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Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed

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Abstract

Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate change impact studies of this nature.

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Acknowledgments

The authors are thankful to the National Institute of Water and Atmospheric Research (NIWA), New Zealand for providing the precipitation data used in this study and the Higher Education Commission (HEC) of Pakistan for funding this research work.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, The University of Auckland, Auckland, 1142, New Zealand

    Muhammad Zia Hashmi, Asaad Y. Shamseldin & Bruce W. Melville

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  1. Muhammad Zia Hashmi
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  2. Asaad Y. Shamseldin
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  3. Bruce W. Melville
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Correspondence to Muhammad Zia Hashmi.

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Hashmi, M.Z., Shamseldin, A.Y. & Melville, B.W. Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess 25, 475–484 (2011). https://doi.org/10.1007/s00477-010-0416-x

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  • DOI: https://doi.org/10.1007/s00477-010-0416-x

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