a case study on drought

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Mongabay Series: Connected Environments , Flood and drought

Southern India’s 2016-2018 drought was the worst in 150 years

• A severe drought that hit southern India during 2016-2018 was the worst to hit the region over the past 150 years and was associated with a deficit in the northeastern monsoon.
• Drought conditions linked to northeastern monsoonal rainfall across southern India are associated with cool phases of the tropical Indo-Pacific Ocean. Cool phases of the Pacific Ocean are known as La Niña.
• If severe droughts in southern India are linked to La Niña, they could potentially be predicted, said an independent expert.

Southern India was hit by severe drought from 2016 to 2018 arising from low rainfall during the northeast monsoon, which occurs during the winter. So severe was the impact that a water crisis erupted in Chennai, India’s sixth-largest city of 11 million inhabitants, as four of the city’s major reservoirs went bone-dry and groundwater levels plummeted. In the summer of 2019, a “Day Zero” was declared and residents scrambled to obtain water from tankers. 

Now, after examining rainfall data over the past 150 years, researchers in India and the US conclude that the 2016-2018 northeast monsoon drought was unprecedented with more than 40 percent deficit in northeast monsoonal rainfall during the three years. 

The recent drought was worse than the Great Drought of 1874-1876 that led to crop failure, which in turn resulted in the Great Madras Famine of 1876 to 1878 that claimed millions of lives. The team demonstrates that cool phases in the equatorial Indian and Pacific Oceans are associated with the rainfall deficit. 

“The consecutive failure of the northeast monsoon can result in a water crisis in Southern India,” lead author Vimal Mishra, associate professor at Indian Institute of Technology, Gandhinagar, told Mongabay-India, adding that “it has considerable implications to agricultural productivity.” 

While India receives most of its annual rainfall during the Indian summer monsoon (June to September), southern India receives about 40 percent of its rainfall from October to December in what is known as the northeastern monsoon (NEM) or the winter monsoon. It is crucial for drinking water and agriculture contributing to the livelihood of millions. 

The southern Indian states of Andhra Pradesh, Karnataka and Tamil Nadu continuously declared drought from 2016 to 2018 linked to low northeast monsoonal rainfall. Over 60 percent of the rural population in southern India is engaged in agriculture and relies on rainfall from the winter monsoon. 

Failure of the northeast monsoon 

How severe was the recent drought compared to those Southern India has experienced in the past? What are the causes of the deficit in the northeast monsoon? Mishra’s team sought to answer these questions. 

To investigate the long-term history of NEM droughts in the region, the team used rainfall observations from the India Meteorology Department from 1870 to 2018. Data on total water storage was obtained from NASA’s Gravity Recovery and Climate Experiment (GRACE) satellites for April 2002 to June 2017 while the GRACE Follow-On (GRACE-FO) mission provided data for 2018 onwards.

Over the past 150 years, there were five main periods of drought with more than 29 percent deficit in rainfall (1876, 2016, 1938, 1988, and 1974 in order of severity). Looking at single year rainfalls, 1876 was the driest year with a precipitation deficit of 69 percent followed by 2016 with a deficit of 63 percent. But when considering cumulative rainfall over three years, 2016 to 2018 was the worst NEM drought with a precipitation deficit of 45 percent while the 1874 to 1876 drought, or the Great Drought as it is known, was the second-worst with a deficit of 37 percent.

The GRACE satellite indicated that total water loss in Southern India in December 2016 was 79 cubic kilometres (km3) while the GRACE-FO data showed that the loss was 46.5 km3 in June 2017 and 41.7 km3 in June 2019. Loss in total water storage likely resulted in significant depletion of groundwater in the region, say the authors.

What factors were associated with deficits in the northeast monsoon?

The team examined sea surface temperatures (SST), sea-level pressure and wind fields during the winter monsoon to understand how circulation patterns affect variability in northeast monsoonal rainfall. Sea surface temperature over the equatorial Indian and Pacific Oceans affects year-to-year variability of the northeast monsoon, explained Mishra. “SST anomalies cooler than normal are linked to a weak northeast monsoon.”

In 2016 and 2017, cool SST anomalies prevailed in the tropical Indo-Pacific Ocean and were associated with La Niña in the central Pacific, the researchers observed. La Niña is a climate pattern that occurs irregularly every two to seven years. During La Niña, the surface waters over the equatorial Pacific Ocean are cool and this affects global weather patterns.

At the same time, the researchers noted anomalous cooling was seen in the Indian Ocean. Such patterns along with those seen in sea-level pressure and surface-air temperatures gave rise to anomalous westerlies in the equatorial Indian Ocean, which weakened moisture transport from the Bay of Bengal during the northeast monsoon, explained the authors.

Interestingly, the study revealed that out of five of the major droughts that struck southern India over the past 150 years, four occurred during La Niña.

Deepti Singh, assistant professor at Washington State University, who was not connected with the study, notes that the paper “links the recent severe, multi-year drought primarily to La Niña conditions in the tropical Pacific Ocean in 2016-2017 and 2017-18.”

This finding “implies that there is potential to predict them a few months in advance since La Niña events can be predicted with some skill in the summer,” said Singh, adding that “this means that stakeholders can prepare for and mitigate their impacts.”

While the study does not explain what made the 2016-2018 drought one of the strongest on record, “it demonstrates that natural climate variability can lead to extreme events.” She stresses that a better understanding of these drivers can inform our ability to predict severe droughts in the future. “Timely predictions of such events can help better manage and potentially reduce their societal impacts,” Singh says. 

“This is particularly important since extreme La Niña conditions are projected to become more frequent with warming and if this link holds, it might mean increasing drought risks to the region, which will likely be worsened by hotter conditions. ”

Mishra, V., Thirumalai, K., Jain, S., & Aadhar, S. (2021). Unprecedented drought in South India and recent water scarcity.  Environmental Research Letters ,  16 (5), 054007.

Banner image: Climate change can increase the frequency of drought conditions in India. Photo by Christopher Michel/Flickr.

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Urban growth is leading to more intense droughts for most of the world’s cities – and Sydney is a case study for areas at risk

Associate Professor in Environmental Science, Western Sydney University

Disclosure statement

Ian A. Wright has received funding from local state and Australian Government and the water industry. He previously worked for Sydney Water and Sydney Catchment Authority.

Western Sydney University provides funding as a member of The Conversation AU.

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The growth of cities worldwide is contributing to more intense drought conditions in many cities, including Sydney, a new Chinese study has found. This is adding to urban heat and water stress. These important findings point to the need to improve how we design and build cities to make them more liveable and resilient.

The study has used a massive 40 years of weather station data collected from urban and rural areas around the globe. Larger cities and those with less green cover are associated with even greater worsening of drought.

The Greater Sydney region was one of six cities selected from around the world for additional, more detailed model simulations. These explore how urbanisation is making local drought conditions worse in Sydney and the other cities. On January 4 2020, the western Sydney suburb of Penrith was the hottest place on Earth that day. It reached a scorching 48.9°C degrees.

A few parts of the world, such as the US west coast, Mediterranean and South-East Queensland, bucked the global trends. This was attributed to cities that cluster near the coast in areas where the ocean cools the land and sea breezes bring moisture to these cities.

How cities affect heat and moisture levels

This new investigation is highly relevant as more than half of the world’s people ( 56% ) now live in cities.

The study adds to our growing knowledge that urban development has many adverse impacts on the natural environment. We know cities affect local microclimates in many ways. Urban areas have previously been shown to influence cloud development .

And it’s well known urban areas can be hotter than non-urban areas. It’s called the urban heat island effect .

This effect is due to the loss of natural vegetation and its replacement by man-made materials. Buildings, roads, parking areas and other infrastructure absorb the sun’s heat during the day and reflect heat in the day and night, increasing the overall temperature of the city.

Urban development also changes the movement and storage of water in urban catchments. Known as the urban stream syndrome , it’s largely due to the human-made impervious surfaces. Roads, roofs, parking areas, footpaths and other artificial surfaces cover much of our cities.

Impervious surfaces reduce the natural soaking of rainwater into the soil. As a result, these hard man-made surfaces contribute to dry and hot urban soils.

There is a close link between air temperature and the amount of moisture the air can hold. This is a function of physics. As air temperature rises (as it does in urban areas) the air can hold about 7% more water vapour for every 1°C degree increase.

This is having far-reaching effects around the world. One result is that heavy rain and storms are becoming more common and intense .

For a short time after heavy rain, hard urban surfaces transform most of the rain into runoff. This can cause flash flooding in cities. But afterwards the soils and few remaining plants and trees often still need watering to make up for the lack of water soaking into the ground.

Loss of urban plants has big impacts

The new study adds to our knowledge by showing urban areas might also suffer more intense droughts due to the effects of urban development itself. This is linked to higher air temperatures as a result of the urban heat island effect and also to dryer conditions from the closely related urban dry island effect .

Important exceptions were found, including South-East Queensland cities, where urban areas can be strongly influenced by being close to the ocean.

The research highlights the substantial role plants play in urban air temperature and air moisture. This is due to plant evapotranspiration . This process drives their uptake of moisture from the soil.

The water flows through their tissues to their leaves and then is released as water vapour into the surrounding air. As well as providing the plant with nutrients, this process of “evapotranspiration” helps cool the plant. At the same time, evaporating water from the leaves adds moisture to the air and has a natural cooling effect.

The research paper states:

[T]he loss of vegetation often associated with urbanization further decreases urban evapotranspiration, resulting in the intensification of local atmospheric dryness.

Shading by plants, and particularly trees, also has a major influence by cooling air, soil and urban materials .

As urban growth leads to fewer plants and more buildings and artificial surfaces, this reduces the cooling effects from plants. Fewer plants transpiring also results in a loss of air moisture.

What’s the solution for cities?

This research is very complex. But, importantly, it has used real data from a large number of weather stations in cities and surrounding rural areas worldwide. The data used daily rainfall and temperature records collected over four decades (1980-2020).

Analysis of real data has been used to substantiate the theory that urban areas can increase the intensity of droughts.

Why is this important? Many cities are already struggling to provide enough water for their residents. Even mega-cities, such as Mexico City, are approaching “day zero” when they could effectively run out of water.

What can we do about this? We need to apply our knowledge about the broad benefits of urban green spaces . These parks, reserves and gardens are important for urban communities to connect with nature .

This new study shows how important these urban green spaces also are to help reduce the severity of droughts.

• Climate change
• Water security
• Urban development
• Urban growth
• Urban heat islands
• Urban green space
• Soil moisture
• Urban cooling
• Better Cities
• Urban water use

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Yale Climate Connections

Climate change and droughts: What’s the connection?

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For tens of millions of Americans, drought has become an ever-present natural disaster.

That’s particularly true in the Western United States. Because of the West’s largely semi-arid and desert climates, droughts are natural occurrences across the region. However, regional climate isn’t the only culprit in drought activity. Climate change, namely rising average temperatures driven by human-generated emissions of heat-trapping greenhouse gases, is contributing to droughts, too.

Warmer temperatures lead to drying

Global warming increases the risk of drought in several ways.

For one, water generally evaporates more quickly at higher temperatures. For that reason, hotter weather can result in drier soils. As high air temperatures sap liquid water from soils and plant leaves, transforming it into atmospheric water vapor via a process called transpiration, ground-level drying will increase in some regions. (Ironically, this additional atmospheric moisture triggers heavier downpours in other regions , which explains why the overall trend in the U.S. has been toward wetter conditions.)

Higher air temperatures not only encourage drought conditions to build but also intensify them. What might have otherwise been a mild or moderate drought in a cooler world will become, in a warmer world, more severe as a result of increased evaporation.

Warming also diminishes snowfall, an essential water resource for the estimated 1.9 billion residents of the Northern Hemisphere who depend on snowpacks, or snow reservoirs that store water during the cooler months and release it when it’s needed in the warmer, drier months. Rising temperatures increase the fraction of winter precipitation that falls as rain rather than snow and also shorten the cold season, so there’s less time for snow to even occur. Such was the case in 2015, the fourth-warmest year in the contiguous U.S. , when a snow drought reduced the April snowpack in the Sierra Nevada mountain range to a mere 5% of its historical average water content — its lowest snowpack in 500 years .

Seasonal melting of snowpacks can be thrown off-kilter, too. As average temperatures warm above freezing earlier in the spring, snowmelt occurs sooner and faster than usual. And rapid melting results in a shorter period during which soils and plants are kept moist.

Another way a warmer atmosphere can disrupt precipitation is by shifting storm tracks. Ordinarily, low-pressure systems known as extratropical cyclones form between 30 and 60 degrees latitude north and south of the equator. But as the climate warms globally, storms are shifting toward the poles. This means that weather features such as atmospheric rivers, which supply as much as 50% of annual precipitation to states in the Western U.S., could cease to pass over regions where their moisture is much-needed.

Is global warming causing more droughts?

Scientists see a clear correlation between droughts and global warming. But a correlation between two events doesn’t always mean one caused the other. For example, ice cream sales often increase around the time that baseball game attendance rises, but that does not mean that eating ice cream causes people to attend baseball games. Nor does it mean that attending baseball games causes people to eat ice cream.

It can be tricky to attribute an increase in droughts to global warming because droughts are variable. In other words, they can occur every year or every few years, last for years or decades, and cause varying levels of dryness. That makes it difficult to distinguish random events from those possibly shaped by human-caused warming. However, the more drought dovetails with trends of increasing temperature, decreasing precipitation, and with computer model projections, the more confident scientists are in pointing to climate change.

In a 2020 study in the journal Science, for example, researchers observed how human-caused climate change is contributing to the 21st-century megadrought in the Western U.S. and northern Mexico by evaluating trends in modeled temperature, relative humidity, and precipitation data between 1901 and 2018. According to the study’s findings, human-caused warming accounts for 46% of this drought’s severity.

What about the rest of the world? Scientists have been cautious about linking human activities to global drought patterns, largely because drought hasn’t occurred as uniformly worldwide as it has across individual regions. That said, building evidence supports the climate change-drought connection on a global scale.

According to an August 2021 report by the Intergovernmental Panel on Climate Change , scientists have high confidence that for every half degree Celsius (0.9 degree Fahrenheit) the atmosphere warms , noticeable increases will occur in some regions in the intensity and frequency of droughts that harm agriculture and ecosystems. Similarly, the report notes that extreme agricultural and ecological drought events that used to occur once every 10 years are now 1.7 times more likely than they were from 1850 to 1900, before humans heavily influenced the climate.

How drought-prone communities can endure future dry spells 

While the intricacies of the climate change-drought connection are still being uncovered, scientists tend to agree on one thing: Droughts will likely become more intense into the 2050s and beyond. The likelihood of megadroughts – droughts lasting 10 years or more – is also projected to increase from its current 12% to more than 60%, a NASA study warns.

