PAUWES Data Portal
Gambia
Keywords
Regions
Contact for the resource
Provided by
Years
Formats
Representation types
Update frequencies
status
Scale
-
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.
-
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.
-
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
-
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 Speed at 100 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 m/s. 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 values are calculated from NWP output extracted parameters at a level close to 100 meter: U = West-East component of the wind speed V = South-North component of the wind speed. 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= 2.95 m/s Maximum= 9.16 m/s Mean= 6.5 m/s StdDev= 0.852 m/s
-
The Government of The Gambia is committed to reducing poverty and improving the well-being of its population. This commitment is driven by the Government’s long-term strategy, Vision 2020, which is being executed through a series of medium-term development plans since 1994. The Programme for Accelerated Growth and Employment (PAGE) is The Gambia’s development strategy and investment programme for 2012 to 2015. PAGE 2012-2015 is based on Vision 2020 and various sector strategies, and is consistent with the Paris Declaration’s resolutions on aid effectiveness and the ownership of development. PAGE is the main interface between the Government and The Gambia’s development partners and is fully aligned with the Millennium Development Goals (MDGs) and is a medium term strategic plan leading to a developed and prosperous Gambia. The focus of PAGE is to accelerate pro-poor growth and generate employment. The implementation of PAGE 2012-15 will be done through the Priority Action Plan (PAP) that will require private sector participation and heavy financial support from development partners. This support will help consolidate the gains of recent years, boost employment, and sustain development in The Gambia. The preparation of PAGE was highly participatory at all levels of society ensuring national ownership. It was coordinated by the Ministry of Finance and Economic Affairs.
-
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
-
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
-
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
-
NAPAs provide a process for the LDCs to identify priority activities that respond to their urgent and immediate needs with regard to adaptation to climate change - those needs for which further delay could increase vulnerability or lead to increased costs at a later stage. The rationale for NAPAs rests on the limited ability of the LDCs to adapt to the adverse effects of climate change. In the NAPA process, prominence is given to community-level input as an important source of information, recognizing that grassroots communities are the main stakeholders. NAPAs use existing information and no new research is needed. They are action-oriented, country-driven, are flexible and based on national circumstances. In order to effectively address urgent and immediate adaptation needs, NAPA documents are presented in a simple format, easily understood both by policy-level decision-makers and the public.