Austral summer rainfall over the period 1991/1992 to 2010/2011 was dynamically downscaled by the weather research and forecasting (WRF) model at 9 km resolution for South Africa. Lateral boundary conditions for WRF were provided from the European Centre for medium-range weather (ECMWF) reanalysis (ERA) interim data. The model biases for the rainfall were eval-uated over the South Africa as a whole and its nine prov-inces separately by employing three different convective parameterization schemes, namely the (1) Kain-Fritsch (KF), (2) Betts-Miller-Janjic (BMJ) and (3) Grell-Devenyi ensemble (GDE) schemes. All three schemes have gener-ated positive rainfall biases over South Africa, with the KF scheme producing the largest biases and mean absolute errors. Only the BMJ scheme could reproduce the intensity of rainfall anomalies, and also exhibited the highest cor-relation with observed interannual summer rainfall variability. In the KF scheme, a significantly high amount of moisture was transported from the tropics into South Africa. The vertical thermodynamic profiles show that the KF scheme has caused low level moisture convergence, due to the highly unstable atmosphere, and hence con-tributed to the widespread positive biases of rainfall. The negative bias in moisture, along with a stable atmosphere and negative biases of vertical velocity simulated by the GDE scheme resulted in negative rainfall biases, especially over the Limpopo Province. In terms of rain rate, the KF scheme generated the lowest number of low rain rates and the maximum number of moderate to high rain rates associated with more convective unstable environment. KF and GDE schemes overestimated the convective rain and underestimated the stratiform rain. However, the simulated convective and stratiform rain with BMJ scheme is in more agreement with the observations. This study also docu-ments the performance of regional model in downscaling the large scale climate mode such as El Nin˜o Southern Oscillation (ENSO) and subtropical dipole modes. The correlations between the simulated area averaged rainfalls over South Africa and Nino3.4 index were -0.66, -0.69 and -0.49 with KF, BMJ and GDE scheme respectively as compared to the observed correlation of -0.57. The model could reproduce the observed ENSO-South Africa rainfall relationship and could successfully simulate three wet (dry) years that are associated with La Nin˜a (El Ni˜no) and the BMJ scheme is closest to the observed variability. Also, the model showed good skill in simulating the excess rainfall over South Africa that is associated with positive sub-tropical Indian Ocean Dipole for the DJF season 2005/2006.

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Windblown transport and deposition of dust is widely recognized as an important physical and chemical concern to climate, human health and ecosystems. Sistan is a region located in southeast Iran with extensive wind erosion, severe desertification and intense dust storms, which cause adverse effects in regional air quality and human health. To mitigate the impact of these phenomena, it is vital to ascertain the physical and chemical characteristics of airborne and soil dust. This paper examines for the first time, the mineralogical and chemical properties of dust over Sistan by collecting aerosol samples at two stations established close to a dry-bed lake dust source region, from August 2009 to August 2010. Furthermore, soil samples were collected from topsoil (0-5 cm depth) at several locations in the dry-bed Hamoun lakes and downwind areas. These data were analyzed to investigate the chemical and mineralogical characteristics of dust, relevance of inferred sources and contributions to air pollution. X-Ray Diffraction (XRD) analysis of airborne and soil dust samples shows that the dust mineralogy is dominated mainly by quartz (30-40%), calcite (18-23%), muscovite (10-17%), plagioclase (9-12%), chlorite (~6%) and enstatite (~3%), with minor components of dolomite, microcline, halite and gypsum. X-Ray Fluorescence (XRF) analyses of all the samples indicate that the most important oxide compositions of the airborne and soil dust are SiO 2 , CaO, Al 2 O 3 , Na 2 O, MgO and Fe 2 O 3 , exhibiting similar percentages for both stations and soil samples. Estimates of Enrichment Factors (EF) for all studied elements show that all of them have very low EF values, suggesting natural origin from local materials. The results suggest that a common dust source region can be inferred, which is the eroded sedimentary environment in the extensive Hamoun dry lakes lying to the north of Sistan.

