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Forecasting Future Groundwater Recharge from Rainfall Under Different Climate Change Scenarios Using Comparative Analysis of Deep Learning and Ensemble Learning Techniques

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Abstract

Groundwater is the most reliable source of freshwater for household, industrial, and agricultural usage. However, anthropogenic interventions in the water cycle have disrupted sustainable groundwater management. This research aims to comprehend the future of groundwater recharge predominantly due to rainfall under changing climate. In this study, predictors of groundwater recharge such as precipitation, land use land cover (LULC), soil type, land slope, temperature, potential evapotranspiration, and aridity index (ArIn) were used for the Punjab region of India over the duration of 34 years, from 1986 to 2019. To simulate future conditions, various climate change scenarios from the CMIP6 report have been incorporated. Different Artificial Intelligence and Deep Learning models, ranging from the straightforward Linear Regression model to the intricate Extreme Gradient Booting (XGBoost), used these parameters as input. Statistical analysis of the models showed that XGBoost is most effective in predicting the groundwater recharge phenomena. Correlation studies revealed precipitation to be the primary contributor to recharge, followed by the ArIn, while soil type and slope are found to have the strongest inverse correlation. The models’ resilience and performance were investigated by conducting a k-fold cross-validation analysis. The pattern of groundwater recharge is forecasted for the years 2020 to 2035 across Punjab with different climate change scenarios. The study demonstrates how the Punjab area is mirroring its current status around Shared Socioeconomic Pathway (SSP) 370. Groundwater level estimates confirmed its strong correlation with and dependence on groundwater recharge. The analysis is strengthened by comparing the AI-predicted groundwater recharge with the Central Ground Water Board (CGWB) Punjab’s annual estimate.

Key points

• Data-driven deep learning models can model groundwater recharge with high accuracy without extensive aquifer parameter data requirement.

• Pronounced effect of climate change on groundwater recharge in the future pertaining to the different climate change scenarios (SSPs).

• Forecasted groundwater recharge and level data shows significant match with the CGWB, Govt. of India’s estimates and observed data.

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Data Availability

Groundwater time series data cannot be provided by the authors directly as per the data usage agreement with the CGWB, Govt. of India. The data can, however, be downloaded from the CGWB website (http://cgwb.gov.in/ground-water-level-monitoring). The authors used codes created by them using open source Python language (van Rossum G and the Python development team, 2023) for all their analysis. The codes along with some weather data, soil, slope and LULC data can be found on the repository (https://doi.org/10.5281/zenodo.8362108).

Code Availability

The authors used codes created by them using open source Python language (van Rossum G and the Python development team, 2023) for all their analysis. The codes along with some weather data, soil, slope and LULC data can be found on the repository (https://doi.org/10.5281/zenodo.8362108).

Abbreviations

LULC:

Land use land cover

XGBoost:

Extreme gradient booting

ArIn:

Aridity index

WB:

Water balance

WTF:

Water table fluctuation

AI:

Artificial intelligence

ML:

Machine learning

DL:

Deep learning

ANN:

Artificial neural network

GA:

Genetic algorithm

MI:

Mutual information

SVM:

Support vector machine

NARX:

Non-linear autoregressive networks with exogenous input

LSTM:

Long short-term memory

CNN:

Convolutional neural network

GWS:

Groundwater storage

CMIP6:

Coupled model intercomparison project phase 6

IPCC:

Intergovernmental panel on climate change

SSP:

Shared socioeconomic pathway

WCRP:

World climate research programme

PHRED:

Punjab water resources and environment directorate

BCM:

Billion cubic meters

CGWB:

Central ground water board

GoI:

Government of India

IMD:

Indian meteorological department

P:

Precipitation

S :

Slope

R2 :

Coefficient of determination

PET:

Potential evapotranspiration

NASA:

National aeronautics and space administration

S t :

Soil type

USGS:

United States geological survey

DEM:

Digital elevation model

MSE:

Mean squared error

NSE:

Nash-Sutcliffe efficiency

MAE:

Mean absolute Error

MLP:

Multilayer Perceptron

LGBM:

Light gradient boosting model

GBR:

Gradient boosting regressor

DTR:

Decision tree regressor

GCM:

Global climate model

RCM:

Regional climate model

LS:

Linear scaling

PT:

Power transformation

DM:

Distribution mapping

NPQM:

Non-parametric quantile mapping

QGIS:

Quantum geographic information system

MOLUSCE:

