Climate change negatively impacts the livelihoods of indigenous communities across the world, including those located on the African continent. This Comment reports on how five African indigenous communities have been impacted by climate change and the adopted adaptation mechanisms. Local knowledge use for climate-change adaptation by African indigenous communitiesGlobally, there are an estimated 370 million indigenous people 1 whose livelihoods are being negatively affected by climate change 2 by means of an increased frequency and intensity of extreme weather events such as droughts, floods, storms, cyclones, as well as heatwaves, among others 1 . While climate change is an environmental challenge that developed countries have largely contributed toward from anthropogenic activities, the negative impacts are being felt among poorer countries, particularly vulnerable indigenous communities who ordinarily live low carbon lifestyles 1,3 . Additionally, many indigenous communities have been confined to the least productive and most delicate lands because of historical, social, political, and economic exclusion 4 . Furthermore, less consideration has been given to indigenous groups during formulation of climate-change mitigation strategies, making them vulnerable to its effects 5 . Notwithstanding, many indigenous communities have enduringly used various indigenous and local knowledge (ILK)-derived coping mechanisms passed from generation to generation.Here, we provide examples of the various climate-change-related challenges faced by five African indigenous communities (Afar, Borana, Endorois, Fulani, and Hadza) and the various adaptation mechanisms they use. After examining African indigenous communities in the context of international trends, we offer a broader outline of the role indigenous communities can play in combating climate change by conserving environmental resources in their lands and territories, including a

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This study aimed to analyze the probability of the occurrence of dry/wet spell rainfall using the Markov chain model in the Upper Awash River Basin, Ethiopia. The rainfall analysis was conducted in the short rainy (Belg) and long rainy (Kiremt) seasons on a dekadal (10–day) scale over a 30-year period. In the Belg season, continuous, three-dekad dry spells were prevalent at all stations. Persistent dry spells might result in meteorological, hydrological, and socio-economic drought (in that order) and merge with the Kiremt season. The consecutive wet dekads of the Kiremt season indicate a higher probability of wet dekads at all stations, except Metehara. This station experienced a short duration (dekads 20–23) of wet spells, in which precipitation is more than 50% likely. Nevertheless, surplus rainwater may be recorded at Debrezeit and Wonji only in the Kiremt season because of a higher probability of wet spells in most dekads (dekads 19–24). At these stations, rainfall can be harvested for better water management practices to supply irrigation during the dry season, to conserve moisture, and to reduce erosion. This reduces the vulnerability of the farmers around the river basin, particularly in areas where dry spell dekads are dominant.

Understanding the timing and variability of rainfall is crucial for the effective management of water resources in river basins dominated by rainfed agricultural practices. Our study aimed to characterize rainfall and analyze the trends in the length of wet spells (LWS) in the Upper Awash River Basin—one of the most water-stressed river basins in Ethiopia. We applied statistical descriptors and a Mann–Kendall (MK) test to determine the onset, end, and LWS for the small (Belg) and main (Kiremt) rainy seasons across different landscapes of the basin. We observed highly stable rainfall onsets in all stations during both seasons. However, unlike the Kiremt season, the LWS in the Belg season was too short and unreliable for rainfed agriculture. Based on the MK test, an increasing monotonic trend in LWS during the Kiremt season was detected only in the mountainous landscape of the basin. In contrast, we observed no trends in the remaining stations in the Upper Valley region of the basin, despite the linear regressions inferring an upward or downward pattern. Our findings provide accurate climatological information for the effective development of rainwater management strategies in the Upper Awash River Basin.

Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR), merged satellite-gauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with ground-observed data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia—Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a non-parametric Mann–Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANN-CDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling–Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANN-CDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland sub-basin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANN-CDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the sub-basins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland sub-basin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency-, flood-, and drought-monitoring studies. However, some may require bias corrections to improve the data quality.

Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.

The rise in global freshwater consumption, inefficient water use and population growth are the main drivers of water scarcity and food insecurity. This study analyzed the green, blue and economic water productivity (GWP, BWP and EWP, respectively) of the rainfed (teff, maize and sorghum) and irrigated (sugarcane) crops along with green water scarcity (GWS) in the Upper Awash Basin using water footprints (WFs) approaches. Teff has the lowest average GWP (∼0.3 kg/m3) and the highest average WF (4205 m3/ton) between 2000 and 2010 with an average EWP of ∼0.3 USD/m3 which is higher than those of other rainfed cereal crops (maize and sorghum). The highest GWS of maize was recorded in Metehara (269 mm/growing period) and the lowest in Debrezeit (70 mm/growing period) with an intermediate value of 90 and 117 mm in Wonji and Melkassa, respectively. All the rainfed crops have lower EWP (less than 0.3 USD/m3) compared with the irrigated sugarcane in the basin. This study demonstrates that increasing the value per unit of green/blue water through increasing yield per unit of supply and switching from high to low WFs crops has significant implications for addressing the water scarcity problem by setting a WP benchmark in the basin.

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