analysis and synthesis of data about drought in limpopo

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Drought patterns: their spatiotemporal variability and impacts on maize production in Limpopo province, South Africa

• Original Paper
• Open access
• Published: 07 December 2022
• Volume 67 , pages 133–148, ( 2023 )

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• Nicole Costa Resende Ferreira   ORCID: orcid.org/0000-0002-3098-5993 1 ,
• Reimund Paul Rötter 1 ,
• Gennady Bracho-Mujica 1 ,
• William C. D. Nelson 1 ,
• Quang Dung Lam 1 ,
• Claus Recktenwald 2 ,
• Isaaka Abdulai 1 ,
• Jude Odhiambo 3 &
• Stefan Foord 4  

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Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa’s summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize.

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Introduction

Droughts affect different regions globally, with a range of negative impacts affecting multiple socioeconomic and environmental sectors, including agriculture (Vicente-Serrano 2006 ; Ferreira et al. 2021a ), water resources (Ferreira and Chou 2018 ; Ferreira et al. 2021b ), and forestry (Copenheaver et al. 2011 ), among others. Due to global climate change, droughts are likely to become more frequent and more severe in many regions (Dai 2011 ), as a consequence of the projected global warming with changes in circulation patterns (e.g., Kornhuber et al. 2019 ), increased evapotranspiration, changes in rainfall patterns, accelerated hydrological cycle with increased rainfall intensity, etc. (Drumond et al. 2019 ; Fischer and Knutti 2014 ; Intergovernmental Panel on Climate Change (IPCC) 2019 ; Lobell et al. 2013 ). High temperatures are expected to result in higher water deficits during the summer season, leading to decreased soil moisture and more frequent and severe agricultural droughts (Adams and Peck 2009 ; Park et al. 2018 ).

Large-scale droughts have occurred worldwide at different times throughout historical record (Dai 2011 ; Trnka et al. 2018 ), yet the damage has increased substantially in recent decades (Moravec et al. 2021 ). In arid and semi-arid areas of southern Africa, droughts are common and frequent (Park et al. 2018 ; Meza et al. 2021 ; Mahlalela et al. 2020 ) causing significant economic losses (Vogel et al. 2000 ) and increasing food insecurity in the region (Verschuur et al. 2021 ). Since 1970, Southern Africa has observed more intense, widespread and more extended droughts (Richard et al. 2001 ; Burls et al. 2019 ). In this context, it is important to unravel the spatiotemporal patterns and severity of drought at different scales to support the design and adjustment of climate change mitigation and adaptation measures.

The agriculture sector depends on climate to guarantee crop productivity, profitability, and quality. Lobell et al. ( 2008 ) concluded that agricultural production will mainly be negatively affected by climate change and will impede the ability of many regions to achieve the necessary gains for future food security, as was also recently found for the main wheat producing and exporting regions worldwide (Trnka et al. 2019 ). In southern Africa, maize is predominantly grown in smallholder farming systems, where over 90% of the production systems are rainfed; and also, the maize cultivated by commercial farmers in South Africa is mainly rainfed (Bationo and Waswa 2011 ). Smallholder maize farming systems in the dry savanna areas, as found in Limpopo (Rötter et al. 2021 ), are particularly vulnerable to climate variability and change (Adger et al. 2007 ; Cairns et al. 2013 ; Conway et al. 2015 ). This could have a huge impact on local food security due to the importance of these areas to the agricultural sector. While many studies show that climate change will increase drought frequency and severity, the direction and extent of these changes and related crop yields depend on the region and season. For this reason, the use of different drought metrics might be needed to provide robust estimates of related risks (Cook et al. 2020 ).

This paper aims to study drought patterns in the Limpopo region (South Africa) and evaluate their spatiotemporal patterns and how these are likely to change under future climatic conditions to signal potential repercussions on crop yields. In particular, we will look at 30-year time horizons and consider different emissions scenarios and global climate models. The other important and closely related objective is how drought may potentially affect maize crop production in two representative sites in Limpopo, with contrasting conditions. To quantify climate change’s impact on maize development and yield, we applied the crop growth simulation model WOFOST (Boogaard et al. 1998 ).

Materials and methods

Study area and maize climatic requirements.

The study area comprises parts of the Limpopo province, South Africa (SA). This region is known as one of the hottest provinces in the country (Kruger and Shongwe 2004 ), with frequent and severe droughts due to high temperatures and unreliable rainfall (Maponya and Mpandeli 2012 ; Maposa et al. 2021 ). The region presents mostly a subtropical climate, with a contrasting environment favorable for the cultivation of grain crops, tropical fruits, and vegetables. We focused this study on two sites: Univen and Syferkuil (Fig.  1 ). These sites were chosen due to the contrasting environmental conditions (i.e., soil and climate characteristics) and long-term data availability.

Experimental sites Syferkuil and Univen in Limpopo, SA. Total precipitation (mm) and mean temperatures (°C) monthly climatology (period: 1984–2014). Missing values are shown in gray

The interannual variability of total accumulated precipitation per year is higher at Univen (Online Resource 1 ) than at Syferkuil. In both sites, there is a clear seasonal pattern in precipitation, from October until March, but Univen is a warmer site. In Syferkuil, there is a distinct increase in air temperature from 2003 onwards, especially in January.

In SA, several circulation phenomena influence climate variability, including El Niño Southern Oscillation (ENSO) phenomenon—with contrasting impacts associated with El Niño and La Niña phases (Reason and Jagadheesha 2005 ; Gizaw and Gan 2017 ). As a general rule, the El Niño phase tends to lead to drier conditions, whereas the La Niña phase tends to lead to wetter conditions (Reason and Jagadheesha 2005 ; Phillips et al. 1998 ; Nicholson and Kim 1997 ; Janowiak 1988 ). The spatial extent of drought-prone regions of SA may increase in the future due to an increase in the frequency of El Niño episodes under a warmer climate (Diaz et al. 2001 ; Perry et al. 2017 ; Cai et al. 2015 ; Power et al. 2013 ). Pomposi et al. ( 2018 ) verified that strong and moderate-to-weak El Niño events tend to increase dry days in southern Africa. The same authors concluded that the likelihood of southern Africa receiving less than average precipitation is approximately 80% for strong El Niño events compared to just over 60% for moderate-to-weak El Niño events. A detailed comparison of the precipitation patterns of average years with El Niños and strong El Niños is given in the supplementary material (Online Resources 2 and 3 ). In years with strong El Niño events, especially the south-eastern region become drier than normal, and in the north-eastern region, strong El Niños have an opposite effect. Therefore, when looking at the country’s average yield, the ENSO impact for some regions may be masked, leading to little overall effect on yield (as shown, e.g., in Mozambique, Angola, Zambia). In other countries, such as South Africa and Botswana, where we observe drought patterns exclusively in years of strong El Niños, the relationship between drought and yield is more consistent on a year-to-year basis.

Regarding maize production in SA, the Limpopo region plays an essential role. A great share of its maize production (62%) is provided by smallholder farmers (LEDET, 2016 ). According to Agbiz ( 2016 ), maize is the most cultivated grain crop in SA, followed by soybeans, wheat, sunflower, and sugar cane (FAOSTAT 2019 ). The average maize yield (t.ha −1 ) from 1990 to 2019 for Limpopo and SA, and their relationship with El Niño can be found at Online Resource 4 . In the last decade, the average commercial yield in Limpopo exceeded the average values in SA, emphasizing the region’s importance in the national agricultural development. The climate conditions in Limpopo and especially the low mean annual precipitation are known as factors limiting yields attainable under rainfed conditions (Conway et al. 2015 ; Trambauer et al. 2014 ).

Limpopo is one of those areas of SA frequently prone to drought events (Dlamini 2013 ). The current climate variability as observed in Limpopo and the expected future climatic change may impose higher future risks to crop production. The climate vulnerability in SA is also emphasized by the fact that most maize production is rainfed, with less than 10% produced under irrigation (Baloyi 2011 ). The location and time of the year/length of the growing season are critical factors that determine the potential impacts of climate change on crop production (Gbetibouo and Hassan 2005 ). Each crop has climatic requirements including crop water requirements, which mainly depend on the crop’s genetic characteristics, stage of growth, and duration of the growth cycle.

Climate projections

Projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6) and the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) provide climate scenarios based on different Shared Socio-Economic Pathways (SSP) (O’Neill et al. 2014 , 2020 ). These climate scenarios can be used to investigate the implications of long-term climatic changes for designing robust policies in an environment of interacting complex systems and uncertainty (Hall et al. 2016 ; Harrison et al. 2015 ; O’Neil et al. 2014 ).

For this study, we selected the scenarios SSP1-2.6, SSP3-7.0, and SSP5-8, which can be considered optimistic, intermediate, and pessimistic climate change scenarios, respectively. We used BIAS-adjusted precipitation data (Lange 2019 ) for the historical and future periods. Climate change projections were divided into two 30-year time-slices, from 2021 to 2050 and 2051 to 2080. The climate models were selected according to their availability: IPSL-CM6A-LR, GFDL-ESM4, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The models have a horizontal resolution of 0.5° × 0.5°, and for convenience, they will be named as IPSL, GFDL, MPI, MRI, and UKESM, respectively. To evaluate the climate models’ performance, we used simulated historical climate data (1981–2010) and observed climate data (1984 to 2014) and calculated the root mean squared error (RMSE) and the mean bias error (MBE). Different meteorological drought indices were calculated, and an ensemble mean model was created for assessing the temporal and spatial patterns of drought.

Drought analyses

Masih et al. ( 2014 ) presented a review of droughts on the African continent from 1900 to 2013, indicating that droughts have become more frequent, intense, and widespread during the last 50 years. In SA, droughts occur often and during different times of the year in all climatic zones, with different intensity, spatial extent, and duration (Rouault and Richard 2003 ). In the Limpopo province, drought imposes a considerable risk since large parts of the province have a semi-arid climate with low, erratic rainfall (Maponya and Mpandeli 2012 ).

Several indices are commonly used as proxies to capture different drought patterns based on climatic information. Those indices were developed to characterize drought considering different approaches based on its magnitude, duration, frequency, and intensity (Heim 2000 , 2002 ; Vicente-Serrano et al. 2010 ; Dai 2011 ; Edossa et al. 2016 ; Rouault and Richard 2003 ).

In this study, six indices were selected to represent different drought conditions: PRCPTOT (total precipitation accumulated per month, mm), DD (dry days: the number of days without precipitation), LDP (longest dry period: the number of consecutive days without precipitation), LWP (longest wet period: number of consecutive days with precipitation), RX5D (maximum consecutive 5-day precipitation within a month, mm), and SPI (standardized precipitation index for classification of drought severity).

Indices were calculated for the maize growing period in the study area (i.e., from October to March). This period was chosen since the main maize planting time is between mid-October and mid-December (Matimolane 2018 ). Each index can help to understand drought patterns in a different way and thus jointly provide the basis for the design of effective adaptation/mitigation measures.

Crop simulation modelling

Climate extremes, such as drought, have several impacts on crop performance, affecting among others, the sowing dates, nutrient management practices, and eventually the actual yield obtained. In this context, process-based crop models are widely used tools for predicting crop growth and yield on the basis of crop characteristics and their interaction with prevailing weather and soil conditions. These tools can support current and future agricultural field management and national decision-making, e.g., the widely applied modelling platforms APSIM (Keating et al. 2003 ), DSSAT (Jones et al. 2003 ), and WOFOST (Van Ittersum et al. 2003 ). It is expected that future changes in temperature and precipitation regimes will be directly reflected by changes in crop yields all over the world, whereby negative yield impacts are likely to be prevalent in many regions, including most African countries (Abraha and Savage 2006 ; Porter et al. 2014 ; Waha et al. 2013 ).

Among the crop simulation models that have been applied in Africa, we chose the World Food Studies (WOFOST 7.1) model for simulating daily crop growth and spring maize yield under rainfed conditions in SA under different climate change scenarios (Ma et al. 2013 ; Boogard et al. 2013 , 1998 ; de Wit et al. 2019 ). The WOFOST model simulates the phenological development of different crops, from emergence to maturity, considering the crop genetic properties and environmental conditions (Hadiya et al. 2018 ). It comprises different processes such as phenological development, light interception, CO2 assimilation, transpiration, respiration, partitioning of assimilates to the various organs, dry matter, and yield formation (Boogard et al. 1998 ; Hadiya et al. 2018 ). The WOFOST model has been applied and is continuously being evaluated and extended for different crops all over the world (Dobermann et al. 2000 ; Palosuo et al. 2011 ; Rötter et al. 2012 ; Cheng et al. 2016 ; de Wit et al. 2019 ) . Previous calibration and validation of the WOFOST model for different regions in Africa can be found at Liu ( 2015 ), Wolf et al. ( 2015 ), Kassie et al. ( 2014 ), Rötter and van Keulen ( 1997 ), and Ogutu et al. ( 2018 ).

