AbstractStatistical downscaling is useful for managing scale and resolution problems in outputs from Global Climate Models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a Supporting Vector Classifier (SVC) and first-order two-state Markov Chain to generate the occurrence and a Support Vector Regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Down-Scaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using ten meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd-years for calibration and even-years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd-years for calibration and even-years for validation can reduce the influence of possible climate change-induced non-stationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, therefore this method will need to be modified for extension into a multi-site domain.
Journal of Hydrometeorology – American Meteorological Society
Published: Jul 13, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera