• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.740-746
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• 2010

Rainwater has been used in many countries as a way of minimizing water availability problems. Rainwater harvesting system (RHS) has been successfully implemented as alternative water supply sources even in Korea. Although RHS is an effective alternative to water supply, its efficiency is often heavily influenced by temporal distribution of rainfall. Since natural precipitation is a random process and has probabilistic characteristics, it will be more appropriate to describe these probabilistic features of rainfall and its relationship with design storage capacity as well as supply deficit of RHS. This study presents the methodology to establish the relationships between storage capacities and deficit rates using probability distributions. In this study, the real three-story building was considered and nine scenaries were developed because the daily water usage pattern of the study one was not identified. GEV, Gumbel and the generalized logistic distribution ware selected according to the results of Kolmogorov-Smirnov test and Chi-Squared test. As a result, a set of curves describing the relationships under different exceedance probabilities were generated as references to RHS storage design. In case of the study building, the deficit rate becomes larger as return period increases and will not increase any more if the storage capacity becomes the appropriate quantity. The uncertainties between design storage and the deficit can be more understood through this study on the probabilistic relationships between storage capacities and deficit rates.

• Journal of Korean Society on Water Environment
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• v.22 no.6
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• pp.1068-1074
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• 2006

For receiving water quality protection a control systems of urban drainage for CSOs reduction is needed. Examples in combined sewer systems include downstream storage facilities that detain runoff during periods of high flow and allow the detained water to be conveyed by an interceptor sewer to a centralized treatment plant during periods of low flow. The design of such facilities as storm-water detention storage is highly dependant on the temporal variability of storage capacity available as well as the infiltration capacity of soil and recovery of depression storage. For the continuous long-term analysis of urban drainage system this study used analytical probabilistic model based on derived probability distribution theory. As an alternative to the modeling of urban drainage system for planning or screening level analysis of runoff control alternatives, this model has evolved that offers much ease and flexibility in terms of computation while considering long-term meteorology. This study presented rainfall and runoff characteristics of the subject area using analytical probabilistic model. Runoff characteristics manifested the unique characteristics of the subject area with the infiltration capacity of soil and recovery of depression storage and was examined appropriately by sensitivity analysis. This study presented the average annual CSOs, number of CSOs and event mean CSOs for the decision of storage volume.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.49-66
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• 2024

Extreme rainfall will become intense due to climate change, increasing inundation risk to agricultural land. Hydrological and hydraulic simulations for the entire watershed were conducted to analyze the impact of climate change. Rainfall data was collected based on past weather observation and SSP (Shared Socio-economic Pathway)5-8.5 climate change scenarios. Simulation for flood volume, reservoir operation, river level, and inundation of agricultural land was conducted through K-HAS (KRC Hydraulics & Hydrology Analysis System) and HEC-RAS (Hydrologic Engineering Center - River Analysis System). Various scenarios were selected, encompassing different periods of rainfall data, including the observed period (1973-2022), near-term future (2021-2050), mid-term future (2051-2080), and long-term future (2081-2100), in addition to probabilistic precipitation events with return periods of 20 years and 100 years. The inundation area of the Aho-Buin district was visualized through GIS (Geographic Information System) based on the results of the flooding analysis. The probabilistic precipitation of climate change scenarios was calculated higher than that of past observations, which affected the increase in reservoir inflow, river level, inundation time, and inundation area. The inundation area and inundation time were higher in the 100-year frequency. Inundation risk was high in the order of long-term future, near-term future, mid-term future, and observed period. It was also shown that the Aho and Buin districts were vulnerable to inundation. These results are expected to be used as fundamental data for assessing the risk of flooding for agricultural land and downstream watersheds under climate change, guiding drainage improvement projects, and making flood risk maps.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Journal of Korean Society of Disaster and Security
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• v.8 no.1
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• pp.15-19
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• 2015

In rainfall-runoff models employed in hydrological applications, runoff amount is estimated through temporal delay of effective precipitation based on a linear system. Its amount is resulted from the linearized ratio by analyzing the convolution multiplier. Furthermore, in case of kernel density estimate (KDE) used in probabilistic analysis, the definition of the kernel comes from the convolution multiplier. Individual data values are smoothed through the kernel to derive KDE. In the current study, the roles of the convolution multiplier for KDE and rainfall-runoff models were revisited and their similarity and dissimilarity were investigated to discover the mathematical applicability of the convolution multiplier.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.713-723
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• 2019

