Evaluating rainfall estimates derived from soil moisture using soil hydraulic properties over the Korean Peninsula

Rainfall is an essential element within hydrological systems, serving as the primary source of moisture on the land surface. Recent climate change-induced extreme weather events have increased the spatiotemporal variability of rainfall, highlighting the demand for diverse rainfall monitoring methods. This study used the SM2RAIN algorithm with soil physical properties (infiltration rate and soil moisture nonlinearity coefficient) to generate a rainfall dataset for the Korean Peninsula. By incorporating soil physical properties into SM2RAIN, the SM2RAIN-Soil Moisture Active and Passive (SM2RAIN-SMAP) dataset was created from SMAP L4 soil moisture. The accuracy of SM2RAIN-SMAP was compared with the Global Precipitation Mission Integrated Multi-satellitE Retrievals (GPM-IMERG) and SM2RAIN-Advanced SCATterometer (SM2RAIN-ASCAT). Rainfall estimated using soil physical property parameters showed good agreement with point-scale rainfall observations with a Spearman rank correlation (Rs) of 0.7. A comparison between SM2RAIN-SMAP and SM2RAIN-ASCAT revealed that the highest correlation occurred in spring (0.77), with an average correlation of 0.65 across all seasons. An analysis of rainfall estimation performance by the land cover type revealed that SM2RAIN-SMAP performed better in croplands, whereas SM2RAIN-ASCAT showed superior performance in forests. The performance difference was attributable to the influence of vegetation interception effects and systematic errors in soil moisture products, which vary depending on sensors. The proposed approach used relatively simple calculations to improve the accuracy of rainfall monitoring and has the potential to provide diverse and reliable rainfall data for hydrometeorological research and disaster management in monsoon regions. This physics-based approach offers an alternative to the traditional empirical calibration methods used in SM2RAIN.