青藏高原蒸发皿蒸发量时空变化特征及其成因的定量分析_英文_

青藏高原蒸发皿蒸发量时空变化特征及其成因的定量_英文_ J. Geogr. Sci. 2011, 21(4): 594-608 DOI: 10.1007/s11442-011-0866-1 ? 2011 Science Press Springer-Verlag Identification of dominant climate factor for pan evaporation trend in the Tibetan Plateau 1,2121LIU Xiaomang, ZHENG Hongxing, ZHANG Minghua, LIU Changming 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 2. Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA Abstract: Despite the observed increase in global temperature, observed pan evaporation in many regions has been decreasing over the past 50 years, which is known as the “pan evaporation paradox”. The “pan evaporation paradox” also exists in the Tibetan Plateau, ?2 where pan evaporation has decreased by 3.06 mm a(millimeter per annum). It is necessaryto explain the mechanisms behind the observed decline in pan evaporation because the Ti- betan Plateau strongly influences climatic and environmental changes in China, Asia and even in the Northern Hemisphere. In this paper, a derivation based approach has been used to quantitatively assess the contribution rate of climate factors to the observed pan evapora- tion trend across the Tibetan Plateau. The results showed that, provided the other factors ?2 remain constant, the increasing temperature should have led to a 2.73 mm aincrease in panevaporation annually, while change in wind speed, vapor pressure and solar radiation should ?2?2 ?2have led to a decrease in pan evaporation by 2.81 mm a, 1.96 mm aand 1.11 mm a respectively from 1970 to 2005. The combined effects of the four climate variables have re- ?2 sulted in a 3.15 mm adecrease in pan evaporation, which is close to the observed pan evaporation trend with a relative error of 2.94%. A decrease in wind speed was the dominant factor for the decreasing pan evaporation, followed by an increasing vapor pressure and de- creasing solar radiation, all of which offset the effect of increasing temperature across the Tibetan Plateau. Keywords: pan evaporation trend; reference evapotranspiration; attribution; the Tibetan Plateau 1 Introduction Pan evaporation is a measure of the evaporative demand over terrestrial surfaces. One of the expected consequences of global warming is that the increasing near-surface air temperature should lead to the increased evaporative demand (Roderick et al., 2009). However, de- creases in pan evaporation over the last years have been reported in the United States (Pe- terson et al., 1995), former Soviet Union (Peterson et al., 1995; Golubev et al., 2001), China (Liu et al., 2004; Xu et al., 2006; Wang et al., 2007; Zheng et al., 2009), Canada (Burn and Received: 2011-02-25 Accepted: 2011-03-18 Foundation: The European Commission (Call FP7-ENV-2007-1), No.212921; National Basic Research Program of China, No.2010CB428406 Author: Liu Xiaomang (1983–), Ph.D, specialized in global change and hydrological process. E-mail: ximliu@ucdavis.edu www.geogsci.com springerlink.com/content/1009-637X Hesch, 2007), Australia (Roderick and Farquhar, 2004; Roderick et al., 2007; Rayner 2007), New Zealand (Roderick and Farquhar, 2005), India (Chattopadhyay and Hulme, 1997), Thailand (Tebakari et al., 2005), Ireland and United Kingdom (Stanhill and Möller, 2008). The increasing temperature accompanying with decreasing pan evaporation was called “pan evaporation paradox” (Brutsaert and Parlange, 1998), which has attracted many research interests during the past decade. The explanations of the “pan evaporation paradox” have been concluded in different re- gions using different methods (Table 1). The three methods are generally used in the litera- ture: the correlation analysis, detrending and derivation based approach. The correlation analysis method calculates the correlation coefficients between evaporation and climate factors (Liu et al., 2004; Burn and Hesch 2007; Stanhill and Möller, 2008), or derive a re- gression