A comparison of low flow regionalisation methods—catchment grouping

Laaha, Gregor; Blöschl, Günter

doi:10.1016/j.jhydrol.2005.09.001

Cited by 164 publications

(152 citation statements)

References 17 publications

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“…Haines et al, 1988;Holmes et al, 1999;Viglione et al, 2006) or low flow percentiles (e.g. Nathan and McMahon, 1990;Laaha and Bloeschl, 2006;Vezza et al, 2010) or the entire flow duration curve (e.g. Singh et al, 2001;Ley et al, 2011;Patil and Stieglitz, 2011;Sauquet and Catalogne, 2011).…”

Section: E Toth: Catchment Classificationmentioning

confidence: 99%

Catchment classification based on characterisation of streamflow and precipitation time series

Toth

2013

Hydrol. Earth Syst. Sci.

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Abstract. The formulation of objective procedures for the delineation of homogeneous groups of catchments is a fundamental issue in both operational and research hydrology. For assessing catchment similarity, a variety of hydrological information may be considered; in this paper, gauged sites are characterised by a set of streamflow signatures that include a representation, albeit simplified, of the properties of fine time-scale flow series and in particular of the dynamic components of the data, in order to keep into account the sequential order and the stochastic nature of the streamflow process.The streamflow signatures are provided in input to a clustering algorithm based on unsupervised SOM neural networks, obtaining groups of catchments with a clear hydrological distinctiveness, as highlighted by the identification of the main patterns of the input variables in the different classes and the interpretation of their interrelations. In addition, even if no geographical, morphological nor climatological information is provided in input to the SOM network, the clusters exhibit an overall consistency as far as location, altitude and precipitation regime are concerned.In order to assign ungauged sites to such groups, the catchments are represented through a parsimonious set of morphometric and pluviometric variables, including also indexes that attempt to synthesise the variability and correlation properties of the precipitation time series, thus providing information on the type of weather forcing that is specific to each basin. Following a principal components analysis, needed for synthesizing and better understanding the morpho-pluviometric catchment properties, a discriminant analysis finally assigns the ungauged catchments, through a leave-one-out cross validation, to one of the above identified hydrologic response classes. The approach delivers a quite satisfactory identification of the membership of ungauged catchments to the streamflow-based classes, since the comparison of the two cluster sets shows a misclassification rate of around 20 %.Overall results indicate that the inclusion of information on the properties of the fine time-scale streamflow and rainfall time series may be a promising way for better representing the hydrologic and climatic character of the study catchments.

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Section: E Toth: Catchment Classificationmentioning

confidence: 99%

Catchment classification based on characterisation of streamflow and precipitation time series

Toth

2013

Hydrol. Earth Syst. Sci.

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“…In the second step the independent variables for the regression equations were selected for each region using a stepwise procedure. Laaha and Blöschl (2006) investigated several catchment grouping strategies when developing regression equations to estimate low flow indices in Austria. The results showed that a catchment grouping based on seasonality gave the best performance.…”

Section: Regional Regression Analysismentioning

confidence: 99%

“…It is necessary to define either geographically continuous regions or regions defined by catchment and climate characteristics. Smakhtin (2001) and Laaha (2006) give reviews of strategies to define homogeneous regions. These include weighted cluster analysis (Nathan and McHahon (1990) regression tree and residual pattern analysis (Laaha and Blöschl, 2006).…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Low Flow Estimates in Ungauged Catchments Using Regional Regression and the HBV-Model

Engeland

Hisdal

2009

Water Resour Manage

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Estimates of a low flow index in ungauged catchments calculated by a regional regression model and a regional hydrological model were compared for a study region southwestern Norway. The regression method was based on a relationship between the low flow index and an optimal set of catchment descriptors, established using stepwise linear regression for homogeneous subregions. Subregions were distinguished according to the season in which the lowest flow occurs, winter (May to October) or summer (November to April), and the average July temperature was found to be the best index for determining the low flow season for ungauged catchments. Catchment descriptors characterising the presence of lakes and bogs, in addition to catchment length and indicators of climatic conditions, were found to be important in the regression models. A cross-validation procedure was used to evaluate the predictive performance of the model in ungauged catchments. A gridded version of HBV, a daily rainfall-runoff model was also applied as a regional hydrological model and was calibrated using the average Nash-Sutcliffe coefficient for log-transformed streamflow as the calibration criterion. A comparison of the two methods in 21 independent catchments indicates that the regression method generally gives better estimates of Qc in ungauged

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“…The regionalization of the low-flow regime is a central topic in this research area (see e.g., Smakthin, 2001;Castellarin et al, 2004;Laaha and Blöschl, 2006a;Castiglioni et al, 2009), as it can be conveniently applied to surface water assessment in ungauged basins. The scientific literature reports a number of regionalization studies in which the availability of surface water and other hydrological attributes can be evaluated at a given ungauged site on the basis of the data collected at basins that are hydrologically similar to the site of interest (see e.g., Vogel and Kroll, 1992;Ludwig and Tasker, 1993;Furey et al, 2000;Brath et al, 2002;Brath et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging

Castiglioni

Castellarin

Montanari

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Recent studies highlight that spatial interpolation techniques of point data can be effectively applied to the problem of regionalization of hydrometric information. This study compares two innovative interpolation techniques for the prediction of low-flows in ungauged basins. The first one, named Physiographical-Space Based Interpolation (PSBI), performs the spatial interpolation of the desired streamflow index (e.g., annual streamflow, low-flow index, flood quantile, etc.) in the space of catchment descriptors. The second technique, named Topological kriging or Top-kriging, predicts the variable of interest along river networks taking both the area and nested nature of catchments into account. PSBI and Top-kriging are applied for the regionalization of Q 355 (i.e., a low-flow index that indicates the streamflow that is equalled or exceeded 355 days in a year, on average) over a broad geographical region in central Italy, which contains 51 gauged catchments. The two techniques are cross-validated through a leave-one-out procedure at all available gauges and applied to a subregion to produce a continuous estimation of Q 355 along the river network extracted from a 90m elevation model. The results of the study show that Top-kriging and PSBI present complementary features. Top-kriging outperforms PSBI at larger river branches while PSBI outperforms Top-kriging for headwater catchments. Overall, they have comparable performances (Nash-Sutcliffe efficiencies in cross-validation of 0.89 and 0.83, respectively). Both techniques provide plausible and accurate predictions of Q 355 in ungauged basins and represent promising opportunities for regionalization of low-flows.

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A comparison of low flow regionalisation methods—catchment grouping

Cited by 164 publications

References 17 publications

Catchment classification based on characterisation of streamflow and precipitation time series

Catchment classification based on characterisation of streamflow and precipitation time series

A Comparison of Low Flow Estimates in Ungauged Catchments Using Regional Regression and the HBV-Model

Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging

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