Modules for land use change evaluation

ASCE:

American society of civil engineers

IWRM:

Integrated water resources management

MAR:

Managed aquifer recharge

BOD:

Biochemical oxygen demand

COD:

Chemical oxygen demand

EIA:

Environmental impact assessment

T mean :

Mean temperature (oC)

T min :

Minimum temperature (oC)

T max :

Maximum temperature (oC)

\(\frac{1000* {R}_{a}}{\lambda * {\rho }_{w}}\) :

Extraterrestrial solar radiation expressed in mm equivalent of water per day

\(\lambda\) :

Latent heat of vaporization, approximately 2.45 (MJ/kg)

References

  • Alehu BA, Bitana SG (2023) Assessment of Climate Change Impact on Water Balance of Lake Hawassa Catchment. Environ Process 10:14. https://doi.org/10.1007/s40710-023-00626-x

    Article  Google Scholar 

  • Allen RG, Pruitt WO (1986) Rational use of the FAO Blaney-Criddle Formula. American Society of Civil Engineers

  • Anwar MT, Winarno E, Hadikurniawati W, Novita M (2020) Rainfall prediction using Extreme Gradient Boosting. Journal of Physics: Conference Series, Volume 1869, 2nd Annual Conference of Science and Technology

  • Azari T, Samani N (2018) Modelling the Neuman’s well function by an artificial neural network for the determination of unconfined aquifer parameters. Comput GeoSci 22:1135–1148

    Article  Google Scholar 

  • Azimi S, Moghaddam MA, Monfared SAH (2019) Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering. J Contam Hydrol 220:6–17

    Article  CAS  Google Scholar 

  • Bhattarai N, Lobell DB, Singh B, Fishman R, Kustas WP, Pokhrel Y, Jain M (2023) Warming temperatures exacerbate groundwater depletion rates in India. Science Advances9,1401.

  • Cartwright I, Cendón D, Currell M, Meredith K (2017) A review of radioactive isotopes and other residence time tracers in understanding groundwater recharge: possibilities, challenges, and limitations. J Hydrol 555:797–811

    Article  CAS  Google Scholar 

  • CGWB (2020–2021) Groundwater Year Book Punjab and Chandigarh (UT), Central Ground Water Board, Department of Water Resources, River Development and Ganga Rejuvenation Ministry of Jal Shakti, Government of India

  • CGWB, Punjab SAS, Nagar (2022) Ground water resources of Punjab state. Ground Water Management Circle Water Resources Department. and Central Ground Water Board North Western Region Chandigarh

  • Das B, Pal SC (2020) Assessment of groundwater recharge and its potential zone identification in groundwater–stressed Goghat–I block of Hugli District, West Bengal, India. Environment, Development and Sustainability 22:5905–5923

  • Diancoumba MO, Bokar H, Toure A, Kelome NC, Preko K (2020) Characterization of Groundwater recharge using the Water table fluctuation method in the Koda Catchment. Int J Adv Earth Sci Eng Volume 8(1):665–681

    Article  Google Scholar 

  • Doble RC, Crosbie RS (2017) Review: current and emerging methods for catchment-scale modelling of recharge and evapotranspiration from shallow groundwater. Hydrogeol J 25:3–23

    Article  Google Scholar 

  • Ferreira LB, Cunhaa FF, Oliveiraa RA, Filhob EIF (2019) Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach. J Hydrol 572:556–570

    Article  Google Scholar 

  • Gong C, Zhang Z, Wang W, Duan L, Wang Z (2021) An assessment of different methods to determine specific yield for estimating groundwater recharge using lysimeters. Sci Total Environ 788:147799

    Article  CAS  Google Scholar 

  • Huang F, An Z, Moran MJ, Liu F (2020) Recognition of typical antibiotic residues in environmental media related to groundwater in China (2009 – 2019). J Hazard Mater 399:122813

    Article  CAS  Google Scholar 

  • Ighalo JO, Adeniyi AG, Marques G (2021) Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis. Model Earth Syst Environ 7:669–681

    Article  Google Scholar 

  • IPCC (2022) Climate Change Impacts, Adaptation and Vulnerability Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change

  • Khorram S, Jehbez N (2023) A hybrid CNN-LSTM Approach for Monthly Reservoir inflow forecasting. Water Resour Manage 37:4097–4121

    Article  Google Scholar 

  • Kim I, Lee J (2022) Performance Analysis of ANN Prediction for Groundwater Level Considering Regional-Specific Influence Components.–Groundwater Vol. 60, No. 3pages 344–361

