This file was generated on 2026-02-18 by Valery Bessely Stanislas Kouassi A GENERAL INFORMATION 1. Title of the dataset: West Africa Dams and Reservoirs Dataset 2. Brief description of the research project and its aims: The West Africa Dams and Reservoirs Dataset, also called Harmonized Dataset for Dams and Reservoirs in West Africa is a West Africa-specific compiled dataset developed through the integration of twelve existing datasets. This harmonized dataset aims to address the heterogeneous data records of West African dams and reservoirs in existing datasets while enhancing the accessibility of dam and reservoir information for policy development, investment planning, and regional-scale analyses. The dataset contains 1,429 georeferenced dams and 1,258 reservoirs (with a minimum surface area of 0.57 × 10⁻³ km²), 38 attributes, and an estimated total reservoir surface area of 14,038 km² and a cumulative storage capacity of 267,369 MCM. The West Africa Dams and Reservoirs Dataset is provided in shapefile and CSV formats for both dam and reservoir entries: WA_Dams (point locations of dams in West Africa) and WA_Reservoirs (polygon representations of reservoirs in West Africa) available for download and future updates. 3. Author Information A. Investigator Contact Information Name: Valery Bessely Stanislas Kouassi Institution: Graduate Research Programme on Climate Change and Water Resources (GRP CCWR), West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), University of Abomey-Calavi, Benin / Department of Geography, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany Address: Meckenheimer Allee 172, 53115 Bonn Email:valerykouassi.vk@gmail.com / s85vkoua@uni-bonn.de Name: Blé Anouma Fhorest Yao Institution: Unité de Formation et de Recherche Sciences et Gestion de l’Environnement, Université Nangui Abrogoua, Abidjan, Côte d’Ivoire Address: Abobo-Adjamé, 02 B.P. 801 Abidjan 02, Côte d'Ivoire Email: ybafci@gmail.com Name: Gneneyougo Emile Soro Institution: Unité de Formation et de Recherche Sciences et Gestion de l’Environnement, Université Nangui Abrogoua, Abidjan, Côte d’Ivoire Address: Abobo-Adjamé, 02 B.P. 801 Abidjan 02, Côte d'Ivoire Email: ge_soro@yahoo.fr Name: Albert Bi Tié Goula Institution: Unité de Formation et de Recherche Sciences et Gestion de l’Environnement, Université Nangui Abrogoua, Abidjan, Côte d’Ivoire Address: Abobo-Adjamé, 02 B.P. 801 Abidjan 02, Côte d'Ivoire Email: goulaba2002@yahoo.fr Name: Nelly Carine Kelome Institution: Département des Sciences de la Terre, Université d’Abomey-Calavi, Abomey-Calavi, Benin Address: 01 BP 526. Abomey-Calavi - Bénin Email: nkelome@yahoo.fr Name: Julian Klaus Institution: Department of Geography, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany Address: Meckenheimer Allee 172, 53115 Bonn Email: jklaus@uni-bonn.de B. Project Supervisor (Principal Investigator) Contact Information Name: Julian Klaus Institution: Department of Geography, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany Address: Meckenheimer Allee 172, 53115 Bonn Email: jklaus@uni-bonn.de C. In case of questions related to this dataset, please contact: Name: Valery Bessely Stanislas Kouassi Institution: Graduate Research Programme on Climate Change and Water Resources (GRP CCWR), West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), University of Abomey-Calavi, Benin / Department of Geography, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany Address: Meckenheimer Allee 172, 53115 Bonn Email:valerykouassi.vk@gmail.com / s85vkoua@uni-bonn.de 4. Date of data collection: 2025-03-01 to 2025-07-30 5. Information about funding sources that supported the collection of the data: This research was conducted within the framework of the WASCAL Ph.D. program in Climate Change and Water Resources (GRP CC&WR), funded by the German Federal Ministry of Education and Research (BMBF) (https://wascal.org/). Additional financial support was generously provided by the University of Bonn through the Argelander Scholarships for doctoral candidates from universities in Africa, Latin America, and South/East Asia (https://www.uni-bonn.de/en/research-and-teaching/support-for-researchers-and-teachers/research-funding/university-grants/argelander-scholarships-phd-global-south), which significantly facilitated the realization of this research. 