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Droughts are a recurring long-term problem in West Africa, but in recent years the region has also experienced a significant increase in damaging floods. Increasing trends in extreme discharges have been shown, reflected by an increase in return level magnitude since 1980s in Sahelian rivers. Our study targets flood prediction within the regional catchment of the Niger River at Niamey, the capital of Niger. The river hydrograph has changed significantly since the drought between the 1970s and 1980s, evolving from a single peak to a two-hump hydrograph: a first flood, coming mainly from three direct Sahelian tributaries of the right bank of the Niger River, and a second one, coming from the more remote Guinean basin. Predicting floods in Niamey is not straightforward because of the complexity of the hydrological system, which combines non-stationarity and the difficulty of deconvoluting the two floods and separate their own trends.The objective of the study is to quantitatively assess the hydrological hazard on the Niger River at Niamey based on a flow data set covering the period 1950-2020 by developing a statistical modeling approach that allows to integrate both the hydrological complexity and the non-stationarity of floods. An important question addressed by the proposed approach is to evaluate the contribution of considering hydrological complexity in non-stationary statistical modeling. This is achieved by defining several flood samples and proposing different non-stationary models adapted to their complexity.

There is a large agreement that global warming induces changes of precipitation regimes of different nature and amplitude depending on the timescale considered. This question is of special concern regarding extreme rainfall that might have critical socio-environmental consequences. A unified framework is proposed here for detecting trends in extreme rainfall. It is based on the GEV distribution, whose parameters depend both on a simple scaling formulation to account for multiple time durations of rainfall and on time to account for the non-stationarity deriving from climatic trends. The implementation of the model is illustrated in the Sahel region by analyzing 30 in situ rainfall series of 28 years measured at time-steps from 2 to 24 h. While the separate analysis of the point series proves inconclusive for detecting trends at any of the time-steps considered, the inclusion of all the series and time-steps into the proposed unified model allows trends to be detected at a high level of confidence (p-value < 1%). This trend essentially appears in the scale parameter of the regional GEV distribution, involving a 15 to 20% increase of the 10-year rainfall in 28 years, and a 23 to 30% increase of the 100-year rainfall. The main advantages of the proposed framework are (i) its parsimony, allowing for reducing the uncertainty associated with the model inference; (ii) its capacity for detecting trends either in the mean and/or in the variability of the extreme events; and (iii) its ability for producing non-stationary Intensity-Duration-Frequency curves that are coherent over a range of durations of accumulation.

Due to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global warming. However, they are computationally costly, limiting uncertainty evaluation in ensembles and covered time periods. This is in contrast to the Climate Model Intercomparison Project (CMIP) 5 and 6 ensembles, which cannot capture relevant convective processes, but provide a range of plausible projections for atmospheric drivers of rainfall change. Here, we quantify the sensitivity of extreme rainfall within West African storms to changes in atmospheric rainfall drivers, using both observations and a CP projection representing a decade under the Representative Concentration Pathway 8.5 around 2100. We illustrate how these physical relationships can then be used to reconstruct better-informed extreme rainfall changes from CMIP, including for time periods not covered by the CP model. We find reconstructed hourly extreme rainfall over the Sahel increases across all CMIP models, with a plausible range of 37-75% for 2070-2100 (mean 55%, and 18-30% for 2030-2060). This is considerably higher than the +0-60% (mean +30%) we obtain from a traditional extreme rainfall metric based on raw daily CMIP rainfall, suggesting such analyses can underestimate extreme rainfall intensification. We conclude that process-based rainfall scaling is a useful approach for creating time-evolving rainfall projections in line with CP model behaviour, reconstructing important information for medium-term decision making. This approach also better enables the communication of uncertainties in extreme rainfall projections that reflect our current state of knowledge on its response to global warming, away from the limitations of coarse-scale climate models alone.

In recent years, West Africa has experienced an increasing number of flood disasters, urging governments and decision makers to take adaptation measures, particularly in cities where the population is growing very fast.The present study focuses on the hydrological hazard of the city of Niamey (capital of Niger) along the Niger River. The regime of the Niger river at Niamey is marked by two successive floods: a first flood (July-August) resulting from the contributions of local Sahelian catchment (Sahelian red flood) and a second flood (February-March) resulting from the contribution of upstream catchments (Guinean black flood). Past studies have shown how the 1st flood, which was almost non-existent in the 1950s and 1960s, has predominated since the 1980s causing the most damaging floods due to a combined effect of climate and land use/cover changes. However, there is no in-depth study on statistical flood modelling, which is an essential step in any flood management strategy. The aim of the study is to evaluate how Niger discharge regime has changed since the 1950s with a specific focus on floods with the aim of deciphering the impact of Guinean and local flows in a statistical model.To do so, an original method is first proposed to isolate local flows from Guinean flows in the Niamey discharge data series. This separation allows us to distinguish three typologies of maximum annual floods according to whether they are generated by Guinean flows only, local flows only or a mix of the two. This observation leads us to propose different models of extreme discharge in Niamey based on the non-stationary extreme value theory able to consider both the typology of the floods and their temporal evolution.

International audience; Due to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global warming. However, they are computationally costly, limiting uncertainty evaluation in ensembles and covered time periods. This is in contrast to the Climate Model Intercomparison Project (CMIP) 5 and 6 ensembles, which cannot capture relevant convective processes, but provide a range of plausible projections for atmospheric drivers of rainfall change. Here, we quantify the sensitivity of extreme rainfall within West African storms to changes in atmospheric rainfall drivers, using both observations and a CP projection representing a decade under the Representative Concentration Pathway 8.5 around 2100. We illustrate how these physical relationships can then be used to reconstruct better-informed extreme rainfall changes from CMIP, including for time periods not covered by the CP model. We find reconstructed hourly extreme rainfall over the Sahel increases across all CMIP models, with a plausible range of 37%–75% for 2070–2100 (mean 55%, and 18%–30% for 2030–2060). This is considerably higher than the +0–60% (mean +30%) we obtain from a traditional extreme rainfall metric based on raw daily CMIP rainfall, suggesting such analyses can underestimate extreme rainfall intensification. We conclude that process-based rainfall scaling is a useful approach for creating time-evolving rainfall projections in line with CP model behaviour, reconstructing important information for medium-term decision making. This approach also better enables the communication of uncertainties in extreme rainfall projections that reflect our current state of knowledge on its response to global warming, away from the limitations of coarse-scale climate models alone.