  • Kouadri S, Pande CB, Panneerselvam B, Moharir KN, Elbeltagi A (2022) Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models. Environ Sci Pollut Res 29:21067–21091

    Article  CAS  Google Scholar 

  • Kumar S, Roshni T, Himayoun DA (2019) Comparison of emotional neural network (ENN) and Artificial neural network (ANN) Approach for Rainfall-Runoff Modelling. Civil Eng J 5(10)

  • Leone G, Ginolfi M, Esposito L et al (2024) Relationships between River and Groundwater Flow in an alluvial plain by Time Series Analysis and Numerical modeling. https://doi.org/10.1007/s11269-024-03795-y. Water Resources Management

  • Li H, Lu Y, Zheng C, Zhang X, Zhou B, Wu J (2020) Seasonal and inter-annual variability of Groundwater and their responses to Climate Change and Human activities in Arid and Desert areas: a Case Study in Yaoba Oasis, Northwest China. Water 12(1):303

    Article  CAS  Google Scholar 

  • Li H, Li M, Miao C, Si B, Lu Y (2023) Field variation of groundwater recharge and its uncertainty via multiple tracers’ method in deep loess vadose zone, vol 876. Science of The Total Environment

  • Malekian A, Chitsaz N (2021) Chap. 4 - concepts, procedures, and applications of artificial neural network models in streamflow forecasting. Adv Streamflow Forecast Pages 115–147

  • Marvel K et al (2019) Twentieth-century hydroclimate changes consistent with human influence. Nature 569:59–65

    Article  CAS  Google Scholar 

  • Mezouari AE, Fazziki AE, Sadgal M (2022) A hybrid artificial neural network: an optimization-based framework for smart groundwater governance. Water Supply 22(5):5237–5252

    Article  Google Scholar 

  • Molajou A, Nourani V, Afshar A et al (2021) Optimal design and feature selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling. Water Resour Manage 35:2369–2384

    Article  Google Scholar 

  • Mulla S, Pande CB, Singh SK (2024) Times Series forecasting of Monthly Rainfall using Seasonal Auto Regressive Integrated moving average with EXogenous variables (SARIMAX) Model. https://doi.org/10.1007/s11269-024-03756-5. Water Resources Management

  • Naghibi SA, Hashemi H, Berndtsson R, Lee S (2020) Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors. J Hydrol 589:125197

    Article  Google Scholar 

  • Nguyen DH, Le XH, Heo JY, Bae DH (2021) Development of an Extreme Gradient Boosting Model Integrated with Evolutionary algorithms for Hourly Water Level Prediction. IEEE Access 9:125853–125867

    Article  Google Scholar 

  • Ni L, Wang D, Wu J, Wang Y, Tao Y, Zhang J, Liu J (2020) Streamflow forecasting using extreme gradient boosting model coupled with gaussian mixture model. J Hydrol 586:124901

    Article  Google Scholar 

  • Nordin NFC, Mohd NS, Koting S, Ismail Z, Sherif M, El-Shafie A (2021) Groundwater quality forecasting modelling using artificial intelligence: a review. Groundw Sustainable Dev 14:100643

    Article  Google Scholar 

  • Parviz L, Rasouli K, Torabi Haghighi A (2023) Improving hybrid models for precipitation forecasting by combining nonlinear machine learning methods. Water Resour Manage 37:3833–3855

    Article  Google Scholar 

  • Peng S, Wang C, Li Z et al (2023) Climate change multi-model projections in CMIP6 scenarios in Central Hokkaido. Sci Rep 13:230

    Article  CAS  Google Scholar 

  • Pham QB, Kumar M, Di Nunno F et al (2022) Groundwater level prediction using machine learning algorithms in a drought-prone area. Neural Comput Appl 34:10751–10773

    Article  Google Scholar 

  • Pointet T (2022) The United Nations World Water Development Report on groundwater, a synthesis. Hydrosci J 108 (1)

  • Pratama AD, Dwiputra DS, Nurkholis A et al (2021) Factors affecting hydrochemistry of Karst Springs and their relationship to Aquifer Development. Environ Process 8:1379–1413

    Article  CAS  Google Scholar 

  • Punjab Water Resources and Environment Directorate (PHRED), Chandigarh (2014) A Report on Status on groundwater quality in Punjab based on PDS HP-II project, HG-WR (201)

