As an important part of resilience, disaster prevention and mitigation capabilities of the exposure units are a key consideration in disaster economic loss assessment. Based on the data collected from Lizhong Town in the Lixiahe region of Jiangsu Province, the initial depth-damage scatter diagrams and fitted curves were drawn for residential property, industrial property, business, infrastructure, and agriculture. Then the modified depth-damage scatter diagrams and fitted curves in line with the actual damage situation were drawn by taking into consideration disaster prevention and mitigation capabilities. The results show that: (1) At the significance level of 0.05, the correlation between submersion depth and depth-damage rate under different scenarios for residential property, industrial property, business, infrastructure, and agriculture is a power function; (2) Taking into consideration disaster prevention and mitigation capabilities, economic losses of different exposure units caused by flood disaster decreased. Residential property flood losses were reduced by 34% at the submersion depth of 3 m; industrial property losses were reduced by 17% at the submersion depth of 2 m; business losses were reduced by 24% at the submersion depth of 3 m; and infrastructure losses were reduced by 11% at the submersion depth of 2 m. The influence of disaster prevention and mitigation capabilities on residential property flood losses was most obvious. This study may serve as a useful supplement for research on flood depth-damage relation and provide references for disaster prevention and mitigation decisions and disaster risk management in similar areas.

This study systematically analyzed the results of current integrated method for social-ecological vulnerability research and found that the comprehensive index method can represent the overall level of social and ecological vulnerabilities using indicators of the two particular aspects of vulnerability in certain time period. But such method cannot reflect the degree of interaction and direction of influence of indicators. Correlation analysis can indicate a quantitative relationship between indicators, but is unable to identify causality. Least square method can generate variable coefficient of explanatory indicators when the indicators have changed, but due to the subjectivity in selecting indicators as explanatory variables or explained variables, the direction of influence between variables still cannot be determined. Given these problems, this article explores the feasibility of using instrumental variable (IV) method to reveal interactions between social and ecological vulnerabilities. By application in a case study at the country level in China , the result shows that the IV method can reveal the direction of influence between indicators of social vulnerability and ecological vulnerability, which overcomes the problem identified above. From 1980s to 2000s, the impact of ecological vulnerability on social vulnerability (NPPI→SoVI) decreased, the corresponding significant impact provinces decreased from 5 to 2; the impact of social vulnerability on ecological vulnerability (SoVI→NPPI) increased, and the corresponding significant impact provinces increased from 1 to 7. A quantitative method that indicates the direction of influence is expected to explain how social vulnerability and ecological vulnerability interact and affect one another.