Spring precipitation forecasting with exhaustive searching and LASSO using climate teleconnection for drought management

Abstract

Drought is defined as a prolonged regional precipitation deficiency. Significant drought conditions often occur during spring in South Korea since abundant water resources are only available in summer, and failed water management is often reversed in the following spring. Since precipitation is the major driver of spring drought over South Korea, forecasting spring precipitation is essential for drought management. However, spring precipitation forecasting is difficult due to the difficulty of finding explanatory variables and the lack of long hydrological records for application to complex models, including nonlinear or deep learning models. When the teleconnection between the spring precipitation and globally gridded climate variables is used, there are many available candidates, and an appropriate procedure must be used to select reliable predictors. Therefore, this study aims to develop a simple forecasting model, finding reliable predictors for spring precipitation in South Korea, and using the teleconnection to the globally gridded climate variable of mean sea level pressure (MSLP). First, the accumulated spring precipitation (ASP), the median of 93 weather stations in South Korea, was identified, and two-pair combinations of teleconnected global winter MSLP, denoted Df4m, were considered candidate predictors. Then, exhaustive searching for explanatory variables was performed with correlation analysis between the ASP and Df4m to find reliable predictors. The 37 Df4m variables have high correlations of 0.55. The 37 Df4m variables were categorized into three regions: Arctic Ocean, South Pacific, and South Africa. Variables in the same region are similar, and the multicollinearity problem is unavoidable. Thus, the least absolute shrinkage and selection operator (LASSO) model was applied by choosing five Df4m variables, and the forecasting value agreed with the observed value (R2 = 0.72). Other models, including the multiple regression model and ElasticNet, did not perform significantly superior to the LASSO model. Therefore, by exhaustively searching many globally gridded candidates, the LASSO model is a good alternative to forecasting spring precipitation over South Korea with the ASP by finding reliable predictors and producing skillful results with limited records. The forecasting result using the LASSO model and Df4m variables can be widely used for drought management. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.