case study of drought in africa

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Review of Meteorological Drought in Africa: Historical Trends, Impacts, Mitigation Measures, and Prospects

Brian ayugi.

1 Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044 China

2 Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center On Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing, University of Information Science and Technology, Nanjing, 210044 China

3 Organization of African Academic Doctors (OAAD), Off Kamiti Road, P.O. Box 25305-00100, Nairobi, Kenya

Emmanuel Olaoluwa Eresanya

4 Department of Marine Science and Technology, Federal University of Technology, P.M.B. 704, Akure, Nigeria

5 Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Science, Haizhu District, 164 Xingangdong Road, Guangzhou, China

Augustine Omondi Onyango

6 Institute of Atmospheric Physics, Chinese Academy of Sciences, International Center for Climate and Environment Sciences (ICCES), University of the Chinese Academy of Sciences, College of Earth and Planetary Science, Beijing, China

Faustin Katchele Ogou

7 Laboratory of Atmospheric Physics, Department of Physics, Faculty of Science and Technology, University of Abomey-Calavi, Godomey, Benin

Eucharia Chidinma Okoro

8 Department of Physics and Astronomy, University of Nigeria, Nsukka, Nigeria

9 Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences, Chaoyang District, Beijing, 100012 China

Charles Obinwanne Okoye

10 Department of Zoology and Environmental Biology, Faculty of Biological Sciences, University of Nigeria, Nsukka, Nigeria

11 Biofuel Institute, School of Environment and Safety Engineering, Jiangsu University, Zhenjiang, 212013 China

Chukwuma Moses Anoruo

Victor nnamdi dike.

12 Energy, Climate, and Environment Science Group, Imo State Polytechnic Umuagwo, Imo, Ohaji, PMB 1472, Owerri, Nigeria

Olusola Raheemat Ashiru

13 Key Laboratory of Geophysics and Georesources, Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, China

Mojolaoluwa Toluwalase Daramola

14 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Beijing, 100101 China

Richard Mumo

15 Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Plot 10071, Private Bag 16, Palapye, Botswana

Victor Ongoma

16 International Water Research Institute, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150 Ben Guerir, Morocco

This review study examines the state of meteorological drought over Africa, focusing on historical trends, impacts, mitigation strategies, and future prospects. Relevant meteorological drought-related articles were systematically sourced from credible bibliographic databases covering African subregions in the twentieth and twenty-first centuries (i.e. from 1950 to 2021), using suitable keywords. Past studies show evidence of the occurrence of extreme drought events across the continent. The underlying mechanisms are mostly attributed to complex interactions of dynamical and thermodynamical mechanisms. The resultant impact is evidenced in the decline of agricultural activities and water resources and the environmental degradation across all subregions. Projected changes show recovery from drought events in the west/east African domain, while the south and north regions indicate a tendency for increasing drought characteristics. The apparent intricate link between the continent’s development and climate variability, including the reoccurrence of drought events, calls for paradigm shifts in policy direction. Key resources meant for the infrastructural and technological growth of the economy are being diverted to develop coping mechanisms to adapt to climate change effects, which are changing. Efficient service delivery to drought-prone hotspots, strengthening of drought monitoring, forecasting, early warning, and response systems, and improved research on the combined effects of anthropogenic activities and changes in climate systems are valuable to practitioners, researchers, and policymakers regarding drought management in Africa today and in the future.

Introduction

Weather and climate have a huge impact on our lives, affecting practically every socio-economic area. As a result, many countries, particularly those whose economies rely significantly on rain-fed agriculture, are vulnerable to climate variability and change. This is the situation in most African countries (Niang et al., 2014 ). Unfortunately, the majority of these countries are extremely vulnerable to climate change and have limited adaptive capacity to cope with the impacts of climate change. Hydrological extremes, especially droughts and floods, are responsible for the loss of lives and destruction of property. Drought occurrence is mostly determined by rainfall performance in a given location, with droughts occurring in areas of both low and high rainfall (Wilhite & Glantz, 1985 ). As a result, it impacts people and the environment in all climatic zones, as well as practically every socio-economic sector. Droughts are projected to become more frequent and have a greater impact due to climate change in areas of Africa that are already water-stressed (Dai, 2011a , b ; Hulme, 1992 ; IPCC 2014 , 2018 ; Niang et al., 2014 ). For instance, Ogou et al. ( 2017 ) showed that drought frequency has increased over northern sub-Saharan Africa. Similarly, due to continuous global warming, widespread droughts have been identified in various locations, with a noteworthy increase in recent decades (Dai, 2011a ; Dai, 2013 ; IPCC, 2014 ; Sheffield et al., 2012 ; Trenberth et al., 2014 ). Drought has affected several nations in Europe (Bradford, 2000 ; Hoerling et al., 2012 ; Spinoni et al., 2015 ), North America (AghaKouchak et al., 2014 ; Cook et al., 2007 ; Swalm et al. 2012 ), Asia (Cai et al., 2015 ; Liang et al., 2014 ; Sun et al., 2016 ), Australia (Chiew et al., 2014 ; Rahmat et al., 2015 ), and Africa (Dai, 2011a ; Hulme, 1992 ; Lyon & DeWitt, 2012 ). Most significantly, Africa, southern Europe, and eastern Australia have recorded an increase in drought events, owing primarily to decreased precipitation associated with decadal fluctuations in the Pacific and western Indian Ocean (Dai, 2013 ; Dai & Zhao, 2017 ; Hua et al., 2016 ).

Different definitions have been put forward in connection to the varying conditions under which droughts occur depending on the discipline. Regardless of the contextual differences, it is clear that most droughts are associated with rainfall deficiency that results in water shortages in all cases. Similarly, droughts are best described based on their geographical coverage, intensity, and duration. Droughts are broadly categorized into four groups: meteorological, agricultural, hydrological, and socio-economic. Meteorological droughts are quite common, and they are primarily classified by the extent of dryness in a given location and the length of the dry period. Although agricultural drought is linked to a lack of water needed to support crops, the drought does not always coincide with meteorological drought. On the other hand, hydrological drought is limited to the level of streamflow that can meet the demand. A study by Wilhite and Glantz ( 1985 ) gives a detailed description of this specific drought phenomenon. In a recent study, Adisa et al. ( 2019 ) noted that three-quarters of the total publications on drought over Africa between 1980 and 2020 focused on agricultural and hydrological droughts, while the remaining fraction was based on socio-economic and meteorological studies.

In this review, the case studies and discussions are based on meteorological drought. This is because most agricultural activities that support over 80% of livelihoods across the African continent are regulated solely by weather and climate. Climate change can influence precipitation (meteorological) droughts through changes in atmospheric water-holding capacity, circulation patterns, and moisture supply (Ukkola et al., 2020 ). Furthermore, changes in atmospheric dynamics and modes of variability such as the El Niño–Southern Oscillation (ENSO) can further influence regional precipitation patterns together with changes in evapotranspiration that show trends over lands and oceans (Roderick et al., 2014 ; Trenberth et al., 2014 ). Thus, meteorological droughts can result in negative anomalies in water supply, and changes leading to more drought occurrences at regional scales influenced by the complex interactions of the different processes. Meanwhile, in comparison between agricultural, hydrological, or socio-economic drought, meteorological drought is most prevalent and thus affects all sectors of the economy and ecosystem.

Given that drought is dependent on many factors, its measurement remains a challenge. Several indices are utilized in measuring it. The common indices include the Standardized Precipitation Evapotranspiration Index (SPEI, Vicente-Serrano et al., 2010 ), Palmer Drought Severity Index (PDSI, Palmer, 1965 ), Standardized Precipitation Index (SPI, McKee et al., 1993 ), Standardized Anomaly Index (SAI, Katz & Glantz, 1986 ), Soil Moisture Anomaly (SMA, Bergman et al., 1988 ), Palmer Z Index (Palmer, 1965 ), Aridity Index (AI, Baltas, 2007 ), Combined Drought Indicator (CDI, Sepulcre-Canto et al., 2012 ), and Normalized Difference Vegetation Index (NDVI, Tarpley et al., 1984 ). Previous studies have discussed these indices at length (Wilhite et al., 2007 ; WMO and GWP, 2016 ). The indices chosen are determined by the dataset available and the accuracy required. Although drought is difficult to quantify because it is challenging to predict, its monitoring, policy reform, and asset management are critical for avoiding drought emergencies (Thomas et al., 2020 ). Seasonal weather forecasts that are specially tailored to drought monitoring systems are crucial to mitigating the impacts of droughts. Inadequate and reliable climate data, as Naumann et al. ( 2014 ) point out, create difficulty in drought monitoring in Africa and globally.

Some studies that investigated drought events over the African continent as a whole leave out countries that are prone to drought (Adisa et al., 2019 ; Masih et al., 2014 ). For instance, Adisa et al. ( 2019 ) noted that the continent experienced droughts in the years 1984, 1989, 1992, and 1997; however, this pattern varied based on the climate zone. Masih et al. ( 2014 ) investigated the drought occurrences over the continent between 1900 and 2013. Their study showed that drought has increased in frequency, intensity, and spread in the last 50 years. The study pointed out the years 1972/1973, 1983/1984, and 1991/1992 as extremely dry years across the continent. In a recent study, Ngcamu and Chari ( 2020 ) reported that droughts pose a high risk to people’s nutritious food security across sub-Saharan Africa. However, the past, actual, and future states of drought, along with their historical trends, impacts, mitigation, and prospects, are still poorly covered across the entire continent. Meanwhile, an extensive understanding of droughts across Africa is necessary for decision- and policymaking for both regional and continental organizations.

Therefore, this study aims to review case studies that address the occurrence of meteorological drought over Africa (Fig.  1 ), focusing on observed and underlying causes, impacts, mitigation measures, and prospects in the future. Given that Africa’s rainfall varies greatly in space, the continent is divided into regions of nearly homogeneous climate: West Africa (WAF), East Africa (EAF), Southern Africa (SAF), and Northern Africa/Sahara (SAH). This study is among the pioneer studies to focus on this topic, especially the projections and mitigation of drought impacts over the entire continent. The outcome of this study will help identify successful adaptation case studies as well as the analysis of projected drought for informed decision-making.

The African topographical map with delineated regions marked “SAH”, “WAF”, “EAF”, and “SAF” represents the regions under consideration for this study

Data Collection Methods

Data were sourced from existing peer-reviewed studies and book chapters published in various databases (search engines), namely Web of Science, SCOPUS, Google Scholar, JSTOR, and AGRIS. The search engines utilized were chosen for their broad coverage of up-to-date studies and interdisciplinary academic content (Spires et al., 2014 ). The data were sourced for the period covering 1950 to 2021, representing the unequivocal historical warming of the climate system as represented by the Intergovernmental Panel on Climate Change (IPCC, 2014 ) and the current period of observed and projected changes in climate. Therefore, the review of the recent decades suitably provides for reflection on significant trends and progress made in understanding the meteorological drought across the continent. Moreover, the global dataset from the Emergency Events Database (EM-DAT) website ( https://public.emdat.be/ ) was assessed for the relevant information on drought situations at the country/regional/continental levels, focusing on estimating the impact of drought events on livelihoods and food security. From the available data collected, the study employed a systematic literature review (SLR) technique to assess the historical trends, impacts, mitigation measures, and future prospects of meteorological drought across the African continent. SLR is a literature review method that is mainly used to examine the state of knowledge related to a topic (Ford et al., 2011 ). The approach is increasingly being employed in climate change discourse in order to understand the most up-to-date state of knowledge and to identify directions for further research exploits (Mcdowell et al., 2016 ). The present study followed the standards of the SLR technique in selecting and examining literature found in the selected literature database. For instance, we conducted the SLR following identification of literature using keywords including “Drought”, “Rainfall”, “Agriculture”, “Health”, “Environment”, “Economy”, and “Africa” for the selection of publications. Subsequently, the selected studies were analysed using both qualitative (thematic analysis) and quantitative (descriptive statistics) methods to explore all possible responses using the defined research questions for this study. A similar approach was employed in a study that systematically reviewed how smallholder agricultural systems’ vulnerability to changing climate is assessed in Africa (Williams et al., 2018 ).

Observed Variations and Underlying Causes of Drought Over Africa

West africa.

The West African Sahel is a semi-arid transition zone located between the Sahara Desert and humid tropical Africa. The region is characterized by a strong inter-annual meridional rainfall gradient and high rainfall variability. The annual rainfall amounts vary across the latitudes, from the humid Guinea coast to the northernmost locations. Rainfall variability over the region is mostly associated with the West African monsoon, which is the advection of moisture from the Gulf of Guinea, occurring during the summer months (July to September) as a result of the northward migration of the Intertropical Convergence Zone (ITCZ, Nicholson et al., 2018 ). The rainfall pattern is known to have been affected by a pronounced multi-decadal drought episode with unprecedented severity in recorded history between the late 1960s and early 1980s (Losada et al., 2012 ; Nicholson 2018 ; Nicholson et al., 2018 ; Ogou et al., 2019 ).

The drought events have caused numerous deaths and destroyed property, hampering development and economic growth in the region, as farming activities in the region are largely dependent on rainfall. The plight of the affected population attracted the attention of international aid organizations as well as the scientific community, which have encouraged research activities aimed at understanding the characteristics of the extreme in terms of causal mechanisms and future prospects. Nonetheless, ensuing studies attributed the Sahel drought to a number of factors. Early concerns focused on the influence of land-use practices (Charney, 1975 ), but later observational (Folland et al., 1986 ) and modelling (Biasutti et al., 2008 ; Caminade & Terray, 2010 ; Hoerling et al., 2006 ; Rowell, 2001 ) studies related both inter-annual and decadal-scale Sahel drought changes to sea surface temperature (SST) changes. In particular, strong links were found with inter-hemispheric (north–south) temperature gradients in the tropical Atlantic and SST in the tropical Pacific and Indian oceans. This relationship between the north–south SST gradient (the south and north oceans warmed and cooled after 1970) on a global scale is thought to have forced the Sahel drought on a decadal timescale. Hastenrath ( 1990 ) suggested that the increase in the cross-equatorial SST gradients in the Atlantic with the ITCZ location is also important. On the other hand, Herceg et al. ( 2007 ) highlighted the influence of the homogeneous warming of the tropical SST on the Sahelian drought through a warming of the free troposphere, affecting deep convection over West Africa.

Bader and Latif ( 2011 ) presented evidence that the dry conditions that persisted over the west Sahel in 1983 were mainly forced by high Indian Ocean SST that were probably remnants of the strong 1982/1983 El Niño event. The study further demonstrates that the Indian Ocean significantly affects inter-annual rainfall variability over the west Sahel and, as such, is the main forcing for the drought over the western Sahel. Indeed, several investigations have associated teleconnection between ENSO and rainfall variability over the Sahel (Rodríguez-Fonseca et al., 2015 ; Rowell, 2001 ), and the significance of this link during the observed drought was highlighted by Janicot et al. ( 2001 ).

Interestingly, both observational and numerical-modelling studies in recent years have suggested a recovery in precipitation over the Sahel (Fontaine et al., 2011 ; Lebel and Ali 2009 ; Nicholson et al., 2018 ; Sanogo et al., 2015 ; Sylla et al., 2016a ; Sylla et al., 2016b ). This implies that, in contrast to the widespread drying of the 1960s–1980s, the Sahel may have witnessed significant increases in precipitation during the subsequent years (Dike et al., 2020 ).

East Africa

The study of drought characteristics over equatorial East Africa (EAF) is particularly important owing to the region’s large inter-annual variability in the amount of rainfall received. Additionally, a large portion of the EAF landmass is classified as arid and semi-arid land (ASAL) despite being in the tropics and, as such, is susceptible to extreme rainfall variations, especially during the drought. Observational evidence over EAF shows that the mean rainfall for the major season, the long rains [March to May (MAM)], is on the decline over recent decades, and with it a widespread trend towards an arid condition (Lyon, 2014 ; Ongoma and Chen, 2017 ; Seleshi & Zanke, 2004 ). Nicholson ( 2014 ) reported widespread below-normal rainfall in the years 1998, 2000, 2005/2006, 2007, 2008, 2009, and 2011 for both the long and short rain seasons. The long local rains are locally referred to as masika in Kenya and Tanzania and gu in Somalia. Over the Ethiopian region, it is usually termed belg. On the other hand, October to December (OND) rain is known as short rain, locally known as vuli, der, and krempt over Kenya, Somalia, and Ethiopia, respectively (Nicholson, 2018 ). Such a trend is particularly worrying, as the population largely depends on rain-fed agriculture for food production, and the sector still has one of the largest shares of employment (Salami and Kamara, 2010 ).

Each drought event has a visible impact on the region’s economy, poses threats to lives, and degrades the natural environment. As an example, recent drought episodes of 2010/2011 and 2016 created a food shortage for over 10 million people, leading to the loss of lives and livelihoods (Uhe et al., 2017 ). Haile et al. ( 2020a , b ) reported increased drought frequencies in Eritrea, parts of Ethiopia, South Sudan, Sudan, and Tanzania, while Rwanda, Burundi, and parts of Uganda experienced smaller droughts in the second half of the twentieth century. The study also reported that a longer-timescale drought (SPI 6) persisted longer than the short-timescale droughts. Future projections of drought also paint a grim picture, as drought events are likely to increase by 16%, 36%, and 54% under the low, medium, and high emission scenarios, while extreme droughts are expected to cover a larger area (Haile et al. 2020a , b ; Tan et al. 2020 ).

