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Article

How Has the Recent Climate Change Affected the Spatiotemporal Variation of Reference Evapotranspiration in a Climate Transitional Zone of Eastern China?

1
School of Civil Aviation, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
2
School of Resources and Environment, Anhui Agricultural University, Hefei 230036, China
3
China Meteorological Administration•Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, China
4
Henan Institute of Meteorological Sciences, Zhengzhou 450003, China
5
Anhui Public Meteorological Service Center, Anhui Meteorological Bureau, Hefei 230031, China
6
The Weather Modification Center of Henan Province, Zhengzhou 450003, China
7
Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
8
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Jiangsu Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2022, 11(5), 300; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11050300
Submission received: 20 March 2022 / Revised: 28 April 2022 / Accepted: 2 May 2022 / Published: 6 May 2022 / Corrected: 8 March 2023

Abstract

:
Reference evapotranspiration (ET0) is essential for agricultural production and crop water management. The recent climate change affecting the spatiotemporal variation of ET0 in eastern China continues to still be less understood. For this purpose, the latest observed data from 77 meteorological stations in Anhui province were utilized to determine the spatiotemporal variations of ET0 by the use of the Penman–Monteith FAO 56 (PMF-56) model. Furthermore, the Theil–Sen estimator and the Mann–Kendall (M–K) test were adopted to analyze the trends of ET0 and meteorological factors. Moreover, the differential method was employed to explore the sensitivity of ET0 to meteorological factors and the contributions of meteorological factors to ET0 trends. Results show that the ET0 decreased significantly before 1990, and then increased slowly. The ET0 is commonly higher in the north and lower in the south. ET0 is most sensitive to relative humidity (RH), except in summer. However, in summer, net radiation (Rn) is the most sensitive factor. During 1961–1990, Rn was the leading factor annually, during the growing season and summer, while wind speed (u2) played a leading role in others. All meteorological factors provide negative contributions to ET0 trends, which ultimately lead to decreasing ET0 trends. During 1991–2019, the leading factor of ET0 trends changed to the mean temperature (Ta) annually, during the growing season, spring and summer, and then to Rn in others. Overall, the negative contributions from u2 and Rn cannot offset the positive contributions from Ta and RH, which ultimately lead to slow upward ET0 trends. The dramatic drop in the amount of u2 that contributes to the changes in ET0 in Region III is also worth noting.

1. Introduction

Evapotranspiration (ET) is a crucial portion of the hydrologic cycle. It participates in surface runoff, groundwater recharge and other key processes, and plays a pivotal role in climate change, hydrological research and irrigation water management [1,2,3]. Reference crop evapotranspiration (ET0) is the potential evapotranspiration that is further specified in terms of crop characteristics. Doorenbos et al. [4] defined ET0 as the ET of vast grassland with uniform and normal growth, completely covering the surface and providing sufficient water at a height of 8–15 cm. Subsequently, Allen et al. [5] introduced the concept of ET0 and defined it as the ET of an ideal 12 cm crop with a fixed canopy resistance of 70 s·m−1 and an albedo of 0.23 (very similar to green grassland with an open surface, uniform height, vigorous growth, complete coverage of the ground and a sufficient supply of soil moisture). Since then, ET0 has been widely used in the fields of agronomy, agriculture, irrigation and ecology [6].
Currently, the Penman–Monteith FAO (the Food and Agriculture Organization of the United Nations) 56 (PMF-56) model, a modified Monteith equation [7], has been used broadly for estimating ET0 worldwide based on its solid theoretical base and wide applications [8]. In the PMF-56 model, the climatic factors (i.e., temperature, humidity, wind speed and radiation) are the main influencing elements for ET0. However, in recent decades, climate change, especially global warming, has stimulated worldwide concerns [9,10,11], which have also led to changes in ET0 in different parts of the world [12,13,14,15,16]. Although under the influence of global warming, ET0 is not only affected by temperature, but also by other elements, and the coupling of multiple factors ultimately determines the increasing or decreasing trends of ET0. Therefore, quantifying the impact of meteorological factors on ET0 trends is very essential. In recent studies, quantitative methods have mainly been used to assess the effects of meteorological factors on ET0 trends, for example, the multiple regression analysis [17], partial correlation analysis and stepwise regression [18], detrending method [2,19,20,21,22,23], sensitivity coefficient method and differential method [1,3,14,24,25]. As the aforementioned methods, the differential method can effectively quantify the actual contributions of climate factors to ET0 trends; therefore, it has been successfully applied in earlier studies.
In recent decades, most scholars have quantified the contributions of meteorological factors to ET0 trends worldwide, such as China [26,27,28,29,30,31], Slovenia [16], Spain [32], Iran [33], Bangladesh [34], etc. However, the dominant factors of ET0 may shift under the changing climate [14,35]. Li et al. [36] pointed out that pan evaporation exhibited a distinct downward trend before 1993 and then reversed in Northwest China. Similarly, Han et al. [35] found a downward trend of ET0 before 1991 and an upward trend after in the Jing-Jin-Ji region of North China, and the dominant factor contributing to ET0 shifted from wind speed to mean temperature. Wang et al. [14] revealed that the increasing ET0 trend after the 1990s over China could be attributed to the increasing air temperature, and the most sensitive factor to ET0 was specific humidity. However, in Southwest China, the sunshine duration was the main contributor to ET0 trends in the growing season from 1961 to 1996, and the relative humidity was the dominant variable for the increasing ET0. Although research scholars have conducted relevant studies on ET0 [14,37,38], ET0 varies significantly among diverse regions and the trends of meteorological factors are essential to analyze the variation of ET0. Thus, it is necessary to conduct regional studies of ET0, especially in eastern China, where there is still a certain knowledge gap in the systematic study of ET0. This work intends to close the knowledge gap in the existing literature. Our study also offers a rational theoretical basis for regional agricultural water management and irrigation planning.
The Yangtze River Delta urban agglomeration is one of the six major urban agglomerations in the world. It is an active economic development region, with the highest degree of openness and the strongest innovation ability in China. In 2019, the State Council of the People’s Republic of China (PRC) issued the planning scope of the Yangtze River Delta, which officially extended to all the cities in the four provinces of Jiangsu, Zhejiang, Anhui and Shanghai. Anhui province is adjacent to the Yangtze River Delta and is also one of the four provinces mentioned above to have witnessed rapid economic development over the past few decades. Furthermore, Anhui province is one of the 13 major granary provinces, with a total grain output of 34.15 billion kilograms, ranking sixth in China. Among them, the growth rate of the total grain output ranks first among 13 major grain provinces [39]. Thus, an accurate estimation of ET0, a precise evaluation of its spatiotemporal distribution characteristics and variation trends, as well as the exploration of its influencing factors have scientific implications for agricultural production planning, water resource management, and ecological protection.
Based on the above discussion, we propose the hypothesis that the ET0 trends and its dominant factors in Anhui province have changed over the past 59 years. To verify this hypothesis, the goals of this research are (1) to investigate the spatiotemporal characteristics of ET0 and meteorological factors in Anhui province; (2) to clarify the sensitivities of ET0 to meteorological factors; (3) to determine the dominant factors of ET0 trends and their internal mechanisms driving ET0 variations. The outcomes of this research would enhance our understanding of climate change and provide theoretical support for agricultural production and crop water resource management in similar regions worldwide.

