This study develops a computational framework that integrates a flood simulation model, a response surface method, and an optimization technique for an optimal flood control design. The objective function was the minimization of the difference between simulated flooding depths and pre-specified allowable flooding depths, with simulated flooding depths constrained to less than allowable flooding depths in the control regions. The design variables were the design capacities of hydraulic structures. The flood simulation model was based on two-dimensional shallow water flow equations for open channel flow, rainfall, overland flow flooding, and river interactions. The response surface method was combined with the central composite design method to construct approximate functions for the objective and the constraint functions in terms of the design variables. Sequential quadratic programming was used to obtain the optimal design variables with the minimum value of the approximated objective function. Actual flood data from Typhoon Morakot for the Linbian River was used to verify the flood simulation model. The integrated computational framework was then used to calculate the optimal upstream interception strengths for controlling downstream flooding under hydrologic conditions associated with a 100-year return period storm. Numerical examples demonstrated that the proposed computational framework can efficiently provide optimal designs for practical flood control problems.

This article presents a procedure to improve the accuracy of calculated stiffness matrix of a structure based on the identified modal parameters from its measured responses. First, a continuous wavelet transform is applied to the measured responses of a structure, and the state–space model can be reconstructed by the wavelet coefficients of acceleration that can be obtained from the measured noisy responses. The modal parameters are identified using the subspace approach. Second, the identified mode shapes are corrected via Gram–Schmidt process. Finally, the identified natural frequencies and the corrected mode shapes in previous steps are utilized to build the stiffness matrix of structure. The accuracy of the proposed approach is numerically confirmed, and the noise effects on the ability to precisely identify the stiffness matrix are also investigated. The measured data of two eight-story steel frames in a shaking table test are analyzed to demonstrate the applicability of the procedure to real structures.

Identifying the structural modal parameters based on the measured responses of structure is a major application for structural health monitoring. This study presents a procedure based on Gram-Schmidt process to improve the accuracy of the calculated stiffness matrix of a structure. The continuous wavelet transform is applied to the measured responses of a structure and the time series model could be reconstructed to denoise for the measured noisy responses. The identified modal parameters would be obtained from the coefficient in time series model. Next, correcting the identified mode shapes via Gram-Schmidt process. Finally, the structural stiffness matrix can be created via the identified natural frequencies and the corrected mode shapes. The accuracy of this procedure is numerically confirmed; the effects of the wavelet parameters and noise on the ability to accurately estimate the stiffness matrix are also investigated.

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.

This work presents the use of a Gabor transform to determine the instantaneous modal parameters (natural frequencies, damping ratios and mode shapes) of a structure from its dynamical responses, such as free vibration or earthquake response. The Gabor transform is applied to the measured acceleration responses of a structural system for reconstructing the time-varying time series model in time-frequency domain. The instantaneous modal parameters of a time-varying system can be directly estimated from the time-varying model coefficients. The proposed procedure is further applied to process the dynamic responses of three-storey steel frames with or without nonlinear elements from the simulated earthquake events via SAP2000 to identify the differences among the frames. The linear or nonlinear behaviors of these steel frames under earthquake input are accurately reflected in the instantaneous natural frequencies that are obtained by the proposed approach.

This study modifies a real-time correction method for water stage forecasts (named the RTEC_TS&KF model) using the time series method developed by Wu et al. (Stoch Environ Res Risk Assess 26:519-531, 2012) (named the RTEC_TS model), by incorporating the Kalman filter (KF) model. The RTEC_TS&KF model adjusts the corrected water stage forecasts resulting from the RTEC_TS model by taking into account the uncertainties in the model structure/inputs as well as the measurement bias. In detail, the water stage forecasts are corrected by separately adding the forecasted errors by the times series model and KF method into the stage forecasts. As compared to the results from the RTEC_TS model using the forecasted and observed water stages for Typhoons Morakot (2009), Saola (2012) and Soulik (2013), the RTEC_TS&KF model not only effectively lessens the uncertainties in regard to the water stage forecasts, but also consistently presents high correction performance of water level forecasts for various rainstorm events. This reveals that the RTEC_TS&KF model is superior to the RTEC_TS model in the correction of water stage forecasts. In the future, the RTEC_TS&KF model will be applied in the real-time corrections of other hydrological variates, such as the outflow of a reservoir, in the case of observation being provided on time.

