Skip to main content
SpringerLink
Log in

Simulation of Evapotranspiration Based on BEPS-TerrainLab V2.0 from 1990 to 2018 in the Dajiuhu Basin

  • Published:
Chinese Geographical Science Aims and scope Submit manuscript

Abstract

Accurate estimations of evapotranspiration (ET) are essential for understanding land-atmosphere coupling and atmosphere-underlying surface energy and water vapor exchanges. Based on input data processing, this paper simulates the temporal and spatial variation of ET in the Dajiuhu Basin from 1990 to 2018 using the BEPS-TerrainLab V2.0 model. Compared with the ET measured by an eddy covariance (EC) tower, the model explained 80.1% of the ET variation. From 1990 to 2018, the average annual ET in the Dajiuhu Basin was 1262.7 mm/yr indicating a downward trend (−27.12 mm/yr). In 2005, a sudden change point was observed based on the Mann-Kendall (MK) test and 3-year moving t-test. Around 2005, the downward trend in ET slowed and the proportional trend of ET to precipitation changed from upward trend to downward trend. Regarding spatial distribution, the ET in the basin's central part was smaller than that in the basin's surrounding area, the ET of the southern slope was higher than that of the northern slope, and the decrease in the ET rate on the sunny side was lower than that on the shady side. ET decreased as the elevation increased, with the fastest decrease observed between 2184 and 2384 m. For different landcover types, the average ET exhibited the following order: deciduous forest > mixed forest > wetland > grass > agriculture land. Decreased solar radiation is the main reason for the decreased ET in the Dajiuhu Basin, followed by increased wind speed and relative humidity, which together contribute 83.9% to the ET trend. This paper provides a theoretical basis for the study of ET changes and the mechanism of ET and provides a decision-making reference for water resource management in the Dajiuhu Basin and even the South-to-North Water Transfer Project.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bagui S C, 2005. Combining pattern classifiers: methods and algorithms. Technometrics, 47(4): 517–518. doi: https://doi.org/10.1198/tech.2005.s320

    Google Scholar 

  • Baldocchi D, Falge E, Gu L H et al., 2001. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82(11): 2415–2434. doi: https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2

    Google Scholar 

  • Bian Y M, Dai H Y, Zhang Q L et al., 2020. Spatial distribution of potential evapotranspiration trends in the Inner Mongolia Autonomous Region (1971–2016). Theoretical and Applied Climatology, 1460(1): 1161–1169. doi: https://doi.org/10.1007/s00704-020-03154-y

    Google Scholar 

  • Biau G, Scornet E, 2016. A random forest guided tour. Test, 25(2): 197–227. doi: https://doi.org/10.1007/s11749-016-0481-7

    Google Scholar 

  • Bowen I S, 1926. The ratio of heat losses by conduction and by evaporation from any water surface. Physical Review, 27(6): 779–787. doi: https://doi.org/10.1103/physrev.27.779

    Google Scholar 

  • Breiman L, 2001. Random forests. Machine Learning, 45(1): 5–32. doi: https://doi.org/10.1023/A:1010933404324

    Google Scholar 

  • Chattopadhyay N, Hulme M, 1997. Evaporation and potential evapotranspiration in India under conditions of recent and future climate change. Agricultural and Forest Meteorology, 87(1): 55–73. doi: https://doi.org/10.1016/s0168-1923(97)00006-3

    Google Scholar 

  • Che H Z, Shi G Y, Zhang X Y et al., 2005. Analysis of 40 years of solar radiation data from China, 1961–2000. Geophysical Research Letters, 32(6): L06803. doi: https://doi.org/10.1029/2004gl022322

    Google Scholar 

  • Chen J M, Cihlar J, 1995. Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index. Applied Optics, 34(27): 6211–6222. doi: https://doi.org/10.1364/ao.34.006211

    Google Scholar 

  • Chen J M, Liu J, Cihlar J et al., 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling, 124(2–3): 99–119. doi: https://doi.org/10.1016/s0304-3800(99)00156-8

    Google Scholar 

  • Chen J M, Chen X Y, Ju W M et al., 2005. Distributed hydrological model for mapping evapotranspiration using remote sensing inputs. Journal of Hydrology, 305(1–4): 15–39. doi: https://doi.org/10.1016/j.jhydrol.2004.08.029

    Google Scholar 

  • Cheng L, Xu Z X, Wang D B et al., 2011. Assessing interannual variability of evapotranspiration at the catchment scale using satellite — based evapotranspiration data sets. Water Resources Research, 47(9): W09509. doi: https://doi.org/10.1029/2011WR010636

    Google Scholar 

  • Cleugh H A, Leuning R, Mu Q Z et al., 2007. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, 106(3): 285–304. doi: https://doi.org/10.1016/j.rse.2006.07.007

    Google Scholar 

  • Costa M H, Biajoli M C, Sanches L et al., 2010. Atmospheric versus vegetation controls of Amazonian tropical rain forest evapotranspiration: are the wet and seasonally dry rain forests any different? Journal of Geophysical Research: Biogeosciences, 115(G4): G04021. doi: https://doi.org/10.1029/2009jg001179

    Google Scholar 

  • Cui T X, Sun R, Qiao C et al., 2017. Estimating diurnal courses of gross primary production for maize: a comparison of sun-induced chlorophyll fluorescence, light-use efficiency and process-based models. Remote Sensing, 9(12): 1267. doi: https://doi.org/10.3390/rs9121267

    Google Scholar 

  • De Dios V R, Roy J, Ferrio J P et al., 2015. Processes driving nocturnal transpiration and implications for estimating land evapotranspiration. Scientific Reports, 5: 10975. doi: https://doi.org/10.1038/srep10975

    Google Scholar 

  • Ding Y H, Liu Y J, Liang S J et al., 2014. Interdecadal variability of the East Asian winter monsoon and its possible links to global climate change. Journal of Meteorological Research, 28(5): 693–713. doi: https://doi.org/10.1007/s13351-014-4046-y

    Google Scholar 

  • Dirmeyer P A, Gao X, Zhao M et al., 2006. GSWP-2: multimodel analysis and implications for our perception of the land surface. Bulletin of the American Meteorological Society, 87(10): 1381–1398. doi: https://doi.org/10.1175/bams-87-10-1381

    Google Scholar 

  • Du Haibo, Wu Zhengfang, Li Ming, 2010. Analysis of Climate Change and Sudden Changes in Changchun in Recent 57 Years: Agriculture and technology, 30(1): 52–58. (in Chinese)

    Google Scholar 

  • Dyer A J, 1961. Measurements of evaporation and heat transfer in the lower atmosphere by an automatic eddy — correlation technique. Quarterly Journal of the Royal Meteorological Society, 87(373): 401–412. doi: https://doi.org/10.1002/qj.49708737311

    Google Scholar 

  • Fan J L, Wu L F, Zhang F C et al., 2016. Climate change effects on reference crop evapotranspiration across different climatic zones of China during 1956–2015. Journal of Hydrology, 542: 923–937. doi: https://doi.org/10.1016/j.jhydrol.2016.09.060

    Google Scholar 

  • Feng T C, Su T, Ji F et al., 2018. Temporal characteristics of actual evapotranspiration over China under global warming Journal of Geophysical Research: Atmospheres, 123(11): 5845–5858. doi: https://doi.org/10.1029/2017jd028227

    Google Scholar 

  • Fisher J B, Tu K P, Baldocchi D D, 2008. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3): 901–919. doi: https://doi.org/10.1016/j.rse.2007.06.025

    Google Scholar 

  • Fisher J B, Melton F, Middleton E et al., 2017. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resources Research, 53(4): 2618–2626. doi: https://doi.org/10.1002/2016WR020175

    Google Scholar 

  • Fyfe J C, Meehl G A, England M H et al., 2016. Making sense of the early-2000s warming slow down. Nature Climate Change, 6(3): 224–228. doi: https://doi.org/10.1038/nclimate2938

    Google Scholar 

  • Govind A, Chen J M, Margolis H et al., 2009. A spatially explicit hydro-ecological modeling framework (BEPS-TerrainLab V2.0): model description and test in a boreal ecosystem in eastern North America. Journal of Hydrology, 367(3–4): 200–216. doi: https://doi.org/10.1016/j.jhydrol.2009.01.006

