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Groundwater levels and resiliency mapping under land cover and climate change scenarios: a case study of Chitravathi basin in Southern India

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Abstract

Chitravathi basin in India is facing significant challenges as its groundwater resources are facing the impact of land cover and climate change. This study explores the impact of land cover and climate change on groundwater levels and groundwater recharge in the basin using CMIP6 GCMs climate projections data. Taylor Skill Score (TSS) and Rating Metric (RM) were used to rank the GCMs. The top four ranked GCMs, i.e., MPI-ESM1-2-LR, EC-Earth3, MPI-ESM1-2-HR, and INM-CM5-0 were found to produce the most accurate projections under scenarios SSP2-4.5 and SSP5-8.5. Cellular Automata-Artificial Neural Network (CA-ANN) was used to develop future LULC maps. SWAT model was applied for estimating the future groundwater recharge and was calibrated and validated for discharge data, giving the values of R2 = 0.84 and 0.82 and NSE = 0.81 and 0.80 during calibration and validation, respectively. A steady-state groundwater flow model, MODFLOW, was employed to estimate future groundwater levels. Based on the projected groundwater recharge and levels, a resiliency map of the basin was developed. The results indicated that by 2060, under SSP2-4.5 scenario, groundwater levels in the basin would decrease by 54 m, while under the SSP5-8.5 scenario, the decrease would be 62 m. The groundwater resiliency for both SSPs would be poor in 2060. This research will help design and implement adaptation measures to mitigate the impacts of land cover and climate change on Chitravathi basin’s groundwater resources. These findings will help to protect and preserve the basin’s groundwater supplies.

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Data availability

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., & Kløve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733–752.

    Article  Google Scholar 

  • Adhikari, R. K., Mohanasundaram, S., & Shrestha, S. (2020). Impacts of land-use changes on the groundwater recharge in the Ho Chi Minh city, Vietnam. Environmental Research, 185, 109440.

    Article  CAS  Google Scholar 

  • Ahmed, K., Sachindra, D. A., Shahid, S., Iqbal, Z., Nawaz, N., & Khan, N. (2020). Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmospheric Research, 236, 104806.

    Article  Google Scholar 

  • Alam, S., Gebremichael, M., Li, R., Dozier, J., & Lettenmaier, D. P. (2019). Climate change impacts on groundwater storage in the Central Valley, California. Climatic Change, 157(3-4), 387–406.

    Article  Google Scholar 

  • Alattar, M. H., Troy, T. J., Russo, T. A., & Boyce, S. E. (2020). Modeling the surface water and groundwater budgets of the US using MODFLOW-OWHM. Advances in Water Resources, 143, 103682.

    Article  Google Scholar 

  • Aneesha Satya, B., Shashi, M., & Deva, P. (2020). Future land use land cover scenario simulation using open-source GIS for the city of Warangal, Telangana, India. Applied Geomatics, 12, 281–290.

    Article  Google Scholar 

  • Chen, W., Jiang, Z., & Li, L. (2011). Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. Journal of Climate, 24(17), 4741–4756.

    Article  Google Scholar 

  • Congalton, R. G., Oderwald, R. G., & Mead, R. A. (1983). Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing, 49(12), 1671–1678.

    Google Scholar 

  • Dawid, W., & Bielecka, E. (2022). GIS-based land cover analysis and prediction based on open-source software and data. Quaestiones Geographicae, 41(3), 75–86.

    Google Scholar 

  • Dey, A., Sahoo, D. P., Kumar, R., & Remesan, R. (2022). A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin. International Journal of Climatology, 42(16), 9215–9236.

    Article  Google Scholar 

  • Dosdogru, F., Kalin, L., Wang, R., & Yen, H. (2020). Potential impacts of land use/cover and climate changes on ecologically relevant flows. Journal of Hydrology, 584, 124654.

    Article  Google Scholar 

  • Ebrahimi, R. S., Eslamian, S., & Zareian, M. J. (2023). Groundwater level prediction based on GMS and SVR models under climate change conditions: Case study—Talesh Plain. Theoretical and Applied Climatology, 151(1-2), 433–447.

    Article  Google Scholar 

  • Erturk, A., Ekdal, A., Gurel, M., Karakaya, N., Guzel, C., & Gonenc, E. (2014). Evaluating the impact of climate change on groundwater resources in a small Mediterranean watershed. Science of the Total Environment, 499, 437–447.

    Article  CAS  Google Scholar 

  • Foody, G. M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment, 239, 111630.

    Article  Google Scholar 

  • Foster, S., & MacDonald, A. (2014). The ‘water security ‘dialogue: why it needs to be better informed about groundwater. Hydrogeology Journal, 22(7), 1489–1492.

    Article  Google Scholar 

  • Garg, V., Aggarwal, S. P., Gupta, P. K., Nikam, B. R., Thakur, P. K., Srivastav, S. K., & Senthil Kumar, A. (2017). Assessment of land use land cover change impact on hydrological regime of a basin. Environmental Earth Sciences, 76(18), 1–17.

    Article  Google Scholar 

  • Garner, G., Hannah, D. M., & Watts, G. (2017). Climate change and water in the UK: Recent scientific evidence for past and future change. Progress in Physical Geography, 41(2), 154–170.

    Article  Google Scholar 

  • Ghaith, M., & Li, Z. (2020). Propagation of parameter uncertainty in SWAT: A probabilistic forecasting method based on polynomial chaos expansion and machine learning. Journal of Hydrology, 586, 124854.

    Article  Google Scholar 

  • Harbaugh, A. W., Banta, E. R., Hill, M. C., & McDonald, M. G. (2000). Modflow-2000, the u. S. Geological survey modular ground-water model-user guide to modularization concepts and the ground-water flow process.

  • Hu, B., Teng, Y., Zhang, Y., & Chen, Z. (2019). The projected hydrologic cycle under the scenario of 936 ppm CO 2 in 2100. Hydrogeology Journal, 27(1), 31–53.

    Article  CAS  Google Scholar 

  • IPCC (Intergovernmental Panel on Climate Change). 2013. Climate change 2013: The physical science basis. Contribution of working group i to the fifth assessment report of the intergovernmental panel on climate change.

  • Isaacson, D. L., & Madsen, R. W. (1985). Markov chains- theory and applications (p. 270). ROBERT E. KRIEGER PUBLISHING COMPANY, INC..

    Google Scholar 

  • Kamaraj, M., & Rangarajan, S. (2022). Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environmental Science and Pollution Research, 29(57), 86337–86348.

    Article  Google Scholar 

  • Kaur, N., Kaur, S., Kaur, P., & Aggarwal, R. (2021). Impact of climate change on groundwater levels in Sirhind Canal Tract of Punjab, India. Groundwater for Sustainable Development, 15, 100670.

    Article  Google Scholar 

  • Kayet, N., Chakrabarty, A., Pathak, K., Sahoo, S., Mandal, S. P., Fatema, S., et al. (2019). Spatiotemporal LULC change impacts on groundwater table in Jhargram, West Bengal, India. Sustainable Water Resources Management, 5, 1189–1200.

    Article  Google Scholar 

  • Khalid, K., Ali, M. F., Abd Rahman, N. F., Mispan, M. R., Haron, S. H., Othman, Z., & Bachok, M. F. (2016). Sensitivity analysis in watershed model using SUFI-2 algorithm. Procedia Engineering, 162, 441–447.

    Article  Google Scholar 

  • Kumar, S., Narjary, B., Vivekanand, Islam, A., Yadav, R. K., & Kamra, S. K. (2022). Modeling climate change impact on groundwater and adaptation strategies for its sustainable management in the Karnal district of Northwest India. Climatic Change, 173(1-2), 3.

    Article  Google Scholar 

  • Malekinezhad, H., & Banadkooki, F. B. (2018). Modeling impacts of climate change and human activities on groundwater resources using MODFLOW. Journal of Water and Climate Change, 9(1), 156–177.

    Article  Google Scholar 

  • Mensah, J. K., Ofosu, E. A., Yidana, S. M., Akpoti, K., Kabo-bah, A. T. (2022). Integrated modeling of hydrological processes and groundwater recharge based on land use land cover, and climate changes: a systematic review. Environmental Advances, 8, 100224.

  • Mishra, V., Bhatia, U., & Tiwari, A. D. (2020). Bias-corrected climate projections for South Asia from coupled model intercomparison project-6. Scientific Data, 7(1), 338.

    Article  Google Scholar 

  • Moghaddasi, P., Kerachian, R., & Sharghi, S. (2022). A stakeholder-based framework for improving the resilience of groundwater resources in arid regions. Journal of Hydrology, 609, 127737.

    Article  Google Scholar 

  • Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models’ part I—A discussion of principles. Journal of Hydrology, 10(3), 282–290.

    Article  Google Scholar 

  • NGWA (National Groundwater Association) (2016). NGWA consensus definitions of groundwater sustainability and resilience.

  • Noori, A. R., & Singh, S. K. (2021). Status of groundwater resource potential and its quality at Kabul, Afghanistan: A review. Environmental Earth Sciences, 80, 1–13.

    Article  Google Scholar 

  • Norouzi Khatiri, K., Niksokhan, M. H., Sarang, A., & Kamali, A. (2020). Coupled simulation-optimization model for the management of groundwater resources by considering uncertainty and conflict resolution. Water Resources Management, 34, 3585–3608.

    Article  Google Scholar 

  • Ostad-Ali-Askari, K., Ghorbanizadeh Kharazi, H., Shayannejad, M., & Zareian, M. J. (2019). Effect of management strategies on reducing negative impacts of climate change on water resources of the Isfahan-Borkhar aquifer using MODFLOW. River Research and Applications, 35(6), 611–631.

    Article  Google Scholar 

  • Ouhamdouch, S., Bahir, M., Ouazar, D., Carreira, P. M., & Zouari, K. (2019). Evaluation of climate change impact on groundwater from semi-arid environment (Essaouira Basin, Morocco) using integrated approaches. Environmental Earth Sciences, 78, 1–14.

    Article  CAS  Google Scholar 

  • Pandey, V. P., Dhaubanjar, S., Bharati, L., & Thapa, B. R. (2019). Hydrological response of Chamelia watershed in Mahakali Basin to climate change. Science of the Total Environment, 650, 365–383.

    Article  CAS  Google Scholar 

  • Pathak, A. A., & Dodamani, B. M. (2019). Trend analysis of groundwater levels and assessment of regional groundwater drought: Ghataprabha River Basin, India. Natural Resources Research, 28, 631–643.

    Article  Google Scholar 

  • Patil, N. S., Chetan, N. L., Nataraja, M., & Suthar, S. (2020). Climate change scenarios and its effect on groundwater level in the Hiranyakeshi watershed. Groundwater for Sustainable Development, 10, 100323.

    Article  Google Scholar 

  • Persaud, E., Levison, J., MacRitchie, S., Berg, S. J., Erler, A. R., Parker, B., & Sudicky, E. (2020). Integrated modelling to assess climate change impacts on groundwater and surface water in the Great Lakes Basin using diverse climate forcing. Journal of Hydrology, 584, 124682.

    Article  Google Scholar 

  • Roy, P., Pal, S. C., Chakrabortty, R., Chowdhuri, I., Saha, A., & Shit, M. (2022). Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation. Science of The Total Environment, 849, 157850.

    Article  CAS  Google Scholar 

  • Sadhwani, K., Eldho, T. I., & Karmakar, S. (2023). Investigating the influence of future landuse and climate change on hydrological regime of a humid tropical river basin. Environmental Earth Sciences, 82(9), 210.

