We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization.
- Authors
Chen, Y.; Li, J.; Xu, H.
- Abstract
Physically based distributed hydrological models discrete 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, and are regarded to have the potential to improve the catchment hydrological processes 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, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so param eter 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 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 models capability in cathcment 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 Liuxihe model as the study model, which is a physically based distributed hydrological particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively. model proposed for catchment flood forecasting, the improverd Particle Swarm Opti mization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coeffcients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO al gorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.
- Subjects
FLOOD forecasting; HYDROLOGIC models; PARAMETER estimation; METEOROLOGICAL precipitation; PREDICTION models
- Publication
Hydrology & Earth System Sciences Discussions, 2015, Vol 12, Issue 10, p10603
- ISSN
1812-2108
- Publication type
Article
- DOI
10.5194/hessd-12-10603-2015