Water conservancy projects have the functions of flood control, power generation, water supply, and irrigation, and play a vital role in the survival and development of human society [...]
The temporal heterogeneity of rainfall is substantial in urban catchments, and it often has huge impacts on stormwater simulation and management. Using a design storm with a fixed pattern may cause uncertainties in hydrological modeling. Here, we propose an event-based stochastic parametric rainfall simulator (ESPRS) for stormwater simulation in a sponge city with green roofs, permeable pavements, and bioretention cells. In the ESPRS, we used five distributions to fit the measured rainfall events and evaluated their performance using Akaike’s Information Criterion, Anderson—Darling goodness-of-fit test, and p-values. The vast rainfall time series data generated using the ESPRS were used to run the storm water management model for outflow simulations in the catchment, thus revealing the influence of temporal rainfall characteristics on the hydrological responses. The results showed the following: (1) The ESPRS outperforms the Chicago method in predicting extreme precipitation events, and its control factors are the rainfall peak period, rainfall peak fraction, and cumulative rainfall fraction at the peak period. (2) The best-fit functions for the rainfall depth in each period have different distributions, mostly being in lognormal, gamma, and generalized extreme value distributions. (3) Rear-type precipitation events with high peak fractions are the most negative pattern for outflow control. The developed ESPRS can suitably reproduce rainfall time series for urban stormwater management.
The deformation properties of concrete arch dams are affected by the synergistic effects of multiple factors, featuring strong, multidimensional spatialtemporal evolution and distribution characteristics. This paper proposes a zoned safety monitoring model for arch dam deformation based on spatialtemporal similarity and model optimization to evaluate the deformation safety state of arch dam structures. First, zoned clustering of the deformation monitoring points at different locations of an arch dam was performed using a panel data multi-index clustering method to determine the deformation laws at different positions of the dam. Next, multipoint comprehensive displacements of the deformation properties of each zone were extracted using principal component analysis to extract the uniform deformation law of the monitoring point in each zone. Finally, we adopted Bayesian model selection (BMS) and Bayesian model averaging (BMA) for the regression model set, considering the uncertainty of the model. The engineering case study showed that BMA yielded robust and effective prediction results for the deformation of the arch dam. The analysis of the zoned deformation mechanism indicated that the deformation of the arch dam followed the general rule. The temperature component of the arch dam was mainly reflected in the middle with a hysteresis effect, and the time-dependent component was evident in both sides of the dam shoulder. The arch dam deformation safety monitoring model proposed in this study has strong robustness and interpretability, which can provide valuable technical support for analyzing the evolution of arch dam deformation properties.
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