COUPLING DROUGHT FORECASTING WITH THE SWAT HYDROLOGY MODEL TO DEVELOP A DECISION MAKING TOOL

FOR WATER RESOURCE CONSERVATION

RACHEL MCDANIEL, BIOLOGICAL AND AGRICULTURAL ENGINEERING DEPARTMENT, TEXAS A&M

CLYDE MUNSTER

BIOLOGICAL AND AGRICULTURAL ENGINEERING DEPARTMENT

TEXAS A&M

TOM COTHREN

SOIL AND CROP SCIENCES DEPARTMENT

TEXAS A&M

JOHN NIELSEN-GAMMON

ATMOSPHERIC SCIENCES DEPARTMENT

TEXAS A&M

PROBLEM

• Estimated the US suffers $6 - $8 billion in drought damages on average

• 2011 Texas drought cost $7.6 billion in agricultural sector alone

• Estimated the US suffers $6 - $8 billion in drought damages on average

• 2011 Texas drought cost $7.6 billion in agricultural sector aloneDroughts are costly

• Estimated there are more droughts that affected at least 1% of the population than any other natural disaster

• Estimated there are more droughts that affected at least 1% of the population than any other natural disaster

Droughts affect many people

• Texas’ 1950’s drought is used for planning in the state• Longer and more severe droughts have been identified• Texas’ 1950’s drought is used for planning in the state• Longer and more severe droughts have been identified

Worse droughts than those used for planning

• Climate change: Increasing drought intensity, duration and severity

• Rising population: Greater demand for water supplies

• Climate change: Increasing drought intensity, duration and severity

• Rising population: Greater demand for water supplies

Impact of drought is increasing

GOAL

To create an early warning system/decision making tool (EWS/DM) to help agricultural producers better prepare

for drought conditions and manage water resources

DECISION MAKING PROCESS

ProblemIdentification

Define Objective

Data Collection

Data Analysis

Develop Alternative Solutions

Select Solution

Solution Implementation

Follow-up

Was the problem solved?Was the objective achieved?

DECISION MAKING PROCESS

ProblemIdentification

Define Objective

Data Collection

Data Analysis

Develop Alternative Solutions

Select Solution

Solution Implementation

Follow-up

Was the problem solved?Was the objective achieved?

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

DroughtLimits on water for irrigation

Agricultural

Production

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

DroughtLimits on water for irrigation

Agricultural

Production

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

Develop drought tool

Generate forecasts EWS/DM Tool

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

• Parameters• Drought

method• Java program

Develop Drought Tool Generate Forecasts EWS/DM Tool

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

• 2 weeks – 3 months• Weather• Hydrologic /plant

conditions

• Parameters• Drought method• Java program

Generate ForecastsDevelop Drought Tool EWS/DM Tool

SWAT

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

• Parameters• Drought method• Java program

• Combine forecasts and drought tool

• Generate report/maps

EWS/DM ToolDevelop Drought Tool

• 2 weeks – 3 months

• Weather• Hydrologic/plant

conditions

Generate Forecasts

Cotton Drought Rankings

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

Case Study: Upper Colorado River Basin (UCRB)

Highly managed watershed

2 diversion dams

Man-made lakes/reservoirs for water supply

Wastewater reuse

Average streamflow: 44 cfs (1.25 cms)

Primary landuses: Rangeland and cropland

Average cotton yields: 200 – 700 lbs/ac

Average % irrigated cotton by county: 2% - 60%

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

LanduseNLCD

Soils STATSGO

ElevationUSGS

30m DEM

Reservoirs / Dams TWDB / CRMWD

CropsVarious

TemperatureNOAA

PrecipitationNOAA

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

LanduseNLCD

Soils STATSGO

ElevationUSGS

30m DEM

Reservoirs / Dams TWDB / CRMWD

Crop information Various sources

SWAT

LanduseNLCD

Soils STATSGO

ElevationUSGS

30m DEM

Reservoirs / Dams TWDB / CRMWD

CropsVarious

TemperatureNOAA

PrecipitationNOAA

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

LanduseNLCD

Soils STATSGO

ElevationUSGS

30m DEM

Reservoirs / Dams TWDB / CRMWD

Crop information Various sources

SWAT

StreamflowUSGS

Crop YieldsUSDA - NASS

LanduseNLCD

Soils STATSGO

ElevationUSGS

30m DEM

Reservoirs / Dams TWDB / CRMWD

CropsVarious

TemperatureNOAA

PrecipitationNOAA

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

Model Statistical Analysis

Drought Determination

Drought Forecast Accuracy

PBIAS: Determines whether the model is typically over- or under-predicting.

NS: Determines how well the observed vs. modeled data fits a line with a slope of 1.

R²: Determines the proportion of the observed variance that is explained by the model.

