The Shuffled Complex Evolution—University of Arizona (SCE-UA) is a classical algorithm in the field of hydrology and water resources, but it cannot solve constrained optimization problems directly. Using penalty functions has been the preferred method to handle constraints, but the appropriate selection of penalty parameters and penalty functions can be challenging. To enhance the universality of the SCE-UA, we propose the Constrained Shuffled Complex Evolution Algorithm (CSCE) to conveniently and effectively solve inequality-constrained optimization problems. Its performance is compared with the SCE-UA using the adaptive penalty function (SCEA) on 14 test problems with inequality constraints. It is further compared with seven other algorithms on two test problems with low success rates. To demonstrate its effect in hydrologic model calibration, the CSCE is applied to the parameter optimization of the Xinanjiang (XAJ) model under synthetic data and observed data. The results indicate that the CSCE is more advantageous than the SCEA in terms of the success rate, stability, feasible rate, and convergence speed. It can guarantee the feasibility of the solution and avoid the problem of deep soil tension water capacity (WDM)<0 in the optimization process of the XAJ model. In the case of synthetic data, the CSCE can accurately find the theoretical optimal parameters of the XAJ model under the given constraints. In the case of observed data, the XAJ model optimized by the CSCE can effectively simulate the hourly rainfall-runoff events of the Hexi Basin and achieves mean Nash efficiency coefficients greater than 0.75 in the calibration period and the validation period.

Under the combined influence of climate change, accelerated urbanization, and inadequate urban flood defense standards, urban pluvial flooding has become an increasingly severe issue. This not only poses significant challenges to social stability and economic development but also makes accurate flood risk assessment crucial for improving urban flood control and drainage capabilities. This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. By constructing a hydrological–hydrodynamic coupled model using the SWMM and LISFLOOD-FP, this study evaluates the drainage capacity of the pipe network and surface inundation characteristics under both historical and design rainfall scenarios. An agent-based model (ABM) is developed to analyze the dynamic risks and vulnerabilities of population and building agents under different rainfall scenarios, capturing macroscopic emergent patterns from individual behavior rules and analyzing them in both time and space dimensions. Additionally, using multi-source remote sensing data, dynamic population vulnerability, and flood hazard processes, a quantitative dynamic flood risk analysis is conducted based on cloud models. The results demonstrated the following: (1) PSO performed best in calibrating the SWMM in the study area, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.93 to 0.69. (2) Drainage system capacity was low, with over 90% of the network exceeding capacity in scenarios with return periods of 1 to 100 years. (3) The vulnerability of people and buildings increased with higher flood intensity and duration. Most affected individuals were located on roads. In Event 6, 11.41% of buildings were at risk after 1440 min; in the 20-year flood event, 26.69% of buildings were at risk after 180 min. (4) Key features influencing vulnerability included the DEM, PND, NDVI, and slope. High-risk areas in the study area expanded from 36.54% at 30 min to 38.05% at 180 min.

Urban floods increasingly pose threats to transportation systems, residential areas, and public safety, which has created an urgent need for rapid urban flood susceptibility mapping (UFSM) under varying rainfall conditions. In this study, five tree-based machine learning models, namely, random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical boosting (CatBoost), and gradient boosting decision tree (GBDT) models, are evaluated for rapid UFSM generation under 18 rainfall scenarios with different return periods and rainfall patterns. Model performance is assessed on the basis of accuracy (ACC), F1 score (F1), precision (PRE), and recall (REC). Among the five models, the RF model performs best overall, with ACC, F1, PRE, and REC values ranging from 0.97 to 0.99, and is the most robust under the 50- and 100-year rainfall scenarios. The GBDT and CatBoost models perform poorly under low-return-period scenarios, whereas the XGBoost and LightGBM models perform better under high-return-period conditions but remain inferior to the RF model. Feature importance analysis reveals elevation, topographic position index, and curvature as the main controlling factors. These findings provide an efficient framework for rapid UFSM generation and offer useful support for urban flood risk management and planning.

The complementary operation of hydropower and photovoltaic power, aimed at meeting real-time demand, has led to frequent adjustments in power generation, causing significant fluctuations in hydrological systems and adversely affecting fish reproduction. The traditional hydrological regime alteration assessment index is based on index of hydrologic alternation (IHA) and mostly focuses on annual and daily runoff alterations. This study proposes a new set of indicators considering the characteristics of subdaily hydrological regime alterations, including magnitude, rate of change, duration, frequency, and timing. Using the hourly outflow from Longyangxia, an analysis of indicator redundancy was conducted. The alteration of the indicators before and after hydropower and photovoltaic operation was then analyzed using the cumulative probability distribution curve. Additionally, a concentration index was introduced to analyze the variations in hydrological impacts during different months. The results show that the hydro–photovoltaic complementary operation changed the subdaily natural flow regime, significantly increasing the rate of flow increase or decrease and the duration, with most indexes increasing by more than 100% compared with the natural flow regime. Furthermore, the concentration values of the indexes for the hydro–photovoltaic complementary operation were less than 10, indicating a more significant impact on the subdaily flow regime throughout the year. This research provides crucial data for mitigating ecological impacts under multi-source complementary scheduling.