WOFOST requires as input data: daily weather, soil information, and crop characteristics. Among the input data needed are station name, latitude, longitude, altitude, minimum temperature, maximum temperature, hours of bright sunshine duration or global radiation, wind speed at 2 m, rainfall amount, and vapor pressure. The soil characteristics required are soil texture and soil moisture volumetric fraction at field capacity (cm 3 .cm −3 ), at permanent wilting point (cm 3 .cm −3 ), and at saturation (cm 3 .cm −3 ). To calibrate the model for a given crop cultivar, it is required to have information about crop phenology, maximum leaf area index (LAImax), biomass partitioning pattern, final biomass, and grain yield; if possible, data on soil moisture content in the root zone at some point in time will allow us to cross-check soil water balance calculations.

Sowing dates and crop emergence can have a considerable impact on crop performance. Usually, farmers plant flexibly within a sowing window depending on the location. We considered three different dates of crop emergence, 15 October (julian day 288), 15 November (319), and 15 December (349), based on crop calendars for the given regions. The sites are classified as sandy clay loams, and the soil properties were taken from the recent high resolution (30 m × 30 m) digital soil map iSDA ( 2020 ). Online Resource 5 describes the full set up of the model runs applied in this research.

Besides using different emergence dates, we used different climate scenarios to identify how climate change may affect maize production in the region. We used data from the climate models considering the historical period (1981–2010) and the future projections SSP1-2.6, SSP3-7.0, and SSP5-8.5 (2021–2050, 2051–2080). We simulated potential yield (Yp) and water-limited yield (Ywl). We evaluated the water-limited yield (t.ha −1 ), yield gap (calculated as the difference between potential yield and water-limited yield) (t.ha −1 ), grain filling period (defined as the period between the day of flowering and the harvest) (days), and cycle duration (days). Model annual outputs were evaluated to understand how climate change, and more specifically drought occurrence will affect maize yield year-to-year variability.

Results and discussion

Drought climatology.

We examined the drought climatology using precipitation data in the Limpopo province to verify the variations of long-term annual drought patterns according to historical observations as well as historical weather simulations. Such analysis is essential to identify the years with drought conditions (DC) and identify differences between the two experimental sites (Online Resource 6 ).

The highest errors in the models are identified for the climate zone represented by Univen site, which has higher amounts of rainfall than the climate zone represented by Syferkuil. In Univen, models underestimate precipitation, as seen from the accumulated precipitation (PRCPTOT) and maximum consecutive 5-day precipitation (RX5D) indices in the NDJF season. In 1999/2000, at Univen, the PRCPTOT index showed a big difference between observed and simulated data. While observations indicated a high value (436.5 mm) of monthly accumulated precipitation (NDJF season), the climate models used in this study were unable to represent this well. A similar pattern is also seen in the RX5D index. The PRCPTOT values in November and December of 1999 were below long-term climatic means (0 and 69 mm, respectively), yet, in January and February of 2000, the highest values recorded in the subregion were observed, with accumulated precipitation of 783 and 894 mm, respectively. These high amounts of precipitation resulted in a disastrous flooding, causing losses of human lives, as well as considerable economic losses (Khandlhela and May 2006 ). Recktenwald ( 2019 ) reported that the southern summer season of 1991/1992 was dry, with droughts occurring in Limpopo. The results agree with the observed drought record, which indicates major droughts in 1991–1992 and 2004–2005 (Walz et al. 2020 ; Meza et al. 2021 ). At Univen, model simulations show an increase in DD in 1999, while according to observed data, there was a decrease in DD. At Syferkuil, climate model simulations underestimated DD. The longest wet period index (LWP) shows great variations among the models (e.g., for 1995 and 2005); hence, it appears that the climate model ensemble cannot adequately capture observed extremes. Regarding the longest dry period index (LDP), at Syferkuil, the models indicated low LDP values, while the observations showed high values in NDJF. The standardized precipitation index (SPI) index also shows great variations across the years and between both sites, which can have several implications for agricultural production. Similar results were found by Manatsa et al. ( 2010 ) for Zimbabwe.

The definition of drought conditions (DC) for each site was calculated based on specific quantiles (q10 and q90, Online Resource 7 ). According to the historical simulations, the most critical values are not associated with a specific month or associated with one region only. However, the months of October and March seem to be very problematic in both areas. The driest conditions of accumulated monthly precipitation (PRCPTOT) are found at the Univen site, mainly in October (q10 is 28.4 mm). October is also the month with the lowest accumulated precipitation values in 5 days (q10 is 17.2 mm) and the shortest wet period (q10 is 2 days). October and November are usually the beginning of the rainy season, and droughts in November can be reflected in delayed sowing, as changing planting dates is a common drought adaptation measure applied by farmers in the Limpopo region (May, 2019 ). At the Univen site, March appears to be the month with the worst drought conditions when considering the number of days without rainfall (DD) and consecutive days without rainfall (LDP).

At Syferkuil, October presents DC due to the low accumulated rainfall in November (q10 PRCPTOT is 35.7 mm), low accumulated rainfall in 5 days (q10 RX5DAY is 19.5 mm), and low values of the longest wet period (2.2 days). October is also the month with the most critical DC, with the highest dry days (26 days) and consecutive dry days (17.2 days). February, on the other hand, presents high values of accumulated rainfall. In general, the Univen site presented more severe DC than the Syferkuil site.

Evaluation of observed against modelled climate data

Figure  2 indicates the RMSE and the MBE for October to March for the six drought indices. The PRCPTOT index showed positive MBE at Univen and negative at Syferkuil, which indicates the overestimation of the index by climate models at Univen and underestimation at Syferkuil. The largest RMSE occurs in Univen in February. The RX5D also has a higher RMSE for the Univen site, whereby January and February are the months with the biggest errors. The MBE of the DD index indicates underestimation at Univen and overestimation at Syferkuil. The smallest RMSE in the LDP index occurs at Syferkuil, while for the LWP, the smallest errors occur at Univen. Considering the SPI index, the smallest MBE is found for January at Univen and for February at Syferkuil.

RMSE and MBE for Univen (red) and Syferkuil (blue), calculated based on historical simulations and observed data (1984–2014). The boxplots indicate the errors of the different models for each index and each month (from October to March) considered in this analysis. PRCPTOT represents the total precipitation (mm), DD is the number of dry days (days), LDP is the longest dry period (days), LWP is the longest wet period (days), RX5D is the maximum consecutive 5-day precipitation (mm), and SPI is the standardized precipitation index (-)

In general, the RMSE is lower for the ensemble mean compared to the individual models. The same result is found for MBE, which tends to approach zero when using the ensemble. This observation confirms the suggestion that multi-member ensemble tend to compensate for errors (Rötter et al. 2011 ; Wallach et al. 2016 ). The patterns of underestimation or overestimation depend on the index studied, the model evaluated, and the climatic “subregion.” Variations in errors among the models indicate projections’ uncertainty, which is reflected in the ensemble. As shown in Fig.  2 , the climate models exhibit differences reflected in model ensemble prediction uncertainty, as they are different numerical system realizations with different types and patterns of errors (Wallach et al. 2016 ). To reduce uncertainties in the predictions, we applied the mean values of multimember model ensembles in the next steps of this analysis to obtain more robust results (Martre et al. 2015 ).

Historical and future drought patterns

To study drought patterns and their shifts in the future, we assessed indices across the Limpopo region. We evaluated the indices using the 30-year averages of the baseline (1981–2010) and the future time-slices (2021–2050 and 2051–2080) from the model ensemble. In Fig.  3 , we present only the scenario SSP5-8.5. However, results for the scenarios SSP1-2.6 and SSP3-7.0 are available in the supplementary material (Online Resources 8 – 13 ).

Ensemble 30-year average of the index: a PRCPTOT, b LWP, and c SPI in the Limpopo province for historical simulation (baseline) and future scenario SSP5-8.5 (2021–2050, 2051–2080). Sites are represented with a black circle (Syferkuil), and a black triangle (Univen)

Relatively small changes in PRCPTOT are expected for future climate scenarios (Fig.  3a ). October is the month with the lowest precipitation values, and the rainy season seems to start in November. The most remarkable future changes occur in January (mostly in SSP5-8.5, 2051–2080), with increasing precipitation, and October, with decreasing precipitation. In general, we can observe that for both sites, shifts in precipitation seasonality may occur, which will reflect in changes in future agricultural practices. In October, the precipitation in Syferkuil may reduce from 50.2 mm (1981–2010) to 42.8 and 35 mm (SSP5-8.5, 2021–2050 and 2051–2080). In Univen, this reduction is from 44.3 mm to 37.4 and 27.6 mm (SSP5-8.5, 2021–2050 and 2051–2080). For November, February, and March, different patterns are found according to the time-slice. In December and January, we identify a trend to increase precipitation in both locations. In December, this increase is from 105.3 mm (baseline) up to 115.7 mm (SS5-8.5, 2051–2080) in Syferkuil, and from 100.8 mm up to 109.2 mm (SSP5-8.5, 2051–2080) in Univen. In January, this increase is from 126.2 mm up to 136.1 mm (SS5-8.5, 2051–2080) in Syferkuil and from 148.1 mm up to 163.5 mm (SSP5-8.5, 2051–2080) in Univen. This will certainly have impacts over the maize yield, as we discuss in the “Impacts of climate change on maize” section.

Regarding the LWP index (Fig.  3b ), it is expected decreasing of LWP in north Limpopo in November and south Limpopo in October (mostly in SSP5-8.5, 2051–2080). In December, the increasing or decreasing of LWP depends on the climate scenario evaluated. There is an increase of LWP in the north region (January) and south region (February), according to the scenario SSP5-8.5 in the time-slice 2051–2080. We found the highest values of RX5D in the baseline period in January and February, in the region of Univen and Syferkuil (Online Resource 9 ). In January, we identified a trend of increasing RX5D in the future (2051–2080). In November, the ensemble indicated that the Northeast of Limpopo tends to get drier in the future. The evaluation of DD suggests increasing DD (online resource 11 ) in the Northeast of Limpopo, mostly in October and November. Also, it is shown decreasing in DD in the future in the southeast region, mainly in February. The months of October and March are the months with higher DD values (higher than 26 days). The highest LDP in the region in the baseline period is identified in October and March, while the lowest values are in December (online resource 12 ). The greatest future changes show increasing LDP in the Northeast region, mostly in SSP5-8.5 (2051–2080).

Regarding the SPI index (Fig.  3 c), the South and Central regions of Limpopo tend to have more problems related to droughts. In the future, the index indicates increases in droughts in October in the southern region. In November, a decrease in droughts in the northern region and an increase in the south of the region are expected. December is currently the month with the most problems concerning droughts. However, there are different trends for December according to different future scenarios. According to current climate conditions, January is not a month prone to drought. Still, it may become drier in the future, mainly in the southern region of Limpopo, as indicated by SSP1-2.6 and SSP3-7.0 (2051–2080).

We conclude, therefore, that both subregions are likely to have more severe drought conditions in the future than during the baseline period 1981–2010. Other studies, which evaluated different indexes, come to similar conclusions. For example, Gizaw and Gan ( 2017 ) used the Palmer drought severity index to analyze changes in droughts, and they concluded that most South African areas would shift to a drier climate in the 2050s and 2080s. The results agree with Schulze et al. ( 2001 ), who reported that in SA, which is already a water-stressed country, climate change is expected to increase the variability of rainfall events and amplify weather extremes.

To identify future changes in droughts frequency, we evaluated drought conditions (DC) based on the quantiles thresholds. We calculate the number of years in the future with DC and compare it to the current DC. Figure  4 indicates the number of years with DC in Univen and Syferkuil, according to the historical simulation and SSP's, in different 30-year time-slices. We can see that there are some local differences regarding the frequency of DC (historical and future) at the two sites.