The observed increase in drought extremes is the subject of heightened research effort, and different theories have been advanced in an attempt to explain the phenomenon. Most studies point to ENSO as the primary factor causing seasonal drought, with El Niño (La Niña) episodes enhanced (suppressed) in the region. However, Lyon ( 2014 ) found that even during the OND season when ENSO influence is strongest, it accounts for less than half of the rainfall variance, thus pointing to influence from other sources. Their study reported that the post-1998 decline in the MAM was strongly driven by natural multi-decadal variability in the tropical Pacific Ocean. There has been a debate as to whether human intervention has played a role in creating the situation. However, considering that the drying trend experienced in the region is small compared to natural variability, Yang et al. ( 2014 ) attributed the cause of this trend to human-induced climate change, especially over a period as short as a few decades. Similarly, Lott et al. ( 2013 ) revealed that the impact of the 2010–2011 droughts was worsened by human intervention but did not find any evidence of human influence. However, Funk et al. ( 2014 ) do not completely exonerate the anthropogenic influence.

The aforementioned study argues that warming of the western Pacific because of human influence may enhance SST gradients along the tropics that are associated with the cold phase of the Pacific Decadal Oscillation (PDO), thereby increasing the drought during the MAM season. Williams and Funk ( 2011 ) attribute the decreasing rainfall trend to increased warming of the Indian Ocean SST, which extends the warm pool and Walker circulation westward, causing anticyclonic moisture flow over east Africa and disrupting moisture influx into the region. On the other hand, Hastenrath et al. ( 2007 ) linked the 2005 drought to the fast-moving westerlies that are often accompanied by anomalously cold waters in the northwestern and warm anomalies in the southeastern Indian Ocean. Recently, Wainwright et al. ( 2019 ) linked the reduction to the delayed onset and earlier withdrawal of the rain band over the region. A detailed study of the recent progress of drought occurrences, causes, impacts, and resilience was well enumerated in a recent study (Haile et al. 2019 ).

Northern Africa/Sahara

Due to its geographical location and climatic conditions, North Africa (NA) is typically a dry region by nature. Approximately 70% of the area is desert, which is hostile to life and normal anthropological activities, with annual precipitation of less than 50 mm and an arid climate with annual precipitation of less than 150 mm (Babaousmail et al., 2021 ; Radhouane, 2013 ). Drought is thus a recurrent phenomenon in the NA region, causing civilizations to collapse and mass migrations. In the past four decades, drought episodes in NA have gradually become more widespread and prolonged, with worrying socio-economic and environmental effects (Kaniewski et al., 2012 ; World Bank, 2017 ). Drought trends over Northern Africa have been caused by the interaction of complex processes and feedback mechanisms. Examples include El Niño events, increased vertical thermal instability from the warming troposphere, and changes in the Atlantic Ocean that result in below-normal summer rainfall (Caminde & Terray, 2010 ; Dai & Zhao, 2017 ). Meanwhile, many studies have concluded that the drought episodes in the Sahel are mainly driven by southward warming of the Atlantic Ocean and persistent warming of the Indian Ocean. Moreover, the shift in the ITCZ contributed to the region’s dry anomaly (Caminade & Terray, 2010 ; Giannini et al., 2008 ; Zeng, 2003 ). Human influence as a result of land-use change, which alters the land surface feedback mechanism, is also noted as a factor (Zheng et al., 1997 ). Other studies have suggested the impact of aerosol emissions as a key driver of the Sahel droughts (Moulin & Chiapello, 2004 ). In addition, human-induced greenhouse gas emissions are considered a contributory factor to ocean warming (Dai, 2013 ).

Southern Africa

Drought is among the most destructive natural disasters in Southern Africa, with the region experiencing an escalation in the spatial extent of drought since the 1970s (Rouault & Richard, 2005 ). The bulk of the current research in the region has concentrated on the protracted droughts of the 1980s and 1990s (Jury & Levey, 1992 ; Landman & Mason, 1999 ; Lindesay & Vogel, 1990 ; Mason & Jury, 1997 ; Tyson & Dyer, 1978 ). The consequences of drought over Southern Africa vary across regions. The socio-economic impacts are usually severe in a region with annual rainfall of less than 500 mm (Mason & Jury, 1997 ; Richard & Poccard, 1998 ; Rouault & Richard, 2003 ). Consequently, drought is a risk to water management and agriculture in the region. Knowledge of the effects of droughts in Southern Africa is of utmost importance because agriculture is the basic economic activity for the majority of the population in these countries (Jury, 2002 ; Washington & Downing, 1999 ). ENSO warm events have been associated with drought, resulting in diverse impacts over much of Southern Africa (Cane et al., 1997 ; Enfield, 1989 ; Ogallo, 1980 ). The 1982–1983 ENSO event, for instance, helped to exacerbate the prevailing dry conditions in much of the subcontinent (Bhalotra, 1985 ; Dent et al., 1987 ; Taljaard, 1989 ). Rainfall unpredictability in Southern Africa has been connected with atmospheric circulation configurations and interchanges in easterly and westerly flows, the connections between tropical and temperature structures, and the difference in pressure systems over Marion and Gough Island. Prolonged heat waves and droughts are interconnected, in most cases, by the prevalence of fundamental anticyclonic circulation over the country. A study on drought characteristics within the twenty-first century showed that ENSO caused over 66% of the extreme drought occurrences in Southern Africa (Rouault & Richard, 2005 ). The effect of ENSO on the region’s climate was also reported to have intensified since the 1970s. The ENSO SST effect on dry conditions in Southern Africa was examined by Gore et al. ( 2020 ), who revealed a weakening effect of El Niño and a strengthening effect of La Niña on the Walker circulation, resulting in drier and wetter conditions, respectively. It was reported that the El Niño and La Niña conditions altered the moisture flux circulation, thus impacting the drought characteristics over the southern region of Africa.

Impacts of Drought on Agriculture, Water, Environments, and Human Health

According to the International Disaster Database (EM-DAT), the drought occurrences between 1950 and 2021 affected close to half a billion people on the African continent, with about 700,000 recorded deaths and damage of about 6.6 billion USD (Fig.  2 ). This gradual shift is probably the consequence of climate change. Generally, agricultural activities, such as livestock, forestry, and fisheries, are prone to droughts, which severely affect food supplies and livelihoods, especially for smallholder farmers and the rural poor. When drought occurs, it is the primary sector to be influenced and the most significantly affected of all economic sectors. Moreover, the drought impacts have led to a decline in crop output, an upsurge in fire hazards, increased livestock mortality, and decreased water volume and level (World Bank, 2017 ). It can also act as a risk multiplier, destabilizing populations, amplifying uneven access to water services and water resources, and reinforcing perceptions of marginalization (World Bank, 2017 ). Low-income earners are more vulnerable to droughts than average members of the population (World Bank, 2017 ).

Drought impacts in Africa from 1950 to2021 (EM-DAT; https://public.emdat.be/ )

Impacts of Drought on Agriculture

Agricultural activities are Africa’s main source of revenue, particularly in the sub-Saharan region. The long-term viability of agricultural activities is limited by their reliance on hydro-climatic variability. This has led to either dry or wet conditions for crop survival. The dry (drought) condition is the most devastating of agricultural activities (Habiba et al., 2012 ; Narasimhan & Srinivasan, 2005 ). Hence, researchers have evaluated the various impacts of drought on Africa. Droughts are a frequent occurrence in the agricultural areas of Eastern and Southern Africa (Winkler et al., 2017 ).

Droughts have caused enormous damage in many regions of Africa. According to the World Bank ( 2012 ), the 2000 drought caused a decline in peanut revenues from 68.4 to 17.4 billion FCFA, accounting for a 74% decline over WAF alone. In the same year, revenues from millet and sorghum fell from 30 to 12 billion FCFA, a 60% decrease (World Bank, 2012 ). Droughts are reported to affect crops not only through a decline in productivity but also through a reduction in the quality of the grains produced (Gautier et al., 2016 ). Hazard events have a negative impact on agriculture (Rojas et al., 2011 ). The main staple food in sub-Saharan Africa, in particular maize, has been vulnerable to drought based on the drought exposure index (DEI) and crop sensitivity index (CSI) (Kamali et al., 2018 ). These authors noted that a higher (lower) crop drought vulnerability index (CDVI) indicated lower (higher) vulnerability (Fig.  3 ). One of the most popular strategies implemented by governments in the region to cushion the effect of drought is the provision of emergency endowments in the form of food aid, school feeding programmes, and the creation of temporary employment for people in the region hard-hit by the drought. This is imperative for reducing starvation as well as saving lives, but this approach has been shown to have several limitations. A paradigm shift of focus to a more proactive strategy that is more effective in risk reduction and social resilience is highly needed.

Spatial distribution of maize drought vulnerability based on the five types of crop drought vulnerability indices (CDVI). CDVI is based on linking a DEI PCP to CSI and b DEI PCP–PET to CSI. PCP and PET stand for precipitation and potential evapotranspiration, respectively. The figure is

adapted from Kamali et al. ( 2018 )

Impacts of Drought on the Environment

In Africa, population growth has become a serious concern, leading to a scarcity of natural resources and worsening socio-economic development (Ahmadalipour & Moradkhani, 2018 ; Ahmadalipour 2018 ). Drought impacts have led to poor soil fertility, affecting agricultural productivity in most sub-Saharan African countries. Environmental stresses emanating from drought vulnerability are the leading cause of biodiversity losses in most African agro ecosystems (Horn & Shimelis, 2020 ; Abdelmalek & Nouiri, 2020 ). For instance, South and West African countries have experienced severe drought impacts on their environment, which included the deracination of the region’s vegetation from their prototype biomes, significant loss of biodiversity, and plant mortality (Lawal et al., 2019 ).

At present, some parts of Southern and Eastern Africa have witnessed a rapid decrease in precipitation, and critical irrigation supply is on the verge of collapse due to a lack of environmental monitoring and assessments by stakeholders (Ayugi et al., 2020 ). Moreover, Mediterranean areas have experienced severe impacts such as water scarcity stress, rainfall variability, and decreased agricultural production, which may worsen under the perceived climate change prognosis (Abdelmalek & Nouiri, 2020 ).

Recent studies have suggested incorporating several strategies such as environmental reclamation involving the advancement of ecosystem services, biodiversity improvement, and soil and water conservation and management suitable for Africa to adapt to drought conditions. In addition, improved monitoring and assessments and understanding of the sources and impacts of droughts are essential for developing resilience to the environmental consequences of drought (Haile et al., 2019 ). Meanwhile, most African countries have developed mitigation initiatives on food security and environmental issues emanating from drought and climate change. Examples include the West Africa drought-monitoring centre, on behalf of the Economic Community of West African States (ECOWAS), which incorporates several international initiatives on climate change, food security, and environmental monitoring that allow them to be updated on the best accessible and applicable technologies and procedures, similar to their counterparts in Eastern and Southern Africa (Traore et al., 2014 ).

Impacts of Drought on the Economy

According to Livingston et al. ( 2011 ) and the Organisation for Economic Co-operation and Development (OECD)/Food and Agriculture Organization (FAO) ( 2016a ; b ), with the exception of Southern Africa and the majority of North African countries, nearly all of Africa is dependent on subsistence agriculture. Although the share of agriculture in the gross domestic product (GDP) has been declining, the sector still accounts for about 30% of GDP and employs about 70% of the African labour force. This practice involves direct dependence on annual rainfall, natural vegetation, and water reserves for livelihood. The economic landscape of most African countries depends heavily on the dynamics of climate change, of which drought is an integral part. The vulnerability of the African economy and key sectors driving economic performance, such as agriculture, forestry, energy, tourism, and coastal and water resources, to climate change has been substantial (Abidoye & Odusola, 2015 ; Abidoye et al., 2012 ).

The IPCC ( 2014 ) has predicted an average increase of 1–3 °C in temperature for most parts of Africa, with a corresponding increase in surface evapotranspiration and a decrease in average annual precipitation. The impacts of this trend will result in an increase in drought conditions across most parts of the continent. Drought episodes in many African countries adversely affect both energy security and economic growth across the continent. This is so because the majority of African nations still depend on hydrothermal power plants for electricity and waterways for transportation of goods and services, as well as agricultural practices. There are probably no other factors that affect agricultural production as much as adverse weather conditions, especially droughts. Some areas that have experienced extreme droughts in the last few years include the Horn of Africa, East and Central Africa, and parts of Southern Africa (Masih et al., 2014 ). Even where droughts have not been as severe, rainfall tends to be unreliable, resulting in lower agricultural outputs and economic decline. Dell et al. ( 2012 ) considered the economies of 136 countries over a period of 54 years (1950–2003). They found that the impact of higher temperatures on economic growth in poor countries was significant, with a 1 °C rise in temperature in a given year reducing economic growth by 1.3% on average. Besides, it affects growth output, but it also reduces growth rates. Lastly, higher temperatures have wide-ranging effects, reducing agricultural and industrial output and increasing political instability (fallout from migration).

Impacts of Drought on Human Health

With the recent climate change projections, the occurrence of drought, intensifying in severity, duration, and the way people are adversely affected, is speculated to be on the increase in the coming decades (Christenson et al., 2014 ; IPCC, 2013 ; Rockström & Falkenmark, 2015 ). Drought is among the most severe phenomena that disturb the world today, particularly in Africa. It seems a formidable task to document the effects of drought on human health due to its complexity in assigning a start and end time and knowing that the generated impacts tend to accumulate over a long period (Stanke et al., 2013 ). Most of the increasing drought impacts on human health in Africa could be attributed indirectly to several factors, for instance, civil wars, bad political policies, adverse weather trends, and diseases like COVID-19 and HIV. However, some of the effects of prolonged drought can have an immediate and direct impact on health as a result of severe heat waves that cause heat stroke and other health issues (Smith et al., 2014 ).

According to Stanke et al. ( 2013 ), the drought-related health effects are strongly dependent on the severity of the drought, baseline population vulnerability, existing health facilities, and the availability of resources to migrate the affected population during the events. Some of the drought-related health impacts include nutrition-related effects (general malnutrition and mortality, micronutrient malnutrition, and anti-nutrient consumption), water-related diseases (including E. coli , cholera, and algal bloom), airborne and dust-related diseases (including silo gas exposure and coccidioidomycosis), vector-borne diseases (including malaria, dengue, and West Nile virus), mental health effects (including distress and other emotional consequences), and other health effects (including wildfire, effects of migration, and damage to infrastructure). Indirect health hazards that relate to large-scale migration and forced displacement result from extreme weather events such as drought in African countries or cross-border (Kumari et al., 2018 ; Serdeczny et al., 2017 ). On the other hand, the severity of the 2011 and 2017/2018 droughts experienced in Eastern Africa led to famine, increased malnutrition in children under age 5, and enhanced mortality (ACAPS, 2018 ; National Drought Management Authority, 2018 ).

Kristina et al. ( 2020 ) inferred that the countries situated in the Horn of Africa, namely Somalia, Kenya, and Ethiopia, are highly vulnerable to climate change, such as prolonged droughts. They concluded that internally displaced persons (IDPs) are more exposed to health challenges such as malnutrition, undernutrition, lack of vaccination, gender-based violence, and mental health disorders. Besides, the treatment of some of these diseases is inadequate, which results in insufficient access to vital health services for the IDPs. Low-income areas, for example, sub-Saharan Africa, exhibit a low adaptive capacity to the multiple underlying factors caused by droughts, such as food insecurity that threatens the livelihoods of people, and inadequate access to clean water, health care, and education (Hartmann & Sugulle, 2009 ; Niang et al., 2014 ; Opiyo et al., 2015 ).

Mitigation Strategies

The vulnerability of Africa to climate change is driven by a range of factors that include weak adaptive capacity, high dependence on ecosystem goods for livelihoods, and less developed agricultural production systems. Efforts towards drought resilience via policy approaches, environmental rehabilitation, and agricultural productivity and water resources development are thus needed. Designing active responses to drought is more important than reactive responses, and the active responses should be based on risk management rather than crisis management (Haile et al., 2019 ). Examples of cases where key resources meant for the infrastructural and technological growth of the economy are being diverted to develop coping mechanisms to adapt to climate change effects should be discouraged. In contrast, drought mitigation interventions should be made in terms of preparedness for coping, and the creation of early warning awareness and the development of skilled personnel should be encouraged.

In response to the anticipated changes, the following proposed mitigation measures may be undertaken to avoid the loss of lives and societal infrastructure. Steps such as collaborating with countries that have advanced agricultural technologies suitable for harsh climates, deepening collaboration in areas of research on agricultural technologies and water conservation, and focusing on climate change mitigation strategies, as well as capacity-building, education, training, and public awareness on climate-related issues, should be prioritized and appropriately coordinated across African countries. Clearly, rain-fed agriculture has limits and is insufficient to feed the world’s growing population or to generate long-term economic growth. In addition, there is increasing competition for water for various uses, especially with the rapid growth of urban populations.

