyclically (seasonal variation such as in-channel vegetation growth in the summer or

freezing of stream water in the winter). Changing stream conditions may also occur as

long-term permanent changes to the geometry of the stream channel (USGS 1982).

The USGS also takes into consideration the presence, absence, or submergence of

section controls; section controls are defined as stable and permanent natural or artificial

structural features in a stream channel. The section controls are capable of maintaining a

40

consistent stage-discharge measurement for the stream gauge over a limited range of flow

conditions (USGS 1982). Observations of section controls are used to adjust the rating

curve (USGS 1982). Additionally, the USGS advocates use of an indirect method of peak

discharge calculation, e.g. the slope-conveyance method, and a field determination of the

gauge height of zero flow (USGS 1982). While these methods serve only as imprecise

estimates, they may be a better alternative to extrapolation of a curved function based on

direct discharge measurements only.

In this study, the first method discussed was followed. Thus, a 3rd

order

polynomial function was the primary tool for development of the rating curves

(Sivapragasam and Muttil 2005). It is surmised here that the first method allows for a

faster and more objective determination of the stage-discharge relationship. As stated

previously, the 3rd

order polynomial allows for a closer fit with the observed stage-

discharge values (Sivapragasam and Muttil 2005). To correct for poor simulation of very

low flows by the 3rd

order polynomial, linear functions were applied to the very low

ranges of stage to achieve a precise alignment with the stage estimated to correspond to

zero flow (USGS 1982). In cases where the polynomial, due to its S-shape (non-

monotonic trend) began to differ from the expected monotonic hydraulic trend in the

upper portion of the stage range, a logarithmic curve was used to extend the rating curve

to the upper reach of the stage range.

Trial Rating Curve Testing and Selection of Final Rating Curves

Trial rating curves for each University-operated site were established first using

the previously described method, and then analyzed for accuracy by comparing observed

41

between-site water yield differences. Based on those results, the rating curve definition

method was adjusted as necessary before selection of the final rating curve.

Initial trials of rating curve fits were tested by calculating the flow for a four

month time-series (March – June 2010) and comparing total water yield between all four

University-operated sites (HCW Sites #1-3, and 5) and the USGS-calculated water yield

at Site #4. For Sites #3 and #5, initial trial rating curves produced unrealistic water yields

relative to other sites. To determine whether the initial trial rating curves were realistic or

not, between-site water yields (over the four-month period) were compared to between-

site discharge differences measured on same days. In these cases, the curve fitting

method was adjusted to more realistically match expected between-site water yield

differences. This analysis resulted in adjustments to the rating curves at Site #3 and Site

#5. For Site #3, the logarithmic portion of the rating curve was re-calculated through

least-squares regression to adjust stage-discharge measurement in the upper portion of the

rating. Specific rating curve functions for each site are shown in Appendix A.

Computation of the Continuous Streamflow Record

The observed streamflow regime at Site #5 exhibited a variable backwater

phenomenon (USGS 1982); this observation was evidenced by the location of numerous

(26 out of 56 total) streamflow measurements that plotted to the right of the general

monotonic trend of the data in the stage-discharge plot (USGS 1982) (Appendix A). The

USGS has published methods to develop direct measures of streamflow estimation at

gauging sites that are affected by variable backwater (USGS 1982). The two methods

described by the USGS are the slope-index method and the velocity-index method. The

42

slope-index method uses continuous measurement of the water surface slope (an estimate

of the energy slope) in conjunction with the stage measurement to determine the

discharge record (USGS 1982). The velocity-index method uses a continuous

measurement of stream velocity at a point or points in the stream channel in conjunction

with the stage measurement to determine the discharge (USGS 1982). An unsuccessful

attempt was made to develop a slope-rating function for Site #5 using the slope-index

method. The USGS gauge at Site #4 served as an auxiliary gauge for determination of

water surface slope.

To account for periods in which the variable backwater effect resulted in

streamflow at high stages but with much lower than expected discharge, a linear

regression model (R2

= 0.95) was developed to correlate the stage at Site #4 with the

stage at Site #5. This linear regression model was then used to simulate stage at Site #5

during periods of extreme difference in the trend time-series, thus resulting in a

hydrograph with corrected backwater-affected streamflow.

To then prepare the observed data for analysis of SWAT-modeled output, stage

data were averaged on both a daily and monthly scale for each gauge site. The final rating

curves were then applied to the stage data to calculate a continuous record of streamflow

during year 2009-2010. Post-processed (i.e. approved) daily mean streamflow for Site #4

was obtained from the USGS.

43

MODEL IMPLEMENTATION

SWAT Model Configuration

In this study, a general framework for SWAT model development and evaluation,

designed with the agency or practitioner in watershed management in mind, is described,

tested, and evaluated. Due to practical limits on time, computer resources, access to

accurate datasets, as well as specialized experience and knowledge in modeling

techniques and hydrologic processes, SWAT model end-users (agencies and individuals)

may not have the ability to incorporate sophisticated research-grade methods to optimize

and evaluate their models.

In consideration of these constraints, several rules guided model development in

this study. Model input datasets for land use / land cover, soils, climate, and topography

were selected from publically and widely available datasets that do not require

considerable pre-processing in a geographic information system (GIS). Default model

input parameters were used when configuring the model (Neitsch et al. 2004). An

automatic calibration method, already built-in to the ArcSWAT version 2.3.4 software

package, was used to optimize the parameters in the model that govern the modeled

output of estimated streamflow. Since automatic calibration methods are typically time-

intensive (Kumar and Merwade (2009) reported two weeks of runtime on a single

computer), the parameters that control the automatic calibration routine were set such that

the time required for successful completion would take less than 24 hours. The automatic

calibration routine was run using a single desktop-class computer equipped with two

44

Intel® Core™ 2 Duo CPUs (E8600) running at 3.33 GHz and with 3.21 GB of RAM

(random access memory).

The 1:250,000 scale State Soil Geographic Database (STATSGO) was selected to

supply a soil dataset to parameterize the SWAT model (Figure 8). STATSGO is a

national-scale geo-referenced soil dataset that is included in the ArcSWAT software

package (Neitsch et al. 2004), and it was assumed to be the soil data reference of choice

for end-users. In addition, a higher resolution national-scale geo-referenced soil dataset,

the 1:24,000 scale Soil Survey Geographic Database (SSURGO), was used in other

model runs because it can be freely downloaded via the internet from U.S government

sources (Figure 8). However, the SSURGO dataset requires pre-processing (appending of

SSURGO soil data to the included national-scale STATSGO soil property database) and

the creation of a lookup text file to associate SSURGO soils with the soil property

records added to the SWAT soil database (Di Luzio et al. 2002). Fortunately, an easy-to-

use, free software program named SWATioTools™ is available for automatic pre-

processing of SSURGO soil data. The software program was developed and is shared via

website by Dr. Alexei Sheshukov of Kanas State University.

The 2001 NLCD used in this study was the most current national-scale publically

available LULC dataset at the time of this study. The 2006 National Land Cover Dataset

was released February 16, 2011. While the state of Missouri Spatial Data Information

Service (MSDIS) has produced a LULC dataset dated 2005, the more widely applicable

NLCD dataset was chosen over the more up-to-date dataset from MSDIS because use of

MSDIS data requires the model end-user to pre-process the data before input into the

model. Pre-processing requires creation of a text lookup file to associate MSDIS land use

45

classifications with the land use classification codes in the SWAT database (Di Luzio et

al. 2002). To automate the process for the end-user, SWAT includes a pre-defined text

lookup file for the 2001 NLCD. The 2001 NLCD text lookup file associates each NLCD

land use / land cover class with a default management file from either the SWAT land

cover / plant growth database (e.g. Forest-Deciduous (FRSD) or Hay (HAY)) or the

SWAT urban database (e.g. Residential – Medium Density (URMD)). Default

management settings were not adjusted. In this study, the most widely-applicable LULC

dataset that involved the most efficient method for the end-user was applied.

To define the number of sub-basins in the SWAT model of the HCW, two

schemes were used: 1) accepting the default value determined in ArcSWAT for the

minimum contributing source area used to define each sub-basin outlet, resulting in 34

sub-basins (based on a 10 m resolution DEM from the National Elevation Dataset); and

2) reducing the number of sub-basins using the built-in tools in ArcSWAT to six, one for

each of the five gauging stations and one below Site #5 at the outlet of Hinkson Creek

(Figure 8). Reducing the number of sub-basins in the SWAT model configuration

increases the computational efficiency of the model by generally reducing the quantity of

hydrologic response units (HRUs) because most SWAT computations occur at the HRU

level (Neitsch et al. 2005).

When defining the number of HRUs, the percent threshold values prescribed in

the SWAT manual (Neitsch et al. 2005) were used to limit the number of HRUs created:

a minimum of 20% of the sub-basin area for each land use class and a minimum of 10%

sub-basin area for each soil type (Neitsch et al. 2005). HRUs were not subdivided by

slope. The default setting in ArcSWAT uses a single slope value for all HRUs in each

46

sub-basin. In accordance with the modeling approach used in this study, the default

and/or recommended settings in the model were chosen, and computational efficiency

was optimized.

47

Figure 8. Comparison of (1) low resolution six sub-basin and high resolution 34 sub-basin watershed discretization schemes and (2) low resolution STATSGO soil data and high resolution SSURGO soil used in SWAT modeling.

48

Five climate input data approaches were selected to use in model configurations.

For the first set (Weather Generator), SWAT‟s built-in weather generator algorithm,

WXGEN (Neitsch et al. 2005), was used to simulate all the climate parameters (daily

values for precipitation, solar radiation, wind speed, relative humidity, minimum and

maximum air temperature). WXGEN by default used multi-year averaged climate data

from nearby Moberly, Missouri, to the north of the watershed, and from nearby Jefferson

City, Missouri, south of the watershed. Two of the five climate datasets used a single

climate station for daily climate input. One of these datasets (Sanborn Field) was

populated with data from the Sanborn Field climate station located in sub-basin #4 in a

small experimental agricultural field surrounded by a highly urbanized (university)

setting. To compare the urban-influenced climate input from the state-university-operated

Sanborn Field climate station, another single station climate input dataset (South Farms)

was taken from the South Farms climate station, located 0.5 km outside the HCW,

approximately 5.5 km southeast of Site #3, in an open, expansive agricultural

experimental area. Finally, two sets of multiple station climate input datasets were

produced using the HCW network of five hydroclimate stations. One of these datasets

(HCW-5 Climate) included all five stations. A second dataset (HCW-4 Climate) was

created including only the climate stations from Sites #2-5. This decision was made to

account for suspected precipitation undercatch at Site #1, possibly due to a fetch problem

from nearby woodland (Figure 9). Site #1 climate data other than precipitation (solar

radiation, wind speed, air temperature, and relative humidity) was not used in order to

avoid any other possible fetch problems with the Site #1 climate data.

49

Figure 9. Photograph of Site #1 climate station dated February 2011, showing the surrounding woodland, a potential source of precipitation undercatch. Hinkson Creek Watershed, Missouri, U.S.A.

ArcSWAT designates an input climate dataset with the nearest sub-basin by

determining in GIS the spatial proximity between the sub-basin and the climate station

(Winchell et al. 2009). All 20 combinations of the two watershed discretization schemes,

the two soil data sets STATSGO and SSURGO, and the five climate datasets were

configured in SWAT and ran for the period from 2001-2010. A “warm-up” period where

the model is run but not analyzed is recommended when running SWAT, allowing the

50

model to equilibrate to ambient watershed hydrologic conditions (e.g. soil antecedent

moisture condition) (Ahl et al. 2008). Only the two-year period of model simulated data

from 2009-2010, in which observed streamflow data was available, was used for analysis.

The specific configuration of each run is listed in Table 4.

Table 4. List of all 20 SWAT model configurations tested, the abbreviated names of each model, and the defining characteristics of each model. Configurations are systematically listed in order of increasing input data resolution.

Model Configuration

No. of

Sub-basins Soil Dataset

No. of

HRUs Climate Dataset

6_ST_WGN 6 STATSGO 22 Weather Generator

6_ST_SAN 6 STATSGO 22 Sanborn Field

6_ST_SFM 6 STATSGO 22 South Farms

6_ST_HCW4 6 STATSGO 22 HCW-4 Climate


6_SS_WGN 6 SSURGO 40 Weather Generator

6_SS_SAN 6 SSURGO 40 Sanborn Field

6_SS_SFM 6 SSURGO 40 South Farms

6_SS_HCW4 6 SSURGO 40 HCW-4 Climate


34_ST_WGN 34 STATSGO 108 Weather Generator

34_ST_SAN 34 STATSGO 108 Sanborn Field

34_ST_SFM 34 STATSGO 108 South Farms



34_SS_WGN 34 SSURGO 206 Weather Generator

34_SS_SAN 34 SSURGO 206 Sanborn Field

34_SS_SFM 34 SSURGO 206 South Farms



Evaluation of Uncalibrated Model Runs

To evaluate the goodness-of-fit of the twenty uncalibrated model configurations,

multiple statistical indicators were used. In addition to calculating previously published

51

goodness-of-fit indicators (Moriasi et al. 2007; ASCE 1993; Legates and McCabe 1999):

the percent bias (PBIAS) (Moriasi et al. 2007), [the root mean squared error (RMSE), the

mean absolute error (MAE), the squared-error based Nash Sutcliffe efficiency (NSE)

(Nash and Sutcliffe 1970), and the absolute-error based modified Nash and Sutcliffe

Efficiency (NSE1) (Legates and McCabe 1999)] for the set of 20 uncalibrated model

configurations, versions of the RMSE, MAE, NSE, and NSE1 that use ranked data series

rather than time series, were developed. The additional goodness-of-fit measures were

designed to measure the fit of modeled flow output with the observed flow duration

curve.

The additional measures are based on the sum of the squared error after ranking

(SSQR) automatic calibration objective function developed by van Griensven and

Bauwens (2003), which was incorporated into the built-in SWAT auto-calibration

method. The SSQR was used in a SWAT automatic calibration study by Van Liew et al.

(2005). In automatic calibration, an objective function, an indicator of the deviation

between a measured and simulated data series, is minimized using a computer algorithm

designed to find an optimal set of input parameter values (van Griensven and Bauwens

2003). The SSQR is given by the following equation:

nj

simulatedjmeasuredj xxSSQR,1

2

,,

(1)

where n represents the total number of paired values in a data series, j, sorted (ranked) by

magnitude, xmeasured is the measured value, and xsimulated is the model-simulated value. The

key difference between the use of SSQR in previous publications (van Griensven and

Bauwens 2003; Van Liew et al. 2005) and its use in this study is that the SSQR was

52

further developed into a tool for comparison of H/WQ models, rather than simply a tool

for parameter optimization in an automatic calibration method. As stated by van

Griensven and Bauwens (2003), the SSQR measures the model fitting of the distribution

of flows through time, and ensures that the full range of flows are represented in an

automatically calibrated model. Van Griensven and Bauwens (2003) further identified a

key advantage of the SSQR function over measures of the error in a time series; daily

H/WQ models in “small” basins may be more appropriately evaluated where errors in the

timing of peak flow events causes high residuals, while flows may be otherwise well-

represented. The new measures based on the SSQR, are identical mathematically to their

original non-ranked counterparts (RMSE, MAE, NSE, and NSE1) except that both the

observed and modeled streamflow data are sorted (ranked) according to the magnitude of

the discharge. The ranked versions of the aforementioned formulas are termed the R-

RMSE, R-MAE, R-NSE, and R-NSE1.

PBIAS, a measure in percentage terms of the overall model bias, i.e. the error in

total mass, is given by the following equation:

ni

measuredi

ni

simulatedimeasuredi

x

xx

PBIAS

,1

,

,1

,,

)100(

(2)

where n represents the total number of paired values (measured and model-simulated

streamflow) in a time-series, i, xmeasured is the measured value, and xsimulated is the model-

simulated value. A value of 0% represents optimal model goodness-of-fit. The RMSE is a

measure of the average error between observed and simulated values in a time-series and

53

is calculated as the square root of the squared errors and presented in the units of the

quantity of interest (e.g. m3/s for streamflow), as follows:

ni

simulatedimeasuredi xxn

RMSE,1

2

,,

1

(3)

where n represents the total number of paired values in a time-series, i, xmeasured is the

measured value, and xsimulated is the model-simulated value. A value of 0 represents

optimal model goodness-of-fit. The MAE, another measure of the average error between

observed and simulated values in a time-series, calculated as the absolute value of the

errors and presented in the units of the quantity of interest (e.g. m3/s for streamflow), is

given by the following equation:

ni

simulatedimeasuredi xxn

MAE,1

,,

1

(4)



optimal model goodness-of-fit. The NSE is a dimensionless statistic that compares the

relative magnitude of the residual variance to the measured data variance (Moriasi et al.

2007). The NSE is given by the following equation:

ni

meanmeasuredi

ni

simulatedimeasuredi

xx

xx

NSE

,1

2

,

,1

2

,,

1

(5)


measured value, xsimulated is the model-simulated value, and xmean is the mean of the

observed values. A NSE value of 1.0 represents optimal model fit, and a value of ≤0.0

54

indicates that the model simulated values are less accurate than a baseline model. The

baseline model is traditionally represented by the linear function f(x) equal to xmean,

where f(x) is equal to the quantity of interest and xmean is equal to the mean value of the

observed time-series (Nash and Sutcliffe 1970). The NSE1 is a dimensionless statistic

based on the NSE that calculates the absolute values of the errors in the time series rather

than the squared errors (Legates and McCabe 2007). The NSE1 is given by the following

equation:

ni

meanmeasuredi

ni

simulatedimeasuredi

xx

xx

NSE

,1

,

,1

,,

1 1

(6)



observed values. Similar to the NSE, A value of 1.0 represents optimal model fit, and a

value of ≤0.0 indicates a poor model-simulated fit to observed (Moriasi et al. 2007). a

value of 1.0 represents optimal model fit, and a value of ≤0.0 indicates that the model

simulated values are less accurate than a baseline model. The baseline model is

represented by the linear function f(x) equal to xmean, where f(x) is equal to the quantity of

interest and xmean is equal to the mean value of the observed time-series (Nash and

Sutcliffe 1970). The R-RMSE, a measure of the average error between observed and

simulated values in a series ranked by magnitude, calculated as the square root of the

squared errors and presented in the units of the quantity of interest (e.g. m3/s for

streamflow), is given by the following equation:

55

nj

simulatedjmeasuredj xxn

RMSER,1

2

,,

1

(7)

where n represents the total number of paired values in a ranked series, j, xmeasured is the


optimal model goodness-of-fit. The R-MAE, an additional measure of the average error

between observed and simulated values in a ranked series, calculated as the absolute

value of the errors and presented in the units of the quantity of interest (e.g. m3/s for

streamflow), is given by the following equation:

nj

simulatedjmeasuredj xxn

MAER,1

,,

1

(8)



optimal model goodness-of-fit. The R-NSE is a dimensionless statistic based on the NSE

that calculates the errors in a ranked series rather than a time series. The R-NSE is given

by the following equation:

nj

meanmeasuredj

nj

simulatedjmeasuredj

xx

xx

NSER

,1

2

,

,1

2

,,

1

(9)



observed values. Similar to the NSE, the value of 1.0 represents optimal model fit, and a

value of ≤0.0 indicates that the mean observed value is a better predictor than the model-

simulated values. Finally, the R-NSE1 is a modified version of the dimensionless statistic

56

R-NSE that calculates the absolute values of the errors in the ranked series rather than the

squared errors. The R-NSE1 is given by the following equation:

nj

meanmeasuredj

nj

simulatedjmeasuredj

xx

xx

NSER

,1

,

,1

,,

1 1

(10)



observed values. Similarly, a value of 1.0 represents optimal model fit, and a value of

≤0.0 indicates that the mean observed value is a better predictor than the model-simulated

values.

To aid in the interpretation of statistical goodness-of-fit indicators used in H/WQ

model evaluation, indicators were associated with a graphical or physical concept that

closely matched the error represented by the statistical indicator. In Table 5, the

aforementioned goodness-of-fit indicators were identified by a suitable conceptual

analog, either the mass balance, the hydrograph, or the flow duration curve. These

analogous associations were previously identified by van Griensven and Bauwens (2003).

While van Griensven and Bauwens (2003) used three sets of objective functions for

automatic streamflow calibration, one each fitting the visual/conceptual analogs

described here, they did not make a direct comparison with the subjective

visual/graphical approach often used in manual calibration (Srinivasan et al. 1998; Santhi

et al. 2001) and in interpretation of model fit by the end-user. Accordingly, use of a

conceptual analog may serve to improve understanding of how to appropriately interpret

these statistics.

57

Table 5. Descriptive table showing key characteristics of goodness-of-fit indicators used for model evaluation in this study.

