Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario

Jongsung Kim, Myungjin Lee, Heechan Han, Donghyun Kim, Yunghye Bae and Hung Soo Kim
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Jongsung Kim: Institute of Water Resources System, Inha University, Incheon 22201, Korea
Myungjin Lee: Institute of Water Resources System, Inha University, Incheon 22201, Korea
Heechan Han: Blackland Research and Extension Center, Texas A&M AgriLife, Temple, TX 76502, USA
Donghyun Kim: Department of Civil Engineering, Inha University, Incheon 22201, Korea
Yunghye Bae: Department of Civil Engineering, Inha University, Incheon 22201, Korea
Hung Soo Kim: Department of Civil Engineering, Inha University, Incheon 22201, Korea

Sustainability, 2022, vol. 14, issue 8, 1-20

Abstract: Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.

Keywords: climate change; convolution neural network; spatial downscaling; teleconnection; quantile mapping (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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