饱和蒸汽压差（Vapor Pressure Deficit，VPD）是大气干旱的重要表征指标，同时也是生态系统光合作用的重要气候调节因子。其作为驱动植被生态变化的主力之一，在黄河流域植被演变和生态演化中发挥着重要作用。建立长序列、高精度、高光滑度的VPD时空变化数据集可为黄河流域植被演变机理，生态环境保护等研究提供科学的基础数据支撑。本数据集基于全国蒸汽压数据和1km分辨率DEM数据，利用气象插值软件ANUSPLIN及Python语言代码，通过插值（以高程为协变量）、投影转换、裁剪等处理生成。数据空间范围为黄河流域，空间分辨率为1km，时间范围为1980年至2019年，时间分辨率为月。坐标系为GCS_WGS_1984。数据格式为NETCDF，即.nc格式。
", "ds_source": "数据集的生成需要黄河流域各气象站点饱和水汽压和实际水汽压值，黄河流域DEM。1）各气象站点的饱和蒸汽压差通过给定温度下空气饱和水汽压与实际水汽压作差求得。空气饱和水汽压和实际水汽压数据来自于国家气象科学数据中心（http://www.nmic.cn/）；2）插值计算时，作为协变量的DEM数据来自中国科学院地理科学与资源研究所的全国DEM 1km、500m和250m数据（SRTM 90m）（https://www.resdc.cn/data.aspx?DATAID=123）。
", "ds_process_way": "本数据集的处理流程如下：1）基于黄河流域102个气象站点的空气饱和水汽压和实际水汽压数据，作差求得各站点的饱和蒸汽压差值；2）利用气象插值软件ANUSPLIN，以地理高程为协变量，对各气象站点的饱和蒸气压差数据进行批量插值处理，得到空间分辨率为1km的逐月饱和蒸汽压差数据；3）利用Python语言代码及Arcpy库对数据进行投影转换、裁剪、数据格式转换等处理得到最终数据集。
", "ds_quality": "为提高数据的精度，在数据处理过程中采取以下措施：1）作为协变量的DEM数据范围大于黄河流域边界，以减少插值边界误差；2）插值处理时，对气象站点VPD数据进行开方运算，降低其峰度，再使用薄盘样条函数进行拟合，在保证处理结果为非负值的基础上提高插值精度。基于ANUSPLIN软件插值处理后，GCV很小，模型残差比MRR和信噪比SNR（信号自由度与剩余自由度之比很小，信号自由度小于站点一半，且模型成功率判断中无*提示，表示处理得到的数据精度合理，质量可靠。
", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2019-01-01 00:00:00", "ds_acq_place": "黄河流域气象站点", "ds_acq_lon_east": 119.20805555555556, "ds_acq_lat_south": 32.17611111111111, "ds_acq_lon_west": 95.88055555555555, "ds_acq_lat_north": 41.83861111111111, "ds_acq_alt_low": 1.0, "ds_acq_alt_high": 6853.0, "ds_share_type": "login-access", "ds_total_size": 5402947040, "ds_files_count": 41, "ds_format": "netCDF", "ds_space_res": "1000", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "无", "ds_thumbnail": "80688476-9fe3-47d5-a5f6-ebf21cfe9b7b.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "nc数据可使用ArcGIS、ArcMap等软件打开制图，并可用Matlab软件进行提取处理。数据坐标系统建议使用WGS84。", "ds_from_station": null, "organization_id": "b412fac3-1f10-4eb6-9d10-c51bcea30d0c", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 3, "publish_time": "2024-01-09 16:51:23", "last_updated": "2025-05-29 11:38:53", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.YRIVER.DB4159.2024", "i18n": { "en": { "title": "Dataset of Monthly Saturated Vapor Pressure Deficit with 1km Resolution in the Yellow River Basin (1980-2019)", "ds_format": "netCDF", "ds_source": "The generation of the dataset requires the saturated vapor pressure and actual vapor pressure values from various meteorological stations along the Yellow River basin, as well as the Digital Elevation Model (DEM) of the Yellow River basin. \n
1) The saturated vapor pressure deficit at each meteorological station is calculated by subtracting the actual vapor pressure from the saturated vapor pressure of the air at a given temperature. The data for saturated vapor pressure and actual vapor pressure are sourced from the National Meteorological Information Center (http://www.nmic.cn/); \n
2) During interpolation calculations, the DEM data used as covariates are sourced from the 1km, 500m, and 250m national DEM data (SRTM 90m) provided by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (https://www.resdc.cn/data.aspx?DATAID=123).
