S HORT COMMUN I CAT I ON

Using Geo-detector to attribute spatio-temporal variation of panevaporation across China in 1961–2001

Tao Yang1,2 | Fubao Sun1,3,4 | Wenbin Liu1 | Hong Wang1 | Tingting Wang1,2 | Changming Liu1

1Key Laboratory of Water Cycle and Related LandSurface Processes, Institute of GeographicSciences and Natural Resources Research, ChineseAcademy of Sciences, Beijing, China2College of Resources and Environment,University of Chinese Academy of Sciences,Beijing, China3Research School of Qilian Mountain Ecology,Hexi University, Zhangye, China4Center for Water Resources Research, ChineseAcademy of Sciences, Beijing, China

CorrespondenceFubao Sun, Key Laboratory of Water Cycle andRelated Land Surface Processes, Institute ofGeographic Sciences and Natural ResourcesResearch, Chinese Academy of Sciences, Beijing100101, China.Email: sunfb@igsnrr.ac.cn

Funding informationNational Research and Development Program ofChina, Grant/Award Number: 2016YFA0602402,2016YFC0401401; Key Research Program of theChinese Academy of Sciences, Grant/AwardNumber: ZDRW-ZS-2017-3-1; National NaturalSciences Foundation of China, Grant/AwardNumber: 41601035; CAS Pioneer Hundred TalentsProgram

Declining pan evaporation (Epan) across many continents in the past decadesdespite global warming is gaining attention of hydrology and climate science com-munities. While Geo-detector is considered useful for attributing the spatio-tempo-ral variation of a certain geography-related element, it has not been applied forEpan attribution. Based on the Geo-detector, we first attribute the spatio-temporalvariation of Epan across China in 1961–2001. The results show that relativehumidity is the primary factor influencing the temporal dynamics of Epan, fol-lowed by sunshine duration, maximum air temperature and wind speed. In thenorthwest China (south China and east China), wind speed, air temperature and rel-ative humidity (relative humidity, sunshine duration, wind speed and air tempera-ture) are the dominant factors of Epan variations. The results obtained arecomparable to the previous studies using other statistical methods (i.e., the partialdifferential method, Pearson correlation coefficient and multiple regression), whichreveal the usefulness of Geo-detector for attributing and diagnosing the spatio-tem-poral variations of Epan. Moreover, more detailed spatial information and interac-tions among meteorological elements can be obtained using Geo-detector. Thesefindings should provide helpful references for understanding the mass and energybudgets at different spatial–temporal scales under global warming.

KEYWORDS

attribution, climate change, Geo-detector, pan evaporation, spatio-temporalvariation

1 | INTRODUCTION

Pan evaporation (Epan) is a useful tool for agricultural irri-gation, water conservation and climate change monitoring(Dobson et al., 2002; Stanhill, 2002; Rotstayn et al., 2006;Martí et al., 2015; Liu and Sun, 2016). Over the past severaldecades, decline in Epan has been observed worldwide(McVicar et al., 2012), e.g., China (Zhang et al., 2015),Australia (Roderick et al., 2009) and America (Hobbinset al., 2004). This phenomenon is called the “pan evapora-tion paradox” because it defies the general expectation ofintensified evaporation under rising global mean surfacetemperature (e.g., Rotstayn et al., 2006; Wang et al., 2017).The attribution of declining Epan with respect to other

meteorological variables is thus of great interests to hydrol-ogy and climate science communities.

Numerous methods have been adopted to attribute thedynamics of Epan over past decades, for example, the multi-ple regression analysis (Liu et al., 2004), partial differentialmethod (Li et al., 2013), variance-based method (Xu et al.,2016) and sensitivity analysis method (Zhang et al., 2014;Zhang et al., 2015). For instance, some studies suggestedthat the observed variation in Epan is related to changes inrelative humidity over different periods (Zhang et al., 2001;2015; Wang et al., 2012; Peng et al., 2018). Li et al. (2013)found that Epan declined over northwest China before 1993mainly due to decreasing wind speed, while air humidity andwind speed are the key drivers of rising Epan after 1993.

