Research & Initiatives

Advancing the development of physically-based hydrological/hydrogeological models by exploiting the potential of Earth Observation​

Numerical predictive tools, especially physically-based distributed hydrological/hydrogeological computational models, offer the key opportunities to monitor, predict, and understand the physical processes of the water cycle, thus providing evidence-based support for water security and sustainable water resources management. However, hydrological/hydrogeological modelling often suffers from uncertainties in climate forcing data and deficiencies in model physics and parameters. With recent advances in satellite soil moisture collections (e.g., SMOS and SMAP) and satellite terrestrial water storage (TWS) detection (GRACE/GRACE-FO), there is an increased demand for exploiting the potential of those remotely sensed hydrologic products. To this end, this research theme aims to develop advanced data assimilation schemes for integrating satellite soil moisture and TWS with hydrological models, especially fully integrated surface-subsurface flow models. The developed assimilation systems will significantly advance the development of hydrological/hydrogeological models and improve our ability to monitor and predict the variability of hydrological processes and surface water-groundwater-soil moisture resources, therefore leading to a better understanding of the physical processes that govern water movement in the global and regional hydrologic systems. This project is supported by the Canadian Space Agency (CSA) Grant 21SUESMVAS.

 

Quantifying the magnitude and causes of the spatial and temporal variations in surface water and groundwater resources through the combined use of satellite, modelling, and field data

Spatial and seasonal changes in surface water and groundwater resources play an important role in driving the impacts of climate and global changes on human settlements and infrastructure. This research theme aims to monitor and quantify the spatiotemporal variations in the physical processes that control soil moisture (shallow and deep), surface water flow, groundwater flow, and surface-subsurface water exchange between the surface and subsurface flow regimes using the developed hydrological modelling and satellite assimilation system, in conjunction with the remotely sensed products and in situ measurements. By the integrated and multi-scale assessment, we can address a series of cutting-edge science questions: how the water cycle behavior is changing for the target basins in response to climate change, what controls the state of coupling between various components of the flow and transport processes and the magnitude of the interactions between the surface and subsurface flow regime, and what controls the transitions of the surface-subsurface flow interaction from one state to another. This research is funded by NSERC Discovery Grant. 

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Quantifying the impacts of uncertainties in precipitation datasets on surface water and groundwater resources modelling

In practice, surface and ground water resources modelling and forecasting are often difficult because we have neither perfect hydrological computational models nor perfect meteorological forcing data. Precipitation is the most crucial hydrological driver. Quantifying the uncertainties in precipitation products is of great importance for the identification of datasets that can offer the greatest benefit toward hydrological/hydrogeological modelling. We investigate the performance of state-of-the-art gridded precipitation products from different development pillars (gauge and/or remote sensing observations, reanalysis products, observation-adjusted numerical weather prediction or reanalysis precipitation, etc.) for the target basins and answer a series of fundamental questions, such as: what are the specific error characteristics associated with the various state-of-the-art gridded precipitation products? how does the precipitation error information vary with the season and precipitation intensity? The work complements the implementation of satellite data assimilation system in hydrological models in terms of improving surface water and groundwater resources modelling. The assessment results can advance understanding of uncertainties induced via precipitation forcing data into hydrological modeling, thus providing evidence-based support for developing ensemble methods for water resources modeling and predictions. This research is jointly supported by NSERC DG and UTM's Bridge Funding.  

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