Appendix 21 Application of Simulation Models, Uncertainty ... · EIA METHODS - APPLICATION OF SIMULATION MODELS - SENSITIVITY AND UNCERTAINTY ANALYSIS . ... The levels of modelling

APPENDIX 21

EIA METHODS - APPLICATION OF SIMULATION MODELS - SENSITIVITY AND UNCERTAINTY ANALYSIS

Suncor Energy Inc. - i - Sensitivity and Uncertainty Analysis Voyageur South Project July 2007

TABLE OF CONTENTS

SECTION PAGE

1 INTRODUCTION......................................................................................................... 1

2 HYDROLOGICAL SIMULATION PROGRAM – FORTRAN MODEL AND SOURCES OF ERROR IN MODEL OUTPUT ............................................................ 3

3 APPROACHES TO SENSITIVITY AND UNCERTAINTY ANALYSES ....................... 6 3.1 GENERAL........................................................................................................................6 3.2 SENSITIVITY ANALYSIS ................................................................................................6 3.3 UNCERTAINTY ANALYSIS ............................................................................................7

4 SENSITIVITY ANALYSIS............................................................................................ 9 4.1 PARAMETER VALUES ...................................................................................................9 4.2 RESULTS ........................................................................................................................9

4.2.1 Beaver River Watershed..................................................................................9 4.2.2 Muskeg River Watershed...............................................................................18 4.2.3 Conclusions....................................................................................................32

5 UNCERTAINTY ANALYSIS ...................................................................................... 33 5.1 KEY MODEL PARAMETERS........................................................................................33 5.2 PROBABILITY DISTRIBUTIONS FOR MODEL PARAMETERS..................................39 5.3 RESULTS ......................................................................................................................41

5.3.1 Muskeg River .................................................................................................41 5.3.2 Application of Uncertainty Analysis to Poplar Creek Flow Forecasts -

Voyageur South Project Far Future Snapshot...............................................45

6 CONCLUSIONS........................................................................................................ 49

7 GLOSSARY AND ABBREVIATIONS........................................................................ 50 7.1 GLOSSARY...................................................................................................................50 7.2 ABBREVIATIONS..........................................................................................................52

8 REFERENCES.......................................................................................................... 55

LIST OF TABLES

Table 1 Possible Ranges of Hydrological Simulation Program – Fortran Hydrologic Parameters for the Oil Sands Region.................................................10

Table 2 Results of Sensitivity Analysis of Hydrological Simulation Program – Fortran Model Parameters for Beaver River..........................................................16

Table 3 Results of Sensitivity Analysis of Hydrological Simulation Program – Fortran Model Parameters for Muskeg River ........................................................19

Table 4 Calibrated, Upper and Lower Ranges of Model Parameter Range Used in Uncertainty Analysis of Hydrological Simulation Program – Fortran Model ..........34

Table 5 Parameter Range for Uncertainty Analysis of Upper Zone Storage Parameter (LZSN)..................................................................................................34

Table 6 Parameter Range for Uncertainty Analysis of Upper Zone Storage Parameter (UZSN).................................................................................................35

Suncor Energy Inc. - ii - Sensitivity and Uncertainty Analysis Voyageur South Project July 2007

Table 7 Parameter Range for Uncertainty Analysis of Infiltration Index Parameter (INFILT)..................................................................................................................35

Table 8 Parameter Range for Uncertainty Analysis of Interflow Parameter (INTFW) .................................................................................................................36

Table 9 Parameter Range for Uncertainty Analysis of Interflow Recession Parameter (IRC).....................................................................................................36

Table 10 Parameter Range for Uncertainty Analysis of Groundwater Recession Parameter (AGWRC).............................................................................................36

Table 11 Parameter Range for Uncertainty Analysis of Snow Evaporation Factor (SNOEVP)..............................................................................................................37

Table 12 Parameter Range for Uncertainty Analysis of Shade Factor (SHADE).................37 Table 13 Parameter Range for Uncertainty Analysis of Soil Infiltration Rate (INFILT).........38 Table 14 Sensitivity of Model Outputs to the Number of Monte-Carlo Simulation for

Uncertainty Analysis – Muskeg River ....................................................................42 Table 15 Sensitivity of Model Outputs to the Number of Monte-Carlo Simulation for

Uncertainty Analysis – Poplar Creek .....................................................................45

Suncor Energy Inc. - iii - Sensitivity and Uncertainty Analysis Voyageur South Project July 2007

LIST OF FIGURES

Figure 1 Percent Change to Annual Mean Discharge of Beaver River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................13

Figure 2 Percent Change to Mean Winter Runoff of Beaver River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................14

Figure 3 Percent Change to 10-Year Peak Flood of Beaver River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................15

Figure 4a Percent Change to Annual Mean Discharge of Muskeg River Associated With ±5% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................24

Figure 4b Percent Change to Annual Mean Discharge of Muskeg River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................25

Figure 5a Percent Change to Mean Winter Runoff of Muskeg River Associated With ±5% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................26

Figure 5b Percent Change to Mean Winter Runoff of Muskeg River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................27

Figure 6a Percent Change to 7Q10 Low Flow of Muskeg River Associated With ±5% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................28

Figure 6b Percent Change to 7Q10 Low Flow of Muskeg River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................29

Figure 7a Percent Change to 10-Year Peak Flood of Muskeg River Associated With ±5% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................30

Figure 7b Percent Change to 10-Year Peak Flood of Muskeg River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters ............................................................................................................31

Figure 8 Parameter Distribution for Index of Mean Soil Infiltration (INFILT) for Lowland Sandy Sub-Surface Soil ..........................................................................40

Figure 9 Parameter Distribution for Interflow Recession Parameter (IRC)..........................41 Figure 10 Sensitivity of Model Outputs to the Number of Monte-Carlo Simulations

for Uncertainty Analysis – Muskeg River...............................................................43 Figure 11 Comparison Uncertainty Analysis and Calibrated Model Values –

Cumulative Frequency Plot – Muskeg River .........................................................44 Figure 12 Sensitivity of Model Outputs to the Number of Monte-Carlo Simulations

for Uncertainty Analysis – Poplar Creek................................................................46 Figure 13 Comparison of the Results Uncertainty Analysis and Calibrated Model

Values – Cumulative Frequency Plot – Poplar Creek ...........................................47

Suncor Energy Inc. - 1 - Sensitivity and Uncertainty Analysis Voyageur South Project July 2007

1 INTRODUCTION

The simulation models used for the aquatic components of the oil sands project Environmental Impact Assessments (EIAs) include MODFLOW for groundwater and Hydrological Simulation Program – Fortran (HSPF) for surface water hydrology and quality. One key aspect of the simulation analysis is the sensitivity and uncertainty of the modelling results to the model inputs and parameters. The Terms of Reference (TOR) for the Voyageur South Project EIA (AENV 2007) requires definition of the levels of sensitivity and uncertainty of the models, including sources of error and relative accuracy.

This technical appendix describes the approach used for analyzing the sensitivity and uncertainty associated with the surface water modelling results using HSPF. This analysis excludes the other types of assessment uncertainty (e.g., climate change and mine plan change) that might affect the Voyageur South Project impact analysis. Climate change has been discussed in another appendix (Appendix 3).

The terms “sensitivity analysis” and “uncertainty analysis” are often combined or used interchangeably. For greater clarity, these terms are defined below.

Sensitivity Analysis

Most of the variation in a model’s output is generally caused by a small number of model parameters and inputs. The primary objective of the sensitivity analysis presented below is to identify which parameters, of the approximately thirty used by HSPF, have the greatest influence on model output. The approach used in the analysis involved varying each parameter individually within a range and examining the resulting relative changes in the model output.

Uncertainty Analysis

The HSPF model is used in EIAs to predict daily flows for a 50+ year period of record for a future mine configuration, using the calibrated HSPF flow model and an understanding of the land surface types, waterbodies and watercourse characteristics. Flow statistics (e.g., the mean annual flow, 10-year flood peak and 7Q10 flows) are calculated from this flow record. The objective of the uncertainty analysis provided below is to evaluate the difference between the expected value of these flow statistics (calculated from many model runs) and the flow statistics calculated by a single model run using the calibrated and/or “best estimates” of model parameters. The latter approach is used to predict flow statistics for each mine snapshot in the EIA.


The levels of modelling uncertainty can be specified in terms of probability distributions that reflect the degree of confidence in the expected values of the simulated parameters. Model formulation can also introduce uncertainty. However, for the purpose of this sensitivity and uncertainty analysis, it is assumed that the model is deterministic and that the output uncertainties are solely introduced via the specifications of the model parameters.


2 HYDROLOGICAL SIMULATION PROGRAM – FORTRAN MODEL AND SOURCES OF ERROR IN MODEL OUTPUT

The HSPF model is a set of equations, input values, parameters and variables that characterize the hydrological processes in a watershed. The implementation of HSPF in the Oil Sands Region includes the following:

• calibration of the model parameters using data from gauged watersheds;

• validation of the calibrated model with the data sets that have not been used in the calibration; and

• application of the calibrated model to watersheds within the Oil Sands Region (i.e., use as a regional model) for making probabilistic flow predictions to support EIAs (Golder 2003).

The term calibration refers to the process of providing a given set of input information to a model, and then adjusting the different “knobs and dials” until the model output matches up with observed data collected from the system being simulated, be that a river, lake or aquifer.

Model validation can occur in one of three ways:

1) A new set of input information is fed into the calibration model, without adjusting any of the model’s “knobs or dials”. The resulting model output is then compared to observed data to see how well the model performed. Output from a well-calibrated model should match closely to the observed data, whereas output from a poorly calibrated model will not.

2) If a calibrated model is used to predict how conditions will change after a given activity takes place, then the model can be validated by looking at how conditions actually did change after the event took place. In other words, looking to see if what was predicted to happen really did happen

or

3) A second model can be used to check the results of the first. Good agreement between the predictions from both models would suggest that the original model is behaving appropriately.


For reference, the Cumulative Environmental Management Association (CEMA) End Pit Lake subcommittee has adopted the third approach to validate its pit lake models, whereas the HSPF model used to complete the hydrology assessment has been validated using the first method. It was originally calibrated using information from the Beaver River and Jackpine Creek watersheds, and it was then validated using information from the Steepbank River and Joslyn Creek (Golder 2003).

As with any model, the calibration and application of HSPF to the Oil Sands Region is subject to errors from three main sources:

• model structure;

• model parameters and input data; and

• numerical solutions.

Errors associated with model structure are largely dependent on empirical constants or model parameters that are specified through model calibration processes. Since model parameters are calibrated, the errors associated with the model structure and numerical solutions have been implicitly accounted for in the calibrated model parameters. The main source of error (in practice) is in the specification of model parameters and input data.

Sources of potential errors in input data include the following:

• lack of measurements (e.g., no precipitation data within the study area watershed);

• measurement errors (e.g., error in recorded stream flow);

• use of inadequate or inappropriate sampling scales (e.g., use of point estimates of precipitation); and

• data transformation errors (e.g., transfer of precipitation data from gauged watershed to a distant ungauged watershed).

For the Oil Sands Region, the Fort McMurray Airport climate station is the only long-term continuously recording climate station, located about 60 to 100 km from most oil sands developments. Hence, the calibration and application of HSPF were based on the assumption that the climatic input data from this station (corrected for elevation) can be used as surrogates for any watershed within the Oil Sands Region. This assumption implies that the climate in the Oil Sands Region is regionally homogeneous and the statistics of the precipitation data, one


of the model’s more important climatic input, are representative of regional precipitation statistics.

Calibrated values of model parameters are only estimates, since the actual values are not known with certainty. Hence, given reliable model input data, the simulated flows from a particular land segment (watershed) are heavily dependent on model parameters that may interact strongly. It is likely that non-unique parameter sets can be derived that are almost equally good simulators of the calibrated system. The importance of incorporating uncertainty analysis associated with model parameters into hydrological models has been emphasized by many authors (Beck 1987; Reckhow 1994; Haan et al. 1995).


