PROGRESS IN GEOGRAPHY ›› 2013, Vol. 32 ›› Issue (4): 532-537.doi: 10.11820/dlkxjz.2013.04.005

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Parameter uncertainty of HIMS model and its influence factor analysis

LIU Lifang1, LIU Changming1,2, WANG Zhonggen2, JIANG Yan3, ZHANG Yongqiang4, SANG Yanfang2, WANG Hu5   

  1. 1. College ofWater Sciences, Beijing Normal University, Beijing 100875, China;
    2. Key Laboratory ofWater Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, CAS, Beijing 100085, China;
    4. CSIRO Land andWater, Canberra, ACT 2601, Australia;
    5. Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
  • Received:2012-10-01 Revised:2013-01-01 Online:2013-04-25 Published:2013-04-25

Abstract: Parameter uncertainty of hydrological model and its influence factor analysis have important significance in hydrological forecasting for ungauged basins. In this paper, the parameter uncertainty of HIMS model was examined by employing generalized likelihood uncertainty estimation (GLUE) method based on the simulation results of daily rainfall runoff data from the three catchments in Australia, and the influence factors of HIMS model parameters were discussed. It was found that parameters of HIMS model were all sensitivity parameters. And then the parameters distribution of HIMS model and the physical attributions in different catchments were compared. The results showed that the value of soil moisture storage capacity (Wsm) in the catchment was related to the precipitation, forest coverage and available water capacity in lay 1. As the precipitation, forest coverage and available water capacity in lay 1 rising, the value of Wsm increased. The larger values of runoff generation coefficient (R) and (r) in the catchment were due to the large soil saturated hydraulic conductivity and high forest coverage. The value of actual evapotranspiration coefficient (ε) was large in the catchment with low temperature and forest coverage. The value of Muskingum model coefficient (C2) was large when soil saturated hydraulic conductivity and forest coverage were high.

Key words: catchment physical attributions, GLUE, HIMS model, parameter uncertainty analysis