6. Language of the dataset: English 7. Geographic location of data collection: West Africa (latitudes 0° N to 20° N and longitudes 20° W to 20° E ) B DATA & FILE OVERVIEW 1. File List: WA_Dams-Res-dens_Comp-and-Orig.png: figure comparing spatial and size-class densities in compiled and original datasets in West Africa; Kouassi_WA_Dams-Res_Dataset_Readme.txt: Readme file of the West Africa Dams and Reservoirs Dataset providing detailed information on the dataset; WA_Dams: point locations of dams in West Africa provided in CSV and shapefile formats; WA_Reservoirs: polygon representations of reservoirs of dams in West Africa provided in CSV and shapefile formats. 2. Are there multiple versions of the dataset? No 3. Relationship between files: WA_Dams-Res-dens_Comp-and-Orig.png highlights the improvement obtained after consolidating the original datasets into one dataset. It suggests better spatial completeness and scale coverage compared to individual datasets, although residual biases from the sources remain in the compiled dataset. WA_Dams and WA_Reservoirs contain identical attribute information; however, WA_Dams represents dam locations as points, whereas WA_Reservoirs represents the associated reservoirs as polygons. 4. Additional related data collected that was not included in the current data package: The field campaign data collected in the Upper Bandama watershed (Côte d’Ivoire), which were used for watershed scale quality assessment of the dataset in the dataset paper (in preparation), are not included in the current data package. C SHARING/ACCESS INFORMATION 1. Was data derived from another source? Yes: Global Information System on Water and Agriculture of the Food and Agriculture Organization (FAO AQUASTAT); https://www.fao.org/aquastat/en/databases/dams; last access date: 2025-07-30 Global Lakes and Wetlands Database (GLWD-1 (Level 1) & GLWD-2 (Level 2)); https://www.worldwildlife.org/our-work/science/global-lakes-and-wetlands-database/; last access date: 2025-07-30 HydroLAKES; https://www.hydrosheds.org/products/hydrolakes; last access date: 2025-07-30 GlObal GeOreferenced Database of Dams (GOODD); https://www.globaldamwatch.org/goodd; last access date: 2025-07-30 Future Hydropower Reservoirs and Dams Database (FHReD); https://www.globaldamwatch.org/fhred; last access date: 2025-07-30 Global Reservoirs and Dams version 1.3 (GranD V1.3); https://www.globaldamwatch.org/grand/; last access date: 2025-07-30 Global River Obstruction Database version 1.1 (GROD v1.1); https://zenodo.org/records/5793918; last access date: 2025-07-30 Reservoir and Lake Surface Area Timeseries (ReaLSAT); https://doi.org/10.5281/zenodo.7614815; last access date: 2025-07-30 Georeferenced global Dams And Reservoirs (GeoDAR); https://zenodo.org/records/6163413; last access date: 2025-07-30 Global Dam Tracker (GDAT); https://zenodo.org/records/7616852; last access date: 2025-07-30 Global Dam Watch (GDW database (V1)); https://www.globaldamwatch.org/database; last access date: 2025-07-30 Global Lakes/Reservoirs Surface Extent Dataset (GLRSED); https://zenodo.org/records/14190225; last access date: 2025-07-30 2. Licenses/restrictions placed on the data: Creative Commons Attribution 4.0 International (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/deed.en. 3. Links to publications that cite or use the data: Bai, B., Mu, L., and Tan, Y.: A Global Lakes/Reservoirs Surface Extent Dataset (GLRSED): An Integration of Multi‐Source Data, Geoscience Data Journal, 