The “natural resources sensitivity” symbolizes the ecosystem vitality and degree of conservation in 2010. Deforestation and loss of water resources quality may render certain areas more sensitive to climate stressors on account of the loss of normal vegetation cover, the depletion of biodiversity, the reduction in ecosystem services and significant loss of beneficial assets. The index results from the first cluster of the Principal Component Analysis preformed among 16 potential variables. The analysis identify five dominant variables, namely “water availability per capita”, “net primary production”, “forest accessibility”, “vegetation continuity” and “climatic resources availability”, assigning respectively the weights of 0.19, 0.21, 0.165, 0.21 and 0.225. Before to perform the analysis the variables “water availability per capita”, “forest accessibility” and “vegetation continuity” were log transformed to shorten the extreme variation and then together with the other two variables were score-standardized (converted to distribution with average of 0 and standard deviation of 1; all variables with inverse method) in order to be comparable. The 6 arc-minute grid “water availability per capita” of 2005 was computed by sum of the run-off and discharge grids produced by World Water Development Report II and then sampled at 0.5 arc-minutes. A focal statistic ran with a radius of 55 cells (about 50 Km). This had a smoothing effect and represents some of the extend influence of major rivers as a resources for local people. To calculate the available water per capita it was then divided by the population. The 5 arc-minute grid “net primary production” of 2000 was gathered from FAO GeoNetwork and sampled at 0.5 arc-minute. Also in this case a focal statistic ran with a radius of 22 cells (about 20 Km) in order to represents the extend effect of primary production as natural resources for local people. The 0.5 arc-minute grid “forest accessibility” was build using the grid of travel distance in minutes to large cities (which one with population greater than 50,000 people), produced by the European Commission and the World Bank to represent the connectivity in 2000, and the grid of forest occurrence, extracted from the FAO Global Land Cover-SHARE dataset of 2014. The result measures the distance in minutes between forest and cities, thus is a proxy for remoteness and naturalness of forest. The 0.125 arc-minute grid “vegetation continuity” of 2010 were collected from University of Maryland and NASA and sampled at 0.5 arc-minute. A focal statistic ran with a radius of 55 cells (about 50 Km). This had a smoothing effect and represents some of the extend influence of vegetation concentration as a resources for local people. Finally the 0.5 arc-minute grid “climatic resources availability” was produced within the ClimAfrica project. The “water availability per capita” represents the potential water available per people in a certain area. We can consider the area with small values more sensitive to climatic stress, because lack a buffer of water resources, precious in a prevalently rain-fed agricultural system like in Africa. The “net primary production” and the “vegetation continuity” are proxies of the potential vegetal productivity available in a certain area. Moreover “vegetation continuity” is an indicator of abundance of natural ecosystem services that can reduce the sensitivity of human-environment systems. The “forest accessibility” assessing the distance between human and natural system measure the anthropogenic degree of a forest. A forest recording a high anthropogenic degree (thus near in terms of minute from a city) may potentially be threaded by human activity and thus represent a fragile ecosystem. Finally the “climatic resources availability” is an indicator of the climatic potential for biomass production. It is based on the climatically determined biomass productivity index that is a proxy for the atmospheric energy available for biomass production, as expressed by accumulated temperature, adjusted for drought stress. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

Vulnerable population identified by the nutritional status of women (BMI) as indicator for food security, in sample of households in East Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

On 8 November 2013, Typhoon Haiyan made landfall in the Philippines causing widespread devastation, tremendous loss of life and catastrophic impact on infrastructure and natural resources. As a result of the devastating damage to agriculture and fisheries sectors and the severe impact on lives and livelihoods FAO has declared Level 3 Emergency Response. In response to the call for needs assessment and coordinating humanitarian assistance in agriculture and fisheries sectors the FAO, Land and Water Division Geospatial Unit collected a number of datasets from various sources including FAO, WFP, UNITAR-UNOSAT, EC-JRC, USDA, USGS as well as updated reports from national agencies as well as the National Disaster Risk Reduction and Management council. Severity zones were mapped using data from the EC-JRC updates based on the Public Storm Warning Signals (PSWC) in four main levels: S1-very high for PSWC #1, S2-high for PSWC #2, S3-medium for PSWC #3 and S4-medium-low for PSWC#4. The severity area mask was calibrated using remote sensing data combined with the data reported on November 13, 2013 by the National Disaster Risk Reduction and Management Council of Philippines. The severity intensity rate was classified proportionally with the affected population figures. The data on crop production, harvested area, yield for major crops were collected from FAO, Global Spatial Database of Agricultural Land-Use Statistics AgroMaps ( http://kids.fao.org/agromaps ), FAOSTAT ( http://faostat.fao.org/) and Bureau of Agricultural Statistics of Philippines (http://countrystat.bas.gov.ph ). Data on arable land and permanent crops were derived from the FAO Global Land Cover Share database (Beta-version) and data on the area by district were derived by the FAO Global Administrative Units Layer, GAUL version 2013-12 (www.fao.org/geonetwork). In addition data on crop calendars to identify crop growing stage were collected from the FAO-IIASA Global Agro-Ecological Zones Data portal, GAEZ (www.fao.org/nr/gaez) , FAO Crop Calendar from Data@Fao.org (https://data.apps.fao.org/) and FAO Agricultural Market Information System, AMIS (http://www.amis-outlook.org/) .Philippines rice crop calendar was provided by the Philippines Rice Research Institute (PhilRice-DA), International Rice Research Institute (IRRI). The assessment was done by first georefencing, harmonizing and creating a central database in the UTM WGS 84 reference system. Areas of crop growth stages were analyzed according to their stage at the time of the event. Major season and secondary season for rice were considered for analysis at the time of the event. The areas of standing rice at the moment of the event were mapped using the district level crop calendars for the major season. Also the planted rice areas for the second season were mapped. The affected area was then calculated considering both these areas which were considered as area loss, calibrated by the severity intensity rate class. The extent (ha) and fraction (%) of the potential affected crop areas was calculated by intersecting the tropical cyclone severity areas with the administrative layers, arable land and permanent crops, annual harvested area, yield and production for year 2012 (which was used as a proxy to assess the planted area). Major crops which were affected include Rice Paddy, Coconut and Sugar Cane. The maps show the impacts of the tropical cyclone on these crops. The outputs were classified in 5 classes of affected areas: less the 10%, 10-25%, 25-50%, 50-75%, and >75%. The tabular information provides information on the estimated affected area for Rice Paddy (extent in ha and share in percentage) by Typhoon “YOLANDA” (HAIYAN) severity Level and by District (updated on 15 November 2013). The affected planted areas for each severity zone were calculated as a fraction of the planted area versus by severity zone by administrative unit area. In addition, the analysis was done for major livestock, pig, poultry and cattle, affected population. Sources: Agromaps, FAOSTAT, CountrySTAT Philippines, GAEZ, AMIS, GLC-SHARE, FAO NRL Geospatial Unit datasets, Data@FAO.ORG, EC-JRC, ESA GlobCover, UNITAR, UNOSAT, WFP, National Disaster Risk Reduction and Management Council, Republic of Philippines, Philippines rice crop calendar, Philippines Rice Research Institute (PhilRice-DA), International Rice Research Institute (IRRI) sponsored by the DA-National Rice Program via the Rice Self Sufficiency Program (RSSP) and the Global Rice Science Partnership (GRiSP), the CGIAR Research Program on Rice.