Number of years with drought conditions (DC) in Univen and Syferkuil. Colors indicate the difference between future scenarios and historical simulation. Shades of red show drier conditions in the future compared to historical simulation, and blue shades show wetter conditions in the future

In Univen, the worst DC tends to occur in October, with an increase of DC indicated by all evaluated indices. In the scenario SSP5-8.5 (2051–2081), PRCPTOT presented 19 years with DC, representing an increase of 16 years in the 30-year time-slice (around 53.3%). In the RX5D index, we observe an increase of 12 years, and DD presented an increase of 9 years. There is an agreement in drought patterns among the climate scenarios. There is a decrease in PRCPTOT, LWP, and RX5D and an increase in DD in November. The worst DC also occurs in SSP5-8.5 (2051–2080).

At Syferkuil, the worst future DC occurs in October, although with lower values than Univen. In SSP5-8.5 (2051–2080), results show an increase of 13 years with DC related to PRCPTOT in the 30-year time-slice, 9 years in the RX5D index, and 6 years in DD and LDP indices. In January, we identified a decrease in LWP and RX5D and increasing in DD. Although January is a month with an increasing rainfall trend in Syferkuil, there was also an increase in DC in all scenarios, according to some indices. The DD index shows an increase of DC from 3 years throughout historical simulations to 9 years in a future scenario and RX5D an increase from 3 to 7 years (in the worst-case-scenario).

We conclude that the Univen site represents the subregion with the most remarkable changes, increasing DC in the future. It was also the subregion that presented the worst DC in the historical period, which is a reason for concern, primarily due to the potential impacts of DC on maize production.

Impacts of climate change on maize

Water-limited yield was similar among sites (Fig.  5 ). However, the future projections for Univen indicate a reduction in yield, regardless of the climate scenario assessed. The highest reduction and lowest yields ​​are found under the SSP5-8.5 (2051–2080) scenario. It is also noteworthy that for baseline yield simulations, the runs with an emergence date at day 288 resulted in the highest yield (~ 7.33 t.ha −1 ), as this coincides with the crop with closed canopy being exposed to high global radiation levels with sufficient moisture in an optimum manner under the given baseline climate. However, in the scenario SSP5-8.5 (2051–2080), this start date led to the worst yield performance (~ 4.88 t.ha −1 ), which is likely due to shifts in the rainfall season, as can be derived from negative changes in drought indices (Figs. 3 and 4 ) at the start of the rainy season under future conditions. In Fig.  4 , we highlighted that the total amount of precipitation in October tends to decrease, regardless of the climate scenario, in both sites. In December and January, we expect an increase in future precipitation. Due to this future shift in the rainy season, a delay in the emergence date seen to be a good strategy to cope with climate changes and maintain reasonable yields. The lowest yield reduction in SSP5-8.5 is found in runs with an emergence date of 349 days, which can also be related to the smaller future changes in total precipitation in December (Figs. 3 and 4 ).

Historical and future water-limited maize yield (t.ha − 1), yield gap (Yp-Ywl) (ton/ha), grain filling period (days), and growth duration (days), in Univen and Syferkuil, according to different emergence dates (15 October (DOY 288), 15 November (319), and 15 December (349) days). The dotted grey lines indicate the median values, and the shade of grey indicates the maximum and minimum values of the historical simulation

Mangani et al. ( 2018 ) evaluated two versions of the crop model CropSyst to simulate crop yield in SA and concluded that in climate change scenarios (2030 and 2050), a decrease in maize yield is expected due to the increase of drought severity. However, the understanding of the period when these droughts will be more severe is also important to create mitigation and adaptation measures under climate change scenarios. By comparing the simulated yields and their changes under future conditions with those of the drought indices (Fig.  5 ), we derive that in the future, drier conditions in October may strongly affect the yield of the Univen region, with the worst scenarios for early sowing and the period 2051–2080 (scenarios SSP3-7.0 and SSP5-8.5). A possible alternative could be to delay the sowing and emergence dates to November or December to reduce yield losses. Considering the SSP5-8.5 scenario (2051–2080) as an example, the yield values ​​at Univen differ considerably depending on the crop emergence date: between 4.88 t.ha −1 (DOY 288) and 5.41 (319) up to 5.55 (349). At Syferkuil, most yield simulations for future conditions also indicate reduced yield, with the worst scenarios in 2051–2080 (SSP3-7.0 and SSP5-8.5). For the baseline period, the yield variability is smaller. We conclude that shifting emergence dates under future conditions can considerably impact yield at both sites significantly by taking into account shifts in seasonality and adjusting accordingly by later sowing to reduce potential yield losses.

The highest values of yield losses found in Syferkuil and Univen were in SSP5-8.5 (2051–2080). In Syferkuil, these losses were − 15.5% (considering the EM 288), − 18.6% (319), and − 10.7% (349). In Univen, the yield losses were − 50.3% (288), − 31.2 (319), and − 19.2 (349). The situations that could represent an increase were found in Syferkuil (SSP1-2.6), with a yield increase of 2.9% (2021–2050, EM 288) and 1.6% (2051–2080, EM 319).

The differences between simulated potential land water-limited yields can serve as an indication of the degree of long-term average water-limitation and how that shifts under climate change scenarios and alternative sowing/emergence date. The simulations show that Univen has lower differences between potential yield and water-limited yield than Syferkuil, and these values do not have a significant relationship with emergence dates. In summary, these differences tend to decrease in Syferkuil and increase in Univen. Regarding the grain filling period, Univen presented fewer days than Syferkuil. This duration tends to decrease in both sites in the future, with the worst scenarios in 2051–2080 (SSP3-7.0 and SSP5-8.5). The cycle duration is higher when EM is 288 in both sites, although the differences between EM are not high. The site Syferkuil has a higher cycle duration than Univen, although both areas indicate a decrease in cycle duration in future scenarios, mainly in SSP3-7.0 and SSP5-8.5 (2051–2080).

The occurrence of drought events in the future may affect yield and climate change adaptation, or more generally future management practices. Assessing the period in which droughts occur is essential as it can affect different maize growth stages, causing damages or sub-optimum growth conditions that call for specific adaptations. When maize is exposed to drought conditions during the vegetative stage, yield losses can reportedly range from 32% to as high as 92% (Atteya 2003 ). Other authors have found yield losses for the reproductive stage or early grain filling ranging from 63 to 87% (Kamara et al. 2003 ) and for the late grain-filling and ripening period from 79 to 81% (Monneveux et al. 2006 ). The occurrence of other agroclimatic extreme events also threatens food security as they may affect food crop production worldwide (see, e.g., Rötter et al. 2018 ). Mangani et al. ( 2019 ) used climate change scenarios and crop models to study potential climate change impacts and concluded that maize yield is expected to be reduced in the future (2051–2080) in SA. Similar results were reported by Cammarano et al. ( 2020 ) for commercial maize farming in the free state of SA. Masupha and Moeletsi ( 2018 ) used drought indicators to study how future droughts may limit maize production in SA and concluded that drought remains a threat to rainfed maize production in the Luvuvhu River catchment area.

Climate-induced changes in productivity due to droughts are already perceived by farmers in the Mopani district of the Limpopo Province. In her master thesis, May ( 2019 ) conducted a survey and concluded that most farmers from the four villages surveyed (Ndengeza, Makhushane, Mafarana, and Gabaza) who perceived changes in the climate over the past decade and increased frequency of extreme years also reported negative effects on their maize yields. The survey also confirmed that in Mafarana, Gabaza, and Ndengeza, for most farmers, October and November is the usual sowing period. In the drier village Makhushane, most farmers are sowing their maize later, in November and December.

Some improved agro-technologies (seasonal weather forecast-based sowing; more drought-tolerant maize cultivars) and management practices (combinations of sowing date and cultivar choice depending on the onset of rains) in the future could be utilized to minimize the impacts of droughts in future maize production. This would require still higher investments in climate information services and in breeding climate-resilient maize cultivars using advanced breeding tools (Rötter et al. 2015 ; Cairns et al. 2018 ; Hoffmann et al. 2018 ) and/or the judicious and site-specific choice of climate-smart interventions, such as cereal-legume intercropping and crop rotations (Swanepoel et al. 2018 ; Rapholo et al. 2019 ; Hoffmann et al. 2020 ).

Conclusions

We aimed to characterize drought patterns and to evaluate their spatiotemporal variability and potential impacts on maize production in the Limpopo province with a closer look at two different climatic subregions. Climate models and drought indicators were used to quantify droughts and changes in their frequency in the future. This was then linked to the quantification of yield impacts for different sowing/crop emergence dates using the climate data in conjunction with the dynamic crop simulation model WOFOST.

The key messages of this research are as follows:

Current drought conditions (DC): Compared to Syferkuil, Univen showed the driest conditions of PRCPTOT. October appears as the month with the worst drought conditions, considering PRCPTOT, LWP, and RX5D. In Syferkuil, October is also the month with the worst DC indicated by DD and LDP. In Univen, DD and LDP have the worst DC in March.

Climate models performance: The estimation of drought indices with a model ensemble was better than those with individual models. The patterns of underestimation or overestimation depend on the index studied, the model evaluated, and the region.

Historical and future drought patterns: The climate scenarios indicate small changes in the future for the PRCPTOT index. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will present worse DC in the future.

Historical and future frequency of droughts: The worst DC tends to occur in October, considering all the evaluated indices. Univen site was the site with the greatest future changes, with the increasing of DC.

Drought’s impacts on maize production: The yield tends to decline in the future considering all emergence dates. We conclude that future changes in the emergence date seem to impact yield in both sites significantly. A possible alternative is to delay the emergence date to November or December to reduce the yield losses. The grain filling period, as well as the cycle duration, tends to decrease in the future. The cycle duration is higher when EM is 288 in both sites.

Understanding historical drought patterns and the future perspective is important for the implementation of drought plans and mitigation measures, to promote sustainable management options, and to support crop ideotype design. Current and future drought conditions in October indicate that droughts will increase in this period, mostly in the mid-end century. The increase in future drought conditions will have a direct impact on maize production, representing a risk for food security in the region. The results found in this paper can contribute to specific measures to support improvement of maize production in SA, considering changes in future drought patterns and their effect on yield, grain filling, and cycle duration.

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Acknowledgements

The authors acknowledge the farmers and SALLnet project partners in Limpopo for the information shared.

Open Access funding enabled and organized by Projekt DEAL. NCRF and GBM are grateful for the funding support from BARISTA (grant no 031B0811A), and RPR and WCDN are grateful for the received funding from SALLnet project (grant no 01LL1802A) via BMBF.

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Nicole Costa Resende Ferreira, Reimund Paul Rötter, Gennady Bracho-Mujica, William C. D. Nelson, Quang Dung Lam & Isaaka Abdulai

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Claus Recktenwald

Department of Soil Science, University of Venda, Thohoyandou, 0950, South Africa

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Department of Zoology, University of Venda, Thohoyandou, 0950, South Africa

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The authors NCRF, RR, and GBM contributed to the study conception and design. Material preparation, data collection, and analysis were performed by NCRF. The manuscript was written by NCRF, and all authors reviewed and approved the final manuscript.

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Ferreira, N.C.R., Rötter, R.P., Bracho-Mujica, G. et al. Drought patterns: their spatiotemporal variability and impacts on maize production in Limpopo province, South Africa. Int J Biometeorol 67 , 133–148 (2023). https://doi.org/10.1007/s00484-022-02392-1

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DOI : https://doi.org/10.1007/s00484-022-02392-1

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Developing timely and actionable drought forecasts for the Limpopo River Basin Southern Africa is highly drought-prone, and its agricultural and hydrological systems are vulnerable. Climate forecasts provide tools for decision-making and adaptation to climate extreme events. This report presents the preliminary results regarding the development of seasonal drought forecasts for the Limpopo River basin. Using multiple climate-relevant datasets, a diagnosis of the climate of the Limpopo basin was carried out, and the relevance of using the SPEI drought index for characterizing droughts was also assessed. The results showed strong climatic seasonality, in addition to the strong relationship between the seasonal drought conditions captured by SPEI. Outputs from four climate models, gridded rainfall observations, and a machine-learning method were used to generate a real-time experimental probabilistic forecast of rainfall in the Limpopo basin. Finally, the next steps are presented to meet the objectives of the Initiative, strengthening the capacities of the Limpopo Watercourse Commission.

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Drought patterns: their spatiotemporal variability and impacts on maize production in Limpopo province, South Africa

Nicole costa resende ferreira.

