Improvement of springtime streamflow simulations in a boreal environment by incorporating snow-covered area derived from remote sensing data

Snow estimation is the major source of errors for spring streamflow simulations in Quebec, Canada. The objective of the study is to improve melting discharge estimation computed with the operational MOHYSE hydrological model by integrating remote sensing snow-cover area in its snow module (SPH-AV). The satellite-derived snow-cover area (SCA) (MODIS & IMS) is first compared with in situ snow depth data measurements and simulated snow-cover area. Results show that the remote sensing products underestimate the snow-cover area on the mainly forested study region. A direct-insertion method of daily satellite SCA images is developed based on an empirical snow water equivalent threshold compensating, on a pixel-by-pixel basis, for the small amount of snow that satellite sensors can not identify during the melting period. This approach improves the streamflow simulation for spring periods (25th March to 25th of May) over 4 years (2004-2007) with a Nash-Sutcliffe coefficient enhancement of 0.11 and a root mean square error (RMSE) improvement of 21% on the Du Nord watershed, for which the threshold was optimized. The threshold found on the Du Nord basin was then directly applied on another watershed (Aux Ecorces basin) for validation. The simulated streamflow is significantly improved as compared to the observed streamflow for these 4 years (mean Nash increase from 0.72 to 0.85 and RMSE decrease by 22%). The method improves streamflow peaks identification as much as 36% on the Du Nord watershed and 19% on the Aux Ecorces watershed. (C) 2010 Elsevier B.V. All rights reserved.