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DESCRIPTIONApplication 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. - PowerPoint PPT Presentation
Application of Stage IV Precipitation Data to Estimate Spatially Variable Recharge for a Groundwater Flow Model Heather MoserMentor: Dr. William Simpkins
Groundwater for Meteorologists
Groundwater and the atmosphere: very similar!Both are fluidsFlows from high to low potential(USGS)
Groundwater for MeteorologistsRecharge: precipitation that percolates through soil to the water tableWhy is it important?Sustains vital fresh water sourceDrinking waterIrrigationIndustry
Recharge EstimationFor groundwater flow modeling, recharge is:
estimated based on local factorsassumed to be uniform everywhere in model domainused as the tweaking term
HypothesesIf 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?
How Much Recharge?Difficult to measure directly
Large variability over space and time
Only 10% to 20% of precipitation actually reaches water table in MidwestEvapotranspirationOverland flowTile Drainage?
Agricultural Tile Drainage(Mark Tomer, USDA)
GFLOW2-D Groundwater flow modelSteady-state and single-layerAnalytic ElementNon-griddedGroundwater flow interpolated between line sinks (stream segments)Allows for heterogeneity (inhomogeneities)
Recharge Scenario 1: RainfallStage IV Precipitation data from NWSGridded dataset (4 km resolution)Multisensor productQuality controlled
Estimate recharge as 10% and 20% of annual precipitation for three years (6 total)
2002 Stage IV DataAnnual Rainfall TotalsRainfall Map by Quantiles
2003 Stage IV DataAnnual Rainfall TotalsRainfall Map by Quantiles
2004 Stage IV DataAnnual Rainfall TotalsRainfall Map by Quantiles
Recharge Scenario 2: RORAUSGS FORTRAN programInput USGS streamflow to calculate recharge as average over a watershedSix gaging stations selected to cover watersheds in domainContinuous streamflow records from 1996-2004Recharge averaged over entire period for mean stateRecharge calculated empirically -- about of stream discharge exceeding baseflow
R = 2(Q2 - Q1)K 2.3026R = Recharge (L/t)Q2 = Discharge after storm event (L3/t)Q1 = Discharge before storm event (L3/t)K = Recession index constant
Recharge Scenario 2: RORA
Recharge Scenario 3: UniformControl to test spatial variabilityAverage of all RORA watershed recharge valuesApproximation of mean recharge based on real data7.09 in/yr(21.4% of mean annual ppt)
UniformLarge errors in modeled head found in certain locations
Calibration required to account for variable soil hydraulic conductivityImpact of glacial formations
UniformRORAStage IVMAE = S |modeled - observed| N(mean absolute error)
Discussion of ResultsWhat happened with Stage IV?Inaccurate rainfall estimation led to inaccurate recharge estimationRainfall data from years tested may not adequately reflect current hydraulic head levelsRecharge based on rainfall alone does not consider geologic factors
Discussion of ResultsWhy did RORA and uniform show better results?Recharge estimates from streamflow do reflect geologic conditionsUniform field based on RORA dataMean conditions rather than time sensitive
ConclusionsSpatially 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.
Future WorkPut rainfall and soil data together
Account for effective hydraulic conductivity
Test a watershed where tile drainage does not effect aquifer
AcknowledgementsDr. William SimpkinsDaryl HerzmannLucie MacalisterUSDA Soil Tilth LabIowa USGS