随着大数据技术的蓬勃发展，对河水流态进行大样本水文分析正变得可行，这可以从全局的角度对水文过程得出可靠的结论。然而，目前还缺乏一个全面的全球大样本数据集，用于分析溪流状态的组成部分。本文介绍了一个新的时间序列数据集，该数据集是根据数据质量控制后的每日河流记录计算得出的全球河流指数。该数据集包含全球41263条河流河段的79个指数，涵盖流态的七个主要组成部分（即量级、频率、持续时间、变化率、时序、变异性和衰退），以年度和多年为尺度。数据集涵盖了2022年之前的河流流量指数值。时间序列数据集的时间跨度为1806年至2022年，平均长度为36年。与现有的全球数据集相比，该全球数据集涵盖了更多的站点和更多的指数，尤其是那些表征河流机制的频率、持续时间、变化率和衰退的指数。有了这个数据集，无需花费时间处理原始的河流流量记录，就能更轻松地开展河流水文研究。这个全面的数据集将成为水文界的宝贵资源，有助于开展广泛的研究，如集水区的水文行为研究、数据稀缺地区的水流状态预测，以及从全球角度研究水流状态的变化。
", "ds_source": "用于建立全球河流指数时间序列数据集的每日溪流记录收集自 9 个数据源，即 全球河流排水中心 (GRDC)、美国地质调查局 (USGS) 国家水资源信息系统、加拿大国家水资源数据档案 (HYDAT)、巴西国家水务局 (ANA)、智利气候与适应性研究中心 (CCCRR)、北极大河观测站 (ArcticGRO)、中国水文年鉴 (CHY)、印度水资源信息系统 (WRIS)，以及澳大利亚气象局 (BOM) 的澳大利亚水资源数据）。除《中国水文年鉴》外，这些数据源均可公开获取。由于中国水文年鉴中的原始流量记录限制访问且难以收集，因此只收集了一些典型流域的流量数据，包括中国七大流域的 30 个站点。在这些数据源中，USGS、HYDAT、ArcticGRO 和 BOM 提供了高质量的记录标志。
", "ds_process_way": "在水流记录收集中，1990-2022 年有 6000 多个站的记录长达 32 年，缺失率小于 5%；1920-2022 年有约 800 个站的记录长达 102 年，缺失率小于 5%。至于最近记录年份，约有 12 000 个站点的记录在 2022 年结束，而约有 17 000 个站点的记录在 2000 年后缺失）。图 3c 显示了从 1900 年到 2022 年每年不同记录缺失比例的站点数量。所有曲线都显示出相似的趋势。观测站数量从 1900 年开始逐渐增加，1978 年左右达到峰值，1979 年至 2013 年保持波动但相对稳定，2014 年至 2022 年有所减少。从 1900 年到 2022 年，80% 以上有记录的站点每年都没有记录缺失。此外，50%以上的站点记录长度超过 30 年，并且从 1900 年到 2022 年每年都没有记录缺失。从 1975 年到 2018 年，约有 15000 个站点每年都没有记录缺失。该数据集是根据数据质量控制后的每日河流记录计算得出的全球河流指数。
", "ds_quality": "为标准化起见，在收集流场记录时将原有的标志转化为四个标志，即可靠、可疑、无标志和缺失。对于没有质量标记的数据库，可用记录标记为无标记，缺失记录标记为缺失。对于质量较差标志或无标志的记录，一些研究，采用自动检测方法识别并删除不合理的流量值，包括连续等值和异常值。
", "ds_acq_start_time": "1806-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "全球范围", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -45.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1233917922, "ds_files_count": 2, "ds_format": "excel、pdf", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "82cb45b4-d0d2-4f65-8c8f-a2eff815326c.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.40", "170.45" ], "quality_level": 3, "publish_time": "2024-03-26 13:59:56", "last_updated": "2026-01-14 10:55:30", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.07227", "i18n": { "en": { "title": "A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)", "ds_format": "excel、pdf", "ds_source": "The daily stream records used to establish the global river index time series dataset were collected from nine data sources, namely the Global River Drainage Center (GRDC), the United States Geological Survey (USGS) National Water Resources Information System, the Canadian National Water Resources Data Archive (HYDAT), the Brazilian National Water Agency (ANA), the Chilean Center for Climate and Adaptation Research (CCCRR), the Arctic River Observatory (ArcticGRO), the China Hydrological Yearbook (CHY), the Indian Water Resources Information System (WRIS), and the Australian Water Resources Data from the Australian Bureau of Meteorology (BOM). Except for the China Hydrological Yearbook, these data sources are publicly available. Due to the limited access and difficulty in collecting the original flow records in the Chinese Hydrological Yearbook, only flow data from some typical river basins, including 30 stations in seven major river basins in China, were collected. Among these data sources, USGS, HYDAT, ArcticGRO, and BOM provide high-quality record markers.
