SpringerLink
Log in

Modeling and Forecasting Climate Change Impact on Groundwater Fluctuations in Northwest Bangladesh

  • Chapter
  • First Online:
Climate Vulnerability and Resilience in the Global South

Part of the book series: Climate Change Management ((CCM))

  • 576 Accesses

Abstract

Water is essential for life but its availability at a sustainable quality and quantity is threatened by many factors, in which climate plays a leading role. Northwest Bangladesh is a severely drought prone area in the country. For forecasting the impact of climate change on groundwater table (GwT) in the drought-prone areas two data sets: (i) weekly groundwater table time series from Bangladesh Water Development Board (BWDB), and (ii) yearly temperature and rainfall (mm) from Bangladesh Meteorological Department (BMD) from January 1991 to December 2018 were collected. The findings showed the superiority of the nonlinear autoregressive modeling with exogenous inputs (NARX) based approach for groundwater tables in arid groundwater aquifer system revealed that the coefficient of determination (R2) lies between 0.547 and 0.854 in the validation period. However, compare to other models applied on the same data sets, the proposed model reduced 50% of the mean absolute error (MAE). Increasing trend in maximum temperature is a cause of high evaporation as well as uncertainty of trans-boundary water movement was found to be strongly influencing the depletion of groundwater level. The results show that Bayesian Regularization (BR) is the most accurate method for forecasting groundwater table with an error of ±0.43 m.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 117.69
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 160.49
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 160.49
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Adeloye AJ, De Munari A (2006) Artificial neural network based generalized storage–yield–reliability models using the Levenberg–Marquardt algorithm. J Hydrol 326(1–4):215–230

    Article  Google Scholar 

  • Adhikary SK, Das SK, Saha GC, Chaki T (2013) Groundwater drought assessment for barind irrigation project in Northwestern Bangladesh. In: 20th International congress on modelling and simulation, Adelaide, Australia, pp 1–6

    Google Scholar 

  • Ahmed AU (2006) Bangladesh climate change impacts and vulnerability. Climate change cell. Department of Environment Component 4b, Comprehensive Disaster Management Programme, Dhaka, Bangladesh

    Google Scholar 

  • Ahmed AU, Neelormi S (2008) Climate change, loss of livelihoods and forced displacements in Bangladesh: Whither facilitated international migration. Campaign for Sustainable Rural Livelihoods and Centre for Global Change, Dhaka, Bangladesh. Retrieved 12 Nov 2012

    Google Scholar 

  • Akther H, Ahmed MS, Rasheed KBS (2009) Spatial and temporal analysis of groundwater level fluctuation in Dhaka city, Bangladesh. Asian J Earth Sci 2(2):49–57

    Article  Google Scholar 

  • Aldrich J (1995) Correlations genuine and spurious in Pearson and Yule. Stat Sci 10(4):364–376

    Article  Google Scholar 

  • Ali MH, Abustan I, Rahman MA, Haque AAM (2012) Sustainability of groundwater resources in the North-Eastern Region of Bangladesh. Water Resour Manage 26(3):623–641

    Article  Google Scholar 

  • Anderson MP, Woessner WW, Hunt RJ (2015) Applied groundwater modeling: simulation of flow and advective transport. Academic Press

    Google Scholar 

  • Bandyopadhyay A, Bhadra A, Raghuwanshi NS, Singh R (2009) Temporal trends in estimates of reference evapotranspiration over India. J Hydrol Eng 14(5):508–515

    Article  Google Scholar 

  • Bates B, Kundzewicz Z, Wu S (2008) Climate change and water. Intergovernmental Panel on Climate Change Secretariat

    Google Scholar 

  • Bear J, Cheng AHD (2010) Modeling groundwater flow and contaminant transport, vol 23. Springer Science & Business Media

    Google Scholar 

  • Bindoff NL, Stott PA, AchutaRao KM, Allen MR, Gillett N, Gutzler D, Mokhov II (2013) Detection and attribution of climate change: from global to regional

    Google Scholar 

  • Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  • Cadenas E, Rivera W, Campos-Amezcua R, Heard C (2016) Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies 9(2):109

