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Influences of El Nino southern oscillation (ENSO) on weather systems have increased the frequency and amplitude of extreme events over the last century. This even continues to exacerbate the already warming earth, with 2014–2016 which coincided with the strongest El Nino years observed as the warmest period in recent past. This study presents an approach of characterizing droughts at various timescales and establishes teleconnections between ENSO and drought severity in Botswana. The study uses Standardized Precipitation Evaporation Index (SPEI) at timescales of 1, 3, 6, 12 and 24 month to characterize droughts and Pearson’s correlations to study the teleconnections between SPEIs and ENSO. Results from the study reveal that extreme droughts are a rare occurrence in Botswana though it is more prone to moderate droughts at 12 month SPEI with a probability of 19% in the north. The highest severe drought probability was 7% recorded in the east. Linear trends indicate increasing dryness of around 0.8% per decade. These results have demonstrated that warm sea surface temperatures combined with negative Southern Oscillation Index correspond to persistent negative SPEI values and thus are likely to result in dry conditions. Significant correlations were observed in the mid austral summer in December and January. Due to this relationship, the drought early warning systems could use ENSO as one of the instruments for predicting drought over the study area and hence in its management.

Semiarid areas exhibit high climate variability whose frequency has increased in the recent past. This variability ultimately influences the spatial and temporal characteristics of droughts which in turn require a reliable drought index that can adequately characterize drought both in time and space. This study applied two drought indices viz.: Standardized precipitation index (SPI) and Standardized precipitation and evapotranspiration index (SPEI) at timescales of 1-, 3-, 6-, 12-, 18- and 24-months for a period of 1960–2016. The monotonic changes in drought severity were studied using Mann-Kendall Z-statistic and Sen's slope estimator. Both drought indices were able to detect the historical drought events of 1961/62–1965/66, 1980/81–1986/87, 1991/92, 2001/02–2005/06, 2009/10–2012/13 and 2014/15–2015/16. These events mainly coincided with El Nino years, hence the influence of El Nino southern oscillation (ENSO) on drought occurrence could not be ruled out. The drought evolutions were characterized by low frequency and longer durations at timescales of 12-, 18-, and 24-months. It was also observed that SPI overestimated drought severity during dry winter months while SPEI showed higher spatial coverage of drought vulnerability compared to SPI. Spatial and temporal trends of droughts showed significant wetting trends during the period 1960–1979 while during the entire period it was mostly drying tendencies after 1979/80. Pandamatenga and Shakawe showed higher vulnerability towards drying conditions during the period of analysis. The association between SPI and SPEI was also investigated using Spearman's rank correlation. Results from this analysis indicate that SPI accounts for >50% of the variations in SPEI and the association was strongest (96%) at 12-months timescale. The influence of temperature on drought evolutions was demonstrated at Francistown with SPI recording positive trends and SPEI negative ones. This study concludes that with the ongoing global warming, SPEI proves more robust in characterizing droughts in semiarid areas. It is hoped that findings from this study will augment ongoing efforts of mitigating negative impacts of increased incidences of climate variability especially in areas of agriculture and water resource management.

Uncertainties in rainfall have increased in the recent past exacerbating climate risks which are projected to be higher in semiarid environments. This study investigates the associated features of rainfall such as rain onset, cessation, length of the rain season (LRS), and dry spell frequency (DSF) as part of climate risk management in Botswana. Their trends were analysed using Mann-Kendall test statistic and Sen’s Slope estimator. The rainfall-evapotranspiration relationships were used in formulating the rain onset and cessation criteria. To understand some of the complexities arising from such uncertainties, artificial neural network (ANN) is used to predict onset and cessation of rain. Results reveal higher coefficients of variation in onset dates as compared to cessation of rain. Pandamatenga experiences the earliest onset on 28th of November while Tsabong the latest on 14th of January. Likewise, earliest cessation is observed at Tshane on 22nd of February and the latest on 30th of March at Shakawe. The shortest LRS of 45 days is registered at Tsabong whereas the northern locations show LRS greater than 100 days. Stations across the country experience strong negative correlation between onset and LRS of − 0.9. DSF shows increasing trends in 50% of the stations but only significant at Mahalapye, Pandamatenga, and Shakawe. Combining the LRS criteria and DSF, Kasane, Pandamatenga, and Shakawe were identified to be suitable for rainfed agriculture in Botswana especially for short to medium maturing cereal varieties. Predictions of onset and cessation indicate the possibility of delayed onset by 2–5 weeks in the next 5 years. Information generated from this study could help Botswana in climate risk management in the context of rainfed farming.

