بررسی روند و زمان تغییر روند بارش حوضه دریاچه ارومیه

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه بیرجند

2 دانشگاه شهید چمران اهواز

3 دانشگاه ارومیه

چکیده

تغییرات اقلیمی در عصر حاضر یکی از چالش‌های مهم زیست محیطی می‌باشد. درک ما از تأثیرات بشر روی محیط، به ویژه آن‌هایی که در ارتباط با گرم شدن ناشی از افزایش گازهای گلخانه‌ای هستند، نشان می‌دهد که تعدادی از پارامترها به احتمال زیاد در حال تغییر می‌باشند. بنابراین بررسی روند تغییرات زمانی بارش در برنامه‌ریزی و طراحی دقیق‌تر پروژه‌های آبی در آینده از اهمیت زیادی برخوردار می‌باشد. در این مطالعه روند تغییرات بارش حوضه دریاچه ارومیه در دو مقیاس سالانه و ماهانه با استفاده از آزمون ناپارامتری من - کندال اصلاح شده با حذف کامل ساختار خودهمبستگی مورد بررسی قرار گرفت. بدین منظور 42 ایستگاه بارانسنجی (24 ایستگاه در استان آذربایجان غربی و 18 ایستگاه در استان آذربایجان شرقی) در حوضه دریاچه ارومیه انتخاب و پس از تکمیل دوره آماری 1392-1363 مورد بررسی قرار گرفتند. نتایج بررسی بارش حوضه دریاچه ارومیه نشان داد که حوضه دریاچه ارومیه در طی سال‌های مورد بررسی ترکیبی از روند کاهشی و افزایشی را تجربه کرده است. روند کاهشی بارش اغلب در ایستگاه‌های مناطق غربی حوضه و مناطق غرب و جنوب غرب دریاچه ارومیه دیده شد. روند افزایشی بارش نیز در مناطق جنوبی و شمال شرقی حوضه دریاچه ارومیه مشاهده گردید. نتایج بررسی زمان تغییر روند در سری بارش حوضه دریاچه ارومیه نشان داد که شکست در سری بارش در اکثر ایستگاه‌هایی که کاهش روند بارش را تجربه کرده‌اند بین سال‌های 1371 تا 1377 بوده است. هم‌چنین نتایج پهنه‌بندی آماره Z آزمون من – کندال اصلاح شده نشان داد که در مقیاس سالانه مناطق تحت تأثیر توده هوای قطبی – قاره‌ای ورودی به ایران از روند کاهشی در بارش سالانه برخوردار هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation the Trend and Trend Chang Point of Urmia Lake Basin Precipitation

نویسندگان [English]

  • Mohammad Nazeri Tahrudi 1
  • Farshad Ahmadi 2
  • Keivan Khalili 3
1 Birjand University
2 Shahid Chamran University of Ahvaz
3 Urmia University
چکیده [English]

