Ethiopia baseline water stress

Aridity index (AI) is an effective estimator of drought status, and spatiotemporally continuous long-term AI dataset is critical for drought assessment and applications.This dataset offers valuable support for research on dryland ecosystems, agriculture, and climate change, offering critical insights to address global environmental and sustainability challenges. Resolution ;0.05 × 0.05   in the resolution  ( referenced year 2022)

This dataset contains the Ethiopian dataset on the Irrigated Lands , from the Waterdata , Global Irrigated Area for Mapping for the nominaL year of 2000 using satellite images at the resolution of about 5000 metres

Level of water stress (SDG 6.4.2) by major river basin (reference year 2018). This map provides a global representation of the level of water stress spatially disaggregated by major river basin. The indicator 6.4.2 is calculated as the ratio between (a) the amount of total freshwater resources withdrawn in the three economic sectors (Agriculture, Service and Industry) and (b) the total renewable freshwater resources after detracting the amount of water needed to support existing environmental services, also indicated as environmental flows. All the variables are expressed in million m3. The purpose of this indicator is to show the degree to which water resources are being exploited to meet the water demand and it measures the pressure on water resources at basin level. High values of water stress mean more water users are competing for limited water supplies. Low water stress indicates minimal potential impact on resource sustainability and on potential competition among users. Major river basins with an indicator's level lower than 25% have no water stress. Those basins with a water stress greater than 75% have a high or critical water stress. The spatial disaggregation of the indicator by river basin allow the identification of hotspots where actions can be prioritized and reveal that the area affected by a high or critical water stress spans across all continents with the only exception of Oceania. The analysis is based on the national water withdrawal data available in AQUASTAT for year 2018. The total renewable freshwater resources have been assessed using the GloBWat model (https://hess.copernicus.org/articles/19/3829/2015/) and the environmental Flows have been estimated applying the percentages available on the GEFIS portal 2021 (http://eflows.iwmi.org/). Further details on the methodology applied can be found at: https://ucl.scienceopen.com/document/read?vid=18caef6a-0b8f-4e0c-a3e6-807e55d075a2. **Data publication:** 2022-03-11 **Contact points:** Resource Contact: [Jippe Hoogeveen](mailto:jippe.hoogeveen@fao.org) Resource Contact: [Michela Marinelli](mailto:michela.marinelli@fao.org) Metadata Contact: [AQUASTAT](mailto:aquastat@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download - Level of Water Stress (SDG 6.4.2) by major river basin (ESRI shapefile)

Livelihood zone maps define geographic areas of a country where people generally share similar options for obtaining food and income and similar access to markets. An understanding of geographic livelihood systems is a key component in Vulnerability Assesment for both Drought and Floods - The livelihood zones of resolution approximately 5000 metres ( 0.05×0.05)   Referenced  September 2019

This dataset is the output from the Global Flood Model intercomparison project undertaken on behalf of the Global Flood Partnership. The methodology is described in Trigg et al., 2016, "The credibility challenge for global fluvial flood risk analysis," published in the Environmental Research Letters Journal. Each file is a geospatial, WGS84, 1/1200 decimal degrees resolution (~90m) GeoTIFF raster with a classified integer value representing how many out of the 6 models agree that a cell is wet (note 0 - dry/no data)

Aridity index (AI) is an effective estimator of drought status, and spatiotemporally continuous long-term AI dataset is critical for drought assessment and applications.This dataset offers valuable support for research on dryland ecosystems, agriculture, and climate change, offering critical insights to address global environmental and sustainability challenges. Resolution ;0.05 × 0.05   in the resolution  ( referenced year 2022)