Received: 10 June 2018 Revised: 18 October 2018 Accepted: 20 December 2018 Published on: 24 January 2019

DOI: 10.1002/joc.5976

Int J Climatol. 2019;39:2833–2840. wileyonlinelibrary.com/journal/joc © 2018 Royal Meteorological Society 2833

Wang et al. (2017) indicated that Epan decrease was attrib-uted to the changes in wind speed and solar radiation. Xuet al. (2016) discovered that Epan was more sensitive toradiation in East China. Moreover, McVicar et al. (2012)reported that the governing climatic factors on the atmo-spheric evaporative demand changed both spatially andtemporally.

Geo-detector method (Wang et al., 2010) is an attribu-tion method developed in the medical geography field. Itwas implemented to assess the association between a healthoutcome (disease or mortality) and feasible risk factors (pol-lution and economic statues) (Cao et al., 2013). This methodis based on the hypothesis that the dependent variable wouldexhibit similar spatial or temporal distribution with the corre-lated factors, and can be used to diagnose the interactionbetween correlated variables. This method demonstrated sat-isfactory performance in analysing spatial–temporal hetero-geneity, such as significance in ecological zones (Wanget al., 2016), physiographic division (Luo et al., 2016), spa-tial variation in carbon sink (Xu et al., 2017). This study tar-gets to verify the feasibility of Geo-detector for attributingEpan variation both spatially and temporally. To the best ofour knowledge, this is the first study to interpret dynamics inEpan via Geo-detector method.

Based on the Geo-detector method, this study aims to(a) demonstrate its applicability for attributing Epan variation;(b) revisit the climatic drivers of Epan variation across Chinaand their spatial heterogeneity. This manuscript is organized asfollows: section 2 introduces the data sets used in this study,that is, the observed Epan records and the related meteorologi-cal data. Section 3 describes the Geo-detector and trend analy-sis methods. Detailed attribution results are described anddiscussed in section 4, followed by conclusions.

2 | DATA AND METHODOLOGY

2.1 | Data sets

Daily meteorological records of 756 meteorological stationsfrom the National Climate Center of China MeteorologicalAdministration (http://www.cma.gov.cn/) were used in thisstudy. These stations have complete records of almost allmeteorological variables, including near surface mean, mini-mum and maximum air temperature (Ta, Tmin, Tmax) at2 m height, wind speed (Ws) measured at 10 m height, airpressure (Ps), relative humidity (Rh), sunshine duration(Sd) and D20 pan evaporation (Epan) during the period1961–2001. The D20 pan evaporation is a Chinese metalmicro-pan (20 cm in diameter, 10 cm height, installed70 cm above the ground on a wooden platform) (Li et al.,2013; Liu and Sun, 2016). Relative to the US Class A pan,D20 pan is much smaller and contains shallower water (itswater level is replenished to 2 cm, sometimes to 3 cm) (Liuand Sun, 2016). The D20 evaporation pan network was

established across China back in the 1950s, and was laterreplaced by D601 pan (pan evaporation with 0.62 m indiameter) after 2001. The number of meteorological siteswith valid measurements was rather uneven in the 1950s(which increased from 147 sites in 1951 to 651 in 1960)(Wang et al., 2018). Thus, current study focuses on theperiod 1961–2001 throughout the manuscript. As the overallconsecutive observations are important, we conduct furtherquality controls based on the length of available data (themissing data) and location of stations, etc. First, we per-formed temporal consistency control using information ofdata length and select 551 (out of the 756) meteorologicalstations with consecutive observations (missing data <3%).Second, for the selected stations, we reconstructed the miss-ing data from the observations of nearby sites using theinverse distance weighted method (Bartier and Keller,1996). Furthermore, we selected 420 grid boxes of 1 × 1�

(longitude by latitude), and each grid box should cover atleast one station with uninterrupted meteorological observa-tions (note that the lack of observation data over centralChina was due to the data quality in this area). If a grid boxcontains more stations, we would take an average for thosestations to represent the observations in that grid box. Fromabove, we reduced the influence of temporal inhomogeneityand spatial autocorrelation in the data and ensured the robustof overall statistics (Liu and Sun, 2016; Zhang et al., 2016).