A conservation mindset is one of the best defenses against drought and its associated risks of wildfire, crop failure, energy crises, and more. Whether you’re preparing for a drought or are already experiencing one, strengthen your resilience by taking these actions:

• Become drought-aware. Keep up with current drought conditions by visiting the National Integrated Drought Information System , and use the Drought Risk Atlas to explore how susceptible your region is to drought.
• Xeriscape lawns and city green spaces. Replacing traditional lawn vegetation with native, drought-tolerant plants reduces a home’s outdoor water demand by 50-70% , according to National Geographic. 
• Repair leaky indoor and outdoor faucets. A seemingly small leak that drips once per second can waste 2,700 gallons of water a year, according to the American Red Cross .  
• Install green infrastructure. Green streets, green roofs, and porous pavements allow whatever rain that does fall to slowly soak into the ground and replenish local groundwater reserves rather than be lost to storm drains.
• Improve your home’s energy efficiency. Since water is needed to generate hydroelectric power and for cooling in other types of energy production, power grids can easily become strained during droughts. Taking care to fully load dishwashers and washing machines, use “light wash” settings, and limit power consumption during peak times (4 p.m. to 9 p.m. local time) can help your community avoid preemptive power shutoffs, or worse, blackouts.
• Build an emergency water supply in your pantry . The CDC recommends storing at least one gallon of water per person per day (half a gallon for drinking; half for personal use). Visit their website for tips on how to safely store drinking water .

Although drought is an immense concern now and in the future, taking small actions such as these can have cascading benefits.

Editor’s note: This story was originally published Aug. 18, 2021 and was updated May 8, 2023.

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Examining optimized machine learning models for accurate multi-month drought forecasting: A representative case study in the USA

• Research Article
• Published: 13 August 2024

Cite this article

• Mohammed Majeed Hameed   ORCID: orcid.org/0000-0002-9366-9162 1 , 2 ,
• Siti Fatin Mohd Razali   ORCID: orcid.org/0000-0003-2757-6141 1 , 3 ,
• Wan Hanna Melini Wan Mohtar   ORCID: orcid.org/0000-0002-5684-5577 1 , 3 &
• Zaher Mundher Yaseen   ORCID: orcid.org/0000-0003-3647-7137 4  

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The Colorado River has experienced a significant streamflow reduction in recent decades due to climate change, resulting in pronounced hydrological droughts that pose challenges to the environment and human activities. However, current models struggle to accurately capture complex drought patterns, and their accuracy decreases as the lead time increases. Thus, determining the reliability of drought forecasting for specific months ahead presents a challenging task. This study introduces a robust approach that utilizes the Beluga Whale Optimization (BWO) algorithm to train and optimize the parameters of the Regularized Extreme Learning Machine (RELM) and Random Forest (RF) models. The applied models are validated against a KNN benchmark model for forecasting drought from one- to six-month ahead across four hydrological stations distributed over the Colorado River. The achieved results demonstrate that RELM-BWO outperforms RF-BWO and KNN models, achieving the lowest root-mean square error (0.2795), uncertainty ( U 95  = 0.1077), mean absolute error (0.2104), and highest correlation coefficient (0.9135). Also, the current study uses Global Multi-Criteria Decision Analysis ( GMCDA ) as an evaluation metric to assess the reliability of the forecasting. The GMCDA results indicate that RELM-BWO provides reliable forecasts up to four months ahead. Overall, the research methodology is valuable for drought assessment and forecasting, enabling advanced early warning systems and effective drought countermeasures.

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This research relies on data obtained from open access sources, which can be accessed at https://www.usgs.gov/ .

Abbes AB, Inoubli R, Rhif M, Farah IR (2023) Combining deep learning methods and multi-resolution analysis for drought forecasting modeling. Earth Sci Inform 16:1811–1820. https://doi.org/10.1007/S12145-023-01009-4/METRICS

Article   Google Scholar  

Aghelpour P, Varshavian V (2021) Forecasting different types of droughts simultaneously using multivariate standardized precipitation index (MSPI), MLP neural network, and imperialistic competitive algorithm (ICA). Complexity. https://doi.org/10.1155/2021/6610228

Ali S, Basit A, Makanda TA et al (2023) Improving drought mitigation strategies and disaster risk reduction through MODIS and TRMM-based data in relation to climate change over Pakistan. Environ Sci Pollut Res 30:40563–40575. https://doi.org/10.1007/S11356-023-25138-X/METRICS

Almikaeel W, Čubanová L, Šoltész A (2022) Hydrological drought forecasting using machine learning–Gidra River Case Study. Water (Basel) 14(3):387

Google Scholar  

Alomar MK, Khaleel F, Aljumaily MM et al (2022) Data-driven models for atmospheric air temperature forecasting at a continental climate region. PLoS One 17:e0277079

Article   CAS   Google Scholar  

Alshahrani MA, Laiq M, Noor-ul-Amin M et al (2024) A support vector machine based drought index for regional drought analysis. Sci Rep 14(1):1–12. https://doi.org/10.1038/s41598-024-60616-3

Awadh SM, Al-Mimar H, Yaseen ZM (2021) Groundwater availability and water demand sustainability over the upper mega aquifers of Arabian Peninsula and west region of Iraq. Environ Dev Sustain 23:1–21. https://doi.org/10.1007/s10668-019-00578-z

Banadkooki FB, Singh VP, Ehteram M (2021a) Multi-timescale drought prediction using new hybrid artificial neural network models. Nat Hazards 106:2461–2478

Banadkooki FB, Singh VP, Ehteram M (2021b) Multi-timescale drought prediction using new hybrid artificial neural network models. Nat Hazards 106:2461–2478. https://doi.org/10.1007/s11069-021-04550-x

Basak A, Rahman ATMS, Das J et al (2022) Drought forecasting using the Prophet model in a semi-arid climate region of western India. Hydrol Sci J 67:1397–1417

Bazrafshan J, Hejabi S, Rahimi J (2014) Drought monitoring using the multivariate standardized precipitation index (MSPI). Water Resour Manag 28:1045–1060. https://doi.org/10.1007/s11269-014-0533-2

Behar O, Khellaf A, Mohammedi K (2015) Comparison of solar radiation models and their validation under Algerian climate – the case of direct irradiance. Energy Convers Manag 98:236–251. https://doi.org/10.1016/j.enconman.2015.03.067

Bovo M, Agrusti M, Benni S et al (2021) Random forest modelling of milk yield of dairy cows under heat stress conditions. Animals 11:1305. https://doi.org/10.3390/ANI11051305

Breiman L (2001) Random forests. Mach Learn 45:5–32

Citakoglu H, Coşkun Ö (2022) Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey. Environ Sci Pollut Res 29:75487–75511. https://doi.org/10.1007/s11356-022-21083-3

Cohen M, Christian-Smith J, Berggren J (2013) Water to supply the land: irrigated agriculture in the Colorado River Basin. Pac Inst 1–93

Cohn JP (2001) Resurrecting the dammed: a look at Colorado River restoration. Bioscience 51:998–1003. https://doi.org/10.1641/0006-3568(2001)051[0998:RTDALA]2.0.CO;2

Danandeh Mehr A, Rikhtehgar Ghiasi A, Yaseen ZM et al (2023a) A novel intelligent deep learning predictive model for meteorological drought forecasting. J Ambient Intell Humaniz Comput 14:10441–10455. https://doi.org/10.1007/s12652-022-03701-7

Danandeh Mehr A, Tur R, Alee MM et al (2023b) Optimizing extreme learning machine for drought forecasting: water cycle vs. bacterial foraging. Sustainability 3923(15):3923. https://doi.org/10.3390/SU15053923

Danso-Abbeam G, Okolie CC, Ojo TO, Ogundeji AA (2024) Understanding drought impacts on livelihoods and risk management strategies: South African smallholder farmers’ perspectives. Nat Hazards 1–21. https://doi.org/10.1007/S11069-024-06561-W/METRICS

Deng W, Zheng Q, Chen L (2009) Regularized extreme learning machine. 2009 IEEE symposium on computational intelligence and data mining, CIDM 2009 - Proceedings 389–395. https://doi.org/10.1109/CIDM.2009.4938676

Deo RC, Tiwari MK, Adamowski JF, Quilty JM (2017) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Env Res Risk Assess 31:1211–1240. https://doi.org/10.1007/s00477-016-1265-z

Despotovic M, Nedic V, Despotovic D, Cvetanovic S (2015) Review and statistical analysis of different global solar radiation sunshine models. Renew Sustain Energy Rev 52:1869–1880. https://doi.org/10.1016/j.rser.2015.08.035

Dikshit A, Pradhan B, Alamri AM (2020) Temporal hydrological drought index forecasting for New South Wales, Australia using machine learning approaches. Atmosphere (Basel) 11(6):585

Dong L, Zuo X, Xiong Y (2024) Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis. PLoS One 19:e0298664. https://doi.org/10.1371/JOURNAL.PONE.0298664

Ehsan SD (2023) Hydrodynamic modelling: estuary dynamic implication to morphological changes. J Kejuruter 35:635–645

Elbeltagi A, Kumar M, Kushwaha NL et al (2023a) Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India. Stoch Env Res Risk Assess 37:113–131. https://doi.org/10.1007/S00477-022-02277-0/METRICS

Elbeltagi A, Pande CB, Kumar M et al (2023b) Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models. Environ Sci Pollut Research 30(15):43183–43202. https://doi.org/10.1007/S11356-023-25221-3

Fox EW, Ver Hoef JM, Olsen AR (2020) Comparing spatial regression to random forests for large environmental data sets. PLoS One 15:e0229509. https://doi.org/10.1371/JOURNAL.PONE.0229509

Ghebrezgabher MG, Yang T, Yang X, Wang C (2019) Assessment of desertification in Eritrea: land degradation based on Landsat images. J Arid Land 11:319–331

Gholizadeh R, Yılmaz H, Danandeh Mehr A (2022) Multitemporal meteorological drought forecasting using Bat-ELM. Acta Geophys. https://doi.org/10.1007/s11600-022-00739-1

Hameed MM, Alomar MK, Khaleel F, Al-Ansari N (2021a) An extra tree regression model for discharge coefficient prediction: novel, practical applications in the hydraulic sector and future research directions. Math Probl Eng 2021:7001710. https://doi.org/10.1155/2021/7001710

Hameed MM, AlOmar MK, Mohd Razali SF et al (2021b) Application of artificial intelligence models for evapotranspiration prediction along the Southern Coast of Turkey. Complexity 2021:1–20. https://doi.org/10.1155/2021/8850243

Hameed MM, AlOmar MK, Al-Saadi AAA, AlSaadi MA (2022a) Inflow forecasting using regularized extreme learning machine: Haditha reservoir chosen as case study. Stoch Env Res Risk Assess. https://doi.org/10.1007/s00477-022-02254-7

Hameed MM, Khaleel F, AlOmar MK et al (2022b) Optimising the selection of input variables to increase the predicting accuracy of shear strength for deep beams. Complexity 2022:6532763. https://doi.org/10.1155/2022/6532763

Hameed MM, Mohd Razali SF, Wan Mohtar WHM, Yaseen ZM (2023a) Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm. Stoch Env Res Risk Assess 37:4963–4989. https://doi.org/10.1007/S00477-023-02548-4/METRICS

Hameed MM, Razali SFM, Mohtar WHMW et al (2023b) Machine learning models development for accurate multi-months ahead drought forecasting: case study of the Great Lakes, North America. Plos One 18:e0290891. https://doi.org/10.1371/JOURNAL.PONE.0290891

Hameed MM, Mohd Razali SF, Wan Mohtar WHM et al (2024) Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: a comparative study and trend analysis in tropical region. Heliyon 10:e22942. https://doi.org/10.1016/J.HELIYON.2023.E22942

Han L, Zhang Q, Zhang Z et al (2021) Drought area, intensity and frequency changes in China under climate warming, 1961–2014. J Arid Environ 193:104596. https://doi.org/10.1016/J.JARIDENV.2021.104596

Hannoun D, Tietjen T (2023) Lake management under severe drought: Lake Mead, Nevada/Arizona. JAWRA J Am Water Resour Assoc 59:416–428. https://doi.org/10.1111/1752-1688.13090

Hasan HH, Razali SF, Muhammad NS, Ahmad A (2022) Modified hydrological drought risk assessment based on spatial and temporal approaches. Sustainability 14(10):6337

He Q, Wang M, Liu K et al (2023) Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning. Weather Clim Extrem 40:100567. https://doi.org/10.1016/J.WACE.2023.100567

Hosseinzadeh P, Nassar A, Boubrahimi SF, Hamdi SM (2023) ML-based streamflow prediction in the Upper Colorado River Basin using climate variables time series data. Hydrology 10(2):29

Houssein EH, Sayed A (2023) Dynamic candidate solution boosted Beluga Whale Beluga Whale Optimization for biomedical classification. Mathematics 11(3):707

Jahangir MH, Zarfeshani A, Danehkar S (2024) Numerical comparison of streamflow drought index (SDI) and standardized streamflow index (SSI) for evaluation of Isfahan drought status. Geol Ecol Landsc 1–14. https://doi.org/10.1080/24749508.2024.2359775

Jehanzaib M, Bilal Idrees M, Kim D, Kim T-W (2021) Comprehensive evaluation of machine learning techniques for hydrological drought forecasting. J Irrig Drain Eng 147:04021022. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001575

Ji Y, Fu J, Lu Y, Liu B (2023) Three-dimensional-based global drought projection under global warming tendency. Atmos Res 291:106812. https://doi.org/10.1016/J.ATMOSRES.2023.106812

Khiem NM, Takahashi Y, Yasuma H et al (2022) A novel machine learning approach to predict the export price of seafood products based on competitive information: the case of the export of Vietnamese shrimp to the US market. PLoS One 17:e0275290

Kumar V, Sharma KV, Pham QB et al (2024) Advancements in drought using remote sensing: assessing progress, overcoming challenges, and exploring future opportunities. Theoret Appl Climatol 2024:1–38. https://doi.org/10.1007/S00704-024-04914-W

Larivière B, Van den Poel D (2005) Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst Appl 29:472–484. https://doi.org/10.1016/j.eswa.2005.04.043

Littell JS, Peterson DL, Riley KL et al (2016) A review of the relationships between drought and forest fire in the United States. Glob Chang Biol 22:2353–2369. https://doi.org/10.1111/gcb.13275

Liu C, Yang C, Yang Q, Wang J (2021) Spatiotemporal drought analysis by the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in Sichuan Province, China. Sci Rep 11(1):1–14. https://doi.org/10.1038/s41598-020-80527-3

