Climate adaptation in the public health sector in Africa: Evidence from United Nations Framework Convention on Climate Change National Communications

Climate change has potential to affect human health in various ways. Extreme temperatures and cold both result in deaths, while the changing habitats favouring the breeding of vectors could result in the spread of diseases such as malaria, cholera and typhus. This article reviews climate change adaptation measures in the African public health sector. The evidence is drawn from National Communications of 21 countries as submitted to the United Nations Framework Convention on Climate Change (UNFCCC). This article combines the literature review and grounded theory approaches with data obtained from the UNFCCC National Communications. Among key adaptation measures emerging from the work are weather-based forecasting and early warning systems, public education and awareness, putting in place appropriate policies, surveillance, research and monitoring as well as improving public health infrastructure and technology. The study recommends that African nations should commit to address health impacts of climate change through the implementation of appropriate adaptation measures

Prediction of inflows into Lake Kariba using a combination of physical and empirical models

Seasonal climate forecasts are operationally produced at various climate prediction centres around the world. However, these forecasts may not necessarily be objectively integrated into application models in order to help with decision-making processes. The use of hydro- meteorological models may be proven effective for reservoir operations since accurate and reliable prediction of reservoir inflows can provide balanced solution to the problems faced by dam or reservoir managers. This study investigates the use of a combination of physical and empirical models to predict seasonal inflows into Lake Kariba in southern Africa. Two predictions systems are considered. The first uses antecedent seasonal rainfall totals over the upper Zambezi catchment as predictor in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method uses predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model (CGCM) downscaled to the inflows. Forecast verification results are presented for five run-on 3-month seasons; from September to June over an independent hindcast period of 14 years (1995/6 to 2008/9). Verification is conducted using the relative operating characteristic (ROC) and the reliability diagram. In addition to the presented verification statistics, the hindcasts are also evaluated in terms of their economic value as a usefulness indicator of forecast quality for bureaucrats and to the general public. The models in general perform best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season). Moreover, the prediction system that uses the output of the CGCM is superior to the simple statistical approach. An additional forecast of a recent flooding event (2010/11), which lies outside of the 14-year verification window, is presented to further demonstrate the forecast system’s operational capability during a season of high inflows that caused societal and infrastructure problems over the region.Applied Center for Climate and Earth Systems Science (ACCESS)http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-00882017-05-30hb201

Hydrometeorological research in South Africa : a review

Water resources, particularly in arid and semi-arid regions of the world are of great concern, as they are closely linked to the wellbeing of humankind. Sophisticated hydrological prediction tools are required to assess climatic and hydrometeorological conditions, as they impact the sustainability of water resources as well as water availability. Research and data collection activities from multi-hydrometeorological sensors (e.g., gauges, radars, satellites) form the basis for quantifying the impact of extreme episodes along the hydrologic phases that manifest in terms of the magnitude, duration and frequency of floods, droughts and other hydrometeorological hazards that affect water resources management. A number of hydrometeorological research activities have been reported in the literature by various researchers and research groups globally. This contribution presents (a) a review of the hydrometeorology resource landscape in South Africa; (b) an analysis of the hydrometeorology services and products in South Africa; (c) a review of the hydrometeorological research that has been conducted in South Africa for the last four decades; and (d) highlights on some of the challenges facing the sustained advancement of research in hydrometeorology in South Africa.http://www.mdpi.com/journal/wateram201

The hydrometeorology of the Kariba catchment area based on the probability distributions

In this paper, monthly, maximum seasonal, and maximum annual hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data series from the Kariba catchment area of the Zambezi River basin, Zimbabwe, have been analyzed in order to determine appropriate probability distribution models of the underlying climatology from which the data were generated. In total, 16 probability distributions were considered and the Kolmogorov–Sminorv (KS), Anderson–Darling (AD), and chi-square (x2) goodness-of-fit (GoF) tests were used to evaluate the best-fit probability distribution model for each hydrometeorological data series. A ranking metric that uses the test statistic from the three GoF tests was formulated and used to select the most appropriate probability distribution model capable of reproducing the statistics of the hydrometeorological data series. Results showed that, for each hydrometeorological data series, the best-fit probability distribution models were different for the different time scales, corroborating those reported in the literature. The evaporation data series was best fit by the Pearson system, the Lake Kariba water levels series was best fit by theWeibull family of probability distributions, and the rainfall series was best fit by the Weibull and the generalized Pareto probability distributions. This contribution has potential applications in such areas as simulation of precipitation concentration and distribution and water resources management, particularly in the Kariba catchment area and the larger Zambezi River basin, which is characterized by (i) nonuniform distribution of a network of hydrometeorological stations, (ii) significant data gaps in the existing observations, and (iii) apparent inherent impacts caused by climatic extreme events and their corresponding variability.http://www2.ametsoc.org/ams/index.cfm/publications/journals/earth-interactions/2015-10-31hb201

