This study evaluated 20 general circulation models (GCMs) of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), which provide the prediction results for the period of 2006 to 2014, the period from which the observation data (the Global Precipitation Climatology Project (GPCP) data) are available. Both the GCM predictions of precipitation and the GPCP data were compared for three data structures-the global, zonal, and grid mean-with conventional statistics like the root mean square error (RMSE) and the pattern correlation coefficient of the cyclostationary empirical orthogonal functions (CSEOFs). As a result, it was possible to select a GCM which showed the best performance among the 20 GCMs considered in this study. Overall, the NorSM1-M model was found to be the most similar to the GPCP data. Additionally, the IPSL-CM5A-LR, BCC-CSM, and GFDL-CMS models were also found to be quite similar to the GPCP data.
A rainfall event, simplified by a rectangular pulse, is defined by three components: the rainfall duration, the total rainfall depth, and mean rainfall intensity. However, as the mean rainfall intensity can be calculated by the total rainfall depth divided by the rainfall duration, any two components can fully define the rainfall event (i.e., one component must be redundant). The frequency analysis of a rainfall event also considers just two components selected rather arbitrarily out of these three components. However, this study argues that the two components should be selected properly or the result of frequency analysis can be significantly biased. This study fully discusses this selection problem with the annual maximum rainfall events from Seoul, Korea. In fact, this issue is closely related with the multicollinearity in the multivariate regression analysis, which indicates that as interdependency among variables grows the variance of the regression coefficient also increases to result in the low quality of resulting estimate. The findings of this study are summarized as follows: (1) The results of frequency analysis are totally different according to the selected two variables out of three. (2) Among three results, the result considering the total rainfall depth and the mean rainfall intensity is found to be the most reasonable. (3) This result is fully supported by the multicollinearity issue among the correlated variables. The rainfall duration should be excluded in the frequency analysis of a rainfall event as its variance inflation factor is very high.
핵심용어 : 레인가든, 유출저감, 저영향개발, 침투성능
AbstractThis study conducted a field experiment to estimate the characteristics of the rain garden installed at the site near Haman, also proposed a one-dimensional model to simulate the infiltration and runoff from the rain garden. This model was used to evaluate the rain garden using the rainfall data after the installation and during the last 10 years. Also, this model was applied to the annual maximum rainfall events to quantify the size of the impervious area that the rain garden can offset the adverse effect. The results are summarized below. (1) Hydraulic conductivity of the rain garden was estimated to be about 0.0188 m/hr by the variable-stage experiment. Also, the simulation experiment using the last 10 years rainfall data over the entire roof area showed that the infiltration amount is about 90.38% out of the total rainfall. (2) Infiltration simulation of the annual maximum rainfall events during last 10 years showed that the rain garden can offset the impervious area with its size about 30 times of the rain garden surface.
Rainwater harvesting is generally assumed to collect rainwater from the roof or ground. However, this study shows that this structural limitation of rainwater harvesting can be overcome by employing a building wall. The rainfall on a building wall is called wind-driven rain (WDR), which is the target for the rainwater harvesting addressed in this study. To prove the possibility of WDR harvesting, this study prepared three different gauges to collect the rainwater from a building wall. These gauges are like miniature buildings used to collect the WDR on the building wall inside a storage tank at the bottom. The WDR harvesting gauges were located on the rooftop of the Engineering Building, Korea University, and a total of 15 rainfall events were observed during the rainy season in Korea from June to September 2020. Our analysis of the collected data confirms the significant role of the building wall in rainwater harvesting. For a building height of 0.5 m, the rainwater additionally harvested from the wall was about 40% that from the roof, which became about 70% for the height of 1.0 m and about 90% for the height of 1.5 m. In addition, Cho et al. (2020)’s empirical equation for estimating the WDR is found to be useful for estimating the amount of rainwater harvested from the building wall. The correlation coefficients between the measurements and estimates were estimated to be high as 0.94, 0.92 and 0.91 for building heights of 0.5 m, 1.0 m, and 1.5 m, respectively.
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