East Sahel Research Articles

An Evaluation of 20-m ESA-CCI S2 Prototype LC Product

Agricultural production represents a key element in food security analysis. However, precious early warning time is often lost before the agricultural statistics are processed and released. The objective of the current study is to assess the performance of the 20-m ESA-CCI (European Space Agency - Climate Change Initiative) S2 prototype LC map classification in comparing it with Sentinel 2A 10 m or Google Earth images. In case the results turn out satisfactory the cropland class could be use as a crop mask to estimate crop production. The assessment method consists of 1) selecting six equal size polygons so that the Sahelian and Sudanian zones of West Africa are represented at each one of the western, central and eastern basins of the Sahel 2) generating four hundred dots randomly overlaid on the sample polygon 3) making extractions at dot location from the map under assessment and using Sentinel 2A 10 m or Google Earth to identify and count through visual interpretation the dots that fall over cropland and the ones that fall on other classes. The last two steps are repeated 10 times to bring the size of the validation point sample to four thousand for each of the 6 sample polygons. Our major concern being the assessment of the cropland classification, the number of classes is reduced to two (cropland and other) with the precision and the recall used as performance indicators. Analysis of the precision indicates that the classification of cropland in the Sahelian zone is less than 3% correct in East Sahel, less than 7% in West Sahel and about a third of the time correct for the Center Sahel. This is easily explained by the fact sand dunes and degraded land that makes up a significant part of the area have been mistakenly taken for cropland. Even in the Sudanian zone where the classification performance is better, the highest precision indicates that cropland classification is incorrect for a little over 28% of cases. Therefore, the ESA-CCI S2 prototype land cover map can’t be used as a crop mask.

Robust assessment of the time of emergence of precipitation change in West Africa

The time of emergence (TOE) of climate change is defined as the time when a new climate state emerges from a prior one. TOE assessment is particularly relevant in West Africa, a region highly threatened by climate change and urgently needing trustworthy climate predictions. In this paper, the TOE of precipitation change in West Africa is assessed for the first time, by analyzing 6 precipitation metrics (cumulated precipitation, number of wet and very wet days, onset and length of the rainy season) computed from the output of 29 state-of-the-art climate models. In West Sahel, climate conditions characterized by reduced occurrence of wet days are likely to emerge before 2036, leading to the possible emergence of a dryer climate in 2028–2052. In East Sahel, a wetter precipitation regime characterized by increased occurrence of very wet days is likely to emerge before 2054. Results do not provide a clear indication about a possible climate shift in the onset and length of the rainy season. Although uncertainty in climate model future projections still limits the robust determination of TOE locally, this study provides reliable time constraints to the expected climate shift in West Africa at the sub-regional scale, supporting adaptation measures to the future change in the precipitation regime.

Rainfall intensification in tropical semi-arid regions: the Sahelian case

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.

Predicting intra-seasonal fluctuations of NDVI phenology from daily rainfall in the East Sahel: a simple linear reservoir model

ABSTRACTShort-term predictive models are essential for understanding the temporal relationships between the variability of impulsive daily rainfall and the attendant dynamics of vegetation development in rangelands, particularly for the water-limited, semi-arid East Sahel. After identifying selected rain-dependent sites in southeast Sudan, this report applies a procedure to predict seasonal biomass development in response to impulse-like daily storm events using a simple linear reservoir model as an analogue for the coupled soil–plant–atmosphere ecosystem. Key parameters are the characteristic time () of the reservoir and the number (n) of elementary reservoirs in series. Surface vegetative biomass is represented by the normalized difference vegetation index (NDVI) provided by the MODIS satellite MOD13A2 16 day 1 × 1 km product. Our specific metric is the commonly used differential NDVI, or δNDVI, the difference between the actual NDVI value and its long-term fallow season baseline. We review the concept of the modelling method, with selected application to 15 years (2000−2014) of NDVI data from a 51 × 51 km study area in the East Sahel, centred on the standard WMO Gauge 62752 at Gedaref City in southeast Sudan. Results indicate that as few as four – even as few as two – model parameters might be used to describe intra-seasonal to multiple-year vegetation dynamics. Allowing for inter-annually variable parameters, the predicted results exhibit a coefficient of determination of 0.96. An alternative formulation using a single set of four, seasonally dependent model parameters, globally determined from 15 years of observed reference data, results in a coefficient of determination of 0.90; a version using only two, globally determined model parameters works almost as well for the type of phenology characteristic of this area. In addition to enabling a number of applications that would benefit from the availability of a synthetic phenological time series that is solely dependent on daily rainfall, one of the most important applications may be for infilling data gaps in the observed time series, which for this area of the Sahel are typically encountered during early-season green-up; a critical time for most NDVI studies. We found that the 1 year lagged autocorrelation coefficient, and some version of the conventional rain use efficiency (RUE) factor with its associated annual correlation coefficient, are essential adjunct screening parameters when identifying sites with the highest inter-annual variability in biomass production (typical of the most natural rain-fed sites in this area).