Moreover, an increase in land vegetation cover will be of great importance. With anticipated changes in various regions, such as an increase (decrease) in drought events (rainfall occurrences), measuring such enhanced tree coverage will likely retain soil moisture and help reduce such impacts. Meanwhile, plans to create new settlement schemes may be put in place to avoid more loss of lives. This is due to the expected surge in the frequency of landslides in regions that will experience flood extremes due to earth mass movements, especially in the hilly areas, which could eventually affect dams and riverbanks. Other measures could be prioritized, such as creating new opportunities for research centres to find suitable crops to be grown in new land areas to enhance food security. This is because climate change will create a shift in farming systems. Regions that are predominantly arid and semi-arid (ASALs) will likely experience an increase in rainfall, thereby creating new opportunities for agricultural activities. New crops that are able to survive in new areas will enhance food security in the region that is considered food-insecure.

The impact of climate change on infrastructures, such as the lost resilience of buildings, roads, and other artefacts owing to an increase in temperature and precipitation, will call for new innovative approaches in engineering science to find suitable materials that can withstand high temperatures and more rainfall as compared to the traditional raw materials used over the last century. In the health sector, the health risk associated with climate change will vary according to age and gender. Anticipated climate-related health risks either directly or indirectly influence the vulnerable population, such as those that depend on climatic conditions (malaria, diarrhoea, and cholera). This will require more financial, human, and technological resources to be allocated to the health sector to research and improve awareness campaigns on possible adaptation measures. Lastly, the increase in drought severity towards the end of the century calls for far-reaching measures to ensure appropriate coping mechanisms are put in place. For instance, the hotspot regions in most African countries will affect the community that is already reeling from the ASAL environment’s unbearable conditions. With the projected changes of an intense increase in aridity conditions, the support systems of community livelihoods will be affected. Thus, African countries must increase their investment in irrigation infrastructure and water-conserving technologies such as drip irrigation, dam construction, and rainwater harvesting so as to avoid more catastrophic impacts.

Future Prospects of Drought Observation and Monitoring

Accurate monitoring of drought situations remains a challenge due to the lack of ground-based datasets across most parts of Africa. Moreover, the modelling uncertainties continue to persist in most global climate models, mainly from the Coupled Model Intercomparison Project (CMIP3/5/6). For instance, in simulations of future climate variations and changes over West Africa, most of the CMIP3 models project modest increases (or decreases) in summer precipitation (Cook, 2008 ) over the Sahel (Guinea coast). Subsequent generations of CMIPs have strengthened our confidence in this notion (Ajayi & Ilori, 2020 ; Almazroui et al., 2020 ; Monerie et al., 2020 ), albeit with some notable uncertainties (Bichet et al., 2020 ; Monerie, et al., 2020 ; Sylla et al., 2016a , 2016b ). Using downscaled climate models, a number of studies reached a consensus that the region is prone to significant drought hazards in the future (Ahmadalipour et al., 2019 ; Ajayi & Ilori, 2020 ), with a more severe impact under global warming (Quenum et al., 2019 ; Sylla et al., 2016a , 2016b ). Nevertheless, drought events are projected to increase in the coastal parts of Liberia, Cameroon, Mali, Burkina Faso, Niger, Ivory Coast, Benin, Nigeria, and Chad (Quenum et al., 2019 ). This indicates that by the end of the twenty-first century, drought will be more severe over the region than it was in the recent past (Ahmadalipour et al., 2019 ), while global warming will intensify its impact even in the near future (Diasso & Abiodun, 2018 ; Klutse et al., 2018 ).

Meanwhile, studies that have also employed a subset of extreme rainfall indices (consecutive dry days and total precipitation; CDD/PRCPTOT) as defined by the World Meteorological Organization (Zhang et al., 2011 ) to investigate future prospects of drought over West Africa have reported significant changes in CDD and PRCPTOT (Akinsanola & Zhou, 2019 ; Akinsanola et al., 2020 ; Klutse et al., 2018 ; Quenum et al., 2019 ). To illustrate, Akinsanola and Zhou ( 2019 ) reported a statistically significant decrease in total summer precipitation and a significant increase in CDD over the region, which underscores that drought events will be more pronounced in the future. Interestingly, Klutse et al. ( 2018 ) also concluded that enhanced warming would reduce mean precipitation across the region and increase CDD over the Guinea Coast subregion known for its humid features. In terms of changes in future precipitation variability over the region, Akinsanola et al. ( 2020 ) projected a remarkably robust increase in CDD over West Africa over a wide range of timescales.

The foregoing demonstrates that drought events will be more severe in the future as a result of a significant decrease in mean precipitation and a robust increase in CDD. Notably, the projected increase in drought occurrence may influence already fragile ecosystems and agriculture in the region (Klutse et al., 2018 ). Although there is an inter-model spread, which implies uncertainties in the presented projections, multi-model ensemble projections strengthen our confidence in the projected increase in drought events over West Africa. Using an ensemble of ten members, Ahmadalipour et al. ( 2019 ) quantified drought risk ratios across Africa and found that an increase in future drought risk across the continent is probable. This indicates that if no climate change adaptation policy is implemented, the unprecedented drought hazard will impact more severely on the vulnerable population (Ahmadalipour et al., 2019 ; Akinsanola & Zhou, 2019 ; Klutse et al., 2018 ). Furthermore, mitigation strategies such as reforestation have the potential to reduce future warming by 0.1–0.8 °C while increasing precipitation by 0.8–1.2 mm per day over the region (Diasso & Abiodun, 2018 ). As a result, this could serve as a wake-up call to relevant stakeholders to take a broad approach to mitigate the impact of increased drought events over West Africa.

Existing studies have noted emerging issues related to possible changes in the drought situation over the East African region. Most studies show consistent results, with a likely increase in drought duration and moderate incidence, with fewer occurrences of extreme events across possible scenarios (i.e. RCP4.5 and 8.5) (Gidey et al., 2018 ; Haile et al., 2020a , 2020b ). Examination of projected changes in drought frequency and severity depicts possible manifestations of severe to extreme drought occurrences that are expected to intensify during 2071–2100 (Haile et al., 2020a , 2020b ; Tan et al., 2020 ). For instance, using CMIP5 models, most studies projected an increase in drought episodes towards the end of the century by 16%, 36%, and 54% under RCP 2.6, 4.5, and 8.5 scenarios, respectively (Haile et al., 2020a , 2020b ). Spatially, drought events, duration, frequency, and intensity would intensify in regions such as Sudan, Tanzania, Somalia, and South Sudan, but generally decrease in Kenya, Uganda, and Ethiopian highlands. Interestingly, Nguvava et al. ( 2019 ) noted an increase in the intensity and frequency of SPEI droughts over East Africa, while SPI demonstrated a weak change for intensity and frequency of droughts. Overall, projections show that the drought changes over East Africa follow the concept of the “dry gets drier and wet gets wetter”. The findings agree with the recent similar studies that were based on recent GCM output of CMIP6 models (Ayugi et al., 2022 ). The resultant implications of projected changes will affect cross-cutting sectors that support livelihoods (i.e. economy, infrastructure, health, agriculture, and energy).

Northern Africa

Most of North Africa is dry because of its terrain (geographical location) and climatic conditions. Many parts of the region are covered by desert, which is unfriendly to anthropogenic activities due to its harsh weather. These harsh conditions are expected to worsen if necessary steps are not taken to curb the nemesis. The effects of drought manifest in the reduced rainfall from the established long-term average that spreads over a specified spatial scale for a definite period and negatively influences human activities (FAO, 2018 ). The drought problem in North Africa has existed for several hundreds or thousands of years. Mauritania experienced severe drought over the Sahel region in the 1910s, 1940s, and again in 1968. This was referred to as “the great famine” and “exchanging children for maize”. A similar severe drought in 2011 resulted in poor harvests, high food prices, and the loss of livestock, and in 2013, the worst drought in 15 years, contributed to the food crisis.

Under the Representative Concentration Pathway (RCP) 8.5 scenario, Northern African countries, particularly Morocco, Algeria, and Tunisia, are unmistakably projected to become global hotspots for drought by the end of the twenty-first century (Dai, 2013 ; Orlowsky & Seneviratne, 2013 ; Prudhomme et al., 2014 ; Sillmann et al., 2013 ). Moreover, recent studies based on socio-economic pathways (SSPs) of CMIP6 equally project a sharp decline in precipitation trends over the region and a steady increase in aridity, leading to an intense upsurge in ecological droughts with medium confidence (IPCC, 2021 ). It is worth noting that the projections of future droughts suffer from outsized model uncertainties and also fundamentally depend on the methodology and baseline periods chosen. The projections for more severe and intense drought conditions around the Mediterranean and Northern Africa are consistent across various research (IPCC, 2012 ; World Bank, 2014 ).

There is an emerging concern that the current global warming may intensify the severity of droughts in Southern Africa. Studies have shown that drought severity escalates with temperature increase (Dai et al., 2004 , 2011a , b ; Sheffield & Wood, 2008 ; Vicente-Serrano et al., 2010 ; Washington & Preston, 2006 ). For example, Dai et al. ( 2004 ) revealed that between 1972 and 2004, global warming increased global dry areas by 20–38%, and reported that the temperature rise of 1–3 °C in 1950–2008 reduced the annual rainfall in most regions of Africa, including Southern Africa (Dai, 2011a ). Furthermore, precipitation characteristics are anticipated to continue to vary towards stronger and intermittent spells, which are expected to transform into more recurrent and severe water-related life-threatening events (Simonovic, 2009 ). Subsequently, most studies project a robust drying signal but with varying magnitudes from one location to another (Kusangaya et al., 2014 ; Maúre et al., 2018 ). To illustrate, recent studies that analysed the impacts of global warming levels on regional drought showed that the decrease in precipitation is insignificant at 1 °C, but the magnitude and spatial extent of the decrease becomes larger as global warming increases to 2 °C and 4 °C warming, respectively (Abiodun et al., 2019 ). Despite the extensive research conducted over this region on possible changes in drought, further studies still need to establish how to reduce the uncertainty in most models and thereby improve the credibility and applicability of the results. Moreover, future studies could examine the contribution of the atmospheric processes to the different drought projections and also extend more studies on hydrological droughts (i.e. stream flows) in the most vulnerable rivers in the Southern African regions. Table ​ Table1 1 documents the summary information of drought impact type, main characteristics, key mitigation measures, and future prospects over Africa.

Summary information of drought impact type, main characteristics, key mitigation measures, and future prospects over Africa

SST sea surface temperature, ITCZ Intertropical Convergence Zone, ENSO El Niño–Southern Oscillation, EAF East Africa, WAF West Africa, SAH Northern Africa/Sahara, SAF Southern Africa

The impacts of changing climate have consequences on nearly all socio-economic sectors that are dependent on it. This leaves many African countries vulnerable to climate variability and change, especially those whose economies are heavily reliant on rain-fed agriculture. Drought occurrence is mainly dependent on rainfall performance in a given locality, with occurrence in low and high rainfall areas. This work reviews meteorological drought over Africa, focusing on the past occurrences, their impacts, the projected changes, and mitigation of drought impacts on regional scales over Africa. Well-documented drought occurrences over most regions of Africa have played a critical role in developing tailor-made coping mechanisms. However, few studies have so far established a clear pathway for the expected changes, due to various limitations. Projected changes show recovery from drought events in the west and east African domain, while the south and north regions indicate a tendency for increasing drought characteristics. Progressive developments in climate models with limited uncertainty call for a more in-depth analysis of mechanisms regulating projected drought patterns. The proposed adaptation case studies are well-documented studies on projected drought occurrences for informed decision-making. Key resources meant for the infrastructural and technological growth of the economy have been diverted to develop coping mechanisms to adapt to climate change effects, which in itself is changing. Thus, in the long term, African countries must increase their investment in areas that deal with environmental conservation, healthcare infrastructure, smart agribusiness approaches, and water-conserving technologies such as drip irrigation, dam construction, and rainwater harvesting. This review adds to the existing information and scientific understanding with proposed mitigation measures to curb the adverse impacts of drought over the continent.

Acknowledgements

The authors wish to thank their respective institutions of affiliation for their support in various forms, which contributed to their completion of this project.

Author Contributions

This study was initiated by EOE and BA. Conceptualization and methodology were developed by all members. Subsections were drafted as follows: VO and RM wrote the introduction section and reviewed the first draft. VND and COO wrote observed variation and underlying causes of drought over West Africa. AOO and BA wrote on East Africa, while EOE drafted sections for South Africa and North Africa. Impacts of drought; Agriculture was drafted by KFO and OEC, Environment by COO, Economy by APC and RM, and Health by AOR. The section for future prospects and challenges in Africa was drafted by VND, BOA, EOE, COO, and MA. The conclusion and abstract were drafted by BA.

This work was supported by the Organization of African Academic Doctors (OAAD) and a grant from the Postdoctoral Research Foundation of Jiangsu Province (Grant no. 2191012100301).

Declarations

All authors agree that the review article be published with no competing interest amongst members.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Emmanuel Olaoluwa Eresanya, Email: moc.liamg@44leunammeaynasere , Email: [email protected] .

Eucharia Chidinma Okoro, Email: moc.liamg@44leunammeaynasere , Email: [email protected] .

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• Published: 22 December 2022

Unprecedented droughts are expected to exacerbate urban inequalities in Southern Africa

• Maria Rusca   ORCID: orcid.org/0000-0003-4513-3213 1 ,
• Elisa Savelli   ORCID: orcid.org/0000-0002-8948-0316 2 , 3 ,
• Giuliano Di Baldassarre   ORCID: orcid.org/0000-0002-8180-4996 2 , 3 ,
• Adriano Biza   ORCID: orcid.org/0000-0001-6165-8939 4 , 5 &
• Gabriele Messori   ORCID: orcid.org/0000-0002-2032-5211 2 , 3 , 6 , 7  

Nature Climate Change volume  13 ,  pages 98–105 ( 2023 ) Cite this article

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• Climate-change adaptation
• Socioeconomic scenarios
• Water resources

Climate change-related drought risks are intensifying in many urban areas, making stakes particularly high in contexts of severe vulnerability. Yet, how social power, differential agency and economic visions will shape societal responses to droughts remains poorly understood. Here, we build a social-environmental scenario of the possible impacts of an unprecedented drought in Maputo, which epitomizes a Southern African city with highly uneven development and differential vulnerability across urban areas. To build the scenario, we draw on theoretical insights from critical social sciences and take Cape Town (2015–2017) as a case-in-point of a locally unprecedented drought in Southern Africa. We show that future droughts in Southern Africa will probably polarize urban inequalities, generate localized public health crises and regress progress in water access. Climate policies must address these inequalities and develop equitable water distribution and conservation measures to ensure sustainable and inclusive adaptation to future droughts.

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Data availability

The qualitative data supporting the findings of this Analysis are available within the Analysis and its Supplementary Information (Extended case study: Maputo and Extended case study: Cape Town). Some qualitative data are not publicly available due to ethical restrictions (that is, they contain information that could compromise the anonymity of research participants). These data are available from the corresponding author ([email protected]) on reasonable request. Anonymized data will be made available within a month from the request. Data on the filling levels of the water reservoirs of the two cities are available at the City of Cape Town Data portal ( https://cip.csag.uct.ac.za/monitoring/bigsix.html ), the Direcção Nacional de Gestão de Recursos Hídricos (National Directorate of Water Resources, Mozambique, https://www.dngrh.gov.mz/index.php/publicacoes/boletins-de-bacias-hidrograficas ) and the Biblioteca Digital de Teses e Dissertações (Digital Dissertation Repositiry, https://repositorio.bc.ufg.br/tede/handle/tede/10365 ). The Standardized Precipitation Evapotranspiration Index (SPEI) data can be retrieved from SPEIbase ( https://spei.csic.es/ ).

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Acknowledgements

M.R., E.S. and G.D.B. were supported by the European Union H2020 research and innovation programme, ERC Grant No. 771678 (HydroSocialExtremes); G.M. was supported by European Union H2020 research and innovation programme ERC grant no. 948309 (CENÆ); A.B. was supported by the Netherlands Organisation for Scientific Research (NWO) grant agreement W07.69.109. M.R.’s fieldwork in Maputo was supported by Marie Skłodowska-Curie grant agreement No. 656738 (INHAbIT Cities) and A.B.’s by NWO 07.69.109.

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Elisa Savelli, Giuliano Di Baldassarre & Gabriele Messori

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M.R. and G.M. conceived and designed the study. M.R., E.S. and A.B. undertook fieldwork in Maputo and Cape Town; M.R., E.S. and G.M. wrote the paper; all authors analysed and interpreted data and G.M., E.S. and M.R. developed the figures. All authors contributed to the revision.

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Extended data figures

Extended data fig. 1.

Twelve-month SPEI index for the cities of Cape Town (blue line) and Maputo (red line). The thick lines show the 13-month running mean of filling levels (%) of the reservoirs supplying Cape Town 61 and Maputo 142 . The labels on the x-axis indicate the center point of each year.

Extended Data Fig. 2

Summary of the phenomena, locations and authors of the case studies mapped in Extended Data Fig. 3 . See refs. 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 .