International audience; Niamey, the capital of Niger, is particularly prone to floods, since it is on the banks of the Niger River, which in its middle basin has two flood peaks: one in summer (the red flood) and one in winter (the black flood). In 2020, the Niger River in Niamey reached its all-time highest levels following an abundant rainy season. On the other hand, the floods in Niamey have been particularly frequent in the last decade, a symptom of a change in hydroclimatic behaviour already observed since the end of the great droughts of the 1970s and 1980s and which is identified with the name of Sahelian Paradox. This study, starting from the analysis of the 2020 flood and from the update of the rating curve of the Niamey hydrometric station, analyses the rainfall–runoff relationship on the Sahelian basins of the Medium Niger River Basin (MNRB) that are at the origin of the local flood. The comparative analysis of runoffs, annual maximum flows (AMAX) and runoff coefficients with various rainfall indices calculated on gridded datasets allowed to hydroclimatically characterise the last decade as a different period from the wet one before the drought, the dry one and the post-drought one. Compared to the last one, the current period is characterised by a sustained increase in hydrological indicators (AMAX +27%) consistent with the increase in both the accumulation of precipitation (+11%) and the number (+51%) and magnitude (+54%) of extreme events in the MNRB. Furthermore, a greater concentration of rainfall and extremes (+78%) in August contributes to reinforcing the red flood’s positive anomalies (+2.23 st.dev in 2020). The study indicates that under these conditions the frequency of extreme hydrological events in Niamey will tend to increase further also because of the concurrence of drivers such as river-bed silting and levee effects. Consequently, the study concludes with the need for a comprehensive flood-risk assessment on the Niamey city that considers both recent hydroclimatic trends and urbanisation dynamics in flood zones hence defining the most appropriate risk-reduction strategies.

Pan-Africa convection-permitting regional climate model simulations have been performed to study the impact of high resolution and the explicit representation of atmospheric moist convection on the present and future climate of Africa. These unique simulations have allowed European and African climate scientists to understand the critical role that the representation of convection plays in the ability of a contemporary climate model to capture climate and climate change, including many impact-relevant aspects such as rainfall variability and extremes. There are significant improvements in not only the small-scale characteristics of rainfall such as its intensity and diurnal cycle, but also in the large-scale circulation. Similarly, effects of explicit convection affect not only projected changes in rainfall extremes, dry spells, and high winds, but also continental-scale circulation and regional rainfall accumulations. The physics underlying such differences are in many cases expected to be relevant to all models that use parameterized convection. In some cases physical understanding of small-scale change means that we can provide regional decision-makers with new scales of information across a range of sectors. We demonstrate the potential value of these simulations both as scientific tools to increase climate process understanding and, when used with other models, for direct user applications. We describe how these ground-breaking simulations have been achieved under the U.K. Government’s Future Climate for Africa Programme. We anticipate a growing number of such simulations, which we advocate should become a routine component of climate projection, and encourage international coordination of such computationally and human-resource expensive simulations as effectively as possible.

Stochastic rainfall generators aim to reproduce the main statistical features of rainfall at small spatial and temporal scales. The simulated synthetic rainfall series are recognized as suitable for use with impact analysis in water, agricultural, and ecological management. Convection-driven precipitation, dominant in certain regions of the world such as the intertropical belt regions, presents properties that require specific consideration when modeling: (i) strong rainfall intermittency, (ii) high variability of intensities within storms, (iii) strong spatiotemporal correlation of intensities, and (iv) marked seasonality of storm properties. In this article, improvements for an existing stochastic generator of rainfall fields that models convective storms are presented. Notable novelties include (i) the ability to model precipitation event timing, (ii) an improved temporal disaggregation scheme representing the rainfall distribution at subevent scales, and (iii) using covariates to reflect seasonal changes in precipitation occurrence and marginal distribution parameters. Extreme values are explicitly considered in the distribution of storm event intensities. The simulator is calibrated and validated using 28 years of 5-min precipitation data from the 30-rain-gauge AMMA-CATCH network in the Sahelian region of southwest Niger. Both large propagative systems and smaller local convective precipitation are generated. Results show that simulator improvements coherently represent the local climatology. The simulator can generate scenarios for impact studies with accurate representation of convective precipitation characteristics.

International audience; In distributed land surface modeling (LSM) studies, uncertainty in the rainfields that are used to force models is a major source of error in predicted land surface response variables. This is particularly true for applications in the African Sahel region, where weak knowledge of highly time/space-variable convective rainfall in a poorly monitored region is a considerable obstacle to such developments. In this study, we used a field-based stochastic rainfield generator to analyze the propagation of the rainfall uncertainty through a distributed land surface model simulating water and energy fluxes in Sahelian ecosystems. Ensemble time/space rainfields were generated from field observations of the local AMMA-CATCH-Niger recording raingauge network. The rainfields were then used to force the SEtHyS-Savannah LSM, yielding an ensemble of time/space simulated fluxes. Through informative graphical representations and innovative diagnosis metrics, these outputs were analyzed to separate the different components of flux variability, among which was the uncertainty represented by ensemble-wise variability. Scale dependence was analyzed for each flux type in the water and energy budgets, producing a comprehensive picture of uncertainty propagation for the various flux types, with its relationship to intrinsic space/time flux variability. The study was performed over a 2530 km 2 domain over six months, covering an entire monsoon season and the subsequent dry-down, using a kilometer/daily base resolution of analysis. The newly introduced dimensionless uncertainty measure, called the uncertainty coefficient, proved to be more effective in describing uncertainty patterns and relationships than a more classical measure based on variance fractions. Results show a clear scaling relationship in uncertainty coefficients between rainfall and the dependent fluxes, specific to each flux type. These results suggest a higher sensitivity to rainfall uncertainty for hydrological than for agro-ecological or meteorological applications, even though eddy fluxes do receive a substantial part of that source uncertainty.

