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!