2.2 | Modified Mann–Kendall test and Theil–Senmedian trend

Mann–Kendall (MK) test (Mann, 1945; Kendall, 1975) is arank-based nonparametric method, which has been recom-mended for investigating climate change and variability bythe World Meteorological Organization (Ziegler et al., 2003;Zhang et al., 2012). It is less sensitive to outlier(s), but it canbe affected by the autocorrelation of time series (Zhang et al.,2015). Hamed and Rao (1998) recommended a modified MK(MMK), which is robust in the presence of proposed autocor-relation and has been widely used in previous studies in thelast half-a-century (McVicar et al., 2012; Zhang et al., 2015;Liu and Sun, 2016). In this study, we applied the MMKmethod to examine the statistical significance of annual andseasonal trends in the observed Epan and meteorological vari-ables. While MMK measures the significance of a trend,Theil–Sen median trend analysis method (Theil, 1950; Sen,1968) derives the change rates of meteorological factors from1961 to 2001. Theil–Sen median trend is effectively com-bined with the MMK method and is particularly useful for thetrend estimation in short time series. It calculates the medianslopes of pairwise time series data (Jiang et al., 2015). It canbe expressed as follows:

Sf ¼ medianMj −Mi

j− i

� �, 1961 ≤ i < j ≤ 2001, ð1Þ

2834 SUN ET AL.

where Sf refers to the Theil–Sen median, Mi and Mj representthe values of a meteorological variable at years of i and j,respectively. When of Sf > 0, the detected meteorologicalvariable presents an increasing trend. Otherwise, it displaysa declining trend.

2.3 | Attributing spatio-temporal variation usingGeo-detector method

In this study, for the first time, we implement the Geo-detectormethod (Wang et al., 2010; Xu et al., 2017) to attribute Epanvariation to the related meteorological variables at annual and sea-sonal scales (spring, MAM; summer, JJA; autumn, SON; winter,DJF). The Geo-detector is different from the conventional mathe-matical correlation or regression, which is good for analysing thecategorical data (Cao et al., 2013; Luo et al., 2016) and diagnos-ing the interaction between the correlated variables. In Geo-detector approach, if a factor F is associated with a dependentvariable D, then D would exhibit a similar distribution (spatial ortemporal) to that of F. The higher the similarity between the pat-terns of F and D, the higher the sensitivity of D to F. In a realisticcase, the degree of spatial correspondence between F and D ismeasured by the power of determinant (PF), which is defined as

PF ¼ 1−1nσ2

Xmr¼1

nrσr2, ð2Þ

where n and σ2 denote the total sample size and the total dis-persion variance of D, respectively. The study area is stratifiedinto m zones, namely, r = 1, … , m. The stratification dependson the property of suspected determinant (F). In addition,nr and σr denote the sample points number and the varianceof D in the rth zone, respectively, based on the hypothesisσr

2 6¼ 0, with PF 2 [0, 1]. In a perfect case, in which factorF completely explains the pattern of D, the value of

D would be uniform across each zone stratified by F,spatial variance of D within all zones would be 0, andPF would be 1, where PF = 0 indicates that F is completelyunrelated to D. Higher values of PF indicate higher affinityof F and D, namely, which factor is more strongly associ-ated with the spatio-temporal distribution of D and thuscould be a dominator. Hence, Geo-detector method is aconvenient tool for attribution studies. Details of Geo-detector method are available in Wang et al. (2010). More-over, Geo-detector analysis can be conducted using freesoftware downloadable from http://geodetector.org/.

3 | RESULTS AND DISCUSSION

3.1 | Trends in Epan and relevant meteorologicalvariables

The annual trends of Epan in selected meteorological gridsare detected in the period 1961–2001 (Figure 1). In China,the averaged change rate of Epan is −0.011 mmday−1 year−1, which is consistent with recent studies(Liu et al., 2011; Wang et al., 2017). Annual Epan exhibitedsignificant decline (increase) at approximately 57% (9%)grids, which distribute across almost the entire country (somein northeast China, northwest China and south China).