12, https://doi.org/10.1002/gdj3.285, 2025. FAO: AQUASTAT – FAO’s global information system on water and agriculture: geo-referenced database on dams, FAO, https://www.fao.org/aquastat/en/databases/dams, last access: 11 February 2025, 2021. Khandelwal, A., Karpatne, A., Ravirathinam, P., Ghosh, R., Wei, Z., Dugan, H. A., Hanson, P. C., and Kumar, V.: ReaLSAT, a global dataset of reservoir and lake surface area variations, Scientific data, 9, https://doi.org/10.1038/s41597-022-01449-5, 2022. Lehner, B. and Döll, P.: Development and validation of a global database of lakes, reservoirs and wetlands, Journal of Hydrology, 296, 1–22, https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004. Lehner, B., Beames, P., Mulligan, M., Zarfl, C., Felice, L. de, van Soesbergen, A., Thieme, M., Garcia de Leaniz, C., Anand, M., Belletti, B., Brauman, K. A., Januchowski-Hartley, S. R., Lyon, K., Mandle, L., Mazany-Wright, N., Messager, M. L., Pavelsky, T., Pekel, J.-F., Wang, J., Wen, Q., Wishart, M., Xing, T., Yang, X., and Higgins, J.: The Global Dam Watch database of river barrier and reservoir information for large-scale applications, Scientific data, 11, 1069, https://doi.org/10.1038/s41597-024-03752-9, 2024. Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management, Frontiers in Ecol & Environ, 9, 494–502, https://doi.org/10.1890/100125, 2011. Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nature communications, 7, 13603, https://doi.org/10.1038/ncomms13603, 2016. Mulligan, M., van Soesbergen, A., and Sáenz, L.: GOODD, a global dataset of more than 38,000 georeferenced dams, Scientific data, 7, 31, https://doi.org/10.1038/s41597-020-0362-5, 2020. Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations, Earth Syst. Sci. Data, 14, 1869–1899, https://doi.org/10.5194/essd-14-1869-2022, 2022. Yang, X., Pavelsky, T. M., Ross, M. R. V., Januchowski‐Hartley, S. R., Dolan, W., Altenau, E. H., Belanger, M., Byron, D., Durand, M., van Dusen, I., Galit, H., Jorissen, M., Langhorst, T., Lawton, E., Lynch, R., Mcquillan, K. A., Pawar, S., and Whittemore, A.: Mapping Flow‐Obstructing Structures on Global Rivers, Water Resources Research, 58, https://doi.org/10.1029/2021WR030386, 2022. Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L., and Tockner, K.: A global boom in hydropower dam construction, Aquat Sci, 77, 161–170, https://doi.org/10.1007/s00027-014-0377-0, 2015. Zhang, A. T. and Gu, V. X.: Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information, Scientific data, 10, 111, https://doi.org/10.1038/s41597-023-02008-2, 2023. D METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: We carried out a literature review to identify, and inventory the existing datasets containing information on dams and reservoirs in West Africa. We consulted online scholarly literature datasets of peer-reviewed journal articles, theses and dissertations, books, conference papers, and technical reports. The online scholarly literature datasets used were Google Scholar (https://scholar.google.com/) and SCOPUS (https://www.scopus.com/). Search strings were defined using the keywords of this study such as dams, reservoirs, datasets, water management, and West Africa along with their synonyms, and related words to ensure comprehensive coverage of pertinent literature. Based on this review, we selected datasets that are open-access (table 1) with standardized file formats (CSV or XLS and SHP) for compilation. Table 1: List of datasets selected to compile the West Africa dam-reservoir dataset. Datasets used for the compilation; Accessibility (license); Coverage; Format; References Global Information System on Water and Agriculture of the Food and Agriculture Organization, FAO AQUASTAT; CC BY 4.0.; Global; XLS; FAO (2021) Global Lakes and Wetlands Database, GLWD-1 (Level 1) & GLWD-2 (Level 2); CC BY 4.0.; Global; SHP; Lehner and Döll (2004) - ,HydroLAKES; CC BY 4.0.; Global; SHP; Messager et