Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

Y Chen,J Li,H Xu

doi:10.5194/hess-20-375-2016

Abstract

Abstract. 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.

Highlights

Improving flood forecasting capability has long been the goal of the global hydrological community, and catchment hydrological models are the main tools for flood forecasting
physically based distributed hydrological models (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, having the potential to improve the catchment hydrological process simulation and prediction capability (Ambroise et al, 2006)
The method has been tested in two catchments in southern China with different sizes, and the results show that the improved particle swarm optimization (PSO) algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting

Summary

Introduction

Improving flood forecasting capability has long been the goal of the global hydrological community, and catchment hydrological models are the main tools for flood forecasting. With the development of remote sensing and GIS techniques, highresolution terrain data such as those from the Shuttle Radar Topography Mission digital elevation model (DEM) database (Falorni et al, 2005; Sharma et al, 2014), the USGS land use type database (Loveland et al, 1991, 2000), the FAO soil type database (http://www.isric.org), and precipitation estimated by digital weather radar (Fulton et al, 1998; Chen et al, 2009) have been prepared and freely available globally This largely facilitated the development of physically based distributed hydrological models (PBDHMs). At the same time, the so-called semi-distributed hydrological models have been proposed, such as the SWAT model (Arnold et al, 1994), TOPMODEL model (Beven et al, 1995), HRCDHM model (Carpenter et al, 2001), and others, with model complexity between the lumped model and distributed model

Methods

Findings

Discussion

Conclusion