Advertisement

Journal of Mountain Science

, Volume 16, Issue 6, pp 1381–1395 | Cite as

Reference evapotranspiration concentration and its relationship with precipitation concentration at southern and northern slopes of Tianshan Mountains, China

  • Fa-rong Huang
  • Tao Yang
  • Qian Li
  • Si-si Li
  • Lan-hai LiEmail author
  • Suwannee Adsavakulchai
Article
  • 13 Downloads

Abstract

The investigation of concentration characteristics of reference evapotranspiration (ETref) is important for water resources management. The concentration index (CI), concentration degree (CD) and concentration period (CP) are used to investigate the concentration characteristics of ETref and the relationship between ETref concentration and precipitation concentration at sub-monthly timescale based on the daily climatic variables from 1966 to 2015 in 27 meteorological stations at the southern and northern slopes of Tianshan Mountains in China. It was found that the CI of ETref is about 0.40 and less concentrated than precipitation in the study area. At the southern slope, the maximum ETref appears in late June and is earlier than the maximum precipitation (early July), ETref distributes more equally than precipitation, and the CI, CD and CP of these two variables do not show significant change based on the Mann-Kendall test. At the northern slope, both the maximum ETref and precipitation appear in early July, and ETref is more dispersed than precipitation. During the study period, the maximum ETref at the northern slope tends to appear earlier due to the impacts of wind speed, relative humidity, sunshine duration, and air temperature. ETref concentration does not match the precipitation concentration in the study area, particularly at the southern slope. The mismatch between ETref and precipitation concentration within a year reveals the water resources pressure on environmental, social and economic sustainability in the study area.

Keywords

Concentration Reference evapotranspiration Precipitation Trend analysis Tianshan Mountains 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgement

This study was funded by the West Light Foundation of the Chinese Academy of Sciences (2016-QNXZ-B-13), the open project of the Xinjiang Uygur Autonomous Region Key Laboratory (2017D04010), the natural science foundation of Xinjiang Uygur Autonomous Region (2017D01B52) and the Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) (No. XDA2004030202). We are grateful to the supports from Tianshan Station for Snow cover and Avalanche Research, Chinese Academy of Sciences, for data collection and analysis. The authors thank Dr. L. X. LI from Ontario Veterinary Medical Association for her linguistic assistance of this manuscript.

