This dataset shows sub-areas (sub-catchments) for West Africa and the estimated hydropower potential as well as several other interesting attributes for hydro power development. This dataset is not intended for local studies but only for regional comparison. The dataset was created using the following methodology: 1. Creation of the sub-catchment boundaries using the river network and the Hydrosheds 15s flow direction grid. Outlet points of sub-catchments were defined as the last cell point of river reaches, where the accumulated upstream catchment size exceeds 3000 km². In coastal areas the threshold was reduced to 1000 km². Outlet points were manually adjusted at large reservoirs. 2. Water balance and climate change variables were joined into GIS from the simulation outputs of a Fortran water balance model. Climate change scenario simulation results are based on the CORDEX Africa ensemble (RCP4.5 and RCP8.5). 3. Theoretical hydropower potential data were aggregated to sub-area values from detailed simulation results at the river network. 4. Attractive regions were identified using a classification system based on hydropower potential. 5. Hydropower plant types are based on a rough classification system using river network data, including mean annual discharge, seasonality in discharge and river slope. Local site studies may result in different suitable plant types. 6. Preferred machine types are based on a rough classification system using river slope and plant type. The following tools were used for creating this dataset: • ArcGIS 9.2: main GIS tool • ArcView 3.1: specific tasks with large attribute tables where more recent GIS versions fail • Fortran: main processing tool for various tasks o Pre-processing of GPCC and satellite precipitation data o Water balance modelling o Sub-catchment outlets definition • MS Excel: some data pre-processing and visualization • Libre Office: dbf file manipulation • CDO: Climate Data Operators for processing of CORDEX-Africa climate model data • Shell scripts: For automatic file processing of climate model data • Batch scripts: For automatic calls to Fortran programs For each sub-area the following attributes are available (units in brackets): • NB: ID number of sub-area • AREA: Local size (km²) of sub-area • PRECIP_Y: Mean annual precipitation (mm) in the period 1998-2014 • ETA_Y: Mean annual actual evapotranspiration (mm) simulated for the period 1998-2014 • RUNOFF_Y: Mean annual runoff (mm) simulated for the period 1998-2014 • TEMP_Y: Mean annual air temperature (°C) in the period 1998-2014 • P_2035_P25: Change in future mean annual precipitation in % (2026-2045 vs. 1998-2014) for the lower quartile projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • P_2035_P50: Change in future mean annual precipitation in % (2026-2045 vs. 1998-2014) for the median projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • P_2035_P75: Change in future mean annual precipitation in % (2026-2045 vs. 1998-2014) for the upper quartile projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • P_2055_P25: Change in future mean annual precipitation in % (2046-2065 vs. 1998-2014) for the lower quartile projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • P_2055_P50: Change in future mean annual precipitation in % (2046-2065 vs. 1998-2014) for the median projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • P_2055_P75: Change in future mean annual precipitation in % (2046-2065 vs. 1998-2014) for the upper quartile projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • E_2035_P25: Change in future mean annual actual evapotranspiration in % (2026-2045 vs. 1998-2014) for the lower quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • E_2035_P50: Change in future mean annual actual evapotranspiration in % (2026-2045 vs. 1998-2014) for the median simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • E_2035_P75: Change in future mean annual actual evapotranspiration in % (2026-2045 vs. 1998-2014) for the upper quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • E_2055_P25: Change in future mean annual actual evapotranspiration in % (2046-2065 vs. 1998-2014) for the lower quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • E_2055_P50: Change in future mean annual actual evapotranspiration in % (2046-2065 vs. 1998-2014) for the median simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • E_2055_P75: Change in future mean annual actual evapotranspiration in % (2046-2065 vs. 1998-2014) for the upper quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • R_2035_P25: Change in future mean annual runoff in % (2026-2045 vs. 1998-2014) for the lower quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • R_2035_P50: Change in future mean annual runoff in % (2026-2045 vs. 1998-2014) for the median simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • R_2035_P75: Change in future mean annual runoff in % (2026-2045 vs. 1998-2014) for the upper quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • R_2055_P25: Change in future mean annual runoff in % (2046-2065 