Staff Research Highlight - A dynamic meshing scheme for integrated hydrologic modeling to represent evolving landscapes
Aquanty is pleased to introduce a novel dynamic meshing scheme for integrated hydrologic modelling with HydroGeoSphere to better represent evolving landscapes. The approach addresses a major challenge in modelling human-altered environments, particularly in regions undergoing rapid changes such as open-pit mining sites, land reclamation zones, or urban developments. Traditional hydrologic models often rely on static mesh geometries, limiting their ability to capture changes in topography and subsurface structure over time. This research proposes a more flexible, adaptive framework capable of simulating surface and subsurface hydrologic responses to complex engineering activities.
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 - Effects of soil heterogeneity and preferential flow on the water flow and isotope transport in an experimental hillslope
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.
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.
Staff Research Highlight - Development of a fully integrated hydrological fate and transport model for plant protection products: incorporating groundwater, tile drainage, and runoff
This research investigates how the integrated hydrological modelling of plant protection products (PPPs) such as pesticides can provide a more comprehensive understanding of their environmental behavior across groundwater, surface water, and tile drainage systems.
Staff Research Highlight - Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site
This research specifically utilizes the Korea CO₂ Storage Environmental Management (K-COSEM) test site to develop methodologies that address the challenges posed by uncertainty in detecting subsurface leakages.
Staff Research Highlight - Climate Change Impacts on Mountain Snowpacks
We’re happy to highlight a new publication co-authored by Aquanty’s senior data scientist, Dr. Andre Erler, focused on the expected impacts that climate change will have on snow depth in mountainous regions. This research relies solely on regional climate projections and employs a very similar model configuration (WRF version 4). The WRF simulations used in this paper are described in detail in Erler & Peltier (2017).
Staff Research Highlight - Great Lakes Basin Heat Waves
We’re pleased to highlight new research co-authored by C1W collaborators at Aquanty (Andre Erler) and the University of Toronto (Dr. Richard Peltier). This new paper explores the effects of climate change and greenhouse gases on extreme heat events in the Great Lakes region.
Staff Research Highlight - Future snow changes over the Columbia Mountains, Canada, using a distributed snow model
This paper, co-authored by Andre Erler and researchers from the University of Northern British Columbia, investigates climate change impacts on snow depth using a distributed snow model called SnowModel. Snowmelt is an essential water source for communities, and seasonal snow accumulation in many regions is decreasing with each passing year. Water managers, communities, and policymakers can benefit from improved snow modeling forecasts to inform their decision making and understand vulnerabilities to their water supply systems.