Mozambique water data

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT

Water resources and water infrastructure in Mozambique including rivers, lakes, boreholes, wells and water points. Use this layer for water access analysis, WASH programme planning, drought monitoring, flood risk assessment, and water resource management.

Adjusted shape file with position and names of dams in Mozambique watersheds.

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected surface waters in Matutuine District, Maputo Province, Mozambique as observed from a Sentinel-1 image acquired on 14 Mar. 2023 at 18:30 local time and using an automated analysis with a machine learning method. Within the analyzed area of about 2,800 km2, about 30 km2 of land appear to be flooded. Water extent appears to have decreased by about 33 km2 since 2 Mar. 2023. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the Bela Vista district, with ~300 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected water using Sentinel-2 acquired on 21 March 2023, 09:55 local time over the intersection between Shire river and Zambezi rivers in Sofala, Tete and Zambezia Provinces of Mozambique. Within the cloud free analyzed areas of about 20,000 km², a total of about 2,400 km² of lands appear to be affected with flood waters. Based on Worldpop population data and flood water extent, ~180,000 people are potentially exposed or living close to flooded areas mainly located in Mutarara district, Tete province with ~62,000 people, Caia district, Sofala province with ~38,200 people and Mopeia district, Zambezia province with ~34,000 people This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected surface waters in Sofala and Zambezia Provinces, Mozambique as observed from a Sentinel-1 image acquired on 17 Mar. 2023 at 03:09 local time. Within the analyzed area of about 27,000 km2, about 1,800 km2 of lands appear to be flooded. Water extent appears to have increased of about 900 km2 since the 13 March 2023. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in Zambezia province, including the district of Namacurra, with ~39,00 people, Nicoadala with ~32,000 people and Maganja Da Costa with ~ 23,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected water using Sentinel-2 acquired on 26 March 2023, 09:55 local time over the intersection between Shire river and Zambezi rivers in Sofala, Tete and Zambezia Provinces of Mozambique. Within the cloud-free analyzed areas of about 20,000 km², a total of about 1,800 km² of land appear to be affected by flood waters. Water extent appears to have decreased of about 600 km2 since 21 March 2023. Based on Worldpop population data and flood water extent, ~120,000 people are potentially exposed or living close to flooded areas mainly located in Mutarara district, Tete province with ~46,000 people, Mopeia district, Zambezia province with ~22,000 people, and Caia district, Sofala province with ~20,00 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates cumulative satellite-detected water using VIIRS in Mozambique between 27 to 31 March 2023. Within the cloud-free analyzed areas of about 600,000 km², a total of about 12,000 km² of land appear to be affected by flood waters. Water extent appears to have decreased by about 6,300 km2 since 21-25 March 2023. Based on Worldpop population data and the maximal flood water extent, ~650,000 people are potentially exposed or living close to flooded areas mainly located in Zambezia province with ~220,000 people and Sofala province with ~140,000 people. This preliminary analysis has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates cumulative satellite-detected water using VIIRS in Mozambique between 15 to 19 March 2023. Within the cloud free analyzed areas of about 600,000 km², a total of about 21,000 km² of lands appear to be affected with flood waters. Based on Worldpop population data and the maximal flood water extent ~1,400,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates cumulative satellite-detected water using VIIRS in Mozambique between 21 to 25 March 2023. Within the cloud free analyzed areas of about 620,000 km², a total of about 18,300 km² of lands appear to be affected with flood waters. Water extent appears to have decreased of about 2,700 km2 since the 15-19 March 2023. Based on Worldpop population data and the maximal flood water extent, ~1,300,000 people are potentially exposed or living close to flooded areas mainly located in Zambezia province with ~480,000 people, Nampula province with ~210,000 people and Sofala province with ~ 210,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected surface waters in Zambezia Provinces, Mozambique as observed from a Sentinel-1 image acquired on 24 March 2023 at 05:00 local time and using an automated analysis with a machine learning method. Within the analyzed area of about 20,000 km2, about 330 km2 of land appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Maganja Da Costa, with ~10,000 people, Cidade De Quelimane with ~4,500 people, Inhassunge with ~ 3,500 people and Maquival with ~ 3,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code TC20230221MOZ, GDACS Id: 1000961** This map illustrates satellite-detected surface waters in Sofala and Zambezia Provinces, Mozambique as observed from an RCM-2 image acquired on 13 Mar. 2023 at 05:01 local time. Within the analyzed area of about 24,000 km2, about 900 km2 of land appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in Zambezia province, including the district of Namacurra, with ~27,200 people, Nicoadala with ~22,500 people, and Cidade De Quelimane with ~ 15,600 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: TC20220311MOZ** This map illustrates satellite-detected surface waters in Nante, Macuze & Namacurra posto, Zambezia Province, Mozambique as observed from a TerraSAR-X image acquired on 16 March 2022 at 05:10 local time. Within the analyzed area of about 12,400 ha, about 3,180 ha of lands appear to be flooded. Based on GRID3 settlement extent data and the detected surface waters, about 60 ha of the urban area is potentially exposed to floodwaters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: TC20220126MWI** This map illustrates satellite-detected surface waters in Malawi and Mozambique as observed from a Sentinel-1 image acquired on 26 January 2021 at 05:16 local time and using an automated analysis with Artificial Intelligence based methods. Within the analyzed area of about 42,000 km2, about 610 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the districts of Chikwawa and Nsanje, Southern region, Malawi with respectively ~ 33,000 people and ~14,300 people, & in Cidade De Tete, Tete province, Mozambique with ~8,200 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: TC20220311MOZ** Este mapa ilustra água superficias detectadas por satélite nos distritos de Mossuril, Mogincual e Liúpo, Província de Nampula em Moçambique, no dia 13 de março, 2022, as 17:44 hora local. Dentro da área analisada de cerca de 14,000 km2, um total de 110 km2 aparece inundada. Baseado nos dados de população do Worldpop e na águas superficiais, cerca de 12,000 pessoas estão potencialmente expostos ou vivem perto das áreas inundadas. Esto sao análises preliminar e ainda não foram verificados no terreno. Por favor, envie comentários United Nations Satellite Centre (UNOSAT). Nota importante: Análises de inundaçãoes a partir de imagens radar podem subestimar a presença de águas acumuladas em áreas construídas e áreas de vegetação densa devido às propriedades de retrodifusão do sinal radar.

