Titlebook: Climate Variability and Change in Africa; Perspectives, Experi Jonathan I. Matondo,Berhanu F. Alemaw,Wennegouda J Book 2020 Springer Nature

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書目名稱Climate Variability and Change in Africa影響因子(影響力)




書目名稱Climate Variability and Change in Africa影響因子(影響力)學科排名




書目名稱Climate Variability and Change in Africa網(wǎng)絡公開度




書目名稱Climate Variability and Change in Africa網(wǎng)絡公開度學科排名




書目名稱Climate Variability and Change in Africa被引頻次




書目名稱Climate Variability and Change in Africa被引頻次學科排名




書目名稱Climate Variability and Change in Africa年度引用




書目名稱Climate Variability and Change in Africa年度引用學科排名




書目名稱Climate Variability and Change in Africa讀者反饋




書目名稱Climate Variability and Change in Africa讀者反饋學科排名

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An Overview of Dynamical Downscaling of Global Reanalyses for Improved Climate Reconstruction Over Ds. This is, however, a challenge for data-poor parts of the globe like Africa where adaptive capacity is also a challenge. Global datasets are also often not sufficient, as they come in coarse resolutions that are limited. Global reanalyses continue to help alleviate this challenge when they are dow

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Framework of Best Practice for Climate Change Adaptation in Africa: The Water–Development Nexusperspective to systematically chart out ways for adaptive capacity building. Africa’s vulnerability to climate change and variability has direct impacts on water availability, access and use. Water is the source of food and livelihood security for millions of its population. The future of food and l

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Relationships Among Surface Water Resources in the WR90, WR2005 and WR2012 Datasets of South Africa rces (WR) of South Africa; i.e. WR90, WR2005 and WR2012. MAR is the hydrological catchment response. It was found that linear models were generally suitable to correlate any pair of datasets. The level of linearity was measured by the coefficient of determination (R.), hence correlation coefficient.

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