Climate variability and change is expected to affect agricultural productivity among other sectors. Studying the influence of this variability on crop production is one measure of generating climate change resilience strategies. In this study, the influence of climate variability on crop yield is investigated by determining the degree of association between climatic indices and crop yields of maize and sorghum using spearman’s rank correlation. The climatic indices used in this study are aridity index (AI), standardised precipitation evapotranspiration index (SPEI) at timescales of 1, 3, 6 and 12 months and southern oscillation index (SOI) representing El Nino southern oscillation (ENSO) influence on local climate. Local rainfall characteristics are expressed through length of the rainy season (LRS). Results reveal that ENSO influence is the most dominant across Botswana accounting for 85% and 78% variations in maize and sorghum yields respectively. Whereas AI and SPEI accounts for 70% and 65% variations in maize and sorghum respectively, LRS accounts for only 50% and 62% respectively. To facilitate agricultural planning, crop yield projections have been made using artificial neural network (ANN) models. The ANN projections indicate a likelihood of maize and sorghum yields declining by 51% and 70% respectively in the next 5 years. The high association between ENSO and crop yields in Botswana could further facilitate yield projections. Information generated from this study is useful in agricultural planning and hence strengthens farmers’ strategies in mitigating impacts of climate variability and change in semiarid areas.

In semi-arid catchments, hydrological modeling, water resources planning and management are hampered by insufficient spatial rainfall data which is usually derived from limited rain gauge networks. Satellite products are potential candidates to augment the limited spatial rainfall data in these areas. In this paper, the utility of the Tropical Rainfall Measuring Mission (TRMM) product (3B42 v7) is evaluated using data from the Notwane catchment in Botswana. In addition, rainfall simulations obtained from a multi-site stochastic rainfall model based on the generalised linear models (GLMs) were used as additional spatial rainfall estimates. These rainfall products were compared to the observed rainfall data obtained from six (6) rainfall stations available in the catchment for the period 1998–2012. The results show that in general the two approaches produce reasonable spatial rainfall estimates. However, the TRMM products provided better spatial rainfall estimates compared to the GLM rainfall outputs on an average, as more than 90% of the monthly rainfall variations were explained by the TRMM compared to 80% from the GLMs. However, there is still uncertainty associated mainly with limited rainfall stations, and the inability of the two products to capture unusually high rainfall values in the data sets. Despite this observation, rainfall indices computed to further assess the daily rainfall products (i.e. rainfall occurrence and amounts, length of dry spells) were adequately represented by the TRMM data compared to the GLMs. Performance from the GLMs is expected to improve with addition of further rainfall predictors. A combination of these rainfall products allows for reasonable spatial rainfall estimates and temporal (short term future) rainfall simulations from the TRMM and GLMs, respectively. The results have significant implications on water resources planning and management in the catchment which has, for the past three years, been experiencing prolonged droughts as shown by the drying of Gaborone dam (currently at a record low of 1.6% full), which is the main source of water supply to the city of Gaborone and neighbouring townships in Botswana.

This study is conducted over the data-poor Limpopo basin centered over southern Africa using reanalysis downscaled to useful resolution. Reanalysis products are of limited value in hydrological applications due to the coarse spatial scales they are available at. Dynamical downscaling of these products over a domain of interest offers a means to convert them to finer spatial scales in a dynamically consistent manner. Additionally, this downscaling also offers a way to resolve dominantatmospheric processes, leading to improved accuracy in the atmospheric variables derived. This study thus evaluates high-resolution downscaling of an objectively chosen reanalysis (ERA-I) over the Limpopo basin using Weather Research and Forecasting (WRF) as a regional climate model. The model generally under-estimates temperature and over-estimates precipitation over the basin, although reasonably consistent with observations. The model does well in simulating observed sustained hydrological extremes as assessed using the Standardized Precipitation Index (SPI) although it consistently under-estimates the severity ofmoisture deficit for the wettest part of the year during the dry years. The basin's aridity index (I) is above the severe drought threshold during summer and is more severe in autumn. This practically restricts rain-fed agriculture to around 3 months in a year over the basin. This study presents possible beneficial use of the downscaled simulations foroptimal hydrologic design and water resources planning in data scarce parts of the world.

