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Identification and Analysis of Drought Propagation of Groundwater During Past and Future Periods

Author

Listed:
  • Mahdi Soleimani Motlagh

    (University of Kashan)

  • Hoda Ghasemieh

    (University of Kashan)

  • Ali Talebi

    (University of Yazd)

  • Khodayar Abdollahi

    (Shahrekord University)

Abstract

Analysis of the characteristics and propagation behaviors of groundwater drought at different aquifer sites during past and future periods needs a proper understanding regarding its relation with meteorological droughts. Use of a robust technique of modeling (stochastic models) allowed groundwater level and precipitation to be forecasted and then the droughts were computed and analyzed using Standardized Precipitation Index (SPI). In this research, Aleshtar Plain was selected as a case study. Analysis in this region was carried out by hierarchy and K-means clustering (5 clusters), because of the regional investigation of groundwater drought and large number of boreholes. The performance results of models showed that best forecasting models in cluster 1, 2, 3, 4 and 5 were Auto-Regressive (AR)(1), AR(2), Moving Average (MA)(2), Mixed Autoregressive–Moving Average (ARMA)(2,2) and AR(2), respectively. Furthermore, the most appropriate model for precipitation within the study plain was ARMA(1, 2). Investigation of the relationship between meteorological and groundwater drought indicated that the strongest correlation between two types of droughts was for clusters 4 and 1 with a correlation coefficient of 0.76 and 0.63, respectively. Also, the lowest correlation was for cluster 2 with a correlation coefficient of 0.51. The results of cumulative periods related to the maximum correlation between SPI and Standardized Groundwater Level Index (SGI) showed that clusters 1 to 3 corresponded with cumulative 24-month periods of SPI and this magnitude for clusters 4 and 5 were 18 and 12 months, respectively. Moreover, results of maximum drought severity showed there was low variability between clusters considering the extreme droughts (SGI ≤ −2) during the study period. For the future period, drought severity results showed that groundwater drought of 2019 may happen with moderate value in cluster 5, severe values in clusters 2, 1 and 4, respectively, and extreme value in cluster 3. Hydrogeological evidence of the sites and results of autocorrelation structure of SGI series confirmed the time taken by meteorological drought for propagation into groundwater. Furthermore, results showed that the aquifer is controlled more by hydraulic diffusivity factor. so it would be expected that drought propagation time into groundwater would be long for the Western part and relatively short for sites located in the East, South tending to center and partially north of the aquifer. In general, these results represent an early warning system for groundwater drought preparation and mitigation of its impacts in a future time.

Suggested Citation

  • Mahdi Soleimani Motlagh & Hoda Ghasemieh & Ali Talebi & Khodayar Abdollahi, 2017. "Identification and Analysis of Drought Propagation of Groundwater During Past and Future Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 109-125, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1513-5
    DOI: 10.1007/s11269-016-1513-5
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    Cited by:

    1. Hone-Jay Chu, 2018. "Drought Detection of Regional Nonparametric Standardized Groundwater Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3119-3134, July.
    2. Farman Ullah Khan & Faridoon Khan & Parvez Ahmed Shaikh, 2023. "Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms," Future Business Journal, Springer, vol. 9(1), pages 1-11, December.
    3. Saeed Azimi & Erfan Hassannayebi & Morteza Boroun & Mohammad Tahmoures, 2020. "Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4703-4724, December.
    4. Wang, Fei & Lai, Hexin & Li, Yanbin & Feng, Kai & Zhang, Zezhong & Tian, Qingqing & Zhu, Xiaomeng & Yang, Haibo, 2022. "Identifying the status of groundwater drought from a GRACE mascon model perspective across China during 2003–2018," Agricultural Water Management, Elsevier, vol. 260(C).

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