  • Qin G, Liu J, Xu S, Wang T (2020) Water quality assessment and pollution source apportionment in a highly regulated river of Northeast China. Environ Monit Assess 192:446

    Article  CAS  Google Scholar 

  • Rajaee T, Ebrahimi H, Nourani V (2019) A review of the artificial intelligence methods in groundwater level modeling. J Hydrol 572:336–351

    Article  Google Scholar 

  • Reinecke R, Schmied HM, Trautmann T, Andersen et al (2021) Uncertainty of simulated groundwater recharge at different global warming levels: a global-scale multi-model ensemble study. Hydrol Earth Syst Sci 25:787–810

    Article  CAS  Google Scholar 

  • Rodell M, Velicogna I, Famiglietti. J. S (2009) Satellite-based estimates of groundwater depletion in India. Nat Volume 460:999–1002

    Article  CAS  Google Scholar 

  • Roshni T, Jha MK, Deo RC, Vandana A (2019) Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modelling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System. Water Resour Manage 33:2381–2397

    Article  Google Scholar 

  • Sahoo S, Russo TA, Elliott J, Foster I (2017) Machine learning algorithms for modelling ground water level changes in agricultural regions of the U.S. Water Resour Res Volume 53:3878–3895

    Article  Google Scholar 

  • Sarma R, Singh SK (2022) A comparative study of Data-driven models for Groundwater Level forecasting. Water Resour Manage 36:2741–2756

    Article  Google Scholar 

  • Singha S, Pasupuleti S, Singha SS, Singh R, Kumar S (2021) Prediction of groundwater quality using efficient machine learning technique. Chemosphere 276:130265

    Article  CAS  Google Scholar 

  • Tang T, Jiao D, Chen T, Gui G (2022) Medium- and long-term precipitation forecasting Method based on Data Augmentation and Machine Learning Algorithms in IEEE Journal of selected topics. 15:1000–1011in Applied Earth Observations and Remote Sensing

  • Todd D, Mays L (2005) Groundwater Hydrology. 3rd Edition, John Wiley and Sons, Inc., Hoboken, 652 p

  • Wakode HB, Baier K, Jha R, Azzam R (2018) Impact of urbanization on groundwater recharge and urban water balance for the city of Hyderabad, India. International Soil and Water Conservation Research, pp 651–662

  • Wunsch A, Liesch T, Broda S (2021) Groundwater Level forecasting with Artificial neural networks: a comparison of LSTM, CNN and NARX. Hydrology and Earth System Sciences 2020 – 552

  • Yadav B, Gupta PK, Patidar N, Himanshu SK (2020) Ensemble modelling framework for groundwater level prediction in urban areas of India, vol 712. Science of The Total Environment, p 135539

  • Yang X, Zhou J, Zhang Q et al (2024) Evaluation and interpretation of runoff forecasting models based on hybrid deep neural networks. https://doi.org/10.1007/s11269-023-03731-6. Water Resources Management

  • Zhang Z, Wang W, Gong C, Zhang M (2020) A comparison of methods to estimate groundwater recharge from bare soil based on data observed by a large-scale lysimeter. Hydrol Process 34(13):2987–2999

    Article  Google Scholar 

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Acknowledgements

The authors would like to express their gratitude and appreciation to the Central Groundwater Board (CGWB), Chandigarh Office for providing the dataset needed for this study.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. PhD student, Department of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India

    Dolon Banerjee

  2. Assistant Professor, Department of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India

    Sayantan Ganguly

  3. Department of Computer Science, Sant Longowal Institute of Engineering & Technology, Longowal, Sangrur, Punjab, 148106, India

    Shashwat Kushwaha

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  1. Dolon Banerjee
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Contributions

Conceptualisation: D.B., S.G.; Methodology: D.B., S.G.; Formal analysis and investigation: D.B., S.G., S.K.; Software: D.B., S.K.; Visualization: D.B., S.G., S.K.; Writing - original draft preparation: D.B., S.G.; Writing ‐ review and editing: D.B., S.G.; Supervision: S.G.

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Correspondence to Sayantan Ganguly.

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Banerjee, D., Ganguly, S. & Kushwaha, S. Forecasting Future Groundwater Recharge from Rainfall Under Different Climate Change Scenarios Using Comparative Analysis of Deep Learning and Ensemble Learning Techniques. Water Resour Manage (2024). https://doi.org/10.1007/s11269-024-03850-8

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