Thesis (M.Sc.(Agriculture (Agricultural Economics)) -- University of Limpopo, 2022 / Understanding the factors that influence farmers' decisions to take preventive measures against natural hazards provides insight that can be used to develop user-specific interventions to support their adaptation processes. The use of Protection Motivation Theory in analysing climate risk adaptation behaviour is driven by the increase in climate change, which is projected to increase the frequency and severity of climate related risks such as heatwaves, floods, and droughts. Given the importance of livestock in rural communities, information about their adaptation must be prioritised; yet, this is not the case, as most climate change adaptation research focus on crop production. The main aim of the study was to analyse the drought risk preparedness of smallholder livestock farmers in the Limpopo Province's Blouberg Local Municipality. The study's specific objectives were to identify and describe the socioeconomic characteristics of smallholder livestock farmers in the Blouberg Local Municipality, as well as to determine the drought coping and adaptation strategies used by them and to evaluate the protection motivation theory components influencing that coping and adaptation behaviour. The study collected primary cross-sectional data from 130 smallholder livestock farmers in the Blouberg Local Municipality using a semi-structured questionnaire. The farmers' drought risk coping and adaptation strategies were described using descriptive statistics, while multiple linear regression was used to test whether protection motivation theory variables influence the adaptation and coping choices of smallholder livestock farmers in Blouberg Local Municipality. According to the findings, smallholder livestock farmers in Blouberg Local Municipality use four measures on average to protect their livestock against drought. With an R2 adjusted of 0.70, protection motivation theory variables explain 70% of the variation in farmer protection motivation. Perceived risk probability, perceived severity, perceived self-efficacy, and perceived costs were significant variables associated with farmers' protection motive. It is recommended that interventions meant to increase drought risk resilience of the farmers should prioritise early warning signals to increase perceived probability of the farmers, create platforms for information exchange to increase perceived severity, teach farmers methods practically to increase perceived self-efficacy and keep the price of utilising measures low to decrease perceived cost / Risk and Vulnerability Science Centre (RVSC)

MENVSC / Department of Geography and Geo-Information Sciences / South Africa was badly affected by the recent 2015/16 severe drought. Water levels in dams declined drastically resulting in decimation of livestock herds and widespread crop failure. Mopani District Municipality is comprised of many agricultural activities that contribute to the economy and social development of the country. The study evaluated the nature of the drought hazard - its impacts, vulnerability and adaptation strategies employed by rural communities of Mopani District. The study used a mixed method approach with both quantitative and qualitative datasets. The district was divided into two distinct climatic areas, the eastern lowveld which includes the Greater-Giyani, Ba-Phalaborwa and Maruleng Local Municipalities and the western highveld which includes Greater- Tzaneen and Greater- Letaba Local Municipalities. Questionnaires were administered among community members whilst key informant interviews were conducted among relevant government and municipal officials. Anomalies in long term climate data were analysed to determine the frequency and intensity of drought in the district. Drought characterisation was done using a Standardised Precipitation and Evapotranspiration Index whilst vegetation anomaly maps, maize yields and dam level data were used to analyse the impacts of drought across the district. Levels of vulnerability to drought were determined using the Household Vulnerability Index. Spatially distinct patterns of drought conditions across the district were remarkable with wet conditions on the western highveld along the escarpment and harsh dry conditions towards the eastern lowveld. It was found that nearly half the time there is some form of drought or another in the district which may be linked to the remote El Nino phenomenon. Community vulnerabilities have a direct impact on human welfare and different strategies are employed to adapt to drought hazards both at community and district levels. The study showed a link between drought hazard extent and vulnerability. Community members are adapting using conservation agriculture, selling fire-wood, accessing boreholes and rearing chickens, amongst other means to survive in these harsh climatic conditions. Local government intervention strategies include supply of seeds and fertilisers, selling fodder at a cheaper price and supplying water using trucks. The findings of this study contribute to disaster risk reduction efforts in Mopani District Municipality / NRF

