IGAD Climate Prediction and Applications Center (ICPAC)

This layer shows the movement path of 2020 Tropical Cyclone Gati in Somalia. TC Gati originated from the Bay of Bengal and became the strongest ever documented tropical storm to hit Somalia. It made landfall at Ras Hafun (Northeast of Somalia) with maximum sustained winds of 170Km/hr and was classified as a Category 2 storm. Tropical Cyclone Gati was the strongest storm ever recorded in the northern Indian Ocean and wreaked unimaginable damage on people and property. GATI left a trail of destruction across Bari and Sanaag regions of Somalia, disproportionately affecting coastal communities. Authorities estimated about 180,000 people (30,000 households) to have 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 damage reported to infrastructure, livelihoods, and social services (telecommunication, electricity, roads, schools). Resultant flooding burst the sewerage system and increased the risk of diseases among the affected population. The worst hit areas were Baargaal, Foocaar, Garduush, Hurdiya, and Xaafuun, Foocaar, Garduush and Garan Hoose were worst hit villages in the Indian Ocean, Bosaso / Qandala in the Gulf of Aden, and Baarmadowe. 

Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.

Dekadal (10 days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.

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) No information provided

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) No information provided

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) No information provided

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) No information provided

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) No information provided

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) No information provided

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) No information provided

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)   No information provided

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) No information provided

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) No information provided

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) No information provided

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   No information provided

This dataset measures relative water demand. Higher values indicate more competition among users.The dataset has a resolution of 0.05 pixels about 5000 metres ( referenced 2023) The baseline water stress (BWS) layer, developed as part of WRI's Aqueduct Water Risk Atlas, measures the ratio of total water withdrawals relative to the annual available renewable surface water supplies. BWS serves as a good proxy for water-related challenges more broadly, given that areas of higher water stress will likely be subject to higher depletion of surface and groundwater resources and more competition amongst users, as well as the associated impacts on water quality and other ecosystem services. Watersheds with high baseline water stress may warrant greater need to take appropriate action to respond to watershed risks. A long time series of supply (1950–2010) was used to reduce the effect of multi-year climate cycles and ignore complexities of short-term water storage (e.g., dams, floodplains) for which global operational data are nonexistent. Baseline water stress thus measures chronic stress rather than drought stress. Watersheds with less than 0.012 m/m2 /year of withdrawal and 0.03 m/m2 /year of available blue water were masked as “arid and low water use” since watersheds with low values were more prone to error in the estimates of baseline water stress. Additionally, although current use in such catchments is low, any new withdrawals could easily push them into higher stress categories. No information provided

Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.

Dekadal(10days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.

Dekadal(10days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.

Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.

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

Tropical cyclones hitting Somalia Districts for the period between 1984 to 2020. This layer indicates the frequency of cyclone occurence within specific regions. 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

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.

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.

In December 24, 1992 Tropical Cyclone 12A struck southern Bari region,bringing beneficial rainfall to the normally arid areas. This was the fourth cyclone in the Arabian Sea and the twelfth in the North Indian Ocean in 1992. The tropical disturbance that became TC12A first formed in the Maldives near the equatorial trough.  The peak intensity reached by this cyclone was 50 kt (26 m/s) just prior to landfall. TC12A weakened rapidly after making landfall in Somalia, bringing much needed rain to a dry country. It dissipated over land. Bari region was the most affected region by this storm.