Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

China’s coastal areas suffer from typhoon attacks every year. Rainstorms induced by typhoons characteristically are high intensity with a large amount of rain and usually induce floods and waterlogging in the affected area. Guangdong province has the highest frequency of typhoon hits in China. It has a special geographical position as well as unique climatic features, but the typhoon flood disaster risk has not been fully assessed in this area. This article attempts to fill this gap by providing a comprehensive risk assessment for the area. By combining the Analytical Hierarchy Process (AHP) and multi-factor analysis through geographic information system (GIS) and the comprehensive weighted evaluation, the typhoon flood disaster risk is evaluated from four different aspects with seventeen indicators. A comprehensive study of the typhoon flood disaster risk is carried out, and the risk maps with a resolution of 1 km² have been made. There is a good coherence between the typhoon flood risk map and historical records of typhoon floods in Guangdong province. The results indicate that the comprehensive typhoon flood disaster risk in the coastal regions of Guangdong province is obviously higher than in the Northern mountainous areas. Chaoshan plain and Zhanjiang city have the highest risk of typhoon flood disaster. Shaoguan and Qingyuan cities, which are in the Northern mountainous areas, have the lowest risk. The spatial distribution of typhoon flood disaster risks shows that it has certain regulations along the coast and rivers, but it may be affected by economic and human activities. This article is significant for environmental planning and disaster management strategies of the study area as well as in similar climatic regions in other parts of the world.

Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1–15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

Past research shows that physically based distributed hydrological model has the advantage of better representing the basin characteristics and the hydrologic processes to potentially simulate/predict river basin flood. But how to physically derive model parameters directly from terrain data and to acquire channel cross-sectional size are still difficult jobs in physically based distributed hydrological modeling that prevented its operational application in river basin flood forecasting. To deal with these challenges, this paper first presents a physically based, distributed hydrological model for river basin flood forecasting/simulation, called the Liuxihe model. Then a method for estimating channel cross-sectional size was proposed that utilizes a readily accessible public data set acquired by remote sensing techniques, which could be employed by other physically based, distributed hydrological models also. Finally, a method for deriving model parameters was proposed that adjusts model parameters with i...