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org. Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.

Urbanization resulting from sharply increasing demographic pressure and infrastructure development has made the populations of many tropical areas more vulnerable to extreme rainfall hazards. Characterizing extreme rainfall distribution in a coherent way in space and time is thus becoming an overarching need that requires using appropriate models of intensity–duration–frequency (IDF) curves. Using a 14 series of 5 min rainfall records collected in Senegal, a comparison of two generalized extreme value (GEV) and scaling models is carried out, resulting in the selection of the more parsimonious one (four parameters), as the recommended model for use. A bootstrap approach is proposed to compute the uncertainty associated with the estimation of these four parameters and of the related rainfall return levels for durations ranging from 1 to 24 h. This study confirms previous works showing that simple scaling holds for characterizing the temporal scaling of extreme rainfall in tropical regions such as sub-Saharan Africa. It further provides confidence intervals for the parameter estimates and shows that the uncertainty linked to the estimation of the GEV parameters is 3 to 4 times larger than the uncertainty linked to the inference of the scaling parameter. From this model, maps of IDF parameters over Senegal are produced, providing a spatial vision of their organization over the country, with a north to south gradient for the location and scale parameters of the GEV. An influence of the distance from the ocean was found for the scaling parameter. It is acknowledged in conclusion that climate change renders the inference of IDF curves sensitive to increasing non-stationarity effects, which requires warning end-users that such tools should be used with care and discernment.

The Sahel has experienced strong climate variability in the past decades. Understanding its implications for natural and cultivated ecosystems is pivotal in a context of high population growth and mainly agriculture-based livelihoods. However, efforts to model processes at the land–atmosphere interface are hindered, particularly when the multi-decadal timescale is targeted, as climatic data are scarce, largely incomplete and often unreliable. This study presents the generation of a long-term, high-temporal resolution, multivariate local climatic data set for Niamey, Central Sahel. The continuous series spans the period 1950–2009 at a 30-min timescale and includes ground station-based meteorological variables (precipitation, air temperature, relative and specific humidity, air pressure, wind speed, downwelling long- and short-wave radiation) as well as process-modelled surface fluxes (upwelling long- and short-wave radiation, latent, sensible and soil heat fluxes and surface temperature). A combination of complementary techniques (linear/spline regressions, a multivariate analogue method, artificial neural networks and recursive gap filling) was used to reconstruct missing meteorological data. The complete surface energy budget was then obtained for two dominant land cover types, fallow bush and millet, by applying the meteorological forcing data set to a finely field-calibrated land surface model. Uncertainty in reconstructed data was expressed by means of a stochastic ensemble of plausible historical time series. Climatological statistics were computed at sub-daily to decadal timescales and compared with local, regional and global data sets such as CRU and ERA-Interim. The reconstructed precipitation statistics, ∼1 °C increase in mean annual temperature from 1950 to 2009, and mean diurnal and annual cycles for all variables were in good agreement with previous studies. The new data set, denoted NAD (Niamey Airport-derived set) and publicly available, can be used to investigate the water and energy cycles in Central Sahel, while the methodology can be applied to reconstruct series at other stations. (Resume d'auteur)

In Sahelian countries, a vast number of people are still affected every year by flood despite the efforts to prevent or mitigate these catastrophic events. This phenomenon is exacerbated by the incessant population growth and the increase of extreme natural events. Hence, the development of flood management strategies such as flood hazard mapping and Early Warning Systems has become a crucial objective for the affected nations. This study presents a comprehensive hazard assessment of the Nigerien reach of the Sirba River, the main tributary Middle Niger River. Hazard thresholds were defined both on hydrological analysis and field effects, according to national guidelines. Non-stationary analyses were carried out to consider changes in the hydrological behavior of the Sirba basin over time. Data from topographical land surveys and discharge gauges collected during the 2018 dry and wet seasons were used to implement the hydraulic numerical model of the analyzed reach. The use of the proposed hydraulic model allowed the delineation of flood hazard maps as well the calculation of the flood propagation time from the upstream hydrometric station and the validation of the rating curves of the two gauging sites. These significative outcomes will allow the implementation of the Early Warning System for the river flood hazard and risk reduction plans preparation for each settlement.

The West African Monsoon (WAM) has undergone drastic changes in the recent past causing dramatic impacts on populations. After 30 years of devastating drought, the last two decades experienced a growing number of damaging floods concurrent with ongoing episodes of water shortages and famines. This raised the issue of a more extreme climate in the Sahel with more intense rainfall and dry spells. This chapter gives an overview of progress on documenting the rainfall extremes in the Sahel, a subject that has long been ignored in the literature. It focuses on statistical characteristics of extremes, on their trends in the context of WAM decadal variability and on the physical mechanisms involved in their occurrence. Based on recent literature and original analyses of daily rain gauge records in the central Sahel, a significant intensification of the Sahelian rainfall regime is confirmed over the last 15 years: more extreme events, larger size storms, accompanied by a deficit in the occurrence of less intense events. From a composite of 20 extreme storms in Ouagadougou, some key atmospheric conditions are shown to drive extreme precipitation, including an intense southerly monsoon flux coincident with strong African easterly waves and a large-scale moist anomaly at the regional scale.

