Monitoring annual precipitation changes in Dezful plain with statistical analysis and time series
الموضوعات :
Yaser Sabzevari
1
(Ph.D. student, water department, Isfahan University of Technology)
Saeid Eslamian
2
(Water department, isfahan university of technology)
Keyvan Moradalivand
3
(Center of education and research agriculture and natural resources of Lorestan)
الکلمات المفتاحية: predict, ARIMA, Dezful, Trend, Man-Kendall,
ملخص المقالة :
Background and objective:Predicting and studying the trend of climate variables in the future plays an important role in the optimal management of water resources. Different methods are used to determine the trend of change. One of the most common methods of trend change analysis is time series analysis. Time series is a set of observations about a variable that is measured at discrete points in time, usually at equal distances, and arranged in chronological orderMaterials and methods:In the present study, the trend of precipitation changes in Dezful plain during 32 years was investigated and by selecting the appropriate time series model, a forecast was made for the next ten years. Man-Kendall’s non-parametric test was used to investigate the trend of precipitation changes.Results and conclusion:The result of this test showed that the annual precipitation of Dezful had a decreasing trend due to having a Man-Kendall statistic of -1.6. To select the appropriate time series model, data preparation (trend elimination and normalization) was performed first. Data stagnation was assessed with autocorrelation (ACF) and partial autocorrelation (PACF) charts. Using the differentiation method, the data became static (eliminating the mean trend) by applying one-time differentiation. By static data, random models were used to predict the average annual precipitation. Then, by fitting different Arima models and considering the criteria of T, P-VALUE less than 0.05 and Bayesian information criterion (BIC), the Arima model (3,1,1) was selected as the most appropriate model and to verify this the model was predicted for the period 2011 to 2018. The validation results showed that the prediction of this model is acceptable according to the actual values. Then, based on this model, a forecast was made for the next ten years from 2019 to 2028, which is predicted that the precipitation trend will decrease for the next period.
Abdullah Nejad, K. (2015). Time series models for prediction of monthly precipitationl (Case study: Hashem Abad Station of Gorgan). Geospatial Space, 17: 15-25. (in Farsi)
Ahmadi, F. (2004). Estimation of annual precipitation of Khorasan province using time series. Master's thesis. 284 pages. (in Farsi)
Bashari, M. & Vafakhah, M. (2010). Comparison of different methods of time series analysis in predicting discharge of Karkhkh watershed. Irrigation and water engineering, 2: 65-77.
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.Dodange, I., Abedi Kupai, J. and Gohari, A. (2011). Application of time series models to determine the process of future climate parameters in the direction of water resources management. Agricultural Sciences and Technology of Agriculture and Natural Resources, 59: 59-74.
Burlando, P., Montanari, A., & Ranzi, R. (1996). Forecasting of storm rainfall by combined use of radar, rain gages and linear models. Atmospheric research, 42(1-4), 199-216. https://doi.org/10.1016/0169-8095(95)00063-1
Ghaderpour, E., Pagiatakis, S. D., and Hassan, Q. K. (2021). A survey on change detection and time series analysis with applications. Applied Sciences, 11(13), 6141. https://doi.org/10.3390/app11136141
Ghahraman, N. & Gharehkhani, A. (2011). Estimation of random time patterns of time series in estimating evaporation from a bath (Case study: Shiraz Station). Water Research in Agriculture, 1: 32-81. (in Farsi)
Ghane Ezabadi, N., Azhdar, S., & Jamali, A. A. (2021). Analysis of dust changes using satellite images in Giovanni NASA and Sentinel in Google Earth Engine in western Iran. Journal of Nature and Spatial Sciences (JONASS), 1(1), 17-26. https://dx.doi.org/10.30495/jonass.2021.680327
Jamali, A. A., Kalkhajeh, R. G., Randhir, T. O., & He, S. (2022). Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni. Journal of Environmental Management, 302, 113970. https://doi.org/10.1016/j.jenvman.2021.113970
Jamali, A. A., Naeeni, M. A. M., & Zarei, G. (2020). Assessing the expansion of saline lands through vegetation and wetland loss using remote sensing and GIS. Remote Sensing Applications: Society and Environment, 20, 100428. https://doi.org/10.1016/j.rsase.2020.100428
Jamali, A. A., Tabatabaee, R., & Randhir, T. O. (2021). Ecotourism and socioeconomic strategies for Khansar River watershed of Iran. Environment, Development and Sustainability, 23(11), 17077-17093. https://doi.org/10.1007/s10668-021-01334-y
Kendall, M.G. (1975). Rank Correlation Methods, London, Charles Griffin.
