Wetland ecological health is assessed using an indicator system that comprehensively considers aquatic environments, soil conditions, biological communities, and socioeconomic factors. In this study, the assessment unit basins and baseline wetland sections were established, and a Pressure-State-Response (PSR) framework comprising twelve indicators was applied to evaluate the ecological health of riverine wetlands. The assessment units were delineated as small watersheds at a spatial scale appropriate for riverine wetland management. In addition, alluvial zones were introduced as a geomorphic reference to delineate baseline wetland sections, defined as the potential wetland space against which human-induced degradation was assessed. The indicator grades may vary depending on the study area, and the state indicators were analyzed using three to five indicators per basin. The assessment results of 80 unit basins in the upper Geum River Basin, South Korea, revealed that one unit basin rated as Grade 1 (Very healthy), 29 unit basins as Grade 2 (Healthy), 47 unit basins as Grade 3 (Moderate), three as Grade 4 (Vulnerable), and none as Grade 5 (Highly vulnerable). Unit basins with higher population density, urbanization rate, and cultivated land ratio generally showed lower wetland ecological health grades. Unit basins adjacent to the mainstream tended to be relatively healthy whereas some tributary unit basins with high urbanization rates and pollution loads showed degraded ecological conditions. This study provides a scientific foundation for prioritizing restoration efforts and establishing sustainable wetland management and conservation policies.

Insurers and reinsurers face a structural blind spot in typhoon-exposed emerging markets as human fatality risk is material for underwriting, parametric triggers, and reputational capital, yet is rarely quantified due to sparse and unreliable data. This study introduces a Typhoon Fatality Risk Index (TFRI) specifically designed for catastrophe risk assessment in data-scarce regions. Unlike traditional catastrophe models that focus on economic loss, TFRI directly quantifies fatality risk using a data-driven, transparent framework suitable for pricing, capital allocation, and portfolio management. Climate drivers such as Niño Sea Surface Temperature (SST) indices and Deep learning (DL)-assisted, hydrological discharge values are integrated via entropy weighting to reflect hazard intensity and variability. Gaussian Process Regression (GPR) also shows that fatality risk exhibits complex, nonlinear dynamics influenced by a few dominant variables, underscoring the importance of integrating climatic and hydrological data in risk models. This framework demonstrates significant potential to improve risk assessment and capital allocation for (re)insurers and overall decision-makers operating in data-scarce regions.

Smart water grid technologies have been widely adopted as a key component of digital transformation in water resource management, where real-time household water consumption data collected from smart water meters serve as fundamental inputs. However, these datasets often contain numerous outliers and missing values due to communication errors, which degrade data reliability and hinder accurate analysis. This study proposes an improved framework for outlier detection and missing data imputation tailored to the characteristics of cumulative household water consumption data. The proposed imputation methods were evaluated against conventional approaches using error metrics, and the results demonstrated significant improvements in accuracy, with RMSE values substantially lower than those of the reference method. In addition, prediction models with varying levels of complexity were explored to examine how improved data quality influences forecasting performance. The results indicate that, although data preprocessing enhances data reliability, prediction performance remains limited due to the inherent variability and stochastic nature of household water consumption data. Prediction models with varying levels of complexity were constructed and evaluated using the corrected datasets. The performance of the models varied depending on dataset characteristics, and no single model consistently outperformed others. Overall, this study highlights the critical role of data quality improvement in smart water management systems and provides practical insights into missing data imputation, while suggesting that further advancements in prediction require additional explanatory variables and more sophisticated modeling approaches.

Wetlands play an important role in cycling water resources, organic matter, water purification, and ecosystem conservation. Understanding flow regimes is essential for effective wetland management because it significantly influences wetland succession and circulation. This study aims to develop a methodology for quantifying flow regimes of the Jangdan Wetland (Imjin River) and the Binae Wetland (Namhan River). The study utilized water level duration curve (LDC) based on historical water level data from 2008 to 2023 for the Binae Wetland and from 2003 to 2023 for the Jangdan Wetland to analyze hydrological flow regimes. Flow regime diagrams describing the inundation characteristics over time, were then constructed using the LDC and the digital elevation models (DEM) of the wetlands. To understand the relationship between vegetation communities and hydrologic regime in the wetland, this study classified nine vegetation zones based on the tolerance of associated plant species to inundation (in weeks or months) reported for plant species in previous studies. The study revealed that the Jangdan Wetland were dry primarily on most days but experienced complete inundation during flooding events. In contrast, the Binae Wetlands were found to be frequently inundated, with approximately 40 % of the area experiencing regular flooding, while 20 % of the higher elevation areas were only inundated once every few years.