The results of trend in Epan also showed considerable spa-tial inhomogeneity at grid scale (Figure 2). Significantly,decreasing trends are found across China. Consistent withZhang et al. (2012), increasing trends are merely found innortheast China, south China and Tibetan Plateau. It is essentialto understand the underlying causes behind the spatio-temporalvariation of Epan in the study period (i.e., 1961–2001), thus wepresent the variations of related meteorological variables inFigure 2 and the trend significance in Figure S1, Supporting

FIGURE 1 Spatial distribution of trends in observed Epan in 1961–2001. The filled triangles denote that the trends in Epan are statistically significant at95% confidence level [Colour figure can be viewed at wileyonlinelibrary.com]

SUN ET AL. 2835

Information. The maximum (minimum) change rate of Epanwas found in summer (−0.017 mm day−1 year−1) (winter:−0.003 mm day−1 year−1) over the study period. Moreover,the changes in meteorological variables exhibited significantspatial inhomogeneity. For example, relative humiditydecreased in north China, east China, south China and somegrids in central and northeast China while increased mainly innortheast China and Tibetan Plateau. Mean and minimum airtemperature showed substantially increasing trends in northernChina and south China over the study period. Decline in maxi-mum air temperature was found in some regions such as southChina, central China and east China. Compared to Tibetan Pla-teau and south China, wind stilling was more remarkable inmajority regions over China (Peng et al., 2018). Sunshineduration declined mainly in north China, central China, eastChina and south China while increased in Tibetan Plateau,northwest China, south China and northeast China.

3.2 | Attributing the spatial variation of Epan

To study what caused the spatial variation in Epan in thestudy period, we first take the change rates of Epan at eachgrid with meteorological stations as the input of Geo-detector

(Figure 3). We estimate the influence of major meteorologicalvariables on spatial variations in Epan change rates and theirinteractions (Table S1, which are not robust due to the lack ofobserved data) using the Geo-detector method at both annual

FIGURE 2 Change rates of Epan and relevant meteorological variables at annual scale in 1961–2001. The subfigure (a) shows 551 selected meteorologicalstations (red solid point) and 420 selected grid boxes (1 × 1�, blue squares, created with at least one station). The characters denote different subregions, thatis, A = northeast China, B = north China, C = east China, D = south China, E = central China, F = Tibet and G = northwest China. Others express:(b) pan evaporation (Epan); (c) mean temperature (Ta); (d) relative humidity (Rh); (e) wind speed (Ws); (f ) minimum air temperature (Tmin); (g) maximumtemperature (Tmax); (h) air pressure (Ps); (i) sunshine duration (Sd). Black edges of grids denote statistically significant trend at 95% confidence level [Colourfigure can be viewed at wileyonlinelibrary.com]

FIGURE 3 Radar diagram showing the contributions (%) ofmeteorological variables to spatial variation of Epan in change rates for theentire China at annual and seasonal scales. The upward triangles denote theinfluences are statistically significant at 95% confidence level [Colour figurecan be viewed at wileyonlinelibrary.com]

2836 SUN ET AL.

and seasonal scales. At the annual scale, relative humidity isthe dominant meteorological factor influencing spatial varia-tion in Epan change rates, followed by wind speed. At theseasonal scales (except winter), relative humidity (maximumair temperature) are the drivers of spatial variations in Epanchange rates. In particular, wind speed has a dominant role inspring, while maximum and daily mean air temperature arethe key factors of the spatial variation of Epan in summer.These results indicate the significance of relative humidity atregional scale (Dai, 2006) which could affect regional watermanagement. Moreover, spatial inhomogeneity in Epanchange rates was more sensitive in summer than other seasons(annual, spring, autumn and winter).