Liu Y, Shan F, Yue H, Wang X (2023) Characteristics of drought propagation and effects of water resources on vegetation in the karst area of Southwest China. Sci Total Environ 891:164663. https://doi.org/10.1016/J.SCITOTENV.2023.164663

Lloyd-Hughes B, Saunders MA (2002) A drought climatology for Europe. Int J Climatol 22:1571–1592. https://doi.org/10.1002/JOC.846

Malik A, Tikhamarine Y, Sammen SS et al (2021) Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms. Environ Sci Pollut Res 28:39139–39158. https://doi.org/10.1007/s11356-021-13445-0

Mamata RC, Ramlia A, Yazidb MRM et al (2022) Slope stability prediction of road embankment using artificial neural network combined with genetic algorithm. Jurnal Kejuruteraan 34:165–173

Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of L\’evy stable stochastic processes. Phys Rev E 49:4677–4683. https://doi.org/10.1103/PhysRevE.49.4677

McCoy AL, Pitt J, Keaton Wilson J et al (2023) A survey of the Bureau of Reclamation’s decree accounting reports in the Lower Colorado River Basin. J Water Resour Plan Manag 149:4022085. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001626

Mishra V (2020) Long-term (1870–2018) drought reconstruction in context of surface water security in India. J Hydrol (amst) 580:124228. https://doi.org/10.1016/j.jhydrol.2019.124228

Mishra AK, Desai VR (2006) Drought forecasting using feed-forward recursive neural network. Ecol Modell 198:127–138. https://doi.org/10.1016/j.ecolmodel.2006.04.017

Mukhawana MB, Kanyerere T, Kahler D, Masilela NS (2023) Application of the standardised streamflow index for hydrological drought monitoring in the Western Cape Province, South Africa: a case study in the Berg River catchment. Water 15:2530. https://doi.org/10.3390/W15142530

Nones M, Hamidifar H, Shahabi-Haghighi SMB (2024) Exploring EM-DAT for depicting spatiotemporal trends of drought and wildfires and their connections with anthropogenic pressure. Nat Hazards 120:957–973. https://doi.org/10.1007/S11069-023-06209-1/METRICS

Richardson D, Black AS, Irving D et al (2022) Global increase in wildfire potential from compound fire weather and drought. NPJ Clim Atmos Sci 5:23. https://doi.org/10.1038/s41612-022-00248-4

Robison J, Bratrschovsky K, Latcham J et al (2014) Challenge and response in the Colorado River Basin. Water Policy 16:12–57. https://doi.org/10.2166/wp.2014.003

SafarianZengir V, Sobhani B, Sayad A (2020) Modeling and monitoring of drought for forecasting it, to reduce natural hazards atmosphere in western and northwestern part of Iran, Iran. Air Qual Atmos Health 13:119–130. https://doi.org/10.1007/s11869-019-00776-8

Schmidt JC, Yackulic CB, Kuhn E (2023) The Colorado River water crisis: its origin and the future. WIREs Water n/a:e1672. https://doi.org/10.1002/wat2.1672

Shamshirband S, Hashemi S, Salimi H et al (2020) Predicting standardized streamflow index for hydrological drought using machine learning models. Eng Appl Comput Fluid Mech 14:339–350. https://doi.org/10.1080/19942060.2020.1715844

Tallaksen LM, Van Lanen HAJ, Hisdal H et al (2004) Hydrological drought processes and estimation methods for streamflow and groundwater editors: chapter 5: hydrological drought characteristics. Hydrol Drought Characteristics 48(5):139–198

Tang H, Xu Y, Lin A et al (2020) Predicting green consumption behaviors of students using efficient firefly grey wolf-assisted k-nearest neighbor classifiers. IEEE Access 8:35546–35562. https://doi.org/10.1109/ACCESS.2020.2973763

Thomas T, Nayak PC, Ventakesh B (2022) Integrated assessment of drought vulnerability for water resources management of Bina basin in Central India. Environ Monit Assess 194:1–31. https://doi.org/10.1007/S10661-022-10300-8/METRICS

Udall B, Overpeck J (2017) The twenty-first century Colorado River hot drought and implications for the future. Water Resour Res 53:2404–2418. https://doi.org/10.1002/2016WR019638

Vano JA, Udall B, Cayan DR et al (2014) Understanding uncertainties in future Colorado River streamflow. Bull Am Meteorol Soc 95:59–78. https://doi.org/10.1175/BAMS-D-12-00228.1

Wang GC, Zhang Q, Band SS et al (2022) Monthly and seasonal hydrological drought forecasting using multiple extreme learning machine models. Eng Appl Comput Fluid Mech 16:1364–1381. https://doi.org/10.1080/19942060.2022.2089732

Wei W, Zhang X, Liu C et al (2023) A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data. Environ Sci Pollut Res 30:17865–17887. https://doi.org/10.1007/s11356-022-23200-8

Wheeler KG, Udall B, Wang J et al (2022) What will it take to stabilize the Colorado River? Science (1979) 377:373–375

CAS   Google Scholar  

Xiao M, Udall B, Lettenmaier DP (2018) On the causes of declining Colorado River streamflows. Water Resour Res 54:6739–6756. https://doi.org/10.1029/2018WR023153

Xie W, Wang J, Xing C et al (2020) Adaptive hybrid soft-sensor model of grinding process based on regularized extreme learning machine and least squares support vector machine optimized by Golden Sine Harris Hawk optimization algorithm. Complexity 2020:6457517. https://doi.org/10.1155/2020/6457517

Xu D, Zhang Q, Ding Y, Huang H (2020) Application of a Hybrid ARIMA–SVR model based on the SPI for the forecast of drought—a case study in Henan Province, China. J Appl Meteorol Climatol 59:1239–1259. https://doi.org/10.1175/JAMC-D-19-0270.1

Yevjevich VM (1967) An Objective approach to definitions and investigations of continental hydrologic droughts

Zhang Z (2016) Introduction to machine learning: k-nearest neighbors. Ann Transl Med 4(11):218

Zhang R, Chen ZY, Xu LJ, Ou CQ (2019) Meteorological drought forecasting based on a statistical model with machine learning techniques in Shaanxi province, China. Sci Total Environ 665:338–346. https://doi.org/10.1016/J.SCITOTENV.2019.01.431

Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl Based Syst 251:109215. https://doi.org/10.1016/J.KNOSYS.2022.109215

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Acknowledgements

The authors would like to express their gratitude to the U.S. Geological Survey (USGS) for providing the data used in this research. The researchers would also thank Green Engineering and Net Zero Solution (GREENZ), Universiti Kebangsaan Malaysia (UKM), and Al-Maarif University for technically supporting this research.

Universiti Kebangsaan Malaysia funded this study.

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Hameed, M.M., Mohd Razali, S.F., Wan Mohtar, W.H.M. et al. Examining optimized machine learning models for accurate multi-month drought forecasting: A representative case study in the USA. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-34500-6

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Advancing scientific understanding of climate, improving society’s ability to plan and respond

New Case Study: Drought in Central Texas

• May 28, 2013

Heading into the 2013 summer season the reservoir system on the Lower Colorado River was at even lower levels than at that same time in 2011. For the second year in a row the Lower Colorado River Authority (LCRA) had not released water for downstream agricultural uses that had an ‘interruptible’ standing under water rights provisions, which meant they could be curtailed. Urban users had purchased ‘firm’ water, available in a drought, resulting in the perception that there was plenty of water and creating tension with downstream agricultural users. Challenges persisted both in instituting an ethic of water conservation and in funding utility operations when selling less water.  The 2-page case study document is one of the outcomes of a collaboration between Federal and NGO partners (NOAA’s Sectoral Application Research Program, EPA, the Water Environment Research Foundation, the Water Research Federation, Concurrent Technologies Corporation, and NOBLIS). These groups are working together to convene a series of workshops in communities that have experienced extreme events (i.e., drought, flood, sea level rise, freezing weather, and cascading impacts from multiple events). 

The objectives of the workshops are to better understand how water utilities had planned for, and responded to, these events, and to learn how they are preparing for future events. An additional goal was to gather information on how they use forecasts and what kind of information they would like to have for planning. The case study summarizes these findings and also includes lessons learned, a list of useful tools and resources cited by stakeholders who participated in the workshop, and an ‘information needs’ section that will help inform NOAA’s climate data and information services.

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Urban growth leads to more intense droughts for many world cities—Sydney is a case study for areas at risk

by Ian A. Wright, The Conversation

The growth of cities worldwide is contributing to more intense drought conditions in many cities, including Sydney, a new Chinese study has found. This is adding to urban heat and water stress. These important findings point to the need to improve how we design and build cities to make them more livable and resilient.

The study has used a massive 40 years of weather station data collected from urban and rural areas around the globe. Larger cities and those with less green cover are associated with even greater worsening of drought.

The Greater Sydney region was one of six cities selected from around the world for additional, more detailed model simulations. These explore how urbanization is making local drought conditions worse in Sydney and the other cities. On January 4, 2020, the western Sydney suburb of Penrith was the hottest place on Earth that day. It reached a scorching 48.9°C degrees.

A few parts of the world, such as the US west coast , Mediterranean and South-East Queensland, bucked the global trends. This was attributed to cities that cluster near the coast in areas where the ocean cools the land and sea breezes bring moisture to these cities.

How cities affect heat and moisture levels

This new investigation is highly relevant as more than half of the world's people ( 56% ) now live in cities.

The study adds to our growing knowledge that urban development has many adverse impacts on the natural environment. We know cities affect local microclimates in many ways. Urban areas have previously been shown to influence cloud development .

And it's well known that urban areas can be hotter than non-urban areas. It's called the urban heat island effect .

This effect is due to the loss of natural vegetation and its replacement by man-made materials. Buildings, roads, parking areas and other infrastructure absorb the sun's heat during the day and reflect heat in the day and night, increasing the overall temperature of the city.

Urban development also changes the movement and storage of water in urban catchments. Known as the urban stream syndrome , it's largely due to the human-made impervious surfaces. Roads, roofs, parking areas, footpaths and other artificial surfaces cover much of our cities.

Impervious surfaces reduce the natural soaking of rainwater into the soil. As a result, these hard man-made surfaces contribute to dry and hot urban soils.

There is a close link between air temperature and the amount of moisture the air can hold. This is a function of physics. As air temperature rises (as it does in urban areas) the air can hold about 7% more water vapor for every 1°C degree increase.

This is having far-reaching effects around the world. One result is that heavy rain and storms are becoming more common and intense .

For a short time after heavy rain, hard urban surfaces transform most of the rain into runoff. This can cause flash flooding in cities. But afterwards, the soils and few remaining plants and trees often still need watering to make up for the lack of water soaking into the ground.

Loss of urban plants has big impacts

The new study adds to our knowledge by showing urban areas might also suffer more intense droughts due to the effects of urban development itself. This is linked to higher air temperatures as a result of the urban heat island effect and also to dryer conditions from the closely related urban dry island effect .

Important exceptions were found, including South-East Queensland cities, where urban areas can be strongly influenced by being close to the ocean.

The research highlights the substantial role plants play in urban air temperature and air moisture. This is due to plant evapotranspiration . This process drives their uptake of moisture from the soil.

The water flows through their tissues to their leaves and then is released as water vapor into the surrounding air. As well as providing the plant with nutrients, this process of "evapotranspiration" helps cool the plant. At the same time, evaporating water from the leaves adds moisture to the air and has a natural cooling effect.

The research paper states:

"[T]he loss of vegetation often associated with urbanization further decreases urban evapotranspiration, resulting in the intensification of local atmospheric dryness. "

Shading by plants, and particularly trees, also has a major influence by cooling air, soil and urban materials .

As urban growth leads to fewer plants and more buildings and artificial surfaces, this reduces the cooling effects from plants. Fewer plants transpiring also results in a loss of air moisture.

What's the solution for cities?

This research is very complex. But, importantly, it has used real data from a large number of weather stations in cities and surrounding rural areas worldwide. The data used daily rainfall and temperature records collected over four decades (1980–2020).

Analysis of real data has been used to substantiate the theory that urban areas can increase the intensity of droughts.

Why is this important? Many cities are already struggling to provide enough water for their residents. Even mega-cities, such as Mexico City, are approaching "day zero" when they could effectively run out of water.

What can we do about this? We need to apply our knowledge about the broad benefits of urban green spaces . These parks, reserves and gardens are important for urban communities to connect with nature.

This new study shows how important these urban green spaces also are to help reduce the severity of droughts.

Provided by The Conversation

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Understanding and Managing Drought-Induced Ecological Transformations

CASC scientists explore how drought can cause lasting transformational changes in terrestrial ecosystems. 

Drought can reshape ecosystems and cause long-lasting changes, like shifts in the composition of the area’s species and functions. Known as “ecological transformations,” these changes include forests becoming grasslands or salt marshes turning into mudflats. As climate change alters drought patterns, these transformations are expected to become more common, presenting challenges for natural resource managers who are not accustomed to managing the transformed landscape. 

A new publication in BioScience , led by Northern Rocky Mountain Science Center Biologist Wynne Moss co-authored by Southeast, North Central, South Central, and Alaska CASC researchers, with funding support from the National, Alaska and North Central CASCs, explores how drought can cause ecological transformations. The authors highlight examples from various terrestrial ecosystems, including Alaskan boreal forests, California chaparral, and Amazonian forests. 

The researchers examined ways drought can transform the dominant vegetation in ecosystems by affecting processes like mortality, growth rates, recruitment, and competitive dynamics. They also highlight how shifts in drought severity or frequency can lead to transformations. For example, more frequent droughts have already led to transformations where slow-growing tree species experience a second drought while still recovering from the first. The study also emphasizes different ways that drought interacts with landscape features, land use, invasive species, pests, grazing, and fire to cause transformations, and how these interactions could make future transformations more likely. 

To better prepare and manage for ecological transformations, the researchers suggest three general directions for future research and management: First, studying long-term drought impacts, specifically those that persist after the drought ends. One way to do this would be to revisit locations of previous studies to assess long-term dynamics and to identify early signs of major changes. Second, studying shifts in species composition after drought. Understanding which species are most likely to be harmed or helped by drought can help predict ecological changes and guide useful interventions for managers. Finally, comparing the responses of different ecosystem types to the same drought events or experimental conditions can be useful for understanding how risk of transformation varies across different ecosystems.  

Unlike other extreme events that can cause ecological transformations, drought is expected to occur widely, even in locations where average yearly precipitation will increase. This CASC-supported research can help managers prepare for different possible outcomes of drought.   

Read additional coverage of this publication, “ 21st-Century Droughts Are Transforming Ecosystems ” on drought.gov.   