equation between them (Chattopadhyay and Hulme, 1997). Therefore, the higher the correlation coefficients between a climate factor and the evaporation, the impact of the factor on the change of evaporation is more important. The detrending method, on the other Table 1 Pan evaporation trend, attribution method and dominant factor of change in pan evaporation from vari- ous regions a Etrend Attribution Dominant Pan type , pan Region and period Reference bc?2 factor methodsite number (mm a) USA, 1948–1993 746 DTR Peterson et al., 1995 Class-A, –2.2 CA CA RH India, 1961–1992 Class-A, 19 –12 Chattopadhyay and Hulme, 1997 Former Soviet Union, DTR CA Class-A, 8 –3.7 Golubev et al., 2001 1960–1990 φ2 0, 85 China, 1955–2000 CA R–2.9 Liu et al., 2004 s ET, 150 Yangtze River Basin ref–3.1 Detrend Xu et al., 2006 R sChina, 1960–2000 Yangtze River Basin φ 20, 115–3.0 CA Wang et al., 2007 R sChina, 1961–2000 Haihe River Basin –4.9 CE U Zheng et al., 2009 φ2 0, 45 China, 1957–2001 Tibetan Plateau, –4.6 Detrend U Zhang et al., 2007 φ2 0, 75 1966–2003 Tibetan Plateau, ET, 101 CA 2006 –1.3 U Chen et al., ref1961–2000 Australia, 1975–2004 Class-A, 41 –2.0 CE Rodick et al., 2007 U U DetrendClass-A, 17 –4.0 Rayner, 2007 Australia, 1975–2004 Class-A, 27 –10.5 No analysis Tebakari et al., 2005 Thailand, 1982–2001 –2.0 No analysis Roderick and Farquhar, 2005 New Zealand, 1970s–2000 Class-A, 19 Canada, 1965–2000 –1.0 Burn and Hesch, 2007 CA Class-A, 4 U (May to Septemeber) Ireland, 1965–2002 Class-A, 8 4 decrease, 4 increase CA RStanhill and Möller, 2008 s R UK, 1900–1968 MO tank, 8 6 decrease, 2 increase CA Stanhill and Möller, 2008 s aPan type: “Class-A” means the US Weather Bureau?s Class-A pan, which is circular, 120.7 cm in diameter and 25.4 cm in depth and mounted 15 cm above ground level (Brouwer and Heibloem, 1986). “ φ20 ” means a metal pan, 20 cm in diameter and 10 cm high, installed 70 cm above the ground (Fu, 2004). “MO tank” means British Meteorological Office (MO) tank, 180 × 180 × 60 cm, installed 6 cm above the surrounding soil (Symons, 1867). “ET” means calculated refreference evapotranspiration by Penman-Monteith method (Allen, 1998). bAttribution Method: “CA” means correlation analysis method. “Detrend” means detrend analysis method. “CE” means combination equation method. All the three methods are introduced in this paper. cDominant factor: “DTR” means diurnal temperature range. “RH” means relative humidity. “R” means solar radiation. s“U” means wind speed. 596 Journal of Geographical Sciences hand, tries to detect the dominant factors for evaporation change by evaluating the effects of the climate factor trends. The detrended climate factors leading to the greatest difference in evaporation is then considered as the main reason of evaporation change (Xu et al., 2006; Zhang et al., 2007; Rayner, 2007). The derivation based approach, which depends on the derivation coefficients of evaporation to climate factors, most recently had been successfully used in quantifying the contribution of climate factors to evaporation changes (Roderick and Farquhar, 2002; Roderick et al., 2007; Zheng et al., 2009). The Tibetan Plateau plays an important role in climate change because it shows critical impacts on climate in Asia and elsewhere in the Northern Hemisphere (Ma et al., 2009). Long-term decreasing trend of pan evaporation had been detected in the Tibetan Plateau (Zhang et al., 2007), which implicated a change of energy budget in the Plateau. It has been reported that, on top of temperature, wind speed may be the dominant factor for pan evapo- ration change in the Plateau. The conclusion was drawn basing on the correlation analysis (Chen et al., 2006) and the detrend method (Zhang et al., 2007). It was great impressive but did not quantitatively provide the contribution rate of each climate factors to change in pan evaporation. The purpose of this paper is to quantitatively assess the impacts of climate factor on the observed changes in pan evaporation in the Tibetan Plateau, regarding solar radiation, tem- perature, vapor pressure and wind speed. The spatial patterns and interannual variation of the contribution rate for each climate factors to the pan evaporation change across the Ti- betan Plateau will also be addressed. 