Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method |
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| Affiliation: | 1. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan S4S 0A2, Canada;2. School of Environment, Beijing Normal University, Beijing 100875, China;3. Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada;4. Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2, Canada;5. State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;6. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;1. College of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, Fujian Province, China;2. Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada;3. College of Applied Mathematics, Xiamen University of Technology, Xiamen 361024, Fujian Province, China;4. Jackson School of Geosciences, The University of Texas at Austin, 2305 Speedway, Stop C1160, Austin, TX 78712-1692, United States;1. Department of Environmental Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Sino-Canada Energy and Environmental Research Center, North China Electric Power University, Beijing 102206, China;3. School of Environment, Beijing Normal University, Beijing 100875, China;4. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Sask. S4S 7H9, Canada;5. State Grid Henan Economic Research Institute, Zhengzhou 450052, China |
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| Abstract: | In this study, a hybrid sequential data assimilation and probabilistic collocation (HSDAPC) approach is proposed for analyzing uncertainty propagation and parameter sensitivity of hydrologic models. In HSDAPC, the posterior probability distributions of model parameters are first estimated through a particle filter method based on streamflow discharge data. A probabilistic collocation method (PCM) is further employed to show uncertainty propagation from model parameters to model outputs. The temporal dynamics of parameter sensitivities are then generated based on the polynomial chaos expansion (PCE) generated by PCM, which can reveal the dominant model components for different catchment conditions. The maximal information coefficient (MIC) is finally employed to characterize the correlation/association between model parameter sensitivity and catchment precipitation, potential evapotranspiration and observed discharge. The proposed method is applied to the Xiangxi River located in the Three Gorges Reservoir area. The results show that: (i) the proposed HSDAPC approach can generate effective 2nd and 3rd PCE models which provide accuracy predictions; (ii) 2nd-order PCE, which can run nearly ten time faster than the hydrologic model, can capably represent the original hydrological model to show the uncertainty propagation in a hydrologic simulation; (iii) the slow (Rs) and quick flows (Rq) in Hymod show significant sensitivities during the simulation periods but the distribution factor (α) shows a least sensitivity to model performance; (iv) the model parameter sensitivities show significant correlation with the catchment hydro-meteorological conditions, especially during the rainy period with MIC values larger than 0.5. Overall, the results in this paper indicate that uncertainty propagation and temporal sensitivities of parameters can be effectively characterized through the proposed HSDAPC approach. |
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| Keywords: | Uncertainty Particle filter Probabilistic collocation method Sensitivity analysis Maximal information coefficient Hydrologic model Monte Carlo simulation |
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