globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2735
论文题名:
Climate and southern Africa's water–energy–food nexus
作者: Declan Conway
刊名: Nature Climate Change
ISSN: 1758-793X
EISSN: 1758-6913
出版年: 2015-08-21
卷: Volume:5, 页码:Pages:837;846 (2015)
语种: 英语
英文关键词: Environmental social sciences
英文摘要:

In southern Africa, the connections between climate and the water–energy–food nexus are strong. Physical and socioeconomic exposure to climate is high in many areas and in crucial economic sectors. Spatial interdependence is also high, driven, for example, by the regional extent of many climate anomalies and river basins and aquifers that span national boundaries. There is now strong evidence of the effects of individual climate anomalies, but associations between national rainfall and gross domestic product and crop production remain relatively weak. The majority of climate models project decreases in annual precipitation for southern Africa, typically by as much as 20% by the 2080s. Impact models suggest these changes would propagate into reduced water availability and crop yields. Recognition of spatial and sectoral interdependencies should inform policies, institutions and investments for enhancing water, energy and food security. Three key political and economic instruments could be strengthened for this purpose: the Southern African Development Community, the Southern African Power Pool and trade of agricultural products amounting to significant transfers of embedded water.

Numerous challenges coalesce to make southern Africa emblematic of the connections between climate and the water–energy–food nexus, which has important economic influence throughout the region. Physical and socioeconomic exposure to climate is high in socioeconomically vulnerable areas and crucial sectors, such as agriculture, but also in energy generation and mining. For example, almost 100% of electricity production in the Democratic Republic of Congo (DRC), Lesotho, Malawi and Zambia is from hydropower. Hydropower further comprises a major component of regional energy security through extensive sharing as part of the Southern African Power Pool (SAPP). The region's population is concentrated in areas exposed to high levels of hydrometeorological variability1 and is projected to roughly double by 20502. Of the 13 mainland countries and Madagascar (Table 1) that comprise the Southern African Development Community (SADC), six are defined as low income, three as lower-middle income and four as upper-middle income, according to the World Bank classification (using 2012 gross national income per capita). There are few quantified examples of the links between climate and economic activity in the region, although South Africa experienced a decrease in gross domestic product (GDP) in the 1983 El Niño year3, and economic modelling studies in Malawi and Zambia indicate that the severe 1992 drought caused a drop in GDP of approximately 7–9% and adversely affected household poverty4. Climate variability has important consequences for resource management in the region, including for non-equilibrium production systems such as rangeland ecology5, irrigation6 and lakes7. Southern Africa is also a region where seasonal climate forecasts can potentially benefit areas where sustained forecast skill is demonstrated. Seasonal climate forecasting has been the subject of many studies in sub-Saharan Africa (SSA)8, 9 and the Southern Africa Regional Climate Outlook Forum provides advance information about the likely character of seasonal climate. Yet, despite more than a decade of research on hydrological applications of seasonal forecasts, there is limited evidence of their operational use in the water sector9. With ongoing climate change, annual precipitation, soil moisture and runoff are likely to decrease, while rising temperatures could increase evaporative demand in large parts of the region10 (Fig. 1).

Table 1: Economic indicators and climate-sensitive economic activities across water, energy and food.

We characterize exposure as the interaction between characteristics of the climate system (particularly interannual rainfall variability) and a country's dependence on climate-sensitive economic activities such as the share of agriculture in GDP, the proportion of rainfed agricultural land and the energy contribution from hydroelectric sources (Table 1; Fig. 3). South Africa's GDP is larger than that of the other 12 southern African economies combined. The direct contribution of agriculture to the economy is lowest (<10%) in South Africa, Botswana, Swaziland, Namibia, Angola and Lesotho, 13% in Zimbabwe and more than 20% in the other countries. If agricultural processing were included in agricultural GDP, the shares would be substantially larger in most, if not all, SADC countries. The share of cropland equipped for irrigation is low in most of the region, with the exception of Madagascar, South Africa and Swaziland (Table 1). The contribution of hydropower to energy production is very high overall (Fig. 3), but varies considerably across the region, from 1.5% in South Africa to more than 30% in Madagascar, Swaziland and Zimbabwe, and to almost 100% in DRC, Lesotho, Malawi and Zambia. Reliable electricity production is at risk during prolonged droughts and also during extreme flood events, when dam safety is an additional risk. More than 90% of South Africa's energy generation is coal-based22, well above the rest of the region. Coal-fired power plants with wet cooling systems consume far more water than most other energy technologies22. Thus, South Africa's main energy utility, Eskom, uses about 2% of the country's freshwater resources, mainly for coal-fired power stations23. Coal mining and energy generation from coal both substantially impact water quality and availability24. To reduce these impacts, Eskom has implemented a dry-cooling system in two existing and all new power stations25, enabling a 15-fold reduction in water use.

