HGS RESEARCH HIGHLIGHT – Upscaling Hydrological Processes for Land Surface Models with a Two-Hydrologic-Variable Model: Application to the Little Washita Watershed
The authors have used a 3D HydroGeoSphere model of a heavily studied sub-catchment (the Little Washita Watershed, Oklahoma) as a reference point to test the validity of much simpler modelling approaches. Results of the 3D HydroGeoSphere model are compared against a simpler 2D hillslope model, also constructed using HydroGeoSphere.
HGS RESEARCH HIGHLIGHT – Predicting Watershed Scale Surface Water Quality Targets With a Combined Fully-Integrated Groundwater-Surface Water Model and Machine Learning Approach
The poster highlights some very interesting research at the nexus of physics based integrated hydrologic modelling (using HydroGeoSphere) and machine learning/artificial intelligence techniques. Here the authors have paired an HGS model of the South Nation Watershed (SNW) with a Random Forest (RF) algorithm trained to predict spatially varying concentrations of nitrate and E. Coli throughout the watershed. For a completely novel approach toward large scale water quality prediction, the results were very encouraging!
HGS RESEARCH HIGHLIGHT - Simulating Climate Change Impacts on Surface Water Resources within a Lake Affected Region using Regional Climate Projections
This study aims to assess the impact of climate change on water resources in a large watershed within the Laurentian Great Lakes region, using the fully‐integrated surface‐subsurface model HydroGeoSphere. The hydrologic model is forced with an ensemble of high‐resolution climate projections from the Weather Research and Forecasting model (WRF). The latter has been extended with an interactive lake model (FLake) to capture the effect of the Great Lakes on the regional climate. The WRF ensemble encompasses two different moist physics configurations at resolutions of 90km, 30km, and 10km, as well as four different initial and boundary conditions, so as to control for natural climate variability. The integrated hydrologic model is run with a representative seasonal cycle, which effectively controls natural climate variability, while remaining computationally tractable with a large integrated model.