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Estimating Water Demands for Irrigation Districts on the Lower Colorado River David Kracman University of Texas at Austin December 7, 2000.

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Presentation on theme: "Estimating Water Demands for Irrigation Districts on the Lower Colorado River David Kracman University of Texas at Austin December 7, 2000."— Presentation transcript:

1 Estimating Water Demands for Irrigation Districts on the Lower Colorado River David Kracman University of Texas at Austin December 7, 2000

2 Colorado River Basin Albers Conical Equal Area Projection

3 The Highland Lakes

4 Lake Buchanan and Lake Travis Lake Buchanan/Buchanan Dam Lake Travis/Mansfield Dam

5 Rice-Growing Irrigation Districts 500,000 acre-ft diverted for rice irrigation each year

6 Organization of Presentation Definition of Study Area Description of Water Demand Regression GIS Applications and Data Acquisition Preliminary Regression Results Conclusions Future Work Acknowledgments

7 Definition of Study Area All shapefiles projected to Albers Conic Equal Area Approximate irrigation district boundaries from LCRA

8 Counties in Study Area

9 Water Demand Regression Developed by Dr. Quentin Martin, LCRA

10 Delay Factor Variable t i = day of first diversion in year i TE = earliest diversion of any year TL = latest first diversion of any year

11 Gross Lake Evaporation Closely related to pan evaporation More data available than pan evap Texas Water Development Board

12 Rainfall 4 National Weather Service stations near study area Used data from nearest station to fill data gaps

13 Total Acreage Rice Acreage Data available for first and second crops Planted acreage a function of economic, weather, other factors

14 Water Demand Dependant variable in regression Measured in acre-ft Garwood Irrigation Pump

15 GIS Applications Gross Lake Evaporation Data

16 GIS Applications National Weather Service Station Locations

17 Regression Design

18 Regression Analysis SPSS Statistical Package

19 Regression Results R 2 improved from 0.49 to 0.787 Std Errors of coefficients improved from 0.5896 and 0.1386 to 0.275 and 0.113 respectively

20 Conclusions GIS can aid in development of regression for predicting water use in rice-growing irrigation districts These regressions have potential to improve existing regressions, and may be incorporated into optimization models

21 Future Work Develop regressions for other irrigation districts, for all relevant months Incorporate results into optimization model Continue to fill data gaps

22 Questions? (My niece, Molly)

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