金沙江堵江堰塞事件及其地貌环境效应研究进展Research Progress of Landslide Dam Events of Jinsha River and Its Geomorphologic and Environmental Effects

2013 ◽  
Vol 03 (01) ◽  
pp. 8-17 ◽  
Author(s):  
段 立曾
Keyword(s):  
Environmental Effects ◽  
Landslide Dam ◽  
Research Progress ◽  
Jinsha River

Research progress in artificial upwelling and its potential environmental effects

2015 ◽  
Vol 59 (2) ◽  
pp. 236-248 ◽  
Author(s):  
YiWen Pan ◽  
Wei Fan ◽  
DaHai Zhang ◽  
JiaWang Chen ◽  
HaoCai Huang ◽  
...  
Keyword(s):  
Environmental Effects ◽  
Research Progress

scholarly journals Meso/Micro-texture analysis of the landslide-dam outburst sediments in the Upper Jinsha River, SE Tibetan Plateau

2019 ◽  
Vol 72 (Special issue) ◽  
pp. 81-91 ◽  
Author(s):  
Jian Chen ◽  
Zhijiu Cui ◽  
Chao Liu ◽  
Wendy Zhou ◽  
Ruichen Chen
Keyword(s):  
Tibetan Plateau ◽  
Texture Analysis ◽  
Landslide Dam ◽  
Jinsha River

scholarly journals Modeling Flood Peak Discharge Caused by Overtopping Failure of a Landslide Dam

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 921
Author(s):  
Hechun Ruan ◽  
Huayong Chen ◽  
Tao Wang ◽  
Jiangang Chen ◽  
Huibin Li
Keyword(s):  
Landslide Dam ◽  
Calculation Model ◽  
Peak Discharge ◽  
Mitigation Measures ◽  
Dam Failure ◽  
Jinsha River ◽  
Simulation Accuracy ◽  
Flood Peak ◽  
Overtopping Failure ◽  
Dam Overtopping

Overtopping failure often occurs in landslide dams, resulting in the formation of strong destructive floods. As an important hydraulic parameter to describe floods, the peak discharge often determines the downstream disaster degree. Based on 67 groups of landslide dam overtopping failure cases all over the world, this paper constructs the calculation model for peak discharge of landslide dam failure. The model considers the influence of dam erodibility, breach shape, dam shape and reservoir capacity on the peak discharge. Finally, the model is compared with the existing models. The results show that the new model has a higher accuracy than the existing models and the simulation accuracy of the two outburst peak discharges of Baige dammed lake in Jinsha River (10 October 2018 and 3 November 2018) is higher (the relative error is 0.73% and 6.68%, respectively), because the model in this study considers more parameters (the breach shape, the landslide dam erodibility) than the existing models. The research results can provide an important reference for formulating accurate and effective disaster prevention and mitigation measures for such disasters.

Mass movement and formation process analysis of the two sequential landslide dam events in Jinsha River, Southwest China

Landslides ◽  
2019 ◽  
Vol 16 (11) ◽  
pp. 2247-2258 ◽  
Author(s):  
Hai-bo Li ◽  
Shun-chao Qi ◽  
Hao Chen ◽  
Hai-mei Liao ◽  
Yi-fei Cui ◽  
...  
Keyword(s):  
Formation Process ◽  
Southwest China ◽  
Process Analysis ◽  
Mass Movement ◽  
Landslide Dam ◽  
Jinsha River

scholarly journals Mapping Outburst Floods Using a Collaborative Learning Method Based on Temporally Dense Optical and SAR Data: A Case Study with the Baige Landslide Dam on the Jinsha River, Tibet

2021 ◽  
Vol 13 (11) ◽  
pp. 2205
Author(s):  
Zhongkang Yang ◽  
Jinbing Wei ◽  
Jianhui Deng ◽  
Yunjian Gao ◽  
Siyuan Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landslide Dam ◽  
Flood Mapping ◽  
Outburst Flood ◽  
Backscattering Coefficient ◽  
Sar Images ◽  
Jinsha River ◽  
Outburst Floods ◽  
Sar Data ◽  
Sentinel 2

Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping using remote sensing images in recent years, but little attention has been devoted to outburst flood mapping. The short-duration nature of these events and observation constraints from cloud cover have significantly challenged outburst flood mapping. This study used the outburst flood of the Baige landslide dam on the Jinsha River on 3 November 2018 as an example to propose a new flood mapping method that combines optical images from Sentinel-2, synthetic aperture radar (SAR) images from Sentinel-1 and a Digital Elevation Model (DEM). First, in the cloud-free region, a comparison of four spectral indexes calculated from time series of Sentinel-2 images indicated that the normalized difference vegetation index (NDVI) with the threshold of 0.15 provided the best separation flooded area. Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness’s changes caused by the flood. We carried out the flood range prediction model based on the random forest algorithm. Training samples consisted of 13 feature vectors obtained from the Hue-Saturation-Value color space, backscattering coefficient differences/ratio, DEM data, and a label set from the flood range prepared from Sentinel-2 images. Finally, a field investigation and confusion matrix tested the prediction accuracy of the end-of-flood map. The overall accuracy and Kappa coefficient were 92.3%, 0.89 respectively. The full extent of the outburst floods was successfully obtained within five days of its occurrence. The multi-source data merging framework and the massive sample preparation method with SAR images proposed in this paper, provide a practical demonstration for similar machine learning applications using remote sensing.

Imaging molecules adsorbed onto electrodes under potential control by scanning probe microscopy

1991 ◽  
Vol 49 ◽  
pp. 376-377 ◽  
Author(s):  
N.J. Tao ◽  
J.A. DeRose ◽  
P.I. Oden ◽  
S.M. Lindsay
Keyword(s):  
Surface Charge ◽  
Environmental Effects ◽  
Scanning Probe Microscopy ◽  
Statistical Significance ◽  
Electrochemical Methods ◽  
Surface Structures ◽  
Scanning Probe ◽  
Potential Control ◽  
Afm Imaging ◽  
Special Circumstances

Clemmer and Beebe have pointed out that surface structures on graphite substrates can be misinterpreted as biopolymer images in STM experiments. We have been using electrochemical methods to react DNA fragments onto gold electrodes for STM and AFM imaging. The adsorbates produced in this way are only homogeneous in special circumstances. Searching an inhomogeneous substrate for ‘desired’ images limits the value of the data. Here, we report on a reversible method for imaging adsorbates. The molecules can be lifted onto and off the substrate during imaging. This leaves no doubt about the validity or statistical significance of the images. Furthermore, environmental effects (such as changes in electrolyte or surface charge) can be investigated easily.

Sign in / Sign up

Export Citation Format

Share Document