Revisiting the Pan Evaporation Trend in China During 1988–2017

Shen, Jiaju; Yang, Hanbo; Li, Sien; Liu, Ziwei; Cao, Yongqiang; Yang, Dawen

doi:10.1029/2022jd036489

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…As pointed out by Mozny et al (2020), the ongoing growth of evaporation demand has been significant, mainly in the last 15 years. Shen et al (2022) revealed that most stations in southern China experienced a significant increase of annual PE during 1988–2017. Niu et al (2021) reported that increasing trends of annual mean PE occurred in East China, South China, Southwest China and west of Northwest China from 1994 to 2017.…”

Section: Discussionmentioning

confidence: 99%

Identifying multivariate controls of pan evaporation variations in subregions of Yangtze River Basin using multiple wavelet coherence

Yang,

He,

et al. 2023

Hydrological Processes

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In this study, temporal variations of pan evaporation (PE) and its optimal combination of controlling factors are investigated in the subregions of Yangtze River Basin (YRB) by using multiple wavelet coherence (MWC). For this purpose, the YRB is firstly divided into nine relatively homogeneous PE subregions by combining empirical orthogonal functions with fuzzy C‐means clustering. After regionalization, temporal variations of regional PE are analysed in each of subregions. Results reveal that regional annual PE has an upward trend in all subregions, and seasonal PE in almost the subregions for four different seasons. The wavelet transform coherence (WTC) analysis shows that vapour pressure deficit (VPD) is the primary controlling factor of PE variations in all subregions. In single factor analysis, VPD, sunshine duration (SSD) and 0 cm ground temperature (ZGT) have a higher covariance with PE than other influencing factors. For two‐factor analysis, the APS (air pressure)‐VPD combination, and for three‐factor analysis, the AWS (average wind speed)‐ ZGT‐VPD combination are the most common best explanatory variables on PE variations. A comparison of the results from the WTC and MWC indicates that PE variations can be explained more accurately by one, two, or three controlling factors. The combination of factors that optimally explains PE variations largely varies with subregions but with VPD as a dominant factor either alone or in combination for all subregions. Moreover, the optimal factor combination for a subregion is not necessarily combined by the first, second and/or third dominant factors for that subregion. Our results can provide useful insights into how multiple temporal dependency of PE is linked with meteorological factors in subregions of the YRB, particularly for PE modelling under changing climate conditions.

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Section: Discussionmentioning

confidence: 99%