2 Study area and data The Tibetan Plateau, known as “the Roof of the World”, is the largest geomorphologic unit on the Eurasian continent. The Tibetan Plateau extends approximately 2700 km from west to 2 east and 1400 km from south to north, with a total area of more than 2.5 million km(Zheng et al., 2000). In recent years, significant climate change was detected in the Tibetan Plateau, such as the maximum and minimum temperatures were increasing with diurnal temperature range decreasing, annual precipitation and vapor pressure deficits were also increasing (Xie et al., 2010), surface solar radiation was decreasing after the 1980s (You et al., 2010), snow cover fraction was slightly decreasing (Pu and Xu, 2009), glacier was shrinking (Piao et al., 2010). The climate change in the Tibetan Plateau has strongly affected its environment and is closely related to global climate change (Zheng and Li, 1999). In this study, the routine meteorological records of 75 national meteorological stations for the period from 1970 to 2005 were used (Figure 1). The dataset retrieved from the National Climatic Centre (NCC) of China Meteorological Administration (CMA) includes daily ob- servations of maximum, minimum and average air temperature (T, T, T) at 2 m maxminmeanheight, wind speed (U) measured at 10 m height, vapor pressure (VP) at 2 m height, sun- shine duration, precipitation and pan evaporation (E). Ewas measured using a metal panpan pan, 20 cm in diameter and 10 cm high, installed 70 cm above the ground. Elevations of the 75 stations vary between 1583 m (No.56533) and 4800 m (No.55294), and the elevations of 50 stations are above 3000 m. Of the 75 stations, 11 have solar radiation records. To esti- mate the reference evapotranspiration, the measured wind speed was transferred to wind Figure 1 Map of the Tibetan Plateau and location of the meteorological stations (Squares indicate meteorologi- cal stations with solar radiation data) speed at 2 m height by the wind profile relationship introduced by Allen et al. (1998). For the entire Plateau, the average values were obtained by the Kriging interpolating method in ArcGIS9.3 based on the station observation. 3 Methodology 3.1 Trend detection The rank-based non-parametric Mann–Kendall statistical test (Mann, 1945; Kendall, 1975) has been commonly used for trend detection (Yue and Wang, 2002; Zheng et al., 2007) due to its robustness for non-normally distributed and censored data, which are frequently en- countered in hydro-climatic time-series. In this paper, the method was used to detect the trends of pan evaporation, temperature, solar radiation, vapor pressure and wind speed in the Tibetan Plateau. At the significant level of α=0.05, a positive Mann-Kendal statistics Z lar- ger than 1.96 indicates an significant increasing trend, while a negative Z lower than –1.96 indicates a significant decreasing trend. The linear regression approach was used to detect the trend of series x against time t. For the linear regression function (i.e. x=a+bt), we have dx/dt=b, in which the slope b can be considered an indicator describing the trend of the variable concerned and estimated as: 2n n n n n ?? ?? ? ? ? ? ?? 2 ??n t ? t = n x t? xt (1) b ? ? ? ? ? ? ? ? ? ? i i i i i ????? i =1 ? ? i =1?i =1 ? ? ?? ? ?? i ? i =1 ?i =1 3.2 Trend attribution For a function y=f(x, x,…), the variation of the dependent variable y can be expressed by 12 the differential equation as: 598 Journal of Geographical Sciences ?f ′dy == f dx (2) ?? i i i ?x idx ′ where xis the ith independent variable and f= ?f / ?x. Moreover, as y varies with time i ii t, we can rewrite Eq.