Prediction of streamflow regimes over large geographical areas: interpolated flow–duration curves for the Danube region

Castellarin, Attilio; Persiano, Simone; Pugliese, Alessio; Aloe, Alberto; Skøien, Jon Olav; Pistocchi, Alberto

doi:10.1080/02626667.2018.1445855

Cited by 18 publications

(38 citation statements)

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%

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

et al. 2020

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Runoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity-based, regression-based, and hydrological signature-based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post-PUB period. However, there is still plenty of room to improve the prediction capability in data-sparse regions (e.g., further verification and proof of multi-modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi-source remote-sensing information.

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Section: Hydrological Regionalization Methodsmentioning

confidence: 99%

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

et al. 2020

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“…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%

A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations

Pugliese

Persiano

Bagli

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).

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“…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%

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics

Costa

Zanardo

Bagli

et al. 2020

Water Resources Research

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A topographic index (flood descriptor) that combines the scaling of bankfull depth with morphology was shown to describe the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: (1) Can functional relationships be established between a flood descriptor and geomorphic and climatic-hydrologic catchment characteristics? (2) If so, can they be used for low-complexity predictive modeling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high-resolution flood modeling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine, and Danube) serve as training data set, while those of the Rhône river basin in Europe serve as testing data set. Two return periods are considered, the 10-and 10,000-year. Prediction of envelope flood extents and flood-prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10-and the 10,000-year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics, the prediction of envelope flood extents may be achieved for a given region, including ungauged basins. Plain Language Summary Topographic features can be extracted from a digital terrain to identify floodplains. In turn, the classification of these features can be used to represent flood extents. The classification, however, depends on existing flood maps to be used as the reference for its calibration in a small portion of the study area, before flood extents can be extrapolated. This work seeks to improve this dependence on the existence of a reference and the assumption that extrapolation can be performed without considering the physical differences between areas. To do so, a machine learning model was developed using the classification outcomes and characteristics of four river basins in Europe (Thames, Weser, Rhine, and Danube) and was tested in the Rhône river basin in Europe, showing promising results. This development should help stakeholders to allocate time and money better, by giving them nearly instantaneous views of areas that can potentially be affected by floods, with an associated probability of occurrence, a physical basis for extrapolation, and without needing a reference flood map for calibration for each case.

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Prediction of streamflow regimes over large geographical areas: interpolated flow–duration curves for the Danube region

Cited by 18 publications

References 37 publications

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics

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