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.

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.

Coastal erosion is a major natural hazard along the northeastern shoreline (i.e., Yilan County) of Taiwan. Analyses of the evolution of the 0 m isobath of the Yilan County coastline indicate that erosion and accretion are occurring north and south of Wushi Fishery Port, respectively, because of jetty and groin construction. Topographic and bathymetric surveys involving the measurement of 43 cross sections were conducted in 2006, 2012, 2013, and 2019. The cross-shore profile comparisons reveal that the erosion of onshore dunes is significant in the northern Jhuan River estuary. Due to the establishment of a nature reserve in the southern Lanyang River estuary, the sediments are carried northward by tidal currents, and accretion is inevitable in the northern Lanyang River estuary. The results of the bathymetric surveys also suggest that the shoreline of Yilan County tends to accrete in summer because of abundant sediment from the rivers; however, it is eroded in winter, owing to the large waves induced by the northeast monsoon. Additionally, the calculated net volume of erosion and accretion between each pair of cross sections shows that the length of coastline impacted by estuarine sediment transport is approximately 2 km long from north to south along the coastline of the Lanyang River estuary.

Taiwan is in a hazard-exposed area where people often suffer through typhoons, earthquakes, and landslides, and must face the challenges of environmental and climate changes in ongoing and future developments. Taiwan has implemented an integrated and interdisciplinary project, which is titled Civil IoT Taiwan, for better disaster risk management and risk communication with all stakeholders by cooperating closely with authorities, scientists, and industry. The purposes of this project are to raise public risk awareness to reduce disaster damage and loss and sustainably increase the social, economic, and environmental impacts. For measuring the social impacts of the Civil IoT Taiwan, the social return on investment (SROI) is an evaluation tool to demonstrate the outcomes and impacts of Civil IoT Taiwan to measure its social effects. The SROI ratio of this project is 1.12. Civil IoT Taiwan has just implemented the first development stage in establishing infrastructure for monitoring and sensing; thus, the significant changes and impacts on society, economics, and the environment will be evaluated in the next phase. This ongoing project will also involve more stakeholders for more sustainable and resilient environmental governance in future development.

In this study, long-term wind fields during 1991–2010 from the Climate Forecast System Reanalysis (CFSR) were dynamically downscaled over Taiwan and its offshore islands at a 5 km horizontal resolution using the Weather Research and Forecasting (WRF) model. Simulations of the 10 m (above sea level) dynamically downscaled winds served as the atmospheric forcing for driving a fully coupled wave-circulation model. The sea states of the waters surrounding Taiwan during 1991–2010 were hindcasted to evaluate the offshore wave energy resources and optimal wave energy hotspots. This study reveals that the southeastern offshore waters of Taiwan and the Central Taiwan Strait exhibited the highest mean wave power density (WPD), exceeding 20 kW/m. The annual mean WPD, incidence of the hourly WPD greater than or equal to 4 kW/m, monthly variability index and coefficient of variation of the WPD indicated that the sea areas located between Green Island and Orchid Island (OH_1), southeast of Orchid Island (OH_2), south of the Hengchun Peninsula (OH_3), and north of the Penghu Islands (OH_4) were the optimal hotspots for deploying wave energy converters. The most energetic months were October for OH_1 and OH_2 and November for OH_3 and OH_4, while the wave power was weak from March to June for OH_1, OH_2 and OH_3 and in May for OH_4. The wave direction is prevailingly east-northeast for OH_1, OH_2 and OH_3 and nearly northeast for OH_4. These phenomena reveal that wave power in the waters offshore Taiwan is induced primarily by the northeast (winter) monsoon. The exploitable annual WPD was estimated to be 158.06, 182.89, 196.39 and 101.33 MWh/m for OH_1, OH_2, OH_3 and OH_4, respectively.