Classification of drainage basins according to their physical characteristics; an application for flood frequency analysis in Scotland

Acreman, M.; Sinclair, C. D.

doi:10.1016/0022-1694(86)90134-4

Cited by 179 publications

(86 citation statements)

References 9 publications

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“…Cluster analysis is one of the popular statistical methods for combining catchments into groups (e.g. Acreman and Sinclair, 1986;Burn, 1997). The idea of cluster analysis is to identify groups (regions) in such a way that the similarity of catchments within one region is maximized while similarity between regions is minimized.…”

Section: Introductionmentioning

confidence: 99%

Catchment classification by runoff behaviour with self-organizing maps (SOM)

Ley

Casper

Hellebrand

et al. 2011

Hydrol. Earth Syst. Sci.

115

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Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation.In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in RhinelandPalatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment.Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments.As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67 % between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

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Section: Introductionmentioning

confidence: 99%

Catchment classification by runoff behaviour with self-organizing maps (SOM)

Ley

Casper

Hellebrand

et al. 2011

Hydrol. Earth Syst. Sci.

115

View full text Add to dashboard Cite

show abstract

“…These include: regional flood frequency analysis (Acreman and Sinclair, 1986;Burn, 1997;Merz and Blöschl, 2005), parameter regionalization for lumped hydrologic models (Burn and Boorman, 1993;Merz and Blöschl, 2004), regional low flow predictions (Nathan and McMahon, 1990;Laaha and Blöschl, 2006), and water quality assessment (Wolock et al, 2004). A common goal in many of these studies involves the transfer of hydrologic information, such as flood quantiles (Burn and Goel, 2000), model parameters (Oudin et al, 2008;Zhang and Chiew, 2009) etc., from gauged to ungauged catchments.…”

Section: Introductionmentioning

confidence: 99%

Hydrologic similarity among catchments under variable flow conditions

Patil

Stieglitz

2011

Hydrol. Earth Syst. Sci.

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Abstract. An assessment of regional similarity in catchment stream response is often needed for accurate predictions in ungauged catchments. However, it is not clear whether similarity among catchments is preserved at all flow conditions. We address this question through the analysis of flow duration curves for 25 gauged catchments located across four river basins in the northeast United States. The coefficient of variation of streamflow percentiles is used as a measure of variability among catchments across flow conditions. Results show that similarity in catchment stream response is dynamic and highly dependent on flow conditions. Specifically, within each of the four basins, the coefficient of variation is high at low flow percentiles and gradually reduces for higher flow percentiles. Analysis of the inter-annual variation in streamflow percentiles shows a similar reduction in variability from low flow to high flow percentiles. Greater similarity in streamflows is observed during the winter and spring (wet) seasons compared to the summer and fall (dry) seasons. Results suggest that the spatial variability in streamflow at low flows is primarily controlled by the dominance of high evaporative demand during the warm period. On the other hand, spatial variability at high flows during the cold period is controlled by the increased dominance of precipitation input over evapotranspiration. By evaluating variability over the entire range of streamflow percentiles, this work explores the nature of hydrologic similarity from a seasonal perspective.

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“…S. Water Resources Council, 1981], the iterative search technique [Wiltshire, 1985], the fractional membership method [Acreman and Sinclair, 1986], the region-of-influence method [Burn, 1990], and the method of hierarchical regions [Gabriele and Arnell, 1991].…”

Section: Introductionmentioning

confidence: 99%

“…[5] Regions and pooling groups are usually delineated according to similarity of catchments in physiographic and climatic characteristics [Mosley, 1981;Wiltshire, 1985;Acreman and Sinclair, 1986;Ando, 1990;Pearson, 1991;Institute of Hydrology, 1999;Ouarda et al, 2001]. In recent years, similarity measures based on flood seasonality have become popular alternatives for identifying hydrologically homogeneous pooling groups Black and Werritty, 1997;Burn, 1997;Lecce, 2000;Castellarin et al, 2001;Cunderlik andBurn, 2002a, 2002b;Ouarda et al, 2001, Cunderlik et al, 2004a.…”

Section: Introductionmentioning

confidence: 99%

Switching the pooling similarity distances: Mahalanobis for Euclidean

Cunderlik

Burn

2006

Water Resources Research

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[1] In recent years, catchment similarity measures based on flood seasonality have become popular alternatives for identifying hydrologically homogeneous pooling groups used in regional flood frequency analysis. Generally, flood seasonality pooling measures are less prone to errors and are more robust than measures based on flood magnitude data. However, they are also subject to estimation uncertainty resulting from sampling variability. Because of sampling variability, catchment similarity in flood seasonality can significantly deviate from the true similarity. Therefore sampling variability should be directly incorporated in the pooling algorithm to decrease the level of pooling uncertainty. This paper develops a new pooling approach that takes into consideration the sampling variability of flood seasonality measures used as pooling variables. A nonparametric resampling technique is used to estimate the sampling variability for the target site, as well as for every site that is a potential member of the pooling group for the target site. The variability is quantified by Mahalanobis distance ellipses. The similarity between the target site and the potential site is then assessed by finding the minimum confidence interval at which their Mahalanobis ellipses intersect. The confidence intervals can be related to regional homogeneity, which allows the target degree of regional homogeneity to be set in advance. The approach is applied to a large set of catchments from Great Britain, and its performance is compared with the performance of a previously used pooling technique based on the Euclidean distance. The results demonstrate that the proposed approach outperforms the previously used approach in terms of the overall homogeneity of delineated pooling groups in the study area.

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Classification of drainage basins according to their physical characteristics; an application for flood frequency analysis in Scotland

Cited by 179 publications

References 9 publications

Catchment classification by runoff behaviour with self-organizing maps (SOM)

Catchment classification by runoff behaviour with self-organizing maps (SOM)

Hydrologic similarity among catchments under variable flow conditions

Switching the pooling similarity distances: Mahalanobis for Euclidean

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