- Abdollahnejad Kamel, A. (2015). Time series models in forecasting monthly rainfall (Case study: Hashemabad station, Gorgan). Geographical Space Planning, 5(17), 15-25. (In Persian).
- Adamowski, J., & Chan, H. F. (2011). A comparison of artificial neural network and time series models for forecasting daily water levels at hydrological monitoring stations. Water Quality Research Journal of Canada, 46(2), 175-185.
- Alizadeh, A. A. (2011). Principles of applied hydrology (32nd ed.). Imam Reza University Press. (In Persian).
- Azizi, G. (2005). Investigation of droughts, wet periods, and the possibility of their prediction using time series model in Hormozgan Province. Geographical Research Quarterly, 79, 48-61. (In Persian).
- Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control (2nd ed.). Holden-Day.
- Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: forecasting and control (3rd ed.). Prentice Hall.
- Burlando, P., Montana, A., & Raze, R. (1996). Forecasting of storm rainfall by combined use of radar, rain gages and linear models. Atmospheric Research, 42, 199-211.
- Cryer, J. D. (1992). Time series analysis (H. A. Niroomand, Trans.). Mashhad University Publication. (In Persian).
- Dastorani, M., Mirzavand, M., Dastorani, M. T., & Sadatjejad, S.J. (2016). Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition. Natural Hazards, 81, 1811-1827. https://doi.org/10.1007/s11069-016-2163-x
- Dodangeh, A., Abedi Koupai, J., & Gohari, S. A. (2012). Application of time series modeling to investigate future climatic parameters trend for water resources management purposes. Journal of Water and Soil Science, 16(59), 59-74. (In Persian). http://jstnar.iut.ac.ir/article-1-2198-fa.html
- Erdem, E., & Shi, J. (2011). ARMA-based approaches for forecasting the tuple of wind speed and direction. Applied Energy, 88(4), 1405-1414. https://doi.org/10.1016/j.apenergy.2010.10.031
- Faruk, D. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), 586-594. https://doi.org/10.1016/j.engappai.2009.09.015
- Fouladmand, H. (2010). Monthly evaporation–transpiration prediction using artificial neural network in Fars agricultural stations. Journal of Water and Soil Science, 20(1), 157-169. (In Persian).
- Ghahreman, N., & Gharehkhani, A. (2011). Evaluation of random time series models in estimating pan evaporation (case study: Shiraz station). Journal of Water Research in Agriculture, 25(1), 75-81. (In Persian). https://sid.ir/paper/196848/fa
- Hannan, E. J. (1971). Multiple time series. Wiley.
- Hejabi, S., & Bazrafshan, J. (2013). Evaluation of six types of stochastic models skill in modeling and forecasting the standardized precipitation index time series. Journal of Water Research in Agriculture, 27(3), 429-454. (In Persian). https://doi.org/10.22092/jwra.2013.128847
- Hipel, K. W., & McLeod, A. I. (1994). Time series modeling of water resources and environmental systems. Elsevier.
- Hisdal, H., & Tallaksen, L. M. (2003). Estimation of regional meteorological and hydrological drought characteristics: a case study for Denmark. Journal of Hydrology, 281(3), 230-247.
- Kersik, N. (2001). Hydrogeology and groundwater modeling to solve problems (M. Chitchian & H. A. Kashkooli, Trans.). Shahid Chamran University Press. (In Persian).
- Khalili, K., Fakhri-Fard, A., & Hessari, B. (2007). Analysis of intensity-duration-frequency curves, drought frequency and reservoir design for agriculture and drinking water. In Proceedings of the 3rd National Congress on Civil Engineering University of Tabriz, Tabriz, Iran. (In Persian).
- Laux, P., Vogl, S., Qiu, W., Knoche, H. R., & Kunstmann, H. (2011). Copula-based statistical refinement of precipitation in RCM simulations over complex terrain. Hydrology and Earth System Sciences, 15, 2401-2419.
