本数据集来源于塔克拉玛干沙漠南缘地下水埋深梯度样地（2025.6.6）调查，旨在揭示地下水埋深变化对荒漠土壤水分、养分及有机碳组分的影响。数据设置2.5、4.5和11.0 m三个地下水埋深水平，每个地下水埋深水平包含15条土壤样品记录，共45条记录。数据指标包括硝态氮、铵态氮、无机氮、有效磷、速效钾、土壤含水量、土壤pH，以及细颗粒有机碳、粗颗粒有机碳、矿物结合态有机碳和其他活性或稳定性有机碳组分。本数据集可用于研究极端干旱区地下水埋深对土壤水分供应、养分有效性及土壤有机碳组分分配的影响。\n
本数据集以CSV格式存储，共包含45条土壤样品记录和14个数据字段。数据按照地下水埋深进行组织，包括2.5、4.5和11.0 m三个地下水埋深水平，每个埋深水平包含15条样品记录。主要字段包括地下水埋深（WD，m）、硝态氮（NN，mg·kg⁻¹）、铵态氮（AN，mg·kg⁻¹）、无机氮（N，mg·kg⁻¹）、有效磷（AP，mg·kg⁻¹）、速效钾（AK，mg·kg⁻¹）、土壤含水量（SWC，%）、土壤pH、细颗粒有机碳（fPOC，mg·kg⁻¹）、粗颗粒有机碳（cPOC，mg·kg⁻¹）、矿物结合态有机碳（MAOC，mg·kg⁻¹），以及ROC ROC（mg·kg⁻¹）、LOC（g·kg⁻¹）和RC（g·kg⁻¹）等有机碳组分或碳库指标。数据可用于比较不同地下水埋深条件下荒漠土壤水分、养分及不同有机碳组分的变化特征，为揭示地下水埋深变化对荒漠土壤碳库及养分状况的影响提供基础数据。", "ds_source": "
本数据来源于新疆策勒荒漠草地生态系统国家野外科学观测研究站的野外采样与实验室（2025.6.6）测定。设置2.5、4.5和11.0 m三个地下水埋深梯度，对应位置分别为37°01′18″N、80°42′29″E，37°00′40″N、80°42′13″E和37°00′33″N、80°42′25″E。每个埋深随机选取3个样地，沿S形路线布设15个采样点，样方10 m×10 m，相邻样方间隔20 m；每3个相邻采样点的0–20 cm土壤按四分法混合形成1个复合样品，最终每个埋深获得5个重复，共15个复合样品。样品运回实验室后去除植物残体和石砾，过2 mm筛，一部分风干用于土壤理化性质和碳组分测定，另一部分4 ℃保存用于微生物量和酶活性测定。具体方法参见韩雪茹等《塔克拉玛干沙漠南缘地下水埋深对荒漠土壤碳组分的影响》。", "ds_process_way": "
以复合土壤样品为基本数据单元进行整编，按照地下水埋深和重复顺序建立唯一编号，建议采用“WD2.5-01～05、WD4.5-01～05、WD11.0-01～05”，其中WD表示地下水埋深，数字表示埋深值，01～05表示5个独立重复。统一变量名称、英文缩写、计量单位和小数位数。数据录入后逐项核查样品编号、重复记录、缺失值、单位和明显录入错误；疑似异常值返回原始测定记录复核，真实观测值予以保留，不进行人为插补。", "ds_quality": "
野外调查采用统一的样地选择、S形布点、采样深度和复合取样方法，3个地下水埋深均设置5个独立复合样品，以保证不同埋深间采样尺度一致。土壤理化性质和碳组分测定参照《土壤农业化学分析方法》（鲁如坤，2000）：土壤含水量采用烘干法，有效磷采用钼锑抗比色法，有效钾采用火焰光度法，SOC采用重铬酸钾-浓硫酸外加热法；cPOC、fPOC和MAOC采用六偏磷酸钠分散法，活性有机碳采用高锰酸钾氧化法。数据产出后核查样品编号、单位、缺失值、重复值和异常值，疑似异常记录与原始实验记录进行复核，以保证数据的完整性、可比性和可追溯性。", "ds_acq_start_time": "2025-06-06 00:00:00", "ds_acq_end_time": "2025-06-06 00:00:00", "ds_acq_place": "策勒县", "ds_acq_lon_east": 80.72916666666667, "ds_acq_lat_south": 37.01583333333333, "ds_acq_lon_west": 80.72916666666667, "ds_acq_lat_north": 37.01583333333333, "ds_acq_alt_low": 1318.0, "ds_acq_alt_high": 1318.0, "ds_share_type": "apply-access", "ds_total_size": 16926, "ds_files_count": 0, "ds_format": "*.xlsx", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "64c97fef-6f75-4b32-8368-b16472e76a1d.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "c49c8ab1-d3df-4dd2-b84b-8709ba45d418", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 0, "publish_time": "2026-09-03 11:39:03", "last_updated": "2026-09-03 11:39:03", "protected": false, "protected_to": "2027-08-27 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.db7770.2026", "i18n": { "en": { "title": "Soil nutrients and organic carbon fractions across groundwater-depth gradients on the southern margin of the Taklimakan Desert (2025)", "ds_format": "*.xlsx", "ds_source": "
This data comes from field sampling and laboratory measurement (2025.6.6) at the National Field Scientific Observation and Research Station of the Desert Grassland Ecosystem in Cele, Xinjiang. Three groundwater burial depth gradients of 2.5, 4.5 and 11.0 m were set, and the corresponding positions were 37°01′18 \"N, 80°42′29\" E, 37°00′40 \"N, 80°42′13\" E and 37°00′33 \"N, 80°42′25\" E respectively. Three sample sites were randomly selected for each burial depth, and 15 sampling points were arranged along the S-shaped route, with a sample square of 10 m×10 m, and an interval of 20 m between adjacent sample squares; the 0 - 20 cm soil at each three adjacent sampling points was mixed according to quartering method to form 1 composite sample, and finally 5 replicates were obtained for each burial depth, making a total of 15 composite samples. After the samples were transported back to the laboratory, plant residues and gravel were removed, passed through a 2 mm sieve, and part was air-dried for determination of soil physical and chemical properties and carbon composition, and the other part was stored at 4 ° C for determination of microbial biomass and enzyme activity. For specific methods, please refer to Han Xueru et al.'s \"Impact of groundwater burial depth on desert soil carbon composition in the southern edge of the Taklimakan Desert.\"", "ds_quality": "