3 APPROACHES TO SENSITIVITY AND UNCERTAINTY ANALYSES

3.1 GENERAL

The assessment of the modelling uncertainty associated with model parameters and input data is normally conducted by defining the model parameters and input data through probability models. In general, the domain of such a probability model includes a set of conditions that describes the time, locations and ranges of the uncertain variable for which the probabilistic model is valid. In addition, the relationship between the size of uncertainties and the size of the measured variable, and any restrictions imposed on the pattern of uncertainties by other variables need to be defined within the domain of the probabilistic model.

There continues to be limited input data for watersheds and the lack of an extensive body of knowledge on hydrologic processes in the Oil Sands Region. Therefore, it is difficult to establish probabilistic models that will include all these characteristics for defining uncertainties associated with model parameters and, particularly, for input data such as precipitation and temperature. As a result, it is deemed appropriate to assess the implication of errors due to input data through a sensitivity analysis.

3.2 SENSITIVITY ANALYSIS

A sensitivity analysis provides an indication of the effects of changes in model parameter values on the response of the model-simulated values. The sensitivity analysis proceeds in a systematic manner, whereby the value of a given parameter is varied while the values of all other parameters are held constant. The Beaver River and Muskeg River watersheds were selected to investigate the sensitivity of the simulated HSPF model output to changes in parameter values. These two watersheds are subject to the key hydrologic processes and include topographic characteristics that are relevant and representative of the Oil Sands Region. The sensitivity analysis of each parameter was performed by applying a constant change to a selected parameter value for each sub-basin in these two watersheds. The pre-development case (natural conditions) was used as the Baseline Case.

The sensitivity analysis involved the following steps:

• Selection of key reference points (i.e., Water Survey of Canada [WSC] gauging station 07DA018 for the Beaver River and WSC gauging


station 07DA008 for the Muskeg River) for evaluating sensitivity of the model output.

• Selection of an appropriate range of variation for the model parameters. For the sensitivity analysis of the HSPF model, the effects of four scenarios were evaluated (i.e., ±5 and 10% changes in parameter values from the calibrated or estimated values). The range of variation must be small enough that it does not affect the underlying structure of the calibrated model. It is assumed that the parameters are essentially independent of one another over a small range of variation.

• Running the HSPF model by varying one model parameter at a time and keeping the other model parameter values unchanged from the calibrated or estimated values.

• Analyzing each of the simulated flow series to obtain the key model output flow parameters associated with each flow series (i.e., mean annual flow, 10-year flood peak flow, mean winter flow and 7Q10 low flow).

• Ranking the sensitivity of model output to the same level of change in the model parameters.

• Identifying the most sensitive model parameters.

3.3 UNCERTAINTY ANALYSIS

The levels of uncertainty in the most sensitive model parameters, as determined from the sensitivity analysis, can be specified in terms of probability distributions that reflect the degree of confidence in their expected values. The most common type of uncertainty analysis is sampling-based, in which the model is executed repeatedly for random combinations of values sampled from the probability distributions (assumed known) of the more sensitive model parameters. These probability distributions are derived from available sources of information, such as expert opinions or literature.

Several researchers have compared the accuracy, applicability and computational demands of various uncertainty analysis techniques (e.g., Thomas 1982; Doctor 1989; Binley and Beven 1991). The estimation of uncertainty in model output due to uncertainties in the calibrated HSPF model parameters has been performed using the Monte-Carlo simulation. The Monte-Carlo simulation approach is a simple and straightforward (but computing intensive) method that does not require the model to be linear but provides a reliable estimate of model uncertainty.


The uncertainty analysis involved the following steps:

• Specification or estimation of a probability distribution associated with each key model parameter based on professional judgment, literature review and relevant field information, if available.

• Use of the Monte-Carlo technique to generate random values for each of the sensitive model parameters. Generate the required number of random combinations for valid statistical analysis (the number of random combinations generated can range in the hundreds to thousands). Complete a sensitivity analysis to determine the number of runs above which the incremental increase in statistical significance is relatively minor. Run the model with each random combination of parameters.

• Analysis of each simulated flow series to obtain the key flow parameters (e.g., mean annual flow, 10-year flood peak flow and 7Q10 low flow) associated with that flow series. Repeat for each random combination of model parameters. Complete a frequency analysis of the series of mean values of each flow parameter. Estimate the difference between the mean flow parameter from the standard calibrated model and the average of the means from all the generated runs. Assess the statistical significance of the difference.

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r Energy Inc. - 9 - Sensitivity and Uncertainty Analysis r South Project July 2007

4 SENSITIVITY ANALYSIS

4.1 PARAMETER VALUES

The sensitivity of the HSPF model output to 23 pervious land segment parameters was examined by increasing and decreasing the model parameters, one at a time, by ±5 and 10% of the calibrated parameter values. The effects of these changes were then evaluated on the simulated mean annual runoff, 7-day 10-year return period (7Q10) low-flow runoff, mean winter runoff and 10-year peak flood discharge.

Several parameters (Interflow recession parameter (IRC), interception parameter (CEPSC), upper zone nominal soil moisture storage (UZSN), lower zone nominal soil moisture storage (LZSN) and base groundwater recession coefficient (time invariant) [AGWRC]) could not be increased or decreased by 5 or 10% because they would then exceed their expected ranges (i.e., upper and lower bound) as described in the literature (EPA-823-R00-012, July 2000 [U.S. EPA 2000]). In such cases, the parameter values were limited to the maximum and minimum values, as defined in Table 1.

4.2 RESULTS

4.2.1 Beaver River Watershed

The effects of changing the calibrated values of each HSPF model parameter applied to the Beaver River watershed are given in Figures 1 to 3 and Table 2 as the relative sensitivity or percent change from the calibrated model output. Key observations are as follows:

• For the Beaver River watershed, which is predominantly an upland watershed, the top 10 parameters (excluding precipitation multiplication factor [PAF] and snow catch factor [SNOWCF]) to which the model output is most sensitive are: IRC, AGWRC, interflow inflow parameter (INTFW), index to mean soil infiltration rate (INFILT), temperature below which precipitation occurs as snow under saturated conditions (TSNOW), UZSN, fraction of groundwater inflow to deep aquifer recharge (DEEPFR), fraction of land segment shaded from solar radiation by tree and slopes (SHADE), LZSN and factor to adjust evaporation (sublimation) from snowpack (SNOEVP). Specific model outputs are each sensitive to a sub-set of these parameters:


Table 1 Possible Ranges of Hydrological Simulation Program – Fortran Hydrologic Parameters for the Oil Sands Region

Parameter Description (a) Parameter Name Unit Min.

Value(b)Max.

Value(b) Function of Comments

Snow Simulation latitude of pervious land segment LAT degrees -90 90 location positive for northern hemisphere

mean elevation of pervious land segment MELEV m 0 2,135 topography used in convective heat flux equation

fraction of land segment shaded from solar radiation by tree and slopes SHADE none 0 0.8 forest cover,

topography controls radiation to and from the snowpack

snow catch factor SNOWCF none 1 2 gauge type, characteristics, location calibrated to snow depth observations

maximum snowpack depth at which entire land segment is covered with snow (water equivalent)

COVIND mm 2.54 254 topography, climate higher for mountainous watersheds

density of new snow relative to water RDCSN none 0.05 0.3 climate, air temperature adjust with field snow density data, if available

temperature below which precipitation occurs as snow under saturated conditions

TSNOW °C -1 4.5 climate, topography increasing TSNOW increases snow accumulation, while reducing TSNOW reduces snow accumulation

factor to adjust evaporation (sublimation) from snowpack SNOEVP none 0 0.5 climate, topography

snow evaporation is not large in most watersheds, but is important parameter in Oil Sands Region where windy, low humidity conditions are common

factor to adjust the rate of heat transfer from the atmosphere to the snowpack due to condensation and convection

CCFACT none 0# 8 climate

CCFACT can vary through a considerable range to account for uncertain meteorological conditions on snowpack; calibrated to change rate/timing of snowmelt

maximum liquid water holding capacity in snowpack MWATER mm/mm 0.005 1# climate

it is a function of the mass of ice layers; the size, shape and spacing of snow crystals; and the degree of channelization and honeycombing of the snowpack to allow liquid water accumulation

ground heat daily melt rate MGMELT mm/d 0 2.54 climate, geology usually small under frozen ground conditions


Table 1 Possible Ranges of Hydrological Simulation Program – Fortran Hydrologic Parameters for Oil Sands Region (continued)


Value(b)Max.


Rainfall-Runoff Simulation fraction of land covered by coniferous forest that continue to transpire in winter

FOREST none 0 0.95 forest cover it may affect watershed, water balance when SNOW subroutine is active. However, water balance is relatively insensitive to this parameter

lower zone nominal soil moisture storage LZSN mm 1.25# 380 soils, climate

calibration parameter, it is related to both precipitation patterns and soil characteristics in the region

index to mean soil infiltration rate INFILT mm/hr 0.025 12.5 soils, land use

calibration parameter, controls the overall division of the available moisture from precipitation into surface and subsurface flow and storage components

length of overland flow LSUR m 30 2,000# topography

estimated from high-resolution topo maps or Geographic Information System (GIS). LSUR is important for hydrograph shape and peak flows for small watershed

slope of overland flow plane SLSUR m/m 0.001 0.3 topography estimated from high resolution topo maps or GIS. LSUR is important for hydrograph shape and peak flows for small watershed

time variant groundwater recession coefficient KVARY 1/mm 0 0.2 baseflow recession

variation used when recession rate varies with groundwater levels (i.e., varying from day to day)

base groundwater recession coefficient (time invariant) AGWRC none 0.85 0.999 baseflow recession calibration parameter; this parameter has an

effect on the base flow from a watershed

temperature below which evapotranspiration (ET) is reduced to 50% due to low temperature in winter time

PETMAX °C 0 9 climate, vegetation reduce ET near freezing, when SNOW subroutine is active

temperature below which ET is set to zero when winter temperature approaches freezing point

PETMIN °C -1 4.5 climate, vegetation reduce ET near freezing, when SNOW subroutine is active

exponent in infiltration equation INFEXP none 1 3 soil variability usually default to 2.0

ratio of max/mean infiltration capacity INFILD none 1 3 soil variability usually default to 2.0

fraction of groundwater inflow to deep aquifer recharge DEEPFR none 0 0.5 geology, groundwater

recharge accounts for subsurface losses to deep aquifers


Table 1 Possible Ranges of Hydrological Simulation Program – Fortran Hydrologic Parameters for Oil Sands Region (continued)


Value(b)Max.


fraction of remaining ET by riparian vegetation as active groundwater enters stream bed

BASETP none 0 0.2 riparian vegetation if significant riparian vegetation is not present, this parameter does not have significant effect on water balance

fraction of remaining ET from shallow groundwater AGWETP none 0 0.2 marsh/wetlands extent this parameter has a negligible effect on both

annual water balance and base flow

interception storage capacity CEPSC mm 0.25 45# vegetation type/density, land use monthly values usually used

upper zone nominal soil moisture storage UZSN mm 1.25 50 surface soil conditions,

land use accounts for near surface retention

Manning's N (roughness) for overland flow NSUR none 0.05 0.5 surface conditions,

residue

monthly values often used for croplands. Is important for hydrograph shape and peak flows for small watershed

interflow inflow parameter INTFW none 1 25# soils, topography, land use

calibration parameter, based on hydrograph separation; INTFW is a coefficient that determines the amount of water which enters the ground from surface detention storage and becomes interflow as opposed to direct overland flow and upper zone storage

interflow recession parameter IRC none 0.3 0.98# soils, topography, land use

often start with a value of 0.7, and then adjust; IRC affects the rate at which interflow is discharged from storage

lower zone ET parameter LZETP none 0.1 0.9 vegetation type/density, root depth

calibration parameter, represents index to lower zone evapotranspiration

(a) HSPF Hydrologic Parameter ranges are set based on Basin's Technical Note 6 (U.S. EPA 2000). (b) # indicates range of parameters outside the range of Basin’s Technical Note 6 and established based on Golder's Calibration for Oil Sands Region (Golder 2003).


