کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4978300 | 1452264 | 2016 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method
ترجمه فارسی عنوان
عدم قطعیت پارامتر و دینامیک زمانی حساسیت برای مدل های هیدرولوژیکی: آمیختگی داده های ترکیبی متوالی و روش کالاکاسیون احتمالی
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کلمات کلیدی
عدم قطعیت، فیلتر ذرات، روش همبستگی احتمالی، تجزیه و تحلیل میزان حساسیت، ضریب اطلاعات حداکثر، مدل هیدرولوژیکی شبیه سازی مونت کارلو،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 86, December 2016, Pages 30-49
Journal: Environmental Modelling & Software - Volume 86, December 2016, Pages 30-49
نویسندگان
Y.R. Fan, G.H. Huang, B.W. Baetz, Y.P. Li, K. Huang, Z. Li, X. Chen, L.H. Xiong,