Khalili, A. S., & Bazrafshan, C. (2004). An analysis of the changes in annual, seasonal, and monthly rainings in five Iranian old stations in the past one seventeen and sixteen years. Desert, 9(1), 25-25.Khazaei, M., & Mirzaei. M.R. (2014). Forecasting of climate variables by analyzing time series of Zohreh Watershed. Applied Geosciences Research, 34: 233-250. (in Farsi)
Khorrami, M., & Bozorgnia, A. (2017). Time series analysis with Minitab software Sokhan gostar press, 336 pages. (in Farsi)
Mann, B. H.(1945). Non-Parametric Test Against Trend. Econometrica, 13. https://doi.org/10.2307/1907187
Mishra, S., Swain, D. K., Mishra, D., & Santra, G. H. (2021). Time Series Analysis of Rainfall for Puri District of Odisha Using ARIMA Modelling. In Intelligent and Cloud Computing (pp. 419-426). Springer, Singapore. https://doi.org/10.1007/978-981-15-6202-0_43
Momani, P. E. N. M., & Naill, P. E. (2009). Time series analysis model for rainfall data in Jordan: Case study for using time series analysis. American Journal of Environmental Sciences, 5(5), 599.. https://doi.org/10.3844/ajessp.2009.599.604
Niromand, H.A. (2012). Analysis of time series: one-dimensional and multivariate methods. Mashhad, Ferdowsi University Press, 602 pages. (in Farsi)
Ouma, Y. O., Cheruyot, R., & Wachera, A. N. (2022). Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin. Complex & Intelligent Systems, 8(1), 213-236. https://doi.org/10.1007/s40747-021-00365-2
Pasquini, A. I., Lecomte, K. L., Piovano, E. L., & Depetris, P. J. (2006). Recent rainfall and runoff variability in central Argentina. Quaternary International, 158(1), 127-139. https://doi.org/10.1016/j.quaint.2006.05.021
Rhif, M., Ben Abbes, A., Farah, I. R., Martínez, B., & Sang, Y. (2019). Wavelet transform application for/in non-stationary time-series analysis: a review. Applied Sciences, 9(7), 1345. https://doi.org/10.3390/app9071345
Ruhf, R. J., & Cutrim, E. M. (2003). Time series analysis of 20 years of hourly precipitation in southwest Michigan. Journal of Great Lakes Research, 29(2), 256-267. https://doi.org/10.1016/S0380-1330(03)70431-6
Salas, (1996). Applied Time Series in Hydrology, Mc Grew Hill.
Shahid, S. (2010). Rainfall variability and the trends of wet and dry periods in Bangladesh. International Journal of climatology, 30(15), 2299-2313. https://doi.org/10.1002/joc.2053
Soltani Gordfaramarzi, S., Saberi, A. and Qeysouri, M. (2017). Determine the best time series model for predicting annual precipitation of selected stations in West Azarbaijan province. Applied Geosciences Research, 44: 105-87. (in Farsi)
Veisipour, H., Masoumpour, J., Sahne B. and Yousefi, Y. (2010). The prediction of precipitation and temperature trend using time series models (ARIMA). Geosciences Research, 12: 77-69.
Wang, D., Shannon, M. C., Grieve, C. M., & Yates, S. R. (2000). Soil water and temperature regimes in drip and sprinkler irrigation, and implications to soybean emergence. Agricultural Water Management, 43(1), 15-28. https://doi.org/10.1016/S0378-3774(99)00057-8