Figure 1 Percent Change to Annual Mean Discharge of Beaver River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters

-20 -10 0 10 20 30

PAF -10% 10%

PAF -5% 5%

SNOWCF 10%

ATMP 3°C

TSNOW 8.1(a)% -10%

ATMP 1°C

AGWRC 1.6(a)~10% -10%~0(b)

UZSN 10% -10%~0(b)

DEEPFR 0.1

CEPSE 0(a)~10% -10%

SHADE 10% -10%

SNOEVP 10% -10%

LZSN 10% -10%~0(b)

FOREST 10% -10%

COVIND 10% -10%

LZETP 10% -10%

CCFACT -10% 10%

INFILT 10% -10%

AGWETP 10% -10%

BASETP 0(a)~10% -10%

MWATER 10% -10%

INTFW -10% 0(a)~10%

IRC 4.3~6.5%(a) -10%

LSUR 10% -10%

NSUR 10% -10%

SLSUR -10% 10%

Annual Mean Discharge [m³/s](a) Parameter bounded by upper bounds.

(b) Parameter bounded by lower bounds.

Note: PAF = Precipitation Adjust Factor.

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75


Figure 2 Percent Change to Mean Winter Runoff of Beaver River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters

0 25 50 150 200

3°C ATMP

-10% 4.3~6.5%(a) IRC

-10%~0(b) 1.6(a)~10% AGWRC

1°C ATMP

8.1(a)% -10% TSNOW

-10% 10% PAF

0.1 DEEPFR

-10% 10% SHADE

-10% 10% CCFACT

10% -10% COVIND

-5% 5% PAF

0(a)~10% -10% CEPSE

10% -10% MWATER

10% SNOWCF

-10% 10% INFILT

10% -10%~0(b) LZSN

10% -10%~0(b) UZSN

10% -10% FOREST

10% -10% SNOEVP

10% -10% LZETP

10% -10% AGWETP

0(a)~10% -10% INTFW

-10% 0(a)~10% BASETP

10% -10% LSUR

10% -10% NSUR

-10% 10% SLSUR

Winter (11/15~4/14) Mean Discharge [m³/s](a) Parameter bounded by upper bounds.



0.03 0.05 0.07 0.09 0.11 0.13 0.15 0.17

SuncoVoyageu

-30 -20 -10 0 10 20 30

PAF -10% 10%

IRC 4.3~6.5%(a) -10%

PAF -5% 5%

SNOWCF 10%

ATMP 3°C

INTFW 0(a)~10% -10%

ATMP 1°C

INFILT 10% -10%

(a)

(b)


Figure 3 Percent Change to 10-Year Peak Flood of Beaver River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters


SHADE -10% 10%

TSNOW -10% 8.1(a)%

LZSN 10% -10%~0(b)

SNOEVP 10% -10%

LSUR 10% -10%

NSUR 10% -10%

FOREST 10% -10%

UZSN 10% -10%~0(b)

AGWRC 1.6(a)~10% -10%~0(b)

MWATER 10% -10%

SLSUR -10% 10%

LZETP 10% -10%

COVIND 10% -10%

CEPSE -10% 0(a)~10%

CCFACT -10% 10%

DEEPFR 0.1

BASETP -10% 0(a)~10%

AGWETP -10% 10%

10 Year Return Discharge [m³/s]

Parameter bounded by upper bounds.

Parameter bounded by lower bounds.

12 14 16 18 20 22 24


Table 2 Results of Sensitivity Analysis of Hydrological Simulation Program – Fortran Model Parameters for Beaver River

Discharge Change to Discharge

Winter Mean

Annual Mean

10-Year Peak

100-Year Peak

Winter Mean

Annual Mean

10-Year Peak

100-Year Peak Scenario Parameter Description

Parameter Change

[%] [m³/s] [m³/s] [m³/s] [m³/s] [%] [%] [%] [%]

1 PAF precipitation multiplication factor 10% 0.063 0.716 23.472 60.145 12.6 27.7 30.8 23.6

2 LZETP lower zone evapotranspiration parameter 10% 0.056 0.556 17.801 48.312 -0.5 -0.8 -0.8 -0.7

3 SHADE shade factor 10% 0.059 0.555 18.775 53.928 5.5 -1.0 4.6 10.8

4 SNOWCF snow catch factor 10% 0.055 0.614 20.188 47.736 -2.9 9.5 12.5 -1.9

5 COVIND snow cover index 10% 0.054 0.555 17.812 48.040 -4.4 -1.0 -0.7 -1.3

6 TSNOW temperature of the snow 8.1%(a) 0.050 0.541 18.596 49.107 -10.9 -3.5 3.6 0.9

7 SNOEVP snow evaporation parameter 10% 0.056 0.551 17.536 49.092 -1.1 -1.6 -2.3 0.9

8 CCFACT the heat exchange factor 10% 0.057 0.563 17.981 47.832 1.8 0.5 0.2 -1.7

9 MWATER liquid water storage 10% 0.055 0.560 17.708 47.584 -2.8 -0.1 -1.3 -2.2

10 FOREST forest cover 10% 0.056 0.554 17.672 48.828 -1.1 -1.2 -1.5 0.3

11 LZSN lower zone nominal storage parameter 10% 0.055 0.552 17.767 47.591 -1.9 -1.5 -1.0 -2.2

12 INFILT infiltration 10% 0.057 0.559 16.924 45.599 2.3 -0.3 -5.7 -6.3

13 LSUR length of overland flow 10% 0.056 0.560 17.555 46.961 0.0 0.0 -2.2 -3.5

14 SSUR slope of overland flow 10% 0.056 0.560 18.130 49.516 0.0 0.0 1.0 1.7

15 AGWRC groundwater recession parameter 1.6(a)~10% 0.106 0.548 18.029 49.656 88.1 -2.1 0.5 2.0

16 DEEPFR deep groundwater loss +0.1 0.052 0.548 17.929 48.738 -7.0 -2.2 -0.1 0.1

17 BASETP base flow evaporation 0(a)~10% 0.056 0.559 17.953 48.617 0.1 -0.2 0.1 -0.1

18 AGWETP groundwater evaporation 10% 0.056 0.559 17.944 48.625 -0.6 -0.2 0.0 -0.1

19 CEPSE interception parameter 0(a)~10% 0.054 0.554 18.036 48.259 -3.1 -1.2 0.5 -0.9

20 UZSN upper zone nominal storage parameter 10% 0.055 0.549 17.813 48.505 -1.3 -2.1 -0.7 -0.3

21 NSUR roughness of overland flow 10% 0.056 0.560 17.555 46.961 0.0 0.0 -2.2 -3.5

22 INTFW interflow parameter 0(a)~10% 0.056 0.561 16.662 44.470 -0.3 0.1 -7.1 -8.6

23 IRC interflow recession parameter 4.3~6.5%(a) 0.130 0.560 16.317 60.799 131.6 -0.1 -9.1 24.9

24 PAF precipitation multiplication factor -10% 0.051 0.421 12.456 33.173 -8.9 -24.8 -30.6 -31.8


Table 2 Results of Sensitivity Analysis of Hydrological Simulation Program – Fortran Model Parameters for Beaver River (continued)


Winter Mean

Annual Mean

10-Year Peak

100-Year Peak

Winter Mean

Annual Mean

10-Year Peak

100-Year Peak Scenario Parameter Description

Parameter Change

[%] [m³/s] [m³/s] [m³/s] [m³/s] [%] [%] [%] [%]

25 LZETP lower zone evapotranspiration parameter -10% 0.057 0.566 18.095 49.009 0.8 0.9 0.9 0.7

26 SHADE shade factor -10% 0.056 0.570 16.988 41.904 -0.9 1.7 -5.3 -13.9

27 COVIND snow cover index -10% 0.058 0.567 18.066 48.797 2.9 1.2 0.7 0.3

28 TSNOW temperature of the snow -10% 0.074 0.584 17.583 45.225 31.3 4.3 -2.0 -7.1

29 SNOEVP snow evaporation parameter -10% 0.057 0.570 18.490 48.537 1.1 1.7 3.1 -0.3

30 CCFACT the heat exchange factor -10% 0.053 0.556 17.884 49.362 -5.5 -0.7 -0.3 1.4

31 MWATER liquid water storage -10% 0.058 0.561 18.069 47.225 3.0 0.1 0.7 -3.0

32 FOREST forest cover -10% 0.057 0.567 18.256 48.984 1.2 1.2 1.7 0.6

33 LZSN lower zone nominal storage parameter -10%~0(b) 0.057 0.570 18.506 50.764 1.9 1.6 3.1 4.3

34 INFILT infiltration -10% 0.055 0.562 19.344 52.139 -2.4 0.3 7.8 7.1

35 LSUR length of overland flow -10% 0.056 0.560 18.364 50.510 0.0 0.0 2.4 3.8

36 SSUR slope of overland flow -10% 0.056 0.560 17.727 47.725 0.0 0.0 -1.2 -1.9

37 AGWRC groundwater recession parameter -10%~0(b) 0.033 0.573 18.182 49.141 -40.5 2.3 1.3 1.0

38 BASETP base flow evaporation -10% 0.056 0.562 17.931 48.733 -0.1 0.2 -0.1 0.1

39 AGWETP groundwater evaporation -10% 0.056 0.562 17.941 48.710 0.6 0.3 0.0 0.1

40 CEPSE interception parameter -10% 0.058 0.570 18.023 49.518 3.3 1.7 0.5 1.7

41 UZSN upper zone nominal storage parameter -10%~0(b) 0.057 0.573 18.246 49.454 1.3 2.2 1.7 1.6

42 NSUR roughness of overland flow -10% 0.056 0.560 18.364 50.510 0.0 0.0 2.4 3.8

43 INTFW interflow parameter -10% 0.056 0.560 19.678 52.793 0.3 -0.1 9.7 8.5

44 IRC interflow recession parameter -10% 0.058 0.561 21.498 46.160 3.8 0.1 19.8 -5.2 (a) Parameter bounded by upper bounds. (b) Parameter bounded by lower bounds.

SuncoVoyageu


• An increase in IRC by 4 to 6% results in an increase of up to about 132% in mean winter flow. A 10% decrease in IRC will result in a 4% decrease in mean winter flow.

• An increase in AGWRC by 1.6 to 10% results in an increase of up to about 88% in mean winter flow. A 10% decrease in AGWRC will result in 40% decrease in mean winter flow.

• An increase in IRC or AGWRC will have much more of an effect on mean winter runoff than a similar decrease in these parameters.

• An increase of 10% in model parameters TSNOW (index temperature below which precipitation occurs as snow), SHADE (shade factor) and SNOEVP (snow evaporation) results in a decrease of less than 10% in mean annual runoff.

• Changes in INTFW (interflow recession parameter), IRC and INFILT (index of infiltration parameter) are the three model parameters that have the most effect on the 10-year flood peak discharge compared to other HSPF model parameters.

• An increase or decrease in the upper zone storage parameter (UZSN) has an effect on the mean annual runoff since water in the upper zone storage is predominantly lost to evapotranspiration.

4.2.2 Muskeg River Watershed

The effects of changing the calibrated values of each model parameter in HSPF applied to the Muskeg River watershed are given in Table 3 and in Figures 4a to 7b, as the relative sensitivity or percent change from the calibrated model output. Key observations are as follows:

• Unlike the largely upland Beaver River watershed, the Muskeg River watershed is a combination of upland (53%) and lowland (47%) areas. The top 10 parameters (excluding PAF and SNOWCF) to which the model output is most sensitive are: IRC, AGWRC, INTFW, INFILT, TSNOW, UZSN, DEEPFR, SHADE, LZSN and SNOEVP. Specific model outputs are each sensitive to a sub-set of these parameters.

