• Atmosphere
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• v.21 no.4
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• pp.481-495
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• 2011

This study is performed to consider the threshold values of heavy rain warning in Korea using 98 surface meteorological station data and 590 Automatic Weather System stations (AWSs), damage data of National Emergency Management Agency for the period of 2005 to 2009. It is in need to arrange new criteria for heavy rain considering concept of rainfall intensity and rainfall damage to reflect the changed characteristics of rainfall according to the climate change. Rainfall values from the most frequent rainfall damage are at 30 mm/1 hr, 60 mm/3 hr, 70 mm/6 hr, and 110 mm/12 hr, respectively. The cumulative probability of damage occurrences of one in two due to heavy rain shows up at 20 mm/1 hr, 50 mm/3 hr, 80 mm/6 hr, and 110 mm/12 hr, respectively. When the relationship between threshold values of heavy rain warning and the possibility of rainfall damage is investigated, rainfall values for high connectivity between heavy rain warning criteria and the possibility of rainfall damage appear at 30 mm/1 hr, 50 mm/3 hr, 80 mm/6 hr, and 100 m/12 hr, respectively. It is proper to adopt the daily maximum precipitation intensity of 6 and 12 hours, because 6 hours rainfall might be include the concept of rainfall intensity for very-short-term and short-term unexpectedly happened rainfall and 12 hours rainfall could maintain the connectivity of the previous heavy rain warning system and represent long-term continuously happened rainfall. The optimum combinations of criteria for heavy rain warning of 6 and 12 hours are 80 mm/6 hr or 100 mm/12 hr, and 70 mm/6 hr or 110 mm/12 hr.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.825-838
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• 2014

The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.

• Journal of the Korean Wood Science and Technology
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• v.47 no.3
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• pp.311-323
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• 2019

The number of newly constructed traditional Korean houses, i.e., Hanoks, and light-frame buildings is increasing. However, related research is limited owing to the lack of awareness regarding safety evaluations. Therefore, this study conducted an outdoor exposure test to accurately evaluate wooden constructions. Spruce, pine, and fir (SPF) material was monitored for a year, wherein the SPF material was artificially dried under 18% moisture content, and its physical properties and color differences were measured once a month. Large differences were observed in the material's weight and moisture content, which are indexes sensitive to daily range and rainfall; however, no significant difference was found for other basic properties in the pre and post test results. Herein, $L^*$, $a^*$, and $b^*$ values represent color differences; these values exhibited a general decrease after the test. Such differences were attributed to the loss of lignin in the wood. The color difference value was high between the months of May and July, when the daily range and rainfall significantly fluctuated. Multiple regression analysis was performed on the $a^*$ value (redness indicator), daily range, rainfall, and ultraviolet index. The results indicated that the daily range influenced redness the most. According to the estimated regression equation, the daily range and redness are positively correlated. Based on the results, the types and influence of independent variables on color difference are expected to change as the wood's duration of outdoor exposure and the amount of data obtained both increase.

• Water for future
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• v.13 no.1
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• pp.39-48
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• 1980

The purpose of this study was to set up the accurate way of estimating the frequency of the max. daily rainfall of the Korean rivers. The area selected for study were Han.Naktong, Geum, Yeongsan, and Seomjin River. The following five methods of the rainfallfrequency analysis were applied to twenty four subgrouped regions in the basins; 2 parameter lognormal, 3 parameter lognormal, Type I extremal(Gumbel method), Pearson Type III, and log-Pearson Type III. The regression equations were established between the depth of max. daily rainfall given 6 reccirrence interval(100, 50, 20, 10, 5, 2) and the basin area.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.2
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• pp.63-74
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• 2019

The objective of this study was to evaluate the influence of rainfall observation network on daily dam inflow using artificial neural networks(ANNs). Chungju Dam and Soyangriver Dam were selected for the study watershed. Rainfall and dam inflow data were collected as input data for construction of ANNs models. Five ANNs models, represented by Model 1 (In watershed, point rainfall), Model 2 (All in the Thiessen network, point rainfall), Model 3 (Out of watershed in the Thiessen network, point rainfall), Model 1-T (In watershed, area mean rainfall), Model 2-T (All in the Thiessen network, area mean rainfall), were adopted to evaluate the influence of rainfall observation network. As a result of the study, the models that used all station in the Thiessen network performed better than the models that used station only in the watershed or out of the watershed. The models that used point rainfall data performed better than the models that used area mean rainfall. Model 2 achieved the highest level of performance. The model performance for the ANNs model 2 in Chungju dam resulted in the $R^2$ value of 0.94, NSE of 0.94 $NSE_{ln}$ of 0.88 and PBIAS of -0.04 respectively. The model-2 predictions of Soyangriver Dam with the $R^2$ and NSE values greater than 0.94 were reasonably well agreed with the observations. The results of this study are expected to be used as a reference for rainfall data utilization in forecasting dam inflow using artificial neural networks.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.99-110
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• 2000

In this study we proposed a simple method for generating multi-site daily rainfall based on the 1-order Markov chain and considering the spatial correlation. The occurrence of rainfall is simulated by a simple 1st-order Markov chain and its intensity to be chosen randomly from the observed data. The spatial correlation between sites could be conserved as the rainfall intensity at each site is to be chosen consistently with the target site in time through generation. It is found that the generated daily rainfall data reproduce genera] characteristics of the observed data such as average, standard deviation, average number of wet and dry days, but the clustering level in time is somewhat loosened. Thus, the lag-I correlation coefficient of the generated data gave smaller value than the observed, also the average lengths of wet run and dry run and the wet-to-wet and dry-to-dry probabilities were a bit less than the observed. This drawback seems to be overcome somewhat by choosing a proper site representing overall basin characteristics or by use of more detailed states of rainfall occurrence.

