On the Pattern and Attribution of Pan Evaporation over China (1951–2021)

Hong Wang aKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Hong Wang in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-5921-2946
,
Fubao Sun aKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
bCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

Search for other papers by Fubao Sun in
Current site
Google Scholar
PubMed
Close
,
Tingting Wang aKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Tingting Wang in
Current site
Google Scholar
PubMed
Close
,
Yao Feng aKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Yao Feng in
Current site
Google Scholar
PubMed
Close
,
Fa Liu aKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Fa Liu in
Current site
Google Scholar
PubMed
Close
, and
Wenbin Liu aKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Search for other papers by Wenbin Liu in
Current site
Google Scholar
PubMed
Close
Free access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

Pan evaporation (Epan) serves as a monitorable method for estimating potential evaporation, evapotranspiration, and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, the monthly Epan was calculated over China during 1951–2021, resulting in an average R2 of 0.93 ± 0.045 and an RMSE of 21.48 ± 6.06 mm month−1. The trend of Epan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of Epan trends, the Epan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of Epan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted Epan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in Epan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in Epan. These results show the complex and dynamic nature of Epan and underscore the need for continued monitoring and in-depth analysis of its drivers.

Significance Statement

The primary objective of this study is to explore the spatiotemporal patterns and potential driving factors of pan evaporation in China based on constructing a comprehensive dataset of pan evaporation. This is important because pan evaporation is an important indicator of the water cycle, which is currently undergoing modifications and is expected to become more pronounced as the climate continues to warm. Our findings showed that the patterns of pan evaporation were characterized by its drivers. As the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to the pattern and attribution of pan evaporation.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Tingting Wang, wangtt@igsnrr.ac.cn

Abstract

Pan evaporation (Epan) serves as a monitorable method for estimating potential evaporation, evapotranspiration, and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, the monthly Epan was calculated over China during 1951–2021, resulting in an average R2 of 0.93 ± 0.045 and an RMSE of 21.48 ± 6.06 mm month−1. The trend of Epan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of Epan trends, the Epan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of Epan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted Epan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in Epan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in Epan. These results show the complex and dynamic nature of Epan and underscore the need for continued monitoring and in-depth analysis of its drivers.

Significance Statement

The primary objective of this study is to explore the spatiotemporal patterns and potential driving factors of pan evaporation in China based on constructing a comprehensive dataset of pan evaporation. This is important because pan evaporation is an important indicator of the water cycle, which is currently undergoing modifications and is expected to become more pronounced as the climate continues to warm. Our findings showed that the patterns of pan evaporation were characterized by its drivers. As the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to the pattern and attribution of pan evaporation.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Tingting Wang, wangtt@igsnrr.ac.cn

1. Introduction

Evaporation plays an important role in the exchange and cycling of energy, water, and heat in the atmosphere, and long-term measurements of its annual rates can serve as an excellent indicator of the intensity of the water cycle (Brutsaert and Parlange 1998; Huntington 2006; Roderick and Farquhar 2002). Significant progress has been made in actual evapotranspiration observation techniques, particularly with eddy covariance measurements, offering advantages of high temporal resolution and broad applicability for advancing evaporation research (Cunliffe et al. 2022; Xue et al. 2023). However, practical adoption may be hindered by factors such as high cost, complex equipment requirements, and dependence on reference flux data. For instance, the ChinaFLUX observation network (http://www.chinaflux.org/enn/index.aspx), with 83 towers, faces challenges in providing long-term and large-scale datasets due to its relatively short construction period. Observing evapotranspiration presents challenges, and the commonly used estimation methods such as Penman’s hypothesis (Allen et al. 1998; Penman 1948), complementary theory (Brutsaert and Parlange 1998), and the coupled water–energy balance (Budyko 1974; Fu 1981; Yang et al. 2006) rely on potential evapotranspiration as a crucial parameter. Pan evaporation (Epan) is widely employed to estimate potential evapotranspiration, wherein the observed evaporation rate is multiplied by an empirically derived Epan coefficient (Abtew et al. 2011; Lugato et al. 2013; McMahon et al. 2013). Meanwhile, notwithstanding the difference between Epan and the evapotranspiration of cropped surfaces, the pan has proved its practical value and has been used successfully to estimate reference crop evapotranspiration (ETo) by observing the evaporation loss from a water surface and applying an empirical pan coefficient to relate Epan to ETo, which is one of the methods recommended by the Food and Agriculture Organization (FAO) (Allen et al. 1998). Moreover, it is essential to acknowledge that the pan coefficient is influenced by local climate and physical conditions (Chiew et al. 1995; Sabziparvar et al. 2010). Observation networks of Epan have been established globally at meteorological stations since the 1950s, owing to the pan’s simplicity, low cost, and wide variety of applications (Lim et al. 2016, 2013; Wang and Dickinson 2012). Earth’s water cycle is currently undergoing modifications, and it is anticipated that these changes will become more pronounced as the climate continues to warm (Elbaum et al. 2022).

Pans provide a measurement of the integrated effect of radiation, wind, temperature, and humidity on the evaporation from an open water surface. As the average global temperature increases, it is generally expected that the air will become drier and that evaporation from terrestrial water bodies will increase (Limjirakan and Limsakul 2012; Roderick and Farquhar 2002). Paradoxically, decreases in Epan have been observed in the past few decades across the world with different climates between the 1950s and 1990s, with a decreasing rate from −1 to −4 mm yr−2 (Brutsaert and Parlange 1998; Limjirakan and Limsakul 2012; Matsoukas et al. 2011; Peterson et al. 1995; Roderick et al. 2009, 2007; Stephens et al. 2018; Wang and Dickinson 2012). Several key physical drivers of evaporation include temperature, radiation, vapor pressure deficit (VPD), and wind speed (Penman 1948; Roderick et al. 2007; Stephens et al. 2018). Initially, Peterson et al. (1995) proposed that the decline in Epan was a result of reduced solar radiation due to an increase in aerosol concentration and cloud cover. A complementary theory was presented by Brutsaert and Parlange (1998), highlighting that the decline in Epan could be a sign of increased regional moisture availability, resulting in decreased sensible heat and energy available for Epan. However, further studies by Roderick et al. (2007) suggested that the primary cause of Epan decreases was due to reductions in wind speed, which may not always remain the dominant driver of Epan changes. While Epan had been decreasing for several decades, recent studies have shown that these trends have either plateaued or reversed in some regions, which has been linked to an increase in VPD (Li et al. 2013; Stephens et al. 2018). VPD describes the difference between the water vapor pressure at saturation and the actual water vapor pressure for a given temperature. The increasing trend of global temperatures in recent decades has caused a significant increase in land surface VPD, resulting in an increased atmospheric demand for evaporation water (Grossiord et al. 2020; Massmann et al. 2019; Monteith 1965; Penman 1948; Yuan et al. 2019).

The D20 pans were the primary choice for evaporation observations from the 1950s until 2001 in China. Subsequently, the E-601B pans have replaced the D20 pans in most stations. The Epan observed by E601 and D20 has a good linear relationship, which is the most direct method for reconstructing the evaporation data. However, there were few stations that observed Epan of E601 and D20 simultaneously. Moreover, the E-601B pans are unsuitable for observation when evaporation rates are low or when water freezes during winter, except for those located in northern regions. Utilization of different types of evaporation pans has resulted in a lack of continuous observations in recent years, making it necessary to explore effective reconstruction techniques to supplement Epan observations (Wang et al. 2019). To estimate atmospheric evaporation demand, several physical models have been developed, including the Penman–Monteith model, which is recommended by the Food and Agriculture Organization (Allen et al. 1998), as well as the Priestley–Taylor (Kingston et al. 2009; Priestley and Taylor 1972) and Hargreaves–Samani (Thompson et al. 2014) models. With the advancement of computing technology, machine learning methods, such as random forest (Lu et al. 2018), artificial neural networks (Goyal et al. 2014; Keskin et al. 2009; Kim et al. 2012), multivariate adaptive regression splines (Ghaemi et al. 2019), and support vector machines (Kisi 2015), have demonstrated the potential in reconstructing Epan (Guan et al. 2020; Kisi and Heddam 2019; Qasem et al. 2019; Seifi and Soroush 2020). However, it is important to consider their limitations, particularly in scenarios where multiple complex factors influence evaporation and their poor interpretability or “black box” phenomenon makes attribution challenging (Coyle and Weller 2020; Yu et al. 2021). Therefore, a physically based tool, called PenPan, for reconstructing the monthly Epan was developed (Roderick et al. 2007; Rotstayn et al. 2006). The model is grounded on mass and energy balances and is based on the Penman equation for potential evapotranspiration (Penman 1948). It incorporates the aerodynamic and radiative components based on Thom et al. (1981) and Linacre (1994), respectively. The PenPan model has undergone extensive validation and has been adeptly adapted for use with the D20 evaporation pan in China (Feng et al. 2018; Liu and Sun 2016; Wang et al. 2018c). Additionally, the model’s efficacy has also been demonstrated in its ability to perform attribution analysis.

