Effectiveness of BMPs considering

uncertainties in weather patterns

in a pasture dominated watershed

Li-Chi Chiang, M. Gitau, and I. Chaubey

Purdue University

Conservation Effectiveness Assessment Program (CEAP)

Funding Agency: USDA-CSREES

Grant No.: 2005-48619-03334

Introduction

Non-point source pollution (NPS): The impact of

agricultural practices on water quality.

BMPs: Adverse impact of agricultural production

on surface water and ground water can be

minimized by implementing best management

practices (BMPs)

Watershed models: evaluate BMP performance.

Weather uncertainty: Water quality can be

affected by weather.

Objectives

Evaluate the effectiveness of various

structural and nonstructural BMPs in

improving water quality, interactions among

BMPs in reducing pollutants of concern.

Evaluate impacts of weather uncertainty on

water quality improvements in a pasture

dominated watershed.

Location: Within the Illinois

River basin located in

Northwest Arkansas and

Eastern Oklahoma

Area: 32 km^2

Major tributaries: Moores

Creek and Beatty Branch

Creek

Primary concern: Phosphorus from land

application of animal manure

Study Area: Lincoln Lake Watershed

Land use:

Pasture 36%

Forest 39%

Urban 12%

Land Use Change (1992-2004)

BMPs implemented (1992-2005)

Soil and Water Assessment Tool (SWAT)

Predict the long-term impacts of land use

management on water, sediment and

agricultural chemical yields at different scales

in a complex watershed.

172 BMP scenarios were modeled.

250 weather conditions for possible future

weather (2004-2028) by using a Monte Carlo

Approach.

BMP Scenarios Modeled in SWAT

Nutrient management

poultry

litter

alum-

amended

litter

No application

spring 1, 1.5, 2 ton/acre

summer 1, 1.5, 2 ton/acre

fall 2, 2.5, 3 ton/acre

grazing and

pasture

management

no grazing

optimum grazing

over grazing

Buffer

width

0 m

15 m

30 m

3 BMP categories(total of 171 BMP scenario combinations) +

2004 baseline => 172 BMP scenarios

19 3 3

Condor Approach

High performance computing system

Number of runs

172 BMP scenarios, 250 weather

realization/scenario = 43,000 SWAT runs

Time/run = 8-10 minutes on LINUX (at least 5,700

CPU hrs in a single machine)

1-2 hours to finish one scenario with 250 weather

realizations in Condor

More information can be found in the lecture (Title: Computational

approaches to evaluating BMP scenarios considering stochasticity

of weather) given by Dr. M. Gitau in SW-25 session

Statistics Analysis (ANOVA)

Two-way ANOVA was used to analyze

multiple factors (3 factors in nutrient

management, grazing management and

buffer strips) that impacted on water

quality.

Tukey method (α = 0.05) was used to

determine the multiple pairwise difference.

Results

Annual sediment

and nutrient

losses at

pastured lands.

Min.

CV

Max.

CV

Range

of CV

Base

-line

Over

grazing

TS 0.69 0.83 0.14

TN 0.40 1.05 0.55

Sol.

P

0.55 0.85 0.30

TP 0.57 0.80 0.23

Annual sediment and water quality from

different land use area (FRST, UCOM, URBN, PAST)

Range of annual total sediment (ton/ha), nitrogen (kg/ha), soluble

phosphorus (kg/ha) and total phosphorus loss (kg/ha) from

various defined land use categories.

Individual effect of BMP:

Fertilizer management (time-amount)

Amount (tons/acre)

Time Spring SP1 SP1.5 SP2

Summer SU1 SU1.5 SU2

Fall FL2 FL2.5 FL3

Interaction effects of BMPs:

Grazing and Buffer management

Grazing management

NG: no grazing

OG: optimum grazing

OVG: over grazing

Buffer width

0 m

15 m

30 m

Interaction effects of BMPs:

Fertilizer, Grazing and Buffer management

Interaction effects of BMPs:

Fertilizer, Grazing and Buffer management

Conclusions

Objective 1: Evaluate the BMPs performance and

interactions among BMPs.

Fall fertilizer application resulted in the greatest TN

losses.

The fertilization timing had less impact on TS and TP

than TN.

Over grazing had greater impact on TN than on TP

because of the heavy manure loading which

increased nitrogen mineralization.

Buffer strip was the most influential BMP for

reducing TS and TP losses.

Conclusions

Objective 2: Evaluate impacts of weather

uncertainty on water quality improvements.

Weather uncertainty has the most impact on TN

loss, then TP and TS.

When over grazing is applied, the BMPs

performance can be greatly affected by stochastic

weather conditions.

Questions?