2. Materials and methods

2.1. Study Area

Anhui province (114°54′~119°27′ E and 29°41′~34°38′ N), located in the lower Yangtze River Basin and middle Huai River Basin of eastern China, is a transitional zone between the warm temperate zone and the subtropical zone. North of the Huai River belongs to the warm temperate zone with a subhumid monsoon climate, while the south belongs to the subtropical humid monsoon climate zone. Anhui province has a warm and humid climate with four distinct seasons, and the average annual temperature and precipitation are 14~17 °C and 800~1600 mm, respectively. The precipitation is characterized by more south and less north, more mountains and fewer plains and hills. The summer precipitation is abundant, accounting for 40~60% of the annual precipitation. The total area of the province is 139,600 km2, accounting for about 1.45% of the land area and ranking third in eastern China, as well as twenty-second in China [40]. The terrain in the study area is higher in the south and lower in the north. The southern region is mainly hills and mountains, while the northern is principally plain land (Figure 1a). The Yangtze River and Huai River run through Anhui province for 416 km and 430 km, respectively, dividing the province into three natural regions, namely, the Huaibei Plain, the Jianghuai Hills and southern Anhui mountains. The primary land use type is croplands, with a proportion of approximately 58% of the province’s area, followed by grasslands, forests, etc. (Figure 1b).

2.2. Data Sources

The meteorological datasets adopted here were the routine meteorological observation data from 77 weather stations in Anhui province during 1961–2019, which were provided by the China Integrated Meteorological Information Sharing System (CIMISS) of the China Meteorological Administration (CMA), including daily average temperature (Ta, °C), maximum and minimum temperature (Tmax and Tmin, °C), relative humidity (RH, %), sunshine duration (SD, h) and wind speed at 10 m height (u10, m·s−1). Quality control of the datasets was already conducted by the National Meteorological Information Center (NMIC) of CMA. In addition, the whole year was divided into five study periods, namely, the growing season (April to October), spring (March to May), summer (June to August), autumn (September to November) and winter (December to February of next year).

2.3. ET0 Calculation Procedure

The PMF-56 model recommended by the FAO was employed to calculate the ET0 in this research [5], which is an international accepted method for calculating ET0. The specific Equation (1) was as follows:
ET 0 = 0.408 Δ ( R n G ) + γ 900 T a + 273 u 2 ( e s e a ) Δ + γ ( 1 + 0.34 u 2 )
where ET0 refers to the daily reference evapotranspiration (mm·d−1), Δ refers to the slope of the curve of the saturation vapor pressure at air temperature Ta (kPa·°C−1), Rn refers to the net radiation (MJ·m−2·d−1), G refers to the soil heat flux density (MJ·m−2·d−1), γ refers to the psychrometric constant (kPa·°C−1), Ta refers to the daily air temperature at 2 m height (°C), u2 refers to the wind speed at a height of 2 m (m·s−1) and es and ea refer to the saturation vapor pressure and actual vapor pressure, respectively (kPa).
Due to the missing observation data of solar radiation (Rs, MJ·m−2·d−1), Angstrom formula [41] was adopted in this work to estimate Rs through SD data. The detailed Equation (2) was as follows:
R s = ( a s + b s SD SD max ) R a
where Rs and Ra denote total daily solar radiation and extraterrestrial radiation, respectively (MJ·m−2·d−1), SD and SDmax denote daily sunshine duration and maximum possible sunshine duration, respectively (h), and as and bs are regression coefficients, according to the research of Chen et al. [42], with the values of 0.19 and 0.53, respectively.
Meanwhile, while lacking the SD data, the radiation formula put forward by Hargreaves et al. [43] was adopted in this study to calculate Rs through Tmax and Tmin; the specific Equation (3) was as follows:
R s = k RS × ( T max T min ) × R a
where kRS denotes empirical coefficient, and the value in the inland area was usually 0.16 [43,44,45]. Moreover, the performance of this method was verified in our earlier research [1].
Then, the Rn could be calculated in the following Equations (4)–(6):
R n = R ns R nl
R ns = ( 1 α ) R s
R nl = σ ( T max , K 4 + T min , K 4 2 ) ( 0.34 0.14 e a ) ( 1.35 R s R s 0 0.35 )
where Rns and Rnl are the incoming net short wave radiation and the outgoing net long wave radiation, respectively (MJ·m−2·d−1), α is the reference crop albedo (with value of 0.23), σ is the Stephen Boltzmann’s constant (4.903 × 10−9 MJ·K−4·m−2·d−1), Tmax,K and Tmin,K are the maximum and minimum absolute temperature within 24 h (K = °C + 273.16) and Rs0 is the clear sky radiation (MJ·m−2·d−1); for the specific calculation procedure, please refer to work of Allen et al. [5].
As the routine observation data adopted in this study only included u10, in order to obtain the u2 and the convenience of calculation, our work adopted the wind speed conversion Equation (7) proposed by Allen et al. [5]:
u 2 = u z 4.87 ln ( 67.8 z 5.42 )
where u2 denotes the wind speed with the height of 2 m above the ground plane (m·s−1).
In addition, detailed calculations using Equations (8)–(12) of Δ , γ , e s and e a were as follows:
Δ = 4098 × [ 0.6108 exp ( 17.27 T a T a + 237.3 ) ] ( T a + 237.3 ) 2
γ = 0.00163 P λ
P = 101.3 ( 273 + T a 0.0065 h 273 + T a ) 5.26
e s = 0.6108 exp ( 17.27 T a T a + 237.3 )
e a = e s × RH
where λ is the latent heat of vaporization with the value of 2.45 (MJ·kg−1), P is the atmospheric pressure (kPa) and h is the elevation above the sea level (m).

