Prediction of hydrological drought using the GRI index and linear random time series models (Study area: Ardabil Plain aquifer)

Document Type : Research paper

Authors

1 Former MSc student, University of Tehran, Tehran, Iran.

2 Professor of Water Engineering Department, University of Tabriz, Tabriz, Iran.

3 Post Doctral student of Water Engineering Department, University of Tabriz, Tabriz, Iran.

10.22034/hydro.2025.64314.1321

Abstract

The study investigated hydrological droughts in the Ardabil plain aquifer. The monthly groundwater level observations in 48 wells in the statistical period of 2004–2021 were used for this aim. The Thiessen polygon approach was applied here to transform point data to the regional form. GRI indexes were computed in different time scales, which are 1, 3, 6, 9, and 12 months for regional groundwater level data. Then the GRI time series were modeled. Model performances were evaluated using the Nash-Sutcliffe (NS) and Akaike Information Criteria (AIC) to find the appropriate model. Based on observational data, the parameters of the models were evaluated. Drought was analyzed for each of the time series separately. To predict drought, the fitted model for each of the time series was used. Results showed that the longest drought period length in the three-month timeframe belonged to the period 2004 to 2007; however, it was in the period 2015 to 2021 using the 6-month time span. Using the GRI1 and GRI9 scales, the largest severity of wetness and drought spells were calculated to be about 1.87 and -3.21, respectively. Modeling of GRI time series showed that all-time series have a seasonal trend, and therefore, they were fitted using the SARIMA (p, d, q) (P, D, Q) model. Results revealed that SARIMA (5,1,0) (0,2,2) is the most appropriate model for the GRI3 series. Values of model performances of the mentioned model using the observed data were about NS=0.53 and AIC=-96.6. Therefore, it can be concluded that time series models have relatively suitable precision in predicting GRI series with different time windows in the Ardabil plain.

