HGS RESEARCH HIGHLIGHT – Numerical analysis of thermal response tests with groundwater flow and heat transfer model
This research, co-authored by J. Raymond, L. Gosselin, R. Lefebvre, and Aquanty’s René Therrien, explores how thermal response tests (TRTs) can be enhanced by employing HydroGeoSphere (HGS), our advanced modelling platform, to simulate coupled groundwater flow and heat transfer processes under complex geological settings. The study investigates the limitations of traditional line-source models, particularly in heterogeneous subsurface conditions, and introduces a numerical modelling approach to improve the accuracy of TRT analyses.
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.
"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.
HGS RESEARCH HIGHLIGHT - Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources
Online data acquisition, data assimilation and integrated hydrological modelling have become more and more important in hydrological science. In this study, we explore cloud computing for integrating field data acquisition and stochastic, physically-based hydrological modelling in a data assimilation and optimisation framework as a service to water resources management. For this purpose, we developed an ensemble Kalman filter-based data assimilation system for the integrated hydrological model HydroGeoSphere, which is able to run in a cloud computing environment.