Extended Data Fig. 3

Locations of the case studies examined for the Theoretical Synthesis (Pillar 1).

Supplementary information

Supplementary information.

Rationale for extended case studies, Extended Case Study: Maputo and Extended Case Study: Cape Town.

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Rusca, M., Savelli, E., Di Baldassarre, G. et al. Unprecedented droughts are expected to exacerbate urban inequalities in Southern Africa. Nat. Clim. Chang. 13 , 98–105 (2023). https://doi.org/10.1038/s41558-022-01546-8

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IGAD case study

Key Messages

• East Africa is one of the regions in the world most affected by droughts. Recent trends seem to show an increase in risk, while longer term projections do not provide clear trends for the whole region and for individual countries.
• Most countries are low income, with some lower middle income countries. Some have diverse agro-ecological conditions, but most of the area in the region as a whole and in all individual countries being assessed is arid or semi-arid lands (ASALs), which has repercussions for national drought vulnerability.
• Food security (reduction in food quantity and quality, and even famine) is the biggest threat presented by droughts in East Africa, provoked by losses in agricultural and livestock production and in income, and compounded by already low income and lack of income diversification, problems surrounding water quantity and quality, and weak local and national food markets. Drought events combined with low local coping capacities and state failure, civil war and political interference have provoked some of the worst nature-based humanitarian disasters of the 21st century.
• These factors also affect the medium and long term impacts, such as loss of assets, human (child) development, conflict, migration, self-help will and thus recovery and development. Other important impacts are more localised: hydro-electric generation, and impacts on sensitive aquatic and terrestrial eco-systems (sometimes with repercussions on tourism or development e.g. eco-system services like water retention and biodiversity)
• Early warning systems have been adopted in the whole region, but require more bottom-up linkages with local communities, and need to be connected with constant monitoring of ever-changing vulnerabilities (i.e. not a once-of static vulnerability assessment only). Key to their effectiveness is mutual trust of stakeholders and stringent use (i.e. no politically-motivated manipulation or arbitrary regard of the results) including cooperation with international early warning systems.
• Pastoralism is one adaptation to the harsh and varying conditions, but is weakened by a range of factors including less open transhumance routes, reduced reserve areas, higher population densities of people and animals, and overall vegetation degradation caused by drought. Nevertheless, (improved) pastoralism must be part of the solution mix in the region.
• Local populations and communities are familiar with resilience strategies including agricultural practices (like natural resource management), income diversification and infrastructure development (small dams, wells, roads, markets, slaughterhouses), which are partially and slowly implemented. In many of these areas, they need additional support, such as in agriculture (breeding, irrigation, agroforestry, water saving cropping, on-farm water harvesting), landscape management (planning tools, water management and larger-scale water harvesting, community forestry), local private and public infrastructure investments.
• Water management in the region needs integrated water management, from watershed to surface and groundwater use, water harvesting, dam construction, irrigation, animal and human use, electricity generation, etc. The large dams in particular have international perspectives and constitute risks for international conflict, needing very careful planning, policy dialogue and conflict resolution. But also smaller structures need to be embedded into conflict sensitive user planning.
• Local informal solidarity networks play a big role in cushioning the impacts of drought and other risks. However, poverty and lack of non-financial capacities limit local efforts. During intense droughts, social protection (cash or food aid) is thus and still elementary and often the combined result of national and international interventions. Emergency aid and longer-term social protection are additional entry points for ‘building (back) better’, partially blurring the borders between development and disaster relief.
• Local and regional conflicts over water, grazing lands and local land use are frequent and strongly exacerbated during droughts. Conflict-sensitivity in all activities and during all periods of drought-resilience building is indispensable.
• Food markets are weak and weakly integrated so that during droughts, food prices rise (while meat markets plummet). Market integration must be improved, which includes not to overly rely on subsistence production in normal times. Also, local food reserves should be promoted, public and private.
• Linked to that, general economic development and diversification away from drought-dependent income sources is a (albeit long-term) pathway to more resilience and food market integration.
• Financial instruments add to resilience in several forms: beneath the standard insurance instruments, also savings are important buffers, and access to credit before, during and after droughts, with conditions designed according to temporal needs and without harming financial sustainability of the institutions.
• Energy systems should be diversified, so that drought does not overly hurt economic activities, and water in reservoirs can be used for irrigation. Bioenergy through careful management of (encroaching) shrubs and trees in rangelands could be one option.
• There is an important need to better synchronise and harmonise sectoral drought preparedness and emergency interventions.
• Overarching these sectoral instruments for more drought resilience, there is a need for clear division and attribution of responsibilities and accountability, coordination, harmonisation, communication, monitoring and evaluation. These efforts need separate (sector independent) support (capacity building and development, funding, political will and highest level), and personal and organisational continuity. Both seems to be lacking at times, but more research would be needed to follow this up at national and sub-national levels.
• Regional organisations (like the IGAD) and international Early Warning Systems (EWS) (FEWS-Net, large NGOs) are important elements of drought risk management in this region, regional cooperation success stories can be seen, but cooperation is still less than optimal.

This case study is a contribution to the GAR Special Report on Drought 2021.

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Climate Change Made East Africa’s Drought 100 Times as Likely, Study Says

The findings starkly show the misery that the burning of fossil fuels, mostly by rich countries, inflicts on societies that emit almost nothing by comparison.

By Raymond Zhong

Two and a half years of meager rain have shriveled crops, killed livestock and brought the Horn of Africa, one of the world’s poorest regions, to famine’s brink . Millions of people have faced food and water shortages . Hundreds of thousands have fled their homes, seeking relief. A below-normal forecast for the current rainy season means the suffering could continue.

Human-caused climate change has made droughts of such severity at least 100 times as likely in this part of Africa as they were in the preindustrial era, an international team of scientists said in a study released Thursday . The findings starkly illustrate the misery that the burning of fossil fuels, mostly by wealthy countries, inflicts on societies that emit almost nothing by comparison.

In parts of the nations hit hardest by the drought — Ethiopia, Kenya and Somalia — climate hazards have piled on top of political and economic vulnerabilities. The region’s string of weak rainy seasons is now the longest in around 70 years of reliable rainfall records. But according to the study, what has made this drought exceptional isn’t just the poor rain, but the high temperatures that have parched the land.

The study estimated that periods as hot and dry as the recent one now have a roughly 5 percent chance of developing each year in the region — a figure that is poised to rise as the planet continues to warm, said Joyce Kimutai, principal meteorologist at the Kenya Meteorological Department and the study’s lead author. “We’re likely to see the combined effect of low precipitation with temperatures causing really exceptional droughts in this part of the world.”

Climate groups have for years pointed to the calamity in East Africa as evidence of the immense harm inflicted on poor regions by global warming from emissions of heat-trapping gases. The new analysis could give more ammunition to those urging polluter nations to pay for the economic damage attributable to their emissions.

“This vital study shows that climate change is not just something our children need to worry about — it’s already here,” said Mohamed Adow, the director of Power Shift Africa, a think tank in Nairobi, Kenya. “People on the front lines of the climate crisis need, and deserve, financial help to recover and rebuild their lives.”

At United Nations climate talks last year in Egypt, diplomats from nearly 200 countries agreed to establish a fund to help vulnerable nations cope with climate disasters.

“Now we must ensure that the fund is made fit for purpose,” said Harjeet Singh, head of political strategy for Climate Action Network International. “This means rich nations and big polluters paying their share to bring the fund to life and to ensure that adequate money reaches those affected on the ground before it is too late.”

In Somalia in particular, the dryness has compounded the instability caused by years of armed conflict . There, the drought may have caused 43,000 excess deaths last year, according to estimates issued last month . Nearly half of these were among children younger than 5.

The new analysis was conducted by Dr. Kimutai and 18 other researchers as part of World Weather Attribution, a scientific collaboration that tries to untangle the influence of human-induced climate change on specific heat waves, floods and other episodes of extreme weather. The study has not yet been published in a peer-reviewed journal, though it relies on methods that are widely used and accepted by researchers.

Scientists know that global warming is increasing the average likelihood and severity of certain kinds of wild weather in many regions. But to understand how it has affected a particular one-off event, they need to dig deeper. It’s like smoking and cancer: The two are undeniably linked, but not all smokers develop cancer, and not all cancer patients were smokers. Each person is slightly different, and so is every weather event.

To determine the effects of global warming on individual weather episodes, climate researchers use computer simulations to compare the global climate as it really is — with billions of tons of carbon dioxide pumped into the atmosphere by humans over decades — and a hypothetical climate without any of those emissions.

The authors of the new study examined the drought in East Africa by looking at data on average rainfall over 24 months and during both of the region’s wet seasons, one between March and May and the other between October and December. Their mathematical models showed that climate change had made springtime rains as weak as the recent ones about twice as likely. The models also showed that climate change was having the opposite effect on the fall rainy seasons, making them wetter. And they indicated no effect on combined rainfall over two-year periods.

A different picture emerged, however, when the researchers looked at both rainfall and evapotranspiration, or how much water leaves the soil because of warm temperatures. Their models showed that global warming had made combinations of high evapotranspiration and poor rainfall as severe as the recent spell at least 100 times as likely as they were before the Industrial Revolution.

Scientists are getting a much better grasp on the atmospheric conditions that lead the rains to fail above the Horn of Africa, and on how global warming might be affecting them.

In recent decades, when the Pacific Ocean has experienced La Niña conditions, the trade winds strengthen and push warm water from the ocean’s eastern end toward its western one. Heat builds up in the western equatorial Pacific around Indonesia, causing moist air to rise from the sea surface and form thunderstorms. This in turn affects the circulation of air above the Indian Ocean, which draws more moisture from the western end of that ocean toward the eastern end, and leaves less to fall as rain above the Horn of Africa.

Climate change has been steadily heating up the surface of the western Pacific, which amplifies this sequence of events and increases the odds of poor rains in East Africa during La Niña periods.

Improved scientific understanding has helped forecasters predict the recent weak rainfall in East Africa months in advance, said Chris Funk, a climate scientist and director of the Climate Hazards Center at the University of California, Santa Barbara.

“That’s light-years ahead of where we were in 2010 or 2016,” he said, referring to years that preceded past droughts in the region.

Policymakers in East Africa need to help communities become better equipped to recover from future droughts — for instance, by encouraging the use of drought-tolerant crops and livestock, said Phoebe Wafubwa Shikuku, an adviser in Nairobi with the International Federation of Red Cross Red Crescent Societies. “Drought will continue to happen,” she said. “Now we have to look at, How do we address the various impacts ?”

Raymond Zhong is a climate reporter. He joined The Times in 2017 and was part of the team that won the 2021 Pulitzer Prize in public service for coverage of the coronavirus pandemic. More about Raymond Zhong

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Article  

• Volume 12, issue 1
• ESSD, 12, 753–769, 2020
• Peer review
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A pan-African high-resolution drought index dataset

Simon dadson, feyera hirpa, thomas lees, diego g. miralles, sergio m. vicente-serrano.

Droughts in Africa cause severe problems, such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security on Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectional perspective that includes crops, hydrological systems, rangeland and environmental systems. Such assessments are essential for policymakers, their advisors and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high-resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole of Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time Series (TS) datasets, Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project and root zone soil moisture modelled by GLEAM. Agreement found between coarse-resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with an average correlation coefficient ( R ) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information for the study of drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) via the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a).

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Peng, J., Dadson, S., Hirpa, F., Dyer, E., Lees, T., Miralles, D. G., Vicente-Serrano, S. M., and Funk, C.: A pan-African high-resolution drought index dataset, Earth Syst. Sci. Data, 12, 753–769, https://doi.org/10.5194/essd-12-753-2020, 2020.

Drought is a complex phenomenon that affects natural environments and socioeconomic systems in the world (von Hardenberg et al., 2001; Vicente-Serrano, 2007; Van Loon, 2015; Wilhite and Pulwarty, 2017). Impacts include crop failure, food shortage, famine, epidemics and even mass migration (Wilhite et al., 2007; Ding et al., 2011; Zhou et al., 2018). In recent years, severe events have occurred across the world, such as the 2003 central Europe drought (García-Herrera et al., 2010), the 2010 Russian drought (Spinoni et al., 2015), the 2011 Horn of Africa drought (Nicholson, 2014), the 2000 drought in southeastern Australia (van Dijk et al., 2013; Peng et al., 2019c), the 2013–2014 California drought (Swain et al., 2014), the 2014 North China drought (Wang and He, 2015) and the 2015–2017 southern Africa drought (Baudoin et al., 2017; Muller, 2018). Widespread negative effects of these droughts on natural and socioeconomic systems have been reported afterwards (Wegren, 2011; Arpe et al., 2012; Griffin and Anchukaitis, 2014; Mann and Gleick, 2015; Dadson et al., 2019; Marvel et al., 2019). Thus, there is a clear need to improve our knowledge about the spatial and temporal variability of drought, which provides a basis for quantifying drought impacts and the exposure of society, the economy, and the environment over different areas and timescales (Pozzi et al., 2013; AghaKouchak et al., 2015).

Generally, drought is defined as a temporal anomaly characterized by a deficit of water compared with long-term conditions (Mishra and Singh, 2010; Van Loon, 2015). Droughts can typically be grouped into five types: meteorological (precipitation deficiency), agricultural (soil moisture deficiency), hydrological (runoff and/or groundwater deficiency), socioeconomic (social response to water supply and demand) and environmental or ecologic (Keyantash and Dracup, 2002; AghaKouchak et al., 2015; Crausbay et al., 2017). These different drought categories involve different event characteristics in terms of timing, intensity, duration and spatial extent, making it very difficult to characterize droughts quantitatively (Panu and Sharma, 2002; Lloyd-Hughes, 2014; Vicente-Serrano, 2016). For this reason numerous drought indices have been proposed for precise applications, and reviews of the available indices have been provided by previous studies, such as Heim Jr. (2002), Keyantash and Dracup (2002), and Mukherjee et al. (2018). Van Loon (2015) noted that there is no best drought index for all types of droughts because every index is designed for a specific drought type, thus multiple indices are required to capture the multifaceted nature of drought. Nevertheless, the Standardized Precipitation Index (SPI) is recommended by the World Meteorological Organization (WMO) for drought monitoring, which is calculated based solely on long-term precipitation data over different time spans (McKee et al., 1993). The advantages of SPI are its relative simplicity and its ability to characterize different types of droughts given the different times of response of different usable water sources to precipitation deficits (Kumar et al., 2016; Zhao et al., 2017). However, information on precipitation is inadequate to characterize drought; in most definitions, drought conditions also depend on the demand of water vapour from the atmosphere. More recently, Vicente-Serrano et al. (2010) proposed an alternative drought index for SPI, which is called Standardized Precipitation Evapotranspiration Index (SPEI). Compared to SPI, it considers not only the precipitation supply but also the atmospheric evaporative demand (Beguería et al., 2010; Vicente-Serrano et al., 2012b). This makes the index more informative of the actual drought effects over various natural systems and socioeconomic sectors (Vicente-Serrano et al., 2012b; Bachmair et al., 2016, 2018; Kumar et al., 2016; S. Sun et al., 2016, 2018; Peña-Gallardo et al., 2018a, b).

For the calculation of SPEI, high-quality and long-term observations of precipitation and atmospheric evaporative demand are necessary. These observations may either come from ground-based station data or gridded data, such as satellite and reanalysis datasets. For example, the SPEIbase (Beguería et al., 2010) and the Global Precipitation Climatology Centre Drought Index (GPCC-DI) (Ziese et al., 2014) both provide SPEI datasets at a global scale. SPEIbase provides gridded SPEI with a 50 km spatial resolution and is calculated from Climatic Research Unit (CRU) Time Series (TS) datasets, which are produced based on measurements from more than 4000 ground-based weather stations across the world (Harris et al., 2014). The SPEI dataset provided by GPCC-DI has a spatial resolution of 1 ∘ and was generated from GPCC precipitation (Becker et al., 2013; Schneider et al., 2016) and National Oceanic and Atmospheric Administration (NOAA)'s Climate Prediction Center (CPC) temperature dataset (Fan and Van den Dool, 2008). Both of these datasets have been applied for various drought-related studies at global and regional scales (e.g. Chen et al., 2013; Vicente-Serrano et al., 2013, 2016; Isbell et al., 2015; Q. Sun et al., 2016; Deo et al., 2017). However, these global SPEI datasets' spatial resolution are too coarse to be applied at district or sub-basin scales (Vicente-Serrano et al., 2017). A sub-basin-scale quantification of drought conditions is particularly crucial in regions such as Africa, in which geospatial data and drought indices can be essential to manage existing drought-related risks (Vicente-Serrano et al., 2012a) and where in situ measurements are scarce (Trambauer et al., 2013; Masih et al., 2014; Anghileri et al., 2019). Over last century, Africa has been severely influenced by intense drought events, which has led to food shortages and famine in many countries (Anderson et al., 2012; Yuan et al., 2013; Sheffield et al., 2014; Awange et al., 2016; Funk et al., 2018; Nicholson, 2018; Gebremeskel et al., 2019). Therefore, the availability of a high-resolution drought index dataset may contribute to an improved characterization of drought risk and vulnerability and minimize its impact on water and food security by supporting policymakers, water managers and stakeholders. Conveniently, with the advancement of satellite technology, the estimation of precipitation and evaporation from remote sensing datasets is becoming more accurate (Fisher et al., 2017). In particular, the long-term Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) (Funk et al., 2015a) precipitation dataset and Global Land Evaporation Amsterdam Model (GLEAM) (Miralles et al., 2011) evaporation dataset provide high-quality data for near-real-time drought monitoring. Here, we use CHIRPS and GLEAM datasets to develop a pan-African high spatial resolution (5 km) SPEI dataset, which may be useful to inform drought relief management strategies for the continent. The dataset covers the period from 1981 to 2016 and it is comprehensively inter-compared with soil moisture, vegetation index and coarse-resolution SPEI datasets.

