RESUMO
The coincidence of flood flows in a mainstream and its tributaries may lead to catastrophic floods. In this paper, we investigated the flood coincidence risk under nonstationary conditions arising from climate changes. The coincidence probabilities considering flood occurrence dates and flood magnitudes were calculated using nonstationary multivariate models and compared with those from stationary models. In addition, the "most likely" design based on copula theory was used to provide the most likely flood coincidence scenarios. The Huai River and Hong River were selected as case studies. The results show that the highest probabilities of flood coincidence occur in mid-July. The marginal distributions for the flood magnitudes of the two rivers are nonstationary, and time-varying copulas provide a better fit than stationary copulas for the dependence structure of the flood magnitudes. Considering the annual coincidence probabilities for given flood magnitudes and the "most likely" design, the stationary model may underestimate the risk of flood coincidence in wet years or overestimate this risk in dry years. Therefore, it is necessary to use nonstationary models in climate change scenarios.
RESUMO
The determination of characteristic flow velocity is a hydrodynamic problem needs to be solved in the application of geomorphologic instantaneous unit hydrograph (GIUH) for runoff simulation in areas with no or limited data. In this study, 120 watersheds are collected to construct a regression model; 85 of these basins are used for regression analysis, and the 35 remaining basins are utilized to verify the feasibility of the constructed model. Random forest algorithm is applied to screen out important geomorphologic factors from the 16 extracted factors that may affect flow velocity. Multivariate regression is used to establish the numerical relationship between velocity and the selected factors. Sensitivity analysis of each adopted factor in the constructed model is conducted using the LH-OAT method. The rationality and feasibility of the regression model are validated by comparing the flow velocity calculation with a previous approach, which is also calculated based on geomorphological parameters. Subsequently, the runoff simulation based on the GIUH model is evaluated using the proposed technique. Results demonstrate that the proposed formula possesses high fitting accuracy and can be easily used to calculate flow velocity and generate GIUH.
