Improving Operational Flood Forecasts in the US Northern Plains Region through Assimilation of Ponded Water Retrievals
A better accounting for ponding within the land surface water balance can improve predictability for runoff and streamflow, with consequent benefits for society through improved river forecasting and decision-making in water management and emergency response to flooding events. There are currently no quantitive estimates of the volume of water detained on the landcape during such ponding events, and we lack a sufficiently comprehensive understanding of the phenomenon for practical enhancement of operational forecasting. The upper midwestern US terrain and land use characteristics provide an ideal setting to demonstrate and achieve practical application of NASA remote sensing in the operational flood forecasting context.
NCAR has teamed up with scientists from Purdue and NASA, as well as forecasters from the NWRFC, to use satellite data primarily from the MODIS sensors (see below right) and LandSat to derive estimates of surface ponded water volume and extent, and use them to update real-time streamflow forecasting models. It has upgraded the NASA Land Information System (LIS) -based VIC hydrology model to include surface ponding schemes, and is beginning to evaluate strategies for improving hydrologic simulation and prediction through data assimilation of the ponded water datasets. A website describing project objectives, information and datasets can be found at: http://www.ral.ucar.edu/staff/wood/nasa_thp/.