    Article  Google Scholar 

  • Shrestha, S., Neupane, S., Mohanasundaram, S., & Pandey, V. P. (2020). Mapping groundwater resiliency under climate change scenarios: A case study of Kathmandu Valley, Nepal. Environmental Research, 183, 109149.

    Article  CAS  Google Scholar 

  • Sinha, R. K., & Eldho, T. I. (2018). Effects of historical and projected land use/cover change on runoff and sediment yield in the Netravati River basin, Western Ghats, India. Environmental Earth Sciences, 77, 1–19.

    Article  Google Scholar 

  • Sishodia, R. P., Shukla, S., Graham, W. D., Wani, S. P., & Garg, K. K. (2016). Bi-decadal groundwater level trends in a semi-arid south Indian region: Declines, causes and management. Journal of Hydrology: Regional Studies, 8, 43–58.

    Google Scholar 

  • Sule, B. F., & Ayenigba, S. E. (2017). Application of GMS-MODFLOW to investigate groundwater development potential in River Meme catchment, Kogi State, Nigeria. International Journal of Sciences, 6(09), 39–51.

    Article  Google Scholar 

  • Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183–7192.

    Article  Google Scholar 

  • Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., Van Beek, R., Wada, Y., et al. (2013). Ground water and climate change. Nature Climate Change, 3(4), 322–329.

    Article  Google Scholar 

  • Wang, B., Zheng, L., Liu, D. L., Ji, F., Clark, A., & Yu, Q. (2018). Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia. International Journal of Climatology, 38(13), 4891–4902.

    Article  Google Scholar 

  • Wunsch, A., Liesch, T., & Broda, S. (2022). Deep learning shows declining groundwater levels in Germany until 2100 due to climate change. Nature Communications, 13(1), 1221.

    Article  CAS  Google Scholar 

  • Yen, H., White, M. J., Jeong, J., Arabi, M., & Arnold, J. G. (2015). Evaluation of alternative surface runoff accounting procedures using SWAT model. International Journal of Agricultural and Biological Engineering, 8(3), 64–68.

    Google Scholar 

  • Yifru, B. A., Chung, I. M., Kim, M. G., & Chang, S. W. (2021). Assessing the effect of land/use land cover and climate change on water yield and groundwater recharge in East African Rift Valley using integrated model. Journal of Hydrology: Regional Studies, 37, 100926.

    Google Scholar 

  • Yousefi, H., Zahedi, S., Niksokhan, M. H., & Momeni, M. (2019). Ten-year prediction of groundwater level in Karaj plain (Iran) using MODFLOW2005-NWT in MATLAB. Environmental Earth Sciences, 78, 1–14.

    Article  Google Scholar 

  • Zeydalinejad, N. (2022). Artificial neural networks vis-à-vis MODFLOW in the simulation of groundwater: A review. Modeling Earth Systems and Environment, 8(3), 2911–2932.

    Article  Google Scholar 

  • Zeydalinejad, N. (2023). An overview of the methods for evaluating the resilience of groundwater systems. Methods X, 10, 102134.

  • Zuo, D., Xu, Z., Yao, W., Jin, S., Xiao, P., & Ran, D. (2016). Assessing the effects of changes in land use and climate on runoff and sediment yields from a watershed in the Loess Plateau of China. Science of the Total Environment, 544, 238–250.

    Article  CAS  Google Scholar 

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  1. Department of Civil Engineering, National Institute of Technology (NIT), Rourkela, Odisha, India

    Nathi Ajay Chandra & Sanat Nalini Sahoo

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  1. Nathi Ajay Chandra
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Nathi Ajay Chandra conducted the research, analyzed the results, and drafted the manuscript. Sanat Nalini Sahoo corrected the manuscript and guided the study.

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Correspondence to Sanat Nalini Sahoo.

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Chandra, N.A., Sahoo, S.N. Groundwater levels and resiliency mapping under land cover and climate change scenarios: a case study of Chitravathi basin in Southern India. Environ Monit Assess 195, 1394 (2023). https://doi.org/10.1007/s10661-023-11995-z

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