为解决冬季结冰河道冰厚、水位、温度、冰密集度及流凌速度等多参数一体化连续监测难的问题，本项目通过系统集成方法，研发了非接触式定点凌情雷达监测装置，开展定点凌情多参数监测，之后通过技术延伸，研发了飞航式测冰雷达系统，开展非接触式断面冰厚测量，最后研究了凌情图像智能分析技术，可以快速识别出视频图像中的流凌密集度和流凌速度。
\n三项技术都经过了实践应用：非接触式定点凌情雷达系统已安装于黄河什四份子弯道，2019年～2021年连续监测了2个凌汛期，2020～2021年凌汛期经过13次人工比测，雷达监测冰厚平均误差0.012m，且成功监测到2019年～2020年黄河首封位置冰塞水位变化，发出封河预警信息；飞航式测冰雷达系统于2020年和2021年分别对黄河内蒙河段开展了20个和7个大断面的冰厚测量；凌情图像智能分析系统已对黄河什四分弯道无人机和定点雷达系统采集的视频图像进行了流凌密集度和流凌速度识别，得到了弯道流凌期密集度和流凌速度的变化趋势。本数据集共包含3个excel文件。
", "ds_source": "本数据集采是多时空多要素凌情快速采集与动态监测技术，定点式全天候凌情动态数据采集技术及装备现场监测获取得到。数据集共包含2019-2022年定点冰情雷达历史数据3个年度的冰情数据，共计64344组数据，数据集完全开放共享。技术参数及精度，技术参数包括：水位、冰厚、水温、冰温、流凌密度、流凌速度等凌汛要素的在线采集。测量要素达到的精度分别为：水位±0.01m，冰厚±0.01m，水温±0.2℃，冰温±0.2℃，流凌密度±5%，流凌速度±0.02m/s。
", "ds_process_way": "每日观测数据通过“非接触式定点凌情雷达系统”自动采集。数据自动存储在云上。
", "ds_quality": "观测数据可靠，数据质量良好。2019年～2021年连续监测了2个凌汛期，2020～2021年凌汛期经过13次人工比测，雷达监测冰厚平均误差0.012m。
", "ds_acq_start_time": "2019-11-19 00:00:00", "ds_acq_end_time": "2022-02-28 00:00:00", "ds_acq_place": "内蒙古托克托县黄河什四份弯道观测站", "ds_acq_lon_east": 111.02777777777777, "ds_acq_lat_south": 40.278888888888886, "ds_acq_lon_west": 111.02777777777777, "ds_acq_lat_north": 40.278888888888886, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 433143, "ds_files_count": 3, "ds_format": "excel", "ds_space_res": null, "ds_time_res": "分", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "75fef3bc-32c6-476a-9ddf-bbc9cb8a1205.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "c55e1118-387b-4a0c-8dc5-70aa438c430d", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.iceflood.db2365.2022", "subject_codes": [ "170.55" ], "quality_level": 3, "publish_time": "2022-07-30 15:16:29", "last_updated": "2025-05-29 11:43:02", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.iceflood.db2365.2022", "i18n": { "en": { "title": "Tuoketuo Ice and Hydrological Conditions Measured Dataset (2019–2022)", "ds_format": "Excel", "ds_source": "This dataset was obtained through multi-temporal and multi-element rapid ice condition acquisition and dynamic monitoring technologies, combined with fixed-point, all-weather ice condition dynamic data collection technologies and equipment deployed in the field. It comprises three years (2019–2022) of historical ice condition data from fixed-point radar monitoring, totaling 64,344 data entries, with full open access. Technical parameters and measurement accuracies include real-time acquisition of ice flood elements such as water level, ice thickness, water temperature, ice temperature, ice concentration, and ice drift velocity. Measurement accuracies are as follows: water level ±0.01m, ice thickness ±0.01m, water temperature ±0.2℃, ice temperature ±0.2℃, ice concentration ±5%, and ice drift velocity ±0.02m/s.
", "ds_quality": "The observational data are reliable with robust quality. Two consecutive ice flood seasons were monitored from 2019 to 2021. During the 2020–2021 ice flood season, 13 manual calibration tests demonstrated an average ice thickness measurement error of 0.012 meters for the radar system.
", "ds_ref_way": "", "ds_abstract": "To address the challenges in achieving integrated continuous monitoring of multiple parameters including ice thickness, water level, temperature, ice concentration, and ice drift velocity in ice-covered river channels during winter, this project developed a non-contact fixed-point ice condition radar monitoring device through systematic integration methodologies, enabling multi-parameter monitoring of localized ice conditions. Subsequently, by extending the technology, an airborne ice-measuring radar system was designed to perform non-contact cross-sectional ice thickness measurements. Finally, intelligent ice condition image analysis technology was investigated to rapidly identify ice concentration and drift velocity in video imagery.
\nAll three technologies have been practically implemented: The non-contact fixed-point ice condition radar system was installed at the Shisifenzi Bend of the Yellow River, continuously monitoring two ice flood seasons from 2019 to 2021. During the 2020–2021 ice flood season, 13 manual calibration tests confirmed an average ice thickness measurement error of 0.012 meters for the radar system. It successfully captured ice-jam-induced water level variations at the initial freezing location of the Yellow River between 2019 and 2020, issuing river-freezing early warnings. The airborne ice-measuring radar system conducted ice thickness surveys across 20 and 7 large cross-sections in the Inner Mongolia reach of the Yellow River during 2020 and 2021, respectively. The intelligent ice condition image analysis system processed video footage captured by drones and fixed radar systems at the Shisifenzi Bend, identifying ice concentration and drift velocity while revealing temporal trends of these parameters during ice drift periods. This dataset comprises three Excel files.
", "ds_time_res": "分", "ds_acq_place": "Shisifen bend observation station of the Yellow River in Tuoketuo County, Inner Mongolia", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The daily observational data are automatically collected via the \"non-contact fixed-point ice condition radar system\" and stored in the cloud.
", "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_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "冰厚", "空气耦合雷达", "流凌密度集", "流凌速度", "黄河水" ], "ds_subject_tags": [ "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河什四分弯道" ], "ds_time_tags": [ 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "张宝森", "email": "zbshnzz@163.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": "张宝森", "email": "zbshnzz@163.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": "张宝森", "email": "zbshnzz@163.com", "work_for": "黄河水利委员会黄河水利科学研究院", "country": "中国" } ], "category": "水文" }