Optimizing Parameters for the Downscaling of Daily Precipitation in Normal and Drought Periods in South Korea

Abstract

One important factor that affects the performance of statistical downscaling methods is the selection of appropriate parameters. However, no research on the optimization of downscaling parameters has been conducted in South Korea to date, and existing parameter selection methods are dependent on studies conducted in other regions. Moreover, several large-scale predictors have been used to predict abnormal phenomena such as droughts, but in the field of downscaling, parameter optimization methods that are suitable for drought conditions have not yet been developed. In this study, by using the K-nearest analog methodology, suitable daily precipitation downscaling parameters for normal and drought periods were derived. The predictor variables, predictor domain, analog date size, time dependence parameters, and parameter sensitivity values that are representative of South Korea were presented quantitatively. The predictor variables, predictor domain, and analog date size were sensitive to the downscaling performance in that order, but the time dependency did not affect the downscaling process. Regarding calibration, the downscaling results obtained based on the drought parameters returned smaller root mean square errors of 1.3–28.4% at approximately 70% of the stations compared to those of the results derived based on normal parameters, confirming that drought parameter-based downscaling methods are reasonable. However, as a result of the validation process, the drought parameter stability was lower than the normal parameter stability. In the future, further studies are needed to improve the stability of drought parameters.

Downscaling Daily Reference Evapotranspiration Using a Super-Resolution Convolutional Transposed Network

“…Understanding the spatiotemporal patterns and hydrological influences of climate change is essential for ensuring the prudent planning and effective management of water resources [1,2]. To tackle the challenge of scale discrepancy in spatial resolutions within climate data sets [3], downscaling coarse-resolution climate data sets to finer resolutions [4][5][6][7][8][9] is an approach designed to enhance the spatial resolution of data sets originating from general circulation models and numerical weather models.…”

Downscaling epidemiological time series data for improving forecasting accuracy: An algorithmic approach

“…This method relies on dynamical or statistical approaches and is extensively utilized in the field of meteorology, climatology, and remote sensing [1,2]. Significant exploration of the downscaling methods has been done in the field of geology and climatology to enhance the out of existing models like the General Circulation Model (GCM) [3][4][5][6][7][8], Regional Climate Model (RCM) [9], Integrated Grid Modeling System (IGMS) [10], System Advisor Model (SAM) [10] and to make it usable for the forecast of geographically significant region and time. Several methods has been used to downscale these data such as BCC/RCG-Weather Generators (BCC/RCG-WG) [11][12][13], and Statistics Downscaling Model (SDSM) [11,[14][15][16][17][18][19], Bayesian Model Averaging (BMA) [20].…”