Model Goodness-of-Fit

Indicator

Visual/Conceptual

Analog

Source of error

measured

Type of

error

measured

Standardized

for

Comparison

Percent Bias (PBIAS) Mass Balance Total Mass (Total

Water Yield)

Overall

Error (Bias)

Yes

Root Mean Square Error

(RMSE)

Hydrograph Event Magnitude

and Timing

Squared

Error

No

Mean Absolute Error

(MAE)


and Timing

Absolute

Error

No

Nash Sutcliffe Efficiency

(NSE)


and Timing

Squared

Error

Yes

Modified Nash Sutcliffe

Efficiency (NSE1)


and Timing

Absolute

Error

Yes

Ranked Root Mean Square

Error (R-RMSE)

Flow Duration

Curve

Percent of Time

Flow Exceeded

Squared

Error

No

Ranked Mean Absolute

Error (R-MAE)

Flow Duration

Curve

Percent of Time

Flow Exceeded

Absolute

Error

No

Ranked Nash Sutcliffe

Efficiency (R-NSE)

Flow Duration

Curve

Percent of Time

Flow Exceeded

Squared

Error

Yes

Ranked Modified Nash

Sutcliffe Efficiency

(R-NSE1)

Flow Duration

Curve

Percent of Time

Flow Exceeded

Absolute

Error

Yes

Indicators such as the Percent Bias (PBIAS) measure the difference between the

sums of the modeled and observed streamflow for the period of record under study; then

divide the difference by the sum of the observed flow. The output of the PBIAS thus

gives an indication of how well the modeled water yield compares with the observed

water yield. A close fit for PBIAS (PBIAS near 0%) indicates that observed and modeled

mass balance are very similar (Moriasi et al. 2007). With respect to hydrograph fit, the

study examines four indicators: two absolute-error based statistics, one which reports in

the units of the quantity of interest (MAE), and one which reports a standardized value

comparable across different gauging stations through normalization (NSE1, Legates and

McCabe 1999); and, also, two squared-error based statistics, the RMSE and the NSE

58

(Nash and Sutcliffe 1970). The NSE and NSE1 range from -∞ to 1, with a score of 0

indicating that the summed error, squared or absolute, between the modeled and observed

data is equal to the summed error between the mean value of the observed data and the

actual observed data (Legates and McCabe 1999). Metrics for evaluating flow duration

curve fit, based on the SSQR objective function of van Griensven and Bauwens (2003),

are introduced and are similar to the above mentioned set of four hydrograph fit

measures. In contrast to the set of hydrograph fit measures, the introduced metrics

compare paired values of observed and modeled streamflow after ranking each dataset in

order of magnitude.

In this study, the SWAT model is evaluated using the three classes of goodness-

of-fit statistical measures: total mass fit, hydrograph fit, and flow duration fit. Van Liew

et al. (2005) reported that there are tradeoffs when implementing the SWAT model

between achieving accurate representation of the total mass balance, hydrograph event

response, and the full range of flows. Accordingly, Van Liew et al. (2005) suggested

pursuing a modeling implementation approach that is best suited to the needs or problems

being addressed by the model; accordingly, a suitable balance in terms of amount, timing,

and distribution of the hydrologic or water quality variable may be attained. Furthermore,

van Griensven and Bauwens (2003) argued that achievement of a satisfactory hydrograph

fit alone does not imply that the hydrological processes, i.e. the division of surface runoff,

lateral flow (interflow), and groundwater input, have been adequately described by the

model. Therefore, while developing the auto-calibration method now made available with

SWAT, van Griensven and Bauwens (2003) incorporated three objective functions to

optimize the modeled streamflow output: one to minimize error in the overall mass

59

balance, one to minimize the errors for each event in the time-series, and one to minimize

error in the ranked streamflow series. In agreement with the finding of the

aforementioned studies and in accordance with the modeling approach described in the

introduction, this study used measures from each of the three classes of goodness-of-fit

indicators.

The normalized (dimensionless) statistics (PBIAS, NSE, NSE1, R-NSE, and R-

NSE1), capable of standardized comparison of model performance across different

gauging stations and watersheds, were used to rank the 20 uncalibrated model

configurations in terms of goodness-of-fit. In addition, direct comparisons were made

between the two watershed discretization schemes, the two soil datasets, and the five

climate datasets by averaging the goodness-of-fit indicators for each model configuration

using, for example, SSURGO soil data, and STATSGO soil data, as was done by Kumar

and Merwade (2009).

Test of the Built-In Automatic Calibration Method in SWAT

The ParaSol (Parameter Solutions) automatic calibration method built into SWAT

and developed by van Griensven (2002) is based on the Shuffled Complex Evolution

algorithm developed by the University of Arizona (SCE-UA). SCE-UA is a genetic

algorithm (Eckhardt and Arnold 2001) that was developed and shown by Duan et al.

(1984) to be highly efficient in finding the optimal set of model parameters. As described

by Eckhardt and Arnold (2001), the SCE-UA method considers a sample of points

(models with varying parameter values) spread throughout the parameter space (limited

by the upper and lower bounds assigned to each parameter). The points are conceived as

60

individual members of a biological population with specific genetic information

(parameter values) that evolve towards optimal fitness (i.e. the minimum of an objective

statistical criterion) through reproduction, mutation, and genetic recombination

(shuffling). An initial sample is sub-divided into multiple sub-samples (complexes) that

generate new sets of points (offspring) via the downhill simplex procedure of Nelder and

Mead (1965). The likelihood of an individual reproducing is based on the fitness of the

individual. The algorithm approaches an optimal set of parameters through mutation (the

simplex procedure), reproduction (replacement of parents by offspring according to

fitness), and genetic recombination (shuffling), a regular re-arrangement of individual

points into new complexes. Through this process, genetic algorithms are able to

efficiently and automatically optimize parameters used in H/WQ models (Duan et al.

1994; Eckhardt et al. 2001).

In this study, the auto-calibration routine was set to optimize the model based on

observed daily streamflow from the Site #4 streamflow gauge for the two-year period

2009-2010. Site #4 was chosen because it is a publically available USGS-operated gauge

and has been in operation since 1967. Accordingly, Site #4 was assumed to have a better

developed rating curve relative to the HCW Sites #1-3 and #5 (in operation since January

2009). A well-developed rating curve is necessary for accurate continuous streamflow

estimation. A publically available gauge also fits with the aforementioned practical

modeling framework aimed at end-users that was presented in the Introduction.

A split-sample method, i.e. the use of separate time periods or spatial locations of

observed flow data, one used for calibration of the parameters with the other serving as

an independent test, or validation, of the calibrated parameters, is typically used for

61

SWAT model validation (Gassman et al. 2007; Neitsch et al. 2005). In most examples of

SWAT model calibration and validation in the scientific literature, the calibration and

validation periods are separated by time (Gassman et al. 2007).

The method used in this study, however, was the more rarely used spatial split-

site calibration and validation method. In this method, data from one streamflow gauge

are used for model calibration and data from other gauges are used for model validation

(Gassman et al. 2007). It is surmised here that split-time sample methods are best suited

to forecasting applications, while split-site sample methods, used in this study, are best

suited for prediction in ungauged basins applications. This conclusion is based on the fact

that Nash and Sutcliffe (1970), in their discussion of the principles of conceptual,

physically based hydrologic modeling, were focused on two broad model applications:

flood event forecasting and forecasting the effects of management works on flow regime.

In these applications, model validation using the split-time method serves to directly test

the forecasting ability of the model. Prediction in ungauged basins or sub-basins,

conversely, a stated goal of the SWAT model (Neitsch et al. 2005) and not mentioned in

Nash and Sutcliffe (1970), is an application of spatial prediction or interpolation rather

than future forecasting and, accordingly, such applications may be best tested using split-

site validation.

The potential disadvantage to automatic calibration methodology is its intensive

demand on computing resources (e.g. two weeks of runtime reported by Kumar and

Merwade (2009)). Thus, the control parameters (Table 6) for the SCE-UA automatic

calibration built-in SWAT were chosen to conserve computational runtime. Based on the

experiment-based recommendations of Duan et al. (1994), the number of complexes in

62

the initial population (NGS) was set equal to the number of parameters being optimized

plus one. This value for NGS was suggested by the experiment-based findings of Duan et

al. (1994). Also, the number of evolution steps taken by each complex before shuffling

(NSPL) was set equal to twice the number of parameters being optimized plus one per the

recommendations of Duan et al. (1994). Parameters particular to the SWAT

implementation of the SCE-UA, KSTOP (set to 1), PCENTO (set to 0.01), and MAXN

(set to 1440) were set to levels that would constrain model runtime with a goal of

successful completion of the calibration procedure in under 24 hours.

63

Table 6. Settings used in ArcSWAT when running the built-in automatic calibration method.

SWAT Auto-Calibration Setting Value

Auto-Calibration Method (ICLB) Parasol only

Objective error function (OFMET) First Auto-Calibration: Sum of the

squared error (SSQ) for daily mean

discharge at Site #4 during year 2009-

2010

Second Auto-Calibration: Sum of the

squared error (SSQ) and the sum of the

squared error after ranking (SSQR) for

daily mean discharge at Site #4 during

year 2009-2010

Maximum number of simulations allowed (MAXN) 1440 (24 hour runtime at 1 minute per

simulation)

Maximum number of shuffling loops allowed following

initial loop in which the global objective criterion does

not change (KSTOP)

1

Percentage by which the global objective criterion must

change (PCENTO)

1%

Number of complexes in the initial population (NGS) 7 (Number of parameters to be

optimized plus one)

Number of evolution steps taken by each complex

before shuffling (NSPL)

13 (Twice the number of parameters to

be optimized plus one)

To further reduce runtime, a set of only six (out of a total of 26 streamflow-related

parameters in SWAT (Neitsch 2005) were selected for optimization (Table 7). These

parameters and their calibration bounds were identical to the set of six parameters used

by Van Liew et al. (2005), which were based on model parameter sensitivity analyses by

van Griensven (2002). This parameterization approach was applied to the entire

watershed as was done by Van Liew et al. (2005). Van Liew et al. (2005) reported

modeling results with automatic calibration that produced satisfactory results (as

measured by PBIAS and NSE) in one day of runtime. Kumar and Merwade (2009), on

64

the other hand, optimized a larger set of 14 flow parameters when using the built-in

SWAT auto-calibration tool and reported that the auto-calibration took two weeks using a

single desktop computer equipped with an Intel® Core 2 Duo™ 2.66 GHz processor with

2.00 GB of RAM. This study used the smaller and more computationally efficient set of

six parameters used by Van Liew et al. (2005). As suggested by the Law of Diminishing

Returns (a basic concept in economics), a smaller set of optimized parameters may be

more computationally efficient in terms of improving model accuracy than a larger set of

parameters, as was demonstrated by the comparison of Van Liew et al. (2005) and

Kumar and Merwade (2009).

The six SWAT input parameters selected for optimization (Table 7) were

ALPHA_BF, the baseflow alpha, or recession, factor; CN2, the initial curve number

condition II value, which governs surface runoff volumes; GW_DELAY, the

groundwater delay factor, which governs the length of time between percolation of water

from the soil profile and entry into the shallow groundwater aquifer; GW_REVAP, the

groundwater revap coefficient, which regulates the rate of “revap,” the movement of

water from the shallow aquifer back to the soil profile through evapotranspiration;

RCHRG_DP, the deep aquifer percolation fraction, which governs the amount of water

that permanently seeps out of the watershed‟s shallow aquifer; and REVAPMN, the

threshold water depth in the shallow aquifer for “revap” to occur (Neitsch et al. 2005).

65

Table 7. The six input parameters in SWAT selected for optimization with the built-in automatic calibration procedure, their initial values, and the variation settings.

SWAT Input

Parameter Input Parameter Description

Variation

Method

Bounds

Lower Upper

ALPHA_BF Baseflow alpha factor (days) Replace

value

0 1

CN2 Initial Curve Number Condition II

value

Multiply

default

parameter

value

-50% 50%

GW_DELAY Groundwater delay factor (days) Replace

value

0 500

GW_REVAP Groundwater revap coefficient Replace

value

0.02 0.2

RCHRG_DP Deep aquifer percolation fraction Replace

value

0 1

REVAPMN Threshold water depth in the shallow

aquifer for revap to occur (mm)

Replace

value

0 500

One model configuration arbitrarily selected from the set of twenty model

configurations was optimized using the automatic calibration routine using two separate

schemes. In the first scheme, the model was optimized for sum of the squared error

(SSQ) for daily streamflow, and in the second scheme was optimized for the sum of the

square error (SSQ) in conjunction with the sum of the squared error after ranking (SSQR)

for daily flow. When using the SWAT auto-calibration tool, other researchers optimized

streamflow for only a single objective function (Van Liew et al. 2005; Van Liew et al.

2007; Kumar and Merwade 2009; Zhang et al. 2009; Setegn et al. 2010); unlike van

Griensven and Bauwens (2003), who optimized streamflow using three different

objective functions: the SSQ, the SSQR, and the total mass controller (TMC). The TMC

objective function is not currently available for use in SWAT.

The previously described SSQ objective function, which measures the hydrograph

fit, is given by the following equation:

66

ni

simulatedimeasuredi xxSSQ,1

2

,,

(1)

where n represents the total number of paired values in the time series, i, xmeasured is the

measured value, and xsimulated is the model-simulated value. The SSQR objective function,

which measures the flow duration fit, is given by the following equation:

nj

simulatedjmeasuredj xxSSQR,1

2

,,

(11)

where n represents the total number of paired values in a data series, j, sorted (ranked) by

magnitude, xmeasured is the measured value, and xsimulated is the model-simulated value.

Finally, the TMC objective function, which measures the total mass fit, is given by the

following equation:

1)100(

,1

,

,1

,

ni

simulatedi

ni

measuredi

x

x

absTMC

(12)

where n represents the total number of paired values in the time series, i, xmeasured is the

measured value, and xsimulated is the model-simulated value.

To analyze the data, the modeled output for the selected uncalibrated model

configuration was ranked with the optimal models identified using the two automatic

calibration (both the single objective (SSQ) and the multiple objective (SSQ and SSQR)

methods) model runs following the same ranking system used to evaluate the

uncalibrated model configurations.

The SWAT auto-calibration method automatically identifies the optimal (best) set

of parameters for use in the model. The arbitrarily selected model chosen was configured

67

with 34 sub-basins, SSURGO soil data, and the HCW-4 climate dataset. The values for

the „best‟ set of parameters are stored in a text file named „bestpar.out‟. The model was

re-run using the „best‟ set of parameters by executing the „ReRun Calibrated Model‟

command in the ArcSWAT 2.3.4 extension for ArcGIS 9. The simulated daily

streamflow output for the model configured with the „best‟ set of parameters was then

obtained from the „output.rch‟ text file.

68

CHAPTER III

RESULTS

OBSERVED CLIMATE

The observed climate dataset indicates a wet two-year (2009-2010) study period

relative to historical values. The total two-year precipitation ranged from 1574 mm at

HCW Site #1 to 2709 mm at both HCW Site #3 and Sanborn Field, with a mean of 1234

mm in 2009 and 1243 mm in 2010 (1239 mm/year average) (Table 8). When excluding

Site #1, two-year precipitation totals ranged from 2481 mm at Site #2 to 2709 mm at both

HCW Site #3 and Sanborn Field, with a mean of 1300 mm in 2009 and 1328 mm in 2010

(1314 mm/year average). As reported earlier, historical mean annual precipitation was

1016 mm per year at the time of this study.

Daily maximum air temperature during the study period ranged from 29.4 °C at

HCW Site #1 to 37.3 °C at Sanborn Field with a mean of 32.1 °C (Table 8). The daily

maximum air temperature reported at Sanborn Field and South Farms, 37.3 °C and 36.2

°C, respectively, was substantially higher than the HCW sites, which ranged from 29.4

°C to 30.9 °C. Daily minimum air temperatures during the study period ranged from -

21.3 °C at South Farms to -15.7 °C at Site #3, with a mean of -18.5 °C. The daily

minimum air temperature reported at Sanborn Field and South Farms, -20.6 °C and 21.3

°C, respectively, was notably lower than the HCW sites, which ranged from -18.8 °C to -

15.7 °C.

69

Daily mean relative humidity ranged from 67.9 % at Sanborn Field to 77.4 % at

HCW Site #1, with a mean for all seven stations of 73.3 % (Table 8). Daily mean wind

speed ranged from 0.6 m/s at Site #1 to 3.1 m/s at South Farms, with a mean of 1.4 m/s.

The daily mean wind speed reported at Sanborn Field and South Farms, 2.0 m/s and 3.1

m/s, respectively, was considerably higher than the HCW sites, which ranged from 0.6

m/s to 1.3 m/s. Daily mean solar radiation ranged from 10.0 MJ/m2 at Site #1 to 13.8

MJ/m2 at South Farms with a mean of 12.4 MJ/m

2. Excluding Site #1, solar radiation

values ranged from 11.3 MJ/m2 at Site #4 to 13.8 MJ/m

2 at South Farms with a mean of

12.8 MJ/m2. Graphical time-series of all climate data are presented in Figures 10 through

15.

Table 8. Summary of climate data during 2009-2010 for five hydroclimate stations in the Hinkson Creek Watershed, Missouri, U.S.A. and the MU Agricultural Experimental Station‟s Sanborn Field and South Farms weather stations. Values for standard deviation are shown in parentheses.

2009 - 2010

Climate Data

Statistic HCW

#1

HCW

#2

HCW

#3

HCW

#4

HCW

#5

Sanborn

Field

South

Farms

Precipitation

(mm)

2009-10

total

1573.7 2481.3 2708.9 2670.9 2498.5 2708.9 2699.6

Maximum Air

Temperature

(°C)

max

daily

29.4 30.6 30.9 30.0 30.6 37.3 36.2

Minimum Air

Temperature

(°C)

min

daily

-18.8 -17.9 -15.7 -17.3 -18.2 -20.6 -21.3

Relative

Humidity (%)

daily

mean

77.4

(10.8)

74.3

(11.1)

72.0

(11.9)

75.2

(10.7)

74.7

(10.9)

67.9

(15.0)

71.5

(13.5)

Wind Speed

(m/s)

daily

mean

0.6

(0.4)

1.2

(0.7)

1.3

(0.6)

0.8

(0.4)

1.0

(0.7)

2.0

(0.7)

3.1

(1.3)

Solar

Radiation

(MJ/m2)

daily

mean

10.0

(5.8)

12.5

(7.7)

13.1

(8.2)

11.3

(7.1)

13.3

(7.9)

12.9

(7.4)

13.8

(7.6)

70

Figure 10. Daily precipitation at each of seven climate stations used in modeling of the Hinkson Creek Watershed, Missouri, U.S.A.

71

Figure 11. Daily maximum air temperature at each of seven climate stations used in modeling of the Hinkson Creek Watershed, Missouri, U.S.A.

72

Figure 12. Daily minimum air temperature at each of seven climate stations used in modeling of the Hinkson Creek Watershed, Missouri, U.S.A.

73

Figure 13. Daily mean relative humidity at each of seven climate stations used in modeling of the Hinkson Creek Watershed, Missouri, U.S.A.

74

Figure 14. Daily mean wind speed at each of seven climate stations used in modeling of the Hinkson Creek Watershed, Missouri, U.S.A.

75

Figure 15. Daily total solar radiation at each of seven climate stations used in modeling of the Hinkson Creek Watershed, Missouri, U.S.A.

76

DEVELOPED STAGE-DISCHARGE RATING CURVES

Rating curve determination for the streamflow gauges at Sites #1-3 and #5 was

based on a total of 34 streamflow measurements at Site #1, 33 streamflow measurements

at Site #2, 31 streamflow measurements at Site #3, and 56 streamflow measurements at

Site #5, 23 of which were identified as variable backwater influenced. Rating curve

determination at Site #4 was completed by the U.S. Geological Survey, using standard

operating procedures as described by USGS (1982; 2010).

To evaluate final rating curves, the Nash Sutcliffe Efficiency (NSE) was

calculated for each University-operated streamflow gauge. Because the NSE is very

sensitive to extreme outliers, one extreme outlier streamflow measurement at both Sites

#1 and #3, each likely due to measurement error, was eliminated from the calculation of

the NSE. In addition, flow measurements identified as variable backwater influenced at

Site #5 were excluded from the calculation of the NSE. Figure 16 shows each final rating

curve and its NSE. The mathematical functions for each rating curve are shown in

Appendix A. The NSE for the university-operated gauging stations ranged from 0.92 at

Site #1 to 0.99 at Sites #2 and #3. The NSE for the Site #5 rating curve (excluding

backwater measurements) was 0.95. A NSE, or a similar measures of rating curve

goodness-of-fit, for the USGS-operated Site #4 rating curve is not made available by the

USGS.

77

Figure 16. Plot of final rating curves used for continuous streamflow estimation at all five gauging stations that also shows streamflow measurements taken at each gauging station in the Hinkson Creek Watershed, Missouri, U.S.A. For the USGS-operated gauging station, Site #4, only available information is shown.

78

OBSERVED STREAMFLOW

Observed streamflow for each of the five streamflow gauging stations located in

the HCW are presented in this text using summary statistics, flow duration curves, and

hydrographs (Tables 9-10, Figures 17-20).

During the 2009-2010 study period, daily mean streamflow at Site #1 ranged from

0.03 m3/s to 77.52 m

3/s, with a mean of 1.18 m

3/s (median of 0.20 m

3/s). Site #2 ranged

from 0.12 m3/s to 185.35 m



3/s). Site #3

ranged from 0.5 m3/s to 115.2 m



3/s). Site

#4 ranged from 0.02 m3/s to 177.8 m



3/s).

Streamflow at Site #5 ranged from 0.10 m3/s to 141.5 m


3/s

(median of 0.71 m3/s).

Monthly mean streamflow for the same periods reported above in daily terms,

between January 2009 and December 2010, ranged at Site #1 from 0.03 m3/s to 3.83

m3/s, with a mean of 1.18 m


3/s). Site #2 ranged from 0.13 m

3/s to

9.56 m3/s, with a mean of 2.11 m


3/s). Site #3 ranged from 0.5 m

3/s

to 6.50 m3/s, with a mean of 2.07 m


3/s). Site #4 ranged from 0.24

m3/s to 10.31 m



3/s). Streamflow at Site #5

ranged from 0.30 m3/s to 12.80 m



3/s).

79

Table 9. Descriptive statistics for observed daily streamflow in 2009-2010, Hinkson

Creek Watershed, Missouri, U.S.A.

Statistic Observed Daily Streamflow Data

Site #1 Site #2 Site #3 Site #4 Site #5

MIN (m3/s) 0.024 0.115 0.046 0.020 0.099

MEAN (m3/s) 1.184 2.105 2.066 3.516 4.540

STDEV (m3/s) 4.926 10.246 7.630 12.424 14.001

MEDIAN (m3/s) 0.196 0.178 0.362 0.510 0.708

MAX (m3/s) 77.518 185.347 115.218 177.830 141.523

Figure 17. Flow duration curve for observed daily mean streamflow at five gauging stations in the Hinkson Creek Watershed, Missouri, U.S.A.

