Parameters optimization on DHSVM model based on a genetic algorithm

doi:10.1007/s11707-009-0040-6

Front Earth Sci Chin

2009, Vol. 3

Issue (3) : 374-380 https://doi.org/10.1007/s11707-009-0040-6

RESEARCH ARTICLE

Parameters optimization on DHSVM model based on a genetic algorithm

Changqing YAO¹, Zhifeng YANG²(

)

1. Institute of Scientific and Technical Information of China, Beijing 100038, China; 2. State Key Laboratory of Water Environment Simulation, School of environment, Beijing Normal University, Beijing 100875, China

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Abstract

Due to the multiplicity of factors including weather, the underlying surface and human activities, the complexity of parameter optimization for a distributed hydrological model of a watershed land surface goes far beyond the capability of traditional optimization methods. The genetic algorithm is a new attempt to find a solution to this problem. A genetic algorithm design on the Distributed-Hydrology-Soil-Vegetation model (DHSVM) parameter optimization is illustrated in this paper by defining the encoding method, designing the fitness value function, devising the genetic operators, selecting the arithmetic parameters and identifying the arithmetic termination conditions. Finally, a case study of the optimization method is implemented on the Lushi Watershed of the Yellow River Basin and achieves satisfactory results of parameter estimation. The result shows that the genetic algorithm is feasible in optimizing parameters of the DHSVM model.

Keywords genetic algorithm DHSVM parameters Optimization Yellow River Basin

Corresponding Author(s): YANG Zhifeng,Email:zfyang@bnu.edu.cn

Issue Date: 05 September 2009

Cite this article:

Changqing YAO,Zhifeng YANG. Parameters optimization on DHSVM model based on a genetic algorithm[J]. Front Earth Sci Chin, 2009, 3(3): 374-380.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-009-0040-6
https://academic.hep.com.cn/fesci/EN/Y2009/V3/I3/374

Tab.1 Optimized parameters of DHSVM

Fig.1 Technical route of programming

Tab.2 Combinations of Genetic operation parameters

Fig.2 Convergence stability of genetic algorithm

Tab.3 Comparison of results

Fig.3 Comparison between measured average run-off and calculated run-off monthly of Lushi Hydrological Station in 1980-1990

1	Arola A, Lettenmaier D P (1999). Effects of subgrid spatial heterogeneity on GCM-scale land surface energy and moisture fluxes. Water Resources Research , 35(1): 255-264
2	Cheng C T, OUB C P, Chau K W (2002). Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. Journal of hydrology , 268: 72-86 doi: 10.1016/S0022-1694(02)00122-1
3	Leung L R, Wigmosta M S (1999). Potential climate change impacts on mountain watersheds in the Pacific Northwest. Journal of American Water Resource Association , 35: 1463-1471 doi: 10.1111/j.1752-1688.1999.tb04230.x
4	Mckinney D C, Min D L (1994). Genetic algorithm solution of groundwater management models. Water Resources Research , 30(6): 1897-1906 doi: 10.1029/94WR00554
5	Michalewicz Z (2000). Evolved procedure-genetic algorithm and data coding union. Beijing: Scientific press. (in Chinese)
6	Nijssen B, Haddeland I, Lettenmaier D P (1997). Point evaluation of a surface hydrology model for BOREAS. Journal of geophysical research , 102: 29367-29378 doi: 10.1029/97JD01217
7	Storck P, Bowling L, Wetherbee P, Lettenmaier D (1998). Application of a GIS-based distributed hydrology model for prediction of forest harvest effects on peak stream flow in the Pacific Northwest. Hydrologic Processes , 12: 889-904 doi: 10.1002/(SICI)1099-1085(199805)12:6<889::AID-HYP661>3.0.CO;2-P
8	Wang Q J (1991). The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resources Research , 27(9): 2467-2471 doi: 10.1029/91WR01305
9	Wang S R, Huang R H, Ding Y H (2002a). Improvements of adistributed hydrology model DHSVM and its climatological-Hydrological off-line simulation experiments. ACTA Meteorologica sinica , 60(3): 290-300 (in Chinese)
10	Wang S R, Huang R H, Ding Y H (2002b). Numerical simulation experiments by nesting Hydrology model DHSVM with regional climate model regcm2/China. ACTA Meteorologica sinica , 60(4): 421-427 (in Chinese)
11	Westrick, K.J., Storck P, Mass C F (2002). Description and evaluation of a hydro meteorological forecast system for mountainous watersheds. Weather and Forecasting , 17: 250-262 doi: 10.1175/1520-0434(2002)017<0250:DAEOAH>2.0.CO;2
12	Wigmosta M S, Lettenmaier D P (1999). A comparison of simplified methods for routing topographically-driven subsurface flow. Water Resources Research , 35: 255-264 doi: 10.1029/1998WR900017
13	Wigmosta M S, Perkins W (2002). User's Guide for the Distributed Hydrology-Soil-Vegetation Model. Meteorology. Washington: Pacific Northwest National Laboratory
14	Wigmosta M S, Vail L, Lettenmaier D P (1994). A distributed hydrology-vegetation model for complex terrain. Water Resources Research , 30(6): 1665-1679 doi: 10.1029/94WR00436
15	Xiong L H, Guo S L (2004). Distributed Watershed Hydrological Model. Beijing: China Water Power Press (in Chinese)
16	Yang X H, Lu G H, Li J Q (2002). Applications of the hybrid accelerating generic algorithm to parameter optimization of basin model. Advances in Water Science , 13: 340-344 (in Chinese)
17	Yang X H, Shen Z Y (2005). Intelligent Algorithms and Their Applications in Resources and Environmental System Modeling. Beijing: Beijing Normal University press (in Chinese)
18	Yao C Q, Yang Z F, Zhao Y W (2006). Study on integration of distributed hydrology soil vegetation model and GIS. Journal of Soil and Water Conservation , 20(1):168-171 (in Chinese)
19	Zhou M, Sun S D (2002). Genetic algorithm principle and application. Beijing: National defense industry press (in Chinese)

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