وردایی دهه‌ای تبخیر در ارتباط با تغییرپذیری برخی عناصر اقلیمی در حوضۀ زاینده رود

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری تغییرات آب و هوایی دانشگاه زنجان، زنجان، ایران.

2 استاد گروه جغرافیا ، دانشکده علوم انسانی، دانشگاه زنجان، زنجان، ایران.

3 استادیار گروه جغرافیا ، دانشکده علوم انسانی، دانشگاه زنجان، زنجان، ایران.

چکیده

تبخیر فرایند انتقال رطوبت از سطح زمین - منابع رطوبتی به جو می‌باشد. شناخت و ارزیابی تبخیر، یک بخش مهم در محاسبه بیلان آب و مدیریت صحیح منابع آب می‌باشد. از منظر اقلیمی، عوامل متعددی در فرایند تبخیر دخالت دارند که از مهم‌ترین آنها می‌توان به تابش و دمای هوا، سرعت باد و رطوبت نسبی اشاره کرد. تغییرپذیری تبخیر در مقیاس‌های زمانی مختلف به‌ویژه طی فرایند و روند گرمایش زمین، از جمله مظاهر در خور توجه تغییرات اقلیمی است. بررسی تغییرات دهه-ای تبخیر و فراسنج‌های موثر بر آن، نقش موثری در برنامه‌ریزی میان‌مدت و مدیریت منابع آب ایفا می‌کند. هدف از پژوهش حاضر واکاوی تغییرات دهه‌ای تبخیر در ارتباط با فراسنج‌های موثر (دما، خشکی و حرکت هوا) بر این فراسنج می-باشد. بدین‌منظور از پایگاه داده‌های روزانه تبخیر، دما، رطوبت‌نسبی و باد، حاصل میان‌یابی ایستگاه‌های همدید، اقلیم‌شناسی هواشناسی و باران‌سنجی وزارت نیرو طی بازه زمانی 2018-1969 با تفکیک مکانی 4 × 4 کیلومتر استفاده شد. نتایج نشان داد فراسنج دما نسبت به سایر فراسنج‌ها نقش مهم‌تر و موثرتری بر فرایند تبخیر دارد، ولی میزان سهم این فراسنج از دهه اول (1978- 1969) به‌سمت دهه‌های انتهایی رو به کاهش است. رطوبت از میان عناصر جوی جایگاه دوم را به‌لحاظ تاثیر بر فرایند تبخیر دارد که بالعکس دما نقش آن از دهه‌های ابتدایی به‌سمت دهه انتهایی (2018-2009) رو به افزایش است. همین روند در مورد فرایند باد نیز تکرار شده و نقش آن از دهه اول (1978- 1969) به‌سمت دهه انتهایی (2018- 2009) رو به افزایش است.

کلیدواژه‌ها


عنوان مقاله [English]

Decadal Variation of Evaporation in Relation with the Variability of Some Climatic Elements in the Zayandeh Roud Basin

نویسندگان [English]

  • Narges Hesami 1
  • Hossein Asakereh 2
  • Kohzad Raispour 3
1 PhD student of climatology, Zanjan University, Zanjan, Iran .
2 professor of climatology, Zanjan University, Zanjan, Iran.
3 Assistant professor, Zanjan University, Zanjan, Iran.
چکیده [English]

Introduction

Evaporation is the process of transferring moisture from the earth's surface - the water bodies to the atmosphere. Recognition and evaluation of evaporation is an important part in calculating water balance and proper management of water resources. From a climatic point of view, several factors are involved in the evaporation process, the most important of which are radiation and air temperature, wind speed and relative humidity. Evaporation variability at different time scales, especially during the process of global warming trends, is one of the considerable manifestations of climate change. Investigating the decadal changes in evaporation and the associated effective parameters play an effective role in the medium-term planning and management of water resources. The main purpose of current study is to analysis the decadal changes of evaporation in relation with the effective parameters (temperature, humidity, and wind) on the parameter.

