The aim of this study was to evaluate hydrologic impacts of potential climate and land use changes in a mountainous watershed in South Korea. The climatic data predicted by MIROC3.2 HiRes GCM A1B for three time periods

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Using large-scale analyses, the effect of tropical cyclone–trough interaction on tropical cyclone (TC) intensity change is readdressed by studying the evolution of upper-level eddy flux convergence (EFC) of angular momentum and vertical wind shear for two TCs in the western North Pacific [Typhoons Prapiroon (2000) and Olga (1999)]. Major findings include the following: 1) In spite of decreasing SST, the cyclonic inflow associated with a midlatitude trough should have played an important role in Prapiroon’s intensification to its maximum intensity and the maintenance after recurvature through an increase in EFC. The accompanied large vertical wind shear is concentrated in a shallow layer in the upper troposphere. 2) Although Olga also recurved downstream of a midlatitude trough, its development and maintenance were not strongly influenced by the trough. A TC could maintain itself in an environment with or without upper-level eddy momentum forcing. 3) Both TCs started to decay over cold SST in a large EFC and vertical wind shear environment imposed by the trough. 4) Uncertainty of input adds difficulties in quantitative TC intensity forecasting.

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[1] This paper presents a statistical model to forecast seasonal tropical cyclone activity. In order to give a comprehensive view of seasonal tropical cyclone activity, we include not only the number of total tropical cyclones but also the number of typhoons and the NTA (Normalized Typhoon Activity) index as the predictands. The model is based on a multiple linear regression model in which the final predictors are selected with respect to minimizing the prediction error rather than simply fitting with past data. The model is expanded into ensemble prediction by considering the uncertainty of the single and deterministic forecast. The probability forecast based on the ensemble model shows reasonably good skill with respect to reliability and relative operating characteristics.

Understanding catchment response to rainfall events is important for accurate runoff estimation in many water-related applications, including water resources management. This study introduced a hybrid model, the Tank-least squared support vector machine (LSSVM), that incorporated intermediate state variables from a conceptual tank model within the least squared support vector machine (LSSVM) framework in order to describe aspects of the rainfall-runoff (RR) process. The efficacy of the Tank-LSSVM model was demonstrated with hydro-meteorological data measured in the Yongdam Catchment between 2007 and 2016, South Korea. We first explored the role of satellite soil moisture (SM) data (i.e., European Space Agency (ESA) CCI) in the rainfall-runoff modeling. The results indicated that the SM states inferred from the ESA CCISWI provided an effective means of describing the temporal dynamics of SM. Further, the Tank-LSSVM model’s ability to simulate daily runoff was assessed by using goodness of fit measures (i.e., root mean square error, Nash Sutcliffe coefficient (NSE), and coefficient of determination). The Tank-LSSVM models’ NSE were all classified as “very good” based on their performance during the training and testing periods. Compared to individual LSSVM and Tank models, improved daily runoff simulations were seen in the proposed Tank-LSSVM model. In particular, low flow simulations demonstrated the improvement of the Tank-LSSVM model compared to the conventional tank model.

The distributed hydrologic model has been considerably improved due to rapid development of computer hardware technology as well as the increased accessibility and the applicability of hydro-geologic information using GIS. It has been acknowledged that physically-based distributed hydrologic model require significant amounts of data for their calibration, so its application at ungauged catchments is very limited. In this regard, this study was intended to develop a distributed hydrologic model (S-RAT) that is mainly based on conceptually grid-based water balance model. The proposed model shows advantages as a new distributed rainfall-runoff model in terms of their simplicity and model performance. Another advantage of the proposed model is to effectively assess spatio-temporal variation for the entire runoff process. In addition, S-RAT does not rely on any commercial GIS pre-processing tools because a built-in GIS preprocessing module was developed and included in the model. Through the application to the two pilot basins, it was found that S-RAT model has temporal and spatial transferability of parameters and also S-RAT model can be effectively used as a radar data-driven rainfall-runoff model.