1 Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Georg-August-Universität Göttingen, Grisebachstraße 6, 37077 Göttingen, Germany

Reimund Paul Rötter

Gennady bracho-mujica, william c. d. nelson, quang dung lam, claus recktenwald.

2 Kasisi Agricultural Training Center (KATC), Kasisi Mission, Farm 591, Lusaka, Zambia

Isaaka Abdulai

Jude odhiambo.

3 Department of Soil Science, University of Venda, Thohoyandou, 0950 South Africa

Stefan Foord

4 Department of Zoology, University of Venda, Thohoyandou, 0950 South Africa

Associated Data

Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa’s summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00484-022-02392-1.

Introduction

Droughts affect different regions globally, with a range of negative impacts affecting multiple socioeconomic and environmental sectors, including agriculture (Vicente-Serrano 2006 ; Ferreira et al. 2021a ), water resources (Ferreira and Chou 2018 ; Ferreira et al. 2021b ), and forestry (Copenheaver et al. 2011 ), among others. Due to global climate change, droughts are likely to become more frequent and more severe in many regions (Dai 2011 ), as a consequence of the projected global warming with changes in circulation patterns (e.g., Kornhuber et al. 2019 ), increased evapotranspiration, changes in rainfall patterns, accelerated hydrological cycle with increased rainfall intensity, etc. (Drumond et al. 2019 ; Fischer and Knutti 2014 ; Intergovernmental Panel on Climate Change (IPCC) 2019 ; Lobell et al. 2013 ). High temperatures are expected to result in higher water deficits during the summer season, leading to decreased soil moisture and more frequent and severe agricultural droughts (Adams and Peck 2009 ; Park et al. 2018 ).

Large-scale droughts have occurred worldwide at different times throughout historical record (Dai 2011 ; Trnka et al. 2018 ), yet the damage has increased substantially in recent decades (Moravec et al. 2021 ). In arid and semi-arid areas of southern Africa, droughts are common and frequent (Park et al. 2018 ; Meza et al. 2021 ; Mahlalela et al. 2020 ) causing significant economic losses (Vogel et al. 2000 ) and increasing food insecurity in the region (Verschuur et al. 2021 ). Since 1970, Southern Africa has observed more intense, widespread and more extended droughts (Richard et al. 2001 ; Burls et al. 2019 ). In this context, it is important to unravel the spatiotemporal patterns and severity of drought at different scales to support the design and adjustment of climate change mitigation and adaptation measures.

The agriculture sector depends on climate to guarantee crop productivity, profitability, and quality. Lobell et al. ( 2008 ) concluded that agricultural production will mainly be negatively affected by climate change and will impede the ability of many regions to achieve the necessary gains for future food security, as was also recently found for the main wheat producing and exporting regions worldwide (Trnka et al. 2019 ). In southern Africa, maize is predominantly grown in smallholder farming systems, where over 90% of the production systems are rainfed; and also, the maize cultivated by commercial farmers in South Africa is mainly rainfed (Bationo and Waswa 2011 ). Smallholder maize farming systems in the dry savanna areas, as found in Limpopo (Rötter et al. 2021 ), are particularly vulnerable to climate variability and change (Adger et al. 2007 ; Cairns et al. 2013 ; Conway et al. 2015 ). This could have a huge impact on local food security due to the importance of these areas to the agricultural sector. While many studies show that climate change will increase drought frequency and severity, the direction and extent of these changes and related crop yields depend on the region and season. For this reason, the use of different drought metrics might be needed to provide robust estimates of related risks (Cook et al. 2020 ).

This paper aims to study drought patterns in the Limpopo region (South Africa) and evaluate their spatiotemporal patterns and how these are likely to change under future climatic conditions to signal potential repercussions on crop yields. In particular, we will look at 30-year time horizons and consider different emissions scenarios and global climate models. The other important and closely related objective is how drought may potentially affect maize crop production in two representative sites in Limpopo, with contrasting conditions. To quantify climate change’s impact on maize development and yield, we applied the crop growth simulation model WOFOST (Boogaard et al. 1998 ).

Materials and methods

Study area and maize climatic requirements.

The study area comprises parts of the Limpopo province, South Africa (SA). This region is known as one of the hottest provinces in the country (Kruger and Shongwe 2004 ), with frequent and severe droughts due to high temperatures and unreliable rainfall (Maponya and Mpandeli 2012 ; Maposa et al. 2021 ). The region presents mostly a subtropical climate, with a contrasting environment favorable for the cultivation of grain crops, tropical fruits, and vegetables. We focused this study on two sites: Univen and Syferkuil (Fig.  1 ). These sites were chosen due to the contrasting environmental conditions (i.e., soil and climate characteristics) and long-term data availability.

Experimental sites Syferkuil and Univen in Limpopo, SA. Total precipitation (mm) and mean temperatures (°C) monthly climatology (period: 1984–2014). Missing values are shown in gray

The interannual variability of total accumulated precipitation per year is higher at Univen (Online Resource 1 ) than at Syferkuil. In both sites, there is a clear seasonal pattern in precipitation, from October until March, but Univen is a warmer site. In Syferkuil, there is a distinct increase in air temperature from 2003 onwards, especially in January.

In SA, several circulation phenomena influence climate variability, including El Niño Southern Oscillation (ENSO) phenomenon—with contrasting impacts associated with El Niño and La Niña phases (Reason and Jagadheesha 2005 ; Gizaw and Gan 2017 ). As a general rule, the El Niño phase tends to lead to drier conditions, whereas the La Niña phase tends to lead to wetter conditions (Reason and Jagadheesha 2005 ; Phillips et al. 1998 ; Nicholson and Kim 1997 ; Janowiak 1988 ). The spatial extent of drought-prone regions of SA may increase in the future due to an increase in the frequency of El Niño episodes under a warmer climate (Diaz et al. 2001 ; Perry et al. 2017 ; Cai et al. 2015 ; Power et al. 2013 ). Pomposi et al. ( 2018 ) verified that strong and moderate-to-weak El Niño events tend to increase dry days in southern Africa. The same authors concluded that the likelihood of southern Africa receiving less than average precipitation is approximately 80% for strong El Niño events compared to just over 60% for moderate-to-weak El Niño events. A detailed comparison of the precipitation patterns of average years with El Niños and strong El Niños is given in the supplementary material (Online Resources 2 and 3 ). In years with strong El Niño events, especially the south-eastern region become drier than normal, and in the north-eastern region, strong El Niños have an opposite effect. Therefore, when looking at the country’s average yield, the ENSO impact for some regions may be masked, leading to little overall effect on yield (as shown, e.g., in Mozambique, Angola, Zambia). In other countries, such as South Africa and Botswana, where we observe drought patterns exclusively in years of strong El Niños, the relationship between drought and yield is more consistent on a year-to-year basis.

Regarding maize production in SA, the Limpopo region plays an essential role. A great share of its maize production (62%) is provided by smallholder farmers (LEDET, 2016 ). According to Agbiz ( 2016 ), maize is the most cultivated grain crop in SA, followed by soybeans, wheat, sunflower, and sugar cane (FAOSTAT 2019 ). The average maize yield (t.ha −1 ) from 1990 to 2019 for Limpopo and SA, and their relationship with El Niño can be found at Online Resource 4 . In the last decade, the average commercial yield in Limpopo exceeded the average values in SA, emphasizing the region’s importance in the national agricultural development. The climate conditions in Limpopo and especially the low mean annual precipitation are known as factors limiting yields attainable under rainfed conditions (Conway et al. 2015 ; Trambauer et al. 2014 ).

Limpopo is one of those areas of SA frequently prone to drought events (Dlamini 2013 ). The current climate variability as observed in Limpopo and the expected future climatic change may impose higher future risks to crop production. The climate vulnerability in SA is also emphasized by the fact that most maize production is rainfed, with less than 10% produced under irrigation (Baloyi 2011 ). The location and time of the year/length of the growing season are critical factors that determine the potential impacts of climate change on crop production (Gbetibouo and Hassan 2005 ). Each crop has climatic requirements including crop water requirements, which mainly depend on the crop’s genetic characteristics, stage of growth, and duration of the growth cycle.

Climate projections

Projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6) and the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) provide climate scenarios based on different Shared Socio-Economic Pathways (SSP) (O’Neill et al. 2014 , 2020 ). These climate scenarios can be used to investigate the implications of long-term climatic changes for designing robust policies in an environment of interacting complex systems and uncertainty (Hall et al. 2016 ; Harrison et al. 2015 ; O’Neil et al. 2014 ).

For this study, we selected the scenarios SSP1-2.6, SSP3-7.0, and SSP5-8, which can be considered optimistic, intermediate, and pessimistic climate change scenarios, respectively. We used BIAS-adjusted precipitation data (Lange 2019 ) for the historical and future periods. Climate change projections were divided into two 30-year time-slices, from 2021 to 2050 and 2051 to 2080. The climate models were selected according to their availability: IPSL-CM6A-LR, GFDL-ESM4, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The models have a horizontal resolution of 0.5° × 0.5°, and for convenience, they will be named as IPSL, GFDL, MPI, MRI, and UKESM, respectively. To evaluate the climate models’ performance, we used simulated historical climate data (1981–2010) and observed climate data (1984 to 2014) and calculated the root mean squared error (RMSE) and the mean bias error (MBE). Different meteorological drought indices were calculated, and an ensemble mean model was created for assessing the temporal and spatial patterns of drought.

Drought analyses

Masih et al. ( 2014 ) presented a review of droughts on the African continent from 1900 to 2013, indicating that droughts have become more frequent, intense, and widespread during the last 50 years. In SA, droughts occur often and during different times of the year in all climatic zones, with different intensity, spatial extent, and duration (Rouault and Richard 2003 ). In the Limpopo province, drought imposes a considerable risk since large parts of the province have a semi-arid climate with low, erratic rainfall (Maponya and Mpandeli 2012 ).

Several indices are commonly used as proxies to capture different drought patterns based on climatic information. Those indices were developed to characterize drought considering different approaches based on its magnitude, duration, frequency, and intensity (Heim 2000 , 2002 ; Vicente-Serrano et al. 2010 ; Dai 2011 ; Edossa et al. 2016 ; Rouault and Richard 2003 ).

In this study, six indices were selected to represent different drought conditions: PRCPTOT (total precipitation accumulated per month, mm), DD (dry days: the number of days without precipitation), LDP (longest dry period: the number of consecutive days without precipitation), LWP (longest wet period: number of consecutive days with precipitation), RX5D (maximum consecutive 5-day precipitation within a month, mm), and SPI (standardized precipitation index for classification of drought severity).

Indices were calculated for the maize growing period in the study area (i.e., from October to March). This period was chosen since the main maize planting time is between mid-October and mid-December (Matimolane 2018 ). Each index can help to understand drought patterns in a different way and thus jointly provide the basis for the design of effective adaptation/mitigation measures.

Crop simulation modelling

Climate extremes, such as drought, have several impacts on crop performance, affecting among others, the sowing dates, nutrient management practices, and eventually the actual yield obtained. In this context, process-based crop models are widely used tools for predicting crop growth and yield on the basis of crop characteristics and their interaction with prevailing weather and soil conditions. These tools can support current and future agricultural field management and national decision-making, e.g., the widely applied modelling platforms APSIM (Keating et al. 2003 ), DSSAT (Jones et al. 2003 ), and WOFOST (Van Ittersum et al. 2003 ). It is expected that future changes in temperature and precipitation regimes will be directly reflected by changes in crop yields all over the world, whereby negative yield impacts are likely to be prevalent in many regions, including most African countries (Abraha and Savage 2006 ; Porter et al. 2014 ; Waha et al. 2013 ).

Among the crop simulation models that have been applied in Africa, we chose the World Food Studies (WOFOST 7.1) model for simulating daily crop growth and spring maize yield under rainfed conditions in SA under different climate change scenarios (Ma et al. 2013 ; Boogard et al. 2013 , 1998 ; de Wit et al. 2019 ). The WOFOST model simulates the phenological development of different crops, from emergence to maturity, considering the crop genetic properties and environmental conditions (Hadiya et al. 2018 ). It comprises different processes such as phenological development, light interception, CO2 assimilation, transpiration, respiration, partitioning of assimilates to the various organs, dry matter, and yield formation (Boogard et al. 1998 ; Hadiya et al. 2018 ). The WOFOST model has been applied and is continuously being evaluated and extended for different crops all over the world (Dobermann et al. 2000 ; Palosuo et al. 2011 ; Rötter et al. 2012 ; Cheng et al. 2016 ; de Wit et al. 2019 ) . Previous calibration and validation of the WOFOST model for different regions in Africa can be found at Liu ( 2015 ), Wolf et al. ( 2015 ), Kassie et al. ( 2014 ), Rötter and van Keulen ( 1997 ), and Ogutu et al. ( 2018 ).

