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Spatiotemporal distributions of pan evaporation and the influencing factors in China from 1961 to 2017

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Abstract

Pan evaporation (EVP) is an important element of the hydrological cycle and exhibits a close relationship with climate change. In this study, the generalized regression neural network (GRNN) model and extreme gradient boosting (Xgboost) model were applied to estimate the monthly EVP. The spatiotemporal distributions of EVP and influencing factors in China and eight subregions from 1961 to 2017 were analyzed. The root mean square error (RMSE) of all GRNN models was approximately 10%, and the Nash–Sutcliffe efficiency (NSE) coefficient was larger than 0.94 in different subregions. The annual mean EVP in all subregions and throughout China showed decreasing trends before 1993, while EVP increasing trends occurred in East China (EC), South China (SC), Southwest China (SWC), west of Northwest China (WNC), and throughout China after 1994. Subsequently, the variable importance in projection (VIP) between EVP and climatic factors obtained by partial least squares (PLS) regression and the relative contribution calculated by Xgboost stepwise regression analysis (SRA) were used to investigate the climatic parameter sensitivity to EVP. The results indicated that the combined effects of the vapor pressure deficit (VPD), sunshine duration (SSD), and wind speed (WIN) were the main reasons for the variations in EVP across China. At the seasonal scale, SSD, WIN, relative humidity (RHU), and VPD were the most sensitive climatic factors to EVP in different seasons. In addition, the Pacific decadal oscillation (PDO) index showed a significant negative correlation with EVP, and the El Niño 3.4 (N3.4) and East Atlantic/Western Russia (EA/WR) indices revealed positive correlations in most regions from 1961 to 1993, while the North Atlantic oscillation (NAO) was negatively correlated with EVP. Moreover, N3.4 and Atlantic multidecadal oscillation (AMO) were positively correlated with EVP from 1994 to 2017. Finally, the yearly number of heatwave events (HWN) was highly correlated with EVP because of the high VPD and SSD levels during the heatwave event periods.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Alduchov OA, Eskridge RE (1996) Improved Magnus form approximation of saturation vapor pressure. J Appl Meteorol 35(4):601–609

    Google Scholar 

  • Aydaraili X, Huang M (2011) Comparative analysis of observed data between E-601 evaporator and 20 cm pan in Tianshan River Basin. J Chin Hydrol 31(4):76–80

    Google Scholar 

  • Azorin-Molina C, Vicente-Serrano SM, Sanchez-Lorenzo A, McVicar TR, Morán-Tejeda E, Revuelto J, Kenawy AE, Martín-Hernández N, Tomas-Burguera M (2015) Atmospheric evaporative demand observations, estimates and driving factors in Spain (1961–2011). J Hydrol 523:262–277

    Google Scholar 

  • Bai H, Lu X, Yang X, Huang J, Mu X, Zhao G, Gui F, Yue C (2020) Assessing impacts of climate change and human activities on the abnormal correlation between actual evaporation and atmospheric evaporation demands in southeastern China. Sustain Cities Soc 56:102075

    Google Scholar 

  • Barnston AG, Livezey RE (1987) Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon Weather Rev 115(6):1083–1126

    Google Scholar 

  • Brutsaert W, Parlange MB (1998) Hydrologic cycle explains the evaporation paradox. Nature 396(6706):30–30

    CAS  Google Scholar 

  • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250

    Google Scholar 

  • Chang FJ, Chang LC, Kao HS, Wu GR (2010) Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network. J Hydrol 384(1–2):118–129

    Google Scholar 

  • Cohen S, Ianetz A, Stanhill G (2002) Evaporative climate changes at bet Dagan, Israel, 1964–1998. Agric For Meteorol 111(2):83–91

    Google Scholar 

  • Cong ZT, Yang DW, Ni GH (2009) Does evaporation paradox exist in China? Hydrol Earth Syst Sci 13(3):357–366

    Google Scholar 

  • da Silva VDPR (2004) On climate variability in Northeast of Brazil. J Arid Environ 58(4):575–596

    Google Scholar 

  • Ding Z, Wang Y, Lu R (2018) An analysis of changes in temperature extremes in the Three River Headwaters region of the Tibetan Plateau during 1961–2016. Atmos Res 209:103–114

    Google Scholar 

  • Enfield DB, Mestas-Nuñez AM, Trimble PJ (2001) The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental US. Geophys Res Lett 28(10):2077–2080

    Google Scholar 

  • Fan J, Wu L, Zhang F, Xiang Y, Zheng J (2016) Climate change effects on reference crop evapotranspiration across different climatic zones of China during 1956–2015. J Hydrol 542:923–937

    Google Scholar 

  • Farrés M, Platikanov S, Tsakovski S, Tauler R (2015) Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation. J Chemom 29(10):528–536