In recent years, West Africa has witnessed an increasing number of damaging floods that raise the question of a possible intensification of the hydrological hazards in the region. In this study, the evolution of extreme floods is analyzed over the period 1950–2015 for seven tributaries in the Sudano-Guinean part of the Senegal River basin and four data sets in the Sahelian part of the Niger River basin. Non-stationary Generalized Extreme Value (NS-GEV) distributions including twelve models with time-dependent parameters plus a stationary GEV are applied to annual maxima of daily discharge (AMAX) series. An original methodology is proposed for comparing GEV models and selecting the best for use. The stationary GEV is rejected for all stations, demonstrating the significant non-stationarity of extreme discharge values in West Africa over the past six decades. The model of best fit most commonly selected is a double-linear model for the central tendency parameter ( μ ), with the dispersion parameter ( σ ) modeled as either stationary, linear, or a double-linear. Change points in double-linear models are relatively consistent for the Senegal basin, with stations switching from a decreasing streamflow trend to an increasing streamflow trend in the early 1980s. In the Niger basin the trend in μ is generally positive since the 1970s with an increase in slope after the change point, but the change point location is less consistent. The recent increasing trends in extreme discharges are reflected in an especially marked increase in return level magnitudes since the 1980s in the studied Sahelian rivers. The rate of the increase indicated by the study results raises urgent considerations for stakeholders and engineers who are in charge of river basin management and hydraulic works sizing.

Since 1950, the Niger River basin has gone through three main climatic periods: a wet period (1950–1960), an extended drought (1970–1980) and since 1990 a recent partial recovery of annual rainfall. Hydrological changes co-occur with these rainfall fluctuations. In most of the basin, the rainfall deficit caused an enhanced discharge deficit, but in the Sahelian region the runoff increased despite the rainfall deficit. Since 2000 the Sahelian part of the Niger has been hit by an increase of flood hazards during the so-called red flood period. In Niamey city, the highest river levels and the longest flooded period ever recorded occurred in 2003, 2010, 2012 and 2013, with heavy casualties and property damage. The reasons for these changes, and the relative role of climate versus land use–land cover (LULC) changes are still debated and are investigated in this paper. The evolution of the Niger red flood in Niamey from 1950 to 2012 is analysed based on long-term records of rainfall (three data sets based on in situ and/or satellite data) and discharge, and a hydrological model. The model is first run with the present LULC conditions in order to analyse solely the effect of rainfall variability. The impact of LULC and drainage area modification is investigated in a second step. The simulations based on the current surface conditions are able to reproduce the observed trend in the red flood occurrence and intensity since the 1980s. This has been verified with three independent rainfall data sets and implies that rainfall variability is the main driver for the red flood intensification observed over the last 30 years. The simulation results since 1953 have revealed that LULC and drainage area changes need to be invoked to explain the changes over a 60-year period.

Dans un monde ou la population est de plus en plus exposee aux risques naturels, l'evaluation des aleas hydro-meteorologiques est un sujet societal majeur. La cartographie de l'alea pluviometrique est necessaire a la fois pour la gestion du risque inondation et pour le dimensionnement d'ouvrages hydrauliques. La principale problematique lors de l'estimation de telles cartes est de prendre en compte l'information locale fournie par les series pluviometriques ponctuelles mais aussi de respecter une certaine coherence regionale.Deux approches basees sur la theorie des valeurs extremes sont comparees ici, avec une application a la cartographie des precipitations extremes en Afrique de l'Ouest. Les deux approches se basent sur la loi GEV (Generalized Extreme Value) utilisee pour modeliser les pluies maximales journalieres annuelles. La premiere approche (LFI, Local Fit and Interpolation) consiste en une interpolation des parametres de la loi GEV estimes separement a chaque station. La seconde approche (SMLE, Spatial Maximum Likelihood Estimation) estime par maximum de vraisemblance directement les parametres de la loi GEV spatialement sur toute la region en utilisant des covariables spatiales.En utilisant l'information de 126 pluviometres journaliers sur la periode 1950-1990, 5 methodes LFI et 3 methodes SMLE sont comparees. Les differentes methodes sont d'abord evaluees en calibration. Puis, la capacite predictive et la robustesse des methodes sont evaluees par une validation croisee et par une validation independante en sous reseaux. L'approche SMLE - en particulier lorsque le cumul pluviometrique inter-annuel moyen est utilise comme covariable - produit de meilleurs resultats pour les differents scores calcules.Grâce a la serie de Niamey, pour laquelle on dispose de 104 ans de donnees, il est egalement montre que l'approche SMLE est beaucoup moins sensible a l'effet d'echantillonnage temporel grâce a l'utilisation de l'information spatiale fourni par les stations alentours.

We propose in this paper a statistical framework to study the evolution of the co-occurrence of extreme daily rainfall in West Africa since 1950. We consider two regions subject to contrasted rainfall regimes: Senegal and the central Sahel. We study the likelihood of the 3% largest daily rainfall (considering all days) in each region to occur simultaneously and, in a 20year moving window approach, how this likelihood has evolved with time. Our method uses an anisotropic max-stable process allowing us to properly represent the co-occurrence of daily extremes and including the possibility of a preferred direction of co-occurrence. In Senegal, a change is found in the 1980s, with preferred co-occurrence along the E-50-N direction (i.e., along azimuth 50 degrees) before the 1980s and weaker isotropic co-occurrence afterward. In central Sahel, a change is also found in the 1980s but surprisingly with contrasting results. Anisotropy along the E-W direction is found over the whole period, with greater extension after the 1980s. The paper discusses how the co-occurrence of extremes can provide a qualitative indicator on change in size and propagation of the strongest storms. This calls for further research to identify the atmospheric processes responsible for such contrasted changes in storm properties. Plain Language Summary We propose in this paper a statistical framework to study the evolution of the co-occurrence of extreme daily rainfall in West Africa since 1950. We consider two regions subject to contrasted rainfall regimes: Senegal and the central Sahel. In Senegal, a change is found in the 1980s, with preferred co-occurrence along the E-50-N direction (i.e., along azimuth 50 degrees) before the 1980s and weaker isotropic co-occurrence afterward. In the central Sahel, a change is also found in the 1980s but surprisingly with contrasting results. Anisotropy along the E-W direction is found over the whole period, with greater extension after the 1980s. The paper discusses how the co-occurrence of extremes can provide a qualitative indicator on change in size and propagation of the strongest storms. This calls for further research to identify the atmospheric processes responsible for such contrasted changes in storm properties.

International audience; An anticipated consequence of ongoing global warming is the intensification of the rainfall regimes meaning longer dry spells and heavier precipitation when it rains, with potentially high hydrological and socio-economic impacts. The semi-arid regions of the intertropical band, such as the Sahel, are facing particularly serious challenges in this respect since their population is strongly vulnerable to extreme climatic events. Detecting long term trends in the Sahelian rainfall regime is thus of great societal importance, while being scientifically challenging because datasets allowing for such detection studies are rare in this region. This study addresses this challenge by making use of a large set of daily rain gauge data covering the Sahel (defined in this study as extending from 20°W-10°E and from 11°N-18°N) since 1950, combined with an unparalleled 5 minute rainfall observations available since 1990 over the AMMA-CATCH Niger observatory. The analysis of the daily data leads to the assertion that a hydro-climatic intensification is actually taking place in the Sahel, with an increasing mean intensity of rainy days associated with a higher frequency of heavy rainfall. This leads in turn to highlight that the return to wetter annual rainfall conditions since the beginning of the 2000s—succeeding the 1970-2000 drought—is by no mean a recovery towards the much smoother regime that prevailed during the 1950s and 1960s. It also provides a vision of the contrasts existing between the West Sahel and the East Sahel, the East Sahel experiencing a stronger increase of extreme rainfall. This regional vision is complemented by a local study at sub-daily timescales carried out thanks to the 5 minute rainfall series of the AMMA-CATCH Niger observatory (12000 km2). The increasing intensity of extreme rainfall is also visible at sub-daily timescales, the annual maximum intensities have increased at an average rate of 2%-6% per decade since 1990 for timescales ranging from 5 min to 1 hour. Both visions—regional/long term/daily on the one hand, and local/27/years/sub-daily, on the other—converge to the conclusion that, rather than a rainfall recovery, the Sahel is experiencing a new era of climate extremes that roughly started at the beginning of this century.

Bien que souvent associee aux problematiques de secheresse, l'Afrique de l'Ouest a ete touchee par de nombreuses inondations marquantes pendant la derniere decennie faisant de la gestion des crues une preoccupation majeure dans la region. Les inondations peuvent se produire a des echelles de temps et d'espace tres variables qui dependent de la variabilite spatio-temporelle du forcage pluviometrique : (i) des pluies intenses associees aux systemes convectifs de meso-echelle pouvant generer sur quelques dizaines de km² et en quelques heures des cumuls pluvio­metriques exceptionnels (ii) des accumulations pluviometriques sur des durees de 5 a 20 jours sur une grande partie de la region provocant des crues inhabituelles sur les bassins d'echelle regionale.L'interet des courbes IDSF (Intensite-Duree-Surface-Frequence) est double : elles permettent d'aider au dimensionnement d'ouvrages hydrauliques en estimant le niveau de retour des fortes pluies pour differentes agregations temporelles et spatiales mais aussi de caracteriser la severite d'une averse spatio-temporellement. L'obtention de telles courbes a partir de reseaux pluviometriques necessite des series longues, un reseau dense et une haute frequence d'enregistrement. En Afrique de l'Ouest, le reseau AMMA-CATCH Niger situe dans la region de Niamey permet de disposer de ces caracteristiques puisque 30 series au pas de temps 5 minutes sur un degre-carre (~10,000 km2) pour la periode 1990-2012 sont disponibles.Dans cette etude, les courbes IDSF sont obtenues en decomposant l'abattement temporel (IDF) de l'abattement spatial (ARF - Areal Reduction Factor). Differents modeles de courbes IDF ont ete testes sur des pluies maximales annuelles pour differentes durees d'agregation. Le modele IDF retenu est base sur le concept d'invariance d'echelle (simple scaling) pour decrire l'abattement temporel et modelise la distribution des intensites par une loi GEV. Ce cadre est parcimonieux et permet d'adopter le concept de mise a l'echelle dynamique (dynamic scaling) pour obtenir des coefficients d'abattement spatiaux (ARF). Les courbes IDSF obtenues decrivent ainsi la distribution des pluies extremes pour des pas de temps allant de 1 h a 24 h et des pas d'espace de 1 km² a 2500 km².

Intensity–duration–area–frequency (IDAF) curves are increasingly demanded for characterising the severity of storms and for designing hydraulic structures. Their computation requires inferring areal rainfall distributions over the range of space scales and timescales that are the most relevant for hydrological studies at catchment scale. In this study, IDAF curves are computed for the first time in West Africa, based on the data provided by the AMMA-CATCH Niger network, composed of 30 recording rain gauges having operated since 1990 over a 16 000 km2 area in south-western Niger. The IDAF curves are obtained by separately considering the time (intensity–duration–frequency, IDF) and space (areal reduction factor, ARF) components of the extreme rainfall distribution. Annual maximum intensities are extracted for resolutions between 1 and 24 h in time and from point (rain gauge) to 2500 km2 in space. The IDF model used is based on the concept of scale invariance (simple scaling) which allows the normalisation of the different temporal resolutions of maxima series to which a global generalised extreme value (GEV) is fitted. This parsimonious framework allows one to use the concept of dynamic scaling to describe the ARF. The results show that coupling a simple scaling in space and time with a dynamical scaling that relates to space and time allows one to satisfactorily model the effect of space–time aggregation on the distribution of extreme rainfall.

