We review the recent progress in dynamical and statistical downscaling approaches for west African precipitation and perform a regional climate model (RCM) intercomparison using the novel multi‐model RCM data set from the Ensembles‐based Predictions of Climate Changes and Their Impacts (ENSEMBLES) and African Monsoon Multidisciplinary Analyses (AMMA) projects. Present RCMs have distinct systematic errors in terms of west African precipitation varying in amplitude and pattern across models. This is also reflected in a relatively large spread in projected future precipitation trends. Altogether, the ENSEMBLES RCMs indicate a prevailing drying tendency in sub‐Saharan Africa. Statistical post‐processing of simulated precipitation is a promising tool to reduce systematic model errors before application in impact studies. Copyright © 2011 Royal Meteorological Society

Monsoon rainfall is central to the climate of West Africa, and understanding its variability is a challenge for which satellite rainfall products could be well suited to contribute to. Their quality in this region has received less attention than elsewhere. The focus is set on the scales associated with atmospheric variability, and a meteorological benchmark is set up with ground-based observations from the African Monsoon Multidisciplinary Analysis (AMMA) program. The investigation is performed at various scales of accumulation using four gauge networks. The seasonal cycle is analyzed using 10-day-averaged products, the synoptic-scale variability is analyzed using daily means, and the diurnal cycle of rainfall is analyzed at the seasonal scale using a composite and at the diurnal scale using 3-hourly accumulations. A novel methodology is introduced that accounts for the errors associated with the areal–time rainfall averages. The errors from both satellite and ground rainfall data are computed using dedicated techniques that come down to an estimation of the sampling errors associated to these measurements. The results show that the new generation of combined infrared–microwave (IR–MW) satellite products is describing the rain variability similarly to ground measurements. At the 10-day scale, all products reveal high regional and seasonal skills. The day-to-day comparison indicates that some products perform better than others, whereas all of them exhibit high skills when the spectral band of African easterly waves is considered. The seasonal variability of the diurnal scale as well as its relative daily importance is only captured by some products. Plans for future extensive intercomparison exercises are briefly discussed.

The evaluation of rainfall products over the West African region will be an important component of the Megha‐Tropiques (MT) Ground Validation (GV) plan. In this paper, two dense research gauge networks from Benin and Niger, integrated in the MT GV plan, are presented and are used to evaluate several currently available global or regional satellite‐based rainfall products. Eight products—the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction Center Morphing method (CMORPH), Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) 3B42 real‐time and gauge‐adjusted version, Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center (CPC) African Rainfall Estimate (RFE), Estimation des Precipitation par SATellite (EPSAT), and Global Precipitation Climatology Project One Degree Daily estimate (GPCP‐1DD)—are compared to the ground reference. The comparisons are carried out at daily, 1° resolution, over the rainy season (June–September), between the years 2003 and 2010. The work focuses on the ability of the various products to reproduce salient features of the rainfall regime that impact the hydrological response. The products are analysed on a multi‐criteria basis, focusing in particular on the way they distribute the rainfall within the season and by rain rate class. Standard statistical diagnoses such as the correlation coefficient, bias, root mean square error and Nash skill score are computed and the inter‐annual variability is documented. Two simplified hydrological models are used to illustrate how the nature and structure of the product error impact the model output in terms of runoff (calculated using the Soil Conservation Service method, SCS, in Niger) or outflow (calculated with the ‘modèle du Génie Rural à 4 paramètres Journalier’, GR4J model, in Benin). Copyright © 2013 Royal Meteorological Society

Rainfall-induced landslides are a common and significant source of damages and fatalities worldwide. Still, we have little understanding of the quantity and properties of landsliding that can be expected for a given storm and a given landscape, mostly because we have few inventories of rainfall-induced landslides caused by single storms. Here we present six new comprehensive landslide event inventories coincident with well identified rainfall events. Combining these datasets, with two previously published datasets, we study their statistical properties and their relations to topographic slope distribution and storm properties. Landslide metrics (such as total landsliding, peak landslide density, or landslide distribution area) vary across 2 to 3 orders of magnitude but strongly correlate with the storm total rainfall, varying over almost 2 orders of magnitude for these events. Applying a normalization on the landslide run-out distances increases these correlations and also reveals a positive influence of total rainfall on the proportion of large landslides. The nonlinear scaling of landslide density with total rainfall should be further constrained with additional cases and incorporation of landscape properties such as regolith depth, typical strength or permeability estimates. We also observe that rainfall-induced landslides do not occur preferentially on the steepest slopes of the landscape, contrary to observations from earthquake-induced landslides. This may be due to the preferential failures of larger drainage area patches with intermediate slopes or due to the lower pore-water pressure accumulation in fast-draining steep slopes. The database could be used for further comparison with spatially resolved rainfall estimates and with empirical or mechanistic landslide event modeling.

In the West African Sahel, two paradoxical hydrological behaviors have occurred during the last five decades. The first paradox was observed during the 1968–1990s ‘Great Drought’ period, during which runoff significantly increased. The second paradox appeared during the subsequent period of rainfall recovery (i.e., since the 1990s), during which the runoff coefficient continued to increase despite the general re-greening of the Sahel. This paper reviews and synthesizes the literature on the drivers of these paradoxical behaviors, focusing on recent works in the West African Sahelo/Sudanian strip, and upscaling the hydrological processes through an analysis of recent data from two representative areas of this region. This paper helps better determine the respective roles played by Land Use/Land Cover Changes (LULCC), the evolution of rainfall intensity and the occurrence of extreme rainfall events in these hydrological paradoxes. Both the literature review and recent data converge in indicating that the first Sahelian hydrological paradox was mostly driven by LULCC, while the second paradox has been caused by both LULCC and climate evolution, mainly the recent increase in rainfall intensity.