Data and Methods

To this end, daily databases of evaporation, temperature, relative humidity and wind speed, with a spatial separation of 4 × 4 km, obtained from stations (synoptic, climatology and rain gauge) data interpolation for the period 1969-2018 were used. In order to check the changes of the studied variables, the spatial average daily distribution of each variable for the whole period and for the five decades studied was estimated and analyzed. In addition, multivariate standard regression was used to track changes in the role and importance of each of the three climatic parameters studied (humidity, wind and temperature) over the decades of evaporation. first, each of the variables is standardized in relation to their mean and standard deviation as follows:
Then the following regression model was used for K the independent variable to justify the spatial variation of evaporation ( ):

Results and Discussion
In Zayandehrood catchment, with increasing altitude, decreasing temperature and consequently reducing annual evapotranspiration can be observed. As altitude decreases from northwest to southeast, climatic elements such as heat and evaporation increase and some others such as humidity and precipitation decrease. And the cold and humid climate is gradually giving way to the hot and dry climate. The lowest average temperature is observed in the northwest of the basin and mountainous areas And the temperature rises to the southeast of the range. Therefore, we see areas with lower temperature, less evaporation and vice versa Which can be deduced from the high correlation (0.9) of these two parameters. This relationship is reversed in the case of moisture. That is, the highest relative humidity is observed in the northwest of the study area; Towards the southeast, the humidity decreases. After temperature, the moisture element has the highest correlation with the evaporation meter (0.79). The relationship between wind speed and evaporation is lower but more direct than other parameters. In the western half of the study area, its amount is less and in the eastern half of the basin, its speed increases.
Also, The results showed that the temperature parameter has a more important and effective pattern on the evaporation process than other parameters, but the share of this parameter has been decreasing since the first decade (1978-1969) towards the last decade. Humidity have ranks second among atmospheric elements in terms of its effect on the evaporation process that converse to temperature plays an increasing role from the early decades to the final decade (2019-2009). The same trend is repeated for the wind and its role is increasing from the first decade (1978-1969) to the last decade (2018-2019).

Conclusion
In this study, an attempt was made to use multivariate regression, Analyze the relationship between these parameters and the evaporation parameter in the period 2018-1969 And determine the most important parameter affecting evaporation in the study area. Then, the changes of decades of the relationship between these three variables and evaporation were discussed. Based on the general characteristics of this basin, it was found that the temperature and wind in the end parts of the basin is higher than the northern parts. Similarly, the rate of evaporation is lower in the northern half and higher in the southern half Conversely, the amount of moisture in the northern parts of the study area is higher than the southern parts. The results also show that The most effective parameter in relation to evaporation is the temperature parameter. This relationship was significant at 95% confidence level in the whole basin. . Moisture of the second share and Wind has the last category in the evaporation process. Research findings also showed The role of temperature is declining from the first decade to the last decade and Conversely, the share of humidity and wind is increasing.

کلیدواژه‌ها [English]