    Article  Google Scholar 

  • Cao G, Zheng C (2016) Signals of short-term climatic periodicities detected in the groundwater of North China Plain. Hydrol Process 30(4):515–533

    Google Scholar 

  • Chan RW, Yuen JK, Lee EW, Arashpour M (2015) Application of Nonlinear-Autoregressive-Exogenous model to predict the hysteretic behaviour of passive control systems. Eng Struct 85:1–10

    Article  Google Scholar 

  • Chen H, Guo S, Xu CY, Singh VP (2007) Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin. J Hydrol 344(3–4):171–184

    Article  Google Scholar 

  • Chen S, Billings SA, Grant PM (1990) Non-linear system identification using neural networks. Int J Control 51(6):1191–1214

    Article  Google Scholar 

  • Coulibaly P, Anctil F, Aravena R, Bobée B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896

    Article  Google Scholar 

  • Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309(1–4):229–240

    Article  Google Scholar 

  • Denton F, Wilbanks TJ, Abeysinghe AC, Burton I, Gao Q, Lemos MC, Warner K (2014) Climate-resilient pathways: adaptation, mitigation, and sustainable development. Clim Change 1101–1131

    Google Scholar 

  • Du Bui D, Kawamura A, Tong TN, Amaguchi H, Nakagawa N (2012) Spatio-temporal analysis of recent groundwater-level trends in the Red River Delta, Vietnam. Hydrogeol J 20(8):1635–1650

    Article  Google Scholar 

  • Ghanbari RN, Bravo HR (2011) Coherence among climate signals, precipitation, and groundwater. Groundwater 49(4):476–490

    Article  Google Scholar 

  • Granger IVCW, Hyung N, Jeon Y (2001) Spurious regressions with stationary series. Appl Econ 33(7):899–904

    Article  Google Scholar 

  • Granger CW (2010) Spurious regressions. Macroeconometrics and time series analysis. Palgrave Macmillan, London, pp 265–268

    Google Scholar 

  • Granger CW, Newbold P, Econom J (1974) Spurious regressions in econometrics. In: Baltagi BH (ed) A companion of theoretical econometrics, pp 557–561

    Google Scholar 

  • Guzman SM, Paz JO, Tagert MLM (2017a) The use of NARX neural networks to forecast daily groundwater levels. Water Resour Manage 31(5):1591–1603. https://doi.org/10.1007/s11269-017-1598-5

  • Guzman SM, Paz JO, Tagert MLM (2017b) The use of NARX neural networks to forecast daily groundwater levels. Water Resour Manage 31(5):1591–1603

    Article  Google Scholar 

  • Guzman SM, Paz JO, Tagert MLM, Mercer AE (2019) Evaluation of seasonally classified inputs for the prediction of daily groundwater levels: NARX networks vs support vector machines. Environ Model Assess 24(2):223–234

    Article  Google Scholar 

  • Haji HT (2011) Impact of climate change on surface water availability in the Upper Vaal River Basin. Doctoral dissertation, Tshwane University of Technology

    Google Scholar 

  • Humphrey, G., Lambert, M., & Maier, H. 2005, Bayesian training of artificial neural networks used for water resources modeling

    Google Scholar 

  • IPCC, WG, I, II, III (2007) Climate change 2007: synthesis report. Geneva, Switzerland

    Google Scholar 

  • Izady A, Davary K, Alizadeh A, Nia AM, Ziaei AN, Hasheminia SM (2013) Application of NN-ARX model to predict groundwater levels in the Neishaboor Plain, Iran. Water Resour Manage 27(14):4773–4794

    Article  Google Scholar 

  • Jahan CS, Mazumder QH, Islam ATMM, Adham MI (2010) Impact of irrigation in Barind area, NW Bangladesh-an evaluation based on the meteorological parameters and fluctuation trend in groundwater table. J Geol Soc India 76(2):134–142

    Article  Google Scholar 

  • Kamruzzaman M, Kabir ME, Rahman ATMS, Jahan CS, Rahman MS, Mazumder QH (2016b) Modeling of agricultural drought risk pattern using Markov chain and GIS in the western part of Bangladesh. Environment, Development and Sustainability, Springer. https://doi.org/10.1007/s10668-016-9898-0