This map illustrates satellite-detected flood waters over Save River in Mozambique as observed from the Radarsat-2 images acquired on 17 February 2017 and 05 February 2017. A decrease of surface waters was observed in the 17 February 2017 image compared to the 05 February 2017 image along the Save river. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT.

**UNOSAT code: TC20220124MOZ** This map illustrates satellite-detected surface waters in Malawi and Mozambique as observed from a Sentinel-1 image acquired on 02 February 2022 at 10:04 local time. The flood analysis were carried out by using an automated analysis with Artificial Intelligence based methods. Within the analyzed area of about 145,978 km2, about 859 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the districts of Mutarara, Cidade de Quelim and Morrumbala with respectively ~ 33,000 people, ~ 11,000 people, and with ~16,600 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: TC20220311MOZ** This map illustrates satellite-detected surface waters in Maganja da Costa, Moaquival and Namacurra Districts, Nampula Province, Mozambique as observed from a RCM-1 image acquired on 15 March 2022 at 05:10 local time. Within the analyzed area of about 1500 km2, about 237 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 22,600 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: FL20251229MOZ** This map illustrates the flood extent derived from Sentinel-1 imagery acquired on 31 December 2025, using an automated machine-learning-based analysis. Within the analysed area of approximately 32,000 km², an estimated 930 km² of land appears to be affected by floodwaters. Based on WorldPop spatial demographic data, approximately 4,200 people are exposed to or living in close to the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20251229MOZ, GDACS ID: 1103694** This map illustrates the flood extent derived from Sentinel-1 imagery acquired on 5 January 2026, using an automated machine-learning-based analysis. Within the analysed area of approximately 25,000 km², an estimated 870 km² of land appears to be affected by floodwaters, representing an increase of about 100 km² compared to 31 December 2025 within the same analysed area. Based on WorldPop spatial demographic data, approximately 7,300 people are exposed to or living in close proximity to the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20251229MOZ** This map illustrates the flood extent derived from Sentinel-1 imagery acquired on 23 January 2026. Within the analysed area of approximately 43,500 km², an estimated 3,000 km² of land appears to be affected by floodwaters. Based on WorldPop spatial demographic data, approximately 230,000 people are exposed to or living near the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: TC20220311MOZ** This map illustrates satellite-detected surface waters in Maganja Da Costa, Namacurra & Nicoadala Districts, Zambezia Province, Mozambique as observed from a TerraSAR-X image acquired on 22 March 2022 at 05:01 local time. Within the analyzed area of about 6,174 km2, about 496 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 49,100 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: TC20220124MOZ** This map illustrates satellite-detected surface waters in Zambezia Province, Mozambique as observed from a Sentinel-1 image acquired on 28 January 2021 at 05:00 local time and using an automated analysis with Artificial Intelligence based methods. Within the analyzed area of about 44,000 km2, about 860 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population are mainly located in the districts of Maganja Da Costa with ~22,500 people, Namacurra with ~17,000 people and Pebane with ~7,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: FL20251229MOZ, GDACS ID: 1103694** This map illustrates cumulative satellite-detected water extent derived from Sentinel-1 imagery acquired on 23 and 24 January 2026. Within the analysed area of approximately 64,000 km², an estimated 4,800 km² of land appears to be affected by floodwaters. Based on WorldPop spatial demographic data, approximately 252,000 people are exposed to or living near the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: TC20220311MOZ** This map illustrates satellite-detected surface waters in Mossuril, Mogincual and Liúpo Districts, Nampula Province, Mozambique as observed from a Sentinel-1 image acquired on 13 February 2022 at 17:44 local time. Within the analyzed area of about 14,000 km2, about 110 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 12,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: TC20220311MOZ** This map illustrates satellite-detected surface waters in Maganja Da Costa, Namacurra & Nicoadala Districts, Zambezia Province, Mozambique as observed from a Sentinel-1 image acquired on 17 March 2022 at 05:00 local time. Within the analyzed area of about 6,970 km2, about 442 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 36,500 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

