Abdou Ali

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...

Pluriannual comparisons of satellite‐based rainfall products over the Sahelian belt for seasonal vegetation modeling

The Sahel corresponds to the transition from the dry arid desert to wet savannahs, where vegetation exhibits a well-marked seasonal cycle in response to the West African Monsoon. Precipitation data sets with high spatial and temporal resolutions are therefore relevant to investigate the dynamics of the Sahelian vegetation. Three satellite-based precipitation products (TRMM3B42, RFE2.0, and CMORPH) are compared and tested against kriged rain gauge measurements. The objective is to evaluate their capability to retrieve the main precipitation characteristics during the rainy season. Comparisons are performed over a 4 year period (2004-2007) at spatial resolutions of 0.25° × 0.25° or 0.5° × 0.5° by looking at sensitive criteria for vegetation: spatial distribution of the rainfall field, precipitation frequency, dry spell distribution, and precipitation amounts. Intercomparisons between satellite data sets are conducted over the Sahelian belt (10°N-20°N; 20°W-35°E) at a 1-10 day time scale, while comparisons with 10 day kriged rain gauge measurements are performed over a smaller area (10°N-17.5°N; 17.5°W-2.5°E). The precipitation spatial distributions are in good agreement between satellite products and with the kriged data. Considering the daily frequency, the satellite products show a high agreement between them (∼80%). The TRMM3B42 product exhibits the lowest number of rainy days, and RFE2.0 exhibits the highest. The CMORPH product overestimates rainfall amounts, while TRMM3B42 and RFE2.0 are both in good agreement with the kriged data. The impacts of these distinctive behaviors on simulated vegetation are investigated by comparisons with MODIS LAI, considering vegetation dynamics and amounts. The studied criteria of precipitation fields appear as a critical issue for Sahelian vegetation modeling.

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...