scholarly journals Changes in Characteristics of Late-Summer Precipitation over Eastern China in the Past 40 Years Revealed by Hourly Precipitation Data

2010 ◽  
Vol 23 (12) ◽  
pp. 3390-3396 ◽  
Author(s):  
Rucong Yu ◽  
Jian Li ◽  
Weihua Yuan ◽  
Haoming Chen
Keyword(s):  
Yangtze River ◽  
Rainfall Intensity ◽  
Eastern China ◽  
Late Summer ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Hourly Rainfall

Abstract Using hourly station rain gauge data during 1966–2005, the authors studied changes in the characteristics of the late-summer (July–August) rainfall, which has exhibited a so-called southern flooding and northern drought (SFND) pattern over eastern China in recent decades. Although the rainfall amount and frequency have significantly increased (decreased) in the mid–lower reaches of the Yangtze River valley (North China) during this period, the rainfall intensity has decreased (increased). This finding differs from previous results based on daily data, which showed that the rainfall intensity has increased in the mid–lower reaches of the Yangtze River valley. In this region, the mean rainfall hours on rainy days have increased because of the prolonged rainfall duration, which has led to an increased daily rainfall amount and to a decreased hourly rainfall intensity. Results also show that the SFND pattern is mostly attributed to changes in precipitation with moderate and low intensity (≤10 mm h−1), which contributes 65% (96%) of rainfall amount to the “flooding” (“drought”) in the mid–lower reaches of the Yangtze River valley. Neither frequency nor amount of strong intensity (>20 mm h−1) rainfall exhibits the SFND pattern.

scholarly journals Effect of Spring Precipitation on Summer Precipitation in Eastern China: Role of Soil Moisture

2017 ◽  
Vol 30 (22) ◽  
pp. 9183-9194 ◽  
Author(s):  
Li Liu ◽  
Renhe Zhang ◽  
Zhiyan Zuo
Keyword(s):  
Soil Moisture ◽  
Yangtze River ◽  
Asian Summer Monsoon ◽  
Eastern China ◽  
Summer Precipitation ◽  
Northeastern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Spring Precipitation

The relation of spring (March–May) to summer (July–August) precipitation in eastern China is examined using observed data. It is found that when spring precipitation from the lower and middle reaches of the Yangtze River valley to northern China (the YRNC region) is higher (lower), more (less) summer precipitation occurs in northeastern China and the lower and middle reaches of the Yangtze River valley, and less (more) in southeastern China. The analysis of physical mechanism showed that higher (lower) spring precipitation in the YRNC region is closely related to wet (dry) spring soil moisture, which decreases (increases) the surface temperature and sensible heat flux in late spring. Because the memory of spring soil moisture in the YRNC region reaches about 2.4 months, the surface thermal anomaly lasts into the subsequent summer, resulting in a weak (strong) East Asian summer monsoon. A weak East Asian summer monsoon corresponds to an anomalous anticyclone and a cyclone over southeastern and northeastern China, respectively, in the lower troposphere. The anomalous anticyclone depresses the summer precipitation in southeastern China, and the anomalous cyclone promotes precipitation over northeastern China. The abnormal northerly and southerly winds associated with the anomalous cyclone and anticyclone, respectively, converge in the lower and middle reaches of the Yangtze River valley, inducing more summer precipitation there.

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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