%0 Dataset
%T Tuoketuo Ice and Hydrological Conditions Measured Dataset (2019–2022)‌
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/75fef3bc-32c6-476a-9ddf-bbc9cb8a1205
%W NCDC
%R 10.12072/ncdc.iceflood.db2365.2022
%A zhang baosen
%K Ice thickness;air coupled radar;flow ice density set;flow ice velocity;Yellow River water
%X 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.All 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.