黄河中上游是我国气候变化的敏感区和生态保护的关键区，探究该地区全球变暖背景下极端降水事件变化对于保障区域生态安全和防灾减灾具有重要意义。本文基于1961—2020年气象站点的逐日降水观测资料，经数据筛选、质量检测和异常值剔除，选取并计算了10个极端降水指数，分时段、分区域统计得到黄河中上游地区极端降水指数数据集。经过单位转换、剔除数据缺失站点、插补缺失值等质量控制以及在时间序列和空间格局上的数据验证，数据集具有较高的准确性和可靠性。本数据集可用于分析黄河中上游地区极端降水事件时空特征及其对气候变化的响应研究，也可为评估该地区水资源安全及旱涝灾害风险提供基础数据支撑
", "ds_source": "为方便进一步处理和使用，数据集结果文件存储为CSV格式，以极端降水指数的中文名称命名。每个数据文件包含该极端降水指数在不同时间尺度（年份、基准时段、最近时段和全时段）和空间尺度（各个站点、七个二级水资源分区、黄河上游、黄河中游和黄河中上游）上的具体数值。
", "ds_process_way": "经过数据筛选、质量检测和异常值去除，通过时间和区域统计得到黄河中上游极端降水指数数据集。经过单位转换、缺失数据站点移除、缺失值插值、时间序列和空间格局数据验证等质量控制，数据集具有较高的准确率和可靠性。
", "ds_quality": "为保证数据的准确性和可靠性，本数据集采取了数据本身和识别极端降水事件过程中的质量控制，体现在以下两方面：一是在整理元数据过程中，对出现的异常值、特殊值进行处理，并将数据统一通用单位；二是在生产极端降水指数过程中，通过软件自动检测和人工校验两者相结合的方式，对数据进行严格的质量检查。
", "ds_acq_start_time": "1961-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "黄河中上游", "ds_acq_lon_east": 113.78333333333333, "ds_acq_lat_south": 33.63333333333333, "ds_acq_lon_west": 95.96666666666667, "ds_acq_lat_north": 40.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 13109677, "ds_files_count": 2, "ds_format": ".shp、.jpg、.csv", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "3bbe9008-d7b3-4b15-b1b4-0d63feaec126.jpg", "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": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-01-25 11:09:21", "last_updated": "2025-04-23 09:37:45", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.14089", "i18n": { "en": { "title": "Dataset of Extreme Precipitation Index in the Upper and Middle Reaches of the Yellow River (1961-2020)", "ds_format": "", "ds_source": "For the convenience of further processing and use, the dataset result file is stored in CSV format and named after the Chinese name of the extreme precipitation index. Each data file contains specific values of the extreme precipitation index at different time scales (year, reference period, nearest period, and full period) and spatial scales (each station, seven secondary water resource zones, upper reaches of the Yellow River, middle reaches of the Yellow River, and upper middle reaches of the Yellow River)", "ds_quality": "
To ensure the accuracy and reliability of the data, this dataset adopts quality control measures for both the data itself and the identification of extreme precipitation events. This is reflected in the following two aspects: firstly, in the process of organizing metadata, abnormal and special values are processed, and the data is unified into a common unit; Secondly, in the process of producing extreme precipitation indices, strict quality checks are conducted on the data through a combination of software automatic detection and manual verification", "ds_ref_way": "", "ds_abstract": "
The upper and middle reaches of the Yellow River are sensitive areas to climate change and key areas for ecological protection in China. Exploring the changes in extreme precipitation events in this region under the background of global warming is of great significance for ensuring regional ecological security and disaster prevention and reduction. This article is based on daily precipitation observation data from meteorological stations from 1961 to 2020. After data screening, quality testing, and outlier removal, 10 extreme precipitation indices were selected and calculated. The extreme precipitation index dataset for the upper and middle reaches of the Yellow River was obtained by time period and regional statistics. After quality control measures such as unit conversion, removal of missing data sites, interpolation of missing values, and data validation on time series and spatial patterns, the dataset has high accuracy and reliability. This dataset can be used to analyze the spatiotemporal characteristics of extreme precipitation events in the upper and middle reaches of the Yellow River and their response to climate change. It can also provide basic data support for evaluating water resource security and the risk of droughts and floods in the region
", "ds_time_res": "", "ds_acq_place": "The middle and upper reaches of the Yellow River", "ds_space_res": "", "ds_projection": "", "ds_process_way": "After data screening, quality testing, and outlier removal, the extreme precipitation index dataset for the upper and middle reaches of the Yellow River was obtained through time and regional statistics. After quality control measures such as unit conversion, removal of missing data sites, interpolation of missing values, and validation of time series and spatial pattern data, the dataset has high accuracy and reliability", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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": [ "极端降水指数", "多时空尺度", "黄河中上游" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河中上游" ], "ds_time_tags": [ 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 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, 2020 ], "ds_contributors": [ { "true_name": "陈超冰", "email": "chaobing231@163.com", "work_for": "曲阜师范大学地理与旅游学院", "country": "中国" }, { "true_name": "贺山峰", "email": "heshanfeng@163.com", "work_for": "曲阜师范大学地理与旅游学院", "country": "中国" }, { "true_name": "李铮", "email": "lizheng00828@163.com", "work_for": "曲阜师范大学地理与旅游学院", "country": "中国" }, { "true_name": "邱兰兰", "email": "qiulanlan1982@163.com", "work_for": "曲阜师范大学地理与旅游学院", "country": "中国" }, { "true_name": "冯爱青", "email": "fengaq.14b@igsnrr.ac.cn", "work_for": "国家气候中心", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "贺山峰", "email": "heshanfeng@163.com", "work_for": "曲阜师范大学地理与旅游学院", "country": "中国" } ], "ds_managers": [ { "true_name": "陈超冰", "email": "chaobing231@163.com", "work_for": "曲阜师范大学地理与旅游学院", "country": "中国" } ], "category": "生态" }