Hsiang-Lin Yu

Rainfall-driven inlet clogging is a phenomenon where inlets of stormwater networks are covered with debris, fallen leaves, or trash flushed by street runoffs due to heavy rainfall. Surface runoffs are thus unable to enter sewer networks that are still under capacity, leading to greater flood risks. To provide a feasible way to reflect such inlet clogging in pluvial urban inundation modeling, a new event-based reverse computing approach using simulated and surveyed data is proposed. The performance of this approach in the aspect of accuracy improvement is then discovered in a flood-vulnerable area. The optimal way for delineating clogged inlets is first explored. Next, the possible range of clogging factors used to represent capacity-reduced effects in inlets is decided by comparing the simulated and reverse-computed flood volumes. The impact of inlet clogging on urban inundation physics is also assessed. The results indicate that, for the study site, considering inlets surrounded by trees to be 10–40% clogged (median of 25%) will increase overall accuracy. Furthermore, inlet clogging reduces drained discharges entering sewer networks, thereby increasing surface water volumes by about 10–20%, diminishing spurious sewer surcharge, and decreasing sewer water depths. Interestingly, greater drained discharges are found in clogged inlets at local depressions, even though most clogged inlets have smaller drained discharges. In summary, the present approach is demonstrated to be a useful tool to account for rainfall-driven inlet clogging in urban inundation modeling.

The generalized likelihood uncertainty estimation (GLUE) method is widely used in many engineering applications due to its ease of implementation. Nevertheless, it has several drawbacks due to the subjectivity inherent in the statistically informal likelihood function, the cutoff threshold for identifying the behavioral solutions, and the probability in the posterior distribution. Hence, although the standard GLUE‐based approach for wind energy probabilistic forecasting includes more measured data within the confidence intervals than the Monte Carlo (MC)‐based approach, its performance becomes less in months with apparent wind speed fluctuations. For this reason, the present study extends the standard GLUE‐based approach with three novel ways to include more behavior data points in the posterior distribution so that the result can be more representative. The resultant improved GLUE‐based approach is assessed and compared with the standard GLUE‐based and MC‐based approaches through the 15 scenario test cases. Based on the results, the 50% and 90% confidence intervals of the proposed approach both match measured data better than the standard GLUE‐based and MC‐based approaches. Also, the proposed approach gives better predictions under all training and validation timespans. Thus, the improved GLUE‐based approach is proven to be more effective and robust in accessing wind energy uncertainties.

To provide accurate and efficient forecasting of fluvial flooding assessment in the river basin, the present study links the well-known CA-based urban inundation modeling (2D-OFM-CA) with a one-dimensional river flow model (1D-RFM) as a coupled 1D-2D river–overland modeling. Rules to delineate the geometric linking between the 1D-RFM and 2D-OFM-CA along embankments are developed. The corresponding exchanged water volume across an embankment is then computed by using the free and submerged weir flow formulas. The applicability of the proposed coupled model on fluvial flooding assessment is then assessed and compared with a well-recognized commercial software (HEC-RAS model) through an idealized fluvial case and an extensively studied real-scale fluvial case in the Severn River Basin. Based on the simulated results concerning the numerical accuracy, the coupled model is found to give similar results in the aspects of the river flow and overland flow modeling in both two study cases, which demonstrates the effectiveness of the linking methodology between the 1D-RFM and 2D-OFM-CA. From the viewpoint of numerical efficiency, the coupled model is 47% and 41% faster than the HEC-RAS model in the two cases, respectively. The above results indicate that the coupled model can reach almost the same accuracy as the HEC-RAS model with an obvious reduction in its computational time. Hence, it is concluded that the coupled model has considerable potential to be an effective alternative for fluvial flooding assessment in the river basin.

This study proposes a novel framework that couples the general likelihood uncertainty estimation (GLUE) method with a deterministic forecasting approach to conduct a new uncertainty analysis approach for assessing the energy production of operational wind turbines installed in the Jhongtun wind farm at Penghu (an island in the middle of Taiwan Strait). The 10‐year measured data of wind speeds and energy output collected on these wind turbines is divided into two 5‐year data sets for the present analysis framework of execution and validation to demonstrate the predictability of the GLUE method. The present study considers 15 scenario testing cases with various time periods, i.e., twelve months, the strong‐wind (October‐March) regime, the weak‐wind (April‐September) regime, and one year, for the framework to investigate the applicability of the GLUE method on long‐term wind energy forecasting. In the execution framework, the 5‐year measured data is used by the GLUE method to access the uncertainties involved in the deterministic approach (i.e., the shape and scale parameters of the Weibull wind speed distribution (WWSD), the performance curve, and the capacity factor) with two confidence intervals of 50% and 90%. The framework is then validated by the measured capacity factors in the last 5‐year data and compared with the results of the uncertainty analysis approach by the Monte Carlo (MC) approach to discover the applicability of the new uncertainty analysis approach. From the simulated results, it is found that the proposed uncertainty analysis approach provides predictions of confidence intervals that match the measured data better than the MC‐based uncertainty analysis approach. Specifically, the proposed approach can match the measured capacity factors in all the simulated scenarios. Conversely, the MC‐based approach is found to create narrow confidence intervals that cannot completely capture the measured capacity factors, particularly for the strong‐wind, weak‐wind, and one‐year scenarios. Therefore, this novel uncertainty analysis approach is proven to be useful in predicting the uncertainties of wind energy production.

Currently, for modeling two-dimensional (2D) solute transport during pluvial and fluvial floods, the finite volume (FV) models are widely used because of their strong ability to handle steep concentration and velocity gradients from the flow advection term. However, heavy computational requirements are subsequently introduced which limit the numerical efficiency. To further increase numerical efficiency but keep the required accuracy, this study proposes a novel Solute Transport Modeling based on Cellular Automata framework (STMCA) to simulate solute transport due to the flow advection, turbulent diffusion, and material decay mechanisms in several sets of explicit algebraic equations. Four studied cases involving steep gradients of solute concentration and velocities in steady/unsteady violent flow conditions are used to compare the accuracy of the STMCA approach with a Godunov-type FV solute transport approach with a total variation diminishing (TVD) scheme. Then, the performances of the two approaches on water quality modeling are assessed through the E. Coli transport modeling during pluvial/fluvial floods on a real-scale terrain. The proposed STMCA approach is found to achieve almost the same accuracy as the FV approach. As to the numerical efficiency, the STMCA approach is faster than the FV approach by 289.6–328.6%. Hence, the proposed STMCA approach is proven to be an effective tool for simulating solute transport.