Guo Xiao-jiao; Wang Wen-zhong; Li Cheng-xi; Wang Wei; Shi Jian-sheng; Miao Ying; Hao Xing-bo; Yuan Dao-xian

doi:10.19637/j.cnki.2305-7068.2022.03.005

doi: 10.19637/j.cnki.2305-7068.2022.03.005

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出版历程
- 收稿日期: 2021-11-06
- 录用日期: 2022-06-18
- 刊出日期: 2022-09-15

Temporal variations of reference evapotranspiration and controlling factors: Implications for climatic drought in karst areas

1.
Key Laboratory of Groundwater Sciences and Engineering, Ministry of Natural Resources, Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
2.
Ecogeological Survey Center of China Geological Survey, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
3.
China University of Geosciences (Wuhan), Wuhan 430074, China
4.
Key Laboratory of Karst Dynamics, Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin 541004, Guangxi, China
5.
Geological Exploration Institute of Shandong Zhengyuan, China Metallurgical Geology Bureau, Jinan 250101, China
6.
Hebei Coal Geology Bureau fourth Geological Brigade, Zhangjiakou 075100, Hebei, China

More Information

Corresponding author: ww@mail.cgs.gov.cn

摘要

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Abstract: Variations in reference evapotranspiration (ET₀) and drought characteristics play a key role in the effect of climate change on water cycle and associated ecohydrological patterns. The accurate estimation of ET₀ is still a challenge due to the lack of meteorological data and the heterogeneity of hydrological system. Although there is an increasing trend in extreme drought events with global climate change, the relationship between ET₀ and aridity index in karst areas has been poorly studied. In this study, we used the Penman–Monteith method based on a long time series of meteorological data from 1951 to 2015 to calculate ET₀ in a typical karst area, Guilin, Southwest China. The temporal variations in climate variables, ET₀ and aridity index (AI) were analyzed with the Mann–Kendall trend test and linear regression to determine the climatic characteristics, associated controlling factors of ET₀ variations, and further to estimate the relationship between ET₀ and AI. We found that the mean, maximum and minimum temperatures had increased significantly during the 65-year study period, while sunshine duration, wind speed and relative humidity exhibited significant decreasing trends. The annual ET₀ showed a significant decreasing trend at the rate of −8.02 mm/10a. However, significant increase in air temperature should have contributed to the enhancement of ET₀, indicating an “evaporation paradox”. In comparison, AI showed a slightly declining trend of −0.0005/a during 1951–2015. The change in sunshine duration was the major factor causing the decrease in ET₀, followed by wind speed. AI had a higher correlation with precipitation amount, indicating that the variations of AI was more dependent on precipitation, but not substantially dependent on the ET₀. Although AI was not directly related to ET₀, ET₀ had a major contribution to seasonal AI changes. The seasonal variations of ET₀ played a critical role in dryness/wetness changes to regulate water and energy supply, which can lead to seasonal droughts or water shortages in karst areas. Overall, these findings provide an important reference for the management of agricultural production and water resources, and have an important implication for drought in karst regions of China.
- Reference evapotranspiration /
- Aridity index /
- Penman–Monteith method /
- Sunshine duration /
- Guilin

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Figure 1. Annual and inter-annual variations of ET₀ for Guilin, China from 1951 to 2015

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Figure 2. Monthly change of precipitation, air temperature and ET₀

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Figure 3. Seasonal variations of ET₀ during (a) spring, (b) summer, (c) autumn, and (d) winter in Guilin, China from 1951 to 2015

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Figure 4. Trend in the annual aridity index (AI) for Guilin, China during 1951–2015. Dotted line represents the linear fitting line.

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Figure 5. Seasonal variations in the aridity index in (a) spring, (b) summer, (c) autumn, and (d) winter for Guilin, China from 1951 to 2015. Dotted line is the linear trend.

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Figure 6. Annual variability trends of meteorological factors during 1951–2015. (a) precipitation, (b) air temperature, (c) wind speed, (d) relative humidity, (e) mean vapor pressure, (f) sunshine duration, (g) minimum temperature, and (h) maximum temperature. Red dotted line represents linear regression of different meteorological variables, and gray dotted line represents 65-year average value.