    Google Scholar 

  • Goward S N, Markham B, Dye D G et al., 1991. Normalized difference vegetation index measurements from the advanced very high resolution radiometer. Remote Sensing of Environment, 35(2–3): 257–277. doi: https://doi.org/10.1016/0034-4257(91)90017-z

    Google Scholar 

  • He H L, Wang S Q, Zhang L et al., 2019. Altered trends in carbon uptake in China's terrestrial ecosystems under the enhanced summer monsoon and warming hiatus. National Science Review, 6(3): 505–514. doi: https://doi.org/10.1093/nsr/nwz021

    Google Scholar 

  • Howell T A, Schneider A D, Jensen M E, 1991. History of lysimeter design and use for evapotranspiration measurements. Proceedings of Lysimeters for Evapotranspiration and Environmental Measurements. Honolulu: ASCE, 1–9.

    Google Scholar 

  • Jackson R D, Reginato R J, Idso S B, 1977. Wheat canopy temperature: a practical tool for evaluating water requirements. Water Resources Research, 13(3): 651–656. doi: https://doi.org/10.1029/wr013i003p00651

    Google Scholar 

  • Jarvis P G, 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society B, Biological Sciences, 273(927): 593–610. doi: https://doi.org/10.1098/rstb.1976.0035

    Google Scholar 

  • Jing Cairui, Fu Aimin, 1986. A preliminary study on pleistocene glacial remants in Dajiuhu region of Shennongjia. Journal of Central China Normal University (Natural Sciences), 20(3): 345–356. (in Chinese)

    Google Scholar 

  • Jung M, Reichstein M, Ciais P et al., 2010. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318): 951–954. doi: https://doi.org/10.1038/nature09396

    Google Scholar 

  • Kirkby M, 1975. Hydrograph modelling strategies. In: Peel R et al. (eds). Processes in Physical and Human Geography. Woburn, Mass: Butterworth-Heinemann, 69–90.

    Google Scholar 

  • Li C, Wu P T, Li X L et al., 2017. Spatial and temporal evolution of climatic factors and its impacts on potential evapotranspiration in Loess Plateau of Northern Shaanxi, China. Science of the Total Environment, 589: 165–172. doi: https://doi.org/10.1016/j.scitotenv.2017.02.122

    Google Scholar 

  • Liu Huiping, Tang Xiaochun, Sun Donghuai et al., 2001. Palynofloras of the Dajiuhu Basin in Shennongjiamountains during the last 12.5ka. Acta Micropalaeontologica Sinica, 18(1): 101–109. (in Chinese)

    Google Scholar 

  • Liu Haiwen, Zhou Tianjun, Zhu Yuxiang et al., 2012, The strengthening East Asia summer monsoon since the early 1990s: Chinese Science Bulletin, 57(13): 1553–1558. (in Chinese)

    Google Scholar 

  • Liu J, Chen J M, Cihlar J, 2003. Mapping evapotranspiration based on remote sensing: an application to Canada's landmass. Water Resources Research, 39(7): 1189. doi: https://doi.org/10.1029/2002wr001680

    Google Scholar 

  • Liu Mingliang, 2001. Land-use/Land-cover Change and Terrestrial Ecosystem Phytomass Carbon Pool and Production in China. Beijing: Chinese Academy of Sciences. (in Chinese)

    Google Scholar 

  • Lu Wei, Fan Wenyi, Tian Tian, 2016. Parameter optimization of BEPS model based on the flux data of the temperate deciduous broad-leaved forest in northeast China. Chinese Journal of Applied Ecology, 27(5): 1353–1358. (in Chinese)

    Google Scholar 

  • Lu Z X, Zhao Y, Wei Y P et al., 2019. Differences among evapotranspiration products affect water resources and ecosystem management in an Australian catchment. Remote Sensing, 11(8): 958. doi: https://doi.org/10.3390/rs11080958

    Google Scholar 

  • Luo K S, Tao F L, Deng X Z et al., 2017. Changes in potential evapotranspiration and surface runoff in 1981–2010 and the driving factors in Upper Heihe River Basin in northwest China. Hydrological Processes, 31(1): 90–103. doi: https://doi.org/10.1002/hyp.10974