  • Climate change
  • zayandeh roud basin
  • Multivariate Regression
  • Evaporation process
  • Variation
Asakereh, H and seifipour, Z. 2012. Spatial modeling of annual precipitation in Iran. 29:6-9. [In Persian]
Asakereh, H., Masoudian, S.A and Tarkarani, F. 2021. A Discrimination of Roles of Internal and External Factors on the Decadal Variation of Annual Precipitation in Iran over Recent Four Decades (1975-2016). Physical geography research quarterly. 53: 91-107. [In Persian]
Asakereh, H. 2008. Kriging Application in Climatic Element Interpolation A Case Study: Iran Precipitation in 1996.12.16. geography and development. 12: 25-42. [In Persian]
Asakereh, H., 2011. Basics of statistical climatology. Zanjan university publication. PP 545. [In Persian]
Asadi, Mehdi and Karami, Mokhtar. 2020. Evaluation of Evapotranspiration Using Satellite Images and SEBAL Algorithm (Case Study: Eastern Azerbaijan Province). Ecohydrology. 1: 17-27. [In Persian]
Ahmadi Moradi, M., Heidari Motlagh, A., Nasrolahi, A. H and Saeedi, M. 2019. Regression analysis of reference evapotranspiration using climatic variables in Al-Shatar weather conditions. Engineering, Natural Resources and Environment, Tehran. [In Persian]
Alizadeh, A. 2011. The principles of applied hydrology. Emam reza university publication. Pp 911. [In Persian]
Alizadeh, A., Sayari, N., Hesami Kermani, M.R. Banayan, M and Hosseini Ali, F. 2010. Investigating the potential effects of climate change on agricultural water sources and uses (case study: Kashf-Roud River catchment). Water and soil, 24: 815:835. [In Persian]
Alijani, B. 2011. Climatology of Iran. Samt publication.PP 230. [In Persian]
Arab slghar, A. A., Dehghan, H., sedghi, H and Naderianfar, M. 2011. Prediction of annual evapotranspiration using meteorological data in a number of stations in semi-arid regions of Iran. Water resources engineering. 8: 21-30. [In Persian]
Almedeij, J. 2012. Modeling pan evaporation for Kuwait by multiple linear regression. The Scientific World Journal, 9: 10-11.
Adnan, R.M., Liang, Z., Heddam, S., Zounemat-Kermani, M., Kisi, O., Li, B. 2020. Least square support vector machine and multivariate adaptive regression splines for stremflow prediction in mountaionous basin using hydro- meteorological data as inputs. Hydrology. 586: 124371.
Alexandersson, H and Moberg, A .1997. Homogenization of Swedish temperature data. Part I: homogeneity test for linear trends. Journal of Climatology. 24: 643–662.
Alexandersson, H.1986. A homogeneity test applied to precipitation data. Journal of Climatol. 6: 661–675.
Babaeian. I., Kouhi. M. 2012. Agroclimatic Indices Assessment over Some Selected Weather Stations of Khorasan Razavi Province Under Climate Change Scenarios. 26: 953- 967. [In Persian]
Barkganpour, S., Ghorbani, kh., Salarijazi, M and Rezaeighale, L. 2021.Climate Research. 46: 57-72. [In Persian]
Baguis, P., Roulin, E., Willems, P and Ntegeka, V. 2010. Climate change scenarios for precipitation and potential evapotranspiration over central Belgium. Theoretical and Applied Climatology. 99: 273-286.
Bandyopadhyay, A., Bhadra, A., Raghuwanshi, N.S and Singh R. 2009. Temporal trends in estimates of reference evapotranspiration over India. Journal of Hydrologic Engineering. 14:508-515.
Dongsheng, Z., Zheng, D., Shaohong, W and Zhengfang, W. 2007. Climate changes in northeastern china during last four decades. Chinese Geographical Sciences. 17:317-324.
Dehghanisanj, H., Yamamoto, T. and Rasiah, V. 2004. Assessment of evapotranspiration estimation models for use in semi-arid environments. Agric. Water, Manage. 64: 91-106.
Farokhzadeh, B., Chobeh., S and Bazrafshan, O. 2020. Evaluating the effects of climate change on standardized precipitation-evaporation and transpiration index (case study: Lethian watershed). Rainwater catchment system. 26: 59-72. [In Persian]
Ghayoor, H.A and Masoudian, S.A. 1996. Investigating the system of total annual precipitation changes in Iran. Nivar. 29: 27-60. [In Persian]
Ghareh khani, A., ghahreman, N. 2010. Seasonal and Annual Trend of Relative Humidity and Dew Point Temperature in Several Climatic Regions of Iran, water and soil. 4: 636-646. [In Persian]
Ghasemi A. R. Timori, M and Timori, F. 2019. Estimating reference evaporation and transpiration of Tabriz and Ardabil stations using principal component analysis method. Geographic space. 65: 215-232. [In Persian]
Gharehloo, R and Ghasemifar, E., 2019. Spatial variability of evapotranspiration regards to extreme temperatures using remote sensing data in Iran. 23: 193-212. [In Persian]
Ghaemi, H., Zarin, A and Khoshakhlagh, F. 2012. Climatology of arid regions. Samt publication. pp 419. [In Persian]
Goudarzi, M., salahi, B and Hsseini, S.A. 2018. Estimation of Evaportranspiration rate due to climate change in the Urmia Lake basin. Watershed mansgement science. 41: 1-12. [In Persian]