2.4. Sensitivity Coefficient

The differential equation method developed by McCuen [46] was adopted to calculate the sensitivities of ET0 to meteorological factors in the following Equation (13):
S ( v i ) = lim Δ v i 0 ( Δ ET 0 / ET 0 Δ v i / v i ) = ET 0 v i × v i ET 0
where S(vi) denotes the sensitivity of ET0 to meteorological factor (vi), the positive (negative) sensitivity represents the ET0 increases (decreases) with vi and the absolute value of S(vi) denotes the influence of vi to ET0. For the detailed calculation processes, please refer to work of Chu et al. [2].

2.5. Contributions of Meteorological Factors to ET0

As shown in Formula (1), the ET0 is a function of meteorological factors. Therefore, the Ta, RH, u2 and Rn were selected as four main meteorological factors affecting ET0. Moreover, this study employed the differential equation method to assess the contribution of four main meteorological factors to ET0 based on PMF-56 model. Precise Equation (14) showed as follows:
dET 0 dt = ET 0 T a dT a dt + ET 0 RH dRH dt + ET 0 u 2 du 2 dt + ET 0 R n dR n dt + ε
Equation (14) can be abbreviated to the below Equation (15):
C _ ET 0 = C ( T a ) + C ( RH ) + C ( u 2 ) + C ( R n ) + ε
where C_ET0 denotes the ET0 trend, C(Ta), C(RH), C(u2) and C(Rn) refer to the contribution of Ta, RH, u2 and Rn to ET0, respectively, and ε indicates the deviation between C_ET0 and ET0 calculated by using Theil–Sen estimator (T_ET0). The contribution of each meteorological factor to ET0 could be computed by multiplying the result of Equation (13), the trend of each meteorological factor during the study period and the length of the corresponding study period (i.e., 365 or 366 days for the annual calculation, 214 days for growing season, 92 days for both spring and summer, 91 days for autumn, 90 or 91 days for winter) [1,3].

2.6. Trend Analysis

The Mann–Kendall (M–K) test was recommended for hydrometeorological time series data analysis [47,48,49]. Thus, it was adopted here to estimate trends of ET0 and the four main meteorological factors [3]. The null hypothesis H0 was that in a series of data (xi, i = 1, 2, …, n), xi was independent and evenly distributed. The alternative hypothesis H1 was that a monotonic tendency consisted of X. The statistical S and standardized test statistics Z were calculated in the following Equations (16) and (17):
S = i = 1 n 1 j = i + 1 n sgn ( x j x i )
sgn ( x j x i ) = { + 1 if   ( x j x i ) > 0 0 if   ( x j x i ) = 0 1 if   ( x j x i ) < 0
where xi and xj are the value of year i and j, respectively, and n is the data length. The S obeyed normal distributions (n ≥ 8), the calculation of average value E(S) and variance Var(S) were given below in Equations (18)–(20):
E ( S ) = 0
Var ( S ) = 1 18 [ n ( n 1 ) ( 2 n + 5 ) p = 1 q t p ( t p 1 ) ( 2 t p + 5 ) ]
where q refers to same group number and tp represents to the value in pth step.
Z = { S 1 Vas ( S ) if   S > 0 0 if   S = 0 S + 1 Vas ( S ) if   S < 0
in which Z is the change trend of time series’ data and Z > 0 (Z < 0) denotes the increasing (decreasing) trend. If | Z | > Z ( 1 α / 2 ) , then the hypothesis was rejected and the time series data showed a significant changing trend. Moreover, Z ( 1 α / 2 ) was the standard normal deviation in the standard normal distribution chart. When the α = 5% and α = 1% were the significant levels, the Z ( 1 α / 2 ) values were 1.96 and 2.58, respectively.
The Theil–Sen estimator was used to determine the magnitude of the variation trends of ET0 and meteorological factors [50,51]. Detailed calculation Equation (21) was as follows:
β = Median ( x j x i j i ) , 1 < i < j
where β is the calculated slope of data series;   x i and x j represent the sequence data corresponding to time i and j, respectively; the positive (negative) β indicates the increasing (decreasing) trend. Spatial distribution map was prepared by inverse distance weighting (IDW) method within ArcGIS platform (version 10.2).

3. Results

3.1. Spatiotemporal Variations of ET0

3.1.1. Temporal Scale

As shown in Figure 2, the ET0 exhibited a significant decreasing trend before 1990 (−3.89 mm·a−2) and then increased slowly (0.62 mm·a−2) throughout the entire region. In subregions, the ET0 presented significant decreasing trends before 1990, and the decrease magnitudes ranked in the order: Region I > Region II > Region III. After 1990, the ET0 showed a slowly decreasing trend in Region I, while it exhibited slow increasing trends in other regions, being especially higher in Region III. Because of the definition of the World Meteorological Organization (WMO) of the standard climate standard (i.e., the 30-year average) and the opposite change trend of ET0 around 1990 in this study, we divided the entire study period into two periods, namely, 1961–1990 and 1991–2019.
Detailed temporal trends of ET0 are also shown in Table 1. Before 1990, the temporal trends of ET0 on an annual timescale, growing season, and summer were similar. During spring, autumn and winter, the downward trends of ET0 were not significant in most regions. After 1990, ET0 exhibited slow upward trends annually, during the growing season, spring and summer, while it showed downward trends during autumn and winter. It is worth noting that only the ET0 trend in Region III in spring was significant.