3.3 | Attributing the temporal variation of Epan

Based on the Geo-detector method, we quantified the contri-bution of each meteorological variable to the temporal varia-tion of Epan in each grid box at annual and seasonal scalesin the study period. For each grid box, we put temporalobservation series with meteorological stations into Geo-detector, and derived dominant meteorological variable(s)(Figure 4). The results showed high consistency with themultiple regression analysis (Figure S1) and Pearsoncorrelation coefficient (Figure S2). Comparing the results ofGeo-detector with those of the two methods (i.e., Pearsoncorrelation coefficient and multiple regression), we noticedthat the main discrepancy is at annual scale. This discrepancy

FIGURE 4 Spatial distribution of the dominant meteorological variable governing Epan variation across 420 grid boxes at seasonal (a, b, c, d) and annual(e) scales in 1961–2001 [Colour figure can be viewed at wileyonlinelibrary.com]

SUN ET AL. 2837

might originate from the basic mechanism of the Geo-detectormethod. This is because the Geo-detector method is sensitiveto the temporal fluctuation of the data series. The annual aver-aged values would lessen the temporal fluctuation and giveunstable results (Figure 4). The relative humidity, sunshineduration and wind speed are equally important on affectingthe temporal changes of Epan in the study period (Table 1).Changes of annual Epan at grid boxes near Hu Line (Yueet al., 2003, also called Heihe-Tengchong Line or Aihui-Tengchong Line, proposed by Huanyong Hu in 1935, is a linecrossing the northeast to southwest of China. On the left andright sides, there are huge differences in population, economicand natural environment), are more sensitive to wind speed.At the seasonal scales, relative humidity was also the domi-nant meteorological variable influencing Epan variation, fol-lowed by sunshine duration, maximum air temperature andwind speed. In spring, changes in Epan at grid boxes near HuLine were mostly dominated by relative humidity. Moreover,Epan variations at some grid boxes located in north China,south China and northeast China (east China, central China,south China and northeast China) were sensitive to windspeed (sunshine duration). In summer, Epan variation at mostof the grid boxes near Hu Line was governed by relativehumidity. Epan variations were mainly influenced by windspeed in northwest China; by air temperature, sunshine dura-tion and wind speed in central China, south China and eastChina. In autumn, relative humidity played an overwhelmingrole on Epan variation for the entire China, except for somegrid boxes in northwest China (air temperature), south China(sunshine duration) and east China (sunshine duration). Inwinter, Epan variation at grid boxes in north China, east chinaand central China was mostly dominated by relative humidity.Epan variation at some grid boxes in south China was mainlyinfluenced by sunshine duration; while that in northeastChina, northwest China and Tibetan Plateau (north China andnorthwest China) was governed by air temperature (windspeed).

Our results confirmed that sunshine duration has a domi-nant effect on Epan variation in humid regions (Figure 4),which supports the significance of radiation on Epan changein energy-limited regions (Roderick et al., 2009). Interest-ingly, compared to sunshine duration, relative humidityplays a more crucial role in temporal evolutions of Epanacross both water-limited and energy-limited regions. Liu

et al. (2004) illustrated that the sensitivity of Epan to radia-tion might be influenced by dry conditions. In addition,Thomas (2000) and Zhang et al. (2011) indicated that rela-tive humidity accounts for most of the local variance of ref-erence evaporation in China. Similar results were alsoobtained by Zhang et al. (2015), Peng et al. (2018) andVicente-Serrano et al. (2018). The Geo-detector showedgood performance in Epan attribution over China with theobtained results in agreement with previous studies and othertwo methods (multiple regression analysis and Pearson cor-relation coefficient), suggesting its feasibility and veracity inattribution-style studies.

Based on above analysis, spatial distribution of changerates in relative humidity (followed by wind speed) playedthe dominant role in the temporal and spatial inhomogeneityof Epan change rates in China over the study period.Thomas (2000) also stressed that changes of potential evapo-transpiration in the winter and pre-monsoon seasons aremost strongly associated with changes in relative humidity.Our results can be interpreted using the conclusions of Brut-saert and Parlange (1998) and Liu et al. (2004), that is,increasing surface runoff and soil wetness due to increasingprecipitation would generate more actual evaporation, andsuppress pan evaporation (Brutsaert and Parlange, 1998; Liuet al., 2004). Nevertheless, such adverse relation was notobvious, especially in the south (south and east China) andnortheast China, where sunshine duration and other meteoro-logical variables (such as wind speed and maximum air tem-perature) have considerable influences beside relativehumidity (Zhang et al., 2015). For instance (Figure 4), ontop of relative humidity, wind speed and temperature havesome impact on the temporal variation of Epan in northwestChina. In this sense, Geo-detector was reasonable and feasi-ble tool for attributing the spatio-temporal variation of Epan.Note that it is difficult to account for other factors(e.g., human activities, changing surface covers, complexinteraction of air masses, and etc.) in current study due tolimited long-term measurements.