(2) as: dxdy ?f i ′ ==f (3) ?? dt ?xdtdt i dxi i let TR= dy / dt and TR= dx/ dt be the long-term trend in y and x, then Eq.(3) can bei y i i rewritten as: ′(4) TR= fTR= ? ?y ii C( x)if TRand TRare estimated as the slope of the linear regression for y and xagainst time t y i i i given in Eq.(1), C(x) can then be estimated as the contribution rate of xto the long-term ii trend in y, which exactly equals to the product of partial derivative and long-term trend in x. iAccording to Eq.(4), with known form of the function, it is therefore easy to estimate the contribution rate of xto y. In terms of pan evaporation, it is widely accepted that there exists i rather good linear relationship with reference evapotranspiration, expressed as: E = K ET+ K(5) pan p ref c where Kand Kare regression coefficients, and Eis pan evaporation and ETis refer- p c pan ref ?1ence evapotranspiration (mm a). The reference evapotranspiration is defined as the poten- tial evapotranspiration of a hypothetical surface estimated by the Penman-Monteith method, where the land cover is hypothetical reference grass with an assumed height of 0.12 m, a ?1 fixed surface resistance of 70 s m, and an albedo of 0.23. FAO recommends that referenceevapotranspiration can be estimated as (Allen et al., 1998): 900 0.408Δ(R ? G) + U ? (VP ? n s T + 273 mean γVP) ET =(6) ref Δ+ γ (1 + ?10.34U ) where ETis reference evapotranspiration (mm d), Ris net radiation at reference surface ref n ?2 ?1?2 ?1(MJ md), G is soil heat flux density (MJ md), Tis daily mean temperature (?), mean ?1U is the wind speed at 2 meters height (m s), VPis saturated vapor pressure (kPa), VP is s actual vapor pressure (kPa), Δ is the slope of vapor pressure curve versus temperature ?1?1(kPa ?) and γ is psychrometric constant (kPa ?). Rrepresents the difference between n incoming net shortwave radiation (R) and outgoing net long-wave radiation (R). Ris nsnlns estimated from surface solar radiation (R): s R= (1 ? (7) ns λ)R s where λ (=0.23) is the albedo of the reference grassland, alfalfa. Ris estimated S s R = (a + b(8) )R s s a s as:N where S is the actual duration of sunshine (h), N is the maximum possible duration of sun- shine or daylight hours (h) (S/N is thus the relative sunshine duration), and Ris the extra- a ?2 ?1terrestrial radiation intensity (MJ md). The coefficients a(=0.22) and b(=0.55) were s s estimated from measured solar radiation and sunshine hours at the 11 radiation stations (Figure 1) by using nonlinear least squares data fitting by the Gauss-Newton method. Following Eq.(6), for contribution rate to reference evapotranspiration trend can be ap- proximately estimated below (Zheng et al., 2009): dET?ET?ET?ET ?ET dRdTdU dVP refref ref ref ref s mean + δ+ +(9) = + dt ?Rdtdt ?U dt ?VP d t?T means 2 With the relation between pan evaporation and reference evapotranspiration shown in Eq.(5), the contribution rate of climate factors to long-term trend in Ecan be expressed as: pan dE / dt = K C (R) + K C (T) + K C (U ) + K C (VP) + ε(10a) pan p s p mean p p or simplified as: ′′′′TR= dE / dt = C(R) + C(T) + C (U ) + C(10b) pan pan s mean 2 (VP) + εwhere TRis the long-term trend in Eand can be estimated with Eq.(1) by the observed pan pan data, and C′(R), C′(T), C′(U) and C′(VP) are individual contributions to the long-smeanterm trends in Edue to a change in R, T, U and VP respectively; ε is the error pan smeanitem. Fur- thermore, the individual proportional contribution of climate variables to the long-term trend in Ecan be estimated as: pan ′′C C ( x) ×100% = ×(11) ρ ( x) = ( x) ′′′C C (R) + C (T) + C (U ) + pans mean 2 100%′C (VP) where x may be R, T, U or VP, and Cis the estimated total contribution to the pan smeanpan evaporation trend. 