Publications

Bergeron, Jean; Royer, Alain; Turcotte, Richard; Roy, Alexandre (2014). Snow cover estimation using blended MODIS and AMSR-E data for improved watershed-scale spring streamflow simulation in Quebec, Canada. HYDROLOGICAL PROCESSES, 28(16), 4626-4639.

Abstract
Estimation of the amount of water stored in snow is a principal source of error for spring streamflow simulations in snow-dominant regions. Measuring this variable throughout large and often remote areas using snow surveys is an expensive task since they are practically point measurements. Remote sensing is an alternative method, which can cover much larger areas in little time, but further research is required to reduce uncertainties on snow water equivalent (SWE) estimations, especially during the melting period. However, optical-near infrared (NIR) and passive microwave remote sensing can detect snow cover area (SCA) with greater certainty, which can be used as a proxy for SWE. The two datasets work in complementary ways considering their spatial resolutions and cloud cover limitations. This study developed an SCA product from blended passive microwave (Advanced Microwave Scanning Radiometer - Earth Observing System: AMSR-E) and optical-NIR (Moderate Resolution Imaging Spectroradiometer: MODIS) remote sensing data to improve estimates of streamflow caused by snowmelt during the spring period. The blended product was assimilated in a snowmelt model (SPH-AV) coupled with the MOHYSE hydrological model through a modified direct insertion method. SCA estimated from AMSR-E data was first compared with in situ snow-depth measurements and SCA estimated with MODIS. Results showed an agreement of over 95% between AMSR-E-derived and cloud-free MODIS-derived SCA products in the spring. Comparison with ground stations confirmed the underestimation of snow cover by AMSR-E. Assimilation of the blended snow product in SPH-AV coupled with MOHYSE yielded an overall improvement of the Nash-Sutcliffe coefficient comparable with simulations with no updates, which is comparable to results driven by biweekly snow surveys. Assimilation of remotely sensed passive microwave data was also found to have little positive impact on streamflow simulation due to the difficulty of differentiating melting snow from snow-free surfaces. (C) 2013 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.

DOI:
10.1002/hyp.10123

ISSN:
0885-6087; 1099-1085