Based on the PenPan model, this study is to construct an Epan dataset for China, to capture its temporal and spatial variations, and to examine the underlying factors that drive these variations. In section 2, we provide a description of the study’s data and methods. In section 3, we simulate Epan and analyze its spatiotemporal pattern and attribution of variations. Last, the conclusions are drawn.

2. Data and methods

Figure 1a displays the spatial locations of meteorological stations used in this study. The daily surface data for 1951–2020 were collected from 2410 meteorological stations available at the China Meteorological Data Service Center (CMDC) (http://data.cma.cn/). The meteorological parameters considered in this study included atmospheric pressure, sunshine hours, relative humidity, air temperature measured 2 m above ground level, wind speed measured at a height of 10 m above ground level, and Epan. The Epan was measured using a D20 pan with a diameter of 20 cm and a height of 10 cm (Fig. 1b). All collected data underwent strict quality control measures, including data integrity check, climate consistency check, internal consistency check, time consistency check, space consistency check, multisource data consistency check, and human–computer interaction check, among others (http://data.cma.cn/). After excluding stations that were impacted by site relocation, resulting in changes to wind speed and other meteorological parameters, a total of 1786 stations were identified for inclusion in the study. To minimize the statistical deviation due to nonuniform station distribution, we defined grid cells with a spatial resolution of 1° × 1° and identified effective grid cells that contained at least one station. A total of 641 effective grids were selected. For a grid containing more than one observation station, the average was used for calculation and analysis.

Fig. 1.
Fig. 1.

(a) The locations of meteorological stations and selected grids, containing at least one station each (spatial resolution of 1° × 1°), and (b) the D20 pan.

Citation: Journal of Hydrometeorology 24, 11; 10.1175/JHM-D-23-0066.1

The PenPan model is a modified version of the Penman equation originally proposed in 1948 for estimating potential evapotranspiration (Penman 1948). The model incorporates both aerodynamic and radiative components, which are based on the works of Thom et al. (1981) and Linacre (1994), respectively, and are modified for Class A evaporation pans (Roderick et al. 2007; Rotstayn et al. 2006). The PenPan model has a physical mechanism and its driving factors are meteorological data with long-term measurements. The model has also been successfully adapted for use with the D20 evaporation pan in China, as demonstrated by studies conducted by Li et al. (2013), Liu and Sun (2016), and Sun et al. (2018). Specifically, the model is described in detail in the following sections:
Epan=Epr+Epa=sS+aγRnλ+aγS+aγfq(u)D,
where Epan is the pan evaporation (mm month−1); Epr (mm month−1) and Epa (mm month−1) are the radiative and aerodynamic components of Epan, respectively; S (Pa K−1) is the slope of the saturation vapor pressure es (Pa) at air temperature Ta (°C + 273.15 = K); a is the ratio of effective surface areas for heat and vapor transfer (≈4.2 for D20); γ is the psychrometric constant (≈67 Pa K−1); λ is the latent heat of vaporization (≈2.45 MJ kg−1); D is the vapor pressure deficit (VPD; Pa) at 2 m above the ground; and fq(u) is an empirical vapor transfer function (mm s−1 Pa−1) (Thom et al. 1981). The term Rn is the net irradiance of the pan (MJ m−2 month−1) and is calculated as follows:
Rn=(1Ap)Rsp+RlinRlout,
where Ap is the pan albedo (=0.14) and Rsp is the incoming shortwave irradiance of the pan (MJ m−2 month−1), and can be estimated as
Rsp=[Pradfdir+2(1fdir)+α]Rs,
where Prad is the pan radiation factor for the extra direct irradiance intercepted by the walls of the pan and α is the albedo of short grass at the meteorological stations (≈0.23). The term fdir is the fraction of the global solar radiation Rs (MJ m−2 month−1), estimated by empirical functions based on measured Rs and diffuse radiation Rd (MJ m−2 month−1) of meteorological stations in China (Wang et al. 2018b). For the northern region (about latitude ≤ 35°N and longitude ≥ 93°E),
fdir=B0+B1×RsRa,
and for the southern region,
fdir=C0+C1×RsRs,
where
B0=1.02,B1=1.13,C0=0.20,andC1=0.40,
where B0, B1, C0, and C1 are the parameters for the model and Ra (MJ m−2 month−1) is the extraterrestrial solar radiance estimated using the approach of FAO56 (Allen et al. 1998) as follows:
Ra=24(60)πGscdr[ωssin(φ)sin(δ)+cos(φ)cos(δ)sin(ωs)]×Nday,
where Gsc is the solar constant (=0.082 MJ m−2 min−1), dr is the inverse relative distance between Earth and the Sun, ωs is the angle at sunset hours, φ is the latitude (rad), δ is the solar declination (rad), and Nday is the number of days of the month. Rlin (MJ m−2 month−1) and Rlout (MJ m−2 month−1) are the incoming and outgoing longwave irradiances calculated using the FAO56 approach (Allen et al. 1998). The PenPan model’s performance was evaluated using the coefficient of determination (R2) and root-mean-square error (RMSE).
The Mann–Kendall (MK) test method was utilized to assess the statistical significance of the annual Epan increasing or decreasing trend, with a confidence level set at 90%. Furthermore, in the spatial grid trend analysis, we addressed the issue of multiple testing by employing the false detection rate (FDR) procedure proposed by Wilks (2016). This procedure effectively controls the FDR, mitigating the occurrence of spurious features and reducing overstatements in the results (Delgado-Torres et al. 2023; Grigorev et al. 2023; Tochimoto and Yanase 2023). The algorithm processes the collection of p values from N local hypothesis tests pi, with i = 1, …, N, which are subsequently sorted in ascending order. These sorted p values are denoted using parenthetical subscripts, such that p(1)p(2)p(N). The local null hypotheses are rejected if their respective p values do not exceed a threshold level pFDR* that depends on the distribution of the sorted p values (Wilks 2016):
pFDR*=maxi=1,,N[p(i):p(i)(iN)αFDR],
where αFDR is the chosen control level for the FDR, with αFDR = 0.1 in this study. The threshold pFDR* for rejecting local null hypotheses is the largest p(i) that is no larger than the fraction of αFDR specified by i/N. Traditional statistical analyses were performed using SPSS and MATLAB. SigmaPlot and ArcGIS were used to perform geostatistical analyses and produce figures.

3. Results and discussion

a. Validation of the monthly pan evaporation simulation based on the PenPan model

Based on the PenPan model, the monthly Epan in China was calculated and the scatter diagram between the calculated and observed Epan is shown in Fig. 2a. The diagram shows a close agreement between the observed and calculated values, with the majority of points lying near the 1:1 line. A linear relationship was observed between the calculated and observed Epan, with y = 0.98x + 7.70, R2 = 0.93 ± 0.045, and RMSE = 21.48 ± 6.06 mm month−1. The spatial distribution of R2 is shown in Fig. 2b, which indicates that 98.28% of grids have R2 values greater than 0.80, whereas 81.41% of grids have R2 values greater than 0.90. The grids with small R2 values were mainly found in the southwest region of China, with the smallest R2 value being no less than 0.66. The spatial distribution of RMSE is shown in Fig. 2c, which shows that the grids with RMSE greater than 30 mm month−1 were primarily located in the northwestern region, where the annual Epan is approximately 2000 mm yr−1 (Fig. 2d) and RMSE accounted for 1.78% on average.

Fig. 2.
Fig. 2.

The statistical comparison between the observed and calculated monthly pan evaporation (Epan) over China. (a) The scatter density plot between the observed values and the values calculated by the PenPan model during 1951–2021. The legends are the counts, with the red color representing more and the blue color representing less. The (b) R2 and (c) RMSE values between the simulation and observation of Epan during 1951–2021. (d) The annual average Epan during 1961–2021.

