A Study of Correcting Climate Model Daily Rainfall Product Using Quantile Mapping in Upper Ping River Basin, Thailand
The study of impact of climate change on water resources is significantly increasing to evaluate its effect in regional or local scale. Regional Climate Models (RCMs) are major physical functioning tools to downscale and simulate future climate projections under various scenarios. However, most RCMs’ products present of uncertainties and generate systematic and random biases. A need of post-processing, bias correction, is inevitable to produce a dependability RCM’s products. It is obvious that bias correction performance is location dependent. In this study, a widely used Quantile Mapping (QM) technique is applied over upper Ping River basin to correct daily rainfall from MM5-RCM. Different distributions between transformation of QM are tested. Mixed distribution between Bernoulli-Weibull, Bernoulli-Gamma and non-parametric transformation are performed. The derived transformation with mixed distributions are included Bernoulli distribution in order to consider the probability of number of rain and no-rain days. Rather than predetermined distribution function, non-parametric transformation might also yield a likely better estimation due to it freedom of fitting distribution. Overall, bias correction methods are generally improved and reduce climate model output bias which is needed to be done before quantifying any impacts of climate change study.
KeywordsClimate Model Daily Rainfall Quantile Mapping Bias Correction upper Ping River Basin
Unable to display preview. Download preview PDF.
This work is supported by Faculty of Engineering, Burapha University Research Fund under Grant No. WJP. 1/2562. Moreover, the data used in this work was supported by Thai Meteorological Department (TMD), Thai Royal Irrigation Department (RID) and Department of Water Resources for proving observed daily rainfall data. The climate change scenarios were retrieved from ECHAM5 and CCSM3 climate models which developed by The Max Planck Institute (MPIM, Germany) and US National Center for Atmospheric Research (NCAR, USA) respectively.
- Clement Bennett, J., Grose, M., Post, D., Ling, F., Corney, S., & Bindoff, N. (2011). Performance of quantile-quantile bias-correction for use in hydroclimatological projections.Google Scholar
- Gudmundsson, L. (2016). qmap: Statistical transformations for post-processing climate model output.R package version 1.0-4 (Version 1.0-4).Google Scholar
- Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods. Hydrol. Earth Syst. Sci., 16(9), 3383-3390. https://doi.org/10.5194/hess-16-3383-2012
- Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., & Haerter, J. O. (2010). Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology, 395(3), 199-215. doi: https://doi.org/10.1016/j.jhydrol.2010.10.024CrossRefGoogle Scholar
- Wuthiwongyothin, S., Jang, S., Kei, I., & Kavvas, M. L. (2017). The Effects of Climate Change on Hydrology based on Dynamically Downscaling and Physically-Based Hydrology Model at Upper Ping. Internet Journal of Society for Social Management Systems, 11(1), sms17-2360.Google Scholar