GeoServer Web Map Tile Service A compliant implementation of WMTS service. WMTS OGC WMTS 1.0.0 NONE NONE http://geoserver.org/com MapX Admin Geneva Switzerland info@mapx.org KVP KVP KVP Chlorophyll-b (μg/L) This layer presents the total green algal biomass of the each sample lakes -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-RBUMB-E1LWM-KF4AP image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wind speed at 10m above the ground (m/s) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF)to gather monthly averages of daily averages of wind speed on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-CDTBT-QULES-ERDYO image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Official development assistance for biodiversity - Period 2002 - 2022 (MSSD indicator 10) In 2022, total official development assistance for biodiversity received in the Mediterranean region (9 countries) was about 1429 million constant 2022 US dollars (in 2018 it was about 350 million constant 2018 US dollars), equivalent to 5.4 dollars per capita (1.1 dollars per capita in 2018). Similar to SDG Indicator 15.a.1 -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-Z6XQL-DAXRF-G6TSI image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Dredging By Zone At 200 Nautical Miles From Shore This layer provides annual data on total volume dredged per zone at 200 nautical miles from shore. It also provides information on the flags of the vessels engaged in dredging activities and the acronyms of the dredging vessel types in a given zone. The data processing and calculation of the zonal statistics was carried out by UNEP/GRID-Geneva in September 2023. -180.0 -90.0 180.0 90.0 MX-RZI-PB2-J4Z-3NM-H3E:MX-TM216-MK1NM-O7R09 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Fragile States Index 2018 (Fund For Peace) The Fragile States Index, produced by The Fund for Peace, is a critical tool in highlighting not only the normal pressures that all states experience, but also in identifying when those pressures are pushing a state towards the brink of failure. By highlighting pertinent issues in weak and failing states, The Fragile States Index—and the social science framework and software application upon which it is built—makes political risk assessment and early warning of conflict accessible to policy-makers and the public at large. More information available on: <a href="http://fundforpeace.org/fsi/2018/04/24/fragile-states-index-2018-annual-report/" target="_blank">http://fundforpeace.org/fsi/2018/04/24/fragile-states-index-2018-annual-report/</a> -180.0 -90.0 180.0 90.0 MX-ZU2-F8I-ZVS-JCK-FBQ:MX-T0DOH-N3IVM-FFXSS image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Health facilities points (HOTOSM) Health facilities (points) as provided by Humanitarian OpenStreetMap Team (HOT). -180.0 -90.0 180.0 90.0 MX-SZX-UBT-FVM-R1V-BDJ:MX-AMYNN-3Y9MB-D5M6D image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wind speed at 10m above the ground (m/s) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF)to gather monthly averages of daily averages of wind speed on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-K3AMA-NWLAK-Q6LRO image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Population with access to safely managed drinking water service in 2022 (MSSD indicator 14) Estimates are available only for 15 countries in the Mediterranean region and there is no data for most of the Southern and Eastern countries. Among the 15 countries where data is available, 96% to 100% of 7 countries' population have access to safely managed drinking water services in 2022. Lebanon is the country the most at risk since only 48% of the population has access to safely managed drinking water services in 2022. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-B1W41-XABN7-RS1IQ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Water resources management (SCP Indicator 6) This indicator measures the extent to which integrated water resources management (IWRM) is implemented in 4 main areas: 1) policies, 2) institutions, 3) management tools, and 4) financing. This indicator is tracked by MedWaves (UNEP/MAP SCP/RAC) and is part of the Regional Action Plan on Sustainable Consumption and Production (SCP) in the Mediterranean. For more information, please check the SCP Factsheets at: <a href="https://switchmed.eu/policy/indicator-factsheets-on-scp-in-the-mediterranean/"target=_blank>Factsheet n.6 (Water resources management) </a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-JCXPM-RY85B-PM2CM image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Global Power Plants Database The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights for one’s own analysis. Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. As of June 2018, the database includes around 28,500 power plants from 164 countries. It will be continuously updated as data becomes available. The most recent release of the Global Power Plant Database 1.1 includes the addition of two countries (China and Fiji), over 3,000 power plants, and nearly 1300 gigawatts of power capacity. We highly recommend using version 1.1, available online as of June 2018. The methodology for the dataset creation is given in the World Resources Institute publication "<a href="https://www.wri.org/publication/global-power-plant-database" target="_blanck">A Global Database of Power Plants</a>". Associated code for the creation of the dataset can be found on <a href="https://github.com/wri/global-power-plant-database" target="_blanck">GitHub</a>. Version: 1.1.0 Source: Citation: Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. 2018. Global Power Plant Database. Published on <a href="http://resourcewatch.org/" target="_blanck">Resource Watch</a> and <a href="https://earthengine.google.com/" target="_blanck">Google Earth Engine</a>. -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-LRCU5-5J50K-11O8V image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Urban population living in informal settlements, or inadequate housing in 2022 in % (MSSD indicator 18) In the most countries with available data, the mean value of mediterranean urban population living in slums is decreasing reaching 8% in 2022 (22,7% in 2002). Some national social housing programs allowed reducing the part of the population without access to an adequate dwelling and living in districts without essential services: e.g. this part fell from 36.7% to 3.8% in Egypt between 2002 and 2022. In Lebanon, the population living in slums in 2022 is approximately 4.5%, compared to 92,4% in 2002. From 2002 to 2022 the situation also improved in Albania to less than 3% of inhabitants living in slums (25% in 2002). On the other hand, the situation in The Syrian Arab Republic is getting worse with an increasing rate of population living in inadequate housing. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-OVAIL-2FYXG-AX6B9&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 18b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-VBTLZ-PJR92-4UHA7 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Water withdrawal intensity (SCP Indicator 4) It measures the ratio between total freshwater withdrawn by all major sectors (agriculture; forestry and fishing; manufacturing; electricity industry; and services) and the total renewable freshwater resources, after taking into account environmental water flow requirements. This indicator is tracked by MedWaves (UNEP/MAP SCP/RAC) and is part of the Regional Action Plan on Sustainable Consumption and Production (SCP) in the Mediterranean. For more information, please check the SCP Factsheets at: <a href="https://switchmed.eu/policy/indicator-factsheets-on-scp-in-the-mediterranean/"target=_blank>Factsheet n.4 (Water withdrawal intensity) </a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-IYXN7-SV8D5-JSRIL image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Upstream Storage Upstream storage measures the water storage capacity available upstream of a location relative to the total water supply at that location. Higher values indicate areas more capable of buffering variations in water supply (i.e. droughts and floods) because they have more water storage capacity upstream. For further information on this dataset visit <a href="https://www.wri.org/our-work/project/aqueduct/" target="_blank">Aqueduct</a>. Citation: Gassert, F., M. Landis, M. Luck, P. Reig, and T. Shiao. 2014. “Aqueduct Global Maps 2.1.” Working Paper. Washington, DC: World Resources Institute. Available online at <a href="http://www.wri.org/publication/aqueduct-metadata-global" target="_blank">http://www.wri.org/publication/aqueduct-metadata-global</a>. -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-JNV36-JG583-PGJ9O image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Surface Temperature No abstract provided -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-SDEQL-MFFHF-GEBLX image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 GMBA v2 0 standard 300 This is the "300" layer of the GMBA v2.0 standard version, which is the main version of the GMBA Inventory v2.0, where the polygons represent actual (only slightly generalized) mountain ranges (according to the GMBA Definition v2.0). It contains a subset of 291 ('Standard') non overlapping major mountain systems and ranges -180.0 -90.0 180.0 90.0 MX-IXP-F6R-H5U-GA8-DTT:MX-EW588-5FDQ7-EVNPJ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Mediterranean maritime protected areas in 2021 (MSSD Indicator 7) In 2018, there were a strong boost towards achieving the Aichi target 11 and the SDG 14 through the declaration of the Spanish cetacean corridor MAP. At the regional level 1.126 Marine Protected Area are declared, among them 257 have a national designation status (MAPAMED 2021). Similar to SDG Indicator 14.5.1 -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-D1K4D-4XWXH-10U9W image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Nutrient Pollution - Dissolved inorganic nitrogen (DIN) in rivers per river basins (risk category) Nitrogen is an important cause of river nutrient pollution caused mainly by agricultural activities (fertiliser use and wastes from livestock), urban wastewater, and atmospheric deposition of nitrogen. Contamination by nutrients (particularly forms of nitrogen and phosphorous) increases the risk of eutrophication in rivers, which can pose a threat to environmental and human health (e.g. algal blooms, decreases in dissolved oxygen, increase in toxins making water unsafe for humans and wildlife, etc.). Five risk categories were developed based on published national river water quality criteria, with a risk factor of 5 being the highest risk for eutrophication and 1 the lowest. This indicator represents annual-scale mean river nitrogen concentration for the entire basin. Output is in kg N per year normalized by basin area. Results output is average for the basin, but most input data sets (for sources of pollutants) are calculated at 0.5 degree grids. River concentrations were then calculated as yield divided by water runoff. Basin area – part of NEWS inputs based on STN30 watershed delineations, presented in km2. This assessment is originally derived for Global NEWS 2 basins (Mayorga et al, 2010; Seitzinger et al, 2010) by assuming that the mean annual concentration at the basin mouth (where the mainstream river drains to the coast or to endorheic terminal points) is representative of river channel concentrations across the basin. -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-7CT6D-HOZMM-HOIKG image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Countries with sustainable consumption and production (SCP) national action plans, or with SCP mainstreamed as a priority (SCP Indicator 19) This indicator enables the quantification and monitoring of a country’s progress in the policy cycle process of binding and non-binding policy instruments aimed at supporting sustainable consumption and production (SCP). This indicator is tracked by MedWaves (UNEP/MAP SCP/RAC) and is part of the Regional Action Plan on Sustainable Consumption and Production (SCP) in the Mediterranean. For more information, please check the SCP Factsheets at: <a href="https://switchmed.eu/policy/indicator-factsheets-on-scp-in-the-mediterranean/"target=_blank>Factsheet n.19 (SCP action plans) </a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-KJWF2-RBLY9-2NLQN image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Biodiversity hotspots (Conservation International, 2016) Version 2016.1. The biodiversity hotspots are regions known to hold especially high numbers of species found nowhere else, yet their remaining habitat combined covers a little more than two percent of Earth's land surface. According to the criteria developed by Myers et al. (2000), a hotspot must meet two thresholds in order to qualify: 1) it must have at least 1500 endemic, native vascular plant species, and 2) it must have already lost at least 70% of its primary, native vegetation. In the updated analysis, Mittermeier et al. (2004) recognize 34 hotspots which together hold 50% of the world's plant species and 42% of all terrestrial vertebrates as endemics. As evidence of their urgency for global conservation, hotspots also hold exceptionally high numbers of threatened vertebrates, including 50% of threatened mammals, 73% of threatened birds and 79% of threatened amphibians as endemics. There are an estimated two billion people living in the hotspots, with 300 million people within less than 10 km of existing protected areas. PURPOSE OF THE DATASET: To show the location and extent of the 35 biodiversity hotspots (highly threatened biogeographic regions with high-biodiversity endemism), as defined by Mittermeier et al. (2011). SUPPLEMENTARY INFORMATION: The ecoregional classification of the World Wildlife Fund-U.S. is the most widely-used bioregional classification system for global scale bioregionalization (Wikramanayake et al. 2002). In order to facilitate analysis, interoperability, and collaboration, we have gone to considerable lengths to ensure that the boundaries of hotspots correspond directly to those of the World Wildlife Fund-U.S. ecoregions (Olson et al. 2001). Thus, our calculations of hotspot extents are based on ecoregional data. In total, the hotspots incorporate 374 ecoregions, 43% of the total. The only significant exceptions to the match between hotspots and ecoregions are in three cases where recent maps update ecoregional boundaries: the Albertine Rift (Plumptre et al. 2003); Maputaland-Pondoland-Albany (van Wyk & Smith 2001); and the Caucasus (Krever et al. 2001). "Hotspots Revisited 2011", Conservation International 2012. -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-ML9PZ-PZ1SI-WVV85 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Lake Names This layer shows the names of the lakes that were sampled during the summer of 2015 for the European Multi Lake Survey -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-BVM6G-LJRIK-SJ3S7 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wind speed at 10m above the ground (m/s) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF)to gather monthly averages of daily averages of wind speed on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-XFT7B-6JP7I-K7B2A image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Projected change in water stress (RCP8.5) 2030 With the goal of producing information for decadal-scale planning, adaptation, and investment, the Aqueduct Water Stress Projections model potential changes in future demand and supply of water over the next 3 decades. Global indicators were developed for water demand (withdrawal and consumptive use), water supply, water stress (the ratio of water withdrawal to supply), and intra-annual (seasonal) variability for the periods centered on 2020, 2030, and 2040 for each of 2 climate scenarios, RCP4.5 and RCP8.5, and 2 shared socioeconomic pathways, SSP2 and SSP3. Source: Luck, M., M. Landis, F. Gassert. 