2.1.1  CHIRPS

CHIRPS is a recently developed high-resolution daily, pentadal, dekadal and monthly precipitation dataset (Funk et al., 2015a). It was produced by blending a set of satellite-only precipitation values (CHIRP) with additional monthly and pentadal station observations. CHIRP is based on infrared cold cloud duration (CCD) estimates calibrated with the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis version 7 (TMPA 3B42 v7) and the Climate Hazards group Precipitation climatology (CHPclim). CHP clim (Funk et al., 2015a, b) is based on station data from the Food and Agriculture Organization (FAO) and the Global Historical Climate Network (GHCN). Compared with other global precipitation datasets, such as Multi-Source Weighted-Ensemble Precipitation (MSWEP) (Beck et al., 2017) and Global Precipitation Climatology Project (GPCP) (Adler et al., 2003), CHIRPS has several advantages: a long period of record, high spatial resolution (5 km), low spatial biases and low temporal latency. It has been widely validated and applied in various applications (e.g. Shukla et al., 2014; Maidment et al., 2015; Duan et al., 2016; Zambrano-Bigiarini et al., 2017; Rivera et al., 2018). In particular, it was recently validated over East Africa and Mozambique and demonstrated good performance compared to other precipitation datasets (Toté et al., 2015; Dinku et al., 2018). Furthermore, CHIRPS was specifically designed for drought monitoring over regions with deep convective precipitation, scarce observation networks and complex topography (Funk et al., 2014). Several studies (e.g. Toté et al., 2015; Guo et al., 2017) have used CHIRPS for drought monitoring. Its high spatial resolution makes it particularly suitable for local-scale studies, such as sub-basin drought monitoring, especially in areas with complex topography. The detailed description of the dataset was provided by Funk et al. (2015a). In this study, daily CHIRPS precipitation from 1981 to 2016 was used.

2.1.2  GLEAM

GLEAM is designed to estimate land surface evaporation and root zone soil moisture from remote sensing observations and reanalysis data (Miralles et al., 2011; Martens et al., 2017). Specifically, the Priestley–Taylor equation is used to calculate potential evaporation within GLEAM based on near-surface temperature and net radiation, while the root zone soil moisture is obtained from a multilayer water balance driven by precipitation observations and updated with microwave soil moisture estimates (Martens et al., 2017). The actual evaporation is estimated by constraining potential evaporation with a multiplicative evaporative stress factor based on root zone soil moisture and vegetation optical depth (VOD) estimates. GLEAM version 3a (v3a) provides global daily potential and actual evaporation, evaporative stress conditions, and root zone soil moisture from 1980 to 2018 at spatial resolution of 0.25 ∘ (Martens et al., 2017) (see http://www.gleam.eu , last access: 29 March 2020). GLEAM datasets have already been comprehensively evaluated against FLUXNET observations and used for multiple hydro-meteorological applications (Greve et al., 2014; Miralles et al., 2014; Trambauer et al., 2014; Forzieri et al., 2017; Lian et al., 2018; Richard et al., 2018; Vicente-Serrano et al., 2018; Zhan et al., 2019). In particular, two recent studies detected global drought conditions based on GLEAM potential and actual evaporation data (Vicente-Serrano et al., 2018; Peng et al., 2019b). For this study, the GLEAM potential evaporation and root zone soil moisture were used.

Table 1 Categories of dry and wet conditions indicated by SPEI values.

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Figure 1 Spatial patterns of 3-month and 12-month SPEI at high spatial resolution (5 km) and coarse spatial resolution (50 km) in June 1995. The high spatial resolution SPEI (SPEI-HR) is based on CHIRPS precipitation and GLEAM potential evaporation, while the coarse spatial resolution SPEI (SPEI-CRU) is calculated from CRU TS datasets.

2.1.3  CRU-TS

The global gridded CRU-TS datasets provide most widely used climate variables, including precipitation, potential evaporation, diurnal temperature range, maximum and minimum temperature, mean temperature, frost day frequency, cloud cover, and vapour pressure (Harris et al., 2014). The CRU TS datasets were produced using angular distance weighting (ADW) interpolation based on monthly meteorological observations collected at ground-based stations across the world. The recently released CRU TS version 4.0.1 covers the period 1901–2016 and provides monthly data at 50 km spatial resolution. The CRU TS datasets have been widely used for various applications since their release (e.g. van der Schrier et al., 2013; Chadwick et al., 2015; Delworth et al., 2015; Jägermeyr et al., 2016). The SPEIbase dataset was generated from CRU TS datasets (Beguería et al., 2010). In this study, the CRU TS precipitation and potential evaporation from 1981 to 2016 was used.

2.1.4  GIMMS NDVI

The Normalized Difference Vegetation Index (NDVI) can serve as a proxy of vegetation status and has been widely applied to investigate the effects of drought on vegetation (e.g. Rojas et al., 2011; Vicente-Serrano et al., 2013, 2018; Törnros and Menzel, 2014). The Global Inventory Monitoring and Modeling System (GIMMS) NDVI was generated based on Advanced Very-High-Resolution Radiometer (AVHRR) observations and has accounted for various deleterious effects, such as orbital drift, calibration loss and volcanic eruptions (Beck et al., 2011; Pinzon and Tucker, 2014). For the current study, the latest version of GIMMS NDVI (3g.v1) was used, which covers the time period from 1981 to 2015 at biweekly temporal resolution and 8 km spatial resolution (Pinzon and Tucker, 2014).

Figure 2 Correlation ( p <0.05 ) between SPEI-HR and SPEI-CRU, with the number indicating different months.

Figure 3 Box plot of the correlation ( p <0.05 ) between SPEI-HR and SPEI-CRU for each month of the entire record. The results here are based on 6-month SPEI, and the red line in each box represents the median.

2.2  Methods

2.2.1  spei calculation.

The SPEI proposed by Vicente-Serrano et al. (2010) has been used for a wide variety of agricultural, ecological and hydro-meteorological applications (e.g. Schwalm et al., 2017; Naumann et al., 2018; Jiang et al., 2019). It accounts for the impacts of evaporation demand on droughts and inherits the simplicity and multi-temporal characteristics of SPI. The procedure for SPEI calculation includes the estimation of a climatic water balance (namely the difference between precipitation and potential evaporation), the aggregation of the climatic water balance over various timescales (e.g. 1, 3, 6, 12, 24 months or more) and a fitting to a certain parameter distribution. As suggested by Beguería et al. (2014) and Vicente-Serrano and Beguería (2016), the log-logistic probability distribution is best for SPEI calculation, from which the probability distribution of the difference between precipitation and potential evaporation can be calculated as suggested by Vicente-Serrano et al. (2010) and Beguería et al. (2014). The negative and positive SPEI values indicate dry and wet conditions, respectively. Table 1 summarizes the category of dry and wet conditions based on SPEI values. In this study, the CHIRPS and GLEAM datasets were used for SPEI calculation at high spatial resolution (5 km). For comparison, the SPEI at 50 km was also calculated based on CRU TS datasets for the same 1981–2016 period. It should be noted that the SPEI over sparsely vegetated and barren areas were masked out based on the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) (Friedl et al., 2010) because SPEI is not reliable over these areas (Beguería et al., 2010, 2014; Zhao et al., 2017).

2.2.2  Evaluation criteria

The SPEIbase dataset (Beguería et al., 2010) was calculated with CRU TS dataset, which has been evaluated and applied by many studies (e.g. Chen et al., 2013; Vicente-Serrano et al., 2013; Isbell et al., 2015; Q. Sun et al., 2016; Greenwood et al., 2017; Um et al., 2017). The newly generated SPEI at high spatial resolution based on CHIRPS and GLEAM (SPEI-HR) is compared temporally and spatially to the SPEI calculated from CRU TS datasets. In addition, the NDVI can also serve as an indicator for drought and vegetation health and to assess the performance of drought indices (Vicente-Serrano et al., 2013; Aadhar and Mishra, 2017). Furthermore, root zone soil moisture is an ideal hydrological variable for agricultural (soil moisture) drought monitoring. The recently released root zone soil moisture (RSM) from GLEAM v3 provides a great opportunity to evaluate whether soil moisture drought is well represented by SPEI. To facilitate direct comparison between SPEI, NDVI and RSM, both NDVI and RSM are standardized by subtracting their corresponding (1981–2016) mean and expressed the resulting anomalies as numbers of standard deviations. This standardization has been applied by many studies to evaluate drought indices (Anderson et al., 2011; Mu et al., 2013; Zhao et al., 2017). The correlation between SPEI and the standardized NDVI and RSM is quantified using Pearson's correlation coefficient ( R ). In addition, the high-resolution SPEI from GLEAM and CHIRPS is also resampled to the same grid size of SPEI from CRU TS in order to quantify their correlation and disentangle whether the added value of the former arises from its increased accuracy or higher resolution. In the following section, the high-resolution (5 km) SPEI is referred to as SPEI-HR, while the coarse 50 km resolution SPEI is referred to as coarse spatial resolution SPEI (SPEI-CRU).

3.1  Inter-comparison between high- and coarse-resolution SPEI

Figure 1 shows the spatial distribution of SPEI-HR and SPEI-CRU at different resolutions for an example month (June 1995). Figure 1a, b show the 3-month SPEI and 12-month SPEI, respectively. It can be seen that the high-resolution and coarse-resolution SPEI display quite similar dry and wet patterns over the whole of Africa for both temporal scales. However, as expected, the SPEI-HR shows much more spatial detail that, as a result, reflects mesoscale geographic and climatic features, which highlights the advantages of this new dataset. The differences in patterns between 3-month and 12-month SPEI indicate the different water deficits caused by different aggregation timescales, which can further separate agricultural, hydrological, environmental and other droughts. For example, in June 1995, southern Africa showed persistent dry conditions over a prolonged period, while western Africa only showed a short-term drought.

Figure 4 Spatial maps of correlation between SPEI and root zone soil moisture (RSM) for 6-month SPEI: (a)  SPEI-HR and (b)  SPEI-CRU. The time series of the African area mean RSM and SPEI are shown in  (c) , where R refers to the correlation coefficient. The correlations shown here are all significant at the 95 % confidence level.

Table 2 The correlation ( p <0.05 ) between area mean RSM and SPEI at different timescales.

In order to quantify how different SPEI-HR is from SPEI-CRU, the correlation between them is calculated for each grid cell over the whole study period. Figure 2 shows the correlations for timescales 1, 3, 6, 9, 12, 24, 36 and 48 months. In general, the SPEI-HR and SPEI-CRU agree well in terms of temporal variability with high positive correlations over most of Africa for every timescale. However, relatively low correlations appear in central Africa, and they become lower as the SPEI timescale increases. This region has very few station observations. It should be noted that the correlations shown here are statistically significant, with p  values of less than 0.05. In addition, the average correlation between 6-month SPEI-CRU and SPEI-HR for each month of the year is summarized in Fig. 3 using a box plot. In general, positive correlations with a median larger than 0.6 ( p <0.05 ) are found for every month. There are no substantial differences in correlations between different months. Figure A1 in Appendix shows additional box plots for SPEI at other timescales.

Figure 5 Spatial maps of the correlation between SPEI and NDVI for 6-month SPEI: (a)  SPEI-HR and (b)  SPEI-CRU. The time series of area mean NDVI and SPEI are shown in  (c) , where R refers to the correlation coefficient. The correlations shown here are all significant at the 95 % confidence level.

Table 3 The correlation ( p <0.05 ) between area mean NDVI and SPEI at different timescales.

3.2  Comparison against root zone soil moisture and NDVI

To gain more insights into their significance and applicability, the SPEI datasets are compared with NDVI and RSM. Figure 4 shows the results of the spatial and temporal comparison between 6-month SPEI and RSM as indicated by Törnros and Menzel (2014). Figure 4a, b display the correlation ( p <0.05 ) of SPEI-HR and SPEI-CRU against RSM during the whole time period, respectively. In general, both SPEI-HR and SPEI-CRU show strong correlations with RSM over the whole African continent. Compared to SPEI-CRU, the SPEI-HR shows higher correlations, particularly over central Africa. Since Sect. 3.1 shows that relatively large discrepancy between SPEI-CRU and SPEI-HR exists over central Africa, the results presented here suggest a potentially better performance of SPEI-HR compared with SPEI-CRU in this region.

The time series of SPEI and RSM, averaged over the entire study area, are shown in Fig. 4c, together with the corresponding correlations. It can be seen that both SPEI-HR and SPEI-CRU agree well with each other and with the RSM dynamics. Consistent with the results from the spatial correlation analysis, the SPEI-HR and SPEI-CRU show similar results when compared with RSM ( R =0.77 for SPEI-HR; R =0.72 for SPEI-CRU). Furthermore, the scatter plots between 6-month SPEI and RSM for the entire data record are shown in Appendix Fig. A2, where positive and significant correlations with RSM are found for both SPEI-HR ( R =0.51 ) and SPEI-CRU ( R =0.42 ). To explore the correlation between RSM and different timescales of SPEI, Table 2 summarizes the correlation value calculated in the same way as Fig. 4c. It can be seen that the highest correlations against RSM are found at 3- and 6-month timescales. It should be noted that satellite-data-driven estimates of root zone soil moisture are more suitable for evaluating SPEI compared to satellite-based top-layer soil moisture or reanalysis soil moisture data (Mo et al., 2011; Xu et al., 2018).

Figure 6 Evolution of the spatial patterns of 6-month SPEI-HR, NDVI and root zone soil moisture (RSM) during the 2011 East Africa drought  (a) and 2002 southern Africa drought  (b) .

Similar to the above analysis between SPEI and RSM, the comparison of results between SPEI and NDVI is shown in Fig. 5. First, Fig. 5a, b present the spatial distribution of the correlations ( p <0.05 ) between SPEI-HR and NDVI and between SPEI-CRU and NDVI, respectively. While correlations are overall lower than for RSM, it can be seen that both SPEI datasets are positively correlated with NDVI over most of the continent. It is also clear that SPEI-HR shows higher correlations. The time series comparison between the area mean SPEI and NDVI is shown in Fig. 5c. Both SPEI-HR and SPEI-CRU show agreement with NDVI, with R =0.54 and R =0.47 , respectively. In addition, the comparison between 6-month SPEI and NDVI for the entire data record was also calculated, with R =0.24 for SPEI-HR and R =0.21 for SPEI-CRU significant at 95 % confidence level (Fig. A3). While these correlations are admittedly low, overall results suggest that the SPEI has a positive relation with NDVI, which is also reported by previous studies (e.g. Törnros and Menzel, 2014; Vicente-Serrano et al., 2018). The lower correlations against NDVI than against RSM are likely due to complex physiological processes associated with vegetation and the fact that ecosystem state is driven by multiple variables other than water availability (Nemani et al., 2003). Furthermore, there are also clearly documented lags between precipitation and NDVI, with NDVI time series typically peaking 1 or even 2 months after the period of maximum rainfall (Funk and Brown, 2006). Finally, Table 3 summarizes the correlation between SPEI and NDVI at different timescales. Compared with the results presented in Table 2 for RSM, the correlation with NDVI shown in Table 3 is also generally lower, and the highest correlations appear between 9- and 24-month SPEI ( R >0.5 ).

Altogether, the comparisons between SPEI and RSM and between SPEI and NDVI indirectly indicate the validity of the generated SPEI datasets. Therefore, the generated high-resolution SPEI-HR from satellite products has the potential to improve upon the state of the art of drought assessment over Africa.

3.3  Patterns of SPEI, RSM and NDVI during specific drought events

Most of Africa has suffered severe droughts in past decades (Naumann et al., 2014; Blamey et al., 2018). Among them, the 2011 East Africa drought (Anderson et al., 2012; AghaKouchak, 2015) and 2002 southern Africa drought (Masih et al., 2014) were extremely severe and had devastating effects on the natural and socioeconomic environment. Taking these two events as case studies, the spatial patterns of the newly developed high-resolution 6-month SPEI-HR are analysed, together with the variability in NDVI and RSM. Figure 6a, b show the evolution of 6-month SPEI, NDVI and RSM during the 2011 East Africa and the 2002 southern Africa drought, respectively. The 6-month periods end in the named month, with the 6-month June 2011 SPEI values based on data for January to June. In general, these three variables reflect the progressive dry-out during the events. For example, strong, severe drought is revealed by the SPEI with values less than −1.5 , coinciding with a decline in NDVI and RSM from June to September 2011 over East Africa; the drought was offset in October. Similarly, dry and wet conditions variations during the 2002 southern Africa drought were also captured by the three variables. Despite differences over space and time, results here demonstrate that the generated SPEI-HR captures the main drought conditions that are reflected by negative anomalies in NDVI and RSM and can thus be used to study local drought-related processes and societal impacts in Africa.