The actual Evapotranspiration and Interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the ETIa in a given year. The data is provided in near real time from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-AETI-A Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See ETIa by dekad for further information. The annual total is obtained by taking the ETIa in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The actual Evapotranspiration and Interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the ETIa in a given month. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-AETI-M Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See ETIa by dekad for further information. The monthly total is obtained by taking the ETIa in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each month. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The actual Evapotranspiration and Interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the average daily ETIa in a given dekad. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-AETI-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of the ETIa is based on the ETLook model described in Bastiaanssen et al. (2012). See the methodology of the three components (E, T and I) for further information. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Evaporation (E) data component is the actual evaporation of the soil surface. The value of each pixel represents the total annual evaporation for that specific year. The data is provided in near real time from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-E-A Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See evaporation (E) by dekad for further information. The annual total is obtained by taking the E in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Evaporation (E) data component (dekadal, in mm/day) is the actual evaporation of the soil surface. The value of each pixel represents the average daily actual evaporation for that specific dekad. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: mm Scale Factor : 0.1 Map code : L3-E-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of the evaporation is based on the ETLook model described in Bastiaanssen et al. (2012). It uses the Penman-Monteith equation, adapted to remote sensing input data. The Penman-Monteith equation predicts the rate of total evaporation and transpiration using commonly measured meteorological data (solar radiation, air temperature, vapour content and wind speed). It has become the FAO standard for calculating the actual and reference evapotranspiration (see FAO Irrigation and Drainage paper 56, Allen et al. 1998). The following data is used for calculating evaporation: - Daily: incoming solar radiation and weather data (temperature, humidity, wind speed and precipitation); - Dekadal: NDVI, surface albedo and soil moisture stress; - Seasonal: Land Cover; - Static: Digital Elevation Model. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Transpiration (T) data component is the actual transpiration of the vegetation canopy. The value of each pixel represents the total annual transpiration for that specific year. The data is provided in near real time from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-T-A Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See transpiration (T) by dekad for further information. The annual total is obtained by taking the T in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The transpiration (T) data component (dekadal, in mm/day) is the actual transpiration of the vegetation canopy. The value of each pixel represents the average daily actual transpiration for that specific dekad. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: mm Scale Factor : 0.1 Map code : L3-T-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of the transpiration is based on the ETLook model described in Bastiaanssen et al. (2012). It uses the Penman-Monteith equation, adapted to remote sensing input data. The Penman-Monteith equation predicts the rate of total evaporation and transpiration using commonly measured meteorological data (solar radiation, air temperature, vapour content and wind speed). It has become the FAO standard for calculating the actual and reference evapotranspiration (see FAO Irrigation and Drainage paper 56, Allen et al. 1998). The following data is used for calculating transpiration: - Daily: incoming solar radiation and weather data (temperature, humidity, wind speed and precipitation); - Dekadal: NDVI, surface albedo and soil moisture stress; - Seasonal: Land Cover; - Static: Digital Elevation Model. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Interception is the process where rainfall is captured by the leaves. Part of this captured rainfall will evaporate again. The Interception (I) data component represents the evaporation of intercepted rainfall from the vegetation canopy. The value of each pixel represents the total annual interception for that specific year. The data is provided in near real time from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-I-A Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See interception (I) by dekad for further information. The annual total is obtained by taking the I in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Interception (I) data component (dekadal, in mm/day) represents the evaporation of intercepted rainfall from the vegetation canopy. Interception is the process where rainfall is captured by the leaves. Part of this captured rainfall will evaporate again. The value of each pixel represents the average daily evaporated interception for that specific dekad. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: mm Scale Factor : 0.1 Map code : L3-I-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** Evaporation of intercepted rainfall requires energy that is not available for transpiration or evaporation. Interception is a function of the vegetation cover and precipitation. Interception is relatively high with a small amount of precipitation, with the fraction intercepted decreasing quickly as precipitation increases. The maximum interception is determined by the LAI. The following data is used for calculating interception: - Daily: incoming solar radiation and weather data (temperature, humidity and precipitation); - Dekadal: NDVI. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage