2018
DOI: 10.1080/02626667.2018.1445855
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Prediction of streamflow regimes over large geographical areas: interpolated flow–duration curves for the Danube region

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Cited by 18 publications
(38 citation statements)
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References 37 publications
(55 reference statements)
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“…Their final results demonstrated similar performances with both approaches, showing that the geomorphology‐based inversion approach is as reliable as the spatial proximity approach. Different from the regionalization comparison studies, the studies on the spatial proximity approach combining with other regionalization methods like physical similarity (Razavi & Coulibaly, 2017), regression‐based (Castellarin et al, 2018; Steinschneider, Yang, & Brown, 2015) method, as well as new techniques such as data assimilation (Pugliese et al, 2018), machine learning (Hong, Zhang, Wang, Qian, & Hu, 2017), have also gradually appeared in the last decade. At the same time, new improvements based on traditional spatial proximity approaches such as three‐dimensional canonical Kriging (Castellarin, 2014) and the streamflow–streamflow (Q–Q) method (Andréassian, Lerat, Le Moine, & Perrin, 2012) showed good performance in some areas, which needs further evaluation.…”
Section: Hydrological Regionalization Methodsmentioning
confidence: 99%
“…Their final results demonstrated similar performances with both approaches, showing that the geomorphology‐based inversion approach is as reliable as the spatial proximity approach. Different from the regionalization comparison studies, the studies on the spatial proximity approach combining with other regionalization methods like physical similarity (Razavi & Coulibaly, 2017), regression‐based (Castellarin et al, 2018; Steinschneider, Yang, & Brown, 2015) method, as well as new techniques such as data assimilation (Pugliese et al, 2018), machine learning (Hong, Zhang, Wang, Qian, & Hu, 2017), have also gradually appeared in the last decade. At the same time, new improvements based on traditional spatial proximity approaches such as three‐dimensional canonical Kriging (Castellarin, 2014) and the streamflow–streamflow (Q–Q) method (Andréassian, Lerat, Le Moine, & Perrin, 2012) showed good performance in some areas, which needs further evaluation.…”
Section: Hydrological Regionalization Methodsmentioning
confidence: 99%
“…Kroll and Song, 2013;Salinas et al, 2013;Wan Jaafar et al, 2011;Srinivas et al, 2008), even though different validation schemes might be preferred in other regions (see e.g. Pugliese et al, 2016;Castellarin et al, 2018).…”
Section: Structure Of the Analysismentioning
confidence: 99%
“…Recent literature shows the significant potential of krigingbased techniques for performing regional prediction of streamflow indices in ungauged locations (Skøien et al, 2006;Castiglioni et al, 2011;Pugliese et al, 2014). Among such techniques, topological kriging, or top-kriging (see Skøien et al, 2006), has shown high prediction accuracy and excellent adaptability to a variety of water-related applications, such as prediction of low-flow indices (Castiglioni et al, 2009), interpolation of river temperatures (Laaha et al, 2013), estimation of flood quantiles (Archfield et al, 2013), regionalization of flow-duration curves (Castellarin, 2014;Castellarin et al, 2018;Pugliese et al, 2014Pugliese et al, , 2016, estimation of daily runoff in ungauged basins (Parajka et al, 2015) and reconstruction of historical daily streamflow series (Farmer, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…In this study, and differently from Jafarzadegan and Merwade (2017), a deterministic classification of the GFI, based on a specific objective function, is used to derive a TH for a larger number of training elementary catchments (i.e., hydrological units defined as the “portion of basin directly drained by a river stretch, between two confluences, or from the headwater to the first confluence” in Castellarin et al, 2018; see Figure S1 in the supporting information for an illustration of this concept) of four different major river basins in Europe with significantly different characteristics and for two return periods (the 10‐ and 10,000‐year). Values of TH correspond to unique envelope flood extents: in other words, the GFI layer isolines that best envelope a given benchmark flood hazard map.…”
Section: Introductionmentioning
confidence: 99%