Metalearning Approach Coupled with CMIP6 Multi-GCM for Future Monthly Streamflow Forecasting
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 6
Abstract
Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercomparison Project CMIP6 multi-Global Climate Model (GCM). Five global climate models and three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the metalearning model to predict the streamflow changes in the Kurau River from 2021 to 2080. The model fitted reasonably well, with Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), percent bias (PBias), and RMS Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976–1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995–2005). Future projections of multi-GCM over the 2021–2080 period under three SSPs predicted an increase in rainfall for all months except April–June during the dry period (off-season), with a higher increase occurring during the wet period (main season). Temperature projections indicated an increase in maximum and minimum temperatures under all SSP scenarios, with a higher increase of approximately 2.0°C under SSP5-8.5 predicted during the 2051–2080 period relative to the baseline period of 1976–2005. The model predicted that the seasonal changes in streamflow of two planting periods range between and 7.1% and between 1.2% and 5.9% during the off-season and the main season, respectively. A significant streamflow decrease was predicted in April and May for all SSP scenarios due to high temperatures during the off-season, with SSP5-8.5 being the worst. The impact assessment of climate variabilities on the availability of water resources is vital to identify appropriate adaptation strategies to deal with an expected increase in irrigation demand due to global warming in the future. The predicted future streamflow under the potential climate change impacts is crucial for the Bukit Merah Reservoir to establish suitable operational policies for irrigation release.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data sets of gauged rainfalls and streamflow of the Kurau River, Perak, Malaysia, that support the findings of this study can be requested from the Department of Irrigation and Drainage (DID) or from the corresponding author upon reasonable request, and meteorological data (temperature, relative humidity, and wind speed) can be bought from the Malaysian Meteorological Department (MMD). The global climate models under AR5 can be downloaded from the CMIP6 climate experiments website: https://esgf-node.llnl.gov/search/cmip6/.
Acknowledgments
This research was supported by the Ministry of Higher Education (MOHE), Universiti Putra Malaysia (UPM), and Universiti Teknologi Malaysia (UTM). The authors are grateful to the Department of Irrigation and Drainage (DID) and the Malaysian Meteorological Department (MMD) for providing gauged hydrometeorological data for the study.
References
Abbasian, M., S. Moghim, and A. Abrishamchi. 2019. “Performance of the general circulation models in simulating temperature and precipitation over Iran.” Theor. Appl. Climatol. 135 (3–4): 1465–1483. https://doi.org/10.1007/s00704-018-2456-y.
Abdulkareem, J. H., B. Pradhan, W. N. A. Sulaiman, and N. R. Jamil. 2018. “Review of studies on hydrological modelling in Malaysia.” Model. Earth Syst. Environ. 4 (4): 1577–1605. https://doi.org/10.1007/s40808-018-0509-y.
Adib, M. N. M., M. K. Rowshon, M. A. Mojid, and I. Habibu. 2020. “Projected streamflow in the Kurau River Basin of Western Malaysia under future climate scenarios.” Sci. Rep. 10 (1): 8336. https://doi.org/10.1038/s41598-020-65114-w.
Ahmadi, H., N. Rostami, and A. Dadashi-roudbari. 2020. “Projected climate change in the Karkheh Basin, Iran, based on CORDEX models.” Theor. Appl. Climatol. 142 (1–2): 661–673. https://doi.org/10.1007/s00704-020-03335-9.
Ali, M., and R. Prasad. 2019. “Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition.” Renewable Sustainable Energy Rev. 104 (Dec): 281–295. https://doi.org/10.1016/j.rser.2019.01.014.
Anandhi, A., A. Frei, D. C. Pierson, E. M. Schneiderman, M. S. Zion, D. Lounsbury, and A. H. Matonse. 2011. “Examination of change factor methodologies for climate change impact assessment.” Water Resour. Res. 47 (3): 1–10. https://doi.org/10.1029/2010WR009104.
Ardabili, S., A. Mosavi, and A. R. Várkonyi-Kóczy. 2020. “Advances in machine learning modeling reviewing hybrid and ensemble methods.” In Vol. 101 of Engineering for Sustainable Future. INTER-ACADEMIA 2019, edited by A. Várkonyi-Kóczy. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-36841-8_21.
Arnold, J. G., et al. 2012. “SWAT: Model use, calibration, and validation.” Asabe 55 (4): 1491–1508. https://doi.org/10.13031/2013.42256.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Cao, J., B. Wang, Y.-M. Yang, L. Ma, J. Li, B. Sun, Y. Bao, J. He, X. Zhou, and L. Wu. 2018. “The NUIST earth system model (NESM) version 3: Description and preliminary evaluation.” Geosci. Model Dev. 11 (7): 2975–2993. https://doi.org/10.5194/gmd-11-2975-2018.
