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: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-01-01
|
| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/20/375/2016/hess-20-375-2016.pdf |
| Summary: | 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. |
|---|---|
| ISSN: | 1027-5606 1607-7938 |