• Magazine of the Korean Society of Agricultural Engineers
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• v.30 no.2
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• pp.95-104
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• 1988

This study refers to the development of a hydrologic model simulating daily inflow and release rates for inigation reservoirs. A daily - based model is needed for adequate operation of an irrigation reservoir sufficing the water demand for paddy fields which is closely related to meteorological conditions. And the objective of this study is to develop a reservoir release rate model and then to calibrata the parameters. The release rates model considers daily water demands , water supply for transplanting, minmum release for maintaining canal flow, and maxirnun and regular flooding depth for determining effective rainfall on paddy fields. Each of the factors in the model was regarded as a lumped pararuter representing the average condition of a whole irrigated area. The water demand was estimated form the potential evapotranspiration by Penman method, the effective rainfall, and the infiltration on paddy fields. The release model was found to be capable of adequately simulating daily reservoir releases based on meteorological data.

• Atmosphere
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• v.33 no.1
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• pp.1-20
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• 2023

Holding the longest observation data from April 1904, Busan is one of the essential points to understand the climate variability of the Korean Peninsula without missing data since implementing the modern weather observation of the South Korea. Busan is featured by coastal areas and affected by various climate factors and fluctuations. This study aims to investigate climate variability and changes in climatic variables, extremes, and several weather indexes. The statistically significant change points in daily mean rainfall intensity and temperature were found in 1964 and 1965. Based on the change point detection, 117 years were divided into two periods for daily mean rainfall intensity and temperature, respectively. In the long-term temperature analysis of Busan, the increasing trend of the daily maximum temperature during the period of 1965~2021 was larger than the daily mean temperature and the daily minimum temperature. Applying Ensemble Empirical Mode Decomposition, daily maximum temperature is largely affected by the decadal variability compared to the daily mean and minimum temperature. In addition, the trend of daily precipitation intensity from 1964~2021 shows a value of about 0.50 mm day^-1, suggesting that the rainfall intensity has increased compared to the preceding period. The results in extremes analysis demonstrate that return values of both extreme temperatures and precipitation show higher values in the latter than in the former period, indicating that the intensity of the current extreme phenomenon increases. For Wet-Bulb Globe Temperature (effective humidity), increasing (decreasing) trend is significant in Busan with the second (third)-largest change among four stations.

• Journal of Korean Society of Forest Science
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• v.99 no.4
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• pp.534-545
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• 2010

To examine 1) rainfall pattern (i.e., type and intensity) regulating surface erosion on hillslopes in postwildfire area and 2) its effect on variation in sediment yield along the gradient of severity wildfire regimes and elapsed years, we surveyed the amount of sediment yield with respect to daily or net-effective rainfall in 9 plots in eastern coastal region, Republic of Korea. Before field investigation, all plots classified into three groups: low-, mixed- and high-severity wildfire regimes (3 plots in each group). We found that, with decreasing wildfire regimes and increasing elapsed years, the rainfall type regulating surface erosion changed from daily rainfall to net-effective rainfall (considering rainfall continuity) and its intensity increased continuously. In general, wildfires can destroy the stabilized forest floors, and thus rainfall interception by vegetation and litter layer should be reduced. Wildfires can also decrease soil pores in forest floors, and thus infiltration rates of soil are reduced. These two processes lead to frequent occurrence of overland flows required to surface erosion, and sediment yields in post-wildfire areas should increase linearly with increasing rainfall events. With the decreasing severity wildfire regimes and the increasing elapsed years, these processes should be stabilized, and therefore their sediment yields also decreased. Our findings on variations in sediment yields caused by the wildfire regimes and the elapsed years suggest understanding of hydrogeomorphic and ecologic diversities in post-wildfire areas, and these should be carefully examined for both watershed management and disaster prevention.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3B
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• pp.277-284
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• 2011

The limitations of existing Markov chain model for reproducing extreme rainfalls are a known problem, and the problems have increased the uncertainties in establishing water resources plans. Especially, it is very difficult to secure reliability of water resources structures because the design rainfall through the existing Markov chain model are significantly underestimated. In this regard, aims of this study were to develop a new daily rainfall simulation model which is able to reproduce both mean and high order moments such as variance and skewness using a piecewise Kernel-Pareto distribution. The proposed methods were applied to summer and fall season rainfall at three stations in Han river watershed in Korea. The proposed Kernel-Pareto distribution based Markov chain model has been shown to perform well at reproducing most of statistics such as mean, standard deviation and skewness while the existing Gamma distribution based Markov chain model generally fails to reproduce high order moments. It was also confirmed that the proposed model can more effectively reproduce low order moments such as mean and median as well as underlying distribution of daily rainfall series by modeling extreme rainfall separately.