Variance analysis of forecasted streamflow maxima in a wet temperate climate
Variance analysis of forecasted streamflow maxima in a wet temperate climate
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DOI：
10.1016/j.jhydrol.2018.03.038
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发表时间：
2018-05
影响因子：
6.4
通讯作者：
N. A. Aamery;J. Fox;M. Snyder;C. Chandramouli
中科院分区：
文献类型：
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作者：
N. A. Aamery;J. Fox;M. Snyder;C. Chandramouli
将全球气候模型、水文模型和极值分析结合起来提供了一种预测径流最大值的方法，但结果难以捉摸的方差结构阻碍了应用的信心。事实证明，利用预测和控制模拟之间的相对变化直接校正预测偏差可以边缘化水文不确定性，减少模型偏差，并在预测月平均和年平均水流时消除系统方差，从而促使我们对最大水流进行研究。我们使用排放情景的实现、全球气候模型类型和项目阶段、降尺度方法、偏差校正、极值方法以及水文模型输入和参数化来评估径流最大值的方差结构。结果表明，径流最大值的相对变化并不依赖于年度最大值与所应用的峰值超阈值方法的系统方差，尽管我们强调研究人员在应用峰值超阈值方法时严格遵守极值理论的规则。无论采用哪种方法，极值模型拟合都会给投影增加方差，并且方差是重现期的递增函数。与平均流量的相对变化不同，结果表明最大值相对变化的方差取决于所有测试的气候模型因素以及水文模型输入和校准。集合预测预测 2050 年水流最大值将增加，预测标准误差显着，包括所研究的湿温带地区 2 年、20 年和 100 年水流事件增加 +30(±21)、+38(±34) 和 +51(±85)%。最大值预测的方差主要由气候模型因素和极值分析决定。
Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima’s relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.