Stormwater and flood simulation of sponge city and LID mitigation benefit assessment.

In the context of global climate change and the influence of human activities, the concept of "sponge city" is put forward to realize the purification, collection, and reuse of rainwater. The effective evaluation of LID facilities in sponge cities is of great guiding significance for the promotion and construction of sponge cities. IFMS (Integrated Flood Modeling System) Urban was selected to construct the rainstorm simulation. LID parameters were added to simulate the improvement of urban waterlogging after the construction of sponge city. A reasonable disaster loss assessment method was used to calculate the disaster mitigation benefit brought by the construction of sponge city. Through the comparison of the inundation situation before and after LID facilities' construction, it can be concluded that the mitigation effect of LID facilities on the overall inundation area of the city decreases with the increase of rainfall recurrence period, with the maximum reduction rate reaching 13.63% in the 5-year recurrence period and the minimum reduction rate of 11.06% in the 50-year recurrence period. LID facilities have a better disaster reduction effect for rainfall events with a small recurrence period than for rainfall events with a large recurrence period.

Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method.

Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modeling. A general Bayesian framework is proposed that combines the CA model with observed data to identify unknown sources of river pollution. To reduce the computational burden of the Bayesian inference, a CA contaminant transport model is developed to efficiently simulate pollutant concentration values in the river. These simulated concentration values are then used to calculate the likelihood function of available measurements. The Markov chain Monte Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters, which is a sampling-based method that enables the estimation of complex posterior distributions. The suggested methodology is applied to a real case study of the Fen River in Yuncheng City, Shanxi Province, Northern China, and it estimates the release time, release mass, and source location with relative errors below 19%. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources.