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Gambia

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  • Numbers of households and population by Local Government Areas, Districts, and Settlements, 2013, The Gambia

  • Monthly evaporation raw data from station Basse in The Gambia.

  • Raw data from several climate stations in The Gambia. Monthly minimum and maximum temperature and rainfall from the stations: - Banjul - Basse - Fatoto - Janjanbureh - Jenoi - Kaur - Kerewan - Sapu - Sibanor - Yundum

  • The 2000 National Greenhouse Gas Inventory of The Gambia shows national emission total of about 20.02 Million Tons CO2 Equivalent (TCO2E) and per capita emissions of 13.5 TCO2E. This is insignificant compared to other country emissions. However, as a Party to the Climate Change Convention and its Kyoto Protocol, Gambia is willing to participate in mitigating global emissions and their concentrations in the atmosphere with the first step of conducting a mitigation assessment and developing this NAMA document. Trend analysis of climate data from 1951 to date shows a progressively warming and drier Gambia. Using General Circulation Model outputs, national temperatures are projected to increase by about 0.3OC in 2010 to about 3.9OC in 2100. Rainfall is also projected to decrease by about 1% in 2010 to about 54% in 2100. This confirms previous results of in the First National Communication that with increase in temperatures under a warming climate, rainfall in The Gambia would correspondingly decrease. The development challenges of The Gambia will be significant as the country faces complex economic, social and technological choices based on the climate change impacts already enumerated in the preceding paragraph. This is compounded by the inadequate capacities, inadequacies in the existing technologies and the non availability of domestic funding from both the public and private sectors for climate change.

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    Created by NOVELTIS for ECREEE during the ACP-EU project ECOWREX 2: Promoting Sustainable Energy Development through the use of Geospatial Technologies in West Africa This dataset shows the average Wind Power Density at 60 meter high over the year 2013. The average is calculated from hourly time series data generated by NOVELTIS meso-scale Numeric Weather Prediction system. The resolution is 4km x 4km. The unit is W/m2. Projection is latlon, EPSG 4326, WGS 84. This dataset is not indicated for local studies but only for regional comparison. The annual average was calculated from hourly time series data generated by NOVELTIS meso-scale Numeric Weather Prediction system over the full 2013 year. The values are calculated from NWP output extracted parameters: U = West-East component of the wind speed V = South-North component of the wind speed. ALT = inverse density AL = inverse perturbation density According to the following formula: WPD = 1/2* 1/(ALT+AL) * (WS)3 With WS = √(U2 + V2) The 2013 year was selected by NOVELTIS as TMY (typical meteorological year) through a regional climatic analysis for the period from 2000 to 2014. Minimum=32.506 W/m2 Maximum=688.547 W/m2 Mean=150.551 W/m2 StdDev=61.167 W/m2

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    Created by NOVELTIS for ECREEE during the ACP-EU project ECOWREX 2: Promoting Sustainable Energy Development through the use of Geospatial Technologies in West Africa This dataset shows the average Wind Power Density at 80 meter high over the year 2013. The average is calculated from hourly time series data generated by NOVELTIS meso-scale Numeric Weather Prediction system. The resolution is 4km x 4km. The unit is W/m2. Projection is latlon, EPSG 4326, WGS 84. This dataset is not indicated for local studies but only for regional comparison. The annual average was calculated from hourly time series data generated by NOVELTIS meso-scale Numeric Weather Prediction system over the full 2013 year. The values are calculated from NWP output extracted parameters: U = West-East component of the wind speed V = South-North component of the wind speed. ALT = inverse density AL = inverse perturbation density According to the following formula: WPD = 1/2* 1/(ALT+AL) * (WS)3 With WS = √(U2 + V2) The 2013 year was selected by NOVELTIS as TMY (typical meteorological year) through a regional climatic analysis for the period from 2000 to 2014. Minimum=37.158 W/m2 Maximum=780.918 W/m2 Mean=176.734 W/m2 StdDev=66.407 W/m2

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    Created by NOVELTIS for ECREEE during the ACP-EU project ECOWREX 2: Promoting Sustainable Energy Development through the use of Geospatial Technologies in West Africa This dataset shows the average Wind Power Density at 40 meter high over the year 2013. The average is calculated from hourly time series data generated by NOVELTIS meso-scale Numeric Weather Prediction system. The resolution is 4km x 4km. The unit is W/m2. Projection is latlon, EPSG 4326, WGS 84. This dataset is not indicated for local studies but only for regional comparison. The annual average was calculated from hourly time series data generated by NOVELTIS meso-scale Numeric Weather Prediction system over the full 2013 year. The values are calculated from NWP output extracted parameters: U = West-East component of the wind speed V = South-North component of the wind speed. ALT = inverse density AL = inverse perturbation density According to the following formula: WPD = 1/2* 1/(ALT+AL) * (WS)3 With WS = √(U2 + V2) The 2013 year was selected by NOVELTIS as TMY (typical meteorological year) through a regional climatic analysis for the period from 2000 to 2014. Minimum=27.706 W/m2 Maximum=583.235 W/m2 Mean=124.764 W/m2 StdDev=57.928 W/m2

