长期高分辨率的国家降水（P）、土壤水分（SM）和雪水当量数据集对于预测中国洪涝干旱和评估气候变化对河流流量的影响是必要的。当前P，SM和SWE的长期每日或亚每日数据集受到粗略空间分辨率或缺乏局部校正的限制。虽然从全国范围内的水文模拟得出的SM和SWE数据具有良好的空间分辨率并利用了局部强迫数据，但水文模型不能直接使用SM和SWE数据进行校准。
\n在这项研究中，我们产生了每日0.1°1981-2017年中国P、SM和SWE数据集，通过CMPA数据的空间和时间分割进行交叉验证，重建P的中位数Kling-Gupta效率（KGE）在日尺度上所有网格为0.68。在每日尺度下，所有网格在校准中SM的中位数KGE为0.61。对于两个积雪丰富的地区的网格，松花盆地、辽河流域和西北大陆盆地标定的SWE中位数KGEs在日尺度上分别为0.55和-2.41。
\n总体而言，重建数据集在华南和华东地区的P和SM表现优于华北和西部，在东北地区的SWE表现优于其他地区。作为第一个长期0.1°P、SM和SWE的每日数据集结合了来自当地观测的信息和基于卫星的数据基准，这一重建产品对未来国家水文调查的过程很有价值。", "ds_source": "
使用全球背景数据和本地现场数据作为强制输入，以卫星数据为重建基准。", "ds_process_way": "
全球 0.1°和本地 0.25°合并1981-2017年的P数据，重建0.1°的历史P。2008-2017年使用堆叠机器学习模型的中国合并降水分析（CMPA）。重建的P数据用于驱动HBV水文模型模拟1981-2017年的SM和SWE数据。SM 模拟由土壤湿度主动被动 4 级 （SMAP-L4） 数据校准。SWE模拟由中国国家卫星雪深数据集（Che和Dai，2015）和中分辨率成像光谱仪（MODIS）积雪数据校准。", "ds_quality": "
数据质量良好。
", "ds_acq_start_time": "1981-01-01 00:00:00", "ds_acq_end_time": "2017-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.05, "ds_acq_lat_south": 4.0, "ds_acq_lon_west": 73.66666666666667, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 9261732564, "ds_files_count": 7, "ds_format": "tiff", "ds_space_res": "680米", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "WGS_1984", "ds_thumbnail": "1e5d0e90-c3e7-4d09-a4bb-7bec6b7f8849.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45", "170.55" ], "quality_level": 3, "publish_time": "2023-08-25 15:20:43", "last_updated": "2026-01-14 10:48:14", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB3946.2023", "i18n": { "en": { "title": "Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China（1981-2017）", "ds_format": "tiff", "ds_source": "Use global background data and local on-site data as mandatory inputs, using satellite data as the reconstruction benchmark.", "ds_quality": "
The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Long term high-resolution national precipitation (P), soil moisture (SM), and snow water equivalent datasets are necessary for predicting floods, droughts, and assessing the impact of climate change on river flow in China. The current long-term daily or sub daily datasets of P, SM, and SWE are limited by rough spatial resolution or lack of local correction. Although the SM and SWE data obtained from nationwide hydrological simulations have good spatial resolution and utilize local forcing data, hydrological models cannot be directly calibrated using SM and SWE data.
\nIn this study, we generated a daily 0.1°dataset of P, SM, and SWE in China from 1981 to 2017. Cross validation was performed using spatial and temporal segmentation of CMPA data, and the median Kling Gupta efficiency (KGE) of reconstructed P was 0.68 for all grids on a daily scale. At the daily scale, the median KGE of SM in calibration for all grids is 0.61. For the grids of two regions with abundant snow cover, the median SWE KGEs calibrated for the Songhua Basin, Liaohe Basin, and Northwest Continental Basin are 0.55 and -2.41 on a daily scale, respectively.
\nOverall, the P and SM performance of the reconstructed dataset in South and East China is better than that in North and West China, and the SWE performance in Northeast China is better than that in other regions. As the first long-term daily dataset of 0.1°P, SM, and SWE, combining information from local observations and satellite based data benchmarks, this reconstruction product is of great value for future national hydrological process investigations.
", "ds_time_res": "日", "ds_acq_place": "China", "ds_space_res": "680米", "ds_projection": "WGS_1984", "ds_process_way": "Merge P data from 1981 to 2017 between global 0.1°and local 0.25°, and reconstruct the historical P of 0.1°. China Consolidated Precipitation Analysis (CMPA) using Stacked Machine Learning Models from 2008 to 2017. The reconstructed P data is used to drive the HBV hydrological model to simulate SM and SWE data from 1981 to 2017. SM simulation is calibrated using soil moisture active passive level 4 (SMAP-L4) data. The SWE simulation was calibrated using the China National Satellite Snow Depth Dataset (Che and Dai, 2015) and the Moderate Resolution Imaging Spectrometer (MODIS) snow cover data.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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, "ds_topic_tags": [ "雪水当量", "水文重建", "降水", "土壤湿度" ], "ds_subject_tags": [ "地理学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 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 ], "ds_contributors": [ { "true_name": "杨汉波", "email": "yanghanbo@mail.tsinghua.edu.cn", "work_for": "清华大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "杨汉波", "email": "yanghanbo@mail.tsinghua.edu.cn", "work_for": "清华大学", "country": "中国" } ], "ds_managers": [ { "true_name": "杨汉波", "email": "yanghanbo@mail.tsinghua.edu.cn", "work_for": "清华大学", "country": "中国" } ], "category": "其他" }