بررسی روند تغییرات تبخیر و تعیین نقش عوامل مؤثر بر آن با استفاده از روش‌های رگرسیون چندک و رگرسیون چندک بیزی (مطالعه موردی: ایستگاه هاشم‌آباد گرگان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد مهندسی منابع آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

2 دانشیار گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

3 استادیار گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

4 دانشجوی دکترای علوم و مهندسی آب، منابع آب، دانشگاه ارومیه، ارومیه، ایران

چکیده

تبخیر-تعرق از مؤلفه‌های مهم چرخه هیدرولوژیک است که تحت تأثیر عوامل مختلف اقلیمی است که این عوامل خود نیز تحت تأثیر تغییر اقلیم می‌باشند. در این پژوهش به منظور بررسی روند تغییرات تبخیر-تعرق ناشی از عوامل مؤثر برآن از آزمون من-کندال و دو روش رگرسیون چندک و رگرسیون چندک بیزی استفاده شد تا ضمن مقایسه این دو روش در تشخیص روند در چندک‌های مختلف سری زمانی تبخیر-تعرق، دلیل احتمالی آن نیز مشخص شود. برای این منظور از سری‌های فصلی داده‌های هواشناسی ایستگاه سینوپتیک هاشم آباد گرگان در دوره زمانی 1363-1397 استفاده شد. آزمون من-کندال فقط برای فصل زمستان روند کاهشی تبخیر را نشان می‌دهد در حالی که کمترین شیب تغییرات روند در چندک‌های مختلف در این فصل می‌باشد و فصل تابستان دارای بیشترین شیب روند افزایشی تبخیر بین 66/0 تا 14/1 درصد بترتیب برای چندک‌های پایینی تا بالایی می‌باشد. در فصل بهار شیب افزایشی 75/0 در چندک‌های بالایی به تدریج به شیب کاهشی 72/0- تغییر می‌کند. در فصل پاییز چندک‌های پایینی دارای شیب بیشتری تا 66/0- می‌باشند و فصل زمستان چندک‌ها از کمترین شیب بین 14/0- تا 08/0- برخوردارند. نتایج بررسی عوامل مؤثر برتبخیر نیز نشان می‌دهد تغییرات دما سهم بیشتری را در تغییرات تبخیر روزانه دارد و با بزرگ‌تر شدن چندک، شیب افزایشی نیز بیشتر می‌شود. رابطه بین رطوبت نسبی با تبخیر از یک شیب کاهشی پیروی می‌کند و پس از دما در رتبه دوم قرار می‌گیرد. در نهایت می‌توان نتیجه گرفت در ایستگاه هاشم آباد گرگان، تغییرات معنی‌داری در تبخیر رخ داده است که روش رگرسیون چندک این تغییرات را به خوبی آشکار می‌کند. همچنین در فصل تابستان که خشک‌ترین و گرم‌ترین فصل سال می‌باشد، افزایش شدت روند تبخیر در این منطقه با توجه به کشت تابستانه، باعث افزایش مصرف آب در کشاورزی طی سال‌های اخیر شده که با این روند در آینده افزایش بیشتری خواهد یافت.

کلیدواژه‌ها


عنوان مقاله [English]

Analysis of Trend of Evaporation Changes and Determining the Role of Factors Affecting it Using Quantile Regression and Bayesian Quantile regression (Case Study: Hashem-Abad Station, Gorgan)

نویسندگان [English]

  • Sedighe Bararkhanpour 1
  • Khalil Ghorbani 2
  • Meysam Salarijazi 3
  • Laleh Rezaeighale 4
1 Department of Water Engineering, College of Soil and Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Iran, Gorgan
2 Associate Prof. Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources
3 Assistant Pro. Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources
4 Ph.D. Student, Water engineering-Water Resources, Urmia University, Iran,Urmia
چکیده [English]

