汛日产品来源于GRACE陆地储水和极端降水。我们利用GRACE陆地储水降水数据，结合高频滤波、异常检测和洪水潜力指数方法，成功提取了2002年4月1日至2016年8月31日期间全球历史洪水天数，并与达特茅斯洪水观测站（DFO）数据、全球径流数据中心（GRDC）流量数据、新闻报道和社交媒体数据进行了进一步的比较和验证。结果表明，基于GRACE的洪水天数可以覆盖DFO数据库中81%的洪水事件，MODIS提取的87%的洪水事件，并补充了DFO未记录的许多其他洪水事件。此外，与GRDC流量数据得出的洪水事件相比，261个流域的检测概率大于或等于0.5的概率达到62%。这些检测能力和检测结果都很好。我们终于提供了从2002年4月1日到2016年8月31日，覆盖60°S—60°N范围的1°空间分辨率的洪水日产品。本研究为全球洪涝事件的机理分析和归因提供了数据基础。", "ds_source": "
每日GRACE TWS：https://www.tugraz.at/institute/ifg/downloads/gravity-field-models/itsg-grace2018/。\n
降水数据：本研究使用全球降水测量（GPM）数据来计算极端降水量。\n
Dartmouth Flood Observatory ：DFO数据集记录了来自各种新闻报道和政府网站的大型洪水事件。它包含每次洪水的开始 和结束时间、发生国家、大致范围、洪水原因和破坏程度。它是研究全球历史洪水的稀有且有用的产品。\n
MODIS衍生洪水淹没数据：本研究中使用的洪水淹没数据来自2002年4月1日至2016年8月31日期间在南纬60°—北纬60°区域记录的总共807个洪水淹没数据点。本产品使用大气校正的 Terra （MOD09GA/GQ） 和 Aqua 135 （MYD09GA/GQ） MODIS 图像和阈值分析方法（包括 3 天标准法、2 天标准法、3 天 Otsu 和 2 天 Otsu 方法）以及边坡约束（大于 5° 的边坡被遮盖）根据 DFO 记录的洪水事件以 250 m 的空间分辨率提取淹没。将提取结果与Landsat 5、7和8影像的30 m分辨率淹没数据进行对比验证，并进行了洪水图质量控制分析。\n
GRDC 排水量数据：全球径流数据中心是在世界气象145组织主持下运作的国际数据中心。相应的数据集是流量产品，记录了与每次洪水事件相关的平均流量、国家、经度、纬度和河流名称。本研究选择2002年4月1日至2016年8月31日的记录作为验证数据集，对提取的洪水进行验证。为了便于与GRACE衍生的洪水天数进行比较并消除随机误差的影响，我们考虑了HydroSHEDS盆地4级数据的空间平均值150涵盖了流量测量值，并使用下述统计方法将其转换为洪水事件。这些数据可以从 https://www.bafg.de/GRDC/EN/Home/homepage_node.html 获得。", "ds_process_way": "
主要包括数据准备、汛日提取和结果验证三部分。洪水数据提取步骤采用日降水量和每日GRACE TWS数据，洪水验证步骤采用日流量、DFO、MODIS衍生的洪水淹没和社交媒体数据。洪水提取步骤的第二部分主要基于利用高频TWS信号和洪水潜力指数来获取初步可能的洪水天数;然后，使用极端降水约束来获得最终的洪水天数。洪水验证步骤的第三部分包括与从DFO记录和MODIS图像中提取的洪水范围进行比较，与GRDC排放数据得出的洪水进行比较，最后与社交媒体上记录的重大洪水事件进行比较。", "ds_quality": "
本研究利用GRACE TWS和2002年4月1日至2016年8月31日南纬60°至北纬60°的极端降水数据，成功提取了全球洪涝天数。结果在时间和空间上与DFO记录的洪水事件进行了比较，结果表明，我们的检测结果不仅识别了DFO记录的81%的洪水事件，而且还补充了DFO未记录的大量洪水事件。为了进一步验证衍生产品的可靠性，我们将其与从全球GRDC排放数据中提取的洪水事件进行了比较，检测到超过0.5的概率达到了62%。", "ds_acq_start_time": "2002-04-01 00:00:00", "ds_acq_end_time": "2016-08-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -60.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 60.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 5766821233, "ds_files_count": 2, "ds_format": "shp", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "46af7249-d159-4ab9-9c7a-38f0054bc5ce.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" ], "quality_level": 3, "publish_time": "2024-06-20 14:57:29", "last_updated": "2026-01-14 10:33:15", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6523.2024", "i18n": { "en": { "title": "Using the GRACE Land Water Storage and Extreme Precipitation Flood Detection Dataset (2002-2016)", "ds_format": "shp", "ds_source": "
Daily GRACE TWS: https://www.tugraz.at/institute/ifg/downloads/gravity-field-models/itsg-grace2018/ .\n
Precipitation data: This study used Global Precipitation Measurement (GPM) data to calculate extreme precipitation.\n
The Dartmouth Flood Observatory: DFO dataset records large-scale flood events from various news reports and government websites. It includes the start and end time of each flood, the country of occurrence, the approximate scope, the cause of the flood, and the degree of damage. It is a rare and useful product for studying global historical floods.\n
MODIS derived flood inundation data: The flood inundation data used in this study were collected from a total of 807 flood inundation data points recorded in the 60 ° S to 60 ° N latitude region between April 1, 2002 and August 31, 2016. This product uses atmospheric corrected Terra (MOD09GA/GQ) and Aqua 135 (MYD09GA/GQ) MODIS images and threshold analysis methods (including 3-day standard method, 2-day standard method, 3-day Otsu, and 2-day Otsu methods) as well as slope constraints (slopes greater than 5 ° are covered) to extract inundation at a spatial resolution of 250 m based on DFO recorded flood events. The extracted results were compared and validated with 30 meter resolution inundation data from Landsat 5, 7, and 8 images, and quality control analysis of flood maps was conducted.\n
