Analytical Propagation of Runoff Uncertainty Into Discharge Uncertainty Through a Large River Network
Analytical Propagation of Runoff Uncertainty Into Discharge Uncertainty Through a Large River Network
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DOI：
10.1029/2019gl083342
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发表时间：
2019-07-28
影响因子：
5.2
通讯作者：
Farniglietti, James S.
中科院分区：
文献类型：
--
作者：
David, Cedric H.;Hobbs, Jonathan M.;Farniglietti, James S.
淡水通过河流从大陆到海洋的输送传统上是通过在河流模型中路由陆地表面模型的径流来获得排放来估计的。这种范式规定，错误从径流量转移到排放量，但从未推导出从径流量到排放量的不确定性的分析传播。在这里，我们应用统计学的连续性方程在一个河流网络，推导出两个方程，传播的平均值和方差/协方差的径流误差独立。我们验证这些方程在美国西部的河流的案例研究中，第一次，反演观测到的空间分布的径流误差的流量误差。我们的研究结果表明，最大的流量误差源是跨空间的径流误差的联合变异性，而不是单个误差的平均值或幅度。我们的研究结果显着推进科学的误差量化模型为基础的估计河流流量。
The transport of freshwater from continents to oceans through rivers has traditionally been estimated by routing runoff from land surface models within river models to obtain discharge. This paradigm imposes that errors are transferred from runoff to discharge, yet the analytical propagation of uncertainty from runoff to discharge has never been derived. Here we apply statistics to the continuity equation within a river network to derive two equations that propagate the mean and variance/covariance of runoff errors independently. We validate these equations in a case study of the rivers in the western United States and, for the first time, invert observed discharge errors for spatially distributed runoff errors. Our results suggest that the largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors. Our findings significantly advance the science of error quantification in model-based estimates of river discharge.