本数据集为美国本土30分辨率地下水埋深（WTD）空间分布图。研究依托 1914-2023 年多个地下水数据库汇总的百万余口钻井观测数据，采用机器学习方法完成模型训练。研究构建包含 300 棵决策树的随机森林模型，选取 80% 观测样本开展训练；模型输入变量涵盖气候要素（降水量、气温、潜在蒸散发）、地下介质属性（渗透系数、土壤质地）以及地形特征（高程、坡度、至河流距离）。模型验证结果为：相关系数r=0.79，均方根误差（RMSE）为 14.94 米，纳什效率系数（NSE）为 0.62。\n
数据包括：1.中值地下水位深度；2.第25百分位地下水位深度；3.第75百分位地下水位深度；4.ParFlow CONUS 2.0 孔隙度结合时导出的地下水储量。\n
详细介绍见Ma 等（2026）论文。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "1914-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "美国", "ds_acq_lon_east": -126.8875, "ds_acq_lat_south": 21.816944444444445, "ds_acq_lon_west": -64.76777777777778, "ds_acq_lat_north": 53.20277777777778, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 36574112561, "ds_files_count": 2, "ds_format": "GeoTIFF", "ds_space_res": "30m", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "8f38a1e9-60e7-41d6-81b4-bda2fdef6b4b.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.45", "170.55" ], "quality_level": 0, "publish_time": "2026-06-23 09:46:24", "last_updated": "2026-06-23 09:46:24", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.hydrology.db7455.2026", "i18n": { "en": { "title": "A high-resolution water table depth (WTD) map for the contiguous United States", "ds_format": "GeoTIFF", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
A 1-arcsec (~30 m) resolution water table depth (WTD) map for the contiguous United States using machine learning methods trained on over one million well observations compiled from multiple groundwater databases spanning 1914-2023. A random forest model with 300 decision trees was trained on 80% of these data using input variables including climatology (precipitation, temperature, PME), subsurface properties (hydraulic conductivity, soil texture), and topographic features (elevation, slope, distances to streams), achieving test performance of r = 0.79, RMSE = 14.94 m, and NSE = 0.62.\r\n
The dataset includes:1.Median water table depth\r\n;2. 25th percentile water table depth;3. 75th percentile water table depth; 4. Groundwater storage derived from porosity in ParFlow CONUS 2.0.", "ds_time_res": "", "ds_acq_place": "US", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "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_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "allow_update_data": false, "ds_topic_tags": [ "地下水埋深", "随机森林模型", "决策树" ], "ds_subject_tags": [ "地理学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "美国" ], "ds_time_tags": [ 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, 2023 ], "ds_contributors": [ { "true_name": "Laura E. Condon", "email": "lecondon@arizona.edu", "work_for": "Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA", "country": "美国" } ], "ds_meta_authors": [ { "true_name": "Laura E. Condon", "email": "lecondon@arizona.edu", "work_for": "Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA", "country": "美国" } ], "ds_managers": [ { "true_name": "Laura E. Condon", "email": "lecondon@arizona.edu", "work_for": "Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA", "country": "美国" } ], "category": "水文" }