The University of Washington Surface Water Monitor: An experimental platform for national hydrologic assessment and prediction

Over the last decade, great strides have been made in land surface modeling at regional to continental scales. The North American Land Data Assimilation System has developed new approaches for estimating current land surface moisture conditions (e.g., soil moisture, snow and runoff) as well as retrospective reconstructions of the same variables. These science-based products were motivated by a need to improve initialization of numerical weather prediction models, but have many other potential applications both in research and operations. An experimental effort called the Surface Water Monitor (SWM) has melded these advances into a system serving both objectives in the area of water and potentially drought management. The SWM is a continental U.S. implementation of the Variable Infiltration Capacity hydrologic model that combines a retrospective daily analysis of over 90 years with real-time, daily-updating simulations of land surface climate and moisture conditions. The retrospective dataset provides a foundation for research toward understanding hydrologic trends and variability on a national scale since 1915. It also provides an unusually consistent statistical background for interpreting the real-time moisture estimates, enabling their depiction as anomalies or percentiles with respect to historical conditions. The real-time percentile maps and predictions have already become an input to national-scale operational drought management efforts such as the US Drought Monitor and the Climate Prediction Center Drought Outlook. The system is also used for prediction at seasonal lead times, enabling the production of operational hydrologic, drought-oriented forecasts that complement those currently available from operational centers. 1 Corresponding author address: Andrew W. Wood, Civil and Environmental Engineering Dept., University of Washington, Seattle, WA 98115; [email protected]; [email protected]