80

Figure 18. Hydrograph for observed daily mean streamflow at five gauging stations in the Hinkson Creek Watershed, Missouri, U.S.A.

81

Table 10. Descriptive statistics for observed monthly streamflow in 2009-2010, Hinkson Creek Watershed, Missouri, U.S.A.

Statistic Observed Monthly Streamflow Data


MIN (m3/s) 0.026 0.130 0.119 0.238 0.300

MEAN (m3/s) 1.186 2.105 2.069 3.520 4.538

STDEV (m3/s) 1.165 2.458 1.909 3.159 4.060

MEDIAN (m3/s) 0.836 1.373 1.699 2.430 3.349

MAX (m3/s) 3.833 9.556 6.497 10.309 12.799

Figure 19. Flow duration curve for observed monthly mean streamflow at five gauging stations in the Hinkson Creek Watershed, Missouri, U.S.A.

82

Figure 20. Hydrograph for observed monthly mean streamflow at five gauging stations in the Hinkson Creek Watershed, Missouri, U.S.A.

83

UNCALIBRATED MODEL CONFIGURATIONS

In order to compare goodness-of-fit of the 20 uncalibrated model configurations

tested in this study, each of the twenty model configurations were ranked using five

standardized goodness-of-fit indicators, at both daily and monthly scales. Each of the 20

uncalibrated model configurations used default model parameter values, and only varied

in terms of watershed subdivision (number of sub-basins), resolution of the input soil

dataset, and input climate dataset used to force the model.

The standardized goodness-of-fit indicators used to rank the twenty uncalibrated

model configurations were PBIAS (Moriasi et al. 2007), NSE (Nash and Sutcliffe 1970),

NSE1 (Legates and McCabe 1999), R-NSE, and R-NSE1, averaged across all five

streamflow gauging sites (summary data in Table 11, rankings in Tables 15 through 20).

PBIAS was used to measure model goodness-of-fit in terms of total mass; NSE and NSE1

were used to measure goodness-of-fit in terms of event timing and magnitude

(hydrograph fit), based on each the square of the errors (NSE) and the absolute values of

the errors (NSE1); R-NSE and R-NSE1 were used to measure goodness-of-fit in terms of

the percent of time flows were exceeded (flow duration fit), based on each the square of

the errors (R-NSE) and the absolute values of the errors (R-NSE1)

The use of three different types of goodness-of-fit measures was modeled after

the work of van Griensven and Bauwens (2003), and the use of both squared error and

absolute value based statistics is based on the work of Willmott (1984; 1985) and Legates

84

and McCabe (1999). Full descriptive and goodness-of-fit statistics for each model

configuration are presented in Appendix B.

The 20 uncalibrated model configurations ranged from 35.10% to 12.53%

absolute value of PBIAS for daily flow and 35.31% to 12.35% PBIAS for monthly flow,

from a NSE (NSE1) for daily flow of -0.17 (0.07) to 0.19 (0.30), from NSE (NSE1) for

monthly flow of -0.32 (-0.03) to 0.66 (0.47), from a R-NSE (R-NSE1) for daily flow of

0.52 (0.57) to 0.92 (0.72), and from an R-NSE (R-NSE1) for monthly flow of 0.48 (0.44)

to 0.87 (0.65).

When ranked by the mean absolute value of PBIAS across all five sites for daily

streamflow, the 34 sub-basin, STATSGO soil, and HCW-4 climate dataset (using HCW

Sites #2-5) configuration performed the best (PBIAS = 12.53%). When ranked using

NSE for daily flow, the 6 sub-basin, SSURGO, HCW-4 climate (NSE = 0.19) performed

the best; however, using NSE1, the 6 sub-basin STATGSO, HCW-5 climate configuration

performed the best (NSE1 = 0.30). When ranked by R-NSE for daily flow, the 34 sub-

basin, STATSGO, HCW-4 climate configuration and the 34 sub-basin, STATSGO,

Sanborn Field configurations performed close to the observed data, both with a R-NSE of

0.92 and a R-NSE1 of 0.72. Unsurprisingly, the Weather Generator based models

performed poor relative to observed data across all error measures and greatly

underestimated streamflow during the wet years of 2009-2010.

Rankings according to monthly goodness-of-fit statistics indicated a different set

of best performing models. Based on the mean absolute value of PBIAS across all five

sites for monthly streamflow, the best performing model configuration was the 34 sub-

basin, STATSGO soil, and HCW-4 climate dataset configuration, (PBIAS = 12.35%), the

85

same best configuration according to daily PBIAS. When ranked using NSE and NSE1

for monthly streamflow, the 6 sub-basin, STATSGO, South Farms climate (NSE = 0.66,

NSE1 = 0.47) and the 34 sub-basin, STATGSO, South Farms climate (NSE = 0.66, NSE1

= 0.47) configurations performed the best. When ranked by R-NSE for monthly flow, the

6 sub-basin, STATSGO, HCW-4 climate, which also ranked as best using R-NSE for

daily flow, and the 34 sub-basin, STATSGO, HCW-4 climate configurations performed

the best, both with an R-NSE of 0.87. When ranked by R-NSE1, the 34 sub-basin,

STATSGO, HCW-4 climate configuration performed the best (R-NSE1= 0.65); this same

configuration performed the best in daily R-NSE1.

Table 11. Goodness-of-fit model evaluation statistics for all twenty uncalibrated model configurations. Optimal goodness-of-fit values are in bold. Configurations are systematically listed in order of increasing input data resolution. Error measures given in the table are the mean of the measures for all five gauging stations.

Model

Configuration

Total Mass Fit Hydrograph Fit Flow Duration Fit

PBIAS (%)

Daily Flow

PBIAS (%)

Monthly Flow

NSE (NSE1)

Daily Flow

NSE (NSE1)

Monthly Flow

R-NSE (R-NSE1)

Daily Flow

R-NSE (R-NSE1)

Monthly Flow

6_ST_WGN 35.10 35.31 -0.17 (0.08) -0.32 (-0.03) 0.58 (0.64) 0.50 (0.44)

6_ST_SAN 19.54 19.36 0.09 (0.16) 0.54 (0.39) 0.91 (0.70) 0.83 (0.58)

6_ST_SFM 16.71 16.50 0.05 (0.18) 0.66 (0.47) 0.91 (0.70) 0.81 (0.55)

6_ST_HCW4 13.11 12.88 0.15 (0.20) 0.62 (0.43) 0.91 (0.71) 0.87 (0.64)

6_ST_HCW5 23.32 23.47 0.18 (0.30) 0.60 (0.43) 0.85 (0.68) 0.76 (0.60)

6_SS_WGN 34.41 34.61 -0.13 (0.09) -0.29 (-0.02) 0.52 (0.57) 0.50 (0.45)

6_SS_SAN 19.99 19.78 0.16 (0.16) 0.54 (0.37) 0.90 (0.62) 0.81 (0.54)

6_SS_SFM 17.97 17.73 0.10 (0.17) 0.65 (0.44) 0.91 (0.62) 0.78 (0.51)

6_SS_HCW4 14.73 14.40 0.19 (0.18) 0.60 (0.40) 0.90 (0.61) 0.84 (0.60)

6_SS_HCW5 21.92 22.09 0.17 (0.29) 0.57 (0.42) 0.82 (0.62) 0.76 (0.62)

34_ST_WGN 33.34 33.52 -0.17 (0.07) -0.21 (0.04) 0.57 (0.65) 0.49 (0.44)

34_ST_SAN 18.82 18.64 -0.01 (0.13) 0.55 (0.40) 0.92 (0.72) 0.84 (0.59)

34_ST_SFM 16.23 16.04 -0.06 (0.15) 0.66 (0.47) 0.91 (0.72) 0.82 (0.56)

34_ST_HCW4 12.53 12.35 0.06 (0.17) 0.62 (0.43) 0.92 (0.72) 0.87 (0.65)

34_ST_HCW5 22.90 23.07 0.11 (0.28) 0.61 (0.44) 0.87 (0.70) 0.77 (0.61)

34_SS_WGN 33.63 33.80 -0.12 (0.09) -0.19 (0.04) 0.51 (0.58) 0.48 (0.44)

34_SS_SAN 19.70 19.49 0.11 (0.14) 0.55 (0.38) 0.91 (0.64) 0.81 (0.55)

34_SS_SFM 17.71 17.48 0.04 (0.16) 0.66 (0.45) 0.91 (0.64) 0.78 (0.51)

34_SS_HCW4 13.75 13.41 0.13 (0.17) 0.61 (0.41) 0.90 (0.64) 0.85 (0.61)

34_SS_HCW5 21.26 21.45 0.13 (0.28) 0.59 (0.42) 0.83 (0.64) 0.77 (0.63)

* PBIAS shown is the average of the absolute values of the PBIAS at all five gauging stations.

** Absolute-error based statistics are in parentheses.

86

86

87

Direct comparisons were made between 6 sub-basin and 34 sub-basin

configurations by averaging the goodness-of-fit statistics for all 10 configurations

discretized with 6 sub-basins and comparing those statistics with the average goodness-

of-fit statistics for all 10 configurations with 34 sub-basins (Table 12). This procedure,

shown briefly on page 1188 of Kumar and Merwade (2009), aggregates the results of all

models using 6 sub-basins and those using 34 sub-basins, thus isolating the effect of

watershed subdivision.

The 34 sub-basin model configurations outperformed the 6 sub-basin

configurations based on the average absolute value of PBIAS, with PBIAS for daily

(monthly) streamflow of 21.67% (21.59%) and 20.92% (20.84%) for the 6 sub-basin and

34 sub-basin models, respectively, a difference of 0.75 % (0.75%) PBIAS. Using NSE

(NSE1) for daily flow, the 6 sub-basin models outperformed the 34 sub-basin models,

with the 6 sub-basin models obtaining 0.08 (0.18) and the 34 sub-basin models receiving

0.02 (0.08), a difference of 0.06 (0.02). According to monthly NSE results (NSE1),

however, the 34 sub-basin models outperformed the 6 sub-basin models, with the 6 sub-

basin models receiving 0.42 (0.33) and the 34 sub-basin models receiving 0.45 (0.35), a

difference of 0.03 (0.02). Based on R-NSE (R-NSE1) for daily flow, the 34 sub-basin

models outperformed the 6 sub-basin models, with the 6 sub-basin models receiving 0.42

(0.33) and the 34 sub-basin models receiving 0.45 (0.35), a difference of 0.03 (0.02).

Using R-NSE (R-NSE1) for monthly flow, the 34 sub-basin models slightly outperformed

the 6 sub-basin models, with the 6 sub-basin models obtaining R-NSE (R-NSE1) values

of 0.75 (0.55) and the 34 sub-basin models obtaining 0.75 (0.56), a difference of 0.00

(0.01).

88

Table 12. Goodness-of-fit model evaluation statistics comparing model runs with 6 sub-basins and model runs with 34 sub-basins. Error measures given in the table are the mean of the measures for all five gauging stations. Optimal goodness-of-fit values are in bold.

Goodness-of-fit Statistic 6 Sub-basin Models 34 Sub-basin Models

Total Mass Fit Daily PBIAS (%) 21.67 20.92

Monthly PBIAS (%) 21.59 20.84

Hydrograph Fit Daily NSE (NSE1) 0.08 (0.18) 0.02 (0.16)

Monthly NSE (NSE1) 0.42 (0.33) 0.45 (0.35)

Flow Duration Fit Daily R-NSE (R-NSE1) 0.82 (0.65) 0.83 (0.67)

Monthly R-NSE (R-NSE1) 0.75 (0.55) 0.75 (0.56)



Direct comparisons were also made between models configured with the low-

resolution soil dataset, STATSGO, and models configured with the high-resolution,

SSURGO soil dataset. This comparison was made by averaging the goodness-of-fit

statistics for all 10 configurations configured with STATSGO soil data and comparing

those statistics with the average goodness-of-fit statistics for all 10 configurations with

SSURGO data (Table 13).

The STATSGO-configured models outperformed the SSURGO configurations

based on the average absolute value of daily and monthly PBIAS, with a daily (monthly)

PBIAS of 21.08% (21.01%) and 21.51% (21.42%) for the STATSGO and SSURGO

models, respectively, a difference of 0.43% (0.41%) PBIAS. Using daily NSE (NSE1),

the results differed between squared error and absolute error based measures, with the

STATSGO-configured models receiving 0.02 (0.17) and the SSURGO-configured

models receiving 0.08 (0.17), a difference of 0.06 (0.00). According to monthly NSE

(NSE1), again the results differed between squared error and absolute error based

measures, with the STATSGO models obtaining a value of 0.43 (0.35) and the SSURGO

89

models obtaining a value of 0.43 (0.33), a difference of 0.00 (0.02). For the daily R-NSE

(R-NSE1), the STATSGO models outperformed the SSURGO models, with the

STATSGO models obtaining a value of 0.83 (0.69) and the SSURGO models obtaining a

value of 0.81 (0.62), a difference of 0.02 (0.07). Using monthly R-NSE (R-NSE1), the

STATSGO models outperformed the SSURGO models, with the STATSGO models

obtaining values of 0.75 (0.69) and the SSURGO models receiving 0.73 (0.63), a

difference of 0.02 (0.06).

Table 13. Goodness-of-fit model evaluation statistics comparing models using the low resolution STATSGO soil dataset and models using the SSURGO soil dataset. Error measures shown are the mean of the measures for all five gauging stations. Optimal goodness-of-fit values are in bold.

Goodness-of-fit Statistic STATSGO Models SSURGO Models


Monthly PBIAS (%) 21.01 21.42

Hydrograph Fit Daily NSE (NSE1) 0.02 (0.17) 0.08 (0.17)

Monthly NSE (NSE1) 0.43 (0.35) 0.43 (0.33)

Flow Duration Fit Daily R-NSE (R-NSE1) 0.83 (0.69) 0.81 (0.62)

Monthly R-NSE (R-NSE1) 0.75 (0.69) 0.73 (0.63)



A final set of direct comparisons were made between the configurations based on

each climate dataset (Table 14). The averages of the goodness-of-fit statistics for all four

configurations using a) The Weather Generator dataset, b) The single-station Sanborn

Field dataset, c) The single-station South Farms dataset, d) The four-station HCW-4

climate dataset (Sites #2-5), and e) The five-station HCW-5 climate dataset (Sites #1-5)

were compared. In terms of PBIAS for daily and monthly flow, the HCW-4 climate

90

dataset performed the best with a daily (monthly) absolute value of PBIAS of 13.53%

(13.39%), followed by South Farms at 17.16% (16.93%), Sanborn Field at 19.52%

(19.32%), HCW-5 climate at 22.35% (22.52%), and Weather Generator at 34.12%

(34.31%). The Sanborn Field, South Farms, and HCW-4 climate datasets generally

overestimated streamflow in terms of overall model bias, while the Weather Generator

and HCW-5 climate datasets generally underestimated streamflow (Appendix B).

Based on NSE and NSE1 for daily streamflow, however, the HCW-5 climate

dataset performed well relative to the observed dataset with an NSE (NSE1) for daily

flow of 0.15 (0.29), followed by HCW-4 climate at 0.13 (0.18), Sanborn Field at 0.09

(0.15), South Farms at 0.03 (0.13), and Weather Generator at -0.15 (0.08). Based on NSE

and NSE1 for monthly flow, the South Farms climate dataset performed the best with a

NSE (NSE1) for monthly flow of 0.66 (0.46), followed by HCW-4 climate at 0.61 (0.42),

HCW-5 climate at 0.59 (0.43), Sanborn Field at 0.55 (0.38), and Weather Generator at -

0.25 (0.01). According to R-NSE and R-NSE1 for daily streamflow, the Sanborn Field,

South Farms, and HCW-4 climate datasets performed the best, all with a daily R-NSE (R-

NSE1) of 0.91 (0.67), followed by HCW-5 climate at 0.84 (0.66), and Weather Generator

at 0.55 (0.61). Considering the R-NSE and R-NSE1 for monthly streamflow, an

inconclusive comparison resulted, with the HCW-4 and HCW-5 climate datasets

generally performing the best with a monthly R-NSE (R-NSE1) of 0.86 (0.62) and 0.77

(0.62), respectively, followed closely by Sanborn Field at 0.82 (0.56) and South Farms at

0.80 (0.56), and Weather Generator at 0.49 (0.44).

Table 14. Goodness-of-fit model evaluation statistics comparing models using the SWAT weather generator, the single climate dataset from urban-located Sanborn Field, a single climate dataset from rural-located South Farms, four climate datasets from HCW Sites #2-5, and five climate datasets from HCW Sites #1-5. Error measures are the mean of the measures for all five gauging stations. Optimal goodness-of-fit values are in bold.

Goodness-of-fit Statistic Weather Generator Sanborn Field South Farms HCW-4 Climate HCW-5 Climate

Total Mass Fit Daily PBIAS (%) 34.12 19.52 17.16 13.53 22.35

Monthly PBIAS (%) 34.31 19.32 16.93 13.39 22.52

Hydrograph Fit Daily NSE (NSE1) -0.15 (0.08) 0.09 (0.15) 0.03 (0.13) 0.13 (0.18) 0.15 (0.29)

Monthly NSE (NSE1) -0.25 (0.01) 0.55 (0.38) 0.66 (0.46) 0.61 (0.42) 0.59 (0.43)

Flow Duration Fit Daily R-NSE (R-NSE1) 0.55 (0.61) 0.91 (0.67) 0.91 (0.67) 0.91 (0.67) 0.84 (0.66)

Monthly R-NSE (R-NSE1) 0.49 (0.44) 0.82 (0.56) 0.80 (0.53) 0.86 (0.62) 0.77 (0.62)



91

92

Table 15. Uncalibrated model results ranked by a measure of mass balance fit: daily

Percent Bias. The Percent Bias shown is the average of the absolute values of each Percent Bias measure at all five HCW gauging stations.

Mean Absolute Value of

PBIAS for all Sites (%)

Uncalibrated Model Runs

No. of Sub-basins Soil Dataset Climate Dataset

12.53 34 STATSGO HCW-4 Climate


13.75 34 SSURGO HCW-4 Climate


16.23 34 STATSGO South Farms


17.71 34 SSURGO South Farms


18.82 34 STATSGO Sanborn Field


19.70 34 SSURGO Sanborn Field






33.34 34 STATSGO Weather Generator

33.63 34 SSURGO Weather Generator



93

Table 16. Uncalibrated models ranked by measures of hydrograph fit: daily Nash

Sutcliffe Efficiency and Modified Nash Sutcliffe Efficiency.

Mean Daily NSE for all Sites Uncalibrated Model Runs
















-0.010 34 STATSGO Sanborn Field

-0.063 34 STATSGO South Farms

-0.123 34 SSURGO Weather Generator


-0.170 34 STATSGO Weather Generator


Mean Daily NSE1 for all Sites No. of Sub-basins Soil Dataset Climate Dataset





















94

Table 17. Uncalibrated models ranked by measures of flow duration fit: daily Ranked Nash Sutcliffe Efficiency and Ranked Modified Nash Sutcliffe Efficiency.

Mean Daily R-NSE for all Sites Uncalibrated Model Runs






















Mean Daily R-NSE1 for all Sites No. of Sub-basins Soil Dataset Climate Dataset





















95

Table 18. Default uncalibrated model runs ranked by a measure of mass balance fit: monthly Percent Bias. Percent Bias is the average of the absolute values of each Percent Bias measure at all five HCW gauging stations.

Mean Absolute Value of

PBIAS for all Sites (%)

Uncalibrated Model Runs






















96

Table 19. Default uncalibrated model runs ranked by measures of hydrograph fit: monthly Nash Sutcliffe Efficiency and Modified Nash Sutcliffe Efficiency.

Mean NSE for all Sites Uncalibrated Model Runs






















Mean NSE1 for all Sites No. of Sub-basins Soil Dataset Climate Dataset





















97

Table 20. Default uncalibrated model runs ranked by measures of flow duration curve fit:

daily Ranked Nash Sutcliffe Efficiency and Ranked Modified Nash Sutcliffe Efficiency.

Mean R-NSE for all Sites Uncalibrated Model Runs






















Mean R-NSE1 for all Sites No. of Sub-basins Soil Dataset Climate Dataset





















98

AUTOMATIC CALIBRATION COMPARISON

The SCE-UA automatic calibration method built into SWAT was successfully run

in well under 24 hours as hypothesized. Previous studies reported up to two weeks of

runtime on a single computer (Kumar and Merwade 2009); model end-users in the

natural resources profession may find two weeks for automatic calibration impracticable.

With the model configuration selected for calibration, the 34 sub-basin, SSURGO soil,

and HCW-4 climate model, running at 1 minute per simulation, the single objective

automatic calibration (sum of the squared error only) took 691 simulations to successfully

complete while the multiple objective automatic calibration (sum of the squared error and

sum of the squared error after ranking) took 547 simulations to complete. In terms of

shuffled complex evolution (SCE) loops, the single objective calibration ran for 4

shuffling loops following the initial loop while the multiple objective calibration ran for 3

shuffling loops after the initial loop. The automatic calibration routine was run on a

single desktop-class computer with a dual-processor Intel® Core™ 2 Duo CPU rated at

3.33 GHz per core with 3.21 GB of RAM.

The automatic calibration procedure identifies the simulation resulting in the least

goodness-of-fit error. The parameter values used in this simulation are identified as the

„best‟ set of parameters. In this study, the single objective and multiple objective

automatic calibrations resulted in two distinct „best‟ sets of optimized parameters (Table

21).

