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Temporal dynamics of monthly evaporation in Lake Urmia

  • Babak Vaheddoost
  • Kasim Kocak
Original Paper
  • 26 Downloads

Abstract

As a UNESCO biosphere, Lake Urmia is a shallow hypersaline lake which is facing a rapid water surface degradation. Evaporation from the surface of the Lake, as a physical process which accelerates the Lake’s degradation, was evaluated using chaos theory. Seven hydrometeorological stations scattered around the Lake were selected, and a 40-year time span between October 1974 and September 2014 was used at each station. Missing data in time series was removed and the whole time series was tested for consistency, randomness, and presence of trend. Since evaporation at each station was measured by means of class A evaporation pan, time series at each station was multiplied by a pan coefficient to incorporate the effect of saline water and free water surface environment simultaneously. Measurement errors arising from assumption of zero evaporation in winter were removed from the time series using locally weighted scatterplot smoothing method after which unification of time series into a single time series is achieved. Results of the data transformation and information loss were monitored by means of auto-correlation, partial-auto-correlation, mutual information, power spectrum, false nearest neighbor, and correlation dimension. A local prediction method is then used to capture the temporal dynamics of the evaporation with consideration of an appropriate time delay and embedding dimension. Finally, the representative model was projected on a 3-dimensional phase space to evaluate the temporal dynamics of the evaporation. Results indicate that the chaotic approach shows accurate predictions in advance.

Notes

Acknowledgments

The authors are thankful for Iranian Water Resource Management Company for providing evaporation data of Lake Urmia. We also appreciate the valuable comments declared by editor and reviewers which were helpful in developing the quality of the study.

