中国3254个水库共提供512个属性。这些属性是在水库上游集水区提供的，包含:\n
1）两种类型的水库上游集水区（即全集水区和中间集水区），\n
2）集水区地形，\n
3）人为活动，\n
4）气候，\n
5）土地覆盖和利用，\n
6）土壤和地质。\n
我们还生成了 15 个气象变量的时间序列。还提供了关于水库储存异常（3254个水库中92%的数据）、水位（20%）、蒸发 （98%）,水域（99%）的大量数据集来自多源卫星——雷达/激光卫星高度计 + 来自 Landsat + Sentinel-2 的卫星图像。Validation_figures记录所有验证、数字的时间序列以及水位、水面积和存储变化的统计指标。", "ds_source": "
多源卫星——雷达/激光卫星高度计，Landsat，Sentinel-2卫星图像。", "ds_process_way": "
水库水位数据：在 GeoDAR 储层 shapefile中提取高度数据；用全球地表水探测器选择水量大于10%的高度数据；用MAD方法（绝对偏差的中位数）删除了每次传递的异常值；从高度计数据中删除了高度超过 DEM 的异常值；用R包“tsHydro”构建时间序列。\n
水库水表面积数据：计算NDWI（归一化差值水指数）；沿边缘对水的出现进行采样，以消除错误检测到的水；将水发生量剪切到一定发生值，并与水面罩结合，得到最终的水面罩；使用基于量化的时间异常值滤波器来消除水库水域误差。\n
水库集水区边界数据：使用自动出口重新定位算法自动划定一个大集水区数量。\n
流域水平特征数据：对相关属性数据集进行一些必要的数据格式转换；用不同方法从栅格和矢量数据中计算集水区级特征；对于温度和海拔等连续变量，计算均值、最大值、最小值和范围值。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "中国", "ds_acq_lon_east": 140.0, "ds_acq_lat_south": 20.0, "ds_acq_lon_west": 70.0, "ds_acq_lat_north": 60.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 5562114349, "ds_files_count": 8, "ds_format": "shp", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "7ee49db4-aadb-46af-894d-cc6374578d16.png", "ds_thumb_from": 0, "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" ], "quality_level": 3, "publish_time": "2024-04-25 16:35:53", "last_updated": "2026-01-14 10:33:27", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6437.2024", "i18n": { "en": { "title": "A dataset of watershed characteristics of 3254 reservoirs in China", "ds_format": "shp", "ds_source": "
Multi source satellites - radar/laser satellite altimeter, Landsat, Sentinel-2 satellite images.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
3254 reservoirs in China provide a total of 512 attributes. These attributes are provided in the upstream catchment area of the reservoir, including:\n
1) There are two types of upstream catchment areas for reservoirs, namely the complete catchment area and the intermediate catchment area,\n
2) Watershed topography,\n
3) Human activities,\n
4) Climate,\n
5) Land cover and utilization,\n
6) Soil and geology.\n
We also generated time series of 15 meteorological variables. A large dataset on reservoir storage anomalies (92% of data from 3254 reservoirs), water levels (20%), evaporation (98%), and water bodies (99%) is also provided from multiple sources of satellites - radar/laser satellite altimeters+satellite images from Landsat+Sentinel-2. Verifiation_figures records all validation, time series of numbers, and statistical indicators of water level, water area, and storage changes.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Reservoir water level data: Extract height data from GeoDAR reservoir shapefile; Select height data with water volume greater than 10% using global surface water detectors; The MAD method (median of absolute deviation) was used to remove outliers passed each time; Removed outliers with heights exceeding DEM from altimeter data; Build a time series using the R package \"tsHydro\".\n
Reservoir water meter area data: Calculate NDWI (Normalized Difference Water Index); Sampling the appearance of water along the edge to eliminate erroneously detected water; Cut the amount of water generated to a certain value and combine it with the water surface cover to obtain the final water surface cover; Use a quantization based time outlier filter to eliminate errors in reservoir waters.\n
Reservoir watershed boundary data: Use automatic outlet relocation algorithm to automatically delineate a large watershed quantity.\n
Watershed horizontal characteristic data: perform necessary data format conversions on relevant attribute datasets; Calculate catchment area features from raster and vector data using different methods; For continuous variables such as temperature and altitude, calculate the mean, maximum, minimum, and range values.", "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": [ "卫星数据集", "水库水位", "水库蓄水量变化", "水库上游集水区" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [], "ds_contributors": [ { "true_name": "刘德地", "email": "dediliu@whu.edu.cn", "work_for": "武汉大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘德地", "email": "dediliu@whu.edu.cn", "work_for": "武汉大学", "country": "中国" } ], "ds_managers": [ { "true_name": "刘德地", "email": "dediliu@whu.edu.cn", "work_for": "武汉大学", "country": "中国" } ], "category": "水文" }