Introduction: Given the fact that Iran is located in the center of the dryland of earth and is significantly influenced by the deserts of Central Asia and hot dry deserts of Arabia and Africa, is one of the most arid and low rainfall land areas.So is the proper management of water resources is of critical importance. The first step in the proper management of water resources is studying the factors that affected these resources including climate change. In fact climate change is a dynamic process in terms of time and place. Large parts of the Earth's climate as part of their normal variability in short-term and long-term experience. Short-term climate changes due to the difference in terms of average annual values of specific climate variables in average periods such as 30 years. Causes and effects of regional climate change in several parts of the world have been widely studied from various aspects. Among hydrological parameters, precipitation is the most important parameter in the complex hydrologic cycle. Follow the phenomenon of global warming on the Earth's surface, the rainfall pattern has changed.Trends of rainfall in different parts of the world have been studied by many researchers. Due to climate change in Iran and climate change in the Basin of Urmia Lake it seems that evaluation the trend of monthly and annual precipitation and its time of change point in the basin of Urmia Lake changes is important. The goal of this study is evaluatingthe trend and time of the change point trend of monthly and annual precipitation of rain gage stations in Urmia Lake basin.
Material and methods: Lake Urmia is the focus of surplus accumulation of surface currents all the rivers of the basin, with an area of approximately 5750 square kilometers and the average elevation of 1276 m above sea level and is located in the middle of the northern basin. Around of Lake Urmia there are 16 wetlands with an area of 5 to 120 hectares (some have dried up) that mostly have sweet or salty and fresh water and a high value of ecosystems.Urmia Lake Basin is situated in eastern of 44-14 to 47-53 and north of 40-35 to 30-38 coordinates. Urmia Lake Basin rainfall changes is 220 to 900 mm and have mean precipitation about 263 mm that added in central parts of the basin to the highlands.
Trend analysis: The aim of process test is to specify whether an ascending or a descending trend exists in data series. Since parametric tests have some assumptions including normality, stability, and independence of variables, where most of these assumptions do not apply to hydrologic variables, the nonparametric methods are more preferred in meteorological and hydrological studies. The nonparametric methods are less sensitive to extreme values compared to parametric tests in the examination of trends. Nonparametric tests can also be utilized for data time series regardless of linearity or nonlinearity of the trend (Khalili et al. 2014). One of the most well-known nonparametric tests is Mann-Kendall test (Mann 1945; Kendall 1975).
The modified Mann-Kendall test (MMK): The main assumption of Mann-Kendall test is that the sample data has no significant autocorrelation. However, some hydrological series might have a significant autocorrelation coefficient. When a series has a positive autocorrelation coefficient, there is an increased chance for Mann-Kendall test to reveal the existence of a trend in this series. In this case, the null hypothesis i.e. lack of trend is rejected, yet this hypothesis should not actually be. The modified Mann-Kendall test was presented by Hamed and Rao (1998) and has been used by Kumar et al (2009) for the analysis of the trend of Indian rivers. In this method, the effect of all significant autocorrelation coefficients is removed from the time series and is appliesto a series whose autocorrelation coefficients are significant in one or more cases.
Change point test: Pettittest is a non-parametrictest that was developedin 1979byPettit. Themethod is used in order tofind change points ina time series(Salarijazi et al 2012).In this study,thestatisticwas usedtofind asudden change intemperaturedata.Thisstatistic isatest with rank basis and without a distributionin orderto detectsignificantchangesin the mean of the time seriesanditis importantwhenthereis noassumptionabout the change time.
Results and discussion: In this study the trend of monthly and annual precipitation of rain gage stations that located in Urmia Lake basin were investigated using modified Mann-Kendall test. Z values of case study were calculated in two monthly and annual scales. The results of evaluation the trend of precipitation of rain gage stations of Urmia Lake basin showed that in October, December, January, February and March (five months of the year) the trend of precipitation is decreasing and the mean of Z values showed the less than zero values. In April and May there is no sensible changing in precipitation trend. Also the results showed that the March, April and May have a low failure rate and February, December and July have a most of change point of monthly precipitation data. About 60 percentages of the time of change point in precipitation trend are between 1992 and 1998. Also the results showed that two months of May and November there is no changing point in west Urmia Lake rain gage stations. In annual scale the time of changing trend is between 1992 and 1998.
Conclusion: The results of evaluation the trend of Lake Urmia precipitations showed that the Urmia Lake basin has a combination of decreasing and increasing trend in studied time period. The decreasing trend in precipitation often seen in west stations of the basin and west and south-west of Urmia Lake. The increasing trend also seen in south and north-east of Urmia Lake basin. Also the results of zoning the Z values of Mann-Kendall test showed that in annual scale the regions that influenced by polar-continental air mass that they entered Iran have a decreasing trend.

کلیدواژه‌ها [English]