• Surface runoff generally represents a small component of the hydrologic budget (i.e., about 8.5% of the mean annual runoff) in this watershed. Thus, the model output is not sensitive to the model parameters that affect surface runoff.

• Similar to the Beaver River watershed, interflow is a major runoff component from areas with muskeg overlying till. This component is a significant portion of the total runoff from the watershed. Hence, the model outputs are sensitive to the parameters that affect interflow (i.e., IRC and INTFW).


Table 3 Results of Sensitivity Analysis of Hydrological Simulation Program – Fortran Model Parameters for Muskeg River


Winter Mean

Annual Mean

10-Year Peak

100-Year Peak 7Q10 Winter

Mean Annual Mean

10-Year Peak

100-Year Peak

7Q10 Scenario Parameter Description Parameter Change

[m³/s] [m³/s] [m³/s] [m³/s] [L/s] [%] [%] [%] [%] [%]

1 PAF precipitation multiplication factor 5% 0.474 4.389 57.287 91.827 24.543 7.1 13.4 10.4 6.5 20.2

2 LZETP lower zone evapotranspiration parameter 5% 0.442 3.855 51.758 86.229 20.193 -0.3 -0.4 -0.2 0.0 -1.1

3 SHADE shade factor 5% 0.450 3.849 52.832 89.324 21.041 1.6 -0.6 1.8 3.6 3.0

4 SNOWCF snow catch factor 5% 0.446 4.050 55.253 91.429 20.226 0.6 4.6 6.5 6.0 -1.0

5 COVIND snow cover index 5% 0.438 3.854 51.811 87.045 20.350 -1.1 -0.4 -0.1 0.9 -0.4

6 TSNOW temperature of the snow 5% 0.401 3.760 51.898 90.272 17.384 -9.3 -2.9 0.0 4.7 -14.9

7 SNOEVP snow evaporation parameter 5% 0.440 3.844 51.432 85.819 20.272 -0.6 -0.7 -0.9 -0.5 -0.7

8 CCFACT the heat exchange factor 5% 0.446 3.881 51.961 86.013 20.593 0.7 0.3 0.2 -0.3 0.8

9 MWATER liquid water storage 5% 0.437 3.869 51.862 86.404 20.418 -1.2 0.0 0.0 0.2 0.0

10 FOREST forest cover 5% 0.441 3.851 51.500 85.782 20.337 -0.3 -0.5 -0.7 -0.5 -0.4

11 LZSN lower zone nominal storage parameter 5% 0.441 3.848 51.703 85.704 20.063 -0.5 -0.6 -0.3 -0.6 -1.8

12 INFILT infiltration 5% 0.443 3.864 51.325 85.246 20.460 0.1 -0.2 -1.1 -1.2 0.2

13 LSUR length of overland flow 5% 0.443 3.871 51.793 86.116 20.421 0.0 0.0 -0.2 -0.1 0.0

14 SSUR slope of overland flow 5% 0.443 3.871 51.915 86.301 20.423 0.0 0.0 0.1 0.1 0.0

15 AGWRC groundwater recession parameter 1.63(a)~5% 0.836 3.751 49.535 83.333 94.367 88.8 -3.1 -4.5 -3.4 362.1

16 INFEXP infiltration exponential +1 0.448 3.940 54.999 90.612 20.417 1.1 1.8 6.0 5.1 0.0

17 INFILD infiltration coefficient no change 0.443 3.871 51.875 86.239 20.423 0.0 0.0 0.0 0.0 0.0

18 DEEPFR deep groundwater loss +0.05 0.430 3.795 51.513 86.332 19.891 -2.8 -2.0 -0.7 0.1 -2.6

19 BASETP base flow evaporation 0(a)~5% 0.443 3.869 51.870 86.227 20.397 0.0 -0.1 0.0 0.0 -0.1

20 AGWETP groundwater evaporation 5% 0.442 3.865 51.870 86.246 20.369 -0.1 -0.2 0.0 0.0 -0.3

21 CEPSE interception parameter 0(a)~5% 0.437 3.863 51.893 86.104 20.101 -1.4 -0.2 0.0 -0.2 -1.6

22 UZSN upper zone nominal storage parameter 5% 0.437 3.822 51.544 86.410 20.198 -1.2 -1.3 -0.6 0.2 -1.1


Table 3 Results of Sensitivity Analysis of Hydrological Simulation Program – Fortran Model Parameters for Muskeg River (continued)


Winter Mean

Annual Mean

10-Year Peak


Mean Annual Mean

10-Year Peak

100-Year Peak


[m³/s] [m³/s] [m³/s] [m³/s] [L/s] [%] [%] [%] [%] [%]

23 NSUR roughness of overland flow 5% 0.443 3.871 51.793 86.116 20.421 0.0 0.0 -0.2 -0.1 0.0

24 INTFW interflow parameter 0(a)~5% 0.443 3.872 51.318 85.007 20.434 0.0 0.0 -1.1 -1.4 0.1

25 IRC interflow recession parameter 4.3(a)~5% 0.816 3.871 39.993 79.057 50.253 84.4 0.0 -22.9 -8.3 146.1

26 PAF precipitation multiplication factor 10% 0.492 4.935 62.435 94.905 28.090 11.1 27.5 20.4 10.0 37.5

27 LZETP lower zone evapotranspiration parameter 10% 0.439 3.833 51.605 86.368 19.813 -0.9 -1.0 -0.5 0.1 -3.0

28 SHADE shade factor 10% 0.462 3.832 53.156 89.119 21.462 4.3 -1.0 2.5 3.3 5.1

29 SNOWCF snow catch factor 10% 0.435 4.235 58.729 94.145 19.105 -1.7 9.4 13.2 9.2 -6.5

30 COVIND snow cover index 10% 0.432 3.838 51.770 87.737 20.337 -2.4 -0.9 -0.2 1.7 -0.4

31 TSNOW temperature of the snow 8.1%(a) 0.392 3.711 52.094 91.929 17.426 -11.5 -4.1 0.4 6.6 -14.7

32 SNOEVP snow evaporation parameter 10% 0.438 3.816 50.981 85.349 20.059 -1.0 -1.4 -1.7 -1.0 -1.8

33 CCFACT the heat exchange factor 10% 0.449 3.891 52.023 85.819 20.781 1.4 0.5 0.3 -0.5 1.8

34 MWATER liquid water storage 10% 0.434 3.868 51.835 86.650 20.333 -2.0 -0.1 -0.1 0.5 -0.4

35 FOREST forest cover 10% 0.440 3.831 51.130 85.337 20.247 -0.6 -1.0 -1.4 -1.0 -0.9

36 LZSN lower zone nominal storage parameter 10% 0.439 3.827 51.534 85.165 19.676 -0.9 -1.2 -0.7 -1.2 -3.7

37 INFILT infiltration 10% 0.444 3.857 50.822 84.309 20.514 0.2 -0.4 -2.0 -2.2 0.4

38 LSUR length of overland flow 10% 0.442 3.871 51.713 85.994 20.421 -0.1 0.0 -0.3 -0.3 0.0

39 SSUR slope of overland flow 10% 0.443 3.871 51.953 86.360 20.423 0.0 0.0 0.2 0.1 0.0

40 AGWRC groundwater recession parameter 1.6(a)~10% 1.087 3.635 48.729 88.777 317.428 145.4 -6.1 -6.1 2.9 1,454.3

41 DEEPFR deep groundwater loss +0.1 0.418 3.720 51.154 86.367 19.386 -5.5 -3.9 -1.4 0.1 -5.1

42 BASETP base flow evaporation 0(a)~10% 0.443 3.867 51.866 86.215 20.377 0.0 -0.1 0.0 0.0 -0.2

43 AGWETP groundwater evaporation 10% 0.442 3.860 51.866 86.252 20.278 -0.1 -0.3 0.0 0.0 -0.7

44 CEPSE interception parameter 0(a)~10% 0.428 3.823 51.807 86.026 19.143 -3.3 -1.3 -0.1 -0.2 -6.3

45 UZSN upper zone nominal storage parameter 10% 0.432 3.775 51.212 86.579 19.975 -2.4 -2.5 -1.3 0.4 -2.2




Winter Mean

Annual Mean

10-Year Peak


Mean Annual Mean

10-Year Peak

100-Year Peak


[m³/s] [m³/s] [m³/s] [m³/s] [L/s] [%] [%] [%] [%] [%]

46 NSUR roughness of overland flow 10% 0.442 3.871 51.713 85.994 20.421 -0.1 0.0 -0.3 -0.3 0.0

47 INTFW interflow parameter 0(a)~10% 0.443 3.873 50.829 83.885 20.447 0.1 0.0 -2.0 -2.7 0.1

48 IRC interflow recession parameter 4.3~6.5%(a) 0.997 3.872 37.523 77.320 79.155 125.1 0.0 -27.7 -10.3 287.6

49 PAF precipitation multiplication factor -5% 0.431 3.388 46.120 79.398 18.565 -2.5 -12.5 -11.1 -7.9 -9.1

50 LZETP lower zone evapotranspiration parameter -5% 0.446 3.896 52.040 86.097 20.800 0.7 0.6 0.3 -0.2 1.8

51 SHADE shade factor -5% 0.434 3.901 51.431 84.656 20.163 -2.0 0.8 -0.9 -1.8 -1.3

52 COVIND snow cover index -5% 0.445 3.890 52.063 85.841 20.524 0.6 0.5 0.4 -0.5 0.5

53 TSNOW temperature of the snow -5% 0.478 3.963 50.762 78.216 23.323 8.0 2.4 -2.1 -9.3 14.2

54 SNOEVP snow evaporation parameter -5% 0.445 3.899 52.333 86.743 20.644 0.5 0.7 0.9 0.6 1.1

55 CCFACT the heat exchange factor -5% 0.439 3.861 51.801 86.543 20.337 -0.7 -0.3 -0.1 0.4 -0.4

56 MWATER liquid water storage -5% 0.446 3.874 51.897 86.149 20.464 0.8 0.1 0.0 -0.1 0.2

57 FOREST forest cover -5% 0.444 3.892 52.269 86.751 20.532 0.3 0.5 0.8 0.6 0.5

58 LZSN lower zone nominal storage parameter -5%~0(b) 0.444 3.889 52.015 86.771 20.724 0.4 0.5 0.3 0.6 1.5

59 INFILT infiltration -5% 0.442 3.879 52.477 87.395 20.377 -0.1 0.2 1.2 1.3 -0.2

60 LSUR length of overland flow -5% 0.443 3.871 51.959 86.370 20.423 0.0 0.0 0.2 0.2 0.0

61 SSUR slope of overland flow -5% 0.443 3.871 51.833 86.175 20.421 0.0 0.0 -0.1 -0.1 0.0

62 AGWRC groundwater recession parameter -5%~0(b) 0.396 3.932 53.240 86.857 18.368 -10.5 1.6 2.6 0.7 -10.1

63 INFEXP infiltration exponential -1 0.449 3.828 48.742 81.100 20.134 1.5 -1.1 -6.0 -6.0 -1.4

64 INFILD infiltration coefficient -1 0.446 3.919 51.723 86.595 22.030 0.7 1.2 -0.3 0.4 7.9

65 BASETP base flow evaporation -5% 0.443 3.876 51.905 86.328 20.446 0.0 0.1 0.1 0.1 0.1

66 AGWETP groundwater evaporation -5% 0.443 3.877 51.879 86.229 20.499 0.1 0.2 0.0 0.0 0.4

67 CEPSE interception parameter -5% 0.452 3.920 51.993 86.248 21.434 2.2 1.3 0.2 0.0 4.9

68 UZSN upper zone nominal storage parameter -5%~0(b) 0.449 3.922 52.198 86.072 20.629 1.3 1.3 0.6 -0.2 1.0




Winter Mean

Annual Mean

10-Year Peak


Mean Annual Mean

10-Year Peak

100-Year Peak


[m³/s] [m³/s] [m³/s] [m³/s] [L/s] [%] [%] [%] [%] [%]

