Kyung Soo Jun

Development of spatial water resources vulnerability index considering climate change impacts

This study developed a new framework to quantify spatial vulnerability for sustainable water resources management. Four hydrologic vulnerability indices--potential flood damage (PFDC), potential drought damage (PDDC), potential water quality deterioration (PWQDC), and watershed evaluation index (WEIC)--were modified to quantify flood damage, drought damage, water quality deterioration, and overall watershed risk considering the impact of climate change, respectively. The concept of sustainability in the Driver-Pressure-State-Impact-Response (DPSIR) framework was applied in selecting all appropriate indicators (criteria) of climate change impacts. In the examination of climate change, future meteorological data was obtained using CGCM3 (Canadian Global Coupled Model) and SDSM (Statistical Downscaling Model), and future stream run-off and water quality were simulated using HSPF (Hydrological Simulation Program - Fortran). The four modified indices were then calculated using TOPSIS, a multi-attribute method of decision analysis. As a result, the ranking obtained can be changed in consideration of climate change impacts. This study represents a new attempt to quantify hydrologic vulnerability in a manner that takes into account both climate change impacts and the concept of sustainability.

Robust spatial flood vulnerability assessment for Han River using fuzzy TOPSIS with α-cut level set

This study aims to improve the general flood vulnerability approach using fuzzy TOPSIS based on @a-cut level sets which can reduce the uncertainty inherent in even fuzzy multi-criteria decision making process. Since fuzzy TOPSIS leads to a crisp closeness for each alternative, it is frequently argued that fuzzy weights and fuzzy ratings should be in fuzzy relative closeness. Therefore, this study used a modified @a-cut level set based fuzzy TOPSIS to develop a spatial flood vulnerability approach for Han River in Korea, considering various uncertainties in weights derivation and crisp data aggregation. Two results from fuzzy TOPSIS and modified fuzzy TOPSIS were compared. Some regions which showed no or small ranking changes have their centro-symmetric distributions, while other regions whose rankings varied dynamically, have biased (anti-symmetric) distributions. It can be concluded that @a-cut level set based fuzzy TOPSIS produce more robust prioritization since more uncertainties can be considered. This method can be applied to robust spatial vulnerability or decision making in water resources management.

Probabilistic prediction of reservoir storage considering the uncertainty of dam inflow

The well-timed water management is required to reduce drought damages. It is also necessary to induce residents in drought-affected areas to save water. Information on future storage is important in managing water resources based on the current and future states of drought. This study employed a kernel function to develop a probabilistic model for predicting dam storage considering inflow uncertainty. This study also investigated the application of the proposed probabilistic model during the extreme drought. This model can predict a probability of temporal variation of storage. Moreover, the model can be used to make a long-term plan since it can identify a temporal change of storage and estimate a required reserving volume of water to achieve the target storage.

Real-time Flood Stage Forecasting of Tributary Junctions in Namhan River

The backwater effect at a tributary junction increases the risk of flood damage such as inundation and levee overflow. In particular, the rapid increase in water level may cause injury to persons. The purpose of this research is the development of the real-time flood forecasting technique as a part of the non-structural flood damage reduction measures. To this end, the factors causing a water level rising at a junction were examined, and the empirical formula for predicting flood level at a junction was developed using the calculated discharge and water level data from the well-constructed hydraulic model. The water level predictions show that average absolute error is about 0.2~0.3m with the maximum error of 1.0m and peak time can be captured prior to 0~5 hr. From the results of this study, the real-time flood forecasting system of a tributary junction can be easily constructed, and this system is expected to be utilized for reduction of flood inundation damage.

Robust Parameter Set Selection for a Hydrodynamic Model Based on Multi-Site Calibration Using Multi-Objective Optimization and Minimax Regret Approach

A robust parameter set (ROPS) selection method for a hydrodynamic flow model was proposed based on the multi-site calibration by combining multi-objective optimization with the minimax regret approach (MRA). The multi-site calibration was defined by a multi-objective optimization problem for which individual objective functions were used to measure errors at each site. In the hydrodynamic model, coefficients of power functions that show the changing relationships between Manning’s roughness and discharge in each sub-reach were optimized by minimizing the residuals of multiple sites. Different combinations of weights were assigned to sites in the application of an aggregation approach to solve the multi-objective function, and the corresponding Pareto optimal parameter sets were assumed as the ROPS candidates. All performance measures to individual Pareto optimal parameter sets were calculated and the ROPS was determined using MRA. The set which has the lowest maximum regret obtained by averaging the results from calibration and validation was determined as the only ROPS. It was found that the estimated variable roughness and the corresponding computed water levels varied considerably depending on the weights assigned to sites. Using the proposed method, the task to assign proper weights on multiple sites can be easily achieved for multi-site calibration problems. This study provides a multi-criteria decision making method to choose a ROPS that has the lowest potential regret among various alternatives for hydrologic and hydraulic models.