WOFOST requires as input data: daily weather, soil information, and crop characteristics. Among the input data needed are station name, latitude, longitude, altitude, minimum temperature, maximum temperature, hours of bright sunshine duration or global radiation, wind speed at 2 m, rainfall amount, and vapor pressure. The soil characteristics required are soil texture and soil moisture volumetric fraction at field capacity (cm 3 .cm −3 ), at permanent wilting point (cm 3 .cm −3 ), and at saturation (cm 3 .cm −3 ). To calibrate the model for a given crop cultivar, it is required to have information about crop phenology, maximum leaf area index (LAImax), biomass partitioning pattern, final biomass, and grain yield; if possible, data on soil moisture content in the root zone at some point in time will allow us to cross-check soil water balance calculations.

Sowing dates and crop emergence can have a considerable impact on crop performance. Usually, farmers plant flexibly within a sowing window depending on the location. We considered three different dates of crop emergence, 15 October (julian day 288), 15 November (319), and 15 December (349), based on crop calendars for the given regions. The sites are classified as sandy clay loams, and the soil properties were taken from the recent high resolution (30 m × 30 m) digital soil map iSDA ( 2020 ). Online Resource 5 describes the full set up of the model runs applied in this research.

Besides using different emergence dates, we used different climate scenarios to identify how climate change may affect maize production in the region. We used data from the climate models considering the historical period (1981–2010) and the future projections SSP1-2.6, SSP3-7.0, and SSP5-8.5 (2021–2050, 2051–2080). We simulated potential yield (Yp) and water-limited yield (Ywl). We evaluated the water-limited yield (t.ha −1 ), yield gap (calculated as the difference between potential yield and water-limited yield) (t.ha −1 ), grain filling period (defined as the period between the day of flowering and the harvest) (days), and cycle duration (days). Model annual outputs were evaluated to understand how climate change, and more specifically drought occurrence will affect maize yield year-to-year variability.

Results and discussion

Drought climatology.

We examined the drought climatology using precipitation data in the Limpopo province to verify the variations of long-term annual drought patterns according to historical observations as well as historical weather simulations. Such analysis is essential to identify the years with drought conditions (DC) and identify differences between the two experimental sites (Online Resource 6 ).

The highest errors in the models are identified for the climate zone represented by Univen site, which has higher amounts of rainfall than the climate zone represented by Syferkuil. In Univen, models underestimate precipitation, as seen from the accumulated precipitation (PRCPTOT) and maximum consecutive 5-day precipitation (RX5D) indices in the NDJF season. In 1999/2000, at Univen, the PRCPTOT index showed a big difference between observed and simulated data. While observations indicated a high value (436.5 mm) of monthly accumulated precipitation (NDJF season), the climate models used in this study were unable to represent this well. A similar pattern is also seen in the RX5D index. The PRCPTOT values in November and December of 1999 were below long-term climatic means (0 and 69 mm, respectively), yet, in January and February of 2000, the highest values recorded in the subregion were observed, with accumulated precipitation of 783 and 894 mm, respectively. These high amounts of precipitation resulted in a disastrous flooding, causing losses of human lives, as well as considerable economic losses (Khandlhela and May 2006 ). Recktenwald ( 2019 ) reported that the southern summer season of 1991/1992 was dry, with droughts occurring in Limpopo. The results agree with the observed drought record, which indicates major droughts in 1991–1992 and 2004–2005 (Walz et al. 2020 ; Meza et al. 2021 ). At Univen, model simulations show an increase in DD in 1999, while according to observed data, there was a decrease in DD. At Syferkuil, climate model simulations underestimated DD. The longest wet period index (LWP) shows great variations among the models (e.g., for 1995 and 2005); hence, it appears that the climate model ensemble cannot adequately capture observed extremes. Regarding the longest dry period index (LDP), at Syferkuil, the models indicated low LDP values, while the observations showed high values in NDJF. The standardized precipitation index (SPI) index also shows great variations across the years and between both sites, which can have several implications for agricultural production. Similar results were found by Manatsa et al. ( 2010 ) for Zimbabwe.

The definition of drought conditions (DC) for each site was calculated based on specific quantiles (q10 and q90, Online Resource 7 ). According to the historical simulations, the most critical values are not associated with a specific month or associated with one region only. However, the months of October and March seem to be very problematic in both areas. The driest conditions of accumulated monthly precipitation (PRCPTOT) are found at the Univen site, mainly in October (q10 is 28.4 mm). October is also the month with the lowest accumulated precipitation values in 5 days (q10 is 17.2 mm) and the shortest wet period (q10 is 2 days). October and November are usually the beginning of the rainy season, and droughts in November can be reflected in delayed sowing, as changing planting dates is a common drought adaptation measure applied by farmers in the Limpopo region (May, 2019 ). At the Univen site, March appears to be the month with the worst drought conditions when considering the number of days without rainfall (DD) and consecutive days without rainfall (LDP).

At Syferkuil, October presents DC due to the low accumulated rainfall in November (q10 PRCPTOT is 35.7 mm), low accumulated rainfall in 5 days (q10 RX5DAY is 19.5 mm), and low values of the longest wet period (2.2 days). October is also the month with the most critical DC, with the highest dry days (26 days) and consecutive dry days (17.2 days). February, on the other hand, presents high values of accumulated rainfall. In general, the Univen site presented more severe DC than the Syferkuil site.

Evaluation of observed against modelled climate data

Figure  2 indicates the RMSE and the MBE for October to March for the six drought indices. The PRCPTOT index showed positive MBE at Univen and negative at Syferkuil, which indicates the overestimation of the index by climate models at Univen and underestimation at Syferkuil. The largest RMSE occurs in Univen in February. The RX5D also has a higher RMSE for the Univen site, whereby January and February are the months with the biggest errors. The MBE of the DD index indicates underestimation at Univen and overestimation at Syferkuil. The smallest RMSE in the LDP index occurs at Syferkuil, while for the LWP, the smallest errors occur at Univen. Considering the SPI index, the smallest MBE is found for January at Univen and for February at Syferkuil.

RMSE and MBE for Univen (red) and Syferkuil (blue), calculated based on historical simulations and observed data (1984–2014). The boxplots indicate the errors of the different models for each index and each month (from October to March) considered in this analysis. PRCPTOT represents the total precipitation (mm), DD is the number of dry days (days), LDP is the longest dry period (days), LWP is the longest wet period (days), RX5D is the maximum consecutive 5-day precipitation (mm), and SPI is the standardized precipitation index (-)

In general, the RMSE is lower for the ensemble mean compared to the individual models. The same result is found for MBE, which tends to approach zero when using the ensemble. This observation confirms the suggestion that multi-member ensemble tend to compensate for errors (Rötter et al. 2011 ; Wallach et al. 2016 ). The patterns of underestimation or overestimation depend on the index studied, the model evaluated, and the climatic “subregion.” Variations in errors among the models indicate projections’ uncertainty, which is reflected in the ensemble. As shown in Fig.  2 , the climate models exhibit differences reflected in model ensemble prediction uncertainty, as they are different numerical system realizations with different types and patterns of errors (Wallach et al. 2016 ). To reduce uncertainties in the predictions, we applied the mean values of multimember model ensembles in the next steps of this analysis to obtain more robust results (Martre et al. 2015 ).

Historical and future drought patterns

To study drought patterns and their shifts in the future, we assessed indices across the Limpopo region. We evaluated the indices using the 30-year averages of the baseline (1981–2010) and the future time-slices (2021–2050 and 2051–2080) from the model ensemble. In Fig.  3 , we present only the scenario SSP5-8.5. However, results for the scenarios SSP1-2.6 and SSP3-7.0 are available in the supplementary material (Online Resources 8 – 13 ).

Ensemble 30-year average of the index: a PRCPTOT, b LWP, and c SPI in the Limpopo province for historical simulation (baseline) and future scenario SSP5-8.5 (2021–2050, 2051–2080). Sites are represented with a black circle (Syferkuil), and a black triangle (Univen)

Relatively small changes in PRCPTOT are expected for future climate scenarios (Fig.  3a ). October is the month with the lowest precipitation values, and the rainy season seems to start in November. The most remarkable future changes occur in January (mostly in SSP5-8.5, 2051–2080), with increasing precipitation, and October, with decreasing precipitation. In general, we can observe that for both sites, shifts in precipitation seasonality may occur, which will reflect in changes in future agricultural practices. In October, the precipitation in Syferkuil may reduce from 50.2 mm (1981–2010) to 42.8 and 35 mm (SSP5-8.5, 2021–2050 and 2051–2080). In Univen, this reduction is from 44.3 mm to 37.4 and 27.6 mm (SSP5-8.5, 2021–2050 and 2051–2080). For November, February, and March, different patterns are found according to the time-slice. In December and January, we identify a trend to increase precipitation in both locations. In December, this increase is from 105.3 mm (baseline) up to 115.7 mm (SS5-8.5, 2051–2080) in Syferkuil, and from 100.8 mm up to 109.2 mm (SSP5-8.5, 2051–2080) in Univen. In January, this increase is from 126.2 mm up to 136.1 mm (SS5-8.5, 2051–2080) in Syferkuil and from 148.1 mm up to 163.5 mm (SSP5-8.5, 2051–2080) in Univen. This will certainly have impacts over the maize yield, as we discuss in the “Impacts of climate change on maize” section.

Regarding the LWP index (Fig.  3b ), it is expected decreasing of LWP in north Limpopo in November and south Limpopo in October (mostly in SSP5-8.5, 2051–2080). In December, the increasing or decreasing of LWP depends on the climate scenario evaluated. There is an increase of LWP in the north region (January) and south region (February), according to the scenario SSP5-8.5 in the time-slice 2051–2080. We found the highest values of RX5D in the baseline period in January and February, in the region of Univen and Syferkuil (Online Resource 9 ). In January, we identified a trend of increasing RX5D in the future (2051–2080). In November, the ensemble indicated that the Northeast of Limpopo tends to get drier in the future. The evaluation of DD suggests increasing DD (online resource 11 ) in the Northeast of Limpopo, mostly in October and November. Also, it is shown decreasing in DD in the future in the southeast region, mainly in February. The months of October and March are the months with higher DD values (higher than 26 days). The highest LDP in the region in the baseline period is identified in October and March, while the lowest values are in December (online resource 12 ). The greatest future changes show increasing LDP in the Northeast region, mostly in SSP5-8.5 (2051–2080).

Regarding the SPI index (Fig.  3 c), the South and Central regions of Limpopo tend to have more problems related to droughts. In the future, the index indicates increases in droughts in October in the southern region. In November, a decrease in droughts in the northern region and an increase in the south of the region are expected. December is currently the month with the most problems concerning droughts. However, there are different trends for December according to different future scenarios. According to current climate conditions, January is not a month prone to drought. Still, it may become drier in the future, mainly in the southern region of Limpopo, as indicated by SSP1-2.6 and SSP3-7.0 (2051–2080).

We conclude, therefore, that both subregions are likely to have more severe drought conditions in the future than during the baseline period 1981–2010. Other studies, which evaluated different indexes, come to similar conclusions. For example, Gizaw and Gan ( 2017 ) used the Palmer drought severity index to analyze changes in droughts, and they concluded that most South African areas would shift to a drier climate in the 2050s and 2080s. The results agree with Schulze et al. ( 2001 ), who reported that in SA, which is already a water-stressed country, climate change is expected to increase the variability of rainfall events and amplify weather extremes.

To identify future changes in droughts frequency, we evaluated drought conditions (DC) based on the quantiles thresholds. We calculate the number of years in the future with DC and compare it to the current DC. Figure  4 indicates the number of years with DC in Univen and Syferkuil, according to the historical simulation and SSP's, in different 30-year time-slices. We can see that there are some local differences regarding the frequency of DC (historical and future) at the two sites.

