Variations of Meiyu Indicators in the Yangtze-Huaihe River Basin during 1954-2003

To better understand climate variations of Meiyu, some new indicators for theonset and retreat dates, duration, and Meiyu precipitation in the Yangtze-Huaihe River valley areobjectively developed by using observed daily precipitation data from 230 stations in eastern Chinaduring 1954-2003. The rainy season onset and retreat dates in each station can be denned in terms ofthresholds for rainfall intensity and persistence. Then, the onset and retreat dates of the Meiyufor the Yangtze-Huaihe River basin have been determined when more than 40% of stations reach thefirst rainy season thresholds in the study region. Based on the indicators of Meiyu in theYangtze-Huaihe River basin, variations of Meiyu rainfall during 1954-2003 are analyzed. The resultssuggest that Meiyu rainfall in the Yangtze-Huaihe River basin has increased in recent 50 years. Inaddition, interannual and interdecadal variability of Meiyu is also obvious. All the indicatorsdisplay a predominant period of about 3 years.

Typical Circulation Patterns and Associated Mechanisms for Persistent Heavy Rainfall Events over Yangtze–Huaihe River Valley during 1981–2020

Persistent heavy rainfall events(PHREs)over the Yangtze–Huaihe River Valley(YHRV)during 1981–2020 are classified into three types(type-A,type-B and type-C)according to pattern correlation.The characteristics of the synoptic systems for the PHREs and their possible development mechanisms are investigated.The anomalous cyclonic disturbance over the southern part of the YHRV during type-A events is primarily maintained and intensified by the propagation of Rossby wave energy originating from the northeast Atlantic in the mid–upper troposphere and the northward propagation of Rossby wave packets from the western Pacific in the mid–lower troposphere.The zonal propagation of Rossby wave packets and the northward propagation of Rossby wave packets during type-B events are more coherent than those for type-A events,which induces eastward propagation of stronger anomaly centers of geopotential height from the northeast Atlantic Ocean to the YHRV and a meridional anomaly in geopotential height over the Asian continent.Type-C events have“two ridges and one trough”in the high latitudes of the Eurasian continent,but the anomalous intensity of the western Pacific subtropical high(WPSH)and the trough of the YHRV region are weaker than those for type-A and type-B events.The composite synoptic circulation of four PHREs in 2020 is basically consistent with that of the corresponding PHRE type.The location of the South Asian high(SAH)in three of the PHREs in 2020 moves eastward as in the composite of the three types,but the position of the WPSH of the four PHREs is clearly westward and northward.Two water vapor conveyor belts and two cold air conveyor belts are tracked during the four PHREs in 2020,but the water vapor path from the western Pacific is not seen,which may be caused by the westward extension of the WPSH.

Estimation model of winter wheat disease based on meteorological factors and spectral information

Wheat scab(WS,Fusarium head blight),one of the most severe diseases of winter wheat in Yangtze-Huaihe river region,whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and achieve the purpose of reducing yield loss.In the present study,remote sensing monitoring on WS was conducted in 4 counties in Yangtze-Huaihe river region.Sensitive factors of WS were selected to establish the remote sensing estimation model of winter wheat scab index(WSI)based on interactions between spectral information and meteorological factors.The results showed that:1)Correlations between the daily average temperature(DAT)and daily average relative humidity(DAH)at different time scales and WSI were significant.2)There were positive linear correlations between winter wheat biomass,leaf area index(LAI),leaf chlorophyll content(LCC)and WSI.3)NDVI(normalized difference vegetation index),RVI(ratio vegetation index)and DVI(difference vegetation index)which had a good correlation with LAI,biomass and LCC,respectively,and could be used to replace them in modeling.4)The estimated values of the model were consistent with the measured values(RMSE=5.3%,estimation accuracy=90.46%).Estimation results showed that the model could efficiently estimate WS in Yangtze-Huaihe river region.