Predicting Temperature And Precipitation During The Flood Season Based On Teleconnection

Abstract

In recent years, the damages resulting from abnormal hydrometeorological climate have substantially increased over the world due to the climate variability and change. Especially, the flood damage has been severely occurred during the flood season almost every year in Korea. For an example, we had the localized heavy rainfalls for 54 days in flood season of 2020 and had huge property damage and loss of life. Therefore, the study needs to be conducted to improve the predictive power of seasonal time-scale forecasts spanning one to several months for the damage reduction and prevention. In this regard, this study aims to provide a priori predictions (several months ahead) of the climate variable at target sites with a statistical method based on teleconnection with global climatic conditions. Herein, the paradigm of the temperature and precipitation prediction in the Geum river basin in Korea is presented. The purposes of the study are also (1) to analyse the characteristics of summer temperatures and precipitation according to the occurrence of El Niño/La Niña and (2) to suggest a seasonal prediction model that can consider the effects of the occurrence of El Niño and La Niña during the flood season. The model is constructed by classifying the data period into El Niño, La Niña, and neutral status. Then we have shown that the prediction model improves the predictive power for the predictions of climate variables such as temperature and precipitation at midlatitude stations which Korea is located. Therefore, this study demonstrates the possibility of improving the predictive power for forecasting temperature and precipitation by the prediction model considering climate variability.