Monitoring Areal Snow Cover Using NASA Satellite Imagery

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest

Approaches for operational forecasting of short-to-medium range streamflow for snowmelt dominated basins /by Brain Harshburger.

Because of the crucial role snowmelt plays in watersheds around the world, it is important to be able to accurately monitor the snowpack in mountainous areas, as well as produce accurate streamflow forecasts. The main objective of this study is to develop methods to assist in the operational management of water resources in mountainous areas. This is accomplished through the development of new methodologies for generating short-to-medium range (1 to 15 days) deterministic and ensemble streamflow forecasts using an enhanced version of the Snowmelt Runoff Model (SRM). Two enhancements were made to SRM to aid in its operational implementation, including: (1) the use of an antecedent temperature index method to track snowpack cold-content and account for the delay in melt associated with diurnal refreezing, and (2) the use of both maximum and minimum critical temperatures to partition precipitation into rain, snow, or a mixture of rain and snow. The comparison of retrospective model simulations with observed streamflow indicated that the enhancements result in an improvement to model performance. Deterministic streamflow forecasts generated using the model were found to compare well with the observed streamflow at leadtimes up to about 7 days, however there was still some predictability at longer leadtimes. Ensemble forecast were comparable to those found in other studies and were found to improve upon the deterministic forecasts. In addition, ensemble streamflow forecasts are advantageous because they provide the user with information regarding how likely a forecast is to be correct.;In addition to streamflow forecasts, a methodology was developed for the generation of gridded estimates of Snow Water equivalency (SWE) using operationally available surface and remote sensing data. Although the methodology employed in this study is rather simple, and relies solely on regression techniques, the results were encouraging and compare well with those found in other studies, which often utilized more sophisticated spatial interpolation methods. Thus, there is a high potential for this methodology to be applied operationally to mountainous basins around the world.;KEYWORDS: Snow Hydrology; Water Supply; Surface Water Hydrology; Quantitative Modeling; Ensemble Streamflow Forecasting; Snow Water Equivalency; Multivariate Regression; Snow-covered Area; Spatial Distribution.Thesis (Ph. D., Geography)--University of Idaho, December 2009