A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3
- 1Hydrologic Science and Engineering Program, Integrated GroundWater Modeling Center, Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado, USA
- 2Centre for High-Performance Scientific Computing in Terrestrial Systems, Institute for Bio- and Geosciences, Agrosphere (IBG-3), Research Centre Jülich, Jülich, Germany
Abstract. Interactions between surface and groundwater systems are well-established theoretically and observationally. While numerical models that solve both surface and subsurface flow equations in a single framework (matrix) are increasingly being applied, computational limitations have restricted their use to local and regional studies. Regional or watershed-scale simulations have been effective tools for understanding hydrologic processes; however, there are still many questions, such as the adaptation of water resources to anthropogenic stressors and climate variability, that can only be answered across large spatial extents at high resolution. In response to this grand challenge in hydrology, we present the results of a parallel, integrated hydrologic model simulating surface and subsurface flow at high spatial resolution (1 km) over much of continental North America (~ 6.3 M km2). These simulations provide integrated predictions of hydrologic states and fluxes, namely, water table depth and streamflow, at very large scale and high resolution. The physics-based modeling approach used here requires limited parameterizations and relies only on more fundamental inputs such as topography, hydrogeologic properties and climate forcing. Results are compared to observations and provide mechanistic insight into hydrologic process interaction. This study demonstrates both the feasibility of continental-scale integrated models and their utility for improving our understanding of large-scale hydrologic systems; the combination of high resolution and large spatial extent facilitates analysis of scaling relationships using model outputs.