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Annual Precipitation (mm)

L-Moment of Annual Precipitation Analysis in Africa calculated with R function lmom::samlmu with default arguments. See R documention of the function for details.


GOAL 13: Climate action


Other SDGs

GOAL 6: Clean Water and Sanitation


Climate


Source: EC-JRC

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Biomes

This map shows the Terrestrial Biomes, the eight major terrestrial biomes on Earth are each distinguished by characteristic temperatures and amount of precipitation. Comparing the annual totals of precipitation and fluctuations in precipitation from one biome to another provides clues as to the importance of abiotic factors in the distribution of biomes. Temperature variation on a daily and seasonal basis is also important for predicting the geographic distribution of the biome and the vegetation type in the biome. The distribution of these biomes shows that the same biome can occur in geographically distinct areas with similar climates.


GOAL 15: Life on land


Other SDGs


Climate Environment Biodiversity Forests


Source: EC-JRC

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Disaster Risk Reduction (DRR) implementation Index

The indicator for the Disaster Risk Reduction (DRR) activity in the country comes from the score of Hyogo Framework for Action self-assessment progress reports of the countries. HFA progress reports assess strategic priorities in the implementation of disaster risk reduction actions and establish baselines on levels of progress achieved in implementing the HFA's five priorities for action. The risk score ranges from 0-10, where 10 is the highest risk.


GOAL 13: Climate action


Other SDGs


Climate Society


Source: EC-JRC

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Forest Cover

Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2018. For additional information about these results, please see the associated journal article (Hansen et al., Science 2013).


GOAL 15: Life on land


Other SDGs


Climate Environment Biodiversity Forests


Source: Hansen/UMD/Google/USGS/NASA

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Forest Gain

Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2018. For additional information about these results, please see the associated journal article (Hansen et al., Science 2013). Year of gross forest cover loss event: Forest gain during the period 2000–2012, defined as the inverse of loss, or a non-forest to forest change entirely within the study period. Encoded as either 1 (gain) or 0 (no gain).


GOAL 15: Life on land


Other SDGs


Climate Environment Biodiversity Forests


Source: Hansen/UMD/Google/USGS/NASA

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Forest Loss

Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2018. For additional information about these results, please see the associated journal article (Hansen et al., Science 2013). Year of gross forest cover loss event: Forest loss during the period 2000–2018, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. Encoded as either 0 (no loss) or else a value in the range 1–17, representing loss detected primarily in the year 2001–2018, respectively.


GOAL 15: Life on land


Other SDGs


Climate Environment Biodiversity Forests


Source: Hansen/UMD/Google/USGS/NASA

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Nuber of years with Heat Wave Magnitude Index >4 (1981-2018)

Number of years in the period 1981-2018 with HMID (HEat Wave Magnitude Index >=4, Russo et al, 2015). Maps based on ERA% Re-Analysis ERA5 Dataset



Other SDGs

GOAL 13: Climate Action


Climate


Source: EC-JRC

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Recent Shocks

Population affected by natural disasters in the last 3 years


GOAL 10: Reduced inequalities


Other SDGs

GOAL 15: Life on Land


Climate Natural Disasters Society


Source: EC-JRC

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Relative likelihood of hydro-political interactions (Ranking in 1997–2012)

Competition over limited water resources is one of the main concerns for the coming decades. Although water issues alone have not been the sole trigger for warfare in the past, tensions over freshwater management and use represent one of the main concerns in political relations between riparian states and may exacerbate existing tensions, increase regional instability and social unrest. Previous studies made great efforts to understand how international water management problems were addressed by actors in a more cooperative or confrontational way. In this study, we analyze what are the pre-conditions favoring the insurgence of water management issues in shared water bodies, rather than focusing on the way water issues are then managed among actors. We do so by proposing an innovative analysis of past episodes of conflict and cooperation over transboundary water resources (jointly defined as “hydro-political interactions”). On the one hand, we aim at highlighting the factors that are more relevant in determining water interactions across political boundaries. On the other hand, our objective is to map and monitor the evolution of the likelihood of experiencing hydro-political interactions over space and time, under changing socioeconomic and biophysical scenarios, through a spatially explicit data driven index. Historical cross-border water interactions were used as indicators of the magnitude of corresponding water joint-management issues. These were correlated with information about river basin freshwater availability, climate stress, human pressure on water resources, socioeconomic conditions (including institutional development and power imbalances), and topographic characteristics. This analysis allows for identification of the main factors that determine water interactions, such as water availability, population density, power imbalances, and climatic stressors. The proposed model was used to map at high spatial resolution the probability of experiencing hydro-political interactions worldwide. This baseline outline is then compared to four distinct climate and population density projections aimed to estimate trends for hydro-political interactions under future conditions (2050 and 2100), while considering two greenhouse gases emission scenarios (moderate and extreme climate change). The combination of climate and population growth dynamics is expected to impact negatively on the overall hydro-political risk by increasing the likelihood of water interactions in the transboundary river basins, with an average increase ranging between 74.9% (2050 – population and moderate climate change) to 95% (2100 - population and extreme climate change). Future demographic and climatic conditions are expected to exert particular pressure on already water stressed basins such as the Nile, the Ganges/Brahmaputra, the Indus, the Tigris/Euphrates, and the Colorado. The results of this work allow us to identify current and future areas where water issues are more likely to arise, and where cooperation over water should be actively pursued to avoid possible tensions especially under changing environmental conditions. From a policy perspective, the index presented in this study can be used to provide a sound quantitative basis to the assessment of the Sustainable Development Goal 6, Target 6.5 “Water resources management”, and in particular to indicator 6.5.2 “Transboundary cooperation”


GOAL 6: Clean water and sanitation


Other SDGs

GOAL 10: Reduced Inequality


Climate Growth & Inequality Environment Water


Source: EC-JRC

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Solar global horizontal irradiation (kWh/m2)

Yearly average global irradiance on an optimally inclined surface (W/m2) . Solar radiation data consists of the average irradiance over the time period 2005-2015 , taking into account both day and night-time, measured in W/m2.


GOAL 7: Affordable and clean energy


Other SDGs


Climate Energy Energy Production


Source: EC-JRC

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Wind power (W/m2)

Wind power density (W/m2) at 10 m heigth. Wind power density is a measure of the wind resources. Higher mean wind power densities indicate better wind resources.


GOAL 7: Affordable and clean energy


Other SDGs


Climate


Source: Technical University of Denmark (DTU) and World Bank

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