Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods
Abstract
This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE). Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations.
Keyword : climate model, machine learning, precipitation, temperature, Mahanadi Basin
How to Cite
Raj, D. K., & T., G. (2024). Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods. Journal of Environmental Engineering and Landscape Management, 32(4), 270–282. https://doi.org/10.3846/jeelm.2024.22352
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Krishna Kumar, K., Patwardhan, S. K., Kulkarni, A., Kamala, K., Koteswara Rao, K., & Jones, R. (2011). Simulated projections for summer monsoon climate over India by a high-resolution regional climate model (PRECIS). Current Science, 101(3), 312–326.
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Pattanaik, D. R., & Das, A. K. (2015). Prospect of application of extended range forecast in water resource management: A case study over the Mahanadi River basin. Natural Hazards, 77(2), 575–595. https://doi.org/10.1007/s11069-015-1610-4
Perkins, S. E., Moise, A., Whetton, P., & Katzfey, J. (2014). Regional changes of climate extremes over Australia – A comparison of regional dynamical downscaling and global climate model simulations. International Journal of Climatology, 34(12), 3456–3478. https://doi.org/10.1002/joc.3927
Rathjens, H., Bieger, K., Srinivasan, R., & Arnold, J. G. (2016). CMhyd user manual: Documentation for preparing simulated climate change data for hydrologic impact studies. https://swat.tamu.edu/media/115265/bias_cor_man.pdf
Rohini, P., Rajeevan, M., & Mukhopadhay, P. (2019). Future projections of heat waves over India from CMIP5 models. Climate Dynamics, 53(1), 975–988. https://doi.org/10.1007/s00382-019-04700-9
Salvi, K., Villarini, G., Vecchi, G. A., & Ghosh, S. (2017). Decadal temperature predictions over the continental United States: Analysis and enhancement. Climate Dynamics, 49(9–10), 3587–3604. https://doi.org/10.1007/s00382-017-3532-1
Saranya, M. S., & Vinish, V. N. (2021). Evaluation and selection of CORDEX-SA datasets and bias correction methods for a hydrological impact study in a humid tropical river basin, Kerala. Journal of Water and Climate Change, 12(8), 3688–3713. https://doi.org/10.2166/wcc.2021.139
Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 International Conference on Advances in Computing, Communications and Informatics (pp. 1643–1647), Udupi, India. https://doi.org/10.1109/ICACCI.2017.8126078
Swain, S. (2016). Impact of climate variability over Mahanadi River Basin.
Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456–457, 12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052
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Vagropoulos, S. I., Chouliaras, G. I., Kardakos, E. G., Simoglou, C. K., & Bakirtzis, A. G. (2016). Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting. In 2016 IEEE International Energy Conference (pp. 1–6), Leuven, Belgium. https://doi.org/10.1109/ENERGYCON.2016.7514029
Vijayakumar, S., Nayak, A. K., Ramaraj, A. P., Swain, C. K., Geethalakshmi, V., Pazhanivelan, S., Tripathi, R., & Sudarmanian, N. S. (2021). Rainfall and temperature projections and their impact assessment using CMIP5 models under different RCP scenarios for the eastern coastal region of India. Current Science, 121(2), 222–232. https://doi.org/10.18520/cs/v121/i2/222-232
Yeboah, K. A., Akpoti, K., Kabo-bah, A. T., Ofosu, E. A., Siabi, E. K., Mortey, E. M., & Okyereh, S. A. (2022). Assessing climate change projections in the Volta Basin using the CORDEX-Africa climate simulations and statistical bias-correction. Environmental Challenges, 6, Article 100439. https://doi.org/10.1016/j.envc.2021.100439
Almazrouee, A. I., Almeshal, A. M., & Almutairi, A. S. (2020). Long-term forecasting of electrical loads in Kuwait. Applied Science, 10, Article 5627. https://doi.org/10.3390/app10165627
Chaturvedi, S., Rajasekar, E., Natarajan, S., & McCullen, N. (2022). A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India. Energy Policy, 168, Article 113097. https://doi.org/10.1016/j.enpol.2022.113097
