Snow cover estimation using blended MODIS and AMSR‐E data for improved watershed‐scale spring streamflow simulation in Quebec, Canada

Abstract

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.

Monitoring snow wetness in an Alpine Basin using combined C-band SAR and MODIS data

“…MODIS and C-Band SAR have sometimes been used to monitor the snow cover extent of river basins, but both methods were used independently of the other and used only to map the SCA (Nagler et al, 2008;Pettinato et al, 2009). As SAR data is becoming increasingly accessible, with the free access to SENTINEL data and the future launch of the RADARSAT-Constellation, this study aims to provide a first step in developing a new tool for watershed managers, which combines the SCA, a product which demonstrated its usefulness and limitations for reservoir management (Bergeron, Royer, Turcotte, & Roy, 2014), to a second layer of information. We demonstrated that the combination of MODIS and C-Band SAR results in a snow wetness map that is more complete and useful than either method used on their own, while retaining a similar accuracy to MODIS for snow/snow-free pixels.…”

Mountain water cellars: a chemical characterization and quantification of the hydrological processes and contributions from snow, glaciers and groundwater to the Upper Mendoza River basin (~ 32° S), Argentina

“…Both products were obtained through the Earth Observing System Data and Information System (EOSDIS; http: //reverb.echo.nasa.Gov/). By combining them to get one product through Cara algorithm (Cara et al, 2016), and using a threshold of 30% of cloud coverage, which is the commonly used and agreement threshold for accepting or discarding information (Roy et al, 2010;Bergeron et al, 2014). 25…”

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

“…Finally, daily SCA data derived from the spaceborne sensor MODIS/Terra are also considered (Hall et al, 2002). Because of its spatial coverage and relatively high temporal resolution, remotely sensed snow data from MODIS have proven to be valuable in a number of hydrologic studies (Bergeron et al, 2014;Roy et al, 2010;Tang and Lettenmaier, 2010;Andreadis and Lettenmaier, 2006;Clark et al, 2006), including one applied to the Nechako watershed (Marcil et al, 2016). The meteorological observations gathered over a period of 10 years (from 15 August 1990 to 14 August 2000) were used as a basis upon which a synthetic experiment (see below) tested the added value of three types of synthetic observations (streamflow, SWE and SCA) for data assimilation purposes.…”