Seasonal rainfall predictability over the Lake Kariba catchment area

The Lake Kariba catchment area in southern Africa has one of the most variable climates of any major river basin, with an extreme range of conditions across the catchment and through time. Marked seasonal and interannual fluctuations in rainfall are a significant aspect of the catchment. To determine the predictability of seasonal rainfall totals over the Lake Kariba catchment area, this study used the low-level atmospheric circulation (850 hPa geopotential height fields) of a coupled ocean-atmosphere general circulation model (CGCM) over southern Africa, statistically downscaled to gridded seasonal rainfall totals over the catchment. This downscaling configuration was used to retroactively forecast the 3-month rainfall seasons of September-October-November through February-March-April, over a 14-year independent test period extending from 1994. Retroactive forecasts are produced for lead times of up to 5 months and probabilistic forecast performances evaluated for extreme rainfall thresholds of the 25th and 75th percentile values of the climatological record. The verification of the retroactive forecasts shows that rainfall over the catchment is predictable at extended lead-times, but that predictability is primarily found for austral mid-summer rainfall. This season is also associated with the highest potential economic value that can be derived from seasonal forecasts. A forecast case study of a recent extreme rainfall season (2010/11) that lies outside of the verification period is presented as evidence of the statistical downscaling system’s operational capability.The Applied Center for Climate and Earth Systems Science (ACCESS)http://www.wrc.org.zaam201

Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe

In this study, average monthly and annual rainfall totals recorded for the period 1970 to 2010 from a network of 13 stations across the Lake Kariba catchment area of the Zambezi river basin were analyzed in order to characterize the spatial-temporal variability of rainfall across the catchment area. In the analysis, the data were subjected to intervention and homogeneity analysis using the Cumulative Summation (CUSUM) technique and step change analysis using rank-sum test. Furthermore, rainfall variability was characterized by trend analysis using the non-parametric Mann-Kendall statistic. Additionally, the rainfall series were decomposed and the spectral characteristics derived using Cross Wavelet Transform (CWT) and Wavelet Coherence (WC) analysis. The advantage of using the wavelet-based parameters is that they vary in time and can therefore be used to quantitatively detect time-scale-dependent correlations and phase shifts between rainfall time series at various localized time-frequency scales. The annual and seasonal rainfall series were homogeneous and demonstrated no apparent significant shifts. According to the inhomogeneity classification, the rainfall series recorded across the Lake Kariba catchment area belonged to category A (useful) and B (doubtful), i.e., there were zero to one and two absolute tests rejecting the null hypothesis (at 5 % significance level), respectively. Lastly, the long-term variability of the rainfall series across the Lake Kariba catchment area exhibited non-significant positive and negative trends with coherent oscillatory modes that are constantly locked in phase in the Morlet wavelet space.http://link.springer.com/journal/7042017-04-12hb201

Predictability of seasonal rainfall and inflows for Water Resource Management at Lake Kariba

The Lake Kariba catchment area in southern Africa has one of the most variable climates of any major river basin, with an extreme range of conditions across the catchment and through time. The study characterized rainfall variability across the Lake Kariba catchment area, followed by describing prediction models for seasonal rainfall totals over the catchment and for inflows into Lake Kariba. The thesis therefore improved our understanding of rainfall variations over central southern Africa and provided evidence on how seasonal forecasts can be applied in order to potentially improve decision making in dam management. The prediction of the seasons in which floods or droughts are most likely to occur involves studying the characteristics of rainfall and inflows within these extreme seasons. The study started off by analyzing monthly rainfall data through statistical analysis. To determine the predictability of seasonal rainfall totals over the Lake Kariba catchment area, this study used low-level atmospheric circulation of a fully coupled ocean-atmosphere general circulation model over southern Africa, statistically downscaled to seasonal rainfall totals over the catchment. The verification of hindcasts showed that rainfall over the catchment is predictable at extended lead-times. Seasonal climate forecasts need to be integrated into application models in order to help with decision-making processes. The use of hydro-meteorological models may be proven effective for reservoir operations since accurate and reliable prediction of reservoir inflows can provide balanced solution to the problems faced by dam or reservoir managers. In order to reliably predict reservoir inflows for decision-making, the study investigated the use of a combination of physical and empirical models to predict seasonal inflows into the Lake. Two predictions systems were considered. First, antecedent seasonal rainfall totals over the upper Zambezi catchment were used as predictors in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method used predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model downscaled to the inflows. Inflow hindcasts performed best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season).Thesis (PhD)--University of Pretoria, 2015.gm2015Geography, Geoinformatics and MeteorologyPhDUnrestricte

Hydrometeorological Research in South Africa: A Review

Water resources, particularly in arid and semi-arid regions of the world are of great concern, as they are closely linked to the wellbeing of humankind. Sophisticated hydrological prediction tools are required to assess climatic and hydrometeorological conditions, as they impact the sustainability of water resources as well as water availability. Research and data collection activities from multi-hydrometeorological sensors (e.g., gauges, radars, satellites) form the basis for quantifying the impact of extreme episodes along the hydrologic phases that manifest in terms of the magnitude, duration and frequency of floods, droughts and other hydrometeorological hazards that affect water resources management. A number of hydrometeorological research activities have been reported in the literature by various researchers and research groups globally. This contribution presents (a) a review of the hydrometeorology resource landscape in South Africa; (b) an analysis of the hydrometeorology services and products in South Africa; (c) a review of the hydrometeorological research that has been conducted in South Africa for the last four decades; and (d) highlights on some of the challenges facing the sustained advancement of research in hydrometeorology in South Africa