Shiang-Jen Wu

This study aims to develop an ANN-based evaluation model to quantify the effects of climate change on long-term trends in 2D inundation simulations (the SM_QLT_2DIS model). The proposed SM_QLT_2DIS model was developed by improving the ANN-derived two-dimensional inundation simulation model (SM_EID_2D) with Mann–Kendall (MK) long-term trend analysis, considering simulated AR5 hourly typhoon events from 1979 to 2099. Miaoli City in northern Taiwan was regarded as the study area, and the AR5 typhoons across it were treated as the study data. The model application results indicate that the average and maximum hourly inundation depths in the study area exhibit a significant, steadily increasing trend, with a high positive MK estimator (on average 790). Despite a simulated flooding area with a negative MK estimator S (around −620), the coefficient of variation shows an increasing trend (ranging from 0.23 to 0.4). Consequently, the proposed SM_QLT_2DIS model can effectively quantify and assess the impacts of climate change on 2D inundation simulations as a reference for early flood warning and mitigation.

An operational two-dimensional real-time flood forecasting system has been developed to prevent urban inundation in Taiwan, and it uses the Delft-FEWS (Flood Early Warning System) platform to integrate the SOBEK models to forecast flooding. A new generation of smart water level gauges, integrating power charging, recording, transmission, and sensing into one, can monitor the inundation on roads in real-time. This study took Annan District in Tainan City, Taiwan, as the study object and presented the application of the flood forecasting system and smart water level gauges in the urban environment. The collected data from Storm 0611 and Typhoon Megi in 2016 were used to assess the accuracy of the flood forecasting system. The analysis of three water level stations in the drainage showed that the one-dimensional simulation results were fairly accurate. According to the 18 observed and simulated inundation depths on the roads, the true positive, false positive, false negative, and true negative values were 8, 0, 2, and 8, respectively. The accuracy, sensitivity, and precision of the flood forecasting system were 0.889, 0.800, and 1.000, respectively, indicating that the two-dimensional simulation results were highly accurate.

An operational two-dimensional real-time flood forecasting system has been developed in Taiwan to prevent urban inundation. This system takes an hour to come up with forecasts for the next three hours, and the resolution of the forecasts is 40 × 40 m. This study used a large urban area of 126 km² in downtown Taichung City for the case study and adopted the active-cell and multi-grid methods to meet the target by computing from data of a 12-h rainfall within one hour at 20 m × 20 m spatial resolution to provide faster forecasting and more hours for flood preparation. With the active-cell method, the Central Processing Unit (CPU) time was reduced by 65.04% from 659 m 29 s to 230 m 33 s under the 200-year return period storm. Further, with multi-grid methods, the CUP time was reduced by 73.98% from 230 m 33 s to 60 m 0 s. In general, the computing time of this model has been reduced 11-fold. The error validation coefficients of inundation areas were between 89.39~97.45% with an average error of depth between 1.06~3.22 cm.

This study proposes a framework for uncertainty analysis by incorporating explicit numerical solutions of governing equations for flood wave propagation with the expectation operator. It aims at effectively evaluating the effect of variations in initial and boundary conditions on the estimation of flood waves. Spatiotemporal semivariogram models are employed to quantify the correlation of the variables in time and space. The 1D nonlinear kinematic wave equation for the overland flow (named EVO_NS_KWE) is applied in the model development. Model validation is made by comparison with the Monte Carlo simulation model in the calculation of statistical properties of model outputs (ie, flow depths), that is, the mean, standard deviation, and coefficient of variation. The results from the model validation show that the EVO_NS_KWE model can produce excellent approximations of the mean and less satisfactory approximations of the standard deviation and coefficient of variation compared with those obtained by using the Monte Carlo simulation model. It concludes that the uncertainties of flow depths in the domain are significantly affected by variations in the boundary condition. Future application of the EVO_NS_KWE model enables the evaluation of uncertainty in model outputs induced by the initial and boundary condition subject to uncertainty and will also provide corresponding probabilistic information for risk quantification method.

This study proposes an evaluation framework to identify the optimal raingauge network in a watershed using grid-based quantitative precipitation estimation (QPE) with high spatial and temporal resolution. The proposed evaluation framework is based on comparison of the spatial and temporal variation in rainfall characteristics (i.e. rainfall depth and storm pattern) from the gauged data compared with those from QPE. The proposed framework first utilizes cluster analysis to separate raingauges into various clusters based on the locations and rainfall characteristics. Then, a cross-validation algorithm is used to identify the influential raingauge in each cluster based on evaluating performance of fitting weighted spatiotemporal semivariograms of rainfall characteristics from the gauged rainfall to the QPE data. Thus, the influential raingauges for a specific cluster number form the representative network. The optimal raingauge network is the one corresponding to the best fitness performance among the representative networks considered. The study area and data set are the hourly rainfall from 26 raingauges and 1,336 QPE grids for 10 typhoons in the Wu River watershed located in central Taiwan. The proposed evaluation framework suggests that a 10-gauge network is the optimal and can describe a good spatial and temporal variation in the rain field similar to the grid-based QPE from two additional typhoon events.