", "ds_quality": "For standardization purposes, the original markers are converted into four markers when collecting flow field records: reliable, suspicious, unmarked, and missing. For databases without quality markings, available records can be marked as unmarked, and missing records can be marked as missing. For records with poor quality or no markings, some studies use automatic detection methods to identify and remove unreasonable traffic values, including continuous equivalence and outliers.
", "ds_ref_way": "", "ds_abstract": "With the vigorous development of big data technology, it is becoming feasible to conduct large-scale hydrological analysis of river flow patterns, which can draw reliable conclusions about hydrological processes from a global perspective. However, there is currently a lack of a comprehensive global large sample dataset for analyzing the components of stream states. This article introduces a new time series dataset that calculates global river indices based on daily river records after data quality control. This dataset contains 79 indices from 41263 river sections worldwide, covering the seven main components of flow regime (i.e. magnitude, frequency, duration, rate of change, time series, variability, and decline) on an annual and multi-year scale. The dataset covers river flow index values before 2022. The time series dataset spans from 1806 to 2022, with an average length of 36 years. Compared with existing global datasets, this global dataset covers more sites and indices, especially those that characterize the frequency, duration, rate of change, and decline of river mechanisms. With this dataset, it is easier to conduct river hydrological research without spending time processing raw river flow records. This comprehensive dataset will become a valuable resource for the hydrological community, facilitating extensive research such as hydrological behavior studies in catchment areas, prediction of water flow status in data scarce regions, and studying changes in water flow status from a global perspective.
", "ds_time_res": "", "ds_acq_place": "Global scale", "ds_space_res": "", "ds_projection": "", "ds_process_way": "In the collection of water flow records, there were over 6000 stations with records spanning 32 years from 1990 to 2022, with a missing rate of less than 5%; From 1920 to 2022, there were approximately 800 stations recorded for 102 years, with a missing rate of less than 5%. As for the most recent recorded year, approximately 12000 sites had records ending in 2022, while approximately 17000 sites had records missing after 2000. Figure 3c shows the number of sites with different rates of missing records from 1900 to 2022. All curves show similar trends. The number of observation stations gradually increased from 1900, reaching its peak around 1978, fluctuating but relatively stable from 1979 to 2013, and decreasing slightly from 2014 to 2022. From 1900 to 2022, over 80% of recorded sites have no missing records every year. In addition, more than 50% of the sites have record lengths exceeding 30 years, and there have been no missing records from 1900 to 2022 each year. From 1975 to 2018, approximately 15000 sites had no missing records each year. This dataset is a global river index calculated based on daily river records after data quality control.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "流速状态", "频率", "持续时间", "变化速率", "衰退" ], "ds_subject_tags": [ "地图学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球范围" ], "ds_time_tags": [ 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "姜丽光", "email": "jianglg@sustech.edu.cn", "work_for": "南方科技大学环境科学与工程学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "姜丽光", "email": "jianglg@sustech.edu.cn", "work_for": "南方科技大学环境科学与工程学院", "country": "中国" } ], "ds_managers": [ { "true_name": "姜丽光", "email": "jianglg@sustech.edu.cn", "work_for": "南方科技大学环境科学与工程学院", "country": "中国" } ], "category": "水文" }