Stars4Water
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Dataset with article https://www.nature.com/articles/s41467-020-14386-x (https://doi.org/10.1038/s41467-020-14386-x). Despite its small land coverage, urban land and its expansion can have profound impacts on global environments. Therefore, a proper understanding of how future urban land change will affect other land covers is important to alleviate the social and environmental problems that challenge the sustainable developments of human societies. Recently, The Shared Socioeconomic Pathways (SSPs) were adopted by the Coupled Model Intercomparison Project Phase 6 (CMIP6), enabling researchers to conduct unified, comparable multi-scenario simulations and integrate such simulation products into climate change research. The SSPs focus on the key socio-economic factors including demographic dynamics, economic development, technological change, social, cultural, and institutional changes and policies. Here, we present the scenario projections of global urban land expansion under the framework of the shared socioeconomic pathways (SSPs) every 10 years from 2015 to 2100. Our projections feature a fine spatial resolution of 1 km that preserves spatial details and avoids potential distortions in urban land patterns. The objective is to enable the assessment of different scenarios of future urban expansion and their related impacts on a global scale under the latest recognized SSP scenarios.
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This layer represents the political administration level 2 boundaries (level of Commune, and Constituency) in Angola and Namibia, that share geographical overlapping with the Okavango Basin. Source: Generated under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project†document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.
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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.
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This map represents the spatial distribution of land under irrigation which is affected by some degree of salinization. It was produced by combining FAO AQUASTAT country statistics regarding irrigated areas affected by salinization with spatial information on irrigated areas where precipitation is not sufficient to leach away salt residues that are built up in the soil due to irrigation. It was assumed that the risk of salinization of irrigated areas can occur only in areas with an Aridity Index lower than 0.65 (where the Aridity Index is defined as Yearly Precipitation divided by Yearly Reference Evapotranspiration).
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The dataset provides a number of indicators of the potential change, relative to a reference period of the recent past, in hydrological conditions over the 21st Century based on an ensemble of climate and hydrological models. The indicators cover hydrological variables of river discharge, soil moisture, snow water equivalent and groundwater recharge. These indicators are produced by various hydrological models using input variables of historical and projected precipitation, temperature and potential evapotranspiration. The indicators transform data from climate projections into usable information for the European water sector. They were defined in discussion with stakeholder groups working in different areas of the water sector (hydropower, irrigation, water supply) to provide clear information on climate projections for water resources as annual, seasonal and monthly change factors for a range of variables. A range of global climate models and standard projection scenarios (based on latest Copernicus Climate Change Service and Coupled Model Inter-comparison Project Phase 5 climate modelling experiments) were used along a multi-hydrological model approach to produce these indicators. This ensemble approach to the climate and hydrological modelling captures the uncertainty and variability of the hydrological regime. Precipitation and temperature data from five global climate models was downscaled to 5km x 5km resolution with the daily values disaggregated to 3-hourly values. These data were used to force four hydrological models to produce the hydrological variables to derive the indicators. The indicators are given as relative changes for a given 30-year projection window with respect to the reference period estimates of 1971-2010 for Representative Concentration Pathways (RCP) 2.6 and 8.5, for each grid cell. This dataset is produced on behalf of Copernicus Climate Change Service, by UK Centre for Ecology & Hydrology (UKCEH), Helmholtz Centre for Environmental Research (UFZ), Leipzig, Centro Tecnológico del Agua (Cetaqua), Climate Partnership LLC (CPL), Environment Agency (EA), Mediterranean Network of Basin Organisations (MENBO), Norwegian Water Resources & Energy Directorate (NVE).
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This layer represents the political regional boundaries (level of Province, Region and District) in the 4 riparian countries that share geographical overlapping with the Okavango Basin: Angola, Namibia, Botswana and Zimbabwe. Source: Digital Chart of the World (DCW) with further editing corrections for a more accurate spatial definition. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project†document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.
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Grids with monthly values of coefficient of variation of precipitation for the period 1961-1990, at 5 arc min. Based on: New, M., Lister, D., Hulme, M. and Makin, I., 2002: A high-resolution data set of surface climate over global land areas. Climate Research 21:1-25
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This layer represents the political administration level 1 boundaries (level of municipality, (Region) and subdistrict) in the 4 riparian countries, that share geographical overlapping with the Okavango Basin: Angola, Namibia, Botswana and Zimbabwe. Source: Generated under the GIS EPSMO program. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project†document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.
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This layer represents the country boundaries of the 4 riparian countries that share geographical overlapping with the Okavango Basin: Angola, Namibia, Botswana and Zimbabwe. Source: Digital Chart of the World (DCW) with further editing corrections for a more accurate spatial definition. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project†document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.
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This map shows to which extent rainfed and irrigated agricultural systems as identified on SOLAW Map 1.3: "Major agricultural systems" suffer from land and / or water scarcity. Land scarcity in rainfed agriculture was assessed by comparing the rural population density, (obtained from GRUMP 2000, adjusted for UN data, excluding the urban areas indicated on the GRUMP dataset) with the suitability for rainfed crops as mapped for the Global Agro-ecological Zones 2000. Since land that is very suitable for rainfed agriculture can sustain more people than land that is not suitable, it was assumed that each suitability class has its own carrying capacity regarding population. On the map, land is considered scarce if the population density is higher that the highest quintile in the density distribution for each suitability class. Land scarce areas in climates with an Aridity Index lower than 0.5 (where the Aridity Index is defined as Yearly Precipitation divided by Yearly Reference Evapotranspiration) are considered both land and water scarce. Water scarcity in irrigated areas was assessed by combining the Map 1.2: Global distribution of physical water scarcity with the Global Map of Irrigation Areas. The areas equipped for irrigation are considered water scarce if already more than 10% of the renewable water resources in the river basin is consumed by irrigated crops.