Spatiotemporal Variations of Reference Evapotranspiration and Its Climatic Driving Factors in Guangdong, a Humid Subtropical Province of South China

Abstract

It is of great importance to study the changes in reference evapotranspiration (ET0) and the factors that influence it to ensure sustainable and efficient water resource utilization. Daily ET0 data calculated using the Penman–Monteith method from 37 meteorological stations located within Guangdong Province in the humid zone of southern China from 1960 to 2020 were analyzed. The trend analysis and Mann–Kendall test were used to analyze the time series changes in ET0 and major climatic factors (air temperature (T), relative humidity (RH), sunshine duration (SD), and wind speed (u2)) for over 61 years. Sensitivity and contribution analyses were used to evaluate the driving factors of ET0. The main findings of the study are as follows: (1) the trend in average annual ET0 time series in Guangdong slightly increased at a trend rate of 1.61 mm/10a over the past 61 years, with most stations experiencing an increase in ET0. During the same period, air temperature significantly increased, while RH and SD decreased; u2 also decreased. (2) Sensitivity analysis showed that ET0 was more sensitive to RH and T than SD and u2, with ET0 being most sensitive to RH in spring and winter and T in summer and autumn. (3) The contribution analysis showed that T was the dominant factor for ET0 variation in Guangdong, followed by SD. SD was found to be the dominant factor in ET0 changes in areas where the “evaporation paradox” occurred, as well as in spring and summer. The study concludes that the climate in Guangdong became warmer and drier over the past 61 years, and if the current global warming trend continues, it will lead to higher evapotranspiration and drought occurrence in the future.

Effect of Drought and Pluvial Climates on the Production and Stability of Different Types of Peanut Cultivars in Guangdong, China

“…In recent decades, extreme climatic conditions, including seasonal droughts, heavy precipitation, storms, floods, and heat waves, have disrupted food production and frequently caused yield losses, seriously threatening China's food security [30]. Guangdong province, located in the southern mainland of China and near the South China Sea, experiences a higher frequency of extreme climate events due to the combined influence of the ocean and the mainland [31]. The meteorological data from 1960 to 2020 indicate that the climate in Guangdong has become warmer and drier over the past 61 years, with an increased frequency of droughts projected in the future [31].…”

“…Guangdong province, located in the southern mainland of China and near the South China Sea, experiences a higher frequency of extreme climate events due to the combined influence of the ocean and the mainland [31]. The meteorological data from 1960 to 2020 indicate that the climate in Guangdong has become warmer and drier over the past 61 years, with an increased frequency of droughts projected in the future [31]. Furthermore, there has been a significant increase in heavy precipitation events, which are classified as strong and relatively strong events, while there was a notable decrease in relatively weak precipitation events [9].…”

Global Sensitivity of Penman–Monteith Reference Evapotranspiration to Climatic Variables in Mato Grosso, Brazil

“…For example, [23], in their analysis of annual ETo changes in Spain, found that relative humidity (RH), wind speed (WS), and maximum temperature (Tmax) exerted more significant influence on ETo than solar radiation (SRD) and minimum temperature (Tmin). Conversely, in Guangdong, a humid subtropical province in South China, ETo exhibited higher sensitivity to RH and temperature (T) compared to SRD and WS, as reported by [24]. In contrast, in the eastern Himalayan region of Sikkim, India [25], and the Qinghai-Tibet Plateau [26], the most influential parameter affecting ETo estimation was Tmax, followed by SRD, while WS, Tmin, and RH demonstrated varying effects on mean ETo.…”

Spatial-Temporal Pattern and Driving Forces of Fractional Vegetation Coverage in Xiong’an New Area of China from 2005 to 2019

“…Geo-detectors focus on detecting factor differentiations from a spatial perspective [27]. The Mann-Kendall trend method is well-suited for analyzing time series data with continuous increasing or decreasing trends, namely monotonic trends [28]. Another approach, the grey correlation degree analysis method, is proposed based on the grey system theory and is suitable for analyzing data series with time trends.…”