Dlamini, N. S., M. R. Kamal, M. A. B. Soom, M. S. F. B. Mohd, A. F. B. Abdullah, and L. S. Hin. 2017. “Modeling potential impacts of climate change on streamflow using projections of the 5th assessment report for the bernam river basin, Malaysia.” Water 9 (3): 226. https://doi.org/10.3390/w9030226.
Dlamini, N. S., M. K. Rowshon, U. Saha, A. Fikri, S. H. Lai, and M. S. F. Mohd. 2015. “Developing and calibrating a stochastic rainfall generator model for simulating daily rainfall by Markov chain approach.” Jurnal Teknologi 76 (15): 13–19. https://doi.org/10.11113/jt.v76.5946.
Du, K. L., and M. Swamy. 2009. “Combining multiple learners: Data fusion and ensemble learning.” In Neural networks and statistical learning. New York: Springer.
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor. 2016. “Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization.” Geosci. Model Dev. 9 (5): 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016.
Fox, J. T., and D. D. Magoulick. 2019. “Predicting hydrologic disturbance of streams using species occurrence data.” Sci. Total Environ. 686: 254–263. https://doi.org/10.1016/j.scitotenv.2019.05.156.
Gorczyca, M. T., N. C. Toscano, and J. D. Cheng. 2019. “The trauma severity model: An ensemble machine learning approach to risk prediction.” Comput. Biol. Med. 108 (Oct): 9–19. https://doi.org/10.1016/j.compbiomed.2019.02.025.
Guan, H., J. Li, M. Chapman, F. Deng, Z. Ji, and X. Yang. 2013. “Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests.” Int. J. Remote Sens. 34 (14): 5166–5186. https://doi.org/10.1080/01431161.2013.788261.
Gupta, H. V., H. Kling, K. K. Yilmaz, and G. F. Martinez. 2009. “Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling.” J. Hydrol. 377 (1–2): 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003.
Hamed, M. M., M. S. Nashwan, and S. Shahid. 2021. “Inter-comparison of historical simulation and future projections of rainfall and temperature by CMIP5 and CMIP6 GCMs over Egypt.” Int. J. Climatol. 1–17. https://doi.org/10.1002/joc.7468.
Han, J., C. Miao, Q. Duan, J. Wu, X. Lei, and W. Liao. 2020. “Variations in start date, end date, frequency and intensity of yearly temperature extremes across China during the period 1961–2017.” Environ. Res. Lett. 15 (4): 045007. https://doi.org/10.1088/1748-9326/ab7390.
Hassan, Z., S. Shamsudin, S. Harun, M. A. Malek, and N. Hamidon. 2015. “Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: A case study in the northern area of Peninsular Malaysia.” Environ. Earth Sci. 74 (1): 463–477. https://doi.org/10.1007/s12665-015-4054-y.
Hussain, D., and A. A. Khan. 2020. “Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan.” Earth Sci. Inf. 13 (3): 939–949. https://doi.org/10.1007/s12145-020-00450-z.
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. Edited by T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley. Cambridge, UK: Cambridge University Press.
Ishida, H., Y. Oishi, K. Morita, K. Moriwaki, and T. Y. Nakajima. 2018. “Development of a support vector machine based cloud detection method for MODIS with the adjustability to various conditions.” Remote Sens. Environ. 205 (Feb): 390–407. https://doi.org/10.1016/j.rse.2017.11.003.
Ismail, H., M. K. Rowshon, L. S. Hin, and A. F. B. Abdullah. 2020a. “Performance of Hec-HMS and ArcSWAT models for assessing climate change impacts on streamflow at Bernam River Basin in Malaysia. Pertanika.” J. Sci. Technol. 28 (3): 1027–1048.
Ismail, H., M. K. Rowshon, L. S. Hin, A. F. B. Abdullah, and N. M. Nasidi. 2020b. “Assessment of climate change impact on future streamflow at Bernam river basin Malaysia.” IOP Conf. Ser.: Earth Environ. Sci. 540 (1): 012040. https://doi.org/10.1088/1755-1315/540/1/012040.
Kling, H., M. Fuchs, and M. Paulin. 2012. “Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios.” J. Hydrol. 424–425 (Mar): 264–277. https://doi.org/10.1016/j.jhydrol.2012.01.011.
Li, F.-F., Z.-Y. Wang, and J. Qiu. 2019a. “Long-term streamflow forecasting using artificial neural network based on preprocessing technique.” J. Forecasting 38 (3): 192–206. https://doi.org/10.1002/for.2564.
Li, X., J. Sha, and Z.-L. Wang. 2019b. “Comparison of daily streamflow forecasts using extreme learning machines and the random forest method.” Hydrol. Sci. J. 64 (15): 1857–1866. https://doi.org/10.1080/02626667.2019.1680846.
