Flood risk analysis integrating of Bayesian-based time-varying model and expected annual damage considering non-stationarity and uncertainty in the coastal city

Flood disaster is more serious in coastal cities due to the combined impact of rainfall and tides. Accurate assessment of coastal flood risk is essential for planning effective and targeted adaptation under changing environment. The objective of the study is to propose an integrated framework for future flood risk assessment by Bayesian-based time-varying model and expected annual damage (EAD) in the coastal city. To decrease the uncertainty of non-stationary frequency, Bayesian Model Averaging (BMA) and time-varying parameter distribution (TVPD) models were employed to establish the non-stationary distributions of rainfall and tides, and copula function was adopted to determine the joint and co-occurrence probability. Subsequently, to reflect flooding probability and inundation damage simultaneously, the EAD was applied to quantify flood risk by copula function and hydrodynamic model. The variation and uncertainty of flood risk were also investigated under changing environment. Taking Haidian Island in Hainan Province, China as a case study, the results show that the non-stationary distributions of rainfall and tides can be appropriately derived based on BMA and TVPD models. The joint and co-occurrence probabilities increase significantly under non-stationary scenario with the average rates of 33.22 % and 64.82 %, respectively. Moreover, the EAD will be underestimated by 20.56 %–69.84 % in 2030–2060 year without considering non-stationarity, and the uncertainty of EAD rises with the increase of the design year. The approach and result of our study can help decision makers evaluate the future flood risk in the coastal city, and provide support for sustainable flood management to adapt the climate change

Threshold and real-time initiation mechanism of urban flood emergency response under combined disaster scenarios

Scientific and reasonable emergency response initiation mechanisms can provide important support for decision making regarding the emergency management of urban floods. However, there is a lack of a unified paradigm on how to calculate the threshold for emergency response initiation and reasonably initiate emergency response. Therefore, this study proposes a loss-driven urban flood emergency response initiation framework from the perspective of combined disasters. A discrimination mechanism of the emergency response initiation level was established based on the optimal threshold and loss function. And the rainfall event that occurred in Zhengzhou, China, on July 20, 2021, was taken as an example to realize real-time emergency response discrimination and initiation driven by forecast data. Results showed that the initiation time of the Level I emergency response using the proposed method was 9.5 h earlier than the time of the government release, thereby significantly increasing the preparation time for flood management personnel. In addition, the results of the optimal threshold selection indicated that the Natural Breakpoint method was the optimal method for loss threshold partitioning, with the comprehensive evaluation index (CEI) being 3.56–9.53 % higher than those of the K-means, Equal Interval, and Quantile method. These results constitute a reference for urban emergency management and related research.</p

Parameter optimization of SWMM model using integrated Morris and GLUE methods

The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most extensively implemented numerical models for simulating urban runoff. Parameter optimization is essential for reliable SWMM model simulation results, which are heterogeneously sensitive to a variety of parameters, especially when involving complicated simulation conditions. This study proposed a Genetic Algorithm-based parameter optimization method that combines the Morris screening method with the generalized likelihood uncertainty estimation (GLUE) method. In this integrated methodology framework, the Morris screening method is used to determine the parameters for calibration, the GLUE method is employed to narrow down the range of parameter values, and the Genetic Algorithm is applied to further optimize the model parameters by considering objective constraints. The results show that the set of calibrated parameters, obtained by the integrated Morris and GLUE methods, can reduce the peak error by 9% for a simulation, and then the multi-objective constrained Genetic Algorithm reduces the model parameters’ peak error in the optimization process by up to 6%. During the validation process, the parameter set determined from the combination of both is used to obtain the optimal values of the parameters by the Genetic Algorithm. The proposed integrated method shows superior applicability for different rainfall intensities and rain-type events. These findings imply that the automated calibration of the SWMM model utilizing a Genetic Algorithm based on the combined parameter set of both has enhanced model simulation performance