A Bayesian Network-Based Integrated for Flood Risk Assessment (InFRA)

Floods are natural disasters that should be considered a top priority in disaster management, and various methods have been developed to evaluate the risks. However, each method has different results and may confuse decision-makers in disaster management. In this study, a flood risk assessment method is proposed to integrate various methods to overcome these problems. Using factor analysis and principal component analysis (PCA), the leading indicators that affect flood damage were selected and weighted using three methods: the analytic hierarchy process (AHP), constant sum scale (CSS), and entropy. However, each method has flaws due to inconsistent weights. Therefore, a Bayesian network was used to present the integrated weights that reflect the characteristics of each method. Moreover, a relationship is proposed between the elements and the indicators based on the weights called the Integrated Index for Flood Risk Assessment (InFRA). InFRA and other assessment methods were compared by receiver operating characteristics (ROC)-area under curve (AUC) analysis. As a result, InFRA showed better applicability since InFRA was 0.67 and other methods were less than 0.5

Assessment of a Stream Gauge Network Using Upstream and Downstream Runoff Characteristics and Entropy

A method for constructing a stream gauge network that reflects upstream and downstream runoff characteristics is assessed. For the construction of an optimal stream gauge network, we develop representative unit hydrographs that reflect such characteristics based on actual rainfall–runoff analysis. Then, the unit hydrographs are converted to probability density functions for application to entropy theory. This allows a comparison between two cases: one that considers the upstream and downstream runoff characteristics of a core dam area in South Korea, and another that uses empirical formula, which is an approach that has been widely used for constructing the stream gauge network. The result suggests that the case of a stream gauge network that considers upstream and downstream runoff characteristics provides more information to deliver, although the number of selected stream gauge stations of this case is less than that of the case that uses the empirical formula. This is probably because the information delivered from the constructed stream gauge network well represents the runoff characteristics of the upstream and downstream stations. The study area, the Chungju Dam basin, requires 12 stream gauge stations out of the current total of 18 stations for an optimal network that reflects both upstream and downstream runoff characteristics

Utilizing inactive storage in a dam reservoir during extreme drought periods

The purpose of this study is to suggest a structural plan for improving the utilization of inactive storage in dam reservoirs, to mitigate extreme drought. Inactive storage in the dam is composed of emergency storage and dead storage. The emergency storage can be used in emergencies such as drought. But, in general, the dead storage for sedimentation is not used, even in an emergency. Therefore, we developed a methodology to determine how the dead storage space can be partially used during extreme drought periods when the sedimentation has not occurred yet. We call this partial space in a dam reservoir “drought storage”. An accurate analysis of sediment levels needs to be performed before calculating drought storage, and so the present sediment level in the dam reservoir was estimated using SED-2D linked with the RMA-2 model of SMS. After considering the additional available storage capacity based on the estimated sediment level, drought storage was finally determined. We also predicted future sediment levels after 100 years and suggest the amount of drought storage available in the future. As a result, we found that the available drought storage will be lower in the future compared to present drought storage, due to the gradual increase in reservoir sedimentation over time in the dam. Further research may be needed to effectively reduce sedimentation in order to increase the drought storage capacity

Development of Simple Method for Flood Control Capacity Estimation of Dam in South Korea

As flood damage is becoming more frequent and severe worldwide, efficient flood control of dams has been highlighted. The purpose of the study is to establish a simple method for dam operators to estimate the flood control capacity and predischarge level required for flood response. The cumulative probability distribution (CDF) pair with the same return period for 12 major dams located in South Korea were estimated using the frequency matching method. A Ratio of Storage volume to Flood inflow (RSF) concept was suggested and applied for each dam, and they were classified into three types: Linear, Estranged, and Vague according to the water storage characteristics. Using the method presented in this study, we suggested the required amount of flood control capacity and target water level for each dam. The results demonstrated that there is no linear relationship between flood and storage of dam when the ratio of watershed area to a storage capacity of the dam is 2.0 or more, or the ratio of watershed area to flood control capacity is 20.0 or more. Further, it was found that the RSF value is affected by the initial water level of the dam when a high flood inflow was observed for Estranged and Vague types. It is expected that the method presented in this study can be basic information for performing predischarge for flood control in numerous dams