2015. “Aqueduct Water Stress Projections: Decadal projections of water supply and demand using CMIP5 GCMs.” Washington, DC: World Resources Institute. Licence: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution 4.0 International</a> Year of content: 2010-2040 -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-1L2TA-6FXPV-N3QMX image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Water Stress 2040 Business As Usual Water Stress 2040 - Business As Usual -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-87C62-4B90N-795I2 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Armed Conflict Location and Event (ACLED) The Armed Conflict Location & Event Data Project (ACLED) is a disaggregated data collection, analysis, and crisis mapping project. ACLED records the dates, actors, types of violence, locations, and fatalities of all reported political violence and protest events across Africa, South Asia, Southeast Asia, Central Asia, the Caucasus, the Middle East, Europe, and Latin America. Political violence and protest activity includes events that occur within civil wars and periods of instability, public demonstrations, and regime breakdown. ACLED’s aim is to capture the forms, actors, dates, and locations of political violence and protest as it occurs across states. The ACLED team conducts analysis to describe, explore, and test conflict scenarios, and makes both data and analysis free and open to use by the public. This dataset shows conflict events in Africa from 1997 to present. More information available on: <a href="https://www.acleddata.com/" target="_blank">https://www.acleddata.com/</a> -180.0 -90.0 180.0 90.0 MX-KVV-ANS-WBN-QXR-S71:MX-B4DGM-36XSS-CR5CF image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Terrestrial Wilderness Lost (1993 to 2009) This layers shows areas of globally significant wilderness that were lost over the period 1993 - 2009. The data is derived from Terrestrial Wilderness layers. See: <a href="https://www.cell.com/current-biology/abstract/S0960-9822(16)30993-9"target=_blank>Watson JEM, Shanahan DF, Di Marco M, Allan J, Laurance WF, Sanderson EW, et al. Catastrophic Declines in Wilderness Areas Undermine Global Environment Targets. Current Biology. 2016;26: 2929–2934</a>. -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-U7N87-9HLN8-W3ITN image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Contiguous Zone (24 Nautical Mile Limit) The Contiguous Zone (24 Nautical Mile Limit) dataset builds on previous versions of the world's marine boundaries data, and indicates the 24 nautical mile maritime boundary. This dataset also contains digital information about treaties, joint regime, and disputed boundaries. For futher information see: Flanders Marine Institute (2023). Maritime Boundaries Geodatabase: Contiguous Zones (24NM), version 4. Available online at <a href="http://www.marineregions.org/" target="_blank">/www.marineregions.org</a>. <a href="https://doi.org/10.14284/630"target="_blank">https://doi.org/10.14284/630</a> -180.0 -90.0 180.0 90.0 MX-KVV-ANS-WBN-QXR-S71:MX-BUH6I-EQ6X7-LE2OZ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 GEO Mountains Inventory of In Situ Observational Infrastructure v2.0 This layer has been downloaded from the GEO Mountains Inventory of In Situ Observational Infrastructure (https://www.geomountains.org/resources/resources-surveys/inventory-of-in-situ-observational-infrastructure) on November 7th 2022. It is provided under a CC BY 4.0 licence and should be cited as follows: GEO Mountains (2022). Inventory of in situ mountain observational infrastructure, v2.0. DOI: 10.6084/m9.figshare.14899845.v2 . The downloaded .csv has been transformed into a shapefile and uploaded into the WESR-Mountains platform. -180.0 -90.0 180.0 90.0 MX-IXP-F6R-H5U-GA8-DTT:MX-LO5BP-R1N58-62YVQ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total Cloud Cover, (0-1) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of total cloud coverage on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-UY7XZ-71DUW-O64ES image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Water productivity or Water use efficiency change - Period 2005-2020 (SCP Indicator 5) This indicator measures the change in water use efficiency over time. It consists of the change in the ratio of the value added in the volume of water use, over time. It is measured as GDP in constant USD prices divided by annual total water withdrawal. This indicator is tracked by MedWaves (UNEP/MAP SCP/RAC) and is part of the Regional Action Plan on Sustainable Consumption and Production (SCP) in the Mediterranean. For more information, please check the SCP Factsheets at: <a href="https://switchmed.eu/policy/indicator-factsheets-on-scp-in-the-mediterranean/"target=_blank>Factsheet n.5 (Water productivity - use efficiency) </a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-LQEKT-595D2-TQRJW image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Financial services points (HOTOSM) Financial services (points) as provided by Humanitarian OpenStreetMap Team (HOT). -180.0 -90.0 180.0 90.0 MX-SZX-UBT-FVM-R1V-BDJ:MX-WM9SQ-FTYDN-RUC5B image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total Cloud Cover, (0-1) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of total cloud coverage on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-1PYQE-PYKLL-3MAPG image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Mercury sites inventoried in Sierra Leone (UN Environment - November 2018) This dataset shows the mercury sites that have been inventoried in Sierra Leone in 2018 by Juha Ronkainen from UN Environment. -180.0 -90.0 180.0 90.0 MX-IY9-QCF-ILZ-UVO-07Y:MX-5B50G-DD57I-HJHWL image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Main Global Drainage System (TWAP, 2016) This dataset shows the main rivers of countries, as assessed by the Transboundary Water Assessments Programme (TWAP). The <a href="http://twap-rivers.org/">TWAP Rivers Basin</a> (TWAP RB) component is a global assessment of 286 transboundary river basins, aimed at enabling the prioritisation of funds for basins at risk from a variety of issues, covering water quantity, water quality, ecosystems, governance and socio-economics. -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-IDT46-PP3LS-V8JIZ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Bidibidi Refugee Camp Points of Interest (UNHCR, 2019) This data contains -180.0 -90.0 180.0 90.0 MX-WJO-FOV-NNB-1BN-SZN:MX-LUEF3-SYSS2-2KK83 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Uganda County Boundaries (UBoS, 2016) Uganda Counties shapefile produced by Uganda Bureau of Statistics (UBOS) in 2016 in consultation with Ministry of Local Government. Data available to download from <a href="http://ubos.geo-solutions.it/layers/geonode:uganda_counties_2016">Uganda Bureau of Statistics</a> -180.0 -90.0 180.0 90.0 MX-WJO-FOV-NNB-1BN-SZN:MX-HEMTR-XVHS2-SLRBV image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Ecoregions - By Ecoregion Ecoregions are defined as relatively large units of land or water containing a distinct assemblage of natural communities sharing a large majority of species, dynamics, and environmental conditions. The original Terrestrial Ecoregions of the World (TEOW)(see Olsen et al., 2001) was comprised of 867 terrestrial ecoregions, classified into 14 different biomes such as forests, grasslands, or deserts. Ecoregions represent the original distribution of distinct assemblages of species and communities. The revised data shown here – Ecoregions2017 – is unchanged for large sections of Earth's seven biogeographical realms but differs from the original map in four regions: the Arabian Peninsula, some of the desert and drier ecoregions of the African continent, Antarctica, and the southeastern United States. See <a href="https://academic.oup.com/bioscience/article/67/6/534/3102935\" target="_blank">Dinerstein, E., et al. 2017. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. BioScience 67, 534–545.</a> For further information please visit the <a href="https://ecoregions2017.appspot.com/\" target="_blank">RESOLVE Ecoregions2017 appspot</a> Data usage licence: <a href="https://creativecommons.org/licenses/by/4.0/\" target="_blank">CC BY 4.0</a> -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-305PX-2NDC3-NOO27 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Freshwater withdrawal as a proportion of available freshwater resources - Period 1968 - 2021 (MSSD indicator 12) Water stress is constantly increasing in most of the Mediterranean countries. The range of the water stress in the Mediterranean countries is wide: from 10% (even less) in the Balkans to 100% and more in the Southern countries. In Libya, the water stress is over 1634% (more than 16 times the available resources). Similar to SDG Indicator 6.4.2 Source: FAO, Aquastat. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-J9CX9-Y6YST-KFBHB image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Years of schooling in 2022 (MSSD Indicator 2a) The years of schooling have constantly increased since 2010. It slightly decreased in 2022 in some Mediterranean countries (for example Syrian Arab Republic decreased from 9.1 in 2021 to 7.4 in 2022). The difference is 12 years between the Syrian Arab Republic (7.4) and Greece (20). The Mediterranean average (15.1 years ) of years of schooling remains above the global average (8.7 years) and increased by one year between 2021 (14.4) and 2022. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-BL3KS-JUT36-62AK5&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 2b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-Y2AI2-CNBWK-6Y7EX image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Mean Depth (m) No abstract provided -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-5YUOV-E4OQF-PZSUO image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Max Depth (m) No abstract provided -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-CMDYE-INYRT-MMNWJ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Active Fires Assessment (Administrative Level 1) This layer shows the trend of fires occurrence at province level (GADM version 3.6 level 1) from 2003 to present based on MODIS Collection 6 Active Fire Product MCD14ML. The dashboard associated to the layer provides relevant information over 1) the density of fires by Km2 per each administrative unit, 2) the evolution of the monthly cumulated fires per each year, 3) the distribution of fires by land cover type (based on MODIS landcover data per each year) and 3) the number of fires occurring in critical areas such as forests, protected areas (WDPA) and forests in protected areas. The administrative units are identified using GADM version 3.6 polygon layer dissolved at level 1 for provinces. Point data events from NASA were associated to the administrative units and the temporally coherent land cover through a spatial jointure in GRASS GIS. Before the trend is calculated, data were pre-processed in order to obtain the fire density (per km2) of the yearly cumulated fires in each administrative unit. Only the fires attributed to natural events were used for the computation of the trend (only events with "type==0" in NASA's dataset were used if near-real time data had to be used). The computation of the trend is based on the Theil–Sen estimator - a robust method for linear regressions that is insensitive to outliers. The resulting positive and negative values of the slope were then normalized separately to a set of values ranging from -3 (very decreasing trend) to +3 (very increasing trend) using a standard deviation based distribution of the data across different administrative units. Thresholds for normalization were set at 0.1*STD, 0.5*STD and 1*STD where STD is the STD calculated on the positive subset of the slope values for positive values and the STD calculated on the negative subset of the slope values for negative values. Data showing a slope estimate below the first positive and negative threshold are given a value of 0 indicating a stable trend. The same value (0) is also given to those whose trend is not significant using a threshold of 90% (pvalue of Kendall rank correlation coefficient = 0.1). The trend was calculated in R using, especially, the function zyp.sen