Keywords

Main Subjects

References

اکبری نیازی، م.، وردی‌نژاد، و.، بهمنش، ج.، نیک پور، م.، 1402. شبیه‌سازی کیفیت آب رودخانه قره سو با استفاده از مدل Kw2QUAL. هیدروژئولوژی، 8(1): 137-155.
بی‌نام (1391) مطالعات طرح آمایش استان اردبیل. جلد  اول، اردبیل، ایران، 27-85. سازمان مدیریت و برنامه‌ریزی استان اردبیل .
حسینی، ب.، دین پژوه، ی.، نیکبخت، ج.، 1394. تحلیل خشک‌سالی‌های شمالغرب ایران با روش شاخص اکتشاف خشک‌سالی. آب و خاک، 29(2): 295-310.
حسینی، س.، خوش سیمای چنار، م.، 1403. کاربرد الگوریتم های یادگیری ماشین در پیش بینی تراز آب زیرزمینی در آبخوان اردبیل. تحقیقات آب و خاک ایران.  
جانی، ر.، 1398. مدلسازی خوشه‌ای تراز آب زیرزمینی دشت تبریز با استفاده از مدل آریما. هیدروژئولوژی 4(2): 108-130.
خسروی دهکردی، ا.، میرعباسی نجف آبادی، ر.، صمدی بروجنی، ح.، قاسمی دستگردی، ا.، 1398. پایش و پیش‌بینی خشک‌سالی‌های آب زیرزمینی دشت شهرکرد با استفاده از شاخص GRI و مدل زنجیره مارکف. هیدروژئولوژی، 4(1): 111-125.
دانشور‌وثوقی، ف.، دین پژوه، ی.، اعلمی، م ت.، 1390. تأثیر خشک‌سالی بر تراز آب زیر زمینی در دو دهه اخیر (مطالعه موردی: دشت اردبیل). دانش آب و خاک، 21(4): 165–179.
دین پژوه، ی.، فاخری فرد، ا.، حسن پور اقدم، م ع.، بهشتی وایقان، و.، 1394. تحلیل روند تغییرات کیفیت آب زیرزمینی در دشت شبستر – صوفیان. علوم و مهندسی آبیاری، 38(1): 55-69.
دین پژوه، ی.، 1401. خوشه‌بندی چاه‌های مشاهداتی آبخوان دشت خوی از نظر کیفیت آب با استفاده از روشK-Means . هیدروژئولوژی، 7(1): 25-41.
زندی‌فر، س.، فیجانی، ا.، نعیمی،م.،  خسروشاهی، م.، 1398. تغییرات زمانی و مکانی شاخص خشک‌سالی آب زیرزمینی، مطالعه موردی: حوزه آبریز زهره- جراحی.. هیدروژئولوژی، 4(2): 108-130.
طاوسی، ت.، دل‌آرا، ق.، 1389. پهنه‌بندی آب و هوایی استان اردبیل. نیوار، 34(71–70): 47–52.
کرد، م.، اصغری مقدم، ا.، نخعی، م.، 1398. مدلسازی عددی آبخوان دشت اردبیل و مدیریت آن با استفاده از بهینه‌سازی برداشت آب زیرزمینی. هیدروژئولوژی، 4(1): 153–167.
مقصود، ف.، یزدانی، م.ر.، رحیمی، م.، ملکیان، آ.، ذوالفقاری، ع.، 1395. مقایسه کارایی مدل شبکه عصبی مصنوعی، سری زمانی و مدل ترکیبی ANN-ARIMA در مدلسازی و پیش‌بینی شاخص منبع آب زیرزمینی (GRI) (مطالعه موردی: جنوب استان قزوین). مجله علوم و مهندسی آبخیزداری ایران. 10(33): 57-47.
Aghelpour, P., Bahrami-Pichaghchi, H., Varshavian, V., 2021. Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran. Stochastic Environmental Research and Risk Assessment, 35(8), 1615–1635.
Akbari Niari, M., Rezaverdinejad, V., Behmanesh, J., Nikpour, R., 2023. Simulating the Water Quality of Qarasu River Using the QUAL2Kw Model. Hydrogeology, 8(1), 137-155. [In persion]
Ali, Z., Hussain, I., Faisal, M., Nazir, H.M., Hussain, T., Shad, M.Y., Mohamd Shoukry, A., Hussain Gani, S., 2017. Forecasting drought using multilayer perceptron artificial neural network model. Advances in Meteorology, 2017(1), 5681308.
Araghinejad, S., Hosseini-Moghari, S.M., Eslamian, S., 2017. Application of data-driven models in drought forecasting. In Handbook of drought and water scarcity, (pp. 423–440). CRC Press.
Azimi, S., Hassannayebi, E., Boroun, M., Tahmoures, M., 2020. Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach. Water Resources Management, 34, 4703–4724.
Azizi, H.R., Ebrahimi, H., Mohamad vali samani, H., khaki, V., 2021. Effect of Meteorological Drought on Groundwater Resources of Varamin Plain Using SPI, NISTOR and GRI index. Iranian Journal of Irrigation & Drainage, 14(6), 2125–2135.
Batool, A., Kartal, V., Ali, Z., Scholz, M., Ali, F., 2025. A novel regional forecastable multiscalar standardized drought index (RFMSDI) for regional drought monitoring and assessment. Agricultural Water Management, 308, 109289.
Bazrafshan, J., Khalili, A., 2013. Spatial analysis of meteorological drought in Iran from 1965 to 2003. Desert, 18, 63–71.
Box, G.E.P., Jenkins, G.M., 1994. Time Series Analysis: Forecasting and Control. Prentice Hall PTR.
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 1994. Time series analysis, forecasting and control. Englewood Clifs. NJ: Prentice Hall.
Buri, E.S., Keesara, V.R., Loukika, K.N., Sridhar, V., Dzwairo, B., Montenegro, S., 2025. Climate-adaptive optimal water resources management: A multi-sectoral approach for the Munneru river basin, India. Journal of Environmental Management, 374, 124014.
Daneshvar Vousoughi, F., Dinpashoh, Y.,  Aalami, M.T., Jhajharia, D., 2013. Trend analysis of groundwater using non-parametric methods (case study: Ardabil plain). Stochastic Environmental Research and Risk Assessment, 27:547–559. [In persion]
Dikshit, A., Pradhan, B., Huete, A., 2021. An improved SPEI drought forecasting approach using the long short-term memory neural network. Journal of Environmental Management, 283, 111979.
Dinpashoh, F., Fakhari Fard, A., Hassanpoor Eghdam, M.A., Beheshtee Vayghan., V., 2015. Trend Analysis of Groundwater Quality of Shabestar- Soofian Plain. Journal of Irrigation Sciences and Engineering, 38(1), 55-69. [In persion]
