Investigations du caractère multifractal des débits maximaux annuels de crue / Investigation of multifractality of maximum annual flood discharges

Bargaoui, Zoubeïda; Chebchoub, Assia

doi:10.1623/hysj.49.4.549.54426

Cited by 7 publications

(2 citation statements)

References 21 publications

(21 reference statements)

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“…Later Gargouri and Bargaoui [31] investigated the copula model classi cation based on physiographic and geographic catchment characteristics to delineate 22 Tunisian catchments in hydro-physiographical regions They noted that the catchments in a same region are not necessarily geographically contiguous. Subsequently, Bargaoui and Chebchoub [32] applied the multifractal analysis of maximum annual ood discharges at 55 stations of the Tunisian gauging network, they managed to associate the various statistical moments of surface discharges of the basins through a scale-invariant law, a random cascade model was identi ed. They de ned a scale invariance law for the extreme values which was considered a theoretical basis for the empirical models proposed since the sixties and linking the extreme values and their associated risks to the drained areas.…”

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

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“…Subsequently, At 55 stations of the Tunisian gauging network, Bargaoui and Chebchoub 28 applied the multifractal analysis of maximum annual flood discharges. They identified a random cascade model after successfully connecting the various statistical moments of the basins' surface discharges through a scale-invariant law.…”

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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Hydrological Processes

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Investigations du caractère multifractal des débits maximaux annuels de crue / Investigation of multifractality of maximum annual flood discharges

Cited by 7 publications

References 21 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)

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