scholarly journals Dynamic and Full-Time Acquisition Technology and Method of Ice Data of Yellow River

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 176
Author(s):  
Yu Deng ◽  
Chunjiang Li ◽  
Zhijun Li ◽  
Baosen Zhang
Keyword(s):  
Water Level ◽  
Real Time ◽  
Yellow River ◽  
Night Vision ◽  
The Yellow River ◽  
Ice Thickness ◽  
Full Time ◽  
Monitoring Device ◽  
Integrated Monitoring ◽  
Measurement Sensor

Regarding the ice periods of the Yellow River, it is difficult to obtain ice data information. To effectively grasp the ice evolution process in the ice periods of the typical reach of the Yellow River, a fixed-point air-coupled radar remote monitoring device is proposed in this paper. The device is mainly composed of an air-coupled radar ice thickness measurement sensor, radar water level measurement sensor, temperature measurement sensor, high-definition infrared night vision instrument, remote switch control, telemetry communication machine, solar and wind power supply, lightning protection, and slewing arm steel tower. The integrated monitoring device can monitor ice thickness, water level, air temperature, ice surface temperature, and other related parameters in real time. At present, devices have obtained the ice change process of fixed points in ice periods from 2020 to 2021. Through a comparison with manual data, the mean error of the monitoring results of the water level and ice thickness was approximately 1 cm. The device realizes the real-time monitoring of ice thickness and water level change in the whole cycle at the fixed position. Through video monitoring, it can take pictures and videos regularly and realize the connection between the visual river and monitoring data. The research results provide a new model and new technology for hydrological monitoring in the ice periods of the Yellow River, which has broad application prospects.

Effects of Water Level and Salinity on Total Sulfur Contents in Salt Marsh Soils of the Yellow River Delta, China

Wetlands ◽  
2015 ◽  
Vol 36 (S1) ◽  
pp. 137-143 ◽  
Author(s):  
Qiongqiong Lu ◽  
Junhong Bai ◽  
Zhaoqin Gao ◽  
Junjing Wang ◽  
Qingqing Zhao
Keyword(s):  
Salt Marsh ◽  
Water Level ◽  
Yellow River ◽  
Yellow River Delta ◽  
River Delta ◽  
The Yellow River ◽  
Total Sulfur ◽  
The Yellow River Delta

Effects of Water Level and Salinity on TN and TP Contents in Marsh Soils of the Yellow River Delta, China

2012 ◽  
Vol 40 (10) ◽  
pp. 1118-1124 ◽  
Author(s):  
Peipei Liu ◽  
Junhong Bai ◽  
Qiuyi Ding ◽  
Hongbo Shao ◽  
Haifeng Gao ◽  
...  
Keyword(s):  
Water Level ◽  
Yellow River ◽  
Yellow River Delta ◽  
River Delta ◽  
The Yellow River ◽  
The Yellow River Delta

Effect of Water Level and Salinity on Metal Fractionation in Sediments of the Yellow River Delta

Wetlands ◽  
2020 ◽  
Vol 40 (6) ◽  
pp. 2765-2774
Author(s):  
Xiao Li ◽  
Xuehong Wang ◽  
Junbao Yu ◽  
Jisong Yang ◽  
Yang Yu ◽  
...  
Keyword(s):  
Water Level ◽  
Yellow River ◽  
Yellow River Delta ◽  
River Delta ◽  
The Yellow River ◽  
Metal Fractionation ◽  
The Yellow River Delta ◽  
Effect Of Water

scholarly journals Study on the evolution law of density current in the Xiaolangdi Reservoir before the flood in 2018

2018 ◽  
Vol 246 ◽  
pp. 01050
Author(s):  
Xinjie Li ◽  
Yuanjian Wang ◽  
Shaojun Qu ◽  
Ting Wang ◽  
Kunpeng Li
Keyword(s):  
Flow Velocity ◽  
Water Level ◽  
Yellow River ◽  
The Yellow River ◽  
Density Current ◽  
Sediment Characteristics ◽  
Sediment Thickness ◽  
Evolution Law ◽  
Research Results ◽  
Xiaolangdi Reservoir

Sediment problems affect the operation life and comprehensive benefits of a reservoir. In response to the floods in the middle and upper reaches of the Yellow River, the Xiaolangdi Reservoir is subjected to preventative pre-discharge scheduling. Through a prototypical observation of the density current plunging zone of the Xiaolangdi Reservoir in 2018, we collected prototypical data of the water level, water-sediment characteristics, sediment thickness, thalweg points, and flow velocity, and analyzed the pre-dam distribution of density current and scouring-sedimentation characteristics during the flood dispatching period of the Xiaolangdi Reservoir. The research results promoted the understanding of the movement law of the density current of the Xiaolangdi Reservoir and the development of efficient sediment drainage technology for reservoirs.

Analysis and Prediction for Xiaolangdi Reservoir Sand Blocking Operation Period and Water Level of the Lower Yellow River

2011 ◽  
Vol 71-78 ◽  
pp. 1318-1323 ◽  
Author(s):  
Qing An Li ◽  
Han Dong Liu ◽  
Yu Kun Zhao
Keyword(s):  
Water Level ◽  
Flood Control ◽  
Yellow River ◽  
River Channel ◽  
The Yellow River ◽  
Deposition Condition ◽  
Reservoir Water ◽  
Specific Flow ◽  
Operation Period ◽  
Xiaolangdi Reservoir

Xiaolangdi Reservoir undertakes 90% of the Yellow River runoff and 100% of sediment discharge. To ensure the long-term utilization of Xiaolangdi reservoir is the key to prevent the flood in the lower reaches of the Yellow River. Take Luokou Hydrologic Station located in the lower reaches of the Yellow River as an example, conduct an analysis from the aspects of sediment retaining of Xiaolangdi Reservoir, scouring and deposition condition of river channel in the lower reaches of the Yellow River, water level performance for years of different flow of ten times’ water-sediment regulation, predict on the sand blocking operation period of Xiaolangdi Reservoir, water level of specific flow of river channel in the lower reaches of the Yellow River and scouring and deposition condition of river channel, which has played a reference role for the flood control in the lower reaches of the Yellow River.

scholarly journals Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1006 ◽  
Author(s):  
Jiayong Shi ◽  
Fei Yuan ◽  
Chunxiang Shi ◽  
Chongxu Zhao ◽  
Limin Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Yellow River ◽  
Source Region ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
The Yellow River ◽  
Light Rain ◽  
Yellow River Source ◽  
Rain Events

As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.

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