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Comparison of dynamical and statistical rainfall downscaling of CMIP5 ensembles at a small urban catchment scale

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Abstract

Downscaling of future projections of climatic variables from global climate models (GCMs) to urban catchment scales is required for stormwater studies as existing GCMs are unable to predict the rainfall at high temporal and spatial resolutions. In the current study, the capability of dynamical and statistical downscaling methods is evaluated and compared using the rainfall data of current climate and future extreme events at a small urban catchment scale. Regional climate model (RCM) and statistical downscaling model (SDSM) are utilized to downscale rainfall data from twelve GCMs under three representative concentration pathways (RCPs) (i.e., RCP2.6, RCP 4.5 and RCP 8.5). The daily rainfall data (1985–2015) of Lucas Creek catchment located in Auckland, New Zealand is used as a baseline/current climate. The future downscaled rainfall data is analyzed in the 2090s (2071–2100). The results showed that both the methods performed well in downscaling the current climate. For future projections, SDSM underestimated mean daily rainfall at the start of the annual cycle and overestimated towards the middle of the year compared to RCM. Similarly, monthly variance and skewness were overestimated for some months by SDSM. The GCMs of both the methods also showed variations in the future rainfall projections amongst themselves. However, significant alterations in the future rainfall were observed compared to the current climate. Rainfall frequency analysis was performed by applying the Gumbel distribution to the baseline and downscaled data for 1, 2, 3, 4, 5, 10, 20, 30, 50 and 100 years return periods. The investigation revealed that RCM and SDSM show similar results for low return periods and different results for high return periods for the current and future climate. Both the methods forecasted an increase in magnitudes of future events however, RCM projections were lower compared to SDSM. The results illustrate the downscaling abilities of both the methods at a small urban catchment scale however, the contrasting implication in downscaling the rainfall data is related to their different downscaling mechanisms.

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Acknowledgements

We are grateful to the Auckland Council and the National Institute of Water and Atmospheric Research (NIWA), New Zealand for providing observed rainfall and geographical data of the Lucas Creek catchment, and rainfall projections for the Auckland region, respectively.

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  1. Department of Civil and Environmental Engineering, The University of Auckland, Private Bag 92019, Auckland, New Zealand

    Muhammad Saleem Akhter, Asaad Yahia Shamseldin & Bruce William Melville

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  1. Muhammad Saleem Akhter
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Correspondence to Muhammad Saleem Akhter.

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Akhter, M.S., Shamseldin, A.Y. & Melville, B.W. Comparison of dynamical and statistical rainfall downscaling of CMIP5 ensembles at a small urban catchment scale. Stoch Environ Res Risk Assess 33, 989–1012 (2019). https://doi.org/10.1007/s00477-019-01678-y

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