Reanalysis Product-Based Nonstationary Frequency Analysis for Estimating Extreme Design Rainfall

Nonstationarity is one major issue in hydrological models, especially in design rainfall analysis. Design rainfalls are typically estimated by annual maximum rainfalls (AMRs) of observations below 50 years in many parts of the world, including South Korea. However, due to the lack of data, the time-dependent nature may not be sufficiently identified by this classic approach. Here, this study aims to explore design rainfall with nonstationary condition using century-long reanalysis products that help one to go back to the early 20th century. Despite its useful representation of the past climate, the reanalysis products via observational data assimilation schemes and models have never been tested in representing the nonstationary behavior in extreme rainfall events. We used daily precipitations of two century-long reanalysis datasets as the ERA-20c by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the 20th century reanalysis (20CR) by the National Oceanic and Atmospheric Administration (NOAA). The AMRs from 1900 to 2010 were derived from the grids over South Korea. The systematic errors were downgraded through quantile delta mapping (QDM), as well as conventional stationary quantile mapping (SQM). The evaluation result of the bias-corrected AMRs indicated the significant reduction of the errors. Furthermore, the AMRs present obvious increasing trends from 1900 to 2010. With the bias-corrected values, we carried out nonstationary frequency analysis based on the time-varying location parameters of generalized extreme value (GEV) distribution. Design rainfalls with certain return periods were estimated based on the expected number of exceedance (ENE) interpretation. Although there is a significant range of uncertainty, the design quantiles by the median parameters showed the significant relative difference, from −30.8% to 42.8% for QDM, compared with the quantiles by the multi-decadal observations. Even though the AMRs from the reanalysis products are challenged by various errors such as quantile mapping (QM) and systematic errors, the results from the current study imply that the proposed scheme with employing the reanalysis product might be beneficial to predict the future evolution of extreme precipitation and to estimate the design rainfall accordingly

Application of Harmony Search to Design Storm Estimation from Probability Distribution Models

The precision of design storm estimation depends on the selection of an appropriate probability distribution model (PDM) and parameter estimation techniques. Generally, estimated parameters for PDMs are provided based on the method of moments, probability weighted moments, and maximum likelihood (ML). The results using ML are more reliable than the other methods. However, the ML is more laborious than the other methods because an iterative numerical solution must be used. In the meantime, metaheuristic approaches have been developed to solve various engineering problems. A number of studies focus on using metaheuristic approaches for estimation of hydrometeorological variables. Applied metaheuristic approaches offer reliable solutions but use more computation time than derivative-based methods. Therefore, the purpose of the current study is to enhance parameter estimation of PDMs for design storms using a recently developed metaheuristic approach known as a harmony search (HS). The HS is compared to the genetic algorithm (GA) and ML via simulation and case study. The results of this study suggested that the performance of the GA and HS was similar and showed more accurate results than that of the ML. Furthermore, the HS required less computation time than the GA

Evaluation of a Depth-Based Multivariate k

A nonparametric simulation model (k-nearest neighbor resampling, KNNR) for water quality analysis involving geographic information is suggested to overcome the drawbacks of parametric models. Geographic information is, however, not appropriately handled in the KNNR nonparametric model. In the current study, we introduce a novel statistical notion, called a “depth function,” in the classical KNNR model to appropriately manipulate geographic information in simulating stormwater quality. An application is presented for a case study of the total suspended solids throughout the entire United States. The stormwater total suspended solids concentration data indicated that the proposed model significantly improves the simulation performance compared with the existing KNNR model

Safety First? Lessons from the Hapcheon Dam Flood in 2020

Floods change the living environment and threaten public health, while dam construction has often been made to protect and mitigate floods. Meanwhile, an exceptionally high outflow, five times higher than the maximum historical outflow, was discharged on 8 August 2020 from the Hapcheon Dam (HCD), which is located at the middle of the Hwang River, South Korea. As a result, the 2020 flood event occurred in the downstream area, flooding the villages located downstream of the HCD, and damaging agricultural and residential areas. The current study investigates the cause of the flood and how the outflow affected the downstream area. The investigation showed that the Hwang River and the streams connected to the Hwang River experienced piping and overflow in several levees downstream. The frequency analysis of the rainfall upstream and the inflow to the HCD illustrated that the rainfall return periods are only 5–30 years for different durations. The return period of inflow to the HCD was only approximately five years. Sustaining a high-water level before the flooding season for future environmental use caused an exceptionally high outflow. Lowering the water level might have prevented damage to the downstream area. The 2020 flood event provided an imperative lesson to water managers and policymakers, demonstrating that the HCD and downstream safety must be prioritized over water conservation for environmental use

Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea

Chlorophyll-a (Chl-a) is one of the major indicators for water quality assessment and recent developments in ocean color remote sensing have greatly improved the ability to monitor Chl-a on a global scale. The coarse spatial resolution is one of the major limitations for most ocean color sensors including Moderate Resolution Imaging Spectroradiometer (MODIS), especially in monitoring the Chl-a concentrations in coastal regions. To improve its spatial resolution, downscaling techniques have been suggested with polynomial regression models. Nevertheless, polynomial regression has some restrictions, including sensitivity to outliers and fixed mathematical forms. Therefore, the current study applied genetic programming (GP) for downscaling Chl-a. The proposed GP model in the current study was compared with multiple polynomial regression (MPR) to different degrees (2nd-, 3rd-, and 4th-degree) to illustrate their performances for downscaling MODIS Chl-a. The obtained results indicate that GP with R2 = 0.927 and RMSE = 0.1642 on the winter day and R2 = 0.763 and RMSE = 0.5274 on the summer day provides higher accuracy on both winter and summer days than all the applied MPR models because the GP model can automatically produce appropriate mathematical equations without any restrictions. In addition, the GP model is the least sensitive model to the changes in the input parameters. The improved downscaling data provide better information to monitor the status of oceanic and coastal marine ecosystems that are also critical for fisheries and fishing farming

Serial Multiple Mediation Analyses: How to Enhance Individual Public Health Emergency Preparedness and Response to Environmental Disasters

Recent environmental disasters have revealed the government’s limitations in real-time response and mobilization to help the public, especially when disasters occur in large areas at the same time. Therefore, enhancing the ability to prepare for public health emergencies at the grassroots level and extend public health emergency response mechanisms to communities, and even to individual families, is a research question that is of practical significance. This study aimed to investigate mechanisms to determine how media exposure affects individual public health emergency preparedness (PHEP) to environmental disasters; specifically, we examined the mediating role of knowledge and trust in government. The results were as follows: (1) knowledge had a significant mediating effect on the relationship between media exposure and PHEP; (2) trust in government had a significant mediating effect on the relationship between media exposure and PHEP; (3) knowledge and trust in government had significant multiple mediating effects on the relationship between media exposure and PHEP

Stochastic Spatial Binary Simulation with Multivariate Normal Distribution for Illustrating Future Evolution of Umbrella-Shape Summer Shelter under Climate Change

Surface temperature has increased due to the impact of climate change, and the related weather events, such as heat waves and urban heat island, are occurring more frequently than before. Local governments and planners consider these impacts of climate change and try to avoid them. One of the mainly used structural tools is building summer shelters. A critical issue for urban planners is to test how many shelters should be added and how to distribute the shelters to cope with the impact of climate change. Stochastic simulation models can be a good option to randomize locations of shelters and to see how beneficial for living the shelters can be. Therefore, a novel stochastic simulation model is proposed for distributing summer shelters for coping with the climate change impact. This study proposes a stochastic spatial binary simulation with multivariate normal distribution (SSBM) which contains two major procedures consisting of (1) simulation-based derivation of the empirical function and (2) stochastic simulation of spatial binary data with multivariate normal distribution and the derived empirical function. The proposed model is applied to a case study in Jinju City, South Korea, for the umbrella-shape summer shelters (USS). Results concluded that the proposed SSBM reproduced the statistical characteristics of USS and can be a good alternative to model the locations of USS, including the impact of climate change and investigating the evolution of the USS in the future

KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X

Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from the motion technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps and lined shapes. Therefore, a nonparametric estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with synthetically simulated trapezoidal, U-shape and V-shape channels. In addition, the proposed KLR model was compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). The experimental study was performed with the river experiment center in Andong, South Korea. Furthermore, the KLR model was applied to two real case studies in the Migok-cheon stream on Hapcheon-gun and Pori-cheon stream on Yecheon-gun and compared to the other models. With the extensive applications to the feasible river channels, the results indicated that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps as well as different shapes. Finally, the limitation of the UAV-driven demarcation approach was also discussed due to the penetrability of RGB sensors to water