Identification of Flood Reactivity Regions via the Functional Clustering of Hydrographs

Brunner, Manuela Irene; Viviroli, Daniel; Furrer, Reinhard; Seibert, Jan; Favre, Anne‐Catherine

doi:10.1002/2017wr021650

Cited by 22 publications

(25 citation statements)

References 52 publications

(94 reference statements)

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“…Expected changes in these mountainous regions, which are crucial for water supply, point out the potential need for adaptations of water management strategies. Water may need to be stored in reservoirs during winter in order to sustain current summer flows in dependent downstream catchments (Brunner et al, 2019a). A careful evaluation of future regime shifts and their uncertainty can guide decision making on water management and attempt to mitigate the negative impacts of climate change.…”

Section: Resultsmentioning

confidence: 99%

Future streamflow regime changes in the United States: assessment using functional classification

Brunner

Melsen

Newman

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Streamflow regimes are changing and expected to further change under the influence of climate change, with potential impacts on flow variability and the seasonality of extremes. However, not all types of regimes are going to change in the same way. Climate change impact assessments can therefore benefit from identifying classes of catchments with similar streamflow regimes. Traditional catchment classification approaches have focused on specific meteorological and/or streamflow indices, usually neglecting the temporal information stored in the data. The aim of this study is 2-fold: (1) develop a catchment classification scheme that enables incorporation of such temporal information and (2) use the scheme to evaluate changes in future flow regimes. We use the developed classification scheme, which relies on a functional data representation, to cluster a large set of catchments in the conterminous United States (CONUS) according to their mean annual hydrographs. We identify five regime classes that summarize the behavior of catchments in the CONUS: (1) intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Our results show that these spatially contiguous classes are not only similar in terms of their regimes, but also their flood and drought behavior as well as their physiographical and meteorological characteristics. We therefore deem the functional regime classes valuable for a number of applications going beyond change assessments, including model validation studies or predictions of streamflow characteristics in ungauged basins. To assess future regime changes, we use simulated discharge time series obtained from the Variable Infiltration Capacity hydrologic model driven with meteorological time series generated by five general circulation models. A comparison of the future regime classes derived from these simulations with current classes shows that robust regime changes are expected only for currently melt-influenced regions in the Rocky Mountains. These changes in mountainous, upstream regions may require adaption of water management strategies to ensure sufficient water supply in dependent downstream regions. Highlights. Functional data clustering enables formation of clusters of catchments with similar hydrological regimes and a similar drought and flood behavior. We identify five streamflow regime clusters: (1) intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Future regime changes are most pronounced for currently melt-dominated regimes in the Rocky Mountains. Functional regime clusters have widespread utility for predictions in ungauged basins and hydroclimate analyses.

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Section: Resultsmentioning

confidence: 99%

Future streamflow regime changes in the United States: assessment using functional classification

Brunner

Melsen

Newman

et al. 2020

Hydrol. Earth Syst. Sci.

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“…To achieve such a functional data representation, we project the discrete observations, i.e. the mean annual hydrographs at daily resolution, to a set of B-spline basis functions (R-package fda; Ramsay et al, 2014) (see illustration in Figure 1.1 a-c) because B-splines are able to mimic the main characteristics of hydrological regimes (Brunner et al, 2018).…”

Section: Regime Clustering and Classificationmentioning

confidence: 99%

“…B-spline, Fourier, or wavelet bases) coefficients can be used for clustering (Cuevas, 2014). It has been shown in previous studies that functional data representations can be beneficial in the identification of groups of similar hydrographs over a range of temporal scales, such as spring flood events (duration of six months; Ternynck et al, 2016), flood events (duration of several days; Brunner et al, 2018), low flow events (Laaha et al, 2017), diurnal discharges (duration of one day; Hannah et al, 2000), and yearly hydrographs (Merleau et al, 2007;Jamaludin, 2016).…”

Section: Introductionmentioning

confidence: 99%

Future streamflow regime changes in the United States: assessment using functional classification

Brunner¹,

Melsen²,

Newman³

et al. 2020

Preprint

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Abstract. Streamflow regimes are changing and expected to further change under the influence of climate change with potential impacts on flow variability and the seasonality of extremes. However, not all types of regimes are going to change in the same way. Climate change impact assessments can therefore benefit from identifying classes of catchments with similar streamflow regimes. Traditional catchment classification approaches have focused on specific meteorological and/or streamflow indices usually neglecting the temporal information stored in the data. The aim of this study is two-fold: (1) develop a catchment classification scheme that allows for the incorporation of such temporal information and (2) use the scheme to evaluate changes in future flow regimes. We use the developed classification scheme, which relies on a functional data representation, to cluster a large set of catchments in the conterminous United States (CONUS) according to their mean annual hydrographs. We identify five regime classes that summarize the behavior of catchments in the CONUS: (1) Intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Our results show that these spatially contiguous classes are not only similar in terms of their regimes, but also their flood and drought behavior, as well as their physiographical and meteorological characteristics. We therefore deem the functional regime classes valuable for a number of applications going beyond change assessments including model validation studies or the prediction of streamflow characteristics in ungauged basins. To assess future regime changes, we use simulated discharge time series obtained from the Variable Infiltration Capacity hydrologic model driven with meteorological time series generated by five general circulation models. A comparison of the future regime classes derived from these simulations with current classes shows that robust regime changes are expected only for currently melt-influenced regions in the Rocky Mountains. These changes in mountainous, upstream regions may require the adaptation of water management strategies to ensure sufficient water supply in dependent downstream regions.

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“…The faceted system of classification supposes that the data is divided into groups based on independent classification criteriafacets (Makarova and Volkova 2015). The most common methods of classification of river hydrographs are multidimensional and functional analyzes (Ternynck et al 2015, Brunner et al 2018. When using a multidimensional approach, river hydrographs are described by different parameters, the number of which is determined by researchers.…”

Section: Introductionmentioning

confidence: 99%

Faceted classification of the spring flood hydrographs of the Southern Buh river

Khrystiuk¹,

Gorbachova²,

Prykhodkina³

2020

geogrcas

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Faceted classification of the spring flood hydrographs of the Southern Buh river A hydrograph shape reflects the effect of hydrological, hydrobiological, hydromorphological, hydrochemical and other processes acting in river basins. Classification of river hydrographs could help to evaluate the long-term changes in river basins or compare hydrological response of different basins. We present the methodology of the spring flood hydrograph classification and show its application in the Southern Buh River basin, Ukraine. The methodology uses faceted classification employing a multidimensional statistical approach and a controlled number of classes. We analyze daily discharge data for 102 spring flood hydrographs of the Southern Buh River from the period 1914 -2015. Classification of hydrographs taking into account the date of the beginning, peak and end of the spring flood flows and the peak discharge provides 81 classes, while only ten of them contained three and more hydrographs. The most frequent hydrograph shapes are EEEL d (early beginning, peak and end of spring flood, low peak discharge) and LLLH d (late beginning, peak and end of spring flood, high peak discharge) that contain 8 and 10 hydrographs, respectively. While the majority of hydrographs falling in the class LLLH d occurred until the end of the 1960's, the hydrographs from the LLLH d were mostly observed in the last three decades.

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Identification of Flood Reactivity Regions via the Functional Clustering of Hydrographs

Cited by 22 publications

References 52 publications

Future streamflow regime changes in the United States: assessment using functional classification

Future streamflow regime changes in the United States: assessment using functional classification

Future streamflow regime changes in the United States: assessment using functional classification

Faceted classification of the spring flood hydrographs of the Southern Buh river

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