Environment data

# Vietnam Floods: Flood Proxy Maps, Sep 2019 The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and Caltech, in collaboration with the Earth Observatory of Singapore (EOS), produced these flood proxy maps for Vietnam Floods (Sep 2019) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **ARIA-SG Flood Proxy Map: Vietnam Floods, 6 Sep 2019, v0.1 ** - **ARIA-SG Flood Proxy Map: Vietnam Floods, 3 Sep 2019, v0.1 ** **More map details and files:**

# ARIA-SG Flood Proxy Map: Laos Floods, 6 Sep 2019, v0.1 Derived from synthetic aperture radar data acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA) before (3 Apr) and during (6 Sep) the event. Analysed by the ARIA-SG team at the Earth Observatory of Singapore (EOS), using the ARIA-SG system originally developed by NASA-JPL and Caltech. Data processing used an AWS Open Dataset of Copernicus Sentinel-1 data for the Asia region (https://registry.opendata.aws/sentinel1-slc-seasia-pds). **More map details and files:**

# ARIA-SG Flood Proxy Map: India Floods, 30 Sep 2019, v0.1 Derived from synthetic aperture radar data acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA) before (21 May) and during (30 Sep) the event. Analysed by the ARIA-SG team at the Earth Observatory of Singapore (EOS) in collaboration with NASA-JPL and Caltech. **More map details and files:**

# Japan Floods: Flood Proxy Maps, Aug 2019 The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and Caltech, in collaboration with the Earth Observatory of Singapore (EOS), produced these flood proxy maps for Japan Floods (Aug 2019) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **ARIA-SG Preliminary Flood Proxy Map: Japan, 2019 Floods v0.5 P22** (superseded) - **ARIA-SG Preliminary Flood Proxy Map: Japan, 2019 Floods v0.5 P131** **More map details and files:**

# Myanmar Floods: Flood Proxy Maps, Aug 2019 The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and Caltech, in collaboration with the Earth Observatory of Singapore (EOS), produced these flood proxy maps for Myanmar Floods (Aug 2019) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **ARIA-SG Flood Proxy Map: Myanmar, 2019 Floods v2.0 ** - **ARIA-SG Flood Proxy Map: Myanmar, 2019 Floods v1.0 ** **More map details and files:**

# ARIA Flood Proxy Map: India, Bangladesh, 2019 Floods This map shows areas that are likely flooded in northeast India and Bangladesh due to heavy rains brought by the monsoon season, shown by light blue pixels of 30 m (Copernicus Sentinel-1) and 25 m (ALOS-2) pixel size, and is a preliminary result. The map extents are indicated with red and yellow polygons. This map should be used as a guidance to identify areas that are likely flooded, and may be less reliable over urban and vegetated areas. Derived from synthetic aperture radar data acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA) before (15 May) and during (14 Jul) the event, and by the ALOS-2 satellite operated by the Japanese Aerospace Exploration Agency (JAXA) before (25 Mar) and during (15 Jul) the event. Analysed by the ARIA-SG team at the Earth Observatory of Singapore in collaboration with NASA/JPL-Caltech. For more information, please visit **More map details and files:**

# EOS-RS Flood Proxy Map: Indonesia Floods, 18 Mar 2024, v0.4 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Indonesia due to heavy rains that occurred on 13 Mar 2024. This map was derived from synthetic aperture radar (SAR) images acquired by the ALOS-2 satellite operated by the Japan Aerospace Exploration Agency (JAXA) before (5 Feb 2024) and after (18 Mar 2024) the event. The image covers an area indicated by the large white polygon. Each light blue pixel measures about 25m across. Preliminary Validation was done by comparing with news reports. This flood proxy map should be used as a guide to identify areas that are likely flooded, and is less reliable over urban and vegetated areas. Data were analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). ALOS-2 data were provided by Sentinel Asia. **More map details and files:** **More map details and files:** earthobservatory.sg/EOS-RS_ 202403_Indonesia_Floods/ **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Original data ALOS-2 PALSAR-2 Product - JAXA (2024)

# Myanmar Flood Dawei: Damage and Flood Proxy Maps, Oct 2026 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage and flood proxy maps for Myanmar Flood Dawei (Oct 2026) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Dawei, Myanmar, Landslide, 04 October 2026, v0.8 ** - **EOS-RS Flood Proxy Map: Dawei, Myanmar, Floods, 04 October 2026, v0.8** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2026)

# EOS-RS Damage Proxy Map: Philippines, Taal Volcanic Eruption, 12 October 2026, v1.1 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Batangas, Philippines, due to a volcanic eruption from the Taal volcano on 08 October 2026. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (01 June 2026 to 17 September 2026) and after (11 October 2026) the event. The map covers an area indicated by the white polygon. Damage is shown by colored pixels of 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports in selected areas. This map should be used as a guidance to identify damaged areas, and may be less reliable over vegetated or mountainous areas. Scattered pixels over vegetated or mountainous areas may be false positives, and a lack of colored pixels over such areas may not mean no damage. This product contains modified Copernicus Sentinel data (2026), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2026)

The Federal Flood Risk Management Standard (FFRMS) floodplain was developed by the Federal Emergency Management Agency (FEMA) to improve the resilience of communities and federal assets against the impacts of flooding. Through a series of Federal executive orders, the FFRMS was established (EOs 11988 and 13690); cancelled in 2017 (EO 13807); re-established in 2021 (EO 14030) and also mapped as the FFRMS floodplain; and then cancelled again in 2025 (EO 14148). This FFRMS floodplain data set was provided by FEMA at the request of Maryland Department of the Environment and is the same data available through the Flood Standard Support Tool (https://floodstandard.climate.gov/).3-meter DEM resolution terrain resolution used for this mapping. In some cases, 1-meter DEM data may have been re-sampled to a 3-meter DEM resolution. FEMA did this to create a consistent FFRMS data set nationwide. The primary sources of data used by FEMA to develop the FFRMS floodplain were USGS’s The National Map for terrain and FEMA’s National Flood Hazard Layer for regulatory water surface elevations. In Riverine areas, the FFRMS floodplain is derived from the National Flood Hazard Layer using the methodology developed by the Maryland Department of the Environment for production of the Riverine CRAB using a linear interpolation applied between two riverine cross sections from the National Flood Hazard Layer (FEMA 100-year floodplain). A freeboard (2-feet for the 2-foot freeboard map, and 3-feet for the 3-foot freeboard map) was added to the 100-year floodplain water surface elevation at each riverine cross section in the National Flood Hazard Layer data set. The new elevations were then interpolated to create a freeboard elevation gradient. This elevation gradient was used to map the horizontal extents of the FFRMS floodplain boundary based on topographic data. Additional details can be found here: https://www.fema.gov/sites/default/files/documents/fema_ffrms-data-methodology.pdfThe methodology to develop the coastal FFRMS floodplain (water surface) raster utilized the coastal static Base Flood Elevation values from the National Flood Hazard Layer as the water surface. To create an expanded surface, these water surface values were collected at uniformly spaced points along the boundary of the effective coastal 1%-annual-chance flood mapping and expanded inland utilizing Thiessen polygons. The Thiessen polygons were converted to a raster matching the spatial resolution of the terrain DEM, and the difference between the water surface elevation raster and the terrain was computed. Areas where the water surface raster value was higher than the terrain raster value indicate the area of flooding. Each freeboard value (2-foot and 3-foot) was mapped independently in whole foot increments utilizing the prior, lower freeboard value boundary of flooding to establish an expanded surface. One limitation of this methodology is that wave runup was modeled for the 100-year floodplain but not remodeled at the higher water surface elevation conditions.Please note that the CRAB data set shows inundation depths derived as the height difference between the DEM and the water surface elevation, whereas the FFRMS shows water surface elevations only. Their horizontal extents may be compared, but users should be careful comparing any color coding or depth values provided in the two applications.

These data represent a subset of the data available on the paper Flood Insurance Rate Maps (FIRM) as provided by the Federal Emergency Management Agency (FEMA). The Q3 flood data were developed to support floodplain management and planning activities but do not replace the official paper FIRMs. These data are not suitable for engineering applications or site work nor can the data be used to determine absolute delineation of flood boundaries. Instead the data should be used to portray zones of uncertainty and possible risks associated with flooding. ** This service includes data only for areas for which the newer National Flood Hazard Layer are not available.  ** Feature service is also available.

