Abou Amani

A Drought Monitoring and Forecasting System for Sub-Sahara African Water Resources and Food Security

Drought is one of the leading impediments to development in Africa. Much of the continent is dependent on rain-fed agriculture, which makes it particularly susceptible to climate variability. Monitoring drought and providing timely seasonal forecasts are essential for integrated drought risk reduction. Current approaches in developing regions have generally been limited, however, in part because of unreliable monitoring networks. Operational seasonal climate forecasts are also deficient and often reliant on statistical regressions, which are unable to provide detailed information relevant for drought assessment. However, the wealth of data from satellites and recent advancements in large-scale hydrological modeling and seasonal climate model predictions have enabled the development of state-of-the-art monitoring and prediction systems that can help address many of the problems inherent to developing regions. An experimental drought monitoring and forecast system for sub-Saharan Africa is described that is...

Rainfall Estimation in the Sahel. Part II: Evaluation of Rain Gauge Networks in the CILSS Countries and Objective Intercomparison of Rainfall Products

Abstract This study investigates the accuracy of various precipitation products for the Sahel. A first set of products is made of three ground-based precipitation estimates elaborated regionally from the gauge data collected by Centre Regional Agrometeorologie–Hydrologie–Meteorologie (AGRHYMET). The second set is made of four global products elaborated by various international data centers. The comparison between these two sets covers the period of 1986–2000. The evaluation of the entire operational network of the Sahelian countries indicates that on average the monthly estimation error for the July–September period is around 12% at a spatial scale of 2.5° × 2.5°. The estimation error increases from south to north and remains below 10% for the area south of 15°N and west of 11°E (representing 42% of the region studied). In the southern Sahel (south of 15°N), the rain gauge density needs to be at least 10 gauges per 2.5° × 2.5° grid cell for a monthly error of less than 10%. In the northern Sahel, this den...

Rainfall Estimation in the Sahel. Part I: Error Function

Abstract Rainfall estimation in semiarid regions remains a challenging issue because it displays great spatial and temporal variability and networks available for monitoring are often of low density. This is especially the case in the Sahel, a region of 3 million km2 where the life of populations is still heavily dependent on rain for agriculture. Whatever the data and sensors available for rainfall estimation—including satellite IR and microwave data and possibly weather radar systems—it is necessary to define objective error functions to be used in comparing various rainfall products. This first of two papers presents a theoretical framework for the development of such an error function and the optimization of its parameters for the Sahel. A range of time scales—from rain event to annual—are considered, using two datasets covering two different spatial scales. The mesoscale [Estimation des Pluies par Satellite (EPSAT)-Niger (E-N)] is documented over a period of 13 yr (1990–2002) on an area of 16 000 km2...

Rainfall Estimation in the Sahel: What Is the Ground Truth?

Abstract Areal rainfall estimation from ground sensors is essential as a direct input to various hydrometeorological models or as a validation of remote sensing estimates. More critical than the estimation itself is the assessment of the uncertainty associated with it. In tropical regions knowledge on this topic is especially scarce due to a lack of appropriate data. It is proposed here to assess standard estimation errors of the areal rainfall in the Sahel, a tropical region of notoriously unreliable rainfall, and to validate those errors using the data of the EPSAT–Niger experiment. A geostatistical framework is considered to compute theoretical variances of estimation errors for the event-cumulative rainfall, and rain gauge networks of decreasing density are used for the validation. As a result of this procedure, charts giving the standard estimation error as a function of the network density, the area, and the rainfall depth are proposed for the Sahelian region. An extension is proposed for larger tim...

Invariance in the Spatial Structure of Sahelian Rain Fields at Climatological Scales

Abstract The occurrence of rainfall in the semiarid regions is notoriously unreliable and characterized by great spatial variability over a large spectrum of timescales. Based on analytical considerations, an integrated approach is presented here in order to describe the spatial structure of rain fields for timescales used in climatological studies, that is from the daily to the seasonal scales and beyond to the interannual scale. At the scale of the rain event, two factors determine the spatial structure of rain fields. One is the spatial variability of the conditional rainfall H* (H > 0), represented by its variogram γ*e. The other is the intermittency, its spatial structure being described by the indicator variogram γ1. It is shown that the spatial structure of rain fields for time steps larger than the event may be analytically derived from γ*e and γ1, taking into account the anisotropy and nonstationarity that may affect either of these two functions, which are thus two timescale invariants of the ra...

Typology of Rainfall Fields to Improve Rainfall Estimation in the Sahel by the Area Threshold Method

The stratification of rainfall fields to improve specific rainfall models is a subject that has received relatively little attention in the literature. It is shown here that objective classification techniques, based on the intensities and spatial distribution of the rainfall fields, can produce meaningful results in terms of the area threshold method (ATM) model and climatology. Four approaches for rainfall classification, using rain gauge data, are proposed in order to improve the average areal rainfall estimation in the Sahel by the ATM model. Two of them are based on the structural behavior of the rainy area (area where it rains above a given threshold) function against a threshold. Based on this function, a new parameter, called the under profile area (UPA), has been proposed for the classification of rainfall fields. The groups obtained from the method based on this parameter are characterized by different average spatial structures. A significant improvement on the ATM model is observed by considering classification based on the UPA parameter. An average reduction of 34% of the root-mean-square error is observed in a validation term. This improvement is a direct consequence of the fact that the optimal thresholds are different from one group to another, which is an important point when considering the impact of classification on the ATM model. 1. Why a Typology of Rainfall Fields?