vs. 1998-2014) for the lower quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • R_2055_P50: Change in future mean annual runoff in % (2046-2065 vs. 1998-2014) for the median simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • R_2055_P75: Change in future mean annual runoff in % (2046-2065 vs. 1998-2014) for the upper quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • T_2035_P25: Change in future mean annual air temperature in °C (2026-2045 vs. 1998-2014) for the lower quartile projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • T_2035_P50: Change in future mean annual air temperature in °C (2026-2045 vs. 1998-2014) for the median projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • T_2035_P75: Change in future mean annual air temperature in °C (2026-2045 vs. 1998-2014) for the upper quartile projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • T_2055_P25: Change in future mean annual air temperature in °C (2046-2065 vs. 1998-2014) for the lower quartile projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • T_2055_P50: Change in future mean annual air temperature in °C (2046-2065 vs. 1998-2014) for the median projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • T_2055_P75: Change in future mean annual air temperature in °C (2046-2065 vs. 1998-2014) for the upper quartile projection of 30 climate model runs in the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • POWER: Theoretical hydropower potential (MW) for the period 1998-2014 (total of all river reaches located in the sub-area) • POW_MINI: Theoretical hydropower potential (MW) for pico/micro/mini hydropower plants (< 1 MW installed capacity) for the period 1998-2014 • POW_SMALL: Theoretical hydropower potential (MW) for small hydropower plants (1-30 MW installed capacity) for the period 1998-2014 • POW_MEDIUM: Theoretical hydropower potential (MW) for medium/large hydropower plants (>30 MW installed capacity) for the period 1998-2014 • ATT_MINI: Region with theoretical hydropower potential that is attractive (0: no, 1: yes) for pico/micro/mini hydropower plants (< 1 MW installed capacity) • ATT_SMALL: Region with theoretical hydropower potential that is attractive (0: no, 1: yes) for small hydropower plants (1-30 MW installed capacity) • ATT_MEDIUM: Region with theoretical hydropower potential that is attractive (0: no, 1: yes) for medium/large hydropower plants (> 30 MW installed capacity) • PLANT_TYP1: Region suitable (0: no, 1: yes) for hydropower plant type 1 (run-of-river without diversion) • PLANT_TYP2: Region suitable (0: no, 1: yes) for hydropower plant type 2 (run-of-river with diversion) • PLANT_TYP3: Region suitable (0: no, 1: yes) for hydropower plant type 3 (storage without diversion) • PLANT_TYP4: Region suitable (0: no, 1: yes) for hydropower plant type 4 (storage with diversion) • TURBINE: Preferred turbine type (text) • PT_2035_25: Change in future hydropower potential in % (2026-2045 vs. 1998-2014) for the lower quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PT_2035_50: Change in future hydropower potential in % (2026-2045 vs. 1998-2014) for the median simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PT_2035_75: Change in future hydropower potential in % (2026-2045 vs. 1998-2014) for the upper quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PT_2055_25: Change in future hydropower potential in % (2046-2065 vs. 1998-2014) for the lower quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PT_2055_50: Change in future hydropower potential in % (2046-2065 vs. 1998-2014) for the median simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PT_2055_75: Change in future hydropower potential in % (2046-2065 vs. 1998-2014) for the upper quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PL_2035_25: Change in future hydropower potential in % (2026-2045 vs. 1998-2014) of local rivers (having their source in the same sub-area) for the lower quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PL_2035_50: Change in future hydropower potential in % (2026-2045 vs. 1998-2014) of local rivers (having their source in the same sub-area) for the median simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PL_2035_75: Change in future hydropower potential in % (2026-2045 vs. 1998-2014) of local rivers (having their source in the same sub-area) for the upper quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PL_2055_25: Change in future hydropower potential in % (2046-2065 vs. 1998-2014) of local rivers (having their source in the same sub-area) for the lower quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PL_2055_50: Change in future hydropower potential in % (2046-2065 vs. 1998-2014) of local rivers (having their source in the same sub-area) for the median simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5) • PL_2055_75: Change in future hydropower potential in % (2046-2065 vs. 1998-2014) of local rivers (having their source in the same sub-area) for the upper quartile simulation using 30 climate model runs of the CORDEX-Africa ensemble (RCP4.5 and RCP8.5)