    Google Scholar 

  • Major D J, Baret F, Guyot G, 1990. A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing, 11(5): 727–740. doi: https://doi.org/10.1080/01431169008955053

    Google Scholar 

  • Mao F J, Du H Q, Zhou G M et al., 2017. Coupled LAI assimilation and BEPS model for analyzing the spatiotemporal pattern and heterogeneity of carbon fluxes of the bamboo forest in Zhejiang Province, China. Agricultural and Forest Meteorology, 242: 96–108. doi: https://doi.org/10.1016/j.agrformet.2017.03.022

    Google Scholar 

  • Matsushita B, Tamura M, 2002. Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sensing of Environment, 81(1): 58–66. doi: https://doi.org/10.1016/s0034-4257(01)00331-5

    Google Scholar 

  • McVicar T R, Van Niel T G, Li L T et al., 2007. Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences. Journal of Hydrology, 338(3–4): 196–220. doi: https://doi.org/10.1016/j.jhydrol.2007.02.018

    Google Scholar 

  • Mishra V, Kumar R, Shah H L et al., 2017. Multimodel assessment of sensitivity and uncertainty of evapotranspiration and a proxy for available water resources under climate change. Climatic Change, 141(3): 451–465. doi: https://doi.org/10.1007/s10584-016-1886-8

    Google Scholar 

  • Moran M S, Clarke T R, Inoue Y et al., 1994. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 49(3): 246–263. doi: https://doi.org/10.1016/0034-4257(94)90020-5

    Google Scholar 

  • Mu Q Z, Heinsch F A, Zhao M S et al., 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4): 519–536. doi: https://doi.org/10.1016/j.rse.2007.04.015

    Google Scholar 

  • Ning TT, Zhou S, Chang F Y et al., 2019. Interaction of vegetation, climate and topography on evapotranspiration modelling at different time scales within the Budyko framework. Agricultural and Forest Meteorology, 275: 59–68.doi: https://doi.org/10.1016/j.agrformet.2019.05.001

    Google Scholar 

  • Oudin L, Andréassian V, Lerat J et al., 2008. Has land cover a significant impact on mean annual streamflow? An international assessment using 1508 catchments. Journal of Hydrology, 357(3–4): 303–316. doi: https://doi.org/10.1016/j.jhydrol.2008.05.021

    Google Scholar 

  • Piao S L, Sitch S, Ciais P et al., 2013. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Global Change Biology, 19(7): 2117–2132. doi: https://doi.org/10.1111/gcb.12187

    Google Scholar 

  • Roderick M L, Farquhar G D, 2002. The cause of decreased pan evaporation over the past 50 years. Science, 298(5597): 1410–1411. doi: https://doi.org/10.1126/science.1075390-a

    Google Scholar 

  • Roerink G J, Su Z, Menenti M, 2000. S-SEBI: a simple remote sensing algorithm to estimate the surface energy balance. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25(2): 147–157. doi: https://doi.org/10.1016/S1464-1909(99)00128-8

    Google Scholar 

  • Sellers P J, Mintz Y, Sud Y C et al., 1986. A simple biosphere model (SIB) for use within general circulation models. Journal of the Atmospheric Sciences, 43(6): 505–531. doi: https://doi.org/10.1175/1520-0469(1986)043<0505:asbmfu>2.0.co;2

    Google Scholar 

  • Sonnentag O, Chen J M, Roulet N T et al., 2008. Spatially explicit simulation of peatland hydrology and carbon dioxide exchange: influence of mesoscale topography. Journal of Geophysical Research: Biogeosciences, 113(G2): G02005. doi: https://doi.org/10.1029/2007jg000605

    Google Scholar 

  • Stanhill G, Cohen S, 2001. Global dimming: a review of the evidence for a widespread and significant reduction in global radiation with discussion of its probable causes and possible agricultural consequences. Agricultural and Forest Meteorology, 107(4): 255–278. doi: https://doi.org/10.1016/s0168-1923(00)00241-0