Pfaff, MC, et al. 2019. A synthesis of three decades of socio-ecological change in False Bay, South Africa: setting the scene for multidisciplinary research and management. Elem Sci Anth, 7: 32.

Background: Nkomazi local municipality of South Africa is a high risk malaria region having an incidence rate of about 500 cases per 100,000. The aim of this study was to examine the influence of environmental factors on population (age group) at risk of malaria in the study area to enhance quality targeting for prevention of malaria incidence. Hence, the study contributes to the country's aim of eliminating malaria by the year 2018. Methods: R software was used to statistically analyse the data. Using remote sensing technology; a Landsat 8 image of 4th October 2015 was classified using objectbased classification technique and a 5 m resolution spot height data was used to generate digital elevation model of the area. Results: A total of 60,718 malaria cases were notified across 48 health facilities in Nkomazi municipality within the 18 year period of investigation (January 1997 to August 2015). The study found that malaria incidence is 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 amount of irrigated areas, malaria risk increased by almost 39% in the entire study area and by almost 44% within the 2 km buffer of the selected villages. Malaria incidence in the study area is more pronounced within the economically active population of age group 15-64 and the male gender are at higher risk of malaria. The result also indicated that though malaria incidence in the study area is high, the incidence and its case fatality rate have drastically declined over the study period. Conclusion: Hence, a predictive model, based on environmental factors would be useful in the effort towards the elimination of the disease by fostering proper malaria control targeting and resource allocation.

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.

Aim A broad suit of climate data sets is becoming available for use in predictive species modelling. We compare the efficacy of using interpolated climate surfaces [Center for Resource and Environmental Studies (CRES) and Climate Research Unit (CRU)] or highresolution model-derived climate data [Division of Atmospheric Research limited-area model (DARLAM)] for predictive species modelling, using tick distributions from sub-Saharan Africa.Location The analysis is restricted to sub-Saharan Africa. The study area was subdivided into 3000 grids cells with a resolution of 60 · 60 km.Methods Species distributions were predicted using an established multivariate climate envelope modelling approach and three very different climate data sets. The recorded variance in the climate data sets was quantified by employing omnidirectional variograms. To further compare the interpolated tick distributions that flowed from using three climate data sets, we calculated true positive (TP) predictions, false negative (FN) predictions as well as the proportional overlaps between observed and modelled tick distributions. In addition, the effect of tick data set size on the performance of the climate data sets was evaluated by performing random draws of known tick distribution records without replacement. ResultsThe predicted distributions were consistently wider ranging than the known records when using any of the three climate data sets. However, the proportional overlap between predicted and known distributions varied as follows: for Rhipicephalus appendiculatus Neumann (Acari: Ixodidae), these were 60%, 60% and 70%; for Rhipicephalus longus Neumann (Acari: Ixodidae) 60%, 57% and 75%; for Rhipicephalus zambeziensis Walker, Norval & Corwin (Acari: Ixodidae) 57%, 51% and 62%, and for Rhipicephalus capensis Koch (Acari: Ixodidae) 70%, 60% and 60% using the CRES, CRU and DARLAM climate data sets, respectively. All data sets were sensitive to data size but DARLAM performed better when using smaller species data sets. At a 20% data subsample level, DARLAM was able to capture more than 50% of the known records and captured more than 60% of known records at higher subsample levels.Main conclusions The use of data derived from high-resolution nested climate models (e.g. DARLAM) provided equal or even better species distribution modelling performance. As the model is dynamic and process based, the output data are available at the modelled resolution, and are not hamstrung by the sampling intensity of observed climate data sets (c. one sample per 30,000 km 2 for Africa). In addition, when exploring the biodiversity consequences of climate change, these modelled outputs form a more useful basis for comparison with modelled future climate scenarios.

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