Feifeng Cao

Natural disasters present a significant challenge to the productivity of Zhejiang Province. This paper is the first to evaluate social vulnerability to natural disasters in Zhejiang Province and provides a scientific foundation for disaster prevention, mitigation, and risk management. In this paper, we construct an indicator system for evaluating social vulnerability of natural disasters in Zhejiang Province through demand analysis, frequency analysis, and applicability analysis. The methodology employed in this paper reduces errors arising from subjective indicator selection and provides a reference for future international research on evaluating social vulnerability to natural disasters. This study analyzes the spatiotemporal evolution of social vulnerability to natural disasters in 11 cities from 2011 to 2020. The results indicate an overall downward trend of social vulnerability to natural disasters in Zhejiang. Social vulnerability to natural disasters exhibits significant spatial variability. The evaluation can help to bridge the knowledge gap regarding the social vulnerability of Zhejiang Province to natural disasters. The analysis of the spatiotemporal evolution of social vulnerability provides insights into the contributing factors to vulnerability and the effectiveness of past disaster management strategies. The findings of this study can serve as a valuable reference for future research in Zhejiang Province and other regions facing similar challenges. The results can contribute to the advancement of comprehensive knowledge of social vulnerability to natural disasters, which can inform the development of policies and strategies aimed at mitigating disaster risk and promoting effective disaster management globally.

The accurate and efficient estimation of bed roughness using limited historical observational data is well-established. This paper presents a new parameter-inversion model for estimating bed roughness in hydrodynamic models that constrains the roughness distribution between river sections. The impact of various factors on the accuracy of inversed roughness was analyzed through a numerical experiment with the number of measurement stations, observed data amount, initial bed roughness, observational noise, and the weight of the regularization term. The results indicate that increasing the number of measurement stations and the amount of observed data significantly improves the robustness of the model, with an optimal parameter setting of 3 stations and 30 observed data. The initial roughness had little impact on the model, and the model showed good noise resistance capacity, with the error significantly reduced by controlling the smoothness level of inversed roughness using a small weight of the regularization term (i.e., 100). An experiment conducted on a real river using the calibrated model parameters shows a forecasted water level RMSE of 0.041 m, 31% less than that from the Federal Emergency Management Agency. The proposed model provides a new approach to estimating bed roughness parameters in hydrodynamic models and can help in improving the accuracy of water-level forecasting.

Water level simulation for complex water river networks is complex, and existing forecasting models are mainly used for single-channel rivers. In this paper, we present a new data assimilation model based on the ensemble Kalman filter (EnKF) for accurate water level simulation in complex river networks. The EnKF-based data model was tested on simulated water level data from a river network hydrodynamic model and optimized through parameter analysis. It was then applied to a real mountainous single-channel river and plain river network and compared with a data assimilation model based on the extended Kalman filter (EKF). The results showed that the EnKF-based model, with a medium ensemble sample size of 100–150, normal observation noise of 0.0001–0.01 m, and a high standard deviation of 0.01–0.1 m, outperformed the EKF-based model, with a 49% reduction in simulation errors, a 45% reduction in calculation cost, and a 43% reduction in filtering time. Furthermore, the EnKF-based data assimilation model predicted the water level in the plain river network better than the mountainous single-channel river. Around 5 to 8 h were required for data assimilation; afterwards, the model could make accurate predictions covering 20 to 30 h. The EnKF-based data assimilation model offers a potential solution for water level predictions in river networks.

Mountain creek areas are a type of flood-prone area, and the flood control in these areas has not been well understood. This study aims to evaluate the impact of rain patterns on the water level and the establishment of flood control standard in mountain creek areas by conducting a case study. The case study was performed on a typical mountain creek area, called Shiliqu watershed in Hangzhou, China. A MIKE11 model was first established to analyze the required flood control standard through investigating the river level impacted by two rain patterns (the designed rainstorm and the typhoon Fitow). The results show that the designed rainstorm pattern and the typhoon Fitow rain pattern have the single-peak and multi-peak profile, respectively. The peak rainfall value of the designed rainstorm pattern is much higher than that of the typhoon Fitow. However, the large fluctuations under the typhoon Fitow rain pattern causes multi-level overtopping which threats the safety of the flood control. Also, the typhoon Fitow could have a greater influence on the water level of the tributaries of the river than that of the mainstream. The selection of rain pattern on the design of flood control standard in mountain creek areas is discussed.

The natural support capacity (NSC) of water resources is a key aspect of the regional carrying capacity of water resources, and it can reflect the quality and quantity of water resources in a region. This paper aims to evaluate the NSC of water resources using a model based on the principal component analysis (PCA) to benefit the development and utilization of regional water resources. A case study in the Fuyang district, Zhejiang Province, China, was carried out. First, water resources, as dependent variables, were assumed to be linearly influenced by the indicators affecting the NSC of water resources. These indicators were regarded as independent variables for multivariate analysis in this study. Then, the available water resources data for the Fuyang district between 1995 and 2003 were inputted to the model to analyze NSC levels of water resources. The results indicated that the most important parameters influencing the NSC of water resources could be shortlisted to water resources availability, surface water resources, groundwater resources, allowable withdrawal of water resources, and emission intensity of chemical oxygen demanding. Our findings revealed that the NSC of water resources in the Fuyang district fluctuated between 1995 and 1999 and generally declined after 2000, indicating that the issue of water pollution has worsened since 2000. These results are consistent with the field observations and thus shall provide new potential applications of a PCA-based model in evaluating the NSC of water resources and the relevant water resource carrying capacity for similar areas.

Breakup may exert a controlling influence on particle size distributions and particles either are fractured or are eroded particle-by-particle through shear. The shear-induced breakage of fine particles in turbulent conditions is investigated using Taylor-expansion moment method. Their equations have been derived in continuous form in terms of the number density function with particle volume. It suitable for future implementation in computational fluid dynamics modeling.