Guo, X., Gong, X., Shi, J., Guo, J., Dominguez-Villar, D., Lin, Y., Wang, H and Yuan, D. 2021. Temporal variations and evaporation control effect of the stable isotope composition of precipitation in the subtropical monsoon climate region, Southwest China. Hydrology. 599: 126278.
Granger, R. J. 1999. Satellite-derived estimation of evapotranspiration in Gedis basin. Journal of Hydrology. 229: 70-76.
Guo, B., Zhang, J., Gong, H and Cheng, X. 2014. Future climate change impacts on the ecohydrology of Guishui River Basin China, Ecohydrology and Hydrobiology. 1: 55-67.
Garbrecht, J and Van Liew, M. 2004. Trends in precipitation, streamflow, and evapotranspiration in the Great Plains of the United States. Journal of Hydrological Engineering. 9:360-367.
Huo, Z., Xiaoqin, D., Shaoyuan, F., Shaozhong, K and Guanhua, H .2013. Effect of climate change on reference evapotranspiration and aridity index in arid region of China. Journal of Hydrology. 492: 24-34.
Harmsen, E., Miller, N., Schlegel, N. and Gonzalez, JE. 2009. Seasonal climate change impacts on evapotranspiration, precipitation deficite and crop yield in Puerto Rico. Agricultural Water Management 96:1085-1095.
Hosseini. M. R., khalet abadi farahani. A. H. 2017. Evaluation and sensitivity analysis of different methods of daily reference evaporation and transpiration estimation in a cold dry climate. Applied science research. 2: 29-40. [In Persian]
Jafari, M and Dinpapasho. 2017. Evaluation of Multiple Ridge Regression Model to Estimation of Pan Evaporation. Irrigation sciences and endineering. 40: 83-97. [In Persian]
IPCC (Intergovernmental Panel on Climate Change) , 2007,. Climate Change (2007), “The Physical Science Basis, A Contribution of Working Groups. I, to the Forth Assessment Report of the Intergovernmental Panel on Climate Change, Solomon and the Core Writing Team (eds)”. Cambridge University press. Cambridge United Kingdom, and New York, USA.
Ito, Y and Momii, K. 2021. Potential of climate changes on evaporation from a temperate deep lake. Joyrnal of hydrology. 35: 100816
Kisi, O. 2009. Modeling monthly evaporation using two different neural computing techniques. Irrigation Science. 27:417–430.
Kavyani, M. R. and Alijani, B. 2011. The foundations of climatology. Samt publication. pp 582. [In Persian]
Kochakzadeh, M and Nikbakht, J. 2004. Comparison of different methods of reference evapotranspiration estimation in different climates of Iran with the standard FAO-Penman-Monteith method. Agriculture science. 3: 43-57. [In Persian]
Kouhi, M., Sanaei Nejad, H and Amini, M. 2017. The effect of the different ETo estimation methods on reconnaissance drought index (RDI) calculation in several climatic zones of Iran. Climate research. 25-26: 47-66. [In Persian]
Karampoor, M., Yousefi, A., Koohpaye. N. 2015. Relationship between climatic elements with vegetation cover of meadows the Hormozgan province (A case study: Gymnocarpus decander). Natural ecosystems of Iran. 21: 41-48. [In Persian]
Khosravin. M., entezari. A. R., Baaghideh. M and Zandi. R. 2020. Modeling the link between drought, number of rainy days and evaporation and transpiration in Fars province. Environmental research and technology. 7: 1-15. [In Persian]
Liang, L., Lijuan L and Qiang, L. 2010. Temporal variation of reference evapotranspiration during 1961-2005 in the Taoer river basin of Northeast China. Agricultural and Forest Meteorology. 150: 298-306.
Li, Y.F., Min, X and Thong, N.G. 2010. Adaptive ridge regression system for software cost estimating on multi-collinear datasets. The Journal of System and Software, 83: 2332-2343.
Ladlani, I., Hauichi, L., Dhemili, L., Heddem, S and Blouze, Kh. 2012. Estimation of daily refrence evapotranspiration in the north of Algeria using adaptive neuro-Fuzzy inference system (ANFIS) and multiple linear regression Models: a comparative study. Arabian Journal for Science and Engineering, 39: 5959-5969.
Mesquita, J.B.D.F., Lima Neto, I.E., Raabe, A and deAraujo, J.C. 2020. The influence of hydroclimatic conditions and water quality on evaporation rates of a tropical lake. 590: 125456.
Mutiga, J., Su, Zh and Woldai, T. 2010. Using satellite remote sensing to assess evapotranspiration: Case study of the upper Ewaso Ng’iro North Basin, Kenya. International Journal of Applied Earth Observation and Geoinformation 12S (2010) S100–S108.
Mallik, A.P., Jyothy, S.A and Sekhar Reddy, K. C. 2013. Daily reference evapotranspiration estimation using linear regression and Ann models. The Institution of Engineers (India). 4:215–221.
Malik, A and Kumar, A. 2015. Pan evaporation simulation based on daily meteorological data using soft computing techniques and multiple minear megression. Water Resources Management. 29: 1859-1872.