Citation: Journal of Hydrometeorology 24, 11; 10.1175/JHM-D-23-0066.1

The results exhibited performance on par with the Epan prediction reported by Wang et al. (2018c) and Li et al. (2013), with R2 and RMSE values of 0.92 and 26.03 mm month−1 over China and 0.94 and 31 mm month−1 in Xinjiang, respectively. Despite the slightly lower accuracy compared to some machine learning methods, such as the random forest model used in Al-Mukhtar’s (2021) study in Iraq (R2 = 0.99; RMSE = 19.88 mm month−1) and Lu et al.’s (2018) study in Poyang Lake (R2 = 0.96; RMSE = 0.35 mm day−1), these results still provide valuable information for attribution analysis. Therefore, the PenPan model can be considered accurate in predicting the monthly Epan values for the entire country based on meteorological factors such as wind speed, atmospheric pressure, relative humidity, air temperature, and sunshine hours.

b. The spatiotemporal patterns of pan evaporation in the recent 70 years

Figure 3 shows the significance of the annual Epan change. The figure displays red grids for a significant decrease, yellow grids for a significant increase, blue grids for no statistically significant changes, and blank grids for incomplete data. Considering the progressive initiation of Epan observations in 1951 and the gradual expansion of site coverage until 1961, we have chosen data spanning each year from 1951 to 1961 through 2021 for trend and attribution analysis to assess result robustness. Extending the study period back to 1951 enables the capture of a broader range of climate variability and cyclic changes, as well as their impacts on evaporation. Figures 3a–i show the spatial distribution of Epan change trends with complete years of data from 1951 to 2021, from 1952 to 2021, …, and from 1961 to 2021, respectively. It can be seen that due to the different start time of station monitoring, the grids with different years have different distributions. The grids with effective data from 1951 to 2021 are the least, whereas those with data from 1961 to 2021 are the most. While the number of spatial grids with available data varies across the different time periods, the results consistently show a significant decrease in Epan across most regions, with a smaller proportion of grids showing a significant increase and a relatively constant proportion showing no significant change over time. On average, 46.85% of the grids showed no significant changed trend, 42.59% showed a significant decrease, and 10.56% showed a significant increase. From 1951 to 2021, there were 31 effective grids, and among them, 70.97% showed no significant change trend, 25.81% showed a significant decrease, and 3.23% showed a significant increase (Fig. 3a). The number of effective grids increased to 190 during 1954–2021, among which 46.32% showed no significant change trend, 41.05% showed a significant decrease, and 12.63% showed a significant increase (Fig. 3d). During 1961–2021, the number of effective grids increased to 580, of which 51.03% showed no significant change trend, 37.76% showed a significant decrease trend, and 11.21% showed a significant increase (Fig. 3i). The significance of the average annual Epan change and its average linear change rate during different periods are shown in Table S1 in the online supplemental material.

Fig. 3.
Fig. 3.

The significance of the annual Epan change. Significance is for the 90% confidence level and controlling the FDR with αFDR = 0.1. The red grids represent a significant decrease, the yellow grids represent a significant increase, the blue grids represent no statistically significant changes, and the blank grids represent incomplete data. (a) 1951–2021; (b) 1952–2021; (c) 1953–2021; (d) 1954–2021; (e) 1955–2021; (f) 1956–2021; (g) 1958–2021; (h) 1960–2021; (i) 1961–2021.

Citation: Journal of Hydrometeorology 24, 11; 10.1175/JHM-D-23-0066.1

Although Epan has shown a decreasing trend or no significant change over the past 6–7 decades, there have been shifts in the annual average Epan trends around 1961 and 1993. Figure 4 shows the average linear change rate of Epan and its aerodynamic component (Epa) and radiative component (Epr) during 1961–93, 1994–2021, and 1961–2021. The red dot represents a decreasing trend and the blue dot represents an increasing trend, with larger dots indicating greater change rates. It can be seen that most grids showed a decreasing trend during 1961–2021 (Fig. 4a). During 1961–93, most grids (83.79%) showed a decreasing trend (Fig. 4b), while during 1994–2021, most grids (62.59%) showed an increasing trend (Fig. 4c). Similarly, from 1954 to 2021, most grids showed a decreasing trend (Fig. S1a). Although most grids (78.95% showed a decreasing trend from 1954 to 1993 (Fig. S1b), from 1994 to 2021 most grids (58.42% showed an increasing trend (Fig. S1c). The Epan also showed similar trends during different periods, such as 1951–2021, 1952–2021, …, and 1960–2021. Hence, it can be seen that most grids showed a significant decreasing trend before 1993 but began to show an increasing trend after 1994, which has slowed down the Epan trend in the past 60–70 years. Moreover, Fig. 4d shows the linear change rate of Epa from 1961 to 2021, indicating that most grids showed a decreasing trend. Similar to the Epan trend, Epr decreased in most grids (76.03%) during 1961–93 (Fig. 4e), while an increasing trend was observed in most grids (62.59%) during 1994–2021 (Fig. 4f). Furthermore, Fig. 4g shows the linear change rate of Epr during 1961–2021. It can be seen that Epr also showed a decreasing trend in most grids during 1961–93 and an increasing trend during 1994–2021. The Epr exhibits stronger regionalism, which can be attributed to the higher solar radiation in northwest China than in southeast China, and the solar radiation began to reverse from decline to rise around the 1990s (Wang et al. 2018a).

Fig. 4.
Fig. 4.

The average linear change rate of Epan and its aerodynamic component (Epa) and radiative component (Epr) during 1961–2021, 1961–93, and 1994–2021. The red dot represents a decreasing trend and the blue dot represents an increasing trend, with larger dots indicating greater change rates. (a)–(c) Epan during 1961–2021, 1961–93, and 1994–2021; (d)–(f) Epa during 1961–2021, 1961–93, and 1994–2021; (g)–(i) Epr during 1961–2021, 1961–93, and 1994–2021.

Citation: Journal of Hydrometeorology 24, 11; 10.1175/JHM-D-23-0066.1

The annual average Epan showed a decreasing trend (0.1 level) over the past 61–71 years, with a decreasing rate ranging from 1.02 to 1.29 mm yr−2 and an average of 1.13 mm yr−2 (Fig. 5a). In the period before 1993 (1951–93, 1952–93, …, and 1961–93), Epan also showed a statistically significant decreasing trend (0.1 level), with the decrease rate first increasing and then stabilizing as the number of years increases. Specifically, the decrease rate was 1.95 mm yr−2 during 1951–93 and gradually stabilized to about 5.00 mm yr−2 during 1954–93. This was mainly because Epan showed an increasing trend before 1961, primarily due to the offsetting of increasing and decreasing trends before and after 1961. Thus, 1961 and 1993 can be used as subsection points to divide annual Epan into three periods, namely, 1951–2021, 1954–2021, and 1961–2021. Epan showed an increasing trend during 1951–61 and 1954–61, with an increasing rate of 15.28 and 3.83 mm yr−2, respectively, while during 1961–93, Epan showed a significant decreasing trend with slightly different rates due to different effective regions in different periods (Fig. 5b). From 1994 to 2021, Epan showed an increasing trend in 11 periods (1951–2021, 1952–2021, …, and 1961–2021), with an increasing rate ranging from 0.05 to 1.63 mm yr−2 and an average of 1.11 mm yr−2 (Fig. 5b). The Epa also showed a significant (0.1 level) decreasing trend during 1951–2021 (1951–2021, 1952–2021, …, 1961–2021), with the period before 1993 showing a significant decrease and the period 1994–2021 showing a significant increase or no statistically significant change (Fig. 5c). Epr showed no statistically significant changes in the recent 61–71 years, showing significant decreasing trends before 1993 with a small decreasing rate and increasing trends from 1994 to 2021 without any statistically significant changes (Fig. 5d). Overall, the change trend of Epa is more significant and has a decisive effect on the change trend of Epan. The average reduction rate of Epan was 1.08 mm yr−2, with the contribution of Epa reduction accounting for 82.75%, followed by Epr. Further analysis was conducted to determine the contribution of the main influencing factors of Epa.

Fig. 5.
Fig. 5.

The annual average Epan and its aerodynamic component (Epa) and radiative component (Epr) over the past 61–71 years. The blue line represents the period 1951–2021, the black line represents the period 1954–2021, the red line represents the period 1961–2021, and the gray lines with different shades represent other periods.