of the package zyp (for magnitude and intercept of the Sens slope) and MannKendall of the package Kendall (to determine the significative value of the trend through Kendall rank correlation coefficient). -180.0 -90.0 180.0 90.0 MX-CAH-DPJ-WDV-VXO-M4Q:MX-OU7NG-ZNZGA-ZX3K0 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Ecological footprint of the economy in 2022 (MSSD Indicator 1b) In 2022 (last updated data), all Mediterranean countries had ecological deficit meaning that the environment capacity of the region is used up faster than it is renewed. Source: <a href="https://www.obs.planbleu.org/wp-content/uploads/2021/03/SMDD_Dashboard_Version_Mars_2021.pdf"target=_blank> Ecological footprint of the economy in 2017 (MSSD Indicator 1b)</a> See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-ILGL3-3JVRG-BQ7XP&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank> MSSD Indicator 1</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-Y3G96-SAL5S-BDGTB image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Paris Agreement The Paris Agreement is a legally binding international treaty on climate change. It was adopted by 196 Parties at COP 21 in Paris, on 12 December 2015 and entered into force on 4 November 2016. Its goal is to limit global warming to well below 2, preferably to 1.5 degrees Celsius, compared to pre-industrial levels. To achieve this long-term temperature goal, countries aim to reach global peaking of greenhouse gas emissions as soon as possible to achieve a climate neutral world by mid-century. The Paris Agreement is a landmark in the multilateral climate change process because, for the first time, a binding agreement brings all nations into a common cause to undertake ambitious efforts to combat climate change and adapt to its effects. https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-NE9WY-TBVNG-OKD3O image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Myanmar hard to reach village tracts (MIMU 2019) Hard To Reach Village Tracts in Myanmar. The General Administration Department defines the Hard to Reach areas in three categories (Moderate, Hard, Very Hard) according to road accessibility across seasons. -180.0 -90.0 180.0 90.0 MX-WJO-FOV-NNB-1BN-SZN:MX-8S8S0-FNAYW-QQ3CS image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total Cloud Cover, (0-1) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of total cloud coverage on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-S6FU7-6YYWH-6KYXL image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 SDG 15.3.1 - National level [% of degraded land] This map shows the % of land that degraded during the period 2001 to 2015 over total land surface in each country. This dataset has been generated using the SDG 15.3.1 - Land Degradation - Global - 250m which was created based on the Trends.Earth model to asses Land Degradation according to the UN Metadata Guideline for SDG. More information on Trends.Earth: <a href="https://docs.trends.earth/en/latest/for_users/features/landdegradation.html"target=_blank>https://docs.trends.earth/en/latest/for_users/features/landdegradation.html</a> More information on Land Degradation: <a href="https://knowledge.unccd.int/topics/sustainable-development-goals-sdgs/sdg-indicator-1531"target=_blank>https://knowledge.unccd.int/topics/sustainable-development-goals-sdgs/sdg-indicator-1531</a> More information on SDG15.3.1: <a href="https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf"target=_blank>https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf</a> -180.0 -90.0 180.0 90.0 MX-4F8-31V-5DI-FF8-72F:MX-EPNQM-22K3V-F2MJ3 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 City Water Intake (TNC) The City Water Map (CWM) contains information on the water sources for 534 cities internationally. The intake layer shown here includes information about the water intake. or diversion, for these cities, including the type of source the water is taken from. The CWM was used as part of a Science for Nature and People Partnership (SNAPP) working group on Latin America water security. It was also used as part of the Urban Water Blueprint analysis. For further information on the City Water Map please see: <a href="https://www.sciencedirect.com/science/article/pii/S0959378014000880"target="_blank">McDonald, R. I., K. Weber, J. C. Padowski, M. Florke, C. Schneider, P. Green, T. Gleason, S. Eckman, B. Lehner, D. Balk, T. Boucher, G. Grill and M. Montgomery (2014). "Water on an urban planet: Urbanization and the reach of urban water infrastructure." Global Environmental Change 27: 96-105</a>. Access to the complete database can be found via the <a href="https://knb.ecoinformatics.org/#view/doi:10.5063/F1J67DWR"target="_blank">Knowledge Network for Biocomplexity (KNB)</a>. -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-FCOA1-U5EF6-UMYBB image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Animal species in IUCN Red List in 2024 (MSSD indicator 17b) The Mediterranean region is the second largest of 34 biodiversity hotspots in the world. The total number of animal species listed in the IUCN red list (gathering extinct, critically endangered, endangered or vulnerable) has increased in all the Mediterranean countries from 2018 to 2024. Countries with the highest increasing total number of species listed in the IUCN red list are Spain and France with 81 new species listed followed by Greece and Italy with respectively 63 and 66 new species. Greece and Spain are the countries with the most species listed. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-JEOJ1-CRX54-JR3PZ&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 17</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-4W3V5-KI12J-KHZB7 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Uganda District Boundaries (UBoS, 2018) Uganda Counties shapefile produced by Uganda Bureau of Statistics (UBOS) in 2016 in consultation with Ministry of Local Government. Data available for download from Data available to download from <a href=" http://ubos.geo-solutions.it/layers/geonode:uganda_districts_2018">Uganda Bureau of Statistics</a> -180.0 -90.0 180.0 90.0 MX-WJO-FOV-NNB-1BN-SZN:MX-1PV3J-YL69X-6OLM0 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Epilimnetic temperature This layer presents the epilimnetic temperatures of all the lakes that were sampled during the European Multi Lake Survey in summer 2015. The epilimnetic temperature is the averaged temperature over an interpolated temperature profile (0.5cm sampling intervals). The temperature profile extents from the surface layer (approximately at 0.5 cm depth) till the end of the thermocline. The thermocline was defined by each sampling group for each specific sampling location, based on the rate of temperature decrease over increasing depth. The thermocline area was located where a decrease of 1°C per m of depth was recorded. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-G3GQ5-RL0NX-6SLEA image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Sampling Depth (m) No abstract provided -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-KWGSP-EGH9E-VD3OZ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Population with access to safely managed sanitation service in % - Period 2000 - 2022 (MSSD indicator 15) Wide disparities still exist in 2022. Three countries have at least 90% of their population using safely managed sanitation services (Monaco, Israel and Greece) whereas 2 others have around 25% (in Libya and Lebanon). Five countries have at least 80% of their population with access to safely managed sanitation services (Tunisia, Slovenia, Malta, France and Spain). The percentage of population with safely managed sanitation services increased from about 58 % in 2000 to reach about 71,9 % in 2022. Similar to SDG Indicator 6.2.1 -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-HDOBG-2DVXK-F6J47 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Index for Coastal Eutrophication The Index for Coastal Eutrophication Potential (ICEP) is an indicator for the potential of riverine nutrient export to sustain new production of non-diatoms phytoplankton biomass; it is calculated by comparing the N, P and Si loading to the Redfield ratios expressing the requirements of marine diatoms growth. A negative value of the ICEP indicates that Si is present in excess over the other nutrients and would thus indicate a low likelihood of HAB development. Positive values of ICEP indicate an excess of N or P over Si, which may lead to blooms of non-diatom, possibly harmful algae species. The ICEP represents the potential impact of the riverine delivery to the coastal zone. Areas with high chlorophyll-a measurements often correspond to high ICEP values, including the Bay of Bengal, parts of south-east Asia and the Baltic Sea. <a href="http://geonode.iwlearn.org/people/profile/Global_Nutrient_Cycle/?limit=100&offset=0">Source</a> -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-V9NAM-UVB5T-FGULJ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Domestic credit to private sector by banks in 2023 (MSSD Indicator 25) In the Mediterranean region, the share of domestic credit allocated to the private sector in 2023 and trends over time varied across countries, from 12% in Libya to 108% in France. From 2020 to 2023 the mean value of domestic credits for all mediterranean countries constantly decreased from 57.8% to 51.2%. From 2020 to 2023, Türkiye decreased by 34 percentage points while Palestine increased by 19 percentage points. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-7O4JN-M4XCG-T6TO2 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Mineral Operations Outside the United States (USGS, 2010) Mineral facilities and operations outside the United States compiled by the National Minerals Information Center of the USGS. This representation combines source data from five previous publications. -180.0 -90.0 180.0 90.0 MX-ZU2-F8I-ZVS-JCK-FBQ:MX-V7AY6-OCTNO-ZB4FV image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Air temperature 2m above the lake surface (in Kelvin) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of air temperature at 2m above the ground on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-8DBRD-TBOLL-AAUWC image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Domestic material consumption in 2024 (MSSD Indicator 23) The total domestic material consumption in all the mediterranean countries have constantly increased from 2020 to 2024 (+18,2%). From 2020 to 2024, tons of domestic material consumption have increased most in Egypt (+27%), Palestinia (+25%), Türkiye (+22%) and Lebanon (+20%) and less in Syria (+3.5%), Greece (+4.7%), Bosnia & Herzegovina (+6%) and Tunisia (+7%). Similar to SDG Indicator 12.2.2 See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-ZNENU-BP0Z6-25GWF&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 23b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-KL9ZE-7XHZS-13F1W image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Vulnerability to Effects of Climate Change on Fisheries Index for RCP 2.6 (2016-2050) The Fishery Vulnerability Index was developed by Blasiak et al., with support from the World Climate Research Programme, and calculates the vulnerability of fishing markets from the effects of climate change on fisheries around the world, using the Intergovernmental Panel on Climate Change (IPCC) definition of vulnerability as a function of exposure, sensitivity, and adaptive capacity. Data on global sea surface anomaly predictions according to various RCP (Representative Concentration Pathways) scenarios are combined with various socioeconomic data sets that illustrate the sensitivity of countries' fisheries (i.e., number of fishers, fisheries export as percentage of total, proportion of the economically active population (EAP) working as fishers, total fisheries landings, nutritional dependence) and overall adaptive capacity of the country's population/government/fishing industry (i.e., healthy life expectancy, literacy rate + school enrollment, governance, gross domestic product per capita, subsidies per landed monetary volume, proportion of industrial to small-scale fisheries) to determine the overall vulnerability of national fisheries. 147 coastal countries have an index score for different scenarios for 2016-2050 and 2066-2100. Source: SU/UTokyo/JCU/UWaterloo License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution 4.0 International</a> -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-38FDX-UJFEC-Z7SP5 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Eutrophication and hypoxia Full dataset of eutrophic and hypoxic coastal areas. The Interactive Map of Eutrophication & Hypoxia represents 762 coastal areas impacted by eutrophication and/or hypoxia. There are 479 sites identified as experiencing hypoxia, 55 sites that once experienced hypoxia but are now improving, and 228 sites that experience other symptoms of eutrophication, including algal blooms, species loss, and impacts to coral reef assemblages. These data were compiled using a literature search conducted by Dr. Robert Diaz of VIMS and WRI staff. Because this map depends on available data, geographic areas with more data availability (such as the United States) may show relatively more problem areas compared to areas with less data. -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-24ADN-L9LXL-H8FR8 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 World Glacier Inventory WGI The World Glacier Inventory (WGI) database is a snapshot of the glacier distribution in the second half of the 20th century as point shapefile. -180.0 -90.0 180.0 90.0 MX-IXP-F6R-H5U-GA8-DTT:MX-0YBAS-SDMOU-WAHBI image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Water withdrawal per capita in 2021 (MSSD indicator 13) From 2002 to 2019, only 40 % of the Mediterranean countries have increased their water productivity. In 2019, 5 countries were over 100 dollars per cubic meter whereas 12 countries remained under 50 dollars. The graph shows values for 2021 according to the 3 uses: municipal, agricultural, industrial. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-RPRNK-WBFMY-4AUBD image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Last of the Wilds (2009) This layer shows a regionally representative map of wilderness following the well-established ‘Last of the Wild’ methodology. It identifies the 10% area with the lowest human pressure within each of Earth’s 60 biogeographic realms and identifies the ten largest contiguous areas within each realm, along with all contiguous areas > 10,000 km2. See: <a href="https://www.nature.com/articles/sdata2017187"target=_blank>Allan, J.R., Venter, O. & Watson, J.E.M., 2017. Temporally inter-comparable maps of terrestrial wilderness and the Last of the Wild. Scientific Data, 4, p.170187</a>. -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-HUIAM-GGZ5R-0A2XF image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Air temperature 2m above the lake surface (in Kelvin) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of air temperature at 2m above the ground on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-IVAST-HG3NO-KOJMW image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Waste production per capita in 2019 (MSSD indicator 20) In 2016, the overall situation is mixed: high waste generation is the North, high food part in the South and a low recycling rate at regional level. In 2019, in the Northern countries, the range of value is from 1.1 to 1.9 kg/cap/day. In the southern countries, the amount generated is from 0.5 kg/cap/day in Morocco to 0.9 in Lebanon. The highest values are for Cyprus and Israel and above EU countries average (1,34 kg/cap/day) with 1,76 kg/cap/day. In the Northern countries (inc. Israel), the percentage of Food & Organic waste is between 25% and 60% while this rate in the Southern countries is from 49 % in The State of Palestine to 70% in Libya). In 2020, the recycling rate is also widely varying. In the northern countries, the recycling rate raises to 46% in Slovenia, except in Bosnia and Herzegovina with a rate close to 0 (the rate for Israel is 25%). France, Italy and Greece are around 20 to 25%. Croatia, Spain and Cyprus are around 10-17%. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-ZHJBN-BSC20-E03N2 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Marine plastics: non-adjusted plastic concentration (items per km2) This dataset is a compiliation of the results from 10 research monitoring plastic concentration in the oceans through in situ sampling (Suaria et al. 2016; Goldstein et al. 2012; Doyle et al. 2011: Law et al. 2010; Law et al. 2014; Eriksen et al. 2014; Cozar et al. 2015; Cózar et al. 2014; Ruiz-Orejón et al 2016; Collignon et al. 2012). The column PC_number_km2 features a non-adjusted plastic concentration (items per km2); the column PC_g_km2 provides the weight of plastic: Non-adjusted Plastic concentration (g per km2). The values are not adjusted for waves height or winds. -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-OKO9U-BTOBK-8NF05 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Altitude This layer shows the altitude of the sampled lakes -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-P1SGY-XTTBU-4SHKM image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wadi El Ku Basin Boundaries (UN Environment) This map shows the boundaries of the Wadi El Ku basin in Darfur Sudan. -180.0 -90.0 180.0 90.0 MX-M5H-L8I-1AL-ZSQ-HXD:MX-1M2ZP-5GW6S-T2AFB image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total capture - state in 2022 (MSSD Indicator 8) In the Mediterranean, the total capture decreased from 801,000t to 746,000t between 2019 and 2020 and then increased to 971,587t in 2022. Among the 10 major fishing countries in the Mediterranean Sea (annual catch > 20,000 tonnes), Türkiye has seen the largest increase in tons (+280 %) in two years (2020-2022), while Morocco has seen the largest decrease (-39 %). Half of the Mediterranean countries caught less than 5,400t in 2022, while Türkiye captured more than 300,000t, Italy and Tunisia about 130,000t and Algeria 80,000t. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-L11IN-ZOYAG-AT69N&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 8b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-AHA05-HEIU6-QJNNX image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Air temperature 2m above the lake surface (in Kelvin) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of air temperature at 2m above the ground on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-3HTFL-RYCKM-GUHNO image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Ongoing projects in each locality in Sudan Shows the ongoing projects in each locality in Sudan. Locality name, State of the locality, Project name, Project desciption, Agency, Partner, Donor, Budget, Funding, Timing are in the attribute tables to show. Size of the point shows the total number of the projects. -180.0 -90.0 180.0 90.0 MX-M5H-L8I-1AL-ZSQ-HXD:MX-N39JY-TI9FP-Y5AKL image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Domestic material consumption per capita - Period 2000 - 2024 (MSSD Indicator 23b) From 2020-2024, domestic material consumption per capita has constantly increased in all mediterranean countries (+12.8 points). In most countries, DMC per capita increased from 10 to 20%. In Israel, Greece, Libya, Tunisia and Algeria, DMC per capita have sightly increased (between 3% to 9%). From 2020 to 2024 Syria is the only country where DMC per capita have decreased (-11%) while in Lebanon domestic material consumption per capita has increased significantly (+30%). In the EU Med countries in 2019, the DMC - total or no breakdown - per constant 2015 US dollars is lower than 1. It decreased about 0.04 points compared to the situation in 2017. It is between 1 and 2.8 in most southern countries and reached 2.8 and 2.6 respectively in The Syrian Arab Republic and Bosnia & Herzegovina. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-KL9ZE-7XHZS-13F1W&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 23</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-ZNENU-BP0Z6-25GWF image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Urban population living in informal settlements, or inadequate housing in 2020 (MSSD indicator 18b) The view shows the number of inhabitants living in slums in thousands per inhabitants. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-VBTLZ-PJR92-4UHA7&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 18</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-OVAIL-2FYXG-AX6B9 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wastewater Pollution Indicator - Basin level aggregated BCUs (weighted scores based on population and area) - EPI (Environmental Performance Index) The Wastewater Pollution indicator measures the estimated levels of wastewater treatment in Basin Country Units (based on national data), rather than absolute volumes of wastewater polluting waterways. This gives an indication of the risks from pathogens which may be highly relevant to vulnerable populations at local scales. The scores are aggregated at basin-scale to give an indication of threats stemming from poor wastewater treatment performance on a basin level. This indicator is largely based on data and methodology from the Wastewater Treatment Performance indicator developed by the EPI (Environmental Performance Index) team at The Yale Center for Environmental Law & Policy (Malik et al. 2015). This indicator compiles wastewater treatment statistics for 183 countries and was deemed to be the most comprehensive, up to date data source available. EPI Wastewater Treatment Performance Indicator is based on two metrics: wastewater treatment and connection rate (Malik et al. A global indicator of wastewater treatment to inform the Sustainable Development Goals (SDGs), Environmental Science & Policy, Volume 48, April 2015, Pages 172–185). Wastewater is defined as “water that has been used by households, industries, and commercial establishments that, unless treated, no longer serves a useful purpose and may contain contaminants. The EPI Wastewater Treatment Performance indicator is based on two variables: a) wastewater treatment - the amount of wastewater that is treated within a country relative to the amount of wastewater that is collected, generated, or produced; b) connection rate – the population connected to municipal sewerage systems relative to the population living in the country. The indicator assesses national wastewater treatment performance, normalizing treatment scores by the population connected to municipal sewerage systems, using following calculation: “wastewater treatment level x connection rate“ Underlying data sources: - Pinsent Masons Water Yearbook (2013) - United Nations Statistics Division (2011) - OECD (2013) - FAO (2013) - National level data gap-filled from reports and on national statistics websites -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-DI35S-NR0FC-SK68E image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wind speed at 10m above the ground (m/s) We used to the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF)to gather monthly averages of daily averages of wind speed on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-B6RWG-ECAY7-4BG71 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Energy intensity measured in terms of primary energy and GDP 2000 - 2021 (MSSD Indicator 22) In the Mediterranean countries, the energy intensity and the share of renewable energy are improving at different speeds. In 2021, the average energy intensity of the Mediterranean countries (3.3 MJ megajoules per GDP constant 2017 PPP dollar) was above the European average (3.1 MJ). However, disparities among countries remain significant, even among countries with equivalent income levels. Energy intensity in Bosnia and Herzegovina, Algeria and Libya is over 5 MJ while it is lower than 1.21 MJ in Malta. Similar to SDG Indicator 7.3.1 See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-39JDD-KUS3L-JEC20&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 22b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-7BEES-FHLZ5-ABHAE image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Youth literacy rate in 2021 (MSSD Indicator 4) The Mediterranean average of literacy rate of young adults is 98% and above the world average (92%). Since 1990, the literacy rate of young adults has increased significantly in all of the southern and eastern Mediterranean countries and is satisfactory in most of the northern Mediterranean countries. This indicator is no longer a priority criterion put forward by UNESCO. Only 9 Mediterranean countries are covered by this indicator in 2021, whereas in 2019 the data existed for 20 countries. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-UA3HH-4J15U-HT2RO image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 ASGM sites in Eastern DRC - Dashboard Please click on the ASGM site of interest to display summary site information -180.0 -90.0 180.0 90.0 MX-IY9-QCF-ILZ-UVO-07Y:MX-W8SQB-SM644-QVQXV image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Air temperature 2m above the lake surface (in Kelvin) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of air temperature at 2m above the ground on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-OMPJZ-CPFGL-KT1S8 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Nutrient Pollution - Dissolved inorganic phosphorous (DIP) in rivers per river basins (risk category) Phosphorous is an important cause of river nutrient pollution caused mainly by agricultural activities (fertiliser use and wastes from livestock), urban wastewater, and atmospheric deposition of nitrogen. Contamination by nutrients (particularly forms of nitrogen and phosphorous) increases the risk of eutrophication in rivers, which can pose a threat to environmental and human health (e.g. algal blooms, decreases in dissolved oxygen, increase in toxins making water unsafe for humans and wildlife, etc.). Five risk categories were developed based on published national river water quality criteria, with a risk factor of 5 being the highest risk for eutrophication and 1 the lowest. This indicator represents annual-scale mean river phosphorous concentration for the entire basin. Output is in kg P per year normalized by basin area. Results output is average for the basin, but most input data sets (for sources of pollutants) are calculated at 0.5 degree grids. River concentrations were then calculated as yield divided by water runoff. Basin area – part of NEWS inputs based on STN30 watershed delineations, presented in km2. This assessment is originally derived for Global NEWS 2 basins (Mayorga et al, 2010; Seitzinger et al, 2010) by assuming that the mean annual concentration at the basin mouth (where the mainstream river drains to the coast or to endorheic terminal points) is representative of river channel concentrations across the basin. -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-GAAFB-ITOCW-HXTK8 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Carbon dioxide emissions per capita in 2023 (MSSD Indicator 21) The rise in CO2 emissions from 2000 to 2023 was higher than the national objectives in most countries. Since 2020, CO2 emissions have increased in most of the Mediterranean countries except, Lebanon (-13%), Syria (-13%), Slovenia (-12%), Israel (-4%), France and Montenegro (-3%), and Palestinia (-2%). The COVID-19 period contributed to reducing C02 emissions but since 2020 the trend is again rising notably in Libya (+72%), Tunisia (+12.4%) and Albania (+10%). See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-ZNRPT-FF8OG-CK5UA&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 21b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-SDKP9-ZDXF8-4CBU0 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Health facilities polygons (HOTOSM) Health facilities (polygons) as provided by Humanitarian OpenStreetMap Team (HOT). -180.0 -90.0 180.0 90.0 MX-SZX-UBT-FVM-R1V-BDJ:MX-23IHF-ZMRTR-MAUGT image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Adoption and implementation for public access to information in 2023 (MSSD Indicator 27) The adoption and implementation for public access to information is heterogeneous is the Mediterranean. 