Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization
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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-01-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/20/375/2016/hess-20-375-2016.pdf |
| _version_ | 1818364494275936256 |
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| author | Y. Chen J. Li H. Xu |
| author_facet | Y. Chen J. Li H. Xu |
| author_sort | Y. Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T22:05:16Z |
| format | Article |
| id | doaj.art-437cf648758c4ef4beaec48393892b8d |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-13T22:05:16Z |
| publishDate | 2016-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-437cf648758c4ef4beaec48393892b8d2022-12-21T23:29:52ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382016-01-0120137539210.5194/hess-20-375-2016Improving flood forecasting capability of physically based distributed hydrological models by parameter optimizationY. Chen0J. Li1H. Xu2Department of Water Resources and Environment, Sun Yat-sen University, Room 108, Building 572, Guangzhou 510275, ChinaDepartment of Water Resources and Environment, Sun Yat-sen University, Room 108, Building 572, Guangzhou 510275, ChinaBureau of Hydrology and Water Resources of Fujian Province. Fuzhou, Fujian, ChinaPhysically 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.http://www.hydrol-earth-syst-sci.net/20/375/2016/hess-20-375-2016.pdf |
| spellingShingle | Y. Chen J. Li H. Xu Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization Hydrology and Earth System Sciences |
| title | Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization |
| title_full | Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization |
| title_fullStr | Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization |
| title_full_unstemmed | Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization |
| title_short | Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization |
| title_sort | improving flood forecasting capability of physically based distributed hydrological models by parameter optimization |
| url | http://www.hydrol-earth-syst-sci.net/20/375/2016/hess-20-375-2016.pdf |
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