  • Climate change
  • Modified Mann-Kendall
  • Pettitt Test
  • zoning
1- Bandyopadhyay A., Bhadra A., Raghuwanshi N.S., and Singh R. 2009. Temporal Trends in Estimates of Reference Evapotranspiration over India. Journal of Hydrology Engineering. 14(5):508–515.
2- Becker S., Gemmer M., and Jiang T. 2006. Spatiotemporal analysis of precipitation trends in the Yangtze River catchment. Stochastic Environ and Research Risk Assessment. 20:435–444.
3- Burn D.H., Cunderlik J.M., and Pietroniro A. 2004. Hydrological trends and variability in the Liard River basin. Hydrological Sciences. 49(1):53-67.
4- Chen j., Wub X., Finlayson B., Webber M., Wei T., and Li M. 2014. Variability and trend in the hydrology of the Yangtze River, China: Annual precipitation and runoff. Journal of Hydrology. 513:403-412.
5- De Lima M.I.P., Marques A.C., De Lima J.L.M.P., and Coelho F.E.S. 2005. Precipitation Trends in Mainland Portugal in the period 1941-2000. Water in Celtic Countries: Quantity, Quality and Climate Variability - Actas do Fourth Inter-Celtic Colloquium on Hydrology and Management of Water Resources. Guimarães, 11-13 de Julho de 2005, 12 p.
6- Duhan D., and Pandy A. 2013. Statistical analysis of long term spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India. Atmospheric Research. 122:136-149.
7- Forland E.J., Van England A., Ashcroft J., Dahlstrom B., Damaree G., Frich P., Hanssen-Bauer I., Heino R., Jonsson T., Meitus M., Muller-Westemeier G., Palsdottir T., Tuomenvirta H., and Vedin H. 1996. Change in Normal Precipitation in the North Atlantic Region. (2nd edn.) DNMI Report 7/96 Klima.
8- Ghareh Chayi R., Salajegheh A., Noorolahi M., and Khosravi H. 2016. Effects excluding the effect of autocorrelation in analysis trend of hydro-climatic variables (Fars Province). Desert Management, 3(6): 13-24. (In Persian).
9- Hamed K.H., and Rao A.R. 1998. A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology. 204:182–196.
10- Herath S., and Ratnayake U. 2004. Monitoring Rainfall Trends to Predict Adverse Impacts: A Case Study from Sri Lanka (1964- 1993). Global Environ Change. 14:71-79.
11- Kahya E., and Partal T. 2007. Is seasonal precipitation decreasing or increasing in Turkey?. Online Journal of Earth Sciences. 1(1): 43-46.
12- Karmeshu N. 2012. Trend Detection in Annual Temperature & Precipitation using the Mann Kendall Test–A Case Study to Assess Climate Change on Select States in the Northeastern United States.
13- Katiraie P. S., Hajjam S., and Iran Nezhad P. 2007. The share of changes in the frequency and intensity of daily rainfall precipitation trend in Iran over the period 1960 to 2001. Journal of Physics of the Earth and Space, 33(1): 67-83. (in Peesian).
14- Kendall M.G. 1975. Rank Correlation Measures, Charles Griffin, London.
15- Khalili K., Nazeri Tahrudi M., and Khanmohammadi N. 2014. Trend Analysis of Precipitation in Recent two Decades over Iran. Journal of Applied Environment and Biological Science. 4(1s):5-10.
16- Khalili K., Tahoudi M.N., Mirabbasi R., and Ahmadi F. 2016. Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stochastic Environmental Research and Risk Assessment. 30(4):1205-1221.
17- Khaliq M.N., Ouarda T.B.M.J., and Gachon P. 2009. Identification of temporal trends in annual and seasonal low flows occurring in Canadian rivers: The effect of short- and long-term persistence. Journal of Hydrology. 369:183–197.
18- Kousari MR., Ahani H., and Hendi-Zadeh R. 2013. Temporal and spatial trend detection of maximum air temperature in Iran during 1960-2005.Global and Planetary Change. 111:97-110.
19- Kumar S., Merwade V., Kam J., and Thurner K. 2009. Streamflow trends in Indiana: Effects of long term persistence, precipitation and subsurface drains. Journal of Hydrology, 374(1-2): 171-183.
20- Luo Y., Liu S., Fu S.L., Liu J.S., Wang G.Q., and Zhou G.Y. 2008. Trends of precipitation in Beijiang River Basin, Guangdong Province, China, Hydrological Processes. 22: 2377–2386.
21- Mann H.B. 1945. Non-parametric tests against trend. Econometrica, 13:245-259.
22- Manton M.J., Dellamartab P.M., Haylock M.R., Hennessy K.J., Nicholls N., Chambers L.E., Collins D.A., Daw G., Finet A., Gunawan D., Inape K., Isobe H., Kestin T.S., Lefale P., Leyu C.H., Lwin T., Maitrepierre L., Ouprasitwong N., Page C.M., Pahalad J., Plummer N., Salinger M.J., Suppiah R., Tran V.L., Trewin B., Tibig I., and Yee D. 2001. Trends in extreme daily rainfall and temperature in Southeast Asia and the South Pacific: 1961-1998. International Journal of Climatology. 21:269-284.
23- Mirabbasi R., and Dinpashoh Y. 2012. Trend Analysis of Precipitation of NW of Iran Over The Past Half of The Century. Journal of Irrigation Science and Engineering. 35(4):59-73. (In Persian).
24- Mirabbasi R. 2010. Evaluation the time's properties of precipitation trend (Case study: North-west of Iran). Research plan report, East Azarbaijan Regional Water Company. (in Peesian).
25- Pal Al-Tabbaa A. 2009. Trends in seasonal precipitation extremes an indicator of ‘climate change’ in Kerala, India. Journal of Hydrology 367:62–69.
26- Pettitt A.N. 1979. A non-parametric approach to the change-point problem. Applied statistics. 126-135.
27- Saboohi R., Soltani S., and khodagholi M. 2012. Trend analysis of temperature parameters in Iran. Theatrical Applied Climatology. 109:529–547.
28- Sen P.K. 1968. Estimates of the regression coefficients based on Kendall’s tau. Journal of the American Statistical Association. 63(324):1379-1389.
29- Tabari H., and Hosseinzadeh-Talaee P. 2011. Analysis trends in temperature data in arid and semi-arid regions of Iran. Atmospheric Research. 79:1-10.
30- Thiel H. 1950. A rank-invariant method of linear and polynomial analysis, part 3.
31- Torabi H., and Emamgholizadeh S. 2015. Analysis of stream flow and effect of autocorrelation coefficient watershed trend across Karkheh on the trend of flow. Iranian Water Research, 9(1): 143-151
32- Xu Z.X., Takeuchi K., and Ishidaira H. 2003. Monotonic trend and step changes in Japanese precipitation, Journal of Hydrology. 279:144–150.
33- Yue S., and Hashino M. 2003. Long term trends of annual and monthly precipitation in Japan. J. of the American Water Resources Association (JAWRA), 39(3): 587-596.
34- Zarenistanak M., Dhorde AG., Kripalani RH. 2014. Temperature analysis over southwest Iran: trends and projections. Theatrical Applied Climatology. 116:103-117. DOI 10.1007/s00704-013-0913-1.
35- Zhang W., Yan Y., Zheng J., Li L., Dong X., and Cai H. 2009. Temporal and spatial variability of annual extreme water level in the Pearl River Delta region, China. Global and Planetary Change, 69, 35–47.
CAPTCHA Image