The article, published in the journal BioScience , is titled “Drought as an emergent driver of ecological transformation in the twenty-first century.”  CASC Authors on the publication include Jennifer Cartwright (Southeast CASC), Imtiaz Rangwala and Brian Miller (North Central CASC), Caitlin Rottler (South Central CASC), and Jeremy Littell (Alaska CASC). 

Related Content

Drought and Disturbances as Drivers of Long-Term Ecological Transformation and Risk

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Case Study: Dealing with Drought

• Forest L. Reinhardt
• Alison Beard

A farmer debates whether to continue planting or lease his land.

Pete Walker liked to start each morning with a drive around the fields. Of course, he could monitor his crops by scanning computer screens back in the farmhouse, but he liked to see and smell the tilled soil, sprouting tendrils, bushy trees, and ripe produce for himself. He sat back in the seat of his Jeep, sipped his coffee, and looked out at the horizon. The 23,000 acres of Walker Farms stretched before him under a pale blue, invariably cloudless California sky.

• FR Forest L. Reinhardt is the  John D. Black Professor of Business Administration at Harvard Business School. He also serves as faculty chair of the HBS Asia-Pacific Research Center and chair of HBS Executive Education, Asia-Pacific Region.
• AB Alison Beard is a senior editor at Harvard Business Review .

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Monitoring Trends in Air Quality During a Drought Case Study to Improve Public Health Response to Drought Threats

Pacific northwest health & air quality (summer 2023).

Team : Abby Sgan (Project Lead), Greta Bolinger, Tallis Monteiro, Cristina Villalobos-Heredia, Taylor West

Summary : Recent studies have documented a correlation between air quality and drought in the United States, which has been linked with increased aerosols including airborne particulate matter (PM) during drought conditions. This study partnered with local health departments to evaluate trends in air quality in the Pacific Northwest during the evolution of drought conditions using aerosol optical depth (AOD) observations collected by NASA’s Moderate Resolution Imaging Spectroradiometer sensor aboard the Terra and Aqua satellites. These satellite data were analyzed in conjunction with ground-based PM2.5 and PM10 data sourced from the Environmental Protection Agency (EPA)’s network of ground-based monitors and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index. Based on recommendations by local health departments, this study examined air quality trends between 2015 and 2022 in 12 counties within Oregon and Washington that reflected diversity in population density, drought exposure, rural and urban status, and data availability from EPA monitors. Overall, results indicated variation in relationships among drought, satellite, and ground-based air quality data across the study area. This study did not control for the impact of wildfire events on air quality and also did not investigate shorter SPEI aggregation periods, both of which are avenues for future research. This project supplemented research into links between drought and human health and provided health departments with an objective foundation from which they can communicate public health risks to local communities.

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Characteristics of droughts in south africa: a case study of free state and north west provinces.

1. Introduction

2. study area, 3. data and method, 3.1. data sets, 3.2. methodology, 4.1. drought characterization, 4.2. drought categories, 5. discussion, 5.1. implications to the agricultural sector, 5.2. implications to water resources, 6. conclusions, acknowledgments, author contributions, conflict of interests.

• Wilhite, D.A.; Svoboda, M.D.; Hayes, M.J. Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resour. Manag. 2007 , 21 , 763–774. [ Google Scholar ] [ CrossRef ]
• Vicente-Serrano, S.M.; Begueria, S.; Eklundh, L.; Gimeno, G.; Weston, D.; Kenawy, A.E.; Lopez-Moreno, J.I.; Nieto, R.; Ayenew, T.; Konte, D.; et al. Challenges for drought mitigation in Africa: The potential use of geospatial data and drought information systems. App. Geogr. 2012 , 34 , 471–486. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tan, C.; Yang, J.; Li, M. Temporal-spatial variation of drought indicated by SPI and SPEI in Ningxia Hui Autonomous region, China. Atmosphere 2015 , 6 , 1399–1421. [ Google Scholar ] [ CrossRef ]
• Zhang, L.X.; Zhou, T.J. Drought over East Asia: A review. J. Clim. 2015 , 28 , 3375–3399. [ Google Scholar ] [ CrossRef ]
• Wang, L.; Chen, W.; Zhou, W.; Huang, G. Drought in Southwest China: A review. Atmos. Ocean. Sci. Lett. 2015 , 8 , 339–344. [ Google Scholar ]
• Chen, T.; van der Werf, G.R.; de Jeu, R.A.M.; Wang, G.; Dolman, A.J. A global analysis of the impact of drought on net primary productivity. Hydrol. Earth Syst. Sci. 2013 , 17 , 3885–3894. [ Google Scholar ] [ CrossRef ]
• Dracup, J.A.; Lee, K.S.; Paulson, E.D. On the definition of droughts. Water Resour. Res. 1980 , 16 , 297–302. [ Google Scholar ] [ CrossRef ]
• Wilhite, D.A.; Glantz, M.H. Understanding the drought phenomenon: The role of definitions. Water Int. 1985 , 10 , 111–120. [ Google Scholar ] [ CrossRef ]
• Wilhite, D.A. Drought assessment, management, and planning: theory and case studies. In Natural Resource Management and Policy Series ; Dinar, A., Zilberman, D., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1993. [ Google Scholar ]
• Tate, E.L.; Gustard, A. Drought definition: A hydrological perspective. In Drought and Drought Mitigation in Europe ; Advances in Natural and Technological Hazards Research Volume 14; Springer Science and Business Media: Dordrecht, The Netherlands, 2000; pp. 23–48. [ Google Scholar ]
• Pereira, L.S.; Cordery, I.; Iacovides, I. Coping with water scarcity. In Addressing the Challenges ; Springer Science and Business Media: Dordrecht, The Netherlands, 2009; p. 382. [ Google Scholar ]
• Halwatura, D.; Lechner, A.M.; Arnold, S. Drought severity-duration-frequency curves: A foundation for risk assessment and planning tool for ecosystem establishment in post-mining landscapes. Hydrol. Earth Syst. Sci. 2015 , 19 , 1069–1091. [ Google Scholar ] [ CrossRef ]
• Vicente-Serrano, S.M.; Gouveia, C.; Camarero, J.J.; Beguería, S.; Trigo, R.; López-Moreno, J.I.; Azorín-Molina, C.; Pasho, E.; Lorenzo-Lacruz, J.; Revuelto, J.; et al. The response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. USA 2013 , 110 , 52–57. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Byun, H.R.; Wilhite, D.A. Objective quantification of drought severity and duration. J. Clim. 1999 , 12 , 2747–2756. [ Google Scholar ] [ CrossRef ]
• Sims, A.P.; Niyogi, D.D.S.; Raman, S. Adopting drought indices for estimating soil moisture: A North Carolina case study. Geophys. Res. Lett. 2002 , 29 , 24.1–24.4. [ Google Scholar ] [ CrossRef ]
• Svoboda, M.; Lecomte, D.; Hayes, M.; Heim, R.; Gleason, K.; Angel, J.; Rippey, B.; Tinker, R.; Palecki, M.; Stooksbury, D.; et al. The drought monitor. Bull. Am. Meteorol. Soc. 2002 , 83 , 1181–1190. [ Google Scholar ]
• Vergni, L.; Todisco, F. Spatio-temporal variability of precipitation, temperature and agricultural drought indices in central Italy. Agr. For. Meteorol. 2011 , 151 , 301–313. [ Google Scholar ] [ CrossRef ]
• Quiring, S.M. Monitoring drought: an evaluation of meteorological drought indices. Geogr. Compass. 2009 , 3 , 64–88. [ Google Scholar ] [ CrossRef ]
• Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. 2010 , 391 , 202–216. [ Google Scholar ] [ CrossRef ]
• Zargar, A.; Sadiq, R.; Naser, B.; Khan, F.I. A review of drought indices. Environ. Rev. 2011 , 19 , 333–349. [ Google Scholar ] [ CrossRef ]
• Palmer, W.C. Meteorological Drought ; Weather Bureau Research Paper (No. 45); US Department of Commerce: Washington, DC, USA, 1965; p. 58.
• Shafer, B.; Dezman, L. Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. In Proceeding of the 50th Western Snow Conference, Reno, NV, USA, April 1982; pp. 164–175.
• Palmer, W.C. Keeping track of crop moisture conditions, nationwide: The new crop moisture index. Weatherwise 1968 , 21 , 156–161. [ Google Scholar ] [ CrossRef ]
• McKee, T.B.; Doesken, N.J.; Kleist, J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA, 17–22 January 1993; pp. 179–184.
• Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 2010 , 23 , 1696–1718. [ Google Scholar ] [ CrossRef ]
• Potop, V.; Boroneat, C.; Možný, M.; Štěpánek, P.; Skalák, P. Observed spatio-temporal characteristics of drought on various time scales over the Czech Republic. Theor. Appl. Climatol. 2014 , 112 , 3–4. [ Google Scholar ]
• Paulo, A.A.; Pereira, L.S.; Matias, P.G. Analysis of local and regional droughts in southern Portugal using the theory of runs and the Standardised Precipitation Index. In Tools for Drought Mitigation in Mediterranean Regions ; Springer: New York, NY, USA, 2003; pp. 55–78. [ Google Scholar ]
• Paulo, A.A.; Pereira, L.S. Drought concepts and characterization. Comparing drought indices. Water Int. 2006 , 31 , 37–49. [ Google Scholar ] [ CrossRef ]
• Paulo, A.A.; Rosa, R.D.; Pereira, L.S. Climate trends and behavior of drought indices based on precipitation and evapotranspiration in Portugal. Nat. Hazards Earth Syst. Sci. 2012 , 12 , 1481–1491. [ Google Scholar ] [ CrossRef ]
• Törnros, T.; Menzel, L. Leaf Area Index as a function of precipitation within a hydrological model. Hydrol. Res. 2014 , 45 , 660–672. [ Google Scholar ] [ CrossRef ]
• Moreira, E.E.; Paulo, A.A.; Pereira, L.S.; Mexia, J.T. Analysis of SPI drought class transitions using loglinear models. J. Hydrol. 2006 , 331 , 349–359. [ Google Scholar ] [ CrossRef ]
• Paulo, A.A.; Pereira, L.S. Stochastic prediction of drought class transitions. Water Resour. Manag. 2008 , 22 , 1277–1296. [ Google Scholar ] [ CrossRef ]
• Moreira, E.E.; Coelho, C.A.; Paulo, A.A.; Pereira, L.S.; Mexia, J.T. SPI-based drought category prediction using loglinear models. J. Hydrol. 2008 , 354 , 116–130. [ Google Scholar ] [ CrossRef ]
• Pereira, L.S.; Rosa, R.D.; Paulo, A.A. Testing a modification of the Palmer drought severity index for Mediterranean environments. In Methods and Tools for Drought Analysis and Management ; Rossi, G., Vega, T., Bonaccorso, B., Eds.; Springer: Dordrecht, The Netherlands, 2007; pp. 149–167. [ Google Scholar ]
• Davis, J.; Tavasci, D.; Marais, L. Fostering Rural and Local Economic Development in the Free State of South Africa ; Natural Resources Institute, University of Greenwich: London, UK, 2006; pp. 2–10. [ Google Scholar ]
• FSP (Free State Province). Free State Province Provincial Growth and Development Strategy (PGDS) 2005–2014 ; Free State Provincial Government: Bloemfontein, South Africa, 2005; p. 18.
• DAFF (Department of Agriculture, Forestry and Fisheries, South Africa). Abstract of Agricultural Statistics ; National Department of Agriculture, Forestry and Fisheries: Pretoria, South Africa, 2010; p. 145. Available online: www.nda.agric.za/docs/statsinfo/Abstract2010.doc (accessed on 29 May 2016).
• Masigo, A.; Matshego, C. Provincial Report on Education and Training for Agriculture and Rural Development in North-West Province ; National Department of Agriculture: Mafikeng, South Africa, 2005.
• Potop, V.; Možný, M.; Soukup, J. Drought at various time scales in the lowland regions and their impact on vegetable crops in the Czech Republic. Agr. For. Meteorol. 2012 , 156 , 121–133. [ Google Scholar ] [ CrossRef ]
• Morid, S.; Smakhtin, V.; Moghaddasi, M. Comparison of seven meteorological indices for drought monitoring in Iran. Int. J. Climat. 2006 , 26 , 971–985. [ Google Scholar ] [ CrossRef ]
• Beguería, S.; Vicente-Serrano, S.M.; Angulo-Martínez, M. A multiscalar global drought dataset: The SPEIbase: A new gridded product for the analysis of drought variability and impacts. Bull. Am. Meteorol. Soc. 2010 , 91 , 1351–1356. [ Google Scholar ] [ CrossRef ]
• McKee, T.B.; Doesken, N.J.; Kleist, J. Drought monitoring with multiple time scales. In Proceedings of the Ninth Conference on Applied Climatology, Dallas, TX, USA, 15–20 January 1995; pp. 233–236.
• Edwards, D.C.; McKee, T.B. Characteristics of 20th Century Drought in the United States at Multiple Time Scales ; Climatology Report No. 97-2; Colorado State University: Ft. Collins, CO, USA, 1997. [ Google Scholar ]
• Thornthwaite, C. An approach toward a rational classification of climate. Geogr. Rev. 1948 , 38 , 55–94. [ Google Scholar ] [ CrossRef ]
• Kumar, M.N.; Murthy, C.S.; Sesha Sai, M.V.R.; Roy, P.S. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Meteorol. Appl. 2009 , 16 , 381–389. [ Google Scholar ] [ CrossRef ]
• Banimahd, S.; Khalili, D. Factors influencing Markov chains predictability characteristics, utilizing SPI, RDI, EDI and SPEI drought indices in different climatic zones. Water Resour. Manag. 2013 , 27 , 3911–3928. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Vincent, L.A.; Hogg, W.D.; Niitsoo, A. Temperature and Precipitation Trends in Canada during the 20th Century. Atmos. Ocean. 2000 , 38 , 395–429. [ Google Scholar ] [ CrossRef ]
• Yue, S.; Pilon, P.; Phinney, B.; Cavadias, G. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Process. 2002 , 16 , 1807–1829. [ Google Scholar ] [ CrossRef ]
• Theil, H. A rank-invariant method of linear and polynomial regression analysis, I. Nederlands Akad. Wetensch. Proc. 1950 , 53 , 386–392. [ Google Scholar ]
• Theil, H. A rank-invariant method of linear and polynomial regression analysis, II. Nederlands Akad. Wetensch. Proc. 1950 , 53 , 521–525. [ Google Scholar ]
• Theil, H. A rank-invariant method of linear and polynomial regression analysis, III. Nederlands Akad. Wetensch. Proc. 1950 , 53 , 1397–1412. [ Google Scholar ]
• Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am Stat. Assoc. 1968 , 63 , 1379–1389. [ Google Scholar ] [ CrossRef ]

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Botai, C.M.; Botai, J.O.; Dlamini, L.C.; Zwane, N.S.; Phaduli, E. Characteristics of Droughts in South Africa: A Case Study of Free State and North West Provinces. Water 2016 , 8 , 439. https://doi.org/10.3390/w8100439

Botai CM, Botai JO, Dlamini LC, Zwane NS, Phaduli E. Characteristics of Droughts in South Africa: A Case Study of Free State and North West Provinces. Water . 2016; 8(10):439. https://doi.org/10.3390/w8100439

Botai, Christina M., Joel O. Botai, Lucky C. Dlamini, Nosipho S. Zwane, and Elelwani Phaduli. 2016. "Characteristics of Droughts in South Africa: A Case Study of Free State and North West Provinces" Water 8, no. 10: 439. https://doi.org/10.3390/w8100439

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Developing Drought Triggers and Indicators Using the National Water Model: A Case Study to Improve the U.S. Drought Monitor in Support of the Northeast DEWS

Assessments following the 2016 drought in New York and New England broadly identified data gaps related to soil moisture, streamflow, and groundwater levels. To address these shortcomings, this project seeks to demonstrate the utility of integrating historical National Water Model (NWM) reanalyses with bias adjustments derived from soil moisture data from the new, 126-station New York State Mesonet and USGS streamflow observations. The project will evaluate NWM skill over the Northeastern U.S. during historical droughts and build a case study around the region’s extreme 2016 drought. The work will then develop a set of drought indicators and forecasts best-suited for the Northeast DEWS which will be supported beyond the project's completion by the Northeast Regional Climate Center (NRCC).

