Hydrologic Regionalization of Watersheds. I: Methodology Development

Chiang, Shih-Min; Tsay, Ting‐Kuei; Nix, Stephan J.

doi:10.1061/(asce)0733-9496(2002)128:1(3)

Cited by 78 publications

(48 citation statements)

References 19 publications

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“…Information on the effect of complex driving factors on the hydrological response (not always easy to recognise) are in fact embedded in the temporal dynamics of the streamflow series (Chiang et al, 2002;Corduas, 2011). Important differences among the streamflow processes may be highlighted by the analysis of their temporal correlation structure, representable through the global autocorrelation function ACF (or the corresponding power spectrum).…”

Section: E Toth: Catchment Classificationmentioning

confidence: 99%

“…Since the time series autocorrelation functions might differ strongly one from another in shape, their comparison and classification through a visual inspection or a synthesising index is not straightforward. To tackle this issue, recent studies (De Thomasis and Grimaldi, 2001;Chiang et al, 2002;Grimaldi, 2004;Corduas, 2011) proposed to analyze the streamflow temporal dynamics through the parameter sets of linear models estimated on the corresponding streamflow time series. A more parsimonious, but less refined and necessarily approximated, approach is applied here for representing the autocorrelation structure: in addition to the lag-1 autocorrelation coefficient (previously used in regionalisation studies, for example, by Montanari and Toth (2007);Castiglioni et al (2010); Lombardi et al (2012) for the parameterisation of a rainfall-runoff model), it is here proposed to use an index representing the shape of the ACF, i.e.…”

Section: E Toth: Catchment Classificationmentioning

confidence: 99%

“…The goodness of the discriminant analysis is then assessed through the capability of assigning a catchment, on the basis of its morpho-pluviometric attributes only, to the same class to which it would be assigned if its streamflow attributes were known. This approach is similar to that applied by Bhaskar and O'Connor (1989), Chiang et al (2002) and Sanborn and Bledsoe (2006), albeit with different hydrometric and morpho-climatic sets of indices and different cluster analysis techniques.…”

Section: Application To Ungauged Catchmentsmentioning

confidence: 99%

“…The principal component analysis helps to interpret the differences and similarities of the data; in fact each PC describes a specific aspect of the variability of the catchment attributes and the variables with the highest loadings on a PC best explain that "dimension" of the data (Chiang et al, 2002;Sanborn and Bledsoe, 2006). The first PC is positively associated with H min , H med , µ P , σ P , thus representing the influence of elevation, corresponding to higher rainfall values (due to orographic effect), whereas the correlation scaling exponent, α P (and also the X coordinate, since the eastern part of the study region is less markedly rugged and receives less rainfall) contributes with the opposite sign, and therefore with lower values (associated to higher correlation) for increasing altitude.…”

Section: Principal Component Analysis Of Catchment Descriptorsmentioning

confidence: 99%

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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%

Section: E Toth: Catchment Classificationmentioning

confidence: 99%

Section: Application To Ungauged Catchmentsmentioning

confidence: 99%

Section: Principal Component Analysis Of Catchment Descriptorsmentioning

confidence: 99%

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Catchment classification based on characterisation of streamflow and precipitation time series

Toth

2013

Hydrol. Earth Syst. Sci.

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“…Regionalization is a useful technique in which hydrological knowledge at gauged sites is extrapolated to an ungauged site (Chiang et al, 2002). In recent years, a number of regionalization studies have been reported in the literature (Rao and Srinivas, 2006a;Parajka et al, 2007;Pinault and Allier, 2007;Isik and Singh, 2008;Oudin et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Construction of design hyetographs for locations without observed data

Lin

Chen

et al. 2009

Hydrological Processes

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Abstract:A design hyetograph which represents the time distribution of design rainfall depth corresponding to a duration and a return period is essential in hydrologic design. However, for locations without observed data (ungauged sites), construction of design hyetographs is a difficult task because of the lack of data. Hence, an approach based on self-organizing map (SOM) is proposed in this paper to construct design hyetographs at ungauged sites. SOM, which is a special kind of artificial neural networks (ANNs), is a powerful technique for extracting and visualizing salient features of data and for solving classification problems. The proposed approach is composed of three steps: classification, assignment and construction. First, the SOM-based classification is performed to analyse gauged sites' design hyetographs. Second, based on the concept of indicator kriging, a method is developed to assign an ungauged site of interest to a certain cluster. Third, based on the spatial information, the clustering results, and the design hyetographs of gauged sites, the design hyetograph at the site of interest is constructed using the reciprocal-distance-squared method. An application is conducted to assess the advantages of the proposed approach over the conventional approaches. Moreover, cross-validation tests are applied to evaluate the performance of the accuracy and the robustness of the proposed approach. The results confirm the improvement in performance by using the proposed approach instead of conventional approaches. The proposed approach is useful for constructing design hyetographs at ungauged sites.

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A framework for hydrologic classification with a review of methodologies and applications in ecohydrology

2011

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Hydrologic classification is one of the most widely applied tasks in ecohydrology. During the last two decades, a considerable effort has gone into analysis and development of methodological approaches to hydrologic classification. We reviewed the process of hydrologic classification, differentiating between an approach based on deductive reasoning using environmental regionalization, hydrologic regionalization and environmental classification whereby environmental variables assumed to be key determinants of hydrology are analysed and one based on inductive reasoning using streamflow classification whereby hydrologic data are analysed directly. We explored past applications in ecohydrology, highlighting the utility of classifications in the extrapolation of hydrologic information across sparsely gauged landscapes, the description of spatial patterns in hydrologic variability, aiding water resource management, and in the identification and prioritization of conservation areas. We introduce an overarching methodological framework that depicts critical components of the classification process and summarize important advantages and disadvantages of commonly used statistical approaches to characterize and predict hydrologic classes. Our hope is that researchers and managers will be better informed when having to make decisions regarding the selection and proper implementation of methods for hydrologic classification in the future. Copyright © 2011 John Wiley & Sons, Ltd.

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Hydrologic Regionalization of Watersheds. I: Methodology Development

Cited by 78 publications

References 19 publications

Catchment classification based on characterisation of streamflow and precipitation time series

Catchment classification based on characterisation of streamflow and precipitation time series

Construction of design hyetographs for locations without observed data

A framework for hydrologic classification with a review of methodologies and applications in ecohydrology

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