Ensemble flood forecasting based on ensemble NWP and the GMKHM distributed hydrological model

Author:

Wang Lili,Bao Hongjun

Abstract

The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a new opportunity for flood forecast. The GMKHM distributed hydrological model, which is based on a mixed runoff generation model and overland flow routing model based on kinematic wave theory, and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the GMKHM model and the TIGGE database gives a promising tool for the anticipation of flood events several days ahead,, comparable with that driven by raingauge observation.

Publisher

EDP Sciences

Subject

General Medicine

Reference29 articles.

1. Topographic Partition of Watersheds with Digital Elevation Models

2. Bao H. J. Coupling EPS-Hydrologic-Hydraulic Model for Flood Forecasting. Ph. D. Dissertation. Nanjing: Hohai University. (In Chinese), 2009.

3. Bao H. J. Research on the Application of Flood Forecasting and Scheduling Model in the Catchment of Yishusi. M. E. Dissertation. Nanjing: Hohai University. (In Chinese), 2006.

4. Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

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