Number of years with drought conditions (DC) in Univen and Syferkuil. Colors indicate the difference between future scenarios and historical simulation. Shades of red show drier conditions in the future compared to historical simulation, and blue shades show wetter conditions in the future

In Univen, the worst DC tends to occur in October, with an increase of DC indicated by all evaluated indices. In the scenario SSP5-8.5 (2051–2081), PRCPTOT presented 19 years with DC, representing an increase of 16 years in the 30-year time-slice (around 53.3%). In the RX5D index, we observe an increase of 12 years, and DD presented an increase of 9 years. There is an agreement in drought patterns among the climate scenarios. There is a decrease in PRCPTOT, LWP, and RX5D and an increase in DD in November. The worst DC also occurs in SSP5-8.5 (2051–2080).

At Syferkuil, the worst future DC occurs in October, although with lower values than Univen. In SSP5-8.5 (2051–2080), results show an increase of 13 years with DC related to PRCPTOT in the 30-year time-slice, 9 years in the RX5D index, and 6 years in DD and LDP indices. In January, we identified a decrease in LWP and RX5D and increasing in DD. Although January is a month with an increasing rainfall trend in Syferkuil, there was also an increase in DC in all scenarios, according to some indices. The DD index shows an increase of DC from 3 years throughout historical simulations to 9 years in a future scenario and RX5D an increase from 3 to 7 years (in the worst-case-scenario).

We conclude that the Univen site represents the subregion with the most remarkable changes, increasing DC in the future. It was also the subregion that presented the worst DC in the historical period, which is a reason for concern, primarily due to the potential impacts of DC on maize production.

Impacts of climate change on maize

Water-limited yield was similar among sites (Fig.  5 ). However, the future projections for Univen indicate a reduction in yield, regardless of the climate scenario assessed. The highest reduction and lowest yields ​​are found under the SSP5-8.5 (2051–2080) scenario. It is also noteworthy that for baseline yield simulations, the runs with an emergence date at day 288 resulted in the highest yield (~ 7.33 t.ha −1 ), as this coincides with the crop with closed canopy being exposed to high global radiation levels with sufficient moisture in an optimum manner under the given baseline climate. However, in the scenario SSP5-8.5 (2051–2080), this start date led to the worst yield performance (~ 4.88 t.ha −1 ), which is likely due to shifts in the rainfall season, as can be derived from negative changes in drought indices (Figs. ​ (Figs.3 3 and ​ and4) 4 ) at the start of the rainy season under future conditions. In Fig.  4 , we highlighted that the total amount of precipitation in October tends to decrease, regardless of the climate scenario, in both sites. In December and January, we expect an increase in future precipitation. Due to this future shift in the rainy season, a delay in the emergence date seen to be a good strategy to cope with climate changes and maintain reasonable yields. The lowest yield reduction in SSP5-8.5 is found in runs with an emergence date of 349 days, which can also be related to the smaller future changes in total precipitation in December (Figs. ​ (Figs.3 3 and ​ and4 4 ).

Historical and future water-limited maize yield (t.ha − 1), yield gap (Yp-Ywl) (ton/ha), grain filling period (days), and growth duration (days), in Univen and Syferkuil, according to different emergence dates (15 October (DOY 288), 15 November (319), and 15 December (349) days). The dotted grey lines indicate the median values, and the shade of grey indicates the maximum and minimum values of the historical simulation

Mangani et al. ( 2018 ) evaluated two versions of the crop model CropSyst to simulate crop yield in SA and concluded that in climate change scenarios (2030 and 2050), a decrease in maize yield is expected due to the increase of drought severity. However, the understanding of the period when these droughts will be more severe is also important to create mitigation and adaptation measures under climate change scenarios. By comparing the simulated yields and their changes under future conditions with those of the drought indices (Fig.  5 ), we derive that in the future, drier conditions in October may strongly affect the yield of the Univen region, with the worst scenarios for early sowing and the period 2051–2080 (scenarios SSP3-7.0 and SSP5-8.5). A possible alternative could be to delay the sowing and emergence dates to November or December to reduce yield losses. Considering the SSP5-8.5 scenario (2051–2080) as an example, the yield values ​​at Univen differ considerably depending on the crop emergence date: between 4.88 t.ha −1 (DOY 288) and 5.41 (319) up to 5.55 (349). At Syferkuil, most yield simulations for future conditions also indicate reduced yield, with the worst scenarios in 2051–2080 (SSP3-7.0 and SSP5-8.5). For the baseline period, the yield variability is smaller. We conclude that shifting emergence dates under future conditions can considerably impact yield at both sites significantly by taking into account shifts in seasonality and adjusting accordingly by later sowing to reduce potential yield losses.

The highest values of yield losses found in Syferkuil and Univen were in SSP5-8.5 (2051–2080). In Syferkuil, these losses were − 15.5% (considering the EM 288), − 18.6% (319), and − 10.7% (349). In Univen, the yield losses were − 50.3% (288), − 31.2 (319), and − 19.2 (349). The situations that could represent an increase were found in Syferkuil (SSP1-2.6), with a yield increase of 2.9% (2021–2050, EM 288) and 1.6% (2051–2080, EM 319).

The differences between simulated potential land water-limited yields can serve as an indication of the degree of long-term average water-limitation and how that shifts under climate change scenarios and alternative sowing/emergence date. The simulations show that Univen has lower differences between potential yield and water-limited yield than Syferkuil, and these values do not have a significant relationship with emergence dates. In summary, these differences tend to decrease in Syferkuil and increase in Univen. Regarding the grain filling period, Univen presented fewer days than Syferkuil. This duration tends to decrease in both sites in the future, with the worst scenarios in 2051–2080 (SSP3-7.0 and SSP5-8.5). The cycle duration is higher when EM is 288 in both sites, although the differences between EM are not high. The site Syferkuil has a higher cycle duration than Univen, although both areas indicate a decrease in cycle duration in future scenarios, mainly in SSP3-7.0 and SSP5-8.5 (2051–2080).

The occurrence of drought events in the future may affect yield and climate change adaptation, or more generally future management practices. Assessing the period in which droughts occur is essential as it can affect different maize growth stages, causing damages or sub-optimum growth conditions that call for specific adaptations. When maize is exposed to drought conditions during the vegetative stage, yield losses can reportedly range from 32% to as high as 92% (Atteya 2003 ). Other authors have found yield losses for the reproductive stage or early grain filling ranging from 63 to 87% (Kamara et al. 2003 ) and for the late grain-filling and ripening period from 79 to 81% (Monneveux et al. 2006 ). The occurrence of other agroclimatic extreme events also threatens food security as they may affect food crop production worldwide (see, e.g., Rötter et al. 2018 ). Mangani et al. ( 2019 ) used climate change scenarios and crop models to study potential climate change impacts and concluded that maize yield is expected to be reduced in the future (2051–2080) in SA. Similar results were reported by Cammarano et al. ( 2020 ) for commercial maize farming in the free state of SA. Masupha and Moeletsi ( 2018 ) used drought indicators to study how future droughts may limit maize production in SA and concluded that drought remains a threat to rainfed maize production in the Luvuvhu River catchment area.

Climate-induced changes in productivity due to droughts are already perceived by farmers in the Mopani district of the Limpopo Province. In her master thesis, May ( 2019 ) conducted a survey and concluded that most farmers from the four villages surveyed (Ndengeza, Makhushane, Mafarana, and Gabaza) who perceived changes in the climate over the past decade and increased frequency of extreme years also reported negative effects on their maize yields. The survey also confirmed that in Mafarana, Gabaza, and Ndengeza, for most farmers, October and November is the usual sowing period. In the drier village Makhushane, most farmers are sowing their maize later, in November and December.

Some improved agro-technologies (seasonal weather forecast-based sowing; more drought-tolerant maize cultivars) and management practices (combinations of sowing date and cultivar choice depending on the onset of rains) in the future could be utilized to minimize the impacts of droughts in future maize production. This would require still higher investments in climate information services and in breeding climate-resilient maize cultivars using advanced breeding tools (Rötter et al. 2015 ; Cairns et al. 2018 ; Hoffmann et al. 2018 ) and/or the judicious and site-specific choice of climate-smart interventions, such as cereal-legume intercropping and crop rotations (Swanepoel et al. 2018 ; Rapholo et al. 2019 ; Hoffmann et al. 2020 ).

Conclusions

We aimed to characterize drought patterns and to evaluate their spatiotemporal variability and potential impacts on maize production in the Limpopo province with a closer look at two different climatic subregions. Climate models and drought indicators were used to quantify droughts and changes in their frequency in the future. This was then linked to the quantification of yield impacts for different sowing/crop emergence dates using the climate data in conjunction with the dynamic crop simulation model WOFOST.

The key messages of this research are as follows:

• Current drought conditions (DC): Compared to Syferkuil, Univen showed the driest conditions of PRCPTOT. October appears as the month with the worst drought conditions, considering PRCPTOT, LWP, and RX5D. In Syferkuil, October is also the month with the worst DC indicated by DD and LDP. In Univen, DD and LDP have the worst DC in March.
• Climate models performance: The estimation of drought indices with a model ensemble was better than those with individual models. The patterns of underestimation or overestimation depend on the index studied, the model evaluated, and the region.
• Historical and future drought patterns: The climate scenarios indicate small changes in the future for the PRCPTOT index. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will present worse DC in the future.
• Historical and future frequency of droughts: The worst DC tends to occur in October, considering all the evaluated indices. Univen site was the site with the greatest future changes, with the increasing of DC.
• Drought’s impacts on maize production: The yield tends to decline in the future considering all emergence dates. We conclude that future changes in the emergence date seem to impact yield in both sites significantly. A possible alternative is to delay the emergence date to November or December to reduce the yield losses. The grain filling period, as well as the cycle duration, tends to decrease in the future. The cycle duration is higher when EM is 288 in both sites.

Understanding historical drought patterns and the future perspective is important for the implementation of drought plans and mitigation measures, to promote sustainable management options, and to support crop ideotype design. Current and future drought conditions in October indicate that droughts will increase in this period, mostly in the mid-end century. The increase in future drought conditions will have a direct impact on maize production, representing a risk for food security in the region. The results found in this paper can contribute to specific measures to support improvement of maize production in SA, considering changes in future drought patterns and their effect on yield, grain filling, and cycle duration.

Below is the link to the electronic supplementary material.

Acknowledgements

The authors acknowledge the farmers and SALLnet project partners in Limpopo for the information shared.

Author contribution

The authors NCRF, RR, and GBM contributed to the study conception and design. Material preparation, data collection, and analysis were performed by NCRF. The manuscript was written by NCRF, and all authors reviewed and approved the final manuscript.

Open Access funding enabled and organized by Projekt DEAL. NCRF and GBM are grateful for the funding support from BARISTA (grant no 031B0811A), and RPR and WCDN are grateful for the received funding from SALLnet project (grant no 01LL1802A) via BMBF.

Declarations

The authors declare no competing interests.

Contributor Information

Nicole Costa Resende Ferreira, Email: [email protected] .

Reimund Paul Rötter, Email: [email protected] .

Gennady Bracho-Mujica, Email: [email protected] .

William C. D. Nelson, Email: [email protected] .

Quang Dung Lam, Email: [email protected] .

Claus Recktenwald, Email: [email protected] .

Isaaka Abdulai, Email: ed.gdwg@aludbai .

Jude Odhiambo, Email: [email protected] .

Stefan Foord, Email: [email protected] .

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CURRENT ONGOING INITIATIVES

LIMCOM's current ongoing interventions being undertaken include:

• Co-implementation of the USAID funded Southern Africa’s Resilient Waters Programme
• Conjunctive surface-groundwater management of SADC shared waters: generating principles through fit-for-purpose practice to SADC Member States.
• Consolidation and Expansion of the Limpopo Early Warning Flood Forecast System (EWFFS)
• Development of Climate Change Scenarios for the Limpopo River Basin
• Integrated Transboundary River Basin Management for the Sustainable Development of the Limpopo River Basin

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Drought is a major challenge in the Limpopo River basin, having an effect on and, affecting availability and distribution of water for agriculture, industry and other major water uses.

Definitions for Various Drought Types

Meteorological drought is a reduction in rainfall compared with the average over a specified period. A drought is said to occur when a large area receives rainfall less than 75% of normal for an extended period.

Agricultural drought is inadequate supply of the moisture required by a crop during each different growth stage, resulting in impaired growth and reduced yields.

Hydrological drought is the impact of a reduction in rainfall on surface and underground water resources that reduces the supply of water for irrigation, hydro-electrical power generation, and other household and industrial uses.

Socio-economic drought relates to the impact of drought on human activities, including both indirect and direct impacts on agricultural production and the wider economy.