National Flood Hazard Layer

The National Flood Hazard Layer (NFHL) is a geospatial database that contains current effective flood hazard data. FEMA provides the flood hazard data to support the National Flood Insurance Program. You can use the information to better understand your level of flood risk and type of flooding. The simplest way for you to access the flood hazard data, including the NFHL, is through FEMAs Map Service Center (MSC). If you want to explore the current digital effective flood hazard data in a map, the best tool to use is the NFHL Viewer. From the NFHL Viewer, you may view, download, and print flood maps for your location. The NFHL is made from effective flood maps and Letters of Map Change (LOMC) delivered to communities. NFHL digital data covers over 90 percent of the U.S. population.  New and revised data is being added continuously. If you need information for areas not covered by the NFHL data, there may be other FEMA Flood Hazard Products and Services which provide coverage for those areas. A list of the types of data available in the NFHL and information about other ways to access the NFHL may be found in the NFHL GIS Services User Guide. If you need more information about individual tables in the NFHL, the FIRM Database Technical Reference, found at FEMA’s Technical References, includes those details. For step-by-step instructions on how to read a flood map, you may view the How to Read a Flood Insurance Rate Map Tutorial. For more information, please visit the FEMA Flood Map Service Center.

https://coast.noaa.gov/arcgis/rest/services/FloodExposureMapper/CFEM_FEMAFloodZones/MapServerFEMA provides access to the National Flood Hazard Layer (NFHL) through web mapping services. The NFHL is a computer database that contains FEMA.s flood hazard map data. The simplest way for you to access the flood hazard data, including the NFHL, is through FEMAs Map Service Center (MSC). If you want to explore the current digital effective flood hazard data in a map, the best tool to use is the NFHL Viewer. From the NFHL Viewer, you may view, download, and print flood maps for your location The data depict effective flood hazard information and supporting data used to develop the information. The primary flood hazard classification is indicated in the Flood Hazard Zones layer.

This dataset integrates high-resolution layers such as Flood Hazard Zones, Base Flood Elevations, and Letters of Map Revisions (LOMR) to depict geographic areas at risk of flooding. It also includes critical infrastructure data like Levees and Coastal Transects to support precise risk assessment and floodplain management.

View current service status Current details are maintained by EGP Service Status Checker. The National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Revision (LOMRs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). The default base map conforms to FEMA's specification for horizontal accuracy. This map should be considered the best online resource to use for official National Flood Insurance Program (NFIP) purposes when determining locations in relation to regulatory flood hazard information. If a different base map is used with the NFHL, the accuracy specification may not be met and the resulting map should be used for general reference only, and not official NFIP purposes. For FEMA's policy on the use of digital flood hazard data for NFIP purposes see Standards for Flood Risk Products.

The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Datum of 1983.

Monthly and annual burnt area 300m rasters are calculated from 2018 onwards. The source data is the Burnt Area (BA) product from COPERNICUS Land Monitoring Service v4 (300m resolution), reprocessed to extract bushfire occurrence statistics. https://land.copernicus.eu/en/products/vegetation/burnt-area-v4-monthly-300m The data used comes from the data generated by the COPERNICUS terrestrial service, the European Commission's Earth observation programme. The research that led to the current version of the product has received funding from various research and technical development programmes of the European Commission. DN values given by GeoTIFF correspond to the month number of fire occurence for the selected season. GeoTIFF raster files and shapefile vector files are available for download at https://geosahel.info For more information, please visit: https://sigsahel.info ******************************************************************************************************** Afrique de l’Ouest et du Centre – Surfaces brûlées Rasters Surfaces annuelles brûlées calculées à partir de 2018. La source des données est le produit « Burnt Area (BA) » du service de surveillance des terres COPERNICUS v4 (résolution de 300 m), retraité pour extraire des statistiques sur les feux de brousse. https://land.copernicus.eu/en/products/vegetation/burnt-area-v4-monthly-300m Les données utilisées sont issues des données générées par le service terrestre COPERNICUS, le programme d'observation de la Terre de la Commission européenne. La recherche qui a conduit à la version actuelle du produit a reçu un financement de divers programmes de recherche et de développement technique de la Commission européenne. Les valeurs DN fournies par le GeoTIFF correspondent au numéro du mois de survenue des feux pour la saison sélectionnée. Les fichiers raster GeoTIFF et les fichiers vectoriels shapefile sont disponibles au téléchargement sur https://geosahel.info Pour plus d’informations, veuillez consulter : https://sigsahel.info

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)

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)

NASA's nighttime lights imagery has a wide range of uses, including tracking urban growth, assessing electrification, monitoring disasters, and studying the biological impacts of light pollution.

