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Trend analysis of hydro-climatological parameters and assessment of climate impact on dam seepage using statistical and machine learning models

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Abstract

Climate change poses significant threats to the water reservoir, with potential repercussions for Pakistan's economy. This study investigates the impact of climatic variations on seepage in Tarbela dam, Pakistan, by analyzing hydro-climatological factors. The research employs statistical multivariant time series (SARIMA and ARDL) and machine learning (ANN and Catboost) modeling techniques to assess the influence of climatic variability on seepage dynamics. The analysis focuses on annual and seasonal climatic variables from a data set spanning from 2003 to 2015, aiming to explore the relationship between climatic variability and dam seepage. The SARIMA model is utilized to estimate water inflow, temperature, rainfall, and reservoir level, validating the integration of multiple data sets. Machine learning models demonstrate superior performance, achieving high R2 scores during the training, validation, and testing. The findings derived from the ARDL analysis reveal a significant increase in seepage with rising water inflow, rainfall, and reservoir level. Among the machine learning models, CatBoost performs the best. The study recommends implementing appropriate adaptive approaches to mitigate any negative impact on Tarbela dam seepage. Moreover, this research serves as a guide for enhancing the dam's power generation capacity and improving irrigation water management in the face of existing crises in Pakistan.

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Acknowledgements

This research was supported by the Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, School of Geoscience and Info-Physics, Central South University, Changsha, Hunan P.R China and Department of Civil Engineering, International Islamic University, Islamabad. The author extends their appreciation to the National Natural Science Foundation of China, providing research grants (Grants no: 42374180-Qianwei Dai, and 42174178-Bin Zhang). This research also having an additional support from Dr. Khan Zaib Jadoon was provided by National Center of GIS and Space Application (NCGSA), Pakistan under project RF-82-RS&GIS-46, as well as the Directorate of Seismology (Jr. Geologist Babar Saddique) and in-charge of survey and hydrology section, Tarbela Dam Project, Water and Power Development Department (WAPDA), for the provision of research data to establish this research work. Furthermore, thanks to an anonymous reviewer for their constructive comments that greatly improved the quality of the manuscript.

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Authors and Affiliations

  1. School of Geoscience and Info-Physics, Central South University, Changsha, 410083, China

    Muhammad Ishfaque, Qianwei Dai & Syed Muzyan Shahzad

  2. Key Laboratory of Metallogenic Prediction of Nonferrous Metal and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha, 410083, China

    Muhammad Ishfaque & Qianwei Dai

  3. Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Central South University, Changsha, 410083, China

    Muhammad Ishfaque & Qianwei Dai

  4. Department of Mathematics and Statistics, Institute of Southern Punjab, Multan, 60800, Punjab, Pakistan

    Abdul Wahid

  5. Tarbela Dam Project, Water, and Power Development Authority, Khyber Pakhtunkhwa, Pakistan

    Babar Saddique

  6. Department of Civil Engineering, Islamic International University, Islamabad, 44000, Pakistan

    Khan Zaib Jadoon

  7. Department of Geology, Shaheed Benazir Bhutto University, Upper Dir, Khyber Pakhtunkhwa, 18050, Pakistan

    Hammad Tariq Janjuhah

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  1. Muhammad Ishfaque
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Contributions

The author contribution as follows: data collection, IM, data provision, SB, JK, conceptualization, IM, methodology, IM, WA, software, IM, WA, analysis, IM, WA, validation, IM, WA, HTJ, JK, QD, writing—original draft, MI, writing—review and editing, MI, supervision QD, project administration, QD, JK. All author has read and agreed to the published version of the manuscript.

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Correspondence to Muhammad Ishfaque or Qianwei Dai.

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Ishfaque, M., Dai, Q., Wahid, A. et al. Trend analysis of hydro-climatological parameters and assessment of climate impact on dam seepage using statistical and machine learning models. Environ Earth Sci 82, 542 (2023). https://doi.org/10.1007/s12665-023-11216-3

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