This study presents a probabilistic radar rainfall estimation (PRRE) model to quantify the reliability and accuracy of the resulting radar rainfall estimates at ungauged locations from a radar-based quantitative precipitation estimation (QPE) model. This model primarily estimates the quantiles of the radar rainfall errors at ungauged locations by incorporating seven spatiotemporal variogram models with a nonparametric sample quantile estimate method based on the radar rainfall errors at rain gauges. Then, by adding the resulting error quantiles to the radar rainfall estimates, the corresponding radar rainfall quantiles can be obtained. The QPE system Quantitative Precipitation Estimation Using Multiple Sensors (QPESUMS) provides hourly observed and radar precipitation for three typhoons in the Shinmen reservoir watershed in Northern Taiwan, which are used in the model development and validation. The results indicate that the proposed PRRE model can quantify the spatial and temporal variations of radar rainfall estimates at ungauged locations provided by the QPESUMS system. Also, its reliability and accuracy could be evaluated based on a 95% confidence interval and occurrence probability resulting from the cumulative probability distribution established by the proposed PRRE model.

This study proposes a risk assessment framework for quantifying the reliability of the rainfall threshold used in flash flood warning, which should be influenced by the uncertainties in the rainfall characteristics, including rainfall duration, depth, and storm pattern. This risk assessment framework incorporates the correlated multivariate Monte Carlo simulation method, the Sobek 1D–2D hydrodynamic model, and a logistic regression equation to establish a quantile relationship of the rainfall threshold for quantifying reliability of the rainfall threshold. The Shuhu Creek catchment locates in East Taiwan, and its historical hourly rainfall records on eight typhoon events are used as the study area and data. The results from the proposed framework indicate that the variation in the rainfall threshold declines with the duration; 12-h duration associated with a stable coefficient of variance of the rainfall threshold appears to be appropriate for the flash flood warning in the Shuhu Creek catchment. Moreover, the issued rainfall thresholds in the Shuhu Creek catchment by Water Resources Agency in Taiwan have a low exceedance probability. This infers that inundation might occur as the observed rainfall depth approaches the threshold, so that it is necessary to lower the rainfall threshold in accordance with higher exceedance probability in order to achieve the goal of early flood warning.

Piers with back-to-back stems or columns and piers for which part of the foundation becomes exposed as a result of the development of scour over large periods of time or because of severe flood events are fairly common at bridge waterways. The present paper uses eddy-resolving numerical simulations to study flow and turbulence structure at piers of complex shape and/or with multiple components. In particular, the study considers cases with one and two back-to-back pier columns for which the section of the main column is neither circular nor rectangular. In addition to a design case for which the foundation of each pier column is submerged, the study analyzes a case when scour exposes part of the foundation of the main column. The results show that the shape and size of the pier column have a significant effect on the spatial and temporal distributions of the bed friction velocity induced by the horseshoe vortex system. The large-scale shedding behind the main column greatly influences flow structure and increases bed friction velocity around the downstream column for piers with two back-to-back columns that are aligned with the incoming flow direction. The present study shows that the presence of large-scale unsteady coherent structures in the vicinity of the bed around piers of complex shapes results in very complex distributions of the bed friction velocity and in large-scale temporal oscillations of the bed friction velocity. The results of eddy-resolving simulations strongly suggest the need to account for the effect of these large-scale oscillations around the mean value when bed friction velocity distributions are used to estimate the flux of entrained sediment in movable bed simulations that do not resolve the large-scale turbulent flow structures. (C) 2013 American Society of Civil Engineers.

This study proposes a real-time monitoring system to integrate the supportive flood-related information for hazard mitigation purposes. Through integrating photography, communication, geometric information display and network technology, this system can provide the real-time monitoring images at the critical gauge stations, inundation-prone areas and important hydraulic facilities during floods. With the development of mobile monitoring modules, the maneuverability of users and facilities could be significantly improved. Users can easily employ mobile phones to receive or report real-time flood-related information at any place and time. Furthermore, the water-level image recognition method is developed and applied to the selected monitoring stations for acquiring early warning of flood events. In final, an application scenario is given to illustrate how this system can greatly improve the effectiveness and efficiency of emergency responses to flood hazard mitigation for the decision-makers.