Due to the climate change and various rainfall pattern, it is difficult to estimate a rainfall criterion which cause inundation for urban drainage districts. It is necessary to examine the result of inundation analysis by considering the detailed topography of the watershed, drainage system, and various rainfall scenarios. In this study, various rainfall scenarios were considered with the probabilistic rainfall and Huff's time distribution method in order to identify the rainfall characteristics affecting the inundation of the Hyoja drainage basin. Flood analysis was performed with SWMM and two-dimensional inundation analysis model and the parameters of SWMM were optimized with flood trace map and GA (Genetic Algorithm). By linking SWMM and two-dimensional flood analysis model, the fitness ratio between the existing flood trace and simulated inundation map turned out to be 73.6 %. The occurrence of inundation according to each rainfall scenario was identified, and the rainfall criterion could be estimated through the logistic regression method. By reflecting the results of one/two dimensional flood analysis, and AWS/ASOS data during 2010~2018, the rainfall criteria for inundation occurrence were estimated as 72.04 mm, 146.83 mm, 203.06 mm in 1, 2 and 3 hr of rainfall duration repectively. The rainfall criterion could be re-estimated through input of continuously observed rainfall data. The methodology presented in this study is expected to provide a quantitative rainfall criterion for urban drainage area, and the basic data for flood warning and evacuation plan.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.1-13
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• 2019

In this study, we propose rainfall frequency criteria for the development of early-warning system based on the evaluation of the highway debris flow that includes the contents of the rainfall recurrence cycle. The rainfall criterion was recommended based on the results of previous researches and the recommended rainfall criterion was 1 hour, 6 hours, and 3 days. At this time, the study subjects were located in Gangwon area and the probability rainfall of 8 stations in Gangwon area was collected. Also, the probabilistic distribution of the 1 hour, 6 hour, and 3 day rainfall criteria to be used for the early warning for the highway debris flow in Kangwon area was estimated through the probability analysis. In addition, we analyzed the correlation between 3 types of rainfall criteria selected from the rainfall data and the actual destructive damages of debris flow at 12 points in 7 lines of Gangwon highways. At this time, the rainfall criterion on the probability distribution was divided into an average value and a lower limit value. As a result of the review, it was found that the case of using the lower limit value of the rainfall according to the recurrence intervalwell simulates the situation of actual debris flow hazards.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.327-338
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• 2022

This study aims to empirically and statistically predict soil depths across areas affected by landslides. Using soil depth measurements from a landslide area in Korea, two sets of soil depths are calculated using a Z-model based on terrain elevation and a probabilistic statistical model. Both sets of calculation results are applied to derive landslide risk using the saturated infiltration depth ratio of the soil layer. This facilitates analysis of the infiltration of rainfall into soil layers for a rainfall event. In comparison with the probabilistic statistical model, the Z-model yields soil depths that are closer to measured values in the study area. Landslide risk assessment in the study area based on soil depth predictions from the two models shows that the percentage of first-grade landslide risk assessed using soil depths from the probabilistic statistical model is 2.5 times that calculated using soil depths from the Z-model. This shows that soil depths directly affect landslide risk assessment; therefore, the acquisition and application of local soil depth data are crucial to landslide risk analysis.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.2
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• pp.127-136
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• 2011

In this study, annual maximum storm events are proposed to determined by the return periods considering total rainfall and rainfall intensity together. The rainfall series at Seoul since 1961 are examined and the results are as follows. First, the bivariate exponential distribution is used to determine annual maximum storm events. The parameter estimated annually provides more suitable results than the parameter estimated by whole periods. The chosen annual maximum storm events show these properties. The events with the biggest total rainfall tend to be selected in the wet years and the events with the biggest rainfall intensity in the wet years. These results satisfy the concept of critical storm events which produces the most severe runoff according to soil wetness. The average characteristics of the annual maximum storm events said average rainfall intensity 32.7 mm/hr in 1 hr storm duration(total rainfall 32.7 mm), average rainfall intensity 9.7 mm/hr in 24 hr storm duration(total rainfall 231.6 mm) and average rainfall intensity 7.4 mm/hr in 48 hr storm duration(total rainfall 355.0 mm).

• Journal of Korean Society on Water Environment
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• v.29 no.3
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• pp.354-364
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• 2013

The performance of the stormwater wetlands can be significantly influenced by antecedent stormwater in storage at the commencement of a stormevent. As inflows are intermittent and stochastic in nature, the evaluation of the treatment efficiency of a stormwater wetland should be considered by runoff capture and water treatment characteristics during interevent periods. In this study, analytical probabilistic model is applied to identity runoff capture rate and treatment efficiency of the stormwater wetland. To achieve this, continuous rainfall data recorded in Busan for 31 years has been analyzed to derive the runoff capture rate, and 1st order kinetic decay constants ($k_V$, 1/d) are calculated from regression analysis to identify pollutants removal during interevent periods. The results show that about 60.9% of annual average runoff is captured through the stormwater wetland. The annual average treatment efficiencies of SS, BOD, COD, TN and TP is about 11.4, 8.9, 9.8, 4.3 and 9.6%, respectively. The analytical model has been compared with the numerical model and it shows that analytical model is valid. Performance evaluation methods developed in this study has the advantages of considering characteristics of rainfall-runoff, facility type and pollutant removal.