    Google Scholar 

  • Feng Y, Jia Y, Zhang Q, Gong D, Cui N (2018) National-scale assessment of pan evaporation models across different climatic zones of China. J Hydrol 564:314–328

    Google Scholar 

  • Fenner D, Holtmann A, Krug A, Scherer D (2019) Heat waves in Berlin and Potsdam, Germany-long-term trends and comparison of heat wave definitions from 1893 to 2017. Int J Climatol 39(4):2422–2437

    Google Scholar 

  • Gocic M, Trajkovic S (2013) Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Glob Planet Chang 100:172–182

    Google Scholar 

  • Guo D, Westra S, Maier H (2017) Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones. Hydrol Earth Syst Sci 21(4):2107–2126

    Google Scholar 

  • Guo Y, Zhang Y, Ma N, Xu J, Zhang T (2019) Long-term changes in evaporation over Siling Co Lake on the Tibetan Plateau and its impact on recent rapid lake expansion. Atmos Res 216:141–150

    Google Scholar 

  • Guven A, Kisi O (2013) Monthly pan evaporation modeling using linear genetic programming. J Hydrol 503:178–185

    Google Scholar 

  • Hamdani I, Assouline S, Tanny J, Lensky IM, Gertman I, Mor Z, Lensky NG (2018) Seasonal and diurnal evaporation from a deep hypersaline lake: the Dead Sea as a case study. J Hydrol 562:155–167

    Google Scholar 

  • Herath IK, Ye X, Wang J, Bouraima AK (2018) Spatial and temporal variability of reference evapotranspiration and influenced meteorological factors in the Jialing River Basin, China. Theor Appl Climatol 131:1417–1428

    Google Scholar 

  • Hounguè R, Lawin AE, Moumouni S, Afouda AA (2019) Change in climate extremes and pan evaporation influencing factors over Ouémé Delta in Bénin. Climate 7(1):2

    Google Scholar 

  • Hurrell JW (1995) Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science 269(5224):676–679

    CAS  Google Scholar 

  • Jhajharia D, Shrivastava SK, Sarkar DSAS, Sarkar S (2009) Temporal characteristics of pan evaporation trends under the humid conditions of Northeast India. Agric For Meteorol 149(5):763–770

    Google Scholar 

  • Jones PD, Jonsson T, Wheeler D (1997) Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and south-west Iceland. Int J Climatol 17(13):1433–1450

    Google Scholar 

  • Kendall MG (1970) Rank correlation methods, 4th ed. Griffin, Oxford

  • Kim S, Shiri J, Kisi O (2012) Pan evaporation modeling using neural computing approach for different climatic zones. Water Resour Manag 26(11):3231–3249

    Google Scholar 

  • Kisi O (2013) Evolutionary neural networks for monthly pan evaporation modeling. J Hydrol 498:36–45

    Google Scholar 

  • Li Z, Pan N, He Y, Zhang Q (2016) Evaluating the best evaporation estimate model for free water surface evaporation in hyper-arid regions: a case study in the Ejina Basin, Northwest China. Environ Earth Sci 75(4):295

    Google Scholar 

  • Liang K, Bai P, Li J, Liu C (2014) Variability of temperature extremes in the Yellow River Basin during 1961–2011. Quat Int 336:52–64

    Google Scholar 

  • Lin F, Chen X, Yao H (2017) Evaluating the use of Nash-Sutcliffe efficiency coefficient in goodness-of-fit measures for daily runoff simulation with SWAT. J Hydrol Eng 22(11):05017023

    Google Scholar 

  • Liu W, Sun F (2016) Assessing estimates of evaporative demand in climate models using observed pan evaporation over China. J Geophys Res-Atmos 121(14):8329–8349

    Google Scholar 

  • Liu B, Xu M, Henderson M, Gong W (2004) A spatial analysis of pan evaporation trends in China, 1955–2000. J Geophys Res-Atmos 109(D15):102–110

  • Liu X, Luo Y, Zhang D, Zhang M, Liu C (2011) Recent changes in pan-evaporation dynamics in China. Geophys Res Lett 38(13):404–407

    Google Scholar 

  • Liu T, Li L, Lai J, Liu C, Zhuang W (2016a) Reference evapotranspiration change and its sensitivity to climate variables in southwest China. Theor Appl Climatol 125(3–4):499–508

    Google Scholar 

  • Liu X, Yu J, Wang P, Zhang Y, Du C (2016b) Lake evaporation in a hyper-arid environment, northwest of China—measurement and estimation. Water 8(11):527

    Google Scholar 

  • Liu YJ, Chen J, Pan T (2019) Analysis of changes in reference evapotranspiration, pan evaporation, and actual evapotranspiration and their influencing factors in the North China Plain during 1998–2005. Earth and Space Science 6(8):1366–1377