For more information, please contact Sylvia Reeves ( [email protected] ).

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• Published: 15 July 2019

Scope and limitations of drought management within complex human–natural systems

• William K. Jaeger   ORCID: orcid.org/0000-0003-3461-4855 1 ,
• Adell Amos 2 ,
• David R. Conklin 3 ,
• Christian Langpap 1 ,
• Kathleen Moore   ORCID: orcid.org/0000-0002-0720-0457 4 &
• Andrew J. Plantinga 5  

Nature Sustainability volume  2 ,  pages 710–717 ( 2019 ) Cite this article

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• Environmental economics
• Water resources

Growing evidence suggests that drought risk is increasing due to climate change. Evaluation of potential policy responses involves understanding complex economic tradeoffs, hydrologic and social feedbacks, and recognizing how combinations of interventions may have complementary or conflicting effects. This paper explores the potential that coupled human–natural system models have to address these questions. We employ a detailed model of the Willamette River Basin, Oregon, to evaluate the effectiveness of a variety of potential drought policy interventions to conserve or reallocate water during a simulated near-term drought year. The drought year is characterized by early-season low flows that make it impossible to meet water demands. The results indicate that while the policies are effective at conserving water, they have limited ability to mitigate the shortages because the timing and location of conservation responses do not match the timing and location of the shortages.

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Data availability.

Details of the Willamette Envision models and scenarios used for the analysis described in this paper can be found at http://inr.oregonstate.edu/ww2100/data .

Diffenbaugh, N. S., Swain, D. L. & Touma, D. Anthropogenic warming has increased drought risk in California. Proc. Natl Acad. Sci. USA 112 , 3931–3936 (2015).

Article   CAS   Google Scholar  

IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

Van Loon, A. F. et al. Drought in the Anthropocene. Nat. Geosci. 9 , 89 (2016).

Article   Google Scholar  

Chang, H. & Jung, I.-W. Spatial and temporal changes in runoff caused by climate change in a complex large river basin in Oregon. J. Hydrol. 388 , 186–207 (2010).

Jaeger, W. K. et al. Finding water scarcity amid abundance using human–natural system models. Proc. Natl Acad. Sci. USA 114 , 11884–11889 (2017).

Amos, A. L. Developing the law of the river: the integration of law and policy into hydrologic and socio-economic modeling efforts in the Willamette River Basin. Univ. Kans. Law Rev. 62 , 1091 (2013).

Google Scholar  

Levin, S. et al. Social-ecological systems as complex adaptive systems: modeling and policy implications. Environ. Dev. Econ. 18 , 111–132 (2013).

Cai, X., McKinney, D. C. & Lasdon, L. S. Integrated hydrologic-agronomic-economic model for river basin management. J. Water Resour. Plan. Manage. 129 , 4–17 (2003).

Bateman, I. et al. Bringing ecosystem services into economic decision-making: land use in the United Kingdom. Science 341 , 45–50 (2013).

Rabotyagov, S. S. et al. Cost-effective targeting of conservation investments to reduce the northern Gulf of Mexico hypoxic zone. Proc. Natl Acad. Sci. USA 111 , 18530–18535 (2014).

IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

Turner, D. P., Conklin, D. R. & Bolte, J. P. Projected climate change impacts on forest land cover and land use over the Willamette River Basin, Oregon, USA. Clim. Change 133 , 335–348 (2015).

Bigelow, D. P., Plantinga, A. J., Lewis, D. J. & Langpap, C. How does urbanization affect water withdrawals?Insights from an econometric-based landscape simulation. Land Econ. 93 , 413–436 (2017).

Lewis, D. J., Plantinga, A. J., Nelson, E. & Polasky, S. The efficiency of voluntary incentive policies for preventing biodiversity loss. Resour. Energy Econ. 33 , 192–211 (2011).

Kalinin, A. V. Right as Rain? The Value of Water in Willamette Valley Agriculture (Oregon State Univ., 2013).

Seibert, J. Estimation of parameter uncertainty in the HBV model. Nord. Hydrol. 28 , 247–262 (1997).

Turner, D. P. et al. Assessing mechanisms of climate change impact on the upland forest water balance of the Willamette River Basin, Oregon. Ecohydrology 10 , e1776 (2017).

Allen, R. G. & Robison, C. W. Evapotranspiration and Consumptive Irrigation Water Requirements for Idaho (Univ. of Idaho Research and Extension, 2007).

Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. Irrigation and Drainage Paper 56 (FAO, 1998).

Seibert, J. HBV Light Version 2. User’s Manual (Stockholm Univ., 2005).

Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33 , 121–131 (2013).

Rupp, D. E., Abatzoglou, J. T., Hegewisch, K. C. & Mote, P. W. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. J. Geophys. Res. Atmos. 118 , 10,884–10,906 (2013).

Vano, J. A., Kim, J. B., Rupp, D. E. & Mote, P. W. Selecting climate change scenarios using impact-relevant sensitivities. Geophys. Res. Lett. 42 , 5516–5525 (2015).

Wilhite, D. A. Drought policy in the U.S. and Australia: a comparative analysis. J. Am. Water Resour. Assoc. 22 , 425–438 (1986).

Drought Management and Its Impact on Public Water Systems (National Research Council, 1986).

Willamette River Basin Project Biological Opinion (NMFS, 2008).

Moore, K. M. Optimizing Reservoir Operations to Adapt to 21st Century Expectations of Climate and Social Change in the Willamette River Basin, Oregon (Oregon State Univ., 2015).

Harrison, J. Warm Water Blamed for Huge Columbia River Sockeye Die-off (Northwest Power and Conservation Council, 2015).

Faulkner, J. R., Widener, D. L., Smith, S. G., Marsh, T. M. & Zabel, R. W. Survival Estimates for the Passage of Spring-Migrating Juvenile Salmonids through Snake and Columbia River Dams and Reservoirs , 2015 (National Marine Fisheries Service for the Bonneville Power Administration, 2016).

Statistical Report for Fiscal year 2016–2017 (Portland Water Bureau, 2017).

US Community Water System Survey 2000 Vol. 1: Overview (EPA, 2002); www.epa.gov/safewater

U. S. Water Supply and Use in the United States 2008 (EPA, 2008); www.epa.gov/watersense

Wichman, C. J., Taylor, L. O. & von Haefen, R. H. Conservation policies: who responds to price and who responds to prescription? J. Environ. Econ. Manag. 79 , 114–134 (2016).

Mini, C., Hogue, T. S. & Pincetl, S. The effectiveness of water conservation measures on summer residential water use in Los Angeles, California. Resour. Conserv. Recycl. 94 , 136–145 (2015).

Bland, A. Californians are struggling to pay for rising water rates—water deeply. NewsDeeply https://www.newsdeeply.com/water/articles/2018/02/27/californians-are-struggling-to-pay-for-rising-water-rates (2018).

Postel, S. L., Daily, G. C. & Ehrlich, P. R. Human appropriation of renewable fresh water. Science 271 , 785–788 (1996).

Scheierling, S. M., Loomis, J. B. & Young, R. A. Irrigation water demand: a meta-analysis of price elasticities. Water Resour. Res. 42 , W01411 (2006).

Huffaker, R. & Whittlesey, N. A theoretical analysis of economic incentive policies encouraging agricultural water conservation. Int. J. Water Resour. Dev. 19 , 37–53 (2003).

Kelley, A. K. & Beck, R. E. Waters and Water Rights (LexisNexis, 2009).

National Hydrography Dataset (US Geological Survey (USGS), accessed 21 September 2018); http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.php

Bennear, L. S., Lee, J. M. & Taylor, L. O. Municipal rebate programs for environmental retrofits: an evaluation of additionality and cost-effectiveness. J. Policy Anal. Manage. 32 , 350–372 (2013).

Mansur, E. T. & Olmstead, S. M. The value of scarce water: measuring the inefficiency of municipal regulations. J. Urban Econ. 71 , 332–346 (2012).

Brent, D. A., Cook, J. H. & Olsen, S. Social comparisons, household water use, and participation in utility conservation programs: evidence from three randomized trials. J. Assoc. Environ. Resour. Econ. 2 , 597–627 (2015).

Olmstead, S. M. The economics of managing scarce water resources. Rev. Environ. Econ. Policy 4 , 179–198 (2010).

Biological Opinion on the Continued Operation and Maintenance of the Willamette River Basin Project and Effects to Oregon Chub, Bull Trout, and Bull Trout Critical Habitat Designated Under the Endangered Species Act (US Fish and Wildlife Service, 2008).

Amos, A. Freshwater conservation in the context of energy and climate policy: assessing progress and identifying challenges in Oregon and the Western United States. Univ. Denv. Water Law Rev . 12 , 1–22 (2008).

Getches, D., Zellmer, S. & Amos, A. Water Law in a Nutshell 5th edn (West Academic, 2015).

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Acknowledgements

This project was supported by the National Science Foundation (grant Nos. 1039192 (Oregon State University), 1038925 (Portland State University) and 1038899 (University of Oregon)). We also acknowledge support from NOAA’s Climate Program Office under cooperative agreement No. NA15OAR4310145.

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William K. Jaeger & Christian Langpap

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David R. Conklin

School of Marine and Environmental Affairs, University of Washington, Seattle, WA, USA

Kathleen Moore

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Andrew J. Plantinga

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Supplementary Methods, References 1–26, Tables 1–7 and Figs. 1–4.

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Jaeger, W.K., Amos, A., Conklin, D.R. et al. Scope and limitations of drought management within complex human–natural systems. Nat Sustain 2 , 710–717 (2019). https://doi.org/10.1038/s41893-019-0326-y

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Received : 29 October 2018

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Published : 15 July 2019

Issue Date : August 2019

DOI : https://doi.org/10.1038/s41893-019-0326-y

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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] .

• Abdelmalek MB, Nouiri I. Study of trends and mapping of drought events in Tunisia and their impacts on agricultural production. Science of the Total Environment. 2020; 734 :139311. doi: 10.1016/j.scitotenv.2020.139311. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Abidoye BO, Herriges JA, Tobias JL. Controlling for observed and unobserved site characteristics in RUM models of recreation demand. American Journal of Agriculture Economy. 2012; 94 :1070–1093. doi: 10.1093/ajae/aas056. [ CrossRef ] [ Google Scholar ]
• Abidoye BO, Odusola AF. Climate change and economic growth in Africa: An econometric analysis. Journal of African Economy. 2015; 24 :277–301. doi: 10.1093/jae/eju033. [ CrossRef ] [ Google Scholar ]
• Abiodun BJ, Makhanya N, Petja B, Abatan AA, Oguntunde PG. Future projection of droughts over major river basins in Southern Africa at specific global warming levels. Theoretical and Applied Climatology. 2019; 137 :1785–1799. doi: 10.1007/s00704-018-2693-0. [ CrossRef ] [ Google Scholar ]
• ACAPS. (2018). Food insecurity—Ethiopia, Nigeria, Somalia, South Sudan, and Yemen. Thematic Report—February 2018. ACAPS; Geneva, Switzerland.
• Adisa OM, Botai JO, Adeola AM, Botai CM, Hassen A, Darkey D, Tasfamariam AT, Adisa AT, Adisa AF. Analysis of drought conditions over major maize producing provinces of South Africa. Journal of Agriculture Meteorology. 2019; 75 :173–182. doi: 10.2480/agromet.D-18-00049. [ CrossRef ] [ Google Scholar ]
• AghaKouchak AL, Cheng M, Omid F, Alireza, Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophysics Research Letters. 2014; 41 :8847–8852. doi: 10.1002/2014GL062308. [ CrossRef ] [ Google Scholar ]
• Ahmadalipour A, Moradkhani H. Multi-dimensional assessment of drought vulnerability in Africa: 1960–2100. Science of the Total Environment. 2018; 644 :520–535. doi: 10.1016/j.scitotenv.2018.07.023. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ahmadalipour A, Moradkhani H, Castelletti A, Magliocca N. Future drought risk in Africa: Integrating vulnerability, climate change, and population growth. Science of the Total Environment. 2019; 662 :672–686. doi: 10.1016/j.scitotenv.2019.01.278. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ajayi VO, Ilori OW. Projected drought events over West Africa using RCA4 regional climate model. Earth Systematic Environment. 2020; 4 :329–348. doi: 10.1007/s41748-020-00153-x. [ CrossRef ] [ Google Scholar ]
• Akinsanola AA, Zhou W. Projections of West African summer monsoon rainfall extremes from two CORDEX models. Climate Dynamics. 2019; 52 :2017–2028. doi: 10.1007/s00382-018-4238-8. [ CrossRef ] [ Google Scholar ]
• Akinsanola AA, Zhou W, Zhou T, Keenlyside N. Amplification of synoptic to annual variability of West African summer monsoon rainfall under global warming. Climate Atmosphere Science. 2020; 3 :21. doi: 10.1038/s41612-020-0125-1. [ CrossRef ] [ Google Scholar ]
• Almazroui M, Saeed F, Saeed S, Islam NM, Ismail M, Klutse NAB, Siddiqui MM. Projected change in temperature and precipitation over Africa from CMIP6. Earth Systematic Environment. 2020; 4 :455–475. doi: 10.1007/s41748-020-00161-x. [ CrossRef ] [ Google Scholar ]
• Ayugi, B., Shilenje, Z. W., Babaousmail, H., Lim Kam Sian, K. T. C., Mumo, R., Dike, V. N., Iyakaremye, V., & Ongoma, V. (2022). Projected changes in meteorological drought over East Africa inferred from bias-corrected CMIP6 models. Natural Hazards . (Under Review). [ PMC free article ] [ PubMed ]
• Ayugi B, Tan G, Rouyun N, Zeyao D, Ojara M, Mumo L, Babaousmail H, Ongoma V. Evaluation of meteorological drought and flood scenarios over Kenya, East Africa. Atmosphere. 2020; 11 :307. doi: 10.3390/atmos11030307. [ CrossRef ] [ Google Scholar ]
• Babaousmail H, Hou R, Ayugi B, Ojara M, Ngoma H, Karim R, Rajasekar A, Ongoma V. Evaluation of the performance of CMIP6 models in reproducing rainfall patterns over North Africa. Atmosphere (basel) 2021; 12 :475. doi: 10.3390/atmos12040475. [ CrossRef ] [ Google Scholar ]
• Bader J, Latif M. The 1983 drought in the West Sahel: A case study. Climate Dynamics. 2011; 36 :463–472. doi: 10.1007/s00382-009-0700-y. [ CrossRef ] [ Google Scholar ]
• Baltas E. Spatial distribution of climatic indices in northern Greece. Meteorological Applications: A journal of forecasting, practical applications, training techniques and modelling. 2007; 14 (1):69–78. doi: 10.1002/met.7. [ CrossRef ] [ Google Scholar ]
• Bergman DJ, Zeng XC, Hui PM, Stroud D. Effective-medium theory for weakly nonlinear composites. Physical Review B. 1988; 38 :10970. doi: 10.1103/PhysRevB.38.10970. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bhalotra, Y. P. R. (1985). Rainfall maps of Botswana.
• Biasutti M, Held IM, Sobel AH, Giannini A. SST forcings and Sahel rainfall variability in simulations of the twentieth and twentyfirst centuries. Journal of Climate. 2008; 21 (14):3471–3486. doi: 10.1175/2007JCLI1896.1. [ CrossRef ] [ Google Scholar ]
• Bichet A, Diedhiou A, Hingray B, Evin G, Toure GEN, Klutse NAB, Kouadio K. Assessing uncertainties in the regional projections of precipitation in CORDEX-AFRICA. Climatic Change. 2020; 162 (2):583–601. doi: 10.1007/s10584-020-02833-z. [ CrossRef ] [ Google Scholar ]
• Bradford RB. Drought events in Europe. In: Vogt JV, Somma F, editors. Drought and drought mitigation in Europe. Kluwer; 2000. pp. 7–20. [ Google Scholar ]
• Encyclopedia Britannica. http://www.britannica.com . Accessed 25 Nov 2020
• Burns M, Audouin M, Weaver A. Advancing sustainability science in South Africa. South African Journal of Science. 2006; 102 (9-10):379–384. [ Google Scholar ]
• Cai Q, Liu Y, Liu H, Ren J. Reconstruction of drought variability in North China and its association with sea surface temperature in the joining area of Asia and Indian-Pacific Ocean. Palaeogeography, Palaeoclimatology, Palaeoecology. 2015; 417 :554–560. doi: 10.1016/j.palaeo.2014.10.021. [ CrossRef ] [ Google Scholar ]
• Caminade C, Terray L. Twentieth century Sahel rainfall variability as simulated by the ARPEGE AGCM, and future changes. Climate Dynamics. 2010; 35 :75–94. doi: 10.1007/s00382-009-0545-4. [ CrossRef ] [ Google Scholar ]
• Cane MA, Clemen AC, Kaplan A, Kushnir Y, Pozdnyakov D, Seager R, Zebiak SE, Murtugudde R. Twentieth-century Sea surface temperature trends. Science. 1997; 275 :957–960. doi: 10.1126/science.275.5302.957. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Charney JG. Dynamics of deserts and drought in the Sahel. Quarterly Journal Royal Meteorological Society. 1975; 101 :193–202. doi: 10.1002/qj.49710142802. [ CrossRef ] [ Google Scholar ]
• Chiew FHS, Potter NJ, Vaze J, Petheram C, Zhang L, Teng J, Post DA. Observed hydrologic non-stationarity in far south-eastern Australia: Implications for modelling and prediction. Stoch Environment Research Risk Assessment. 2014; 28 :3–15. doi: 10.1007/s00477-013-0755-5. [ CrossRef ] [ Google Scholar ]
• Christenson E, Elliott M, Banerjee O, Hamrick L. Bartram J (2014) Climate-related hazards: A method for global assessment of urban and rural population exposure to cyclones, droughts, and floods. International Journal of Environmental Research and Public Health. 2014; 11 :2169–2192. doi: 10.3390/ijerph110202169. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cook ER, Seager R, Cane MA, Stahle DW. North American drought: Reconstructions, causes, and consequences. Earth-Science Reviews. 2007; 81 :93–134. doi: 10.1016/j.earscirev.2006.12.002. [ CrossRef ] [ Google Scholar ]
• Cook KH. The mysteries of Sahel droughts. Natural Geoscience. 2008; 1 :647–648. doi: 10.1038/ngeo320. [ CrossRef ] [ Google Scholar ]
• Dai A. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008. Journal of Geophysical Research. 2011 doi: 10.1029/2010JD015541. [ CrossRef ] [ Google Scholar ]
• Dai A. Drought under global warming: A review. Wireless Climate Change. 2011; 2 :45–65. doi: 10.1002/wcc.81. [ CrossRef ] [ Google Scholar ]
• Dai A. Increasing drought under global warming in observations and models. Nature Climate Change. 2013; 3 :52–58. doi: 10.1038/nclimate1633. [ CrossRef ] [ Google Scholar ]
• Dai A, Trenberth KE, Qian T. A global dataset of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology. 2004; 5 :1117–1130. doi: 10.1175/JHM-386.1. [ CrossRef ] [ Google Scholar ]
• Dai A, Zhao T. Uncertainties in historical changes and future projections of drought. Part I: Estimates of historical drought changes. Climatic Change. 2017; 144 :519–533. doi: 10.1007/s10584-016-1705-2. [ CrossRef ] [ Google Scholar ]
• Dell M, Jones BF, Olken BA. Temperature shocks and economic growth: Evidence from the last half century. American Economy Journal of Applied Economics: Macroeconomics. 2012; 4 :66–95. doi: 10.1257/mac.4.3.66. [ CrossRef ] [ Google Scholar ]
• Dent MC, Schulze RE, Wills HMM, Lynch SD. Spatial and temporal analysis of the recent drought in the summer rainfall region of southern Africa. Water S. a. 1987; 13 :37–42. [ Google Scholar ]
• Diasso U, Abiodun BJ. Future impacts of global warming and reforestation on drought patterns over West Africa. Theoretical and Applied Climatology. 2018; 133 :647–662. doi: 10.1007/s00704-017-2209-3. [ CrossRef ] [ Google Scholar ]
• Dike VN, Lin ZH, Ibe CC. Intensification of summer rainfall extremes over Nigeria during recent decades. Atmosphere. 2020; 11 :1084. doi: 10.3390/atmos11101084. [ CrossRef ] [ Google Scholar ]
• Ellis JE, Swift DM. Stability of African pastoral ecosystems: Alternate paradigms and implications for development. Journal of Range Management. 1988; 41 (6):450–459. doi: 10.2307/3899515. [ CrossRef ] [ Google Scholar ]
• Enfield DB. El Niño, past and present. Review of Geophysics. 1989; 27 :159–187. doi: 10.1029/RG027i001p00159. [ CrossRef ] [ Google Scholar ]
• FAO. (2018). Drought characteristics and management in North Africa and the Near East . Food and Agriculture Organization of the United Nations Rome, 2018.
• Folland CK, Palmer TN, Parker DE. Sahel rainfall and worldwide sea temperatures, 1901–85. Nature. 1986; 320 :602–607. doi: 10.1038/320602a0. [ CrossRef ] [ Google Scholar ]
• Fontaine B, Roucou P, Monerie PA. Changes in the African monsoon region at medium-term time horizon using 12 AR4 coupled models under the A1b emissions scenario. Atmospheric Sciences Letters. 2011; 12 :83–88. doi: 10.1002/asl.308. [ CrossRef ] [ Google Scholar ]
• Ford JD, Berrang-Ford L, Paterson J. A systematic review of observed climate change adaptation in developed nations. Climate Change. 2011; 106 :327–336. doi: 10.1007/s10584-011-0045-5. [ CrossRef ] [ Google Scholar ]
• Funk C, Hoell A, Shukla S, Bladé I, Liebmann B, Roberts JB, Robertson FR, Husak G. Predicting East African spring droughts using Pacific and Indian Ocean Sea surface temperature indices. Hydrology Earth System Science. 2014; 18 :4965–4978. doi: 10.5194/hess-18-4965-2014. [ CrossRef ] [ Google Scholar ]
• Gautier D, Locatelli B, Corniaux C, Alary V. Global changes, livestock and vulnerability: The social construction of markets as an adaptive strategy. Geography Journal. 2016; 182 :153–164. doi: 10.1111/geoj.12115. [ CrossRef ] [ Google Scholar ]
• Gebrechorkos SH, Hülsmann S, Bernhofer C. Analysis of climate variability and droughts in East Africa using high-resolution climate data products. Global and Planetary Change. 2020 doi: 10.1016/j.gloplacha.2020.103130. [ CrossRef ] [ Google Scholar ]
• Gemenne F, Barnett J, Adger WN, Dabelko GD. Climate and security: Evidence, emerging risks, and a new agenda. Climatic Change. 2014; 123 :1–9. doi: 10.1007/s10584-014-1074-7. [ CrossRef ] [ Google Scholar ]
• Giannini A, Biasutti M, Held IM, Sobel AH. A global perspective on African climate. Climate Change. 2008; 90 :359–383. doi: 10.1007/s10584-008-9396-y. [ CrossRef ] [ Google Scholar ]
• Gidey E, Dikinya O, Sebego R, Segosebe E, Zenebe A. Predictions of future meteorological drought hazard (~ 2070) under the representative concentration path (RCP) 4.5 climate change scenarios in Raya, Northern Ethiopia. Model Earth System and Environment. 2018; 4 :475–488. doi: 10.1007/s40808-018-0453-x. [ CrossRef ] [ Google Scholar ]
• Gore M, Abiodun BJ, Kucharski F. Understanding the influence of ENSO patterns on drought over southern Africa using SPEEDY. Climate Dynamics. 2020; 54 :307–327. doi: 10.1007/s00382-019-05002-w. [ CrossRef ] [ Google Scholar ]
• Habiba U, Shaw R, Takeuchi Y. Farmer's perception and adaptation practices to cope with drought: Perspectives from Northwestern Bangladesh. International Journal of Disaster Risk Reduction. 2012; 1 :72–84. doi: 10.1016/j.ijdrr.2012.05.004. [ CrossRef ] [ Google Scholar ]
• Haile GG, Tang Q, Hosseini-Moghari SM, Liu X, Gebremicael TG, Leng G, Kebede A, Xu X, Yun X. Projected impacts of climate change on drought patterns over East Africa. Earth’s Future. 2020; 8 (7):e2020EF001502. doi: 10.1029/2020EF001502. [ CrossRef ] [ Google Scholar ]
• Haile GG, Tang Q, Li W, Liu X, Zhang X. Drought: Progress in broadening its understanding. Wiley Interdisciplinary Review RNA Water. 2020; 7 :e1407. doi: 10.1002/wat2.1407. [ CrossRef ] [ Google Scholar ]
• Haile GG, Tang Q, Sun S, Huang Z, Zhang X, Liu X. Droughts in East Africa: Causes, impacts and resilience. Earth-Science Review. 2019; 193 :146–161. doi: 10.1016/j.earscirev.2019.04.015. [ CrossRef ] [ Google Scholar ]
• Hartmann I, Sugulle AJ. The impact of climate change on pastoral societies of Somaliland. Heinrich-BöllStiftung; 2009. [ Google Scholar ]
• Hastenrath S. Decadal-scale changes of the circulation in the tropical Atlantic sector associated with Sahel drought. International Journal of Climatology. 1990; 10 :459–472. doi: 10.1002/joc.3370100504. [ CrossRef ] [ Google Scholar ]
• Hastenrath S. Circulation mechanisms of climate anomalies in East Africa and the equatorial Indian Ocean. Dynamic Atmospheres Oceans. 2007; 43 :25–35. doi: 10.1016/j.dynatmoce.2006.06.002. [ CrossRef ] [ Google Scholar ]
