69th SWCS International Annual Conference July 27-30, 2014 Lombard, IL

1. COUPLING DROUGHT FORECASTING WITH THE SWAT
HYDROLOGY MODELTO 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

2. 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 alone
Droughts 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

3. 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

4. DECISION MAKING PROCESS
Problem
Identification
Define
Objective
Data
Collection
Data
Analysis
Develop
Alternative
Solutions
Select
Solution
Solution
Implementation
Follow-up
Was the problem solved?
Was the objective achieved?

5. DECISION MAKING PROCESS
Problem
Identification
Define
Objective
Data
Collection
Data
Analysis
Develop
Alternative
Solutions
Select
Solution
Solution
Implementation
Follow-up
Was the problem solved?
Was the objective achieved?

6. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. Final Tool

7. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
Drought
Limits on
water for
irrigation
Agricultural
Production

8. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
Drought
Limits on
water for
irrigation
Agricultural
Production

9. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
Develop drought
tool
Generate
forecasts
EWS/DM Tool

10. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
• Parameters
• Drought
method
• Java program
Develop Drought
Tool
Generate Forecasts EWS/DMTool

11. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
• 2 weeks – 3 months
• Weather
• Hydrologic /plant
conditions
• Parameters
• Drought method
• Java program
Generate Forecasts
Develop Drought
Tool EWS/DMTool
SWAT

12. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
• Parameters
• Drought method
• Java program
• Combine forecasts
and drought tool
• Generate
report/maps
EWS/DMTool
Develop Drought
Tool
• 2 weeks – 3
months
• Weather
• Hydrologic/plant
conditions
Generate Forecasts
Cotton Drought Rankings

13. 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%

14. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool

15. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
Landuse
NLCD
Soils
STATSGO
Elevation
USGS
30m DEM
Reservoirs / Dams
TWDB / CRMWD
Crops
Various
Temperature
NOAA
Precipitation
NOAA

16. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
Landuse
NLCD
Soils
STATSGO
Elevation
USGS
30m DEM
Reservoirs / Dams
TWDB / CRMWD
Crop information
Various sources
SWAT
Landuse
NLCD
Soils
STATSGO
Elevation
USGS
30m DEM
Reservoirs / Dams
TWDB / CRMWD
Crops
Various
Temperature
NOAA
Precipitation
NOAA

17. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
Landuse
NLCD
Soils
STATSGO
Elevation
USGS
30m DEM
Reservoirs / Dams
TWDB / CRMWD
Crop information
Various sources
SWAT
Streamflow
USGS
CropYields
USDA - NASS
Landuse
NLCD
Soils
STATSGO
Elevation
USGS
30m DEM
Reservoirs / Dams
TWDB / CRMWD
Crops
Various
Temperature
NOAA
Precipitation
NOAA

18. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
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

19. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
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

20. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
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

21. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
Problem
Identification
Define Objectives Define Scope Data Availability
Data Collection
Field Data Interviews Currently
Available
Weather
Forecasting
Canopy Temps
Evapo‐
transpiration
Yields
Hydrologic and Crop
Model
Data Analysis
Drought Triggers Weather Trends Hydrologic Trends Drought Indices Biomass
Production
Solutions
Synthesis Report

22. OVERVIEW
1. Problem
2. Objective
3. Scope
4. Data Availability
5. Data Collection
6. Data Analysis
7. FinalTool
Problem
Identification
Define Objectives Define Scope Data Availability
Data Collection
Field Data Interviews Currently
Available
Weather
Forecasting
Canopy Temps
Evapo‐
transpiration
Yields
Hydrologic and Crop
Model
Data Analysis
Drought Triggers Weather Trends Hydrologic Trends Drought Indices Biomass
Production
Solutions
Synthesis Report

23. PROGRESS AND CHALLENGES
RESULTS, CURRENT WORK, AND NEXT STEPS

24. 0
0.2
0.4
0.6
0.8
1
0
200
400
600
800
1000
2005 2006 2007 2008
Harvestedacres,%of
planted
Cottonyield,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
Harvestedacres,%of
planted
Cottonyield,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.

25. 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)

26. 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)

27. RESULTS – SWAT MODEL
Gauge Data

28. RESULTS – DROUGHTTOOL
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

29. RESULTS – DROUGHTTOOL
Drought Ranking
2007
2002
2011
Median Annual Drought Ranking

30. CURRENTWORK
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
DroughtTool Analysis

31. 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

32. THANKYOU