本数据集来源于黄河源区热喀斯特湖泊集水区（TLC）与非湖泊集水区（NTLC）2023年6月-2025年3月的野外原位观测，涵盖冻土土壤、水文、气象多维度实测数据。在 10–80 cm 冻土活动层分层采集土壤样品并设置重复，依托高精度传感器持续监测土壤水热、地下水位及气象要素。\n
本数据集为黄河源多年冻土区热喀斯特湖流域水文过程2023年6月-2025年3月野外观测数据，包含热喀斯特湖集水区（TLC）与非湖泊对照集水区（nTLC）多维度水文实测资料，共包含 3 张数据表。表 1 不同冻土活动层土壤体积含水量数据记录了活动层不同深度降雨前初始土壤体积含水量、降雨事件引发的土壤含水量变化量以及对应场次降雨量；表 2 冻土活动层地下水位变化及降雨响应数据以单次降雨事件为统计单元，记录次降雨累计量与对应的地下水位变化幅度；表 3 冻土实验流域河流径流‑地下水水位变化数据为连续时序观测结果，同步记录河流水位、热喀斯特湖水位、地下水监测井水位以及时段降雨量。该数据集可用于揭示热喀斯特湖影响下冻土流域土壤水、地下水、湖水与地表径流之间的水力联系，量化降雨入渗补给过程，对比两类集水区水文调蓄能力与降雨响应特征的差异性。", "ds_source": "
本数据集基于黄河源区热喀斯特湖泊集水区（TLC）与邻近非湖泊集水区（NTLC）原位野外观测获取。在 10、20、40、80 cm 冻土活动层分层采集土壤样品测定质地，各深度设 3 组重复；两区土壤质地相近，减少基底差异干扰。研究布设 HOBO 气象记录仪连续观测气象要素，采用 TDR-315H 传感器监测 20–120 cm 土壤体积含水率，NTLC 监测最深至 190 cm，传感器埋设时分层回填，维持原位土壤结构。", "ds_process_way": "
本数据集原始观测采样步长为 15 min，来源于黄河源配对小流域 2023-2025 年野外观测，首先剔除传感器上电后 15 天预热期数据，依据物理阈值剔除时序异常跳变值，将压力水位观测校正换算为统一基准的绝对高程，完成连续时序数据预处理；再以总降雨量≥1 mm 筛选有效降雨事件，统一确定事件起始时刻与降雨结束后水分稳定的终态时刻，分别差分计算得到事件尺度土壤初始含水量、土壤含水量变化量以及地下水位变化幅度，并将高程差值换算为厘米单位，最后通过时序差分复现、同场次降雨量交叉核对开展一致性校验，剔除关键时段大量缺测的无效事件，最终形成数据集。", "ds_quality": "
本数据集土壤含水量测量精度 ±2% 体积含水量，水位计精度 ±0.3 cm，降水未进行风致误差校正，冻土冻胀造成的水位小幅抖动属于野外正常观测现象。", "ds_acq_start_time": "2023-06-01 00:00:00", "ds_acq_end_time": "2025-03-31 00:00:00", "ds_acq_place": "青藏高原东北部", "ds_acq_lon_east": 99.0, "ds_acq_lat_south": 35.65, "ds_acq_lon_west": 95.81666666666666, "ds_acq_lat_north": 33.65, "ds_acq_alt_low": 4301.0, "ds_acq_alt_high": 4900.0, "ds_share_type": "apply-access", "ds_total_size": 52935, "ds_files_count": 5, "ds_format": "*.xlsx，*.docx", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "9a09869b-b83e-40d3-988b-64641e0a3e70.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "e7579b46-aade-4090-80a4-cbf0d8a8eb69", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-10-15 17:38:26", "last_updated": "2026-10-15 17:38:26", "protected": false, "protected_to": "2028-10-14 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.db7849.2026", "i18n": { "en": { "title": "Dataset of Soil Volumetric Water Content and Groundwater Level in Permafrost Active Layers, Source Area of the Yellow River(2023-2025)", "ds_format": "*.xlsx，*.docx", "ds_source": "
This dataset was collected based on in-situ field observations in the TLC (Thermal Karst Lakes) region at the source of the Yellow River, as well as in neighboring non-lake catchment areas. Soil samples were taken at depths of 10, 20, 40, and 80 cm within the permafrost layer to determine soil texture. Each depth was measured three times to minimize interference caused by differences in underlying conditions. A HOBO weather station was deployed in both regions to continuously monitor meteorological parameters. The TDR-315H sensor was used to measure the soil moisture content at depths of 20–120 cm. In the NTLC areas, measurements were conducted up to a depth of 190 cm. The sensors were buried layer by layer to maintain the original soil structure during measurement operations.
