Abstract
Climate Change caused by global warming is a growing public concern throughout the world. It is well accepted within the scientific community that an ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a Multi-Model Ensemble and performed statistical downscaling for 9 GCMs of CMIP3 and CMIP5. The observed precipitation data from 83 stations around the country were interpolated to grid data using the Inverse Distance Weighted method. The precipitation projection was downscaled by the Distribution Mapping for the near-future (2010–2039), the mid-future (2040–2069) and the far-future (2070–2099). The nonlinear autoregressive neural network with exogenous input (NARX) was used to forecast the mean monthly inflow to reservoirs. The projection inflow for the future periods are shown to increase in inflow in the wet season. A possibility of increase in hydrological extreme flood in the wet season may be indicated by these findings.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
World Bank: The World Bank support Thailand’s post-floods recovery effort, World Bank, Washington DC (2011)
Santer, B.D., Taylor, K.E., Glecker, P.J., et al.: Incorporating model quality information in climate change detection and attribution studies. Proc. National Acad. Sci. United States America 106(35), 14778–14783 (2009)
Knutti, R., Abramowitz, G., Collins, M., Eyring, V., Gleckler, P.J., Hewitson, B.M., Mearns, L.: Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections. Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, IPCC Working Group I Technical Support Unit, University of Bern, Bern (2010)
Supharatid, S.: Skill of precipitation projection in the Chao Phraya river Basin by multi-model ensemble CMIP3-CMIP5. Weather and Clim. Extremes 12, 1–14 (2016)
Coulibaly, P., Anctil, F., Bobee, B.: Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J. Hydrol. 230(3), 244–257 (2000)
Chang, F.J., Chen, P.A., Lu, Y.R., Huang, E., Chang, K.Y.: Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control. J. Hydrol. 517, 836–846 (2014)
Teutschbein, C., Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. J. Hydrol. 456, 12–29 (2009)
Nørgård, P.M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural Networks for Modelling and Control of Dynamic Systems-A Practitioner’s Handbook (2000)
Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesús, O.: Neural network design, vol. 20, Boston (1996)
Acknowledgement
The authors wish to thank the Royal Irrigation Department of Thailand and the Thai Meteorological Department for providing their observed precipitation. The Electricity Generating Authority of Thailand for the Bhumibol and Sirikit reservoirs inflow characteristic. Also, the climate model datasets were obtained from PCMDI.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Aribarg, T., Supratid, S. (2017). Simulated Precipitation and Reservoir Inflow in the Chao Phraya River Basin by Multi-model Ensemble CMIP3 and CMIP5. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-61845-6_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61844-9
Online ISBN: 978-3-319-61845-6
eBook Packages: Computer ScienceComputer Science (R0)