Classification-Based Spatiotemporal Variations of Pan Evaporation Across the Guangdong Province, South China Yanhu He, Kairong Lin, Xiaohong Chen, Changqing Ye & Lei Cheng
Water Resources Management An International Journal - Published for the European Water Resources Association (EWRA) ISSN 0920-4741 Volume 29 Number 3 Water Resour Manage (2015) 29:901-912 DOI 10.1007/s11269-014-0850-5
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Author's personal copy Water Resour Manage (2015) 29:901–912 DOI 10.1007/s11269-014-0850-5
Classification-Based Spatiotemporal Variations of Pan Evaporation Across the Guangdong Province, South China Yanhu He & Kairong Lin & Xiaohong Chen & Changqing Ye & Lei Cheng
Received: 2 March 2014 / Accepted: 16 October 2014 / Published online: 23 October 2014 # Springer Science+Business Media Dordrecht 2014
Abstract Change in pan evaporation (Epan) has evoked general scientific interest because it involves both climatic and hydrological effects. The enhanced greenhouse effect may be the casual factor, although the mechanism still needs to be further confirmed. Based on meteorological data (Pan evaporation, rainfall, air temperature, sunshine duration, relative humidity, wind speed, cloud cover, and water vapor pressure) of 85 sites from 1957 to 2006, classification-based spatiotemporal variations of pan evaporation and possible causes were studied in the Guangdong province, South China (with a coastline of 8,500 km), which is one of China’s most prosperous provinces with the largest population. The Guangdong province was spatially divided into 4 parts, i.e., Southwest part, East part, Central part, and Northwest part, according to Epan and seven other climatic factors based on cluster analysis. Results showed that pan evaporation in this study area declined −3.35 mm year−1 on average in time, and mainly decreased from the seashore area to the inland area in space. Results also showed that all the climatic factors can contribute to change in Epan, but their contributions were different over the space. Sunshine duration (SD) and wind speed (WS) had a positive correlation with Epan, while rainfall (R) and air temperature (Ta) were negatively correlated to Epan. Among all the 7 climatic factors, SD was identified as the dominant driving force of Epan change in the Guangdong province. Keywords Pan evaporation . Spatiotemporal variations . Classification . Climatic factor . Guangdong province
Y. He : K. Lin (*) : X. Chen Department of Water Resources and Environment, Geography and Planning School, Sun Yat-sen University, 135 Xingangxi Road, Guangzhou 510275, People’s Republic of China e-mail:
[email protected] Y. He : K. Lin : X. Chen Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong Higher Education Institutes, Guangzhou 510275, China C. Ye College of Environment and plant protection, Hainan University, Haikou 570228, China L. Cheng Water for a Healthy Country Flagship, CSIRO Land and Water, Canberra, ACT, Australia
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1 Introduction Evaporation is not only a climatic factor, but also a part of the hydrological cycle. Especially under the current global climate warming environment, regional evaporation has changed greatly, largely due to the enhanced greenhouse effect caused by excess carbon dioxide emissions (e.g., Farquhar and Roderick 2003). Evaporation may have a significant effect on surface moisture balance, and it is an important component of the hydrological cycle, influencing the availability of water, particularly for agriculture (Hossein and Safar 2011). Therefore, study on the pan evaporation change is very important for monitoring and management of water resources (Kim et al. 2012, 2013). However, the mechanism of change in pan evaporation and its causes across the world are still unclear. Many Studies on global evaporation change and its mechanisms have been conducted (Golubev et al. 2001; Roderick and Farquhar 2002, 2004, 2005; Irmak et al. 2012). The study areas were based on a country scale from the northern or southern hemisphere or on a river-basin scale. Studies have found that pan evaporation in both northern and southern hemispheres has shown a decreasing trend but with a few exceptions (Xu 2001; Cohen et