Theme unsuitability is noted to have inhibited the accuracy of groundwater potential zones (GWPZs) mapping approach, especially in a semi-arid environment where surface water supply is inadequate. This work, therefore presents a geoscience approach for mapping high-precision GWPZs peculiar to the semi-arid area, using Buffalo catchment, Eastern Cape, South Africa, as a case study. Maps of surficial-lithology, lineament-density, drainage-density, rainfall-distribution, normalized-difference-vegetation-index, topographic-wetness-index, land use/land cover, and land-surface-temperature were produced. These were overlaid based on analytical hierarchical process weightage prioritization at a constituency ratio of 0.087. The model categorizes GWPZs into the good (187 km2), moderate (338 km2), fair (406 km2), poor (185 km2), and very poor (121 km2) zones. The model validation using borehole yield through on the coefficient of determination (R2 = 0.901) and correlation (R = 0.949) indicates a significant replication of ground situation (p value < 0.001). The analysis corroboration shows that the groundwater is mainly hosted by a fractured aquifer where the GWPZs is either good (9.3 l/s) or moderate (5.5 l/s). The overall result indicates that the model approach is reliable and can be adopted for a reliable characterization of GWPZs in any semi-arid/arid environment.

Increasing demand for food and environmental stressors are some of the most challenging problems that human societies face today and these have encouraged new studies to examine drought impacts on food production. Seeking to discuss these important issues in the South African context, this study analyzed the impacts of drought on food security in one of the country's largest commercial agricultural land (Free State Province). Earth observation and crop data were acquired from Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) and GrainSA databases, respectively for years 2011/2012–2020/2021 over Free State Province. Two crops namely, maize and sorghum were obtained from the database and analyzed accordingly to quantify drought impacts on the two crops. The result reveals that the years 2015 and 2018 were affected by extreme drought events (&lt;10%) where the majority of the study area was impacted. Years 2011, 2016, 2018, and 2019 were severely affected by drought (&gt;30%) and impacted the agricultural sector in the study area. Findings further revealed that maize production observed the lowest recorded in the year 2014 and 2015 with about 223,600 and 119,050 tons, respectively. More so, results further showed that sorghum production recorded the lowest production in years 2019, 2016, and 2015 with about 23,600, 24,640, and 24,150 tons, in that order during the period of study. The results confirm the impacts of drought on maize and sorghum productions in the year 2015 and other years that recorded the lowest productions during drought years. This development might have impacted food security in the study area, and this outcome will enable decision-making bodies on food security to enhance improved strategy in vulnerable areas.

The impact of climate change over the coming decades will increase the likelihood of many species undergoing genetic alterations or even becoming extinct. Vegetation and belowground organisms are more vulnerable to the intensified impact of climate change due to a possible lack of genetic plasticity and limited mobility. Organisms are inter-dependable in ecosystems; hence, this study focused on the impact of climate change, examining the soil condition in Africa, vegetation responses and the overview of species’ responses to climate change through a bibliometric study and an analysis of remote sensing information. The bibliometric study examines climate change-related literature published from 1999 to 2019, collected from the Web of Science and Scopus database platforms, and this reveals an overall rapid increase in the number of climate change publications in Africa, with South Africa occupying a leading position in all the studied parameters. The spatially based information on soil moisture, temperature and the photosynthetic activities of vegetation affirmed that there is increasing amount of drought in Africa with more impact in northern, southern and eastern Africa. African countries, especially in the above-mentioned regions, need to urgently invest in support programs that will ease the impact of climate change, particularly on food security.

No abstract

No abstract

Connections between water, food, and energy are at the center of long-term economic and environmental development and protection. Water, energy and food are the keys to economic input and a necessary component of economic progress. The adoption of water management policies and techniques that support the sustainable use of resources while promoting economic growth is becoming an important concern, particularly in countries where water and food scarcity are critical or problematic. This study aimed at evaluating Water, Energy and Food Nexus (WEF), and as well as challenges of its implementation. This study looked at the articles that were published on WEF nexus between 2015 and 2021 acquired from the Scopus database, focusing on gaps and implementations. I searched for relevant key terms in the database and the search found hundreds of articles on WEF, of which 28 articles were relevant to the scope of the study and these articles were downloaded as BibTeX file for the analysis and the analysis was done using R programming. A number of insights and implications were identified based on the analyses of the findings of the reviewed research in order to increase the policy relevance and overall implementation of the WEF nexus by public policy and decision-making institutions. To boost uptake of the findings, the study gives an outline of the primary constraints and challenges that restrict the policy relevance of the WEF nexus.

Precise assessment, monitoring and forecasting of drought phenomena are crucial and play a vital role in agriculture and water resources management in the semi-arid region. In this study, Standardized Precipitation Index (SPI) was used to predict the drought in the upper Godavari River basin, India. Ten combinations were used to predict three SPI timescales (i.e., . The historical data of SPI from 2000 to 2019 was divided into training (75 % of the data) and testing (25 % of the data) models for SPI prediction. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to estimate the SPI. The results were compared and analyzed using statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The main results showed that the SMO-SVM poly kernel model precisely predict the SPI-3 (R 2 = 0.819) and SPI-12 (R 2 = 0.968) values at Paithan station; the SPI-3 (R 2 = 0.736) and SPI-6 (R 2 = 0.841) values at Silload station, respectively.The SMO-SVM PUK kernel showed superiority in the prediction of SPI-6 (R 2 = 0.846) at Paithan station and SPI-12 (R 2 = 0.975) at the Silload station. The competition between SVM-SMO poly kernel and SVM-SMO PUK kernel was observed in the prediction of long setting time (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is superior in the estimation of SPI-3 at both stations. The results of the study showed the efficacy of the SVM-SMO algorithm with various kernel functions in the estimation of multiscale SPI and can be helpful in decision making for water resource management and tackle droughts in the semi-arid region of central India.

No abstract