69 NSUR roughness of overland flow -5% 0.443 3.871 51.959 86.370 20.423 0.0 0.0 0.2 0.2 0.0

70 INTFW interflow parameter -5% 0.443 3.870 52.503 87.677 20.403 0.0 0.0 1.2 1.7 -0.1

71 IRC interflow recession parameter -5% 0.422 3.874 58.452 88.673 19.337 -4.6 0.1 12.7 2.8 -5.3

72 PAF precipitation multiplication factor -10% 0.405 2.931 40.174 70.702 17.345 -8.4 -24.3 -22.6 -18.0 -15.1

73 LZETP lower zone evapotranspiration parameter -10% 0.447 3.914 52.165 86.110 21.090 1.0 1.1 0.6 -0.1 3.3

74 SHADE shade factor -10% 0.436 3.941 51.222 82.555 19.963 -1.6 1.8 -1.3 -4.3 -2.3

75 COVIND snow cover index -10% 0.447 3.914 52.530 86.400 20.696 1.0 1.1 1.3 0.2 1.3

76 TSNOW temperature of the snow -10% 0.547 4.040 51.226 79.285 28.069 23.7 4.4 -1.3 -8.1 37.4

77 SNOEVP snow evaporation parameter -10% 0.447 3.927 52.801 87.210 20.780 1.0 1.5 1.8 1.1 1.7

78 CCFACT the heat exchange factor -10% 0.436 3.851 51.766 86.982 20.135 -1.5 -0.5 -0.2 0.9 -1.4

79 MWATER liquid water storage -10% 0.450 3.876 51.871 86.071 20.562 1.6 0.1 0.0 -0.2 0.7

80 FOREST forest cover -10% 0.446 3.913 52.658 87.208 20.642 0.7 1.1 1.5 1.1 1.1

81 LZSN lower zone nominal storage parameter -10%~0(b) 0.446 3.908 52.156 87.299 21.332 0.8 0.9 0.5 1.2 4.5

82 INFILT infiltration -10% 0.442 3.888 53.139 88.649 20.405 -0.1 0.4 2.4 2.8 -0.1

83 LSUR length of overland flow -10% 0.443 3.871 52.046 86.510 20.423 0.1 0.0 0.3 0.3 0.0

84 SSUR slope of overland flow -10% 0.443 3.871 51.787 86.105 20.421 0.0 0.0 -0.2 -0.2 0.0

85 AGWRC groundwater recession parameter -10%~0(b) 0.388 3.961 53.877 86.446 18.349 -12.4 2.3 3.9 0.2 -10.2

86 BASETP base flow evaporation -10% 0.442 3.881 51.938 86.416 20.475 -0.1 0.3 0.1 0.2 0.3




Winter Mean

Annual Mean

10-Year Peak


Mean Annual Mean

10-Year Peak

100-Year Peak


[m³/s] [m³/s] [m³/s] [m³/s] [L/s] [%] [%] [%] [%] [%]

87 AGWETP groundwater evaporation -10% 0.443 3.883 51.883 86.217 20.608 0.1 0.3 0.0 0.0 0.9

88 CEPSE interception parameter -10% 0.459 3.930 51.974 86.346 21.860 3.7 1.5 0.2 0.1 7.0

89 UZSN upper zone nominal storage parameter -10%~0(b) 0.455 3.975 52.519 85.901 20.873 2.7 2.7 1.2 -0.4 2.2

90 NSUR roughness of overland flow -10% 0.443 3.871 52.046 86.510 20.423 0.1 0.0 0.3 0.3 0.0

91 INTFW interflow parameter -10% 0.442 3.869 53.228 89.264 20.414 -0.1 -0.1 2.6 3.5 0.0

92 IRC interflow recession parameter -10% 0.429 3.877 63.164 90.888 19.188 -3.2 0.1 21.8 5.4 -6.0 (a) Parameter bounded by upper bounds. (b) Parameter bounded by lower bounds.


Figure 4a Percent Change to Annual Mean Discharge of Muskeg River Associated With ±5% Change in Hydrological Simulation Program – Fortran Model Parameters

-30 - 20 -10 0 10 20

PAF -5.0% 5.0%

SNOWCF 5.0%

AGWRC 1.63(a)~5% -5%~0(b)

TSNOW 5.0% -5.0%

ATMP 1°C

DEEPFR 0.05

INFEXP -1 1

UZSN 5.0% -5%~0(b)

CEPSE 0(a)~5% -5.0%

INFILD -1

SHADE 5.0% -5.0%

SNOEVP 5.0% -5.0%

LZETP 5.0% -5.0%

LZSN 5.0% -5%~0(b)

FOREST 5.0% -5.0%

COVIND 5.0% -5.0%

CCFACT -5.0% 5.0%

INFILT 5.0% -5.0%

AGWETP 5.0% -5.0%

BASETP 0(a)~5% -5.0%

IRC 4.3(a)~5% -5.0%

MWATER 5.0% -5.0%

INTFW -5.0% 0(a)~5%

LSUR 5.0% -5.0%

NSUR 5.0% -5.0%

SLSUR -5.0% 5.0%

Annual Mean Discharge [m³/s](a) Parameter bounded by upper bounds.(b) Parameter bounded by lower bounds.


2.5 3 3.5 4 4.5 5


Figure 4b Percent Change to Annual Mean Discharge of Muskeg River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters

-30 - 20 -10 0 10 20

PAF -10.0% 10.0%

SNOWCF 10.0%

AGWRC 1.6(a)~10% -10%~0(b)

TSNOW 8.1(a)% -10.0%

DEEPFR 0.1

ATMP 3°C

UZSN 10.0% -10%~0(b)

SHADE 10.0% -10.0%

CEPSE 0(a)~10% -10.0%

SNOEVP 10.0% -10.0%

LZSN 10.0% -10%~0(b)

LZETP 10.0% -10.0%

COVIND 10.0% -10.0%

FOREST 10.0% -10.0%

CCFACT -10.0% 10.0%

INFILT 10.0% -10.0%

AGWETP 10.0% -10.0%

BASETP 0(a)~10% -10.0%

IRC 4.3~6.5%(a) -10.0%

MWATER 10.0% -10.0%

INTFW -10.0% 0(a)~10%

LSUR 10.0% -10.0%

NSUR 10.0% -10.0%

SLSUR -10.0% 10.0%

Annual Mean Discharge [m³/s](a) Parameter bounded by upper bounds.(b) Parameter bounded by lower bounds.


2.5 3 3.5 4 4.5 5


Figure 5a Percent Change to Mean Winter Runoff of Muskeg River Associated With ±5% Change in Hydrological Simulation Program – Fortran Model Parameters

-20 0 20 40 60 8 0 100 120 140

-5%~0(b) 1.63(a)~5% AGWRC

-5.0% 4.3(a)~5% IRC

1°C ATMP

5.0% -5.0% TSNOW

-5.0% 5.0% PAF

+0.05 DEEPFR

0(a)~5% -5.0% CEPSE

-5.0% 5.0% SHADE

1 -1 INFEXP

5.0% -5%~0(b) UZSN

5.0% -5.0% MWATER

5.0% -5.0% COVIND

-5.0% 5.0% CCFACT

0 -1 INFILD

5.0% -5.0% LZETP

5.0% SNOWCF

5.0% -5.0% SNOEVP

5.0% -5%~0(b) LZSN

5.0% -5.0% FOREST

-5.0% 5.0% INFILT

5.0% -5.0% AGWETP

-5.0% 0(a)~5% BASETP

5.0% -5.0% LSUR

5.0% -5.0% NSUR

-5.0% 0(a)~5% INTFW

-5.0% 5.0% SLSUR

Mean Winter (11/15~4/14) Flow [m³/s](a) Parameter bounded by upper bounds.(b) Parameter bounded by lower bounds.


0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1


Figure 5b Percent Change to Mean Winter Runoff of Muskeg River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters

-20 0 20 40 60 80 100 120 140

3°C ATMP

-10%~0(b) 1.6(a)~10% AGWRC

-10.0% 4.3~6.5%(a) IRC

8.1(a)% -10.0% TSNOW

-10.0% 10.0% PAF

0.1 DEEPFR

-10.0% 10.0% SHADE

0(a)~10% -10.0% CEPSE

10.0% -10%~0(b) UZSN

10.0% -10.0% COVIND

10.0% -10.0% MWATER

10.0% SNOWCF

-10.0% 10.0% CCFACT

10.0% -10.0% SNOEVP

10.0% -10.0% LZETP

10.0% -10%~0(b) LZSN

10.0% -10.0% FOREST

-10.0% 10.0% INFILT

10.0% -10.0% AGWETP

-10.0% 0(a)~10% BASETP

-10.0% 0(a)~10% INTFW

10.0% -10.0% LSUR

10.0% -10.0% NSUR

-10.0% 10.0% SLSUR

Mean Winter (11/15~4/14) Flow [m³/s](a) Parameter bounded by upper bounds.



0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1


Figure 6a Percent Change to 7Q10 Low Flow of Muskeg River Associated With ±5% Change in Hydrological Simulation Program – Fortran Model Parameters

0 200 400 600 800 1000 1200 1400

-5%~0(b) 1.63(a)~5% AGWRC

-5.0% 4.3(a)~5% IRC

-5.0% 0.05 PAF

5.0% -5.0% TSNOW

-1 INFILD

1°C ATMP

0a~5% -5.0% CEPSE

-5.0% 5.0% SHADE

+0.05 DEEPFR

5.0% -0.05 LZETP

5.0% -5%~0(b) LZSN

-1 1 INFEXP

5.0% -5%~0(b) UZSN

5.0% -5.0% SNOEVP

5.0% SNOWCF

-5.0% 5.0% CCFACT

5.0% -5.0% FOREST

5.0% -5.0% COVIND

5.0% -5.0% AGWETP

-0.05 5.0% INFILT

5.0% -0.05 MWATER

0(a)~5% -5.0% BASETP

-5.0% 0(a)~5% INTFW

-5.0% 5.0% SLSUR

0.05 -0.05 LSUR

0.05 -0.05 NSUR

7Q10 [l/s](a) Parameter bounded by upper bounds.(b) Parameter bounded by lower bounds.


0 50 100 150 200 250 300


Figure 6b Percent Change to 7Q10 Low Flow of Muskeg River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters

0 200 400 600 800 1000 1200 1400

-10%~0(b) 1.6(a)~10% AGWRC

-10.0% 4.3~6.5%(a) IRC

3°C ATMP

-10.0% 10.0% PAF

8.1(a)% -10.0% TSNOW

0(a)~10% -10.0% CEPSE

10.0% SNOWCF

-10.0% 10.0% SHADE

0.1 DEEPFR

10.0% -10%~0(b) LZSN

10.0% -10.0% LZETP

10.0% -10%~0(b) UZSN

10.0% -10.0% SNOEVP

-10.0% 10.0% CCFACT

10.0% -10.0% COVIND

10.0% -10.0% FOREST

10.0% -10.0% AGWETP

10.0% -10.0% MWATER

-10.0% 10.0% INFILT

0(a)~10% -10.0% BASETP

-10.0% 0(a)~10% INTFW

-10.0% 10.0% SLSUR

10.0% -10.0% LSUR

10.0% -10.0% NSUR

7Q10 [l/s](a) Parameter bounded by upper bounds.(b) Parameter bounded by lower bounds.


