Variation of reference evapotranspiration and its teleconnection with multiple large‐scale climate oscillations in the Yangtze River Delta, China

Abstract

Evapotranspiration is a key factor in regional hydrological processes and water resources management. Long‐term variation of evapotranspiration affects the regional climate wet/dry tendency and agricultural production profoundly. Thus, reference evapotranspiration (ET0) values for 46 meteorological stations in the Yangtze River Delta (YRD) were calculated for 1957–2014 using the FAO‐Penman–Monteith (FAO‐PM) method. The variation patterns of ET0 values were determined based on the principal component analysis (PCA) method. In addition, the methods of cross wavelet transform (CWT), Kendall tau‐b correlation coefficient determination and cross‐correlation method were applied in the assessment of the correlation between ET0 values and large‐scale climate oscillations, such as the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Oceanic Niño3.4 Sea Surface Temperature Index (NINO) and the Indian Ocean Dipole (IOD). Annual ET0 patterns for three dominant geographic subregions of the YRD (the southeastern, northwestern and mid‐eastern) were determined. There were only several discontinuous lower timescale bands between the three change patterns of annual ET0 and climate oscillations. In seasonal scale, the temporal patterns of ET0 changed simultaneously with the NAO, NINO and PDO in spring, the PDO in summer and the NAO in winter. The monthly ET0 was mostly influenced by the NAO and IOD in January, the IOD in February, the IOD and NINO in March, the NINO in June, the PDO in July to October, the NINO, IOD and NAO in October and the NINO in December. The lag times for the ET0 changes were about 0–5 months for the NAO and NINO, 1–2 months for the PDO and 4 months for the IOD.

Spatiotemporal change and attribution of potential evapotranspiration over China from 1901 to 2100

“…Although numerous work have studied PET changes, trends, and attribution in China, they typically used low-density weather station or low-spatial-resolution climatic proxy data (Chai et al, 2018;Chang et al, 2019;Jiang et al, 2019;Li et al, 2019;Liu et al, 2019;Tian et al, 2018;Wang et al, 2019;Xu et al, 2019). Moreover, they often employed the FAO Penman-Monteith model to attribute PET variations to wind speed, solar radiation, TMP, and vapor pressure (Han et al, 2018;Zheng et al, 2009).…”

“…To date, many investigations regarding PET have been completed for China. However, these studies were conducted using datasets based on low-density weather stations with short time series (Chang et al, 2019;Jiang et al, 2019;Li et al, 2019;Liu et al, 2019;Xu et al, 2019) or climatic proxy data with low spatial resolution (Chai et al, 2018;Tian et al, 2018;Wang et al, 2019). Limited knowledge on the detailed spatial patterns of PET over China impedes the development of sustainable strategies to adapt and mitigate the climatic warming of China.…”

Assessing the Climate Tendency over the Yangtze River Delta, China: Properties, Dry/Wet Event Frequencies, and Causes

“…Meanwhile, Principal Component analysis (PCA) was considered to detect the spatial and temporal variation of the SPI. PCA proved to be useful and much simpler to understand and interpret [3,38,39]. Thirdly, the frequencies of wet/dry events were described according to the classification.…”

Possible Linkages of Hydrological Variables to Ocean–Atmosphere Signals and Sunspot Activity in the Upstream Yangtze River Basin

“…Due to its proximity to the Pacific Ocean, the Yangtze River basin is prominently sensitive to such climatic phenomena as El Niño-Southern Oscillation (ENSO) [32][33][34][35][36] and Pacific Decade Oscillation (PDO) [33,35,37,38], which have significant implications for forecasting water resources and climate conditions. In recent years, studies in the Yangtze River basin and its tributaries have made significant advances in the influence of large-scale teleconnection patterns on hydrological variability [33,35,37,[39][40][41][42][43][44][45], while the temporal persistence of these relationships is not yet wholly understood. Additionally, as global climate conditions have changed, the intensity and frequency of ENSO events have increased since the late 1970s [26], and ocean-atmosphere forcing has become one of the most potential sources of global natural variability [1]; however, the influence of large-scale climate variability on precipitation and streamflow in the UYRB has rarely been examined.…”