Existing and planned distribution grid network (11KV-33KV) in the ECOWAS region. The dataset is developed by the ECOWREX team by using the following sources of information: - BENIN: Ministry of Energy (2016) - BURKINA FASO: West African Power Pool (WAPP) GIS database January 2017, SONABEL - CÔTE D'IVOIRE: CI-ENERGIES - GAMBIA: WAPP database (2015) - GHANA: Energy Commission (2015) - GUINEA: Ministry of Energy, Energy Information System Unit (SIE, 2016) - MALI: EnergyInfo platform - https://energydata.info/ (World Bank, 2017) - NIGER: EnergyInfo platform - https://energydata.info/ (World Bank, dataset 2015) - NIGERIA: EnergyInfo platform - https://energydata.info/ (World Bank, dataset 2016), Nigeria Rural Electrification Program (http://rrep-nigeria.integration.org/ ), Facebook model (https://energydata.info/dataset/medium-voltage-distribution-predictive) - SENEGAL: EnergyInfo platform - https://energydata.info/ (World Bank, dataset 2007) - SIERRA LEONE: EnergyInfo platfom - https://energydata.info/ (World Bank, 2017)

Density of identified off-grid households in ROGEP countries by 2030, per country administrative boundary level 1. The households could have access to electricity through off-grid solutions by 2030 according to the least-cost analysis conducted in the framework of the off-grid solar market assessment and private sector support facility design, which was done as an activity of the sub-component 1A of the Regional Off-Grid Electrification Project (ROGEP).

Location of settlements that could have access to electricity through off-grid solutions by 2023 according to the least-cost analysis conducted in the framework of the off-grid solar market assessment and private sector support facility design, which was done as an activity of the sub-component 1A of the Regional Off-Grid Electrification Project (ROGEP).

Location of settlements in West Africa in identified irrigable off-grid areas in 2018, that could still be in off-grid areas in 2023 according to the least-cost analysis conducted in the framework of the off-grid solar market assessment and private sector support facility design, which was done as an activity of the sub-component 1A of the Regional Off-Grid Electrification Project (ROGEP).

Drainage Network and Inland Water Bodies of Togo

Density of identified off-grid villages in ROGEP countries by 2030, per country administrative boundary level 1 or 2. The different types of electricity demand in the villages (households, public institutions, SMEs, etc.) could have access to electricity through off-grid solutions by 2030 according to the least-cost analysis conducted in the framework of the off-grid solar market assessment and private sector support facility design, which was done as an activity of the sub-component 1A of the Regional Off-Grid Electrification Project (ROGEP).

Location of settlements that could have access to electricity through mini-grids in 2023 according to the least-cost analysis conducted in the framework of the off-grid solar market assessment and private sector support facility design, which was done as an activity of the sub-component 1A of the Regional Off-Grid Electrification Project (ROGEP).

Livelihood zoning consists in identifying areas where rural people share relatively homogeneous living conditions, on the basis of a combination of biophysical and socio-economic determinants. The main criteria to establish livelihood zones are: the predominant source of income (livelihood activities); the natural resources available to people and the way they are used; and the prevailing agroclimatic conditions that influence farming activities. Patterns of livelihood vary from one area to another, based on local factors such as climate, soil or access to markets. The analysis delineates geographical areas within which people share similar livelihood patterns: source of living, access to food, farming practices, including crops, livestock and access to markets. The map of livelihood zones is the main output from a participatory mapping workshop and forms the basis for the overall AWM assessment. It describes and geographically locates the different country livelihood contexts, focusing on the main smallholders’ livelihood strategies, their water-related problems and other constraints for development, and the role agricultural water management plays for their livelihoods. An attribute table provides a detailed description of each livelihood zone. (Source: FAO, 2011)

Location of settlements that could be connected to national electricity grids in 2023 according to the least-cost analysis conducted in the framework of the off-grid solar market assessment and private sector support facility design, which was done as an activity of the sub-component 1A of the Regional Off-Grid Electrification Project (ROGEP).