4 Results 4.1 Relations between ETand E ref pan Figures 2 and 3 show the correlation between annual Eand ET. As shown in Figure 2, pan ref 2 Figure 2 Spatial distribution of correlation coefficient (R) between annual Eand ETat the 75 meteoro- pan ref logical stations 600 Journal of Geographical Sciences Figure 3 Relationship between annual Eand ETat the 75 meteorological stations (a) and the entire Tibetan pan ref Plateau (b) from 1970 to 2005 Table 2 Regression functions between Eand ETfor the entire Plateau pan ref 2 Season Regression equation R Spring (MAM) E= 2.857×ET– 227.7 0.92 pan ref Summer (JJA) E= 2.321×ET– 140.36 0.94 pan ref Autumn (SON) E= 1.798×ET+ 25.6 0.87 pan ref Winter (DJF) E= 2.495×ET– 68.2 0.91 pan ref Annual 0.92 E= 2.235×ET– 267.8 pan ref 2 the correlation coefficients Rbetween annual Eand ETwere all above 0.82 at the 75 pan ref 2 stations. Figure 3a shows that Rbetween annual Eand ETof all the 75 stations together pan ref was 0.88, while it was 0.92 over the entire Plateau (Figure 3b). Seasonally, the relations between Eand ETcan be as high as around 0.90 (Table 2). The good agreement be- pan ref tween Eand ETsuggests it was reasonable to use Eq.(10) to estimate contributions to pan ref the long-term trend in E. pan 4.2 Trends in ET, Eand climate variables refpan Figure 4 shows long-term trends in annual ET, Eand meteorological variables for the refpan period 1970–2005. It was found that Edecreased at 49 stations (33 of which showed sig- pan nificant trend), while Eincreased at 26 stations (8 of which with significant trend). In pan comparison with E, the trends of ETare almost the same, which implicates again the panref possibility of using Eq.(10) to estimate the contribution rates. For the entire Tibetan Plateau, the annual ETand Edecreased significantly ( α = 0.05) from 1970 to 2005 with ref pan ETref ?2 ?2 decreasing at a rate of 1.41 mm aand Eat 3.06 mm a(Figure 5 and Table 3). Season- pan ?2?2 ally, as shown in Table 3, Edecreased significantly by 1.10 mm a, 1.11 mm aand 0.60 pan ?2 mm ain spring, summer and autumn, respectively, while the decreasing trend in winter ?2was not significant. Similar to E, ETdecreased significantly by 0.36 mm a, 0.50 mm panref ?2 ?2 aand 0.35 mm arespectively in spring, summer and autumn. Figure 4 Spatial distributions of trends in annual E, ETand meteorological variables at 75 stations from panref 1970 to 2005. Cross symbol indicates an increasing trend (solid cross represents the trend is significant and hol- low cross not significant at the level of α = 0.05); diamond symbol indicates a decreasing trend (solid diamond represents the trend is significant and hollow diamond not significant at the level of α = 0.05). For the four climate factors concerned, Tshowed significant increasing trends at 66 mean stations and wind speed showed significant decreasing trends at 66 stations. Vapor pressure and solar radiation decreased significantly at 63 stations and 41 stations, respectively. It is interesting to note that most of the stations where solar radiation decreased significantly are located in the southeastern part of the Plateau, where Tand Tdid not increase as sig- max mean nificantly as other parts. Considering the lower elevation and higher population density in the southeastern part, complicated feedback mechanisms may exist between human activi- ties, solar radiation and temperature, which need further researches. For the entire Plateau, Figure 5 and Table 3 show that solar radiation and wind speed decreased significantly by ?2?1?2 ?1?2 0.017 MJ mdaand0.018 msa,respectively, while vapor pressure increased by ?2?20.0016 kPa a. T, Tand Tincreased by 0.025, 0.041 and 0.031 ? a, respec- maxmin mean tively. Tincreased more than T(about 1.6 times), which was also found in other re- min max gions of the world (Peterson et al., 1995; Roderick and Farquhar, 2002; Xu et al., 2006). 