Canadian Forest Service Publications
A national-scale assessment of long-term water budget closures for Canada's watersheds. 2014. Wang,S.; McKenney, D.W.; Shang, J.; Li,J. Journal of Geophysical Research: Atmospheres 119:8712-8725.
Issued by: Great Lakes Forestry Centre
Catalog ID: 35606
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This study examined the long-term water budget closures for 370 watersheds over Canada's landmass by using 30 years' (1981–2010) data products recently produced for precipitation (P) gridded using climate station measurements, land surface evapotranspiration (ET), and water surface evaporation (E0) obtained by the Ecological Assimilation of Land and Climate Observations (EALCO) model, and observed streamflow (Q). The results show that 29%, 58%, and 83% of the watersheds were closed within 5%, 10%, and 20% of P, respectively. The positive and negative imbalances among the 370 watersheds are largely offset and the national scale average is −24 mm yr−1, or 4.2% of P. Water budget closures have large variation across the landmass. Regions with sparse or less accurate monitoring of P such as the mountainous region and the Arctic exhibit the largest water imbalances. Further efforts on enhancing the climate observation networks, improving spatial models for P and ET estimates, and streamflow measurements are all likely critical for a better understanding of Canada's water budgets.
Plain Language Summary
A water budget is the quantitative accounting of the amount of water entering, stored within, and leaving a hydrologic system. Improved information on water budgets supports better water resources management. This study examines the national scale long-term water budget for Canada by using state-of-the-art data products generated from remote sensing, modelling, and in situ measurements by ESS and CFS of NRCan and other government agencies. The new technology developed in this study advanced the Earth Observation applications in large scale water mapping. The datasets and results produced filled knowledge gaps in water resources accounting, quantified the uncertainties in the current water datasets, and provided future directions for improving the water-related datasets for Canada.