Xuekai Chen

Addressing the challenge of tracing hidden and transient cross-connections in urban drainage networks, this study develops a SWMM–Bayesian coupled model based on the Py SWMM interface using the Daming Lake area in Jinan as a case study. By employing a Markov Chain Monte Carlo (MCMC) algorithm to drive the interaction between dynamic simulation and statistical inference, the model achieves multidimensional joint posterior estimation of pollution source location (J_x), discharge intensity (M), and discharge timing (T). The results indicate: (1) Model accuracy: The coupled model demonstrates strong source tracing capability, with mean absolute errors below 0.6% in single-parameter inversion. Under multi-parameter joint inversion, the true values of all parameters consistently fall within the 95% confidence intervals. (2) Parameter sensitivity: The influence of MCMC step size on the uncertainty of pollution tracing results is systematically clarified. Discrete source location estimates (J_x) exhibit high robustness to step size variation due to spatial heterogeneity in hydraulic responses, whereas continuous physical parameters (M and T) show strong dependence on the selected step size scale. (3) Practical application: The impact of spatial monitoring network configuration on pollution tracing performance is examined. By deploying a complementary monitoring system integrating trunk and branch pipelines, the inversion accuracy for mass (M) and time (T) parameters is significantly improved by 84.2% and 88.5%, respectively. Overall, the proposed pollution source tracing method for urban drainage networks effectively overcomes the multi-solution challenge in complex network inversion, providing critical technical support for refined urban water environment management.

The prevention and control of non-point source pollution is an important link in managing basin water quality and is an important factor governing the environmental protection of watershed water in China over the next few decades. The control of non-point source pollution relies on the recognition of the amount, location, and influencing factors. The watershed nonpoint source pollution mechanism model is an effective method to address the issue. However, due to the complexity and randomness of non-point source pollution, both the development and application of the watershed water environment model have always focused on the accuracy and rationality of model parameters. In this pursuit, the present study envisaged the temporal and spatial heterogeneity of non-point source pollution caused by the complex underlying surface conditions of the watershed, and the insufficient coverage of hydrological and water quality monitoring stations. A refined watershed non-point source pollution simulation method, combining the Monte Carlo analytic hierarchy process (MCAHP) and the sub-watershed parameter transplantation method (SWPT), was established on the basis of the migration and transformation theory of the non-point source pollution, considering the index selection, watershed division, sub-watershed simulation, and parameter migration. Taking the Erhai Lake, a typical plateau lake in China, as the representative research object, the MCAHP method effectively reduced the uncertainty of the weights of the watershed division indexes compared to the traditional AHP method. Furthermore, compared to the traditional all watershed parameter simulation (AWPS) approach, the simulation accuracy was improved by 40% using the SWPT method, which is important for the prevention and control of non-point source pollution in large-scale watersheds with significant differences in climatic and topographic conditions. Based on the simulation results, the key factors affecting the load of the non-point source pollution in the Erhai watershed were identified. The results showed that the agricultural land in Erhai Lake contributed a majority of the load for several reasons, including the application of nitro phosphor complex fertilizer. Among the different soil types, paddy soil was responsible for the largest pollution load of total nitrogen and total phosphorus discharge into the lake. The zones with slopes of 0–18° were found to be the appropriate area for farming. Our study presents technical methods for the assessment, prevention, and control of non-point source pollution load in complex watersheds.

Due to the influence of climate warming and human activities, extreme precipitation events occur frequently in various parts of the country. In order to reduce their negative impacts, the present study envisaged a systematic and comprehensive analysis of the variation characteristics in extreme precipitation of the Shiwulihe River Basin. Based on the daily precipitation data of the Shiwulihe River Basin in Hefei from 1960 to 2019, six extreme precipitation indexes such as RX1day, RX5day, R20 mm, R95P, SDII and CWD were selected. The variation characteristics in the extreme precipitation were analyzed using linear trend analysis, M-K non-parametric test, sliding T test, Morlet continuous complex wavelet analysis, and Pearson correlation analysis. The results reveal the time-varying characteristics of extreme precipitation in Shiwulihe River Basin.