Figure 3: National rainfall variability and socioeconomic exposure to hydroclimate.
National rainfall variability and socioeconomic exposure to hydroclimate.

a–c, Individual countries. d, Average, minimum and maximum of 13 countries. Data for rainfall interannual variability (COV, %) from ref. 84; hydropower share in energy production (%) from ref. 90; and agriculture (crop and livestock production, forestry, hunting and fishing) value added share of GDP (%) from ref. 90. Note: data for agricultural GDP in Malawi are not available.

Multiyear rainfall variability in southern Africa is higher than in many other parts of the world31, 32. Interannual variability, expressed as the coefficient of variation (COV), is not particularly high on national scales: <20% for most countries, except for Botswana and Namibia, the driest two countries (Fig. 3). However, rainfall shows much greater local variability (local COV exceeds 20% across much of the SADC region), strong seasonality and a range of multi-annual to decadal variations33. At the national level, long-term trends in rainfall between 1901 and 2012 are modest (the linear trend is insignificant relative to the long-term average), without evidence of any clear spatial pattern (Supplementary Table 1). Linear trends during the past two decades show varied behaviour; three countries with wetting trends above 20% of the long-term mean annual rainfall (Botswana, Namibia and Zambia) and Tanzania with a drying trend of 21% (Supplementary Table 1). National-level analysis is likely to obscure local trends and the results are highly sensitive to the period chosen for analysis, particularly in regions with strong multi-annual variability.

National variations in rainfall and temperature have been found to exert major influence on agricultural production in all of SSA, but with considerable regional heterogeneity in the response to rainfall34. Another study for SSA used panel regressions to explore the effects of temperature and precipitation variability on country-level economic growth indicators, and found drought was the most significant climate influence on GDP per capita growth35. We use correlation analysis to explore, for each country, the associations between annual rainfall and national economic activity (GDP annual growth rate) and agricultural production (all cereals and maize — the most significant crop in the region). Fifteen-year sliding correlations are used to examine the temporal stability of associations between variables (see Supplementary Information). There are no statistically significant relationships between annual rainfall and GDP growth rate (Supplementary Table 2). Correlation of rainfall with total production of cereals and maize shows three countries with significant relationships at the 1% level and three at the 5% level (although for DRC it is negative and possibly spurious). The average sliding correlations are somewhat higher (Supplementary Table 3).

Time series data of hydropower production are neither publically available nor easily comparable between sites/countries, making it difficult to assess the importance of climate variability as a driver of energy production fluctuations. A study of the effects of modified reservoir operation on downstream environmental flows of the Zambezi shows considerable variability in observed hydropower production at three sites, but does not consider the role of climate36. Electricity insecurity is known to negatively affect total factor productivity and labour productivity of small and medium-sized enterprises, but the relationship is not simple, with differences between countries and measurement effects37. Studies of specific events highlight the major consequences of drought-induced reductions in electricity production38. Ref. 18 cites examples of drought impact on the Kariba Dam (Zambezi Basin) during 1991–1992, resulting in estimated reductions of US$102 million in GDP and US$36 million in export earnings; and Kenya, where, during 2000, a 25% reduction in hydropower capacity resulted in an estimated 1.5% reduction in GDP. A review of the economics of climate change in Tanzania profiled the consequences of the 2003 drought, which brought the Mtera Dam reservoir levels close to the minimum required for electricity generation39. This prompted the Tanzania Electric Supply Company to approach a private provider to use gas turbine units at huge cost. A more recent World Bank estimate put costs of power shortages in Tanzania at US$1.7 million per day, with an average 63 days per year with power outages39.

Given the links between climate and the water–energy–food nexus in the region, seasonal forecast information can play an important role in guiding nexus-related decision making, depending on forecast skill and utility. Seasonal to interannual variability in southern Africa is high, but so is its predictability relative to other regions, depending on location, time of year40 and phase of the El Niño–Southern Oscillation41 (ENSO). This can be seen by considering the association (Fig. 4a) between NINO3.4 sea surface temperatures — as a representation of ENSO — and gridded rainfall over southern Africa south of 15° S (ref. 41). A state-of-the-art coupled ocean–atmosphere model has some skill in predicting seasonal (December to February, DJF) rainfall over the region at a one-month lead time (DJF forecasts produced in November; Fig. 4b shows areas with statistically significant correlation41; see Supplementary Information). Stronger ENSO associations and the best model performance are found for maximum temperatures (Supplementary Fig. 2). The areas where ENSO impacts significantly and where forecast skill levels are relatively high include the river basins of the Limpopo, Orange, Umgeni and lower Zambezi.

Figure 4: Rainfall and sea surface temperature; Kendall's tau correlations.
Rainfall and sea surface temperature; Kendall's tau correlations.

a, Between concurrent DJF NINO3.4 sea surface temperatures and DJF rainfall for the 30 years from 1982/1983 to 2011/2012. b, Between ECHAM4.5-MOM3-DC2 downscaled seasonal rainfall forecasts for DJF produced in November and observed DJF rainfall. Data for panel b from ref. 41. Correlations significant at the 95% level are shaded. See Supplementary Information.

