River contaminant source identification problems can be formulated as an inverse model to estimate the missing source release history from the observed contaminant plume. In this study, the identification of pollution sources in rivers, where strong advection is dominant, is solved by the global space-time radial basis collocation method (RBCM). To search for the optimal shape parameter and scaling factor which strongly determine the accuracy of the RBCM method, a new cost function based on the residual errors of not only the observed data but also the specified governing equation, the initial and boundary conditions, was constructed for the k-fold cross-validation technique. The performance of three global radial basis functions, Hardy's multiquadric, inverse multiquadric and Gaussian, were also compared in the test cases. The numerical results illustrate that the new cost function is a good indicator to search for near-optimal solutions. Application to a real polluted river shows that the source release history is reasonably recovered, demonstrating that the RBCM with the k-fold cross-validation is a powerful tool for source identification problems in advection-dominated rivers. (C) 2015 Elsevier Ltd. All rights reserved.

A one-dimensional (1D) finite-volume model is developed for simulating nonequilibrium sediment transport in unsteady flow. The governing equations are the 1D mass and momentum conservation equations for sediment-laden flow and the sediment continuity equation for both bed load and suspended-load transport. The Rouse profile is modified to consider the nonequilibrium transport of suspended sediment. The spatial lag between the instantaneous flow properties (e.g., velocity, bed shear stress) and the rate of bed load transport in unsteady flow is quantified by using an adaptation length, which is derived theoretically by applying the momentum principle in the bed load layer. This new method for calculating the adaptation length is verified using data from several experiments and yields better results than other empirical formulas for a wide range of shear stress. The nonequilibrium model is applied to simulate a series of laboratory dam-break flows over erodible beds, and the simulated results agree ...

Urban flooding is a global water disaster resulting from the expansion of urban impervious surfaces and the strengthening of extreme precipitation events, especially in China. Nonetheless, few studies have focused on the spatial distributions of urban flooding characteristics and their variations in the context of climate change. In this study, eight critical metrics (i.e., maximum flooding volume, total overloaded manholes with different flooding volumes or durations, total flooding volume, mean and maximum flooding durations, maximum inundation area, and depth) are adopted to characterize the urban flood events. The impacts of climate change on these metrics are assessed for two periods, the 2030s (2020–2049) and 2070s (2060–2089), and compared with those in the baseline period (1976–2005). The Future Science City Park in Beijing, China, is selected as our study area. The results show that all four flood events are well simulated, with both efficiency coefficients and correlation coefficients being over 0.8. The number of overloaded manholes and the total flooding volume are projected to increase 19.3%–44.8% and 171%–716% under 20‐year rainfall events due to climate change in the two future periods. The spatial distribution of overloaded manholes with different increased flooding volumes is projected to expand to almost the whole area from the region with lowland and limited drainage capacity. Furthermore, the maximum inundation area and depth are projected to increase obviously. This study will be helpful for designing and improving the drainage system, controlling urban flooding, and adapting to climate change.