The frequent occurrence of El Niño Southern Oscillation (ENSO) phenomena complicates the expected rainfall pattern that is normally controlled by the movement of the Inter-Tropical Convergence Zone (ITCZ) (IDRC 2008).

Economic development in developing countries is currently threatened by weather-related disasters such as floods and droughts (World Water Assessment Programme 2009). Water shortages can seriously harm the economy of a country or region, con­straining development and reducing economic growth as financial and hydrological resources are expended to counter the drought. Multi-year droughts can have a lasting legacy, resulting in long-term suffering for agriculturally dependent rural communities and reducing national and regional growth rates significantly.

The map below shows satellite-based measurements of vegetation as an indicator of water stress and drought occurrence in 2007.

SATELLITE-BASED MEASUREMENTS OF VEGETATION AS AN INDICATOR OF DROUGHT FROM 2007 (SEE DESCRIPTION BELOW). SOURCE: NASA GIMMS GROUP AT GODDARD SPACE FLIGHT CENTER 2007

Drought in Southern Africa 2007

Hot, dry weather from January through March 2007 wilted crops in southern Africa. The severe drought produced near-record temperatures that, combined with a lack of rainfall, caused extensive crop damage, particularly in western crop areas, reported the United States Department of Agriculture’s Foreign Agricultural Service. In South Africa, the anticipated yield from the corn crop dropped from ten million tons in December to six million tons in April because farmers couldn’t plant in the dry conditions and many of the crops that were planted wilted in the dry heat. The last South African drought of this magnitude occurred in 1992.

The impact of the drought on vegetation throughout southern Africa is illustrated in this image. The image shows vegetation conditions in March 2007 compared to conditions during the average March between 1999 and 2006 as measured by the SPOT satellite. Brown areas show where plants were less thick or where fewer plants grew than average. Green areas, by contrast, indicate that vegetation was thicker and more lush than average.

Next: Climate of the Limpopo River Basin >

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Evaluation of drought regimes and impacts in the Limpopo basin

2014, Hydrology and Earth System Sciences Discussions

Related Papers

Sintayehu L E G E S S E Gebre

This study has analyzed the climate variability and meteorological drought events over Limpopo River Basin. The Limpopo Basin is shared by four countries, Botswana, South Africa, Zimbabwe, and Mozambique. The total catchment is approximately 408,000 km 2. The main governing factor for rainfall patterns in the basin is the movement of the Inter-tropical Convergence Zone (ITCZ). In this study the drought event has been analyzed using standardized precipitation index (SPI). The SPI quantifies the precipitation deficit for multiple time scales and reflects the impact of droughts on the availability of water resources. The long year`s daily average monthly precipitation for the whole area indicates that the precipitation is variable and there is no any clear trend. The relative percentage change of average monthly precipitation of the 1992-2001 compared to 1961-1991 period using WATCH Climate data of the River basin indicates that, a positive value increase in percentage change is observed for the whole months of the year. High magnitude deviation in maximum and minimum temperature in the month of July 2001 observed with respect to 1961-2000 period. 5.2 and 7.9 degree centigrade respectively. The long term SPI analysis indicates that there was an extended accumulated sever dry condition that is prolonged from 1991 up to 1992 over the basin. Generally, this study indicates that there is a frequent meteorological drought events and unpredictable climate variability in the basin. Therefore farmers should take a precaution to adjust their farming system and to overcome drought events for better agricultural productivity.

Douglas Merrey

Physics and Chemistry of the Earth, Parts A/B/C

Piet Kenabatho

Themba Gumbo

Summary This paper attempts to highlight how implementation of the integrated water resource management approach can reduce livelihood risk, either as a basis for an agricultural intervention, or as an essential planning tool. Three groups of studies are showcased. The first examines conservation agriculture and rainwater harvesting; the second evaluates the productivity, impact and sustainability of the current widespread distribution of low-head drip kits for irrigation at household level and the third case study considers aspects of climate change and livelihood risk. The basic principle illustrated in each of these three studies is that water in agriculture (although this is true in general) is best managed by considering the water cycle. This issue arises because the water cycle is a complex system and the implementation of a livelihood intervention that involves water use - whether in rainfed agriculture, irrigation, domestic supply or elsewhere - has to consider the impacts o...

Florian Pappenberger , Shreedhar Maskey , P. Trambauer

Muumbe K Lweendo , Baohong Lu , Wei Xu

In this study, an integrated approach involving multiple standardized indicators and hydrological modeling (Soil and Water Assessment Tool, SWAT) was evaluated to reconstruct and characterize meteorological, agricultural and hydrological droughts in Upper Kafue River Basin of Zambia during 1984–2013. Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to identify meteorological droughts. Standardized Soil Moisture Index (SSI) was applied to characterize agricultural droughts and Standardized Runoff Index (SRI) was calculated to illustrate hydrological droughts. Input data for SRI and SSI computation was obtained from SWAT model which simulated daily and monthly runoff well with Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R 2) greater than 0.65. The results showed that: (1) all indices were able to detect temporal variability of major drought events in a humid subtropical basin in Southern Africa; (2) SWAT successfully simulated runoff and soil moisture although soil moisture requires further calibration to increase accuracy; (3) the average duration and intensity for meteorological droughts at three-month time scale were lower but frequencies were higher compared to agricultural and hydrological droughts at 3-and 12-month aggregates; and (4) drought events exhibited a negative trend as evaluated by Mann–Kendall on SPEI, indicating an increase in drought severity, and correlation analysis between SPEI and SRI revealed that SPEI at 9–15 months has a strong link with hydrological conditions. This study showed that a comprehensive assessment of droughts by integrating multiple variables provided a versatile tool for drought monitoring and mitigation.

Sustainability

ephias mugari

Understanding the effects of droughts on vegetation and ecosystem services (ES) is important for climate change adaptation. However, drought occurrence varies across space and time. We examined drought dynamics and impacts on vegetation and ES in the semi-arid Limpopo Basin of Botswana. Weather station precipitation, remotely sensed normalized difference vegetation index (NDVI) and participatory mapping exercises provided data for the analyses. Results show that between 1980 and 2015, rainfall anomaly indices of potential drought years ranged between −4.38 and −0.12. The longest spell of below-average rainfall occurred between 1992 and 1996. On average, drought events lasted for 1.9 years and recurred every 2.3 years. Although the overall drought frequency was 3.7 times in every 5 years, drought prevalence increased to 50%, 60% and 70% between 1981–1990, 1991–2000, and 2001–2010, respectively. The wet season average vegetation condition index between 2000 and 2015 revealed the occur...

Christina Botai

miriam murambadoro

Global impacts of drought conditions pose a major challenge towards the achievement of the 2030 Sustainable Development Goals. As a result, a clarion call for nations to take actions aimed at mitigating the adverse negative effects, managing key natural resources and strengthening socioeconomic development can never be overemphasized. The present study evaluated hydrological drought conditions in three Cape provinces (Eastern, Western and Northern Cape) of South Africa, based on the Standardized Streamflow Index (SSI) calculated at 3- and 6-month accumulation periods from streamflow data spanning over the 3.5 decades. The SSI features were quantified by assessing the corresponding annual trends computed by using the Modified Mann–Kendall test. Drought conditions were also characterized in terms of the duration and severity across the three Cape provinces. The return levels of drought duration (DD) and drought severity (DS) associated with 2-, 5-, 10-, 20- and 50-year periods were es...

Jurnal Ilmiah Teknik Mesin

A. Yudi Eka Risano

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How severe is the drought? An analysis of the latest data

Facts are few, opinions plenty, on the cause of the water crisis.

Editor’s summary

• Piotr Wolski, a water scientist at UCT, has used the latest rainfall and best available weather station data to compare the current drought to past ones.
• The data shows that the drought over the water catchment area is the worst on record over one- and three-year periods. Rainfall in 2017 was especially poor.
• This kind of drought is expected once in 300 years (90% confidence interval: 105 to 1,280 years).
• The drought in the city itself is severe, but not the worst on record. Very little of Cape Town’s water is sourced from in the city itself.
• There is a trend towards lower rainfall over the past 84 years in the catchment area. This may be due to human-caused climate change.

The causes of Cape Town’s water crisis are hotly contested. There is a drought, of course. But there are also other reasons brought up in the public discourse, particularly on social media, such as population and water demand growth, unreported agricultural use, invasive species sucking out water in catchments, poor planning and mismanagement of water supply system, and lack of foresight in development of new water sources. Are these significant contributors to the shortage? It’s very difficult to say without dedicated, comprehensive analyses. But facts are few, opinions plenty.

Few people question whether there is a drought: what is questioned is its severity. This has important implications. If the drought has been mild, then it should not have resulted in a major water crisis. If it has been severe, then it has just been a bad ticket on the climate lottery, and all the other factors would be at most aggravating factors but not the main cause. The issue is obviously socially tense, creating wedges between authorities and citizens, between those who institute water restrictions and those who have to bear the brunt of them.

In this article I look at the most up-to-date rainfall data to assess how severe the current drought is. I have tried to answer that question before . But that article was based on very limited data, and was carried out well before 2017’s rainy season had finished. Now I have a more comprehensive dataset, which provides an opportunity to perform a more robust assessment.

There are a number of stations that measure rainfall in the vicinity of the Western Cape Water Supply System (WCWSS) dams. Data for them are available from the Department of Water and Sanitation (DWS) website .

Not all stations in this dataset have good records; there are numerous gaps. It appears that nine stations have data available for recent years, but four are located in the region of the WCWSS dams, and have no significant gaps or systematic errors from 1981 through 2017. Those stations are: Vogel Vallij, Zacharashoek, Theewaterskloof and Kogel Baai.

This is annual rainfall at these stations for 1981 to 2017:

Note that the year in the above figures is taken to be between November and October. This is because October is more or less the end of rainy season, and the last month when an increase in dam levels may be recorded. Also, because at the time of writing this, data are available only till October 2017. Putting the end of the year in October allows me to use the 2017 data without estimating rainfall in November and December.

To make further analyses more robust, instead of analysing data from individual stations, I calculated their simple average. And to account for the fact that the drought is likely of a multi-year character, I created plots showing two-, three- and four-year average rainfall:

These plots tell a consistent story: 2017, as well as the preceding two-, three- and four-year average rainfall in the region are the lowest since 1981. What it means is that the 2017 drought was at least as rare as once in 36 years.

But looking at the last 36 years is not enough. We know that before that there were droughts in the 1920s and 1970s. We need to compare the current drought to the old ones. Can we go back further in time?

The DWS data do not span further back. But data from other sources do. The rainfall data from the South African Weather Service (SAWS) for some stations in the Western Cape go as far back as the late 1800s. Unfortunately, there isn’t any overlap between the DWS and SAWS rain stations, so we cannot simply extend the record of the four stations used above. We need to repeat the analyses on the entire SAWS dataset. If available SAWS data are screened in a similar manner as the DWS data were (i.e. for continuity and consistency of record), we get five stations with data covering the period 1933 to 2017. Only three are located in the WCWSS dam region: Vrugbaar, Rustfontein and Nuweberg.

Similarly to the above analysis, I plot below the mean rainfall for the three stations:

The figures show that 2017 was the lowest rainfall year since 1933. They also show that the mean rainfall in the three preceding years, 2015-2017, was unprecedented. However, the two-year mean as well as the four-year mean leading to 2017, were not the driest.

We can even go back further in time to 1920, but then we can rely on one station only (Vrugbaar). This is how plots for this station look:

Again, the 2017 rainfall for that station, and importantly the mean of 3-years prior to 2017 were lower than in any period experienced by this station since 1920.

Interestingly, if you look at the long-term rainfall data for the stations located near the coast, the situation is slightly different:

Here, although relatively bad, 2017 was not – either alone or in combination with preceding years — the driest on record. But this is not the area where most of our water is supplied from.

So the long-term SAWS data from the WCWSS dams region shows that 2017 and the period 2015 to 2017 were the driest since 1933. This translates into a drought return period of once in 84 years, possibly rarer. But how rare exactly? The statistical analysis I have done to calculate this are explained in my more detailed blog version of this article . My findings are that this kind of drought occurs once in 311 years with a 90% confidence that it falls between 105 and 1,280 years.

This is pretty rare and actually in the same range as the earlier estimates .

Of course, one may question that result in many ways. Are the stations used representative? Perhaps if we had a different set of observations we would get a different result? Does it make sense to use the average of several stations in the analysis? Perhaps there are errors in data?

