PAUWES Data Portal
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The data was collected in the catchment of Lake Cyohoha North to analyze socio-economic impact that the change in Land use/cover and lake degradation have had on smallholder farmers living within this catchment.
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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 3-hourly simulations of WRFv3.5.1, driven by GFDL-ESM2M, are hereby presented.
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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 incident shortwave radiation, obtained from the 3-hourly simulations of WRFv3.5.1, driven by HadGEM2-ES, are hereby presented.
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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 near-surface dew point temperature, obtained from the 3-hourly simulations of WRFv3.5.1, driven by GFDL-ESM2M, are hereby presented.
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This dataset shows the river network for West Africa and the estimated hydropower potential as well as several other interesting attributes. After detailed calculation, the attribute values were rounded to a reasonable accuracy. This dataset is not intended for local studies but only for regional comparison. The dataset was created using the following methodology: 1. The river network was derived from the Hydrosheds 15s dataset: a) A threshold of 10 cells in the flow accumulation grid was used to delineate river reaches. b) Reaches with zero long-term mean annual discharge (rounded at 0.000 m³/s) were removed. c) Some remaining reaches in desert areas were manually removed. d) Small tributary reaches in reservoir water bodies were manually removed. e) The topology between adjacent reaches was computed from nodes and the flow accumulation grid (to determine which of the adjacent reaches is located downstream). 2. Discharge was computed with an annual water balance model using precipitation (TRMM) and potential evapotranspiration (CRU) as inputs. The water balance model was calibrated with observed discharge data of about 400 gauges (Global Runoff Database). The last cellpoint at the river network before a confluence with a downstream reach at 15s Hydrosheds resolution was used to query discharge from the discharge grid. The monthly distribution of discharge was computed by superimposing a specific seasonality derived from observed discharge data. At some rivers (Niger, Bani, Yobe) diversions and evaporation losses were roughly considered. 3. Computation of slope for each river reach: a) Compute length of reaches. b) Aggregate Hydrosheds unconditioned DEM (based on SRTM DEM) from 3s resolution to 15s resolution. After extensive testing (also with ASTER DEM) the 10% percentile of the 3s elevation values within the 15s box was selected as aggregation value. c) Extract upstream and downstream elevation for each reach from the aggregated DEM (see previous step). d) Adjust upstream and downstream reach elevation values with the following method: i. Adjustment of elevation in downstream direction by carving through barriers. ii. Adjustment of elevation in upstream direction by filling sinks. iii. Compute average of previous two steps. e) Smoothing of elevation along the river network. Differentiate between main rivers and tributaries in the smoothing process. Several rounds of smoothing with different settings were applied. f) Consider artificial reservoirs (e.g. Lake Volta) by ensuring zero slope in reservoir water bodies. 4. Slope was computed from length and elevation difference. 5. Gross theoretical hydropower potential (attribute POWER) was computed from mean annual discharge (Q_YEAR) and elevation difference in reach (ELEV_DIFF). Specific hydropower potential (POWER_SPEC) was computed from POWER and reach length. 6. Preferred hydropower plant size is based on a classification scheme using specific hydropower potential (attribute POWER_SPEC) and mean annual discharge (attribute Q_YEAR). The classification scheme is based on an analysis of existing hydropower plants in West Africa and some general considerations for hydropower plant design. 7. River names were manually assigned by comparison with the following data sources: a) GRDC gauges b) Nigeria JICA gauges c) Sierem GIS dataset d) Google maps e) Open Streetmap f) Michelin map (online version) g) Travelmag map (analog version) h) Various other sources (Wikipedia, detailed local maps, etc.) 8. For rivers forming international borders the country entries in the attribute table were manually assigned. The manual editing was required because the GIS datasets (river network and countries) do not align fully accurate at country borders. 9. Future change in annual discharge was computed by running the water balance model using the delta-change method for future precipitation and air temperature. The climate change projections were extracted from 30 Regional Climate Model (RCM) runs included in the CORDEX-Africa ensemble for the representative concentration pathways RCP4.5 and RCP8.5. Future change in potential evapotranspiration was estimated using an empirical relationship between air temperature and potential evapotranspiration. The following tools were used for creating this dataset: • ArcGIS 9.2: main GIS tool • ArcGIS 10.0: Python scripting and model building • ArcView 3.1: specific tasks with large attribute tables where more recent GIS versions fail • GDAL: automatic merging of DEM tiles • Fortran: main processing tool for various tasks o Pre-processing of GPCC and satellite precipitation data o Water balance modelling o Spatial aggregation of DEMs from 3s to 15s resolution o Adjustment of reach elevation (fill sinks, carve through barriers, smoothing) • 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 reach of the river network the following attributes are available (units in brackets): • ARCID: ID number of reach • TOARCID: ID number of next downstream reach • FROMARCID: ID number of dominant upstream reach (largest inflow) • NB: ID number of sub-area • RIVER: River name (English) • RIVER_FREN: River name (French) • COUNTRY_1: Country (ISO code) • COUNTRY_2: Second country (ISO code) if reach forms international border • AREA_Km2: Total upstream catchment area (km²) of reach • LENGTH_Km: Length (km) of reach • EXRIVER: Flag indicating external river originating from another sub-area (0: local river, 1: external river) • ELEV_US_m: Elevation (m) at upstream end of reach • ELEV_DS_m: Elevation (m) at downstream end of reach • ELEV_DIFF: Elevation difference (m) in reach • SLOPE: Slope (m/m) of reach • PWR_MW: Theoretical hydropower potential (MW) for the period 1998-2014 • PWR_MW_KM: Specific hydropower potential (MW/km) for the period 1998-2014 • QYEAR_m3_s: Mean annual discharge (m³/s) simulated for the period 1998-2014 • QJAN_m3_s: Mean monthly discharge (m³/s) 1998-2014 in January • QFEB_m3_s: Mean monthly discharge (m³/s) 1998-2014 in February • QMAR_m3_s: Mean monthly discharge (m³/s) 1998-2014 in March • QAPR_m3_s: Mean monthly discharge (m³/s) 1998-2014 in April • QMAY_m3_s: Mean monthly discharge (m³/s) 1998-2014 in May • QJUN_m3_s: Mean monthly discharge (m³/s) 1998-2014 in June • QJUL_m3_s: Mean monthly discharge (m³/s) 1998-2014 in July • QAUG_m3_s: Mean monthly discharge (m³/s) 1998-2014 in August • QSEP_m3_s: Mean monthly discharge (m³/s) 1998-2014 in September • QOCT_m3_s: Mean monthly discharge (m³/s) 1998-2014 in October • QNOV_m3_s: Mean monthly discharge (m³/s) 1998-2014 in November • QDEC_m3_s: Mean monthly discharge (m³/s) 1998-2014 in December • Q_2035_P25: Change in future mean annual discharge 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) • Q_2035_P50: Change in future mean annual discharge 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) • Q_2035_P75: Change in future mean annual discharge 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) • Q_2055_P25: Change in future mean annual discharge 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) • Q_2055_P55: Change in future mean annual discharge 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) • Q_2055_P75: Change in future mean annual discharge 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) • PLANT_SIZE: Preferred hydropower plant size (0: none, 1: <1MW, 2: 1-30MW, 3: >30MW installed capacity) • LAT: Latitude (decimal degrees North) at end of reach • LON: Longitude (decimal degrees East) at end of reach
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Location of settlements that could have access to electricity through mini-grids 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).
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Bias-corrected data set of daily precipitation for a study region in Burkina Faso and parts of Ghana, Togo and Benin. The precipitation simulations of the CORDEX Africa ensemble have been bias-corrected with a geostatistically based Quantile-Mapping with dry day correction. The distribution parameters probability of rainfall and mean precipitation on wet days were Kriged to the ungauged locations from the observed parameters of point measurements (172 stations in total) for nine season (dry season NDJF and remaining eight months individually). Observed precipitation was modelled with the exponential distribution, RCM precipitation was modeled with a Kernel Density Estimation of the CDF. Future scenarios were bias-corrected with the Double-Quantile-Mapping approach presented in http://onlinelibrary.wiley.com/doi/10.1029/2010WR009689/abstract All models were stretched to the Gregorian calendar (original CORDEX-RCMs are available as 360d/a, 365d/a and 365.25d/a) to facilitate running impact models. - Historical period: 1950-2005 (exception: SMHI-models start 1951) - calibration period - Future period: 2006-2100 (exception: models driven by HadGEM2 end 2099) - validation period - Temporal resolution: daily - Spatial resolution: 0.44° This is a test version - please inform me about any potential problems you may have with the data via manuel.lorenz@geo.uni-augsburg.de
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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, deaccumulated daily outputs of TOA incident longwave radiation, obtained from the 3-hourly simulations of WRFv3.5.1, driven by HadGEM2-ES, are hereby presented.
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The layer provides quantitative information about the inhabitants main points in the different countries for the ECOWAS region
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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 near-surface air temperature, obtained from the 3-hourly simulations of WRFv3.5.1, driven by HadGEM2-ES, are hereby presented.