The high-resolution SPEI dataset is publicly available from the Centre for Environmental Data Analysis (CEDA) from the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a). It covers the whole of Africa at a monthly temporal resolution and 5 km spatial resolution from 1981 to 2016 and is provided with geographic latitude–longitude projection and NetCDF format.

The study presents a newly generated high-resolution SPEI dataset (SPEI-HR) over Africa. The dataset is produced from satellite-based CHIRPS precipitation and GLEAM potential evaporation and covers the entire African continent over the time period from 1981 to 2016 with a spatial resolution of 5 km. The accumulated SPEI, ranging from 1 to 48 months, is provided to facilitate applications from meteorological to hydrological droughts. The SPEI-HR was compared with widely used coarse-resolution SPEI data (SPEI-CRU), GIMMS NDVI and GLEAM root zone soil moisture to investigate its capability for drought detection. In general, the SPEI-HR has good correlation with SPEI-CRU temporally and spatially. They both agree well with NDVI and root zone soil moisture, although SPEI-HR displays higher correlations overall. These results indicate the validity and advantage of the newly developed high-resolution SPEI-HR dataset, and its unprecedentedly high spatial resolution offers important advantages for drought monitoring and assessment at district and river basin level in Africa.

Figure A1 Box plots of the correlation ( p <0.05 ) between SPEI-HR and SPEI-CRU for each month and the entire monthly record.

Figure A2 Scatter plots between 6-month SPEI and RSM for the entire data record. R is correlation coefficient with p <0.05 , and the colours denote the occurrence frequency of values.

Figure A3 Scatter plots between 6-month SPEI and NDVI for the entire data record. R is correlation coefficient with p <0.05 , and the colours denote the occurrence frequency of values.

JP developed the processing algorithm, generated the dataset and drafted the manuscript. DGM and CF produced the GLEAM and CHIRPS data as input. SD, FH, ED and TL supported the generation of the dataset and the analysis of the results. All authors contributed to the discussion, review and revision of this paper.

The authors declare that they have no conflict of interest.

This work is supported by the UK Space Agency's International Partnership Programme (417000001429). Diego G. Miralles acknowledges funding from the European Research Council (ERC) under grant agreement no. 715254 (DRY–2–DRY). Simon Dadson is also funded by the Natural Environment Research Council (NE/M020339/1). Chris Funk is supported by the U.S. Geological Survey's Drivers of Drought program and NASA Harvest Program grant Z60592017.

This research has been supported by the UK Space Agency's International Partnership Programme (grant no. 417000001429).

This paper was edited by Alexander Gelfan and reviewed by two anonymous referees.

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• Introduction
• Data and methodology
• Results and discussion
• Data availability
• Conclusions
• Author contributions
• Competing interests
• Acknowledgements
• Financial support
• Review statement

CASE STUDY: drought resistant farming in Africa

Brad lancaster.

An endless stream of humour, poetic analogies and stories poured out of him. The best story of all was his own.

During an extended trip through southern Africa in the summer of 1995, I had the privilege of meeting a true ecological visionary. His name is Mr Zephaniah Phiri Maseko, but to the Permaculture Trust of Botswana (who directed me to him), as well as to hundreds of people throughout the region, he is known more generally as ‘the man who farms water’.

As a longtime student of sustainability and rainwater harvesting, I’ve found an abundance of simple, inspiring, and highly effective strategies practised in areas having far fewer available resources than the United States. On this trip I’d been through the arid and temperate regions of South Africa, Botswana, and Zimbabwe, with the goal of observing at first hand, proven strategies for sustainable living that I might be able to bring home and adapt to the similar climates of the southwest USA.

Gazing out of the window of a colourful old bus roaring through the countryside of southern Zimbabwe, I was struck by both the beauty of the land and its similarities to my home: rolling hills of yellow grass on red earth, broken up by small thickets of twisting, umbrella-like trees. Almost nine hours later, we crested a pass of low-lying semi-desert vegetation; below us spread a vast, dry prairie veldt capped with barren outcroppings of granite. Trees were sparse. A brilliant expanse of blue sky stretched overhead, reminiscent of the sky above the open grasslands of southeastern Arizona. The bus crept slowly downhill and stopped in Zvishavane, a small rural town in a province of the same name.

The local director of CARE International escorted me to a row of single-storey houses. One of these was the simple office of the Zvishavane Water Resources Project, and there on the porch sat the water farmer himself, reading the Bible. As my ride came to a stop he sprang up, beaming. Here at last was Mr Zephaniah Phiri Maseko. When he learned how far I had travelled to meet him, he burst into wonderful laughter. He explained that lately, visitors from all over the globe seemed to be dropping in about once a week. Then he jumped in his jeep and we drove off together over worn, eroded dirt roads toward his farm. An endless stream of humour, poetic analogies and stories poured out of him. The best story of all was his own.

The Garden of Eden, Mark II

In 1964, Phiri was fired from his job on the railway for being politically active against the white-minority-led Rhodesian government. The government told him that he would never work again. With a family of eight to support, Phiri turned to the only two things he had – an overgrazed and eroding 7.4-acre (three-hectare) family landholding, and the Bible.

He put the Scriptures to use as a kind of gardening manual. Reading Genesis, Phiri was struck by the realisation that everything Adam and Eve needed was provided in the Garden of Eden. ‘So,’ he thought, ‘I must create my own Garden of Eden.’ Gifted also with a firm grasp of geography, however, he realised that Adam and Eve had had the benefit of the Tigris and Euphrates Rivers in their region, while he didn’t have even an ephemeral creek. ‘So,’ he thought, ‘I must also create my own rivers.’

The family farm is located on the north-northeast-facing slope of a hill providing good winter sun to the site (important in the southern hemisphere). The top of the hill is a large exposed granite dome from which stormwater runoff once freely and erosively flowed. The average annual rainfall in the region is just over 22 inches (570mm). However, as Phiri points out, this average is based on extremes. Many years are drought years, when the land is lucky to receive 12 inches (304mm) of rain. When Phiri began farming, it was very difficult to grow crops successfully, let alone make a profit. There were frequent droughts and he had no money for deep wells, pumps, fuel and other equipment needed for groundwater irrigation.

Along with everyone else in the area, Phiri was dependent on the rains for water. Storms always brought him outside to observe how water flowed across his land. He noticed that moisture lingered longer in small depressions and in the upslope of rocks and plants than it did in areas where sheet flow went unchecked. He was struck by a realisation: he could mimic and enhance areas of his land where this was occurring.

Thus began Phiri’s self-education and work in rainwater harvesting, or ‘water farming’. Thirty years later, this humble, hard-working African farmer has managed to create a sustainable system that now provides all the water needs of his land and farm – which has thrived as a result – and his household, from rainfall alone.

Check dams – and ‘immigration centre’

‘You start catchment upstream, before the old deep gullies form downstream,’ said Phiri. Beginning at the top of the watershed, he built unmortared stone walls at random intervals on contour (along lines of equal elevation). These ‘check dam walls’ slow or ‘check’ the flow of storm runoff and disperse the water as it moves through winding paths between the stones. Runoff is then more easily managed because it never gets a chance to build up to more destructive volumes and velocities. Controlled runoff from the granite dome is then directed to unlined reservoirs just below.

The larger of the two reservoirs is what Phiri calls (with a characteristic flair for metaphor) his ‘immigration centre’. ‘It is here that I welcome the water to my farm and then direct it to where it will live in the soil,’ he told me. The water is directed into the soil as quickly as possible. The reservoirs are located at the highest point in the landscape where soil begins to cover the granite bedrock.

Above the reservoirs, the slope is steep, with very little soil. At and below the reservoir, the slope is gentle and soil has accumulated. ‘The soil is like a tin,’ Phiri explains. ‘The tin should hold all water. Gullies and erosion are like holes in the tin that allow water and organic matter to escape. These must be plugged.’

Phiri’s ‘immigration centre’ is also a water gauge. He now knows from his long experience that if it fills three times in a season, enough rain will have infiltrated the soil of his entire farm to support the bulk of his vegetation for two years.

The reservoirs occasionally fill with sand carried in the runoff water. This is used for mixing concrete, or for reinforcing the mass of the reservoir wall. Gravity brings this resource to Phiri free of charge.

Overflow from the smaller reservoir is directed, via a short pipe, to an aboveground ferro-cement (steel-reinforced concrete) cistern that feeds the family’s courtyard garden in dry spells. The family has another cistern, shaded and cooled by a lush, food-producing passion vine. This cistern collects water from the roof of the house for potable use inside.

Aside from these two cisterns, all the water-harvesting structures on the farm enable water to infiltrate directly into the soil. Nothing is wasted. Even all the grey water (used wash water) from an outdoor washbasin is drained to a covered, unmortared, stone-lined, underground cistern where the water is percolated into the soil and made available to the roots of surrounding plants.

Across the farm’s entire watershed, from top to bottom, numerous water-harvesting structures act as nets that collect the flow of surface runoff and quickly infiltrate the water into the soil before it can evaporate. These include check dams, vegetation planted on contour, terraces, berm ’n’ basins (dug out basins and earthen or vegetated berms laid out on contour), and infiltration basins (basins without berms). All these handmade structures catch and put to use water that was once lost to a government-built drainage system.

Many years before, the government of Zimbabwe had built large drainage swales throughout the region. Unlike water-harvesting swales or berm ’n’ basins, these ditches were not placed across the slopes on contour (to retain water), but instead were built so they would drain water off the land. Vast amounts of unhindered monsoon runoff were caught by the drainage swale, carried away to a central drainage, and shot out to the distant floodplain. The erosion problem was addressed, but drought intensified because the area was being robbed of its sole source of water.

From conception to fruition

Phiri turned things around by digging a series of large ‘fruition pits’ (basins about 12 feet long, by three to six feet wide, by four to six feet deep) in the bottoms of all the government drainage swales on his land. Now, when it rains, the pits fill with water and the overflow successively fills one pit after another across his property. Long after the rain stops, water remains in the fruition pits, percolating into the soil.

The fruit of Phiri’s fruition pits takes the form of thatch grasses, fruit trees and timber trees, which are planted in and around the pits. This vegetation provides building materials, cash crops, food, erosion control, shade, and windbreaks. All are watered strictly by rain and the rising groundwater table underground. Growing steadily stronger

Phiri explained that he dug fruition pits to ‘plant’ the water so it could germinate elsewhere. ‘I have taught the trees my system,’ he told me. ‘They understand my language. I put them here and tell them, “Look, the water is there. Now, go and get it”.’ A basin or berm for holding water may be constructed around or beside the trees, but Phiri always places such earthworks at some distance from them, so their roots are encouraged to reach out and grow strong as they seek water.

A truly diverse mix of open-pollinated crops – such as basketry reeds, squash, corn, peppers, eggplant, tomatoes, lettuce, spinach, peas, garlic, onion, beans, passion fruit, mango, guava, and paw paws; along with such indigenous crops and trees as matobve, muchakata, munyii, and mutamba – are planted between the swales and contour berms. This diversity gives his family food security; if some crops fail due to drought, disease or pests, others will survive.

Rather than using hybrid and genetically modified (GMO) seed, Phiri uses open-pollinated varieties to create superior seed stock as he collects, selects and plants seed grown in his own garden. By propagating seed from plants that have prospered off the sporadic rainfall and unique growing conditions of his site, each season his seed becomes better suited to his land and climate. This is another form of water conservation – Phiri is helping his seeds to adapt to living off less water, instead of adapting his farm management to import more water.

Living fertiliser factories pepper the farm, in the form of nitrogen-fixing plants. One example, the edible, leguminous pigeon pea, is also used for animal fodder and mulch. Phiri has found that soils amended with local organic matter and nitrogen-fixing plants infiltrate and hold water much better than those amended with synthetic fertilisers. As he says, ‘You apply fertiliser one year but not the next, and the plants die. Apply manure once and plant nitrogen-fixing plants, and the plants continue to do well year after year. Synthetically-fertilised soil is bitter.’

The abundant food and fruit Phiri produces is anything but bitter. He’s been generous with his abundance, giving away a diverse array of trees to anyone who wants them. Unfortunately, as Phiri points out, the majority of the trees he gives away die when people don’t implement rainwater-harvesting techniques before planting. ‘The land must harvest water to give to the trees, so before you plant trees you must plant water.’

The soil is Phiri’s catchment tank, and it is vast. In times of drought, his neighbours’ wells go dry, even those that are deeper than his. Yet, Phiri says, ‘My wells always have water.’ This is due not only to the particular hydrologic/geologic conditions of his site, but also because he is putting more water into the soil than he takes out.

Except for one well, which is lined and has a hand pump for household water use, all are open and lined with unmortared stone. ‘These wells,’ explains Phiri, ‘are those of an unselfish man. The water comes and goes as it pleases, for you see, in my land it is everywhere.’ During severe drought, Phiri uses a donkey-driven pump to draw from these wells to water annual crops in nearby fields.

A lush wetland lies below the wells at the lowest point of Phiri’s property. Here, three rich aquaculture reservoirs are surrounded by a vibrant soil-stabilising grove of bananas, sugar cane, reeds and grasses. The fish are harvested for food and their manure enriches the water used to irrigate the vegetation. The taller vegetation creates a windbreak around the ponds, reducing water loss to evaporation. The dense, lower-growing grasses filter incoming runoff water.

Rhyming with nature

For years, Phiri was an object of scorn in Zvishavane, standing in opposition to international aid and government programmes that pushed groundwater extraction and export crops.

Phiri’s response – aside from proving his critics wrong with the success of his farm – was to create the Zvishavane Water Resources Project, a non-governmental organisation. The organisation is having a dramatic effect. He influenced CARE International in his region to shift much of its work from giving away imported food to helping people implement his methods, and growing their own food.

When I asked Phiri about the three decades it took him to get his land and his vision to the place it is today, he answered, ‘It’s a slow process, but that’s life. Slowly implement these projects, and as you begin to rhyme with nature, soon other lives will start to rhyme with yours.’

We walked back up toward the house – and stopped midway. Phiri’s eyes were sparkling as he pointed across the fence. His neighbour was in the government’s diversion swale, digging fruition pits on the adjoining property. ‘Look,’ cried my guide, ‘now he is starting to rhyme!’

As an educator in the field of sustainable living, Phiri is an ongoing inspiration. His work and his perspective enabled me to understand what we can accomplish if we choose to live as stewards of the land by truly walking the talk. Phiri shows how water scarcity can be turned to water abundance – by planting the rain both in the soil and in the minds of the people.

‘The land must harvest water to give to the trees,’ says Phiri, ‘so before you plant trees you must plant water’

Brad Lancaster is the author of Rainwater Harvesting for Drylands, available from www.HarvestingRainwater.com

If you would like to support the work of this grassroots project, write to: Mr Zephaniah Phiri Maseko, ZWRP, P.O. Box 118, Zvishavane, Zimbabwe.

This article first appeared in the Ecologist February 2007

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Dimensions of drought: South African case studies

• Published: May 1993
• Volume 30 , pages 93–98, ( 1993 )

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• Drummond J. H. 2  

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The recent drought in southern Africa has underscored the need for detailed analysis of the phenomenon. While geographers have researched the causes and impacts of drought in many African contexts, South Africa and in particular its Bantustans have not received sufficient similar attention. This paper outlines firstly the dimensions of drought in South Africa, including the biophysical and socio-economic factors. Issues such as land-use management, drought planning and relief are interrogated in the South African context. The final section of the paper highlights these debates with specific reference to case studies of past and present drought initiatives in South Africa.

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Case Studies for the RFS Catalytic Grant Projects: Malawi

March 21, 2024.

Under the Resilient Food System (RFS) Programme , UNDP and AGRA co-designed three catalytic grant projects to pilot innovative approaches and model projects to showcase and develop practical methodologies of promoting Green Value Chain Development in East, Southern and West Africa. 

In Malawi, the catalytic grant project “ Sustainable Agriculture Production and Marketing for Rural Transformation (SAPMaRT) ” aimed to showcase a market-led and greening approach in the groundnut value chain food systems transformation pathway through catalysing system change at various levels of the value chain. The green production technologies promoted on this project included planting the groundnuts in double-rows as well as the intercropping of groundnuts with other cereals such as beans and maize, the use of drought and diseases tolerant seeds, the use of inoculants such as nitrofix - an affordable technology that is context relevant to smallholder farmers and is less expensive than conventional fertilizers, and Aflasafe for suppressing the level of aflatoxins in groundnuts. Ensuring consistent extension services facilitated the uptake/adoption of the promoted technologies. Smallholder groundnut producers were also trained to employ postharvest loss-reducing management practices, including the use of Tandala and Mandela cocks that are known to reduce the occurrence of aflatoxins.  

