ESTIMATION OF SNOW AND RAINFALL FOR HYDROLOGICAL ANALYSIS OF A MID-WINTER FLOODING EVENT IN WESTERN UNITED STATES

The overarching goal of this study is to evaluate the potential of multi-sensor snow and rainfall monitoring for hydrological analysis and simulations of flooding events triggered by rain-on-snow. The study focuses on a rain-on-snow event that occurred in early February 2017 in Western US which led to extreme flooding and ultimately dam failure. Snow water equivalent is estimated using a data assimilation system called SNOWDAS developed at the National Oceanic and Atmospheric Administration (NOAA) and used in the analysis of the event. Rainfall estimations from a blended radar- and gauge-based product are used as inputs along with analyzed near-surface air temperature to drive a coupled snow-soil moisture accounting and routing model to simulate discharge at the basin outlet. Satellite-based rainfall from geostationary satellites is also estimated and compared with gauge measurements. Hydrologic simulations conducted during January-March and multi-sensor rainfall estimations showed that the maximum discharge at the basin outlet occurred in early February, coinciding with extreme flooding and light-to-moderate but sustained rain accumulations. Satellite and gauge data confirmed moderate daily rainfall accumulations followed by extensive snowmelt confirmed by SNOWDAS-modeled snow water equivalent changes. Modeled snow water equivalent changes over a two-day period preceding the extreme flooding indicated that extensive snowmelt runoff occurred throughout the basin during the second day whereas during the first day rainfall and potential snowmelt were retained in the snowpack over a large part of the basin. This work demonstrates that light to moderate but sustained amounts of rainfall over snow cover can lead to significant runoff and extreme flooding situations as well as the need to understand distributed snowmelt/runoff mechanisms for improved forecasting of rain-on-snow events.