Theoretical and Applied Climatology

, Volume 116, Issue 1–2, pp 243–257 | Cite as

Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature

  • Zulkarnain Hassan
  • Supiah Shamsudin
  • Sobri Harun
Original Paper


Climate change is believed to have significant impacts on the water basin and region, such as in a runoff and hydrological system. However, impact studies on the water basin and region are difficult, since general circulation models (GCMs), which are widely used to simulate future climate scenarios, do not provide reliable hours of daily series rainfall and temperature for hydrological modeling. There is a technique named as “downscaling techniques”, which can derive reliable hour of daily series rainfall and temperature due to climate scenarios from the GCMs output. In this study, statistical downscaling models are used to generate the possible future values of local meteorological variables such as rainfall and temperature in the selected stations in Peninsular of Malaysia. The models are: (1) statistical downscaling model (SDSM) that utilized the regression models and stochastic weather generators and (2) Long Ashton research station weather generator (LARS-WG) that only utilized the stochastic weather generators. The LARS-WG and SDSM models obviously are feasible methods to be used as tools in quantifying effects of climate change condition in a local scale. SDSM yields a better performance compared to LARS-WG, except SDSM is slightly underestimated for the wet and dry spell lengths. Although both models do not provide identical results, the time series generated by both methods indicate a general increasing trend in the mean daily temperature values. Meanwhile, the trend of the daily rainfall is not similar to each other, with SDSM giving a relatively higher change of annual rainfall compared to LARS-WG.


Root Mean Square Error Climate Scenario Daily Rainfall Monthly Rainfall Weather Generator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work had been financially supported by the Ministry of Higher Education Malaysia, under EScience Fund vote 79385 and Universiti Teknologi Malaysia. The authors would like to thank Malaysia Meteorological Department for providing the data and technical support. Thanks to all software developers, especially to Dawson C. W. (SDSM 4.2) and Semenov M. A. (LARS-WG) for their valuable support and prompt feedbacks through e-mail contacts.


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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Zulkarnain Hassan
    • 1
  • Supiah Shamsudin
    • 2
  • Sobri Harun
    • 1
  1. 1.Faculty of Civil Engineering (FKA)Universiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.Razak School of Engineering and Advanced TechnologyUniversiti Teknologi Malaysia-Kuala LumpurKuala LumpurMalaysia

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