The fundamental assumption of flood frequency analysis is that flood samples are generated by the same flood generation mechanism (FGM). However, flood events are usually triggered by the interaction of meteorological factors and watershed properties, which results in different FMGs. To solve this problem, researchers have put forward traditional two-component mixture distributions (TCMD-T) without clearly linking each component distribution to an explicit FGM. In order to improve the physical meaning of mixture distributions in seasonal snow-covered areas, the ratio of rainfall to flood volume (referred to as rainfall–flood ratio, RF) method was used to classify distinct FGMs. Thus, the weighting coefficient of each component distribution was determined in advance in the rainfall–flood ratio based TCMD (TCMD-RF). TCMD-RF model was applied to 34 basins in Norway. The results showed that flood types can be clearly divided into rain-on-snow-induced flood, snowmelt-induced flood and rainfall-induced flood. Moreover, the design flood and associated uncertainties were also estimated. It is found that TCMD-RF model can reduce the uncertainties of design flood by 20% compared with TCMD-T. The superiority of TCMD-RF is attributed to its clear classification of FGMs, thus determining the weighting coefficients without optimization and simplifying the parameter estimation procedure of mixture distributions.

Substantial uncertainty is inherent in reservoir inflow forecasting, which exerts a potential negative impact on reservoir risk. However, the risk propagation from the inflow forecast uncertainty (IFU) to reservoir operations remains elusive. Thus, a new integrated assessment framework was developed in this study to characterize the risk coupling with flood and electricity curtailment risks that propagate from the IFU to the reservoir operations. First, to incorporate the IFU, an improved Gaussian mixture distribution (IGMD) and Markov chain Monte Carlo (MCMC) algorithm were constructed to model the measured forecast errors and generate ensemble inflow forecasts, respectively. Next, to assess the reservoir risk, the flood risk induced by the IFU overestimation and the electricity curtailment risk related to the IFU underestimation were identified according to the reservoir operation rules. The sub-daily inflow forecast at the Jinping First Stage Hydropower Plant Reservoir of Yalong River, China (Jinping I Reservoir) was selected. The results indicated that the IGMD-based MCMC was capable of deriving robust ensemble forecasts. Furthermore, there was no flood risk (risk rate was zero) induced by the IFU when taking designed reservoir floods with a ≥10-year return period as the benchmark. In contrast, the electricity curtailment risk rate significantly increased up to 41% when considering the IFU. These findings suggested that compared with the flood prevention pressure, the IFU would more likely result in severe electricity curtailment risk at the Jinping I Reservoir.

Due to underlying surface changes (USCs), the changes in the Taojiang River Basin’s flood generation conditions could impact the flooding process in the basin. However, most studies have typically focused on either land-use changes (LUCs) or soil and water conservation measures (SWCMs) to assess the impact of the USCs on floods, which may not provide a more comprehensive understanding of the response of floods to the USCs. To investigate how the USCs have altered the floods in the Taojiang River Basin, located upstream of Poyang Lake, China, the HEC-HMS model, which incorporates the influence of the USCs into the parameter calibration, is established in this study to investigate the flood processes on an hourly scale. The flood peak and the maximum 72 h flood volume are selected as two indexes and are applied to analyze the changes in floods caused by the USCs. The 1981–2020 period is divided into three sub-periods (i.e., 1981–1992, 1993–2007, and 2008–2020) based on the conditions of the USCs. It is found that the two indexes have exhibited decreasing trends, mainly due to the USCs during 1981–2020. Benchmarked against the baseline period of 1981–1992, the two indexes decreased by 3.06% (the flood peak) and 4.00% (the maximum 72 h flood volume) during 1993–2007 and by 5.92% and 7.58% during 2008–2020. Moreover, the impacts of the LUCs and SWCMs are separated through parameter adjustments in the model, revealing that the SWCMs played a dominant role in the USCs in the Taojiang River Basin. The quantification and assessment of the impact of the USCs on floods of different magnitudes revealed that the influence decreases with increasing flood magnitude. The results of this study improve our understanding of how USCs affect the flooding process and therefore provide support for flood control management under changing environments.

When using a differential evolution algorithm to solve the joint flood optimization scheduling problem of cascade reservoirs, a greedy random optimization strategy is prone to premature convergence. Therefore, a new, improved Elite Conservative Differential Evolution Algorithm (ECDE) was proposed in this study. This algorithm divides a population into elite and general populations. The elite population does not undergo differential mutation, whereas the general population uses an adaptive differential mutation strategy based on successful historical information to participate in differential mutation. This elite conservative strategy effectively improves the diversity of the population evolution process and enhances convergence accuracy and stability. In a numerical experiment involving 10 test functions, the proposed ECDE performed the best overall (seven functions had the best stable convergence solution, while the remaining three performed the best), while in the single-objective flood control optimization scheduling problem of cascade reservoirs in the middle reaches of the Gan River, some algorithms could not even stably converge to feasible solutions (taking the 1973 inflow as an example, the peak shaving rate of the ECDE calculation results was 3.4%, 13.72%, and 11.73% higher than those of SHADE, SaDE, and GA, respectively). The proposed ECDE algorithm outperformed the SHADE, SaDE, GA, PSO, and ABC algorithms in terms of both convergence accuracy and stability. Finally, ECDE was used to analyze the multi-objective flood control scheduling problem of cascade reservoirs in the middle reaches of the Gan River, and it was found that the weight setting in multi-objective optimization should follow an upstream priority program or equilibrium programs. Adopting a downstream priority program results in poor upstream flood control performance. The above analysis fully verifies the superiority of the proposed algorithm, which can be used to solve and analyze the joint optimization scheduling problem of cascade reservoirs.