Toward a High-resolution and Long-term Drought Monitoring System in South Korea by Using a Community-based Land Surface Model

Abstract

Drought in South Korea significantly affects agricultural production and recreational activities in this fast-changing economy. This paper describes a long-term (1980-2020) gridded-based (0.01o) surface hydrologic dataset in South Korea at an hourly time step based on simulations from the Korean Land Data Assimilation System (KLDAS). The Modern-Era Retrospective analysis for Research and Application, version 2 (MERRA-2) and the recently fully established Automated Synoptic Observing System (ASOS) of Korean Meteorological Administration (KMA) weather forcing datasets were used to drive the land model. The model was validated against observed soil moisture (10 sites) and evapotranspiration (6 sites) over four major river basins in S. Korea. The error statistics indicated that the ASOS-based hydrologic outputs have been improved compared to those from the baseline MERRA2 simulations. Historical drought events were reconstructed and analyzed using various drought indices such as meteorological, agricultural, and hydrological drought perspectives. In particular, the spatially distributed severity and duration of major droughts in S. Korea were compared to identify new characteristics, which might be induced by the recent climate change. The historical drought stood out as the most catastrophic event considering three factors (severity, duration, and areal extent) at specific years. This dataset is expected to serve as a climatic and hydrologic indicator which contributes to water resources management, and to mitigating agricultural drought, especially in the context of the long-term understanding of drought impacts on crop yield in South Korea. An analysis of agricultural reservoir and dam storage levels based on precipitation deficits is expected to understand the propagation of different types of droughts.