0 50 100 150 200 250 300


Figure 7a Percent Change to 10-Year Peak Flood of Muskeg River Associated With ±5% Change in Hydrological Simulation Program – Fortran Model Parameters

-30 -20 -10 0 10 2 0

IRC 4.3(a)~5% -5.0%

PAF -5.0% 5.0%

ATMP 1°C

SNOWCF 5.0%

INFEXP -1 1

AGWRC 1.63(a)~5% -5%~0(b)

TSNOW -5.0% 5.0%

SHADE -5.0% 5.0%

INTFW 0(a)~5% -5.0%

INFILT 5.0% -5.0%

SNOEVP 5.0% -5.0%

FOREST 5.0% -5.0%

DEEPFR 0.05

UZSN 5.0% -5%~0(b)

COVIND 5.0% -5.0%

LZSN 5.0% -5%~0(b)

LZETP 5.0% -5.0%

INFILD -1

CEPSE 0(a)~5% -5.0%

CCFACT -5.0% 5.0%

LSUR 5.0% -5.0%

NSUR 5.0% -5.0%

SLSUR -5.0% 5.0%

BASETP 0(a)~5% -5.0%

MWATER 5.0% -5.0%

AGWETP 5.0% -5.0%

10 Year Return Discharge [m³/s](a) Parameter bounded by upper bounds.



35 40 45 50 55 60 65


Figure 7b Percent Change to 10-Year Peak Flood of Muskeg River Associated With ±10% Change in Hydrological Simulation Program – Fortran Model Parameters

-30 -20 -10 0 10 20

IRC 4.3~6.5%(a) -10.0%

PAF -10.0% 10.0%

SNOWCF 10.0%

ATMP 3°C

AGWRC 1.6(a)~10% -10%~0(b)

INTFW 0(a)~10% -10.0%

SHADE -10.0% 10.0%

INFILT 10.0% -10.0%

SNOEVP 10.0% -10.0%

FOREST 10.0% -10.0%

DEEPFR 0.1

UZSN 10.0% -10%~0(b)

COVIND 10.0% -10.0%

TSNOW -10.0% 8.1(a)%

LZSN 10.0% -10%~0(b)

LZETP 10.0% -10.0%

LSUR 10.0% -10.0%

NSUR 10.0% -10.0%

CCFACT -10.0% 10.0%

CEPSE 0(a)~10% -10.0%

SLSUR -10.0% 10.0%

BASETP 0(a)~10% -10.0%

MWATER 10.0% -10.0%

AGWETP 10.0% -10.0%

10 Year Return Discharge [m³/s](a) Parameter bounded by upper bounds.(b) Parameter bounded by lower bounds.


35 40 45 50 55 60 65


• Base flow is a major runoff component from areas of sandy subsurface soil and is the dominant runoff component from the Muskeg River watershed during the winter months. Model parameters that affect the base flow (i.e., groundwater recession parameter [AGWRC], and deep groundwater loss [DEEPFR]) have a large effect on land segments representing sandy subsurface soils than in other land segments. These parameters are the dominant model parameters that affect simulated low flows from the Muskeg River watershed.

• Evapotranspiration (ET) losses are primarily from the upper-zone storage. Evapotranspiration can also be satisfied from the lower-zone storage during the growing season. Model parameters that affect the amount of upper zone storage (such as UZSN) and the rate of ET loss from the upper zone have a greater effect on mean annual runoff. Conversely, model parameters that affect the amount of lower-zone storage (such as LZSN) and the rate of ET loss from the lower zone have a greater effect in the growing season than in the non-growing season.

4.2.3 Conclusions

In general, the results of the sensitivity analysis for the Beaver River and Muskeg River watersheds indicate that the most influential model parameters are those that affect groundwater (AGWRC) and interflow (INTFLW and IRC). In addition, model simulation results are sensitive to changes in parameters that influence the amount of precipitation that eventually discharges from groundwater or interflow (INFILT, LZSN and UZSN).

The SHADE and SNOEVP parameters, which are associated with snowmelt runoff, have an effect on mean annual runoff and mean winter runoff. An increase in these parameters results in decreased mean annual runoff and vice versa. An increase in SHADE parameter results in increased mean winter runoff and a decrease in this parameter has little effect on mean winter runoff.

SuncoVoyageu


5 UNCERTAINTY ANALYSIS

5.1 KEY MODEL PARAMETERS

An examination of the sensitivity of simulated flow output from the HSPF model for the Beaver River and Muskeg River watersheds indicate that the model output is most sensitive to the model parameters discussed below. The calibrated values and the range of uncertainty analysis are provided in Tables 4 to 12.

• SHADE – the fraction of land segment shaded from solar radiation by trees or slopes. It controls the fraction of radiation that reaches the snowpack and hence has an effect on the timing of snowmelt runoff. The effect of the timing of snowmelt runoff will in turn affect the peak and volume of runoff as indicated by sensitivity analysis.

The calibrated value of the SHADE parameter for natural watersheds in the Oil Sands Region is about 0.8. This parameter is included in the uncertainty analysis by setting the lower limit to 30% of the calibrated value (i.e., 0.56) and the upper limit to 1.0 (i.e., no solar radiation reaching snowpack) (Tables 4 and 12).

• SNOEVP – a factor to adjust evaporation (or sublimation) from the snowpack. This parameter affects the amount of runoff from a land segment because evaporation from the snowpack reduces the amount of moisture available for runoff from the land segment.

The calibrated values of this parameter for natural watersheds in the Oil Sands Region vary from 0.2 to 0.3. This parameter was the most sensitive parameter for the prediction of mean annual runoff and hence was included in the uncertainty analysis by setting the lower and upper range to ±30% of the calibrated values (Tables 4 and 11).

• LZSN - represents the parameter of lower zone ‘nominal’ moisture storage. In the HSPF model, the actual soil moisture storage (LZS) at any time can be up to three times LZSN; hence LZSN cannot directly affect the amount of moisture storage in the lower zone. This parameter has a noticeable effect on the volume of surface and subsurface runoff. The calibrated value of this parameter varies from 1.3 to 10.2 mm, depending on subsurface soil type. For the uncertainty analysis, the upper limits of the parameter are set to +30% of the calibrated values. The lower limits are set to -30% of the calibrated values except for the sandy subsurface soil for which the lower limits are bound by the lower range defined in Table 5, which defines the lower bounds based on the lower value from Basin's Technical Note 6 (U.S. EPA 2000) or that based on Golder's Calibration for the Oil Sands Region (Golder 2003) [see Table 1].


Table 4 Calibrated, Upper and Lower Ranges of Model Parameter Range Used in Uncertainty Analysis of Hydrological Simulation Program – Fortran Model

Typical Parameter Range Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area

Parameter Lower Range

Upper Range

Silt and Clay

Silt, Clay and Sand Sand Silt and

Clay Silt, Clay and Sand Sand Silt and

Clay Sand CT Site Tailings Sand

Plant Area

LZSN [mm] 1.25 380 7.62 10.16 1.27 7.62 10.16 1.27 7.62 1.27 1.27 1.27 5.08

UZSN [mm] 1.25 50 2.54 2.54 2.54 12.7 12.7 12.7 3.05 3.81 2.54 (upland) 12.7 (lowland) 2.54 2.54

INFILT [mm/hr] 0.02 12.5 0.203 0.254 2.54 0.203 0.254 1.25 0.305 2.921 2.54 (upland) 1.25 (lowland) 2.54 0.03

INTFW 1 25 3.3 3.3 10 8 10 25 3.3 10 13 (upland) 25 (lowland) 10 1

IRC 0.3 0.98 0.94 0.93 0.925 0.925 0.925 0.92 0.94 0.93 0.9275 0.94 0.93

AGWRC [/day] 0.85 0.999 0.983 0.94 0.94 0.87 0.87 0.87 0.983 0.85 0.94 (upland) 0.85 (lowland) 0.94 0.94

SNOWEVP 0 0.5 0.3 0.2 0.2 0.25 0.25 0.25 0.2 0.2 0.2 (upland) 0.25 (lowland) 0.2 0.2

SHADE 0 1 0.8 0.8 0.8 0.8 0.8 0.8 0.2 0.2 0.2 0.2 0.2

Table 5 Parameter Range for Uncertainty Analysis of Upper Zone Storage Parameter (LZSN) Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area

Range Silt and Clay Silt, Clay and

Sand Sand Silt and Clay Silt, Clay and Sand Sand Silt and Clay Sand CT

Site Tailings

Sand Plant Area

lower limit 5.33 7.11 1.25 5.33 7.11 1.25 5.33 1.25 1.25 1.25 3.56

upper limit 9.91 13.20 1.65 9.91 13.20 1.65 9.91 1.65 1.65 1.65 6.60

distribution normal normal Weibull normal normal Weibull normal Weibull Weibull Weibull normal


Table 6 Parameter Range for Uncertainty Analysis of Upper Zone Storage Parameter (UZSN)

Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area Range Silt and

Clay Silt, Clay and

Sand Sand Silt and Clay

Silt, Clay and Sand Sand Silt and Clay Sand CT Site Tailings

Sand Plant Area

lower limit 1.78 1.78 1.78 8.89 8.89 8.89 2.14 2.67 1.78 (upland)

8.89 (lowland) 1.78 1.78

upper limit 3.30 3.30 3.30 16.50 16.50 16.50 3.97 4.95 3.3 (upland)

16.5 (lowland) 3.30 3.30

distribution normal normal normal normal normal normal normal normal normal normal normal

Table 7 Parameter Range for Uncertainty Analysis of Infiltration Index Parameter (INFILT) Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area

Range Silt and Clay


Clay Silt, Clay and

Sand Sand Silt and Clay Sand CT Site Tailings

Sand Plant Area

lower limit 0.2 0.2 1.78 0.2 0.2 0.88 0.2 2.04 1.88 (upland)

0.88 (lowland) 1.88 0.02

upper limit 0.26 0.33 3.3 0.26 0.33 1.63 0.4 3.8 3.3 (upland)

1.63 (lowland) 3.3 0.04

distribution Weibull normal normal Weibull normal normal normal normal normal normal normal


Table 8 Parameter Range for Uncertainty Analysis of Interflow Parameter (INTFW) Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area

Range Silt and Clay


Clay Silt, Clay and

Sand Sand Silt and Clay Sand CT Site Tailings

Sand Plant Area

lower limit 2.3 2.3 7 5.6 7 17.5 2.3 7 7 (upland)

17.5 (lowland) 7 0.7

upper limit 4.3 4.3 13 10.4 13 25 4.3 13 13 (upland)

25 (lowland) 13 1.3


Table 9 Parameter Range for Uncertainty Analysis of Interflow Recession Parameter (IRC) Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area


Sand Sand Silt and Clay Silt, Clay and Sand Sand Silt and Clay Sand CT Site Tailings

Sand Plant Area

lower limit 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75

upper limit 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98

distribution Weibull Weibull Weibull Weibull Weibull Weibull Weibull Weibull Weibull Weibull Weibull

Table 10 Parameter Range for Uncertainty Analysis of Groundwater Recession Parameter (AGWRC) Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area


Sand Sand Silt and Clay Silt, Clay and Sand Sand Silt and Clay Sand CT Site Tailings

Sand Plant Area

lower limit 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85

upper limit 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.999

distribution Weibull Weibull Weibull Weibull Weibull Weibull Weibull Weibull Weibull Weibull Weibull


Table 11 Parameter Range for Uncertainty Analysis of Snow Evaporation Factor (SNOEVP) Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area


Sand Sand Silt and Clay Silt, Clay and Sand Sand Silt and Clay Sand CT

Site Tailings

Sand Plant Area

lower limit 0.2 0.14 0.14 0.18 0.18 0.18 0.14 0.14 0.14 0.14 0.14

upper limit 0.4 0.26 0.26 0.33 0.33 0.33 0.26 0.26 0.33 0.26 0.26


Table 12 Parameter Range for Uncertainty Analysis of Shade Factor (SHADE) Natural Upland Natural Lowland Reclaimed Overburden Reclaimed Area


Sand Sand Silt and Clay Silt, Clay and Sand Sand Silt and Clay Sand CT Site

Tailings Sand

Plant Area

lower limit 0.56 0.56 0.56 0.56 0.56 0.56 0.14 0.14 0.14 0.14 0.14

upper limit 1 1 1 1 1 1 0.26 0.26 0.26 0.26 0.26



• UZSN - represents the parameter of ‘nominal’ upper zone soil moisture storage. Any water storage in the upper zone will be depleted either by evapotranspiration or by percolation to groundwater. This parameter is related to the unique land surface characteristics in the Oil Sands Region (e.g., peat or muskeg). The lowland areas are predominantly covered by thick muskeg that acts to temporarily store runoff from upland areas as well as lowland areas. Hence, the hydrology of lowland areas is mainly governed by this parameter (Table 6).

• INFILT - an index to mean soil infiltration rate. In HSPF, INFILT is the parameter that effectively controls the overall division of the available moisture from precipitation (after interception) into surface and subsurface flow and storage components. Thus, high values of INFILT will produce more water in the lower zone and groundwater, and result in higher baseflow to the stream; low values of INFILT will produce more upper zone and interflow storage water, and thus result in greater direct overland flow and interflow (Table 12).

INFILT is primarily a function of soil characteristics. The range of this parameter for the uncertainty analysis is based on the ranges established for the United States Department of Agriculture (USDA) Soil Conservation Service (SCS) hydrologic soil groups (Donigian and Davis 1978). Values are provided in Table 13.

Table 13 Parameter Range for Uncertainty Analysis of Soil Infiltration Rate (INFILT)

INFILT Estimate SCS Hydrologic Soil Group [inch/hr] [mm/hr]

Runoff Potential

A 0.4 to 1.0 10.0 to 25.0 low B 0.1 to 0.4 2.5 to 10.0 moderate C 0.05 to 0.1 1.25 to 2.5 moderate to high D 0.01 to 0.05 0.25 to 1.25 high

Note: SCS = Soil Conservation Service.

• INTFW - is a coefficient that determines the amount of water that enters the ground from surface detention storage and becomes interflow, as opposed to direct overland flow and upper zone storage. Increasing INTFW increases the amount of interflow and decreases direct overland flow. Hence, this parameter will have little effect on runoff volume but will affect peak flow from the land segment (Table 8).

• IRC - represents the interflow recession and affects the rate at which interflow is discharged from storage, and thus the hydrograph shape in the recession region of the curve between the peak storm flow and base flow (Table 9).


The values of IRC for the Muskeg River watershed reflect the general characteristics of muskeg hydrology, where interflow through muskeg layers may have a detention time of more than four days. As a result, the interflow behaves more like baseflow and hence the higher values of IRC used in the model.

• AGWRC - a time invariant groundwater recession coefficient. It has an effect on the baseflow from a watershed. The sensitivity analysis for this parameter shows that an increase of this parameter to the upper-bound physical limit will result in a significant increase in baseflow and an appreciable decrease in mean annual runoff. On the other hand, a decrease of this parameter to the lower-bound physical limit will have a small effect on the water balance and some effect on low flow (Table 10).

5.2 PROBABILITY DISTRIBUTIONS FOR MODEL PARAMETERS

The selection of appropriate prior probability distributions for specifying the levels of uncertainty in the model parameter values has some degree of subjectivity. Haan (1989) investigated the impact of the form of model input parameters distributions on model output probability distributions predicted by Monte-Carlo simulation. The conclusion was that a good estimate of the mean and variance of the parameters is more important than the actual distribution chosen to represent the parameter value uncertainty.

For the uncertainty analysis of the HSPF model output, the selected parameter distributions are broad enough to ensure that they span the range of parameter observations. For example, the INFILT parameter in the HSPF model represents the index of soil infiltration that depends on soil types for each sub-basin. Given information of sub-surface soil, the range of values for this parameter can be estimated for different soil types based on USDA Soil Conservation Service hydrological soil groups.

For those parameters for which probability distributions are difficult to accurately define, the following assumptions were made:

• The range of parameter values (upper and lower bounds) is set to either:

− the maximum and minimum values based on previous experiences with the application of HSPF (i.e., Table 13); or

− ±30% of the calibrated parameter values.


• If the lower and upper range of parameter values are set to ±30% of the calibrated values, a normal distribution is assumed with a mean equal to the calibrated value and standard deviation equal to one-tenth of the mean. This results in a normal distribution function bounded by ±3 times the standard deviation.

• If either the upper or lower range is dictated by the bound set based on previous experience of model application, a Weibull distribution with pre-defined location, scale and shape parameters is used to reflect some prior knowledge about the expected distribution of the parameter.

Table 4 provides the range defined for each of the parameters considered in the certainty analysis of the HSPF model. Figures 8 and 9 show examples of parameter distributions defined for the infiltration parameter (i.e., INFILT) and the interflow recession parameter (i.e., IRC), respectively.

Figure 8 Parameter Distribution for Index of Mean Soil Infiltration (INFILT) for Lowland Sandy Sub-Surface Soil


Figure 9 Parameter Distribution for Interflow Recession Parameter (IRC)

5.3 RESULTS

5.3.1 Muskeg River

The sensitivity of the predicted uncertainty of model output to the number of runs (or samples) by Monte-Carlo simulation was analyzed by considering six scenarios (i.e., 50 to 500 samples) as shown in Table 14 and Figures 10a to 10d. The results indicate that the model outputs are similar for Monte-Carlo simulations that use 200 or more sample points. The HSPF model was run 500 times. For each run, the probability distribution of each parameter value specified in Section 5.2 was sampled for a random value of the parameter for each sub-basin in the watershed. Hence, spatial uncertainty in the parameters is incorporated in the analysis. The results of the uncertainty analysis for the four model output variables (i.e., mean annual runoff, mean winter runoff, 10-year flood peak and 7Q10 low flow) for the Muskeg River at WSC station 07DA008 are given in Figure 11. Some observations are as follows:

• The calibrated mean annual flow for the Muskeg River watershed at the gauging station is 3.871 m3/s. The expected mean annual flow from the 500 model runs with random parameter values (about the calibrated values) is 3.877 m3/s. The difference between calibrated mean annual runoff and expected mean annual runoff from the uncertainty analysis is very small (i.e., 0.006 m3/s or 0.2%).


• The difference between the calibrated values and expected values obtained from the uncertainty analysis for the mean winter flow, 10-year flood peak flow and 7Q10 low flow for Muskeg River at WSC station are given in Table 14. The expected mean winter and 7Q10 flows are higher than the calibrated values, because an increase in model parameters AGWRC and IRC increases the simulated flows to a greater degree than an equivalent decrease in model parameters will decrease model outputs. On the other hand, the 10-year flood peak discharge obtained from the uncertainty analysis is less than the calibrated value by about 3%, as shown in Table 14. An increase in the model parameters that have an effect on the 10-year flood peak discharge (i.e., IRC, AGWRC and INTFW) has more effect than an equivalent decrease in these model parameters, as shown in Figures 10 and 11. The differences between the outputs from the uncertainty runs and the values used for the assessment are statistically not significant.

Table 14 Sensitivity of Model Outputs to the Number of Monte-Carlo Simulation for Uncertainty Analysis – Muskeg River

Monte-Carlo Simulated Values % Change No. of Samples

50 100 200 300 400 500 Calibrated

Values 50 500

Average [m3/s]

annual mean 3.865 3.868 3.868 3.868 3.868 3.868 3.871 -0.2 -0.1

winter mean 0.508 0.506 0.504 0.504 0.504 0.504 0.443 14.7 13.8

10-year peak flood 49.8 49.8 49.9 50.0 50.0 49.9 51.9 -4.1 -3.7

7Q10 low flow 0.021 0.021 0.021 0.021 0.021 0.021 0.018 16.5 15.7

Standard Deviation [m3/s]

annual mean 0.022 0.022 0.023 0.024 0.024 0.023

winter mean 0.024 0.022 0.021 0.021 0.021 0.020

10-year peak flood 1.001 1.032 1.035 1.052 1.061 1.059

7Q10 low flow 0.0037 0.0018 0.0023 0.0027 0.0024 0.0025


Figure 10 Sensitivity of Model Outputs to the Number of Monte-Carlo Simulations for Uncertainty Analysis – Muskeg River

(a) Mean Annual Runoff

3.810

3.830

3.850

3.870

3.890

3.9103.930

3.950

0 100 200 300 400 500 600Sample Size

Run

off [

m3 /s

]

Calibrated Value

(b) Mean Winter Runoff

0.434

0.454

0.474

0.494

0.514

0.534

0 100 200 300 400 500 600Sample Size

Run

off [

m3 /s

]

Calibrated Value

(c) 10-year Peak Flood

46.1047.10

48.1049.1050.10

51.1052.1053.10

0 100 200 300 400 500 600Sample Size

Floo

d Fl

ow [m

3 /s]

Calibrated Value

(d) 7Q10 Low Flow Runoff

-0.0017

0.0033

0.0083

0.0133

0.0183

0.0233

0 100 200 300 400 500 600Sample Size

Low

Flo

w [m

3 /s]

Calibrated Value


Figure 11 Comparison Uncertainty Analysis and Calibrated Model Values – Cumulative Frequency Plot – Muskeg River

0

0.2

0.4

0.6

0.8

1

3.78 3.8 3.82 3.84 3.86 3.88 3.9 3.92 3.94 3.96

Mean Annual Runoff [m3/s]

Cum

ulat

ive

Freq

uenc

y

50 Samples100 Samples200 Samples300 Samples400 Samples500 Samples

Calibrated Value = 3.871

0

0.2

0.4

0.6

0.8

1

0.417 0.437 0.457 0.477 0.497 0.517 0.537 0.557 0.577 0.597

Mean Winter Flow [m3/s]

Cum

ulat

ive

Freq

uenc

y



0

0.2

0.4

0.6

0.8

1

0.015 0.018 0.021 0.024 0.027 0.03 0.033 0.036 0.039 0.042

7Q10 [m3/s]

Cum

ulat

ive

Freq

uenc

y



0

0.2

0.4

0.6

0.8

1

46 47 48 49 50 51 52 53 54

10-year Return Period Flood [m3/s]

Cum

ulat

ive

Freq

uenc

y 50 Samples100 Samples200 Samples300 Samples400 Samples500 Samples



5.3.2 Application of Uncertainty Analysis to Poplar Creek Flow Forecasts - Voyageur South Project Far Future Snapshot

Sensitivity and uncertainty analyses were conducted to evaluate the modelled estimates of Poplar Creek flow statistics in the far-future time snapshot. The HSPF model was run 397 times using the Monte-Carlo method for varying model parameters as described in previous sections. The results shown in Table 15 and Figures 12a to 12c indicate that the model outputs are similar for Monte-Carlo simulations that use more than 200 sample points. The uncertainty analysis using Monte-Carlo simulation of about 200 samples (or runs) was deemed sufficient to quantify the uncertainty of HSPF model output.

Table 15 Sensitivity of Model Outputs to the Number of Monte-Carlo Simulation for Uncertainty Analysis – Poplar Creek

Monte-Carlo Simulated Values Calibrated % Change

No. of Samples 50 100 200 300 397 Values 50 397

Average [m3/s]

annual mean 0.750 0.751 0.751 0.751 0.751 0.745 0.68 0.72 winter mean 0.323 0.324 0.325 0.324 0.325 0.328 -1.47 -0.96 10-year peak flood 7.597 7.611 7.612 7.622 7.621 8.200 -7.35 -7.06

7Q10 low flow 0.000 0.000 0.000 0.000 0.000 0.000

Standard Deviation [m3/s]

annual mean 0.008 0.009 0.008 0.009 0.009 winter mean 0.009 0.010 0.011 0.011 0.011 10-year peak flood 0.168 0.184 0.180 0.187 0.191

7Q10 low flow 0.000 0.000 0.000 0.000 0.000

For each HSPF model run, the probability distribution of each parameter value specified in Section 5.2 was sampled for a random value of the parameter for each sub-basin in the watershed. Hence, spatial uncertainty in the parameters is incorporated in the analysis. The results of the uncertainty analysis for the four model output variables (i.e., mean annual runoff, mean winter runoff, 10-year flood peak and 7Q10 low flow) for Poplar Creek at the mouth are given in Figure 13. Some observations are as follows:

• The calibrated mean annual flow for the Poplar Creek watershed at the mouth is 0.745 m3/s. The expected mean annual flow from the 397 model runs is about 0.751 m3/s. The difference between the calibrated mean annual runoff and expected mean annual runoff from the uncertainty analysis is very small (i.e., 0.006 m3/s or 0.72%).


