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A. Msilini, P. Masselot, and T. B. M. J. Ouarda

1. Introduction and literature review The main objective of regional frequency analysis (RFA) is the estimation of the return period of extreme hydrological events at target sites where little or no hydrological data are available. Examples of these events include floods and low-flow quantiles which are crucial for infrastructure design and management. In general, RFA comprises two main steps: (i) the delineation of homogenous region (DHR) to determine gauged sites similar to the target one and

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D. Ouali, F. Chebana, and T. B. M. J. Ouarda

also integrated the QR tool when dealing with precipitation analysis, such as Tareghian and Rasmussen (2013) and Choi et al. (2014) . In the present paper, the aim is to investigate the applicability, potential, and benefits of the QR technique in the RFA context. The performance of the proposed approach is evaluated through a rigorous comparison with the classical regression model. To avoid confusion, note that in some studies, for instance, Palmen et al. (2011) and Haddad and Rahman (2012

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Martin Durocher, Fateh Chebana, and Taha B. M. J. Ouarda

are linear combinations of basin characteristics. For instance, principal component regression corresponds to multiple regression that is performed on the outputs of a principal component analysis ( Hastie et al. 2009 ). In this method, the outputs of principal component analysis are the intermediate predictors, and the purpose of this substitution is to overcome multicollinearity problems. Other examples that consider intermediate predictors in RFFA are spatial methods ( Archfield et al. 2013

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M. Tugrul Yilmaz and Wade T. Crow

matching techniques are perhaps the most common. A handful of studies have applied rescaling based on least squares regression techniques ( Crow et al. 2005 ; Crow and Zhan 2007 ) but failed to offer any clear rationale for this choice. Additionally, signal variance-based rescaling, typically applied as a preprocessing step in triple collocation analysis ( Stoffelen 1998 ), also provides a means to rescale datasets using three independent estimates of the same variable. However, this approach has not

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F. Chebana, C. Charron, T. B. M. J. Ouarda, and B. Martel

study of their homogeneity. However, much less attention has been dedicated to the development of new regional estimation methods. In the present study, canonical correlation analysis (CCA) is used to delineate homogenous regions. In GREHYS (1996b) , it was shown that this method produced the best performances in comparison to other ones. Among RFA estimation methods, regression models and index-flood models are commonly used. GREHYS (1996b) showed that their performances are equivalent and are

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Masahiro Ryo, Oliver C. Saavedra Valeriano, Shinjiro Kanae, and Tinh Dang Ngoc

mean the discharge simulated and observed, and the bar over Q indicates the average value over n . e. Multiple regression analysis for NSE Multiple linear regression analysis is finally applied for evaluating the simulation with the different precipitation datasets in terms of the NSE value. We aim to detect under which conditions 1) the performance of the discharge simulation is high during flood events, 2) the assumption of uniform distribution of precipitation ( P uni ) degrades the

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Huade Guan, Xinping Zhang, Oleg Makhnin, and Zhian Sun

) and effective temperature estimated from plant distribution datasets in mountain terrains ( Reger et al. 2011 ). The objectives of this study are to 1) examine an algorithm for temperature mapping which incorporates transformed terrain aspect and slope into a multiple linear regression for mean monthly T max and T min mapping, 2) explore the physical mechanisms behind what is inferred from the regression analysis, 3) compare the performance of regression-based algorithms with a selected local

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Dwi Prabowo Yuga Suseno and Tomohito J. Yamada

specific situation for combined total PWV and atmospheric vertical instability can be represented by different regression curves. Before moving to the next stage (i.e., to measure the performance of MTSAT rainfall estimation by comparing the results with observed rainfall), a cross-correlation analysis was conducted to determine the lag time between them. The grid values at the coordinate locations of station 74181 (33°33′59″N, 133°32′48″E) and station 87321 (32°13′52″N, 131°9′2″E) were extracted from

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Anna-Maria Tilg, Flemming Vejen, Charlotte Bay Hasager, and Morten Nielsen

statistical properties: (i) normal distribution of the residuals, (ii) homoscedasticity of the residuals, and (iii) linearity of the model. Equation (12) did not fulfill these assumptions, and neither did the equation from Wischmeier and Smith (1958) . By training other regression types based on published relationships mentioned in Petrů and Kalibová (2018) , it turned out that all of them failed in this residual analysis. To overcome this issue, regression lines were trained for three RR ranges: RR

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Fahimeh Sarmadi, Yi Huang, Steven T. Siems, and Michael J. Manton

(Prec_Acc) by combining the results of the synoptic classification with a stepwise regression. To this end, the averaged high-elevation SHL rain gauge observations are taken as the “ground truth” for this analysis. As the aim is to develop an operational MOS algorithm, the six predictors and are taken from ACCESS-R instead of the physical soundings. Note that, because of operational management issues, the number of daily soundings at the Wagga Wagga station has been reduced to roughly three per

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