Hydrological models serve as essential tools in hydrological research, allowing us to address practical hydrological issues. This study focuses on the Xunhe Watershed in Shandong Province, China, constructing a distributed Xin’anjiang hydrological model. Furthermore, traditional manual calibration and automatic calibration using the Particle Swarm Optimization (PSO) algorithm were employed to determine model parameters, followed by hydrological simulations, with the aim of investigating the applicability of the distributed Xin’anjiang model in this watershed. The research findings indicate that the distributed Xin’anjiang model accurately simulates the hydrological processes in the Xunhe Watershed. There is a high level of agreement between the observed data and the simulated results, including key indicators such as peak discharge, runoff volume, and peak time. After optimizing the model parameters using the PSO algorithm, the distributed Xin’anjiang model demonstrates improved simulation performance in the Xunhe Watershed. During the calibration period, the mean relative peak discharge error (RPE) is 4.1%, the mean relative runoff error (RRE) is 4.34%, and the average Nash–Sutcliffe efficiency (NSE) for simulating the flood events is 0.89. During the validation period, the mean RPE is 3.82%, the mean RRE is 6.1%, and the average NSE for the process is 0.83. This indicates that the distributed Xin’anjiang model has good applicability in this watershed, providing a reliable reference for flood control and disaster reduction in the Xunhe Watershed.

The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction). The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.

Urban floods have been exacerbated globally, associated with increasing spatial-temporal variations in rainfall. However, compared with rainfall variabilities of intensity and duration, the effect of rainfall movement direction is always ignored. Based on 1313 rainfall scenarios with different combinations of rainfall intensity and rainfall movement direction in the typically rainy city of Shenzhen in China, we find that the effect of rainfall movement direction on the peak runoff may reach up to 20%, which will decrease to less than 5% under heavy rainfall intensity conditions. In addition, our results show that the impact of rainfall movement direction is almost symmetrical and is associated with the direction of the river. The closer rainfall movement direction is to the Linear Directional Mean of rivers, the larger is the peak runoff of section. Our results reveal that rainfall movement direction is significant to urban peak runoff in the downstream reaches, which should be considered in urban hydrological analysis.

The lumped hydrological model and empirical model have the problems of low accuracy and short forecasting period in real-time flood forecasting of small- and medium-sized rivers in a mountainous watershed. The sharing of underlying surface data such as high-resolution DEM, land use data, soil data, and the popularization and application of the Internet of Things, big data, cloud computing, and intelligent calculation methods makes distributed hydrological model an effective method for real-time runoff simulation and prediction. The topographic, kinematic, approximation, and integration (TOPKAPI) model is a distributed hydrological model whose physical mechanism developed gradually in the late 20th century. It has great advantages in real-time flood forecasting in small- and medium-sized watersheds. Based on the data required by the TOPKAPI model, in this study, 26 selected flood events were simulated from 2000 to 2013 at the outlet section of the upper reach of the Zhenjiang River in Guangdong Province, and the effect of application of the model in flood forecasting of small- and medium-sized rivers was evaluated. The results show that the pass rate (considering the peak discharge as the evaluation item) of 18 flood events in the calibration period was 66.67%, and that of 8 flood events in the validation period was 75%, while the mean Nash efficiency coefficient of the selected 26 flood events was 0.789. According to the simulation results, real-time flood forecasting should be closely combined with the dispatching of the small- and medium-sized reservoirs in the basin. The application of the TOPKAPI model can make a scientific and rapid analysis of the flood control situation in the whole basin and provide accurate information and maximum convenience for flood forecasting consultation and decision making. Additionally, it can improve the efficiency of disaster prevention and mitigation work in small- and medium-sized river basins, and has a major significance in enhancing the modernization level of flood forecasting.

The ridge estimation-based dynamic system response curve (DSRC-R) method, which is an improvement of the dynamic system response curve (DSRC) method via the ridge estimation method, has illustrated its good robustness. However, the optimization criterion for the ridge coefficient in the DSRC-R method still needs further study. In view of this, a new optimization criterion called the balance and random degree criterion considering the sum of squares of flow errors (BSR) is proposed in this paper according to the properties of model-simulated residuals. In this criterion, two indexes, namely, the random degree of simulated residuals and the balance degree of simulated residuals, are introduced to describe the independence and the zero mean property of simulated residuals, respectively. Therefore, the BSR criterion is constructed by combining the sum of squares of flow errors with the two indexes. The BSR criterion, L-curve criterion and the minimum sum of squares of flow errors (MSSFE) criterion are tested on both synthetic cases and real-data cases. The results show that the BSR criterion is better than the L-curve criterion in minimizing the sum of squares of flow residuals and increasing the ridge coefficient optimization speed. Moreover, the BSR criterion has an advantage over the MSSFE criterion in making the estimated rainfall error more stable.

Increasing urban extreme rainfall, driven by global climate change and rapid urbanization, highlights the deficiencies of existing studies on urban system responses to flooding. Current functional assessments of floods often neglect spatial variations and the temporal dynamics of urban functions. We first developed a GIS-based spatiotemporal assessment framework that integrates system functional curves and complex network analysis to evaluate urban functional conditions. Focusing on the 20 July 2021 Zhengzhou flood, the High-Performance Integrated Hydrodynamic Modeling System (HiPIMS) coupled with the Storm Water Management Model (SWMM) was used to simulate flood processes and spatiotemporal variations. The results are as follows: (1) Four rainfall events increased inundation areas, with the extreme event (Pr3) causing an 82.4% expansion relative to the initial water surface. (2) Flood stages (absorption, resistance, overload) were classified by maximum flood depth and inundation area, as well as the maximum growth rate of flood depth and inundation area. Grids with medium-level maximum inundation depth predominated, while those with the lowest-level maximum depth growth were least frequent. (3) Overall urban function declined sharply at 17:20, reaching a minimum of 18.2% at 21:00, resulting in a 32.8% total functional loss. (4) Complex network analysis reveals strong temporal co-movement among urban functions, with the temporal correlation network comprising 10 edges (density = 0.667, global efficiency = 0.865). Education and emergency shelter were consistently identified as the most central nodes across different correlation thresholds, indicating potential systemic vulnerability. The framework elucidates the spatiotemporal dynamics of urban function assessment during flooding and provides actionable insights for understanding urban system functioning mechanisms and emergency planning.