The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).

TOPKAPI is a physically based distributed rainfall–runoff model derived upon the assumption that the horizontal flow at a point in the soil, over the slopes and in the channel network can be approximated by means of a kinematic wave model. The TOPKAPI model combines this kinematic approach with a digital elevation model (DEM)‐based description of a basin. Similarly to what is done in the finite element approach, the basic model equations are derived by integrating in space the point process equations up to a finite dimension, the pixel, thus converting the original kinematic partial differential equations into a cascade of finite dimension non‐linear reservoir ordinary differential equations. These new equations are ‘structurally similar’ for all the processes (flow in the soil, over the slopes and in the channel network) and are shown to be representative of the original equations at the new finite pixel scale. With a view to assessing the quality of the approximation, the paper presents a comparison study in which a quasi‐analytical approach based upon the characteristic kinematic wave solution of the original equations is compared to the solution provided by the non‐linear reservoir finite scale approximation. The test is first applied to a simplified theoretical case in order to show the quality of the approximation at small scales. Successively it is applied to two real‐word cases: the Upper Reno catchment based on 400 × 400 m² pixels and the Sieve River catchment using 1 × 1 km² pixels.All the results show that the cascade of non‐linear reservoirs is a good finite dimension approximation of the subsurface flow in the soil, the flow over the slopes and the flow in the channel network, which allows us to properly retain the physical properties of the original equations at finite scales that range from a few metres up to 1 km. Copyright © 2005 John Wiley & Sons, Ltd.

Flooding has been the most severe hazard in China from time immemorial, due to its special geographical, climatic and socio-economic conditions. In recent years, against the background of global climate change and rapid urbanization, extreme hydro-meteorological events have obviously increased in China, in turn affecting the sustainable social and economic development. This paper analyses how modern flood risk management has evolved from the early “build and protect” flood control approach to a broader flood risk management approach by analyzing what has happened in China and the role of major floods in this evolution. The development of flood forecasting models and systems in terms of how they have informed decision-making as flood risk management has evolved over the years. The challenging of recognizing and dealing with forecast uncertainty and flood risk in decision-making is also analyzed in the paper.