3.1.2. Spatial Scale

As shown in Figure 3, the spatial distribution of ET0 during 1961–1990 and 1991–2019 was basically consistent. The ET0 was higher in the north and lower in the south annually, during the growing season, spring and summer. However, the ET0 was higher in the southwest of Regions II and III in both autumn and winter. Before 1990, ET0 annually, during the growing season and summer showed a significant decreasing trend in most regions. After 1990, the ET0 in Region III showed a significant increasing trend annually, during the growing season and spring. Moreover, the ET0 in Region II also exhibited a significant increasing trend in spring. These phenomena are echoed in Table 1.

3.2. Spatiotemporal Variations of Meteorological Factors

To evaluate the impact of meteorological factors on ET0, we analyzed the change trends of meteorological factors. As shown in Figure 4, Ta decreased significantly at the rate of −0.021 °C·a−1 before 1990, and then increased significantly with the rate of 0.034 °C·a−1 (Figure 4a). RH first increased at the rate of 0.071 a−1 before 1990, and then decreased slightly at the rate of 0.070 a−1 (Figure 4b). u2 declined significantly with the rate of −0.020 m·s−1 before 1990, and then slowed down to −0.006 m·s−1 (Figure 4c). Similar to u2, Rn also declined significantly with the rate of −0.017 MJ·m−2·d−1 before 1990, and then slowed down to −0.004 MJ·m−2·d−1 insignificantly (Figure 4d).
From Table 2, Ta exhibited significant decreasing trends in the entire region and each subregion on an annual timescale and in Regions I and II in summer, while showing insignificant decreasing trends in other regions and timescales during 1961–1990. During 1991–2019, Ta showed increasing trends in all regions and study periods, with significant trends exhibited annually, during the growing season, spring, summer and autumn in most regions. RH showed increasing trends before 1990 in most regions, while it showed decreasing trends after 1990, except in autumn. u2 presented significant decreasing trends in most regions during the two time periods, except in Region III after 1990, which exhibited slightly increasing trends. Rn decreased significantly annually, during the growing season, summer and winter, while insignificantly in other periods before 1990. Then, Rn decreased slowly in most regions and study periods, except in spring, which increased slowly after 1990.

3.3. Sensitivity of Meteorological Factors to ET0

3.3.1. Temporal Variation Characteristics

The daily sensitivity coefficient of each meteorological factor to ET0 is shown in Figure 5. As seen from Figure 5, the daily sensitivity coefficient of Ta, RH and Rn (i.e., S(Ta), S(RH) and S(Rn)) first increased and then decreased, while the daily sensitivity coefficient of u2 (S(u2)) showed a gradual changing trend, going downward first and then upward. The magnitude of these four meteorological factors was ranked as follows: S(RH) > S(Rn) > S(Ta) > S(u2). In contrast to S(Ta), S(u2) and S(Rn), S(RH) was negative.
Table 3 presents the seasonal variation of the sensitivities of meteorological factors to ET0. The general change trends of sensitivity coefficients in the seasonal timescale were consistent with those in the daily timescale. During 1961–1990, the ET0 was most sensitive to RH annually, during the growing season, spring, autumn and winter, and the sensitivity magnitude of four meteorological factors was ranked as follows: S(RH) > S(Rn) > S(Ta) > S(u2). However, the ET0 was most sensitive to Rn in summer, followed by Ta, RH and u2 in all regions. Furthermore, the ET0 was most sensitive to Rn in the growing season, followed by RH, Ta and u2 in Region III. In contrast to that during 1961–1990, the ET0 was most sensitive to Rn in the growing season, followed by RH, Ta and u2 during 1991–2019. Moreover, Rn was also the most sensitive factor in spring in Region III. The differences between these two time periods were not distinct.
Figure 6 shows the annual sensitivity coefficients of meteorological factors to ET0. S(Ta) exhibited a slow downward trend during the whole study period, decreased before 1990 and then increased. S(RH) and S(Rn) both presented evident increasing trends in the past 59 years, especially during 1961–1990. Because the S(RH) was negative, as far as the magnitude was concerned, the sensitivity of RH decreased generally. S(u2) was similar to S(Ta), while it increased more obviously after 1990.

3.3.2. Spatial Variation Characteristics

In order to illuminate the sensitivity coefficients of ET0 to meteorological factors, the spatial distribution of annual mean sensitivity coefficients is displayed in Figure 7. As shown in Figure 7, S(Ta) was higher in Region II, especially along the Yangtze River, than in the north of Region I, west of Region II and south of Region III. The spatial distribution of S(RH) was similar to that of S(Ta), while the highest value was located in the central Region III (namely, the Huangshan station). S(u2) was comparatively higher in the north and lower in the south. The S(u2) in the west of Region II was lower than in surrounding regions. Contrary to the spatial distribution of S(Ta) and S(RH), the higher values of S(Rn) were mainly located in the west of Region II and most areas of Region III.

3.4. Contribution of Meteorological Factors to ET0

3.4.1. Validation of Differential Method

To verify the validity of the differential method, we compared the ET0 trends calculated using a differential method (C_ET0) and with those, the Sen slope estimator (E_ET0) in three time periods (during 1961–2019, 1961–1990 and 1991–2019). As shown in Figure 8, the fitting results from the differential method and the Sen slope estimator were all concentrated in a 1:1 line, with an R2 value generally greater than 0.90. Thus, all the above phenomena indicated that the four selected meteorological factors in this research were reasonable and could be well explained by the contributions to ET0 trends by employing the differential method.

3.4.2. Contribution of Meteorological Factors to ET0

The contribution of meteorological factors to the ET0 trend during the two study periods is shown in Figure 9. During 1961–1990, all meteorological factors offered negative contributions to ET0 trends, which ultimately led to decreasing trends for almost all regions and periods (Table 1). Specifically, Rn was the leading factor annually, during the growing season and summer, while u2 played leading roles in spring, autumn and winter. However, RH was the leading factor in Region I annually and Rn was the leading factor in Region III in autumn. During 1991–2019, Ta and RH showed positive contributions to ET0 trends for most regions and periods, except for RH in autumn and winter. In contrast, u2 and Rn devoted negative contributions to ET0 trends, except for Rn in spring and summer and u2 in Region III. Concretely, the main reason for changes in the ET0 was Ta annually, during the growing season, spring and summer, and then Rn in autumn and winter for most regions. Moreover, notably, the contribution magnitude of u2 to ET0 trends dropped sharply in Region III for all seasons. Overall, the negative contributions from u2 and Rn could not offset the positive contributions from Ta and RH, which led to the slow upward ET0 trends in the entire region, eventually, while the upward trend was higher in Region III. Table 4 and Figures S1 and S2 in the Supplementary Materials provide detailed information and the spatial distribution of dominant factors.

