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Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (3 December 2021) | Viewed by 37871

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Special Issue Editors

Prof. Dr. Hongey Chen

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Guest Editor

National Taiwan University, Taipei, Taiwan/National Science and Technology Center for Disaster Reduction, New Taipei, Taiwan
Interests: engineering geology; geohazards; slope development

Dr. Wei-Bo Chen

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Guest Editor

National Science and Technology Center for Disaster Reduction, New Taipei, Taiwan
Interests: hydrodynamic and hydraulic modeling; storm tide and storm wave modeling
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Special Issue Information

Dear Colleagues,

According to the 2020 edition of the United Nations World Water Development Report (UN WWDR 2020), about 74% of all natural disasters were water-related between 2001 and 2018. The total number of deaths exceeded 166,000 only because of floods and droughts during the past 20 years. Additionally, floods and droughts caused total economic damage of almost USD 700 billion and affected over 3 billion people worldwide. Water-induced natural disasters can be categorized as floods, droughts, landslides, storm surges, storm waves, and tsunami and are expected to worsen with climate change. Hence, there is still a growing demand for novel techniques that could be adopted for mitigating water-induced natural disasters.

In order to improve our capabilities and understandings for management, resilience, monitor, analysis, prediction, forecast, and hindcast of water-induced natural disasters, this Special Issue is intended to collect the latest and state-of-the-art studies on floods, droughts, landslides, storm surges, storm waves, and tsunami disasters. Research focusing on model development and applications using state-of-the-art methods is welcome. We look forward to receiving contributions in the form of research articles and reviews for this Special Issue. Topics include but are not limited to the following:

Monitor and prediction of natural disaster due to water-induced natural disasters;
Preparing an emergency evacuation plan for water-induced natural disasters;
Improving disaster resilience to water-induced natural disasters;
Statistical and big data analysis for floods, landslides, storm surges, storm waves, and tsunami disasters;
Artificial intelligence techniques for simulating and predicting water-induced natural disasters;
Risk assessment of future water-induced natural disasters;
Numerical method and its applications to water-induced natural disasters.

Prof. Dr. Hongey Chen
Dr. Wei-Bo Chen
Guest Editors

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Keywords

Monitor and prediction
Emergency evacuation
Disaster resilience
Climate change
Numerical modeling
Statistical and big data analysis
Artificial intelligence techniques

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Published Papers (9 papers)

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Editorial

Jump to: Research

3 pages, 149 KiB

Open AccessEditorial

Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art

by Wei-Bo Chen

Water 2022, 14(8), 1247; https://doi.org/10.3390/w14081247 - 13 Apr 2022

Viewed by 1456

Abstract

According to the 2020 edition of the United Nations World Water Development Report (UN WWDR 2020), about 74% of all-natural disasters were water-related between 2001 and 2018 [...] Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

Research

Jump to: Editorial

12 pages, 3271 KiB

Open AccessCommunication

The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform: A Decade of Climate Research

by Lee-Yaw Lin, Cheng-Ting Lin, Yung-Ming Chen, Chao-Tzuen Cheng, Hsin-Chi Li and Wei-Bo Chen

Water 2022, 14(3), 358; https://doi.org/10.3390/w14030358 - 26 Jan 2022

Cited by 14 | Viewed by 6788

Abstract

Taiwan’s climate change projections have always presented a challenge due to Taiwan’s size and unique meteorological and geographical characteristics. The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform (TCCIP), funded by the Ministry of Science and Technology, Taiwan, is a decade-long climate research project with the most predominant climate data provider and national adaptation policymaking in the country. This paper outlines the evolution of the project. It describes the project’s major achievements, including climate projection arising from participation in the WCRP Coupled Model Inter-comparison Project (CMIP), dynamically and statistically downscaled data with resolutions up to 5 km grid, impact assessments of various themes, such as flooding, as well as the support of national policies through approaches including risk maps, climate data, and knowledge brokering. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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17 pages, 2049 KiB

Open AccessArticle

Assessment of Flood Risk Map under Climate Change RCP8.5 Scenarios in Taiwan

by Yun-Ju Chen, Hsuan-Ju Lin, Jun-Jih Liou, Chao-Tzuen Cheng and Yung-Ming Chen

Water 2022, 14(2), 207; https://doi.org/10.3390/w14020207 - 11 Jan 2022

Cited by 19 | Viewed by 5865

Abstract

Climate change has exerted a significant global impact in recent years, and extreme weather-related hazards and incidents have become the new normal. For Taiwan in particular, the corresponding increase in disaster risk threatens not only the environment but also the lives, safety, and property of people. This highlights the need to develop a methodology for mapping disaster risk under climate change and delineating those regions that are potentially high-risk areas requiring adaptation to a changing climate in the future. This study provides a framework of flood risk map assessment under the RCP8.5 scenario by using different spatial scales to integrate the projection climate data of high resolution, inundation potential maps, and indicator-based approach at the end of the 21st century in Taiwan. The reference period was 1979–2003, and the future projection period was 2075–2099. High-resolution climate data developed by dynamic downscaling of the MRI-JMA-AGCM model was used to assess extreme rainfall events. The flood risk maps were constructed using two different spatial scales: the township level and the 5 km × 5 km grid. As to hazard-vulnerability(H-V) maps, users can overlay maps of their choice—such as those for land use distribution, district planning, agricultural crop distribution, or industrial distribution. Mapping flood risk under climate change can support better informed decision-making and policy-making processes in planning and preparing to intervene and control flood risks. The elderly population distribution is applied as an exposure indicator in order to guide advance preparation of evacuation plans for high-risk areas. This study found that higher risk areas are distributed mainly in northern and southern parts of Taiwan and the hazard indicators significantly increase in the northern, north-eastern, and southern regions under the RCP8.5 scenario. Moreover, the near-riparian and coastal townships of central and southern Taiwan have higher vulnerability levels. Approximately 14% of townships have a higher risk level of flooding disaster and another 3% of townships will become higher risk. For higher-risk townships, adaptation measures or strategies are suggested to prioritize improving flood preparation and protecting people and property. Such a flood risk map can be a communication tool to effectively inform decision- makers, citizens, and stakeholders about the variability of flood risk under climate change. Such maps enable decision-makers and national spatial planners to compare the relative flood risk of individual townships countrywide in order to determine and prioritize risk adaptation areas for planning spatial development policies. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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18 pages, 9681 KiB

Open AccessEditor’s ChoiceArticle

Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan

by Shen Chiang, Chih-Hsin Chang and Wei-Bo Chen

Water 2022, 14(2), 191; https://doi.org/10.3390/w14020191 - 11 Jan 2022

Cited by 31 | Viewed by 4635

Abstract

To better understand the effect and constraint of different data lengths on the data-driven model training for the rainfall-runoff simulation, the support vector regression (SVR) approach was applied to the data-driven model as the core algorithm in the present study. Various features selection strategies and different data lengths were employed in the training phase of the model. The validated results of the SVR were compared with the rainfall-runoff simulation derived from a physically based hydrologic model, the Hydrologic Modeling System (HEC-HMS). The HEC-HMS was considered a conventional approach and was also calibrated with a dataset period identical to the SVR. Our results showed that the SVR and HEC-HMS models could be adopted for short and long periods of rainfall-runoff simulation. However, the SVR model estimated the rainfall-runoff relationship reasonably well even if the observational data of one year or one typhoon event was used. In contrast, the HEC-HMS model needed more parameter optimization and inference processes to achieve the same performance level as the SVR model. Overall, the SVR model was superior to the HEC-HMS model in the performance of the rainfall-runoff simulation. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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26 pages, 9085 KiB