• Hastenrath S, Polzin D, Mutai C. Diagnosing the 2005 drought in equatorial East Africa. Journal of Climate. 2007; 20 :4628–4637. doi: 10.1175/jcli4238.1. [ CrossRef ] [ Google Scholar ]
• Herceg D, Sobel AH, Sun L. Regional modeling of decadal rainfall variability over the Sahel. Climate Dynamics. 2007; 29 :89–99. doi: 10.1007/s00382-006-0218-5. [ CrossRef ] [ Google Scholar ]
• Hoerling M, Eischeid J, Perlwitz J, Quan X, Zhang T, Pegion P. On the increased frequency of Mediterranean drought. Journal of Climate. 2012; 25 :2146–2161. doi: 10.1175/JCLI-D-11-00296.1. [ CrossRef ] [ Google Scholar ]
• Hoerling M, Hurrell J, Eischeid J, Phillips A. Detection and attribution of twentieth-century northern and southern African rainfall change. Journal of Climate. 2006; 19 :3989–4008. doi: 10.1175/JCLI3842.1. [ CrossRef ] [ Google Scholar ]
• Horn LN, Shimelis H. Production constraints and breeding approaches for cowpea improvement for drought prone agro-ecologies in Sub-Saharan Africa. Annals of Agricultural Science. 2020; 65 :83–91. doi: 10.1016/j.aoas.2020.03.002. [ CrossRef ] [ Google Scholar ]
• Hua W, Zhou L, Chen H, Nicholson SE, Raghavendra A, Jiang Y. Possible causes of the Central Equatorial African long-term drought. Environmental Research Letters. 2016; 11 (12):124002. doi: 10.1088/1748-9326/11/12/124002. [ CrossRef ] [ Google Scholar ]
• Hulme M. Rainfall changes in Africa: 1931–1960 to 1961–1990. International Journal of Climatology. 1992; 12 :685–699. doi: 10.1002/joc.3370120703. [ CrossRef ] [ Google Scholar ]
• Intergovernmental Panel on Climate Change (IPCC) Climate change 2007: Impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutik JP, van der Linden PJ, Hasen CE, editors. Contribution of working group II to the fourth assessment report of the IPCC. Cambridge University Press; 2007. [ Google Scholar ]
• IPCC . Climate change 2007: Synthesis report. In: Pachauri RK, Reisinger A, editors. Contribution of working groups I, II and III to the fourth assessment report of the intergovernmental panel on climate change. IPCC Geneva; 2007. [ Google Scholar ]
• IPCC . Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field CB, Barros V, Stocker TF, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM, editors. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge, UK, and New York, NY, USA: Cambridge University Press; 2012. p. 582. [ Google Scholar ]
• IPCC . Climate change 2013: The physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press; 2013. p. 1535. [ Google Scholar ]
• IPCC . Summary for policymakers, climate change 2014: Impacts, adaptation, and vulnerability. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL, editors. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press; 2014. pp. 1–32. [ Google Scholar ]
• IPCC. (2018). Global warming of 1.5° C: An IPCC special report on the impacts of global warming of 1.5° C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Intergovernmental Panel on Climate Change.
• IPCC . Summary for policymakers. In: Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis MI, Huang M, Leitzell K, Lonnoy E, Matthews JBR, Maycock TK, Waterfield T, Yelekçi O, Yu R, Zhou B, editors. Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Cambridge University Press; 2021. [ Google Scholar ]
• Janicot S, Trzaska S, Poccard I. Summer Sahel-ENSO teleconnection and decadal time scale SST variations. Climate Dynamics. 2001; 18 :303–320. doi: 10.1007/s003820100172. [ CrossRef ] [ Google Scholar ]
• Jury, M. R. (2002). Economic impacts of climate variability in South Africa and development of resource prediction models. Journal of Applied Meteorology Climatology 41, 46–55. 10.1175/1520-0450(2002)041<0046:EIOCVI>2.0.CO;2
• Jury, M. R., Levey, K. M., McQueen, C., Parker, B. A., Lee-Thorp, A., Makarau, A., & Pathack, B. (1992). Correlation atlas of climatic determinants for sub-tropical Southern Africa and the SW Indian Ocean . Technical Report, Department Oceanography, University of Cape Town
• Kamali B, Abbaspour KC, Wehrli B, Yang H. Drought vulnerability assessment of maize in Sub-Saharan Africa: Insights from physical and social perspectives. Global and Planetary Change. 2018; 162 :266–274. doi: 10.1016/j.gloplacha.2018.01.011. [ CrossRef ] [ Google Scholar ]
• Kaniewski D, Van Campo E, Weiss H. Drought is a recurring challenge in the Middle East. PNAS. 2012; 109 :3862–3867. doi: 10.1073/pnas.1116304109. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Katz, R. W., & Glantz, M. H. (1986). Anatomy of a rainfall index. Monthly Weather Review, 114 , 764–771. 10.1175/1520-0493(1986)114<0764:AOARI>2.0.CO;2
• Klutse, N. A. B., et al. (2018). Potential impact of 1.5 °C and 2 °C global warming on consecutive dry and wet days over West Africa. Environmental Research Letters, 13 , 055013. 10.1088/1748-9326/aab37b
• Kristina LJ, Kinsman AA, Abdirisak DM, Farah A, Hagos G, Lelekoitien LT, Mohamed OM, Bile KM, Jairus M, Barbara S. Health status and health care needs of drought-related migrants in the horn of Africa—A qualitative investigation. International Journal of Environmental Research and Public Health. 2020; 17 :5917. doi: 10.3390/ijerph17165917. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kumaririgaud, K., de Sherbinin, A., Jones, B., Bergmann, J., Clement, V., Ober, K., Schewe, J., Adamo, S., Mccusker, B., & Heuser, S., et al. (2018). Groundswell: Preparing for internal climate migration . The World Bank, Washington, DC
• Kusangaya S, Warburton ML, Van Garderen EA, Jewitt GP. Impacts of climate change on water resources in southern Africa: A review. Physics and Chemistry of the Earth, Parts A/B/C. 2014; 67 :47–54. doi: 10.1016/j.pce.2013.09.014. [ CrossRef ] [ Google Scholar ]
• Landman WA, Mason SJ. Change in the association between Indian Ocean sea-surface temperatures and summer rainfall over South Africa and Namibia. International Journal of Climatology. 1999; 19 :1477–1492. doi: 10.1002/(SICI)1097-0088(19991115)19:13<1477::AID-JOC432>3.0.CO;2-W. [ CrossRef ] [ Google Scholar ]
• Lawal S, Lennard C, Jack C, Wolski P, Hewitson B, Abiodun B. The observed and model-simulated response of southern African vegetation to drought. Agricultural and Forest Meteorology. 2019 doi: 10.1016/j.agrformet.2019.107698. [ CrossRef ] [ Google Scholar ]
• Lebel T, Ali A. Recent trends in the Central and Western Sahel rainfall regime (1990–2007) Journal of Hydrology. 2009; 375 :52–64. doi: 10.1016/j.jhydrol.2008.11.030. [ CrossRef ] [ Google Scholar ]
• Liang L, Zhao SH, Qin ZH, Ke-Xun HE, Chong C, Luo YX, Zhou XD. Drought change trend using MODIS TVDI and its relationship with climate factors in China from 2001 to 2010. Journal of Integrative Agriculture. 2014; 13 :1501–1508. doi: 10.1016/S2095-3119(14)60813-3. [ CrossRef ] [ Google Scholar ]
• Lindesay JA, Vogel CH. Historical evidence for Southern Oscillation—Southern African rainfall relationships. International Journal of Climatology. 1990; 10 :679–689. doi: 10.1002/joc.3370100703. [ CrossRef ] [ Google Scholar ]
• Livingston, G., Schonberger, S., & Delaney, S. (2011). Sub-Saharan Africa: The state of smallholders in agriculture IFAD Conf. on New Directions for Smallholder Agriculture, Int. Fund for Agricultural Development , Via Paolo Di Dono (Via Paolo DiDono, Rome, Italy, vol. 44, pp. 00142).
• Losada T, Rodriguez-Fonseca B, Mohino E, Bader J, Janicot S, Mechoso CR. Tropical SST and Sahel rainfall: A non-stationary relationship. Geophysics Research Letters. 2012 doi: 10.1029/2012GL052423. [ CrossRef ] [ Google Scholar ]
• Lott FC, Christidis N, Stott PA. Can the 2011 East African drought be attributed to human-induced climate change? Geophysical Research Letters. 2013; 40 :1177–1181. doi: 10.1002/grl.50235. [ CrossRef ] [ Google Scholar ]
• Lyon BJ. Seasonal drought in the greater horn of africa and its recent increase during the March–May long rains. Journal of Climate. 2014; 27 :7953–7975. doi: 10.1175/JCLI-D-13-00459.1. [ CrossRef ] [ Google Scholar ]
• Lyon B, Dewitt DG. A recent and abrupt decline in the East African long rains. Geophysics Research Letters. 2012; 39 :L02702. doi: 10.1029/2011GL050337. [ CrossRef ] [ Google Scholar ]
• Maúre G, Pinto I, Ndebele-Murisa M, Muthige M, Lennard C, Nikulin G, Meque A. The southern African climate under 1.5 C and 2 C of global warming as simulated by CORDEX regional climate models. Environmental Research Letters. 2018; 13 (6):065002. doi: 10.1088/1748-9326/aab190. [ CrossRef ] [ Google Scholar ]
• Masih I, Maskey S, Mussá FEF, Trambauer P. A review of droughts on the African continent: A geospatial and long-term perspective. Hydrology Earth System Science. 2014; 18 :3635–3649. doi: 10.5194/hess-18-3635-2014. [ CrossRef ] [ Google Scholar ]
• Mason SJ, Jury MR. Climatic variability and change over southern Africa: A reflection on underlying processes. Program Physics Geography. 1997; 21 :23–50. doi: 10.1177/030913339702100103. [ CrossRef ] [ Google Scholar ]
• Mcdowell G, Ford J, Jones J. Community-level climate change vulnerability research: Trends, progress, and future directions. Environment Research Letters. 2016; 11 :033001. doi: 10.1088/1748-9326/11/3/033001. [ CrossRef ] [ Google Scholar ]
• McKee TB, Doesken NJ, Kleist J. 9th Conference on applied climatology. American Meteorologic Society; 1993. Drought monitoring with multiple time scales; pp. 233–236. [ Google Scholar ]
• Monerie PA, Sanchez-Gomez E, Gaetani M, Mohino E, Dong B. Future evolution of the Sahel precipitation zonal contrast in CESM1. Climate Dynamics. 2020; 55 :2801–2821. doi: 10.1007/s00382-020-05417-w. [ CrossRef ] [ Google Scholar ]
• Monerie PA, Wainwright CM, Sidibe M, Akinsanola AA. Model uncertainties in climate change impacts on Sahel precipitation in ensembles of CMIP5 and CMIP6 simulations. Climate Dynamics. 2020; 55 :1385–1401. doi: 10.1007/s00382-020-05332-0. [ CrossRef ] [ Google Scholar ]
• Moulin C, Chiapello I. Evidence of the control of summer atmospheric transport of African dust over the Atlantic by Sahel sources from TOMS satellites (1979–2000) Geophysics Research Letters. 2004; 31 :L02107. doi: 10.1029/2003gl018931. [ CrossRef ] [ Google Scholar ]
• Narasimhan B, Srinivasan R. Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agricultural and Forest Meteorology. 2005; 133 :69–88. doi: 10.1016/j.agrformet.2005.07.012. [ CrossRef ] [ Google Scholar ]
• National Drought Management Authority. (2018). National drought early warning bulletin, February . Ministry of Devolution and Planning.
• Naumann G, Dutra E, Barbosa P, Pappenberger F, Wetterhall F, Vogt JV. Comparison of drought indicators derived from multiple data sets over Africa. Hydrology Earth System Science. 2014; 18 :1625–1640. doi: 10.5194/hess-18-1625-2014. [ CrossRef ] [ Google Scholar ]
• Ngcamu BS, Chari F. Drought influences on food insecurity in africa: A systematic literature review. International Journal of Environmental Research Public Health. 2020; 17 :5897. doi: 10.3390/ijerph17165897. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nguvava M, Abiodun BJ, Otieno F. Projecting drought characteristics over East African basins at specific global warming levels. Atmospheric Research. 2019; 228 :41–54. doi: 10.1016/j.atmosres.2019.05.008. [ CrossRef ] [ Google Scholar ]
• Niang I, Ruppel OC, Abdrabo MA, Essel A, Lennard C, Padgham J, Urquhart P. Africa. In: Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL, editors. Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press; 2014. pp. 1199–1265. [ Google Scholar ]
• Nicholson SE. A detailed look at the recent drought situation in the Greater Horn of Africa. Journal of Arid Environments. 2014; 103 :71–79. doi: 10.1016/jaridenv.2013.12.003. [ CrossRef ] [ Google Scholar ]
• Nicholson SE. The ITCZ and the seasonal cycle over equatorial Africa. Bulletin America Meteorology Society. 2018; 99 :337–348. doi: 10.1175/BAMS-D-16-0287.1. [ CrossRef ] [ Google Scholar ]
• Nicholson SE, Funk C, Fink AH. Rainfall over the African continent from the 19th through the 21st century. Global and Planetary Change. 2018; 165 :114–127. doi: 10.1016/j.gloplacha.2017.12.014. [ CrossRef ] [ Google Scholar ]
• OECD, FAO . Agriculture in Sub-Saharan Africa: Prospects and challenges fot the next decade OECD-FAO Agricultural Outlook 2016–2025. OECD Publishing; 2016. pp. 59–95. [ Google Scholar ]
• OECD/FAO . OECD-FAO agricultural outlook 2016–2025. OECD Publishing; 2016. [ Google Scholar ]
• Ongoma V, Chen H. Temporal and spatial variability of temperature and precipitation over East Africa from 1951 to 2010. Meteorology and Atmospheric Physics. 2017; 129 :131–144. doi: 10.1007/s00703-016-0462-0. [ CrossRef ] [ Google Scholar ]
• Ogallo LJ. Rainfall variability in Africa. Monthly Weather Research. 1980; 107 :1133–1139. doi: 10.1175/1520-0493(1979)107<1133:RVIA>2.0.CO;2. [ CrossRef ] [ Google Scholar ]
• Ogou KF, Ma Z-G, Yang Q, Kpaikpai B. Comparison of trends and frequencies of drought in central North China and sub-Saharan Africa from 1901 to 2010. Atmosphere Oceanic Science Letters. 2017; 10 (6):418–426. doi: 10.1080/16742834.2017.1392825. [ CrossRef ] [ Google Scholar ]
• Ogou FK, Yang Q, Duan Y, Ma Z-G. Comparative analysis of interdecadal precipitation variability over central North China and sub Saharan Africa. Atmosphere Oceanic Science Letters. 2019; 12 (2):1–7. doi: 10.1080/16742834.2019.1593040. [ CrossRef ] [ Google Scholar ]
• Opiyo F, Wasonga O, Nyangito M, Schilling J, Munang R. Drought adaptation and coping strategies among the turkana pastoralists of Northern Kenya. International Journal of Disaster Risk Science. 2015; 6 :295–309. doi: 10.1007/s13753-015-0063-4. [ CrossRef ] [ Google Scholar ]
• Orimoloye IR, Ololade OO, Mazinyo SP, Kalumba AM, Ekundayo OY, Busayo ET, Akinsanola AA, Nel W. Spatial assessment of drought severity in Cape Town area, South Africa. Heliyon. 2019; 5 :e02148. doi: 10.1016/j.heliyon.2019.e02148. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Orlowsky B, Seneviratne SI. Elusive drought: Uncertainty in observed trends and short-and long-term CMIP5 projections. Hydrology and Earth System Sciences. 2013; 17 :1765–1781. doi: 10.5194/hess-17-1765-2013. [ CrossRef ] [ Google Scholar ]