The field investigation adopts unified sample site selection, S-shaped layout, sampling depth and composite sampling method. Five independent composite samples are set at each of the three groundwater burial depths to ensure consistent sampling scales between different burial depths. The physical and chemical properties and carbon components of soil are determined in accordance with the Soil Agrochemical Analysis Methods (Lu Rukun, 2000): the soil water content is dried by the drying method, the available phosphorus is molybdenum and antimony resistant colorimetric method, the available potassium is flame photometry method, and the SOC is potassium dichromate-concentrated sulfuric acid external heating method;cPOC, fPOC and MAOC are sodium hexametaphosphate dispersion method, and the active organic carbon is activated by potassium permanganate oxidation method. After the data is output, the sample numbers, units, missing values, duplicate values and abnormal values are checked, and the suspected abnormal records are reviewed with the original experimental records to ensure the integrity, comparability and traceability of the data.", "ds_ref_way": "", "ds_abstract": "
This dataset is derived from the groundwater burial gradient sample plot (2025.6.6) survey on the southern edge of the Taklimakan Desert. It aims to reveal the impact of groundwater burial changes on desert soil moisture, nutrients and organic carbon components. The data sets three groundwater burial depth levels of 2.5, 4.5 and 11.0 m. Each groundwater burial depth level contains 15 soil sample records, for a total of 45 records. Data indicators include nitrate nitrogen, ammonium nitrogen, inorganic nitrogen, available phosphorus, available potassium, soil water content, soil pH, as well as fine particulate organic carbon, coarse particulate organic carbon, mineral-bound organic carbon and other active or stable organic carbon components. This dataset can be used to study the impact of groundwater depth on soil water supply, nutrient availability, and soil organic carbon component allocation in extremely arid areas.\r\n
\tThis dataset is stored in CSV format and contains a total of 45 soil sample records and 14 data fields. The data is organized according to groundwater burial depth, including three groundwater burial depth levels of 2.5, 4.5 and 11.0 m, and each burial depth level contains 15 sample records. The main fields include groundwater depth (WD, m), nitrate nitrogen (NN, mg·kg ³), ammonium nitrogen (AN, mg·kg ³), inorganic nitrogen (N, mg·kg ³), available phosphorus (AP, mg·kg ³), available potassium (AK, mg·kg ³), soil water content (SWC, %), soil pH, fine particulate organic carbon (fPOC, mg·kg ³), coarse particulate organic carbon (cPOC, mg·kg ³), mineral-bound organic carbon (MAOC, mg·kg ³), and organic carbon components or carbon pool indicators such as ROC (mg·kg ³), LOC (g·kg ³) and RC (g·kg ³). The data can be used to compare the change characteristics of desert soil moisture, nutrients and different organic carbon components under different groundwater depth conditions, and provide basic data for revealing the impact of groundwater depth changes on desert soil carbon pools and nutrient status.", "ds_time_res": "", "ds_acq_place": "Celle County", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Composite soil samples are used as the basic data unit for reorganization, and unique numbers are established according to the groundwater burial depth and repetition order. It is recommended to use \"WD2.5-01 ~05, WD4.5-01~05, WD11.0-01~05\", where WD represents the groundwater burial depth, numbers represent the burial depth value, and 01~05 represent 5 independent repeats. Unify variable names, English abbreviations, units of measurement and decimal places. After data entry, the sample numbers, duplicate records, missing values, units and obvious entry errors are checked item by item; suspected abnormal values are returned to the original measurement records for review, and the real observed values are retained, and no artificial imputation is carried out.", "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", "土壤理化性质", "土壤养分", "有机碳" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆", "策勒" ], "ds_time_tags": [ 2025 ], "ds_contributors": [ { "true_name": "张志浩", "email": "zhangzh@ms.xjb.ac.cn", "work_for": "中国科学院新疆生态与地理研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张波", "email": "zhangbo@ms.xjb.ac.cn", "work_for": "中国科学院新疆生态与地理研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "曾凡江", "email": "zengfj@ms.xjb.ac.cn", "work_for": "中国科学院新疆生态与地理研究所", "country": "中国" } ], "category": "生态" }