4. Discussion

In this study, Ta exhibited significant decreasing and increasing trends before and after 1990, respectively, which was similar to that in the whole of China [52] and also surrounding regions, such as the Huai River Basin [53] and Jiangsu province [2]. Ding et al. [52] pointed out that the sum of positive radiative forcing generated by greenhouse gases was the cause of climate warming, and that the surface temperature is likely to continue to rise. On the contrary, the RH first increased and then decreased, which may also be explained by the climate warming in this region (namely, the change trends in Ta). Furthermore, the larger vapor pressure deficit (VPD) caused by climate change from 1983 to 2008 [54] could explain the RH changing trends. Here, the changing trend of u2 was similar to that in mainland China [55]. However, because Region III is mainly a mountainous terrain zone, it was greatly affected by the narrow tube effect, which resulted in a higher wind speed here. These phenomena might be responsible for the relatively lower decreasing trends of u2 during 1961–1990 and slightly increasing trends during 1991–2019. Moreover, Tao et al. [56] reported that urbanization also had an impact on the annual mean wind speed decline in Anhui province after 2000 and its contribution rate reached 40%, particularly in spring. Meanwhile, the attenuation factor u2 might suppress the diffusion of aerosols and strengthen the influence of aerosol emissions on solar dimming [57]. In addition, Qian et al. [58] indicated that a fog-like haze caused by increasing air pollution might absorb or reflect the radiation, resulting in a reduction in surface solar radiation. Similar results were also shown by Ma et al. [59], Qi et al. [60] and Tao et al. [61].
This research also revealed that the ET0 decreased significantly before 1990 and then increased slowly. Similar phenomena occurred in a desertification-prone region of China [62], the Yellow River Basin [31], the Jing-Jin-Ji region of North China [35], Xinjiang province [63] and even mainland China [14,64]. All these changing trends in ET0 were attributed to changes in meteorological factors before and after the change point in specific regions. Generally, in this research, ET0 was most sensitive to RH, followed by Rn, Ta and u2, while the most sensitive factor shifted to Rn in summer, followed by Ta, RH and u2 for most regions. Similar results could be found in the Huai River Basin [1] and Jiangsu province [2]. However, in other regions of China, Xu et al. [27] pointed out that Tmax was, generally, the most sensitive factor for ET0, followed by RH, Rs, Tmin and u2 in the Jing River Basin of Northwest China. Wang et al. [65] reported that the ET0 was more sensitive to Tmax and SD than RH, u2 and Tmin in the three-river headwaters region of the Qinghai–Tibetan Plateau. Li et al. [29] discovered that the RH had the highest sensitivity, followed by Tmax, u2, SD and Tmin. She et al. [31] and Yang et al. [66] both indicated that the ET0 was the most sensitive to Rs, followed by RH and Ta in parts of the Yellow River Basin. From the previous research above, the difference in sensitivity factors of ET0 in different regions of China may have been mainly caused by the lower water vapor carried by the wind in dry regions and the higher humidity of the wind flow in humid regions [64]. However, in summer, and even in the growing season of Anhui province, Ta and Rn increased, while the air pressure and RH decreased, which could explain the transition of the most sensitive factor of ET0 from RH to Rn.
Although ET0 was most sensitive to RH for most regions, the change rate of RH was relatively small compared to other factors. Before 1990, Rn was the leading factor of ET0 trends annually, during the growing season and summer, while u2 was the leading factor in spring, autumn and winter. However, the high contribution of RH to the ET0 trend in Region I in the annual timescale could be interpreted reasonably by its significant increasing trend (Table 2). During 1991–2019, the leading factor of ET0 trends changed to Ta annually, during the growing season, spring and summer, then to Rn in autumn and winter for most regions. Similarly, Han et al. [35] reported that a decreasing sunshine duration and wind speed offset the impact of increasing air temperature, resulting in a decrease in ET0 between 1961 and 1991, while Ta was the most dominant factor contributing to an increase in ET0 in the Jing-Jin-Ji region between 1992 and 2015. Wang et al. [14] reported that the ET0 presented a significant increasing trend after the 1990s in China due to the increasing Ta. In this study, we also demonstrated an interesting phenomenon that the significance of climate variables was proportional to their dominance in ET0 trends. This finding was supported by a similar finding in our previous study on pan evaporation in the Huai River Basin [3]. As shown in Table 2, during 1961–1990, Rn represented significant decreasing trends annually, during the growing season and summer, which may explain its dominant role in the corresponding seasons. Although u2 always showed significant downward trends in these periods, the magnitude of u2 trends in spring, autumn and winter was larger than those in other seasons, which might have been responsible for its dominance in corresponding seasons. However, during 1991–2019, Ta presented prominently increasing trends for most regions and seasons except winter, which could account for its leading role in corresponding seasons. Rn only showed significant trends in autumn and winter for most regions, which corresponded to its dominant position in these two seasons. Furthermore, the insignificant trends of u2 in Region III for all seasons might also decipher its small contributions here. As shown in Figure 1, Region III is mainly a mountainous area with high elevation and the land use types are mainly forest and grassland. Meanwhile, Tao et al. [56] also found that the decreasing trend of u2 of urban stations was significantly greater than that of rural stations in Anhui province, which could give a possible explanation for the insignificant upward trends of u2 in Region III.
Although the effect of meteorological factors on ET0 was well quantified in this study, some uncertainties still existed in this aspect. Firstly, the differential equation method was adopted in this study to determine the contribution of each meteorological factor to ET0 trends. This method assumes that each major climate variable is independent and has been proven to be equivalent to the performance of the detrending method in a previous study [53]. However, each meteorological factor is not completely independent and may interact with one another, and the differential equation method adopts the averaged partial derivatives of each variable to attribute to the ET0 changes, which may also introduce uncertainty in the ultimate results [67]. In addition, considering the complexity of the underlying surface, although the most complete observational data of national meteorological stations were employed in this study, the density of current meteorological stations was still sparse, which was not enough to fully reflect ET0 changes and their causes at the spatial scale. Moreover, the changes in climate factors caused by human activities are likely to eventually lead to ET0 changes. Therefore, human activities, especially land use and cover changes and urbanization, increase the errors and uncertainties of ET0 calculation and attribution [31,68], which needs further research.

