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Climate change impact assessment on flow regime by incorporating spatial correlation and scenario uncertainty

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Abstract

Flooding risk is increasing in many parts of the world and may worsen under climate change conditions. The accuracy of predicting flooding risk relies on reasonable projection of meteorological data (especially rainfall) at the local scale. The current statistical downscaling approaches face the difficulty of projecting multi-site climate information for future conditions while conserving spatial information. This study presents a combined Long Ashton Research Station Weather Generator (LARS-WG) stochastic weather generator and multi-site rainfall simulator RainSim (CLWRS) approach to investigate flow regimes under future conditions in the Kootenay Watershed, Canada. To understand the uncertainty effect stemming from different scenarios, the climate output is fed into a hydrologic model. The results showed different variation trends of annual peak flows (in 2080–2099) based on different climate change scenarios and demonstrated that the hydrological impact would be driven by the interaction between snowmelt and peak flows. The proposed CLWRS approach is useful where there is a need for projection of potential climate change scenarios.

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Acknowledgments

The research work was supported by Singapore’s Ministry of Education (MOE) AcRF Tier 1 Project (Ref No. RG188/14; WBS No. M4011420.030). The authors also appreciate the support from Environmental Process Modelling Centre (Previously named DHI-NTU Centre) under Nanyang Environment and Water Research Institute (NEWRI).

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  1. School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Ave., Singapore, 639798, Singapore

    P. Vallam & X. S. Qin

  2. Environmental Process Modelling Centre (EPMC), Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, Singapore, 637141, Singapore

    X. S. Qin

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Vallam, P., Qin, X.S. Climate change impact assessment on flow regime by incorporating spatial correlation and scenario uncertainty. Theor Appl Climatol 129, 607–622 (2017). https://doi.org/10.1007/s00704-016-1802-1

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