CIWW1km 数据集基于一种基于物理-机器学习耦合模型研制。首先通过融合多源遥感观测数据（包括蒸散发和土壤水分产品）与再分析气象数据，基于土壤水分平衡原理，实现了灌溉用水各组成分量的物理一致性估算（Zhang, Che*, et al., 2026）。在此基础上，引入可解释性机器学习方法，对物理模型估算结果的进行优化，进一步提升灌溉取水量的估算精度（Zhang* et al., 2025）。 CIWW1km 系统刻画了过去二十年来中国灌溉取水的时空演变特征，为水资源管理、农业规划以及人类活动影响下的水文循环研究提供了重要的数据基础。", "ds_source": "
该数据集在像元尺度上提供两个关键变量：灌溉取水深（mm，网格单位面积灌溉用水量）和灌溉取水量（km³，网格的总用水量）。数据以GeoTIFF格式呈现，采用WGS84地理坐标，例如：
CIWW_1km_depth_mm_2000_v2.tif, 表示2000年灌溉取水深，v2表示版本号。
CIWW_1km_depth_mm_2000_01_v2.tif表示2000年1月灌溉取水深。
CIWW_1km_depth_km3_2000_v2.tif, 表示2000年灌溉取水量。
CIWW_1km_depth_mm_2000_01_v2.tif表示2000年1月灌溉取水量。", "ds_process_way": "
利用物理约束的机器学习模型，通过融合灌溉耕地分布、多源遥感蒸散发和土壤水分产品、再分析资料、以及社会经济等数据研制而成。", "ds_quality": "
数集据的时空分辨率及精度优于同类产品。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.0, "ds_acq_lat_south": 4.0, "ds_acq_lon_west": 73.0, "ds_acq_lat_north": 53.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 6305562705, "ds_files_count": 549, "ds_format": "*.tif", "ds_space_res": "1km", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "5e74ffea-2514-41db-b2db-77b9f38449fb.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2026-01-09 11:33:35", "last_updated": "2026-01-09 11:52:41", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7058.2026", "i18n": { "en": { "title": "CIWW1km:China High-Resolution Monthly Irrigation Water Withdrawal Dataset (2000–2020)", "ds_format": "*.tif", "ds_source": "
The dataset integrates multiple data sources, including remote sensing observations, reanalysis products, statistical data, and socioeconomic information.", "ds_quality": "
The spatiotemporal resolution and accuracy of data sets are superior to similar products.", "ds_ref_way": "", "ds_abstract": "
The CIWW1km dataset was developed based on an innovative physics-constrained estimation framework. By integrating multi-source remote sensing observations (including evapotranspiration and soil moisture products) with reanalysis meteorological data, and grounding the analysis in the soil water balance principle, this framework achieves physically consistent estimates of irrigation water withdrawal components (Zhang, Che*, et al., 2026). Building upon this, interpretable machine learning methods were introduced to optimize the spatiotemporal representation of the physical model outputs, thereby enhancing the overall estimation accuracy (Zhang* et al., 2025). CIWW1km systematically characterizes the spatiotemporal evolution of irrigation water withdrawal in China over the past two decades, providing a critical data foundation for water resources management, agricultural planning, and research on the hydrological cycle under human influence.", "ds_time_res": "月", "ds_acq_place": "China", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "
The dataset wasd developed using a physically constrained machine learning model by integrating irrigated cropland distribution, multisource remote sensing evapotranspiration and soil moisture products, reanalysis datasets, and socioeconomic data.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "灌溉用水", "灌溉取水", "CIWW1km", "中国" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "张凌", "email": "zhanglingky@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": "车涛", "email": "chetao@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": "张琨", "email": "zhangkun3@mail.sysu.edu.cn", "work_for": "中山大学", "country": "中国" }, { "true_name": "郑东海", "email": "zhengd@itpcas.ac.cn", "work_for": "中国科学院青藏高原研究所", "country": "中国" }, { "true_name": "马慧", "email": "1790425948@qq.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "胡英屹", "email": "huyingyi@nieer.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": "王艺晓", "email": "2023222982@nwnu.edu.cn", "work_for": "西北师范大学", "country": "中国" }, { "true_name": "赵彦博", "email": "eanbo@lab.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": "李新", "email": "xinli@itpcas.ac.cn", "work_for": "中国科学院青藏高原研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张凌", "email": "zhanglingky@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": "张凌", "email": "zhanglingky@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": "水文" }