Flood frequency analysis of southern Africa: II. Identification of regional distributions

Abstract

Regional frequency analysis of annual maximum flood data comprising 407 stations from 11 countries of southern Africa is presented. Forty-one homogeneous regions are identified. The L-moments of the observed data indicate that the possible underlying frequency distributions are Pearson type 3 (P3), lognormal 3-parameter (LN3), General Pareto (GPA) or General Extreme Value (GEV). Simulation experiments for the selection of the most suitable flood frequency procedure indicate that Pearson type 3/Probability Weighted Moments (P3/PWM) and log-Pearson type 3/Method of Moments (LP3/MOM) are suitable procedures for the region. Analyse frequentielle des crues en Afrique australe, II Identification des distributions régionalesRésumé Ce papier présente l'analyse fréquentielle régionale des crues maximales annuelles de 407 stations de 11 pays d'Afrique Australe. 41 régions homogènes ont été identifiées. Les moments des données observées indiquent que les distributions statistiques possibles sont de type Pearson type 3 (P3), Log-normale à 3-parametres (LN), Pareto généralisée (GPA) ou Valeurs Extrêmes Généralisées (GEV). Les essais de simulation pour la sélection de la meilleure modélisation statistique des fréquences des crues indiquent que les lois Pearson type 3 (Calée selon la méthode des moments pondérés) et Log-Pearson type 3 (Calée selon la méthode des moments ) sont les plus adaptées pour la région.

Estimating extreme dry‐spell risk in the middle Ebro valley (northeastern Spain): a comparative analysis of partial duration series with a general Pareto distribution and annual maxima series with a Gumbel distribution

“…The use of PDS for the stochastic modelling of extremes has yielded good results in the analysis of hydrological variables (Moreno and Roldán, 1999;Alila, 2000;Mkhandi et al, 2000), whereas numerous studies have pointed out that AMS produces a significant loss of data for extreme modelling (Cunnane, 1973;Madsen et al, 1997a,b). Here, the greater efficiency of PDS has been demonstrated for dry-spell series by the lower dispersion of the points in the L-moments ratios diagram.…”

“…In this paper, we have used a PDS sampling in conjunction with a GP distribution, adopting a similar approach to that used in other studies that have sought to model extreme precipitations (Alila, 2000), floods (Mkhandi et al, 2000) or hydrological droughts (ARIDE, 2002). The results obtained have been compared with those obtained when adopting the AMS-G approach for the maximum dry-spell series observed in the study area.…”

An elusive search for regional flood frequency estimates in the River Nile basin

“…A region is considered "acceptably homogeneous" (A) if H 1 < 1; "probably homogeneous" (P) if 1 H 1 < 2; and "definitely heterogeneous" (H) if H 1 2. The GT assumes that a group of catchments form a homogeneous region if the L-CV values are similar to those obtained from synthetic generated data of the assumed parent probability distribution (Mkhandi et al, 1996). Table 2 shows the homogeneity test results for the three test methods.…”

“…Once the homogeneous regions are delineated they can be tested for homogeneity (Wiltshire, 1986). Several approaches such as the one based on the homogeneity index (H 1 ) (Hosking, 1994;Hosking and Wallis, 1993), the S 1 -statistic based homogeneity test (Alila, 1990) and the graphical test (GT) (Mkhandi et al, 1996) can be used for that purpose. In case of acceptable homogeneity (similarity in the peak flow properties), a unique flood frequency distribution (also called growth curve) can be assumed for each region after scaling of the discharge values by the local specific mean peak flow.…”

Modèle statistique des hauteurs de pluies journalières en zone sahélienne: exemple du bassin amont du fleuve Sénégal / Frequency analysis of daily rainfall in the Sahelian area: case of the upstream basin of the Senegal River

“…L'approche la plus connue et la plus courante est l'approche statistique. Elle consiste à rechercher une loi de probabilité qui s'ajuste bien aux valeurs observées de la variable aléatoire à laquelle on s'intéresse après comparaison de différentes lois de probabilités théoriques et méthodes d'estimation à l'aide de tests d'adéquation (Mutua, 1994;Mkhandi et al, 2000).…”