Erosion-based analysis of breaching of Baige landslide dams on the Jinsha River, China, in 2018

Landslides ◽  
2019 ◽  
Vol 16 (10) ◽  
pp. 1965-1979 ◽  
Author(s):  
Limin Zhang ◽  
Te Xiao ◽  
Jian He ◽  
Chen Chen
Keyword(s):  
Jinsha River ◽  
Landslide Dams

scholarly journals Impacts of climate change and LULC change on runoff in the Jinsha River Basin

2020 ◽  
Vol 30 (1) ◽  
pp. 85-102 ◽  
Author(s):  
Qihui Chen ◽  
Hua Chen ◽  
Jun Zhang ◽  
Yukun Hou ◽  
Mingxi Shen ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Jinsha River ◽  
Lulc Change ◽  
Impacts Of Climate Change

scholarly journals Biodiversity of non-Saccharomyces yeasts associated with spontaneous fermentation of Cabernet Sauvignon wines from Shangri-La wine region, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yue Zhao ◽  
Qingyang Sun ◽  
Shusheng Zhu ◽  
Fei Du ◽  
Ruzhi Mao ◽  
...  
Keyword(s):  
Yeast Species ◽  
Its Region ◽  
Cabernet Sauvignon ◽  
Jinsha River ◽  
Spontaneous Fermentation ◽  
Lancang River ◽  
5.8S Rrna ◽  
Colonial Morphology ◽  
26S Rrna ◽  
Wine Region

AbstractShangri-La is a wine region that has the highest altitude vineyards in China. This is the first study investigated the biodiversity of non-Saccharomyces yeasts associated with spontaneous fermentation of Cabernet Sauvignon wines produced from two sub-regions (Lancang River and Jinsha River) of Shangri-La. The culturable yeasts were preliminarily classified based on their colonial morphology on the Wallerstein Laboratory nutrient agar plates. Yeast species were identified by the sequencing of the 26S rRNA D1/D2 region and the 5.8S rRNA ITS region. Twenty-five non-Saccharomyces yeast species belonging to sixteen genera were isolated and identified in Shangri-La wine region. Candida, Hanseniaspora, Pichia, and Starmerella were found in both sub-regions, but the Lancang River showed more diverse yeast species than the Jinsha River. Shangri-La not only exhibited high diversity of non-Saccharomyces yeasts, and furthermore, seven species of non-Saccharomyces yeasts were exclusively found in this region, including B. bruxellensis, D. hansenii, M. guilliermondii, S. vini, S. diversa, T. delbrueckii and W. anomalus, which might play an important role in distinctive regional wine characteristics. This study provide a relatively comprehensive analysis of indigenous non-Saccharomyces yeasts associated with Cabernet Sauvignon from Shangri-La, and has significance for exploring ‘microbial terroir’ of wine regions in China.

Breaches of embankment and landslide dams - State of the art review

2021 ◽  
Vol 216 ◽  
pp. 103597 ◽  
Author(s):  
Qiming Zhong ◽  
Lin Wang ◽  
Shengshui Chen ◽  
Zuyu Chen ◽  
Yibo Shan ◽  
...  
Keyword(s):  
State Of The Art ◽  
Landslide Dams

scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

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