Lateral and lower boundary conditions derived from a suitable global reanalysis dataset forms the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis datasets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational datasets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias corrected WRF simulations are, however, found to be marginally better than mean only bias corrected WRF simulations and raw ERA-I reanalysis driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.

Skillful and high-resolution datasets are critical if climate change is to be fully assimilated into water resources management and planning strategies. This is, however, a challenge for data-poor parts of the globe like Africa where adaptive capacity is also a challenge. Global datasets are also often not sufficient, as they come in coarse resolutions that are limited. Global reanalyses continue to help alleviate this challenge when they are downscaled to useful resolution, especially at the basin scale. This chapter provides an overview of dynamical downscaling of global reanalyses to useful resolution. The usefulness of dynamical downscaling through providing relatively high resolution and skillful datasets is also highlighted as very important for climate impact studies, especially with regard to data-sparse regions of Africa.

Multiple reanalysis datasets currently exist that can provide boundary conditions for dynamic downscaling and simulating local hydro-climatic processes at finer spatial and temporal resolutions. Previous work has suggested that there are two reanalyses alternatives that provide the best lateral boundary conditions for downscaling over southern Africa. This study dynamically downscales these reanalyses (ERA-I and MERRA) over southern Africa to a high resolution (10 km) grid using the WRF model. Simulations cover the period 1981–2010. Multiple observation datasets were used for both surface temperature and precipitation to account for observational uncertainty when assessing results. Generally, temperature is simulated quite well, except over the Namibian coastal plain where the simulations show anomalous warm temperature related to the failure to propagate the influence of the cold Benguela current inland. Precipitation tends to be overestimated in high altitude areas, and most of southern Mozambique. This could be attributed to challenges in handling complex topography and capturing large-scale circulation patterns. While MERRA driven WRF exhibits slightly less bias in temperature especially for La Nina years, ERA-I driven simulations are on average superior in terms of RMSE. When considering multiple variables and metrics, ERA-I is found to produce the best simulation of the climate over the domain. The influence of the regional model appears to be large enough to overcome the small difference in relative errors present in the lateral boundary conditions derived from these two reanalyses.

Future global warming may result in extreme precipitation events leading to crop, environment and infrastructure damage. Rainfall is a major input for the livelihood of peasant farmers in the Aswa catchment where the future rainfall variability, onset and cessation are also likely to be affected. The Aswa catchment has limited rainfall data, therefore, use of secondary datasets from Tropical Rainfall Measuring Mission (TRMM) is considered in this study, based on the close correlation of the recorded and TRMM rainfall. The latter was used to calibrate the statistical downscaling model for downscaling of two general circulation models to simulate future changes in rainfall. These data were analyzed for trends, wet and dry conditions/variability, onset and cessations of rain using the Mann&ndash, Kendall test, Standardized Precipitation Index (SPI) and the cumulative percentage mean rainfall method, respectively. Results show future rainfall is likely to increase, accompanied by increasing variability reaching as high as 118.5%. The frequency of SPI values above 2 (extreme wetness) is to increase above current level during mid and end of the century. The highest rainfall variability is expected especially during the onset and cessation months, which are generally expected to come earlier and later, by up to four and five weeks, respectively. The reliability worsens from the midterm (2036&ndash, 2065) to long term (2066&ndash, 2099). These likely changes in rainfall quantities, variability, onset and cessation months are some of the key rainfall dynamics that have implications for future arable agriculture, environment and water resource availability and planning over the Aswa catchment, as is increasingly the case elsewhere.

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann–Kendall and Sen’s Slope estimator statistics. Association of El Nino Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Nino and La Nina years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.