99

The auto-calibration routine resulted in ALPHA_BF, the baseflow alpha factor,

changing from the default value of 0.048 days to 0.56 days in the single objective

calibration best parameter set and 0.48 days in the multiple objective calibration best

parameter set. The initial Curve Number condition II values were reduced basin-wide by

50% in the single objective calibration, while the Curve Numbers were increased by 7%

in the multiple objective calibration. GW_DELAY, the groundwater delay factor, was

changed from the default value of 31 days to 0.62 days in the single objective calibration

and 211.39 days in the multiple objective calibration. The GW_REVAP parameter, the

groundwater revap (movement of water from the shallow aquifer to the vadose

(unsaturated) zone due to evapotranspiration demand) coefficient, was changed from the

default value of 0.02 to 0.079 in the single objective calibration and 0.163 in the multiple

objective calibration. RCHRG_DP, the deep aquifer percolation fraction, was changed

from the default value of 0.05 to 0.00 in the single objective calibration and 0.44 in the

multiple objective calibration. Finally, REVAPMN, the threshold water depth in the

shallow aquifer for revap to occur, was changed from the default value of 1 mm to 417.4

mm in the single objective calibration and 355.3 mm in the multiple objective calibration.

Results show widely varied selected parameter sets between the single and multiple

objective automatic calibration methods.

100

Table 21. Values for the best set of six input parameters for the selected model configuration optimized first by minimizing the sum of squared error in daily streamflow at Site #4 and second by minimizing the sum of squared error and the sum of squared error after ranking using the built-in automatic calibration method in SWAT.

SWAT Input

Parameter Input Parameter Description

SWAT Default

Value

Best Value

SSQ SSQ & SSQR

ALPHA_BF Baseflow alpha factor (days) 0.048 0.56 0.48

CN2 Initial Curve Number Condition II

value

Varies -50% 7%

GW_DELAY Groundwater delay factor (days) 31 0.62 211.39

GW_REVAP Groundwater revap coefficient 0.02 0.079 0.163

RCHRG_DP Deep aquifer percolation fraction 0.05 0.00 0.44

REVAPMN Threshold water depth in the shallow

aquifer for revap to occur (mm)

1 417.4 355.3

Goodness-of-fit statistics based on streamflow at the USGS-operated Site #4

gauge were calculated to compare the selected uncalibrated configuration, the single

objective optimized model, and the multiple objective optimized model (Table 22).

Statistics for Site #4 were calculated because the SCE-UA automatic calibration

procedure was set to minimize the error in daily streamflow model prediction specifically

at Site #4. The calibration procedure was set to minimize error at Site #4 because it is a

long-term USGS-operated gauge with publically available streamflow data. The USGS

gauge has been in operation for a longer period (beginning in 1967) than all other HCW

streamflow gauges (beginning in 2009). Due to the longer operational period of the

USGS gauge, it was assumed that the USGS-operated Site #4 may serve as the most

suitable reference gauge.

In terms of PBIAS, the multiple objective optimized model performed the best

with an absolute value PBIAS for daily (monthly) flow of 0.50% (0.75%), followed by

101

the uncalibrated model at 11.75% (11.42%), and the single objective optimized model at

16.66% (16.08%). Using NSE and NSE1, the single objective optimized model performed

the best at simulating daily flow, with an NSE (NSE1) of 0.52 (0.35), followed by both

the uncalibrated model at 0.25 (0.24) and the multiple objective optimized model at 0.15

(0.30). Based on NSE and NSE1 for monthly flow, however, the multiple objective

optimized model performed the best with an NSE (NSE1) of 0.81 (0.59), followed by the

uncalibrated model at 0.68 (0.24), and the single objective optimized model at 0.54

(0.40). According to R-NSE and R-NSE1 for daily flow, the multiple objective optimized

model performed the best with an R-NSE (R-NSE1) of 0.97 (0.82), followed by the

uncalibrated model at 0.95 (0.66), and the single objective optimized model at 0.80

(0.63). R-NSE and R-NSE1 values for monthly streamflow indicated the uncalibrated

model and the multiple objective optimized model performed similarly with the

uncalibrated model obtaining an R-NSE (R-NSE1) value of 0.89 (0.67) and the multiple

objective optimized model obtaining a value of 0.89 (0.73). followed by the single

objective optimized model at 0.82 (0.67).

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Table 22. Goodness-of-fit model evaluation statistics for Site #4 comparing the selected uncalibrated model configuration (34 sub-basin, SSURGO soil data, and HCW-4 climate dataset) with single objective (SSQ) and multiple objective (SSQ & SSQR) automatically calibrated runs of the selected model. Optimal goodness-of-fit values are in bold.

Goodness-of-fit Statistic for Site #4 Uncalibrated

Model

SSQ

Calibrated Model

SSQ & SSQR

Calibrated Model

Total Mass Fit Daily PBIAS (%) 11.75 16.66 0.50

Monthly PBIAS (%) 11.42 16.08 0.75

Hydrograph Fit Daily NSE (NSE1) 0.25 (0.24) 0.52 (0.35) 0.15 (0.30)

Monthly NSE (NSE1) 0.68 (0.45) 0.54 (0.40) 0.81 (0.59)

Flow Duration Fit Daily R-NSE

(R-NSE1) 0.95 (0.66) 0.80 (0.63) 0.97 (0.82)

Monthly R-NSE

(R-NSE1) 0.89 (0.67) 0.82 (0.69) 0.89 (0.73)



To validate the automatically calibrated model results on a spatially independent

dataset, average goodness-of-fit statistics for Sites #1-3 and #5 were calculated (Table

23). In general, validation statistics were slightly lower than the statistics calculated for

Site #4, the USGS-operated gauge used for optimization in the automatic calibration

procedure.

In terms of PBIAS for daily (monthly) flow, the multiple objective optimized

model was closest to the observed data with an absolute value PBIAS of 11.11%

(10.98%), followed by the uncalibrated model at 14.24% (13.91%), and the single

objective optimized model at 19.37% (18.76%). Based on NSE and NSE1 for daily flow,

the single objective optimized model was closest to the observed data with an NSE

(NSE1) of 0.45 (0.31), followed by both the uncalibrated model at 0.19 (0.15) and the

multiple objective optimized model at 0.16 (0.20). Based on NSE and NSE1 for monthly

flow, on the other hand, the multiple objective optimized model was closest to the

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observed data with an NSE (NSE1) of 0.70 (0.49), followed by the uncalibrated model at

0.59 (0.40), and the single objective optimized model at 0.44 (0.35). According to R-NSE

and R-NSE1 for daily flow, the multiple objective optimized model was closest to the

observed data with an R-NSE (R-NSE1) of 0.91 (0.77), followed by the uncalibrated

model at 0.89 (0.63), and the single objective optimized model at 0.74 (0.57). R-NSE and

R-NSE1 values for monthly flow indicated similar performance between all three models;

the R-NSE for the uncalibrated model on a monthly basis was (R-NSE1) of 0.84 (0.60),

for the single objective optimized model was 0.79 (0.66), and for the multiple objective

optimized model was 0.86 (0.66).

Table 23. Goodness-of-fit model evaluation statistics for Sites #1-3, and 5 comparing the

selected uncalibrated model configuration (34 sub-basin, SSURGO soil data, and HCW-4 climate dataset) single objective (SSQ) and multiple objective (SSQ & SSQR) automatically calibrated runs of the selected model. Optimal goodness-of-fit values are in bold.

Goodness-of-Fit Statistic for Sites #1-3, 5 Uncalibrated

Model

SSQ Calibrated

Model

SSQ & SSQR

Calibrated Model

Total Mass Fit Daily PBIAS (%) 14.24 19.37 11.11

Monthly PBIAS (%) 13.91 18.76 10.98

Hydrograph Fit Daily NSE (NSE1) 0.19 (0.15) 0.45 (0.31) 0.16 (0.20)

Monthly NSE (NSE1) 0.59 (0.40) 0.44 (0.35) 0.70 (0.49)

Flow Duration Fit Daily R-NSE

(R-NSE1) 0.89 (0.63) 0.74 (0.57) 0.91 (0.77)

Monthly R-NSE

(R-NSE1) 0.84 (0.60) 0.79 (0.66) 0.86 (0.66)



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CHAPTER IV

DISCUSSION

ANALYSIS OF OBSERVED CLIMATE DATA

Well above mean precipitation levels occurred during the study period. Mean

annual precipitation for all seven climate stations used averaged 1239 mm per year (22%

above historical average). When excluding the Site #1 precipitation data that was affected

by undercatch, the other six climate stations used averaged 1314 mm per year (29%

above historical average). Therefore, it is suggested that the modeling results presented in

this study be viewed within this context of well-above mean precipitation. The modeling

results do not reflect potential model performance in years with average precipitation or

performance in unusually dry years.

One clear problem in the climate data that arose was the apparent undercatch of

precipitation at Site #1. This problem was suspected due to the considerable difference in

total precipitation (1574 mm) during the study period measured at the six other climate

stations used in the study (2481 – 2709 mm). It was not surprising that Site #1 would

exhibit lower precipitation total due to fetch since the Site #1 climate station is located

closer to surrounding woodland area than any other site in the study (Figure 8). A simple

linear regression model was developed to correlate precipitation and elevation along a

North-South elevation gradient for six precipitation gauges (Sites #1-5 and a Missouri

Agricultural Experiment Station operated precipitation gauge in Auxvasse, Missouri).

The six gauges ranged in elevation from 172 m to 265 m above sea level. The linear

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regression, however disqualified (coefficient of determination (R2) = 0.010) the

possibility of orographic effects being the sole cause for lower precipitation totals at Site

#1.

The results of the uncalibrated model configuration rankings using PBIAS, a

measure of overall model bias, additionally support the conclusion that the Site #1

climate station was indeed underestimating precipitation (Appendix B). Specifically, all

four model configurations using the HCW-4 climate dataset (Sites #2-5) performed better

based on PBIAS (absolute value of PBIAS for daily (monthly) streamflow ranging from

12.53% (12.35%) to 14.73% (14.40%)) than models using all other climate datasets

(absolute value of PBIAS for daily (monthly) flow ranging from 16.23% (16.04%) to

35.10% (35.31%)). Due to the multiple stations included in the HCW-4 climate dataset,

the results suggest that the HCW-4 best represented the climate parameters in the HCW

through representation of the spatial heterogeneity of climate in the watershed.

The four model configurations using the HCW-5 climate dataset performed

further from the observed data in terms of PBIAS (absolute value of PBIAS for daily

(monthly) flow ranging from 21.26% (21.45%) to 23.32% (23.47%)) than both the single

station South Farms and Sanborn Field climate datasets (absolute value of PBIAS for

daily (monthly) flow ranging from 16.23% (16.04%) to 19.99% (19.78%)). The poor

performance of models configured with the HCW-5 climate dataset relative to the single

station climate datasets suggests that the incorporation of Site #1 climate data in the

HCW-5 dataset considerably reduced model fit with observed data.

The HCW-5 model configurations only outperformed the stochastic weather

generated climate dataset (absolute value of PBIAS for daily (monthly) flow ranging

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from 33.34% (33.52%) to 35.10% (35.31%)). Positive PBIAS values for models with the

HCW-5 climate dataset (Appendix B) indicated underestimation of streamflow; thus

suggesting that the HCW-5 climate dataset precipitation total accordingly was an

underestimate. The poor performance (streamflow underestimation) of the HCW-5

climate dataset may be attributed to the aforementioned precipitation undercatch at Site

#1.

Results also indicated key differences between the climate data collected at HCW

Sites #1-5 and the data collected at the Sanborn Field and South Farms climate stations.

The maximum and minimum daily air temperature values are more extreme (higher

maximum (37.3° at Sanborn Field and 36.2° at South Farms) and lower minimum (-20.6°

at Sanborn Field and -21.3° at South Farms)) at the Sanborn Field and South Farms

climate stations relative to HCW Sites #1-5 (29.4° to 30.9° maximum temperature and -

18.8° to -15.7° minimum temperature). Additionally, daily mean wind speeds were

considerably higher at Sanborn Field (2.0 m/s) and South Farms (3.1 m/s) than at HCW

Sites #1-5 (range from 0.6 – 1.3 m/s).

Both the moderation in temperature and reduced wind speeds at the HCW sites

may be at least partially attributed to the topographic position of the climate stations. The

HCW sites, co-located with stream gauging stations, are located in valley bottom riparian

zones, where air temperatures and wind speeds may be moderated (Dingman 2008;

Campbell and Norman 1999). The more extreme maximum and minimum daily air

temperatures at Sanborn Field and South Farms may also be attributed to the position of

the temperature gauges at approximately 1 m above the ground at those sites, making

those gauges more susceptible to surface heating and cooling processes than the HCW

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temperature gauges, which are positioned approximately 3 m above the ground surface

(Campbell and Norman 1999).

Between May 2009 and July 2009, solar radiation values were relatively low at

HCW Sites #1 and #4 relative to all other sites as shown in Figure 15. The decline in

solar radiation during this period at both sites may be attributed to seasonal growth

patterns of leaf area in trees near the climate stations. At Site #1, multiple trees were

felled towards the end of this period to remove the shading problem. At Site #4, the

climate station was moved to an open field on the opposite side of Hinkson Creek

towards the end of this period to address fetch and canopy shading issues.

ANALYSIS OF OBSERVED STREAMFLOW DATA

Streamflow was perennial rather than intermittent at all five HCW gauging sites

during the two-year study period of this study (2009-2010). The lowest daily mean

streamflow, 0.020 m3/s, was recorded at Site #4. This finding supports the use of the

default value of zero for the CH_K (2) parameter in SWAT. This parameter represents

the effective hydraulic conductivity of the main stream channel in mm/hour (Neitsch et

al. 2005). As stated by Neitsch et al. (2005), the effective hydraulic conductivity for

perennial streams is zero.

Observed streamflow at Site #2 had higher total (mean) streamflow for the 2009-

2010 period (2.105 m3/s) than Site #3 (2.066 m

3/s), located 5.5 km downstream of Site

#2. It is also noted that Site #2 had the highest maximum daily streamflow (185.4 m3/s)

compared to all other sites (daily range 77.5 – 177 m3/s). It is suspected that these

seemingly unrealistic flow values for Site #2 may be due to a large flux or slug of

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bedload sediment in the sand and gravel size-classes that was observed in the Site #2

reach in the Spring of 2009. The bedload sediment flux resulted in the up to 0.5 meter

burial of the orifice of the Accubar® Constant Flow Bubble Gauge during multiple

(approximately five) storm events. The orifice was uncovered following storm events by

shoveling sediment away from the orifice. The large amount of accumulated bedload

sediment that temporarily buried the bubbler orifice on multiple occasions may have

interfered with proper operation of the bubbler gauge, which measures the gauge pressure

(the absolute pressure subtracted by the atmospheric pressure). Sediment covering the

bubbler orifice may have increased the absolute pressure at the bubbler orifice, resulting

in greater than actual computed water levels.

In July 2009, the bubbler was moved approximately 15 meters downstream to

avoid sedimentation. A second possible explanation for the greater discharge at Site #2

may be that the flow at Site #3 has artificially reduced groundwater input due to

impervious surface area in sub-basin #3. Also, there potentially exists an urban

stormwater path(s) that may route water out of sub-basin #3 into sub-basin #4, thereby

bypassing the Site #3 streamflow gauge. No direct evidence of such surface flow paths

exists; further investigation is warranted.

In addition, flow duration curves suggest that Site #2 observed streamflow is

unusual and/or in error. The Site #2 flow duration curve intersects the Site #1 flow

duration curve for daily flow (Figure 17) less than 1 m3/s, and intersects the Site #3 flow

duration curve for monthly flow (Figure 19) above 6 m3/s. The Site #2 flow duration

curve also exhibits an asymptote between ranked days 300 to 730, suggesting that low

(base) flows are far more consistent in frequency and magnitude than all other sites. This

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phenomenon observed in the flow duration curves is a function of the close spatial

proximity between Sites #2 and #3; the sub-basin area between the sites measures 1327

ha, only 6.4% of the total cumulative contributing area at Site #5, the most downstream

gauge in the study, and only 11.6% of the total cumulative contributing area to Site #3.

The phenomenon also suggests that further refinement of the rating curves at Sites #2 and

#3 is needed, particularly in the low water portion (less than 0.2 m3/s discharge) of the

rating curve where the Site #2 flow duration curve asymptotes. Further refinement may

be achieved by conducting regular (e.g. monthly or seasonal) cross-sections at Sites #2

and #3 during periods of low flow (less than 0.2 m3/s discharge).

While the Site #5 stage record was post-processed to correct for variable

backwater effects, further investigation of the variable backwater phenomenon at Site #5

is warranted. Investigation into backwater at Site #5 may permit a more direct

determination of the streamflow at Site #5; for this study, periods with which the effect of

variable backwater on streamflow was acute required correction using a linear regression-

model based on the Site #4 streamflow. A more direct determination of streamflow at

Site #5 may be obtained through the use of a velocity-index rating curve as described by

the USGS (1982). A velocity-index rating curve would require installation of a

continuously recording velocity meter(s) at a point in the stream or at multiple points

along a transverse line (USGS 1982). Another method suggested by the USGS (1982), a

slope index rating curve, proved unsuccessful for calculation of Site #5 backwater-

affected streamflow when using the USGS-operated Site #4 gauge as an auxiliary gauge

for continuous measurement of water surface slope. The failure of this attempt to develop

a slope-rating function for Site #5 is likely due to the appreciable longitudinal curvature

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of the stream reach between Sites #4 and #5; the curvature likely led to the measured

water surface slope to be a poor estimate of the energy slope (USGS 1982). This failure

also may be due to the large proportional difference in cumulative contributing area

between Sites #4 and #5 (14.6%, 2630.1 ha), resulting in markedly different magnitudes

in flow between the two gauging stations.

UNCALIBRATED MODEL PERFORMANCE

Moriasi et al. (2007) proposed standard criteria for judging H/WQ model

performance on a monthly time scale. Based on the proposed Percent Bias (PBIAS)

criteria, 4 of the 20 uncalibrated model configurations may be considered “good” (10% <

PBIAS < 15%), 12 of the 20 may be considered “satisfactory” (15% < PBIAS < 25%),

and 4 of the 20 configurations may be considered “unsatisfactory” (PBIAS > 25%).

These results demonstrate that the SWAT model is capable of achieving

acceptable goodness-of-fit with observed data according to published standard criteria

(Moriasi et al. 2007) without calibration (i.e. parameter optimization) in the Hinkson

Creek Watershed, Missouri, U.S.A., when full-suite observed climate data (i.e.

precipitation, air temperature, solar radiation, wind speed, and relative humidity) are

used. Furthermore, these results may be applicable to other watersheds in Missouri and

the Central U.S.A.

The sole common denominator among the “good” model configurations was the

use of the HCW-4 climate dataset; the sole common denominator among the

“unsatisfactory” model configurations is the use of the Weather Generator climate

dataset. Models using the HCW-4 climate dataset may have performed closest to the

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observed streamflow values as measured by PBIAS because this dataset potentially better

quantified the variability in climate (e.g. precipitation) across the HCW, particularly at a

daily time-scale. Based on the proposed Nash Sutcliffe Efficiency (NSE) criteria of

Moriasi et al. (2007), 4 of the 20 model configurations may be considered “good” (0.65 <

NSE < 0.75), 12 of the 20 model configuration may be considered “satisfactory” (0.50 <

NSE < 0.65), and 4 of the 20 configurations may be considered “unsatisfactory” (NSE <

0.50).

The sole common denominator among the “good” model configurations based on

NSE is the use of the South Farms climate dataset; the sole common denominator among

the “unsatisfactory” model configurations based on NSE is the use of the Weather

Generator climate dataset. For monthly streamflow simulation, models using the well-

sited (negligible fetch problem) South Farms climate dataset most closely matched the

observed streamflow at a monthly scale. These results indicate that a single well-sited

(negligible fetch) and fully-equipped climate station dataset is capable of outperforming

multiple fully-equipped climate stations that better represent the spatial variability in

climate (e.g. precipitation).

EFFECT OF WATERSHED SUBDIVISION AND INPUT DATASET SELECTION ON MODEL FIT

Goodness-of-fit statistical rankings of the uncalibrated model configurations did

not clearly determine the optimal choice for model watershed subdivision, input soil or

climate dataset because the rankings represent the combined effect of watershed

subdivision, input soil, and climate dataset. To overcome this problem, direct

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comparisons were made between watershed discretization schemes, soil datasets, and

climate datasets. Direct comparisons were obtained by, for example, by averaging the

NSE for each model configured with STATSGO soil data and each model configured

with SSURGO soil data, as was briefly demonstrated on page 1188 in Kumar and

Merwade (2009).

Watershed Subdivision

The direct comparison between 6 sub-basin and 34 sub-basin model

configurations indicated that the choice between low and high resolution watershed

discretization had a negligible effect on streamflow simulation (Table 12). This finding of

negligible effect is based on the fact that differences between goodness-of-fit statistical

values were minimal and varied with the goodness-of-fit evaluation measure used. The

differences in PBIAS for daily streamflow between 6 sub-basin and 34 sub-basin models

was 0.43% and for monthly streamflow was 0.41%. The differences between NSE

(NSE1) between 6 sub-basin and 34 sub-basin models for daily streamflow was 0.06

(0.00) and for monthly streamflow was 0.00 (0.02). The differences between R-NSE (R-

NSE1) for 6 sub-basin and 34 sub-basin models for daily streamflow was 0.02 (0.07) and

for monthly streamflow was 0.02 (0.06). A similar case emerged in the direct comparison

of the low resolution STATSGO soil model configurations and the high resolution

SSURGO soil model configurations.

Direct comparisons of the effect of watershed subdivision on SWAT modeled

streamflow accuracy were not available. Arabi et al. (2006) report the effects of

watershed subdivision on sediment and nutrient modeled predictions, but not streamflow.