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Figure 7. Scatter plots of the (a) relationship between ET₀ and aridity index (ET₀/P) and (b) precipitation and aridity index for Guilin, China during 1951–2015. Red dashed line is the fitted line.

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Table 1. Meteorological station site and mean meteorological variables of Guilin, China

Station	Longitude (°E)	Latitude (°N)	Altitude (m)	T_mean	RH (%)	U₁₀ (m s⁻¹)	Sunshine duration (h)	Vapor pressure (hpa)	Precipitation (mm a⁻¹)
Guilin	110.30	25.32	164.40	19.0	75.1	2.4	1533.1	18.1	1902.6
Note: RH represents relative humidity; T_mean is the mean air temperature; U₁₀ is the wind speed at the height of 10 m.

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Table 2. Interdecadal variation in the annual and seasonal reference evapotranspiration (ET₀) in Guilin, China

	Annual ET₀ (mm)	Magnitude (%)	Spring ET₀ (mm)	Magnitude (%)	Summer ET₀ (mm)	Magnitude (%)	Autumn ET₀ (mm)	Magnitude (%)	Winter ET₀ (mm)	Magnitude (%)
1950s	1164.17	−	208.57	−	375.74	−	390.75	−	189.5	−
1960s	1167.32	0.3	207.49	−0.5	386.8	2.9	390.08	−0.2	183.32	−3.3
1970s	1130.66	−3.1	201.95	−2.7	367.04	−5.1	375	−3.9	185.46	1.2
1980s	1117.85	−1.1	201.44	−0.3	371.35	1.2	361.37	−3.6	182.7	−1.5
1990s	1126.85	0.8	205.62	2.1	354.46	−4.5	375.46	3.9	191.49	4.8
2000s	1136.89	0.9	209.48	1.9	362.59	2.3	382.44	1.9	182.99	−4.4
2010–2015	1104.9	−2.8	204.56	−2.3	363.2	0.2	364.44	−4.7	171.9	−6.1
Mean	1135.52	−0.9	205.59	−0.3	368.74	−0.5	377.08	−1.1	183.91	−1.6

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Table 3. Mean, maximum and minimum annual and seasonal ET₀ (Unit: mm)

Values	Annual	Spring	Summer	Autumn	Winter
Mean	1137.9	205.7	369.2	378.0	184.8
Maxmium	1269.5	243.1	424.7	444.2	225.6
Minimum	1010.4	178.5	325.5	320.3	133.7
Ratio (%)	-	18.1	32.4	33.2	16.2

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Table 4. Annual, seasonal changing rates of reference evaporation (ET₀) and climate factors in Guilin, China

Time	ET₀ (mm 10a⁻¹)	Precipitation (mm 10a⁻¹)	Mean temperature (°C 10a⁻¹)	Wind speed (m s⁻¹ 10a⁻¹)	Relative humidity (% 10a⁻¹)	Vapor pressure (hpa 10a⁻¹)	Sunshine duration (h 10a⁻¹)	Minimum temperature (°C 10a⁻¹)	Maximum temperature (°C 10a⁻¹)
Annual	−8.02*	9.93	0.17*	−0.074*	−0.64*	−0.02	−62.90*	0.20*	0.08*
Spring	0.134	−14.39	0.253*	−0.114*	−0.58*	0.086	−84.45	0.279*	0.214*
Summer	−4.231*	28.81	0.102*	−0.023	−0.358*	0.016	−14.62*	0.097*	0.043
Autumn	−2.871	−11.872	0.153*	−0.026	−0.78*	−0.065	−18.27*	0.184*	0.046
Winter	−1.138	7.715	0.154*	−0.143*	−0.727*	−0.026*	−14.695	0.239*	0.017
* indicate significant at the level of 0.05.