PBIAS: Determines whether the model is typically over- or under-predicting.

NS: Determines how well the observed vs. modeled data fits a line with a slope of 1.

R²: Determines the proportion of the observed variance that is explained by the model.

Statistic Acceptable Range

PBIAS -25% - 25%

NS > 0.5

R² > 0.5

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

Model Statistical Analysis

Drought Determination

Drought Forecast Accuracy

1. Precipitation• Compared against the calculated normal

2. Days above a temperature threshold (i.e. 100°F)• Compared against the calculated normal

3. Soil moisture stress• Compared against a stress threshold for

the crop (% depletion)

4. Transpiration stress• Calculated by SWAT

5. Biomass production• Compared against the calculated normal

1. Precipitation• Compared against the calculated normal

2. Days above a temperature threshold (i.e. 100°F)• Compared against the calculated normal

3. Soil moisture stress• Compared against a stress threshold for

the crop (% depletion)

4. Transpiration stress• Calculated by SWAT

5. Biomass production• Compared against the calculated normal

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

Model Statistical Analysis

Drought Determination

Drought Forecast Accuracy

1. Input weather forecast into SWAT

2. Get forecasted hydrologic conditions

3. Evaluate predicted conditions by comparing them to a known drought event

1. Input weather forecast into SWAT

2. Get forecasted hydrologic conditions

3. Evaluate predicted conditions by comparing them to a known drought event

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

Problem Identification

Define Objectives Define Scope Data Availability 

Data Collection

Field Data Interviews Currently Available

Weather Forecasting

Canopy Temps

Evapo‐transpiration

YieldsHydrologic and Crop 

Model

Data Analysis

Drought Triggers Weather Trends Hydrologic Trends Drought Indices Biomass Production

Solutions Synthesis Report

OVERVIEW

1. Problem

2. Objective

3. Scope

4. Data Availability

5. Data Collection

6. Data Analysis

7. Final Tool

Problem Identification

Define Objectives Define Scope Data Availability 

Data Collection

Field Data Interviews Currently Available

Weather Forecasting

Canopy Temps

Evapo‐transpiration

YieldsHydrologic and Crop 

Model

Data Analysis

Drought Triggers Weather Trends Hydrologic Trends Drought Indices Biomass Production

Solutions Synthesis Report

PROGRESS AND CHALLENGESRESULTS, CURRENT WORK, AND NEXT STEPS

00.20.40.60.81

0200400600800

1000

2005 2006 2007 2008

Har

vest

ed a

cres

, % o

f pl

ante

d

Cot

ton

yiel

d, lb

/ac

Midland: Original Observed Data

Observed Modeled Harvested acres

0

0.5

1

1.5

0

200

400

600

800

2005 2006 2007 2008 Har

vest

ed a

cres

, % o

f pl

ante

d

Cot

ton

yiel

d, lb

/ac

Midland: Adjusted Observed Data

Observed Modeled Harvested acres

RESULTS – SWAT MODEL

Midland County, 2008

2008 was a dry year – only 23% of the planted acreage was harvested

2008 had a high average observed yield = 770 lb/ac

The low yield acres that were not harvested were not taken into account. Assuming a yield of zero, the observed yield better follows the harvest trend.

770 * 0.23 = 177 lb/ac

The model simulations better follow the adjusted trend of the observed values because they also take into account acres that were not harvested when simulating the average yield.

RESULTS – SWAT MODEL

Using radar vs. gauge precipitation required a separate calibration

Gauges have a longer record, but…

Gauge Data Radar Data

Average annual precipitation (2005-2012)

Gauge Radar

RESULTS – SWAT MODEL

Preliminary results indicate

1. Streamflows perform similarly (NS = 0.83)

2. Yields performed better with radar data

Average annual cotton yield (lb/ac) by subbasin(2005-2012)

RESULTS – SWAT MODEL

Gauge Data

RESULTS – DROUGHT TOOL

County n Correlation Significance

Andrews 16 -0.50 0.2

Borden 16 -0.67 0.1

Dawson 19 -0.53 0.2

Gaines 19 -0.73 0.05

Howard 19 -0.71 0.1

Martin 19 -0.79 0.05

Midland 16 -0.80 0.05

Mitchell 16 -0.68 0.1

Terry 19 -0.72 0.05

Yoakum 18 -0.59 0.1

All 177 -0.65 0.01

RESULTS – DROUGHT TOOL

Drought Ranking

2007

2002

2011

Median Annual Drought Ranking

CURRENT WORK

1. How long the dataset for the normals calculation should be

2. At what point in the season is the drought value indicative of low yields

3. Does the tool perform better with more spatially distributed inputs

Drought Tool Analysis

NEXT STEPS

Input forecasted weather data into calibrated and validated model and assess performance

Generate a synthesis report with figures and explanations of drought conditions

THANK YOU