On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data

Rainfall data is frequently used as input and analysis data in the field of hydrology. To obtain adequate rainfall data, there should be a rain gauge network that can cover the relevant region. Therefore, it is necessary to analyze and evaluate the adequacy of rain gauge networks. Currently, a complex network analysis is frequently used in network analysis and in the hydrology field, Pearson correlation is used as strength of link in constructing networks. However, Pearson correlation is used for analyzing the linear relationship of data. Therefore, it is now suitable for nonlinear hydrological data (such as rainfall and runoff). Thus, a possible solution to this problem is to apply mutual information that can consider nonlinearity of data. The present study used a method of statistical analysis known as the Brock–Dechert–Scheinkman (BDS) statistics to test the nonlinearity of rainfall data from 55 Automated Synoptic Observing System (ASOS) rain gauge stations in South Korea. Analysis results indicated that all rain gauge stations showed nonlinearity in the data. Complex networks of these rain gauge stations were constructed by applying Pearson correlation and mutual information. Then, they were compared by computing their centrality values. Comparing the centrality rankings according to different thresholds for correlation showed that the network based on mutual information yielded consistent results in the rankings, whereas the network, which based on Pearson correlation exhibited much variability in the results. Thus, it was found that using mutual information is appropriate when constructing a complex network utilizing rainfall data with nonlinear characteristics

Case Study: On Objective Functions for the Peak Flow Calibration and for the Representative Parameter Estimation of the Basin

The objective function is usually used for verification of the optimization process between observed and simulated flows for the parameter estimation of rainfall–runoff model. However, it does not focus on peak flow and on representative parameter for various rain storm events of the basin, but it can estimate the optimal parameters by minimizing the overall error of observed and simulated flows. Therefore, the aim of this study is to suggest the objective functions that can fit peak flow in hydrograph and estimate the representative parameter of the basin for the events. The Streamflow Synthesis And Reservoir Regulation (SSARR) model was employed to perform flood runoff simulation for the Mihocheon stream basin in Geum River, Korea. Optimization was conducted using three calibration methods: genetic algorithm, pattern search, and the Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA). Two objective functions of the Sum of Squared of Residual (SSR) and the Weighted Sum of Squared of Residual (WSSR) suggested in this study for peak flow optimization were applied. Since the parameters estimated using a single rain storm event do not represent the parameters for various rain storms in the basin, we used the representative objective function that can minimize the sum of objective functions of the events. Six rain storm events were used for the parameter estimation. Four events were used for the calibration and the other two for validation; then, the results by SSR and WSSR were compared. Flow runoff simulation was carried out based on the proposed objective functions, and the objective function of WSSR was found to be more useful than that of SSR in the simulation of peak flow runoff. Representative parameters that minimize the objective function for each of the four rain storm events were estimated. The calibrated observed and simulated flow runoff hydrographs obtained from applying the estimated representative parameters to two different rain storm events were better than those retrieved from parameters estimated using a single rain storm event. The results of this study demonstrated that WSSR is adequate in peak flow simulation, that is, the estimation of peak flood runoff. In addition, representative parameters can be applied to a flow runoff simulation for rain storm events that were not involved in parameter estimation

Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models

Development of Simple Method for Flood Control Capacity Estimation of Dam in South Korea

As flood damage is becoming more frequent and severe worldwide, efficient flood control of dams has been highlighted. The purpose of the study is to establish a simple method for dam operators to estimate the flood control capacity and predischarge level required for flood response. The cumulative probability distribution (CDF) pair with the same return period for 12 major dams located in South Korea were estimated using the frequency matching method. A Ratio of Storage volume to Flood inflow (RSF) concept was suggested and applied for each dam, and they were classified into three types: Linear, Estranged, and Vague according to the water storage characteristics. Using the method presented in this study, we suggested the required amount of flood control capacity and target water level for each dam. The results demonstrated that there is no linear relationship between flood and storage of dam when the ratio of watershed area to a storage capacity of the dam is 2.0 or more, or the ratio of watershed area to flood control capacity is 20.0 or more. Further, it was found that the RSF value is affected by the initial water level of the dam when a high flood inflow was observed for Estranged and Vague types. It is expected that the method presented in this study can be basic information for performing predischarge for flood control in numerous dams