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Table 5. Correlation coefficients between annual and seasonal climate factors and potential evapotranspiration in Guilin, China during 1951–2015

	Precipitation	Mean temperature	Wind speed	Relative humidity	Vapor pressure	Sunshine duration	Maximum temperature	Minimum temperature
Annual	−0.517**	0.129	0.414**	−0.584**	−0.525**	0.784**	0.489**	−0.187
Spring	−0.300*	0.555**	−0.01	−0.658**	0.194	0.767**	0.728**	0.349**
Summer	−0.571**	0.451**	0.24	−0.569**	−0.146	0.833**	0.690**	0.046
Autumn	−0.693**	0.510**	0.366**	−0.730**	−0.508**	0.812**	0.718**	0.191
Winter	−0.664**	0.275*	0.470**	−0.836**	−0.640**	0.761**	0.638**	−0.13
** significant correlation at the 99% confidence level (two−tailed); * significant correlation at the 95% confidence level (two−tailed). Noting that the maximum correlation coefficients in each season are shown in bold.

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Table 6. Summary of previously estimated ET₀ or pan evaporation (ET_p) trends in China, and the primary causes of the trend

Location	ET₀ or ET_p trend (mm/10a)	Study period	Cause	Source
China	−3.0, ↓	1955−2000	Solar irradiance	Liu et al. 2004b
China	−8.56, ↓	1961−2008	Wind speed Sunshine duration	Yin et al. 2010
China	−6.02, ↓	1960−2007	−	Liu et al. 2012
China	−3.45, ↓	1956−2015	Wind speed	Fan et al. 2016
China	−6.84, ↓	1961−2013	Wind speed Sunlight duration	Wang et al. 2017
China	< −6.0, ↓	1960−2012	Wind speed	Chai et al. 2018
Yangtze River basin	−12.4, ↓	1960−2000	Net radiation Wind speed	Xu et al. 2006
Yangtze River basin	−3.26, ↓ ET_p: −2.98, ↓	1961−2000	Wind speed Net radiation	Wang et al. 2007
Yangtze River Delta (Eastern China)	22.51, ↑	1957−2014	Relative humidity Wind speed	Xu et al. 2017
Yellow River Basin	0.02, ↑	1961−2006	Air temperature Relative humidity	Liu et al. 2010
Yellow River Basin	−12.9, ↓	1961−2012	Sunshine hours	Zhang et al. 2015
Yellow River Basin	−4.689, ↓	1960−2012	Wind speed Solar radiation	She et al. 2017
Heihe River Basin	2.01, ↑	1961−2014	Relative humidity	Du et al. 2016
Songhua River Basin	4.90, ↑	1961−2010	Mean air temperature	Wen et al. 2014
Wei River Basin	−	1959−2008	Relative humidity Air temperature	Zuo et al. 2012
The North China Plain	ET_p: −7.09, ↓	1981−2013	Radiation Wind speed	Mo et al. 2017
Loess Plateau Region	−10.30, ↓	1961−2012	Wind speed	Zhao et al. 2014
Tibetan Plateau	−9.6, ↓	1960−2012	−	Wang et al. 2014
Qinghai−Tibetan Plateau	−24.0, ↓	1970−2011	Net radiation Wind speed	Zhang et al. 2018b
Northwest China	−30.0, ↓	1955−2008	Wind speed	Huo et al. 2013
Shenzhen City	−	1954−2012	Sunshine hours Vapor pressure deficit	Liu et al. 2015
Yunnan province	−6.50, ↓	1961−2004	Sunshine duration	Fan and Thomas 2013
Guizhou province	−4.476, ↓	1959−2011	Sunshine duration	Gao et al. 2016
Southwest China	−4.34, ↓	1960−2013	Sunshine duration Wind speed	Zhao et al. 2018
Southwest China	−5.13, ↓	1961−2012	Net radiation	Sun et al. 2016
Note: The symbol “↑” and “↓” represent increasing and decreasing trends, respectively. The symbol “−” indicates that ET₀ trend was not estimated. Noting that ET_p specified in the table refers to pan evaporation, and the others represent ET₀ estimated by the FAO56 Penman–Monteith method.

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