Citation: Journal of Hydrometeorology 24, 11; 10.1175/JHM-D-23-0066.1

c. Attribution recognition of the pan evaporation variations

The results indicated that the average rate of Epan decreased from 1961 to 2021 at a rate of −1.06 mm yr−2. It is noteworthy that between 1961 and 1993, the average rate of Epan decrease was −4.59 mm yr−2, with 65.40% of the contribution coming from Epa and 34.60% from Epr. However, from 1994 to 2021, Epan showed a reversal and increased at an average rate of 1.63 mm yr−2, with Epa contributing 75.58% and Epr contributing 24.42%. The study further decomposed the contributions of wind speed, VPD, and temperature within the Epa during the entire period (1961–2021) as well as the subperiods of 1961–93 and 1994–2021, as depicted in Fig. 6. The red dots represent negative contributions, the blue dots represent positive contributions, and the size of the dots denotes the magnitude of the contribution.

Fig. 6.
Fig. 6.

The contributions of wind speed, vapor pressure deficit (VPD), and air temperature during 1961–2021 as well as the subperiods of 1961–93 and 1994–2021. The red dot represents a decreasing trend and the blue dot represents an increasing trend, with larger dots indicating greater contributions. (a)–(c) The contributions of wind speed (U*) during 1961–2021, 1961–93, and 1994–2021. (d)–(f) The contributions of VPD (D*) during 1961–2021, 1961–93, and 1994–2021. (g)–(i) The contributions of air temperature (T*) during 1961–2021, 1961–93, and 1994–2021.

Citation: Journal of Hydrometeorology 24, 11; 10.1175/JHM-D-23-0066.1

Figure 6a shows the contribution of wind speed in different regions during 1961–2021. The results indicated that wind speed predominantly has a negative impact on Epan, with an average contribution of −2.14 mm yr−2. Furthermore, the reduction in wind speed contribution in the north was more substantial than that in the south. Most grids made negative contributions during 1961–93, with an average value of −2.68 mm yr−2 (Fig. 6b). However, from 1994 to 2021, the average contribution of wind speed was −0.20 mm yr−2, and many grids changed from negative to positive contributions (Fig. 6c). These findings suggest that the influence of wind speed on evaporation is weakening over time, mainly due to the continuous decrease in wind speed during 1961–93, but its stagnation and reversal upward during 1994–2021 (Fig. 7d). Figure 6d shows the contribution of VPD in different regions during 1961–2021. The results indicated that VPD makes a positive contribution to Epan in most grids, with an average value of 1.32 mm yr−2, and the contribution is greater in northern and coastal areas. During 1961–93, VPD had a negative contribution in some areas and a positive contribution in others (Fig. 6e), with an average value of −0.35 mm yr−2. However, from 1994 to 2021, most regions had positive contributions (Fig. 6f), with an average value of 1.61 mm yr−2. The contribution of VPD increased over time, primarily due to the slight decrease in VPD during 1961–93, but a significant increase during 1994–2021 (Fig. 7e). Figures 6g–i show the spatial distribution of temperature contribution to Epan change during 1961–2021, 1961–93, and 1994–2021, respectively. The temperature had a negative contribution to Epan during all three periods, with an average contribution from −0.08 to −0.04 mm yr−2.

Fig. 7.
Fig. 7.

The annual Epan and its driving factors wind speed and VPD. The blue line represents the Epan, the black line represents the wind speed, and the red line represents the VPD. (a)–(c) 1951–2021; (d)–(f) 1961–2021. (top) The Epan vs wind speed; (middle) Epan vs VPD; (bottom) wind speed vs VPD.

Citation: Journal of Hydrometeorology 24, 11; 10.1175/JHM-D-23-0066.1

Wind speed, VPD, and temperature in the other 10 periods (1951–2021, 1952–2021, …, and 1960–2021) showed a similar positive and negative contribution trend to Epan change as the period 1961–2021, with slightly different contribution values. For instance, Fig. S2 illustrates the contribution values for the period 1954–2021. The average annual reduction rate of Epan was 1.08 mm yr−2, with an average reduction rate of Epa of 0.89 mm yr−2 in recent 61–71 years of 11 periods. The average contributions of wind speed, VPD, and temperature to Epan reduction were −2.11, 1.32, and −0.08 mm yr−2, respectively. Before 1993, the average annual reduction rate of Epan was 3.48 mm yr−2, with an average reduction rate of Epa of 2.16 mm yr−2. Wind speed and VPD had contributions to the reduction of Epan of −2.21 and −0.01 mm yr−2, respectively. However, the period before 1993 was further divided into two periods: before 1961 and after 1961. Before 1961, both wind speed and VPD made positive contributions to Epan (Figs. 7a,b). During 1961–93, the average reduction rate of Epan was 5.11 mm yr−2, with an average reduction rate of Epa of 3.24 mm yr−2. Wind speed and VPD had an average contribution to the reduction of Epan of −2.88 and −0.37 mm yr−2, respectively. From 1994 to 2021, Epan began to rise with an average increase rate of 1.07 mm yr−2, and the average increase rate of Epa was 0.69 mm yr−2. The average contributions of wind speed and VPD were −0.67 and 1.50 mm yr−2, respectively. It can be seen that before 1961 the increase in wind speed and VPD promoted evaporation. During 1961–93, a slight decrease in VPD, coupled with a significant decrease in wind speed, led to a reduction in evaporation. During 1994–2021, the stagnation of wind speed, coupled with an increase in VPD, promoted an increase in evaporation (Fig. 7).

4. Conclusions

There has been a decreasing trend observed in Epan over the past 71 years. The first period (1951–60) exhibited an increasing trend, whereas the second period (1961–93) displayed a decreasing trend. Notably, during the third period (1994–2021), there was an increase in the trend. The trend of decreasing Epan is more prominent in the spatial distribution of the data than the trend of increasing Epan. The regions with a significant decreasing trend are mainly distributed in the northern and western regions of China, whereas the regions with a significant increasing trend are mainly distributed in the eastern and southern regions. These suggest that the changes in Epan are likely related to regional climate and environmental conditions. It is important to continue monitoring and analyzing the changes in Epan in order to better understand the impacts of climate change and human activities on water resources and ecosystems.

The radiation, wind speed, VPD, and temperature are important factors affecting Epan, and their contributions vary over different time periods. Notably, the dynamic term was found to contribute more than 60% to Epan. Further decomposition of the contributions of wind speed, VPD, and temperature was conducted within the aerodynamic component. The period before 1961 showed a positive contribution of wind speed and VPD to Epan, whereas the period from 1961 to 1993 showed a significant decrease due to a decrease in wind speed. The contribution of wind speed was consistently negative, indicating that slower winds reduce evaporation. From 1994 to 2021, the increase in VPD promoted the increase of Epan, suggesting that drier air promotes evaporation. These findings suggest that changes in climate variables can have significant impacts on evaporation, which can in turn affect water availability and ecosystem functioning. Nonetheless, there remains a considerable degree of uncertainty regarding trends in climate variables, and the available data are limited in terms of spatial and temporal coverage. To effectively manage water resources and mitigate the impacts of climate change, it is crucial to comprehend the relationships between evaporation and its drivers.

Acknowledgments.

This study was supported by the National Natural Science Foundation of China (42025104 and 42022005), the Program for the “Kezhen-Bingwei” Youth Talents (2021RC002) from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, and the Top-Notch Young Talents Program of China (Fubao Sun).

Data availability statement.

The meteorological data used in this study were obtained from meteorological stations of the China Meteorological Data Service Center (CMDC) (http://data.cma.cn/).

REFERENCES

  • Abtew, W., J. Obeysekera, and N. Iricanin, 2011: Pan evaporation and potential evapotranspiration trends in South Florida. Hydrol. Processes, 25, 958969, https://doi.org/10.1002/hyp.7887.

    • Search Google Scholar
    • Export Citation
  • Al-Mukhtar, M., 2021: Modeling the monthly pan evaporation rates using artificial intelligence methods: A case study in Iraq. Environ. Earth Sci., 80, 39, https://doi.org/10.1007/s12665-020-09337-0.

    • Search Google Scholar
    • Export Citation
  • Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp., www.fao.org/docrep/X0490E/X0490E00.htm.

  • Brutsaert, W., and M. B. Parlange, 1998: Hydrologic cycle explains the evaporation paradox. Nature, 396, 30, https://doi.org/10.1038/23845.

    • Search Google Scholar
    • Export Citation
  • Budyko, M. I., 1974: Climate and Life. Elsevier, 507 pp.

  • Chiew, F. H. S., N. N. Kamaladasa, H. M. Malano, and T. A. McMahon, 1995: Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric. Water Manage., 28, 921, https://doi.org/10.1016/0378-3774(95)01172-F.

