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      A high-resolution dataset of water bodies distribution over the Tibetan Plateau

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          Abstract

          Water body (WB) extraction is the basic work of water resources management. Tibetan Plateau is one of the largest alpine lake systems in the world. However, research on the characteristics of water bodies (WBs) is mainly focused on large and medium WBs due to spatial resolution. This research presents a dataset containing a 2-m resolution map of WBs in 2020 based on Gaofen-1 data, and morphometric and landscape indices of WBs across the Tibetan Plateau. The Swin-UNet model is well performed with overall accuracy at 98%. The total area of WBs is 56354.6 km 2 across Tibetan Plateau in 2020. The abundance compared with that from size-abundance relationship indicate WBs in the Tibetan Plateau conformed to the classic power scaling law. We evaluate the influence of spatial-resolution in WB extraction, which shows the dataset could be valuable to fill the gap of existing WBs map, especially for small waters. The dataset is valuable for revealing the spatial patterns of WBs, and understanding the impacts of climate change on water resources in Plateau.

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          High-resolution mapping of global surface water and its long-term changes.

          Jean-François Pekel, Andrew Cottam, Noel Gorelick … (2016)
          The location and persistence of surface water (inland and coastal) is both affected by climate and human activity and affects climate, biological diversity and human wellbeing. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions, statistical extrapolation of regional data and satellite imagery, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal. Losses in Australia and the USA linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.
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            The global abundance and size distribution of lakes, ponds, and impoundments

            J. Downing, Y. T. Prairie, J. J. Cole … (2006)
            Article 0 comments Cited 483 times – based on 0 reviews     Review now
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              The impacts of climate change on water resources and agriculture in China.

              Shilong Piao, Philippe Ciais, Yao Huang … (2010)
              China is the world's most populous country and a major emitter of greenhouse gases. Consequently, much research has focused on China's influence on climate change but somewhat less has been written about the impact of climate change on China. China experienced explosive economic growth in recent decades, but with only 7% of the world's arable land available to feed 22% of the world's population, China's economy may be vulnerable to climate change itself. We find, however, that notwithstanding the clear warming that has occurred in China in recent decades, current understanding does not allow a clear assessment of the impact of anthropogenic climate change on China's water resources and agriculture and therefore China's ability to feed its people. To reach a more definitive conclusion, future work must improve regional climate simulations-especially of precipitation-and develop a better understanding of the managed and unmanaged responses of crops to changes in climate, diseases, pests and atmospheric constituents.
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                Author and article information

                Contributors
                Bing Zhang:
                ORCID: http://orcid.org/0000-0003-0319-7753
                zb@radi.ac.cn
                Journal
                Journal ID (nlm-ta): Sci Data
                Journal ID (iso-abbrev): Sci Data
                Title: Scientific Data
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2052-4463
                Publication date (Electronic): 4 May 2024
                Publication date PMC-release: 4 May 2024
                Publication date Collection: 2024
                Volume: 11
                Electronic Location Identifier: 453
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, , Chinese Academy of Sciences, ; Beijing, 100094 China
                [2 ]International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094 China
                [3 ]China University of Mining & Technology-Beijing, ( https://ror.org/01xt2dr21) Beijing, 100083 China
                [4 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, , Chinese Academy of Sciences, ; Beijing, 100094 China
                [5 ]GRID grid.9227.e, ISNI 0000000119573309, Aerospace Information Research Institute, , Chinese Academy of Sciences, ; Beijing, 100094 China
                [6 ]University of the Chinese Academy of Sciences, ( https://ror.org/05qbk4x57) Beijing, 100049 China
                [7 ]GRID grid.9227.e, ISNI 0000000119573309, Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, , Chinese Academy of Sciences, ; Shenzhen, 518055 China
                [8 ]GRID grid.9227.e, ISNI 0000000119573309, China Remote Sensing Satellite Ground Station, Aerospace Information Research Institute, , Chinese Academy of Sciences, ; Beijing, 100094 China
                Author information
                http://orcid.org/0000-0003-0319-7753
                Article
                Publisher ID: 3290
                DOI: 10.1038/s41597-024-03290-4
                PMC ID: 11069532
                PubMed ID: 38704376
                SO-VID: 2a18eba1-e21c-45fc-b9c1-fbd209e29ec3
                Copyright © © The Author(s) 2024
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 29 January 2024
                Date accepted : 22 April 2024
                Funding
                Funded by: the Director Fund of the International Research Center of Big Data for Sustainable Development Goals (Grant No. CBAS2022DF009) Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 42201145)
                Categories
                Subject: Data Descriptor
                Custom metadata
                issue-copyright-statement © Springer Nature Limited 2024

                Keywords: hydrology,freshwater ecology
                Data availability:
                Keywords: hydrology, freshwater ecology

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