Modeling Continuum. Nonpoint source contaminants affect. Groundwater at Contrasting Spatial Scales

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1 Modeling Agricultural l Impacts on Groundwater at Contrasting Spatial Scales Tom Nolan Sustainable Groundwater in Agriculture June 15-17, 17 1 San Francisco Larry Puckett (USGS, Reston, VA)) Liwang Ma (USDA, Ft. Collins, CO) Chris Green (USGS, Menlo Park, CA)) Randy Bayless (USGS, Indianapolis, IN) Rob Malone (USDA, Ames, IA) Mary Hill (USGS, Boulder, CO) Claire Tiedeman (USGS, Menlo Park, CA) Mike Fienen (USGS, Madison, WI) Kerie Hitt (USGS, Reston, VA) Outline of Talk Modeling Continuum A GW vulnerability modeling continuum Parsimonious models (national scale) Mechanistic models (field scale) Examples Concluding thoughts Data driven Statistical Mechanistic Physically based Process complexity Number of parameters Model Complexity Accuracy Accuracy Prediction Parsimony vs. Complexity Debate A New Modeling Paradigm Model Complexity Accuracy Parameter estimation: How much model complexity is supported by the data? More transparent reporting of model limitations, parameter and prediction uncertainties Increasing emphasis on inverse modeling (e.g., PEST) for mech. models Nonpoint source contaminants affect large areas e.g., Nitrogen cycle Empirical models a good starting point at large spatial scales Complexity (number of parameters) Schwarz et al. (6), UGSS Techniques And Methods Report, Book 6, Chapter B From Dubrovsky et al., in press

2 Modeling Continuum Nonlinear Regression (GWAVA) Data driven Statistical Mechanistic Physically based c gwi N J K n X n exp( jt j ) exp( kzk ) n1 j1 k1 i N Load Transport Attenuation GWAVA is empirical Fewer parameters than a mechanistic model c gwi = mean nitrate concentration for ground-water network i X n = average N load in network i, for source n T j = average transport factor j for network i Z k = average attenuation factor k for network i β n = coefficient for N source n α j = coefficient for transport factor j δ k = coefficient for attenuation factor k ε i = model error for network i Because the model is nonlinear in the parameters, an iterative process is used to find the parameter estimates. GWAVA Calibration (USGS NAWQA) Each network has - wells,6 wells sampled 1991 N = 97 networks after averaging Shallow groundwater (4 m) Nitrate data plus ancillary data (>1 variables analyzed) Calibrated Model Estimated Coefficient Significance Level (p) Parameter Units Nitrogen source (β) Farm fertilizer kg/ha.7. Confined manure kg/ha.45.4 Orchards/vineyards percent Population density people/km.7 <.1 Cropland/pasture/fallow percent Transport to aquifer (α) Water input a km /cm Carbonate rocks binary indicator.56.1 Basalt and volcanic rocks binary indicator Drainage ditch km <.1 Slope percent Glacial till binary indicator Clay sediment percent -.48 <.1 Attenuation (δ) Fresh surface water withdrawal megaliters/day -1.8 <.1 Irrigation tailwater recovery km -8. <.1 Histosol soil type percent Wetlands percent -..6 a ratio of irrigated i i dland dto precipitation it ti Nolan and Hitt, 6, ES&T, 4: GWAVA Model Fit GWAVA Application Discretize the inputs on 1-km grid cells and apply the equation. mg/l erved nitrate, Obse 15 R = :1 line Predicted nitrate, mg/l ls Model residual Normal distribution quantiles Suggests areas of concern for detailed modeling High N load Irrigation Well-drained soils or fractured rocks NO attenuation - Histosols - Wetlands

3 Modeling Continuum NAWQA Small Scale Studies ( Agric. Chem Transport ) Data driven Statistical Mechanistic Physically based RZWQM is highly parameterized ACT: Modeled site in CA was one of NAWQA sites featuring inverse modeling calibration of unsaturated zone fate and transport models CA: sandy, irrigated, depth to water = 6.5 m Root Zone Water Quality Model USDA ARS, Ft. Collins, CO (Laj Ahuja, Liwang Ma) 1D unsaturated zone model that includes N cycling: - Additions (fertilizer, crop residue, roots, fixation ) - Losses (uptake, leaching, runoff, denitrification ) - Transformations (immobilization, mineralization) Extensive management options (N form, N application, irrigation type, many others ) from Puckett and Tesoriero Including N cycling processes means additional model complexity. RZWQM is much more highly parameterized than a regression model. How can we reduce the parameter space? Approach Use PEST parameter a estimation software (John Doherty) with observed data to estimate RZWQM parameters and constrain predictions. Inverse calibration: to soil moisture, soil tension, aqueous nitrate and bromide, soil nitrate, organic matter data from UZ monitoring Evaluation : water flux (recharge), groundwater NO also observed but not used in calibration Parameter ESTimation by PEST Provides simultaneous adjustment of parameters guided by an objective function: n1 n n b sm,i P O st,i P O an,i P O n4 b,i P O n5 n 6 sn,i P O om,i P O b,i sm,i b,i sm,i sn,i st,i sn,i st,i om,i an,i om,i Yields parameter sensitivities and confidence intervals Yields prediction confidence intervals an,i

