Tendances et ruptures au niveau des pluies journalières extrêmes en Côte d'Ivoire (Afrique de l'Ouest)

Résumé

Le changement climatique est une réalité qui affecte plusieurs variables climatiques dont les précipitations. Néanmoins, son impact sur les évènements extrêmes et en particulier sur les pluies journalières extrêmes n'est pas encore certain car peu de travaux y ont été consacrés en Afrique de l'Ouest. Dans ce contexte, il a été proposé de détecter d'éventuels tendances et ruptures dans les propriétés statistiques (moyenne, variance) des pluies journalières extrêmes à l'aide de tests statistiques locaux et régionaux. Pour détecter ces changements, les indices caractérisant la pluie maximale journalière annuelle (PJmaxan), le nombre annuel de jours de pluie dépassant 50 mm (NJsup50) et la contribution des pluies dépassant 50 mm dans les cumuls annuels (R(PJsup50/Pan)) ont été définis. L'analyse de 44 postes pluviométriques en Côte d'Ivoire sur la période 1942–2002 ne montre pas de changement généralisé ni en moyenne, ni en variance. Toutefois, en subdivisant la Côte d'Ivoire en régions climatiques homogènes, des tendances à la baisse ont été observées dans les régions IV (au Nord) et II (au Sud-Est).

Editeur Z.W. Kundzewicz; Assistant editeur G. Mahé

Citation Goula, A.B.T., Gneneyougo Soro, E., Kouassi, W. et Srohourou, B., 2012. Tendances et ruptures au niveau des pluies journalières extrêmes en Côte d'Ivoire (Afrique de l'Ouest). Hydrological Sciences Journal, 57 (6), 1067–1080.     

Quantifying watershed surface depression storage: determination and application in a hydrologic model

Hydrologic models often require correct estimates of surface macro‐depressional storage to accurately simulate rainfall–runoff processes. Traditionally, depression storage is determined through model calibration or lumped with soil storage components or on an ad hoc basis. This paper investigates a holistic approach for estimating surface depressional storage capacity (DSC) in watersheds using digital elevation models (DEMs). The methodology includes implementing a lumped DSC model to extract geometric properties of storage elements from DEMs of varying grid resolutions and employing a consistency zone criterion to quantify the representative DSC of an isolated watershed. DSC obtained using the consistency zone approach is compared to DSC estimated by “brute force” (BF) optimization method. The BF procedure estimates optimal DSC by calibrating DRAINMOD, a quasi‐process based hydrologic model, with observed streamflow under different climatic conditions. Both methods are applied to determine the DSC for relatively low‐gradient coastal plain watersheds on forested landscape with slopes less than 3%. Results show robustness of the consistency zone approach for estimating depression storage. To test the adequacy of the calculated DSC values obtained, both methods are applied in DRAINMOD to predict the daily watershed flow rates. Comparison between observed and simulated streamflow reveals a marginal difference in performance between BF optimization and consistency zone estimated DSCs during wet periods, but the latter performed relatively better in dry periods. DSC is found to be dependent on seasonal antecedent moisture conditions on surface topography. The new methodology is beneficial in situations where data on depressional storage is unavailable for calibrating models requiring this input parameter. Copyright © 2012 John Wiley & Sons, Ltd.     

Evaluation of GEV model for frequency analysis of annual maximum water levels in the coast of United States

The three-parameter generalized-extreme-value (GEV) model has been recommended by FEMA [FEMA (Federal Emergency Management Agency of the United States), 2004. Final Draft Guidelines for Coastal Flood Hazard Analysis and Mapping for the Pacific Coast of the United States. http://www.fema.gov/library/viewRecord.do?id=2188] for frequency analysis of annual maximum water levels in the Pacific coast of the United States. Yet, the GEV model's performance in other coastal areas still needs to be evaluated. The GEV model combines three types of probability distributions into one expression. The probability distributions can be defined by one of the three parameters of the GEV model. In this study, annual maximum water levels at nine water-level stations with long history data (more than 70 years) were chosen for analysis in five coastal areas: Pacific, Northeast Atlantic, East Atlantic, Southeast Atlantic, and Gulf of Mexico coasts. Parameters of the GEV model are estimated by the maximum likelihood estimation (MLE) method. Results indicate that probability distributions are characterized by the GEV Type III model at stations in the Pacific, Northeast, and East Atlantic coastal areas, while they are described by GEV Type II in stations of the Southeast Atlantic and Gulf of Mexico coastal areas. GEV model predictions of extreme water levels show good correlation to observations with correlation coefficients of 0.89 to 0.99. For predictions of 10% annual maximum water levels, the GEV model predictions are very good with errors equal to or less than 5% for all nine stations. Comparison of observations and GEV model estimations of annual maximum water levels for the longest recorded return periods, close to 100 years, revealed errors equal to or less than 5% for stations in the Pacific and Northeast Atlantic coastal areas. However, the errors range from 10% to 28% for other stations located in the East and Southeast Atlantic coasts as well as Gulf of Mexico coastal areas. Findings from this study suggest caution regarding the magnitudes of errors in applying the GEV model to the East and Southeast Atlantic coasts and Gulf of Mexico coast for estimating 100-year annual maximum water levels for coastal flood analysis.