al. 2002; Hossein et al. 2011). Pan evaporation declined in the former Soviet Union, the United States (Peterson et al. 1995), and parts of Asia in different periods (Chattopadhya and Hulme 1997; Tebakari et al. 2005), as well as in Australia and New Zealand since the 1970s (Roderick and Farquhar 2004, 2005). There is no exception in China. Research has been conducted in different parts of the country including the Yellow River basin (Qiu et al. 2003), North China Plain (Guo and Ren 2005), Tibetan Plateau (Zhang et al. 2007), and even the entire country (Liu et al. 2006, 2009; Zeng et al. 2007) for the past 50 years. However, the physical mechanisms of pan evaporation change, especially in the southern seashore area of China, are still open for investigation. There is no doubt that Epan is influenced by associated climatic factors, mainly rainfall (R), air temperature (Ta), wind speed (WS), net radiation (NR), cloud cover (CC), sunshine duration (SD), water vapor pressure (WVP), and relative humidity (RH). However, the mechanism of interaction of each of these factors is very complicated. There is no consensus among scientists as to what climatic factors have caused Epan change. What is of particular interest is the complex relationship between E pan and climatic factors that have been reported in many documents (Peterson et al. 1995; Donald and Hesch 2007). Which of the above factors have the strongest correlation with Epan change? Do they contribute to an increase or decrease in Epan? The findings may be different according to different study areas. Considering previous documents about Epan change around the world and the close correlation between Epan and the regional water resources, further research on annual Epan variability and its possible causes will be helpful to better understand the availability and scientific management and assessment of regional water resources. Thus, the Guangdong province, South China was selected as a study area to address the issues noted above. There are two reasons: first is its geographical location. Located in South China, Guangdong province is on the periphery of the South China Sea, and the characteristics of Epan change in a seashore area may differ from that of inland locations due to different atmospheric circulations. In addition, the study area involves major tributaries of the Pearl River (West River, North River, and East River) and the Pearl River Delta (Fig. 1), which characteristics of hydrological and meteorological changes, especially under the changing environment, have received attention (Chen et al. 2010, 2011; He et al. 2013). Second, Guangdong is one of China’s most prosperous provinces with the largest population. This area is now in a stage of
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industrial transformation. Epan change may contribute greatly to regional water resources and therefore could have a notable effect on the economic, social, and especially agricultural development of China. Therefore, the objectives of this paper are: (1) to reveal spatiotemporal distribution of annual Epan over the past five decades in the Guangdong province using classification-based method; (2) to detect the trend of annual Epan in the study area compared to that of other parts of China; (3) to explore possible causes behind variability of Epan and find the crucial climatic factors that drive the change in Epan in different part of the study area.
2 Datasets and Methods 2.1 The Study Area Guangdong Province is located in Southern China (Fig. 1) with an area of 179,752 km2. The topography is characterized as low-lying terrain. Elevation is high in north and east and low in south (Fig. 1). Four rivers flow across the province including the West River, North River, East River, and Han River. The climate of the province is of a humid subtropical monsoon type, with a mean temperature of 22 °C and annual rainfall of over 1, 300 mm. The surface of the province is dominated by forests, with coverage about 56 % (Zhou et al. 2010). The Province has maintained rapid population growth since the 1980s and occupies an important position in the Chinese economy. Over 100 towns and cities aggregate in the Pearl River Delta (PRD), central part of the Guangdong province.
Fig. 1 Location of the Guangdong province and meteorological sites
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2.2 Data Source A dataset of 85 national meteorological observational sites with daily mean air temperature (Ta) at 1.5 m height, wind speed (WS) measured at 10 m height, relative humidity (RH) at 1.5 m, sunshine duration(SD), cloud cover (CC), water vapor pressure (WVP), and pan evaporation (Epan) of 20 cm diameter, for the period 1957–2006 was used in this study. The data was provided by the National Climatic Center of China Meteorological Administration. Detailed information of the meteorological sites for average annual Epan in this study is provided in Table 1. The sites are well distributed across the Guangdong province. Several steps are conducted to ensure the quality and consistency of the data used in this study. Firstly, we found that in the earliest years of this data set (1951–1956), measurements for some sites are inconsistent or missing for more than 20 consecutive days, so this period was excluded. Secondly, measurements for the period of 1957–2006 are made using the same standards and instrumentation at all sites, ensuring the homogeneity of data. Finally, cases of missing data are found as late as 1960; for the period of 1957–1960, 8 sites reported missing Table 1 Detailed information of the meteorological sites for Pan evaporation in this study Site
Lon (E)
Lat (N)
Site
Lon (E)
Lat (N)
Site
Lon (E)
Lat (N)
Shenzhen
114.1
22.55
Dongguan
113.8
23.03
Gaoyao
112.5
23.05
Jiaoling
116.2
24.65
Huidong
114.7
22.93
Yunfu
112.1
22.93
Pingyuan
115.9
24.58
Xinfeng
114.2
24.05
Luoding
111.6
22.77
Dapu
116.7
24.35
Longmen
114.2
23.73
Xingxing
112.2
22.72
Meixian
116.1
24.3
Conghua
113.6
23.55
Sanshui
112.9
23.17
Xingning Wuhua
115.7 115.8
24.15 23.93
Huaxian Zengcheng
113.2 113.8
23.38 23.3
Nanhai Shunde
113.1 113.3
23.02 22.85
Fengshun
116.2
23.77
Guangzhou
113.3
23.17
Heshan
113
22.77
Raoping
117
23.68
Panyu
113.4
22.95
Xinhui
113.1
22.53 22.53
Chaozhou
116.6
23.67
Lechang
113.3
25.15
Zhongshan
113.3
Jieyang
116.4
23.57
Nanxiong
114.3
25.08
Kaiping
112.7
22.37
Chenhai
116.8
23.47
Renhua
113.7
25.1
Taishan
112.8
22.22
Jiexi
115.8
23.43
Shixing
114.1
24.95
Doumen
113.3
22.22
Nanao Chaoyang
117 116.6
23.43 23.27
Lianzhou Lianlan
112.4 112.3
24.78 24.72
Enping Shangchuandao
112.3 112.8
22.18 21.72
Shantou
116.7
23.37
Qujiang
113.6
24.68
Xinyi
110.9
22.35
Puning
116.2
23.3
Lianshan
112.2
24.57
Yangchun
111.8
22.17
Huilai
116.3
23.03
Yangshan
112.6
24.48
Gaozhou
110.8
21.93
Haifeng
115.3
22.97
Ruyuan
113.3
24.78
Yangjiang
112
21.88 21.65
Lufeng
115.7
22.95
Yingde
113.4
24.18
Maoming
110.9
Shanwei
115.4
22.78
Fogang
113.5
23.87
Huazhou
110.6
21.65
Heping Lianping
114.9 114.5
24.45 24.37
Qingyuan Huaiji
113.1 112.2
23.67 23.92
Lianjiang Dianbai
110.3 111
21.63 21.5
Longchuan
115.3
24.1
Guangning
112.4
23.63
Wuchuan
110.8
21.43
Heyuan
114.7
23.73
Fengkai
111.5
23.45
Suixi
110.3
21.38
Zijin
115.2
23.63
Sihui
112.7
23.35
Zhanjiang
110.4
21.22
Boluo
114.3
23.18
Yunan
111.5
23.25
Leizhou
110.1
20.92
Huiyang
114.4
23.08
Deqin
111.8
23.15
Xuwen
110.2
20.33
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data for some number of days between May and September, accounting for less than 0.1 % of all measurements. No site reported missing data for more than 7 consecutive days, nor for more than 30 total days in the study period. Where data were missing for fewer than 3 consecutive days, a direct interpolation method is adopted to estimate the missing observations. For 3 or more consecutive days with missing data, the stepwise regression method is used to estimate the missing values with all other sites with valid values for those dates. The regression results are good for all measurements and sites with missing observations, with R2 values >0.85 for most regression equations. 