", "ds_quality": "The accuracy of soil moisture measurement in this dataset is ±2% of the volume moisture content. The accuracy of the water level sensor is ±0.3 cm. Precipitation data has not been corrected for wind-induced errors. Any slight fluctuations in water level caused by permafrost expansion are normal phenomena during field observations.", "ds_ref_way": "", "ds_abstract": "
This dataset consists of field observations conducted in the TLC (Hot Karst Lakes Basin) and NTLC (Non-Lakes Basin) regions during June 2023 to March 2025. The observations cover various aspects of soil, hydrology, and meteorology, including data from permafrost soils. Soil samples were collected at depths ranging from 10 to 80 cm, and continuous monitoring was carried out using high-precision sensors to track soil temperature and moisture levels, groundwater levels, and meteorological conditions.
\r\n\r\nThis dataset provides detailed hydrological data from the hot karst lake basins in the permafrost regions of the Yellow River source area, covering the period from June 2023 to March 2025. It includes multi-dimensional hydrological measurements both in the TLC and non-lake basins. There are three tables in total: Table 1 presents data on soil volumetric water content at different depths within the permafrost layer, including initial water content before rainfall, changes in soil water content due to rainfall events, and corresponding rainfall amounts. Table 2 shows changes in groundwater levels and their responses to rainfall, with statistics based on individual rainfall events. Table 3 contains continuous temporal data on river runoff and groundwater level changes in the permafrost experimental basin, along with records of river levels, hot karst lake levels, groundwater monitoring wells, and rainfall amounts over time. This dataset can help elucidate the hydraulic connections between soil water, groundwater, lake water, and surface runoff under the influence of hot karst lakes. It also allows for the quantification of rainfall infiltration processes and comparison of the hydrological regulation capabilities and rainfall response characteristics between the two types of basins.
", "ds_time_res": "", "ds_acq_place": "northeastern part of the Qinghai-Tibet Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The original observational sampling interval for this dataset is 15 minutes. The data comes from field observations conducted in the Yellow River source area during the years 2023 to 2025. First, data from the 15-day pre-warmup period after the sensors were powered on was excluded. Additionally, values that showed sudden changes in timing were removed based on physical thresholds. The pressure water level observations were corrected and converted into a unified reference elevation scale. Continuous temporal data preprocessing was completed accordingly. Then, rainfall events with total precipitation exceeding 1 mm were selected. The start time of each event and the final state after the rainfall ended when the water level stabilized were determined. By calculating the difference between these two times, the initial soil moisture content at the onset of the event, the change in soil moisture content, and the variation in the groundwater level were determined. The elevation differences were converted into centimeters. Finally, consistency checks were performed by comparing the temporal differences and cross-checking the same rainfall events. Invalid events with many missing measurements during critical periods were eliminated, resulting in a complete dataset.
", "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, "created_from": "admin", "ds_topic_tags": [ "冻土", "土壤体积含水量", "地下水位" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原", "黄河源区" ], "ds_time_tags": [ 2023, 2024, 2025 ], "ds_contributors": [ { "true_name": "秦甲", "email": "qinjia418@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王飞腾", "email": "wangfeiteng@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "秦甲", "email": "qinjia418@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王飞腾", "email": "wangfeiteng@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "秦甲", "email": "qinjia418@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王飞腾", "email": "wangfeiteng@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "其他" }