Analysis on Features of Hydrological Variation in the Seasonal Stagnant Lower Reach of Xiangjiang River

2014 ◽  
Vol 1065-1069 ◽  
pp. 3004-3009
Author(s):  
Xiao Gang Pu ◽  
Zu Huai Xu ◽  
Xiao Xiang Feng
Keyword(s):  
Water Level ◽  
Yangtze River ◽  
Water Levels ◽  
Lower Reach ◽  
Middle Reach ◽  
Xiangjiang River ◽  
Water Gradient ◽  
Hydrological Variation ◽  
Temporal And Spatial ◽  
Hydrological Feature

Based on daily measurement of the water levels and flow rates made by hydrologic stations of Xiangtan, Changsha, Xiangyin, Lujiao at lower reach, Xiangjiang River and Chenglingji in the middle reach of Yangtze River from 1980 to 2010, the analysis is made on the conditions of typical hydrologic year at the lower reaches, Xiangjiang River and middle reach of Yangtze River as well as the temporal and spatial correlation of water level of different hydrologic stations of lower reaches of Xiangjiang River and Chenglingji Station and features of variation of water level and water gradient in different typical years. A definition for seasonal stagnant river was given according to the hydrological feature of lower reaches of Xiangjiang River on the base of current domestic and international summaries and definitions regarding stagnant flows.

scholarly journals Monitoring Temporal Change of River Islands in the Yangtze River by Remotely Sensed Data

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1484 ◽  
Author(s):  
Jinyan Sun ◽  
Lei Ding ◽  
Jiaze Li ◽  
Haiming Qian ◽  
Mengting Huang ◽  
...  
Keyword(s):  
Water Level ◽  
Yangtze River ◽  
Temporal Change ◽  
Flood Disaster ◽  
Water Levels ◽  
Sediment Supply ◽  
The Yangtze River ◽  
Flood Warning ◽  
River Islands ◽  
The Impact

The spatial extent and area of river islands are always changing due to the impact of hydrodynamic conditions, sediment supply and human activities. A catastrophic flood disaster was driven by sustained and heavy rainfall around the middle and lower Yangtze River in 18 June to 21 July 2016. The flood resulted in the most serious social-economic loss since 1954 and caused a larger-scale inundation for a short time. It is essential to continuously monitor the dynamics changes of river islands because this can avoid frequent field measurements in river islands before and after flood disasters, which are helpful for flood warning. This paper focuses on the temporal change of three river islands called Fenghuangzhou, Changshazhou, and one uninhabited island in the Yangtze River in 2016. In this study, GF-1 (GaoFen-1) WFV (wide field view) data was used for our study owing to its fine spatial and temporal resolution. A simple NDWI (Normalized Difference Water Index) method was used for the river island mapping. Human checking was then performed to ensure mapping accuracy. We estimated the relationship between the area of river islands and measured water levels using four models. Furthermore, we mapped the spatial pattern of inundation risk of river islands. The results indicate a good ability of the GF-1 WFV data with a 16-m spatial resolution to characterize the variation of river islands and to study the association between flood disaster and river islands. A significantly negative but nonlinear relationship between the water level and the area of the river island was observed. We also found that the cubic function fits best among three models (R2 > 0.8, P < 0.001). The maximum of the inundated area at the river island appeared in the rainy season on 8 July 2016 and the minimum occurred in the dry season on 28 December 2016, which is consistent with the water level measured by the hydrological station. Our results derived from GF-1 data can provide a useful reference for decision-making of flood warning, disaster assessment, and post-disaster reconstruction.

