February 7, 2023 Dataset Open Access

Zhang, Alice Tianbo; Gu, Vincent Xinyi

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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.7616852", 
  "container_title": "Scientific Data", 
  "title": "Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information", 
  "issued": {
    "date-parts": [
      [
        2023, 
        2, 
        7
      ]
    ]
  }, 
  "abstract": "<p><strong>Citation</strong></p>\n\n<p>Zhang, Alice Tianbo, and Vincent Xinyi Gu. 2023. &ldquo;Global Dam Tracker: A Database of More than 35,000 Dams with Location, Catchment, and Attribute Information.&rdquo; <em>Scientific Data</em> 10 (1): 111.</p>\n\n<p><a href=\"https://www.nature.com/articles/s41597-023-02008-2\">https://www.nature.com/articles/s41597-023-02008-2</a></p>\n\n<p>&nbsp;</p>\n\n<p><strong>Description</strong></p>\n\n<p>We present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spanning three decades, we demonstrate how GDAT improves upon existing databases to enable the inter-temporal analysis of the costs and benefits of dam construction on a global scale. Our findings show that over the past three decades, dams have contributed to a dramatic increase in global surface water coverage, especially in developing countries in Asia and South America. This is an important step toward a more systematic understanding of the worldwide impact of dams on local communities. By filling in the data gap, GDAT would help inform a more sustainable and equitable approach to energy access and economic development.</p>", 
  "author": [
    {
      "family": "Zhang, Alice Tianbo"
    }, 
    {
      "family": "Gu, Vincent Xinyi"
    }
  ], 
  "page": "111", 
  "volume": "10", 
  "version": "v1", 
  "type": "dataset", 
  "issue": "1", 
  "id": "7616852"
}