    Google Scholar 

  • Strasser U, Mauser W, 2001. Modelling the spatial and temporal variations of the water balance for the Weser catchment 1965–1994. Journal of Hydrology, 254(1–4): 199–214. doi: https://doi.org/10.1016/s0022-1694(01)00492-9

    Google Scholar 

  • Sun G, McNulty S G, Moore Myers J A et al., 2008. Impacts of multiple stresses on water demand and supply across the Southeastern United States. JAWRA Journal of the American Water Resources Association, 44(6): 1441–1457. doi: https://doi.org/10.1111/j.1752-1688.2008.00250.x

    Google Scholar 

  • Sun R, Chen J M, Zhu Q J et al., 2004. Spatial distribution of net primary productivity and evapotranspiration in Changbaishan Natural Reserve, China, using Landsat ETM+ data. Canadian Journal of Remote Sensing, 30(5): 731–742. doi: https://doi.org/10.5589/m04-040

    Google Scholar 

  • Tabari H, Aeini A, Talaee P H et al., 2012. Spatial distribution and temporal variation of reference evapotranspiration in arid and semi-arid regions of Iran. Hydrological Processes, 26(4): 500–512. doi: https://doi.org/10.1002/hyp.8146

    Google Scholar 

  • Tombul M, Akyürek Z, Sorman A Ü, 2004. Research note: determination of soil hydraulic properties using pedotransfer functions in a semi-arid basin, Turkey. Hydrology and Earth System Sciences, 8(6): 1200–1209. doi: https://doi.org/10.5194/hess-8-1200-2004

    Google Scholar 

  • Van Den Hurk B J J M, Viterbo P, Los S O, 2003. Impact of leaf area index seasonality on the annual land surface evaporation in a global circulation model. Journal of Geophysical Research: Atmospheres, 108(D6): 4191. doi: https://doi.org/10.1029/2002jd002846

    Google Scholar 

  • Vicente-Serrano S M, Azorin-Molina C, Sanchez-Lorenzo A et al., 2014. Sensitivity of reference evapotranspiration to changes in meteorological parameters in S pain (1961–2011). Water Resources Research, 50(11): 8458–8480. doi: https://doi.org/10.1002/2014wr015427

    Google Scholar 

  • Wang J W, Zhang J H, Bai Y et al., 2020. Integrating remote sensing-based process model with environmental zonation scheme to estimate rice yield gap in Northeast China. Field Crops Research, 246: 107682. doi: https://doi.org/10.1016/j.fcr.2019.107682

    Google Scholar 

  • Wang L Z, Cao L G, Deng X J et al., 2014. Changes in aridity index and reference evapotranspiration over the central and eastern Tibetan Plateau in China during 1960–2012. Quaternary International, 349: 280–286. doi: https://doi.org/10.1016/j.quaint.2014.07.030

    Google Scholar 

  • Wang T I, Ochs G R, Clifford S F, 1978. A saturation-resistant optical scintillometer to measure C 2n . Journal of the Optical Society of America, 68(3): 334–338. doi: https://doi.org/10.1364/josa.68.000334

    Google Scholar 

  • Wang TT, Zhang J, Sun F B et al., 2017a. Pan evaporation paradox and evaporative demand from the past to the future over China: a review. WIREs Water, 4(3): e1207. doi: https://doi.org/10.1002/wat2.1207

    Google Scholar 

  • Wang Z L, Xie P W, Lai C G et al., 2017b. Spatiotemporal variability of reference evapotranspiration and contributing climatic factors in China during 1961–2013. Journal of Hydrology, 544: 97–108. doi: https://doi.org/10.1016/j.jhydrol.2016.11.021

    Google Scholar 

  • Wei Z W, Yoshimura K, Wang L X et al., 2017. Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophysical Research Letters, 44(6): 2792–2801. doi: https://doi.org/10.1002/2016gl072235

    Google Scholar 

  • Wigmosta M S, Vail L W, Lettenmaier D P, 1994. A distributed hydrology-vegetation model for complex terrain. Water Resources Research, 30(6): 1665–1679. doi: https://doi.org/10.1029/94WR00436

