Scope, trends and opportunities for sociohydrology research in Africa: A bibliometric analysis

Socio-hydrology research is concerned with the understanding of how humanity interacts with water resources. The purpose of this study was to assess the disparity between global and African trends as well as developments in the research domain of socio-hydrology. From the viewpoint of a multitude of research themes, multi-author collaborations between African and international researchers and the number of publications produced globally, the results reveal that the field of socio-hydrology is still underdeveloped and yet nascent. At a global level, the USA, China, and the Netherlands have the highest number of scientific publications, while in Africa, South Africa dominates, although these scientific publications are significantly much lower than the global output. The output of scientific publications on socio-hydrology research from Africa increased from 2016, with significant output reached in 2019. Water management and supply, hydrological modelling, flood monitoring as well as policies and decision-making, are some of the dominant themes found through keywords co-occurrence analysis. These main keywords may be considered as the foci of research in socio-hydrology. Although socio-hydrology research is still in the early stages of development in Africa, the cluster and emerging themes analysis provide opportunities for research in Africa that will underpin new frontiers of the research agenda encompassing topics such as the (1) impacts of climate change on socio-hydrology; (2) influence of socio-hydrology on water resources such as surface water and groundwater; (3) benefits of socio-hydrological models on river basins and (4) role of socio-hydrology in economic sectors such as agriculture. Overall, this study points to a need to advance socio-hydrology research in Africa in a bid to address pressing water crises that affect sustainable development as well as to understand the feedback mechanisms and linkages between water resources and different sectors of society. Significance:• The field of socio-hydrology is still under-researched in Africa. • Limited research could be attributed to a lack of expertise, resources and data limitations.• Socio-hydrology research is likely to be strengthened through collaborations between Africa and other developed countries.• Existing gaps present opportunities to advance socio-hydrology research in Africa

Variability of satellite derived phenological parameters across maize producing areas of South Africa

Changes in phenology can be used as a proxy to elucidate the short and long term trends in climate change and variability. Such phenological changes are driven by weather and climate as well as environmental and ecological factors. Climate change affects plant phenology largely during the vegetative and reproductive stages. The focus of this study was to investigate the changes in phenological parameters of maize as well as to assess their causal factors across the selected maize-producing Provinces (viz: North West, Free State, Mpumalanga and KwaZulu-Natal) of South Africa. For this purpose, five phenological parameters i.e., the length of season (LOS), start of season (SOS), end of season (EOS), position of peak value (POP), and position of trough value (POT) derived from the MODIS NDVI data (MOD13Q1) were analysed. In addition, climatic variables (Potential Evapotranspiration (PET), Precipitation (PRE), Maximum (TMX) and Minimum (TMN) Temperatures spanning from 2000 to 2015 were also analysed. Based on the results, the maize-producing Provinces considered exhibit a decreasing trend in NDVI values. The results further show that Mpumalanga and Free State Provinces have SOS and EOS in December and April respectively. In terms of the LOS, KwaZulu-Natal Province had the highest days (194), followed by Mpumalanga with 177 days, while NorthWest and Free State Provinces had 149 and 148 days, respectively. Our results further demonstrate that the influences of climate variables on phenological parameters exhibit a strong space-time and common covariate dependence. For instance, TMN dominated in North West and Free State, PET and TMX are the main dominant factors in KwaZulu-Natal Province whereas PRE highly dominated in Mpumalanga. Furthermore, the result of the Partial Least Square Path Modeling (PLS-PM) analysis indicates that climatic variables predict about 46% of the variability of phenology indicators and about 63% of the variability of yield indicators for the entire study area. The goodness of fit index indicates that the model has a prediction power of 75% over the entire study area. This study contributes towards enhancing the knowledge of the dynamics in the phenological parameters and the results can assist farmers to make the necessary adjustment in order to have an optimal production and thereby enhance food security for both human and livestock.The Department of Science and Technology (DST) and The National Research Foundation (NRF).http://www.mdpi.com/journal/sustainabilityam2018Animal and Wildlife SciencesGeography, Geoinformatics and MeteorologyPlant Production and Soil Scienc

