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2016

223 record(s)
 
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  • High resolution (0.11°) regional climate simulations were carried out by the researchers at Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) as part of the West Africa Science Service Center on Climate Change and Adapted Land Use (WASCAL) Project. One of the goals of the WASCAL project is to provide the best accuracy in regional climate simulations over the entire West Africa region for a large proportion of the 21st century. The regional climate model employed in the project was the COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Model) 
version 4.8_clm19 forced by one global circulation model (GCM), the Max Planck Institute Earth System Model (MPI-ESM-LR, Stevens et al. 2013) under the Representative Concentrative Pathways 4.5 (RCP 4.5). Further control runs with ERA-Interim reanalysis products (Dee et al. 2011) were also used for model verification. Therefore, daily outputs of near-surface maximum temperature, obtained from the hourly simulations of CCLMv4.8.19, driven by MPI-ESM-LR, are hereby presented.

  • High resolution (12km) regional climate simulations were carried out by the researchers at Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) as part of the West Africa Science Service Center on Climate Change and Adapted Land Use (WASCAL) Project. One of the goals of the WASCAL project is to provide the best accuracy in regional climate simulations over the entire West Africa region for a large proportion of the 21st century. The regional climate model employed in the project was the Weather Research and Forecasting Model version 3.5.1 (WRFv3.5.1) forced by three global circulation models (GCMs) under the Representative Concentrative Pathways 4.5 (RCP 4.5). The forcing GCMs are: the Max Planck Institute Earth System Model (MPI-ESM-MR, Stevens et al. 2013), the General Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2M, Dunne et al. 2012), and the Hadley Global Environment Model (HadGEM2-ES, Collins et al. 2011). Further control runs with ERA-Interim reanalysis products (Dee et al. 2011) were also carried out for model verification and bias correction. Therefore, daily outputs of deaccumulated TOA outgoing longwave radiation, obtained from the 3-hourly simulations of WRFv3.5.1, driven by GFDL-ESM2M, are hereby presented.

  • High resolution (12km) regional climate simulations were carried out by the researchers at Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) as part of the West Africa Science Service Center on Climate Change and Adapted Land Use (WASCAL) Project. One of the goals of the WASCAL project is to provide the best accuracy in regional climate simulations over the entire West Africa region for a large proportion of the 21st century. The regional climate model employed in the project was the Weather Research and Forecasting Model version 3.5.1 (WRFv3.5.1) forced by three global circulation models (GCMs) under the Representative Concentrative Pathways 4.5 (RCP 4.5). The forcing GCMs are: the Max Planck Institute Earth System Model (MPI-ESM-MR, Stevens et al. 2013), the General Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2M, Dunne et al. 2012), and the Hadley Global Environment Model (HadGEM2-ES, Collins et al. 2011). Further control runs with ERA-Interim reanalysis products (Dee et al. 2011) were also carried out for model verification and bias correction. Therefore, monthly outputs of atmospheric eastward wind, obtained from the 6-hourly simulations of WRFv3.5.1, driven by HadGEM2-ES, are hereby presented.

  • High resolution (12km) regional climate simulations were carried out by the researchers at Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) as part of the West Africa Science Service Center on Climate Change and Adapted Land Use (WASCAL) Project. One of the goals of the WASCAL project is to provide the best accuracy in regional climate simulations over the entire West Africa region for a large proportion of the 21st century. The regional climate model employed in the project was the Weather Research and Forecasting Model version 3.5.1 (WRFv3.5.1) forced by three global circulation models (GCMs) under the Representative Concentrative Pathways 4.5 (RCP 4.5). The forcing GCMs are: the Max Planck Institute Earth System Model (MPI-ESM-MR, Stevens et al. 2013), the General Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2M, Dunne et al. 2012), and the Hadley Global Environment Model (HadGEM2-ES, Collins et al. 2011). Further control runs with ERA-Interim reanalysis products (Dee et al. 2011) were also carried out for model verification and bias correction. Therefore, de-accumulated daily outputs of surface upwelling longwave radiation, obtained from the 3-hourly simulations of WRFv3.5.1, driven by ERA-Interim reanalysis, are hereby presented.