  • Kamruzzaman M, Rahman ATMS, Jahan CS (2015) Adapting crop** systems under changing climate in NW Bangladesh. Lambert Academic Publishing

    Google Scholar 

  • Kamruzzaman M, Rahman ATMS, Kabir ME, Jahan CS, Mazumder QH, Rahman MS (2016a) Spatio-temporal analysis of climatic variables in the western part of Bangladesh, vol 20. Environment, Development and Sustainability, Springer, pp 89–108. https://doi.org/10.1007/s10668-016-9872-x

  • Kendall M (1955) Rank correlation methods, 1st ed. Charles Griffin & Company, Ltd., London

    Google Scholar 

  • Khaki M, Yusoff I, Islami N (2015) Application of the artificial neural network and neuro-fuzzy system for assessment of groundwater quality. CLEAN–Soil Air Water 43(4):551–560

    Article  Google Scholar 

  • Khalek MA, Ahasan MN, Ali MA (2019) Groundwater table volatility forecasting using hybrid wavelet-GARCH model in the Northwest Bangladesh. Int J Stat Sci (IJSS) 17:39–60. ISSN 1683-5603

    Google Scholar 

  • Kingston GB, Lambert MF, Maier HR (2005) Bayesian training of artificial neural networks used for water resources modeling. Water Resour Res 41(12)

    Google Scholar 

  • Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539

    Article  Google Scholar 

  • Konikow LF (2011) Contribution of global groundwater depletion since 1900 to sea‐level rise. Geophys Res Lett 38(17)

    Google Scholar 

  • Lee WK, Resdi TA (2016) Simultaneous hydrological prediction at multiple gauging stations using the NARX network for Kemaman catchment, Terengganu, Malaysia. Hydrol Sci J 61(16):2930–2945

    Article  Google Scholar 

  • Lee YS, Kim TH, Newbold P (2005) Spurious nonlinear regressions in econometrics. Econ Lett 87(3):301–306

    Article  Google Scholar 

  • Lehmann EL, Casella G (2011) Theory of point estimation, 3rd edn. Springer, New York

    Google Scholar 

  • Lin T, Horne BG, Tino P, Giles CL (1996) Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans Neural Networks 7(6):1329–1338

    Article  Google Scholar 

  • Lohani AK, Krishan G (2015) Groundwater level simulation using artificial neural network in southeast Punjab, India. J Geol Geosciences 4(3):206

    Google Scholar 

  • Mann H (1945) Nonparametric tests against trend. Econometrica 13:245–259

    Article  Google Scholar 

  • Mohanty S, Jha MK, Raul SK, Panda RK, Sudheer KP (2015) Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites. Water Resour Manag 29(15):5521–5532

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Nayak PC, Rao YS, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manage 20(1):77–90

    Article  Google Scholar 

  • Patle GT, Singh DK, Sarangi A, Rai A, Khanna M, Sahoo RN (2013) Temporal variability of climatic parameters and potential evapotranspiration. Indian J Agric Sci 83(5):518–524

    Google Scholar 

  • Piotrowski AP, Napiorkowski JJ (2013) A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling. J Hydrol 476:97–111

    Article  Google Scholar 

  • Rahman ATMS, Jahan CS, Mazumder QH, Kamruzzaman M, Hosono T (2017) Drought analysis and its implication in sustainable water resource management in Barind area Bangladesh. J Geol Soc India 89:47–56. https://doi.org/10.1007/s12594-017-0557-3

    Article  Google Scholar 

  • Rahman ATMS, Jahan CS, Mazumder QH, Kamruzzaman M, Hossain A (2016a) Evaluation of spatio-temporal dynamics of water table in NW Bangladesh: an integrated approach of GIS and statistics. Sustain Water Resour Manage 2:297–312, https://doi.org/10.1007/s40899-016-0057-4