Farzin, S., Anaraki, M. V., Naeimi, M., Zandifar, S. (2022). Prediction of groundwater table and drought analysis; a new hybridization strategy based on bi-directional long short-term model and the Harris hawk optimization algorithm. Journal of Water and Climate Change, 13(5), 2233–2254.
Hosseini, B., dinpazhoh, Y., and Nikbakht, J., 2015. Analysis of Droughts of Northwest of Iran Using the Reconnaissance Drought Index. Water and Soil, 29(2), 295-310. [In persion]
Hoseini, S.M. khoshsimaie chenar, M., 2025. Application of machine learning algorithms in groundwater level prediction in the Ardabil aquifer. Iranian Journal of Soil and Water Research. [In persion]
Jani, R., 2020. Cluster Modeling of Groundwater Level of Tabriz Plain Using ARIMA Model. Hydrogeology, 4(2), 74-91. [In persion]
Jenkins, G.M., & Box, G.E.P., 1976. Time series analysis: forecasting and control. (No Title).
Karimi, M., Melesse, A.M., Khosravi, K., Mamuye, M., Zhang, J., 2019. Analysis and prediction of meteorological drought using SPI index and ARIMA model in the Karkheh River Basin, Iran. In Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation, (pp. 343–353). Elsevier.
Khosravi Dehkordi, A., Mirabbasi, R., Samadi Boroujeni, H., Ghasemi Dastgerdi, A.R., 2019. Monitoring and forecasting of groundwater drought in Shahrekord plain using Groundwater Resource Index (GRI) and Markov chain model. Hydrogeology, 4(1), 111-125. [In persion]
Kord, M., Asghari Moghaddam, A., and Nakhaei, M., 2019. Numerical modeling of the Ardabil plain aquifer and its management using optimization of Groundwater extraction. Hydrogeology, 4(1), 153-167. [In persion]
Li, Y., Huang, Y., Li, Y., Zhang, H., Fan, J., Deng, Q., Wang, X., 2024. Spatiotemporal heterogeneity in meteorological and hydrological drought patterns and propagations influenced by climatic variability, LULC change, and human regulations. Scientific Reports, 14(1), 5965.
Maghsoud, F., Yazdani, M.R., Rahimi, M., Malekian, A., Zolfaghari, A., 2016. Performance Comparison of Artificial Neural Network, Time Series and ANN-ARIMA For Groundwater Resources Index (GRI) Forecasting  (Case Study: South of Qazvin Province). Ijwmse, 10(33), 47–57. [In persion]
McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 17(22), 179–183.
Mishra, A.K., Desai, V.R., 2005. Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment, 19, 326–339.
Mishra, A.K., Singh, V.P., 2010. A review of drought concepts Journal of Hydrology, 391(1–2): 202–216.
Mossad, A., Alazba, A.A., 2015. Drought forecasting using stochastic models in a hyper-arid climate. Atmosphere, 6(4), 410–430. 
Nguyen, V.H., Li, Q.F., Nguyen, L.B., 2017. Drought forecasting using ANFIS- a case study in drought prone area of Vietnam. Paddy and Water Environment, 15(3), 605–616.
Rezaiy, R., Shabri, A., 2023. Drought forecasting using W-ARIMA model with standardized precipitation index. Journal of Water and Climate Change, 14(9), 3345–3367.
Salas, J.D., Delleur J.W., Yevjevich, V., Lane, W.L., 1997. Applied Modeling of hydrologic time series. Water Resources Publication. Fourth Edition.
Tavosi, T. and delara, G., 2010. Climate Classification of Ardebil Province. Nivar, 34(71-70), 47-52. [In persion]
Tsakiris, G., Pangalou, D., Vangelis, H., 2007. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resources Management, 21(5), 821–833.
Van Loon, A.F., Van Huijgevoort, M.H.J., Van Lanen, H.A.J., 2012. Evaluation of drought propagation in an ensemble mean of large-scale hydrological models. Hydrology and Earth System Sciences, 16(11), 4057–4078.
Wang, J., Rong, G., Li, K., Zhang, J., 2021. Analysis of drought characteristics in northern Shaanxi based on copula function. Water (Switzerland), 13(11), 20734441.
Wilhite, D.A., Svoboda, M.D., Hayes, M.J., 2007. Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resources Management, 21(5), 763–774.
Xu, D., Zhang, Q., Ding, Y., Huang, H., 2020. Application of a hybrid ARIMA–SVR model based on the SPI for the forecast of drought—a case study in Henan Province, China. Journal of Applied Meteorology and Climatology, 59(7), 1239–1259.
Yan, H., Moradkhani, H., & Zarekarizi, M. 2017. A probabilistic drought forecasting framework: A combined dynamical and statistical approach. Journal of Hydrology, 548, 291–304.
Yuan, M., Gan, G., Bu, J., Su, Y., Ma, H., Liu, X., Zhang, Y., Gao, Y., 2025. A new multivariate composite drought index considering the lag time and the cumulative effects of drought. Journal of Hydrology, 132757.
Zandifar, S., Fijani, E., Naeimi, M., Khosroshahi, M., 2020. Spatiotemporal variations of groundwater drought indices, Case study: Zohreh- Jarrahi watershed. Hydrogeology, 4(2), 108-130. [In persion]
Hydrogeology
Volume 9, Issue 2
Publisher
March 2024
Pages 77-92

Files

Share

How to cite

Statistics