I think this dataset is robust. I have taken care in the preparation of the dataset to find continuous, long-term data. Using averages of several stations reduces the chance that data errors affect the result.

The drought, as manifested by rainfall in the region of WCWSS dams, is indeed very rare, and very severe. Importantly, the analyses reveal that the drought was likely less severe in the coastal plains and in Cape Town itself. A possible reason for that might be a weaker penetration of cold fronts that bring winter rainfall to the region into the higher and distant inland regions.

Before I conclude this story – one more figure to think about:

This figure shows a trend in rainfall in the WCWSS region over the last 84 years. That trend is towards lower rainfall and it has a relatively strong magnitude – 17mm per ten years. It is barely statistically significant, though. The important thing is that this trend may be an expression of human-caused climate change, and may be affecting the magnitude of droughts. Simply, if that trend was not there, the 2017 drought would likely be less severe.

In summary – the analyses presented above, based on the best rainfall data available at this time, show that the drought, manifested through low rainfall in 2015 to 2017, was very rare and severe.

Acknowledgements to SAWS and DWS for data, and Chris Jack and Stefaan Conradie (both CSAG/UCT) for valuable discussions.

A more detailed version of this analysis has been published.

Dr Wolski is a researcher with UCT’s Climate System Analysis Group.

More about Cape Town water crisis

• After the drought: Cape Town’s gushing water 07 September 2020
• Coca-Cola and Cape Town’s sweetheart Day Zero deal 08 May 2020
• Cape Town residents battle spring floods 28 October 2019

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Can we forecast rain for 2018? Or is it totally random?

Dear Editor

Thank you for interesting analysis.

Just a couple of questions from this layman:

Can one forecast on past trends and on the data available (a) what the likelihood is of above average rainfall in the WC in 2018 and (b) if heavy rain is likely, from the monthly data how early in the year these heavy rains may fall following a three-year lower than average rainfall? Or is this totally random?

Sincerely Lorna Jones 25 Jan 2018

Revised data on drought crisis still not very useful

Thank you for publishing this, but I note that a lot of the data begins in 1933. This was the end of the 'great drought' experienced in Cape Town, from 1928-1933.

It would surely have been far more helpful to have data from 1928. The researcher has thus omitted the most important 'drought' period, except for one data set.

Furthermore, the current 'drought' isn't showing significantly lower rainfall that before, is it? It's a few percentage points at most. So when do semantics inform science? Wouldn't it be better to say, 'this is an average 12 year drought, maybe slightly worse than usual, but we can't say if it's really worse than the 1928 drought?'

Sincerely Mark J 25 Jan 2018

Predicting rainfall

GroundUp Editor's Response

In response to Lorna Jones, Piotr Wolski writes:

Extrapolation of a trend is possible, particularly if we know what is the driving force behind it. In this particular case we suspect that it is anthropogenic climate change, so it is not likely that the trend will get reversed in the next couple of years. However, the trend explains very little of the overall variance in data. So any prediction based on the trend will not be very accurate. In other words, the current drought has not been caused by the trend; the trend only magnified it. Similarly, the rainfall in the forthcoming season, and in the seasons following it, will be affected by the trend, but only so slightly. The actual magnitude of that rainfall will be determined by factors that are independent of the trend.

At this stage, we have a relatively good understanding of what atmospheric processes and factors affect the onset and magnitude of rains in the winter rainfall region, but a very limited ability to forecast them. Thus, from a practical point of view, at this stage we have to consider that indeed, it is a coin toss between early/late or wet/dry rainy season. Perhaps we will be able to tell more closer to the actual season, i.e. in May/June.

Sincerely GroundUp Editor 26 Jan 2018

Learning from Perth's water catchment

I come from Perth, a city of 1.6 million on the south western tip of Australia - with a climate almost identical to Cape Town. Originally, our city relied 100% on rainfall flowing into our dams.

However, this all changed when we realised in the 1990's that our climate was changing permanently. We now only get 10% of the stream flow that was flowing into the dams for the first 70 years of last century.

The most illuminating graph can be found here: https://www.watercorporation.com.au/water-supply/rainfall-and-dams/streamflow/streamflowhistorical .

If Cape Town is experiencing a similar trend then this would mean a total change to where you get your water. I trust the lessons learnt in Perth can be applied to Cape Town.

James Marshall Water Engineer

Sincerely James Marshall 6 Feb 2018

Population growth affects water supply

Since Cape Town lies in an area prone to dry spells, one might presume that the city would be prepared for such eventualities. But that appear to be not the case. One factor should be obvious: population growth.

From worldpopulationreview.com :

Year Population Growth(%) Growth 2030 4,322,000 5.65% 231,000 2025 4,091,000 5.98% 231,000 2020 3,860,000 2.22% 84,000 2018 3,776,000 3.17% 116,000 2015 3,660,000 9.42% 315,000 2010 3,345,000 10.54% 319,000 2005 3,026,000 11.45% 311,000 2000 2,715,000 13.41% 321,000 1995 2,394,000 11.09% 239,000 1990 2,155,000 11.95% 230,000

Take a finite resource and distribute it to twice as many people without efforts to increase that finite resource and you might, just might find yourself in a world of hurt.

Sincerely Bruce Hall 16 Feb 2018

You may republish this article, so long as you credit the authors and GroundUp, and do not change the text. Please include a link back to the original article.

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Preprint  

• Preprint hess-2013-432

Evaluation of drought regimes and impacts in the Limpopo basin

Abstract. Drought is a common phenomenon in the Limpopo River basin. In essence, droughts are long–term hydro-meteorological events affecting vast regions and causing significant non-structural damages. In the interest of riparian states' joint integrated water resources development and management of the Limpopo basin, inter regional drought severity and its impacts should be understood. The study focussed on case studies in the basin which is subdivided into four homogeneous regions owing to topographic and climate variations based on the previous work of the same authors. Using the medium range time series of the Standardized Precipitation Index (SPI) as an indicator of drought, for each homogeneous region monthly and annual Severity-Area-Frequency (SAF) curves and maps of probability of drought occurrence were constructed. The results indicated localized severe droughts in higher frequencies, while only moderate to severe low frequency droughts may spread over wider areas in the basin. The region-level Drought-Severity Indices can be used as indicators for planning localized interventions and drought mitigation efforts in the basin. The approach can also be used to develop improved drought indicators, to assess the relationship between drought hazard and vulnerability and to enhance the performance of methods currently used for drought forecasting. Results on the meteorological drought linkage with hydrological and vegetation or agricultural drought indices are presented as means of validation of the specific drought regimes and their localized impact in each homogeneous region. In general, this preliminary investigation reveals that the western part of the basin will face a higher risk of drought when compared to other regions of the Limpopo basin in terms of the medium-term drought. The Limpopo basin is water stressed and livelihood challenges remain at large, thus impacts of droughts and related resilience options should be taken into account in the formulation of regional sustainable water resources development strategies. This study is exciting in the manner that the variations in the sub-basin drought severities are revealed and are used to suggest the corresponding drought monitoring and management strategies. This will have an overall effect in developing a basin-wide framework for integrated drought management as well as water resources development and management, which requires cooperative efforts among the riparian countries of the Limpopo basin.

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4 citations as recorded by crossref.

• Impact of basin-wide dry climate conditions and non-climatic drivers: an isolation approach F. Al-Faraj & M. Scholz 10.2166/wcc.2016.130
• Regional analysis and derivation of copula-based drought Severity-Area-Frequency curve in Lake Urmia basin, Iran B. Amirataee et al. 10.1016/j.jenvman.2017.10.027
• Spatial and temporal distribution of blue water in the Limpopo River Basin, Southern Africa: A case study E. Mosase et al. 10.1016/j.ecohyd.2018.12.002
• Evaluation of Infilling Methods for Time Series of Daily Temperature Data: Case Study of Limpopo Province, South Africa Z. Shabalala et al. 10.3390/cli7070086

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Drought Pushes Millions Into ‘Acute Hunger’ in Southern Africa

The disaster, intensified by El Niño, is devastating communities across several countries, killing crops and livestock and sending food prices soaring.

By Somini Sengupta and Manuela Andreoni

An estimated 20 million people in southern Africa are facing what the United Nations calls “acute hunger” as one of the worst droughts in more than four decades shrivels crops, decimates livestock and, after years of rising food prices brought on by pandemic and war, spikes the price of corn, the region’s staple crop.

Malawi, Zambia and Zimbabwe have all declared national emergencies.

It is a bitter foretaste of what a warming climate is projected to bring to a region that’s likely to be acutely affected by climate change, though scientists said on Thursday that the current drought is more driven by the natural weather cycle known as El Niño than by global warming.

Its effects are all the more punishing because in the past few years the region had been hit by cyclones, unusually heavy rains and a widening outbreak of cholera.

‘Urgent help’ is needed

The rains this year began late and were lower than average. In February, when crops need it most, parts of Zimbabwe, Zambia, Malawi, Angola, Mozambique and Botswana received a fifth of the typical rainfall.

That’s devastating for these largely agrarian countries, where farmers rely entirely on the rains.

In southern Malawi, in a district called Chikwawa, some residents were wading into a river rife with crocodiles to collect a wild tuber known as nyika to curb their hunger. “My area needs urgent help,” the local leader, who identified himself as Chief Chimombo, said.

Elsewhere, cattle in search of water walked into fields still muddy from last year’s heavy rains, only to get stuck, said Chikondi Chabvuta, a Malawi-based aid worker with CARE, the international relief organization. Thousands of cattle deaths have been reported in the region, according to the group.

The first few months of every year, just before the harvest begins in late April and May, are usually a lean season. This year, because harvests are projected to be significantly lower , the lean season is likely to last longer. “The food security situation is very bad and is expected to get worse,” Ms. Chabvuta said.

Local corn prices have risen sharply. In Zambia, the price more than doubled between January 2022 and January of this year, according to the United Nations Food and Agriculture Organization . In Malawi, it rose fourfold.

The F.A.O. pointed out that, in addition to low yields, grain prices have been abnormally high because of the war in Ukraine, one of the world’s biggest grain exporters, as well as weak currencies in several southern African countries, making it expensive to buy imported food, fuel and fertilizers.

Why it’s happening

According to an analysis published Thursday by World Weather Attribution, an international coalition of scientists that focuses on rapid assessment of extreme weather events, the driving force behind the current drought is El Niño, a natural weather phenomenon that heats parts of the Pacific Ocean every few years and tweaks the weather in different ways in different parts of the world. In Southern Africa, El Niños tend to bring below-average rainfall.

El Niño made this drought twice as likely, the study concluded. That weather pattern is now weakening, but a repeat is expected soon.

The drought may also have been worsened by deforestation, which throws off local rainfall patterns and degrades soils, the study concluded.

Droughts are notoriously hard to attribute to global warming. That is particularly true in regions like Southern Africa, in part because it doesn’t have a dense network of weather stations offering detailed historical data.

Scientists are uncertain as to whether climate change played a role in this particular drought. However, there is little uncertainty about the long-term effects of climate change in this part of the world.

The average temperature in Southern Africa has risen by 1.04 to 1.8 degrees Celsius in the past 50 years , according to the Intergovernmental Panel on Climate Change, and the number of hot days has increased. That makes a dry year worse. Plants and animals are thirstier. Moisture evaporates. Soils dry out. Scientific models indicate that Southern Africa is becoming drier overall .

The Intergovernmental Panel on Climate Change calls Southern Africa a climate change “hot spot in terms of both hot extremes and drying.”

The costs of adaptation

To the millions of people trying to cope with this drought, it hardly matters whether climate change or something else is responsible for why the skies have gone dry.

What matters is whether these communities can adapt fast enough to weather shocks.

“It’s really important that resilience to droughts, especially in these parts of the continent, should really be improved,” said Joyce Kimutai, one of the authors of the study and a researcher at the Grantham Institute, a climate and environment center at Imperial College London.

There are existing solutions that need money to put into effect: early warning systems that inform people about what to expect, insurance and other social safety programs to help them prepare, as well as diversifying what farmers plant. Corn is extremely vulnerable to heat and erratic rains.

Golden Matonga contributed reporting.

Somini Sengupta is the international climate reporter on the Times climate team. More about Somini Sengupta

Manuela Andreoni is a Times climate and environmental reporter and a writer for the Climate Forward newsletter. More about Manuela Andreoni

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