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)

GSOCmap is the first global soil organic carbon map ever produced through a consultative and participatory process involving member countries, which makes this map totally new and unique. In fact, the map was prepared by member countries, under the guidance of the Intergovernmental Technical Panel on Soils and the Global Soil Partnership Secretariat. Countries agreed on the methodology to produce the map and were trained on modern tools and methodologies to develop national maps. The Global Soil Partnership then gathered all national maps to produce the final product, ensuring a thorough harmonization process.

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)

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)

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)

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 ( 0.000833)  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)

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)

This layer shows the movement path of 2015 Tropical Cyclone Chapala. On Monday 2 November 2015, Tropical Cyclone Chapala made a landfall in Yemen; however, its effects were also felt across the Gulf of Aden in Somalia where extensive rainfall was experienced in the Northern Bari region in Bosaso district, Puntland. The storm reached maximum wind speed of 130knots.  According to a joint inter-agency rapid assessment more than 500 families (4,000 people) were affected by Tropical Cyclones Chapala and Megh, most affected lived in Gardaful Region, Puntland. No human loss of life was reported, but the rainfall and waves destroyed people’s homes, washed fishing boats and nets, killed livestock (an estimated 3,000 sheep and goats, as well as 200 camels) and caused damage/destruction to public infrastructure including hospitals, roads and schools. It was also estimated that 4,000 people were displaced, with 1,129 people being worst affected, having lost their homes and livelihoods (business, fishing boats, engines and nets), which were swept away by waves. It was reported that there had been extensive damage/destruction to people’s livelihoods, with 80 per cent of villages in Alula and 60 per cent of villages in Af Kalahay Bareda, BiyoCade, Boolimoog, Dhurbo, Fagoora, Geesalay, Murcanyo, Murcanyo, Sayn Weyn, Sayn Yar, Toxiin and Xaabo experiencing loss of livestock and damage to crops and fisheries.

This layer shows the path followed by Tropical Cyclone Bandu. Early on Saturday, 22 May 2010, Tropical Storm 02A moved into the Gulf of Aden and was named "Bandu." At that time, Bandu had maximum sustained winds near 45 knots (52 mph) and was 50 miles north-northwest of Cape Guardafui, Somalia. The storm hit Bari Region in northern Somalia, resulting in flooding and displacement of approximately 12,000 people. TC Bandu quickly dissipated because of cooler waters, higher wind shear and drier air in the Gulf of Aden. It curved southwest and made landfall in northeast Somalia On May 26, OCHA reported flooding in parts of Belet Weyne town, Hiran Region, due to embankment breaches along the Shabelle River as a result of 2010 TC Bandu, displacing an estimated 17,000 people. According to the FAO Somalia Water and Land Information Management Project (SWALIM), flooding also damaged crops and resulted in the displacement of a combined population of nearly 2,000 people in Jamaame town, Lower Juba Region, as well as Afgooye and Kurtunwaarey districts, Lower Shabelle Region.

This was the first documented tropical cyclone to move through the Gulf of Aden.In May 28,1984 TC1A storm struck about 65Km (40 miles) west of Berbera, Somalia and quickly dissipated. The storm had maximum wind speeds of 23m/s before dissipating. This layer shows the occurence of the tropical cyclone in Woqooyi Galbeed province, Somalia. 1984 TC 1A remained a compact system throughout its life. Even at its maximum intensity of 45 kt (23 m/s) between 00002 and 06002 on 27 May, the radius of greater than 30 kt (15 m/s) winds was estimated to be only 60 nm (111 km).  The cyclone moved north-westward until late on the 26th, then turned to the west-southwest and entered the Gulf of Aden in response to a strong subtropical ridge over Saudi Arabia.The storm then transited up the Gulf of Aden until it made landfall at 03002 on 28 May, approximately 35 nm (65 Km) west of Berbera, Somalia. There were no reports of damages or injuries from this system.

This layer shows the movement path for 2015 Tropical Cyclone Megh. Following Tropical Cyclone Chapala, new tropical cyclone Megh originated from the Arabian Sea causing even more rains in parts of Bari region in Puntland and Somaliland. The storm produced a maximum windspeed of 110knots. Areas affected included: Af Kalahay, Alula, Bareda, BiyoCade, Boolimoog, Dhurbo, Fagoora, Geesalay, Murcanyo, Sayn Weyn, Sayn Yar, Toxin and Xaabo. Re-estimated population figures after Tropical Megh, showed 4.9 million people were in need of assistance, 308,700 children under-5 were acutely malnourished, of which 55,800 were severely malnourished and 1.1 million remain in a protracted internal displacement situation.