Ongoing global warming raises the hypothesis of an intensification of the hydrological cycle, extreme rainfall events becoming more frequent. However, the strong time–space variability of extreme rainfall makes it difficult to detect meaningful trends in the regime of their occurrence for recent years. Using an integrated regional approach, it is shown that over the last 10 years, the Sahelian rainfall regime is characterized by a lasting deficit of the number of rainy days, while at the same time the extreme rainfall occurrence is on the rise. As a consequence, the proportion of annual rainfall associated with extreme rainfall has increased from 17% in 1970–1990 to 19% in 1991–2000 and to 21% in 2001–2010. This tends to support the idea that a more extreme climate has been observed over 2001–2010: this climate is drier in the sense of a persisting deficit of rainfall occurrence compared to 1950–1969, while at the same time there is an increased probability of extreme daily rainfall.

Global warming is expected to intensify the hydrologic cycle. Documenting whether significant changes in the extreme precipitation regimes have already happened is consequently one of the challenging topics in climatic research. The high natural variability of extreme precipitation often prevents from obtaining significant results when testing changes in the empirical distribution of extreme rainfall at regional scale. A regional integrated approach is proposed here as one possible answer to this complex methodological problem. Three methods are combined in order to detect regionally significant trends and/or breakpoints in series of annual maximum daily rainfall: (1) individual stationarity tests applied to the raw point series of maxima, (2) a maximum likelihood testing of time-dependent generalized extreme value (GEV) distributions fitted to these series, and (3) a heuristic testing of a regional time-dependent GEV distribution. This approach is applied to a set of 126 daily rain gauges covering the Sahel over the period 1950-1990. It is found that only a few stations are tested as nonstationary when applying classical tests on the raw series, while the two GEV-based models converge to show that the extreme rainfall series indeed underwent a negative breakpoint around 1970. The study evidences the limits of the widely used classical stationarity tests to detect trends in noisy series affected by sampling uncertainties, while using a parametric space and time-dependent GEV efficiently reduces this effect. Showing that the great Sahelian drought was accompanied by a significant decrease of extreme rainfall events is the other main result of this study.

La crue de 2012 a Niamey : un paroxysme du paradoxe du Sahel ? E n aout 2012, le fleuve Niger a connu une crue exceptionnelle dans le bassin du Niger moyen. Plusieurs episodes pluvieux parfois exceptionnels se sont succ ed e dans ce bassin et notamment sur le bassin-versant des affluents burkinab es. Elle a provoqu e de tr es graves inondations dans la r egion de Niamey. L'Autorit e du bassin du Niger (ABN) qui coordonne les activit es de suivi du fleuve, doit eclairer les d ecisions a prendre, tant au niveau r egional que dans les pays concern es. Le bilan humain est terrible puisque 81 personnes ont perdu la vie au Niger ; les d egâts mat eriels sont enormes. Cette crue fait

International audience; This study aims in filling the gap in understanding the relationship between trend and extreme in diurnal and nocturnal temperatures (Tx and Tn) over the Gulf of Guinea area and the Sahel. Time-evolution and trend of Tx and Tn anomalies, extreme temperatures and heat waves are examined using regional and station-based indices over the 1900-2012 and 1950-2012 periods respectively. In investigating extreme temperature anomalies and heat waves, a percentile method is used. At the regional and local scales, rising trends in Tx and Tn anomalies, which appear more pronounced over the past 60 years, are identified over the two regions. The trends are characterized by an intensification of: i) nocturnal/Tn warming over the second half of the 20th century; and ii) diur-nal/Tx warming over the post-1980s. This is the same scheme with extreme warm days and warm nights. Finally annual number of diurnal and nocturnal heat waves has increase over the Gulf of Guinea coastal regions over the second half of the 20th century, and even more substantially over the post-1980s period. Although this trend in extreme warm days and nights is always overestimated in the simulations, from the Coupled Model Intercompar-ison Project Phase 5 (CMIP5), those models display rising trends whatever the scenario, which are likely to be more and more pronounced over the two regions in the next 50 years.

International audience; In this work we apply a recently proposed Bayesian Multiple Target Tracking model to Mesoscale Convective Systems tracking. This stochastic model follows the Multiple Hypothesis Tracking paradigm and can handle a varying number of targets while detecting the target birth, death, split, and merge events. The model is tested experimentally with real MCS targets detected from Meteosat IR data over the Sahelian region. The performance of the stochastic tracking is evaluated by comparing it qualitatively and quantitatively with well established deterministic methods.

The impact of the assimilation of surface soil moisture on the simulations of the physically based hydrological model DHSVM (Distributed Hydrology Soil Vegetation Model) is investigated in this paper for a 12 000 km catchment located in Benin, West Africa. Thanks to a large number of rain gauges spread all over the entire basin, reference simulations are performed from one year of calibration (in 2010) and two years of evaluation (2011 and 2012) based on in situ measurements of streamflow at the outlet and local observations of soil moisture at different soil depths and evapotranspiration. In a second step, several satellite products (PERSIANN, TRMM-3B42RT, and CMORPH) are used instead of in situ precipitation measurements. These products bring too much water (especially PERSIANN and CMORPH), sometimes not at the correct time of the year, which has a large impact on various hydrological variables. In order to correct for the wrong amount of input water brought by the satellite precipitation products, the SMOS satellite soil moisture observations are assimilated in the hydrological model. An optimal interpolation is implemented here using an influence radius in order to replicate the field of view of the SMOS instrument. The assimilation of SMOS data shows a positive impact on the soil moisture at different depths (5, 40, and 80 cm defined in the model), with a decrease of the bias compared to the in situ measurements. Streamflow is also positively impacted with a large improvement of the Nash efficiency coefficient after assimilation (from negative to positive for PERSIANN and CMORPH). Finally, the temporal evolution of the water table depth is also greatly improved (from 0.1–0.3 to 0.8–0.9 for PERSIANN and CMORPH). This work shows that the use of satellite precipitation products into a hydrological model can lead to large errors that can be reduced by assimilating satellite soil moisture, which has a positive impact on the estimation of hydrological variables at deeper layers and at other stages of the water cycle.