• Palmer, W. C. (1965). Meteorological drought  (Vol. 30). US Department of Commerce, Weather Bureau.
• Price, R. A. (2017). Climate change and stability in North Africa. K4D Helpdesk Report 242 . Institute of Development Studies
• Prudhomme C, Giuntoli I, Robinson EL, Clark DB, Arnell NW, Dankers R, Fekete BM, Fransen W, Gerten D, Gosling SN, Hagemann S, Kim H, Maski Y, Satoh Y, Stacke T, Wada Y, Wisser D. Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment. PNAS. 2014; 111 :3262–3267. doi: 10.1073/pnas.1222473110. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Quenum GMLD, Klutse NAB, Dieng D, Laux P, Arnault J, Kodja JD, Oguntunde P. Identification of potential drought areas in West Africa under climate change and variability. Earth System Environment. 2019; 3 :429–444. doi: 10.1007/s41748-019-00133-w. [ CrossRef ] [ Google Scholar ]
• Radhouane, L. (2013). Climate change impacts on North African countries and on some Tunisian economic sectors. Journal of Agriculture Environment International Dev-JAEID 107, 101–113. http://www.iao.florence.it/ojs/index.php/JAEID/article/view/123/106
• Rahmat SN, Jayasuriya N, Bhuiyan M. Development of drought severity-duration-frequency curves in Victoria, Australia. Australasian Journal of Water Resources. 2015; 19 :31–42. doi: 10.7158/13241583.2015.11465454. [ CrossRef ] [ Google Scholar ]
• Richard Y, Poccard IJIJORS. A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. International Journal of Remote Sensors. 1998; 19 :2907–2920. doi: 10.1080/014311698214343. [ CrossRef ] [ Google Scholar ]
• Rockström J, Falkenmark M. Agriculture: Increase water harvesting in Africa. Nature. 2015; 519 :283–285. doi: 10.1038/519283a. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Roderick ML, Sun F, Lim WH, Farquhar GD. A general framework for understanding the response of the water cycle to global warming over land and ocean. Hydrology and Earth System Sciences. 2014; 18 :1575–1589. doi: 10.5194/hess-18-1575-2014. [ CrossRef ] [ Google Scholar ]
• Rodríguez-Fonseca B, Mohino E, Mechoso CR, Caminade C, Biasutti M, Gaetani M, Voldoire A. Variability and predictability of West African droughts: A review on the role of sea surface temperature anomalies. Journal Climate. 2015; 28 :4034–4060. doi: 10.1175/JCLI-D-14-00130.1. [ CrossRef ] [ Google Scholar ]
• Rojas O, Vrieling A, Rembold F. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensors Environment. 2011; 115 :343–352. doi: 10.1016/j.rse.2010.09.006. [ CrossRef ] [ Google Scholar ]
• Rouault M, Richard Y. Intensity and spatial extension of drought in South Africa at different time scales. Water SA. 2003; 29 (4):489–500. [ Google Scholar ]
• Rouault M, Richard Y. Intensity and spatial extent of droughts in southern. Africa. 2005; 32 :2–51. doi: 10.1029/2005GL022436. [ CrossRef ] [ Google Scholar ]
• Rowell DP. Teleconnections between the tropical Pacific and the Sahel. Quarterly Journal of Research Meteorology Society. 2001; 127 :1683–1706. doi: 10.1002/qj.49712757512. [ CrossRef ] [ Google Scholar ]
• Salami, A. A. B., Kamara, & Brixiova, Z. (2010). Small holder agriculture in East Africa: Trends, constraints, and opportunities. African Development Bank Working Papers Series No. 105, African Development Bank, Tunis, Tunisia, p. 52. http://www.afdb.org/fileadmin/uploads/afdb/Documents/
• Sanogo S, Fink AH, Omotosho JA, Ba A, Redl R, Ermert V. Spatio-temporal characteristics of the recent rainfall recovery in West Africa. International Journal of Climatology. 2015; 35 :4589–4605. doi: 10.1002/joc.4309. [ CrossRef ] [ Google Scholar ]
• Schwalm CR, Williams K, Schaefer D, Baldocchi TA, Black AH, Goldstein BE, Law WC, Oechel KT, Paw RL, Scott CA. Reduction in carbon uptake during turn of the century drought in western North America. Nature Geoscience. 2012; 5 :551–556. doi: 10.1038/ngeo1529. [ CrossRef ] [ Google Scholar ]
• Seleshi Y, Zanke U. Recent changes in rainfall and rainy days in Ethiopia. International Journal of Climatology. 2004; 24 :973–983. doi: 10.1002/joc.1052. [ CrossRef ] [ Google Scholar ]
• Sepulcre-Canto G, Horion SMAF, Singleton A, Carrao H, Vogt J. Development of a Combined Drought Indicator to detect agricultural drought in Europe. Natural Hazards and Earth Systems Sciences. 2012; 12 :3519–3531. doi: 10.5194/nhess-12-3519-2012. [ CrossRef ] [ Google Scholar ]
• Serdeczny O, Adams S, Baarsch F, Coumou D, Robinson A, Hare W, Schaeffer M, Perrette M, Reinhardt J. Climate change impacts in Sub-Saharan Africa: From physical changes to their social repercussions. Regional Environmental Change. 2017; 17 :1585–1600. doi: 10.1007/s10113-015-0910-2. [ CrossRef ] [ Google Scholar ]
• Sheffield J, Wood EF. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Climate Dynamics. 2008; 31 :79–105. doi: 10.1007/s00382-007-0340-z. [ CrossRef ] [ Google Scholar ]
• Sheffield J, Wood EF, Roderick ML. Little change in global drought over the past 60 years. Nature. 2012; 491 :435–438. doi: 10.1038/nature11575. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sillmann JV, Kharin V, Zhang XW, Zwiers F, Bronaugh D. Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future Climate Projections. Journal of Geophysics Research. 2013; 118 :2473–2493. doi: 10.1002/jgrd.50188. [ CrossRef ] [ Google Scholar ]
• Simonovic SP. Managing water resources: Methods and tools for a systems approach. Paris: UNESCO; 2009. [ Google Scholar ]
• Smith KR, Woodward A, Campbell-Lendrum D, Chadee DD, Honda Y, Liu QY, Olwoch JM, Revich B, Sauerborn R, Aranda C, et al. Climate change impacts, adaptation, and vulnerability, Pt A: Global and sectoral aspects: Working Group II contribution to the fifth assessment report of the intergovernmental panel on climate change human health: Impacts, adaptation, and co-benefits. Cambridge University Press; 2014. pp. 709–754. [ Google Scholar ]
• Spinoni J, Naumann G, Vogt JV, Barbosa P. The biggest drought events in Europe from 1950 to 2012. Journal of Hydrology: Regional Studies. 2015; 3 :509–524. doi: 10.1016/j.ejrh.2015.01.001. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spires M, Shackleton S, Cundill G. Barriers to implementing planned community-based adaptation in developing countries: A systematic literature review. Climate Development. 2014; 6 :277–287. doi: 10.1080/17565529.2014.886995. [ CrossRef ] [ Google Scholar ]
• Stanke C, Kerac M, Prudhomme C, Medlock J, Murray V. Health effects of drought: A systematic review of the evidence. Plos Currents, Version. 2013; 1 :5. doi: 10.1371/currents.dis.7a2cee9e980f91ad7697b570bcc4b004. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sun S, Chen H, Wang G, Li J, Mu M, Yan G, Zhu S. Shift in potential evapotranspiration and its implications for dryness/wetness over Southwest China. Journal of Geophysical Research: Atmospheres. 2016 doi: 10.1002/2016JD025276. [ CrossRef ] [ Google Scholar ]
• Sylla, M. B., Nikiema, P. M., Gibba, P., Kebe, I., & Klutse, N. A. B. (2016a). Climate change over West Africa: Recent trends and future projections. Springer. 10.1007/978-3-319-31499-03
• Sylla MB, Nikiema PM, Gibba P, Kebe I, Klutse NAB. Climate change over West Africa: Recent trends and future projections. In: Yaro JA, Hesselberg J, editors. Adaptation to climate change and variability in rural West Africa. Springer International Publishing; 2016. pp. 25–40. [ Google Scholar ]
• Taljaard, J. J. (1989). Climate and circulation anomalies in the South African region during the dry summer of 1982/83 . Department of Environment Affairs, Weather Bureau.
• Tan G, Ayugi B, Ngoma HN, Ongoma V. Projections of Future Meteorological Drought Events under representative concentrations pathways (RCPs) of CMIP5 over Kenya, East Africa. Atmosphere Research. 2020 doi: 10.1016/j.atmosres.2020.105112. [ CrossRef ] [ Google Scholar ]
• Tarpley JD, Schneider SR, Money RL. Global vegetation indices from the NOAA-7 meteorological satellite. Journal of Applied Meteorology Climate. 1984; 23 :491–494. doi: 10.1175/1520-0450(1984)023<0491:GVIFTN>2.0.CO;2. [ CrossRef ] [ Google Scholar ]
• Thomas E, Jordan E, Linden K, Mogesse B, Hailu T, Jirma H, Collins G. Reducing drought emergencies in the Horn of Africa. Science Total Environment. 2020; 727 :138772. doi: 10.1016/j.scitotenv.2020.138772. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tierney JE, Smerdon JE, Anchukaitis KJ, Seager R. Multidecadal variability in East African hydroclimate controlled by the Indian Ocean. Nature. 2013; 493 :389–392. doi: 10.1038/nature11785. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Traore SB, Ali A, Tinni SH, Samake M, Garba I, Maigari I, Alhassane A, Samba A, Diao MB, Atta S, Dieye PO, Nacro HB, Bouafou KGM. AGRHYMET: A drought monitoring and capacity building center in the West Africa Region. Weather Climatic Extremes. 2014; 3 :22–30. doi: 10.1016/j.wace.2014.03.008. [ CrossRef ] [ Google Scholar ]
• Trenberth KE, Dai A, van der Schrier G, Jones PD, Barichivich J, Briffa KR, Sheffield J. Global warming and changes in drought. Nature Climate Change. 2014; 4 :17–22. doi: 10.1038/nclimate2067. [ CrossRef ] [ Google Scholar ]
• Tyson PD, Dyer TGJ. The predicted above-normal rainfall of the seventies and the likelihood of droughts in the eighties in South Africa. South Africa Journal of Science. 1978; 74 :372–377. [ Google Scholar ]
• Uhe P, Philip S, Kew S, Shah K, Kimutai J, Mwangi E, van Oldenborgh GJ, Singh R, Arrighi J, Jjemba E, Cullen H. Attributing drivers of the 2016 Kenyan drought. International Journal of Climatology. 2017; 38 :554–568. doi: 10.1002/joc.5389. [ CrossRef ] [ Google Scholar ]
• Ukkola AM, De Kauwe MG, Roderick ML, Abramowitz G, Pitman AJ. Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophysical Research Letters. 2020; 47 (11):e2020GL087820. doi: 10.1029/2020GL087820. [ CrossRef ] [ Google Scholar ]
• Vicente-Serrano SM, Beguería S, López-Moreno JI. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate. 2010; 23 :1696–1718. doi: 10.1175/2009JCLI2909.1. [ CrossRef ] [ Google Scholar ]
• Vogel C. (Mis)management of droughts in South Africa. South African Journal of Science. 1994; 90 :4–6. [ Google Scholar ]
• Wainwright CM, Marsham JH, Keane RJ, Rowell DP, Finney DL, Black E, All RP. 'Eastern African Paradox' rainfall decline due to shorter not less intense Long Rains. Climate Atmosphere Science. 2019; 2 :34. doi: 10.1038/s41612-019-0091-7. [ CrossRef ] [ Google Scholar ]
• Washington R, Downing TE. Seasonal forecasting of African rainfall: Prediction, responses and household food security. Geographical Journal. 1999 doi: 10.2307/3060442. [ CrossRef ] [ Google Scholar ]
• Washington R, Preston A. Extreme wet years over southern Africa: Role of Indian Ocean Sea surface temperatures. Journal of Geophysical Research. 2006; 111 :D15104. doi: 10.1029/2005JD006724. [ CrossRef ] [ Google Scholar ]
• Wilhite DA, Glantz MH. Understanding: The drought phenomenon: The role of definitions. Water International. 1985; 10 :111–120. doi: 10.1080/02508068508686328. [ CrossRef ] [ Google Scholar ]
• Wilhite DA, Svoboda MD, Hayes MJ. Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resource Manage. 2007; 21 :763–774. doi: 10.1007/s11269-006-9076-5. [ CrossRef ] [ Google Scholar ]
• Williams PA, Crespo O, Abu M, Simpson NP. A systematic review of how vulnerability of smallholder agricultural systems to changing climate is assessed in Africa. Environmental Research Letters. 2018; 13 (10):103004. doi: 10.1088/1748-9326/aae026. [ CrossRef ] [ Google Scholar ]
• Williams AP, Funk C. A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa. Climate Dynamics. 2011; 37 :2417–2435. doi: 10.1007/s00382-010-0984-y. [ CrossRef ] [ Google Scholar ]
• Winkler K, Gessner U, Hochschild V. Identifying droughts affecting agriculture in Africa based on remote sensing time series between 2000–2016: Rainfall anomalies and vegetation condition in the context of ENSO. Remote Sensors. 2017; 9 :831. doi: 10.3390/rs9080831. [ CrossRef ] [ Google Scholar ]
• World Bank. (2012). Doing business in the East African economies. IFC/World Bank Rep., 116.
• World Bank. (2014). Turn down the heat: Confronting the new climate normal. Washington, DC: World Bank. Licence: CC BY-NC-ND 3.0 IGO. http://documents.worldbank.org/curated/en/317301468242098870/Main-report
• World Bank. (2017). Beyond scarcity: Water security in the Middle East and North Africa. MENA Development Report, Washington, DC: World Bank. Licence: CC BY 3.0 IGO https://openknowledge.worldbank.org/handle/10986/27659
• World Meteorological Organization (WMO) and Global Water Partnership (GWP) Handbook of drought indicators and indices (M. Svoboda and B.A. Fuchs). Integrated Drought Management Programme (IDMP), Integrated Drought Management Tools and Guidelines Series 2. Geneva. Wilhite, D.A., 1993. Understanding the phenomenon of drought. Hydrology Review. 2016; 12 :136–148. [ Google Scholar ]
• Yang W, Seager R, Cane MA, Lyon B. The East African long rains in observations and models. Journal of Climate. 2014; 27 :7185–7202. doi: 10.1175/JCLI-D-13-00447.1. [ CrossRef ] [ Google Scholar ]
• Zeng N. Drought in the Sahel. Science. 2003; 302 :999–1000. doi: 10.1126/science.1090849. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhang X, Alexander L, Hegerl GC, Jones P, Klein Tank A, Peterson TC, Trewin B, Zwiers FW, et al. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wires Climate Change. 2011; 2 :851–870. doi: 10.1002/wcc.147. [ CrossRef ] [ Google Scholar ]
• Zheng X, Eltahir EAB. The response to deforestation and desertification in a model of West African monsoons. Geophysics Research Letters. 1997; 24 :155–158. doi: 10.1029/96gl03925. [ CrossRef ] [ Google Scholar ]

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