Filters: Tags: Metamodel (X)
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Five MODFLOW-NWT inset models were extracted from the Lake Michigan Basin (LMB) regional model (https://pubs.usgs.gov/sir/2010/5109/). These inset models were designed to serve as a training ground for metamodels of groundwater age in glacial wells. The study areas of the inset models correspond to HUC8 basins. Two of the basins are tributary to Lake Michigan from the east, two are tributary to the lake from the west, and one is located outside the western boundary of the Lake Michigan topographic basin. The inset models inherit many of the inputs to the parent LMB model, such as its hydrostratigraphy and layering scheme, the hydraulic conductivity assigned bedrock layers, the recharge distribution, and water use...
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...
Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large-scale numerical models can be resource intensive. With the data and computer codes in this data release users can (1) reconstruct and run 115 General Simulation Models (GSMs) of groundwater flow, (2) calculate groundwater age metrics at selected GSM cells, (3) train a boosted regression tree model using the provided data, (4) predict...
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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