Dadhwal, V. K., Mahendran, A., Sharma, J. R., Tembhurney, W. M., Joseph, M., Jain, R. K., Singh, H., Paithankar, Y., Manasa Devi, B., & Kalsi, A. P. (2014). Mahanadi Basin. https://indiawris.gov.in/downloads/Mahanadi%20Basin.pdf
Das, P., Zhang, Z., & Ren, H. (2022). Evaluation of four bias correction methods and random forest model for climate change projection in the Mara River Basin, East Africa. Journal of Water and Climate Change, 13(4), 1900–1919. https://doi.org/10.2166/wcc.2022.299
Déandreis, C., Pagé, C., Braconnot, P., Bärring, L., Bucchignani, E., de Cerff, W. S., Hutjes, R., Joussaume, S., Mares, C., Planton, S., & Plieger, M. (2014). Towards a dedicated impact portal to bridge the gap between the impact and climate communities: Lessons from use cases. Climatic Change, 125(3–4), 333–347. https://doi.org/10.1007/s10584-014-1139-7
Dhamodharavadhani, S., & Rathipriya, R. (2019). Region-wise rainfall prediction using mapreduce-based exponential smoothing techniques. Advances in Intelligent Systems and Computing, 750, 229–239. https://doi.org/10.1007/978-981-13-1882-5_21
Fadnavis, S., Mahajan, A. S., Choudhury, A. D., Roy, C., Singh, M., & Biswas, M. S. (2020). Atmospheric aerosols and trace gases. In Assessment of climate change over the Indian region: A report of the Ministry of Earth Sciences (MoES), Government of India (pp. 93–116). Springer. https://doi.org/10.1007/978-981-15-4327-2_5
Fiseha, B. M., Setegn, S. G., Melesse, A. M., Volpi, E., & Fiori, A. (2014). Impact of climate change on the hydrology of Upper Tiber River basin using bias corrected regional climate model. Water Resources Management, 28(5), 1327–1343. https://doi.org/10.1007/s11269-014-0546-x
Graham, A., Pathak Mishra, E., & Anosh Graham, C. (2017). Time series analysis model to forecast rainfall for Allahabad region. ~ 1418 ~ Journal of Pharmacognosy and Phytochemistry, 6(5), 1418–1421.
Haq, M. A. (2022). CDLSTM: A novel model for climate change forecasting. Computers, Materials and Continua, 71(2), 2363–2381. https://doi.org/10.32604/cmc.2022.023059
Ines, A. V. M., & Hansen, J. W. (2006). Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology, 138(1–4), 44–53. https://doi.org/10.1016/j.agrformet.2006.03.009
Intergovernmental Panel on Climate Change. (2022). Impacts, adaptation, and vulnerability: Working group II contribution to the IPCC sixth assessment report of the intergovernmental panel on climate change. Cambridge University Press. https://doi.org/10.1017/9781009325844
IS-ENES3 C4I-Search. (n.d.). Welcome to Climate4Impact! Retrieved January 19, 2024, from https://www.climate4impact.eu/c4i-frontend/
Ishida, K., Kiyama, M., Ercan, A., Amagasaki, M., & Tu, T. (2021). Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks. Journal of Hydroinformatics, 23(6), 1312–1324. https://doi.org/10.2166/hydro.2021.095
Jain, S., Salunke, P., Mishra, S. K., & Sahany, S. (2019). Performance of CMIP5 models in the simulation of Indian summer monsoon. Theoretical and Applied Climatology, 137(1–2), 1429–1447. https://doi.org/10.1007/s00704-018-2674-3
Jose, D. M., Vincent, A. M., & Dwarakish, G. S. (2022). Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques. Scientific Reports, 12(1), 1–25. https://doi.org/10.1038/s41598-022-08786-w
Kannan, S., & Ghosh, S. (2011). Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output. Stochastic Environmental Research and Risk Assessment, 25(4), 457–474. https://doi.org/10.1007/s00477-010-0415-y
Khan, M. M. R., Siddique, M. A. B., Sakib, S., Aziz, A., Tasawar, I. K., & Hossain, Z. (2020). Prediction of temperature and rainfall in Bangladesh using long short term memory recurrent neural networks. In 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (pp. 1–6), Istanbul, Turkey. https://doi.org/10.1109/ISMSIT50672.2020.9254585
Krishna Kumar, K., Patwardhan, S. K., Kulkarni, A., Kamala, K., Koteswara Rao, K., & Jones, R. (2011). Simulated projections for summer monsoon climate over India by a high-resolution regional climate model (PRECIS). Current Science, 101(3), 312–326.
