Remotely Sensed Quantitative Precipitation Estimates in Real-Time for Water Management Applications
Can Reclamation provide radar precipitation estimates of sufficient quality to accurately predict streamflows in near-real-time?
Need and Benefit
Previous rainfall information has been largely obtained from rain gauges, which can be sparse in geographic coverage (particularly in the West) and often do not sample heavy rainfalls. This situation often leads to erroneous precipitation input to hydrologic models that forecast critical water supplies for Reclamation water operations managers.
Radar, unlike gauges, has continuous areal coverage. Nevertheless, while radar rainfall estimation has been done by the operational WSR-88D (NEXRAD) radar network, these estimates are often suspect in areas of complex terrain that have radar beam blockages, and during the cool season when snow is predominant over rain (the original WSR-88D Quantitative Precipitation Estimation - QPE - was not designed for snow). The Precipitation Accumulation Algorithm (PAA) represents an improvement to the standard QPE, offering half-hourly to daily updates of all precipitation types (rain, snow, melting snow) with resolution of about 2 kilometers square.
As we work with real radar data we see effects from several phenomena that affect the quality of PAA estimates: bright band from melting snow, anomalous propagation when refraction makes the radar beam hit the ground, vertical gradient of reflectivity (includes virga and evaporation for precipitation decreases, plus precipitation growth as it nears the surface), and clear air echoes. Furthermore, the NEXRAD occultation and hybrid scan files (both based upon digital terrain) usually need refinement for various blockages and ground targets (trees, towers, buildings, etc) not accounted for in the terrain data. Such problems usually need refinements on an individual case basis.
PAA outputs can serve as input to distributive hydrologic models that estimate streamflows at any point in a basin, thus calculating water volume discharge into main rivers and reservoir systems. Accurate precipitation input to hydrologic models is the most critical element in such models. Such a coupled QPE/hydrologic modeling system is being developed as part of a complementary S&T study entitled Watershed Precipitation and Runoff Enhancement for AWARDS (DS2-FY03-005-RLC). This capability has never been available for water operations management. Such river forecasts will provide decision support to water managers for a) Flood control/spill avoidance and increases in the conservation pool (number and volume) and b) Conservation pool storage and water delivery (volumes). Both a) and b) increase the efficiency of water allocation for flood control, irrigation, and power production.
We are also developing a version of the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPE-SUMS) system. QPE-SUMS combines radar with satellite and gauge data to derive QPEs. The ultimate aim is to arrive at the optimum QPE for the western U.S. by evaluating and culling the best features of both systems, while mitigating errors. Experience gained in these analyses and algorithm adjustments will enable us to easily transfer the optimized radar QPE to other regions within Reclamation.