    Google Scholar 

  • Xie X Y, Li A N, Jin H A et al., 2018. Derivation of temporally continuous leaf maximum carboxylation rate (Vcmax) from the sunlit leaf gross photosynthesis productivity through combining BEPS model with light response curve at tower flux sites. Agricultural and Forest Meteorology, 259: 82–94.doi: https://doi.org/10.1016/j.agrformet.2018.04.017

    Google Scholar 

  • Xu Y, Xu Y P, Wang Y F et al., 2017. Spatial and temporal trends of reference crop evapotranspiration and its influential variables in Yangtze River Delta, eastern China. Theoretical and Applied Climatology, 130(3–4): 945–958. doi: https://doi.org/10.1007/s00704-016-1928-1

    Google Scholar 

  • Yu Tengfei, Feng Qi, Si Jianhua et al., 2017. Evapotranspiration of a Populus euphratica Oliv. forest and its controlling factors in the lower Heihe River basin, Northwest China. Sciences in Cold and Arid Regions, 9(2): 175–182.

    Google Scholar 

  • Yuan W P, Liu S G, Yu G R et al., 2010. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment, 114(7): 1416–1431. doi: https://doi.org/10.1016/j.rse.2010.01.022

    Google Scholar 

  • Zeng ZZ, Piao S L, Lin X et al., 2012. Global evapotranspiration over the past three decades: estimation based on the water balance equation combined with empirical models. Environmental Research Letters, 7(1): 014026. doi: https://doi.org/10.1088/17489326/7/1/014026

    Google Scholar 

  • Zhang L, Dawes W R, Walker G R, 2001. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resources Research, 37(3): 701–708. doi: https://doi.org/10.1029/2000wr900325

    Google Scholar 

  • Zhang Q, Xu C Y, Chen X H, 2011. Reference evapotranspiration changes in China: natural processes or human influences? Theoretical and Applied Climatology, 103(3–4): 479–488. doi: https://doi.org/10.1007/s00704-010-0315-6

    Google Scholar 

  • Zhang R H, 2015. Changes in East Asian summer monsoon and summer rainfall over eastern China during recent decades. Science Bulletin, 60(13): 1222–1224. doi: https://doi.org/10.1007/s11434-015-0824-x

    Google Scholar 

  • Zhang W C, Yan H, Cheng P et al., 2016. Peatland development and climate changes in the Dajiuhu basin, central China, over the last 14,100 years. Quaternary International, 425: 273–281. doi: https://doi.org/10.1016/j.quaint.2016.06.039

    Google Scholar 

  • Zhou Shunwu, 1999. Analysis of climatic trend and jump of summer rainfall in Lhasa during recent 50 years. Tibet Science and Technology, (4): 35–39. (in Chinese)

    Google Scholar 

  • Zhu Jiawen, Zeng Xiaodong, 2015. Comprehensive study on the influence of evapotranspiration and albedo on surface temperature related to changes in the leaf area index. Advances in Atmospheric Sciences, 32(7): 935–942. doi: https://doi.org/10.1007/s00376-014-4045

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China

    Zongfan Wu, Lihua Zhang, Yasheng Fu & Yongming Ma

  2. Guizhou Electric Power Design Research Institute Co, Ltd, Power Construction Corporation of China, Guiyang, 550002, China

    Dandan Liu

  3. School of Earth Sciences, China University of Geosciences, Wuhan, 430074, China

    Kang Zhang & Zhiru Zhu

Authors
  1. Zongfan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lihua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dandan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiru Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yasheng Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yongming Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lihua Zhang.

Additional information

Foundation item

Under the auspices of National Natural Science Foundation of China (No. 41201429), Independent Research Funds of the State Key Laboratory of Biogeology and Environmental Geology (No. GKZ17Y651), Fundamental Research Funds of Geological Processes, Resources and Environment in the Yangtze Basin (No. CUGCJ1808)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Z., Zhang, L., Liu, D. et al. Simulation of Evapotranspiration Based on BEPS-TerrainLab V2.0 from 1990 to 2018 in the Dajiuhu Basin. Chin. Geogr. Sci. 30, 1095–1110 (2020). https://doi.org/10.1007/s11769-020-1160-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11769-020-1160-x

Keywords

Search

Navigation