A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies
<p>Our understanding and predictive capability of streamflow processes largely rely on high-quality datasets that depict a river's upstream basin characteristics. Recent proliferation of large sample hydrology (LSH) datasets has promoted model parameter estimation and data-driven analyses...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-03-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/1559/2024/essd-16-1559-2024.pdf |
| _version_ | 1797246470576930816 |
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| author | Z. Yin P. Lin P. Lin P. Lin R. Riggs G. H. Allen X. Lei Z. Zheng Z. Zheng S. Cai |
| author_facet | Z. Yin P. Lin P. Lin P. Lin R. Riggs G. H. Allen X. Lei Z. Zheng Z. Zheng S. Cai |
| author_sort | Z. Yin |
| collection | DOAJ |
| description | <p>Our understanding and predictive capability of streamflow processes largely rely on high-quality datasets that depict a river's upstream basin characteristics. Recent proliferation of large sample hydrology (LSH) datasets has promoted model parameter estimation and data-driven analyses of hydrological processes worldwide, yet existing LSH is still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of anthropogenic activities. To bridge the gap, we contribute the synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) to complement existing LSH datasets, which covers 21 568 watersheds from 13 agencies for as long as 43 years based on discharge observations scraped from the internet. In addition to annual and monthly streamflow indices, each basin's daily meteorological variables (i.e., precipitation, 2 m air temperature, longwave/shortwave radiation, wind speed, actual and potential evapotranspiration), daily–weekly water storage terms (i.e., snow water equivalence, soil moisture, groundwater percentage), and yearly dynamic descriptors of the land surface characteristics (i.e., urban/cropland/forest fractions, leaf area index, reservoir storage and degree of regulation) are also provided by combining openly available remote sensing and reanalysis datasets. The uncertainties in all meteorological variables are estimated with independent data sources. Our analyses reveal the following insights: (i) the meteorological data uncertainties vary across variables and geographical regions, and the revealed pattern should be accounted for by LSH users; (ii) <span class="inline-formula">∼6</span> % watersheds shifted between human-managed and natural states during 2001–2015, e.g., basins with environmental recovery projects in northeast China, which may be useful for hydrologic analysis that takes the changing land surface characteristics into account; and (iii) GSHA watersheds showed a more widespread declining trend in runoff coefficient than an increasing trend, pointing towards critical water availability issues. Overall, GSHA is expected to serve hydrological model parameter estimation and data-driven analyses as it continues to improve. GSHA v1.1 can be accessed at <a href="https://doi.org/10.5281/zenodo.8090704">https://doi.org/10.5281/zenodo.8090704</a> and <a href="https://doi.org/10.5281/zenodo.10433905">https://doi.org/10.5281/zenodo.10433905</a> (Yin et al., 2023a, b).</p> |
| first_indexed | 2024-04-24T19:43:19Z |
| format | Article |
| id | doaj.art-aad762a5b6524e9dbacc8551b9d8b193 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-24T19:43:19Z |
| publishDate | 2024-03-01 |
| publisher | Copernicus Publications |
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| series | Earth System Science Data |
| spelling | doaj.art-aad762a5b6524e9dbacc8551b9d8b1932024-03-25T10:36:13ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162024-03-01161559158710.5194/essd-16-1559-2024A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studiesZ. Yin0P. Lin1P. Lin2P. Lin3R. Riggs4G. H. Allen5X. Lei6Z. Zheng7Z. Zheng8S. Cai9Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, ChinaInstitute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, ChinaInternational Research Center for Big Data for Sustainable Development Goals, Beijing, ChinaSouthwest United Graduate School, Kunming, Yunnan, ChinaDepartment of Geography, Texas A&M University, College Station, Texas, USADepartment of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USAInstitute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, ChinaKey Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China<p>Our understanding and predictive capability of streamflow processes largely rely on high-quality datasets that depict a river's upstream basin characteristics. Recent proliferation of large sample hydrology (LSH) datasets has promoted model parameter estimation and data-driven analyses of hydrological processes worldwide, yet existing LSH is still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of anthropogenic activities. To bridge the gap, we contribute the synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) to complement existing LSH datasets, which covers 21 568 watersheds from 13 agencies for as long as 43 years based on discharge observations scraped from the internet. In addition to annual and monthly streamflow indices, each basin's daily meteorological variables (i.e., precipitation, 2 m air temperature, longwave/shortwave radiation, wind speed, actual and potential evapotranspiration), daily–weekly water storage terms (i.e., snow water equivalence, soil moisture, groundwater percentage), and yearly dynamic descriptors of the land surface characteristics (i.e., urban/cropland/forest fractions, leaf area index, reservoir storage and degree of regulation) are also provided by combining openly available remote sensing and reanalysis datasets. The uncertainties in all meteorological variables are estimated with independent data sources. Our analyses reveal the following insights: (i) the meteorological data uncertainties vary across variables and geographical regions, and the revealed pattern should be accounted for by LSH users; (ii) <span class="inline-formula">∼6</span> % watersheds shifted between human-managed and natural states during 2001–2015, e.g., basins with environmental recovery projects in northeast China, which may be useful for hydrologic analysis that takes the changing land surface characteristics into account; and (iii) GSHA watersheds showed a more widespread declining trend in runoff coefficient than an increasing trend, pointing towards critical water availability issues. Overall, GSHA is expected to serve hydrological model parameter estimation and data-driven analyses as it continues to improve. GSHA v1.1 can be accessed at <a href="https://doi.org/10.5281/zenodo.8090704">https://doi.org/10.5281/zenodo.8090704</a> and <a href="https://doi.org/10.5281/zenodo.10433905">https://doi.org/10.5281/zenodo.10433905</a> (Yin et al., 2023a, b).</p>https://essd.copernicus.org/articles/16/1559/2024/essd-16-1559-2024.pdf |
| spellingShingle | Z. Yin P. Lin P. Lin P. Lin R. Riggs G. H. Allen X. Lei Z. Zheng Z. Zheng S. Cai A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Earth System Science Data |
| title | A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies |
| title_full | A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies |
| title_fullStr | A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies |
| title_full_unstemmed | A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies |
| title_short | A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies |
| title_sort | synthesis of global streamflow characteristics hydrometeorology and catchment attributes gsha for large sample river centric studies |
| url | https://essd.copernicus.org/articles/16/1559/2024/essd-16-1559-2024.pdf |
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