Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models

Abstract

In this study, long-term precipitation forecasting models capable of reflecting constantly changing climate characteristics and providing forecasts for up to 12 months in advance were developed using lagged correlations with global and local climate indices. These models were applied to predict monthly precipitation in the Han River basin, South Korea. Based on the lead month of forecast, 10 climate indices with high correlations were selected and combined to construct four-variable multiple regression models for monthly precipitation forecasting. The forecast results for the analytical period (2010–2019) showed that predictability was low for some summer seasons but satisfactory for other seasons and long periods. In the goodness-of-fit test results, the Nash–Sutcliffe efficiency (0.48–0.57) and the ratio of the root mean square error to the standard deviation of the observation (0.66–0.72) were evaluated to be satisfactory while the percent bias (9.4–15.5%) was evaluated to be between very good and good. Due to the nature of the statistical models, however, the predictability is highly likely to be reduced if climate phenomena that are different from the statistical characteristics of the past appear in the forecast targets or predictors. The forecast results were also presented as tercile probability information (below normal, normal, above normal) through a comparison with the observation data of the past 30 years. The results are expected to be utilized as useful forecast information in practice if the predictability for some periods is improved.

Investigation of the Effects of Climate Variability, Anthropogenic Activities, and Climate Change on Streamflow Using Multi-Model Ensembles

“…The HRB is the most densely populated basin in Korea, with more than half of the country's population living in this basin, including Seoul, a metropolitan city in South Korea with a population of 25 million. The HRB has continental, mountainous, and humid climatic conditions, with an average elevation of about 406 m, and the mean annual precipitation and temperature are 1300 mm and 12 • C, respectively [28].…”

“…The HRB is the most densely populated basin in Korea, with more than half of the country's population living in this basin, including Seoul, a metropolitan city in South Korea with a population of 25 million. The HRB has continental, mountainous, and humid climatic conditions, with an average elevation of about 406 m, and the mean annual precipitation and temperature are 1300 mm and 12 °C, respectively [28]. The meteorological data, consisting of daily precipitation and temperature (max and min) for the period from 1966 to 2014, were collected from the official website of the Korea Meteorological Administration (http://www.data.kma.go.kr, accessed on 15 July 2021).…”

Prediksi Curah Hujan Bulanan Menggunakan Multi Prediktor Di Zom 313 Sulawesi Tenggara

“…Dalam melakukan pemodelan curah hujan bulanan yang secara konstan mengunakan karateristik unsur lokal klimat dan global dapat disusun dengan memperhatikan korelasi tertunda (lag) [7]. Hasil dari korelasi antara beberapa prediktor dan curah hujan bulanan menghasilkan nilai yang bervariatif yang menunjukkan baik dan tidaknya model prediksi [8].…”

“…Hasil dari korelasi antara beberapa prediktor dan curah hujan bulanan menghasilkan nilai yang bervariatif yang menunjukkan baik dan tidaknya model prediksi [8]. dengan menggunakan model statistikal diharapkan mampu memprediksi dan mengurangi fenomena klimatologis berdasarkan karakteristik statistik untuk memprediksi target dan prediktor [7].…”

Catchment‐scale skill assessment of seasonal precipitation forecasts across South Korea

“…The intensity and frequency of extreme weather events, such as droughts, is expected to increase in a warmer climate (Cook et al, 2018;Mishra & Singh, 2010;Spinoni et al, 2018). These events can cause extensive damages (Raikes et al, 2019) to a wide range of socio-economic sectors (Naumann et al, 2015) such as agriculture (Bodner et al, 2015;Madadgar et al, 2017), hydropower production (Turner & Voisin, 2022), water supply (Baltas, 2011;Kim et al, 2020aKim et al, , 2020b) and water quality (Delpla et al, 2009;Whitehead et al, 2009). In South Korea, an unusually longer drought lasting from 2013 to 2015 (Kwon et al, 2016) caused severe damages including water restriction in the western part of the country, and nation-wide crop failure.…”