Introduction: Climate change and global warming are some of the issues and concerns of human beings today that have important effects on rain, evaporation, runoff, and finally water supply and causes the severity and weakness of these parameters, increasing the occurrence of severe weather events and lack of available water, which causes irreparable damage. Evapotranspiration is an important component in the hydrological cycle that is affected by various factors such as air temperature, relative humidity, wind speed and number of sunshine hours, and these factors are also affected by climate change. Due to the occurrence of climate change in the country in recent decades, it is important to study the changes in the trend of climatic parameters and their role in evapotranspiration in order to apply management methods to reduce evaporation in water resources. The Mann-Kendall trend test is an often-used method to examine changes in the data time series, but this test only expresses changes in the center of the data series, so Quantile and Bayesian multiple regression methods are used to study the trend of changes in different parts of the data time series and also to investigate the role of various parameters on a specific parameter. Therefore, the purpose of the present research is to study and compare the trend of changes in evaporation and climatic parameters affecting it and determining the role of these factors on evaporation using Quantile and Bayesian multiple regression methods at Hashemabad Gorgan station located in Golestan province.
Materials and methods: In the first step, the meteorological data time series of evaporation, and the factors affecting it including average temperature, relative humidity, sunshine hour and wind speed were prepared for Hashemabad Gorgan synoptic station with a statistical period of more than 30 years (1984-2018) and the seasonal data series of these data were formed. The non-parametric Mann-Kendall test was performed to investigate the trend of changes in evapotranspiration and the factors affecting it and then Quantile and Bayesian regression was used to investigate the changes in various quantiles of data series and also to determine the role of changes in different values of each of these meteorological factors on evaporation.
Results and discussion: investigation of the trend in the evaporation data series and the factors affecting it based on the Man-Kendall test shows that the aforementioned trend test shows a decreasing trend in evaporation only for winter, while the lowest changes in trend in different quantiles are in this season, and in Summer the increasing trend in evaporation has the highest slope in the range of 0.66-1.14% for lower and upper quantiles, respectively. In spring, the increasing slope of 0.75 in the upper quantiles gradually changes to a decreasing slope of -0.72. In autumn, the lower quantiles have a higher slope up to -0.66 and in winter, the quantiles have the lowest slope between -0.14 to -0.08. The results of investigating the factors affecting evaporation also show that changes in temperature have a greater share in the changes of daily evaporation and the bigger the quantile, the increasing slope increases. The relationship between relative humidity and evaporation follows a decreasing slope and ranks second after temperature. Finally, it can be concluded that in Hashemabad station of Gorgan, the Mann-Kendall trend test could not detect significant changes that have occurred in the process of the evaporation trend, but these changes have been revealed the best by the Quantile regression method. In summer, which is the driest and hottest season of the year, the trend of increase in evaporation is more intense, and due to summer cultivation in this region, water consumption in agriculture has increased in recent years and with this trend will increase further in the future.
Conclusion: Given the fact that climate change may not have occurred only in the average value of a data series, but extreme events might have occurred in some parts of the series so it is necessary to study different parts of the data series using methods such as Quantile and Bayesian multiple regression and the results of this research emphasizes this matter. Also, due to the necessity of studying evapotranspiration in the application of management methods for water resources, useful and practical results can be brought out by studying the changes in different ranges of evapotranspiration, especially it’s extreme and high values as well as the effects of changes in different ranges of climatic parameters on evaporation.

کلیدواژه‌ها [English]

  • Evaporation
  • climatic parameters
  • quantile regression
  • Bayesian Quantile Regression
  • Mann-Kendall
  1.  

    1. Abbas, S.A., Xuan, Y. and X. Song, 2019, Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions. Journal of Water Resources Management, Vol. 33, pp. 4249–4264.
    2. Adnan, S., Ullah, K. and R. Ahmed, 2020, Variability in meteorological parameters and their impact on evapotranspiration in a humid zone of Pakistan. Journal of Meteorological Applications, Vol. 27, No 2, PP. 1-10.
    3. Allies, A., Demarty, J., Olioso, A., Moussa, I.B., Issoufou, H.B.A., Velluet, C. and B. Cappelaere, 2020, Evapotranspiration estimation in the Sahel using a new ensemble-contextual method. Remote Sensing, Vol. 12, No. 380, pp. 1-34.
    4. Amran, B. and B. Anisa, 2017, Trend Analysis of Precipitation Extreme Related to Climate Change in Province Sulawesi Selatan, Indonesia. International Journal of Applied Engineering Research ISSN, 12, No, 21, PP. 11035-11038.
    5. Asadzadeh, F., Kaki, M. and S. Shakiba, 2017, Trends Analysis of Reference Evapotranspiration in the Synoptic Sitesof Kurdistan Province Using Spearman’s Test. Journal of Iran-Water Resources Research, 13, No. 1, PP. 216-222 (In Persian).
    6. Chakav, Safia., Davari, K. and B. Ghahremani, 1397, Analysis and study of the time trend of evapotranspiration of the reference plant and its effective parameters in several climatic zones in Iran. Conference on Civil Engineering, Architecture and Urban Planning of the Islamic World, Tabriz, (In Persian).
    7. Dunn, R.H., Willett, K.M. and E.P. Parker, 2019, Changes in statistical distributions of sub-daily surface temperatures and wind speed. Journal of Eearth System Dynamics, Vol. 10, No 4, PP. 765-788.
    8. Gao, M. and C. Franzke, 2017, Quantile Regression-based Spatio-temporal Analysis of Extreme Temperature Change in China. Journal of Climate, Vol. 30, PP. 9897-9914.
    9. Ghorbani, Kh., Valizadeh, E. and S. Bararkhanpour, 2018,  Investigation of spatiotemporal trend of the bivariate meteorological drought index, SPEI, in Iran. Journal of  Desert Management, 6, No. 11, pp. 25-38 (In Persian).