Regional climate modelling studies often begin by downscaling a reanalysis dataset in order to simulate the observed climate, allowing the investigation of regional climate processes and quantification of the errors associated with the regional model. To date choice of reanalysis to perform such downscaling has been made based either on convenience or on performance of the reanalyses within the regional domain for relevant variables such as near-surface air temperature and precipitation. However, the only information passed from the reanalysis to the regional model are the atmospheric temperature, moisture and winds at the location of the boundaries of the regional domain. Here we present a methodology to evaluate reanalyses derived lateral boundary conditions for an example domain over southern Africa using satellite data. This study focusses on atmospheric temperature and moisture which are easily available. Five commonly used global reanalyses (NCEP1, NCEP2, ERA-I, 20CRv2, and MERRA) are evaluated against the Atmospheric Infrared Sounder satellite temperature and relative humidity over boundaries of two domains centred on southern Africa for the years 2003–2012 inclusive. The study reveals that MERRA is the most suitable for climate mean with NCEP1 the next most suitable. For climate variability, ERA-I is the best followed by MERRA. Overall, MERRA is preferred for generating lateral boundary conditions for this domain, followed by ERA-I. While a “better” LBC specification is not the sole precursor to an improved downscaling outcome, any reduction in uncertainty associated with the specification of LBCs is a step in the right direction.

Rainfall is a major requirement for many water resources applications, including food production and security. Understanding the main drivers of rainfall and its variability in semi-arid areas is a key to unlocking the complex rainfall processes influencing the translation of rainfall into runoff. In recent studies, temperature and humidity were found to be among rainfall predictors in Botswana and South African catchments when using complex rainfall models based on the generalized linear models (GLMs). In this study, we explore the use of other less complex models such as artificial neural networks (ANNs), and Multiplicative Autoregressive Integrated Moving Average (MARIMA) (a) to further investigate the association between rainfall and large-scale rainfall predictors in Botswana, and (b) to forecast these predictors to simulate rainfall at shorter future time scales (October–December) for policy applications. The results indicate that ANN yields better estimates of forecasted temperatures and ra...

In recent years, the scientific community has been urged to undertake research that can immediately have impact on development issues, including national policies, strategies, and people's livelihoods, among others. While this is a fair call from decision makers, it should also be realized that science by nature is about innovation, discovery and knowledge generation. In this context, there is need for a balance between long term scientific investigations and short term scientific applications. With regard to the former, researchers spend years investigating (or need data of sufficient record length) to provide sound and reliable solutions to a problem at hand while in the latter, it is possible to reach a solution with few selected analyses. In all cases, it is advisable that researchers, where possible should link their studies to topical development issues in their case studies. In this paper, we use a hydrometeorological project in the Notwane catchment, Botswana, to show the importance of linking research to development agenda for mutual benefit of researchers and policy makers. The results indicate that some key development issues are being addressed by the Project and the scope exists to improve the impact of the project.

Climate change is expected to alter rainfall regimes across most parts of the world. The implications of this could be more severe in arid environments where rainfall is limited and highly variable in space and time. However, lack of good quality data, of sufficient record length and spatial coverage usually restricts model development and performance geared towards assessing the effects of climate change in these areas. This paper presents an analysis of rainfall and climate data in order to determine the time of change in rainfall series and identify possible correlations between rainfall and temperature. In addition, the paper aims to make predictions of future rainfall patterns in Botswana. This is achieved by using historical rainfall and climate data from rainfall stations spread across Botswana from 1965 to 2008. In addition, large scale reanalysis data from NCAR/NCEP and El Nino Southern Oscillation (ENSO) data were used to augment the limited observed spatial climate data series when developing a rainfall model. Temperature and ENSO indices have been used to predict rainfall regimes for the present climate. Based on these, the effects of climate change were quantified using a stochastic generalised linear rainfall model (GLM) driven by outputs of global climate models (GCMs). The results indicate that temperature is a significant rainfall predictor in Botswana compared to ENSO indices.

