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Abstract:Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall-runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC) prior to a rainfall event. Soil moisture is one of the most important variables in rainfall-runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN) method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API 5 ), ground-based soil moisture (SSM insitu ), remotely sensed surface soil moisture (SSM), and soil water index (SWI) provided by the advanced scatterometer (ASCAT). To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall-runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI) and ground based soil moisture data (SSM insitu ). In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API 5 by 60% and 66%, respectively. Moreover, the proposed IWC framework with remotely sensed soil moisture indicates an improved Nash-Sutcliffe efficiency from 0.48 to 0.74 for the four catchments in the Korean Peninsula. It can be concluded that the SCS-CN method extended with remotely sensed soil moisture for reducing uncertainty in the runoff prediction and the proxy variables obtained from the soil moisture data provided by the ASCAT can be useful in enhancing the accuracy of runoff prediction over a range of spatial scales.

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In this study, Frequency Domain Reflectometry (FDR) and COSMIC-ray soil moisture (SM) stations were installed at Sungkyunkwan University in Suwon, South Korea. To provide reliable information about SM, soil property test, time series analysis of measured soil moisture, and comparison of measured SM with satellite-based SM product are conducted. In 2014, six FDR stations were set up for obtaining SM. Each of the stations had four FDR sensors with soil depth from 5 cm to 40 cm at 5~10 cm different intervals. The result showed that study region had heterogeneous soil layer properties such as sand and loamy sand. The measured SM data showed strong coupling with precipitation. Furthermore, they had a high correlation coefficient and a low root mean square deviation (RMSD) as compared to the satellite-based SM products. After verifying the accuracy of the data in 2014, four FDR stations and one COSMIC-ray station were additionally installed to establish the Soil Moisture site with FDR and COSMIC-ray, called SM-FC. COSMIC-ray-based SM had a high correlation coefficient of 0.95 compared with mean SM of FDR stations. From these results, the SM-FC will give a valuable insight for researchers into investigate satellite-and model-based SM validation study in South Korea.

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