The rapid development of deep learning (DL) is the most significant contemporary evolution of hydrological science, yet its limitations remain underexplored. This study aims to evaluate the effectiveness of DL-based, traditional, and hybrid hydrological models in streamflow simulation especially in data-deficient regions. Daily discharge flow of the four sub-catchments in Samar, Philippines were modeled using HEC-HMS, Univariate Long-Short Term Memory (LSTM) Network, Classical GR4J and DL-assisted, parametrically- optimized GR4J. Results show that the classical GR4J underestimated the discharge, while the LSTM overestimated peaks. The DL-assisted, parametrically-optimized GR4J achieved the highest consistency and balance between realistic peak and low flow estimation, with NSE = 0.63–0.84, IA = 0.85–0.93, LMI = 0.76–0.82, and low MAPE (≤ 0.03) and RSR (≤ 0.02). Although the Univariate LSTM captured general trends well, it underestimated some peaks despite reaching high values in performance metrics. Many studies highlight DL’s hydrological modeling power but ignore its limits and hybrid model benefits. The results highlight that neither DL nor classical models alone are sufficient as DL-assisted approaches yield more reliable and realistic hydrological simulations, particularly in climate-sensitive, data-deficient regions like Samar.

For flood-prone, developing nations where hydrological data is scarce, an innovative methodological approach is essential. This study aims to explore the potentiality of modelling daily evapotranspiration time series by checking causal relationship among the available climate variables in a flood-prone, data-deficient region like Samar in the Philippines. First, to verify if the available variables (rainfall, air pressure and the four (4) Niño Sea Surface Temperature (SST) Indices) have direct effects to evapotranspiration, a causality test called Convergent Cross-Mapping (CCM) was used. Interestingly, only the Niño SST indices and air pressure were found to have direct effects. Results showed that air pressure and the four (4) Niño SST Indices when combined with Non-Linear Autoregressive Exogenous (NARX) method, can effectively model evapotranspiration. This study raises a significant advancement in evapotranspiration modelling as it is the first to model and pinpoint the potentiality of causal relationship of air pressure and the four (4) Niño SST Indices to daily evapotranspiration time series. This method is found to be potentially suitable for disaster-prone regions where hydrological data is limited.

A simple model that can assess the river rehabilitation using rehabilitation potential index (RPI) of fish habitat has been developed. This model calculates the RPI using three indicators: the habitat suitability index (HSI), niche breadth (NB), and niche overlap (NO). These indicators were estimated by using environmental factors that affect fish habitat in the river and then RPI was obtained by combining the indicators. However, the model showed some limitations. For example, the representative NO was obtained by summing each NO, which does not reflect the difference of the number of fish species in rivers. This study improved the estimation method of NO by using the averaged NO that can consider different kinds of species in each river. In addition, the previous study used standardization method for making three indicator values in the range of 0 to 1. However, the standardized indicators can distort the relative size when we compare with the original data. In this study, we have selected alternative methods to calculate indicators, eliminating the need for standardization. Along with these improvements, this study developed a new RPI estimation model by reviewing various methods and selecting the proper ones. The RPIs of the two models were estimated using fish abundance and habitat environmental factors from 57 sites in Han river, Geum river, and Nakdong river in Korea. We compared the results of the existing model with the new model using root mean square error (RMSE) and mean absolute deviation (MAD). The new model showed the reduced uncertainty and enhanced applicability in comparison to the previous model.