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Kumar and Merwade (2009) do not provide a direct quantitative comparison of results

between model configurations with varying levels of watershed subdivision. To analyze

the modeling results presented in Kumar and Merwade (2009) with respect to the effect

of watershed subdivision on streamflow simulation accuracy, the data presented in Table

4, pg. 1187, in Kumar and Merwade (2009) were re-analyzed using the same methods in

this study (Table 24). Goodness-of-fit measures for total mass fit and hydrograph fit were

available in Kumar and Merwade (2009), but measures for flow duration fit were not

available. Kumar and Merwade subdivided the modeled watershed using various

threshold critical source areas (CSAs). The CSA represents the minimum area for a

stream channel to originate. The CSA was defined by the percentage of overall watershed

area. Only the four CSA percentages (2%, 3%, 5%, and 7%) applied consistently (four

configurations per CSA percentage) in Kumar and Merwade (2009) were analyzed in this

study. Thus, 16 of 24 total model configurations used by Kumar and Merwade were taken

into account. Lower percent CSA values result in finer watershed subdivision (i.e. more

sub-basins). Results are presented for uncalibrated, calibrated, and validated runs of the

16 model configurations. Kumar and Merwade (2009) used a single objective automatic

calibration (sum of the squared error (SSQ) only) method with the built-in SWAT auto-

calibration tool.

The results of the re-analysis confirm the findings determined in this study;

watershed subdivision had a negligible effect on streamflow simulation accuracy. Mean

absolute values of PBIAS for daily streamflow ranged from 6.47% to 8.51% for 16

uncalibrated models; ranged from 3.28% to 9.17% in the single objective (SSQ)

automatically calibrated models; and ranged from 12.42% to 14.11% in the validated

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models. Mean values of NSE ranged from -0.01 to 0.13 for the uncalibrated models;

ranged from 0.59 to 0.69 for the calibrated models; and ranged from 0.57 to 0.60 for the

validated models. These differences in goodness-of-fit may be considered negligible.

Table 24. Goodness-of-fit model evaluation statistics comparing SWAT model configurations with varying levels of watershed subdivision (Kumar and Merwade 1999). Watershed subdivisions are defined by percent contributing source area (CSA). Error measures shown are the mean of the measures for all 24 models.

Statistic 2% CSA 3% CSA 5% CSA 7% CSA

Uncalibrated Models

Total Mass Fit Daily PBIAS (%) 6.47 7.62 8.51 7.89

Hydrograph Fit Daily NSE -0.01 0.05 -0.01 0.13

Calibrated Models


Hydrograph Fit Daily NSE 0.69 0.66 0.59 0.65

Validated Models


Hydrograph Fit Daily NSE 0.60 0.58 0.57 0.58

* Optimal goodness-of-fit values are in bold.

** PBIAS shown is the average of the absolute values of the PBIAS for all model configurations.

*** Lower percent CSA values result in finer watershed subdivision (i.e. more sub-basins).

Soil Data Resolution

Differences between goodness-of-fit values between models configured with

STATSGO soil data and with SSURGO soil data were also negligible and also varied

with the goodness-of-fit evaluation measure used (Table 13). The findings suggested that

the choice between STATSGO and SSURGO soil data had a negligible effect on

streamflow simulation. For both daily and monthly streamflow the difference in PBIAS

was 0.75%. For daily streamflow, the difference in NSE (NSE1) was 0.06 (0.02) for daily

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flow and was 0.03 (0.02) for monthly streamflow. The value of R-NSE (R-NSE1) for

daily streamflow was 0.03 (0.02) for daily flow and for monthly flow was 0.00 (0.01).

Published research corroborates the results of this study in that the resolution of

the soil dataset had negligible influence on the modeled streamflow output (Kumar and

Merwade 2009; Ye et al. 2011). In the 15,535 km2 Xinjiang River basin in China, Ye et

al. (2011) found that varying soil data resolution did not lead to considerable differences

in SWAT streamflow simulation accuracy. For the 2800 km2 St. Joseph River Watershed

in Michigan, Indiana, and Ohio, U.S.A, Kumar and Merwade (2009) report average NSE

(PBIAS) for 12 SWAT model configurations varying in watershed discretization and soil

data resolution (STATSGO vs. SSURGO) of 0.56 (19.4%) with STATSGO soil data and

0.56 (19.0%) for models with SSURGO soil data, a negligible difference of 0.00 (0.4%).

These directly compared goodness-of-fit statistics reported in Kumar and Merwade

(2009) were only calculated for model validation following a single objective automatic

calibration (sum of the squared error (SSQ) only) using the built-in SWAT auto-

calibration tool.

The modeling results presented in Kumar and Merwade (2009) were further

analyzed in this study with respect to the effect of soil resolution on streamflow

simulation accuracy. The data presented in Table 4, pg. 1187, in Kumar and Merwade

(2009) were re-analyzed using the same methods used for direct comparison in this study

(Table 25). All 24 model configurations were taken into account, including 12 model

configurations of the aforementioned 2800 km2 St. Joseph River Watershed (SJRW) and

also 12 model configurations of the 700 km2 Cedar Creek Watershed, a sub-watershed of

the SJRW. The uncalibrated, calibration, and validation model runs were all taken into

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account. As mentioned previously, goodness-of-fit measures for total mass fit and

hydrograph fit were available in Kumar and Merwade (2009), but measures for flow

duration fit were not available. For this re-analysis, a direct comparison was made

between all 12 models configured with STATSGO soil data and all 12 models configured

with SSURGO soil data.

The results of the soil resolution re-analysis confirm the findings determined in

this study; soil resolution had a negligible effect on streamflow simulation accuracy.

Mean absolute values of PBIAS for daily streamflow were 9.71% for STATSGO-

configured models and 7.37% for SSURGO-configured uncalibrated models, a difference

of 2.34%. For the single objective (SSQ) automatically calibrated models, PBIAS for

daily streamflow was 3.88% for STATSGO models and 5.88% for SSURGO models, a

difference of 2.00%. For the validation models, PBIAS for daily streamflow was 13.82%

for STATSGO models and 12.22% for SSURGO models, a difference of 1.60%.

Mean values of NSE for daily streamflow were 0.10 for STATSGO-configured

models and -0.16 for SSURGO-configured uncalibrated models, a difference of 0.26. For

the automatically calibrated model runs, NSE for daily streamflow was 0.66 for

STATSGO models and 0.66 for SSURGO models, a difference of 0.00. For the

validation models, NSE for daily streamflow was 0.58 for STATSGO models and 0.59

for SSURGO models, a difference of 0.01. These differences in goodness-of-fit (i.e. both

PBIAS and NSE) may also be considered negligible.

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Table 25. Goodness-of-fit model evaluation statistics comparing SWAT models configured with low resolution soil data (STATSGO) and high resolution soil data (SSURGO) (Kumar and Merwade 2009). Error measures shown are the mean of the measures for all 24 models.

Statistic STATSGO SSURGO

Uncalibrated Models


Hydrograph Fit Daily NSE 0.10 -0.16

Calibrated Models


Hydrograph Fit Daily NSE 0.66 0.66

Validated Models


Hydrograph Fit Daily NSE 0.58 0.59

It is surmised here that soil resolution does not considerably affect SWAT

streamflow simulation accuracy due to SWAT‟s use of the empirical SCS curve number

method for surface runoff / infiltration volume partitioning (Neitsch et al. 2005). The

SCS curve number method requires input of an empirical, indirectly calculated non-

physical parameter, the curve number (CN). Default curve numbers are supplied in the

SWAT model using a built-in database. The CN is a function of soil permeability, land

use, and antecedent soil moisture conditions. The CN is dynamically adjusted in SWAT

in response to topographic slope and daily changes in soil water content (Neitsch et al.

2005). It is surmised here that a more process-based approach that uses physically

measureable soil parameters to partition surface runoff and infiltration volumes would

increase SWAT‟s sensitivity to input soil data resolution. Increased model sensitivity to

soil data resolution may provide more accurate representation of surface runoff and

infiltration patterns in the modeled watershed.

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While a second optional method for surface runoff / infiltration portioning, the

Green-Ampt-Mein-Larsen infiltration method, is made available in SWAT (Neitsch et al.

2005), its use in the SWAT modeling literature is rare according to Gassman et al.

(2007). Summarizing the few studies that used the Green-Ampt-Mein-Larsen method,

Gassman et al. (2007) concluded that there was no discernible benefit to its use over the

SCS curve number method. Furthermore, use of the Green-Ampt-Mein-Larsen

infiltration method requires sub-daily (e.g. hourly) precipitation input data, which in

many cases may not be available to end-users.

The negligible difference in SWAT modeled streamflow accuracy between the

low and high resolution soil datasets may also be attributed to the high runoff potential of

the soils in the HCW (primarily hydrologic soil groups C and D (Figure 3)) in

combination with the well above mean precipitation during the study period (29% above

historical average when excluding Site #1). A combination of high runoff potential soils

and high soil available water content (due to above average precipitation) may be

expected to cause high levels of surface runoff. Since surface runoff flows overland

rather than through the soil, it may be expected that in this watershed (HCW) during this

study period (2009-2010), the accuracy of the representation of soil hydraulic properties

in the model was not critical to accurate model streamflow simulation. In watersheds with

soils with lower runoff potential (hydrologic soil groups A and B) and under more arid

conditions, soil data resolution may take on increased importance in model streamflow

simulation.

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Climate Data

While study results indicated mixed (according to evaluation statistic used) and

negligible differences in model fit between watershed subdivision schemes and soil

datasets, direct comparisons made between models configured with the five different

climate datasets clearly demonstrated considerable differences in model fit (Table 14). A

direct comparison of PBIAS, the statistical measure of modeled versus observed total

mass, for models configured with each climate dataset indicated that the models

configured with the HCW-4 climate dataset performed the best (mean absolute value of

PBIAS for daily (monthly) flow of 13.53% (13.39%)). This result may be attributed to

the more accurate representation of spatial heterogeneity in climate parameters (e.g.

precipitation) by the multiple station HCW-4 dataset and the HCW-4 dataset‟s exclusion

of the problematic (i.e. precipitation undercatch) Site #1 climate data. Thus, it may be

concluded that multiple station input climate datasets that well-represent spatial climate

heterogeneity may be best suited to modeling applications requiring close fit with the

observed total mass (i.e. mass balance).

The precipitation undercatch in the Site #1 climate dataset further resulted in the

HCW-5 climate dataset obtaining values of PBIAS values for daily and monthly flow

(absolute value of PBIAS for daily (monthly) flow of 22.35% (22.52%)) indicated that

the HCW-5 climate dataset performed further from observed in terms of total mass than

the single station, homogenous Sanborn Field (absolute value of PBIAS for daily

(monthly) of 19.52% (19.32%)) and South Farms climate datasets (absolute value of

PBIAS for daily (monthly) flow of 17.16% (16.93%)). As discussed previously, the poor

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performance (streamflow underestimation) of the HCW-5 climate dataset may be

attributed to the aforementioned precipitation undercatch at Site #1.

Based on measures of hydrograph fit, the squared error NSE and the absolute

error NSE1, however, the HCW-5 climate dataset performed best at the daily scale (NSE

(NSE1) for daily flow of 0.15 (0.29)) while the South Farms climate dataset performed

best on the monthly scale (NSE (NSE1) for monthly flow of 0.66 (0.46)). These results

suggest that multiple climate station datasets, through their spatially heterogeneous

accounting of climate patterns, may be best suited for modeling daily event timing and

magnitude, even if, as in this case, one of the climate stations (Site #1) accounts

inappropriately for the total quantity of precipitation. At a broader monthly time scale,

the results suggest that a single well-sited (i.e. negligible fetch) and representative climate

station dataset like South Farms, even if located 0.5 km outside the watershed under

study, was capable of best modeling monthly event timing and magnitude. With respect

to measures of flow duration fit, the R-NSE and R-NSE1, study results suggest that full-

suite measured climate datasets, whether using a single or multiple stations, are similarly

capable of modeling the distribution of flows through time (R-NSE (R-NSE1) ranging

from 0.77 to 0.86 (0.53 to 0.62), a negligible difference of 0.09 (0.09).

Published studies on the effect of varying quantity and quality of climate stations

in the SWAT model are not available. Studies using other models have found that

multiple climate stations for precipitation measurement reduce the error in modeled

streamflow output. Michaud and Sorooshian (1994) found that precipitation

representation errors resulting from too few climate stations accounted for approximately

half of the errors in streamflow simulation using a rainfall/runoff model. Similarly, Van

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Werkhoven et al. (2008) concluded that representation of the spatial characteristics of

rainfall events strongly controls the value of streamflow observations in distributed

watershed models. The results of this study, however, suggest that the effects of climate

station input depend upon the topographic position of the climate station, the number of

climate stations, and the time-scale and evaluation method used (total mass fit,

hydrograph fit, and flow duration fit).

EVALUATION OF THE SWAT AUTOMATIC CALIBRATION PROCEDURE

For model users seeking to calibrate their models, this study provides support for

use of limited observed data time-series (two years) in model calibration and validation.

This finding was previously supported by Du et al. (2009), who used a discontinuous set

of 41 measures of daily streamflow for calibration and 70 measures of daily streamflow

for validation and achieved satisfactory results (NSE >0.5 for daily flow for both the

calibration and validation datasets). Similarly, based on the criteria set forth by Moriasi et

al. (2007), in this study the SSQ calibrated model performed “satisfactory” in terms of

PBIAS and “satisfactory” in terms of NSE, while the SSQ and SSQR calibrated model

performed “very good” in terms of PBIAS (PBIAS <10% for monthly flow) and “very

good” in terms of NSE (NSE >0.75 for monthly flow) at the calibrated gauging station,

Site #4. The successful values for goodness-of-fit indicators in the study by Du et al.

(2009) and in this study suggest that calibration and validation may be performed

successfully on limited time-series of observed data. The evidence supports use of time

series as limited as two years (730 days) for both calibration and validation at different

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streamflow gauge sites as was done in this study; and 41 days for calibration and 71 days

for validation

Engel et al. (2007) (via Gan et al. 1997) stated that model calibration should

incorporate three to five years of observed data including wet, dry, and average years.

They go on to argue, however, that the amount of data necessary to calibrate a model is

project specific, indicating that shorter time-series may be appropriate. The years

included in this study were constrained by gaps in the streamflow record at the USGS

gauge (inactive between 1991 and mid-2007) and time limits on data collection at the

other four HCW gauging sites. Accordingly, this study is reflective of common

constraints in model evaluation and calibration, in which observed datasets are often

limited (Du et al. 2009; Engel et al. 2007). These constraints may further limit H/WQ

modeling efforts in the future, because, as reported by Simon et al. (2007), the number of

active USGS streamflow gauges decreased from approximately 8,500 in 1989 to

approximately 7,500 in 2006. Thus, the amount of observed streamflow data publically

available for model calibration is decreasing in the U.S.A. and future model calibration

efforts may be further limited. In regards to the HCW, however, future studies will be

able to further address H/WQ model performance during average and dry years as data

become available.

In this study, a single objective automatic calibration using the SSQ as an

objective function, and a multiple objective automatic calibration using both the SSQ and

the SSQR were compared. When van Griensven (2003) originally developed and tested

the automatic calibration method included with SWAT, the model was calibrated using

three different objective functions combined into a single global objective criterion that

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was then optimized using the SCE-UA method (Shuffled Complex Evolution method

developed at the University of Arizona) (Duan et al. 1994): (1) the sum of the squared

error (SSQ) to minimize error in the timing and magnitude of the daily streamflow time-

series, (2) the sum of the squared error after ranking (SSQR) to minimize error in the

ranked distribution of daily streamflow, and (3) the total mass controller (TMC), a

measure of overall model bias, for the mass balance. The three objective functions were

weighted equally when combined into the single global objective criterion for

optimization.

However, only the SSQ and SSQR objective functions are made available in the

publically-distributed version of SWAT (van Griensven 2005). Furthermore, published

studies that have since evaluated the auto-calibration procedure in SWAT have limited

their optimization of streamflow parameters to a single objective function, rather than

using both available objective functions, SSQ and SSQR (Van Liew et al. 2005; Van

Liew et al. 2007; Kumar and Merwade 2009; Setegn et al. 2010).

The goodness-of-fit evaluation at Site #4 for the selected uncalibrated model, the

single objective calibrated model, and the multiple objective calibrated model indicated

that the multiple objective calibrated model performed best in five of six measures. These

measures include the daily and monthly total mass fit (PBIAS), monthly hydrograph fit

(NSE and NSE1), and daily and monthly flow duration fit (R-NSE and NSE1). The single

objective calibrated model only performed best in one of the six measures, daily

hydrograph fit (NSE and NSE1). This finding is unsurprising considering that the single

objective calibration only optimized for the sum of the squared error in daily flow, which

is represented by the daily NSE and NSE1. Furthermore, with respect to the other five of

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six goodness-of-fit measures, the single objective calibrated model generally performed

worse than the uncalibrated model with respect to the other five of six measures. These

results provide a strong argument for use of multiple objective automatic calibration

methods for streamflow predictions including reduction of both error associated with

event timing and magnitude as well as error associated with the distribution of flows

through time. The set of validation statistics calculated on the remaining four gauging

stations show a similar pattern as the calibration statistics calculated using Site #4 only;

however, the values for each goodness-of-fit statistic are generally worse than the set of

calibration statistics, a common finding in H/WQ modeling efforts (Gassman et al. 2007).

Studies that have assessed the auto-calibration tool built into SWAT have

generally used a single objective function for streamflow calibration. Similar to the

results in this study, it was generally found that single objective function calibration

yielded poor overall model accuracy. Van Liew et al. (2005) ran the auto-calibration tool

to optimize streamflow in one scenario using only the SSQ, in another scenario using

only the SSQR, and another using only the TMC. They reported that preliminary results

from the optimization of the TMC alone were so poor that the approach was not included

in the study. They further report poor representation of the mass balance (poor PBIAS

values) when using only the SSQ or using only the SSQR for optimization. Accordingly,

published results suggest that overall model accuracy (incorporating the total mass fit,

hydrograph fit, and flow duration fit) is not well-achieved when using a single objective

function for streamflow calibration.

In this study, the selected best set of parameters found using each automatic

calibration were substantially different, particularly with respect to differences in surface

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and ground water partitioning. The single objective calibration reduced curve numbers

(CN2) basin-wide by the maximum bound, 50%, set by the automatic calibration

procedure; the multiple objective calibration, on the other hand, increased the curve

numbers by 7%. Accordingly, the single objective calibration strongly reduced surface

runoff from storm events, while the multiple objective calibration slightly increased event

surface runoff. The GW_DELAY parameter, which represents the lag time in the vadose

zone between percolation out of the soil profile and recharge of the shallow aquifer

(Neitsch 2005), was reduced from the default value in the single objective calibration,

while it was increased in the multiple objective calibration. Thus, the single objective

calibration (SSQ objective function only) reduced the groundwater response time to

precipitation events, while the multiple objective calibration (SSQ and SSQR objective

functions) lengthened the response time of groundwater flow to precipitation events.

Similarly, Van Liew et al. (2005) found that single objective automatic calibration

resulted in sharp departures in CN2 values, ranging from -13% to -50%. The results in

this study suggest that multiple objective function automatic calibration resulted in a

more conservative change in the CN2 values of 7%.

COMPARISON OF H/WQ MODEL FIT EVALUATION METHODS

The results of the rankings of the twenty uncalibrated model configurations

according to five different goodness-of-fit statistical indicators at both a daily and

monthly time scale suggest that the selection of the optimal model configuration depends

both on the evaluation statistic being used and the temporal scale of the evaluation. The

10 different evaluation methods (daily and monthly PBIAS, daily and monthly NSE,

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daily and monthly NSE1, daily and monthly R-NSE, and daily and monthly R-NSE1

yielded 7 different selected optimal configurations.

Four of the seven optimal model configurations were configured with 6 sub-

basins and three with 34 sub-basins. Six of the seven optimal model configurations were

configured with the low-resolution STATSGO soils data while one was configured the

high-resolution SSURGO soil data. With respect to climate input dataset, three of the

seven optimal configurations were configured with the HCW-4 climate dataset (Sites #2-

5), two configurations were configured with the South Farms dataset, and one optimal

configuration each was configured with the HCW-5 climate dataset (Sites #1-5) and the

Sanborn Field dataset, respectively. The rankings accordingly suggest that no single

statistical evaluation method can fully account for model fit.

The results of this study support the supposition that model evaluation should

incorporate measures of total mass fit, hydrograph fit, and flow duration fit, as was

conducted by van Griensven and Bauwens (2003) and suggested by Van Liew et al.

(2005). This support is provided by the different selection of best input parameter and

model configuration rankings given by each set of statistical measures across multiple

analyses in the study. Support is also provided by the better overall performance of the

multiple objective calibration (better daily and monthly PBIAS, monthly NSE (NSE1),

daily and monthly R-NSE (R-NSE1)) which incorporated a measure of daily flow

duration fit in addition to daily hydrograph fit, compared to the single objective

calibration (better daily NSE (NSE1) only), which solely accounted for hydrograph fit at

the daily scale.

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Willmott (1984 and 1985) and others (e.g. Legates and McCabe 1999) explained

that the squared error indices inflate large-magnitude errors and minimize small-

magnitude errors, thus distorting the error between observed and modeled streamflow

through the square function as shown by Figure 21. With respect to differences in

absolute error and squared error goodness-of-fit measures, this study‟s results showed

that the absolute error measures, NSE1 and R-NSE1, produced narrower ranges of values

for the twenty uncalibrated model configurations, 0.23 and 0.50, respectively, compared

to their squared error counterparts, NSE and R-NSE, which produced ranges of 0.36 and

0.98. It is surmised here that the absolute value measures provide a more conservative

estimate of model performance; they result in lower index values for high-performing

models and higher index values for poor-performing models as was reported by Legates

and McCabe (1999). The results also suggest that model configurations may perform

equally well according to a squared error based measure while performing considerably

different using an absolute error based measure. Thus, absolute value measures may be

well-suited for determining differences in model performance that are obscured by

squared error measures.