Changes in catchment response as a result of climate change and/or variability continue to be of concern to hydrologists and water managers and thus identification and study of changes in rainfall as a major input into the hydrologic system remains pivotal in water resources management and planning for reliable input into modelling catchment response systems. An attempt has been made in this study to analyse long-term rainfall data obtained from five rain gauges (Kabompo, Kasempa, Mwinilunga, Mongu and Kaoma) located in the headstream regions of the Zambezi river basin in Zambia and to determine if these time series belonged to similar regime, have had any significant trends and if there was any homogeneity in trends among stations. To detect change in regime, the data were separately subjected to intervention analysis (using Cumulative Summation or CUSUM technique) and step change analysis (using rank-sum test) and subsequently the trend in each of the time series were determined using Mann–Kendall-statistic. The analyses undertaken to identify possibility of any intervention due to either natural and/or manmade causes, through the CUSUM technique, did not show signs of any major interventions except for Mongu station, where a change in regime was observed around 1980. This was even confirmed through step change analysis using the rank-sum test. Though the five stations showed marginal downward trends, these were not significant. Even, the test of homogeneity in trends observed at different stations showed homogeneity between them. Based on these findings, it was concluded that the rainfall data in the entire sub-basin belonged to a similar climate regime and the rainfall data for the entire period could be used for developing rainfall-response relationship except for Mongu, where the time series from 1981 onwards appear to have been subjected human/instrumental errors and needs to be investigated and updated.

Information on rainfall magnitude at specified recurrence interval is important for agricultural and water resources planning and management besides particularly for a semi-arid country like Botswana where the annual rainfall is erratic with large coefficient of variation. This study which was based on long term annual rainfall data from 1961 to 2003 at 11 synoptic stations across Botswana, concluded that rainfall was increasing up to 1981, after which there has been a decline in the annual rainfall pattern. An overall downward trend shows that the decrease in the rainfall quantities in the recent years, i.e. after 1981, has been very high. Regional analysis showed that all the stations behaved homogeneously and followed a generalised extreme value (GEV) distribution. Rainfall quantiles computed with the total data also showed a decreasing pattern when compared with quantiles estimated using the data series up to 1981. It was found that for specific amounts of rainfall the recurrence interval increased considerably when the total period data were analysed. Overall it can be said that rainfall quantiles with a 10-year recurrence interval will decrease by 2–17%, while rainfall quantiles with a 50-year recurrence interval will decrease by 0–14%.

Forecasting future response behaviour of a semi-arid catchment in terms of runoff coefficient being trivial, an attempt has been made to apply an artificial neural network (ANN) model to forecast the runoff coefficients (ROC) for the rapidly urbanizing Notwane catchment system in Botswana. Runoff coefficients computed from 1978 to 2000, by the water balance technique have been used to develop the optimal network architecture with appropriate choice of the size of input vectors, number of hidden layers and number of neurons in the hidden layers, training algorithms and transfer functions for the network. The developed network has then been used to simulate the runoff coefficients for the above period. Based on its performance in terms of reproducibility of the water balance runoff coefficients, the network was used to forecast the runoff coefficients up to 2020 and it was found that the increase in runoff coefficients is about 1% per year. Based on the weights given by the network to the input variables, it was found that while about 48% contribution came from climatic factors, 52% came from the land use/land cover.

Rapid population growth, accompanied with increase in affluence, continues to put the semi-arid environments’ limited water resources under pressure. This study has attempted to compare population projections for two cities of Botswana (Gaborone and Francistown) using various methods including the Logistic Curve, and Artificial Neural Networks (ANNs). For both the cities, the Geometric Increase, Logistic Curve and ANNs performed better. The ANNs (-0.03 % deviation from observed) was the best for Gaborone City followed by the Geometric Increase (+2.97 % deviation from observed) and Logistic Curve (-3.40 deviation from observed). For the City of Francistown, the Logistic Curve outperformed the Geometric Increase and the ANNs with deviations of 0.03 %, -0.27 %, and +11.6 5 respectively. The findings indicate that the Cohort Component method for population projections should be supplemented with the superior methods of Geometric Increase, Logistic Curve and ANNs. As census data increase with time, ANNs might prove to be the best approach. With improved population projections, determining likely future water demands against available water resources could be improved and this can help feed into water resources planning strategies.