Hydrological and water quality models have been continuously improved for assessing the impacts of climate change. Accordingly, meteorological data with high spatial and temporal resolutions are required. Current climate change scenario-based projections provide meteorological data on monthly and daily scales, which are too coarse for regional climate change assessments. In this study, we propose a temporal downscaling method based on the nearest neighbor search methodology for temporal downscaling from daily time-step meteorological data to hourly time-step data. To verify the temporal downscaling method, historical meteorological data from weather stations located in the Nakdong River basin were used, and the normalized root mean square error (NRMSE) between observational and simulated data was calculated. Consequently, the simulation errors were approximately 2–4% for temperature and precipitation, and approximately 7–16% for wind speed, relative humidity, and solar radiation. Next, using the temporal downscaling method, hourly future climate projection data were derived from the daily SSP5-8.5 climate scenario projections retrieved from weather stations in the target area. According to the downscaled hourly climate projection results, under the SSP5-8.5 climate change scenario, the future annual maximum temperature is projected to increase by up to 7.0 ℃ and 5.6 ℃ at Daegu and Busan weather stations, compared to the reference period maximum temperatures of 36.2 ℃ and 33 ℃, respectively. The annual precipitation duration is projected to decrease by up to 103.3 h (21.25%) and 157.2 h (27.57%), compared to the reference period durations of 486.2 h and 570.2 h, respectively. Annual precipitation intensity is projected to increase by up to 0.71 mm/h (33.5%) and 0.83 mm/h (31.2%), compared to the reference period precipitation intensities of 2.12 mm/h and 2.66 mm/h, respectively. The temporal downscaling method presented in this study is expected to contribute to the preparation of meteorological input data to effectively simulate detailed hourly rainfall runoff and the behavior of water quality factors, thereby improving the assessment of climate change impacts.

In this study, fuzzy mathematics and VIKOR were employed to develop a flood vulnerability assessment system for Ulsan Metropolitan City, South Korea in 2018. HEC-HMS model was used to simulate the major rivers’ runoff in Ulsan Metropolitan City, and HEC-RAS model was used to convert the 1-D runoff simulation results into 2-D results of inundation map, while the simulation results were exhibited through ArcMap. Hazards, sensitivity and adaptivity were selected as the three evaluation indicators for fuzzy comprehensive evaluation, and pressure resilience, state resilience and response resilience were utilized as the indicators for ranking and comparison of the VIKOR method for local governments (administrative districts) of Ulsan Metropolitan City. As the result, Dong-gu district, Ulju-jun County and Nam-gu district showed the best results by both the fuzzy mathematical and the VIKOR method in hazard and pressure, sensitivity and state, adaptivity and response indicator. Fuzzy mathematics and the VIKOR method provide two different ways to study the flood protection capacity of Ulsan Metropolitan City, comparing the historical statistics, VIKOR method’s evaluation results match well with the flooding statistics in Ulsan Metropolitan City.

Abstract Wetlands play an important role in cycling water resources, organic matter, water purification, and ecosystem conservation. Understanding flood regimes is essential for effective wetland management because it significantly influences wetland succession and circulation. This study aims to develop a methodology for quantifying flood regimes of the Jangdan Wetland (Imjin River) and the Binae Wetland (Namhan River). This study analyzed the hydrologic regime of these wetlands using the water level duration curve (LDC) based on the flow duration curve (FDC). Flood regime diagrams describing the inundation characteristics over time, were then constructed using the LDC and the digital elevation models (DEM) of the wetlands. To understand the relationship between vegetation communities and hydrologic regime in the wetland, this study classified nine vegetation zones based on the tolerance of associated plant species to inundation. The study revealed that the Jangdan Wetland were dry primarily on most days but experienced complete inundation during flooding events. In contrast, the Binae Wetlands were found to be frequently inundated, with approximately 40% of the area experiencing regular flooding, while 20% of the higher elevation areas were only inundated once every few years.

In Korea, it is difficult to efficiently manage water resources, due to the variability of rainfall in addition to the increasing outflow due to climate change in recent years. Therefore, if the trends and characteristics of rainfall at each station can be identified in advance, the problems caused by the variability of rainfall can be effectively dealt with. In this study, the data on rainfall characteristics of 64 rainfall stations in the Nakdong river basin were collected for the period 2000 to 2019. The data were analyzed according to the elevation of each station using K-means cluster analysis, and the rainfall trends at each station were identified by homogeneity test and modified Mann-Kendall test. The analysis showed an increasing trend in March, April, November, December, spring and autumn, and a decreasing trend in January, May, September, summer and year. Also, based on the cluster analysis, it was confirmed that when the number of clusters was set to three, the rainfall characteristics were different depending on the elevation of each station. It is believed that linking the characteristics of rainfall by cluster and the results of trend analysis by station derived from the study can be used to come up with a water resource management plan that takes into account the variability of rainfall.

