This study integrated coastal watershed models and combined them with a risk assessment method to develop a methodology to investigate the impact resulting from coastal disasters under climate change. The mid-western coast of Taiwan suffering from land subsidence was selected as the demonstrative area for the vulnerability analysis based on the prediction of sea level rise (SLR), wave run-up, overtopping, and coastal flooding under the scenarios of the years from 2020 to 2039. Databases from tidal gauges and satellite images were used to analyze SLR using Ensemble Empirical Mode Decomposition (EEMD). Extreme wave condition and storm surge were estimated by numerical simulation using the Wind Wave Model (WWM) and the Princeton Ocean Model (POM). Coastal inundation was then simulated via the WASH123D watershed model. The risk map of study areas based on the analyses of vulnerability and disaster were established using the Analytic Hierarchy Process (AHP) technique. Predictions of sea level rise, the maximum wave condition, and storm surge under the scenarios of 2020 to 2039 are presented. The results indicate that the sea level at the mid-western coast of Taiwan will rise by an average of 5.8 cm, equivalent to a rising velocity of 2.8 mm/year. The analysis indicates that the Wuqi, Lukang, Mailiao, and Taixi townships are susceptive, low resistant and low resilient and reach the high-risk level. This assessment provides important information for creating an adaption policy for the mid-western coast of Taiwan.

Water resources in Taiwan come predominantly from rivers. Hence, it is important to understand the impact of future climate scenarios for policymaking. To investigate the impact of accelerating climate change on river flow in Taiwan, a regional flow impact model (RFIM) was developed. The RFIM is based on the radial basis function neural network. It adapts the genetic algorithm for parameter optimisation and the bootstrap method for quantifying uncertainties in the model and its results. The study area is the Taiwan Island, divided into four water resource management regions: North, Middle, South and East. After the RFIMs were developed for different regions, various future weather scenarios predicted from global circulation models were applied. The results suggest that the average discharge increases at a higher rate in the Middle and the East and the uncertainty of future discharge is higher in the Middle and the South of Taiwan Island.

Water resources in Taiwan come predominantly from rivers. Hence, it is important to understand the impact of future climate scenarios for policymaking. To investigate the impact of accelerating climate change on river flow in Taiwan, a regional flow impact model (RFIM) was developed. The RFIM is based on the radial basis function neural network. It adapts the genetic algorithm for parameter optimisation and the bootstrap method for quantifying uncertainties in the model and its results. The study area is the Taiwan Island, divided into four water resource management regions: North, Middle, South and East. After the RFIMs were developed for different regions, various future weather scenarios predicted from global circulation models were applied. The results suggest that the average discharge increases at a higher rate in the Middle and the East and the uncertainty of future discharge is higher in the Middle and the South of Taiwan Island.

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