4.3 Attribution of pan evaporation trend Pan evaporation is an integrated effect of climate factors such as solar radiation, temperature, 602 Journal of Geographical Sciences Figure 5 Variation of average annual E, ETand meteorological variables from 1970 to 2005. Slope repre- panref sents b in Eq.(1), S represents the trend is significant and NS not significant at the level of α = 0.05 by Mann–Kendall test Table 3 Trend in E, ETand meteorological variables within the Tibetan Plateau panref Annual Spring Summer Autumn Winter E pan Slope –1.10 –1.11 –0.60 –0.33 –3.06 ****Z –1.62 –4.18 –2.73 –3.12 –3.27 ET Spring Summer Autumn Winter Annual ref Slope –1.41 –0.36 –0.50 –0.35 –0.14 ****Z –1.47 –3.86 –2.36 –2.92 –3.83 Meteorologi- U VP TTTR smeanmaxmincal variables Slope –0.018 0.0016 –0.017 0.031 0.025 0.041 ******Z –6.45 +3.67 –4.85 +4.37 +2.48 +6.32 Z is the Mann-Kendall test statistic in Eq. (1); „*? means significant trend at the level of α = 0.05 by Mann–Kendall test. vapor pressure, and wind speed. The contribution of change in each climate variable to the long-term trend in ETcan be quantitatively defined as the product of the partial derivative pan and the corresponding trend of the climate variable shown in Eq.(10). For the Tibetan Pla- teau, it can be found that the estimated pan evaporation trends (C) using Eq.(10) fit well pan with that detected from the observed pan evaporation (TR) for all the 75 stations (Figure pan?2 6). The largest absolute error between seasonal TRand Cwas 1.43 mm ain summer pan pan at station No.56146 with a relative error of –15.1%. Annually, the largest absolute error be- ?2 tween TRand Cwas 2.76 mm aat station No.52825 with a relative error of –21.0%. pan pan For the Plateau as a whole, seasonally, Table 4 shows that the absolute errors between TR pan?2and Cin spring, summer, autumn and winter were 0.06, –0.01, –0.01 and –0.05 mm a pan with relative errors of –5.45%, 0.90%, 1.67% and 15.15%, respectively. Annually, the ab- ?2 solute error was –0.09 mm awith a relative error of 2.94%. In conclusion, the derivation based method used herein well represents the long-term pan evaporation trends in the Ti- betan Plateau, which enables the possibility to estimate the contribution rate of each climate factor to pan evaporation changes. Figure 6 Relationship between the observed pan evaporation trend (C) and the calculated pan evaporation pan trend (TR) for different seasons at all the 75 stations from 1970 to 2005 pan Table 4 Contributions of meteorological variables to the long-term trend in E pan Contribution Period TR εpan C′C′C′C′C pan (U)(VP)*(R)(T) S mzzean –1.20 Spring –0.68 –0.05 0.89 –1.04 –1.10 0.06 *Summer –0.66 –0.51 0.83 –1.12 –1.11 –0.01 –0.78 *Autumn –0.34 –0.23 0.54 –0.61 –0.60 –0.01 –0.58 *Winter –0.36 –0.44 –0.04 0.46 –0.38 –0.33 –0.05 *Annual –1.96 –1.11 2.73 –3.15 –3.06 –0.09 –2.81 C Proportional contribution (% ) pan (%) Period ρ(ε) TRρρρρsum pan (%) (U)*(VP)(R)(T) S mean 115.38 Spring 65.38 4.81 –85.58 100.00 94.55 –5.45 *Summer 58.93 45.54 –74.11 100.00 100.90 0.90 69.64 *95.08 55.74 37.70 –88.52 100.00 101.67 1.67 Autumn *Winter 94.74 115.79 10.53 –121.05 100.00 115.15 15.15 *62.22 35.24 –86.67 100.00 102.94 2.94 Annual 89.21 604 Journal of Geographical Sciences Table 4 shows the contributions of each climate variable to the trend in E. Annually the pan?2 increasing Tshould have led to a 2.73 mm aincrease in Eand the change in wind mean pan speed, vapor pressure and solar radiation should have led to a decrease in Eat –2.81 pan ?2?2 ?2mm a, –1.96 mm aand –1.11 mm a, respectively. The combined effects of the four ?2 climate variables resulted in a decrease of 3.15 mm ain pan evaporation. The proportional contribution rates of mean temperature, vapor pressure, wind speed and solar radiation to the long-term trend in annual Ewere –86.67%, 62.22%, 89.21% and 35.24%, respectively. It pan is clear that wind speed was the dominant factor for the decrease in E, followed by