    Gorbani, Kh. 2015, Spatial and seasonal pattern in climate change, temperatures across Iran. Journal of Water and Soil Conservation, Vol. 21,  No. 5, pp. 257-270 (In Persian).

    1. Halabian, A.H. and M. Ravarian, 2017, Study of potential evapotranspiration and climatic parameters affecting it in Quchan city. 4th Iranian Scientific Research Congress on Development and Promotion of Agricultural Sciences, Natural Resources and Environment. (In Persian).
    2. Hamed, K.H. and A.R. Rao, 1998, A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology, Vol. 204, pp. 182-196.
    3. Kendall, M.G. 1975, Rank Auto-correlation Methods, Charles Griffin, London.
    4. Koenker, R. 2005, Quantile Regression, first ed, New York, Cambridge University Press, 2005, pp. 1-25.
    5. Koenker, R. 2006, Quantile regression in R: A vignette. [Available online at http://www.econ.uiuc.edu/~roger/research/rq/vig.pdf.]
    6. Koenker, R. and G. Bassett, 1978, Regression Quantiles. Econometrica, Vol. 46, pp. 33-50.
    7. Kotz, S., Kozubowski, T.J. and K. Podgorski, 2001, The Laplace distribution and generalizations: engineering, and finance. Birkhauser. Boston.
    8. Li, Q., Lin, J. and J.S. Racine, 2013, Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions. Journal of Business and Economic Statistics, 31, PP. 57-65.
    9. Mann, H.B. 1945, Nonparametric Tests Against Trend, Econometrica. Journal of the Econometric Society, PP. 245-259.
    10. Moazenzadeh, R., Mohammadi, B., Shamshirband, S. and K.W. Chau, 2018, Coupling a firefly algorithm with support vector regression to predict evaporation in Northern Iran. Journal of Engineering applications of computational fluid mechanics, Vol. 12, No. 1, pp. 584–597.
    11. Ramsey, A.F. 2020. Probability distributions of crop yields: A bayesian spatial quantile regression approach. American Journal of Agricultural Economics, 102(1):  220-239.
    12. Reich , B. J. and L.B. Smith, 2013, Bayesian quantile regression for censored data. Biometrics, 69, No. 3, pp. 651-660.
    13. Reich, B.J. 2012, Spatiotemporal quantile regression for detecting distributional changes in environmental processes. Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 61, No. 4, pp. 535-553.
    14. Reich, B.J., Fuentes, M. and D.B. Dunson, 2011, Bayesian Spatial Quantile Regression. Journal of the American Statistical Association, 106, No. 493, PP. 6–20.
    15. Smith, L. B. and B.J. Reich, 2013, BSquare: An R package for Bayesian simultaneous quantile regression.
    16. Saeedi, Z., Ataei, H. and M. Javari, 2019, Study of temperature and precipitation changes trend in Koohrang synoptic station (statistical period 1986 to 2015). the second national conference on natural resources and sustainable development in Zagros, Shahrekord. (In Persian).
    17. Salmani, H., Sheikh, V.B., Salman Mahiny, A., Ownegh, M. and A. Fathabadi, 2019, Long-Term Trend Analysis of Climate and Hydrological Series using Removal of the Autocorrelation Statistical Methods in the Eastern Gorganrood Basin, Golestan Province. Journal of Watershed Management Research, 10, No. 19, PP. 58-72 (In Persian).
    18. Tan, X., Gan, T.Y., Chen, S. and B. Liu, 2018, Modeling distributional changes in winter precipitation of Canada using Bayesian spatiotemporal quantile regression subjected to different teleconnections. Journal of Climate Dynamics, 52, No. (3-4), pp. 2105-2124.

     

    1. Torkaman, M., Noroozi, A.A. and M. Homaee, 2019, Analysis of the Trends of Climate Parameters Using Mann Kendal Test (TFPW-MK) In Khuzestan Province. Journal of environmental Sciences Studies, Vol. 4, No. 1, PP. 884-895 (In Persian).
    2. Uranchimeg, S., Kim, J.G., Kim, J.G., Kown, H.H. and S.O. Lee, 2018, A Bayesian Quantile Regression Approach for Nonstationary Frequency Analysis of Annual Maximum Sea Level in a changing Climmate. Journal of Coastal Research, 58, PP. 536-540.