Mauritsen, T., et al. 2019. “Developments in the MPI-M earth system model version 1.2 (MPI-ESM1.2) and its response to increasing .” J. Adv. Model. Earth Syst. 11 (4): 998–1038. https://doi.org/10.1029/2018MS001400.
Mazrooei, A., A. Sankarasubramanian, and A. W. Wood. 2021. “Potential in improving monthly streamflow forecasting through variational assimilation of observed streamflow.” J. Hydrol. 600 (Sep): 126559. https://doi.org/10.1016/j.jhydrol.2021.126559.
Moriasi, D. N., M. W. Gitau, N. Pai, and P. Daggupati. 2015. “Hydrologic and water quality models: Performance measures and evaluation criteria.” Trans. ASABE 58 (6): 1763–1785. https://doi.org/10.13031/trans.58.10715.
Moss, R. H., et al. 2010. “The next generation of scenarios for climate change research and assessment.” Nature 463 (7282): 747–756. https://doi.org/10.1038/nature08823.
Nakicenovic, N., J. Alcamo, A. Grubler, K. Riahi, R. A. Roehrl, H. H. Rogner, and N. Victor. 2000. Special report on emissions scenarios (SRES), A special report of working group III of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.
Nash, E., and V. Sutcliffe. 1970. “River flow forecasting through conceptual models part I—A discussion of principles.” J. Hydrol. 10 (3): 282–290. https://doi.org/10.1016/0022-1694(70)90255-6.
Niu, W.-J., Z.-K. Feng, M. Zeng, B.-F. Feng, Y.-W. Min, C.-T. Cheng, and J.-Z. Zhou. 2019. “Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm.” Appl. Soft Comput. J. 82 (82): 105589. https://doi.org/10.1016/j.asoc.2019.105589.
Rajamoorthy, Y., K. B. A. Rahim, and S. Munusamy. 2015. “Rice industry in Malaysia: Challenges, policies and implications.” Procedia Econ. Finance 31 (15): 861–867. https://doi.org/10.1016/S2212-5671(15)01183-1.
Refshaard, J. C., and B. Storm. 1995. “Computer models of watershed hydrology.” In MIKE SHE, edited by V. P. Singh, 809–846. Littleton, CO: Water Resources Publications.
Riahi, K., et al. 2017. “The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview.” Global Environ. Change 42 (Jan): 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009.
Rowshon, M. K., N. S. Dlamini, M. A. Mojid, M. N. M. Adib, M. S. M. Amin, and S. H. Lai. 2019. “Modeling climate-smart decision support system (CSDSS) for analyzing water demand of a large-scale rice irrigation scheme.” Agric. Water Manage. 216 (May): 138–152. https://doi.org/10.1016/j.agwat.2019.01.002.
Samadianfard, S., S. Jarhan, E. Salwana, A. Mosavi, S. Shamshirband, and S. Akib. 2019. “Support vector regression integrated with fruit fly optimization algorithm for river flow forecasting in lake urmia basin.” Water 11 (9): 1934. https://doi.org/10.3390/w11091934.
Seland, Ø., et al. 2020. “Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations.” Geosci. Model Dev. 13 (12): 6165–6200. https://doi.org/10.5194/gmd-13-6165-2020.
Sheehy, J. E., and P. L. Mitchell. 2013. Designing rice for the 21st century: The three laws of maximum yield. Los Baños, Philippines: International Rice Research Institute.
Shevade, S. K., S. S. Keerthi, C. Bhattacharyya, and K. R. K. Murthy. 2000. “Improvements to the SMO algorithm for SVM regression.” IEEE Trans. Neural Networks 11 (5): 1188–1193. https://doi.org/10.1109/72.870050.
Sheykhmousa, M., M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni. 2020. “Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review.” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13 (Sep): 6308–6325. https://doi.org/10.1109/JSTARS.2020.3026724.
Smola, A. J., and B. Schölkopf. 2004. “A tutorial on support vector regression.” Stat. Comput. 14 (3): 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88.
Suhaila, J., S. M. Deni, W. A. N. Zawiah Zin, and A. A. Jemain. 2010. “Trends in Peninsular Malaysia rainfall data during the southwest monsoon and northeast monsoon seasons: 1975–2004.” Sains Malaysiana 39 (4): 533–542.
Swart, N. C., et al. 2019. “The Canadian Earth System Model version 5 (CanESM5.0.3).” Geosci. Model Dev. 12 (11): 4823–4873. https://doi.org/10.5194/gmd-12-4823-2019.