PhDH / Department of Hydrology and Water Resources / Demand for water resources has been on the increase and is compounded by population growth and related development demands. Thus, numerous sectors are affected by water scarcity and therefore effective management of drought-induced water deficit is of importance. Luvuvhu River Catchment (LRC), a tributary of the Limpopo River Basin in South Africa has witnessed an increasing frequency of drought events over the recent decades. Drought impacts negatively on communities’ livelihoods, development, economy, water resources, and agricultural yields. Drought assessment in terms of frequency and severity using Drought Indices (DI) in different parts of the world has been reported. However, the forecasting and prediction component which is significant in drought preparedness and setting up early warning systems is still inadequate in several regions of the world. This study aimed at characterising, assessing, and predicting drought conditions using DI as a drought quantifying parameter in the LRC. This was achieved through the application of hybrid statistical and machine learning models including predictions via a combination of hybrid models. Rainfall and temperature data were obtained from South African Weather Service, evapotranspiration, streamflow, and reservoir storage data were obtained from the Department of Water and Sanitation while root zone soil moisture data was derived from the NASA earth data Giovanni repository. The Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), Standardised Streamflow Index (SSI), and Nonlinear Aggregated Drought Index (NADI) were selected to assess and characterise drought conditions in the LRC. SPI is precipitation based, SPEI is precipitation and evapotranspiration based, SSI is based on streamflow while NADI is a multivariate index based on rainfall, potential evapotranspiration, streamflow, and storage reservoir volume. All indices detected major historical drought events that have occurred and reported over the study area, although the precipitation based indices were the only indices that categorised the 1991/1992 drought as extreme at 6- and 12- month timescales while the streamflow index and multivariate NADI underestimated the event. The most recent 2014/16 drought was also categorised to be extreme by the standardised indices. The study found that the multivariate index underestimates most historical drought events in the catchment. The indices further showed that the most prevalent drought events in the LRC were mild drought. Extreme drought events were the least found at 6.42%, 1.08%, 1.56%, and 4.4% for SPI, SPEI, SSI, and NADI, respectively. Standardised indices and NADI showed negative trends and positive upward trends, respectively. The positive trend showed by NADI depicts a decreased drought severity over the study period. Drought events were characterised based on duration, intensity, severity, and frequency of drought events for each decade of the 30 years considered in this study i.e. between 1986 – 1996, 1996 – 2006, 2006 – 2016. This was done to get finer details of how drought characteristics behaved at a 10-year interval over the study period. An increased drought duration was observed between 1986 - 1996 while the shortest duration was observed between 1996 - 2006 followed by 2006 - 2016. NADI showed an overall lowest catchment duration at 1- month timescale compared to the standardised indices. The relationship between drought severity and duration revealed a strong linear relationship across all indices at all timescales (i.e. an R2 of between 0.6353 and 0.9714, 0.6353 and 0.973, 0.2725 and 0.976 at 1-, 6- and 12- month timescales, respectively). In assessing the overall utilisation of an index, the five decision criteria (robustness, tractability, transparency, sophistication, and extendibility) were assigned a raw score of between one and five. The sum of the weighted scores (i.e. raw scores multiplied by the relative importance factor) was the total for each index. SPEI ranked the highest with a total weight score of 129 followed by the SSI with a score of 125 and then the SPI with a score of 106 while NADI scored the lowest with a weight of 84. Since SPEI ranked the highest of all the four indices evaluated, it is regarded as an index that best describes drought conditions in the LRC and was therefore used in drought prediction. Statistical (GAM-Generalised Additive Models) and machine learning (LSTM-Long Short Term Memory) based techniques were used for drought prediction. The dependent variables were decomposed using Ensemble Empirical Mode Decomposition (EEMD). Model inputs were determined using the gradient boosting, and all variables showing some relative off importance were considered to influence the target values. Rain, temperature, non-linear trend, SPEI at lag1, and 2 were found to be important in predicting SPEI and the IMFs (Intrinsic Mode Functions) at 1, 6- and 12- month timescales. Seven models were applied based on the different learning techniques using the SPEI time series at all timescales. Prediction combinations of GAM performed better at 1- and 6- month timescales while at 12- month, an undecomposed GAM outperformed the decomposition and the combination of predictions with a correlation coefficient of 0.9591. The study also found that the correlation between target values, LSTM, and LSTM-fQRA was the same at 0.9997 at 1- month and 0.9996 at 6- and 12- month timescales. Further statistical evaluations showed that LSTM-fQRA was the better model compared to an undecomposed LSTM (i.e. RMSE of 0.0199 for LSTM-fQRA over 0.0241 for LSTM). The best performing GAM and LSTM based models were used to conduct uncertainty analysis, which was based on the prediction interval. The PICP and PINAW results indicated that LSTM-fQRA was the best model to predict SPEI timeseries at all timescales. The conclusions drawn from drought predictions conducted in this study are that machine learning neural networks are better suited to predict drought conditions in the LRC, while for improved model accuracy, time series decomposition and prediction combinations are also implementable. The applied hybrid machine learning models can be used for operational drought forecasting and further be incorporated into existing early warning systems for drought risk assessment and management in the LRC for better water resources management. Keywords: Decomposition, drought, drought indices, early warning system, frequency, machine learning, prediction intervals, severity, water resources. / NRF

MESHWR / Department of Hydrology and Water Resources / This study dealt with the influence of climate variability on flood and drought cycles and implications on rainy season characteristics in Luvuvhu River Catchment (LRC) in Limpopo of South Africa. Extreme weather events resulting in hazards such as floods and droughts are becoming more frequent due to climate change. Extreme events affect rainy season characteristics and hence have an influence on water availability and agricultural production. Annual temperature was obtained from Water Research Commission for stations 0723485W, 0766628W and 0766898W from 1950-2013 were used to show/or confirm if there is climate variability in LRC. Daily rainfall data was obtained from SAWS for stations 0766596 9, 0766563 1, 0723485 6 and 0766715 5 were used to detect climate variability and determine the onset, duration and cessation of the rainy season. Streamflow data obtained from the Department of Water and Sanitation for stations A9H004, A9H012, and A9H001 for at least a period of 30 years for each station were used for climate variability detection and determination of flood and drought cycles. Influence of climate variability on floods and droughts and rainy season characteristic were determined in the area of study. Trends were evaluated for temperature, rainfall and streamflow data in the area of study using Mann Kendall (MK) and linear regression (LR) methods. MK and LR detected positive trends for temperature (maximum and minimum) and streamflow stations. MK and LR results of rainfall stations showed increasing trends for stations 0766596 9, and 0766563 1 whereas stations 0723485 6 and 0766715 5 showed decreasing trends. Standardized precipitation index (SPI) was used to determine floods and droughts cycles. SPI results have been classified either as moderately, severely and extremely dry or, moderately, very and extremely wet. This SPI analysis provides more details of dominance of distinctive dry or wet conditions for a rainy season at a particular station. Mean onset of rainfall varied from day 255 to 297, with 0766715 5 showing the earliest onset compared to the rest of the stations. Cessation of rainfall for most of the hydrological years was higher than the mean days of 88, 83 and 86 days in 0766596 9, 0766563 1 and 0723485 6 stations. Mean duration of rainfall varied from 102 to 128, with station 0766715 5 showing shortest duration of rainfall. The results of the study showed that the mean onset, duration and cessation were comparable for all stations except 0766715 5 which had lower values. The study also found that climate variability greatly affects onset, duration and cessation of rainfall during dry years. This led to late onset, early cessation and relatively short duration of the rainfall season. Communities within the catchment must be educated to practice activities such as conservation of indigenous plants, reduce carbon dioxide emissions.