Precipitation forcing is usually the main source of uncertainty in hydrology. It is of crucial importance to use accurate forcing in order to obtain a good distribution of the water throughout the basin. For real-time applications, satellite observations allow quasi-real-time precipitation monitoring like the products PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, TRMM (Tropical Rainfall Measuring Mission) or CMORPH (CPC (Climate Prediction Center) MORPHing). However, especially in West Africa, these precipitation satellite products are highly inaccurate and the water amount can vary by a factor of 2. A post-adjusted version of these products exists but is available with a 2 to 3 month delay, which is not suitable for real-time hydrologic applications. The purpose of this work is to show the possible synergy between quasi-real-time satellite precipitation and soil moisture by assimilating the latter into a hydrological model. Soil Moisture Ocean Salinity (SMOS) soil moisture is assimilated into the Distributed Hydrology Soil Vegetation Model (DHSVM) model. By adjusting the soil water content, water table depth and streamflow simulations are much improved compared to real-time precipitation without assimilation: soil moisture bias is decreased even at deeper soil layers, correlation of the water table depth is improved from 0.09–0.70 to 0.82–0.87, and the Nash coefficients of the streamflow go from negative to positive. Overall, the statistics tend to get closer to those from the reanalyzed precipitation. Soil moisture assimilation represents a fair alternative to reanalyzed rainfall products, which can take several months before being available, which could lead to a better management of available water resources and extreme events.

Since 1950, the Niger River basin went through 3 main climatic periods: a wet period (1950–1960), an extended drought (1970–1980) and since 1990 a partial recovery of the rainfall. Hydrological changes co-occur with these rainfall fluctuations. In most of the basin the rainfall deficit caused an enhanced discharge deficit, but in the Sahelian region the runoff increased despite the rainfall deficit. Since 2000, the Sahelian part of the Niger has been hit by an increase of flood hazards during the so-called Red flood period. In Niamey city, the highest river levels and the longest flooded period ever recorded occurred in 2003, 2010, 2012 and 2013, with heavy casualties and property damage. The reasons for these changes, and the relative role of climate vs. Land Use Land Cover (LULC) changes are still debated and are investigated in this paper. The evolution of the Niger Red flood in Niamey from 1950 to 2012 is analysed based on long term records of rainfall (three data sets based on in situ and/or satellite data) and discharge, and a hydrological model. The model is first run with present LULC conditions in order to analyse solely the effect of rainfall variability. The impact of LULC and drainage area modification is investigated in a second step. The simulations based on the current surface conditions are able to reproduce the observed trend in Red flood occurrence and intensity since the 1980s. This has been verified with three independent rainfall data sets and implies that rainfall variability is the main driver for the Red flood intensification observed over the last 30 years. The simulation results since 1953 reveals that LULC and drainage area changes need to be invoked to explain the changes over a 60 year period.

Dans cette etude, une methode simple et originale est developpee pour produire une cartographie de l'humidite du sol sur la bande sahelienne a une resolution spatiale et temporelle de 10 x 10 km 2 et 15 minutes. La methode est basee sur l'utilisation d'une estimation satellite des precipitations et une cartographie micro-onde journaliere en bande C (6.9 GHz). La motivation premiere de cette etude consiste a obtenir des cartes d'humidite du sol toutes les 15 minutes pour etudier l'influence potentielle de l'etat hydrique des sols sur la convection en Afrique de l'ouest. Un produit satellite de precipitation base sur des mesures de Meteosat est utilise dans le modele API (antecedent precipitation index) pour produire une cartographie de l'humidite du sol. L'incertitude sur l'estimation des precipitations se traduit par des erreurs sur les cartes d'humidite. Une technique d'assimilation utilise les mesures micro-ondes du capteur AMSR-E (NASA) en bande C et un modele d'emission micro-onde. La demarche consiste a obtenir des temperatures de brillance qui s'approchent des observations satellites en ajustant le taux de precipitations entre deux mesures AMSR-E. L'estimation initiale de precipitation peut etre modifiee par un facteur multiplicatif compris entre 0 et 7. La solution est donnee par le taux de pluie qui minimise la difference entre les temperatures de brillance simulees et observees. La methode est evaluee a l'aide de mesures terrain d'humidite du sol obtenues sur trois sites de mesures au Niger, au Mali et au Benin. Les resultats montrent une amelioration de l'estimation d'humidite du sol sur les trois sites.

The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

In West Africa, the concomitant occurrence of extreme droughts and damaging floods points to the urgent need for linking the climate variability at various time scales (daily to decadal) to its impact in terms of water related risks. While hydrological models are key tools to do so, their use in this part of the world is strongly limited by the scarcity of rainfall data. Satellite precipitation products can be used as rainfall input to models in order to make up for this shortage of appropriate surface data. However, these satellite products have their own weaknesses, both in terms of accuracy and resolution. While the accuracy of satellite rainfall estimates has already received a fair amount of attention, little published work deals with the resolution issue. The study presented here is motivated by this lack of attention to the resolution issue. It makes use of the data produced by a very dense rainfall network covering the Ouémé catchment in Benin (14 600 km²), to study the impact of varying the space-time resolution of input rainfields on the output produced by DHSVM (Distributed Hydrology Soils and Vegetation Model), thus mimicking the resolution-induced errors associated with using satellite rainfall input for such physically-based models. The major result of this sensitivity analysis is that the model output is much more sensitive to the time resolution than to the space resolution, at least for this region and for the range of resolutions tested.