    Google Scholar 

  • Ma Q, Zhang J, Sun C, Guo E, Zhang F, Wang M (2017) Changes of reference evapotranspiration and its relationship to dry/wet conditions based on the aridity index in the Songnen Grassland, Northeast China. Water 9(5):316

    Google Scholar 

  • Mann HB (1945) Nonparametric tests against trend. Econometrica J Econ Soc 245–259

  • Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A Pacific interdecadal climate oscillation with impacts on salmon production. Bull Am Meteorol Soc 78(6):1069–1080

    Google Scholar 

  • McVicar TR, Roderick ML, Donohue RJ, Li LT, Van Niel TG, Thomas A, Grieser J, Jhajharia D, Himri Y, Mahowald NM, Meshcerskaya AV, Kruger AC, Rehman S, Dinpashoh Y (2012) Global review and synthesis of trends in observed terrestrial near-surface wind speeds: implications for evaporation. J Hydrol 416:182–205

    Google Scholar 

  • Mwangi HM, Julich S, Patil SD, McDonald MA, Feger KH (2016) Relative contribution of land use change and climate variability on discharge of upper Mara River, Kenya. J Hydrol Reg Stud 5:244–260

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290

    Google Scholar 

  • Ning T, Li Z, Liu W, Han X (2016) Evolution of potential evapotranspiration in the northern Loess Plateau of China: recent trends and climatic drivers. Int J Climatol 36(12):4019–4028

    Google Scholar 

  • Niu Z, Wang L, Fang L, Li J, Yao R (2020) Spatiotemporal variations in monthly relative humidity in China based on observations and CMIP5 models. Int J Climatol 40:6382–6395. https://doi.org/10.1002/joc.6587

    Article  Google Scholar 

  • Pendergrass AG (2018) What precipitation is extreme? Science 360(6393):1072–1073

    CAS  Google Scholar 

  • Qasem SN, Samadianfard S, Kheshtgar S, Jarhan S, Kisi O, Shamshirband S, Chau KW (2019) Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Eng App Comput Fluid Mech 13(1):177–187

    Google Scholar 

  • Qin MS, Zhang Y, Wan SQ, Yue Y, Cheng Y, Zhang B (2021) Impact of climate change on “evaporation paradox” in province of Jiangsu in southeastern China. PLoS One 16(2):e0247278

    CAS  Google Scholar 

  • Rezaie-Balf M, Kisi O, Chua LH (2019) Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation. Hydrol Res 50(2):498–516

    Google Scholar 

  • Roderick ML, Farquhar GD (2004) Changes in Australian pan evaporation from 1970 to 2002. Int J Climatol 24(9):1077–1090

    Google Scholar 

  • Roderick ML, Farquhar GD (2005) Changes in New Zealand pan evaporation since the 1970s. Int J Climatol 25(15):2031–2039

    Google Scholar 

  • Roderick ML, Rotstayn LD, Farquhar GD, Hobbins MT (2007) On the attribution of changing pan evaporation. Geophys Res Lett 34(17):403–408

  • Russo S, Dosio A, Graversen RG, Sillmann J, Carrao H, Dunbar MB, Dunbar MB, Singleton A, Montagna P, Barbola P, Vogt JV (2014) Magnitude of extreme heat waves in present climate and their projection in a warming world. J Geophys Res-Atmos 119(12):500–512

    Google Scholar 

  • Russo S, Sillmann J, Fischer EM (2015) Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environ Res Lett 10(12):124003

    Google Scholar 

  • She D, Xia J, Zhang Y (2017) Changes in reference evapotranspiration and its driving factors in the middle reaches of Yellow River Basin, China. Sci Total Environ 607:1151–1162

    Google Scholar 

  • Shen J, Yang H, Li S, Liu Z, Cao Y, Yang D (2020) Revisiting the pan evaporation trend in China from 1988–2017. Earth Space Sci Open Arch 18:2020. https://doi.org/10.1002/essoar.10505424.1

    Article  Google Scholar 

  • Sudheer KP, Gosain AK, Mohana Rangan D, Saheb SM (2002) Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16(16):3189–3202

    Google Scholar 

  • Sun Z, Ouyang Z, Zhao J, Li S, Zhang X, Ren W (2018) Recent rebound in observational large-pan evaporation driven by heat wave and droughts by the Lower Yellow River. J Hydrol 565:237–247

    Google Scholar 

  • Tabari H, Marofi S (2011) Changes of pan evaporation in the west of Iran. Water Resour Manag 25(1):97–111

    Google Scholar 

  • Thompson DW, Wallace JM (1998) The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys Res Lett 25(9):1297–1300