Martinez, M. D., Serra, C., Burgueño A and Lana, X. 2010. Time trends of daily maximum and minimum temperatures in Catalonia (ne Spain) for the period 1975–2004. Journal of Climatol. 30: 267–290.
Malekinejad, H and pourmohamadi, S. 2012. Developing and performance assessment of a regression equation for estimating reference crop evapotranpiration based on FAO Penman -Moneteith in central Iran. 20: 495-507. Pasture and desert research of Iran. [In Persian]
Mosavi Baigi, M., Ashraf, B and Mianabadi, A. 2010. The assessment of four reference crop evapotranspiration models in a semi-arid climate of Iran to find the best radiation model. Water and soil conservation. 17:85-105. [In Persian]
Nourani, V., Sayyah-fard, M., Taghi Alami, M and Sharghi, E. 2020. Data pre-processing effect on ANN-based prediction intervals construction of evaporation process at different climate regions in Iran. Journal of Hydrology. 588: 125078.
Pandzic, K and Likso, T. 2010. Homogeneity of average annual air temperature time series for Croatia. Journal of Climatology. 30: 1215–1225.
Panda, K.C., Singh, R.M., Thakural, L.N and Sahoo, D.P., 2022. Representative grid location – multivariate adaptive regression spline (RGL-MARS) algorithm for downscaling dry and wet season rainfall.  Hydrology. 605: 127381.
Paul, G., Gowda, P. H., Vara Prasad, P. V., Howell, T. A., Staggenborg, S. A and Neale, C. M. U. 2013. Lysimetric evaluation of SEBAL using high resolution airborne imagery from BEAREX08. Advances in Water Resources. 59:157-168.
Patle, G. T., Chettri, M and Jhajharia, D. 2020. Monthly pan evaporation modelling using multiple liner regression and artificial neural network techniques.  Water supply. 3: 800-808.
Rodrigues, I.S. Costa, C.A.G., lima Neto, I.E and Hopkinson. C.2021. Trends of evaporation in Brazilian tropical reservoirs using remote sensing. Hydrology. 598: 126476.
Sahin, S and Cigizoglu, H. K. 2010. Homogeneity analysis of Turkish meteorological data set. Hydrology Processes. 24: 981–992.
Sawano, Sh., Hotta, N., Komatsu, H., Suzuki, M and Yayama, T. 2007. Forest Environments in the Mekong River Basin.Evaluation of Evapotranspiration in Forested Areas in the Mekong Basin Using GIS Data Analysis. 295: 36-44.
Shirgure, PS and Rajput, G.S.  2012. Prediction of daily pan evaporation using neural networks models. Science Journal Agriculcure. 5:126–137.
Shirgure, PS. 2011. Evaporation modeling with neural networks-A research review. International Journal of Research and Reviews in Applied Sciences. 2:37–47.
Shirsath, P.B and Kumar, A.S. 2009. A comparative study of daily pan evaporation estimation using Ann, regression and climate-based models. Water Resources Management, 24: 1571-1581.
Sanaei Nejad, S.H, Noori, S., Hashemi nia, M. 2011. Estimation of Evapotranspiration Using Satellite Image Data in Mashhad area, 3:540: 547. [In Persian]
Seifi. A., Mirlotfi. S. M and Riahi. H. 2010. Development of a combined model of multiple regression - analysis of main components and factors in the prediction of evaporation - reference transpiration. Water and soil. 6: 1186- 1196. [In Persian]
Sabzi parvar., A. A and Shadmani., M. 2011. Analyzing the process of evaporation and transpiration using Mann-Kendall and Spearman tests in dry areas. 4: 823 – 834. [In Persian]
Salarian, M., Najafi, M., Nagafi, K., Eslamiyan. S. S. Heidari, M. 2014. The most Appropriate Method to Estimate Potential Evapotranspiration in Meteorological Data Scarce Condition in the Warm and Cold Months of the Year (Case Study of Isfahan). Irrigation and drainage. 1: 62-73. [In Persian]
Shikh eslami, N., Ghahreman, B., Mosaedi, A., Davari, K and Mohajerpoor, M. 2014. Estimating Reference Evapotranspiration by Using Principal Component Analysis (PCA) and The Development of a Regression Model (MLR-PCA) (Case Study: Mashhad Station). Water and soil. 2: 420- 429. [In Persian]
Tian, CH., Jiao, W., Beysens, D., Kaseke, K.F., Medici, M. G. Li. F and Wang, L. 2021.Investigating the role of evaporation in dewformation under different climates using 17O-excess. Journal of hydrology. 592: 125847
Tabari, H., Marofi, S., Aeini, A., Hosseinzadeh Talaeea, P and Mohammadi, K. 2011. Trend analysis of reference evapotranspiration in the western half of Iran. Agricultural and Forest Meteorology.151: 128-136.
Vicente-serrano, M. S., Begueria, S., Lopez-Mereno, J. L., Garcia-vera, M. A and Stepanek, P. 2010. A complete daily precipitation database for northeast Spain: reconstruction, quality control, and homogeneity. Journal of Climatology. 30: 1146–1163.
Xu, C., Gong, L., Jiang, T., Chen, D and Singh, V.P. 2006. Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. Journal of Hydrology. 327: 81–93.
Yuting, Y., Songhao, Sh and Lei, J. 2012. Remote sensing temporal and spatial patterns of evapotranspiration and the responses to water management in a large irrigation district of North China. Agricultural.