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首页> 外文期刊>Journal of hydrometeorology >Real-Time Flood Forecasting via Parameter Regionalization and Blending Nowcasts with NWP Forecasts over the Jiao River, China
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Real-Time Flood Forecasting via Parameter Regionalization and Blending Nowcasts with NWP Forecasts over the Jiao River, China

机译：Real-Time Flood Forecasting via Parameter Regionalization and Blending Nowcasts with NWP Forecasts over the Jiao River, China

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摘要

Supertyphoon rainstorms are apposite examples to evaluate the utility of multisource precipitation products in monitoring and forecasting short-duration heavy rainfall and the resulting intense floods. In this study, the record-breaking floods induced by Typhoon Lekima in Jiao River, China, were retrospectively forecasted. The Xinanjiang (XAJ) model was calibrated based on parameter regionalization derived from SOM1k-means clustering. Via XAJ, the performance of the currently prevailing atmosphere reanalysis (CLDASv2 and CMA-CMORP), quantitative precipitation estimation (QPE) (IMERG-ER and PERSIANN-CCS), and quantitative precipitation forecasts (QPFs) (GRAPES_MESO, ECMWF, and GFS) in monitoring and forecasting Lekima rainfall and flood was comprehensively evaluated. A three-component blended ensemble was proposed, by blending QPE nowcasts with the weighted ensemble of QPFs through a transition of the regional GRAPES_MESO, and compared with two conventional two-component blending methods. The results indicated that the parameter regionalization enabled an explicit consideration of the spatial heterogeneity of basin attributes as well as meteo-rology, resulting in a minimum NSE of 0.81. CLDASv2 and CMA-CMORPH provided superior spatiotemporal accuracy with a structural similarity index up to 0.75 and NSE . 0.9 for the flood simulation. PERSIANN-CCS rainfall and the driven flood were seriously underestimated by 70% and 80%, respectively. The real-time application of QPFs during the Lekima flood provided encouraging results with a lead time of 40 h. The three-component blended ensemble method resulted in more stable and accurate flood forecasts, especially for the flood peak on 9 August, which was improved by 80%. Our results are expected to present support for real-time flood preparation and mitigation with practical significance.

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