2.3 Methods Trends in annual Epan, R, Ta, RH, CC, WS, WVP, and SD for each site are estimated using the Mann-Kendall trend test (Mann 1945; Kendall 1975) and linear trend test, and the statistical significance of trends was assessed at the 0.05 level. These two methods were commonly used to detect trends of the hydro-meteorological time series (Zar 1984; Helsel and Hirsch 1992), especially the M-K trend test, which was highly recommended for general use by the World Meteorological Organization, because of its simplicity, and ability to deal with non-normal and missing data distributions and robustness to the effects of outliers and gross data errors (Kahya and Kalayci 2004). The results for these two methods complemented each other. The k-means cluster analysis method was used to categorize annual Epan of the Guangdong province in this study. This method is based on a least sum-of-squares estimation (Jaynes et al. 2003), and clusters the analyzed items with high internal homogeneity and high external or inter-group heterogeneity (Chen et al. 2010). The Inverse Distance Weighted (IDW) method is a simpler numerical method, which is widely used to interpolate climatic factors like Rainfall from point data (Ashoke Basistha et al. 2008). The average values of Epan in the whole province are obtained by the IDW interpolating method based on the observed site, with a power of 0.2 and 12 points in the neighbor site, and a spatial resolution of 0.002×0.002 degree. The moving average is commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles (Zhao et al. 2008). In this study, a 5 year moving average method is used to analyze the annual Epan change in the study area.
3 Results 3.1 Trend in Pan Evaporation Over the Past 50 Years The line trend analysis and the 5 year moving average of mean annual Epan are showed in Fig. 2. It can be seen from Fig. 2 that E pan in the Guangdong province has a decreasing trend from 1957 to 1997 and then a slow increase. On average, annual Epan in the study area decreases at the rate of −3.36 mm year−1 during the period 1957–2006. Overall, E pan in the Guangdong province has declined trend over the past decades, which confirms that the Guangdong province belongs to one of China’s eight climatic regions showing a statistically significant decrease of Epan (Liu et al. 2004). To understand the spatial distribution of annual Epan trends, the trends for each site from 1957 to 2006 are detected by the Mann–Kendall trend test as showed in Fig. 3. It can be seen from Fig. 3, the results show that the trend in annual Epan varies from site to site, and most sites (64 out of 85) show a decreasing trend. More than half of the sites (49 out of 85) show statistically significant decline, while only 8 sites show statistically significant increasing trends, particularly southwestern part of the PRD (Pearl
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River Delta) and in the eastern part of the study area. It also show that most parts of the study area have a decreasing, even significant decreasing, and trend except for part of the PRD, the North River basin, and the Han River basin with an insignificant increasing trend. 3.2 Spatiotemporal Distribution of Pan Evaporation Figure 4 shows the spatial distribution of mean annual Epan in the Guangdong province during different time periods (1960s, 1970s, 1980s, 1990s, and 2000s). Refer to Fig. 4, basically, spatial patterns of Epan are consistent in the five different periods. The general pattern is: a low value center is formed in the north part of the North River basin, East River basin, and West River basin, while the high value zone is mainly situated in the east and the west parts of the Guangdong province, especially the seashore area. Obviously, most parts of the North River basin and the West River basin, located relatively far from the sea, were in the low value zone. The inland and seashore area of the Guangdong province show low and high value of Epan, respectively. However, discrepancies in spatial distribution of Epan in different time periods can be observed as well. Refer to Fig. 4, it can be also seen that the spatial distribution of annual Epan changed towards a more homogenized, and the high value zone moved from Yuexi River basin to the Pearl River delta and Yuedong River basin from 1960s to 2000s. 3.3 Classification of Pan Evaporation and Other