5. Conclusions

In this paper, we found that the ET0 decreased significantly before 1990 (−3.89 mm·a−2) and then increased slowly (0.62 mm·a−2) throughout the Anhui province. Among the meteorological factors affecting ET0 changes, Ta decreased significantly before 1990 and then increased significantly, with RH performing the opposite, while u2 and Rn both declined significantly before 1990 and then slowed down. Ta, RH and Rn daily sensitivity coefficients to ET0 increased first and then decreased, whereas u2 showed a gradual change trend on the opposite. Generally, RH was the most sensitive factor except in summer, when Rn was the most sensitive factor. The four selected meteorological factors were reasonable and could explain well the contributions to ET0 trends by employing the differential method. During 1961–1990, all meteorological factors provided negative contributions to ET0 trends, which ultimately led to decreasing trends for almost all regions and periods. Rn was the leading factor annually, during the growing season and summer, while u2 played a dominant role in other seasons. During 1991–2019, the leading factor of ET0 trends changed to Ta annually, during the growing season, spring and summer, then to Rn in other seasons for most regions. Ta and RH exhibited positive contributions to ET0 trends for most regions and periods, while u2 and Rn showed negative contributions. Overall, the negative contributions from u2 and Rn could not offset the positive contributions from Ta and RH, which, eventually, led to the slow upward ET0 trends. Furthermore, the slightly increasing trends of u2 and its extremely small contributions to the ET0 trends in Region III deserve more attention. The outcomes obtained from this research should help in the understanding of the changing climate and provide a theoretical basis for the agricultural crop production and sustainable management of water resources in similar world regions.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/ijgi11050300/s1, Figure S1 Spatial distribution of dominant factors in Anhui province during 1961–1990, Figure S2 Spatial distribution of dominant factors in Anhui province during 1991–2019.