8 countries (HR, GR, ES, SL, CY, MT, IT, AL) of the northern Mediterranean have ratified the Aarhus convention from 2001 to 2007 whereas very no data is available for 5 countries. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-7YCJJ-VSDMZ-IEVPB image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Terrestrial Wilderness (1993) Terrestrial wilderness is derived from the Human Footprint (terrestrial) for years 1993 and 2009. Wilderness is defined as contiguous areas larger than 10,000 km sq where HFP values are less than 1. As such, the definition of wilderness doesn't preclude human presence in an area, only more significant impacts often resultant from increased human presence. Datasets present here include global wilderness areas in 1993, 2009, and a map of the wilderness areas present in 1993 than were lost by 2009. See: <a href="https://www.cell.com/current-biology/abstract/S0960-9822(16)30993-9"target=_blank>Watson JEM, Shanahan DF, Di Marco M, Allan J, Laurance WF, Sanderson EW, et al. Catastrophic Declines in Wilderness Areas Undermine Global Environment Targets. Current Biology. 2016;26: 2929–2934</a>. -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-XBBSX-D9U0Z-VPNDD image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Gross enrolment in education in 2020 (MSSD Indicator 5) In 2018, the enrolment rate in primary education is over 99% in most Mediterranean countries except in The State of Palestine (98.6%), Türkiye (94.9%) and Croatia (94.6%). In secondary education the gross enrolment rate is over 95% in 11 countries. In 2020 the terminology changed : from “primary, secondary, tertiary” to early “childhood, pre-primary, tertiary”. The analysis is not possible. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-WK84M-GGQTQ-2CX36 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Territorial Sea (12 Nautical Mile Limit) The Territorial Sea (12 Nautical Mile Limit) dataset builds on previous versions of the world's marine boundaries data, and indicates the 12 nautical mile maritime boundary. This dataset also contains digital information about treaties, joint regime, and disputed boundaries. For futher information see: Flanders Marine Institute (2023). Maritime Boundaries Geodatabase: Territorial Seas (12NM), version 4. Available online at <a href="http://www.marineregions.org/" target="_blank">/www.marineregions.org</a>. <a href="https://doi.org/10.14284/633" target="_blank">https://doi.org/10.14284/633</a> -180.0 -90.0 180.0 90.0 MX-KVV-ANS-WBN-QXR-S71:MX-7FZV4-9DY3K-PZCNG image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Public expenditure on research and development in 2022 (MSSD Indicator 26) On average, Mediterranean countries spent 1.65% of their GDP on research and development in 2020 (1.3% in 2019) but it sightly decreasing reaching 1.53% in 2022. The expenditure in EU-mediterranean countries is about 1.36% of GDP in 2022 (1.15% in 2018). These expenditures are increasing quite constantly (+0.12% per year). But data of 2 countries with low rates is not considered. In Israel, Research and Development expenditure is 6% of GDP in 2022 (4.79% in 2018), this percentage is between 1% and 2.5% in Croatia, Greece, Italy, Slovenia, Spain, Türkiye and Egypt. In the other Mediterranean countries it is less than 1%. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-CT9J0-CNCXP-FKV0V image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Global Food Security Index Global score - Period 2012 - 2022 (MSSD Indicator 11) In most Mediterranean countries (except in France, Spain, Israel, Italy, Greece), food affordability rates are higher than availability rate. In these countries, improving food production and farmers income is necessary for better food security. On the contrary, where food affordability is lower, improving employment and income should have a positive effect on food security. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-QJL46-F54J5-1H3ZM image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Carbon dioxide emissions per energy use 2016 (MSSD Indicator 21b) Co2 emissions from fossil fuel continue to rise in most Mediterranean countries. The rise in CO2 emissions from 2000 to 2016 was higher than the national objectives in most countries except in France. From 2000 to 2016, CO2 emissions from energy have decreased mainly in Italy, France, and the Syrian Arab Republic and increased mainly in Turkey, Egypt, and Algeria. Source: <a href="https://www.obs.planbleu.org/wp-content/uploads/2021/03/SMDD_Dashboard_Version_Mars_2021.pdf"target=_blank>MSSD Dashboard</a> See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-SDKP9-ZDXF8-4CBU0&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 21</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-ZNRPT-FF8OG-CK5UA image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total Phosphorus (mg/L) This map includes the analysed Total Phosphorus data of all the EMLS samples. The nutrient analysis was performed in the section of Aquatic Ecology and Water Quality Management in University of Wageningen by the group of Dr. Miquel Lürling and Wendy Beekman-Lukasssen. The samples were analysed using a Skalar SAN+ segmented flow analyser (Skalar Analytical BV, Breda, NL) with UV/persulfate destruction integrated in the system. The protocol of the analysis can be found in the documents. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-8RQQD-MW7E4-7W7CE image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Water Stress 2020 Business As Usual Water stress 2020 - Aqueducts -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-0LXTJ-83KQ9-TK194 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total Nitrogen (mg/L) This map includes the analysed Total Nitrogen data of all the EMLS samples. The nutrient analysis was performed in the section of Aquatic Ecology and Water Quality Management in University of Wageningen by the group of Dr. Miquel Lürling and Wendy Beekman-Lukasssen. The samples were analysed using a Skalar SAN+ segmented flow analyser (Skalar Analytical BV, Breda, NL) with UV/persulfate destruction integrated in the system. The protocol of the analysis can be found in the documents (NNI 1990) -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-BOZQL-KG6SB-GOGPZ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total Cloud Cover, (0-1) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of total cloud coverage on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-ZEKQW-BMF95-ALHIY image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wind speed at 10m above the ground (m/s) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF)to gather monthly averages of daily averages of wind speed on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-6L59P-3EGUX-SQRDC image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Terrestrial Wilderness (2009) Terrestrial wilderness is derived from the Human Footprint (terrestrial) for years 1993 and 2009. Wilderness is defined as contiguous areas larger than 10,000 km sq where HFP values are less than 1. As such, the definition of wilderness doesn't preclude human presence in an area, only more significant impacts often resultant from increased human presence. Datasets present here include global wilderness areas in 1993, 2009, and a map of the wilderness areas present in 1993 than were lost by 2009. See: <a href="https://www.cell.com/current-biology/abstract/S0960-9822(16)30993-9"target=_blank>Watson JEM, Shanahan DF, Di Marco M, Allan J, Laurance WF, Sanderson EW, et al. Catastrophic Declines in Wilderness Areas Undermine Global Environment Targets. Current Biology. 2016;26: 2929–2934</a>. -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-A6C0Y-HPIQY-GZU3K image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Vulnerability to Effects of Climate Change on Fisheries Index for RCP 4.5 (2016-2050) The Fishery Vulnerability Index was developed by Blasiak et al., with support from the World Climate Research Programme, and calculates the vulnerability of fishing markets from the effects of climate change on fisheries around the world, using the Intergovernmental Panel on Climate Change (IPCC) definition of vulnerability as a function of exposure, sensitivity, and adaptive capacity. Data on global sea surface anomaly predictions according to various RCP (Representative Concentration Pathways) scenarios are combined with various socioeconomic data sets that illustrate the sensitivity of countries' fisheries (i.e., number of fishers, fisheries export as percentage of total, proportion of the economically active population (EAP) working as fishers, total fisheries landings, nutritional dependence) and overall adaptive capacity of the country's population/government/fishing industry (i.e., healthy life expectancy, literacy rate + school enrollment, governance, gross domestic product per capita, subsidies per landed monetary volume, proportion of industrial to small-scale fisheries) to determine the overall vulnerability of national fisheries. 147 coastal countries have an index score for different scenarios for 2016-2050 and 2066-2100. Source: SU/UTokyo/JCU/UWaterloo License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution 4.0 International</a> -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-GZGBG-FR91E-WB86C image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Ratifications and level of compliance as reported by Barcelona Convention Contracting Parties in 2023 (MSSD Indicator 6) The Convention for the Protection of the Mediterranean Sea Against Pollution was adopted on 16 February 1976 and entered into force on 12 February 1978. The original Convention has been modified by amendments and the “Convention for the Protection of the Marine Environment and the Coastal Region of the Mediterranean” was adopted on 10 June 1995 and entered into force on 9 July 2004. Disparities exist between countries regarding the ratification of protocols. Morocco and the Syrian Arab Republic are the 2 contracting parties which have at least ratified the Barcelona Convention and all its protocols. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-7X0B0-IXLN3-IXQ27 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 GMBA v2 0 standard basic This is the "basic" layer of the GMBA v2.0 standard version, which is the main version of the GMBA Inventory v2.0, where the polygons represent actual (only slightly generalized) mountain ranges (according to the GMBA Definition v2.0). It contains a subset of 6717 ('Standard') smallest non-overlapping mapping units of the inventory, i.e. those having no 'child' sub-range -180.0 -90.0 180.0 90.0 MX-IXP-F6R-H5U-GA8-DTT:MX-0RWKE-VEHER-7SDGS image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Air temperature 2m above the lake surface (in Kelvin) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of air temperature at 2m above the ground on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-T4G3M-YOPBF-IJH6I image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Number of policies to address nutrient pollution This policy database provides an overview of various policy instruments around the globe that have been implemented to address nutrient pollution. The database includes a wide range of policies that decision-makers can reference when considering options and approaches to reduce nutrient losses and can be searched by policy category, policy type, region, country and/or sector. Policies are categorized as follows (policy taxonomy borrowed from Greenhalgh & Selman 2014 ): -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-FH320-5LBJV-EY910 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Secchi Depth (m) No abstract provided -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-NBRBB-AQ1AF-2KSKD image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Vulnerability to Effects of Climate Change on Fisheries Index for RCP 8.5 (2016-2050) The Fishery Vulnerability Index was developed by Blasiak et al., with support from the World Climate Research Programme, and calculates the vulnerability of fishing markets from the effects of climate change on fisheries around the world, using the Intergovernmental Panel on Climate Change (IPCC) definition of vulnerability as a function of exposure, sensitivity, and adaptive capacity. Data on global sea surface anomaly predictions according to various RCP (Representative Concentration Pathways) scenarios are combined with various socioeconomic data sets that illustrate the sensitivity of countries' fisheries (i.e., number of fishers, fisheries export as percentage of total, proportion of the economically active population (EAP) working as fishers, total fisheries landings, nutritional dependence) and overall adaptive capacity of the country's population/government/fishing industry (i.e., healthy life expectancy, literacy rate + school enrollment, governance, gross domestic product per capita, subsidies per landed monetary volume, proportion of industrial to small-scale fisheries) to determine the overall vulnerability of national fisheries. 