Initial lists of AMMA-2050 user-relevant climate metrics

AMMA-2050 (African Monsoon Multi-disciplinary Analysis 2050) will improve understanding of how the West African monsoon will be affected by climate change in the coming decades – and help West African societies prepare and adapt

The intensification of thermal extremes in west Africa

International audienceThis 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) diurnal/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 Intercomparison 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

A modelling-chain linking climate science and decision-makers for future urban flood management in West Africa

Intensification of the hydrological cycle resulting from climate change in West Africa poses significant risks for the region’s rapidly urbanising cities, but limited research on flood risk has been undertaken at the urban domain scale. Furthermore, conventional climate models are unable to realistically represent the type of intense storms which dominate the West African monsoon. This paper presents a decision-first framing of climate research in co-production of a climate-hydrology-flooding modelling chain, linking scientists working on state-of-the-art regional climate science with decision-makers involved in city planning for future urban flood management in the city of Ouagadougou, Burkina Faso. The realistic convection-permitting model over Africa (CP4A) is applied at the urban scale for the first time and data suggest significant intensification of high-impact weather events and demonstrate the importance of considering the spatio-temporal scales in CP4A. Hydrological modelling and hydraulic modelling indicate increases in peak flows and flood extents in Ouagadougou in response to climate change which will be further exacerbated by future urbanisation. Advances in decision-makers’ capability for using climate information within Ouagadougou were observed, and key recommendations applicable to other regional urban areas are made. This study provides proof of concept that a decision-first modelling-chain provides a methodology for co-producing climate information that can, to some extent, bridge the usability gap between what scientists think is useful and what decision-makers need

Assessing the water balance in the Sahel: Impact of small scale rainfall variability on runoff. Part 2: Idealized modeling of runoff sensitivity

International audienceAs in many other semi-arid regions in the world, the Sahelian hydrological environment is characterized by a mosaic of small endoreic catchments with dry soil surface conditions producing mostly Hortonian runoff. Using an SCS-type event based rainfall–runoff model, an idealized modeling experiment of a Sahelian environment is set up to study the sensitivity of runoff to small scale rainfall variability. A set of 548 observed rain events is used to force the hydrological model to study the sensitivity of runoff to the time and space variability of rainfall input. The rainfall time variability sensitivity analysis shows that preserving the event rain depth without representing the main variabilities of the hyetograph intensities can translate into a runoff error of 65% in the worst case. On a virtual mosaic of 1-km2 catchments covering 10,000 km2, the simulated runoff shows a high sensitivity to a decrease of the spatial resolution of event rain fields from 1 × 1 km2 to 100 × 100 km2. For the catchments characterized by low runoff coefficients, which are the most sensitive to rainfall variability, at the coarsest spatial resolution of 100 × 100 km2, the global runoff computed from the 548 events is underestimated by 50% with respect to the runoff simulated from the 1 × 1 km2 resolution rain fields. The threshold resolution of 20 km was identified as a characteristic spatial scale, over which the performance of the model rapidly decreases. Looking at the influence of the number of available rain gauges, the effect of spatial aggregation depends on the density of the rain gauge network with lower effect for sparser networks

PyTOPKAPI v0.3.0

Final release targeting python

Implementation of the TOPKAPI model in South Africa: Initial results from the Liebenbergsvlei catchment

International audienceFlash floods and droughts are of major concern in Southern Africa. Hydrologists and engineers have to assist decision makers to address the issue of forecasting and monitoring extreme events. For these purposes, hydrological models are useful tools to: • Identify the dominant hydrological processes which influence the water balance and result in conditions of extreme water excess and/or deficit • Assist in generating both short- and long-term hydrological forecasts for use by water resource managers. In this study the physically-based and fully distributed hydrological TOPKAPI model (Liu and Todini, 2002),which has already been successfully applied in several countries in the world (Liu and Todini, 2002; Bartholomes and Todini, 2005; Liu et al., 2005; Martina et al., 2006), is applied in Africa for the first time. This paper contains the main theoretical and numerical components that have been integrated by the authors to model code and presents details of the application of the model in the Liebenbergsvlei catchment (4 625 km2) in South Africa. The physical basis of the equations, the fine-scale representation of the spatial catchment features, the parsimonious parameterisation linked to field/catchment information, the good computation time performance, the modularity of the processes, the ease of use and finally the good results obtained in modelling the river discharges of Liebenbergsvlei catchment, make the TOPKAPI model a promising tool for hydrological modelling of catchments in South Africa

Trend in the Co-Occurrence of Extreme Daily Rainfall in West Africa Since 1950

International audienceWe 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 20 year 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°) 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

Trends in hydrological extremes in the Senegal and Niger Rivers

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Intensity-duration-frequency (IDF) rainfall curves in Senegal

International audienceUrbanization 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