References

  1. Addison PS (1997) Fractals and chaos: an illustrated course. Institute of Physics Publishing, Dirac House, BristolGoogle Scholar
  2. Adeloye AJ, Montaseri M (2002) Preliminary streamflow data analyses prior to water resources planning study. Hydrol Sci J 47(5):679–692CrossRefGoogle Scholar
  3. Baydaroglu O, Kocak K (2014) SVR-based prediction of evaporation combined with chaotic approach. J Hydrol 508:356–363CrossRefGoogle Scholar
  4. Bras RL (1990) Hydrology: an introduction to hydrologic science. Addison-Wesley Publishing Co, New YorkGoogle Scholar
  5. Brutsaert W (2013) Evaporation into the atmosphere: theory, history and applications, vol 1. Springer Science and Business Media, AmsterdamGoogle Scholar
  6. Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74(368):829–836CrossRefGoogle Scholar
  7. Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83(403):596–610CrossRefGoogle Scholar
  8. Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on LOESS. J Off Stat 6(1):3–73Google Scholar
  9. Dehghan S, Kamaneh SAA, Eslamian S, Gandomkar A, Marani-Barzani M, Amoushahi-Khouzani M, Singh VP, Ostad-Ali-Askari K (2017) Changes in temperature and precipitation with the analysis of geomorphic basin chaos in Shiraz, Iran. Int J Constr Res Civ Eng 3(2):50–57Google Scholar
  10. Eslami A, Ghahraman B, Ziaee A, Eslami P (2016) Effect of noise reduction in nonlinear dynamic analysis of maximum daily temperature series in Kerman Station. Iran-Water Resour. Res 12(1):171–185 (In Farsi)Google Scholar
  11. Farzin S, Ifaei P, Farzin N, Hassanzadeh Y, Aalami MT (2012) An investigation on changes and prediction of Urmia Lake water surface evaporation by chaos theory. Int J Environ Res 6(3):815–824Google Scholar
  12. Farzin S, Hajiabadi R, Ahmadi MH (2017) Application of Chaos theory and artificial neural networks to evaluate evaporation from lake’s water surface. J Water and Soi 31(1):61–74Google Scholar
  13. Fuwape IA, Ogunjo ST, Oluyamo SS, Rabiu AB (2017) Spatial variation of deterministic chaos in mean daily temperature and rainfall over Nigeria. Theor Appl Climatol 130(1–2):119–132CrossRefGoogle Scholar
  14. Gao ZK, Yang YX, Fang PC, Jin ND, Xia CY, Hu LD (2015) Multi-frequency complex network from time series for uncovering oil-water flow structure. Sci Rep 5:8222CrossRefGoogle Scholar
  15. Ghorbani MA, Khatibi R, Mehr AD, Asadi H (2018) Chaos-based multigene genetic programming: a new hybrid strategy for river flow forecasting. J Hydrol 562:455–467CrossRefGoogle Scholar
  16. Hashemi M (2008) An independent review: the status of water resources in the Lake Uromiyeh Basin. UNDP/GEF “Conservation of Iranian Wetlands” Project: 37–38Google Scholar
  17. Hassanzadeh E, Zarghami M, Hassanzadeh Y (2012) Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resour Manag 26(1):129–145CrossRefGoogle Scholar
  18. Hegger R, Kantz H, Schreiber T (2018) The TISEAN downloads and updates page. Max Planck Institute for the Physics of Complex Systems (MPIPKS). Dresden, Germany. https://www.pks.mpg.de/~tisean/archive_3.0.0.html. Accessed 26 Jan 2018
  19. Huisman J, Thi NNP, Karl DM, Sommeijer B (2006) Reduced mixing generates oscillations and chaos in the oceanic deep chlorophyll maximum. Nature 439(7074):322–325CrossRefGoogle Scholar
  20. Iranian Department of Environment (2009) Integrated management plan for Lake Urmia Basin: protecting lagoons for people, for environment. Under supervision of UNDP/GEF. 22 May 2009 Urmia, IranGoogle Scholar
  21. Iranian Water Resource Management Company (2015) Daily rainfall report of Iran based on seconder catchment areas. http://wrs.wrm.ir/m3/gozaresh.asp. Accessed 15 Jun 2015
  22. Itoh KI (1995) A method for predicting chaotic time-series with outliers. Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 78(5):44–53Google Scholar
  23. Jensen ME (2010) Estimating evaporation from water surfaces. In CSU/ARS Evapotranspiration Workshop, Fort Collins, CO, pp 1–27Google Scholar
  24. Kamran KV, Khosroshahi SS, Omrani K (2014) Examining the decreasing Urmia Lake water depth and effects that on the environment. Proceedings of International Conference on Agriculture, Environment and Biological Sciences (ICFAE 2014). 4–5 June 2014 Antalya, Turkey, pp 26–29Google Scholar
  25. Kennel MB, Brown R, Abarbanel HD (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 45(6):3403–3411CrossRefGoogle Scholar
  26. Khatibi R, Sivakumar B, Ghorbani MA, Kisi O, Kocak K, Zadeh DF (2012) Investigating chaos in river stage and discharge time series. J Hydrol 414:108–117CrossRefGoogle Scholar
