Une approche floue pour la détermination de la région d'influence d'une station hydrométrique

Bargaoui, Z. K.; Fortin, Vincent; Bobée, Bernard; Duckstein, Lucien

doi:10.7202/705307ar

Cited by 12 publications

(2 citation statements)

References 13 publications

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“…In Tunisia, few hydrological regionalization studies involving catchment classi cation were done. Bargaoui et al [30] applied the ISODATA method to regionalize 39 Tunisian catchments and assess the centennial ood.…”

Section: Introductionmentioning

confidence: 99%

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Chérif

Gargouri-Ellouze

2023

Preprint

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Partitioning methods such as cluster analysis are advantageous in pooling catchments into hydrometric similar regions. However, these study cases are always infrequent in Sud Mediterranean zones and remain under-represented in international publications. This paper illustrates a Tunisian application case, which aims to pool catchments with hierarchical clustering method based on distances calculated in multidimensional physiographical and hydrometric space. Homogeneity of generated clusters is checked by Silhouette index. Current study considers nineteen Tunisian catchments, in a semi-arid climate observed since 1992. Areas and annual average rainfall respectively vary in [1–10 km2 ] and [280–500 mm] ranges. Twelve physiographical attributes and nine rainfall and streamflow signatures are considered in hierarchical partitioning procedure with two clusters. Correlation distance provides the most homogeneous clusters. Statistics demonstrate that: percentage of area affected by anti-erosive practices, percentage of forest cover and catchment’s area are the most discriminant attributes. However, hydrometrical signatures appear to be not relevant. This partitioning highlight two different hydrological behaviors which must be in support of forecasting. Results are promising as a Sud Mediterranean case, where the shortage of hydrometrical data is an occurring problem. They have the advantageous of enabling hydrologic forecasting without need of heavy information.

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Section: Introductionmentioning

confidence: 99%

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Chérif

Gargouri-Ellouze

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In Tunisia, few hydrological regionalization studies involving catchment classification were done. Bargaoui et al 26 applied the ISODATA method to regionalize 39 Tunisian catchments and assess the centennial flood. The copula model classification based on physiographic and geographic catchment characteristics was later investigated by Gargouri and Bargaoui 27 to delineate 22 Tunisian catchments in hydro-physiographical regions.…”

Section: Introductionmentioning

confidence: 99%

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Chérif

Gargouri-Ellouze

2023

Sci Rep

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Partitioning methods such as cluster analysis are advantageous in pooling catchments into hydrometric similar regions. They help overcome data shortage in ungauged catchments, which is a common problem in Sud Mediterranean zones. Without accurate forecasts, it is difficult to assess and manage water resources efficiently this situation won't be of any assistance to hydrology decision-makers. This paper illustrates a Tunisian application case, that aims to pool catchments with a hierarchical clustering algorithm (HCA) based on distances calculated in multidimensional physiographical and hydrometric space. The homogeneity of generated clusters is checked by the silhouette index. Then the distances efficiencies are compared. Nineteen semi-arid Tunisian catchments monitored since 1992 are studied. Twelve physiographical attributes, nine rainfall and streamflow signatures are considered in the HCA with two clusters. Correlation distance provides the most homogeneous clusters. Statistically the: percentage of area affected by anti-erosive practices, percentage of forest cover and catchment area are the most discriminating attributes. However, hydrometrical signatures appear to be irrelevant. These partitions highlight two different hydrological behaviors that must support forecasting. Results are promising in the Sud-Mediterranean case, where the shortage of hydrometrical data is an ongoing problem. They have the advantage of enabling hydrologic forecasting without requiring heavy information.

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Regional flood frequency analysis using kernel‐based fuzzy clustering approach

Basu

Srinivas

2014

Water Resources Research

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Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites.

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Une approche floue pour la détermination de la région d'influence d'une station hydrométrique

Cited by 12 publications

References 13 publications

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Regional flood frequency analysis using kernel‐based fuzzy clustering approach

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