Oliver C. Saavedra Valeriano

Temporal Downscaling of Daily Gauged Precipitation by Application of a Satellite Product for Flood Simulation in a Poorly Gauged Basin and Its Evaluation with Multiple Regression Analysis

The study demonstrates that the temporal downscaling of rain gauge‐measured precipitation with satellite-based precipitation estimates enhances the accuracy of hydrological simulations, especially for flood duration. Multiple regression analysis was examined to predict which hydrometeorological parameters have a significant influence on accuracy. The approach was examined at the Hương River basin in Vietnam (1520km 2 ), which is a mountainous region subject to heavy rainfall. The multisensor algorithm Global Satellite Mapping of Precipitation, version Moving Vector with Kalman (MVK; GSMaP_MVK; Psat), was employedtodownscalethedailygauge precipitationmeasurements into6-htime steps.Discharge in the rainy season in 2006‐09 was simulated by a distributed hydrological model with 6-h time steps with four precipitation datasets: 6-h gauge (Pcontrol), daily uniform gauge (Puni), Psat, and the downscaled satellite product (Pds). Flood simulation with Pds performed better than that with Puni in 14 out of the 18 flood events, being close to the results with Pcontrol (median Nash‐Sutcliffe efficiencies of 0.776, 0.261, and 0.710, respectively). Multiple regression analysis showed that the effectiveness of the downscaling method was significantly related to the bias and random errors of the satellite product. In conclusion, satellite-based precipitation measurements have potential for temporal downscaling of discharge simulations from daily to subdaily resolutions in moderate-sized watersheds that lack subdaily rainfall records, and the degree of simulation improvement can be estimated by statistical analysis. The suggested method can be broadly applied to watersheds where the daily precipitation is measured and when satellite-based precipitation measurements are available.

Near Real-Time Optimization of Multi-Reservoir during Flood Season in the Fengman Basin of China

This study aims to develop a multi-objective optimization model in a multi-reservoir system during flood season using Numerical Weather Predictions (NWPs) outputs (short forecast). The optimization model was coupled with the Water and Energy Budget-based Distributed Hydrological Model that was used to forecast the reservoir inflows. The model was forced by 8-day lead time global deterministic NWPs by Japan Meteorological Agency. The reservoir objective function was established by considering the reservoir and upstream safety, downstream safety and future water use. The model was applied to the Baishan-Fengman multi-reservoir system of Northeast China. The results have demonstrated the model with high efficiency in optimizing reservoir objectives for all of the reservoirs. The sensitivity of the system to lead time and decision time were investigated. With the decreasing of lead time, the dam release peaks decrease and the end water levels increase. This is mainly due to the fact that the model with longer lead time needs to keep storage capacity for detected floods during long lead time period. The variation amplitude of dam releases and water levels decrease with the increasing of decision time due to the smoothing of floods and dam releases during long decision period. The model is easy to operate and is able to be coupled with other hydrological models or earth system models.