@conference {50931,
	title = {Implementation of a remote sensing-snowmelt runoff forecasting system in the Rio Grande Basin},
	booktitle = {EOS Transactions, American Geophysical Union},
	volume = {83},
	number = {47},
	year = {2002},
	month = {November 19, 200},
	pages = {F540},
	address = {San Francisco, CA},
	abstract = {<p>Information on snow water resources is a major concern in river basins where the snowmelt runoff can be a significant contributor to total discharge, even in basins such as the Rio Grande where desert makes up much of the downstream area. Snow resource information is especially useful in the Rio Grande for irrigation, hydropower, and water supply management. The system, being developed for the high elevation portions of the Rio Grande Basin, makes use of satellite snow cover data as well as SNOTEL data from remote, ground-based instrumentation. Several problems in using the new MODIS satellite data have been addressed in order to input it into the modeling system in the basin, including the Bowtie Effect and topographic shadowing (see Gomez-Landesa and Rango, 2002). The snow distribution in March-May in snowmelt headwater basins of the Rio Grande is input into the Snowmelt Runoff Model (SRM) to generate forecasts of daily flows. To allow forecasts earlier in the hydrologic year, the SNOTEL data are relied upon until the snowmelt is underway (see DeWalle et al., 2002) and the satellite data play a larger role. The outflow generated by SRM from the snowmelt basins is then used as input to the SLURP model which is used to provide discharge for the entire basin, as well as for tributaries only affected by rainfall. The utilization of SRM in the snow-fed basins also allows generation of climate-change-affected hydrographs for individual tributaries. The Jemez River Basin (New Mexico), a designated wild river, is being used for validation of both snow cover maps and discharge forecasts, whereas the upper Rio Grande Basin (Colorado) has been selected for evaluation of the climate change scenarios.</p>
},
	author = {A. Rango and E. Gomez-Landesa and Kris Havstad and M. Bleiweiss and Tanksley, K. and D. DeWalle and G. Kite}
}
