HydroGeoSphere Research Highlight - Physics based hydrological modeling to predict soil moisture in a cold climate mesoscale catchment
Aquanty staff recently had the pleasure to meet the newest member of our modelling team over an informal ‘Lunch ‘n’ Learn’ presentation. Keshav Parameshwaran, M.Sc. is a new graduate from the University of Manitoba with a specialization in Groundwater Hydrology and Soil Science. His graduate research was on Physical-based hydrological modelling to forecast soil moisture in a mesoscale catchment in cold climates, specifically the Red River Valley in Manitoba, Canada.
Keep your eye on the Aquanty blog for updates on this research, we hope to see this research published soon.
Abstract:
Knowledge of soil moisture is significant for supporting agricultural production in cold climates, and other ecosystem services. Climate change is expected to produce more fluctuations in precipitation across the globe and cause more frequent extremes in soil moisture, including floods and drought which have major impacts on agriculture and infrastructure. Forecasting can help mitigate the impacts of soil moisture extremes by providing warnings about upcoming extreme events and prompt mitigation measures. This study constructed a physically-based groundwater-surface water model for an agriculturally dominated watershed in the Red River Valley, Manitoba, to determine the soil moisture variability in cold climate in deeper soil layers. A 1D replica of the main watershed model was also created for sensitivity analysis of soil hydraulic parameters that influenced moisture at different depths. Historically available soil moisture data and additional data from installed Sentek probes in observational fields were used for calibration. Statistical analysis was performed by comparing simulated and measured soil moisture. At the surface (5 cm), the sand series in the 1D model had an excellent match and the 3D results produced a good correlation at the surface during calibration and validation. The model results of the deeper layers in the clay soils also showed a good fit during calibration and validation while the sand series showed poor correlation at lower depths. The modelling framework in this study assists to provide valuable insights into different hydrological processes.