This case study documents the process of the implementation of the catalytic grant project. It puts together key lessons, success and/or failure factors, and outlines the project results as part of the process of documenting and disseminating information that can be used by multiple stakeholders including policy and decision-makers, project developers, funding agencies, and the private sector for widescale application of greening principles in food systems particularly in response to the challenges and impacts of climate change and environmental degradation. 

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Global climate change is not a future problem. Changes to Earth’s climate driven by increased human emissions of heat-trapping greenhouse gases are already having widespread effects on the environment: glaciers and ice sheets are shrinking, river and lake ice is breaking up earlier, plant and animal geographic ranges are shifting, and plants and trees are blooming sooner.

Effects that scientists had long predicted would result from global climate change are now occurring, such as sea ice loss, accelerated sea level rise, and longer, more intense heat waves.

"The magnitude and rate of climate change and associated risks depend strongly on near-term mitigation and adaptation actions, and projected adverse impacts and related losses and damages escalate with every increment of global warming." - Intergovernmental Panel on Climate Change

Some changes (such as droughts, wildfires, and extreme rainfall) are happening faster than scientists previously assessed. In fact, according to the Intergovernmental Panel on Climate Change (IPCC) — the United Nations body established to assess the science related to climate change — modern humans have never before seen the observed changes in our global climate, and some of these changes are irreversible over the next hundreds to thousands of years.

Scientists have high confidence that global temperatures will continue to rise for many decades, mainly due to greenhouse gases produced by human activities.

The IPCC’s Sixth Assessment report, published in 2021, found that human emissions of heat-trapping gases have already warmed the climate by nearly 2 degrees Fahrenheit (1.1 degrees Celsius) since 1850-1900. 1 The global average temperature is expected to reach or exceed 1.5 degrees C (about 3 degrees F) within the next few decades. These changes will affect all regions of Earth.

The severity of effects caused by climate change will depend on the path of future human activities. More greenhouse gas emissions will lead to more climate extremes and widespread damaging effects across our planet. However, those future effects depend on the total amount of carbon dioxide we emit. So, if we can reduce emissions, we may avoid some of the worst effects.

"The scientific evidence is unequivocal: climate change is a threat to human wellbeing and the health of the planet. Any further delay in concerted global action will miss the brief, rapidly closing window to secure a liveable future." 2 - Intergovernmental Panel on Climate Change

Future effects of global climate change in the united states:.

Here are some of the expected effects of global climate change on the United States, according to the Third and Fourth National Climate Assessment Reports.

U.S. Sea Level Likely to Rise 1 to 6.6 Feet by 2100

Global sea level has risen about 8 inches (0.2 meters) since reliable record-keeping began in 1880. By 2100, scientists project that it will rise at least another foot (0.3 meters), but possibly as high as 6.6 feet (2 meters) in a high-emissions scenario. Sea level is rising because of added water from melting land ice and the expansion of seawater as it warms.

Climate Changes Will Continue Through This Century and Beyond

Global climate is projected to continue warming over this century and beyond.

Hurricanes Will Become Stronger and More Intense

Scientists project that hurricane-associated storm intensity and rainfall rates will increase as the climate continues to warm.

More Droughts and Heat Waves

Droughts in the Southwest and heat waves (periods of abnormally hot weather lasting days to weeks) are projected to become more intense, and cold waves less intense and less frequent.

Longer Wildfire Season

Warming temperatures have extended and intensified wildfire season in the West, where long-term drought in the region has heightened the risk of fires. Scientists estimate that human-caused climate change has already doubled the area of forest burned in recent decades. By around 2050, the amount of land consumed by wildfires in Western states is projected to further increase by two to six times. Even in traditionally rainy regions like the Southeast, wildfires are projected to increase by about 30%.

Changes in Precipitation Patterns

Climate change is having an uneven effect on precipitation (rain and snow) in the United States, with some locations experiencing increased precipitation and flooding, while others suffer from drought. On average, more winter and spring precipitation is projected for the northern United States, and less for the Southwest, over this century.

Frost-Free Season (and Growing Season) will Lengthen

The length of the frost-free season, and the corresponding growing season, has been increasing since the 1980s, with the largest increases occurring in the western United States. Across the United States, the growing season is projected to continue to lengthen, which will affect ecosystems and agriculture.

Global Temperatures Will Continue to Rise

Summer of 2023 was Earth's hottest summer on record, 0.41 degrees Fahrenheit (F) (0.23 degrees Celsius (C)) warmer than any other summer in NASA’s record and 2.1 degrees F (1.2 C) warmer than the average summer between 1951 and 1980.

Arctic Is Very Likely to Become Ice-Free

Sea ice cover in the Arctic Ocean is expected to continue decreasing, and the Arctic Ocean will very likely become essentially ice-free in late summer if current projections hold. This change is expected to occur before mid-century.

U.S. Regional Effects

Climate change is bringing different types of challenges to each region of the country. Some of the current and future impacts are summarized below. These findings are from the Third 3 and Fourth 4 National Climate Assessment Reports, released by the U.S. Global Change Research Program .

• Northeast. Heat waves, heavy downpours, and sea level rise pose increasing challenges to many aspects of life in the Northeast. Infrastructure, agriculture, fisheries, and ecosystems will be increasingly compromised. Farmers can explore new crop options, but these adaptations are not cost- or risk-free. Moreover, adaptive capacity , which varies throughout the region, could be overwhelmed by a changing climate. Many states and cities are beginning to incorporate climate change into their planning.
• Northwest. Changes in the timing of peak flows in rivers and streams are reducing water supplies and worsening competing demands for water. Sea level rise, erosion, flooding, risks to infrastructure, and increasing ocean acidity pose major threats. Increasing wildfire incidence and severity, heat waves, insect outbreaks, and tree diseases are causing widespread forest die-off.
• Southeast. Sea level rise poses widespread and continuing threats to the region’s economy and environment. Extreme heat will affect health, energy, agriculture, and more. Decreased water availability will have economic and environmental impacts.
• Midwest. Extreme heat, heavy downpours, and flooding will affect infrastructure, health, agriculture, forestry, transportation, air and water quality, and more. Climate change will also worsen a range of risks to the Great Lakes.
• Southwest. Climate change has caused increased heat, drought, and insect outbreaks. In turn, these changes have made wildfires more numerous and severe. The warming climate has also caused a decline in water supplies, reduced agricultural yields, and triggered heat-related health impacts in cities. In coastal areas, flooding and erosion are additional concerns.

1. IPCC 2021, Climate Change 2021: The Physical Science Basis , the Working Group I contribution to the Sixth Assessment Report, Cambridge University Press, Cambridge, UK.

2. IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

3. USGCRP 2014, Third Climate Assessment .

4. USGCRP 2017, Fourth Climate Assessment .

Related Resources

A Degree of Difference

So, the Earth's average temperature has increased about 2 degrees Fahrenheit during the 20th century. What's the big deal?

What’s the difference between climate change and global warming?

“Global warming” refers to the long-term warming of the planet. “Climate change” encompasses global warming, but refers to the broader range of changes that are happening to our planet, including rising sea levels; shrinking mountain glaciers; accelerating ice melt in Greenland, Antarctica and the Arctic; and shifts in flower/plant blooming times.

Is it too late to prevent climate change?

Humans have caused major climate changes to happen already, and we have set in motion more changes still. However, if we stopped emitting greenhouse gases today, the rise in global temperatures would begin to flatten within a few years. Temperatures would then plateau but remain well-elevated for many, many centuries.

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• Open access
• Published: 18 March 2024

Generalizability of machine learning in predicting antimicrobial resistance in E. coli : a multi-country case study in Africa

• Mike Nsubuga 1 , 3 , 5 , 6 ,
• Ronald Galiwango 1 , 3 ,
• Daudi Jjingo 2 , 3 &
• Gerald Mboowa 1 , 3 , 4  

BMC Genomics volume  25 , Article number:  287 ( 2024 ) Cite this article

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Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutilized in these settings, presents a promising avenue. This study leverages ML models trained on whole-genome sequencing data from England, where such data is more readily available, to predict AMR in E . coli , targeting key antibiotics such as ciprofloxacin, ampicillin, and cefotaxime. A crucial part of our work involved the validation of these models using an independent dataset from Africa, specifically from Uganda, Nigeria, and Tanzania, to ascertain their applicability and effectiveness in LMICs.

Model performance varied across antibiotics. The Support Vector Machine excelled in predicting ciprofloxacin resistance (87% accuracy, F1 Score: 0.57), Light Gradient Boosting Machine for cefotaxime (92% accuracy, F1 Score: 0.42), and Gradient Boosting for ampicillin (58% accuracy, F1 Score: 0.66). In validation with data from Africa, Logistic Regression showed high accuracy for ampicillin (94%, F1 Score: 0.97), while Random Forest and Light Gradient Boosting Machine were effective for ciprofloxacin (50% accuracy, F1 Score: 0.56) and cefotaxime (45% accuracy, F1 Score:0.54), respectively. Key mutations associated with AMR were identified for these antibiotics.

As the threat of AMR continues to rise, the successful application of these models, particularly on genomic datasets from LMICs, signals a promising avenue for improving AMR prediction to support large AMR surveillance programs. This work thus not only expands our current understanding of the genetic underpinnings of AMR but also provides a robust methodological framework that can guide future research and applications in the fight against AMR.

Peer Review reports

Antimicrobial resistance (AMR) is a pressing global health challenge that threatens human and animal well-being [ 1 ]. Recognized as a priority by the World Health Organization (WHO) and the United Nations General Assembly [ 2 ], AMR’s unchecked proliferation could lead to catastrophic consequences, with Africa alone projected to account for millions of annual deaths by 2050 [ 3 ]. In 2019, reports showed that AMR all-age death rates were highest in some low- and middle-income countries (LMICs), making AMR not only a major health problem globally but a particularly serious problem for some of the poorest countries in the world [ 4 ].

The WHO launched the Global Antimicrobial Resistance and Use Surveillance System (GLASS) to enhance AMR evidence base for priority pathogens including Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Staphylococcus aureus, Streptococcus pneumoniae, Salmonella spp and others. While capacities for antimicrobial susceptibility testing (AST) exist across Africa, they are unevenly distributed and often limited in scope, particularly in LMICS. The COVID-19 pandemic has however catalyzed the broader adoption of Next-Generation Sequencing (NGS) platforms in Africa, now increasingly available to support a range of disease surveillance programs, including AMR. This technological advance offers a valuable complement to traditional AST methods, although the distribution and accessibility of NGS capabilities remain variable across the continent.

In Uganda, available data indicates concerning levels of drug resistance among E . coli strains (45.62%) with substantial resistance to key antibiotics [ 5 ]. Similarly, in Tanzania and Nigeria, studies have highlighted the growing challenge of AMR, reflecting patterns of resistance that may differ from other regions, thereby necessitating localized surveillance and tailored predictive models [ 6 ]. These countries exemplify the diverse AMR landscape across Africa and underscore the need for enhanced detection methods and strengthening diagnostic programs [ 7 , 8 , 9 ].

Overall, the increasing availability of whole-genome sequence (WGS) data in dedicated databases, exemplified by tools like CARD and Resfinder, has facilitated the identification of antibiotic resistance determinants [ 10 , 11 ]. Existing approaches for detecting AMR from microbial whole-genome sequence data, such as rule-based models relying on identifying causal genes in databases, have high accuracy for some common pathogens but are limited in detecting resistance caused by unknown mechanisms in other major pathogenic strains. Machine learning techniques, including random forest, support vector machines, and neural networks have shown great promise in predicting antimicrobial resistance [ 12 ]. These methods excel in capturing complex patterns within large datasets and can directly learn valuable features from genomic sequence data without relying on assumptions about the underlying mechanisms of AMR. Previous studies using machine learning have demonstrated success in predicting AMR and pathogen invasiveness from genomic sequences [ 13 , 14 , 15 , 16 , 17 , 18 ]. Despite this potential, the application of machine learning for AMR prediction has not been widely explored in LMICs, often due to data scarcity and the underrepresentation of AMR genetic determinants within reference databases [ 19 ].

To bridge this gap, we adopted a cross-continental approach, training machine learning models on data from England and validating them on datasets from Uganda, Tanzania and Nigeria. This strategy aimed to evaluate the efficacy of machine learning in predicting AMR for E. coli and assess the models’ generalizability across diverse African settings and datasets. By leveraging microbial genomic data and advanced machine learning techniques, this study endeavored to enhance the accuracy and efficiency of AMR prediction, thus contributing significantly to the global battle against AMR. This comprehensive analysis provides crucial insights into the practical implementation and scalability of AMR prediction strategies, especially in LMICs where genomic data is limited and the burden of AMR is disproportionately high.

Study design

This was a cross-sectional study utilizing data collected in the past years to explore associations between predictors and outcomes.

Sample size

In this study, two datasets, referred to as the Africa data and the England data of E. coli strains were used.

Data description

The study focused on three antibiotics ciprofloxacin (CIP), ampicillin (AMP) & cefotaxime (CTX). Each of these represented an antibiotic from a different class of antibiotics (penicillins, cephalosporins, and fluoroquinolones). They are broad-spectrum antibiotics with activity against numerous Gram-positive and Gram-negative bacteria, including E. coli. These drugs were selected based on their increasing prevalence of resistance as reported in the GLASS report [ 5 ]. In addition, data on resistance to these drugs was available in the study datasets, making them an ideal choice for the study. The study utilized data from one of the largest complete E. coli datasets that were already available online from the National Center for Biotechnology Information, eliminating the need for additional data collection efforts. We categorised the data into two primary datasets:

England dataset

Comprising of 1,496 samples for ciprofloxacin; 1,428 for cefotaxime; and 1,396 for ampicillin. The dataset was collected from England and consisted of WGS of 1509 E. coli strains and corresponding phenotypic information [ 20 ]. This data was collected in England by the British Society for Antimicrobial Chemotherapy and from the Cambridge University Hospitals NHS Foundation Trust as part of a longitudinal survey of E. coli to contextualize the ST131 lineage within a broader E. coli population. This data was made publicly available by the Wellcome Trust Sanger Institute (Accession: PRJEB4681).

Africa dataset

Comprising of data from Uganda, Tanzania and Nigeria. The first Africa dataset consisted of samples collected from pastoralist communities of Western Uganda to study phylogenomic changes, virulence, and resistant genes. It contained WGS data for a total of 42 E. coli strains [ 21 ]. These were isolated from stool samples from both humans ( n  = 30) and cattle ( n  = 12) collected between January 2018– March 2019. WGS was carried out at facilities of Kenya Medical Research Institute -Wellcome Trust, Kilifi. The data is made publicly available by the author in a repository (DOI https://doi.org/10.17605/OSF.IO/KPHRD ). The second Africa dataset consisted of 73 samples collected from both Uganda ( n  = 40) and Tanzania ( n  = 33) in a study that was unravelling virulence determinants in extended-spectrum beta-lactamase-producing E . coli from East Africa using WGS [ 22 ]. The third dataset consisted 68 samples collected from Nigeria as part of a study looking at WGS data from E . coli isolates from South-West Nigeria hospitals [ 23 ] (Table  1 ).

The samples that had not been screened for AST were removed from the dataset.

Variant calling of whole-genome sequencing data

The raw WGS paired-end reads were first quality checked and filtered by fastp 0.23.4 using its default parameters: adapter detection and trimming, sliding window quality filtering with a threshold of Q20, end trimming for low quality bases and removing reads shorter than 15 bp post-trimming [ 24 ]. The filtered reads were aligned to the E. coli K-12 substr. MG1655 U00096.3 complete genome using Burrows-Wheeler Aligner-mem (0.7.17-r1188) algorithm with default seed length of 19, bandwidth of 100, and off-diagonal X-dropoff of 100 [ 25 ]. BCFtools 1.18 was used for calling variants with a minimum of depth coverage of 10x and allelic frequency of 0.9 [ 26 ]. SAMtools 1.18 was used to sort the aligned reads and BCFtools 1.18 was used to filter the raw variants applying default filtering thresholds, including a minimum read depth of 2, SNP quality of 20 [ 27 ]. The entire bioinformatics workflow was subsequently executed on the Open Science Grid High Throughput Computing infrastructure [ 28 , 29 ].

SNPs pre-processing and encoding

We employed a previously established methodology for constructing the SNP matrix from the VCF files. First, the reference alleles, variant alleles, and their positions from the VCF files were extracted and merged with the isolates based on the position of the reference alleles. A SNP matrix was built where the rows represented the samples, and the columns represented the variant alleles [ 15 ]. The SNPs were converted from characters to numbers through categorical encoding where the categories are converted to numbers. The SNPs were encoded for machine learning using label encoding, where the A, C, G, T in the SNP matrix were converted to 1,2,3,4 (Fig.  1 ). It is acknowledged that certain machine learning models could misconstrue these as ordinal values; however based on previous studies demonstrating minimal performance difference between label, one-hot and Frequency Chaos Game Representation encoding methodologies [ 15 ], label encoding was selected for its computational efficiency in handling large genomic datasets. The missing values encoded as N were converted to 0. The gene positions that had more than 90% as null were removed and the remaining were selected for machine learning. The antibiotic phenotypes were encoded as binary values: ‘S’ for susceptible was mapped to 0, and ‘R’ for resistant was mapped to 1.