    • Search Google Scholar
    • Export Citation
  • Coyle, D., and A. Weller, 2020: “Explaining” machine learning reveals policy challenges. Science, 368, 14331434, https://doi.org/10.1126/science.aba9647.

    • Search Google Scholar
    • Export Citation
  • Cunliffe, A. M., and Coauthors, 2022: Strong correspondence in evapotranspiration and carbon dioxide fluxes between different eddy covariance systems enables quantification of landscape heterogeneity in dryland fluxes. J. Geophys. Res. Biogeosci., 127, e2021JG006240, https://doi.org/10.1029/2021JG006240.

    • Search Google Scholar
    • Export Citation
  • Delgado-Torres, C., and Coauthors, 2023: Multi-annual predictions of the frequency and intensity of daily temperature and precipitation extremes. Environ. Res. Lett., 18, 034031, https://doi.org/10.1088/1748-9326/acbbe1.

    • Search Google Scholar
    • Export Citation
  • Elbaum, E., C. I. Garfinkel, O. Adam, E. Morin, D. Rostkier-Edelstein, and U. Dayan, 2022: Uncertainty in projected changes in precipitation minus evaporation: Dominant role of dynamic circulation changes and weak role for thermodynamic changes. Geophys. Res. Lett., 49, e2022GL097725, https://doi.org/10.1029/2022GL097725.

    • Search Google Scholar
    • Export Citation
  • Feng, Y., Y. Jia, Q. Zhang, D. Gong, and N. Cui, 2018: National-scale assessment of pan evaporation models across different climatic zones of China. J. Hydrol., 564, 314328, https://doi.org/10.1016/j.jhydrol.2018.07.013.

    • Search Google Scholar
    • Export Citation
  • Fu, B.-P., 1981: On the calculation of the evaporation from land surface. Chin. J. Atmos. Sci., 5, 2331, https://doi.org/10.3878/j.issn.1006-9895.1981.01.03.

    • Search Google Scholar
    • Export Citation
  • Ghaemi, A., M. Rezaie-Balf, J. Adamowski, O. Kisi, and J. Quilty, 2019: On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agric. For. Meteor., 278, 107647, https://doi.org/10.1016/j.agrformet.2019.107647.

    • Search Google Scholar
    • Export Citation
  • Goyal, M. K., B. Bharti, J. Quilty, J. Adamowski, and A. Pandey, 2014: Modeling of daily pan evaporation in subtropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Syst. Appl., 41, 52675276, https://doi.org/10.1016/j.eswa.2014.02.047.

    • Search Google Scholar
    • Export Citation
  • Grigorev, V. Y., M. A. Kharlamov, N. K. Semenova, A. A. Sazonov, and S. R. Chalov, 2023: Impact of precipitation and evaporation change on flood runoff over Lake Baikal catchment. Environ. Earth Sci., 82, 16, https://doi.org/10.1007/s12665-022-10679-0.

    • Search Google Scholar
    • Export Citation
  • Grossiord, C., T. N. Buckley, L. A. Cernusak, K. A. Novick, B. Poulter, R. T. W. Siegwolf, J. S. Sperry, and N. G. McDowell, 2020: Plant responses to rising vapor pressure deficit. New Phytol., 226, 15501566, https://doi.org/10.1111/nph.16485.

    • Search Google Scholar
    • Export Citation
  • Guan, Y. Q., B. Mohammadi, Q. B. Pham, S. Adarsh, K. S. Balkhair, K. U. Rahman, N. T. T. Linh, and D. Q. Tri, 2020: A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model. Theor. Appl. Climatol., 142, 349367, https://doi.org/10.1007/s00704-020-03283-4.

    • Search Google Scholar
    • Export Citation
  • Huntington, T. G., 2006: Evidence for intensification of the global water cycle: Review and synthesis. J. Hydrol., 319, 8395, https://doi.org/10.1016/j.jhydrol.2005.07.003.

    • Search Google Scholar
    • Export Citation
  • Keskin, M. E., Ö. Terzi, and D. Taylan, 2009: Estimating daily pan evaporation using adaptive neural-based fuzzy inference system. Theor. Appl. Climatol., 98, 7987, https://doi.org/10.1007/s00704-008-0092-7.

    • Search Google Scholar
    • Export Citation
  • Kim, S., J. Shiri, and O. Kisi, 2012: Pan evaporation modeling using neural computing approach for different climatic zones. Water Resour. Manage., 26, 32313249, https://doi.org/10.1007/s11269-012-0069-2.

    • Search Google Scholar
    • Export Citation
  • Kingston, D. G., M. C. Todd, R. G. Taylor, J. R. Thompson, and N. W. Arnell, 2009: Uncertainty in the estimation of potential evapotranspiration under climate change. Geophys. Res. Lett., 36, L20403, https://doi.org/10.1029/2009GL040267.

    • Search Google Scholar
    • Export Citation
  • Kisi, O., 2015: Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J. Hydrol., 528, 312320, https://doi.org/10.1016/j.jhydrol.2015.06.052.

    • Search Google Scholar
    • Export Citation
  • Kisi, O., and S. Heddam, 2019: Evaporation modelling by heuristic regression approaches using only temperature data. Hydrol. Sci. J., 64, 653672, https://doi.org/10.1080/02626667.2019.1599487.

    • Search Google Scholar
    • Export Citation
  • Li, Z., Y. Chen, Y. Shen, Y. Liu, and S. Zhang, 2013: Analysis of changing pan evaporation in the arid region of Northwest China. Water Resour. Res., 49, 22052212, https://doi.org/10.1002/wrcr.20202.

    • Search Google Scholar
    • Export Citation
  • Lim, W. H., M. L. Roderick, M. T. Hobbins, S. C. Wong, and G. D. Farquhar, 2013: The energy balance of a US Class A evaporation pan. Agric. For. Meteor., 182–183, 314331, https://doi.org/10.1016/j.agrformet.2013.07.001.

    • Search Google Scholar
    • Export Citation
  • Lim, W. H., M. L. Roderick, and G. D. Farquhar, 2016: A mathematical model of pan evaporation under steady state conditions. J. Hydrol., 540, 641658, https://doi.org/10.1016/j.jhydrol.2016.06.048.

    • Search Google Scholar
    • Export Citation
  • Limjirakan, S., and A. Limsakul, 2012: Trends in Thailand pan evaporation from 1970 to 2007. Atmos. Res., 108, 122127, https://doi.org/10.1016/j.atmosres.2012.01.010.

    • Search Google Scholar
    • Export Citation
  • Linacre, E. T., 1994: Estimating U.S. class a pan evaporation from few climate data. Water Int., 19, 514, https://doi.org/10.1080/02508069408686189.

    • Search Google Scholar
    • Export Citation
  • Liu, W., and F. Sun, 2016: Assessing estimates of evaporative demand in climate models using observed pan evaporation over China. J. Geophys. Res. Atmos., 121, 83298349, https://doi.org/10.1002/2016JD025166.

    • Search Google Scholar
    • Export Citation
  • Lu, X., Y. Ju, L. Wu, J. Fan, F. Zhang, and Z. Li, 2018: Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models. J. Hydrol., 566, 668684, https://doi.org/10.1016/j.jhydrol.2018.09.055.

    • Search Google Scholar
    • Export Citation
  • Lugato, E., G. Alberti, B. Gioli, J. O. Kaplan, A. Peressotti, and F. Miglietta, 2013: Long-term pan evaporation observations as a resource to understand the water cycle trend: Case studies from Australia. Hydrol. Sci. J., 58, 12871296, https://doi.org/10.1080/02626667.2013.813947.

    • Search Google Scholar
    • Export Citation
  • Massmann, A., P. Gentine, and C. Lin, 2019: When does vapor pressure deficit drive or reduce evapotranspiration? J. Adv. Model. Earth Syst., 11, 33053320, https://doi.org/10.1029/2019MS001790.

    • Search Google Scholar
    • Export Citation
  • Matsoukas, C., N. Benas, N. Hatzianastassiou, K. G. Pavlakis, M. Kanakidou, and I. Vardavas, 2011: Potential evaporation trends over land between 1983–2008: Driven by radiative fluxes or vapour-pressure deficit? Atmos. Chem. Phys., 11, 76017616, https://doi.org/10.5194/acp-11-7601-2011.

    • Search Google Scholar
    • Export Citation
  • McMahon, T. A., M. C. Peel, L. Lowe, R. Srikanthan, and T. R. McVicar, 2013: Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: A pragmatic synthesis. Hydrol. Earth Syst. Sci., 17, 13311363, https://doi.org/10.5194/hess-17-1331-2013.