  27. Kocak K, Saylan L, Sen O (2000) Nonlinear time series prediction of O3 concentration in Istanbul. Atmos Environ 34(8):1267–1271CrossRefGoogle Scholar
  28. Kocak K, Bali A, Bektasoglu B (2007) Prediction of monthly flows by using chaotic approach. In International congress on river basin management. 22–24 March 2007, Antalya, TurkeyGoogle Scholar
  29. Kohler MA, Nordenson TJ, Fox WE (1955) Evaporation from pans and lakes. US Dept Com Weather Bur Res Paper 38, Washington, D.C.Google Scholar
  30. Kokya BA, Kokya TA (2008) Proposing a formula for evaporation measurement from salt water resources. Hydrol Process 22(12):2005–2012CrossRefGoogle Scholar
  31. Luke A (2018) Heat transfer in evaporation on micro- and macrostructured tubes. In: Innovative heat exchangers. Springer, pp 135–166, ChamGoogle Scholar
  32. McGhee JW (1985) Introductory statistics. West Group, New YorkGoogle Scholar
  33. Millan H, Kalauzi A, Cukic M, Biondi R (2010) Nonlinear dynamics of meteorological variables: multifractality and chaotic invariants in daily records from Pastaza, Ecuador. Theor Appl Climatol 102(1–2):75–85CrossRefGoogle Scholar
  34. Ozgur E, Kocak K (2015) The effects of the atmospheric pressure on evaporation. Acta Geobalcanica 1(1):17–24CrossRefGoogle Scholar
  35. Pahnehkolaei SMA, Alfi A, Sadollah A, Kim JH (2017) Gradient-based water cycle algorithm with evaporation rate applied to chaos suppression. Appl Soft Comput 53:420–440CrossRefGoogle Scholar
  36. Penman HL (1948) Natural evaporation from open water, bare soil and grass. In Proc. R. Soc. Lond. A, vol 193, No. 1032, pp 120–145). The Royal Society, UKGoogle Scholar
  37. Rubel F, Kottek M (2010) Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol Z 19(2):135–141CrossRefGoogle Scholar
  38. Sanchez L, Infante S, Marcano J, Griffin V (2015) Polynomial chaos based on the parallelized ensemble Kalman filter to estimate precipitation states. Stat Opt Inf Comp 3(1):79–95Google Scholar
  39. Santiago Duarte Prieto F, Hernandez Murcia OE, Corzo Perez GA, Santos Granados GR (2017) Chaos analysis of precipitation time series in the upper Magdalena River Basin. In: EGU general assembly conference abstracts, vol 19, p 18496Google Scholar
  40. Sima S, Ahmadalipour A, Tajrishy M (2013) Mapping surface temperature in a hyper-saline lake and investigating the effect of temperature distribution on the lake evaporation. Remote Sens Environ 136:374–385CrossRefGoogle Scholar
  41. Solomatine DP, Velickov S, Wust JC (2001) Predicting water levels and currents in the North Sea using chaos theory and neural networks. In Proceedings of the 29th Congress-International Association for Hydraulic Research. 16–21 September 2001, Beijing, China, pp 353–359Google Scholar
  42. Stosic T, Stosic B, Singh VP (2018) q-triplet for Brazos River discharge: the edge of chaos. Physica A 495:137–142CrossRefGoogle Scholar
  43. Vaheddoost B, Aksoy H (2017) Structural characteristics of annual precipitation in Lake Urmia Basin. Theor Appl Climatol 128(3):919–932CrossRefGoogle Scholar
  44. Vaheddoost B, Aksoy H. (2018, Online First) Interaction of groundwater with Lake Urmia in Iran. Hydrol Process DOI:  https://doi.org/10.1002/hyp.13263
  45. Wang L, He WP, Liao LJ, Wan SQ, He T (2015) A new method for parameter estimation in nonlinear dynamical equations. Theor Appl Climatol 119(1–2):193–202CrossRefGoogle Scholar
  46. World Maps of Köppen-Geiger Climate Classification (2015) World maps and computer animations of our updated Köppen-Geiger climate classification, accessed. http://koeppen-geiger.vu-wien.ac.at/present.htm. Accessed 26 Jul 2015
  47. World Meteorological Organization (1988) Analysing long time series of hydrological data with respect to climate variability. WCAP-3, WMO/TD no. 224. World Metrological Organization, GenevaGoogle Scholar
  48. Wu J, Lu J, Wang J (2009) Application of chaos and fractal models to water quality time series prediction. Environ Model Softw 24(5):632–636CrossRefGoogle Scholar
  49. Wu YP, Zhu CY, Feng GL, Li BL (2018) Mathematical modeling of Fog-Haze evolution. Chaos, Solitons Fractals 107:1–4CrossRefGoogle Scholar
  50. Yekom Consulting Engineers (2002) Management plan for the Lake Uromiyeh ecosystem. 1st Report- EC-IIP, environmental management project for Lake Uromiyeh. Tehran, IranGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Engineering and Natural Sciences, Department of Civil EngineeringBursa Technical UniversityBursaTurkey
  2. 2.Faculty of Aeronautics and Astronautics, Department of Meteorological EngineeringIstanbul Technical UniversityIstanbulTurkey

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