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Heather Moser

Application of Stage IV Precipitation Data to Estimate Spatially Variable Recharge for a Groundwater Flow Model. Heather Moser. Mentor: Dr. William Simpkins. Groundwater for Meteorologists. Groundwater and the atmosphere: very similar! Both are fluids Flows from high to low potential.

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Heather Moser

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  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! • Both are fluids • Flows from high to low potential (USGS)

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

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

  5. Hypotheses • 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?

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

  7. Agricultural Tile Drainage (Mark Tomer, USDA)

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

  9. Recharge Scenario 1: Rainfall • Stage IV Precipitation data from NWS • Gridded dataset (4 km resolution) • Multisensor product • Quality controlled • Estimate recharge as 10% and 20% of annual precipitation for three years (6 total)

  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. Recharge Scenario 2: RORA • USGS FORTRAN program • Input USGS streamflow to calculate recharge as average over a watershed • Six gaging stations selected to cover watersheds in domain • Continuous streamflow records from 1996-2004 • Recharge averaged over entire period for mean state • Recharge calculated empirically -- about ½ of stream discharge exceeding baseflow R = 2(Q2 - Q1)K 2.3026 R = Recharge (L/t) Q2 = Discharge after storm event (L3/t) Q1 = Discharge before storm event (L3/t) K = Recession index constant

  14. Recharge Scenario 2: RORA

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

  16. Model Results Uniform

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

  18. Uniform

  19. Uniform RORA MAE =S|modeled - observed| N (mean absolute error) Stage IV

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

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

  22. Conclusions • Spatially variable recharge based on precipitation did not improve model accuracy. • Other factors may have affected results. • 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. • However, spatial qualities still carry promise.

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

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

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

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