4 | CONCLUSION

By combining the MMK and Geo-detector approaches, wecarried out a comprehensive trend and attribution analysison the spatio-temporal variation of Epan to the correspond-ing meteorological variables across China in 1961–2001.The following conclusions are drawn:

1. The Geo-detector showed good performance in Epanattribution across China with the obtained results inagreement with previous studies, which indicates its fea-sibility and veracity in attribution-style studies.

2. Substantial declines in Epan were found from 1961 to2001 in most areas of China, for example, east China,northwest China and central China. For the entire China,

TABLE 1 Number of grid boxes with Epan variation dominated by aspecific meteorological variable at annual and seasonal scales

Ta Rh Ws Tmin Tmax Ps Sd

Annual 25 146 54 29 51 17 98

Spring 33 177 33 20 74 8 75

Summer 28 181 23 6 42 15 125

Autumn 23 184 23 20 82 7 81

Winter 41 97 74 44 51 26 87

Note. The total grid boxes are 420.

2838 SUN ET AL.

relative humidity is the dominated the temporal variationof Epan in 1961–2001. It was followed by sunshineduration, maximum air temperature and wind speed.Regionally, relative humidity (sunshine duration) playeda crucial role in Epan variation in the northern (southern)China. In the northwest China, Epan variation wereinfluenced by wind speed, air temperature and relativehumidity. The combined influence of relative humidity,sunshine duration, wind speed and air temperature domi-nated Epan variation in the south China and east China.

3. Among all the seasons, summer was the season withEpan variation highly sensitive to the changes in meteo-rological variables. Air temperature dominated the spa-tial variation in Epan change in winter.

ACKNOWLEDGEMENTS

This study was financially supported by the National Researchand Development Program of China (2016YFA0602402 and2016YFC0401401), the Key Research Program of the ChineseAcademy of Sciences (ZDRW-ZS-2017-3-1), the National Nat-ural Sciences Foundation of China (41601035), the CAS Pio-neer Hundred Talents Program (Fubao Sun). The data sets usedin this study are available from the first author upon request(yangt.16b@igsnrr.ac.cn).

REFERENCES

Bartier, P.M. and Keller, C.P. (1996) Multivariate interpolation to incorporatethematic surface data using inverse distance weighting (IDW). Computersand Geosciences, 22(7), 795–799.

Brutsaert, W. and Parlange, M.B. (1998) Hydrologic cycle explains the evapora-tion paradox. Nature, 396(6706), 30–30.

Cao, F., Ge, Y. and Wang, J.F. (2013) Optimal discretization for geographicaldetectors-based risk assessment. GIScience & Remote Sensing, 50(1), 78–92.

Dai, A.G. (2006) Recent climatology, variability, and trends in global surfacehumidity. Journal of Climate, 19(15), 3589–3606.

Dobson, D.P., Meredith, P.G. and Boon, S.A. (2002) Simulation of subductionzone seismicity by dehydration of serpentine. Science, 298(5597),1407–1410.

Hamed, K.H. and Rao, A.R. (1998) A modified Mann–Kendall trend test forautocorrelated data. Journal of Hydrology, 204, 182–196.

Hobbins, M.T., Ramírez, J.A. and Brown, T.C. (2004) Trends in pan evaporationand actual evapotranspiration across the conterminous U.S.: paradoxical orcomplementary? Geophysical Research Letters, 31(13), 1–5. https://doi.org/10.1029/2004GL019846.

Jiang, W.G., Yuan, L.H., Wang, W.J., Cao, R., Zhang, Y.F. and Shen, W.M.(2015) Spatio-temporal analysis of vegetation variation in the Yellow Riverbasin. Ecological Indicators, 51, 117–126.

