Exploring the Factors Controlling the Error Characteristics of the Surface Water and Ocean Topography Mission Discharge Estimates
Exploring the Factors Controlling the Error Characteristics of the Surface Water and Ocean Topography Mission Discharge Estimates
复制标题
地表水和海洋地形任务排放估算误差特征控制因素探讨
作者：
R. Frasson;M. Durand;K. Larnier;C. Gleason;K. Andreadis;M. Hagemann;R. Dudley;D. Bjerklie;H. Oubanas;P. Garambois;P. Malaterre;P. Lin;T. Pavelsky;J. Monnier;C. Brinkerhoff;C. David
地表水和海洋地形（SWOT）卫星任务将测量宽度超过50-100米的河流的宽度、水面高程和坡度。SWOT观察将通过使用简单的流动规律（如Manning - Strickler方程）来估计河流流量，补充原位流量。已经提出了几种设计用于计算未观测到的流动规律参数（如摩擦系数和水深）的流量反演算法，但迄今为止，尚未对控制算法性能的因素进行系统评估。在这里，我们评估了五种算法的性能，预计将用于构建SWOT产品。为了进行这一评估，我们使用了用类似SWOT的错误破坏的水力模型输出创建的综合SWOT观察。提供给算法的先验信息被有意地限制在平均年流量（MAF）的估计中，旨在产生“最坏情况”基准。先验MAF误差是影响算法性能的重要因素，但算法产生的流量估计比MAF偏差更小；因此，放电算法在先验算法的基础上进行了改进。我们首次表明，在控制流量算法性能方面，遥感观测的精度和频率不如先验偏差、河段之间的水力变异性和流量规律精度重要。这里报告的放电误差和误差灵敏度是一个边界基准，代表了最坏可能的预期误差和误差灵敏度。本研究为开发算法性能的预测能力奠定了基础，从而绘制出最坏情况下SWOT放电精度的全球分布。
The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50–100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning‐Strickler equation, complementing in situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g., friction coefficient and bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT‐like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst‐case SWOT discharge accuracy.
影响因子：
5.4
作者：
Brinkerhoff, C. B.;Gleason, C. J.;Lin, P.
通讯作者：
Lin, P.