Illustration of the preprocessing and encoding process of the SNPs. Created with Biorender.com

• Machine learning

We trained eight machine learning algorithms, each selected for its unique capabilities in predictive modeling. The training of these models was conducted individually for each antibiotic, focusing on one antibiotic at a time to ensure the specificity and accuracy of the predictions. Logistic Regression (LR) provided a baseline for binary classification, and Random Forest (RF) and Gradient Boosting (GB) were chosen for their effectiveness in handling high-dimensional data and intricate relationships. Support Vector Machines (SVM) were implemented with a sigmoid kernel, optimized through hyperparameter tuning to a C parameter of 9.795846277645586 and gamma set to ‘auto’. Feed-Forward Neural Networks (FFNNs), designed using Keras 2.12.0, consisted of an input layer with 64 neurons, a hidden layer with 32 neurons, and an output layer with one neuron, using binary cross-entropy loss and the Adam optimizer. The FFNN was trained for 20 epochs with a batch size of 32, with hyperparameter tuning improving its configuration. XGBoost (XGB) with xgboost 1.7.6, LightGBM (LGB) using lightgbm 4.1.0, and CatBoost using catboost 1.2.2 were implemented with default parameters, leveraging their efficiencies with large-scale data.

All models were implemented using Scikit-learn version 1.3.2, except for FFNNs which were implemented in Keras. Hyperparameter tuning was conducted for SVM and FFNNs using scikit-learn’s RandomizedSearchCV, which helped identify the most effective configurations for these models. The training was performed on both originally imbalanced and balanced datasets. For balancing, a simple random down-sampling approach was employed to reduce the majority class, enabling us to assess the impact of class distribution on model performance.

This comprehensive approach, involving diverse algorithms and hyperparameter tuning, allowed for an exhaustive evaluation of predictive models in the detection of AMR, under varied dataset conditions.

Statistical evaluation

The machine learning models were optimized using five times 5-fold stratified cross-validation. For the final evaluation of the data from Africa, the performance was analyzed on the raw public dataset and on a balanced set using a downsampling strategy. The models were evaluated using the receiver operating characteristics curve (ROC) and the area under the curve (AUC). Precision, recall, f1-score, and accuracy for all models were calculated. In order to determine the statistical significance of the differences in AUC scores between models, we employed Tukey’s Honestly Significant Difference (HSD) test [ 30 ]. This test is appropriate for comparing all possible pairs of groups in a family of models without increasing the risk of Type I errors that multiple comparisons may induce. The significance threshold was set at α = 0.05, indicating that differences with p-values less than this threshold were considered statistically significant. The pairwise comparisons were conducted using statsmodels 0.14.0.

Identification of genes

To identify the top 10 most important features for the models mentioned, the methods for calculating feature importance varied between models. For tree-based models like Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost, we utilized the feature_importances attribute, which quantifies the contribution of each feature to the model’s prediction. In Logistic Regression, feature importance was deduced from the absolute values of the coefficients. The SVM model employed the coefficients’ absolute values for linear kernels and SelectKBest with the chi2 method for non-linear kernels. For the Keras Neural Network model, we averaged the absolute values of the weights in the first layer, reflecting the relevance of each feature in the model. The corresponding gene annotations were extracted from the reference genome for the identified SNPs. By examining the functional roles of these genes, an investigation of their potential contribution to antibiotic resistance mechanisms in E. coli was done (Fig.  2 ).

Flowchart showing how genes were identified

Performance of machine learning methods in predicting AMR

We assessed the performance of eight machine learning algorithms, including LR, RF, SVM, GB, XGB, LGB, CatBoost, and FFNN, in predicting antibiotic resistance in E. coli. Multiple metrics, such as accuracy, precision, recall, F1 score, and the area under the receiver operating characteristics (ROC) curve, were used for evaluation (Table  2 ). The models were optimized using 5-fold stratified cross-validation and confidence intervals recorded (Supplementary Material 1 ). Tukey’s Honestly Significant Difference (HSD) test was employed for pairwise comparisons of AUC scores.

For CIP, we evaluated the models’ effectiveness considering the class imbalance issue. We applied a random down-sampling strategy but didn’t observe significant improvements. The FFNN emerged as the top performer with the highest mean AUC score (0.83), while SVM achieved the highest accuracy (0.87). HSD tests revealed significant performance differences between several pairs of models, specifically RF ( p  < 0.001) when compared to all the models.

For AMP, the SVM achieved the highest mean AUC score (0.72). GB had the highest F1 score and precision, and CB and SVM had the highest recall scores.

On the CTX, FFNN stood out with the highest mean AUC score (0.72), while SVM recorded the highest accuracy (0.92). The Random Forest model excelled in precision, and Logistic Regression had the highest F1 score (0.42) (Fig.  3 ).

Performance of different machine learning methods for predicting AMR on England microbial sequence data

Evaluation of the machine learning models on the Africa data

We assessed the generalizability of our machine learning models on an external dataset from Uganda, Nigeria and Tanzania, consisting of up to 170 samples with a severe class imbalance issue. Performance metrics for each model on this dataset (Table  3 ).

In the external validation with the African dataset, the class imbalance presented varied challenges across different antibiotics. For CIP, the Logistic Regression model exhibited an accuracy of 0.55 and precision of 0.59, but a recall of only 0.16. The RF model achieved an accuracy of 0.50 and an AUC-ROC score of 0.53. SVM displayed an accuracy of 0.50, while GB showed an accuracy of 0.52 and a recall of 0.32. XGB had an accuracy of 0.57, and both LGB and CatBoost had accuracies just above 0.55, with CB also attaining an AUC-ROC of 0.58. The FFNN model did not identify any true positives.

For AMP, LR achieved an accuracy of 0.94 and a near-perfect recall. RF had a precision of 0.93 but a lower accuracy of 0.38. SVM’s performance was close to that of LR, with high accuracy and recall but a slightly lower AUC-ROC score of 0.57. GB, XGB, LGB, and CatBoost demonstrated solid accuracy and precision, albeit with varying AUC-ROC scores. The FFNN model’s accuracy was at 0.05.

Regarding CTX, LR recorded an AUC-ROC of 0.39, RF exhibited high precision but low recall, and SVM had a precision of 1. GB had the highest accuracy among the models at 0.22 and the highest AUC-ROC score of 0.57. XGB and LGB showed higher accuracy and recall rates, with LGB achieving the highest recall of 0.38. The FFNN model again showed zero capacity for true positive identification (Fig.  4 ).

Performance of different machine learning methods for predicting AMR on Africa microbial sequence data

Marker genes associated with antibiotic resistance

A crucial part of machine learning in the genomic field is to interpret the model’s results. In our case, the analysis of feature importance and interactions provided insights into which genetic mutations are most influential in predicting antibiotic resistance. For each model, we identified the top 10 features (SNP positions) with the highest importance scores, which reflect their contribution to the accuracy of the model’s predictions.

For instance, in the Logistic Regression model on CIP, the mutation at position ‘3589009’ has the highest importance score, followed by ‘4040529’, ‘1473047’, and so on. These positions potentially have a substantial impact on antibiotic resistance, as mutations in these areas of the gene could probably cause the bacteria to become resistant to specific antibiotics. The exact biological mechanism for this can be complex, involving changes in the gene’s protein product that might render an antibiotic ineffective (Table  4 ).

The models used different ways to calculate these importance scores, which is why they differ between models. Still, positions that are consistently high across different models can be a strong indicator of their significance in conferring antibiotic resistance.

Gene annotation

The identification of genetic SNPs associated with antibiotic resistance can shed light on the underlying genetic mechanisms that contribute to drug resistance in E. coli (Table  5 ). By analyzing the top SNPs from each predictive model, key marker genes that potentially play a role in antibiotic resistance were identified. For CIP, SNPs were identified in the following genes: rlmL, yehB, rrfA, vciQ , and ygjK . For AMP, the implicated genes include rcsD, yjfI, tdcE, ugpB, ugpQ , and ggt . Lastly, for CTX, SNPs were found in ydbA, mltB, lomR, mppA, recD , and glyS . The identified SNPs in these genes underscore the complex and multifactorial nature of antibiotic resistance in E. coli . A variety of biological processes, such as membrane transport, rRNA methylation, DNA repair, and cell wall synthesis, are potentially collectively implicated in the development of resistance. Further experimental validation of these marker genes is warranted to confirm their role in antibiotic resistance.

This study embarked on an explorative journey to understand the generalizability of machine learning models in predicting AMR in E . coli , utilizing datasets from England and multiple African countries. While the models showed promise on the England dataset, the application to the highly imbalanced African dataset illuminated significant challenges. The validation of machine learning models on the African dataset, which had a higher incidence of resistant strains compared to the training data from England, highlighted the challenges and potential of such tools; discrepancies in class distribution impacted performance measures like recall and precision, yet the robustness and real-world applicability of these models were affirmed when they successfully predicted resistance across varied datasets.

In the England dataset, models like SVM (Accuracy: 0.87, AUC-ROC: 0.86) and Logistic Regression (AUC-ROC: 0.77) demonstrated effectiveness. However, the transition to the African dataset, characterized by significant class imbalance, presented a stark contrast. For example, the Random Forest model experienced a decline in accuracy from 0.75 for CIP in the England dataset to 0.50 in the African dataset.

The performance of the models on the African dataset, particularly in terms of recall, highlights potential overfitting to the England dataset and the need for more generalizable models. The disparity in class distribution between the datasets—where the England dataset had a higher proportion of susceptible strains and the African dataset had a higher proportion of resistant strains—presented both challenges and opportunities.

A notable observation in this study is the impressive performance of the models for predicting ampicillin (AMP) resistance in the African dataset, despite their moderate performance on the England dataset. For AMP, models demonstrated substantial accuracy and recall in the African dataset (e.g., Logistic Regression: Accuracy 0.94, Recall 0.99, F1 0.97), highlighting their effectiveness in identifying true resistance cases. This success may be attributed to the distinct resistance mechanisms of AMP, which were perhaps better captured in the training data, leading to more accurate predictions in the validation dataset, or the data representation of the AMP training dataset which might have contained patterns that were more representative of the resistance seen in the African dataset.

Moreover, the process of down-sampling the England dataset for training, while fostering a balanced environment, did not uniformly enhance model performance. While down-sampled models showed a slight improvement for AMP, indicating that down-sampling might enhance the model’s sensitivity to specific resistance patterns associated with AMP, this effect was not as pronounced for CIP and CTX.

The identification of SNPs associated with antibiotic resistance can illuminate the genetic mechanisms driving drug resistance in E. coli . By analyzing the top SNPs from each predictive model, we identified key marker genes potentially involved in antibiotic resistance. For CIP, SNPs were identified in the following genes: ugpC, rlmL, yciQ, ygjK, yehB, rrfA, ytfB , and yjjW . These genes encode for various bacterial functions. For instance, ugpC is part of the glycerol-3-phosphate (G3P) transport system implicated in phospholipid biosynthesis, and RlmL is an enzyme involved in the methylation of ribosomal RNA (rRNA). mdtC is a component of multidrug efflux pump systems that can contribute to antibiotic resistance by actively pumping out antibiotics from bacterial cells. It’s important to note that while machine learning can highlight these genes as candidates, experimental validation is essential to confirm their roles in antibiotic resistance.

Implications and applications

While our research concentrated primarily on three specific antibiotics, the methodology we’ve developed is versatile and readily adaptable for investigating other antibiotics and can be extended to resistance-associated SNPs in a variety of pathogens beyond just bacteria. This flexibility allows for a broader scope of study, opening the door for a comprehensive understanding of AMR mechanisms. In addition, the applicability of our approach extends beyond the realm of infectious diseases, holding promise for other branches of biomedical research, such as predicting resistance to cancer treatments by enabling precise targeted therapy.

Limitations

While this study has provided valuable insights into predicting genotypic resistance to ciprofloxacin, ampicillin, and cefotaxime in E. coli strains, it is important to acknowledge several limitations that should be considered when interpreting the results. First, it is important to acknowledge the inherent limitation of focusing exclusively on SNPs as the single specific genomic factor. Antimicrobial resistance is a complex phenomenon influenced by various genomic drivers including resistance genes, insertion sequences, plasmids and AMR gene cassettes which collectively contribute to the intricate landscape of resistance mechanisms. Our study, by concentrating on SNPs, represents a deliberate simplification to ensure depth and clarity in our ML analysis, driven by data quality and the need for clinically interpretable models. However, we recognise that the exclusive emphasis on SNPs may not capture the entirety of the multifaceted interplay within resistance determinants.

Furthermore, it is worth noting that the validation of the models on Africa data presented some challenges. The availability of whole-genome sequence data from Africa was limited, resulting in a relatively small dataset for model evaluation. Additionally, the African dataset exhibited high-class imbalance, where certain resistance classes were significantly underrepresented. This imbalance can introduce bias and affect the performance metrics of the models. Due to our study’s uniqueness, traditional benchmarking might not capture our nuanced challenges. Future studies should explore alternative methodologies for a comprehensive evaluation of predictive models in diverse contexts.

Moreover, it is important to highlight that the performance of the models in this study is specific to the context of the datasets used, which may not fully represent the diversity and complexity of AMR patterns observed in other regions or populations. Therefore, caution should be exercised when generalizing the findings to different settings. Despite these limitations, this study provides a valuable foundation for future research and highlights areas for improvement and expansion. Incorporating additional variables, addressing the class imbalance, and expanding the dataset to include a more diverse range of sequences would enhance the robustness and applicability of the models. Overall, while the findings of this study contribute to our understanding of genotypic resistance prediction, it is important to recognize these limitations and consider them in the broader context of AMR research.

In conclusion, our study highlights the complex interplay between data composition, model training approaches, and predictive accuracy in the context of AMR. The impressive performance of models for AMP in the African dataset despite their moderate performance in the England dataset underscores the potential of machine learning in AMR prediction, given appropriate training and validation strategies.

The findings from this study serve as a crucial reminder of the complexities involved in applying machine learning models to predict AMR across diverse settings. It emphasizes the importance of developing robust, adaptable, and generalizable machine learning tools, capable of handling varied data landscapes and resistance mechanisms. Future research should focus on integrating larger and more diverse datasets while exploring innovative methods to maintain a balance between dataset size and class distribution, thus advancing the development of machine learning tools in the global fight against antimicrobial resistance.

As the threat of antimicrobial resistance continues to rise, the successful application of these models - particularly on the African dataset, signals a promising avenue for improving AMR detection and treatment strategies. This work thus not only expands our current understanding of the genetic underpinnings of antibiotic resistance but also provides a robust methodological framework that can guide future research and applications in the fight against antimicrobial resistance.

Code Availability

The source code in data preparation and model training is provided on the GitHub page: https://github.com/KingMike100/mlamr .

Data availability

The data that supports the findings of this study is publicly available at EBI from https://www.ebi.ac.uk/ena/browser/view/PRJEB4681 and https://osf.io/kphrd/ .

Abbreviations

Antimicrobial Resistance

Antimicrobial Susceptibility Testing

Ciprofloxacin

Convolutional Neural Network

Comprehensive Resistance Prediction for Tuberculosis:an International Consortium

Deoxyribonucleic Acid

World health Organization Global Antimicrobial Resistance and Use Surveillance system

Logistic Regression

Machine Learning

National Centre for Biotechnology Information

Polymerase Chain Reaction (PCR)

Real-time Polymerase Chain Reaction

Research Ethics Committee

Random Forest

Ribonucleic acid

Support Vector Machine

Uganda National Council for Science and Technology

Whole Genome Sequencing

World Health Organization

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Acknowledgements

The first author MN was funded by the East African Network for Bioinformatics Training (EANBIT) under Fogarty International Center at the U.S. National Institutes of Health (NIH) under award number U2RTW010677 as a Masters scholar. The authors would also like to acknowledge the Open Science Grid (OSG) consortium which provided computational resources to carry out this study. The OSG is supported by the National Science Foundation award number 2030508 and 1836650. G.M. acknowledges the EDCTP2 career development grant which supports the Pathogen detection in HIV-infected children and adolescents with non-malarial febrile illnesses using the metagenomic next-generation sequencing (PHICAMS) approach in Uganda, a project which is part of the EDCTP2 programme from the European Union (Grant number TMA2020CDF-3159). He is also in part supported by the US National Institutes of Health, Fogarty International Center [Grant number: 5U2RTW012116-01]. The views and opinions of the author expressed herein do not necessarily state or reflect those of funders.

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MN: Data analysis, Data Interpretation, Writing-Original draft; GM: Conception, Writing-Final draft; DJ: Conception, Writing-Final draft; RG: Data Interpretation, Writing-Final draft. All authors read and approved the final manuscript.

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Nsubuga, M., Galiwango, R., Jjingo, D. et al. Generalizability of machine learning in predicting antimicrobial resistance in E. coli : a multi-country case study in Africa. BMC Genomics 25 , 287 (2024). https://doi.org/10.1186/s12864-024-10214-4

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Received : 28 September 2023

Accepted : 11 March 2024

Published : 18 March 2024

DOI : https://doi.org/10.1186/s12864-024-10214-4

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