Staff Research Highlight - Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models
We’re pleased to highlight this publication, co-authored by Aquanty’s senior scientist, Hyoun-Tae Hwang, which focuses on the integration of deep learning (DL) models with physics-based hydrological models to enhance the efficiency of estimating spatiotemporal groundwater and surface water conditions.
HGS RESEARCH HIGHLIGHT – The HypoSalar project: Integrating hyporheic exchange fluxes into Atlantic salmon (Salmo salar) spawning habitat models
In this research highlight ultra-fine resolution HydroGeoSphere models are used to simulate hyporheic exchange fluxes in river reaches used by Atlantic salmon for spawning. The HypoSalar project is contributing to demonstrate that the capabilities of HydroGeoSphere are not exclusively related to the field of hydrogeology, but can be used for both fluvial geomorphology and ecological studies due to HydroGeoSphere's flexibility and superior modeling approach.
"Model Convergence and Optimizing Runtimes with HydroGeoSphere" - Aquanty Webinar
A recording of our August 16th, 2023 webinar focused on building high-quality HydroGeoSphere models that are more likely to converge on a solution, and optimizing those models and numerical criteria to reduce model runtimes.
HGS RESEARCH HIGHLIGHT – An adaptive zone-based refinement method for characterizing a highly complex aquifer system model
This new paper by Aquanty senior scientist Hyoun-Tae Hwang introduces an innovative new method to iteratively refine model meshes based on model sensitivity and uncertainty, as calculated by PEST. The paper presents an initial proof-of-concept for this new method, based on the K-COSEM test site located in Eumseong-gun, South Korea.