Kendall, M.G. (1975) Rank Correlation Methods. London: Griffin.Li, Z., Chen, Y.N., Shen, Y.J., Liu, Y.B. and Zhang, S.H. (2013) Analysis of

changing pan evaporation in the arid region of northwest China. WaterResources Research, 49, 2205–2212.

Liu, W.B. and Sun, F.B. (2016) Assessing estimates of evaporative demand inclimate models using observed pan evaporation over China. Journal of Geo-physical Research: Atmospheres, 121(14), 8329–8349. https://doi.org/10.1002/2016JD025166.

Liu, B.H., Xu, M., Henderson, M. and Gong, W.G. (2004) A spatial analysis ofpan evaporation trends in China, 1955–2000. Journal of GeophysicalResearch, 109, D15102. https://doi.org/10.1029/2004JD004511.

Liu, X.M., Luo, Y.Z., Zhang, D., Zhang, M.H. and Liu, C.M. (2011) Recentchanges in pan-evaporation dynamics in China. Geophysical Research Let-ters, 38, L13404.

Luo, W., Jasiewicz, J., Stepinski, T., Wang, J.F., Xu, C.D. and Cang, X.Z.(2016) Spatial association between dissection density and environmental fac-tors over the entire conterminous United States. Geophysical Research Let-ters, 43, 692–700.

Mann, H.B. (1945) Nonparametric tests against trend. Econometrica, 13,245–259.

Martí, P., González-Altozano, P., López-Urrea, R., Mancha, L.A. and Shiri, J.(2015) Modeling reference evapotranspiration with calculated targets.Assessment and implications. Agricultural Water Management, 149, 81–90.

McVicar, T.R., Roderick, M.L., Donohue, R.J., Li, L.T., Van Niel, T.G.,Thomas, A., Grieser, J., Jhajharia, D., Himri, Y., Mahowald, N.M.,Mescherskaya, A.V., Kruger, A.C., Rehman, S., and Dinpashoh, Y. (2012)Global review and synthesis of trends in observed terrestrial near-surface windspeeds: implications for evaporation. Journal of Hydrology, 416–417, 182–205.

Peng, L.Q., Li, D. and Sheffield, J. (2018) Drivers of variability in atmosphericevaporative demand: multiscale spectral analysis based on observations andphysically based modeling. Water Resources Research, 54, 1–20. https://doi.org/10.1029/2017WR022104.

Roderick, M.L., Hobbins, M.T. and Farquhar, G.D. (2009) Pan evaporationtrends and the terrestrial water balance. I. Principles and observations. Geog-raphy Compass, 3(2), 746–760.

Rotstayn, L.D., Roderick, M.L. and Farquhar, G.D. (2006) A simple pan-evaporation model for analysis of climate simulations: evaluation overAustralia. Geophysical Research Letters, 33, L17715.

Sen, P.K. (1968) Estimates of the regression coefficient based on Kendall’s tau.Journal of the American Statistical Association, 63, 1379–1389.

Stanhill, G. (2002) Is the class A evaporation pan still the most practical andaccurate meteorological method for determining irrigation water require-ments? Agricultural and Forest Meteorology, 112, 233–236.

Theil, H. (1950) A rank-invariant method of linear and polynomial regressionanalysis. Indagationes Mathematicae, 12, 85–91.

Thomas, A. (2000) Spatial and temporal characteristics of potential evapotranspi-ration trends over China. International Journal of Climatology, 20, 381–396.

Vicente-Serrano, S.M., Bidegain, M., Tomas-Burguera, M., Dominguez-Castro, F., Kenawy, A.E., McVicar, T.R., Azorin-Molina, C., López-Moreno, J.I., Nieto, R., Gimeno, L., and Giménez, A. (2018) A comparisonof temporal variability of observed and model-based pan evaporation overUruguay (1973–2014). International Journal of Climatology, 38, 337–350.https://doi.org/10.1002/joc.5179.