Drought modes in West Africa and how well CORDEX RCMs simulate them

This study presents the spatial-temporal structure of droughts in West Africa and evaluates the capability of CORDEX regional climate models in simulating the droughts. The study characterize droughts with the standardized evapo-transpiration index (SPEI) computed using the monthly rainfall and temperature data from the Climatic Research Unit (CRU) and CORDEX models simulation datasets. To obtain the spatial-temporal structure of the droughts, we applied the principal component analysis on the observed and simulated SPEIs and retained the first four principal factors as the leading drought modes over West Africa. The relationship between the drought modes and atmospheric teleconnections was studied using wavelet coherence analysis, while the ability of the CORDEX models to simulate the drought modes was quantified with correlation analysis. The analysis of the relationship between drought modes and atmospheric teleconnections is based on SPEI from observation dataset (CRU). The study shows that about 60 % of spatial-temporal variability in SPEI over West Africa can be grouped into four drought modes. The first drought mode features drought over east Sahel, the second over west Sahel, the third over the Savanna, and the fourth over the Guinea coast. Each drought mode is linked to sea surface temperature anomalies (SSTAs) over tropical areas of Pacific, Atlantic, and Indian Oceans. Most CORDEX models reproduce at least two of the drought modes, but only two models (REMO and CNRM) reproduce all the four drought modes. REMO and WRF give the best simulation of the seasonal variation of the drought mode over the Sahel in March-May and June-August seasons, while CNRM gives the best simulation of seasonal variation in the drought pattern over the Savanna. Results of this study may guide in selecting appropriate CORDEX models for seasonal prediction of droughts and for downscaling projected impacts of global warming on droughts in West Africa.

Can climate projection uncertainty be constrained over Africa using metrics of contemporary performance?

Projections of climate change over Africa are highly uncertain, with wide disparity amongst models in their magnitude of local rainfall and temperature change, and in some regions even disparity in the sign of rainfall change. This has significant implications for decision-makers within the context of a vulnerable population and few resources for adaptation. One approach towards addressing this uncertainty is to rank models according to their historical climate performance and disregard those with least skill. This approach is systematically evaluated by defining 23 metrics of model skill and focussing on two vulnerable regions of Africa, the Sahel and the Greater Horn of Africa. Some discrimination in the performance of 39 CMIP5 models is achieved, although divergence amongst metrics in their ranking of climate models implies some uncertainty in using these metrics to robustly judge the models' relative performance. Importantly, when the more capable models are selected by an overall performance measure, projection uncertainty is not reduced because these models are typically spread across the full range of projections (except perhaps for Central to East Sahel rainfall). This suggests that the method’s underlying assumption is false, this assumption being that the modelled processes that most strongly drive errors and uncertainty in projected change are a subset of the processes whose errors are observed by standard metrics of historical climate. Further research must now develop an expert judgement approach that will discriminate models using an in-depth understanding of the mechanisms that drive the errors and uncertainty in projected changes over Africa.

Assessing daily and seasonal satellite rainfall estimates using local gauges for the anomalous 2012 monsoon season in the African East Sahel

Satellite rainfall estimates (SRFEs) are essential for characterizing the seasonal delivery of monsoon rainfall to sub-Saharan Africa. Such data are particularly robust when composited over sufficiently large areas of the Earth's surface (≥2500 km2), and sufficiently long time periods (≥1 month). However, the application of SRFEs to early warning flash-flood and drought-monitoring systems at the hamlet level is less well documented, particularly for areas dominated by intense, small-scale convective storms, which require a higher spatial and temporal resolution than usually expected from standard SRFE products. This report assesses the potential of one of the primary contenders for such applications – the Climate Prediction Center (CPC) Rainfall Estimation Algorithm Version 2.0 (or RFE2) product – posing the specific challenge: How well does the RFE2 product perform on a site-by-site, storm-by-storm basis as a potential tool for early warning flood and drought applications in the East Sahel? Daily gridded (0.1° × 0.1°) RFE2 estimates are compared with daily time series from nine gauges provided by local agencies in the state of Gedaref, Sudan (area of gauge coverage ≈ 160 km × 160 km). Seasonal composites of multiple gauges and multiple RFE2 cells for 2012 (1 April to 31 October) agree quite well. However, daily time series of individual pairs of gauge data and corresponding RFE2 estimates show a remarkable lack of temporal coherence − typically only 10−20% of the daily variance of the RFE2 time series is associated with the gauge data. Moreover, low-pass filtered (30 day cut-off), smoothed versions of the daily RFE2 data do not conform well to the corresponding gauge distributions over the season, and fail to capture a seasonal bimodal rainfall pattern recorded by a cluster of rain gauges. Finally, there is a characteristic difference in the intensity-versus-frequency statistics of RFE2-produced events compared with corresponding gauge observations: RFE2 estimates imply that rain should have occurred on 70% of the days, whereby gauges recorded rainfall on only 35% of the days. On the other hand, extreme storms recorded by the gauge data are typically twice as strong as those estimated by the RFE2 algorithm. These results circumscribe specific limits on the RFE2 product for the purpose of site-by-site, storm-by-storm early warning monitoring of rainfall events, and for resolving details on the intra-seasonal modulation of rainfall patterns.

