Generation of Daily Rainfall Scenario Based on Nonstationary Spatial-Temporal Downscaling Techniques with Multimodel Ensemble of Different GCMs

Abstract:

Recently, extreme weather occurrences associated with climate change are gradually increasing in frequency, causing unprecedented major weather-related disasters. General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the discrepancy between the spatio-temporal scale at which the models deliver output and the scales that are generally required for applied studies has led to the development of various downscaling methods. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a multimodel ensemble of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. In particular, this study uses MMEs from the APEC Climate Center (APCC) as a predictor. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management.

Acknowledgement: This research was supported by a grant (13SCIPA01) from Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and the Korea Agency for Infrastructure Technology Advancement (KAIA).

Keywords: Climate Change, GCM, Hidden Markov Chain Model, Multi-Model Ensemble