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A functional framework for flow-duration-curve and daily streamflow estimation at ungauged sites

机译:一个无流量站点的流量持续时间曲线和每日流量估算的功能框架

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Flow duration curves (FDC) are used to obtain daily streamflow series at ungauged sites. In this study, functional multiple regression (FMR) is proposed for FDC estimation. Its natural framework for dealing with curves allows obtaining the FDC as a whole instead of a limited number of single points. FMR assessment is performed through a case study in Quebec, Canada. FMR provides a better mean FDC estimation when obtained over sites by considering simultaneously all FDC quantiles in the assessment of each given site. However, traditional regression provides a better mean FDC estimation when obtained over given FDC quantiles by considering all sites in the assessment of each quantile separately. Mean daily streamflow estimation is similar; yet FMR provides an improved estimation for most sites. Furthermore, FMR represents a more suitable framework and provides a number of practical advantages, such as insight into descriptor influence on FDC quantiles. Hence, traditional regression may be preferred if only few FDC quantiles are of interest; whereas FMR would be more suitable if a large number of FDC quantiles is of interest, and therefore to estimate daily streamflows. (C) 2018 Elsevier Ltd. All rights reserved.
机译:流量持续时间曲线(FDC)用于获得未开挖站点的日流量序列。在这项研究中,功能多元回归(FMR)被提出用于FDC估计。它的自然曲线处理框架允许获得FDC的整体,而不是有限数量的单点。 FMR评估是通过加拿大魁北克的案例研究进行的。通过在每个给定站点的评估中同时考虑所有FDC分位数,在站点上获得FMR时,可以提供更好的平均FDC估计。但是,当在给定的FDC分位数上通过分别考虑每个分位数的评估中的所有位点而获得时,传统回归可以提供更好的FDC平均估计。平均每日流量估算值相似;但是FMR可为大多数站点提供改进的估计。此外,FMR代表了一个更合适的框架,并提供了许多实际优势,例如深入了解描述符对FDC分位数的影响。因此,如果只关注很少的FDC分位数,则传统回归方法可能更可取。如果感兴趣的是大量FDC分位数,则FMR将更适合,因此可以估算每日流量。 (C)2018 Elsevier Ltd.保留所有权利。

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