GRDC Drainage Data: The Global Runoff Data Center is an international data center operated under the auspices of the World Meteorological Organization 145. The corresponding dataset is a flow product that records the average flow, country, longitude, latitude, and river name associated with each flood event. This study selected records from April 1, 2002 to August 31, 2016 as the validation dataset to validate the extracted floods. In order to facilitate comparison with GRACE derived flood days and eliminate the influence of random errors, we considered the spatial average of 150 from the HydroSHEDS Basin level 4 data, which includes flow measurement values, and converted it into flood events using the following statistical methods. These data can be obtained from https://www.bafg.de/GRDC/EN/Home/homepage_node.html get.", "ds_quality": "
This study successfully extracted global flood days using GRACE TWS and extreme precipitation data from 60 ° S to 60 ° N latitude from April 1, 2002 to August 31, 2016. The results were compared with the flood events recorded by DFO in time and space, and the results showed that our detection not only identified 81% of the flood events recorded by DFO, but also supplemented a large number of flood events not recorded by DFO. To further verify the reliability of the derivative product, we compared it with flood events extracted from global GRDC emission data, and detected a probability of over 0.5 with a probability of 62%.", "ds_ref_way": "", "ds_abstract": "
The products during the flood season are sourced from GRACE land water storage and extreme precipitation. We used GRACE land water storage precipitation data, combined with high-frequency filtering, anomaly detection, and flood potential index methods, to successfully extract the global historical flood days from April 1, 2002 to August 31, 2016. We further compared and validated the data with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Center (GRDC) flow data, news reports, and social media data. The results indicate that GRACE based flood days can cover 81% of flood events in the DFO database, 87% of flood events extracted by MODIS, and supplement many other flood events not recorded by DFO. In addition, compared with the flood events obtained from GRDC flow data, the detection probability of 261 watersheds is greater than or equal to 0.5, with a probability of 62%. These detection capabilities and results are both very good. We finally provide flood day products with a 1 ° spatial resolution covering the range of 60 ° S-60 ° N from April 1, 2002 to August 31, 2016. This study provides a data foundation for the mechanism analysis and attribution of global flood events.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "It mainly includes three parts: data preparation, flood day extraction, and result verification. The flood data extraction step uses daily precipitation and daily GRACE TWS data, while the flood validation step uses daily flow, DFO, MODIS derived flood inundation, and social media data. The second part of the flood extraction process is mainly based on using high-frequency TWS signals and flood potential indices to obtain preliminary possible flood days; Then, extreme precipitation constraints are used to obtain the final number of flood days. The third part of the flood verification process includes comparing the flood range extracted from DFO records and MODIS images, comparing it with the floods obtained from GRDC emission data, and finally comparing it with major flood events recorded on social media.", "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": [ "GRACE", "极端降水", "洪水", "检测" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016 ], "ds_contributors": [ { "true_name": "刘凯", "email": "liukai@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘凯", "email": "liukai@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "刘凯", "email": "liukai@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "水文" }