References

  1. Abolverdi J, Ferdosifar G, Khalili D, et al. (2016) Spatial and temporal changes of precipitation concentration in Fars province, southwestern Iran. Meteorology and Atmospheric Physics 128(2): 181–196.  https://doi.org/10.1007/s00703-015-0414-0 CrossRefGoogle Scholar
  2. Allen RG (2000) Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. Journal of Hydrology 229(1–2): 27–41.  https://doi.org/10.1016/s0022-1694(99)00194-8 CrossRefGoogle Scholar
  3. Allen R G, Pereira L S, Raes D, et al. (1998) Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage paper 56. Rome: Food and Agriculture Organization of the United Nations.Google Scholar
  4. Coscarelli R, Caloiero T (2012) Analysis of daily and monthly rainfall concentration in Southern Italy (Calabria region). Journal of Hydrology 416–417: 145–156.  https://doi.org/10.1016/j.jhydrol.2011.11.047 CrossRefGoogle Scholar
  5. Dong Y, Haimiti Y (2015) Spatio-temporal variability and trend of potential evapotranspiration in Xinjiang from 1961 to 2013. Transactions of the Chinese Society of Agricultural Engineering 31: 153–161. (In Chinese)Google Scholar
  6. Dong Y, Hu J, Wang J, et al. (2016) Study of temporal and spatial variation of the reference crop evapotranspiration in Xinjiang Uygur Autonomous Region during the period from 1961 to 2013. Research of Soil and Water Conservation 23: 304–308. (In Chinese)Google Scholar
  7. El-Shafie A, Alsulami HM, Jahanbani H, et al. (2013) Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure. Stochastic Environmental Research and Risk Assessment 27(6): 1423–1440.  https://doi.org/10.1007/s00477-012-0678-6 CrossRefGoogle Scholar
  8. Folland CK, Karl T, Christy JR, et al. (2001) Observed climate variability and change. In: Houghton JT, et al. (eds.), Climate Change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge. p 944.Google Scholar
  9. Gao ZD, He JS, Dong KB, et al. (2017) Trends in reference evapotranspiration and their causative factors in the West Liao River basin, China. Agricultural and Forest Meteorology 232: 106–117.  https://doi.org/10.1016/j.agrformet.2016.08.006 CrossRefGoogle Scholar
  10. Gocic M, Trajkovic S (2013) Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Global and Planetary Change 100: 172–182.  https://doi.org/10.1016/j.gloplacha.2012.10.014 CrossRefGoogle Scholar
  11. Gong LB, Xu CY, Chen DL, et al. (2006) Sensitivity of the Penman-Monteith reference evapotranspiration to key climatic variables in Changjiang (Yangtze River) Basin. Journal of Hydrology 329: 620–629.  https://doi.org/10.1016/j.jhydrol.2006.03.027 CrossRefGoogle Scholar
  12. Grosso C, Manoli G, Martello M, et al. (2018) Mapping maize evapotranspiration at field scale using SEBAL: A comparison with the FAO method and soil-plant model simulations. Remote Sensing 10(9): 1452.  https://doi.org/10.3390/rs10091452 CrossRefGoogle Scholar
  13. Guo B, Chen ZS, Guo JY, et al. (2016) Analysis of the nonlinear trends and non-stationary oscillations of regional precipitation in Xinjiang, northwestern China, using ensemble empirical mode decomposition. International Journal of Environmental Research and Public Health 13(3): 345.  https://doi.org/10.3390/ijerph13030345 CrossRefGoogle Scholar
  14. Guo LP, Li LH (2015) Variation of the proportion of precipitation occurring as snow in the Tian Shan Mountains, China. International Journal of Climatology 35(7): 1379–1393.  https://doi.org/10.1002/joc.4063 CrossRefGoogle Scholar
  15. Guo XY, Wu ZF, He HS, et al. (2018) Variations in the start, end, and length of extreme precipitation period across China. International Journal of Climatology 38(5): 2423–2434.  https://doi.org/10.1002/joc.5345 CrossRefGoogle Scholar
  16. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. American Society of Agricultural Engineers.  https://doi.org/10.13031/2013.26773
  17. Huo ZL, Dai XQ, Feng SY, et al. (2013) Effect of climate change on reference evapotranspiration and aridity index in arid region of China. Journal of Hydrology 492: 24–34.  https://doi.org/10.1016/j.jhydrol.2013.04.011 CrossRefGoogle Scholar
  18. Jiang FQ, Li XM, Wei BG, et al. (2009) Observed trends of heating and cooling degree-days in Xinjiang Province, China. Theoretical and Applied Climatology 97(3–4): 349–360.  https://doi.org/10.1007/s00704-008-0078-5 CrossRefGoogle Scholar
  19. Jiang P, Gautam MR, Zhu JT, et al. (2013) How well do the GCMs/RCMs capture the multi-scale temporal variability of precipitation in the Southwestern United States? Journal of Hydrology 479: 75–85.  https://doi.org/10.1016/j.jhydrol.2012.11.041 CrossRefGoogle Scholar
  20. Jiang SZ, Liang C, Cui NB, et al. (2019) Impacts of climatic variables on reference evapotranspiration during growing season in Southwest China. Agricultural Water Management 216: 365–378.  https://doi.org/10.1016/j.agwat.2019.02.014 CrossRefGoogle Scholar