The challenges for the water–energy–food nexus posed by interannual variability occur in the context of a gradually changing climate. Even if an international agreement to limit global warming to 2 °C above pre-industrial conditions is successfully developed, climate models project significant changes that exceed the range of natural climate variability (Fig. 1). According to the majority of climate models, most southern African countries will warm more than the global mean, with annual mean temperatures rising by 2 to 3 °C in most cases. Precipitation changes are more uncertain, with both increases and decreases possible. Nevertheless, for most countries the majority of models project decreases in annual precipitation, by 20% or more for some models and countries. Except for the southernmost countries, there is a tendency for models that project most warming to simulate stronger reductions in precipitation. Analysis of extreme precipitation in the climate models used for the Intergovernmental Panel on Climate Change Fourth Assessment Report shows a marked delay in rainy season onset over most of the region and an early end to the season in parts of the region46.

Most nexus studies for southern Africa have been motivated by climate change and assess biophysical impacts for specific sectors, for example, rainfall and irrigation water availability on crop production, or river flow changes on hydropower generation. Some crop models simulate sizable yield losses for southern Africa47, suggesting that the region's food system could be particularly vulnerable to climate change48. However, differences in climate scenarios, impact models, spatial and temporal scales, and processes represented restrict our ability to reliably define impacts for specific sectors and, importantly, secondary effects across the water–energy–food nexus. Nevertheless, an estimate of the range of potential impacts on maize yield (and the wide uncertainty range) can be determined from the 30-member ensemble of global gridded crop models run by the Inter-Sectoral Impact Model Intercomparison Project49 (see Supplementary Information). The simulated maize yield averaged across southern Africa decreases by 15.7 ± 16.3% (rain fed) and 8.3 ± 20.4% (irrigated) by the 2080s relative to the 2000s, that is, a median yield reduction with a substantial range of different outcomes. The wide range is owing to uncertainties in climate and in our understanding of crop response to climate change, particularly the role of increased atmospheric CO2 concentration on photosynthesis. In the top five southern African producers, median impacts are relatively small in the 2020s and 2050s, becoming more substantially negative by the 2080s, with a stronger level of agreement in the sign of change among simulations (Fig. 5). Among these countries, rainfed cultivation is most negatively impacted, highlighting that water stress is an important limiting factor of crop yield in the region. Average crop water use decreases, resulting in a 5.9 ± 20.7% increase in estimated crop water productivity (Supplementary Information; Supplementary Fig. S3) by the 2080s.

Figure 5: Simulated climate change impacts on rainfed and irrigated maize yield in the top five producing countries of southern Africa for the near, medium and long time horizon under Representative Concentration Pathway 8.5.
Simulated climate change impacts on rainfed and irrigated maize yield in the top five producing countries of southern Africa for the near, medium and long time horizon under Representative Concentration Pathway 8.5.

The bottom and top of the box are lower and upper quartiles, respectively; the band near the middle of the box is the median value across each set of simulations, which comprises an ensemble of 30 impact simulations49.

Southern Africa can be characterized as a single economic block of strongly interlinked economies where water, energy and food flow between producers and consumers, which also displays considerable heterogeneity in its natural resource endowments and infrastructure distribution, sociopolitical cohesion and economic development. For both the region and individual nations, this implies significant challenges in attempting to balance supply and demand while maintaining coherent policies towards integrated management of water–energy–food resources. The region is well placed to transfer resources intraregionally to meet energy and food shortfalls. However, rising demand for electricity, food and water throughout southern Africa may sharpen the region's sensitivity to climate-induced shocks. Fifteen transboundary river basins transect the region, including the large Congo and Zambezi basins, shared by nine and eight countries, respectively, as well as many smaller shared catchments. Surface catchments are underlain by an estimated 16 transboundary aquifers62. The origin of the southern African economic block can be tied to the dominant position of South Africa and its history alongside other ex-South African and British colonies such as Swaziland, Zimbabwe, Botswana, Namibia and Zambia. South Africa in particular has great cultural, economic and political influence over its neighbours, making its role as a source (and sometimes a sink) of energy, water and food hegemonic63. This alliance and influence is also shown through the SAPP (South Africa has 77% of SAPP's installed power supply capacity64), the SADC and other agreements.

In responding to the distribution of and demand for water–energy–food resources, three key instruments have emerged. First, the SADC, based in Botswana, addresses how member countries sharing rivers might resolve water allocation priorities through a protocol on shared watercourses65, 66. The presence of significant water demands arising from irrigated agriculture and the Gauteng urban industrial complex in South Africa has led to relatively sophisticated water-sharing agreements such as the Joint Development and Utilization of the Water Resources of Komati River BasinURL:

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标识符: http://119.78.100.158/handle/2HF3EXSE/4621
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Declan Conway. Climate and southern Africa's water–energy–food nexus[J]. Nature Climate Change,2015-08-21,Volume:5:Pages:837;846 (2015).
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