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    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)

  • High resolution (0.11°) regional climate simulations were carried out by the researchers at Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) as part of the West Africa Science Service Center on Climate Change and Adapted Land Use (WASCAL) Project. One of the goals of the WASCAL project is to provide the best accuracy in regional climate simulations over the entire West Africa region for a large proportion of the 21st century. The regional climate model employed in the project was the COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Model) 
version 4.8_clm19 forced by one global circulation model (GCM), the Max Planck Institute Earth System Model (MPI-ESM-LR, Stevens et al. 2013) under the Representative Concentrative Pathways 4.5 (RCP 4.5). Further control runs with ERA-Interim reanalysis products (Dee et al. 2011) were also used for model verification. Therefore, daily outputs of near-surface minimum temperature, obtained from the hourly simulations of CCLMv4.8.19, driven by ERA-Interim reanalysis, are hereby presented.

  • High resolution (12km) regional climate simulations were carried out by the researchers at Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) as part of the West Africa Science Service Center on Climate Change and Adapted Land Use (WASCAL) Project. One of the goals of the WASCAL project is to provide the best accuracy in regional climate simulations over the entire West Africa region for a large proportion of the 21st century. The regional climate model employed in the project was the Weather Research and Forecasting Model version 3.5.1 (WRFv3.5.1) forced by three global circulation models (GCMs) under the Representative Concentrative Pathways 4.5 (RCP 4.5). The forcing GCMs are: the Max Planck Institute Earth System Model (MPI-ESM-MR, Stevens et al. 2013), the General Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2M, Dunne et al. 2012), and the Hadley Global Environment Model (HadGEM2-ES, Collins et al. 2011). Further control runs with ERA-Interim reanalysis products (Dee et al. 2011) were also carried out for model verification and bias correction. Therefore, de-accumulated daily outputs of TOA incident longwave radiation, obtained from the 3-hourly simulations of WRFv3.5.1, driven by GFDL-ESM2M, are hereby presented.

  • High resolution (12km) regional climate simulations were carried out by the researchers at Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) as part of the West Africa Science Service Center on Climate Change and Adapted Land Use (WASCAL) Project. One of the goals of the WASCAL project is to provide the best accuracy in regional climate simulations over the entire West Africa region for a large proportion of the 21st century. The regional climate model employed in the project was the Weather Research and Forecasting Model version 3.5.1 (WRFv3.5.1) forced by three global circulation models (GCMs) under the Representative Concentrative Pathways 4.5 (RCP 4.5). The forcing GCMs are: the Max Planck Institute Earth System Model (MPI-ESM-MR, Stevens et al. 2013), the General Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2M, Dunne et al. 2012), and the Hadley Global Environment Model (HadGEM2-ES, Collins et al. 2011). Further control runs with ERA-Interim reanalysis products (Dee et al. 2011) were also carried out for model verification and bias correction. Therefore, de-accumulated daily outputs of surface downwelling longwave radiation, obtained from the 3-hourly simulations of WRFv3.5.1, driven by HadGEM2-ES, are hereby presented.

  • High resolution (12km) regional climate simulations were carried out by the researchers at Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) as part of the West Africa Science Service Center on Climate Change and Adapted Land Use (WASCAL) Project. One of the goals of the WASCAL project is to provide the best accuracy in regional climate simulations over the entire West Africa region for a large proportion of the 21st century. The regional climate model employed in the project was the Weather Research and Forecasting Model version 3.5.1 (WRFv3.5.1) forced by three global circulation models (GCMs) under the Representative Concentrative Pathways 4.5 (RCP 4.5). The forcing GCMs are: the Max Planck Institute Earth System Model (MPI-ESM-MR, Stevens et al. 2013), the General Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2M, Dunne et al. 2012), and the Hadley Global Environment Model (HadGEM2-ES, Collins et al. 2011). Further control runs with ERA- Interim reanalysis products (Dee et al. 2011) were also carried out for model verification and bias correction. Therefore, monthly outputs of atmospheric upward wind, obtained from the 6-hourly simulations of WRFv3.5.1, driven by HADGEM2-ES, are hereby presented.

  • Bias corrected data of AFRICA CORDEX run, here: ICHEC-EC-EARTH_historical_r12i1p1_CLMcom-CCLM4-8-17_v1. The bias corrected is done based on the concept of a quantile-quantile mapping using a fitted normal distribution and AgMERRA Input Data; Temporal resolution: daily Spatial resolution: 0.25 DD