Climatic Factors According to above analysis, it can be given that the variation of Epan and its relationships with other climatic factors may be different at regional scale. That is because the impact of individual climatic factor, especially the impact of their interactions on Epan can change substantially from one region to another and even in the same region can change temporally and spatially (Irmak et al. 2012). Also, the sensitivity of Epan to the same climatic factor shows significant variation from one location to another. Therefore, to analyze the spatiotemporal variation of Epan and its responses to the regional climatic factors, the whole province is spatially divided into four parts according to Epan and seven other climatic factors based on cluster analysis (Fig. 5). Part 1 (with 12 sites) locates in the southwest area of the Guangdong province, called Southwest part, which mainly includes the Yuexi River basin. Part 2 (with 44 sites) locates in the East of the Guangdong province, called East part, which includes East River basin, Han River Basin and Yuedong River basin. Part 3 (with 7 sites) covers the
y = 0.866x + 1722. R² = 0.002
5 per. Mov.Avg 5 per. Mov.Avg
y = -0.072x + 79.08 R² = 0.385 y = 0.019x + 20.50 R² = 0.392
5 per. Mov.Avg
5 per. Mov.Avg
y = -3.357x + 1691. R² = 0.313
regression trend line regression trend line
regression trend line
regression trend line
1972
y = -0.006x + 2.321 R² = 0.424
y = -0.060x + 68.35 R² = 0.062
y = -2.080x + 1788. R² = 0.050
5 per. Mov.Avg
y = 0.005x + 20.75 R² = 0.039
5 per. Mov.Avg 5 per. Mov.Avg
5 per. Mov.Avg
regression trend line
regression trend line regression trend line
regression trend line
Fig. 2 Trend analyses of mean annual Epan, R, Ta, RH, WS, WVP, CC and SD of the eight Guangdong province from 1957 to 2006
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Fig. 3 Trend of mean annual Epan, R, and SD of each site in the Guangdong province (1957~2006)
transition region of Yuexi River basin and the Pearl River Delta, called Central part. And Part 4 (with 22 sites) locates in the northwest of the Guangdong province, called Northwest part, which includes North River basin and West River basin. For the average annual Epan among the four parts, in general, part 1 has the highest value, and then followed by part 2 and part 3, while part 4 has the lowest value, with all its sites in the inland area (Fig. 5). For each part, the mean annual Epan values including the 5 year moving average are given in Fig. 6. Figure 6 indicates that annual Epan in all of the four parts decrease, and the decrease of Epan in part 1 is much larger than that of any other part at a rate of −6.25 mm year−1, and Epan in parts 2 and 3 decrease at a rate about −3.6 mm year−1, while that of part 4 at a rate of −2.5 mm year−1. More interesting, part 3 and part 4 have almost the same Epan change curves, especially after the 1980s. Meanwhile, Epan in part 1, with an obvious decrease of Epan in part 1 from 1970 to 1997, has a much smaller fluctuation than that of other parts. 3.4 Underlying Factors Firstly, the trends of seven climatic factors during the past 50 years in the Guangdong province are also analyzed using linear trend test method. The results are also showed in Fig. 2. It can be
Fig. 4 Spatial distributions of average Epan during different periods in Guangdong province
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Fig. 5 Cluster analysis of meteorological sites and average pan evaporation in the Guangdong province
