HGS RESEARCH HIGHLIGHT – Sources of surface water in space and time: Identification of delivery processes and geographical sources with hydraulic mixing-cell modeling

HGS RESEARCH HIGHLIGHT – Sources of surface water in space and time: Identification of delivery processes and geographical sources with hydraulic mixing-cell modeling

The paper highlighted this week presents a very interesting post-processing method for HydroGeoSphere models. The results of the HGS model were used as input into the hydraulic mixing-cell (HMC) approach which enables tracking and delineation of the mixing of predefined initial water sources at any location and at any time based on information from the hydraulic flow solution

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HGS RESEARCH HIGHLIGHT – Transit-Time and Temperature Control the Spatial Patterns of Aerobic Respiration and Denitrification in the Riparian Zone

HGS RESEARCH HIGHLIGHT – Transit-Time and Temperature Control the Spatial Patterns of Aerobic Respiration and Denitrification in the Riparian Zone

The paper highlighted this week introduces a novel method of implementing temperature-dependent reactions in a HydroGeoSphere solute transport model by pairing a Lagrangian flow path-reaction model to the results of a 2nd order Runge-Kutta particle tracking analysis.

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HGS RESEARCH HIGHLIGHT – Finite-volume flux reconstruction and semi-analytical particle tracking on triangular prisms for finite-element-type models of variably-saturated flow
Case Studies, HGS, Research Highlight Brayden McNeill Case Studies, HGS, Research Highlight Brayden McNeill

HGS RESEARCH HIGHLIGHT – Finite-volume flux reconstruction and semi-analytical particle tracking on triangular prisms for finite-element-type models of variably-saturated flow

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!

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