Leafminer agromyzid pest distribution over Limpopo province under changing climate

The objective of the study was to assess the impact of climate change on the spatial distribution of leafminer agromyzid pest over Limpopo province, South Africa. In the study the Conformal Cubic Atmospheric Model (CCAM) simulated climate scenarios; (a) the current climatology (1981-2010), (b) projected near future climatology (2041-2070) and (c) the projected distant future climatology (2071-2100) was used. In particular, the linkage between the model simulated temperature and the pest population parameters (that is, the intrinsic rate of increase (rm), net reproduction (ro), mean generation time (tg)) was modeled by empirical functions based on laboratory temperature measurements. The empirical functions (derived from the correlation between temperature and rm, o as well as tg) are used to simulate spatial distribution of leafminer agromyzid pest under changing climate. The present analysis illustrates that leafminer agromyzid pest and climatic factors exhibit a non-linear relationship best described by polynomial function of order two while in general, the influence of climate change on the spatial distribution of leafminer agromyzid pest over Limpopo province is noticeable. This work contributes towards our understanding of the impact of climate change on the population dynamics of leafminer agromyzid pest and hence impacts on tomato production in Limpopo province, South Africa.Canon Collinshttp://www.academicjournals.org/AJARam201

Variability properties of daily and monthly observed near-surface temperatures in Uganda : 1960-2008

Variability and trends in daily and monthly near-surface temperatures in Uganda, collected over the period 1960–2008 (49 years), are analysed. For this purpose, daily observational maximum and minimum temperature records from eight selected stations in Uganda were acquired from the Uganda Meteorological Department (UMD). Data collected by the UMD are quality controlled through a rigorous process before being archived. The data received were tested for homogeneity, gaps were filled and correlation analyses were used for validation of area average series. Statistical techniques (e.g. Mann–Kendall and Linear Regression) were employed to analyse temperature variability and to obtain temperature trends. Findings indicate that intra-annual temperature shows reduced variability over recent decades, but which is not statistically significant. Results also demonstrated that maximum temperatures are more variable compared to minimum temperatures in Uganda. An increasing trend in hot days, hot nights, warm nights and warm spells were also detected. At seven of the stations, annual temperature range and diurnal temperature range trends were found to be negative. The finding that intra-annual and intra-monthly variance is declining suggests that fewer anomalously extreme temperature episodes occur. The gap between maximum and minimum extremes is reducing, which supports the observation that minimum temperatures are on the increase.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-0088hb201

Climatic trends at Namulonge in Uganda : 1947-2009

This paper investigates rainfall and temperature trends at Namulonge parish, in Wakiso district of Uganda using statistical techniques. Daily-observed temperature and rainfall records were aggregated into monthly means over a period of more than 55 years. These records were analyzed in an effort to identify both seasonal trends and shifts in climate. This was achieved by using non- parametric (Mann-Kendall) and parametric (linear regression) techniques. The analysis shows that total rainfall during the March-May season decreased, while maximum temperatures were increasing during the months between April and September, with both trends statistically significant at 5% confidence level. The Mann-Kendall test revealed that the number of wet days reduced significantly. Temperatures were found to be warmer and rainfall higher in the first climate normal compared to the recent 30 years. Results revealed that April was the only month with a statistically significant rainfall trend.http:/www.ccsenet.org/jggam201

Long-run relative importance of temperature as the main driver to malaria transmission in Limpopo Province, South Africa : a simple econometric approach

Malaria in Limpopo Province of South Africa is shifting and now observed in originally non-malaria districts, and it is unclear whether climate change drives this shift. This study examines the distribution of malaria at district level in the province, determines direction and strength of the linear relationship and causality between malaria with the meteorological variables (rainfall and temperature) and ascertains their short- and long-run variations. Spatio-temporal method, Correlation analysis and econometric methods are applied. Time series monthly meteorological data (1998–2007) were obtained from South Africa Weather Services, while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province) and South African Department of Health. We find that malaria changes and pressures vary in different districts with a strong positive correlation between temperature with malaria, r = 0.5212, and a weak positive relationship for rainfall, r = 0.2810. Strong unidirectional causality runs from rainfall and temperature to malaria cases (and not vice versa): F (1, 117) = 3.89, q = 0.0232 and F (1, 117) = 20.08, P < 0.001 and between rainfall and temperature, a bi-directional causality exists: F (1, 117) = 19.80; F (1,117) = 17.14, P < 0.001, respectively, meaning that rainfall affects temperature and vice versa. Results show evidence of strong existence of a long-run relationship between climate variables and malaria, with temperature maintaining very high level of significance than rainfall. Temperature, therefore, is more important in influencing malaria transmission in Limpopo Province.EU project QWeCI (Quantifying Weather and Climate Impacts on health in developing countries; funded by the European Commission’s Seventh Framework Research Programme under the Grant agreement 243964).http://link.springer.com/journal/103932016-03-31hb201

Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda

A methodology has been applied to investigate the spatial variability and trends existent in a mid-twentieth century climatic time series (for the period 1943–1977) recorded by 58 climatic stations in the Albert–Victoria water management area in Uganda. Data were subjected to quality checks before further processing. In the present work, temporal trends were analyzed using Mann–Kendall and linear regression methods. Heterogeneity of monthly rainfall was investigated using the precipitation concentration index (PCI). Results revealed that 53 % of stations have positive trends where 25 % are statistically significant and 45 % of stations have negative trends with 23 % being statistically significant. Very strong trends at 99 % significance level were revealed at 12 stations. Positive trends in January, February, and November at 40 stations were observed. The highest rainfall was recorded in April, while January, June, and July had the lowest rainfall. Spatial analysis results showed that stations close to Lake Victoria recorded high amounts of rainfall. Average annual coefficient of variability was 19 %, signifying low variability. Rainfall distribution is bimodal with maximums experienced in March–April–May and September–October–November seasons of the year. Analysis also revealed that PCI values showed a moderate to seasonal rainfall distribution. Spectral analysis of the time components reveals the existence of a major period around 3, 6, and 10 years. The 6- and 10-year period is a characteristic of September–October–November, March–April– May, and annual time series.http://link.springer.com/journal/704hb201

Assessing industrial development influence on land use/cover drivers and change detection for West Bank East London, South Africa

South Africa’s nationwide socio-economic industrial development zone drive focuses on alleviating of the apartheid social ills legacy. To ensure sustainable industrial ecological development, land-cover monitoring is needed though limited attention has been accorded. This study, aimed at assessing the influence of East London Industrial Development Zone (ELIDZ) on land-use/land-cover (LULC) drivers and detecting LULC changes for 15 years over the West Bank East London. An integration of remote sensing with qualitative approaches was adopted to provide robust temporal and spatial LULC change analysis. Object-based classification was performed on the satellite images for 1998, 2007 and 2013. Normalised Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI) complemented and validated observed land cover changes. The study reveals that industrial development has been a key driver for land-use changes in West Bank. The classification indicated that vegetation (5.97%) and bare land (-9.06%) classes had the highest percentage increase and decrease respectively. Water (0.02%) and bare land (-0.6%) classes had the lowest annual rate of change. Built-up and bare land classes varied considerably. An overall land-cover classification mean accuracy assessment of 97.24% and a mean Kappa coefficient of 0.95 were attained for the entire study period. This study offers the value of integrated methods in monitoring land-cover change to enhance informed decision-making especially in rapidly changing landscapes for conservation purposes.This manuscript stems from the corresponding authors’ postgraduate study and who performed most of the experiments.The University of Pretoria and the United State Geological Survey (USCS).http://www.ripublication.comam2019Geography, Geoinformatics and Meteorolog

The nature of rainfall in the main drainage sub-basins of Uganda

A study of rainfall trends and temporal variations within seven sub- basins of Uganda spanning from 1940 to 2009 has been made. Rainfall climatologies are constructed from observational data, using 36 station records which reflect hydro climatic conditions. Long-term changes in rainfall characteristics were determined by non-parametric tests (Mann-Kendall and Sen’s T tests), coefficient of variation, precipitation concentration Index and drought severity index. Magnitude of change was estimated by applying Sen’s estimator of slope. Decadal variability of rainfall with marked seasonal cycles is evident. Temporal variability of drought patterns is detected. Variations in annual rainfall are low with no significant trends observed in the main drainage sub-basins. Significant trends occur in October, November, December and January. A noticeable decrease in the annual total rainfall was observed mostly in north-western and south-western sub-basins. Rainfall trend in the second normal of June-July-August (JJA) was decreasing in all the main drainage sub-basins.http://www.tandfonline.com/loi/thsj202015-06-30hb201

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

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