seen from Fig. 2, it can be seen that RH, WS, CC, and SD, in the Guangdong province have a decreasing trend from 1957 to 2006. In which, SD has an increasing trend after 1997, which is similar to that of Epan as showed in Fig. 2. In addition, R and WVP have non-significant increase, and Ta has a significant increase from 1957 to 2006. To further analyze the underlying factors causing Epan decrease, correlation analysis between Epan and other climatic factors was conducted in this study. The correlation coefficients between Epan and other climatic factors in the four parts are given in Table 2. We analyzed the correlation coefficients between Epan and regional climatic factors to potentially determine the main factors that caused the decrease in Epan. Table 2 indicates that, among the climatic factors for each part, both SD and WS have a positive correlation with Epan, while R and Ta are negatively correlated to Epan. SD in all the four parts have the largest correlation coefficients and show significant positive correlation with Epan. The correlations between SD and Epan in the four parts are presented in Fig. 7. The correlation coefficients between SD and Epan in parts 1, 2, 3 and 4 are 0.71, 0.51, 0.7 and 0.81 respectively, which is significant at 0.05 level (larger than 0.2875 or less than −0.2875). part 1
part 2
5 per. Mov.Avg
y = -6.252x + 1861. R² = 0.475
5 per. Mov.Avg
regression trend line
y = -3.701x + 1618. R² = 0.164
regression trend line
part 4
part 3 5 per. Mov.Avg
5 per. Mov.Avg
y = -3.568x + 1730. R² = 0.249
regression trend line
y = -2.387x + 1426. R² = 0.184
regression trend line
Fig. 6 Trend analyses of mean annual Epan of four parts in the Guangdong province from 1957 to 2006
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Table 2 The correlation coefficients between Epan and other climatic factors in the 4 parts of the Guangdong Province Part
Climatic factor SD(h)
RH(%)
R(mm)
Ta(°C)
WVP(hpa)
1
0.71*
−0.05
−0.42*
−0.34*
−0.28
2
*
0.51
−0.33
−0.51
−0.03
3
0.70*
0.03
−0.49*
−0.29*
4
0.81*
−0.04
−0.58*
−0.21
−0.42*
*
*
CC(%)
WS(m/s)
0.07
0.35*
0.16
0.23
0.11
−0.38*
−0.19
0.25
−0.48*
0.46*
* significant at the 0.05 level (larger than 0.2875 or less than −0.2875)
Regional difference in correlation between Epan and other climatic factors are also given in Table 2. As for part 1, R, WS and Ta are significantly correlated to Epan at 0.05 level with a correlation coefficient −0.42, 0.35 and −0.34 respectively, which indicates that, besides SD, R, WS and Ta are also the important climatic factors causing Epan decrease in part 1. Similarly in part 2, besides SD, R could also be considered as the crucial climatic factor because of its correlation to Epan is significant at 0.05 level. R, Ta and WVP are significantly correlated to Epan with a negative correlation coefficient in part 3. It can be inferred that R and Ta combined with SD contributed more to the decrease of Epan when compared to other climatic factors. R is still the crucial climatic factor causing Epan decrease in part 4, moreover, WVP should also be considered. That is because both of them have been increasing in the past 50 years and their correlation coefficients to Epan are up to 0.05 level. In a whole, we can confirm that the decline in Epan in the four parts is more due to decrease in SD and WS, and increase in R and Ta when compared to other climatic factors. However, decrease in Epan with respect to change in climatic factors varies spatially. 2600
2000
2300
1700
SD ( h )
SD ( h )
2000 1700 1400 1100 800
y = 0.876x + 434.1 R² = 0.499
1400 1100 800
Part 1
500
y = 0.605x + 640.7 R² = 0.258
Part 2
500 Epan ( mm )
Epan ( mm)
2600
2000
2300
1700
SD ( h )
SD ( h )
2000 1700 1400
1400 1100
1100 800
y = 0.984x + 231.7 R² = 0.494
Part 3
500 Epan ( mm )
800 500
y = 1.299 x - 168 .3 R² = 0.654
Part 4
Epan ( mm )
Fig. 7 Scatter plot between mean annual Epan and SD from 1957 to 2006 in the four parts of the Guangdong province
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From the above analysis, it can be found that correlations between R and Epan, and between SD and Epan are both significant in all parts of the Guangdong province. Figure 3 also shows the spatial distributions of the trends of R and SD in the study area over the past 50 years. Refer to Fig. 3, it can be seen that most sites (70 out of 85) show a decreasing trend, in which, more than half of the sites (45 out of 85) show statistically significant decline for SD, and there are good consistency between the spatial distribution of trend in SD and Epan. According to the above analysis and the perspective of a physical explanation, it can be given that SD could be the key factors dominating the change in Epan.
