The impact of lake and reservoir parameterization on global streamflow simulation.
The impact of lake and reservoir parameterization on global streamflow simulation.
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DOI：
10.1016/j.jhydrol.2017.03.022
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发表时间：
2017-05
影响因子：
6.4
通讯作者：
Beck H
中科院分区：
文献类型：
--
作者：
Zajac Z;Revilla-Romero B;Salamon P;Burek P;Hirpa FA;Beck H
评估了湖泊和水库对全球日径流量的影响。水库影响模型的性能在全球范围内显着。湖泊对模型性能的影响仅限于少数集水区。湖泊和水库降低了全球的回流水平和排放阈值。储层参数对模型性能指标的不确定性有贡献。湖泊和水库影响到径流的时间和大小，因此是水文模型的基本组成部分，特别是在全球洪水预报方面。然而，由于缺乏有关湖泊水文特征和水库运行规则的信息，全球范围内湖泊和水库常规的参数化具有相当大的不确定性。在这项研究中，我们估计的影响，湖泊和水库的全球日径流模拟的空间分布的LISFLOOD水文模型。我们应用最先进的全球敏感性和不确定性分析选定的集水区，以检查不确定的湖泊和水库参数化模型性能的影响。从地球仪周围的390个集水区的径流观测和多个性能指标被用来评估模型的性能。结果表明，相当大的地理变化，湖泊和水库的径流模拟的影响。Nash-Sutcliffe效率（NSE）和Kling-Gupta效率（KGE）指标分别改善了65%和38%的集水区，技能评分中值分别为0.16和0.2，而28%和52%的集水区的评分分别下降，中值分别为-0.09和-0.16。水库对极端高流量的影响在全球范围内是巨大和广泛的，而湖泊的影响在空间上仅限于少数集水区。全局敏感性分析表明，参数的不确定性对模型性能的不确定性有很大影响。储层参数往往造成这种不确定性，尽管不同集水区的影响差异很大。水库参数对模型性能的影响随着水库下游距离的增加而减小，有利于其他参数，特别是地下水相关参数和通道曼宁糙率系数。这项研究强调了在大规模水文模拟中考虑湖泊，特别是水库和使用适当参数化的重要性。
The effects of lakes and reservoirs on global daily streamflow are evaluated. Reservoirs affect model performance substantially in the global domain. Lakes’ effects on model performance are limited to few catchments. Lakes and reservoirs reduce return levels discharge thresholds globally. Reservoir parameters contribute to uncertainty of model performance metrics. Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values −0.09 and −0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning’s roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations.