Climate change and variability increases the probability of frequency, timing, intensity, and duration of flood events. After rainfall, soil moisture is the most important factor dictating flash flooding, since rainfall infiltration and runoff are based on the saturation of the soil. It is difficult to conduct ground-based measurements of soil moisture consistently and regionally. As such, soil moisture is often derived from models and agencies such as the National Weather Service (NWS) use proxy estimates of soil moisture at the surface in order support operational flood forecasting. In particular, a daily national map of Flash Flood Guidance (FFG) is produced that is based on surface soil moisture deficit and threshold runoff estimates. Improved flash flood forecasting requires accurate and high resolution soil surface information. The remote sensing observations of soil moisture can improve the flood forecasting accuracy.  The Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellites are two potential sources of remotely sensed soil moisture data. SMAP is a directed mission within the NASA Earth Systematic Mission Program and is planned to launch in 2015, while SMOS is a Living Planet Programme from the European Space Agency (ESA) and launched in 2009. SMOS measures the microwave radiation emitted from the Earth's surface. SMAP has a similar mission and will use a combined radiometer and high-resolution synthetic aperture radar operating at L-band (1.20-1.41 GHz) to measure surface soil moisture directly. Microwave radiation at this wavelength offers relatively deeper penetration and has lower sensitivity to vegetation impacts. The main objective of this research is to evaluate the contribution of remote sensing technology to quantifiable improvements in flash flood applications.

This study is focused on adding a remote sensing component to the NWS FFG Algorithm. The current operational FFG algorithm applies the precipitation-based Antecedent Precipitation Index (API) and the NWS Hydrology Laboratory - Research Distributed Hydrologic Model (HL-RDHM) to counties across the USA, and when combined with Doppler radar and Multisensor Precipitation Estimation (MPE) data, supports the issuance of Flash Flood Warnings. HL-RDHM is under active and continual development at the National Oceanic and Atmospheric Administration's National Weather Service (NOAA/NWS), Office of Hydrologic Development (OHD) and is used at many River Forecast Centers (RFC) for stream flow, snow pack, and soil moisture modeling. The challenge of this study is employing the direct soil moisture data from SMAP and SMOS to replace the model-calculated soil moisture state which is based on the soil water balance in 4 km x 4 km Hydrologic Rainfall Analysis Project (HRAP) grid cells. In order to determine the value of the satellite data to NWS operations, the streamflow generated by HL-RDHM with and without soil moisture assimilation will be compared to USGS gauge data. Furthermore, we will apply the satellite-based soil moisture data with the FFG algorithm to evaluate how many hits, misses and false alarms are generated. As SMAP has not yet been launched, high resolution (10 km) SMAP test bed data will be used which contains soil moisture and temperature from an integration of a distributed (DEM-based) hydrological model. This study will evaluate the value of remote sensing data in constraining the state of the system for main-stem and flash flood forecasting.