al. (2016) GlObal GeOreferenced Database of Dams, GOODD; CC-0 license; Global; SHP; Mulligan et al. (2020) Future Hydropower Reservoirs and Dams Database, FHReD; Open access; Global; XLS; Zarfl et al. (2015) Global Reservoirs and Dams version 1.3, GranD V1.3; Free for non-commercial use; Global; SHP; Lehner et al. (2011) Global River Obstruction Database version 1.1, GROD v1.1; CC BY 4.0.; Global; SHP; Yang et al. (2022) Reservoir and Lake Surface Area Timeseries; ReaLSAT; CC BY 4.0.; Global; SHP; Khandelwal et al. (2022) Georeferenced global Dams And Reservoirs, GeoDAR; CC BY 4.0.; Global; SHP; Wang et al. (2022) Global Dam Tracker, GDAT; CC BY 4.0.; Global; SHP; Zhang and Gu (2023) Global Dam Watch, GDW database (V1); CC BY 4.0.; Global; SHP; Lehner et al. (2024) Global Lakes/Reservoirs Surface Extent Dataset, GLRSED; ODC Open Database License v1.0; Global; SHP; Bai et al. (2025) 2. Methods for processing the data: We integrated twelve existing datasets (table 1) that are open-access with standardized file formats (CSV or XLS and SHP). Dam point and their associated reservoir polygon entries were merged separately into unified shapefiles using Quantum Geographic Information System software (QGIS, version 3.28.3). For each dataset, we used the most recent version available at the time of the study and combined the versions when they are complementary. For instance, we combined two versions of the Global Lakes and Wetlands Database (GLWD) into a unified dataset by merging them: GLWD Level 1 (GLWD-L1) including large waterbodies (≥50 km² for lakes and ≥0.5 km³ for reservoirs), and GLWD Level 2 (GLWD-L2), which includes smaller waterbodies. We overlaid the two merged files (point and polygon files) on high-resolution satellite basemaps from ESRI Imagery and Google Earth. This was to verify and refine the spatial accuracy of dam and reservoir geographic coordinates and to digitize missing dam points or reservoir polygons as appropriate. The satellite images also enabled us to visually identify natural waterbodies such as rivers and lakes that were subsequently removed manually from the data. We only retained man-made reservoirs with dams. Additionally, we removed duplicate entries, retaining the most complete attribute records. In cases where conflicting data existed for the same dam or reservoir, we prioritized information obtained through field surveys and records that were consistent across multiple selected datasets. We benchmarked the quality of the newly compiled dataset at watershed scale through an extended field study, and statistical analyses. 3. Instrument- and/or software-specific information needed to interpret the data: We used the Quantum Geographic Information System software (QGIS, version 3.28.3, https://qgis.org/). QGIS is a free, open-source desktop Geographic Information System (GIS) software that allows users to create, edit, visualize, analyze, and publish geospatial information. The shapefiles and csv files of the dataset (WA_Dams.csv, WA_Dams.shp, WA_Reservoirs.csv, and WA_Reservoirs.shp) are georeferenced and can be opened and analyzed with any other GIS software such as ArcGIS. The Coordinate Reference System (CRS) used for the dataset is EPSG:4326 (WGS 84). 4. People involved in sample collection, processing, analysis and/or submission: Mr. Abdoulaye Diara, Head of Rural Planning at the Agriculture and Rural Development Department of the National Office of Technical Studies and Development (BNETD) of Côte d’Ivoire and the administrative and traditional authorities, local rural dam management organizations, cooperatives, and user associations in the Upper Bandama watershed (in the Sudanian savannah, Côte d'Ivoire) which supported the field campaign data collection for data quality assessment. 5. Describe any quality-assurance procedures performed on the data: We assessed the quality of the compiled dataset against field campaign data at watershed scale (14,500 km², in the Sudanian savannah). The compiled dataset showed strong spatial and temporal consistency while counting for 32 % of field observed dams, 62 % of the total reservoir area and 59 % of the total storage volume reported from field campaign. This is primarily due to the underestimation of smaller and recently constructed dams and reservoirs highlighting persistent biases inherited from the source datasets. E DATA-SPECIFIC INFORMATION FOR: [WA_Dams.shp], [WA_Dams.csv], [WA_Reservoirs.shp] and [WA_Reservoirs.csv] 1. Variable list including full names and definitions (please spell out abbreviated words) of column headings for tabular data: All dataset files contain the same attribute details. Table 2: Description of the attributes of the Harmonized Dataset for Dams and Reservoirs in West Africa. Variable name(s); Description(s); unit(s) ID; The dam identity (ID) generated in this dataset and is identical to the associated reservoir ID Continent; Continent in which the dam (or reservoir) is located: Africa (West Africa for all entries) Country; Country in which the dam (or reservoir) is located. ISO_Alpha; ISO Alpha-3 codes of the countries as described in the ISO 3166 international standard Admin; Name of administrative unit (district) Alt_Admin; Name of alternative administrative unit Near_City; Name of nearest city to dam (Lehner and Döll, 2004) Dam_Name or Res_Name; Name of dam and is identical to the name of the associated reservoir Alt_Name; Alternative name of dam or reservoir (Lehner et al., 2011) Riv_Name; Name of dammed river (Lehner and Döll, 2004) Main_Basin; Name of main basin (Lehner et al., 2011) Sub_Basin; Name of sub-basin (Lehner et al., 2011) Dam_Year; Year of completion of dam Elevation; Elevation of the reservoir surface (Messager et al., 2016); in meters (m) above sea level Slope_100; Average slope within a 100 meter (m) buffer around the reservoir polygon (Messager et al., 2016); in degrees Shore_Len; Length of shoreline (i.e. polygon outline) (Messager et al., 2016); in km Shore_Dev; Shoreline development. The shoreline development is measured as the ratio between shoreline length and the circumference of a circle with the same area. (Messager et al., 2016) Res_Area; Reservoir surface area (i.e. polygon area) (Messager et al., 2016); in square kilometers (km2) Res_Vol_T; Total reservoir volume; in million cubic meters (1 MCM = 0.001 km3) Res_Vol Reported reservoir volume (Messager et al., 2016); in million cubic meters (1 mcm = 0.001 km3) Dam_Height; Height of dam (Lehner and Döll, 2004); in meters (m) Depth_Avg; Average reservoir depth; in meters (m) Dis_Avg; Average long-term discharge flowing through the reservoir; in m3.s-1. Res_Time; Average residence time of the water, in days Wshd_Area; Area of the watershed associated with the reservoir (Messager et al., 2016); in square kilometers (km2) Irrigation; Used for irrigation (x=yes) Water_Sup; Used for water supply (x=yes) Flood_Cont; Used for flood control (x=yes) Hydropower; Used for hydropower production (x=yes) Navigation; Used for navigation (x=yes) Recreation; Used for recreation (x=yes) Pollution; Used for pollution (x=yes) Livestock; Used for livestock including fish breeding (x=yes) Other_Use; Used for other purposes (x=yes) Main_Use; Main purpose or use of reservoir (Lehner and Döll, 2004) Main_Sourc; Main data source used to identify most of the information of the dam or reservoir Long_X; Longitude of the dam point and reservoir centroid; in decimal degrees Lat_Y; Latitude of the dam point and reservoir centroid; in decimal degrees Importantly, in the WA_Reservoirs files (CSV and shapefile), reservoir entries without ID numbers correspond to artifical reservoirs that do not have associated dam points. 2. Missing data codes/symbols: Missing data are denoted as 'NA' in the CSV files and as 'NULL' in the shapefiles for both WA_Dams and WA_Reservoirs datasets.