Author Contributions

Conceptualization, Meng Li, Ronghao Chu and Yuelin Jiang; Software, Xiuzhu Sha; Validation, Meng Li, Ronghao Chu and Yuelin Jiang; Formal Analysis, Meng Li and Ronghao Chu; Data Curation, Meng Li, Ronghao Chu and Xiuzhu Sha; Writing—Original Draft Preparation, Meng Li and Ronghao Chu; Supervision, Meng Li, Ronghao Chu and Yuelin Jiang; Funding Acquisition, Meng Li, Ronghao Chu and Yuelin Jiang; Writing—Review & Editing, Abu Reza Md. Towfiqul Islam, Yuelin Jiang and Shuanghe Shen. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2018YFD0300905), the Anhui Provincial Natural Science Foundation (1908085QD171; 2108085QD157), the National Natural Science Foundation of China (41905100), the Anhui Agricultural University Science Foundation for Young Scholars (2018zd07), the Anhui Agricultural University Introduction and Stabilization of Talent Fund (yj2018-57) and the Scientific Research Project of the Anhui Meteorological Bureau (KM202003).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location, elevation (a) and land use type (b) of Anhui province in China. Note: The Huai River and Yangtze River divide the Anhui province into three regions, namely, Regions I, II and III (a). The land use type dataset adopted here was MCD12Q1 of MODIS product in 2014, with the spatial resolution of 500 m × 500 m (b).
Figure 1. Location, elevation (a) and land use type (b) of Anhui province in China. Note: The Huai River and Yangtze River divide the Anhui province into three regions, namely, Regions I, II and III (a). The land use type dataset adopted here was MCD12Q1 of MODIS product in 2014, with the spatial resolution of 500 m × 500 m (b).
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Figure 2. Temporal variations of ET0 during 1961–2019 in Anhui province (a) Whole region, (b) Region I, (c) Region II, (d) Region III.
Figure 2. Temporal variations of ET0 during 1961–2019 in Anhui province (a) Whole region, (b) Region I, (c) Region II, (d) Region III.
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Figure 3. Spatial trends of ET0 during 1961–1990 and 1991–2019 in Anhui province. Note: Solid (hollow) rhombus and plus sign indicate significant (insignificant) downward and upward trends, respectively.
Figure 3. Spatial trends of ET0 during 1961–1990 and 1991–2019 in Anhui province. Note: Solid (hollow) rhombus and plus sign indicate significant (insignificant) downward and upward trends, respectively.
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Figure 4. Temporal variations of meteorological factors (a) Ta, (b) RH, (c) u2, (d) Rn during 1961–2019 in Anhui province.
Figure 4. Temporal variations of meteorological factors (a) Ta, (b) RH, (c) u2, (d) Rn during 1961–2019 in Anhui province.
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Figure 5. Changes of daily sensitivity coefficients of meteorological factors to ET0 for the whole period.
Figure 5. Changes of daily sensitivity coefficients of meteorological factors to ET0 for the whole period.
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Figure 6. Changes of annual sensitivity coefficients of meteorological factors (a) Ta, (b) RH, (c) u2, (d) Rn to ET0.
Figure 6. Changes of annual sensitivity coefficients of meteorological factors (a) Ta, (b) RH, (c) u2, (d) Rn to ET0.
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Figure 7. Space distribution of annual mean sensitivity coefficient of ET0 to meteorological factors (a) Ta, (b) RH, (c) u2, (d) Rn.
Figure 7. Space distribution of annual mean sensitivity coefficient of ET0 to meteorological factors (a) Ta, (b) RH, (c) u2, (d) Rn.
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Figure 8. Validation of differential equation method in annual and seasonal timescale during 1961–2019 (a), 1961–1990 (b) and 1991–2019 (c).
Figure 8. Validation of differential equation method in annual and seasonal timescale during 1961–2019 (a), 1961–1990 (b) and 1991–2019 (c).
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Figure 9. Contributions of meteorological factors to ET0 trends during 1961–1990 and 1991–2019.
Figure 9. Contributions of meteorological factors to ET0 trends during 1961–1990 and 1991–2019.
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Table 1. Temporal trends of ET0 during 1961–1990 and 1991–2019 in Anhui province.
Table 1. Temporal trends of ET0 during 1961–1990 and 1991–2019 in Anhui province.
TimeRegionAnnualGrowing SeasonSpringSummerAutumnWinter
ZβZβZβZΒZβZβ
1961–1990Whole−3.53−3.953 ***−2.96−2.861 **−1.07−0.474−2.85−2.242 **−1.61−0.494−1.64−0.390
I−3.93−5.254 ***−3.75−4.148 ***−2.14−1.256 *−3.78−3.066 ***−1.57−0.450−1.50−0.548
II−3.25−3.567 **−2.78−2.672 **−0.82−0.362−2.93−2.233 **−1.68−0.499−1.53−0.343
III−2.96−3.188 **−2.32−2.363 *−1.03−0.258−1.96−1.748 *−1.93−0.554−1.89−0.326
1991–2019Whole0.620.5520.540.3641.260.8090.880.594−1.44−0.478−0.58−0.096
I−0.02−0.059−0.21−0.2860.730.3640.060.046−1.22−0.555−0.66−0.198
II0.210.1990.130.0751.260.8500.960.619−1.67−0.541−0.88−0.155
III1.591.3391.261.1202.041.029 *0.730.551−0.51−0.139−0.36−0.091
Note: Z indicates the M–K test statistic; β is the estimated ET0 slope, β > 0 (β < 0) denotes increasing (decreasing) trend; *, ** and *** denote the significance level of 0.05, 0.01 and 0.001, respectively.
Table 2. Temporal trends of meteorological factors during 1961–1990 and 1991–2019 in Anhui province.
Table 2. Temporal trends of meteorological factors during 1961–1990 and 1991–2019 in Anhui province.
MeteorologicalTimeRegionAnnualGrowing SeasonSpringSummerAutumnWinter
FactorZβZβZβZβZβZβ
Ta1961–1990Whole−2.57−0.022 *−1.64−0.011−0.86−0.007−1.82−0.035−0.54−0.007−0.75−0.010
I−2.36−0.029 *−1.86−0.017−0.75−0.009−2.25−0.039 *−0.04−0.001−0.54−0.004
II−2.71−0.021 **−1.57−0.012−0.57−0.007−2.00−0.036 *−0.57−0.008−1.14−0.012
III−2.50−0.020 *−1.25−0.009−0.96−0.013−1.32−0.019−0.61−0.009−0.61−0.013
1991–2019Whole2.910.036 **2.460.032 *3.280.059 **2.120.030 *2.760.031 **0.840.009
I2.760.037 **2.120.026 *3.060.053 **1.520.0262.310.026 *1.070.015
II2.870.035 **2.270.028 *3.100.062 **1.970.02 9 *2.080.026 *0.730.011
III3.210.038 **2.790.035 **3.170.054 **1.780.0302.980.042 **0.690.011
RH1961–1990Whole1.780.00091.500.00080.140.00022.780.0012 **0.360.00020.610.0008
I2.360.0016 *1.860.00140.890.00112.930.0021 **0.430.00050.540.0012
II1.320.00061.210.0007−0.11−0.00012.460.0012 *0.210.00010.390.0004
