Tsang-Jung Chang

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.

Agricultural development in Kinmen region has long suffered from the absence of a corresponding management unit responsible for irrigation planning and infrastructure maintenance, resulting in the majority of local farmlands relying on natural rainfall for cultivation. Unfortunately, this method is highly susceptible to the impacts of climate change. To address this pressing issue, the government plans to utilize reclaimed water from domestic sources as a supplementary irrigation resource. Within this context, this study aims to devise an irrigation water allocation model to optimally harness the limited water resources.In this study, we simulate the crop rotation of local sorghum and wheat, considering soil, crop, and historical meteorological data. We calculate the variations in crop yield under different irrigation schemes. Additionally, we use historical meteorological data from Kinmen to calculate various simulated climates, testing the benefits of this irrigation plan under more extreme weather conditions. In conclusion, guided by the simulation outcomes and considerations of factors like cost and government procurement prices, we undertake a comparative analysis of the economic benefits under various scenarios and irrigation plans. This analysis aims to pinpoint the optimal irrigation water allocation plan that can be feasibly implemented by local farmers.For this study, we have chosen a 100-hectare demonstration area located in Jinsha Town, Kinmen, as our study area. We will utilize 750 tons of reclaimed water provided daily by the Ronghu Water Resources Recycling Center as the irrigation water source, and the government has already established six agricultural ponds in the area to store water. Following this, our study will proceed with the implementation of the irrigation water allocation plan in the designated demonstration area. Our ultimate aim is for this initiative to serve as a starting point, enabling the systematic expansion of the irrigation water allocation plan to other regions in Kinmen, thereby enhancing the overall irrigation quality.Keywords: Irrigation Water Allocation Model; Reclaimed Water; Rotation Irrigation; Kinmen; Sorghum

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.

To achieve efficient modeling of flood flow and subsequent solute transport in real-world flood events, the present study couples the CA-based shallow water flow (SWFCA) solver of Chang et al. (2022) with a CA-based advanced solute transport (ASTCA) solver as a novel coupled approach. This coupled approach is GPU-parallelized by using OpenCL 2.1 under Nvidia CUDA to enhance efficiency. The accuracy of the coupled approach is verified and compared with a popular finite-volume-based coupled approach consisting of a Godunov-type FV-HLLC model for flood flow and a Godunov-type FV-TVD model for solute transport through four test cases. The efficiency of the coupled approach is next assessed through three real-world flood cases with massive computational cells. From the simulated results, the ASTCA solver is found to have better accuracy than the state-of-the-art FV-TVD model. Regarding the efficiency, the SWFCA and ASTCA solvers respectively outperform the FV-HLLC and FV-TVD models by 1.28–1.33 and 2.90–3.33 times. After GPU parallelization, the SWFCA solver, ASTCA solver, and CA-based coupled approach respectively speed up the simulations by 57.64–76.23, 53.55–69.88, and 56.32–74.15 times, which is a remarkable progress as the simulations are performed on a PC with a normal graphic card. Hence, the proposed approach is a useful tool for real-world flood flow and solute transport simulations.

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.

Modelling urban inundation and its associated health implications is numerous in its many applications. Flood modelling research contains a broad wealth of material, and microbial risk assessment has gained more popularity over the last decade. However, there is still a relative lack of understanding of how the microbial risk can be quantified from urban sewer flooding. This article intends to review the literature encompassing contemporary urban flood modelling approaches. Hydrodynamic and microbial models that can be applied for quantitative microbial risk assessment will be discussed. Consequently, urban sewer flooding will be the focus. This review found that the literature contains a variety of different hazards posed by urban flooding. Yet, far fewer examples encompass microbial risk from sewer system exceedance. To date, there is no evidence of a perfect model or technique, to carry out a quantitative microbial risk assessment from hydrodynamic simulations. The literature details many different methods. We intend to detail the advantages and limitations of each method. Along similar lines, hydraulic data constitutes a large part of the uncertainty which is inherent to this research field. Many studies in the literature detail data paucity and uncertainty in input data. As such, any advancement in this discipline will very likely aid future research.