Analysis of the Three Gorges Reservoir Operation on the Water Level of Yangtze River during the Non-Flood Season

2013 ◽  
Vol 864-867 ◽  
pp. 2207-2212 ◽  
Author(s):  
Jing Zheng
Keyword(s):  
Water Level ◽  
Yangtze River ◽  
Three Gorges Reservoir ◽  
Water Levels ◽  
Three Gorges ◽  
The Yangtze River ◽  
The Three Gorges Reservoir ◽  
Flood Season ◽  
The Three Gorges ◽  
Downstream Area

In the middle and downstream area of the Yangtze River, low water levels had occurred at post-flood season or before the flood season in recent years, since the trial impoundment of the Three Gorges Reservoir (TGR) in 2008. Based on the analysis of the low water levels, both rating curve of main stations in the middle and lower reaches of the Yangtze River and the operation of TGR in the dry season were analyzed in study to reveal the effects of the impoundment of TGR on water level of downstream areas. The research results show that the water supplement of the TGR could raise the downstream water level, which has positive effect on water supplement and navigation in this area.

scholarly journals Quantitative Assessment of the Influences of Three Gorges Dam on the Water Level of Poyang Lake, China

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1519 ◽  
Author(s):  
Dan Wang ◽  
Shuanghu Zhang ◽  
Guoli Wang ◽  
Qiaoqian Han ◽  
Guoxian Huang ◽  
...  
Keyword(s):  
Water Level ◽  
Yangtze River ◽  
Poyang Lake ◽  
Model Simulation ◽  
Water Storage ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
The Yangtze River ◽  
Middle Reach ◽  
Significant Downward Trend

Lakes are important for global ecological balance and provide rich biological and social resources. However, lake systems are sensitive to climate change and anthropogenic activities. Poyang Lake is an important wetland in the middle reach of the Yangtze River, China and has a complicated interaction with the Yangtze River. In recent years, the water level of Poyang Lake was altered dramatically, in particular showing a significant downward trend after the operation of the Three Gorges Dam (TGD) in 2003, thus seriously affecting the lake wetland ecosystem. The operation of the TGD changed both the hydrological regime and the deeper channel in the middle reach of the Yangtze River, and affected the river–lake system between the Yangtze River and Poyang Lake. This study analyzed the change in the water level of Poyang Lake and quantified the contributions of the TGD operation, from the perspectives of water storage and erosion of the deeper channel in the middle reach of the Yangtze River, through hydrodynamic model simulation. The erosion of the deeper channel indicated a significant decrease in annual water level. However, due to the water storage of the TGD in September and October, the discharge in the Yangtze River sharply decreased and the water level of Poyang Lake was largely affected. Especially in late September, early October, and mid-October, the contributions of water storage of the TGD to the decline in the water level of Poyang Lake respectively reached 68.85%, 59.04%, and 54.88%, indicating that the water storage of the TGD was the main factor in the decrease in water level. The erosion of the deeper channel accelerated the decline of the water level of Poyang Lake and led to about 10% to 20% of the decline of water level in September and October. Due to the combined operation of the TGD and more reservoirs under construction in the upper TGD, the long-term and irreversible influence of the TGD on Poyang Lake should be further explored in the future.

scholarly journals A contribution to understanding the turbidity behaviour in an Amazon floodplain

2010 ◽  
Vol 14 (2) ◽  
pp. 351-364 ◽  
Author(s):  
E. Alcântara ◽  
E. Novo ◽  
J. Stech ◽  
J. Lorenzzetti ◽  
C. Barbosa ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Level ◽  
Spatial Data ◽  
Quantitative Information ◽  
Water Levels ◽  
Amazon River ◽  
Spatial And Temporal Variability ◽  
Automatic Monitoring System ◽  
Amazon Floodplain ◽  
Temporal And Spatial

Abstract. Observations of turbidity provide quantitative information about water quality. However, the number of available in situ measurements for water quality determination is usually limited in time and space. Here, we present an analysis of the temporal and spatial variability of the turbidity of an Amazon floodplain lake using two approaches: (1) wavelet analysis of a turbidity time series measured by an automatic monitoring system, which should be improved/simplified, and (2) turbidity samples measured in different locations and then interpolated using an ordinary Kriging algorithm. The spatial and temporal variability of turbidity are clearly related to the Amazon River flood pulses in the floodplain. When the water level in the floodplain is rising or receding, the exchange between the Amazon River and the floodplain is the major driving force in turbidity variability. At high-water levels, turbidity variability is controlled by Lake Bathymetry. When the water level is low, wind action and Lake Morphometry are the main causes of turbidity variability. The combined use of temporal and spatial data shows a good potential for better understanding of the turbidity behaviour in a complex aquatic system such as the Amazon floodplain.

