On the Effect of Nonlinear Recessions on Low Flow Variability: Diagnostic of an Analytical Model for Annual Flow Duration Curves
On the Effect of Nonlinear Recessions on Low Flow Variability: Diagnostic of an Analytical Model for Annual Flow Duration Curves
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非线性衰退对低流量变化的影响：年流量持续时间曲线分析模型的诊断
DOI：
10.1029/2019wr024912
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发表时间：
2019
影响因子：
5.4
通讯作者：
Müller, Marc F.
中科院分区：
文献类型：
--
作者：
Karst, Nathaniel;Dralle, David;Müller, Marc F.
对于做出合理的水资源管理决策，预测一条特定河流将保持在或超过各种流量阈值的水年比例至关重要。径流持续时间曲线(FDCs)使用某一历史时期的所有可用数据简洁地捕捉到这一信息，而年度径流持续时间曲线(AFDCS)则使用每个单独水年的数据。分析AFDCS的种群，特别是这种分布的尾部，可以使水资源管理者更好地为极端径流条件下的年份做好准备。然而，为了捕捉年际径流变化，需要长时间序列的观测，在快速变化和测量不佳的集水区获取长时间序列观测是困难的。通过结合基于过程的模型来构建基于日降雨量统计和径流衰退特征的AFDCS，拟议的方法是朝着解决这一挑战迈出的第一步。结果表明，不同流量分位数的预报性能有很大差异，目前的模型不能很好地捕捉枯水流量的年际变化。数值分析将这些误差归因于储泄关系的非线性，而不是跨尺度径流相关和非泊松降雨，解释了在低流量分位数中常见的重尾行为的起源。我们给出了一个水力发电的案例研究，表明忠实地捕捉年际径流变化和退缩非线性对装机盈利能力具有重要影响。
Predicting the proportion of the water year a given stream will remain at or above various flow thresholds is critically important for making sound water management decisions. Flow duration curves (FDCs) succinctly capture this information using all data available over some historical period, while annual flow duration curves (AFDCs) instead use data from each individual water year. Analyzing the population of AFDCs, and in particular the tails of this distribution, can allow water managers to better prepare for years with extreme streamflow conditions. However, long time series of observations are necessary to capture interannual streamflow variations and are problematic to obtain in rapidly changing and poorly gauged catchments. By incorporating a process‐based model to construct AFDCs based on daily rainfall statistics and flow recession characteristics, the proposed approach is a first step toward addressing this challenge. Results indicate that prediction performance varies substantially across flow quantiles and that the current model fails to properly capture the interannual variability of low flows. Numerical analyses attributed these errors to nonlinearity in storage‐discharge relation, rather than cross‐scale streamflow correlations and non‐Poissonian rainfall, explaining the origin of commonly observed heavy‐tailed behavior in low flow quantiles. We present a case study on hydroelectric power generation, showing that faithfully capturing both interannual streamflow variability and recession nonlinearity has important implications for installation profitability.
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