147 coastal countries have an index score for different scenarios for 2016-2050 and 2066-2100. Source: SU/UTokyo/JCU/UWaterloo License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution 4.0 International</a> -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-PND06-SIUL7-M93DN image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Nutrient Pollution (N & P) The river Nutrient Pollution indicator is divided into two sub-indicators corresponding to nitrogen (N) and phosphorus (P) nutrient forms representative of water quality impairment: dissolved inorganic N and P (DIN, DIP). Each sub-indicator represents annual-scale mean river nutrient concentration for the entire basin. This assessment is originally derived for Global NEWS 2 basins (Mayorga et al, 2010; Seitzinger et al, 2010) by assuming that the mean annual concentration at the basin mouth (where the mainstem river drains to the coast or to endorheic terminal points) is representative of river channel concentrations across the basin. Concentration values for TFDD river basins are calculated based on spatial intersection with Global NEWS 2 basins, as described under ͆Computation͇. The Global NEWS 2 model run used here is referred to as the Realistic Hydrology Model Run for reference year 2000 (RH2000), and corresponds to near-contemporary conditions using the year 2000 as a reference for all basin model inputs and forcings (Mayorga et al, 2010). RH2000 is based on observed climate forcings and river discharge corrections from river gage observations, and was developed to represent contemporary conditions more realistically than the year-2000 reference model run used in the Millennium Ecosystem Scenarios (MEA) assessment presented in Seitzinger et al. (2010) and related publications. Relative risk category. The category are based on combined concentration of N & P in mg N/L or mg P/L. The details are provided in the description document. -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-81SMR-G2KFE-7F5OR image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Toxic Sites Identification Program - Tanzania (PureEarth, 2018) This view shows the work of Pure Earth in Tanzania in the Toxic Sites Identification Program (TSIP). Pure Earth’s work in Tanzania began in 2007. To date, 187 sites have been assessed in Mbeya, Dar es Salaam, Arusha, Kilimanjaro, Geita, Mwanza regions among. The majority of identified sites are found in areas where mining activities are carried out, namely in the Northern and Western regions of the country (Mbeya, Geita, Shinyanga and Mwanza). -180.0 -90.0 180.0 90.0 MX-6PV-1R2-R94-RTF-NZO:MX-CFJ0C-U6WOC-PHU49 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Global Reservoir and Dam (GRanD) database (SEDAC CIESIN, 2011) The Global Reservoir and Dam Database, Version 1 (Revision 01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data. More information on: <a href="http://sedac.ciesin.columbia.edu/data/set/grand-v1-dams-rev01" target="_blank">http://sedac.ciesin.columbia.edu/data/set/grand-v1-dams-rev01</a> -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-CS2DD-OY1F7-QLE5W image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 ASGM regions in countries that have submitted a NAP ASGM-regions-30-countries.shp -180.0 -90.0 180.0 90.0 MX-IY9-QCF-ILZ-UVO-07Y:MX-CIKHM-IMQJC-2APYD image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Land share of organic farming - Period 2000 - 2023 (MSSD indicator 16c) The share of agricultural land used by organic farming is rising in most Mediterranean countries. In 2023, in Italy, this share reached up to more than 18.5 % while it is lower than 4% in 4 countries and even less than 1% in 8 countries. Italy, Slovenia, Spain and Greece are in the first positions in the Mediterranean region for their share of organic farming, and ranked respectively 9th, 15th, 19th and 20th worldwide in terms of proportion of agricultural land used for organic farming. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-5BWL4-CH1P4-27GZ2&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 16</a> <a href="https://app.mapx.org/static.html?language=en&views=MX-T6Q0U-3BSFE-KMDJV&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 16b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-9282A-KNM3B-NAGIY image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total Cloud Cover, (0-1) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of total cloud coverage on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-Q3ATS-NOTPO-S6DC0 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Potential for Tropical Forest Carbon Sequestration - Catchment (NatureServe) This indicator represents the amount of carbon that could be sequestrated (i.e., prevented from entering the atmosphere as carbon dioxide) if deforestation were halted in tropical forests. Higher values indicate that a greater amount of carbon (measured as CO2 equivalents/km2) per unit area could potentially be sequestrated if deforestation were halted in tropical forests, calculated at the river catchment scale. Limitations and caveats: Note that the carbon biomass data from which this indicator is derived only covers the tropics; for areas at the tropical boundary the results only apply to the portion of the region, country, or basin occurring inside tropical region. This indicator provides an estimate of "potential" carbon sequestration when deforestation is completely halted. It is assumed that when deforestation is stopped, the amount of carbon sequestration potential is the same with the same area of existing forest. In reality, the carbon stored in the re-growing forest may be higher or lower, depending on the time frame considered. For more information please visit the <a href="http://dashboard.natureserve.org/metadata/potential-carbon-sequestration"target=_blank>NatureServe Dashboard</a> or <a href="http://www.natureserve.org/"target=_blank>natureserve.org</a>. -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-70XIE-6UJVD-QQS7J image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wastewater pollution indicator - Country level BCU (weighted scores based on population and area) - EPI (Environmental Performance Index) The Wastewater Pollution indicator measures the estimated levels of wastewater treatment in Basin Country Units (based on national data), rather than absolute volumes of wastewater polluting waterways. This gives an indication of the risks from pathogens which may be highly relevant to vulnerable populations at local scales, and give an indication of threats stemming from poor wastewater treatment performance on a country level. This indicator is largely based on data and methodology from the Wastewater Treatment Performance indicator developed by the EPI (Environmental Performance Index) team at The Yale Center for Environmental Law & Policy (Malik et al. 2015). This indicator compiles wastewater treatment statistics for 183 countries and was deemed to be the most comprehensive, up to date data source available. EPI Wastewater Treatment Performance Indicator is based on two metrics: wastewater treatment and connection rate (Malik et al. A global indicator of wastewater treatment to inform the Sustainable Development Goals (SDGs), Environmental Science & Policy, Volume 48, April 2015, Pages 172–185). Wastewater is defined as “water that has been used by households, industries, and commercial establishments that, unless treated, no longer serves a useful purpose and may contain contaminants. The EPI Wastewater Treatment Performance indicator is based on two variables: a) wastewater treatment - the amount of wastewater that is treated within a country relative to the amount of wastewater that is collected, generated, or produced; b) connection rate – the population connected to municipal sewerage systems relative to the population living in the country. The indicator assesses national wastewater treatment performance, normalizing treatment scores by the population connected to municipal sewerage systems, using following calculation: “wastewater treatment level x connection rate“ Underlying data sources: - Pinsent Masons Water Yearbook (2013) - United Nations Statistics Division (2011) - OECD (2013) - FAO (2013) - National level data gap-filled from reports and on national statistics websites -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-NGS7E-T4B7P-7NCNA image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Total Cloud Cover, (0-1) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of total cloud coverage on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-KPXDB-79ZT8-O5FFN image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Potential for Tropical Forest Carbon Sequestration - Country (NatureServe) This indicator represents the amount of carbon that could be sequestrated (i.e., prevented from entering the atmosphere as carbon dioxide) if deforestation were halted in tropical forests. Higher values indicate that a greater amount of carbon (measured as CO2 equivalents/km2) per unit area could potentially be sequestrated if deforestation were halted in tropical forests, calculated at the national scale. Limitations and caveats: Note that the carbon biomass data from which this indicator is derived only covers the tropics; for areas at the tropical boundary the results only apply to the portion of the region, country, or basin occurring inside tropical region. This indicator provides an estimate of "potential" carbon sequestration when deforestation is completely halted. It is assumed that when deforestation is stopped, the amount of carbon sequestration potential is the same with the same area of existing forest. In reality, the carbon stored in the re-growing forest may be higher or lower, depending on the time frame considered. For more information please visit the <a href="http://dashboard.natureserve.org/metadata/potential-carbon-sequestration"target=_blank>NatureServe Dashboard</a> or <a href="http://www.natureserve.org/"target=_blank>natureserve.org</a>. -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-M4JJA-OI6N0-HCAYK image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Mean exploitation ratio by GFCM sub area (MSSD Indicator 8b) The percentage of stocks in overexploitation was around 65% in 2008. It culminated to around 88% from 2012 to 2014 and was close to 75% in 2018. Moreover, an increase in biomass levels is observed in 6 out of 18 stocks, and 46% of assessed stocks now have high relative biomass. A corner is finally being turned on overexploitation of fish stocks in the region's vital fisheries. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-AHA05-HEIU6-QJNNX&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 8</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-L11IN-ZOYAG-AT69N image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Financial services polygons (HOTOSM) Financial services (polygons) as provided by Humanitarian OpenStreetMap Team (HOT). -180.0 -90.0 180.0 90.0 MX-SZX-UBT-FVM-R1V-BDJ:MX-BFXWH-GIRKD-OIHY9 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Active Fires Assessment (Administrative Level 0) This layer shows the trend of fires occurrence at country level (UN_0 UN Cartographic Section level 0) from 2003 to present based on MODIS Collection 6 Active Fire Product MCD14ML. The dashboard associated to the layer provides relevant information over 1) the density of fires by Km2 per each administrative unit, 2) the evolution of the monthly cumulated fires per each year, 3) the distribution of fires by land cover type (based on MODIS landcover data per each year) and 3) the number of fires occurring in critical areas such as forests, protected areas (WDPA) and forests in protected areas. The administrative units are identified using UN Cartographic Section's UN_0 polygon layer for countries. Point data events from NASA were associated to the administrative units and the temporally coherent land cover through a spatial jointure in GRASS GIS. Before the trend is calculated, data were pre-processed in order to obtain the fire density (per km2) of the yearly cumulated fires in each administrative unit. Only the fires attributed to natural events were used for the computation of the trend (only events with "type==0" in NASA's dataset were used if near-real time data had to be used). The computation of the trend is based on the Theil–Sen estimator - a robust method for linear regressions that is insensitive to outliers. The resulting positive and negative values of the slope were then normalized separately to a set of values ranging from -3 (very decreasing trend) to +3 (very increasing trend) using a standard deviation based distribution of the data across different administrative units. Thresholds for normalization were set at 0.1*STD, 0.5*STD and 1*STD where STD is the STD calculated on the positive subset of the slope values for positive values and the STD calculated on the negative subset of the slope values for negative values. Data showing a slope estimate below the first positive and negative threshold are given a value of 0 indicating a stable trend. The same value (0) is also given to those whose trend is not significant using a threshold of 90% (pvalue of Kendall rank correlation coefficient = 0.1). The trend was calculated in R using, especially, the function zyp.sen of the package zyp (for magnitude and intercept of the Sens slope) and MannKendall of the package Kendall (to determine the significative value of the trend through Kendall rank correlation coefficient). -180.0 -90.0 180.0 90.0 MX-CAH-DPJ-WDV-VXO-M4Q:MX-CY3C3-R9YDU-EXGPW image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Projected Sea Level (RCP 8.5) 2100 Global sea level is expected to change due to climatic and non-climatic factors. Here, sea level change for selected locations for the years 2050 and 2100 and RCP 4.5 and 8.5 scenarios are shown in units of cm above levels in the year 2000. The Kopp et al. (2014), or K14, framework combines multiple lines of information to construct probability distributions for key contributors to global mean sea level and relative sea level change. Factors in this model include: thermal expansion; regional ocean dynamics; glacier and ice sheet mass-balance; anthropogenic changes in land-waterstorage; and non-climatic effects such as glacio-isostatic adjustment, tectonics, and sediment compaction. -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-MWQMG-RFT5M-KVJ5V image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Zeaxanthin (μg/L) This layer presents the total cyanobacterial biomass of the each sample lakes -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-WGDIJ-TPKW6-9VSZ2 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Ecological deficit per capita (Biocapacity) - Period 1961 - 2022 (MSSD Indicator 1) "Biocapacity is the ecosystems’ capacity to produce biological materials used by people and to absorb waste material generated by humans, under current management schemes and extraction technologies." In 2022 (last updated data), all Mediterranean countries had ecological deficit meaning that the environment capacity of the region is used up faster than it is renewed. Source: <a href="https://www.obs.planbleu.org/wp-content/uploads/2021/03/SMDD_Dashboard_Version_Mars_2021.pdf"target=_blank> https://www.obs.planbleu.org/wp-content/uploads/2021/03/SMDD_Dashboard_Version_Mars_2021.pdf</a> See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-Y3G96-SAL5S-BDGTB&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD Indicator 1b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-ILGL3-3JVRG-BQ7XP image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Uganda Major Road Networks (OSM, 2019) This view shows the major road networks of Uganda, including primary, secondary, and tertiary roads. The data is an extract of OSM and updated every hour. It uses OSM tags that match corresponding attributes of the WFP data structure (UNSDI-T) and the values of the tags are used as is. This data is crowd-sourced and was downloaded from <a href="https://data.humdata.org/dataset/hotosm_uga_roads">HDX</a> on August 28, 2019. -180.0 -90.0 180.0 90.0 MX-WJO-FOV-NNB-1BN-SZN:MX-J5WA5-2UBRZ-JWIYH image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Bidibidi Refugee Camp Settlement (UNHCR 2019) This data contains the name of different settlements in secondary attributes (users can get access to it by just clicking the area of interest in the map )and which zone they belong to in the first attribute(see the legend). Data source: UNHCR. -180.0 -90.0 180.0 90.0 MX-WJO-FOV-NNB-1BN-SZN:MX-BM19B-2LUQ9-F4PWW image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Chlorophyll-a (μg/L) This layer presents the total algal biomass at the sample lakes. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-ATTVV-JCJFJ-DJLMX image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Green List Initiative project phases in 2020 (MSSD indicator 9b) The development of the UICN Green List aims to provide an accurate framework for protected area’s management. In 2019, 7 protected areas from North Africa were proposed for the Green List of Protected Areas (2 in Morocco; 2 in Algeria; 3 in Tunisia). Source: <a href="https://www.obs.planbleu.org/wp-content/uploads/2021/03/SMDD_Dashboard_Version_Mars_2021.pdf"target=_blank>https://www.obs.planbleu.org/wp-content/uploads/2021/03/SMDD_Dashboard_Version_Mars_2021.pdf</a> See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-NZT4W-P6TGE-71Z89&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 9</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-CILFI-F0WKU-VPJBN image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Projected Sea Level (RCP 4.5) 2100 Global sea level is expected to change due to climatic and non-climatic factors. Here, sea level change for selected locations for the years 2050 and 2100 and RCP 4.5 and 8.5 scenarios are shown in units of cm above levels in the year 2000. The Kopp et al. (2014), or K14, framework combines multiple lines of information to construct probability distributions for key contributors to global mean sea level and relative sea level change. Factors in this model include: thermal expansion; regional ocean dynamics; glacier and ice sheet mass-balance; anthropogenic changes in land-waterstorage; and non-climatic effects such as glacio-isostatic adjustment, tectonics, and sediment compaction. -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-3QK99-XGQG6-DJY37 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Education for sustainable development in 2022 (MSSD Indicator 28) The Mediterranean Strategy on Education for Sustainable Development (MSESD) and its Action Plan was endorsed in 2014. The overall aim is to encourage the countries to develop and incorporate ESD into formal, non-formal and informal education. In October 2022, 16 (73%) Mediterranean countries had a legal framework for ESD. -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-DMILD-26PBV-Z41IB image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Large Marine Ecosystems (LME) Large Marine Ecosystems (LMEs) are regions of ocean space of 200,000 km² or greater, that encompass coastal areas from river basins and estuaries to the outer margins of a continental shelf or the seaward extent of a predominant coastal current. LMEs are defined by ecological criteria, including bathymetry, hydrography, productivity, and tropically linked populations. The LME concept for ecosystem-based management has 5-module approach focused on productivity, fish and fisheries, pollution and ecosystem health, socioeconomics, and governance. -180.0 -90.0 180.0 90.0 MX-KVV-ANS-WBN-QXR-S71:MX-PX2A4-S13QR-JL72G image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Coral Bleaching The ReefBase Coral Bleaching dataset provides observation details of coral bleaching occurrences (or lack thereof) around the world. This dataset was build upon an original bleaching database developed at UNEP-WCMC, and has been maintained and updated regularly by ReefBase since early 2002. Main sources of information for this dataset have been: published records from the literature, postings on the <a href="https://www.coral.noaa.gov/resources/coral-list.html" target="_blanck">coral-list</a>, the ReefBase online <a href="http://www.reefbase.org/contribute/bleachingreport.aspx" target="_blanck">Bleaching Report</a>, and through collaborations with numerous institutions and individuals. ReefBase would like to particularly acknowledge the contributions made by: National Oceanographic and Atmospheric Administration (NOAA), Australian Institute of Marine Science (AIMS), and Great Barrier Reef Marine Park Authority (GBRMPA). -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-WPE1F-U33ST-XE15D image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Green List Initiative protected areas in 2023 (MSSD indicator 9) The Green List of Protected and Conserved Areas has been recognized as a Flagship Initiative under the Mediterranean Strategy for Sustainable Development 2016- 2025, adopted during the 19th meeting of the Contracting Parties to the Barcelona Convention. In 2021, in France 7 new sites have been listed and 9 have been proposed (candidates) while in Italy 3 new sites have been listed and 3 have been proposed (candidates). Source: <a href="https://iucngreenlist.org/explore/#countries"target=_blank> https://iucngreenlist.org/explore/#countries</ <a href="https://portals.iucn.org/library/node/49967"target=_blank> https://portals.iucn.org/library/node/49967</a> See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-CILFI-F0WKU-VPJBN&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 9b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-NZT4W-P6TGE-71Z89 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Air temperature 2m above the lake surface (in Kelvin) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of air temperature at 2m above the ground on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-HFXJE-LGOBK-NFJWI image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Life expectancy at birth in 2022 (MSSD Indicator 2b) The average life expectancy in 2022 was 78 years old for the Mediterranean region, less than in 2018 with 78,3 years old. With an average of 78 years old in 2022, the Mediterranean region was above the world value of 72 years (based on 184 countries). The life expectancy at birth, which accounts for one third of the HDI, shows a gap of 13.9 years between Egypt (70.1) and Italy (84). See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-Y2AI2-CNBWK-6Y7EX&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 2a</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-BL3KS-JUT36-62AK5 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Kyoto Protocol The Kyoto Protocol was adopted on 11 December 1997. Owing to a complex ratification process, it entered into force on 16 February 2005. Currently, there are 192 Parties to the Kyoto Protocol. In short, the Kyoto Protocol operationalizes the United Nations Framework Convention on Climate Change by committing industrialized countries and economies in transition to limit and reduce greenhouse gases (GHG) emissions in accordance with agreed individual targets. The Convention itself only asks those countries to adopt policies and measures on mitigation and to report periodically. The Kyoto Protocol is based on the principles and provisions of the Convention and follows its annex-based structure. It only binds developed countries, and places a heavier burden on them under the principle of “common but differentiated responsibility and respective capabilities”, because it recognizes that they are largely responsible for the current high levels of GHG emissions in the atmosphere. https://unfccc.int/kyoto_protocol -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-41OV9-WQ1WL-5DTZQ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Organic farming growth rate (number of producer) - Period 2006 - 2021 (MSSD indicator 16) Besides, organic farming has become one of the most dynamic agricultural sectors in the European Union, with 14,7 million hectares in 2020 (+0.9 billion of hectares since 2018), i.e. 9.36 % of agricultural land (+1.6% since 2017). Organic farming area is unprecedentedly booming (more than x 4 times since 2000, in a majority of countries) but still only covers less than 5 % of the agricultural land in 2020. In 2019, France and Spain were among the world’s top ten countries with the highest increase of organic land. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-T6Q0U-3BSFE-KMDJV&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 16b</a> <a href="https://app.mapx.org/static.html?language=en&views=MX-9282A-KNM3B-NAGIY&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 16c</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-5BWL4-CH1P4-27GZ2 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Armed Conflict Location and Event: Heatmap (ACLED) Heat map showing the number of fatalities due to conflict events in Africa from 1997 to present. Areas on map are tainted on the basis of the interpolated number of fatalities associated to each event and the number of events that occurred in a given area, which radius changes with zoom. -180.0 -90.0 180.0 90.0 MX-KVV-ANS-WBN-QXR-S71:MX-0BAMT-MXYIM-NHMM0 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Renewable energy share in the total final energy consumption - Period 2000 - 2021 (MSSD Indicator 22b) In Mediterranean countries, the share of renewable energy in total final consumption varies widely in 2021: from 0.12 % in Algeria to 42 % in Albania. Italy, France, Spain, Greece the share of renewable energy in total final consumption varies between 16 to 21 %. In Morocco, Tunisia and Türkiye the share of renewable energy in total final consumption varies from 10 to 12%. In the Balkan countries, the share is between 34% and 42% while it is very low (below 3%) in the Southern countries, especially in the Oil & Gas producing countries. Similar to SDG Indicator 7.2.1 See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-7BEES-FHLZ5-ABHAE&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 22</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-39JDD-KUS3L-JEC20 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Organic producers in 2023 (MSSD indicator 16b) The number of organic producers continues to increase with about + 34,210 from 2021 to 2023 reaching 327,719 producers in 2023. Italy is in the worldwide top ten countries. Some countries have seen the numbers of their organic producers increasing significantly (Greece +64%, Morocco +22,3%) but others faced significant decreases like Algeria (-91%), Lebanon (-60%) and Türkiye (-13 %) from 2021 to 2023. Algeria has seen the number of organic producers decrease from 64 to only 6 in one year. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-5BWL4-CH1P4-27GZ2&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 16</a> <a href="https://app.mapx.org/static.html?language=en&views=MX-9282A-KNM3B-NAGIY&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 16c</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-T6Q0U-3BSFE-KMDJV image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Projected Sea Level (RCP 8.5) 2050 Global sea level is expected to change due to climatic and non-climatic factors. Here, sea level change for selected locations for the years 2050 and 2100 and RCP 4.5 and 8.5 scenarios are shown in units of cm above levels in the year 2000. The Kopp et al. (2014), or K14, framework combines multiple lines of information to construct probability distributions for key contributors to global mean sea level and relative sea level change. Factors in this model include: thermal expansion; regional ocean dynamics; glacier and ice sheet mass-balance; anthropogenic changes in land-waterstorage; and non-climatic effects such as glacio-isostatic adjustment, tectonics, and sediment compaction. -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-K7BCD-BXZMA-LCKGI image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Marine Ecoregions of the World (MEOW) The Marine Ecoregions of the World (MEOW) dataset shows a biogeographic classification of the world’s coastal and continental shelf waters, following a nested hierarchy of realms, provinces and ecoregions. It describes 232 ecoregions, which lie within 62 provinces and 12 large realms. The regions aim to capture generic patterns of biodiversity across habitats and taxa, with regions extending from the coast (intertidal zone) 370 kilometers (200 nautical miles) offshore (or to the 200-m isobath, where this lies further offshore). See: <a href="http://www.bioone.org/doi/full/10.1641/B570707"target=_blank>Spalding MD, Fox HE, et al. 2007. Marine Ecoregions of the World: a bioregionalization of coast and shelf areas. BioScience 57: 573-583</a>. -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-FR3KW-TRWD1-TRE1G image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Projected Sea Level (RCP 4.5) 2050 Global sea level is expected to change due to climatic and non-climatic factors. Here, sea level change for selected locations for the years 2050 and 2100 and RCP 4.5 and 8.5 scenarios are shown in units