Syafrina, A. H., M. D. Zalina, and L. Juneng. 2015. “Historical trend of hourly extreme rainfall in Peninsular Malaysia.” Theor. Appl. Climatol. 120 (1–2): 259–285. https://doi.org/10.1007/s00704-014-1145-8.
Tan, M. L., D. L. Ficklin, A. L. Ibrahim, and Z. Yusop. 2014. “Impacts and uncertainties of climate change on streamflow of the Johor River Basin, Malaysia using a CMIP5 General Circulation Model ensemble.” J. Water Clim. Change 5 (4): 676–695. https://doi.org/10.2166/wcc.2014.020.
Tan, M. L., J. Liang, N. Samat, N. W. Chan, J. M. Haywood, and K. Hodges. 2021. “Hydrological extremes and responses to climate change in the kelantan river basin, malaysia, based on the CMIP6 highresmip experiments.” Water 13 (11): 1472. https://doi.org/10.3390/w13111472.
Tang, K. H. D. 2019. “Climate change in Malaysia: Trends, contributors, impacts, mitigation and adaptations.” Sci. Total Environ. 650 (Feb): 1858–1871. https://doi.org/10.1016/j.scitotenv.2018.09.316.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl. 2012. “An overview of CMIP5 and the experiment design.” Bull. Am. Meteorol. Soc. 93 (4): 485–498. https://doi.org/10.1175/BAMS-D-11-00094.1.
Thissen, W., J. Kwakkel, M. Mens, J. van der Sluijs, S. Stemberger, A. Wardekker, and D. Wildschut. 2017. “Dealing with uncertainties in fresh water supply: Experiences in the Netherlands.” Water Resour. Manage. 31 (2): 703–725. https://doi.org/10.1007/s11269-015-1198-1.
Tongal, H., and M. J. Booij. 2018. “Simulation and forecasting of streamflows using machine learning models coupled with base flow separation.” J. Hydrol. 564 (Sep): 266–282. https://doi.org/10.1016/j.jhydrol.2018.07.004.
USACE-HEC. 2000. Hydrologic modeling system HEC-HMS: Technical reference manual. Washington, DC: USACE.
Vapnik, V. 1995. The nature of statistical learning theory. New York: Springer.
Wan, S., and H. Yang. 2013. “Comparison among methods of ensemble learning.” In Proc., Int. Symp. on Biometrics and Security Technologies, 286–290. New York: IEEE. https://doi.org/10.1109/ISBAST.2013.50.
Wang, D., J. Liu, W. Shao, C. Mei, X. Su, and H. Wang. 2021. “Comparison of CMIP5 and CMIP 6 multi-model ensemble for precipitation downscaling results and observational data: The case of Hanjiang River Basin.” Atmosphere 12 (7): 867. https://doi.org/10.3390/atmos12070867.
Wan Zin, W. Z., S. Jamaludin, S. M. Deni, and A. A. Jemain. 2010. “Recent changes in extreme rainfall events in Peninsular Malaysia: 1971-2005.” Theor. Appl. Climatol. 99 (3–4): 303–314. https://doi.org/10.1007/s00704-009-0141-x.
Wu, C.-H., G.-H. Tzeng, and R.-H. Lin. 2009. “A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression.” Expert Syst. Appl. 36 (3): 4725–4735. https://doi.org/10.1016/j.eswa.2008.06.046.
Wu, M.-C., G.-F. Lin, and H.-Y. Lin. 2014. “Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self-organizing map.” Hydrol. Processes 28 (2): 386–397. https://doi.org/10.1002/hyp.9584.
Yaseen, Z. M., O. Kisi, and V. Demir. 2016. “enhancing long-term streamflow forecasting and predicting using periodicity data component: Application of artificial intelligence.” Water Resour. Manage. 30 (12): 4125–4151. https://doi.org/10.1007/s11269-016-1408-5.
Yazdani, M. R., and A. A. Zolfaghari. 2017. “Monthly river forecasting using instance-based learning methods and climatic parameters.” J. Hydrol. Eng. 22 (6): 04017002. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001490.
Yukimoto, S., et al. 2019. “The meteorological research institute Earth system model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component.” J. Meteorol. Soc. Jpn. 97 (5): 931–965.
Zarrin, A., and A. Dadashi-Roudbari. 2021. “Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble.” Theor. Appl. Climatol. 144 (1–2): 643–660. https://doi.org/10.1007/s00704-021-03568-2.
Zewdie, G. K., D. J. Lary, X. Liu, D. Wu, and E. Levetin. 2019. “Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.” Environ. Monit. Assess. 191 (7): 418. https://doi.org/10.1007/s10661-019-7542-9.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 27 • Issue 6 • June 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 29, 2021
Accepted: Feb 9, 2022
Published online: Apr 4, 2022
Published in print: Jun 1, 2022
Discussion open until: Sep 4, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.