scholarly journals Water Level Reconstruction Based on Satellite Gravimetry in the Yangtze River Basin

2018 ◽  
Vol 7 (7) ◽  
pp. 286 ◽  
Author(s):  
Hok Fok ◽  
Qing He
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Severity Index ◽  
Water Levels ◽  
Drought Severity ◽  
Hydrological Extremes ◽  
The Yangtze River Basin ◽  
Drought Severity Index

The monitoring of hydrological extremes requires water level measurement. Owing to the decreasing number of continuous operating hydrological stations globally, remote sensing indices have been advocated for water level reconstruction recently. Nevertheless, the feasibility of gravimetrically derived terrestrial water storage (TWS) and its corresponding index for water level reconstruction have not been investigated. This paper aims to construct a correlative relationship between observed water level and basin-averaged Gravity Recovery and Climate Experiment (GRACE) TWS and its Drought Severity Index (GRACE-DSI), for the Yangtze river basin on a monthly temporal scale. The results are subsequently compared against traditional remote sensing, Palmer’s Drought Severity Index (PDSI), and El Niño Southern Oscillation (ENSO) indices. Comparison of the water level reconstructed from GRACE TWS and its index, and that of remote sensing against observed water level reveals a Pearson Correlation Coefficient (PCC) above 0.90 and below 0.84, with a Root-Mean-Squares Error (RMSE) of 0.88–1.46 m, and 1.41–1.88 m and a Nash-Sutcliffe model efficiency coefficient (NSE) above 0.81 and below 0.70, respectively. The ENSO-reconstructed water levels are comparable to those based on remote sensing, whereas the PDSI-reconstructed water level shows a similar performance to that of GRACE TWS. The water level predicted at the location of another station also exhibits a similar performance. It is anticipated that the basin-averaged, remotely-sensed hydrological variables and their standardized forms (e.g., GRACE TWS and GRACE-DSI) are viable alternatives for reconstructing water levels for large river basins affected by the hydrological extremes under ENSO influence.

scholarly journals Investigating a complex lake-catchment-river system using artificial neural networks: Poyang Lake (China)

2015 ◽  
Vol 46 (6) ◽  
pp. 912-928 ◽  
Author(s):  
Y. L. Li ◽  
Q. Zhang ◽  
A. D. Werner ◽  
J. Yao
Keyword(s):  
Water Level ◽  
Lake Water ◽  
Yangtze River ◽  
Poyang Lake ◽  
Multiple Stressors ◽  
Resource Planning ◽  
Water Levels ◽  
The Yangtze River ◽  
Large Lake ◽  
Lake Catchment

Lake hydrological simulations using physically based models are cumbersome due to extensive data and computational requirements. Despite an abundance of previous modeling investigations, real-time simulation tools for large lake systems subjected to multiple stressors are lacking. The back-propagation neural network (BPNN) is applied as a first attempt to simulate the water-level variations of a large lake, exemplified by the Poyang Lake (China) case study. The BPNN investigation extends previous modeling efforts by considering the Yangtze River effect and evaluating the influence of the Yangtze River on the lake water levels. Results indicate that the effects of both the lake catchment and the Yangtze River are required to produce reasonable BPNN calibration statistics. Modeling results suggest that the Yangtze River plays a significant role in modifying the lake water-level changes. Comparison of BPNN models to a 2D hydrodynamic model (MIKE 21) shows that comparable accuracies can be obtained from both modeling approaches. This implies that the BPNN approach is well suited to long-term predictions of the water-level responses of Poyang Lake. The findings of this work demonstrate that BPNN can be used as a valuable and computationally efficient tool for future water resource planning and management of the Poyang Lake.

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

Sign in / Sign up

Export Citation Format

Share Document