  • Rahman ATMS, Kamruzzaman M, Jahan CS, Mazumder QH (2016b) Long-term trend analysis of water table using ‘MAKESENS’ model and sustainability of groundwater resources in drought prone Barind Area, NW Bangladesh. J Geol Soc India 87(2):179–193

    Article  Google Scholar 

  • Ramazanipour M, Roshani M (2011) Seasonal trend analysis of precipitation and discharge parameters in Guilan, north of the Iran. In: International Conference on Humanities, Geography and Economics (ICHGE’2011), Pattaya, pp 290–293

    Google Scholar 

  • Sahoo S, Jha MK (2013) Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeol J 21(8):1865–1887

    Article  Google Scholar 

  • Samarasinghe S (2016) Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Auerbach Publications, New York. ISBN 0429115784

    Google Scholar 

  • Sarkar AA, Ali MH (2009) Water table dynamics of Dhaka City and its long-term trend analysis using the “MAKESENS” model. Water Int 34(3):373–382

    Article  Google Scholar 

  • Shahid S, Behrawan H (2008) Drought risk assessment in the western part of Bangladesh. Nat Hazards 46(3):391–413

    Article  Google Scholar 

  • Shamsudduha M, Chandler RE, Taylor RG, Ahmed KM (2009) Recent trends in groundwater levels in a highly seasonal hydrological system: the Ganges-Brahmaputra-Meghna Delta. Hydrol Earth Syst Sci 13(12):2373–2385

    Article  Google Scholar 

  • Siegelmann HT, Horne BG, Giles CL (1997) Computational capabilities of recurrent NARX neural networks. IEEE Trans Syst Man Cybern B (Cybernetics) 27(2):208–215

    Article  Google Scholar 

  • Wilamowski BM, Yu H (2010) Improved computation for Levenberg–Marquardt training. IEEE Trans Neural Networks 21(6):930–937

    Article  Google Scholar 

  • Wunsch A, Liesch T, Broda S (2018) Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). J Hydrol 567:743–758

    Article  Google Scholar 

  • Yu H, Wilamowski BM (2011) Levenberg-marquardt training. In: Industrial electronics handbook, vol 5, no 12, p 1

    Google Scholar 

Download references

Acknowledgements

We would like to acknowledge and sincere appreciation to Bangladesh Water Development Board (BWDB) for given essential data to complete this study. We would also like to express our sincere acknowledgments to the Ministry of Education for monetary support to complete this research project (Project No. PS142260). Finally, we are grateful to the reviewers for their valuable observations to improve this manuscript.

Author information

Authors and Affiliations

  1. Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Md. Abdul Khalek, Md. Mostafizur Rahman, M. Sayedur Rahman & Md. Ayub Ali

  2. Institute of Bangladesh Studies, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Md. Kamruzzaman

  3. Department of Finance, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Zubair Ahmed Shimon

Authors
  1. Md. Abdul Khalek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Mostafizur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Md. Kamruzzaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zubair Ahmed Shimon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Sayedur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Md. Ayub Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md. Abdul Khalek .

Editor information

Editors and Affiliations

  1. Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh

    G. M. Monirul Alam

  2. Sustainable Enterprise Division, Asia Pacific College of Business and Law, Charles Darwin University, Darwin, NT, Australia

    Michael O. Erdiaw-Kwasie

  3. Universidad de la Republica, Montevideo, Uruguay

    Gustavo J. Nagy

  4. Hamburg University of Applied Sciences, Hamburg, Germany

    Walter Leal Filho

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Abdul Khalek, M., Mostafizur Rahman, M., Kamruzzaman, M., Ahmed Shimon, Z., Sayedur Rahman, M., Ayub Ali, M. (2021). Modeling and Forecasting Climate Change Impact on Groundwater Fluctuations in Northwest Bangladesh. In: Alam, G.M.M., Erdiaw-Kwasie, M.O., Nagy, G.J., Leal Filho, W. (eds) Climate Vulnerability and Resilience in the Global South. Climate Change Management. Springer, Cham. https://doi.org/10.1007/978-3-030-77259-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77259-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77258-1

  • Online ISBN: 978-3-030-77259-8

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics

Search

Navigation