vapor pan pressure and solar radiation. Temperature, on the contrary, shows an increasing effect of E, however, the effect has been offset by changes in vapor pressure, wind speed and solar pan radiation. Table 4 also shows the attribution of seasonal Etrend. In spring, the increased Thas pan mean ?2 led to a 0.89 mm aincrease of E, while wind speed, vapor pressure and solar radiation pan?2?2 ?2have led to a decrease of Eat a rate of 1.20 mm a, 0.68 mm aand 0.05 mm a, respec- pan ?2 tively. The combined effects of the four climate factors resulted in a 1.04 mm adecrease in pan evaporation. Among the four factors, wind speed was the dominant factor for the de- crease in E, followed by vapor pressure and solar radiation. In comparison to spring, the pan dominant factors of decreasing Ein summer, autumn and winter are solar radiation, wind pan speed and vapor pressure respectively. Concurrently, changes in Tshould have led to a mean ?2?2 ?2 0.83 mm a, 0.54 mm aand 0.46 mm aincrease of Ein summer, autumn and winter. pan For different locations of the Tibetan Plateau, as shown in Figure 7, annually, wind speed was the dominant factor for Etrend at 36 out of 75 stations, accounting for 48.0% of all pan stations. Temperature was the dominant factor at 26 stations, where pan evaporation in- creased during the period. Vapor pressure and solar radiation were the dominant factors at 11 and 2 stations, respectively. The results indicate that the increasing temperature indeed should have led to the increase of pan evaporation, but this effect has been offset by the de- creasing wind speed and solar radiation and the increasing vapor pressure. Moreover, the decreasing wind speed was the most crucial factor for the decreasing in Eover the Plateau, pan followed by vapor pressure and solar radiation. Figure 7 Contributions (a) and proportional contributions (b) of meteorological variables to pan evaporation trends at the 75 meteorological stations from 1970 to 2005 5 Discussion Evaporation plays an important role in water and energy budget, and decrease in pan evapo- ration could cause changes in the hydrologic cycle over the Tibetan Plateau. Figure 8 shows the variation of average annual precipitation and aridity index over the Tibetan Plateau, where the aridity index was defined as the ratio between annual potential evapotranspiration and precipitation. It can be seen that annual precipitation increased during the period 1970–2005, while aridity index decreased over the same period (Figure 8). This suggests that the climate became warmer and wetter over the Plateau between the years 1970 to 2005. The decreasing pan evaporation could represent a decline in actual evapotranspiration (Pe- terson et al., 1995). However, it should be noted that actual evapotranspiration is not only controlled by energy (potential evapotranspiration) but also by the water availability, espe- cially in dryer regions. Brutsaert and Parlange (1998) suggested that a decrease in pan evaporation could signal an increase in actual evapotranspiration in non-humid regions ac- cording to the Bouchet evaporative complimentary hypothesis (Bouchet, 1963). Though the assumption needs to be further proved and validated (Szilagyi, 2001; Lhomme and Guilioni, 2006; Fu et al., 2009), it has been confirmed to be true in the United States (Lawrimore and Peterson 2000; Hobbins et al., 2004; Walter et al., 2004), Yellow River Basin of China (Liu et al., 2006), Australia (Zhang et al., 2004) and southeastern Turkey (Ozdogan and Salvucci, 2004). Zhang et al. (2007) found that the complementary relationship may exist in the Ti- betan Plateau, where actual evapotranspiration increased accompanying with a decreasing potential evapotranspiration from 1966 to 2001. One may also notice that the solar radiation was weakening and wind speed was slowing down (“solar dimming” and “wind stilling”) in the Tibetan