Filters: Tags: Metamodel (X)
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Groundwater age is an important indicator of groundwater susceptibility to anthropogenic contamination and a key input to statistical models for forecasting water quality. Numerical models can provide estimates of groundwater age, enabling interpretation of measured age tracers. However, to extend to national-scale groundwater systems where numerical models are not routinely available, a more efficient metamodeling approach can provide a less precise but widely applicable estimate of groundwater age, trained to make forecasts based on predictor variables that can be measured independent of numerical models. We trained gradient-boosted regression tree statistical metamodels to MODFLOW/MODPATH derived groundwater...
Widespread nitrate contamination of groundwater in agricultural areas poses a major challenge to sustainable water resources. Efficient analysis of nitrate fluxes across large regions also remains difficult. This study introduces a method of characterizing nitrate transport processes continuously across regional unsaturated zones and groundwater based on surrogate, machine-learning metamodels of an N flux process-based model. The metamodels used boosted regression trees (BRTs) to relate mappable variables to parameters and outputs of a “vertical flux method” (VFM) applied in the Fox-Wolf-Peshtigo (FWP) area in Wisconsin. In this context, the metamodels are upscaling the VFM results throughout the region, and the...
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