Physical-based hydrological modelling to predict soil moisture in a mesoscale catchment in cold climates

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Date
2022-05-16
Authors
Shankara Mahadevan, Keshav Parameshwaran
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Abstract
Knowledge of soil moisture is significant for supporting agricultural production and other ecosystem services in cold climates. 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 a cold climate in deeper soil layers. A 1D replica of the main watershed model was additionally used 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 forecasting. The model results of the deeper layers in the clay soils also showed a good fit during calibration and forecasting while the sand series showed poor correlation at lower depths. The modelling framework in this study provides valuable insights into different hydrological processes.
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Keywords
Soil moisture, Fully-integrated hydrological model, Forecasting, Soil series, Cold climate
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