Staff Research Highlight - Understanding topography-driven groundwater flow using fully-coupled surface-water and groundwater modeling
This research focuses on understanding the dynamics of topography-driven groundwater flow systems using fully-coupled surface–subsurface hydrologic modelling. This study addresses long-standing challenges in representing nested flow systems by simulating interactions between climate, topography, and groundwater without relying on potentially unrealistic, static boundary conditions.
Assessing the Sensitivity of Subsurface Mine-Dewatering Simulations to Surface Water Representation - Aquanty Webinar
Explore how surface water representation shapes subsurface mine-dewatering simulations in our latest webinar with Dr. Andrea Brookfield (University of Waterloo). This session highlights how climate change and surface water interactions can significantly influence dewatering strategies across mining operations.
Using HydroGeoSphere, the webinar compares conventional groundwater-only models with fully integrated surface–subsurface simulations under future climate scenarios. The results reveal important limitations of traditional approaches and show how integrated models provide more accurate insights for long-term mine water management.
Staff Research Highlight - Steady-state density-driven flow and transport: Pseudo-transient parameter continuation
Co-authored by Aquanty’s senior scientist, Hyoun-Tae Hwang, this research presents a new numerical approach for efficiently solving steady-state density-driven flow and transport equations— an important challenge in groundwater modelling, particularly for coastal aquifers affected by seawater intrusion. The research introduces a hybrid technique called pseudo-transient parameter continuation (PTPC), which combines the robustness of pseudo-transient continuation (PTC) methods with the computational efficiency of parameter continuation (PC) strategies.
HydroSphereAI Case Study: Crowe River Near Glen Alda
In late March 2025, the Crowe Valley Conservation Authority issued a Flood Watch across the watershed in response to a forecasted storm system and ongoing spring runoff. Forecasted rainfall, saturated soils, and an above-average snowpack created the right conditions for elevated streamflows, particularly in the Crowe River watershed. With a peak spring snow water equivalent (SWE) of 149.2 mm, the basin was primed for rapid hydrologic response—culminating in a strong single peak on April 6, when flows at Water Survey of Canada hydrometric Station 02HK005 reached 45.2 m³/s.
Staff Research Highlight - Application of Different Weighting Schemes and Stochastic Simulations to Parameterization Processes Considering Observation Error
In this paper co-authored by Aquanty personnel, researchers explore how different weighting schemes and stochastic simulations can enhance the accuracy of parameter estimation processes, ultimately reducing uncertainty in climate change impact assessments.
HydroSphereAI Case Study: Sauble River at Allenford — Spring Melt 2025
Using HydroSphereAI to anticipate and understand flood risks in real time and in retrospect. In late March and in the first days of April 2025, the Grey Sauble Conservation Authority issued an “All Watersheds” Flood Watch in anticipation of significant rainfall and elevated flows. A forecasted weather system was expected to bring up to 50 mm of total precipitation, following weeks of already saturated conditions. For the Sauble River at Allenford (Station 02FA004), this setup resulted in two distinct streamflow peaks within a five-day span— first on March 30, then again on April 3. Looking at both peaks, HydroSphereAI consistently delivered strong performance in predicting the structure and timing of the events.
HydroSphereAI: Next-Generation Hydrological Forecasting & AI-Driven Insights for a Changing Climate
We’re excited to share the recording of our recent webinar, HydroSphereAI: Machine Learning-Driven Insights and Hydrological Forecasting in a Changing Climate.
This session offers a deep dive into how cutting-edge machine learning approaches are transforming streamflow prediction and hydrological forecasting across Canada.
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.
Staff Research Highlight - Assessment of hydraulic and thermal properties of the Antarctic active layer: Insights from laboratory column experiments and inverse modelling
We’re pleased to highlight this publication, co-authored by Aquanty’s senior scientist, Hyoun-Tae Hwang, which investigates the hydraulic and thermal properties of the Antarctic active layer using laboratory column experiments and HydroGeoSphere (HGS) for inverse modeling.
Staff Research Highlight - Water sources and threshold behaviors of streamflow generation in a mountain headwater catchment
We’re pleased to highlight this publication, co-authored by Aquanty’s senior scientist, Hyoun-Tae Hwang, which examines the water sources and threshold behaviours of streamflow generation in a mountain headwater catchment.