JOURNAL ARTICLE

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

Lili WangHongjun Bao

Year: 2018 Journal:   MATEC Web of Conferences Vol: 246 Pages: 01108-01108   Publisher: EDP Sciences

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 km 2 ) 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.

Keywords:
Flood forecasting Meteorology Numerical weather prediction Flood myth Environmental science North American Mesoscale Model Ensemble forecasting Global Forecast System Lead time Surface runoff Rain gauge Quantitative precipitation forecast Flow routing Data assimilation Computer science Precipitation Geology Geography Engineering

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Citation History

Topics

Hydrology and Watershed Management Studies
Physical Sciences →  Environmental Science →  Water Science and Technology
Hydrological Forecasting Using AI
Physical Sciences →  Environmental Science →  Environmental Engineering
Meteorological Phenomena and Simulations
Physical Sciences →  Earth and Planetary Sciences →  Atmospheric Science
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