of cm above levels in the year 2000. The Kopp et al. (2014), or K14, framework combines multiple lines of information to construct probability distributions for key contributors to global mean sea level and relative sea level change. Factors in this model include: thermal expansion; regional ocean dynamics; glacier and ice sheet mass-balance; anthropogenic changes in land-waterstorage; and non-climatic effects such as glacio-isostatic adjustment, tectonics, and sediment compaction. -180.0 -90.0 180.0 90.0 MX-5Z8-45E-K4I-SKH-75H:MX-9C2IH-YHLDP-I2ZB6 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Red List Index - Period 2000 - 2022 (MSSD indicator 17) In 2022, the value of the Red List Index in the Mediterranean countries is above the world value 0.83 except for Israel, Monaco and Montenegro. The Red List Index is above 0.9 in 10 Mediterranean countries. From 2010 to 2022, the Red List Index is decreasing mainly in 4 countries: Albania, Algeria, France and a little bit in Malta, Libya, Lebanon, Montenegro. See also: <a href="https://app.mapx.org/static.html?language=en&views=MX-4W3V5-KI12J-KHZB7&zoomToViews=true&useMaxBounds=true&p=0&b=0&n=44.259&s=26.823&e=43.993&w=-1.129&t3d=false&sat=false&theme=classic_light"target=_blank>MSSD indicator 17b</a> -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-JEOJ1-CRX54-JR3PZ image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Road network (HOTOSM) Road network as provided by Humanitarian OpenStreetMap Team (HOT). -180.0 -90.0 180.0 90.0 MX-SZX-UBT-FVM-R1V-BDJ:MX-L9U3Y-3MLOU-9NLVS image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Annual growth GDP per capita - Period 1960 - 2023 (MSSD Indicator 3) In 2023 (last updated data) the EU Mediterranean countries count for 80% of the Mediterranean GDP. In 2023 the average income per capita in the South and East Mediterranean countries (less than 10,000$) is about 4 times lower than the average income in the EU Mediterranean countries (35 000$). -180.0 -90.0 180.0 90.0 MX-R2F-467-2PL-J9H-CCR:MX-M4O7D-67WRA-GPUB6 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Last of the Wilds (1993) This layer shows a regionally representative map of wilderness following the well-established ‘Last of the Wild’ methodology. It identifies the 10% area with the lowest human pressure within each of Earth’s 60 biogeographic realms and identifies the ten largest contiguous areas within each realm, along with all contiguous areas > 10,000 km2. See: <a href="https://www.nature.com/articles/sdata2017187"target=_blank>Allan, J.R., Venter, O. & Watson, J.E.M., 2017. Temporally inter-comparable maps of terrestrial wilderness and the Last of the Wild. Scientific Data, 4, p.170187</a>. -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-IKEIJ-KG56Z-YXJ4N image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wind speed at 10m above the ground (m/s) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF)to gather monthly averages of daily averages of wind speed on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-AWDTD-NLYFK-4VUFS image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Ecoregions - By Biome Ecoregions are defined as relatively large units of land or water containing a distinct assemblage of natural communities sharing a large majority of species, dynamics, and environmental conditions. The original Terrestrial Ecoregions of the World (TEOW)(see Olsen et al., 2001) was comprised of 867 terrestrial ecoregions, classified into 14 different biomes such as forests, grasslands, or deserts. Ecoregions represent the original distribution of distinct assemblages of species and communities. The revised data shown here – Ecoregions2017 – is unchanged for large sections of Earth's seven biogeographical realms but differs from the original map in four regions: the Arabian Peninsula, some of the desert and drier ecoregions of the African continent, Antarctica, and the southeastern United States. See <a href="https://academic.oup.com/bioscience/article/67/6/534/3102935\" target="_blank">Dinerstein, E., et al. 2017. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. BioScience 67, 534–545.</a> For furhter information please visit the <a href="https://ecoregions2017.appspot.com/\" target="_blank">RESOLVE Ecoregions2017 appspot</a> Data usage licence: <a href=\"https://creativecommons.org/licenses/by/4.0/\"target="_blank">CC BY 4.0</a> -180.0 -90.0 180.0 90.0 MX-EDD-M46-8XZ-MAG-RLC:MX-B65LI-II6KG-ZLC2K image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Projected change in water stress (RCP4.5) 2030 With the goal of producing information for decadal-scale planning, adaptation, and investment, the Aqueduct Water Stress Projections model potential changes in future demand and supply of water over the next 3 decades. Global indicators were developed for water demand (withdrawal and consumptive use), water supply, water stress (the ratio of water withdrawal to supply), and intra-annual (seasonal) variability for the periods centered on 2020, 2030, and 2040 for each of 2 climate scenarios, RCP4.5 and RCP8.5, and 2 shared socioeconomic pathways, SSP2 and SSP3. Source: Luck, M., M. Landis, F. Gassert. 2015. “Aqueduct Water Stress Projections: Decadal projections of water supply and demand using CMIP5 GCMs.” Washington, DC: World Resources Institute. Licence: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution 4.0 International</a> Year of content: 2010-2040 -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-06I8P-A2V1A-9PD7I image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Wind speed at 10m above the ground (m/s) We used the ERA-interm climate forecast model from the European Centre for Medium Range Weather Forecasts (ECMWF) to gather monthly averages of daily averages of wind speed on a scale of 0.75° for each lake. ERA data for each sample location was extracted for the current month that was sampled, in addition to the prior five months. -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-HMVSR-EV6PP-RN4FK image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Marine Plastics: non-adjusted Plastic concentration (g per km2) This dataset is a compiliation of the results from 10 research monitoring plastic concentration in the oceans through in situ sampling (Suaria et al. 2016; Goldstein et al. 2012; Doyle et al. 2011: Law et al. 2010; Law et al. 2014; Eriksen et al. 2014; Cozar et al. 2015; Cózar et al. 2014; Ruiz-Orejón et al 2016; Collignon et al. 2012). The column PC_number_km2 features a non-adjusted plastic concentration (items per km2); the column PC_g_km2 provides the weight of plastic: non-adjusted Plastic concentration (g per km2). The values are not adjusted for waves height or winds. -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-4W6BQ-L2FVY-I24OR image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Surface Area (km2) No abstract provided -180.0 -90.0 180.0 90.0 MX-SHL-MH6-6FZ-X8N-Y2E:MX-UCQ3S-FA3SW-HUG5Z image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Protected Upstream Land Upstream protected land measures the percentage of total water supply that originates from protected ecosystems. Modified land use can affect the health of freshwater ecosystems and have severe downstream impacts on both water quality and quantity. For further information on this dataset visit <a href="https://www.wri.org/our-work/project/aqueduct/" target="_blank">Aqueduct</a>. Citation: Gassert, F., M. Landis, M. Luck, P. Reig, and T. Shiao. 2014. “Aqueduct Global Maps 2.1.” Working Paper. Washington, DC: World Resources Institute. Available online at <a href="http://www.wri.org/publication/aqueduct-metadata-global" target="_blank">http://www.wri.org/publication/aqueduct-metadata-global</a>. -180.0 -90.0 180.0 90.0 MX-70L-6MZ-5FF-DTH-CWW:MX-8MO1U-JCZPP-MWWDL image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Types of policies to address nutrient pollution This policy database provides an overview of various policy instruments around the globe that have been implemented to address nutrient pollution. The database includes a wide range of policies that decision-makers can reference when considering options and approaches to reduce nutrient losses and can be searched by policy category, policy type, region, country and/or sector. Policies are categorized as follows (policy taxonomy borrowed from Greenhalgh & Selman 2014 ): + Environmental outreach & education—including environmental education; public awareness; and technical assistance. + Regulatory approaches—including environmental bans & restrictions; environmental standards; environmental caps & limits; and regulatory frameworks. + Priced-based instruments – including taxes, fees and levies; tax credits & rebates; subsidies, grants & incentive payments; low-interest loans; and income support. + Market-based instruments—including environmental markets (regulated and voluntary); auctions & tenders; and ecolabeling. + Ecosystem preservation and restoration—including ecosystem restoration; protected areas; land purchases; covenants and easements; and stewardship agreements. + Institution and capacity—including institutional capacity; transparency & accountability; bridging Institutions; partnerships; and frameworks & guidance. + Research, monitoring, and evaluation—including research; monitoring; evaluation; and biophysical models. -180.0 -90.0 180.0 90.0 MX-JOJ-8ME-I4T-G9M-I9E:MX-MXQWR-339CX-31BJR image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Uganda Refugee Settlement Zones - OSM Export These datasets contain OpenStreetMap data related to the Refugee Response in northern Uganda. Data model coordinated with UNHCR. The source is surveys and mapping in northern Uganda performed by HOTOSM and partners. OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: refugee='yes' AND (boundary='refugee_camp' OR boundary='administrative' OR landuse='residential') Features may have these attributes: refugee admin_level boundary name type operator operator:type population capacity start_date landuse place name:en official_name loc_name access This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information. -180.0 -90.0 180.0 90.0 MX-WJO-FOV-NNB-1BN-SZN:MX-X75U7-IYVE5-SCKI4 image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 EPSG:4326 urn:ogc:def:crs:EPSG::4326 EPSG:4326:0 2.795411320143589E8 90.0 -180.0 256 256 2 1 EPSG:4326:1 1.3977056600717944E8 90.0 -180.0 256 256 4 2 EPSG:4326:2 6.988528300358972E7 90.0 -180.0 256 256 8 4 EPSG:4326:3 3.494264150179486E7 90.0 -180.0 256 256 16 8 EPSG:4326:4 1.747132075089743E7 90.0 -180.0 256 256 32 16 EPSG:4326:5 8735660.375448715 90.0 -180.0 256 256 64 32 EPSG:4326:6 4367830.1877243575 90.0 -180.0 256 256 128 64 EPSG:4326:7 2183915.0938621787 90.0 -180.0 256 256 256 128 EPSG:4326:8 1091957.5469310894 90.0 -180.0 256 256 512 256 EPSG:4326:9 545978.7734655447 90.0 -180.0 256 256 1024 512 EPSG:4326:10 272989.38673277234 90.0 -180.0 256 256 2048 1024 EPSG:4326:11 136494.69336638617 90.0 -180.0 256 256 4096 2048 EPSG:4326:12 68247.34668319309 90.0 -180.0 256 256 8192 4096 EPSG:4326:13 34123.67334159654 90.0 -180.0 256 256 16384 8192 EPSG:4326:14 17061.83667079827 90.0 -180.0 256 256 32768 16384 EPSG:4326:15 8530.918335399136 90.0 -180.0 256 256 65536 32768 EPSG:4326:16 4265.459167699568 90.0 -180.0 256 256 131072 65536 EPSG:4326:17 2132.729583849784 90.0 -180.0 256 256 262144 131072 EPSG:4326:18 1066.364791924892 90.0 -180.0 256 256 524288 262144 EPSG:4326:19 533.182395962446 90.0 -180.0 256 256 1048576 524288 EPSG:4326:20 266.591197981223 90.0 -180.0 256 256 2097152 1048576 EPSG:4326:21 133.2955989906115 90.0 -180.0 256 256 4194304 2097152 EPSG:900913 urn:ogc:def:crs:EPSG::900913 EPSG:900913:0 5.590822639508929E8 -2.003750834E7 2.0037508E7 256 256 1 1 EPSG:900913:1 2.7954113197544646E8 -2.003750834E7 2.0037508E7 256 256 2 2 EPSG:900913:2 1.3977056598772323E8 -2.003750834E7 2.0037508E7 256 256 4 4 EPSG:900913:3 6.988528299386162E7 -2.003750834E7 2.0037508E7 256 256 8 8 EPSG:900913:4 3.494264149693081E7 -2.003750834E7 2.0037508E7 256 256 16 16 EPSG:900913:5 1.7471320748465404E7 -2.003750834E7 2.0037508E7 256 256 32 32 EPSG:900913:6 8735660.374232702 -2.003750834E7 2.0037508E7 256 256 64 64 EPSG:900913:7 4367830.187116351 -2.003750834E7 2.0037508E7 256 256 128 128 EPSG:900913:8 2183915.0935581755 -2.003750834E7 2.0037508E7 256 256 256 256 EPSG:900913:9 1091957.5467790877 -2.003750834E7 2.0037508E7 256 256 512 512 EPSG:900913:10 545978.7733895439 -2.003750834E7 2.0037508E7 256 256 1024 1024 EPSG:900913:11 272989.38669477194 -2.003750834E7 2.0037508E7 256 256 2048 2048 EPSG:900913:12 136494.69334738597 -2.003750834E7 2.0037508E7 256 256 4096 4096 EPSG:900913:13 68247.34667369298 -2.003750834E7 2.0037508E7 256 256 8192 8192 EPSG:900913:14 34123.67333684649 -2.003750834E7 2.0037508E7 256 256 16384 16384 EPSG:900913:15 17061.836668423246 -2.003750834E7 2.0037508E7 256 256 32768 32768 EPSG:900913:16 8530.918334211623 -2.003750834E7 2.0037508E7 256 256 65536 65536 EPSG:900913:17 4265.4591671058115 -2.003750834E7 2.0037508E7 256 256 131072 131072 EPSG:900913:18 2132.7295835529058 -2.003750834E7 2.0037508E7 256 256 262144 262144 EPSG:900913:19 1066.3647917764529 -2.003750834E7 2.0037508E7 256 256 524288 524288 EPSG:900913:20 533.1823958882264 -2.003750834E7 2.0037508E7 256 256 1048576 1048576 EPSG:900913:21 266.5911979441132 -2.003750834E7 2.0037508E7 256 256 2097152 2097152 EPSG:900913:22 133.2955989720566 -2.003750834E7 2.0037508E7 256 256 4194304 4194304 EPSG:900913:23 66.6477994860283 -2.003750834E7 2.0037508E7 256 256 8388608 8388608 EPSG:900913:24 33.32389974301415 -2.003750834E7 2.0037508E7 256 256 16777216 16777216 EPSG:900913:25 16.661949871507076 -2.003750834E7 2.0037508E7 256 256 33554432 33554432 EPSG:900913:26 8.330974935753538 -2.003750834E7 2.0037508E7 256 256 67108864 67108864 EPSG:900913:27 4.165487467876769 -2.003750834E7 2.0037508E7 256 256 134217728 134217728 EPSG:900913:28 2.0827437339383845 -2.003750834E7 2.0037508E7 256 256 268435456 268435456 EPSG:900913:29 1.0413718669691923 -2.003750834E7 2.0037508E7 256 256 536870912 536870912 EPSG:900913:30 0.5206859334845961 -2.003750834E7 2.0037508E7 256 256 1073741824 1073741824