Figure 12 Sensitivity of Model Outputs to the Number of Monte-Carlo Simulations for Uncertainty Analysis – Poplar Creek

(a) Mean Annual Runoff

0.735

0.740

0.745

0.750

0.755

0 100 200 300 400 500

Sample Size

Run

off [

m3 /s

]

Calibrated Value

(b) Mean Winter Runoff

0.315

0.320

0.325

0.330

0.335

0 50 100 150 200 250 300 350 400 450

Sample Size

Run

off [

m3 /s

] Calibrated Value

(c) 10-year Peak Flood

7.300

7.500

7.700

7.900

8.100

8.300

8.500

0 50 100 150 200 250 300 350 400 450

Sample Size

Run

off [

m3 /s

]

Calibrated Value


Figure 13 Comparison of the Results Uncertainty Analysis and Calibrated Model Values – Cumulative Frequency Plot – Poplar Creek

0

0.2

0.4

0.6

0.8

1

0.72 0.73 0.74 0.75 0.76 0.77 0.78Mean Annual Runoff [m3/s]

Cum

ulat

ive

Freq

uenc

y

50 Samples

100 Samples

200 Samples

300 Samples

397 Samples


0

0.2

0.4

0.6

0.8

1

0.3 0.31 0.32 0.33 0.34 0.35 0.36 0.37

Mean Winter Flow [m3/s]

Cum

ulat

ive

Freq

uenc

y

50 Samples100 Samples200 Samples300 Samples397 Samples


0

0.2

0.4

0.6

0.8

1

7.000 7.200 7.400 7.600 7.800 8.000 8.200 8.400

10-year Return Period Flood [m3/s]

Cum

ulat

ive

Freq

uenc

y

50 Samples100 Samples200 Samples300 Samples397 Samples



• The expected mean winter flow calculated from the uncertainty analysis is approximately 1% lower than the calibrated value.

• The 10-year flood peak discharge obtained from the uncertainty analysis is approximately 7% less than the calibrated value. Increases in the model parameters that have an effect on the 10-year flood peak discharge (i.e., IRC, AGWRC and INTFW) have a greater effect than an equivalent decreases in these model parameters, as shown in Figures 7a and 7b.

• The calibrated 7Q10 low flow for Poplar Creek at the mouth is zero. Hence, the expected 7Q10 low flow obtained using uncertainty analysis for all runs is also zero.


6 CONCLUSIONS

The Muskeg River and Poplar Creek watersheds were used to conduct uncertainty analysis on HSPF model parameters that affect model output. The Monte-Carlo sample method was used. The limits within which the calibration parameter values were randomly sampled were based on the upper and lower limits provided in Table 4.

Model parameters were sampled for each sub-basin (i.e., about 48 sub-watersheds for the Muskeg River basin and about 33 sub-watersheds for Poplar Creek basin). Hence, the model parameters used in the uncertainty analysis are allowed to vary spatially within one model run as well as between the 500 model runs. The modelling uncertainty was evaluated by calculating the expected mean annual runoff, mean winter runoff, 10-year flood peak discharge and 7Q10 low flow at the gauging station on the Muskeg River and at the mouth for Poplar Creek.

The results of the uncertainty analysis support the following conclusions:

• The HSPF model reproduces the variable pattern of the flow recorded on the Muskeg River and Poplar Creek discharge, with the use of randomly selected input parameter sets.

• The HSPF model has a robust performance in simulating streamflows.

• The differences between the outputs from the uncertainty runs and the values used for the assessment are statistically not significant.


7 GLOSSARY AND ABBREVIATIONS

7.1 GLOSSARY

Aquifer A body of rock or soil that contains sufficient amounts of saturated permeable material to yield economic quantities of water to wells or springs. Any water-saturated body of geological material from which enough water can be drawn at a reasonable cost for the purpose required. An aquifer in an arid prairie area required to supply water to a single farm may be adequate if it can supply 1 m3/d. This would not be considered an aquifer by any industry looking for cooling water in volumes of 10,000 m3/d. A common usage of the term aquifer is to indicate the water-bearing material in any area from which water is most easily extracted.

Consolidated Tailings (CT)

Consolidated tailings are prepared by combining mature fine tails with cycloned fresh sand tailings to form a deposit that consolidates relatively quickly in tailings deposits. This mixture is chemically stabilized (to prevent segregation of fine and coarse mineral solids) using gypsum (CaSO4). Also known as composite tailings.

Evaporation The process by which water is changed from a liquid to a vapour.

Evapotranspiration A measure of the ability of the atmosphere to remove water from a location through the processes of evaporation and water loss from plants (transpiration).

Groundwater That part of the subsurface water that occurs beneath the water table, in soils and geologic formations that are fully saturated.

Hydrological Simulation Program – Fortran (HSPF)

The Hydrological Simulation Program – Fortran (HSPF) model is a comprehensive, conceptual, continuous watershed simulation model designed to simulate the water quantity and water quality processes that occur in a watershed. The model can reproduce spatial variability by dividing the basin in hydrologically homogeneous land segments and simulating runoff for each land segment independently, using segment-specific meteorologic input data and watershed parameters.

Hydrology The science of waters of the earth, their occurrence, distribution, and circulation; their physical and chemical properties; and their reaction with the environment, including living beings.


Lowland Areas Areas with ground slopes of less than 0.5% and typically poorly drained.

Monte-Carlo Simulation A problem solving technique used to approximate the probability of certain outcomes by running multiple trial runs, called simulations, using random variables.

Muskeg A soil type comprised primarily of organic matter. Also known as bog peat.

Overburden The soil, sand, silt or clay that overlies a mineral deposit and must be removed before mining.

Peat A material composed almost entirely of organic matter from the partial decomposition of plants growing in wet conditions.

Riparian Refers to terrain, vegetation or simply a position next to or associated with a stream, floodplain or standing waterbody.

Runoff The portion of water from rain and snow that flows over land to streams, ponds or other surface waterbodies. It is the portion of water from precipitation that does not infiltrate into the ground, or evaporate.

Tailings A by-product of oil sands extraction typically comprised of water, sands and clays, with minor amounts of residual bitumen.

Till Sediments laid down by glacial ice.

Upland Areas Areas that have typical ground slopes of 1 to 3% and are better-drainage.

Watershed The entire surface drainage area that contributes water to a lake or river.

Weibull Type of frequency distribution, which is the extreme value type III distribution for minima bounded below by zero.

Wetlands Wetlands are land where the water table is at, near or above the surface or which is saturated for a long enough period to promote such features as wet-altered soils and water tolerant vegetation. Wetlands include organic wetlands or “peatlands,” and mineral wetlands or mineral soil areas that are influenced by excess water but produce little or no peat.


7.2 ABBREVIATIONS

o Temperature in degrees celcius

± Plus or minus

% Percent

7Q10 Lowest annual average 7-day consecutive flow that occurs, on average, once every 10 years

AGWETP Fraction of remaining ET from shallow groundwater

AGWRC Base groundwater recession coefficient (time invariant)

ATMP Air Temperature

BASETP Fraction of remaining ET by riparian vegetation as active groundwater enters stream bed

CCFACT Factor to adjust the rate of heat transfer from the atmosphere to the snowpack due to condensation and convection

CEMA Cumulative Environmental Management Association

CEPSC Interception storage capacity

CEPSE Interception parameter

COVIND Maximum Snowpack depth at which entire land segment is covered with snow (water equivalent)

CT Consolidated Tailings

DEEPFR Fraction of groundwater inflow to deep aquifer recharge

e.g. For example

EIA Environmental Impact Assessment

EPA U.S. Environmental Protection Agency

ET Evapotranspiration

FOREST Fraction of land covered by coniferous forest that continue to transpire in winter

GIS Geographic Information System


HSPF Hydrologic Simulation Program-Fortran

i.e. That is

INFEXP Exponent in infiltration equation

INFILD Ratio of max/mean infiltration capacity

INFILT Index to mean soil infiltration rate

INTFW Interflow inflow parameter

IRC Interflow recession parameter

KM Kilometre

KVARY Time variant groundwater recession coefficient

LAT Latitude of pervious land segment

L/S Litres per second

LSUR Length of overland flow

LZETP Lower zone evapotranspiration parameter

LZS Soil moisture storage

LZSN Lower zone nominal soil moisture storage

m Metre

m3/s Cubic Metres per second

mm Millimetre

mm/d Millimetre per day

mm/mm Millimetre/Millimetre

mm/yr Millimetre per year

min Minimum

max Maximum

MELEV Mean elevation of pervious land segment

MGMELT Ground heat daily melt rate


MODFLOW Three-dimensional, numerical groundwater flow model

MWATER Maximum liquid water holding capacity in snowpack

NSUR Manning's N (roughness) for overland flow

PAF Precipitation adjust factor

PETMAX Temperature below which evapotranspiration is reduced to 50% due to low temperature in winter time

PETMIN Temperature below which evapotranspiration is set to zero when winter temperature approaches freezing point

RDCSN Density of new snow relative to water

SCS USDA Soil Conservation Service

SHADE Fraction of land segment shaded from solar radiation by tree and slopes

SLSUR Slope of overland flow plane

SNOEVP Factor to adjust evaporation (sublimation) from snowpack

SNOWCF Snow catch factor

TOR Terms of Reference

TSNOW Temperature below which precipitation occurs as snow under saturated conditions

USDA United States Department of Agriculture

UZSN Upper zone nominal soil moisture storage

WSC Water Survey of Canada


8 REFERENCES

AENV (Alberta Environment). 2007. Final Terms of Reference: Environmental Impact Assessment Report for Proposed Voyageur South Project. Issued by Alberta Environment, July 2007.

Beck, B. 1987. Water Quality Modelling: A Review of the Analysis of Uncertainty. Water Resour. Res. 23 (8): 1393-1442.

Binley, A.M. and K.J. Beven, 1991. Physically-based modelling of catchment hydrology: A likelihood approach to reducing predictive uncertainty. In Computer Modelling in the Environmental Sciences, 75-88, eds. D.G. Farmer, and M.J. Rycroft. Oxford, England: Clarendon Press.

Doctor, P.G. 1989. Sensitivity and uncertainty analysis for performance assessment modelling. Engng. Geol. 26:411-429.

Donigian, A.S., Jr. and H.H. Davis, Jr. 1978. User's Manual for Agricultural Runoff Management (ARM) Model, U.S. Environmental Protection Agency, EPA- 600/3-78-080.

Golder (Golder Associates Ltd.). 2003. Regional Surface Water Hydrology Study of Re-calibration of HSPF Model prepared for Canadian natural Resources Ltd., Shell Canada Limited, Suncor Energy Inc. and Syncrude Canada Ltd.

Haan, C.T., B. Allred, D.E. Storm, G.J. Sabbagh and S. Prabhu. 1995. Statistical procedure for evaluating hydrologic/water quality models. Transactions of the ASAE 38(3):725-733.

Haan, C. T., 1989. Parametric Uncertainty in Hydrologic Modeling. Trans. ASAE 32(l):137-146.

Reckhow, K.H. 1994. Water quality simulation modelling and uncertainty analysis for risk assessment and decision making. Ecol. Modelling 72:1-20.

Thomas, R.E. 1982. Uncertainty analysis. Battelle Columbus Laboratories, Columbus, Ohio. U.S. Department of Energy, Office of Nuclear Waste Isolation Contract DE-AC606-76-PAO1830-ONWI.


U.S. EPA (United States Environmental Protection Agency). 2000. Estimating Hydrology and Hydraulic Parameters for HSPF, BASINS Technical Note 6. United States Environmental Protection, Office of Water, EPA-823-R00-012, EPA 4305. July 2000.

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Appendix 21 Application of Simulation Models, Uncertainty ... · EIA METHODS - APPLICATION OF SIMULATION MODELS - SENSITIVITY AND UNCERTAINTY ANALYSIS . ... The levels of modelling