수자원 관리 분야의 디지털 전환 기술로서 스마트워터그리드(smart water grid, SWG) 기술이 도입되고 있으며, 수도계량기를 통해 수집되는 실시간 물사용량 자료는 SWG 기술의 기초 자료로 활용되고 있다. 하지만 통신오류 등의 문제로 다수의 이상치와 결측치가 포함된 채 기록되며, 이를 실시간으로 보정하기 위한 기존의 방법론은 온전히 기록된 과거시간대의 값을 그대로 입력하는 등 정확도가 낮은 보정 방법이므로 개선이 필요한 실정이다. 따라서 본 연구에서는 과거 누적 자료의 이상치 처리와 개선된 결측 처리 방법을 제안하고, 실시간 물사용량 자료의 요일별 정보를 이용한 실시간 물사용량 보정 기법을 제안하고자 한다. 기존 결측 처리 방법과 새로 제안하는 결측 처리 방법을 각각 적용하여 보정 결과를 비교 및 평가한 결과, 새로 제안하는 결측 처리 방법의 오차지표(RMSE)는 0.002로 기존 방법론의 오차지표 (RMSE) 0.079 보다 낮게 산정되어 정확성이 개선되었음을 확인하였다. 이후 요일별-시간별 물사용량의 평균을 산정하여 실시간 결측 발생시 대체값으로 사용하는 방법을 제시하였다. 본 연구결과는 효율적인 스마트 물관리의 근거자료로 사용될 수 있으며, 개발된 방법론을 사용하여 결측 보정 오류로 인한 의사결정의 불확실성을 줄일 수 있을 것으로 판단된다.

In the past, damage from natural disasters was limited to the country directly affected, but as the world becomes one economic community, instances of damage spreading to other countries are increasing. Nonetheless, there has been insufficient research on the ripple effect of foreign disaster. This study thus analyzed the ripple effect on the domestic economy from foreign disaster, using a disaster scenario based on cases of China. The ripple effect was quantitatively calculated using an industry input coefficient. The results show that the direct damage was 0.08% of Gross Domestic Product (GDP), and the total amount of damage (including indirect damage) was 0.39% of GDP, thus demonstrating that foreign disaster could cause great damage to the domestic economy.

최근 기후위기 극복 및 산업계의 녹색전환을 위한 친환경 경제성장 전략으로서 녹색산업의 성장이 대두되고 있다. 현재 녹색산업의 산업적 범위 및 정의는 불명확하지만 선택적이고 전략적인 육성전략이 필요한 시점이다. 따라서 집중육성이 필요한 산업에 대한 글로벌가치사슬(GVC) 분석을 하여 산업 생태계를 파악하고 육성전략을 도출할 필요가 있다. 본 연구에서는 녹색산업 관련 선행 연구 및 통계자료, 국내·외 주요 정책 등을 수집하여 녹색산업의 매체별 정의 및 범위를 제시하였다. 또한 통계자료 기반의 정량적 분석과 전문가 설문을 통한 정성적 분석을 통해 GVC분석을 위한 녹색산업 핵심업종으로 태양광산업 및 폐패널 재활용업, 전기차 배터리 제조업 및 폐배터리 재활용업, 스마트 상수관망 운영관리업을 도출하였다.

Since it was recognized as a UNDRR international safety city in 2020, Incheon Metropolitan City has been promoting policies aiming to strengthen resilience strategies for the next 10 years. For the resilience assessment, a Quick Risk Estimation (QRE), which is a risk assessment tool for various disasters, can also be used to support the decision-making of experts on strengthening resilience strategies. However, QRE is unable to provide a detailed risk assessment of a specific disaster such as flood. Therefore, in this study, a flood risk assessment was performed from 2016 to 2019 using the Indicator Based Approach for the 10 cities and counties in Incheon city. The aforementioned method can also support the decision-making of experts for disaster management alongside QRE results. The flood risk assessment in this study consists of four items (hazard, exposure, vulnerability, and capacity) and 11 detailed indicators. The details for each index, item, and flood risk indices were calculated for each evaluation stage. As of 2019, the flood risk index had been calculated for Ganghwa-gun (county), Michuhol-gu (district), Jung-gu, Seo-gu, and Ongjin-gun, among others. The flood risk assessment conducted in this study is believed to be beneficial for a rational decision-making that can support the strengthening of resilience strategies by identifying changes in city- and county-specific situations and risk-exposed indicators in response to flood risks.

As we move towards the more critical age of technology and learning, understanding the underlying dynamics of events such as the unforeseen and unpredictable pandemics in the ecologival system are deemed invaluable and important. In this paper, we examine how daily cases of CoVid-19 behaves chaotically enough to affect a large population. The data used were from the different countries mostly concentrated to Southeast Asia. The authors believe that the data currently available does not allow reliable forecast because of the presence of untested asymptomatic cases, therefore understanding the chaotic dynamics may provide possible reasons or justifications for the prevalent CoVid-19 outbreaks.