Wang, J.F., Li, X.H., Christakos, G., Liao, Y.L., Zhang, T., Gu, X. andZheng, X.Y. (2010) Geographical detectors-based health risk assessment andits application in the neural tube defects study of the Heshun region, China.International Journal of Geographical Information Science, 24, 107–127.

Wang, K.C., Dickinson, R.E. and Liang, S.L. (2012) Global atmospheric evaporativedemand over land from 1973 to 2008. Journal of Climate, 25(23), 8353–8361.

Wang, J.F., Zhang, T.L. and Fu, B.J. (2016) A measure of spatial stratified het-erogeneity. Ecological Indicators, 67, 250–256.

Wang, T.T., Zhang, J., Sun, F.B. and Liu, W.B. (2017) Pan evaporation paradoxand evaporative demand from the past to the future over China: a review.WIREs: Water, 4, 1–13.

Wang, T.T., Sun, F.B., Ge, Q.S., Kleidon, A. and Liu, W.B. (2018) The effect ofelevation bias in interpolated air temperature data sets on surface warming inChina during 1951–2015. Journal of Geophysical Research: Atmospheres,123(4), 2141–2151. https://doi.org/10.1002/2017JD027510.

Xu, Y.P., Pan, S.L., Gao, C., Fu, G.T. and Chiang, Y.M. (2016) Historical panevaporation changes in the Qiantang River basin, east China. InternationalJournal of Climatology, 36(4), 1928–1942. https://doi.org/10.1002/joc.4470.

Xu, Q., Dong, Y.X. and Yang, R. (2017) Influence of different geographical fac-tors on carbon sink functions in the Pearl River Delta. Scientific Reports,7(110), 1–10.

Yue, T.X., Wang, Y.A., Chen, S.P., Liu, J.Y., Qiu, D.S., Deng, X.Z., Liu, M.L.and Tian, Y.Z. (2003) Numerical simulation of population distribution inChina. Population and Environment, 25, 141–163. https://doi.org/10.1023/B:POEN.0000015562.88915.01.

Zhang, Q., Xu, C.Y. and Chen, X.H. (2011) Reference evapotranspiration changesin China: natural processes or human influences? Theoretical and AppliedClimatology, 103, 479–488. https://doi.org/10.1007/s00704-010-0315-6.

Zhang, Q., Sun, P., Singh, V.P. and Chen, X.H. (2012) Spatial-temporal precipi-tation changes (1956–2000) and their implications for agriculture in China.Global and Planetary Change, 82-83, 86-95. https://doi.org/10.1016/j.gloplacha.2011.12.001.

SUN ET AL. 2839

Zhang, D., Hong, H.Y., Zhang, Q. and Nie, R. (2014) Effects of climatic varia-tion on pan-evaporation in the Poyang Lake basin, China. Climate Research,61(1), 29–40. https://doi.org/10.3354/cr01225.

Zhang, Q., Qi, T.Y., Li, J.F., Singh, V.P. and Wang, Z.Z. (2015) Spatiotemporalvariations of pan evaporation in China during 1960–2005: changing patternsand causes. International Journal of Climatology, 35, 903–912. https://doi.org/10.1002/joc.4025.

Zhang, J., Sun, F.B., Xu, J.J., Chen, Y.N., Sang, Y.F. and Liu, C.M. (2016)Dependence of trends in and sensitivity of drought over China (1961–2013)on potential evaporation model. Geophysical Research Letters, 43, 206–213.https://doi.org/10.1002/2015GL067473.

Ziegler, A.D., Sheffield, J., Maurer, E.P., Nijssen, B., Wood, E.F. andLettenmaier, D.P. (2003) Detection of intensification in global- andcontinental-scale hydrological cycles: temporal scale of evaluation. Journalof Climate, 16, 535–547.

SUPPORTING INFORMATION

Additional supporting information may be found online inthe Supporting Information section at the end of the article.

How to cite this article: Yang T, Sun F, Liu W,Wang H, Wang T, Liu C. Using Geo-detector to attri-bute spatio-temporal variation of pan evaporationacross China in 1961–2001. Int J Climatol. 2019;39:2833–2840. https://doi.org/10.1002/joc.5976

2840 SUN ET AL.