Location: Range Management ResearchTitle: Assimilation of AATSR, MERIS and MODIS data in the snowmelt runoff model (SRM) on the upper Rio Grande (USA)) Author
|Rango, Albert - Al|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 12/15/2008
Publication Date: 12/17/2008
Citation: Bleiweiss, M.P., Rampini, A., Pepe, M., Rango, A., Steele, C., Stein, W.L., Schmugge, T. 2008. Assimilation of AATSR, MERIS and MODIS data in the snowmelt runoff model (SRM) on the upper Rio Grande (USA)[abstract]. AGU 2008 Fall Meeting, December 15-19, 2008, San Francisco, California. C21C-0576. CDROM. Interpretive Summary:
Technical Abstract: Current efforts for simulating or forecasting snowmelt are time-consuming and laborious; the AWARE project (A tool for monitoring and forecasting Available WAter REsource in mountain environments) has been motivated by the urgent need to facilitate the prediction of medium-term flows from snowmelt for an effective and sustainable water resources management. Its main goal is to provide innovative tools for monitoring and predicting water availability and distribution in drainage basins where snowmelt is a major component of the annual water balance. The particular objective of the effort reported here is to compare results obtained from the MODIS sensor on NASA Terra and Aqua satellite and next generation sensors AATSR and MERIS on board ESA Envisat satellite. The vehicle for this comparison is the AWARE Geoportal (http://www.aware- eu.info/eng/home.htm) which is a WWW implementation of the Snowmelt Runoff Model (SRM). The river basin chosen for analysis is the Upper Rio Grande of North America. The time period for analysis encompasses the Water Years 2005, 2006, and 2007 (October 2004 - September 2007). The reason for this is to ensure that data from all three sensors are available for use and to investigate variable climate conditions. A successful comparison between the various sensors will help demonstrate that the AWARE approach will facilitate future processing of several years' worth of snow cover data from a variety of sensors that covers large extremes in climate variability. This will allow greater success in developing forecasts and understanding of longer term climate change impacts.