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Figure 21. Graphical diagram showing differences between absolute error and squared error relative to actual errors between modeled and observed data.

129

CHAPTER V

CONCLUSION

FINDINGS ON HYDROCLIMATE IN THE HINKSON CREEK WATERSHED

When excluding the Site #1 precipitation data that was affected by undercatch, the

other six climate stations used in modeling averaged 1314 mm per year (29% above

historical average) for the two-year study period 2009-2010. These unusually high levels

of precipitation during the study period resulted in perennial daily streamflow at all five

gauging stations located on Hinkson Creek during the study period, from Site #1, located

at Rogers Road, Columbia, Missouri, to Site #5, located at Scott Boulevard, Columbia,

Missouri. The lowest daily mean streamflow, 0.020 m3/s, was recorded at Site #4, the

USGS-operated gauge.

The flow duration curves and total two-year total water yields at Sites #2 and #3

suggest that further refinement is needed in the Site #2 and Site #3 rating curves. Further

refinement may be achieved by conducting regular (e.g. monthly or seasonal) cross-

sections at Sites #2 and #3 particularly during periods of low flow (less than 0.2 m3/s

discharge). Additionally, investigation of the variable backwater phenomenon at Site #5

is warranted. Investigation into backwater at Site #5 may permit a more direct

determination of the streamflow at Site #5; rather than the indirect regression modeling

with Site #4 used in this study. It is recommended that a more direct determination of

streamflow at Site #5 should be obtained through the use of a velocity-index rating curve

as described by the USGS (1982). Development of a velocity-index rating curve will

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require installation of a continuously recording velocity meter(s) at a point in the stream

or at multiple points along a transverse line (USGS 1982). An accurate and more directly

determined estimate of streamflow at Site #5 will assist in efforts to quantify the effects

of urbanization in the City of Columbia, Missouri, on streamflow processes in the

Hinkson Creek Watershed (HCW). Such findings may have considerable impact on

future development practices and implementation of Hinkson Creek TMDL‟s (EPA

2011).

RECOMMENDATIONS FOR H/WQ MODEL CONFIGURATION

When the application for model implementation warrants prediction of monthly

values, it is recommended, based on results from this work, that the most representative

climate station (well sited; minimal fetch problem) in or near the watershed be used to

both produce better modeled predictions and also reduce time needed for model

configuration (pre-processing of climate data input files). In the HCW, the South Farms

climate station provided the best overall monthly streamflow predictions in terms of

hydrograph fit (NSE (NSE1)=0.66 (0.46)). When daily estimates are needed, the results of

this study suggest that multiple station climate input is needed for optimal model

performance. Both multiple climate station datasets performed better than all other

climate datasets with respect to hydrograph fit. The HCW-5 climate dataset performed

the best, surprisingly, with an NSE (NSE1)=0.15 (0.29), followed by the HCW-4 climate

dataset with a NSE (NSE1)=0.13 (0.18). The better performance of the HCW-5 over the

HCW-4 climate dataset at the daily scale suggest that better representation of climate

131

heterogeneity, despite precipitation undercatch at one of five climate stations, is

necessary for accurate simulation at fine time scales (i.e. daily).

With respect to watershed subdivision and soil dataset resolution, this study

showed that increased watershed subdivision and soil resolution do not considerably

affect model performance with respect to total mass fit, hydrograph fit, and flow duration

fit. These conclusions are confirmed by a re-analysis of the data in Kumar and Merwade

(2009). Accordingly, to reduce time and effort required for model configuration and

reduce simulation run-time, the lowest spatial watershed discretization and soil resolution

required by the intended modeling application, is recommended. This recommendation is

a particularly important consideration when running automatic calibration routines, which

may require 100s to 1000s of model simulations.

RECOMMENDATIONS FOR H/WQ MODEL CALIBRATION

Based on the proposed standard criteria (Moriasi et al. 2007) for judging H/WQ

model performance on a monthly time scale using Percent Bias (PBIAS) and the Nash-

Sutcliffe Efficiency (NSE), 16 of the 20 uncalibrated model configurations performed

„satisfactorily‟ (PBIAS < 25%; NSE > 0.5). All model configuration using full-suite

observed climate data (i.e. precipitation, air temperature, solar radiation, wind speed, and

relative humidity) met this criteria. Therefore, it is concluded that SWAT is capable of

performing at satisfactory levels with respect to total mass fit and hydrograph fit without

calibration in the HCW and potentially other urbanizing watersheds in the Central U.S.A.

It was shown in this study that the built-in automatic calibration included with

SWAT is capable of running successfully in less than 24 hours on a single desktop-class

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computer, lending support for the wider use of this technology in management

applications. Care must be taken to constrain the automatic calibration routine to

optimize a limited set of parameters (six parameters in this study). In addition, the study

strongly supports the use of multiple objective automatic calibration methods (SSQ and

SSQR) over single objective methods (SSQ only) to ensure optimal model performance

across the evaluation suite of total mass fit, hydrograph fit, and flow duration fit. The

multiple objective calibrated model better predicted monthly flow (PBIAS=0.75%, NSE

(NSE1)=0.81 (0.59), R-NSE (R-NSE1)=0.89 (0.73)) than the single-objective calibrated

model (PBIAS=16.08%, NSE (NSE1)=0.54 (0.40); R-NSE (R-NSE1) = 0.82 (0.67).

Contrary to the conclusions of Van Liew et al. (2005) and Kumar and Merwade

(2009), who recommend use of single objective calibration (SSQ) combined with manual

calibration to correct for poor estimation of the total mass and flow distribution, it is

recommended here that multiple objective streamflow calibration be used to optimize

model performance. Manual calibration is not recommended due to excessive labor

requirements (e.g. Van Liew et al (2005) reported four to six weeks of expert labor

required for manual SWAT calibration).

It is further recommended that the SWAT development team incorporate the Total

Mass Controller (TMC) objective function into the next version of the SWAT model. The

TMC was originally included in Enhanced SWAT (ESWAT), the proprietary version of

SWAT developed by van Griensven and Bauwens (2003). The enhancements made to

SWAT in ESWAT were later incorporated into SWAT 2005, the version of SWAT used

in this study. However, the TMC objective function, which directly accounts for error in

the mass balance, was surprisingly not included in SWAT2005. It is expected that

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inclusion of the TMC objective function will further improve SWAT multiple objective

automatic calibration by enabling simultaneous streamflow parameter optimization of the

sum of the squared error (SSQ), the sum of the squared error after ranking (SSQR), and

the TMC as was done by van Griensven and Bauwens (2003).

RECOMMENDATIONS FOR H/WQ MODEL EVALUATION

In the modeling literature, primary emphasis has been placed on optimizing the

Nash Sutcliffe Efficiency (Nash and Sutcliffe 1970). Results of this study suggest that the

choice of goodness-of-fit objective function or statistical indicator can have considerable

effect on the model development process. Thus, more effective assessment of model

accuracy for research and management applications would be served by accounting for

measures of error in the mass balance (total mass), the hydrograph (event timing and

magnitude), and the flow duration curve (percent of time flow exceeded) as was

presented in this study and was done by van Griensven and Bauwens (2003). As

suggested by Van Liew et al. (2005), optimization should be designed to take into

account model fit with the mass balance, the hydrograph, and the flow duration curve,

allowing development of models that best represent the needs determined by the intended

application, e.g., short-duration storm runoff analysis, low flow assessments, and water

availability evaluations. What Van Liew et al. (2005) ignored was that this capability is

already partially included in the SWAT automatic calibration tool (SSQ and SSQR

objective functions may be run optimized simultaneously), and the software code for

including a third objective function, the TMC, was previously written for use in ESWAT.

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The results of this study also provide an argument for the consideration of

absolute-error-based measures such as the Modified Nash-Sutcliffe Efficiency introduced

by Legates and McCabe (1999). In addition to the strong theoretical argument for the use

of absolute error based goodness-of-fit statistics presented in the literature (Willmott

1984, 1985; Legates and McCabe 1999), this study demonstrated that the choice of NSE

over NSE1, for example, can change the selection of the optimal model configuration, and

consequently, the implementation of H/WQ models. Non-optimal selection of a model

configuration or a set of optimized parameters may result in less accurate H/WQ models.

Reduced model accuracy may lead to poor management decision-making. For example,

the U.S. Department of Agriculture‟s Conservation Effects Assessment Program (CEAP),

which is using the SWAT model to account for the environmental impact of U.S.

Department of Agriculture (USDA) policies, may provide improper estimates of

environmental impact and thus result in inefficient future public policy.

RECOMMENDATIONS FOR FUTURE RESEARCH IN H/WQ MODELING

Continuing data-intensive, multi-site monitoring efforts like the ongoing effort in

the nested HCW will provide opportunities to further evaluate SWAT and other H/WQ

models for simulation of water pollutant transport (e.g. suspended sediment and

nutrients), and to test in-depth multi-site (multiple stream gauges), multi-criteria

(streamflow, sediment, nutrients) (Vazquez 2008), and more-detailed multi-scalar (i.e.

yearly, monthly, and daily) model calibration approaches. Furthermore, many more

multi-configuration modeling studies, (an approach taken here, by Kumar and Merwade

(2009), by Di Luzio et al. (2005), and by Ye et al. (2011)) need to be carried out and

135

published in the scientific literature. It is also recommend that further testing of the four

new goodness-of-fit criteria, introduced in this study, be conducted to further test

appropriateness for use in H/WQ model evaluation.

Finally, the scientific community is encouraged to take into account real-world

limitations natural resource managers (e.g. labor, time, and computational power) must

face when conducting modeling research as was done in this study. As noted by van

Griensven, H/WQ models are in need of further development to reach their potential to

be simple and useful tools for water resource management decision-making.

CONTRIBUTIONS TO SCIENCE

In this thesis, a practical approach to model development (configuration,

calibration, validation, and evaluation methods) is presented. This thesis represents the

first (to the author‟s knowledge) published H/WQ model study to present a

comprehensive multi-configuration, multi-site, and multi-objective approach in SWAT

model implementation. Four introduced goodness-of-fit indicators were introduced to

measure the model fit with the distribution of flows in time (analogous to the flow

duration curve); these new goodness-of-fit indicators are inspired by the work of van

Griensven and Bauwens (2003). A novel model performance ranking method was used to

determine the effect of input resolution, including watershed discretization spatial

resolution, soil dataset spatial resolution, and climate station quantity and quality, and the

effect of temporal scale (daily vs. monthly evaluation). In short, we find that some

applications of H/WQ modeling, particularly daily simulations, will require multiple

climate station input. In addition, multiple objective automatic calibration, which is

136

shown in this study to improve SWAT model performance, requires observed streamflow

data to optimize the model. It is thus recommended that public policy decision makers

and natural resource managers increase investment in hydroclimatic data collection to

help develop and implement models (tools) that can assist in managing many pressing

water resource problems throughout the world.

137

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APPENDIX A: STAGE-DISCHARGE RATING CURVES

Table 26. Equations for rating curves applied in the study in Hinkson Creek Watershed, Missouri, U.S.A. The „y‟ variable is equivalent to discharge in m

3/s; the „x‟ variable is equivalent to stage in m.

HCW

Gauge Site

Linear Rating Polynomial Rating Power Rating

Stage (m) Equation Stage (m) Equation Equation Stage (m)

HCW #1 0 - 0.5523 y = 0.0472x 0.5523 - max y = 12.794x3-17.502x

2+10.57x-2.6548 N/A N/A

HCW #2 0 - 0.464 y = 0.4163x 0.464 - max y = 4.3007x3+21.944x

2-20.288x+4.4526 N/A N/A

HCW #3 0 - 0.11976 y = 1.3590180x 0.11976 - 0.363 y = 17.571x3-7.0615x

2+3.94x-0.238 0.363 - max y = 15.061x

2.5797

HCW #5 N/A N/A N/A N/A entire range y = 5.1365x1.8118

144

145

Figure 22. Detailed plot of Site #1 rating curve and flow measurements.

146


147


148


149

APPENDIX B: COMPLETE MODELING RESULTS

Table 27. Full daily streamflow descriptive and goodness-of-fit statistics for all uncalibrated SWAT model configurations. Average Percent Bias is the average of the absolute values of each Percent Bias measure.