  21. Kendall MG (1975) Rank Correlation Methods. Charles Griffin, London, U.K. p 220.Google Scholar
  22. Khanmohammadi N, Rezaie H, Montaseri M, et al. (2017) The effect of reference-condition-based temperature modification on the trend of reference evapotranspiration in arid and semi-arid regions. Agricultural Water Management 194: 204–213.  https://doi.org/10.1016/j.agwat.2017.09.010 CrossRefGoogle Scholar
  23. Kisi O, Cengiz TM (2013) Fuzzy genetic approach for estimating reference evapotranspiration of Turkey: mediterranean region. Water Resources Management 27(10): 3541–3553.  https://doi.org/10.1007/s11269-013-0363-7 CrossRefGoogle Scholar
  24. Li SS, Wang Q, Li LH (2016) Interdecadal variations of pan-evaporation at the southern and northern slopes of the Tianshan Mountains, China. Journal of Arid Land 8(6): 832–845.  https://doi.org/10.1007/s40333-016-0018-7 CrossRefGoogle Scholar
  25. Li XM, Jiang FQ, Li LH, et al. (2011) Spatial and temporal variability of precipitation concentration index, concentration degree and concentration period in Xinjiang, China. International Journal of Climatology 31: 1679–1693.  https://doi.org/10.1002/joc.2181 Google Scholar
  26. Li Y, Sun CF (2016) Impacts of the superimposed climate trends on droughts over 1961–2013 in Xinjiang, China. Theoretical and Applied Climatology 129: 977–994.  https://doi.org/10.1007/s00704-016-1822-x CrossRefGoogle Scholar
  27. Li Y, Yao N, Chau HW (2017a) Influences of removing linear and nonlinear trends from climatic variables on temporal variations of annual reference crop evapotranspiration in Xinjiang, China. Science of Total Environment 592: 680–692.  https://doi.org/10.1016/j.scitotenv.2017.02.196 CrossRefGoogle Scholar
  28. Li Y, Yao N, Sahin S, et al. (2017b) Spatiotemporal variability of four precipitation-based drought indices in Xinjiang, China. Theoretical and Applied Climatology 129: 1017–1034.  https://doi.org/10.1007/s00704-016-1827-5 CrossRefGoogle Scholar
  29. Liu CM, Zhang D (2011) Temporal and spatial change analysis of the sensitivity of potential evapotranspiration to meteorological influencing factors in China. Acta Geographica Sinica 66: 579–588. (In Chinese)Google Scholar
  30. Liu XM, Zheng HX, Liu CM, et al. (2009) Sensitivity of the potential evapotranspiration to key climatic variables in the Haihe River Basin. Resources Science 31: 1470–1476. (In Chinese)Google Scholar
  31. Mann HB (1945) Nonparametric tests against trend. Econometrica 13(3): 245–249.  https://doi.org/10.2307/1907187 CrossRefGoogle Scholar
  32. Martins DS, Paredes P, Raziei T, et al. (2017) Assessing reference evapotranspiration estimation from reanalysis weather products. An application to the Iberian Peninsula. International Journal of Climatology 37: 2378–2397.  https://doi.org/10.1002/joc.4852 CrossRefGoogle Scholar
  33. Martin-Vide J (2004) Spatial distribution of a daily precipitation concentration index in Peninsular Spain. International Journal of Climatology 24:959–971.  https://doi.org/10.1002/joc.1030 CrossRefGoogle Scholar
  34. Nouri M, Homaee M, Bannayan M (2017) Quantitative trend, sensitivity and contribution analyses of reference evapotranspiration in some arid environments under climate change. Water Resources Management 31: 2207–2224.  https://doi.org/10.1007/s11269-017-1638-1 CrossRefGoogle Scholar
  35. Partal T (2009) Modelling evapotranspiration using discrete wavelet transform and neural networks. Hydrological Processes 23: 3545–3555.  https://doi.org/10.1002/hyp.7448 CrossRefGoogle Scholar
  36. Pu Z, Zhang S (2011) Study on spatial-temporal variation characteristic of summer half year ETo in recent 48 years in Xinjiang. Chinese Journal of Agrometeorology 32(1): 67–72. (In Chinese)Google Scholar
  37. Qin M, Hao L, Sun L, et al. (2017) Climatic controls on watershed reference evapotranspiration vary dramatically during the past 50 years in southern China. Hydrology and Earth System Sciences Discussions 1–40.  https://doi.org/10.5194/hess-2017-241
  38. Rafi Z, Merlin O, Le Dantec V, et al. (2019) Partitioning evapotranspiration of a drip-irrigated wheat crop: Inter-comparing eddy covariance-, sap flow-, lysimeter- and FAO-based methods. Agricultural and Forest Meteorology 265: 310–326.  https://doi.org/10.1016/j.agrformet.2018.11.031 CrossRefGoogle Scholar
  39. Ramarohetra J, Sultan B (2018) Impact of ETo method on the simulation of historical and future crop yields: a case study of millet growth in Senegal. International Journal of Climatology 38: 729–741.  https://doi.org/10.1002/joc.5205 CrossRefGoogle Scholar
  40. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association 63: 1379–1389.  https://doi.org/10.1080/01621459.1968.10480934 CrossRefGoogle Scholar