Comparing satellite RFE data with surface gauges for 2012 extreme storms in African East Sahel

The year 2012 was a relatively ‘wet’ year for many areas of the African Sahel. In spite of earlier predictions of normal or below normal rainfall in the East Sahel early in the season, by September of 2012, humanitarian agencies reported that flash floods in the region had affected almost a quarter million people, destroying tens of thousands of homes. Early evidence of impending conditions was supplied by satellite images largely produced from the geosynchronous EUMETSAT METEOSATs and low earth orbiting satellites of opportunity, which have become critical tools in the arsenal of emergency responders to extreme hydrologic events around the globe. This report is an assessment of the 2012 monsoon season performance of one of these satellite-based tools – the U.S. Climate Prediction Center’s (CPC) Rainfall Estimation (RFE) Algorithm Version 2.0 (RFE 2.0) – expressly developed for purposes of drought forecasting and flood prediction in Africa. We directly compare satellite-based daily RFE 2.0 rainfall estimates with ground-based daily gauge data recorded at 9 separate but closely spaced stations in the East Sahel of Sudan for the April–October, 2012, monsoon season. We find that, although the RFE 2.0 data may reasonably represent seasonal trends and annual rainfall totals when composited over substantial areas, the morphology and intensity of distinct storm events at the local level are poorly characterized, with many misses (i.e. non-detects), or alternatively, false alerts (i.e. false alarms).

Stress transfer among en echelon and opposing thrusts and tear faults: Triggering caused by the 2003Mw= 6.9 Zemmouri, Algeria, earthquake

[1] The essential features of stress interaction among earthquakes on en echelon thrusts and tear faults were investigated, first through idealized examples and then by study of thrust faulting in Algeria. We calculated coseismic stress changes caused by the 2003 Mw = 6.9 Zemmouri earthquake, finding that a large majority of the Zemmouri afterslip sites were brought several bars closer to Coulomb failure by the coseismic stresses, while the majority of aftershock nodal planes were brought closer to failure by an average of ∼2 bars. Further, we calculated that the shallow portions of the adjacent Thenia tear fault, which sustained ∼0.25 m slip, were brought >2 bars closer to failure. We calculated that the Coulomb stress increased by 1.5 bars on the deeper portions of the adjacent Boumerdes thrust, which lies just 10–20 km from the city of Algiers; both the Boumerdes and Thenia faults were illuminated by aftershocks. Over the next 6 years, the entire south dipping thrust system extending 80 km to the southwest experienced an increased rate of seismicity. The stress also increased by 0.4 bar on the east Sahel thrust fault west of the Zemmouri rupture. Algiers suffered large damaging earthquakes in A.D. 1365 and 1716 and is today home to 3 million people. If these shocks occurred on the east Sahel fault and if it has a ∼2 mm/yr tectonic loading rate, then enough loading has accumulated to produce a Mw = 6.6–6.9 shock today. Thus, these potentially lethal faults need better understanding of their slip rate and earthquake history.

Heavy Metal Concentrations In A West African Sahel Reservoir

Heavy metal concentrations were investigated over a period of 12 months in five stations in Alau reservoir, Maiduguri, in the North – east sahel zone of Nigeria. The mean concentrations of zinc, copper, lead, iron and manganese were 0.17 + 0.02 mg/l (range 0.14+ 0.03 - 0.19+ 0.02 mg/l), 0.56 + 0.06 mg/l (range 0.52+ 0.01 - 0.64 + 0.01 mg/l), 0.56 + 0.02 mg/l (range 0.54+ 0.03 - 0.58+ 0.06 mg/l), 0.09 + 0.02 mg/l (range 0.07+ - 0.12+ 0.01 mg/l) and 0.19 + 027 mg/l (range 0.04 . + 0.01- 0.66 + 0.01 mg/l) respectively. Except for lead, mean concentrations varied significantly between stations (P

Mineral contents of extracellular fluids in camel and cattle in the North East Sahel region of Nigeria

The levels of some mineral elements in serum, urine, ruminal fluid and digesta of camels and cattle of the North East Sahel region of Nigeria were evaluated using apparently healthy animals presented for slaughter at the Maiduguri municipal abattoir during the months of November and December, 1993. Serum, urine, and rumen samples were obtained from the animals and analysed for calcium, phosphorus, magnesium, sodium, potassium, and copper. The results showed that the concentrations of all the minerals studied, except sodium, were higher in the ruminal fluid of camels compared to cattle. However, serum concentrations were comparable in both species, with the exception of phosphorus which was lower in cattle, and potassium which was higher in cattle. For urine samples, all the minerals measured were higher in camel than in cattle. Analysis of solid rumen digesta showed that the concentrations of phosphorus, magnesium, copper and calcium, sodium, potassium were respectively higher in camels than cattle digesta. Key Words: Cattle, Camel, Minerals, Sahel Region (Nigerian Veterinary Journal: 2003 24(1): 13-20)