III1.500.00051.000.0004−0.18−0.000041.460.00090.610.00040.890.0006
1991–2019Whole−1.07−0.0008−1.37−0.0009−1.29−0.0016−1.29−0.00090.660.0007−0.06−0.0001
I−0.88−0.0005−0.84−0.0007−0.47−0.0009−0.99−0.00060.470.0007−0.09−0.0001
II−1.18−0.0008−1.18−0.0007−1.48−0.0019−0.88−0.00071.030.0009−0.36−0.0003
III−0.88−0.0005−1.41−0.0010−1.67−0.0016−1.22−0.00080.690.00060.430.0004
u21961–1990Whole−5.67−0.021 ***−5.28−0.019 ***−5.32−0.024 ***−4.78−0.014 ***−5.32−0.023 ***−5.03−0.023 ***
I−5.53−0.025 ***−5.03−0.023 ***−4.92−0.029 ***−4.53−0.020 ***−5.25−0.027 ***−5.35−0.031 ***
II−5.46−0.019 ***−4.78−0.017 ***−5.00−0.023 ***−4.32−0.012 ***−4.89−0.023 ***−4.82−0.021 ***
III−5.71−0.018 ***−5.07−0.016 ***−5.46−0.022 ***−3.71−0.012 ***−5.07−0.019 ***−5.57−0.020 ***
1991–2019Whole−3.55−0.008 ***−3.25−0.007 **−4.60−0.009 ***−2.38−0.005 *−1.97−0.005 *−2.04−0.004 *
I−3.43−0.011 ***−3.28−0.010 **−4.15−0.014 ***−2.91−0.010 **−2.91−0.009 **−1.82−0.006
II−4.18−0.011 ***−3.62−0.011 ***−4.75−0.013 ***−3.36−0.010 ***−2.53−0.009 *−2.68−0.007 **
III1.030.0011.480.0030.470.0011.030.0031.520.0030.580.001
Rn1961–1990Whole−3.00−0.017 **−2.36−0.020 *−0.71−0.007−2.57−0.047 *−1.14−0.006−2.53−0.010 *
I−3.03−0.016 **−2.50−0.021 *−1.00−0.010−3.32−0.053 ***−0.75−0.003−3.07−0.009 **
II−2.71−0.018 **−2.28−0.021 *−0.75−0.008−2.60−0.048 **−1.00−0.007−2.60−0.010 **
III−2.78−0.016 **−2.32−0.019 *−0.61−0.003−1.96−0.040 *−1.75−0.012−2.28−0.008 *
1991–2019Whole−0.92−0.005−0.69−0.0080.840.008−0.51−0.010−2.57−0.017 *−2.46−0.008 *
I−0.66−0.004−0.92−0.0070.580.003−0.62−0.011−1.89−0.012−2.57−0.009 *
II−0.96−0.006−0.99−0.0110.660.006−0.54−0.009−2.49−0.017 *−2.31−0.008 *
III−0.32−0.003−0.06−0.0011.630.0130.280.005−2.49−0.016 *−1.67−0.006
Note: Z indicates the M–K test statistic; β refers to the estimated slope of meteorological factor, β > 0 (β < 0) denotes the increasing (decreasing) trend; *, ** and *** indicate significance level of 0.05, 0.01 and 0.001, respectively.
Table 3. Sensitivities of ET0 to meteorological factors during 1961–1990 and 1991–2019.
Table 3. Sensitivities of ET0 to meteorological factors during 1961–1990 and 1991–2019.
RegionSeason1961–19901991–2019
S(Ta)S(RH)S(u2)S(Rn)S(Ta)S(RH)S(u2)S(Rn)
WholeAnnual0.489−1.0330.0740.7090.492−0.8860.0820.731
Growing season0.660−0.8240.0490.7780.641−0.6830.0600.791
Spring0.499−0.9710.0490.7240.507−0.7530.0660.740
Summer0.728−0.6910.0370.8170.692−0.6280.0440.830
Autumn0.578−1.0780.0900.7070.575−0.9060.0980.726
Winter0.152−1.3980.1190.5880.194−1.2530.1210.628
IAnnual0.474−1.0240.1020.6520.477−0.8710.1040.690
Growing season0.666−0.8170.0700.7370.643−0.6640.0720.770
Spring0.505−0.9030.0790.6580.511−0.7130.0860.698
Summer0.736−0.7130.0540.7830.696−0.6180.0530.814
Autumn0.565−1.0950.1150.6600.559−0.9040.1190.692
Winter0.090−1.3930.1590.5070.144−1.2430.1600.556
IIAnnual0.499−1.0800.0750.7010.501−0.9200.0860.720
Growing season0.669−0.8610.0510.7710.650−0.7130.0640.780
Spring0.507−1.0190.0480.7180.516−0.7770.0710.728
Summer0.737−0.7200.0380.8130.701−0.6570.0460.823
Autumn0.589−1.1230.0950.6940.584−0.9430.1040.713
Winter0.164−1.4640.1200.5800.203−1.2970.1250.616
IIIAnnual0.483−0.9540.0520.7630.487−0.8380.0600.777
Growing season0.640−0.7580.0330.8200.626−0.6440.0450.823
Spring0.482−0.9300.0290.7780.491−0.7360.0460.785
Summer0.707−0.6190.0250.8490.676−0.5810.0330.854
Autumn0.568−0.9790.0650.7630.570−0.8460.0740.773
Winter0.176−1.2880.0900.6600.211−1.1850.0880.696
Note: The bold font represents the most sensitive factor.
Table 4. Contributions of meteorological factors to ET0 trends during 1961–1990 and 1991–2019.
Table 4. Contributions of meteorological factors to ET0 trends during 1961–1990 and 1991–2019.
RegionSeason1961–19901991–2019
C(Ta)C(RH)C(u2)C(Rn)C(Ta)C(RH)C(u2)C(Rn)
WholeAnnual−0.746−0.772−0.842−1.2230.9930.499−0.459−0.267
Growing season−0.317−0.533−0.470−1.0630.6120.304−0.274−0.225
Spring−0.088−0.049−0.221−0.0850.4860.309−0.1600.146
Summer−0.368−0.424−0.150−1.1580.2650.159−0.094−0.023
Autumn−0.041−0.094−0.268−0.1360.201−0.133−0.105−0.300
Winter−0.047−0.077−0.196−0.1060.047−0.018−0.061−0.095
IAnnual−0.959−1.560−1.277−1.2040.9100.454−0.785−0.181
Growing season−0.438−1.101−0.738−1.1180.4830.122−0.490−0.068
Spring−0.115−0.311−0.355−0.1190.4550.111−0.2720.087
Summer−0.471−0.693−0.257−1.2900.2150.140−0.1800.062
Autumn0.000−0.164−0.374−0.0620.161−0.152−0.197−0.233
Winter−0.021−0.145−0.282−0.0960.051−0.030−0.113−0.093
IIAnnual−0.713−0.528−0.781−1.3040.9800.572−0.715−0.333
Growing season−0.294−0.381−0.424−1.1100.5920.307−0.461−0.306
Spring−0.0570.050−0.207−0.1140.5230.399−0.2380.126
Summer−0.402−0.378−0.116−1.2230.2650.146−0.170−0.085
Autumn−0.044−0.048−0.268−0.1180.170−0.170−0.179−0.304
Winter−0.055−0.034−0.187−0.1170.0420.015−0.094−0.103
IIIAnnual−0.611−0.387−0.505−1.1611.0430.2120.009−0.098
Growing season−0.216−0.244−0.273−0.9810.6760.2600.052−0.095
Spring−0.0930.037−0.123−0.0520.4420.277−0.0170.260
Summer−0.227−0.240−0.093−0.9770.2640.1160.0350.094
Autumn−0.064−0.065−0.156−0.2220.260−0.1170.033−0.320
Winter−0.052−0.060−0.129−0.1020.048−0.0740.001−0.079
Note: The bold font represents the most dominant factor.
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Li, M.; Chu, R.; Sha, X.; Islam, A.R.M.T.; Jiang, Y.; Shen, S. How Has the Recent Climate Change Affected the Spatiotemporal Variation of Reference Evapotranspiration in a Climate Transitional Zone of Eastern China? ISPRS Int. J. Geo-Inf. 2022, 11, 300. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11050300

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Li M, Chu R, Sha X, Islam ARMT, Jiang Y, Shen S. How Has the Recent Climate Change Affected the Spatiotemporal Variation of Reference Evapotranspiration in a Climate Transitional Zone of Eastern China? ISPRS International Journal of Geo-Information. 2022; 11(5):300. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11050300

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Li, Meng, Ronghao Chu, Xiuzhu Sha, Abu Reza Md. Towfiqul Islam, Yuelin Jiang, and Shuanghe Shen. 2022. "How Has the Recent Climate Change Affected the Spatiotemporal Variation of Reference Evapotranspiration in a Climate Transitional Zone of Eastern China?" ISPRS International Journal of Geo-Information 11, no. 5: 300. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11050300

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