    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., 1965: Evaporation and environment. Symp. Soc. Exp. Biol., 19, 205234.

  • Penman, H. L., 1948: Natural evaporation from open water, bare soil and grass. Proc. Roy. Soc. London, 193A, 120145, https://doi.org/10.1098/rspa.1948.0037.

    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., V. S. Golubev, and P. Y. Groisman, 1995: Evaporation losing its strength. Nature, 377, 687688, https://doi.org/10.1038/377687b0.

    • Search Google Scholar
    • Export Citation
  • Priestley, C. H. B., and R. J. Taylor, 1972: On the assessment of surface heat flux and evaporation using large scale parameters. Mon. Wea. Rev., 100, 8192, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Qasem, S. N., S. Samadianfard, S. Kheshtgar, S. Jarhan, O. Kisi, S. Shamshirband, and K.-W. Chau, 2019: Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Eng. Appl. Comput. Fluid Mech., 13, 177187, https://doi.org/10.1080/19942060.2018.1564702.

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., and G. D. Farquhar, 2002: The cause of decreased pan evaporation over the past 50 years. Science, 298, 14101411, https://doi.org/10.1126/science.1075390-a.

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., L. D. Rotstayn, G. D. Farquhar, and M. T. Hobbins, 2007: On the attribution of changing pan evaporation. Geophys. Res. Lett., 34, L17403, https://doi.org/10.1029/2007GL031166.

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., M. T. Hobbins, and G. D. Farquhar, 2009: Pan evaporation trends and the terrestrial water balance. I. Principles and observations. Geogr. Compass, 3, 746760, https://doi.org/10.1111/j.1749-8198.2008.00213.x.

    • Search Google Scholar
    • Export Citation
  • Rotstayn, L. D., M. L. Roderick, and G. D. Farquhar, 2006: A simple pan-evaporation model for analysis of climate simulations: Evaluation over Australia. Geophys. Res. Lett., 33, L17715, https://doi.org/10.1029/2006GL027114.

    • Search Google Scholar
    • Export Citation
  • Sabziparvar, A.-A., H. Tabari, A. Aeini, and M. Ghafouri, 2010: Evaluation of class a pan coefficient models for estimation of reference crop evapotranspiration in cold semi-arid and warm arid climates. Water Resour. Manage., 24, 909920, https://doi.org/10.1007/s11269-009-9478-2.

    • Search Google Scholar
    • Export Citation
  • Seifi, A., and F. Soroush, 2020: Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran. Comput. Electron. Agric., 173, 105418, https://doi.org/10.1016/j.compag.2020.105418.

    • Search Google Scholar
    • Export Citation
  • Stephens, C. M., T. R. Mcvicar, F. M. Johnson, and L. A. Marshall, 2018: Revisiting pan evaporation trends in Australia a decade on. Geophys. Res. Lett., 45, 11 16411 172, https://doi.org/10.1029/2018GL079332.

    • Search Google Scholar
    • Export Citation
  • Sun, Z., Z. Ouyang, J. Zhao, S. Li, X. Zhang, and W. Ren, 2018: Recent rebound in observational large-pan evaporation driven by heat wave and droughts by the Lower Yellow River. J. Hydrol., 565, 237247, https://doi.org/10.1016/j.jhydrol.2018.08.014.

    • Search Google Scholar
    • Export Citation
  • Thom, A. S., J.-L. Thony, and M. Vauclin, 1981: On the proper employment of evaporation pans and atmometers in estimating potential transpiration. Quart. J. Roy. Meteor. Soc., 107, 711736, https://doi.org/10.1002/qj.49710745316.

    • Search Google Scholar
    • Export Citation
  • Thompson, J. R., A. J. Green, and D. G. Kingston, 2014: Potential evapotranspiration-related uncertainty in climate change impacts on river flow: An assessment for the Mekong River basin. J. Hydrol., 510, 259279, https://doi.org/10.1016/j.jhydrol.2013.12.010.

    • Search Google Scholar
    • Export Citation
  • Tochimoto, E., and W. Yanase, 2023: Structural and environmental characteristics of western Baiu frontal depressions. J. Climate, 36, 23492365, https://doi.org/10.1175/JCLI-D-22-0515.1.

    • Search Google Scholar
    • Export Citation
  • Wang, H., F. Sun, and W. Liu, 2018a: Spatial and temporal patterns as well as major influencing factors of global and diffuse horizontal irradiance over China: 1960–2014. Sol. Energy, 159, 601615, https://doi.org/10.1016/j.solener.2017.11.038.

    • Search Google Scholar
    • Export Citation
  • Wang, H., F. Sun, T. Wang, and W. Liu, 2018b: Estimation of daily and monthly diffuse radiation from measurements of global solar radiation a case study across China. Renewable Energy, 126, 226241, https://doi.org/10.1016/j.renene.2018.03.029.

    • Search Google Scholar
    • Export Citation
  • Wang, K., and R. E. Dickinson, 2012: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys., 50, RG2005, https://doi.org/10.1029/2011RG000373.

    • Search Google Scholar
    • Export Citation
  • Wang, K., X. Liu, Y. Li, X. Yang, P. Bai, C. Liu, and F. Chen, 2019: Deriving a long-term pan evaporation reanalysis dataset for two Chinese pan types. J. Hydrol., 579, 124162, https://doi.org/10.1016/j.jhydrol.2019.124162.

    • Search Google Scholar
    • Export Citation
  • Wang, T., F. Sun, J. Xia, W. Liu, Y. Sang, and H. Wang, 2018c: An experimental detrending approach to attributing change of pan evaporation in comparison with the traditional partial differential method. J. Hydrol., 564, 501508, https://doi.org/10.1016/j.jhydrol.2018.07.021.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2016: “The stippling shows statistically significant grid points”: How research results are routinely overstated and overinterpreted, and what to do about it. Bull. Amer. Meteor. Soc., 97, 22632273, https://doi.org/10.1175/BAMS-D-15-00267.1.

    • Search Google Scholar
    • Export Citation
  • Xue, K., and Coauthors, 2023: Estimating ecosystem evaporation and transpiration using a soil moisture coupled two-source energy balance model across FLUXNET sites. Agric. For. Meteor., 337, 109513, https://doi.org/10.1016/j.agrformet.2023.109513.

    • Search Google Scholar
    • Export Citation
  • Yang, D., F. Sun, Z. Liu, Z. Cong, and Z. Lei, 2006: Interpreting the complementary relationship in non-humid environments based on the Budyko and Penman hypotheses. Geophys. Res. Lett., 33, L18402, https://doi.org/10.1029/2006GL027657.

    • Search Google Scholar
    • Export Citation
  • Yu, F., C. Wei, P. Deng, T. Peng, and X. Hu, 2021: Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles. Sci. Adv., 7, eabf4130, https://doi.org/10.1126/sciadv.abf4130.

    • Search Google Scholar
    • Export Citation
  • Yuan, W. P., and Coauthors, 2019: Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv., 5, eaax1396, https://doi.org/10.1126/sciadv.aax1396.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • Abtew, W., J. Obeysekera, and N. Iricanin, 2011: Pan evaporation and potential evapotranspiration trends in South Florida. Hydrol. Processes, 25, 958969, https://doi.org/10.1002/hyp.7887.

    • Search Google Scholar
    • Export Citation
  • Al-Mukhtar, M., 2021: Modeling the monthly pan evaporation rates using artificial intelligence methods: A case study in Iraq. Environ. Earth Sci., 80, 39, https://doi.org/10.1007/s12665-020-09337-0.

    • Search Google Scholar
    • Export Citation
  • Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp., www.fao.org/docrep/X0490E/X0490E00.htm.

  • Brutsaert, W., and M. B. Parlange, 1998: Hydrologic cycle explains the evaporation paradox. Nature, 396, 30, https://doi.org/10.1038/23845.

    • Search Google Scholar
    • Export Citation
  • Budyko, M. I., 1974: Climate and Life. Elsevier, 507 pp.

  • Chiew, F. H. S., N. N. Kamaladasa, H. M. Malano, and T. A. McMahon, 1995: Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric. Water Manage., 28, 921, https://doi.org/10.1016/0378-3774(95)01172-F.

    • Search Google Scholar
    • Export Citation
  • Coyle, D., and A. Weller, 2020: “Explaining” machine learning reveals policy challenges. Science, 368, 14331434, https://doi.org/10.1126/science.aba9647.

