An approach to spatially distributed snow modelling of the Sacramento and San Joaquin basins, California

This project developed an approach to spatially distribute snow modeling and monitoring procedures for estimating snowmelt inputs to the Sacramento and San Joaquin liver basins, California. The Distributed Snow Process Model (DSPM) implements a temperature index algorithm, SSARR-grid, for estimating total snow water equivalent (SWE) and liquid water arriving at the ground surface each hourly time step in each model cell with 2 km spatial resolution. DSPM used interpolated air temperature and precipitation measurements, as well as initial snow conditions derived from ground stations and satellite data. Air temperature interpolation incorporated hourly ambient lapse rates based on observed air temperatures and elevation in an inverse-distance-squared technique. Precipitation interpolation used an inverse-distance-squared method. We developed a linear function that specified the melt factor used by SSARR-grid in each cell depending on the accumulated temperature index. In this test, DSPM relied on maps of SWE for initiation and validation. We estimated the spatial distribution of SWE by combining estimates of snow cover area (SCA) with interpolations of SWE based on snow sensor and course measurements. SCA retrieval algorithms used measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) to estimate snow extent as fractional cover per pixel. SWE interpolations for this project used an algorithm similar to the temperature interpolation The results showed that SWE maps predicted by DSPM over periods up to several days agree well with maps produced from AVHRR merged with ground measurements.