The interest in renewable energy to replace fossil fuel is increasing as the problem caused by climate change has become more severe. In this study, small hydropower (SHP) was evaluated as a resource with high development value because of its high energy density compared to other renewable energy sources. SHP may be an attractive and sustainable power generation environmental perspective because of its potential to be found in small rivers and streams. The power generation potential could be estimated based on the discharge in the river basin. Since the river discharge depends on the climate conditions, the hydropower generation potential changes sensitively according to climate variability. Therefore, it is necessary to analyze the SHP potential in consideration of future climate change. In this study, the future prospect of SHP potential is simulated for the period of 2021 to 2100 considering the climate change in three hydropower plants of Deoksong, Hanseok, and Socheon stations, Korea. The results show that SHP potential for the near future (2021 to 2040) shows a tendency to be increased, and the highest increase is 23.4% at the Deoksong SPH plant. Through the result of future prospect, we have shown that hydroelectric power generation capacity or SHP potential will be increased in the future. Therefore, we believe that it is necessary to revitalize the development of SHP to expand the use of renewable energy. In addition, a methodology presented in this study could be used for the future prospect of the SHP potential.

Instances of flood damage caused by extreme storm rainfall due to climate change and variability have been showing an increasing trend. Particularly, a flood forecasting and warning system has been recognized as an important nonstructural measure for flood damage reduction, including loss of life. Flood forecasting and warning have been performed by the forecasts of flood discharge and flood stage using the physically based rainfall-runoff models. However, recently, studies involving the application of a machine learning-based flood forecasting models, which addresses the limitations of extant physically based flood stage forecasting models, have been performed. We may require various case studies to determine more accurate methods. Therefore, this study performed the real-time forecasting of the river water level or stage at the Gurye station of the Sumjin river with lead times of 1, 3, and 6 h by applying a long short-term memory (LSTM)-based deep learning model. In addition, the applicability of the LSTM model was evaluated by comparing the results with those from widely used models based on support vector machine and multilayer perceptron. Consequently, we noted that the LSTM model exhibited a relatively better forecasting performance. Therefore, the applicability of the LSTM model should be extensively studied for flood forecasting applications.

In this study, we proposed a method to quantitatively evaluate the improvement degree of disaster prevention capability through investment of the recovery cost in areas damaged by heavy rain storms. Pyeongtaek, Gwangju, Pocheon, and Hwaseong cities, where heavy rain damage had most frequently occurred during the last 10 years, were selected as the study areas, and damages and recovery cost data for 10 years were collected. The total rainfall and 5-antecedent rainfall during the damage period were used to define the amount of disaster rainfall. A disaster rainfall-damage equation was established to assess disaster prevention capability before and after the investment of the recovery cost. Then, we calculated virtual damage before the investment based on the equation and obtained the damage reduction benefit by the damage difference from before and after the investment. The ratio of recovery cost and damage reduction benefit was assessed to ascertain the improvement degree of disaster prevention capability. From the results, we found that disaster prevention capability can be improved in the range of about 19 to 61 percent by the investment of recovery costs over the past 10 years. Therefore, the results of this study could be used as basic data to quantitatively evaluate the effects of recovery costs and establish an effective disaster management plan. Keywords: Heavy Rain Damage, Recovery Cost, Disaster Prevention Capability, Damage Reduction Benefit

Many researches are conducted to evaluate benefit from local disaster prevention projects. However, since such evaluation can only be assessed at the local district level, there is a limitation to applying this assessment to the local government's disaster prevention management in that it requires the local characteristics. To compensate for this, in this study, the effect of disaster damage reduction through local governments’ local disaster prevention budget was estimated, and that used for disaster damage reduction in the area was calculated. Further, the effect was applied to the damage prediction model that is typical local government-level disaster prevention management, and heavy rain damage prediction was done considering the effect. As a result of applying it to the study area, the effect was prominent in Pyeongtaek-si and Pocheon-si in Gyeonggi-do. Also, considering the results of the effect, it was confirmed that error in the heavy rain damage prediction function was reduced. The results of this study can be used to establish disaster prevention budget management and disaster mitigation plans. Keywords: Disaster Prevention Budget, Disaster Damage Reduction Effect, Heavy Rain Damage Prediction