Open AccessFeature PaperArticle

Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps

by Yi-Chung Chen, Tzu-Yin Chang, Heng-Yi Chow, Siang-Lan Li and Chin-Yu Ou

Water 2022, 14(2), 155; https://doi.org/10.3390/w14020155 - 7 Jan 2022

Cited by 10 | Viewed by 2886

Abstract

Recent climate change has brought extremely heavy rains and widescale flooding to many areas around the globe. However, previous flood prediction methods usually require a lot of computation to obtain the prediction results and impose a heavy burden on the unit cost of the prediction. This paper proposes the use of a deep learning model (DLM) to overcome these problems. We alleviated the high computational overhead of this approach by developing a novel framework for the construction of lightweight DLMs. The proposed scheme involves training a convolutional neural network (CNN) by using a radar echo map in conjunction with historical flood records at target sites and using Grad-Cam to extract key grid cells from these maps (representing regions with the greatest impact on flooding) for use as inputs in another DLM. Finally, we used real radar echo maps of five locations and the flood heights record to verify the validity of the method proposed in this paper. The experimental results show that our proposed lightweight model can achieve similar or even better prediction accuracy at all locations with only about 5~15% of the operation time and about 30~35% of the memory space of the CNN. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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13 pages, 16503 KiB

Open AccessArticle

A Strategy to Optimize the Implementation of a Machine-Learning Scheme for Extreme Meiyu Rainfall Prediction over Southern Taiwan

by Jung-Lien Chu, Chou-Chun Chiang, Li-Huan Hsu, Li-Rung Hwang, Yi-Chiang Yu, Kuan-Ling Lin, Chieh-Ju Wang, Shih-Hao Su and Ting-Shuo Yo

Water 2021, 13(20), 2884; https://doi.org/10.3390/w13202884 - 14 Oct 2021

Cited by 4 | Viewed by 2181

Abstract

This study aims to propose a strategy to optimize the performance of the Support Vector Machine (SVM) scheme for extreme Meiyu rainfall prediction over southern Taiwan. Variables derived from Climate Forecast System Reanalysis (CFSR) dataset are the candidates for predictor selection. A series of experiments with different combinations of predictors and domains are designed to obtain the optimal strategy for constructing the SVM scheme. The results reveal that the accuracy (ACC), positive predictive values (PPV), probability of detection (POD), and F1-score can exceed 0.6 on average. Choosing the predictors associated with the Meiyu system and determine the domain associated with the correlations between selected predictors and predictand can improve the forecast performance. Our strategy shows the potential to predict extreme Meiyu rainfall in southern Taiwan with lead times from 16 h to 64 h. The F1-score analysis further demonstrates that the forecast performance of our scheme is stable, with slight inter-annual fluctuations from 1990 to 2019. Higher performance would be expected when the north of the South China Sea is characterized by stronger southwesterly flow and abundant low-level moisture for a given year. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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31 pages, 17050 KiB

Open AccessArticle

3D GIS Platform for Flood Wargame: A Case Study of New Taipei City, Taiwan

by Wen-Ray Su, Yong-Jun Lin, Chun-Hung Huang, Chun-Hung Yang and Yuan-Fan Tsai

Water 2021, 13(16), 2211; https://doi.org/10.3390/w13162211 - 13 Aug 2021

Cited by 6 | Viewed by 3196

Abstract

Wargames have been promoted by local governments in Taiwan since 2009, as they require far fewer resources than full-scale exercises. Previously, wargame scenarios were divulged before their launch, enabling participants to formulate response plans in advance, which made them ineffective. Currently, wargames in which scenarios are not shared in advance require a common platform for debriefing players regarding their planned actions. Owing to its geographical location, Taiwan is prone to significant flooding disasters. To assist in making countermeasures, we created a 3D GIS-based Flood Wargame Assistance Platform (FWGAP) for conducting rapid spatial analyses. Flooded areas are estimated in the FWGAP in three ways: (1) using a digital terrain model (DTM) with designated flood center and depth; (2) applying historical flooding spots; and (3) potential flooding maps. A FWGAP can estimate affected and vulnerable populations and has functions for locating resources such as shelters and hospitals near the flooded areas. Its integrated use of closed-circuit televisions, Google Street View maps, and 3D buildings to display flooded areas realistically ensures greater fidelity. This study reports on the city- and the district-level applications of the FWGAP. The results of the survey undertaken indicates that 56% of the participants agreed that the FWGAP enables disaster relief resources to be located on a GIS map. About half of the participants believed that a no-script flooding wargame using a FWGAP could help to identify problems in standard operation procedures and promote greater horizontal coordination among departments. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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26 pages, 7712 KiB

Open AccessFeature PaperArticle

Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

by Wen-Dar Guo, Wei-Bo Chen, Sen-Hai Yeh, Chih-Hsin Chang and Hongey Chen

Water 2021, 13(7), 920; https://doi.org/10.3390/w13070920 - 27 Mar 2021

Cited by 18 | Viewed by 4963

Abstract

Time-series prediction of a river stage during typhoons or storms is essential for flood control or flood disaster prevention. Data-driven models using machine learning (ML) techniques have become an attractive and effective approach to modeling and analyzing river stage dynamics. However, relatively new ML techniques, such as the light gradient boosting machine regression (LGBMR), have rarely been applied to predict the river stage in a tidal river. In this study, data-driven ML models were developed under a multistep-ahead prediction framework and evaluated for river stage modeling. Four ML techniques, namely support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR), and LGBMR, were employed to establish data-driven ML models with Bayesian optimization. The models were applied to simulate river stage hydrographs of the tidal reach of the Lan-Yang River Basin in Northeastern Taiwan. Historical measurements of rainfall, river stages, and tidal levels were collected from 2004 to 2017 and used for training and validation of the four models. Four scenarios were used to investigate the effect of the combinations of input variables on river stage predictions. The results indicated that (1) the tidal level at a previous stage significantly affected the prediction results; (2) the LGBMR model achieves more favorable prediction performance than the SVR, RFR, and MLPR models; and (3) the LGBMR model could efficiently and accurately predict the 1–6-h river stage in the tidal river. This study provides an extensive and insightful comparison of four data-driven ML models for river stage forecasting that can be helpful for model selection and flood mitigation. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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19 pages, 20428 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

An Operational High-Performance Forecasting System for City-Scale Pluvial Flash Floods in the Southwestern Plain Areas of Taiwan

by Tzu-Yin Chang, Hongey Chen, Huei-Shuin Fu, Wei-Bo Chen, Yi-Chiang Yu, Wen-Ray Su and Lee-Yaw Lin

Water 2021, 13(4), 405; https://doi.org/10.3390/w13040405 - 4 Feb 2021

Cited by 15 | Viewed by 3856

Abstract

A pluvial flash flood is rapid flooding induced by intense rainfall associated with a severe weather system, such as thunderstorms or typhoons. Additionally, topography, ground cover, and soil conditions also account for the occurrence of pluvial flash floods. Pluvial flash floods are among the most devastating natural disasters that occur in Taiwan, and these floods always /occur within a few minutes or hours of excessive rainfall. Pluvial flash floods usually threaten large plain areas with high population densities; therefore, there is a great need to implement an operational high-performance forecasting system for pluvial flash flood mitigation and evacuation decisions. This study developed a high-performance two-dimensional hydrodynamic model based on the finite-element method and unstructured grids. The operational high-performance forecasting system is composed of the Weather Research and Forecasting (WRF) model, the Storm Water Management Model (SWMM), a two-dimensional hydrodynamic model, and a map-oriented visualization tool. The forecasting system employs digital elevation data with a 1-m resolution to simulate city-scale pluvial flash floods. The extent of flooding during historical inundation events derived from the forecasting system agrees well with the surveyed data for plain areas in southwestern Taiwan. The entire process of the operational high-performance forecasting system prediction of pluvial flash floods in the subsequent 24 h is accomplished within 8–10 min, and forecasts are updated every six hours. Full article

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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