Environmental factors and population at risk of malaria in Nkomazi municipality, South Africa

OBJECTIVE : Nkomazi local municipality of South Africa is a high-risk malaria region with an incidence rate of about 500 cases per 100 000. We examined the influence of environmental factors on population (age group) at risk of malaria. methods R software was used to statistically analyse data. Using remote sensing technology, a Landsat 8 image of 4th October 2015 was classified using object-based classification and a 5-m resolution. Spot height data were used to generate a digital elevation model of the area. RESULTS : A total of 60 718 malaria cases were notified across 48 health facilities in Nkomazi municipality between January 1997 and August 2015. Malaria incidence was highly associated with irrigated land (P = 0.001), water body (P = 0.011) and altitude ≤400 m (P = 0.001). The multivariate model showed that with 10% increase in the extent of irrigated areas, malaria risk increased by almost 39% in the entire study area and by almost 44% in the 2-km buffer zone of selected villages. Malaria incidence is more pronounced in the economically active population aged 15–64 and in males. Both incidence and case fatality rate drastically declined over the study period. CONCLUSION : A predictive model based on environmental factors would be useful in the effort towards malaria elimination by fostering appropriate targeting of control measures and allocating of resources.This study was supported by the EU project ‘Quantifying Weather and Climate Impacts on health in developing countries’, an European Commission’s Seventh Framework Research Programme by providing a 2-year student bursary to the primary author. We acknowledge the support of the University of Pretoria, Centre for Sustainable Malaria Control and of the Earth and Atmospheric Remote Sensing Research Group, University of Pretoria.http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-31562017-05-31hb2016Centre for Environmental StudiesCentre for Geoinformation ScienceGeography, Geoinformatics and Meteorolog

Scope, trends and opportunities for sociohydrology research in Africa : a bibliometric analysis

Socio-hydrology research is concerned with the understanding of how humanity interacts with water resources. The purpose of this study was to assess the disparity between global and African trends as well as developments in the research domain of socio-hydrology. From the viewpoint of a multitude of research themes, multi-author collaborations between African and international researchers and the number of publications produced globally, the results reveal that the field of socio-hydrology is still underdeveloped and yet nascent. At a global level, the USA, China, and the Netherlands have the highest number of scientific publications, while in Africa, South Africa dominates, although these scientific publications are significantly much lower than the global output. The output of scientific publications on socio-hydrology research from Africa increased from 2016, with significant output reached in 2019. Water management and supply, hydrological modelling, flood monitoring as well as policies and decision-making, are some of the dominant themes found through keywords co-occurrence analysis. These main keywords may be considered as the foci of research in socio-hydrology. Although socio-hydrology research is still in the early stages of development in Africa, the cluster and emerging themes analysis provide opportunities for research in Africa that will underpin new frontiers of the research agenda encompassing topics such as the (1) impacts of climate change on socio-hydrology; (2) influence of socio-hydrology on water resources such as surface water and groundwater; (3) benefits of socio-hydrological models on river basins and (4) role of socio-hydrology in economic sectors such as agriculture. Overall, this study points to a need to advance socio-hydrology research in Africa in a bid to address pressing water crises that affect sustainable development as well as to understand the feedback mechanisms and linkages between water resources and different sectors of society.https://sajs.co.zadm2022Geography, Geoinformatics and MeteorologyUP Centre for Sustainable Malaria Control (UP CSMC

Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa

There has been a conspicuous increase in malaria cases since 2016/2017 over the three malaria-endemic provinces of South Africa. This increase has been linked to climatic and environmental factors. In the absence of adequate traditional environmental/climatic data covering ideal spatial and temporal extent for a reliable warning system, remotely sensed data are useful for the investigation of the relationship with, and the prediction of, malaria cases. Monthly environmental variables such as the normalised difference vegetation index (NDVI), the enhanced vegetation index (EVI), the normalised difference water index (NDWI), the land surface temperature for night (LSTN) and day (LSTD), and rainfall were derived and evaluated using seasonal autoregressive integrated moving average (SARIMA) models with different lag periods. Predictions were made for the last 56 months of the time series and were compared to the observed malaria cases from January 2013 to August 2017. All these factors were found to be statistically significant in predicting malaria transmission at a 2-months lag period except for LSTD which impact the number of malaria cases negatively. Rainfall showed the highest association at the two-month lag time (r=0.74; P<0.001), followed by EVI (r=0.69; P<0.001), NDVI (r=0.65; P<0.001), NDWI (r=0.63; P<0.001) and LSTN (r=0.60; P<0.001). SARIMA without environmental variables had an adjusted R2 of 0.41, while SARIMA with total monthly rainfall, EVI, NDVI, NDWI and LSTN were able to explain about 65% of the variation in malaria cases. The prediction indicated a general increase in malaria cases, predicting about 711 against 648 observed malaria cases. The development of a predictive early warning system is imperative for effective malaria control, prevention of outbreaks and its subsequent elimination in the region

Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa

The advancement, availability and high level of accuracy of satellite data provide a unique opportunity to conduct environmental and epidemiological studies using remotely sensed measurements. In this study, information derived from remote sensing data is used to determine breeding habitats for Anopheles arabiensis which is the prevalent mosquito species over Nkomazi municipality. In particular, we have utilized the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) coupled with land surface temperature (LST) derived from Landsat 5 TM satellite data. NDVI, NDWI and LST are considered as key environmental factors that influence the mosquito habitation. The breeding habitat was derived using multi-criteria evaluation (MCE) within ArcGIS using the derived environmental metric with appropriate weight assigned to them. Additionally, notified malaria cases were analysed and spatial data layers of water bodies, including rivers and dams, were buffered to further illustrate areas at risk of malaria. The output map from the MCE was then classified into three classes which are low, medium and high areas. The resulting malaria risk map depicts that areas of Komatieport, Malelane, Madadeni and Tonga of the district are subjected to high malaria incidence. The time series analysis of environmental metrics and malaria cases can help to provide an adequate mechanism for monitoring, control and early warning for malaria incidence.The EU project QWeCI (Quantifying Weather and Climate Impacts on health in developing countries) and the European Commission’s Seventh Framework Research Programme under the [grant number 243964]).http://www.tandfonline.com/loi/rsag202017-12-30hb2016Geography, Geoinformatics and Meteorolog

Assessment of the Dissimilarities of EDI and SPI Measures for Drought Determination in South Africa

This study examines the (dis)similarity of two commonly used indices Standardized Precipitation Index (SPI) computed over accumulation periods 1-month, 3-month, 6-month, and 12-month (hereafter SPI-1, SPI-3, SPI-6, and SPI-12, respectively) and Effective Drought Index (EDI). The analysis is based on two drought monitoring indicators (derived from SPI and EDI), namely, the Drought Duration (DD) and Drought Severity (DS) across the 93 South African Weather Service&rsquo;s delineated rainfall districts over South Africa from 1980 to 2019. In the study, the Pearson correlation coefficient dissimilarity and periodogram dissimilarity estimates were used. The results indicate a positive correlation for the Pearson correlation coefficient dissimilarity and a positive value for periodogram of dissimilarity in both the DD and DS. With the Pearson correlation coefficient dissimilarity, the study demonstrates that the values of the SPI-1/EDI pair and the SPI-3/EDI pair exhibit the highest similar values for DD, while the SPI-6/EDI pair shows the highest similar values for DS. Moreover, dissimilarities are more obvious in SPI-12/EDI pair for DD and DS. When a periodogram of dissimilarity is used, the values of the SPI-1/EDI pair and SPI-6/EDI pair exhibit the highest similar values for DD, while SPI-1/EDI displayed the highest similar values for DS. Overall, the two measures show that the highest similarity is obtained in the SPI-1/EDI pair for DS. The results obtainable in this study contribute towards an in-depth knowledge of deviation between the EDI and SPI values for South Africa, depicting that these two drought indices values are replaceable in some rainfall districts of South Africa for drought monitoring and prediction, and this is a step towards the selection of the appropriate drought indices

Application of Artificial Neural Network for Predicting Maize Production in South Africa

The use of crop modeling as a decision tool by farmers and other decision-makers in the agricultural sector to improve production efficiency has been on the increase. In this study, artificial neural network (ANN) models were used for predicting maize in the major maize producing provinces of South Africa. The maize production prediction and projection analysis were carried out using the following climate variables: precipitation (PRE), maximum temperature (TMX), minimum temperature (TMN), potential evapotranspiration (PET), soil moisture (SM) and land cultivated (Land) for maize. The analyzed datasets spanned from 1990 to 2017 and were divided into two segments with 80% used for model training and the remaining 20% for testing. The results indicated that PET, PRE, TMN, TMX, Land, and SM with two hidden neurons of vector (5,8) were the best combination to predict maize production in the Free State province, whereas the TMN, TMX, PET, PRE, SM and Land with vector (7,8) were the best combination for predicting maize in KwaZulu-Natal province. In addition, the TMN, SM and Land and TMN, TMX, SM and Land with vector (3,4) were the best combination for maize predicting in the North West and Mpumalanga provinces, respectively. The comparison between the actual and predicted maize production using the testing data indicated performance accuracy adjusted R2 of 0.75 for Free State, 0.67 for North West, 0.86 for Mpumalanga and 0.82 for KwaZulu-Natal. Furthermore, a decline in the projected maize production was observed across all the selected provinces (except the Free State province) from 2018 to 2019. Thus, the developed model can help to enhance the decision making process of the farmers and policymakers

Analysis of agro-climatic parameters and their influence on maize production in South Africa

This study analyzed the variability of the agro-climatic parameters that impact maize production across different seasons in South Africa. To achieve this, four agro-climatic variables (precipitation, potential evapotranspiration, minimum, and maximum temperatures) were considered for the period spanning 1986–2015, covering the North West, Free State, Mpumalanga, and KwaZulu-Natal (KZN) provinces. Results illustrate that there is a negative trend in precipitation for North West and Free State provinces and positive trend in maximum temperature for all the provinces over the study period. Furthermore, the results showed that among other agro-climatic parameters, minimum temperature had the most influence on maize production in North West, potential evapotranspiration (combination of the agro-climatic parameters), minimum and maximum temperature influenced maize production in KZN while maximum temperature influenced maize production in Mpumalanga and Free State. In general, the agro-climatic parameters were found to contribute 7.79, 21.85, 32.52, and 44.39% to variation in maize production during the study period in North West, Free State, Mpumalanga, and KZN, respectively. The variation in maize production among the provinces under investigation could most likely attribute to the variation in the size of the cultivated land among other factors including soil type and land tenure system. There were also difference in yield per hectare between the provinces; KZN and Mpumalanga being located in the humid subtropical areas of South Africa had the highest yield per hectare 5.61 and 4.99 tons, respectively, while Free State and North West which are in the semi-arid region had the lowest yield per hectare 3.86 and 3.03 tons, respectively. Understanding the nature and interaction of the dominant agro-climatic parameters discussed in the present study as well as their impact on maize production will help farmers and agricultural policy makers to understand how climate change exerts its influence on maize production within the study area so as to better adapt to the major climate element that either increases or decreases maize production in their respective provinces.The University of Pretoria through the Animal change project and DST for providing bursary through a grant that was received by University of Pretoria.http://link.springer.com/journal/7042019-11-01hj2019Animal and Wildlife SciencesGeography, Geoinformatics and MeteorologyPlant Production and Soil Scienc