4 Useful Metrics (Hill and Tiedeman, 7) Composite scaled sensitivities (CSS) Normalized CSS =CSS/CSS j max Insensitivity threshold 1 Parameter correlation coefficients (PCC) Nonuniqueness threshold.98 Highly correlated/insensitive parameters cause numerical instability Soil hydraulics Adjusted Parameters Saturated K, cm/hr Water content at field capacity, vol. Bulk density, g/cm N cycling Simulation PEST estimated Parameter depth (cm) value ks ks ks 16.8 ks4 4. ks wfc1 Same as above wfc.11 wfc.8 wfc4.4 wfc5.4 bd1 Same as above 1.45 bd 1.67 bd 1.47 bd4 1.1 bd r45 Global.1 95% Confidence Intervals on Parameter Estimates al dence interva nd 95% confi Param meter value a ks1 ks ks ks4 ks5 wfc1 wfc wfc wfc4 wf wfc5 bd1 bd bd bd4 bd5 r45 R45 lit. values CSS S/CSSmax Parameter Sensitivity and Correlation ks1 ks ks ks4 ks5 wfc1 wfc wfc wfc4 wfc5 bd1 bd bd bd4 bd5 r45 Soil hydraulic parameters generally more sensitive Three most sensitive: BD > WFC1 > BD1 Correlation: PCC = -.94 for WFC1 and BD1 PCC = -.84 for KS and BD 1 CSS S/CSS max 1 1 Normalized CSS vs. CI Width PCC % Prediction Intervals* for Observations Not Used in Calibration Annual water flux, cm/yr Observed 4. Predicted 8.1 (.1, 48.5) Average groundwater nitrate, mg/l Observed 4. Predicted 18.7 (16.1, 7.1) CI width/parameter estimate *PEST uses nonlinear calibration constrained method (Vecchia and Cooley, WRR, 1987)

5 Moistur re content, cm /cm /1/4 /1/4 Calibrated Results (Soil Moisture) C. m measured. m predicted 5/1/4 California (Irrigated) 7/1/4 9/1/4 11/1/4 1/1/5 Moisture content, cm /cm M1.49 m measured.49 m predicted Maryland 4/1/ 6/1/ 8/1/ 1/1/ 1/1/ /1/4 4/1/4 6/1/4 8/1/4 1/1/4 Nolan et al., 1, JEQ, 9: N Mass Balance (major pathways shown) Additions, kg N ha -1 yr -1 California Maryland Fertilizer 15 7 Manure.. Incorporated residue Dead roots. 11 Fixation. 99 Losses, kg N ha -1 yr -1 Deep seepage Plant uptake Transformations, kg N ha -1 yr -1 Net mineralization 54 9 Immobilization. 1.4 Organic storage pools, time-averaged kg N ha -1 m -1 Plant residue. 7 Soil humus 88 17,1 Microorganisms 9 1 Inorganic storage, time-averaged kg N ha -1 m -1 Nitrate 19 Concluding Thoughts Fate and transport models are used in higher tier risk assessments, but How much model complexity is supported by the data? More transparent t reporting of model limitations, parameter and prediction uncertainties ti needed. d Thank you for your attention. Questions? Overall Model Fit Observation group California Maryland Soil moisture (cm cm ) Relative RMSE Index of agreement Soil tension (cm) Relative RMSE Index of agreement.4.86 Nitrate (mg L 1 ) Relative RMSE.4. Index of agreement.6.86 Bromide (mg L 1 ) Relative RMSE.57.6 Index of agreement Insights Gained Leaching Patterns at SANJ Almond Orchard /L entration, mg Nitrate conce Nitrate C 5 Irrigation 14 Precipitation /1/1 1/1/ 4/1/ 7/1/ 1/1/ 1/1/ 4/1/ 7/1/ 1/1/ 1/1/4 4/1/4 7/1/4 1/1/4 1/1/ nput, cm/d Water i

6 NAWQA Small Scale Studies ( Agric. Chem Transport ) CA NE ACT Calibrated UZ models at sites California site: sandy, irrigated, 6.5 m to water. UZ monitoring sites MD