4 Discussion In this study, we found that Epan in the Guangdong province declines along with decreasing SD and WS, and increasing R, Ta (Fig. 3, Table 2), which indicated that they would be the major reason for the decline in Epan. Besides, land cover could also play an important role in Epan change. Fast urbanization in the Guangdong province brings about a reduction in irrigated areas, which may be related to the decrease of vapor pressure of near surface air and then a decline in RH. Epan in the Guangdong province has been decreasing since the 1960s with the lowest value in the 1990s, while recovering during the 2000s (Figs. 2 and 5). The average reduction rate in irrigated areas during 1980–2000 was far greater than that of the 2000s (−1.3 % in 1980–2000, −0.25 % in the 2000s; 2.13 million hectares in 1980, 1.89 million hectares in 1990, 1.64 million hectares in 2000, 1.68 million hectares in 2010). Thus, the change in irrigated areas correlates with Epan in the past 30 years. Although the physical mechanism linking irrigated areas and Epan is still unclear. In addition, another reason of change in Epan may be attributed to the increasing atmospheric CO2 and aerosols in the air, along with population expansion and fast economic development, especially since 1980. The abundant CO2 decreases the diurnal temperature range and then helps the average RH to remain constant and insensitive to the increasing temperature; hence Epan declines although Ta shows an increasing trend. On the other hand, increasing aerosol concentration (more than 5.22 million vehicles) decreases SD. Some previous studies (Roderick and Farquhar 2004, 2005) tried to attribute this to the enhanced greenhouse effect; however, more complete investigations of trends in Epan in more parts of the world are still on the way. Another finding showed in Fig. 4 is a decline in evaporation from seashore area to inland in the study area, which may suggest a hint that a distance to the ocean may be the key factor dominating the Epan change, although we lack a similar report on evaporation from seashore areas to inland. There should be an expectation that compound effects of associated climatic factors in Epan are more dramatic and intense. Overall, the findings of this study not only shows us the dominant driving force of Epan in the Guangdong province, but also indicates that each climatic factor has a more or less impact on the Epan change and their roles vary based on regional characteristics. Thus, it is suggested that more climatic factors should be considered when it comes to the physically-based equations to estimate pan evaporation.
5 Conclusion This study analyzes the statistical spatiotemporal Epan change and its implications in the Guangdong province over the past 50 years. Mann-Kendall trend test and linear trend test
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are used to detect trends within the climatic series, and the study on classification-based variation of Epan and its relationship with climatic factors are conducted based on the cluster analysis method, which would help to reveal the characteristic of the Epan spatial distribution and its causes in the Guangdong province. It can be concluded that: (1) the Guangdong province has obvious spatial characteristics for climatic factors, and it is classified into four parts, i.e., Southwest part, East part, Central Part, and Northwest part. (2) Epan has decreased over the past 50 years in the Guangdong province with a rate of −3.35 mm year −1. Especially in Southwest part (mainly in Yuexi River basin), Epan decreases most with a rate of −6.25 mm year -1; (3) Epan mostly decreases from seashore area to inland in the Guangdong province, and Southwest part has the highest value of Epan, then followed by East part and Central Part, while there is the lowest value in Northwest part. (4) In all the four parts, SD decreases significantly and has the largest positive correlation with Epan among all climatic factors, which is considered as the key climatic factor causing Epan decrease. Besides, increase in R and Ta and decrease in WS also contribute to the decrease in Epan in the Guangdong province, and WVP is also the crucial climatic factor leading to the decrease of Epan particularly in both Central Part and Northwest part. Pan evaporation provides a foundation for application in ecology, hydrology, agriculture, and engineering. However, to better understand the changes in surface moisture balance, actual evaporation closely related to potential evaporation is needed. Moreover, because of the complexity of the various physical causes of evaporation, it is difficult to give a quantitative description for each climatic factor of its influence on Epan change. Other related climatic factors including net radiation may contribute greatly to Epan change in spite of no quantitative analyses of this hypothesis due to a lack of observations on solar radiation and aerosols. However, a lack of such observations can be addressed in further modeling and monitoring studies. These should be considered and investigated in detail in further studies. Acknowledgments The research is financially supported by the National Natural Science Foundation of China (Grant No. 51379223 and 51210013), and the Pearl-River-New-Star of Science and Technology supported by Guangzhou City (Grant No.: 2011J2200051). The authors would like to express their sincere gratitude to two anonymous referees for their constructive comments and useful suggestions that helped us to improve the quality of this manuscript.
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