Mavume, A. F., Banze, B. E., Macie, O. A., & Queface, A. J. (2021). Analysis of climate change projections for mozambique under the representative concentration pathways. Atmosphere, 12(5), Article 588. https://doi.org/10.3390/atmos12050588
McHugh, C., Coleman, S., Kerr, D., & McGlynn, D. (2019). Forecasting day-ahead electricity prices with a SARIMAX model. In 2019 IEEE Symposium Series on Computational Intelligence (pp. 1523–1529), Xiamen, China. https://doi.org/10.1109/SSCI44817.2019.9002930
Mujumdar, P. P., & Ghosh, S. (2008). Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the Mahanadi River, India. Water Resources Research, 44(6), 1–15. https://doi.org/10.1029/2007WR006137
Pattanaik, D. R., & Das, A. K. (2015). Prospect of application of extended range forecast in water resource management: A case study over the Mahanadi River basin. Natural Hazards, 77(2), 575–595. https://doi.org/10.1007/s11069-015-1610-4
Perkins, S. E., Moise, A., Whetton, P., & Katzfey, J. (2014). Regional changes of climate extremes over Australia – A comparison of regional dynamical downscaling and global climate model simulations. International Journal of Climatology, 34(12), 3456–3478. https://doi.org/10.1002/joc.3927
Rathjens, H., Bieger, K., Srinivasan, R., & Arnold, J. G. (2016). CMhyd user manual: Documentation for preparing simulated climate change data for hydrologic impact studies. https://swat.tamu.edu/media/115265/bias_cor_man.pdf
Rohini, P., Rajeevan, M., & Mukhopadhay, P. (2019). Future projections of heat waves over India from CMIP5 models. Climate Dynamics, 53(1), 975–988. https://doi.org/10.1007/s00382-019-04700-9
Salvi, K., Villarini, G., Vecchi, G. A., & Ghosh, S. (2017). Decadal temperature predictions over the continental United States: Analysis and enhancement. Climate Dynamics, 49(9–10), 3587–3604. https://doi.org/10.1007/s00382-017-3532-1
Saranya, M. S., & Vinish, V. N. (2021). Evaluation and selection of CORDEX-SA datasets and bias correction methods for a hydrological impact study in a humid tropical river basin, Kerala. Journal of Water and Climate Change, 12(8), 3688–3713. https://doi.org/10.2166/wcc.2021.139
Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 International Conference on Advances in Computing, Communications and Informatics (pp. 1643–1647), Udupi, India. https://doi.org/10.1109/ICACCI.2017.8126078
Swain, S. (2016). Impact of climate variability over Mahanadi River Basin.
Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456–457, 12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Tsai, Y. T., Zeng, Y. R., & Chang, Y. S. (2018). Air pollution forecasting using RNN with LSTM. In 2018 IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (pp. 1074–1079), Athens, Greece. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00178
Vagropoulos, S. I., Chouliaras, G. I., Kardakos, E. G., Simoglou, C. K., & Bakirtzis, A. G. (2016). Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting. In 2016 IEEE International Energy Conference (pp. 1–6), Leuven, Belgium. https://doi.org/10.1109/ENERGYCON.2016.7514029
Vijayakumar, S., Nayak, A. K., Ramaraj, A. P., Swain, C. K., Geethalakshmi, V., Pazhanivelan, S., Tripathi, R., & Sudarmanian, N. S. (2021). Rainfall and temperature projections and their impact assessment using CMIP5 models under different RCP scenarios for the eastern coastal region of India. Current Science, 121(2), 222–232. https://doi.org/10.18520/cs/v121/i2/222-232
Yeboah, K. A., Akpoti, K., Kabo-bah, A. T., Ofosu, E. A., Siabi, E. K., Mortey, E. M., & Okyereh, S. A. (2022). Assessing climate change projections in the Volta Basin using the CORDEX-Africa climate simulations and statistical bias-correction. Environmental Challenges, 6, Article 100439. https://doi.org/10.1016/j.envc.2021.100439