  41. Shi P, Wu M, Qu SM, et al. (2015) Spatial distribution and temporal trends in precipitation concentration indices for the southwest China. Water Resources Management 29(11): 3941–3955.  https://doi.org/10.1007/s11269-015-1038-3 CrossRefGoogle Scholar
  42. Shi WL, Yu XZ, Liao WG, et al. (2013) Spatial and temporal variability of daily precipitation concentration in the Lancang River basin, China. Journal of Hydrology 495: 197–207.  https://doi.org/10.1016/j.jhydrol.2013.05.002 CrossRefGoogle Scholar
  43. Shiri J, Kişi Ö, Landeras G, et al. (2012) Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). Journal of Hydrology 414–415: 302–316.  https://doi.org/10.1016/j.jhydrol.2011.11.004 CrossRefGoogle Scholar
  44. Silva BKN, Lucio PS (2015) Characterization of risk/exposure to climate extremes for the Brazilian Northeast—case study: Rio Grande do Norte. Theoretical and Applied Climatology 122(1–2): 59–67.  https://doi.org/10.1007/s00704-014-1275-z CrossRefGoogle Scholar
  45. Sneyers R (1975) Sur l’analyse statistique des séries d’observations. WMO TechNote.Google Scholar
  46. Sun SL, Chen HS, Wang GJ, et al. (2016) Shift in potential evapotranspiration and its implications for dryness/wetness over Southwest China. Journal of Geophysical Research: Atmospheres 121(16): 9342–9355.  https://doi.org/10.1002/2016jd025276 Google Scholar
  47. Tabari H, Marofi S, Aeini A, et al. (2011) Trend analysis of reference evapotranspiration in the Western half of Iran. Agricultural and Forest Meteorology 151(2): 128–136.  https://doi.org/10.1016/j.agrformet.2010.09.009 CrossRefGoogle Scholar
  48. Tan XZ, Shao DG (2017) Precipitation trends and teleconnections identified using quantile regressions over Xinjiang, China. International Journal of Climatology 37(3): 1510–1525.  https://doi.org/10.1002/joc.4794 CrossRefGoogle Scholar
  49. Theil H (1950) A rank invariant method of linear and polynomial regression analysis: part 3. Nederlands Akad. Wetensch. Proc. 53: 1397–1412.Google Scholar
  50. Trenberth KE, Jones PD, Ambenje P, et al. (2007) Observations: surface and atmospheric climate change. In: Solomon S, et al. (eds.), Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge.Google Scholar
  51. Wei WS, Hu RJ (1990) Precipitation and climate conditions of Tianshan Mountains. Arid Land Geography 13: 29–36. (In Chinese)Google Scholar
  52. Wu MH, Wang HJ, Sun GL, et al. (2016) Formation and risk analysis of meteorological disasters in Xinjiang. Arid Land Geography 39: 1212–1220. (In Chinese)Google Scholar
  53. Xie P, Gu YL, Zhang YH, et al. (2017) Precipitation and drought characteristics in Xinjiang during 1961–2015. Arid Land Geography 40: 332–339. (In Chinese)Google Scholar
  54. Xu CY, Gong LB, Jiang T, et al. (2006) Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. Journal of Hydrology 327(1–2): 81–93.  https://doi.org/10.1016/j.jhydrol.2005.11.029 CrossRefGoogle Scholar
  55. Yu ZB, Jiang P, Gautam MR, et al. (2015) Changes of seasonal storm properties in California and Nevada from an ensemble of climate projections. Journal of Geophysical Research: Atmospheres 120(7): 2676–2688.  https://doi.org/10.1002/2014jd022414 Google Scholar
  56. Zhang LJ, Qian YF (2003) Annual distribution features of precipitation in China and their interannual variations. Acta Meteorological Sinica 17: 146–163.Google Scholar
  57. Zhang N (2018) High-efficient water-saving irrigation development and 13th Five-Year Plan in Xinjiang Uygur Autonomous Region. China Water Resources 13: 36–38. (In Chinese)Google Scholar
  58. Zhang Q, Singh VP, Li JF, et al. (2012) Spatio-temporal variations of precipitation extremes in Xinjiang, China. Journal of Hydrology 434–435: 7–18.  https://doi.org/10.1016/jjhydrol.2012.02.038 CrossRefGoogle Scholar
  59. Zhang Q, Sun P, Li JF, et al. (2015) Spatio-temporal properties of droughts and related impacts on agriculture in Xinjiang, China. International Journal of Climatology 35: 1254–1266.  https://doi.org/10.1002/joc.4052 CrossRefGoogle Scholar
  60. Zhang Q, Xu CY, Marco G, et al. (2009) Changing properties of precipitation concentration in the Pearl River basin, China. Stochastic Environmental Research and Risk Assessment 23:377–385.  https://doi.org/10.1007/s00477-008-0225-7 CrossRefGoogle Scholar
  61. Zhang SF, Hua D, Meng XJ, et al. (2011) Climate change and its driving effect on the runoff in the “Three-River headwaters” region. Acta Geographica Sinica 66: 13–24. (In Chinese)Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina
  2. 2.Ili station for Watershed Ecosystem ResearchChinese Academy of SciencesXinyuanChina
  3. 3.Research Center for Ecology and Environment of Central AsiaChinese Academy of SciencesUrumqiChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.School of EngineeringUniversity of the Thai Chamber of CommerceBangkokThailand

Personalised recommendations