On the Relationship between Teleconnections and Taiwan's Streamflow: Evidence of Climate Regime Shift and Implications for Seasonal Forecasting

Chen, Chia-Jeng; Lee, Tsung-Yu

doi:10.5194/hess-2016-216

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…When comparing the results of the previous studies that analyzed correlations between precipitation in the Korean Peninsula and global climate indices with the results presented in Figure 8, common results were found for some indices although the areas and periods were different. Cho et al [9] used the indices of AO (5) in February, AO (8) in May, and NAO (6) in June for predicting monthly precipitation in the Andong Dam basin. Kim and Kim [24] analyzed correlations between the global climate indices and the monthly precipitation of 60 weather stations in South Korea and found that the indices of AO (5) and NAO (6) were highly correlated with the precipitation in February and June, respectively.…”

Section: Application Frequency Of Climate Indicesmentioning

confidence: 99%

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

Kim¹,

Lee²,

Lee³

et al. 2020

Water

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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.

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Section: Application Frequency Of Climate Indicesmentioning

confidence: 99%