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Abstract:Previous ensemble streamflow prediction (ESP) studies in Korea reported that modelling error significantly affects the accuracy of the ESP probabilistic winter and spring (i.e. dry season) forecasts, and thus suggested that improving the existing rainfall-runoff model, TANK, would be critical to obtaining more accurate probabilistic forecasts with ESP. This study used two types of artificial neural network (ANN), namely the single neural network (SNN) and the ensemble neural network (ENN), to provide better rainfall-runoff simulation capability than TANK, which has been used with the ESP system for forecasting monthly inflows to the Daecheong multipurpose dam in Korea. Using the bagging method, the ENN combines the outputs of member networks so that it can control the generalization error better than an SNN. This study compares the two ANN models with TANK with respect to the relative bias and the root-mean-square error. The overall results showed that the ENN performed the best among the three rainfall-runoff models. The ENN also considerably improved the probabilistic forecasting accuracy, measured in terms of average hit score, half-Brier score and hit rate, of the present ESP system that used TANK. Therefore, this study concludes that the ENN would be more effective for ESP rainfall-runoff modelling than TANK or an SNN.

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The overall purpose of this study was to examine individuals' experiences of running an ultramarathon. Following pilot work data were collected with six people who entered the 2012 Canadian Death Race. Participants were interviewed before the race, took photographs and made video recordings during the race, wrote a summary of their experience, and attended a focus group after the race. The research team also interviewed participants during the race. Before the race participants had mixed emotions. During the race they experienced numerous stressors (i.e., cramping and injuries, gastrointestinal problems, and thoughts about quitting). They used coping strategies such as making small goals, engaging in a mental/physical battle, monitoring pace, nutrition, and hydration, and social support. After the race, nonfinishers experienced dejection or acceptance whereas finishers commented on the race as a major life experience. These findings provide some insights into factors involved in attempting to complete ultramarathons and offer some implications for applied sport psychology.

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This study conducted a broad review of the pre-and post-processor methods for ensemble streamflow prediction using a Korean case study. Categorical forecasts offered by the Korea Meteorogical Administration and deterministic forecasts of a regional climate model called Seoul National University Regional Climate Model(SNURCM) were selected as climate forecast information for the pre-processors and incorporated into Ensemble Streamflow Prediction(ESP) runs with the TANK hydrologic model. The post-processors were then used to minimize a possible error propagated through the streamflow generation. The application results show that use of the post-processor more effectively reduced the uncertainty of the no-processor ESP than use of the pre-processor, especially in dry season.

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