Summary The mesoscale variability of the Sahelian rainfall is analysed from a series of 30 high time resolution rainfall series covering 13 years and a 110 × 160 km2 area in the region of Niamey. It is shown that the convective scale variability is strongly influencing the spatial pattern of rainfields at larger time and spatial scales. This means that a proper assessment of the rainfall patterns at the mesoscale in the Sahel requires raingauge networks with a sufficient density to allow sampling this convective scale variability. This is usually not the case with operational networks whose density is in the order of 1–2 gauges per 10,000 km2. Computations carried out here show that the areal rainfall estimation error increases from 3% to 16% at the annual scale and from 21% to 113% at the event scale when the number of stations over a 100 × 100 km2 area decreases from 12 to 1. While being highly variable in space, the Sahelian rainfall is also highly intermittent in time. An analysis of the series of tipping bucket times leads to compute that 50% of the annual rain falls in less than 4 h with intensities larger than 35 mm/h. Areal rainfall statistics are compared to point rainfall statistics for event accumulated rainfall. The implication of these results for studying the influence of rainfall intermittency on runoff is discussed in a final section, as an introduction to a companion paper.

Resume En zone tropicale, les conditions de surface continentales sont un facteur important pour l'etablissement du regime de mousson qui genere les pluies tropicales. Il y a donc une forte interaction entre dynamique du climat et cycle de l'eau. L'exemple de l'Afrique de l'Ouest, encore sous l'influence de la plus forte secheresse observee au XX e sur le globe, est ici traite plus en detail pour illustrer cette interaction et les questions, notamment d'echelle, qui se posent lorsque l'on cherche a etablir des scenarios hydroclimatiques pour le futur. Pour citer cet article : T. Lebel, T. Vischel, C. R. Geoscience 337 (2005).

In a world of increasing exposure of populations to natural hazards, the mapping of extreme rainfall remains a key subject of study. Such maps are required for both flood risk management and civil engineering structure design, the challenge being to take into account the local information provided by point rainfall series as well as the necessity of some regional coherency. Two approaches based on the extreme value theory are compared here, with an application to extreme rainfall mapping in West Africa. The first approach is a local fit and interpolation (LFI) consisting of a spatial interpolation of the generalized extreme value (GEV) distribution parameters estimated independently at each station. The second approach is a spatial maximum likelihood estimation (SMLE); it directly estimates the GEV distribution over the entire region by a single maximum likelihood fit using jointly all measurements combined with spatial covariates. Five LFI and three SMLE methods are considered, using the information provided by 126 daily rainfall series covering the period 1950-1990. The methods are first evaluated in calibration. Then the predictive skills and the robustness are assessed through a cross validation and an independent network validation process. The SMLE approach, especially when using the mean annual rainfall as covariate, appears to perform better for most of the scores computed. Using the Niamey 104 year time series, it is also shown that the SMLE approach has the capacity to deal more efficiently with the effect of local outliers by using the spatial information provided by nearby stations.

The Mediterranean basin is a particularly vulnerable region to climate change, featuring a sharply contrasted climate between the North and South and governed by a semi-enclosed sea with pronounced surrounding topography covering parts of the Europe, Africa and Asia regions. The physiographic specificities contribute to produce mesoscale atmospheric features that can evolve to high-impact weather systems such as heavy precipitation, wind storms, heat waves and droughts. The evolution of these meteorological extremes in the context of global warming is still an open question, partly because of the large uncertainty associated with existing estimates produced by global climate models (GCM) with coarse horizontal resolution (~200 km). Downscaling climatic information at a local scale is, thus, needed to improve the climate extreme prediction and to provide relevant information for vulnerability and adaptation studies. In this study, we investigate wind, temperature and precipitation distributions for past recent climate and future scenarios at eight meteorological stations in the French Mediterranean region using one statistical downscaling model, referred as the "Cumulative Distribution Function transform" (CDF-t) approach. A thorough analysis of the uncertainty associated with statistical downscaling and bi-linear interpolation of large-scale wind speed, temperature and rainfall from reanalyses (ERA-40) and three GCM historical simulations, has been conducted and quantified in terms of Kolmogorov-Smirnov scores. CDF-t produces a more accurate and reliable local wind speed, temperature and rainfall. Generally, wind speed, temperature and rainfall CDF obtained with CDF-t are significantly similar with the observed CDF, even though CDF-t performance may vary from one station to another due to the sensitivity of the driving large-scale fields or local impact. CDF-t has then been applied to climate simulations of the 21st century under B1 and A2 scenarios for the three GCMs. As expected, the most striking trend is obtained for temperature (median and extremes), whereas for wind speed and rainfall, the evolution of the distributions is weaker. Mean surface wind speed and wind extremes seem to decrease in most locations, whereas the mean rainfall value decreases while the extremes seem to slightly increase. This is consistent with previous studies, but if this trend is clear with wind speed and rainfall data interpolated from GCM simulations at station locations, conversely CDF-t produces a more uncertain trend.

High-resolution rain fields are a prerequisite to many hydrometeorological studies. For some applications, the required resolution may be as fine as 1 km in space and 5 min in time. At these scales, rainfall is strongly intermittent, variable in space, and correlated in time because of the propagation of the rainy systems. This paper compares two interpolation approaches to generate high-resolution rain fields from rain gauge measurements: (i) a classic interpolation technique that consists in interpolating independently the rain intensities at each time step (Eulerian kriging) and (ii) a simple dynamic interpolation technique that incorporates the propagation of the rainy systems (Lagrangian kriging). For this latter approach, three propagation models are tested. The different interpolation techniques are evaluated over three climatically contrasted areas in West Africa where a multiyear 5-min rainfall dataset has been collected during the African Monsoon Multidisciplinary Analyses (AMMA) campaigns. The dynamic interpolation technique is shown to perform better than the classic approach for a majority of the rainy events. The performances of the three propagation models differ from one another, depending on the evaluation criteria used. One of them provides a satisfactory time of arrival of rainfall but slightly smooths the rain intensities. The two others reproduce well the rain intensities, but the time of arrival of the rain is sometimes delayed. The choice of an appropriate propagation algorithm will thus depend on the operational objectives underlying the rain field generation.