- Malakoutian, M. M. A., Samaei, S. Y., Khaksar, M., & Malakoutian, Y. (2022). A prediction of future flows of ephemeral rivers by using stochastic modeling (AR autoregressive modeling). Sustainable Operations and Computers, 3, 330–335. https://doi.org/10.1016/j.susoc.2022.05.003
- Mirzavand, M., & Bagheri, R. (2020). The water crisis in Iran: Development or destruction? World Water Policy, 00, 1–9. https://doi.org/10.1002/wwp2.12023
- Mirzavand, M., & Ghazavi, R. (2015). A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resources Management, 29, 1315–1328. https://doi.org/10.1007/s11269-014-0875-9
- Mousavi, S., Banihabib, M., & Bandari, R. (2011). Prediction of daily inflow to dam reservoir using time series models. In Proceedings of the 15th National Seminar on Irrigation and Evaporation Reduction. Kerman, Iran. (In Persian).
- Padilla, A., Pulido-Bosch, A., Cavache, M., & Vallejos, A. (1996). The ARMA model applied to the flow of Karst Spring. Water Resources Bulletin, 32, 917-928.
- Rezanejad Keshteli, M., Babanezhad, M., & Amini, A. (2016). Fitting the seasonal time series model to the rivers discharge in time domain (Case study: Atrak River). Journal of Water and Soil Conservation, 22(6), 307-315. (In Persian). https://doi.org/20.1001.1.23222069.1394.22.6.20.2
- Sabaghian, R., & Sharifi, M. B. (2009). The use of stochastic models in river flow simulation and prediction of annual average river discharge using time series analysis. In Proceedings of the First International Conference on Water Resources Management. Shahroud University of Technology, Shahroud, Iran. (In Persian).
- Sabzevary, Y., & Abedi Koupaei, J. (2022). Trend and time series analysis of reference evapotranspiration (Case study: Khorram Abad Plain). Extension and Development of Watershed Management, 10(37), 35-46. (In Persian).
- Sabziparvar, A. A., Mokhtari, B., Sadeghifar, M., Saghai, S., Ershad-Fath, F., & Norouz-Valashedi, R. (2014). Estimation of daily pan evaporation using time series models. Watershed Engineering and Management Journal, 6(1), 42-51. (In Persian).
- Salas, J. D. (1993). Analysis and modeling of hydrological time series. In D. R. Maidment (Ed.), Handbook of hydrology. McGraw-Hill.
- Shirmohammadi, B., Vafakhah, M., Moosavi, V., & Moghaddamnia, A. (2013). Application of several data-driven techniques for predicting groundwater level. Water Resources Management, 27, 419-432. https://doi.org/10.1007/s11269-012-0194-y.
- Thomas, H. A., & Fiering, M. B. (1962). Mathematical synthesis of stream flow sequences for the analysis of river basin by simulation. Harvard University Press.
- Soleimani Motlagh, M., Ghasemieh, H., Talebi, A., & Abdollahi, K. (2017). Identification and Analysis of Drought Propagation of Groundwater During Past and Future Periods. Water Resources Management, 31, 109-125. https://doi.org/https://doi.org/10.1007/s11269-016-1513-5
- Thompstone, R. M., Hipel, K. W., & McLeod, A. I. (1985). Forecasting quarter-monthly river flow. Water Resources Bulletin, 21, 731-741.
- Tsonis, A. A. (2001). Probing the linearity and nonlinearity in the transitions of the atmospheric circulation. Nonlinear Processes in Geophysics, 8, 341-345.
- Wang, W., Van Gelder, P. H., Vrijling, J. K., & Ma, J. (2005). Testing and modeling autoregressive conditional heteroskedasticity of streamflow processes. Nonlinear Processes in Geophysics, 12, 55-66.
- Yaseen, Z. M., El-Shafie, A., Jaafar, O., Afan, H. A., & Sayl, K. N. (2015). Artificial intelligence-based evapotranspiration models: A review. Water Resources Management, 29(7), 2587-2611.
- Yurekli, K., Kurung, A., & Ozturk, F. (2005). Testing the residuals of an ARIMA model on the Cekerek stream watershed in Turkey. Turkish Journal of Environmental Science, 29, 61-74.
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