    Google Scholar 

  • Trenberth KE (1984) Signal versus noise in the Southern Oscillation. Mon Weather Rev 112(2):326–332

    Google Scholar 

  • Vincent LA, Wang XL, Milewska EJ, Wan H, Yang F, Swail V (2012) A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis. J Geophys Res-Atmos 117(D18):110–122

  • Wang J, Wang Q, Zhao Y, Li H, Zhai J, Shang Y (2015) Temporal and spatial characteristics of pan evaporation trends and their attribution to meteorological drivers in the Three-River Source Region, China. J Geophys Res-Atmos 120(13):6391–6408

    Google Scholar 

  • Wang L, Kisi O, Zounemat-Kermani M, Li H (2017a) Pan evaporation modeling using six different heuristic computing methods in different climates of China. J Hydrol 544:407–427

    Google Scholar 

  • Wang T, Zhang J, Sun F, Liu W (2017b) Pan evaporation paradox and evaporative demand from the past to the future over China: a review. Wiley Interdiscip Rev Water 4(3):1207

    Google Scholar 

  • Wang H, Yan H, Zeng W, Lei G, Ao C, Zha Y (2020) A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China. J Hydrol 582:124545

    Google Scholar 

  • Xiao M, Zhang Q, Singh VP (2015) Influences of ENSO, NAO, IOD and PDO on seasonal precipitation regimes in the Yangtze River Basin, China. Int J Climatol 35(12):3556–3567

    Google Scholar 

  • Xing W, Wang W, Shao Q, Yu Z, Yang T, Fu J (2016) Periodic fluctuation of reference evapotranspiration during the past five decades: does evaporation paradox really exist in China? Sci Rep 6(1):1–12

    Google Scholar 

  • Xu YP, Pan S, Gao C, Fu G, Chiang YM (2016) Historical pan evaporation changes in the Qiantang River Basin, East China. Int J Climatol 36(4):1928–1942

    Google Scholar 

  • Xu S, Yu Z, Yang C, Ji X, Zhang K (2018) Trends in evapotranspiration and their responses to climate change and vegetation greening over the upper reaches of the Yellow River Basin. Agric For Meteorol 263:118–129

    Google Scholar 

  • Yan Z, Wang S, Ma D, Liu B, Lin H, Li S (2019) Meteorological factors affecting pan evaporation in the Haihe River Basin, China. Water 11(2):317

    Google Scholar 

  • Yang T, Sun F, Liu W, Wang H, Wang T, Liu C (2019) Using geo-detector to attribute spatio-temporal variation of pan evaporation across China in 1961–2001. Int J Climatol 39(5):2833–2840

    Google Scholar 

  • You Q, Jiang Z, Kong L, Wu Z, Pepin N (2017) A comparison of heat wave climatologies and trends in China based on multiple definitions. Clim Dyn 48(11–12):3975–3989

    Google Scholar 

  • Zhang Y, Wallace JM, Battisti DS (1997) ENSO-like interdecadal variability: 1900–93. J Clim 10(5):1004–1020

    Google Scholar 

  • Zhang T, Chen Y, Paw U, K. T. (2019) Quantifying the impact of climate variables on reference evapotranspiration in Pearl River Basin, China. Hydrol Sci J 64(16):1944–1956

    Google Scholar 

  • Zhao Z, Wang H, Wang C, Li W, Chen H, Deng C (2020) Changes in reference evapotranspiration over Northwest China from 1957 to 2018: variation characteristics, cause analysis and relationships with atmospheric circulation. Agric Water Manag 231:105958

    Google Scholar 

  • Zhou H, Han S, Liu W (2020) Evaluation of two generalized complementary functions for annual evaporation estimation on the Loess Plateau, China. J Hydrol 587:124980

    Google Scholar 

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (41975044) and the Fundamental Research Funds for National Universities, China University of Geosciences, Wuhan. We thank the National Meteorological Information Center (NMIC), China Meteorological Administration (CMA), for providing the climate data.

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  1. Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, China

    Zigeng Niu, Lunche Wang, Xinxin Chen, Liu Yang & Lan Feng

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  1. Zigeng Niu
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  2. Lunche Wang
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Contributions

Zigeng Niu: data curation, writing (original draft preparation), and revision. Lunche Wang: conceptualization, methodology, software, reviewing and editing, supervision, project administration, funding acquisition, and revision. Xinxin Chen: methodology, software, and supervision. Liu Yang: methodology, software, revision. Lan Feng: software and funding acquisition.

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Correspondence to Lunche Wang.

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Niu, ., Wang, L., Chen, X. et al. Spatiotemporal distributions of pan evaporation and the influencing factors in China from 1961 to 2017. Environ Sci Pollut Res 28, 68379–68397 (2021). https://doi.org/10.1007/s11356-021-15386-0

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