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    Created by NOVELTIS for ECREEE during the ACP-EU project ECOWREX 2: Promoting Sustainable Energy Development through the use of Geospatial Technologies in West Africa This dataset shows the average temperature at 2 meter over the year 2013. The average is calculated from hourly time series data generated by NOVELTIS meso-scale Numeric Weather Prediction system. The resolution is 4km x 4km. The unit is degree Celcius. Projection is latlon, EPSG 4326, WGS 84. This dataset is not indicated for local studies but only for regional comparison. The annual average was calculated from hourly time series data generated by NOVELTIS meso-scale Numeric Weather Prediction system over the full year 2013. The parameter extracted from the NWP output is T2 = Temperature at 2 meter. The 2013 year was selected by NOVELTIS as TMY (typical meteorological year) through a regional climatic analysis for the period from 2000 to 2014. Minimum=20.633 °C, Maximum=29.597°C Mean=26.395 °C StdDev=1.431 °C

  • Occupying a total area of 11,300 sq km, with a population density of 130 persons per sq km, The Republic of the Gambia is one of the most densely populated countries on continental Africa. Because The Gambia possesses only minimal commercial mineral resources and manufacturing sector, agriculture is the primary source of livelihood for many Gambians, employing more than 68% of the workforce and accounting for about 40% of the Gambia’s export earnings contributing about 26% of the Gross Domestic Product (GDP). Agriculture is predominantly subsistence and rain-fed with farmers relying on traditional shifting cultivation and livestock management practices. Over the last fifty years cropland area increased from under 100,000 ha to over 300,000 at the expense of natural woodland and wetland ecosystems. Over 51% of The Gambia’s population resides in urban areas. Driven by variable and degrading climate, decline in agricultural productivity in rural areas, and changes in economic activity (tourism, petty trade and small scale manufacturing) in the ecologically favorable West Coast Region, urban population has increased from 110,000 in 1973 to 680,000 in 2003. Between 1980 and 2001, built-up area in the Gambia has increased from 2,725 ha to more than 19,000 ha with over 50% of the increase occurring in Kombo (KMC and the districts of Kombo). The Gambia’s climate is Sahelian characterized by high variability in the amount and distribution of annual precipitation. Analysis of long-term climate data shows that the past 50 years have seen a decrease in total amount of precipitation, length of rainy season, and increase in length and frequency of extreme weather events such as droughts and dust storms. The low-lying topography, combined by high dependence on subsistence rain-fed agriculture and inadequate drainage and storm water management system in a context of rapidly expanding un-regulated urban expansion has placed the Gambia among those countries most vulnerable to climate change. This study examines threats associated with anthropogenic climate change; vulnerable ecosystems and ecosystem services; and examines how to integrate responses to climate change and adaptation measures into strategies for poverty reduction, to ensure sustainable development.

  • With a national electrification rate of an estimated 40 per cent and with certain rural areas having an electrification rate as low as 6 per cent, the time is ripe in The Gambia for the Rural Electrification with Renewable Energy (RE) Nationally Appropriate Mitigation Action (NAMA). A number of building blocks have already been put in place in the country. The 2013 Renewable Energy Act provides the framework for both on and off-grid renewable energy tariffs and net metering, as well as establishing a national RE Fund. There has been development of pilot renewable energy projects as well as diesel powered multi-function platforms, which provide energy access for economic activities in rural areas. The NAMA has five key objectives which are: 1. Increase the level of renewable energy (for electricity) and contribute to the national long-term target of increasing the share of renewable energy within the power generation sector. 2. Reduce greenhouse gas emissions in the power generation sector. 3. Increase the rural population’s access to sustainable electricity. 4. Encourage an increase in rural community income generation, and improve rural livelihoods. 5. Increase the level of private sector participation within the power sector. These objectives will be accomplished through a number of activities, divided into Phase 1 and Phase 2. Phase 1 activities will include the establishment of two types of ventures which will connect unelectrified rural communities: RE Community Energy Centres (RE-CEC) and RE Micro-Grids (RE-MGs). Phase 2 ventures will comprise RE systems which will displace thermal generation at existing regional grids (referred to as RE Displacement Systems—RE-DIS) and RE independent power producers (RE-IPPs).