scholarly journals Correction: Wu, Q.-L.; et al. Estimation of the Potential Infestation Area of Newly-Invaded Fall Armyworm Spodoptera Frugiperda in the Yangtze River Valley of China. Insects 2019, 10, 298

Insects ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 521
Author(s):  
Qiu-Lin Wu ◽  
Li-Mei He ◽  
Xiu-Jing Shen ◽  
Yu-Ying Jiang ◽  
Jie Liu ◽  
...  
Keyword(s):  
Spodoptera Frugiperda ◽  
Yangtze River ◽  
Fall Armyworm ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River

The authors wish to make the following correction to this paper [...]

scholarly journals Estimation of the Potential Infestation Area of Newly-invaded Fall Armyworm Spodoptera Frugiperda in the Yangtze River Valley of China

Insects ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 298 ◽  
Author(s):  
Qiu-Lin Wu ◽  
Li-Mei He ◽  
Xiu-Jing Shen ◽  
Yu-Ying Jiang ◽  
Jie Liu ◽  
...  
Keyword(s):  
Yangtze River ◽  
Southern China ◽  
Meteorological Data ◽  
Fall Armyworm ◽  
Northern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
Flight Behavior ◽  
The Yangtze River ◽  
Self Powered

The fall armyworm (FAW), native to the Americas, has rapidly invaded the whole of Southern China since January 2019. In addition, it can survive and breed in the key maize- and rice- growing area of the Yangtze River Valley. Furthermore, this pest is also likely to continue infiltrating other cropping regions in China, where food security is facing a severe threat. To understand the potential infestation area of newly-invaded FAW from the Yangtze River Valley, we simulated and predicted the possible flight pathways and range of the populations using a numerical trajectory modelling method combining meteorological data and self-powered flight behavior parameters of FAW. Our results indicate that the emigration of the first and second generations of newly-invaded FAW initiating from the Yangtze River Valley started on 20 May 2019 and ended on 30 July 2019. The spread of migratory FAW benefitted from transport on the southerly summer monsoon so that FAW emigrants from the Yangtze River Valley can reach northern China. The maize-cropping areas of Northeastern China, the Korean Peninsula and Japan are at a high risk. This study provides a basis for early warning and a broad picture of FAW migration from the Yangtze River Valley.

scholarly journals Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3294
Author(s):  
Chentao He ◽  
Jiangfeng Wei ◽  
Yuanyuan Song ◽  
Jing-Jia Luo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Yangtze River ◽  
Climate Models ◽  
Summer Precipitation ◽  
Yangtze River Valley ◽  
River Valley ◽  
Training Data ◽  
The Yangtze River ◽  
Learning Methods

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions.

Contribution of Water Vapor to the Record-Breaking Extreme Meiyu Rainfall along the Yangtze River Valley in 2020

2021 ◽  
Vol 35 (4) ◽  
pp. 557-570
Author(s):  
Licheng Wang ◽  
Xuguang Sun ◽  
Xiuqun Yang ◽  
Lingfeng Tao ◽  
Zhiqi Zhang
Keyword(s):  
Water Vapor ◽  
Yangtze River ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River
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