Statistic Observed Daily Streamflow Data


MIN (m3/s) 0.024 0.115 0.046 0.020 0.099

MEAN (m3/s) 1.184 2.105 2.066 3.516 4.540

MEDIAN (m3/s) 0.196 0.178 0.362 0.510 0.708

MAX (m3/s) 77.518 185.347 115.218 177.830 141.523

Statistic 6 Subbasin - STATSGO - Weather Generator Run

Site #1 Site #2 Site #3 Site #4 Site #5 Mean - All Sites

MIN (m3/s) 0.013 0.015 0.011 0.026 0.015

MEAN (m3/s) 0.854 1.195 1.412 2.347 2.749

MEDIAN (m3/s) 0.357 0.547 0.647 1.196 1.378

MAX (m3/s) 29.590 30.830 31.770 39.700 50.630

PBIAS (%) 27.894 43.245 31.636 33.257 39.447 35.096

RMSE (m3/s) 5.583 10.688 8.250 13.294 15.195

MAE (m3/s) 1.602 2.723 2.644 4.458 5.595

NSE -0.285 -0.088 -0.169 -0.145 -0.178 -0.173

NSE1 0.009 0.137 0.060 0.070 0.110 0.078

R-RMSE (m3/s) 2.670 7.958 5.021 8.700 9.186

R-MAE (m3/s) 0.471 1.293 0.928 1.789 2.402

R-NSE (AVG) 0.741 0.420 0.603 0.536 0.601 0.580

R-NSE (MED) 0.751 0.440 0.622 0.562 0.629 0.601

R-NSE1 (AVG) 0.709 0.590 0.670 0.627 0.618 0.643

150

Statistic 6 Subbasin - STATSGO - Sanborn Field Run


MIN (m3/s) 0.079 0.099 0.107 0.173 0.205

MEAN (m3/s) 1.734 2.262 2.594 4.060 4.666

MEDIAN (m3/s) 0.815 1.037 1.182 1.901 2.141

MAX (m3/s) 73.680 98.950 115.200 181.800 211.400

PBIAS (%) -46.444 -7.466 -25.588 -15.453 -2.766 19.543

RMSE (m3/s) 4.639 8.947 7.239 11.001 15.459

MAE (m3/s) 1.505 2.473 2.402 3.624 5.397

NSE 0.113 0.237 0.100 0.216 -0.219 0.090

NSE1 0.070 0.217 0.146 0.244 0.142 0.164

R-RMSE (m3/s) 1.828 5.942 2.448 3.933 3.876

R-MAE (m3/s) 0.651 0.986 0.717 1.155 1.744

R-NSE (AVG) 0.931 0.799 0.960 0.963 0.909 0.912

R-NSE (MED) 0.934 0.806 0.962 0.965 0.915 0.916

R-NSE1 (AVG) 0.597 0.688 0.745 0.759 0.723 0.702

Statistic 6 Subbasin - STATSGO - South Farms Run


MIN (m3/s) 0.076 0.093 0.102 0.174 0.225

MEAN (m3/s) 1.680 2.196 2.531 3.976 4.621

MEDIAN (m3/s) 0.786 1.004 1.150 1.867 2.141

MAX (m3/s) 64.110 86.150 100.600 158.700 185.500

PBIAS (%) -41.841 -4.328 -22.525 -13.062 -1.778 16.707

RMSE (m3/s) 4.771 9.134 7.432 11.319 15.673

MAE (m3/s) 1.439 2.424 2.325 3.564 5.343

NSE 0.062 0.205 0.051 0.170 -0.253 0.047

NSE1 0.110 0.232 0.173 0.257 0.150 0.185

R-RMSE (m3/s) 1.371 5.317 1.845 3.240 4.522

R-MAE (m3/s) 0.573 0.991 0.687 1.304 1.971

R-NSE (AVG) 0.945 0.794 0.961 0.952 0.884 0.907

R-NSE (MED) 0.947 0.801 0.963 0.955 0.892 0.912

R-NSE1 (AVG) 0.646 0.686 0.756 0.728 0.686 0.700

151

Statistic 6 Subbasin - STATSGO - HCW-5 Climate Run


MIN (m3/s) 0.008 0.004 0.031 0.175 0.219

MEAN (m3/s) 0.949 1.239 1.553 3.061 3.732

MEDIAN (m3/s) 0.315 0.394 0.520 1.476 1.817

MAX (m3/s) 67.170 90.160 106.300 167.300 196.800

PBIAS (%) 19.913 41.127 24.808 12.935 17.804 23.317

RMSE (m3/s) 4.346 8.902 6.797 10.461 14.440

MAE (m3/s) 1.186 2.052 1.964 3.152 4.772

NSE 0.222 0.245 0.206 0.291 -0.064 0.180

NSE1 0.267 0.350 0.302 0.343 0.241 0.300

R-RMSE (m3/s) 2.356 6.602 3.667 5.399 6.150

R-MAE (m3/s) 0.454 1.245 0.827 1.427 2.117

R-NSE (AVG) 0.902 0.725 0.886 0.905 0.831 0.850

R-NSE (MED) 0.906 0.735 0.891 0.911 0.842 0.857

R-NSE1 (AVG) 0.719 0.606 0.706 0.702 0.663 0.679



MIN (m3/s) 0.077 0.100 0.112 0.181 0.216

MEAN (m3/s) 1.607 2.101 2.415 3.923 4.593

MEDIAN (m3/s) 0.801 1.013 1.150 1.985 2.279

MAX (m3/s) 75.940 101.900 118.000 179.000 208.500

PBIAS (%) -35.723 0.196 -16.892 -11.561 -1.160 13.106

RMSE (m3/s) 4.425 8.747 6.894 10.598 14.992

MAE (m3/s) 1.436 2.345 2.288 3.506 5.257

NSE 0.193 0.271 0.184 0.272 -0.147 0.155

NSE1 0.112 0.257 0.187 0.269 0.164 0.198

R-RMSE (m3/s) 1.820 6.049 2.530 4.104 4.237

R-MAE (m3/s) 0.533 1.013 0.685 1.205 1.932

R-NSE (AVG) 0.947 0.798 0.968 0.963 0.897 0.915

R-NSE (MED) 0.949 0.805 0.969 0.965 0.904 0.919

R-NSE1 (AVG) 0.670 0.679 0.756 0.749 0.693 0.709

152

Statistic 6 Subbasin - SSURGO - Weather Generator Run


MIN (m3/s) 0.036 0.042 0.046 0.074 0.062

MEAN (m3/s) 0.868 1.200 1.415 2.356 2.821

MEDIAN (m3/s) 0.444 0.723 0.868 1.437 1.648

MAX (m3/s) 29.110 30.180 30.980 33.560 45.160

PBIAS (%) 26.706 42.992 31.483 32.985 37.872 34.408

RMSE (m3/s) 5.416 10.573 8.074 13.068 14.984

MAE (m3/s) 1.573 2.681 2.584 4.365 5.526

NSE -0.209 -0.065 -0.120 -0.106 -0.145 -0.129

NSE1 0.028 0.151 0.081 0.090 0.121 0.094

R-RMSE (m3/s) 3.098 8.373 5.516 9.106 9.536

R-MAE (m3/s) 0.613 1.500 1.181 2.111 2.702

R-NSE (AVG) 0.671 0.378 0.532 0.482 0.562 0.525

R-NSE (MED) 0.684 0.399 0.554 0.510 0.593 0.548

R-NSE1 (AVG) 0.621 0.525 0.580 0.560 0.570 0.571



MIN (m3/s) 0.053 0.063 0.067 0.093 0.090

MEAN (m3/s) 1.732 2.261 2.596 4.087 4.743

MEDIAN (m3/s) 1.052 1.342 1.520 2.353 2.552

MAX (m3/s) 73.210 97.510 113.500 180.100 212.500

PBIAS (%) -46.227 -7.404 -25.663 -16.222 -4.456 19.995

RMSE (m3/s) 4.379 8.739 6.806 10.543 15.035

MAE (m3/s) 1.529 2.496 2.418 3.652 5.384

NSE 0.209 0.273 0.204 0.280 -0.153 0.163

NSE1 0.055 0.209 0.140 0.238 0.144 0.157

R-RMSE (m3/s) 1.927 6.209 2.782 4.567 4.681

R-MAE (m3/s) 0.703 1.337 0.978 1.616 2.274

R-NSE (AVG) 0.938 0.775 0.956 0.952 0.877 0.900

R-NSE (MED) 0.941 0.783 0.958 0.955 0.886 0.905

R-NSE1 (AVG) 0.565 0.576 0.652 0.663 0.638 0.619

153

Statistic 6 Subbasin - SSURGO - South Farms Run


MIN (m3/s) 0.047 0.056 0.061 0.087 0.091

MEAN (m3/s) 1.692 2.212 2.551 4.025 4.721

MEDIAN (m3/s) 0.992 1.278 1.467 2.280 2.552

MAX (m3/s) 64.340 85.560 99.770 158.300 187.800

PBIAS (%) -42.825 -5.116 -23.475 -14.476 -3.978 17.974

RMSE (m3/s) 4.575 8.981 7.086 10.999 15.394

MAE (m3/s) 1.480 2.468 2.365 3.621 5.369

NSE 0.137 0.232 0.138 0.216 -0.209 0.103

NSE1 0.085 0.218 0.159 0.245 0.146 0.171

R-RMSE (m3/s) 1.398 5.509 2.089 3.607 4.907

R-MAE (m3/s) 0.620 1.298 0.987 1.716 2.362

R-NSE (AVG) 0.963 0.781 0.965 0.949 0.869 0.906

R-NSE (MED) 0.965 0.789 0.967 0.952 0.878 0.910

R-NSE1 (AVG) 0.617 0.589 0.649 0.642 0.624 0.624

Statistic 6 Subbasin - SSURGO - HCW-5 Climate Run


MIN (m3/s) 0.004 0.000 0.030 0.106 0.113

MEAN (m3/s) 0.964 1.259 1.576 3.116 3.825

MEDIAN (m3/s) 0.439 0.559 0.766 1.775 2.065

MAX (m3/s) 70.970 94.390 110.300 171.300 203.400

PBIAS (%) 18.571 40.185 23.700 11.384 15.759 21.920

RMSE (m3/s) 4.422 8.959 6.858 10.468 14.441

MAE (m3/s) 1.207 2.070 1.993 3.195 4.771

NSE 0.194 0.235 0.192 0.290 -0.064 0.170

NSE1 0.254 0.344 0.292 0.334 0.241 0.293

R-RMSE (m3/s) 2.546 6.811 4.034 5.980 6.713

R-MAE (m3/s) 0.547 1.357 0.999 1.761 2.424

R-NSE (AVG) 0.876 0.715 0.848 0.873 0.791 0.821

R-NSE (MED) 0.881 0.725 0.855 0.880 0.806 0.829

R-NSE1 (AVG) 0.662 0.570 0.645 0.633 0.614 0.625

154



MIN (m3/s) 0.060 0.071 0.078 0.105 0.111

MEAN (m3/s) 1.628 2.128 2.445 3.985 4.693

MEDIAN (m3/s) 1.009 1.298 1.489 2.377 2.667

MAX (m3/s) 79.430 105.500 121.400 182.400 214.500

PBIAS (%) -37.491 -1.093 -18.354 -13.319 -3.371 14.726

RMSE (m3/s) 4.327 8.653 6.705 10.357 14.785

MAE (m3/s) 1.479 2.397 2.324 3.555 5.268

NSE 0.228 0.287 0.228 0.305 -0.115 0.187

NSE1 0.086 0.241 0.174 0.259 0.162 0.184

R-RMSE (m3/s) 1.909 6.289 2.856 4.782 5.083

R-MAE (m3/s) 0.638 1.355 0.997 1.720 2.465

R-NSE (AVG) 0.945 0.783 0.951 0.943 0.855 0.896

R-NSE (MED) 0.947 0.791 0.954 0.946 0.865 0.901

R-NSE1 (AVG) 0.606 0.571 0.646 0.641 0.608 0.614



MIN (m3/s) 0.002 0.001 0.000 0.004 0.000

MEAN (m3/s) 0.892 1.232 1.450 2.383 2.792

MEDIAN (m3/s) 0.388 0.532 0.659 1.193 1.328

MAX (m3/s) 26.340 27.580 28.520 46.710 58.370

PBIAS (%) 24.672 41.462 29.819 32.227 38.499 33.336

RMSE (m3/s) 5.445 10.662 8.256 13.416 15.382

MAE (m3/s) 1.613 2.750 2.678 4.501 5.655

NSE -0.222 -0.083 -0.171 -0.166 -0.207 -0.170

NSE1 0.003 0.129 0.048 0.061 0.101 0.068

R-RMSE (m3/s) 2.943 8.144 5.111 8.511 8.765

R-MAE (m3/s) 0.470 1.262 0.894 1.733 2.294

R-NSE (AVG) 0.682 0.393 0.579 0.571 0.644 0.574

R-NSE (MED) 0.694 0.413 0.599 0.594 0.669 0.594

R-NSE1 (AVG) 0.710 0.600 0.682 0.639 0.635 0.653

155



MIN (m3/s) 0.067 0.086 0.093 0.159 0.175

MEAN (m3/s) 1.719 2.246 2.578 4.047 4.645

MEDIAN (m3/s) 0.759 0.973 1.122 1.841 2.030

MAX (m3/s) 81.010 106.600 122.800 191.600 221.700

PBIAS (%) -45.170 -6.702 -24.809 -15.104 -2.311 18.819

RMSE (m3/s) 5.026 9.227 7.662 11.517 16.133

MAE (m3/s) 1.588 2.537 2.496 3.735 5.537

NSE -0.041 0.189 -0.008 0.141 -0.328 -0.010

NSE1 0.019 0.196 0.113 0.221 0.119 0.134

R-RMSE (m3/s) 1.717 5.710 2.297 3.674 3.489

R-MAE (m3/s) 0.607 0.876 0.702 1.129 1.643

R-NSE (AVG) 0.925 0.827 0.957 0.964 0.907 0.916

R-NSE (MED) 0.928 0.833 0.959 0.966 0.913 0.920

R-NSE1 (AVG) 0.625 0.723 0.750 0.765 0.739 0.720



MIN (m3/s) 0.063 0.080 0.089 0.165 0.200

MEAN (m3/s) 1.667 2.182 2.517 3.976 4.623

MEDIAN (m3/s) 0.718 0.932 1.078 1.800 2.025

MAX (m3/s) 70.410 92.710 107.100 167.400 195.000

PBIAS (%) -40.724 -3.673 -21.863 -13.068 -1.816 16.229

RMSE (m3/s) 5.188 9.457 7.886 11.883 16.402

MAE (m3/s) 1.531 2.493 2.431 3.692 5.508

NSE -0.110 0.148 -0.068 0.085 -0.372 -0.063

NSE1 0.054 0.210 0.136 0.230 0.124 0.151

R-RMSE (m3/s) 1.259 5.019 1.716 3.019 4.392

R-MAE (m3/s) 0.520 0.910 0.656 1.254 1.934

R-NSE (AVG) 0.936 0.822 0.955 0.952 0.877 0.908

R-NSE (MED) 0.938 0.828 0.957 0.955 0.886 0.913

R-NSE1 (AVG) 0.678 0.712 0.767 0.738 0.692 0.718

156



MIN (m3/s) 0.000 0.007 0.034 0.164 0.197

MEAN (m3/s) 0.944 1.279 1.593 3.048 3.686

MEDIAN (m3/s) 0.286 0.395 0.503 1.374 1.639

MAX (m3/s) 73.770 97.740 113.800 181.300 210.800

PBIAS (%) 20.273 39.213 22.862 13.314 18.820 22.896

RMSE (m3/s) 4.632 9.071 7.076 10.891 14.976

MAE (m3/s) 1.253 2.095 2.027 3.260 4.864

NSE 0.116 0.216 0.140 0.232 -0.144 0.112

NSE1 0.226 0.336 0.279 0.320 0.226 0.278

R-RMSE (m3/s) 2.153 6.337 3.423 5.185 5.953

R-MAE (m3/s) 0.411 1.191 0.757 1.378 2.027

R-NSE (AVG) 0.925 0.762 0.905 0.914 0.828 0.867

R-NSE (MED) 0.928 0.770 0.910 0.919 0.840 0.873

R-NSE1 (AVG) 0.746 0.623 0.731 0.713 0.678 0.698



MIN (m3/s) 0.067 0.090 0.100 0.172 0.194

MEAN (m3/s) 1.597 2.090 2.404 3.858 4.496

MEDIAN (m3/s) 0.733 0.941 1.083 1.890 2.149

MAX (m3/s) 83.380 109.700 125.800 193.200 222.700

PBIAS (%) -34.879 0.714 -16.365 -9.727 0.982 12.533

RMSE (m3/s) 4.793 9.006 7.299 11.119 15.624

MAE (m3/s) 1.516 2.408 2.382 3.649 5.375

NSE 0.053 0.227 0.085 0.199 -0.245 0.064

NSE1 0.063 0.237 0.153 0.239 0.145 0.167

R-RMSE (m3/s) 1.653 5.809 2.333 3.882 3.994

R-MAE (m3/s) 0.519 0.937 0.681 1.177 1.847

R-NSE (AVG) 0.948 0.827 0.968 0.966 0.888 0.920

R-NSE (MED) 0.950 0.833 0.970 0.968 0.896 0.923

R-NSE1 (AVG) 0.679 0.703 0.758 0.754 0.706 0.720

157



MIN (m3/s) 0.023 0.029 0.024 0.032 0.010

MEAN (m3/s) 0.882 1.214 1.429 2.385 2.849

MEDIAN (m3/s) 0.503 0.724 0.839 1.406 1.620

MAX (m3/s) 24.560 25.590 26.390 39.070 51.550

PBIAS (%) 25.542 42.338 30.817 32.186 37.247 33.626

RMSE (m3/s) 5.285 10.535 8.053 13.160 15.135

MAE (m3/s) 1.559 2.682 2.591 4.397 5.567

NSE -0.151 -0.057 -0.114 -0.122 -0.169 -0.123

NSE1 0.036 0.150 0.079 0.083 0.115 0.093

R-RMSE (m3/s) 3.356 8.585 5.611 8.917 9.189

R-MAE (m3/s) 0.625 1.484 1.156 2.039 2.598

R-NSE (AVG) 0.599 0.337 0.501 0.513 0.600 0.510

R-NSE (MED) 0.615 0.359 0.524 0.540 0.628 0.533

R-NSE1 (AVG) 0.614 0.530 0.589 0.575 0.587 0.579

Statistic 34 Subbasin - SSURGO - Sanborn Field Run


MIN (m3/s) 0.047 0.056 0.058 0.078 0.077

MEAN (m3/s) 1.722 2.252 2.587 4.094 4.743

MEDIAN

(m3/s) 0.989 1.281 1.459 2.250 2.437

MAX (m3/s) 77.060 101.400 117.400 186.100 218.600

PBIAS (%) -45.387 -6.990 -25.238 -16.430 -4.472 19.703

RMSE (m3/s) 4.596 8.890 7.041 10.860 15.461

MAE (m3/s) 1.563 2.522 2.459 3.704 5.465

NSE 0.130 0.247 0.149 0.236 -0.220 0.108

NSE1 0.034 0.201 0.126 0.228 0.131 0.144

R-RMSE (m3/s) 1.788 6.042 2.610 4.258 4.259

R-MAE (m3/s) 0.658 1.250 0.925 1.486 2.113

R-NSE (AVG) 0.947 0.795 0.963 0.961 0.886 0.910

R-NSE (MED) 0.949 0.802 0.965 0.963 0.894 0.915

R-NSE1 (AVG) 0.593 0.604 0.671 0.690 0.664 0.645

158



MIN (m3/s) 0.043 0.050 0.053 0.075 0.082

MEAN (m3/s) 1.681 2.203 2.541 4.039 4.727

MEDIAN (m3/s) 0.945 1.231 1.423 2.220 2.448

MAX (m3/s) 67.670 88.980 103.200 163.900 193.500

PBIAS (%) -41.914 -4.660 -23.011 -14.867 -4.114 17.713

RMSE (m3/s) 4.809 9.157 7.338 11.341 15.842

MAE (m3/s) 1.517 2.495 2.408 3.682 5.456

NSE 0.047 0.201 0.075 0.167 -0.280 0.042

NSE1 0.062 0.210 0.144 0.232 0.132 0.156

R-RMSE (m3/s) 1.260 5.319 1.939 3.373 4.680

R-MAE (m3/s) 0.583 1.213 0.934 1.637 2.265

R-NSE (AVG) 0.967 0.800 0.968 0.955 0.872 0.912

R-NSE (MED) 0.968 0.807 0.970 0.957 0.881 0.917

R-NSE1 (AVG) 0.640 0.616 0.668 0.659 0.640 0.644



MIN (m3/s) 0.000 0.011 0.041 0.105 0.110

MEAN (m3/s) 0.962 1.303 1.620 3.113 3.793

MEDIAN (m3/s) 0.407 0.563 0.777 1.664 1.917

MAX (m3/s) 75.070 99.110 115.000 183.900 215.300

PBIAS (%) 18.754 38.083 21.559 11.461 16.449 21.261

RMSE (m3/s) 4.589 9.035 6.993 10.761 14.814

MAE (m3/s) 1.241 2.082 2.019 3.250 4.825

NSE 0.132 0.222 0.160 0.250 -0.119 0.129

NSE1 0.233 0.340 0.282 0.322 0.233 0.282

R-RMSE (m3/s) 2.437 6.663 3.870 5.760 6.506

R-MAE (m3/s) 0.509 1.332 0.961 1.681 2.313

R-NSE (AVG) 0.891 0.738 0.864 0.884 0.791 0.833

R-NSE (MED) 0.895 0.747 0.870 0.890 0.806 0.842

R-NSE1 (AVG) 0.685 0.578 0.658 0.650 0.632 0.641

159



MIN (m3/s) 0.055 0.069 0.073 0.102 0.107

MEAN (m3/s) 1.620 2.120 2.437 3.930 4.610

MEDIAN (m3/s) 0.966 1.252 1.441 2.277 2.479

MAX (m3/s) 84.060 110.200 126.100 195.000 226.400

PBIAS (%) -36.772 -0.711 -17.964 -11.753 -1.526 13.745

RMSE (m3/s) 4.541 8.795 6.935 10.739 15.236

MAE (m3/s) 1.514 2.417 2.366 3.636 5.326

NSE 0.150 0.263 0.174 0.253 -0.184 0.131

NSE1 0.064 0.234 0.159 0.242 0.153 0.170

R-RMSE (m3/s) 1.764 6.129 2.696 4.553 4.855

R-MAE (m3/s) 0.610 1.270 0.932 1.621 2.330

R-NSE (AVG) 0.952 0.804 0.957 0.947 0.851 0.902

R-NSE (MED) 0.954 0.811 0.959 0.950 0.861 0.907

R-NSE1 (AVG) 0.623 0.598 0.669 0.662 0.629 0.636

160

Table 28. Full monthly streamflow descriptive and goodness-of-fit statistics for all

uncalibrated SWAT model configurations. Average Percent Bias is the average of the absolute values of each Percent Bias measure.