", "ds_quality": "To improve the accuracy of the data, the following measures were taken during the data processing: 1) The DEM data used as covariates have a range larger than the boundaries of the Yellow River basin, in order to reduce interpolation boundary errors; 2) During the interpolation process, the vapor pressure deficit (VPD) data from meteorological stations were subjected to a square root operation to reduce their kurtosis, and then fitted using a thin plate spline function. This improved the interpolation accuracy while ensuring that the processing results were non-negative. After interpolation processing based on the ANUSPLIN software, the Generalized Cross-Validation (GCV) value was very small, and the Model Residual Ratio (MRR) and Signal-to-Noise Ratio (SNR, the ratio of signal degrees of freedom to residual degrees of freedom) were also favorable. The signal degrees of freedom were less than half of the number of stations, and there were no asterisks in the model success rate judgment, indicating that the processed data had reasonable accuracy and reliable quality.
", "ds_ref_way": "", "ds_abstract": "Vapor Pressure Deficit (VPD) is an important indicator of atmospheric drought and also a crucial climatic regulatory factor for photosynthesis in ecosystems. As one of the main drivers of vegetation ecological changes, it plays a significant role in vegetation evolution and ecological development in the Yellow River Basin. Establishing a long-term sequence of VPD spatio-temporal variation datasets with high precision and smoothness can provide scientific basic data support for research on vegetation evolution mechanisms and ecological environmental protection in the Yellow River Basin. This dataset is based on national vapor pressure data and 1km resolution DEM data, and is generated through interpolation (with elevation as a covariate), projection transformation, clipping, and other processing steps using the meteorological interpolation software ANUSPLIN and Python code. The spatial scope of the data covers the Yellow River Basin, with a spatial resolution of 1km. The temporal scope spans from 1980 to 2019, with a temporal resolution of one month. The coordinate system used is GCS_WGS_1984. The data format is NETCDF, specifically in .nc format.
", "ds_time_res": "月", "ds_acq_place": "Meteorological Stations in the Yellow River Basin", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "The processing workflow for this dataset is as follows: 1) Based on the data of saturated vapor pressure and actual vapor pressure from 102 meteorological stations along the Yellow River basin, the saturated vapor pressure deficit at each station is calculated by subtracting the actual vapor pressure from the saturated vapor pressure; 2) Using the meteorological interpolation software ANUSPLIN, with geographic elevation as a covariate, batch interpolation processing is performed on the saturated vapor pressure deficit data from each meteorological station to obtain monthly saturated vapor pressure deficit data with a spatial resolution of 1km; 3) Using Python code and the Arcpy library, processes such as projection transformation, clipping, and data format conversion are performed on the data to obtain the final dataset.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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, "ds_topic_tags": [ "大气水分", "空气饱和水汽压", "饱和蒸汽压差" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河流域" ], "ds_time_tags": [ 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 ], "ds_contributors": [ { "true_name": "靳晓辉", "email": "xiaohui7360@126.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "樊玉苗", "email": "fanyumiao1990@sina.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "张铭琪", "email": "1536983402@qq.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "胡亚伟", "email": "huyawei168@126.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "王辉辉", "email": "whh1320@126.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "杨蕾", "email": "1554967065@qq.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "贾倩", "email": "244045860@qq.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "靳晓辉", "email": "xiaohui7360@126.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "樊玉苗", "email": "fanyumiao1990@sina.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "张铭琪", "email": "1536983402@qq.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "胡亚伟", "email": "huyawei168@126.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "王辉辉", "email": "whh1320@126.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "杨蕾", "email": "1554967065@qq.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" }, { "true_name": "贾倩", "email": "244045860@qq.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "张铭琪", "email": "1536983402@qq.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" } ], "category": "生态" }