This study proposes a parameter-calibration method (SA_GA) for the conceptual rainfall–runoff model using a real-value coding genetic algorithm (GA) which takes into account runoff estimation sensitivity to model parameters; this process is carried out using the standardized regression equation. The proposed SA_GA method treats the standardized values of model parameters as the real-value code and adopts a multinomial trial process with a probability of selecting genes for the crossover and mutation resulting from the runoff estimation sensitivity to the model parameters. A 19-parameter conceptual rainfall–runoff model, Sacramento Soil Moisture Accounting (SAC-SMA) model, and seven rainstorm events recorded in the Baj-Hang River watershed of South Taiwan are applied in the model development and validation. The results indicate that SA_GA is superior to a simple genetic algorithm (SGA) as regards the calculation of fi tness values associated with the optimal parameters under various GA operators. In addition, by comparing the performance indices of estimated runoff with the calibrated optimal parameters by SA_GA and SGA with the different number of calibration rainstorm events, SA_GA can provide efficient and robust optimal parameters. These parameters not only estimate reliable and accurate runoff, but also capture the varying trends of discharge in time.

This study proposes a real-time error correction method for the forecasted water stage using a combination of forecast errors estimated by the time series models, AR(1), AR(2), MA(1) and MA(2), and the average deviation model to update the water stage forecast during rainstorm events. During flood forecasting and warning operations, the proposed real-time error correction method takes advantage of being individually and continuously implemented and the results not being updated to the hydrological model and hydraulic routings so as to save computational time by recalibrating the parameters of the proposed methods with real-time observation. For model validation, the current study adopts the observed and forecasted data on a severe typhoon, Morakot, collected at eight water level gauges in Southern Taiwan and provided by the flood forecast system FEWS_Taiwan, which is linked with the reliable quantitative precipitation forecast (QPF) at 3 h of lead time provided by the Center Weather Bureau in Taiwan, as the model validation. The results of numerical experiments indicate that the proposed real-time error correction method can effectively reduce the errors of forecasted water stages at the 1-, 2-, and 3-h lead time and so enhance the reliability of forecast information issued by the FEWS_Taiwan. By means of real-time estimating potential forecast error, the uncertainties in hydrology, modules as well as associated parameters, and physiographical features of the river can be reduced.

This work proposes a risk analysis model to evaluate the risk of underestimating the predicted peak discharge, i.e. the exceedance of probability due to the uncertainties in rainfall information (rainfall depth, duration, and storm pattern) and the parameters of the rainfall-runoff model (Sacramento Soil Moisture Accounting model, SAC-SMA) during the flooding prevention and warning operation. The proposed risk analysis model is combined with the multivariate Monte Carlo simulation method and the Advance First-Order Second-Moment method (AFOSM). The observed rainfall and discharge measured at Yu-feng Basin study area in Shihmen reservoir watershed is used in the model development and application. The results of the model application indicate that the proposed risk analysis model can analyze the sensitivity of the uncertainty factors for the predicted peak discharge and evaluates the variation of the probability of exceeding the predicted peak discharge with respect to the rainfall depth and storm duration. In addition, the result of risk analysis for a real rainstorm event, Typhoon Morakot, shows that the proposed model successfully explores the risk of underestimating the predicted peak discharge using SAC-SMA and forecasted rainfall information and provides a probabilistic forecast of the peak discharge.

(1) National Taipei University of Technology, Institute of Engineering Technology, Civil Engineering, Taiwan (coop.shen@gmail.com), (2) National Taipei University of Technology,Civil Engineering, Taiwan, (3) National Center for High-Performance Computing,Taiwan (hclien@nchc.org.tw, sjwu@nchc.org.tw), (4) Water Resources Agency (WRA), Ministry of Economic Affairs (MOEA), Taiwan (mjhorng@wra.gov.tw)