1. Application of Stage IV Precipitation Data to Estimate Spatially Variable Recharge for a Groundwater Flow Model Heather Moser Mentor: Dr. William Simpkins

2. Groundwater for Meteorologists Groundwater and the atmosphere: very similar! Groundwater and the atmosphere: very similar! Both are fluids Both are fluids Flows from high to low potential Flows from high to low potential (USGS)

3. Groundwater for Meteorologists Recharge: precipitation that percolates through soil to the water table Recharge: precipitation that percolates through soil to the water table Why is it important? Why is it important? Sustains vital fresh water source Sustains vital fresh water source Drinking water Drinking water Irrigation Irrigation Industry Industry (USGS)

4. For groundwater flow modeling, recharge is: For groundwater flow modeling, recharge is: estimated based on local factors estimated based on local factors assumed to be uniform everywhere in model domain assumed to be uniform everywhere in model domain used as the “tweaking” term used as the “tweaking” term Recharge Estimation

5. Hypotheses If recharge is spatially variable to reflect actual conditions, will incorporating it improve groundwater modeling accuracy? If recharge is spatially variable to reflect actual conditions, will incorporating it improve groundwater modeling accuracy? Is radar precipitation data useful to estimate recharge for modeling? How does it compare to other methods of estimating recharge? Is radar precipitation data useful to estimate recharge for modeling? How does it compare to other methods of estimating recharge?

6. How Much Recharge? Difficult to measure directly Difficult to measure directly Large variability over space and time Large variability over space and time Only 10% to 20% of precipitation actually reaches water table in Midwest Only 10% to 20% of precipitation actually reaches water table in Midwest Evapotranspiration Evapotranspiration Overland flow Overland flow Tile Drainage? Tile Drainage?

7. Agricultural Tile Drainage (Mark Tomer, USDA)

8. GFLOW 2-D Groundwater flow model 2-D Groundwater flow model Steady-state and single-layer Steady-state and single-layer Analytic Element Analytic Element Non-gridded Non-gridded Groundwater flow interpolated between line sinks (stream segments) Groundwater flow interpolated between line sinks (stream segments) Allows for heterogeneity (inhomogeneities) Allows for heterogeneity (inhomogeneities)

9. Recharge Scenario 1: Rainfall Stage IV Precipitation data from NWS Stage IV Precipitation data from NWS Gridded dataset (4 km resolution) Gridded dataset (4 km resolution) Multisensor product Multisensor product Quality controlled Quality controlled Estimate recharge as 10% and 20% of annual precipitation for three years (6 total) Estimate recharge as 10% and 20% of annual precipitation for three years (6 total) YearType Rainfall (mean) 1 - 2 2002 1. Stage IV: 2. Gages: 28.51 in 30.03 in -1.53 in 2003 1. Stage IV: 2. Gages: 26.49 in 28.32 in -1.83 in 2004 1. Stage IV: 2. Gages: 28.55 in 34.47 in -5.92 in

10. 2002 Stage IV Data Annual Rainfall Totals Rainfall Map by Quantiles

11. 2003 Stage IV Data Annual Rainfall Totals Rainfall Map by Quantiles

12. 2004 Stage IV Data Annual Rainfall Totals Rainfall Map by Quantiles

13. USGS FORTRAN program USGS FORTRAN program Input USGS streamflow to calculate recharge as average over a watershed Input USGS streamflow to calculate recharge as average over a watershed Six gaging stations selected to cover watersheds in domain Six gaging stations selected to cover watersheds in domain Continuous streamflow records from 1996-2004 Continuous streamflow records from 1996-2004 Recharge averaged over entire period for mean state Recharge averaged over entire period for mean state Recharge calculated empirically -- about ½ of stream discharge exceeding baseflow Recharge calculated empirically -- about ½ of stream discharge exceeding baseflow R = 2(Q 2 - Q 1 )K 2.3026 2.3026 R = Recharge (L/t) Q 2 = Discharge after storm event (L 3 /t) Q 1 = Discharge before storm event (L 3 /t) K = Recession index constant Recharge Scenario 2: RORA

15. Control to test spatial variability Control to test spatial variability Average of all RORA watershed recharge values Average of all RORA watershed recharge values Approximation of mean recharge based on real data Approximation of mean recharge based on real data Recharge Scenario 3: Uniform 7.09 in/yr (21.4% of mean annual ppt)

16. Uniform Model Results

17. Uniform Large errors in modeled head found in certain locations Large errors in modeled head found in certain locations Calibration required to account for variable soil hydraulic conductivity Calibration required to account for variable soil hydraulic conductivity Impact of glacial formations Impact of glacial formations Alluvial Materials Alluvial Materials

18. Uniform

19. UniformRORA Stage IV MAE =  |modeled - observed| N (mean absolute error)

20. Discussion of Results What happened with Stage IV? What happened with Stage IV? Inaccurate rainfall estimation led to inaccurate recharge estimation Inaccurate rainfall estimation led to inaccurate recharge estimation Rainfall data from years tested may not adequately reflect current hydraulic head levels Rainfall data from years tested may not adequately reflect current hydraulic head levels Recharge based on rainfall alone does not consider geologic factors Recharge based on rainfall alone does not consider geologic factors

21. Discussion of Results Why did RORA and uniform show better results? Why did RORA and uniform show better results? Recharge estimates from streamflow do reflect geologic conditions Recharge estimates from streamflow do reflect geologic conditions Uniform field based on RORA data Uniform field based on RORA data Mean conditions rather than time sensitive Mean conditions rather than time sensitive

22. Conclusions Spatially variable recharge based on precipitation did not improve model accuracy. Spatially variable recharge based on precipitation did not improve model accuracy. Other factors may have affected results. Other factors may have affected results. Spatially variable recharge from streamflow did slightly improve over uniform distribution. Spatially variable recharge from streamflow did slightly improve over uniform distribution. Radar-derived rainfall estimates are still not accurate enough to be useful for hydrological modeling. Radar-derived rainfall estimates are still not accurate enough to be useful for hydrological modeling. However, spatial qualities still carry promise. However, spatial qualities still carry promise.

23. Future Work Put rainfall and soil data together Put rainfall and soil data together Account for effective hydraulic conductivity Account for effective hydraulic conductivity Test a watershed where tile drainage does not effect aquifer Test a watershed where tile drainage does not effect aquifer

24. Acknowledgements Dr. William Simpkins Dr. William Simpkins Daryl Herzmann Daryl Herzmann Lucie Macalister Lucie Macalister USDA Soil Tilth Lab USDA Soil Tilth Lab Iowa USGS Iowa USGS

25. Questions? miraje@iastate.edu miraje@iastate.edu http://www.meteor.iastate.edu/~miraje/thesis http://www.meteor.iastate.edu/~miraje/thesis