MCDM Approach for Identifying Urban Flood Vulnerability under Social Environment and Climate Change

ABSTRACT Lee, G.; Choi, J., and Jun, K.S., 2017. MCDM approach for identifying urban flood vulnerability under social environment and climate change. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 209–213. Coconut Creek (Florida), ISSN 0749-0208. This study examines a method for conducting flood vulnerability assessments based on urban environmental and social characteristics. In other words, the purpose of this study is to analyze the characterization of flood damage to a city and determine assessment factors, thereby establishing a vulnerability assessment procedure. In order to consider urban environments in the future, the trend for socio-economic changes and changes in rainfall patterns due to climate change are included. The established assessment procedure consists of the following three steps: criteria and weights determination using Delphi and Entropy methods, data acquisition and construction of database, and vulnerability quantification and priority decision making. Future precipitation is estimated from the RCP scenario, and the indexes that indicate social and economic changes in a region, such as population change and urbanization, are calculated using a scenario based on the data from Statistics Korea. In this study, the assessment method was applied to the Dorimcheon basin in Seoul, Korea, where inland flooding caused substantial damage in 2011. It was found that the influence of increased precipitation (due to climate change) on flooding risk was less than that of the increase in vulnerability factors caused by social and economic development. The approach used in this study can suggest preferred regions or objects for urban flood management by considering characteristics of each region and recognizing intrinsic vulnerability factors in advance.

Evaluation of Probabilistic Storage Prediction Model (PSPM) for Optimal Reservoir Operation during a Drought

ABSTRACT Kwon, M.; Jun, K.S., and Kim, T.-W., 2017. Evaluation of probabilistic storage prediction model (PSPM) for optimal reservoir operation during a drought. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 314–318. Coconut Creek (Florida), ISSN 0749-0208. The Probabilistic Storage Prediction Model (PSPM) is a model that probabilistically predicts the future reservoir storages considering the uncertainty of natural inflow. This study simulated reservoir operation using the PSPM and evaluated the usefulness of the PSPM compared to the actual reservoir operation during the recent severe drought of the Chungju Dam basin in South Korea. The initial storage was set to observed storage at the end of January 2015, and the reservoir operation for achieving target storage at the end of June was simulated for various achievement probabilities. The differences between the simulated storages and the actual storage at the end of June 2015 was as large as 14–20% of effective storage capacity of the reservoir. The maximum supply reduction for achieving target storage simulated for the achievement probability of 0.8 was less than actual maximum supply reduction. This is possible by storing more water in advance to prepare for more severe drought. PSPM can offer valuable information as a decision-making tool, which will enable reservoir managers to secure water in advance, and thus mitigate severe drought damages.

Estimation of probable flood discharge in tidal river using unsteady flow model

ABSTRACT Lee, G., Kim, J. and Jun, K.S., 2013. Estimation of probable flood discharge in tidal river using unsteady flow model A method for estimating the probable flood discharge using unsteady flow model was developed. The method consists of two steps. Firstly, the unsteady flow model is applied to the current stream condition to simulate the peak discharge for each historical flood event. Then, the probable flood discharge is calculated by conducting a frequency analysis on the annual maximum series or partial time series of simulated peak discharges. The proposed method avoids the estimation of the probable rainfall and application of a rainfall-runoff model, and the accompanying uncertainty can thus be eliminated. The proposed method was applied to the main stream reach of the Han River in South Korea. The downstream reach of the Han River is greatly influenced by strong tidal variations, and it has another unique feature in that it contains two submerged weirs—Jamsil and Singok. The unsteady flow model was developed by modifying the existing FLDWAV model so as to model the characteristics of the Han River in the context of computational river hydraulics. Historical flood events were simulated and the probable flood discharges along the river reach was computed through partial duration series frequency analysis.

Development and Application of Drought Index Based on Accumulative Pattern of Daily Rainfall

This study proposed a new drought index considering the accumulative pattern of daily rainfall, i.e., Rainfall Accumulation Drought Index (RADI). The RADI can be easily calculated at daily scale by comparing the long-term averaged cumulative rainfall to the observed cumulative rainfall for a specific duration. This study evaluated the availability of the RADI in the field of monitoring short-term and long-term droughts by investigating the spatial and temporal variability and the recurrence cycle of drought in South Korea. To present the short-term and long-term droughts, the various SPIs with different durations should be used in practice. However, the RADI can present and monitor both short-term and long-term droughts as a single index. By investigating the national average of the RADI, specific drought patterns of 20-year cycle were identified in this study. This study also proposed a five-level drought classification considering occurrence probability that would be a suitable alternative as a drought criterion for drought forecast/response.

Analysis of Annual Maximum Daily Rainfall Using RCP Climate Change Scenario in Korean Peninsula

This study analyzes daily rainfall within the Korean Peninsula under RCP scenario. To compare RCP scenarios with recent rainfall, atotal of 88 rainfall stations across Korean Peninsula, including 61 stations in South Korea and 27 in North Korea, with no missingdaily rainfall data from 1981 to 2010 (SP0) were selected, and the RCP scenario was separated into three periods: 2011-2040 (SP1),2041-2070 (SP2), and 2071-2100 (SP3). Comparisons between scenarios, and between the recent observed data (SP0) and each of theRCP scenarios were performed using the mean and maximum of the annual maximum daily rainfall for each period. The parametersof the Gumbel distribution at each station for the observed SP0 period were estimated by using a probability weighted momentmethod. Using these parameters, the return period was estimated for the maximum rainfall in the RCP scenario for each period.Across all scenarios and in all periods, more rainfall stations registered an increase in the maximum rainfall than in the mean rainfall,and North Korea has much higher rates of increases in the mean rainfall than does South Korea. Ratios of rainfall stations with areturn Period over 300-year in SP1, SP2, and SP3 are 14%, 28% and 35%, and ratios of rainfall stations with a return period over1,000-year among those are especially high: SP1 64%, SP2 63% and SP3 69%.Key words : Climate change, RCP scenario, Rainfall frequency, Annul maximum daily rainfall