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首页> 外文期刊>Theoretical and applied climatology >Estimation of extreme quantiles at ungaged sites based on region-of-influence and weighting approaches to regional frequency analysis of maximum 24-h rainfall
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Estimation of extreme quantiles at ungaged sites based on region-of-influence and weighting approaches to regional frequency analysis of maximum 24-h rainfall

机译:基于影响的区域频率分析的区域频率分析,估计未折叠网站的极端定量估计最大24小时降雨

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摘要

Lack of adequate and reliable data for estimating the extreme values of hydrological variables at ungaged sites has always been one of the issues facing hydrologists in designing and planning water resource projects. Regionalizing the considered hydrological variable, finding an acceptable relationship for estimating its extreme values at ungaged sites using given data of other stations, and applying their available attributes are the solutions for the mentioned issue. In this study, historical data of maximum 24-h rainfall (M24-hR) covering the statistical period of 30 years (1979-2008) were collected and used from 63 rainfall gaging stations situated at Lake Urmia basin, northwestern Iran. Afterwards, using the method of region-of-influence (ROI) regionalization, the study area was regionalized through the geographic attributes of the stations (including latitude, longitude, elevation above mean sea level, and distance to the center of Lake Urmia). Then, all possible situations were considered for providing an appropriate regression relationship to estimate the extreme quantiles of M24-hR at ungaged sites by defining various scenarios of weighting to the geographic attributes and rainfall quantiles. The results showed that among different defined weighting scenarios, weighting to both stations and attributes in the at-site situation had an effective impact on forming an appropriate regression relationship for the estimation of extreme quantiles at ungaged sites. However, in the regional situation, a scenario considering no weight for both stations and attributes resulted in the most acceptable estimation of the quantiles with the lowest error (MSE = 1.29 mm). Further, the study showed that in most scenarios, the extreme quantiles estimated by means of regional regression relationships at ungaged sites (MSE = 1.29~1.75 mm) resulted in lower errors than the at-site ones (MSE = 1.35~7.64 mm).
机译:缺乏用于估计未折叠站点的水文变量极值的足够和可靠的数据一直是设计和规划水资源项目中水文学家的问题之一。区域化所考虑的水文变量,找到可接受的关系,用于使用其他站的给定数据估计其在未良出的站点的极端值,并应用其可用属性是所提到的问题的解决方案。在本研究中,收集了涵盖了30岁统计期(1979-2008)的最大24小时降雨(M24-HR)的历史数据,并从位于伊朗西北乌斯兰湖盆地湖的63个降雨名录站中使用。之后,使用影响区域的方法(ROI)区域化,研究区域通过站点的地理属性(包括纬度,经度,平均海平面上方的高度,以及与乌利湖中心的距离)来区齐地。然后,考虑所有可能的情况,以提供适当的回归关系,以通过定义对地理属性和降雨量的各种权重的各种场景来估计未折叠站点的极端量程。结果表明,在不同的定义加权情景中,在现场情况中的站点和属性的加权对形成适当的回归关系进行了有效的影响,以估计在未验度位点。然而,在区域情况下,考虑两个站和属性没有重量的场景导致数量最低的误差(MSE = 1.29 mm)的量级估计。此外,该研究表明,在大多数情况下,通过未折叠位点(MSE = 1.29〜1.75mm)的区域回归关系估计的极值估计导致比现场误差更低(MSE = 1.35〜7.64 mm)。

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