    • Search Google Scholar
    • Export Citation
  • Cunliffe, A. M., and Coauthors, 2022: Strong correspondence in evapotranspiration and carbon dioxide fluxes between different eddy covariance systems enables quantification of landscape heterogeneity in dryland fluxes. J. Geophys. Res. Biogeosci., 127, e2021JG006240, https://doi.org/10.1029/2021JG006240.

    • Search Google Scholar
    • Export Citation
  • Delgado-Torres, C., and Coauthors, 2023: Multi-annual predictions of the frequency and intensity of daily temperature and precipitation extremes. Environ. Res. Lett., 18, 034031, https://doi.org/10.1088/1748-9326/acbbe1.

    • Search Google Scholar
    • Export Citation
  • Elbaum, E., C. I. Garfinkel, O. Adam, E. Morin, D. Rostkier-Edelstein, and U. Dayan, 2022: Uncertainty in projected changes in precipitation minus evaporation: Dominant role of dynamic circulation changes and weak role for thermodynamic changes. Geophys. Res. Lett., 49, e2022GL097725, https://doi.org/10.1029/2022GL097725.

    • Search Google Scholar
    • Export Citation
  • Feng, Y., Y. Jia, Q. Zhang, D. Gong, and N. Cui, 2018: National-scale assessment of pan evaporation models across different climatic zones of China. J. Hydrol., 564, 314328, https://doi.org/10.1016/j.jhydrol.2018.07.013.

    • Search Google Scholar
    • Export Citation
  • Fu, B.-P., 1981: On the calculation of the evaporation from land surface. Chin. J. Atmos. Sci., 5, 2331, https://doi.org/10.3878/j.issn.1006-9895.1981.01.03.

    • Search Google Scholar
    • Export Citation
  • Ghaemi, A., M. Rezaie-Balf, J. Adamowski, O. Kisi, and J. Quilty, 2019: On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agric. For. Meteor., 278, 107647, https://doi.org/10.1016/j.agrformet.2019.107647.

    • Search Google Scholar
    • Export Citation
  • Goyal, M. K., B. Bharti, J. Quilty, J. Adamowski, and A. Pandey, 2014: Modeling of daily pan evaporation in subtropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Syst. Appl., 41, 52675276, https://doi.org/10.1016/j.eswa.2014.02.047.

    • Search Google Scholar
    • Export Citation
  • Grigorev, V. Y., M. A. Kharlamov, N. K. Semenova, A. A. Sazonov, and S. R. Chalov, 2023: Impact of precipitation and evaporation change on flood runoff over Lake Baikal catchment. Environ. Earth Sci., 82, 16, https://doi.org/10.1007/s12665-022-10679-0.

    • Search Google Scholar
    • Export Citation
  • Grossiord, C., T. N. Buckley, L. A. Cernusak, K. A. Novick, B. Poulter, R. T. W. Siegwolf, J. S. Sperry, and N. G. McDowell, 2020: Plant responses to rising vapor pressure deficit. New Phytol., 226, 15501566, https://doi.org/10.1111/nph.16485.

    • Search Google Scholar
    • Export Citation
  • Guan, Y. Q., B. Mohammadi, Q. B. Pham, S. Adarsh, K. S. Balkhair, K. U. Rahman, N. T. T. Linh, and D. Q. Tri, 2020: A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model. Theor. Appl. Climatol., 142, 349367, https://doi.org/10.1007/s00704-020-03283-4.

    • Search Google Scholar
    • Export Citation
  • Huntington, T. G., 2006: Evidence for intensification of the global water cycle: Review and synthesis. J. Hydrol., 319, 8395, https://doi.org/10.1016/j.jhydrol.2005.07.003.

    • Search Google Scholar
    • Export Citation
  • Keskin, M. E., Ö. Terzi, and D. Taylan, 2009: Estimating daily pan evaporation using adaptive neural-based fuzzy inference system. Theor. Appl. Climatol., 98, 7987, https://doi.org/10.1007/s00704-008-0092-7.

    • Search Google Scholar
    • Export Citation
  • Kim, S., J. Shiri, and O. Kisi, 2012: Pan evaporation modeling using neural computing approach for different climatic zones. Water Resour. Manage., 26, 32313249, https://doi.org/10.1007/s11269-012-0069-2.

    • Search Google Scholar
    • Export Citation
  • Kingston, D. G., M. C. Todd, R. G. Taylor, J. R. Thompson, and N. W. Arnell, 2009: Uncertainty in the estimation of potential evapotranspiration under climate change. Geophys. Res. Lett., 36, L20403, https://doi.org/10.1029/2009GL040267.

    • Search Google Scholar
    • Export Citation
  • Kisi, O., 2015: Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J. Hydrol., 528, 312320, https://doi.org/10.1016/j.jhydrol.2015.06.052.

    • Search Google Scholar
    • Export Citation
  • Kisi, O., and S. Heddam, 2019: Evaporation modelling by heuristic regression approaches using only temperature data. Hydrol. Sci. J., 64, 653672, https://doi.org/10.1080/02626667.2019.1599487.

    • Search Google Scholar
    • Export Citation
  • Li, Z., Y. Chen, Y. Shen, Y. Liu, and S. Zhang, 2013: Analysis of changing pan evaporation in the arid region of Northwest China. Water Resour. Res., 49, 22052212, https://doi.org/10.1002/wrcr.20202.

    • Search Google Scholar
    • Export Citation
  • Lim, W. H., M. L. Roderick, M. T. Hobbins, S. C. Wong, and G. D. Farquhar, 2013: The energy balance of a US Class A evaporation pan. Agric. For. Meteor., 182–183, 314331, https://doi.org/10.1016/j.agrformet.2013.07.001.

    • Search Google Scholar
    • Export Citation
  • Lim, W. H., M. L. Roderick, and G. D. Farquhar, 2016: A mathematical model of pan evaporation under steady state conditions. J. Hydrol., 540, 641658, https://doi.org/10.1016/j.jhydrol.2016.06.048.

    • Search Google Scholar
    • Export Citation
  • Limjirakan, S., and A. Limsakul, 2012: Trends in Thailand pan evaporation from 1970 to 2007. Atmos. Res., 108, 122127, https://doi.org/10.1016/j.atmosres.2012.01.010.

    • Search Google Scholar
    • Export Citation
  • Linacre, E. T., 1994: Estimating U.S. class a pan evaporation from few climate data. Water Int., 19, 514, https://doi.org/10.1080/02508069408686189.

    • Search Google Scholar
    • Export Citation
  • Liu, W., and F. Sun, 2016: Assessing estimates of evaporative demand in climate models using observed pan evaporation over China. J. Geophys. Res. Atmos., 121, 83298349, https://doi.org/10.1002/2016JD025166.

    • Search Google Scholar
    • Export Citation
  • Lu, X., Y. Ju, L. Wu, J. Fan, F. Zhang, and Z. Li, 2018: Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models. J. Hydrol., 566, 668684, https://doi.org/10.1016/j.jhydrol.2018.09.055.

    • Search Google Scholar
    • Export Citation
  • Lugato, E., G. Alberti, B. Gioli, J. O. Kaplan, A. Peressotti, and F. Miglietta, 2013: Long-term pan evaporation observations as a resource to understand the water cycle trend: Case studies from Australia. Hydrol. Sci. J., 58, 12871296, https://doi.org/10.1080/02626667.2013.813947.

    • Search Google Scholar
    • Export Citation
  • Massmann, A., P. Gentine, and C. Lin, 2019: When does vapor pressure deficit drive or reduce evapotranspiration? J. Adv. Model. Earth Syst., 11, 33053320, https://doi.org/10.1029/2019MS001790.

    • Search Google Scholar
    • Export Citation
  • Matsoukas, C., N. Benas, N. Hatzianastassiou, K. G. Pavlakis, M. Kanakidou, and I. Vardavas, 2011: Potential evaporation trends over land between 1983–2008: Driven by radiative fluxes or vapour-pressure deficit? Atmos. Chem. Phys., 11, 76017616, https://doi.org/10.5194/acp-11-7601-2011.

    • Search Google Scholar
    • Export Citation
  • McMahon, T. A., M. C. Peel, L. Lowe, R. Srikanthan, and T. R. McVicar, 2013: Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: A pragmatic synthesis. Hydrol. Earth Syst. Sci., 17, 13311363, https://doi.org/10.5194/hess-17-1331-2013.

    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., 1965: Evaporation and environment. Symp. Soc. Exp. Biol., 19, 205234.

