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Application and evaluation of a snowmelt runoff model in the Tamor River basin, Eastern Himalaya using a Markov Chain Monte Carlo (MCMC) data assimilation approach

机译:马尔可夫链蒙特卡罗(MCMC)数据同化方法在喜马拉雅东部塔莫尔河流域融雪径流模型的应用和评估

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Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree-day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash–Sutcliffe metric ~0.84, annual volume bias < 3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002–2006 period is estimated to be 29.7 ± 2.9% (which includes 4.2 ± 0.9% from snowfall that promptly melts), whereas 70.3 ± 2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000–5500 m range contributes the most to basin runoff, averaging 56.9 ± 3.6% of all snowmelt input and 28.9 ± 1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt rainfall contributions to total outflow but that this derives from the degree-day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds. Copyright © 2013 John Wiley & Sons, Ltd.
机译:先前的研究已经引起人们对山区流域发生的重大水文变化的关注,在山区流域,水文学以冰冻圈过程为主。建模是理解这些变化的重要工具,但是由于地面观测的缺乏和跨时空的模型参数的不确定性,在山区地形中尤其具有挑战性。这项研究利用马尔可夫链蒙特卡洛数据同化方法来检查和评估尼泊尔喜马拉雅东部塔莫尔河流域应用的概念,度日积雪径流模型的性能。融雪径流模型使用2002年至2006年的每日流量进行了校准,具有相当高的精度(平均纳什–萨特克利夫度量值〜0.84,年体积偏差<3%)。马尔可夫链蒙特卡罗方法限制了模型最敏感的参数(例如失效率和后退系数),并使模型拟合和性能最大化。与使用观测站降水的模拟相比,使用插值降水数据集进行的模拟流量模拟可减少降雨的分数贡献。据估计,塔莫尔河流域2002年至2006年期间融雪对总径流量的平均贡献为29.7%±2.9%(其中包括迅速融化的降雪造成的4.2%±0.9%),而70.3%±2.6%的贡献来自于雨量。平均而言,在4000-5500 m范围内的高程带对流域径流的贡献最大,平均占全部融雪输入量的56.9%±3.6%,占径流总降雨量输入量的28.9%±1.1%。与使用观测站降水的模拟相比,使用插值降水数据集进行的模型模拟流量可减少降雨融雪的分数贡献。模型实验表明,水文图本身并不限制对融雪降雨对总流出量的贡献的估计,但这是基于度-天融化模型得出的。最后,我们证明了数据同化方法可用于量化和减少与模型参数有关的不确定性,从而为此类山区流域的融雪和降雨贡献提供不确定性范围。版权所有©2013 John Wiley&Sons,Ltd.

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