Statistic Observed Monthly Streamflow Data


MIN (m3/s) 0.026 0.130 0.119 0.238 0.300

MEAN (m3/s) 1.186 2.105 2.069 3.520 4.538

MEDIAN

(m3/s) 0.836 1.373 1.699 2.430 3.349

MAX (m3/s) 3.833 9.556 6.497 10.309 12.799



MIN (m3/s) 0.066 0.083 0.092 0.130 0.186

MEAN (m3/s) 0.851 1.191 1.409 2.343 2.745

MEDIAN (m3/s) 0.629 0.961 1.119 2.024 2.474

MAX (m3/s) 2.630 3.008 3.304 5.246 6.182

PBIAS (%) 28.291 43.416 31.900 33.442 39.503 35.310

RMSE (m3/s) 1.490 2.786 2.174 3.433 4.430

MAE (m3/s) 1.139 1.790 1.581 2.442 3.130

NSE -0.635 -0.284 -0.297 -0.181 -0.191 -0.317

NSE1 -0.206 0.005 -0.061 0.032 0.089 -0.028

R-RMSE (m3/s) 0.630 1.970 1.281 2.269 3.184

R-MAE (m3/s) 0.381 1.117 0.864 1.446 2.158

R-NSE (AVG) 0.708 0.358 0.550 0.484 0.385 0.497

R-NSE (MED) 0.732 0.410 0.566 0.539 0.433 0.536

R-NSE1 (AVG) 0.596 0.379 0.421 0.427 0.372 0.439

161



MIN (m3/s) 0.172 0.220 0.243 0.367 0.409

MEAN (m3/s) 1.733 2.260 2.592 4.057 4.662

MEDIAN

(m3/s) 1.649 2.145 2.465 3.868 4.417

MAX (m3/s) 4.309 5.650 6.464 10.081 11.605

PBIAS (%) -46.093 -7.375 -25.321 -15.243 -2.744 19.355

RMSE (m3/s) 0.899 1.743 1.292 1.894 2.567

MAE (m3/s) 0.672 1.138 0.931 1.417 1.796

NSE 0.405 0.497 0.542 0.641 0.600 0.537

NSE1 0.288 0.367 0.375 0.439 0.477 0.389

R-RMSE (m3/s) 0.616 1.175 0.677 1.019 1.225

R-MAE (m3/s) 0.551 0.837 0.619 0.916 1.008

R-NSE (AVG) 0.721 0.771 0.874 0.896 0.909 0.834

R-NSE (MED) 0.744 0.790 0.879 0.907 0.916 0.847

R-NSE1 (AVG) 0.417 0.535 0.585 0.637 0.707 0.576



MIN (m3/s) 0.154 0.193 0.216 0.345 0.404

MEAN (m3/s) 1.678 2.194 2.529 3.972 4.617

MEDIAN (m3/s) 1.475 1.930 2.240 3.515 4.090

MAX (m3/s) 3.541 4.639 5.351 8.362 9.778

PBIAS (%) -41.473 -4.219 -22.239 -12.836 -1.742 16.502

RMSE (m3/s) 0.736 1.554 1.085 1.592 2.356

MAE (m3/s) 0.540 1.004 0.799 1.242 1.753

NSE 0.601 0.600 0.677 0.746 0.663 0.658

NSE1 0.429 0.442 0.464 0.508 0.490 0.466

R-RMSE (m3/s) 0.573 1.371 0.716 1.066 1.510

R-MAE (m3/s) 0.531 0.903 0.666 0.976 1.250

R-NSE (AVG) 0.758 0.689 0.859 0.886 0.862 0.811

R-NSE (MED) 0.778 0.714 0.864 0.898 0.872 0.826

R-NSE1 (AVG) 0.438 0.498 0.553 0.613 0.636 0.548

162



MIN (m3/s) 0.031 0.039 0.122 0.411 0.461

MEAN (m3/s) 0.947 1.238 1.551 3.058 3.727

MEDIAN (m3/s) 0.687 0.905 1.164 2.770 3.353

MAX (m3/s) 3.485 4.577 5.388 8.434 9.881

PBIAS (%) 20.143 41.208 25.024 13.130 17.858 23.473

RMSE (m3/s) 0.724 1.782 1.134 1.729 2.652

MAE (m3/s) 0.514 1.096 0.850 1.358 1.948

NSE 0.614 0.474 0.647 0.701 0.573 0.602

NSE1 0.455 0.390 0.430 0.462 0.433 0.434

R-RMSE (m3/s) 0.444 1.600 0.859 1.351 1.967

R-MAE (m3/s) 0.282 0.908 0.573 0.900 1.480

R-NSE (AVG) 0.855 0.576 0.798 0.817 0.765 0.762

R-NSE (MED) 0.867 0.611 0.805 0.837 0.784 0.781

R-NSE1 (AVG) 0.701 0.495 0.615 0.643 0.569 0.605



MIN (m3/s) 0.181 0.233 0.260 0.408 0.458

MEAN (m3/s) 1.604 2.096 2.409 3.916 4.585

MEDIAN (m3/s) 1.464 1.894 2.160 3.589 4.139

MAX (m3/s) 4.464 5.870 6.743 10.280 11.878

PBIAS (%) -35.205 0.430 -16.468 -11.253 -1.050 12.881

RMSE (m3/s) 0.779 1.656 1.103 1.724 2.484

MAE (m3/s) 0.612 1.089 0.817 1.335 1.793

NSE 0.553 0.546 0.667 0.702 0.626 0.619

NSE1 0.352 0.395 0.452 0.471 0.478 0.429

R-RMSE (m3/s) 0.489 1.079 0.518 1.030 1.356

R-MAE (m3/s) 0.437 0.718 0.468 0.826 1.069

R-NSE (AVG) 0.824 0.807 0.926 0.894 0.888 0.868

R-NSE (MED) 0.839 0.823 0.929 0.905 0.897 0.879

R-NSE1 (AVG) 0.537 0.601 0.686 0.673 0.689 0.637

163



MIN (m3/s) 0.062 0.079 0.094 0.165 0.231

MEAN (m3/s) 0.865 1.197 1.412 2.353 2.817

MEDIAN (m3/s) 0.736 0.991 1.187 2.186 2.556

MAX (m3/s) 2.762 3.192 3.492 4.880 6.072

PBIAS (%) 27.070 43.149 31.737 33.166 37.926 34.610

RMSE (m3/s) 1.466 2.766 2.146 3.393 4.376

MAE (m3/s) 1.107 1.777 1.551 2.446 3.144

NSE -0.583 -0.266 -0.263 -0.153 -0.162 -0.286

NSE1 -0.172 0.012 -0.041 0.031 0.085 -0.017

R-RMSE (m3/s) 0.634 1.964 1.289 2.276 3.109

R-MAE (m3/s) 0.369 1.114 0.839 1.395 2.100

R-NSE (AVG) 0.704 0.361 0.544 0.481 0.414 0.501

R-NSE (MED) 0.728 0.413 0.561 0.536 0.460 0.540

R-NSE1 (AVG) 0.609 0.380 0.437 0.447 0.389 0.453



MIN (m3/s) 0.127 0.158 0.172 0.239 0.258

MEAN (m3/s) 1.730 2.258 2.593 4.083 4.738

MEDIAN

(m3/s) 1.721 2.237 2.566 4.032 4.639

MAX (m3/s) 3.977 5.223 5.998 9.490 11.107

PBIAS (%) -45.837 -7.286 -25.359 -15.981 -4.414 19.775

RMSE (m3/s) 0.893 1.726 1.280 1.901 2.516

MAE (m3/s) 0.703 1.148 0.994 1.491 1.755

NSE 0.413 0.507 0.551 0.638 0.616 0.545

NSE1 0.255 0.361 0.333 0.409 0.489 0.370

R-RMSE (m3/s) 0.643 1.286 0.740 1.127 1.370

R-MAE (m3/s) 0.571 0.904 0.692 1.010 1.116

R-NSE (AVG) 0.696 0.726 0.850 0.873 0.886 0.806

R-NSE (MED) 0.721 0.749 0.855 0.886 0.895 0.821

R-NSE1 (AVG) 0.395 0.497 0.535 0.600 0.675 0.541

164



MIN (m3/s) 0.108 0.135 0.150 0.221 0.250

MEAN (m3/s) 1.689 2.210 2.547 4.020 4.715

MEDIAN (m3/s) 1.632 2.138 2.441 3.845 4.382

MAX (m3/s) 3.581 4.708 5.494 8.742 10.346

PBIAS (%) -42.404 -4.970 -23.140 -14.208 -3.908 17.726

RMSE (m3/s) 0.754 1.549 1.103 1.636 2.297

MAE (m3/s) 0.576 1.007 0.871 1.359 1.748

NSE 0.581 0.603 0.667 0.732 0.680 0.652

NSE1 0.390 0.440 0.415 0.462 0.491 0.440

R-RMSE (m3/s) 0.625 1.429 0.794 1.212 1.621

R-MAE (m3/s) 0.571 0.953 0.736 1.090 1.384

R-NSE (AVG) 0.713 0.662 0.827 0.853 0.841 0.779

R-NSE (MED) 0.736 0.690 0.834 0.868 0.853 0.796

R-NSE1 (AVG) 0.395 0.470 0.506 0.568 0.597 0.507



MIN (m3/s) 0.020 0.022 0.116 0.303 0.341

MEAN (m3/s) 0.963 1.257 1.573 3.113 3.821

MEDIAN (m3/s) 0.696 0.920 1.235 2.730 3.602

MAX (m3/s) 3.661 4.815 5.658 8.930 10.552

PBIAS (%) 18.838 40.291 23.947 11.573 15.794 22.089

RMSE (m3/s) 0.783 1.807 1.207 1.802 2.613

MAE (m3/s) 0.556 1.123 0.899 1.383 1.909

NSE 0.549 0.459 0.601 0.675 0.586 0.574

NSE1 0.412 0.376 0.397 0.452 0.445 0.416

R-RMSE (m3/s) 0.443 1.596 0.878 1.423 1.977

R-MAE (m3/s) 0.254 0.888 0.551 0.881 1.466

R-NSE (AVG) 0.856 0.578 0.789 0.797 0.763 0.757

R-NSE (MED) 0.868 0.613 0.796 0.819 0.782 0.775

R-NSE1 (AVG) 0.731 0.506 0.630 0.651 0.573 0.618

165



MIN (m3/s) 0.147 0.183 0.202 0.302 0.341

MEAN (m3/s) 1.625 2.123 2.439 3.978 4.687

MEDIAN (m3/s) 1.575 2.048 2.329 3.885 4.591

MAX (m3/s) 4.141 5.441 6.281 9.705 11.372

PBIAS (%) -36.939 -0.843 -17.907 -13.021 -3.283 14.398

RMSE (m3/s) 0.814 1.670 1.143 1.775 2.461

MAE (m3/s) 0.670 1.125 0.889 1.410 1.755

NSE 0.512 0.538 0.641 0.684 0.633 0.602

NSE1 0.290 0.374 0.404 0.441 0.489 0.400

R-RMSE (m3/s) 0.529 1.178 0.599 1.132 1.460

R-MAE (m3/s) 0.477 0.785 0.533 0.939 1.202

R-NSE (AVG) 0.794 0.770 0.902 0.872 0.871 0.842

R-NSE (MED) 0.811 0.789 0.905 0.885 0.881 0.854

R-NSE1 (AVG) 0.495 0.564 0.642 0.628 0.650 0.596



MIN (m3/s) 0.056 0.071 0.081 0.118 0.185

MEAN (m3/s) 0.890 1.229 1.447 2.380 2.789

MEDIAN (m3/s) 0.709 0.988 1.175 2.189 2.640

MAX (m3/s) 2.328 2.730 3.218 5.410 6.354

PBIAS (%) 25.020 41.603 30.056 32.393 38.540 33.522

RMSE (m3/s) 1.372 2.681 2.083 3.356 4.353

MAE (m3/s) 1.019 1.692 1.472 2.357 3.033

NSE -0.387 -0.190 -0.191 -0.129 -0.150 -0.209

NSE1 -0.079 0.059 0.013 0.066 0.118 0.035

R-RMSE (m3/s) 0.669 2.001 1.282 2.220 3.115

R-MAE (m3/s) 0.420 1.133 0.837 1.404 2.106

R-NSE (AVG) 0.670 0.337 0.549 0.506 0.411 0.495

R-NSE (MED) 0.698 0.391 0.566 0.559 0.458 0.534

R-NSE1 (AVG) 0.556 0.370 0.438 0.443 0.387 0.439

166



MIN (m3/s) 0.157 0.202 0.225 0.349 0.382

MEAN (m3/s) 1.718 2.244 2.577 4.045 4.642

MEDIAN (m3/s) 1.629 2.116 2.432 3.838 4.365

MAX (m3/s) 4.288 5.628 6.441 10.056 11.603

PBIAS (%) -44.844 -6.624 -24.557 -14.902 -2.296 18.645

RMSE (m3/s) 0.883 1.732 1.277 1.880 2.556

MAE (m3/s) 0.655 1.128 0.920 1.407 1.782

NSE 0.426 0.504 0.553 0.646 0.604 0.546

NSE1 0.307 0.373 0.383 0.442 0.482 0.397

R-RMSE (m3/s) 0.600 1.167 0.661 1.008 1.209

R-MAE (m3/s) 0.535 0.821 0.603 0.902 0.985

R-NSE (AVG) 0.735 0.774 0.880 0.898 0.911 0.840

R-NSE (MED) 0.757 0.793 0.885 0.909 0.918 0.852

R-NSE1 (AVG) 0.434 0.543 0.595 0.643 0.713 0.586



MIN (m3/s) 0.138 0.177 0.200 0.333 0.384

MEAN (m3/s) 1.665 2.180 2.515 3.972 4.619

MEDIAN (m3/s) 1.462 1.915 2.226 3.513 4.074

MAX (m3/s) 3.569 4.666 5.362 8.422 9.859

PBIAS (%) -40.379 -3.577 -21.589 -12.848 -1.784 16.036

RMSE (m3/s) 0.720 1.544 1.070 1.583 2.348

MAE (m3/s) 0.525 1.000 0.788 1.235 1.746

NSE 0.618 0.606 0.686 0.749 0.666 0.665

NSE1 0.444 0.444 0.472 0.511 0.492 0.472

R-RMSE (m3/s) 0.557 1.362 0.702 1.057 1.495

R-MAE (m3/s) 0.516 0.890 0.651 0.969 1.246

R-NSE (AVG) 0.771 0.693 0.865 0.888 0.864 0.816

R-NSE (MED) 0.790 0.718 0.870 0.900 0.875 0.830

R-NSE1 (AVG) 0.453 0.505 0.563 0.616 0.637 0.555

167



MIN (m3/s) 0.024 0.056 0.139 0.388 0.428

MEAN (m3/s) 0.943 1.278 1.591 3.043 3.680

MEDIAN (m3/s) 0.689 0.926 1.193 2.769 3.245

MAX (m3/s) 3.510 4.639 5.448 8.740 10.154

PBIAS (%) 20.505 39.305 23.091 13.549 18.901 23.070

RMSE (m3/s) 0.725 1.745 1.091 1.739 2.665

MAE (m3/s) 0.514 1.084 0.831 1.377 1.941

NSE 0.612 0.496 0.673 0.697 0.569 0.610

NSE1 0.456 0.397 0.442 0.454 0.435 0.437

R-RMSE (m3/s) 0.439 1.574 0.819 1.300 1.932

R-MAE (m3/s) 0.275 0.888 0.550 0.874 1.460

R-NSE (AVG) 0.858 0.590 0.816 0.831 0.773 0.774

R-NSE (MED) 0.870 0.623 0.823 0.849 0.791 0.791

R-NSE1 (AVG) 0.708 0.506 0.631 0.653 0.575 0.615



MIN (m3/s) 0.169 0.219 0.246 0.385 0.426

MEAN (m3/s) 1.594 2.085 2.398 3.850 4.487

MEDIAN (m3/s) 1.433 1.862 2.127 3.485 4.056

MAX (m3/s) 4.466 5.870 6.743 10.435 12.078

PBIAS (%) -34.365 0.946 -15.944 -9.387 1.115 12.351

RMSE (m3/s) 0.767 1.646 1.090 1.722 2.490

MAE (m3/s) 0.603 1.081 0.807 1.327 1.793

NSE 0.566 0.552 0.674 0.703 0.624 0.624

NSE1 0.361 0.399 0.459 0.474 0.478 0.434

R-RMSE (m3/s) 0.474 1.070 0.504 0.955 1.374

R-MAE (m3/s) 0.428 0.705 0.453 0.719 1.059

R-NSE (AVG) 0.834 0.811 0.930 0.909 0.885 0.874

R-NSE (MED) 0.848 0.826 0.933 0.918 0.894 0.884

R-NSE1 (AVG) 0.547 0.608 0.696 0.715 0.692 0.651

168



MIN (m3/s) 0.056 0.071 0.087 0.158 0.233

MEAN (m3/s) 0.880 1.211 1.426 2.381 2.846

MEDIAN (m3/s) 0.729 1.003 1.258 2.167 2.577

MAX (m3/s) 2.391 2.803 3.103 5.169 6.358

PBIAS (%) 25.857 42.465 31.042 32.348 37.286 33.800

RMSE (m3/s) 1.357 2.675 2.062 3.319 4.301

MAE (m3/s) 0.998 1.678 1.444 2.357 3.041

NSE -0.356 -0.184 -0.167 -0.104 -0.123 -0.187

NSE1 -0.057 0.067 0.031 0.066 0.115 0.044

R-RMSE (m3/s) 0.688 2.031 1.313 2.244 3.064

R-MAE (m3/s) 0.423 1.141 0.836 1.355 2.057

R-NSE (AVG) 0.651 0.317 0.527 0.496 0.430 0.484

R-NSE (MED) 0.680 0.373 0.545 0.549 0.475 0.524

R-NSE1 (AVG) 0.552 0.365 0.439 0.463 0.401 0.444



MIN (m3/s) 0.121 0.150 0.164 0.231 0.248

MEAN (m3/s) 1.720 2.250 2.585 4.090 4.739

MEDIAN (m3/s) 1.698 2.216 2.544 4.020 4.609

MAX (m3/s) 3.950 5.202 5.977 9.530 11.152

PBIAS (%) -45.018 -6.881 -24.943 -16.194 -4.435 19.494

RMSE (m3/s) 0.879 1.721 1.269 1.888 2.505

MAE (m3/s) 0.688 1.141 0.980 1.478 1.744

NSE 0.431 0.510 0.558 0.643 0.619 0.552

NSE1 0.271 0.365 0.342 0.414 0.493 0.377

R-RMSE (m3/s) 0.629 1.282 0.729 1.113 1.344

R-MAE (m3/s) 0.558 0.894 0.682 0.999 1.093

R-NSE (AVG) 0.708 0.728 0.854 0.876 0.890 0.811

R-NSE (MED) 0.732 0.750 0.860 0.889 0.899 0.826

R-NSE1 (AVG) 0.409 0.503 0.542 0.604 0.682 0.548

169



MIN (m3/s) 0.101 0.128 0.142 0.214 0.240

MEAN (m3/s) 1.679 2.200 2.538 4.034 4.721

MEDIAN (m3/s) 1.612 2.113 2.416 3.813 4.311

MAX (m3/s) 3.572 4.694 5.481 8.805 10.411

PBIAS (%) -41.516 -4.525 -22.688 -14.603 -4.048 17.476

RMSE (m3/s) 0.740 1.544 1.091 1.622 2.288

MAE (m3/s) 0.562 1.000 0.858 1.342 1.732

NSE 0.597 0.605 0.674 0.736 0.682 0.659

NSE1 0.405 0.444 0.424 0.468 0.496 0.448

R-RMSE (m3/s) 0.611 1.422 0.783 1.198 1.597

R-MAE (m3/s) 0.557 0.942 0.725 1.083 1.362

R-NSE (AVG) 0.725 0.665 0.832 0.856 0.845 0.785

R-NSE (MED) 0.748 0.693 0.838 0.872 0.857 0.801

R-NSE1 (AVG) 0.410 0.476 0.513 0.571 0.604 0.515



MIN (m3/s) 0.015 0.051 0.145 0.292 0.324

MEAN (m3/s) 0.961 1.301 1.617 3.108 3.788

MEDIAN (m3/s) 0.700 1.026 1.299 2.749 3.416

MAX (m3/s) 3.677 4.857 5.700 9.270 10.854

PBIAS (%) 19.021 38.196 21.816 11.694 16.515 21.449

RMSE (m3/s) 0.777 1.768 1.159 1.799 2.623

MAE (m3/s) 0.551 1.096 0.877 1.401 1.903

NSE 0.555 0.483 0.631 0.676 0.583 0.586

NSE1 0.417 0.390 0.411 0.445 0.446 0.422

R-RMSE (m3/s) 0.437 1.570 0.847 1.360 1.930

R-MAE (m3/s) 0.256 0.873 0.531 0.832 1.434

R-NSE (AVG) 0.860 0.592 0.803 0.815 0.774 0.769

R-NSE (MED) 0.871 0.625 0.810 0.834 0.792 0.786

R-NSE1 (AVG) 0.728 0.515 0.644 0.670 0.583 0.628

170



MIN (m3/s) 0.141 0.177 0.197 0.290 0.322

MEAN (m3/s) 1.616 2.115 2.431 3.922 4.602

MEDIAN (m3/s) 1.544 2.017 2.298 3.696 4.400

MAX (m3/s) 4.152 5.443 6.286 9.885 11.569

PBIAS (%) -36.233 -0.468 -17.524 -11.421 -1.413 13.412

RMSE (m3/s) 0.799 1.659 1.130 1.774 2.469

MAE (m3/s) 0.658 1.115 0.877 1.384 1.740

NSE 0.529 0.544 0.650 0.685 0.630 0.608

NSE1 0.303 0.380 0.412 0.451 0.494 0.408

R-RMSE (m3/s) 0.517 1.171 0.588 1.051 1.456

R-MAE (m3/s) 0.467 0.774 0.522 0.828 1.173

R-NSE (AVG) 0.803 0.773 0.905 0.889 0.871 0.848

R-NSE (MED) 0.819 0.791 0.909 0.901 0.881 0.860

R-NSE1 (AVG) 0.505 0.569 0.650 0.672 0.659 0.611

171

Table 29. Full daily and monthly streamflow descriptive and goodness-of-fit statistics for

the selected uncalibrated SWAT model configuration, the sum of square error (SSQ) optimized model, and the sum of squared error (SSQ) and sum of squared error after ranking (SSQR) optimized model. Average Percent Bias shown is the average of the absolute values of each Percent Bias measure.

Daily Statistic Uncalibrated - 34 Subbasin - SSURGO - HCW-4 Climate Run


MIN (m3/s) 0.055 0.069 0.073 0.102 0.107

MEAN (m3/s) 1.620 2.120 2.437 3.930 4.610

MEDIAN (m3/s) 0.966 1.252 1.441 2.277 2.479

MAX (m3/s) 84.060 110.200 126.100 195.000 226.400

PBIAS (%) -36.772 -0.711 -17.964 -11.753 -1.526 13.745

RMSE (m3/s) 4.541 8.795 6.935 10.739 15.236

MAE (m3/s) 1.514 2.417 2.366 3.636 5.326

NSE 0.150 0.263 0.174 0.253 -0.184 0.131

NSE1 0.064 0.234 0.159 0.242 0.153 0.170

R-RMSE (m3/s) 1.764 6.129 2.696 4.553 4.855

R-MAE (m3/s) 0.610 1.270 0.932 1.621 2.330

R-NSE (AVG) 0.952 0.804 0.957 0.947 0.851 0.902

R-NSE (MED) 0.954 0.811 0.959 0.950 0.861 0.907

R-NSE1 (AVG) 0.623 0.598 0.669 0.662 0.629 0.636

Monthly Statistic Uncalibrated - 34 Subbasin - SSURGO - HCW-4 Climate Run


MIN (m3/s) 0.141 0.177 0.197 0.290 0.322

MEAN (m3/s) 1.616 2.115 2.431 3.922 4.602

MEDIAN (m3/s) 1.544 2.017 2.298 3.696 4.400

MAX (m3/s) 4.152 5.443 6.286 9.885 11.569

PBIAS (%) -36.233 -0.468 -17.524 -11.421 -1.413 13.412

RMSE (m3/s) 0.799 1.659 1.130 1.774 2.469

MAE (m3/s) 0.658 1.115 0.877 1.384 1.740

NSE 0.529 0.544 0.650 0.685 0.630 0.608

NSE1 0.303 0.380 0.412 0.451 0.494 0.408

R-RMSE (m3/s) 0.517 1.171 0.588 1.051 1.456

R-MAE (m3/s) 0.467 0.774 0.522 0.828 1.173

R-NSE (AVG) 0.803 0.773 0.905 0.889 0.871 0.848

R-NSE (MED) 0.819 0.791 0.909 0.901 0.881 0.860

R-NSE1 (AVG) 0.505 0.569 0.650 0.672 0.659 0.611

172

Daily Statistic SSQ-Optimized 34 Subbasin - SSURGO - HCW-4 Climate Run


MIN (m3/s) 0.000 0.000 0.000 0.000 0.001

MEAN (m3/s) 1.695 2.218 2.548 4.102 4.792

MEDIAN

(m3/s) 0.232 0.311 0.368 0.741 1.012

MAX (m3/s) 45.990 59.700 68.300 104.100 123.900

PBIAS (%) -43.151 -5.402 -23.362 -16.657 -5.546 18.824

RMSE (m3/s) 3.715 7.995 5.433 8.586 9.887

MAE (m3/s) 1.287 2.052 1.963 3.139 3.819

NSE 0.431 0.391 0.493 0.522 0.501 0.468

NSE1 0.204 0.350 0.302 0.345 0.393 0.319

R-RMSE (m3/s) 2.886 7.349 4.168 6.620 6.612

R-MAE (m3/s) 0.843 1.340 1.102 1.779 2.411

R-NSE (AVG) 0.757 0.587 0.798 0.801 0.830 0.755

R-NSE (MED) 0.767 0.601 0.808 0.812 0.842 0.766

R-NSE1 (AVG) 0.479 0.576 0.608 0.629 0.617 0.582

Monthly

Statistic

SSQ-Optimized 34 Subbasin - SSURGO - HCW-4 Climate Run


MIN (m3/s) 0.106 0.144 0.173 0.293 0.347

MEAN (m3/s) 1.688 2.208 2.537 4.086 4.776

MEDIAN (m3/s) 1.084 1.420 1.636 2.712 3.300

MAX (m3/s) 6.649 8.648 9.849 15.240 17.292

PBIAS (%) -42.264 -4.907 -22.627 -16.083 -5.249 18.226

RMSE (m3/s) 1.115 1.574 1.478 2.132 2.337

MAE (m3/s) 0.807 0.935 1.066 1.526 1.717

NSE 0.084 0.590 0.401 0.545 0.669 0.458

NSE1 0.146 0.480 0.285 0.395 0.500 0.361

R-RMSE (m3/s) 0.777 0.647 0.910 1.349 1.253

R-MAE (m3/s) 0.501 0.447 0.506 0.783 0.855

R-NSE (AVG) 0.556 0.931 0.773 0.818 0.905 0.796

R-NSE (MED) 0.592 0.936 0.781 0.837 0.912 0.812

R-NSE1 (AVG) 0.469 0.751 0.660 0.690 0.751 0.664

173

Daily Statistic SSQ & SSQR-Optimized 34 Subbasin - SSURGO - HCW-4 Climate Run


MIN (m3/s) 0.219 0.271 0.294 0.478 0.506

MEAN (m3/s) 1.427 1.873 2.156 3.499 4.150

MEDIAN (m3/s) 0.414 0.532 0.614 0.990 1.083

MAX (m3/s) 90.170 118.200 135.300 209.400 242.500

PBIAS (%) -20.459 11.028 -4.361 0.501 8.600 8.990

RMSE (m3/s) 4.896 9.011 7.445 11.423 16.274

MAE (m3/s) 1.412 2.243 2.212 3.368 5.175

NSE 0.012 0.227 0.048 0.155 -0.351 0.018

NSE1 0.127 0.290 0.214 0.298 0.177 0.221

R-RMSE (m3/s) 1.126 3.917 1.347 2.085 4.764

R-MAE (m3/s) 0.416 0.756 0.548 0.878 1.344

R-NSE (AVG) 0.948 0.854 0.969 0.972 0.884 0.925

R-NSE (MED) 0.950 0.859 0.970 0.973 0.892 0.929

R-NSE1 (AVG) 0.743 0.761 0.805 0.817 0.786 0.782

Monthly

Statistic

SSQ & SSQR-Optimized 34 Subbasin - SSURGO - HCW-4 Climate Run


MIN (m3/s) 0.343 0.435 0.480 0.730 0.781

MEAN (m3/s) 1.424 1.869 2.152 3.494 4.145

MEDIAN (m3/s) 1.098 1.433 1.635 2.727 3.223

MAX (m3/s) 4.390 5.761 6.634 10.420 12.110

PBIAS (%) -20.035 11.208 -4.014 0.749 8.662 8.934

RMSE (m3/s) 0.606 1.513 0.917 1.378 2.302

MAE (m3/s) 0.494 1.033 0.720 1.024 1.635

NSE 0.729 0.621 0.769 0.810 0.678 0.722

NSE1 0.476 0.426 0.517 0.594 0.524 0.507

R-RMSE (m3/s) 0.384 1.118 0.516 1.065 1.582

R-MAE (m3/s) 0.348 0.729 0.377 0.683 1.191

R-NSE (AVG) 0.892 0.793 0.927 0.886 0.848 0.869

R-NSE (MED)

R-NSE1 (AVG) 0.631 0.594 0.747 0.729 0.654 0.671

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