  • Penman, H. L., 1948: Natural evaporation from open water, bare soil and grass. Proc. Roy. Soc. London, 193A, 120145, https://doi.org/10.1098/rspa.1948.0037.

    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., V. S. Golubev, and P. Y. Groisman, 1995: Evaporation losing its strength. Nature, 377, 687688, https://doi.org/10.1038/377687b0.

    • Search Google Scholar
    • Export Citation
  • Priestley, C. H. B., and R. J. Taylor, 1972: On the assessment of surface heat flux and evaporation using large scale parameters. Mon. Wea. Rev., 100, 8192, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Qasem, S. N., S. Samadianfard, S. Kheshtgar, S. Jarhan, O. Kisi, S. Shamshirband, and K.-W. Chau, 2019: Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Eng. Appl. Comput. Fluid Mech., 13, 177187, https://doi.org/10.1080/19942060.2018.1564702.

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., and G. D. Farquhar, 2002: The cause of decreased pan evaporation over the past 50 years. Science, 298, 14101411, https://doi.org/10.1126/science.1075390-a.

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., L. D. Rotstayn, G. D. Farquhar, and M. T. Hobbins, 2007: On the attribution of changing pan evaporation. Geophys. Res. Lett., 34, L17403, https://doi.org/10.1029/2007GL031166.

    • Search Google Scholar
    • Export Citation
  • Roderick, M. L., M. T. Hobbins, and G. D. Farquhar, 2009: Pan evaporation trends and the terrestrial water balance. I. Principles and observations. Geogr. Compass, 3, 746760, https://doi.org/10.1111/j.1749-8198.2008.00213.x.

    • Search Google Scholar
    • Export Citation
  • Rotstayn, L. D., M. L. Roderick, and G. D. Farquhar, 2006: A simple pan-evaporation model for analysis of climate simulations: Evaluation over Australia. Geophys. Res. Lett., 33, L17715, https://doi.org/10.1029/2006GL027114.

    • Search Google Scholar
    • Export Citation
  • Sabziparvar, A.-A., H. Tabari, A. Aeini, and M. Ghafouri, 2010: Evaluation of class a pan coefficient models for estimation of reference crop evapotranspiration in cold semi-arid and warm arid climates. Water Resour. Manage., 24, 909920, https://doi.org/10.1007/s11269-009-9478-2.

    • Search Google Scholar
    • Export Citation
  • Seifi, A., and F. Soroush, 2020: Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran. Comput. Electron. Agric., 173, 105418, https://doi.org/10.1016/j.compag.2020.105418.

    • Search Google Scholar
    • Export Citation
  • Stephens, C. M., T. R. Mcvicar, F. M. Johnson, and L. A. Marshall, 2018: Revisiting pan evaporation trends in Australia a decade on. Geophys. Res. Lett., 45, 11 16411 172, https://doi.org/10.1029/2018GL079332.

    • Search Google Scholar
    • Export Citation
  • Sun, Z., Z. Ouyang, J. Zhao, S. Li, X. Zhang, and W. Ren, 2018: Recent rebound in observational large-pan evaporation driven by heat wave and droughts by the Lower Yellow River. J. Hydrol., 565, 237247, https://doi.org/10.1016/j.jhydrol.2018.08.014.

    • Search Google Scholar
    • Export Citation
  • Thom, A. S., J.-L. Thony, and M. Vauclin, 1981: On the proper employment of evaporation pans and atmometers in estimating potential transpiration. Quart. J. Roy. Meteor. Soc., 107, 711736, https://doi.org/10.1002/qj.49710745316.

    • Search Google Scholar
    • Export Citation
  • Thompson, J. R., A. J. Green, and D. G. Kingston, 2014: Potential evapotranspiration-related uncertainty in climate change impacts on river flow: An assessment for the Mekong River basin. J. Hydrol., 510, 259279, https://doi.org/10.1016/j.jhydrol.2013.12.010.

    • Search Google Scholar
    • Export Citation
  • Tochimoto, E., and W. Yanase, 2023: Structural and environmental characteristics of western Baiu frontal depressions. J. Climate, 36, 23492365, https://doi.org/10.1175/JCLI-D-22-0515.1.

    • Search Google Scholar
    • Export Citation
  • Wang, H., F. Sun, and W. Liu, 2018a: Spatial and temporal patterns as well as major influencing factors of global and diffuse horizontal irradiance over China: 1960–2014. Sol. Energy, 159, 601615, https://doi.org/10.1016/j.solener.2017.11.038.

    • Search Google Scholar
    • Export Citation
  • Wang, H., F. Sun, T. Wang, and W. Liu, 2018b: Estimation of daily and monthly diffuse radiation from measurements of global solar radiation a case study across China. Renewable Energy, 126, 226241, https://doi.org/10.1016/j.renene.2018.03.029.

    • Search Google Scholar
    • Export Citation
  • Wang, K., and R. E. Dickinson, 2012: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys., 50, RG2005, https://doi.org/10.1029/2011RG000373.

    • Search Google Scholar
    • Export Citation
  • Wang, K., X. Liu, Y. Li, X. Yang, P. Bai, C. Liu, and F. Chen, 2019: Deriving a long-term pan evaporation reanalysis dataset for two Chinese pan types. J. Hydrol., 579, 124162, https://doi.org/10.1016/j.jhydrol.2019.124162.

    • Search Google Scholar
    • Export Citation
  • Wang, T., F. Sun, J. Xia, W. Liu, Y. Sang, and H. Wang, 2018c: An experimental detrending approach to attributing change of pan evaporation in comparison with the traditional partial differential method. J. Hydrol., 564, 501508, https://doi.org/10.1016/j.jhydrol.2018.07.021.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2016: “The stippling shows statistically significant grid points”: How research results are routinely overstated and overinterpreted, and what to do about it. Bull. Amer. Meteor. Soc., 97, 22632273, https://doi.org/10.1175/BAMS-D-15-00267.1.

    • Search Google Scholar
    • Export Citation
  • Xue, K., and Coauthors, 2023: Estimating ecosystem evaporation and transpiration using a soil moisture coupled two-source energy balance model across FLUXNET sites. Agric. For. Meteor., 337, 109513, https://doi.org/10.1016/j.agrformet.2023.109513.

    • Search Google Scholar
    • Export Citation
  • Yang, D., F. Sun, Z. Liu, Z. Cong, and Z. Lei, 2006: Interpreting the complementary relationship in non-humid environments based on the Budyko and Penman hypotheses. Geophys. Res. Lett., 33, L18402, https://doi.org/10.1029/2006GL027657.

    • Search Google Scholar
    • Export Citation
  • Yu, F., C. Wei, P. Deng, T. Peng, and X. Hu, 2021: Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles. Sci. Adv., 7, eabf4130, https://doi.org/10.1126/sciadv.abf4130.

    • Search Google Scholar
    • Export Citation
  • Yuan, W. P., and Coauthors, 2019: Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv., 5, eaax1396, https://doi.org/10.1126/sciadv.aax1396.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (a) The locations of meteorological stations and selected grids, containing at least one station each (spatial resolution of 1° × 1°), and (b) the D20 pan.

  • Fig. 2.

    The statistical comparison between the observed and calculated monthly pan evaporation (Epan) over China. (a) The scatter density plot between the observed values and the values calculated by the PenPan model during 1951–2021. The legends are the counts, with the red color representing more and the blue color representing less. The (b) R2 and (c) RMSE values between the simulation and observation of Epan during 1951–2021. (d) The annual average Epan during 1961–2021.

  • Fig. 3.

    The significance of the annual Epan change. Significance is for the 90% confidence level and controlling the FDR with αFDR = 0.1. The red grids represent a significant decrease, the yellow grids represent a significant increase, the blue grids represent no statistically significant changes, and the blank grids represent incomplete data. (a) 1951–2021; (b) 1952–2021; (c) 1953–2021; (d) 1954–2021; (e) 1955–2021; (f) 1956–2021; (g) 1958–2021; (h) 1960–2021; (i) 1961–2021.

  • Fig. 4.

    The average linear change rate of Epan and its aerodynamic component (Epa) and radiative component (Epr) during 1961–2021, 1961–93, and 1994–2021. The red dot represents a decreasing trend and the blue dot represents an increasing trend, with larger dots indicating greater change rates. (a)–(c) Epan during 1961–2021, 1961–93, and 1994–2021; (d)–(f) Epa during 1961–2021, 1961–93, and 1994–2021; (g)–(i) Epr during 1961–2021, 1961–93, and 1994–2021.