Tropical Cyclone Gati originated from the Bay of Bengal. On 22 November 2020 at around 13:00 UTC, TC Gati became the strongest ever documented tropical storm to hit Somalia. Gati made landfall at Ras Hafun (Northeast of Somalia) with maximum sustained winds of 170Km/hr and was classified as a Category 2 storm (or category 2 hurricane on the Saffir-Simpson scale). This layers shows the occurence of TC Gati in two regions of Somali, Sanaag and Bari Regions. TC Gati primarily impacted parts of Bari region in Puntland State and then Sanaag region in Somaliland. Authorities reported rainfall totals from the storm to be greater than the amounts normally seen for the whole year. In a 24-hour period, Bosaso recorded 128mm of rain and Balidhidin 103mm. By 25th November 2020, TC Gati had dissipated but it left trail of destruction across Bari and Sanaag regions of Somalia. Authorities estimated 180,000 people (30,000 households) had been affected in Puntland Regional State, with 42,000 people (7,000 households) displaced and at least eight people killed and unknown number injured, with considerable damages reported to infrastructure, livelihoods, and social services (telecommunication, electricity, roads, schools). Resultant flooding bursted the sewerage system and increased risk of diseases among the affected population. By the end of December, the storm had killed 9 people and over 63 000 livestock (sheep and goats), and affected around 183 000 people.  The heavy rains caused the Ceel-Daahir River in Puntland to flood, blocking vehicles from bringing crucial supplies. In some towns such as Xaafun, Hurdiye, and Karduush people reported losing their entire herds of livestock. There were also reports of the destruction of 120 fishing vessels in these areas resulting in the loss of livelihood for an estimated 460 fishermen.

Tropical cyclones hitting Somalia Regions for the period between 1984 to 2020. This layer indicates the frequency of cyclone occurence within specific regions of Somalia. The cyclones recorded include: 1984 Tropical Cyclone 1A 1984 Tropical Cyclone 4B 1992 Tropical Cyclone 12A 1994 Tropical Cyclone 05A 1997 Tropical Cyclone 03A 2004 Tropical Cyclone Agni 2010 Tropical Cyclone Bandu 2012 Tropical Cyclone Murjan 2013 Tropical Cyclone 3A 2015 Tropical Cyclone Chapala 2015 Tropical Cyclone Megh 2018 Tropical Cyclone Sagar 2019 Tropical Cyclone Pawan 2020 Tropical Cyclone Gati

This layer shows movement path of 2019 Tropical Cyclone Pawan in Somalia. A Tropical Storm initially named 06A formed in the northern Indian Ocean and later developed into a Tropical Cyclone named Pawan after sustaining wind speeds of more than 39mph (48kph) and heavy rain for two days. TC Pawan spread its clouds as far northwest as Oman and Yemen on its way to Somalia. It made landfall in Somalia on 7th December 2019 on the Coastal side of Puntland (Bossaso, Garowe).   The worst hit areas by 2019 TC Pawan included Nugaal Region (Eyl and Dangorayo Districts), Karkaar (Qardho District) and Bari Region (Alula, Iskushuban, and Baargaal Districts) who are under Garowe Somalia Red Crescent Society (SRCS) Branch) and the Coastal villages of Hafun, Iskushuban, Baargaal, Quandala and Alula Districts in Bari Region (under Bosasso SRCS Branch). Other areas affected include the coastal villages in Bari Region including Hafun, Iskushuban, Baargaal, Quandala and Alula districts. Most affected households needed urgent humanitarian assistance as they were already living in dire conditions prior to the crisis. The destruction and flooding caused by TC Pawan increased the vulnerability of communities and heightened the impact of the ever -challenging climatic extremes. The assessment estimated that 35,600 households, representing a population of 213,600 people had been affected. The assessment further estimated the affected population to represented 60-70% of the entire Bari and Nugaal Regions.

1984 Tropical Cyclone 04B was a weak storm that struck eastern Somalia in Mudug province, and quickly dissipated. The storm had a maximunm windspeed of 18m/s before dessipating. The cyclone was detected on 23/11/1984. Mudug landfall was on 7/12/1984. There were no reports of damages or injuries from this storm.