Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Dams and reservoirs are among the most widespread human-made infrastructures on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GlObal geOreferenced Database of Dams, GOODD) or detailed attributes for a limited dam quantity or region (e.g., GRanD (Global Reservoir and Dam database) and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD), maintained by the International Commission on Large Dams (ICOLD), documents nearly 60 000 dams with an extensive suite of attributes. Unfortunately, the WRD records provide no geographic coordinates, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dams And Reservoirs (GeoDAR) dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at <a href="https://doi.org/10.5281/zenodo.6163413">https://doi.org/10.5281/zenodo.6163413</a> (Wang et al., 2022). GeoDAR v1.0 holds 22 560 dam points georeferenced from the WRD, whereas v1.1 consists of (a) 24 783 dam points after a harmonization between GeoDAR v1.0 and GRanD v1.3 and (b) 21 515 reservoir polygons retrieved from high-resolution water masks based on a one-to-one relationship between dams and reservoirs. Due to geocoding challenges, GeoDAR spatially resolved <span class="inline-formula">∼</span> 40 % of the records in the WRD, which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we may provide assistance in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. Despite this limit, GeoDAR, with a dam quantity triple that of GRanD, significantly enhances the spatial details of smaller but more widespread dams and reservoirs and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modeling, water resource management, ecosystem health, and energy planning.

Highlights

  • Since around the 1950s, the world has seen an unprecedented boom in large dam construction as a response to the evergrowing human demands for water and energy (Chao et al, 2008; Wada et al, 2017)

  • GeoDAR v1.0 is a collection of 21,051 dam points georeferenced exclusively for International Commission on Large Dams (ICOLD) World Register of Dams (WRD) (Fig. 6a)

  • We focus on the retrieved reservoir polygons for comparing how GeoDAR v1.1 represents the reservoir areas in the entire ICOLD WRD

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Summary

Introduction

Since around the 1950s, the world has seen an unprecedented boom in large dam construction as a response to the evergrowing human demands for water and energy (Chao et al, 2008; Wada et al, 2017). Dams and their impounded reservoirs are ubiquitous across many global basins, providing multiple services that range from hydropower and flood control to water supply and navigation (Belletti et al, 2020; Biemans et al, 2011; Boulange et al, 2021; Doll et al, 2009; 40 Grill et al, 2019) These benefits were, often gained at the costs of fragmenting river systems, submerging arable lands, displacing population, and disturbing climate regimes (Carpenter et al, 2011; Cretaux et al, 2015; Degu et al, 2011; Grill et al, 2019; Latrubesse et al, 2017; Nilsson and Berggren, 2000; Tilt et al, 2009; Vorosmarty et al, 2003; Wang et al, 2017). These WRD records are considered to be “complete” to the extent of contributions from willing nations and water authorities (Wada et al, 2017)

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