Plateau, but the hydrological was accelerating due to increasing precipitation and actual evapotranspiration. It may be because that the increase in greenhouse gas (GHG) concentrations has a significant effect on the energy driving hydrologic cycle (Allen and Ingram, 2002). However, the complex feedbacks between hydrological cycle and GHG ra- diation forcing need further exploration. Figure 8 Variation of annual precipitation and aridity index over the Tibetan Plateau As mentioned above, in addition to temperature, wind speed is an important and dynamic factor for evaporation, and any reduction in wind speed could contribute to decline in pan evaporation. Observed wind speed has decreased over the last years in many regions of the world (Gulev et al., 1999; Xu et al., 2006; Burn and Hesch, 2007; Roderick et al., 2007; Pryor et al., 2009; Jiang et al., 2010; McVicar et al., 2008, 2010). However, the reasons at- tributed to the change in wind speed have been far less studied than temperature and pre- 606 Journal of Geographical Sciences cipitation. Some researchers interpreted that tall buildings associated with urbanization may cause wind speed reduction (Lam, 2006; Xu et al., 2006). This is reasonable, however, an- thropogenic impacts within the Tibetan Plateau are far less serious than most regions of the world and the impact of urbanization on wind speed can be assumed to be negligible. Therefore, other reasons must have led to the decrease in observed wind speed in the Tibetan Plateau. The main reason for decrease in surface wind speeds is expected to be due to changes in atmospheric circulation (Pryor et al., 2010; Jiang et al., 2010). In the context of global warming, the differences of the sea level pressure and near-surface temperature be- tween the Asian continent and the Pacific Ocean are getting significantly smaller and the East Asian trough has shifted eastward and northward and has also weakened (IPCC, 2007). Additionally, East Asian winter and summer monsoons, which strongly control the climate in China, are weakening. The correlation coefficient between the decreasing wind speed in China and the weakening of the winter monsoons and summer monsoons over East Asia is rather high (Jiang et al., 2010). The decline of wind speed within the Tibetan Plateau may potentially be due to the changes in global atmospheric circulation. 6 Conclusions In this study the spatial and temporal variation of pan evaporation (E) and related climate pan variables over the Tibetan Plateau has been detected for the period 1970–2005. It has been found that both annual and seasonal pan evaporation decreased during the period. With the application of the derivation based approach, the contribution rate of wind speed, solar ra- diation, temperature, and vapor pressure to pan evaporation trend in the Tibetan Plateau has been estimated. The results showed that the increasing temperature indeed should have led to the increase of pan evaporation, but the combined effects of wind speed, solar radiation, and vapor pressure was greater than that of temperature on the change of pan evaporation. In annual scale, the decreasing trend in wind speed was the most crucial factor for the decreas- ing trend detected in Eover the Plateau, followed by vapor pressure and solar radiation. In pan seasonal scale, the decreasing trend in wind speed was the dominant factor for decreasing Ein spring and autumn, while changes in solar radiation and vapor pressure was the pan dominant factor in summer and winter, respectively. Acknowledgements This research was supported by the European Commission (Call FP7-ENV-2007-1 Grant nr. 212921) as part of the CEOP-AEGIS project and National Basic Research Program of China (2010CB428406). References Allen M R, Ingram W J, 2002. Constraints on future changes in climate and the hydrologic cycle. 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