This paper develops a systematic hazard interaction classification based on the geophysical environment that natural hazards arise from – the hazard-forming environment. According to their contribution to natural hazards, geophysical environmental factors in the hazard-forming environment were categorized into two types. The first are relatively stable factors which construct the precondition for the occurrence of natural hazards, whilst the second are trigger factors, which determine the frequency and magnitude of hazards. Different combinations of geophysical environmental factors induce different hazards. Based on these geophysical environmental factors for some major hazards, the stable factors are used to identify which kinds of natural hazards influence a given area, and trigger factors are used to classify the relationships between these hazards into four types: independent, mutex, parallel and series relationships. This classification helps to ensure all possible hazard interactions among different hazards are considered in multi-hazard risk assessment. This can effectively fill the gap in current multi-hazard risk assessment methods which to date only consider domino effects. In addition, based on this classification, the probability and magnitude of multiple interacting natural hazards occurring together can be calculated. Hence, the developed hazard interaction classification provides a useful tool to facilitate improved multi-hazard risk assessment.

Precipitation from tropical cyclones (TCs) can cause massive damage from inland floods and is becoming more intense under a warming climate. However, knowledge gaps still exist in changes of spatial patterns in heavy TC precipitation. Here we define a metric, DIST30, as the mean radial distance from centers of clustered heavy rainfall cells (> 30 mm/3 h) to TC center, representing the footprint of heavy TC precipitation. There is significant global increase in DIST30 at a rate of 0.34 km/year. Increases of DIST30 cover 59.87% of total TC impact areas, with growth especially strong in the Western North Pacific, Northern Atlantic, and Southern Pacific. The XGBoost machine learning model showed that monthly DIST30 variability is majorly controlled by TC maximum wind speed, location, sea surface temperature, vertical wind shear, and total water column vapor. TC poleward migration in the Northern Hemisphere contributes substantially to the DIST30 upward trend globally.

In recent years, measures proposed to address urban flooding caused by extreme rainfall often demand substantial investment, restricting their broad implementation. This study quantitatively assessed the inundation situations of 138 capital cities under both normal and extreme rainfall conditions. Using machine learning techniques, we found that grey infrastructure—closely commensurate with a city’s economic development—dominates flood reduction during normal rainfall events. However, during extreme precipitation, as rainfall intensity rises, the marginal effectiveness of grey infrastructure declines markedly. In contrast, green infrastructure and topography—less commensurate with economic development—play increasingly critical roles in mitigating urban flooding. These findings suggest that economic development has a limited impact on urban flooding during extreme rainfall events. Rationally utilizing topography and enhancing green spaces provides a cost-effective nature-based solution, which is particularly important for urban planning in low- and middle-income countries undergoing rapid urbanization.

Flood disasters are one of the most serious meteorological disasters in China. With the rapid development of information technology, individual monitoring tools could not meet the need for flood disaster monitoring. Therefore, a new integrated air-space-ground method, based on combined satellite remote sensing, unmanned aerial vehicle remote sensing and field measurement technology, has been proposed to monitor and assess flood disasters caused by a dam failure in Poyang County, Jiangxi Province. In this paper, based on an air-space-ground investigation system, the general flooded areas, severely affected areas, and more severely affected areas were 53.18 km², 12.61 km² and 6.98 km², respectively. The size of the dam break gap was about 65 m and 34.7 m on 22 and 23 June. The assessment precision was better than 98%, and the root mean square error (RMSE) was 0.86 m. The method could meet the needs for flood disaster information at different spatiotemporal scales, such as macro scale, medium scale and local small scale. The integrated monitoring of flood disasters was carried out to provide the whole process and all-round information on flood evolution dynamics, the disaster development process for flood disaster monitoring and emergency assessment, and holographic information for emergency rescue and disaster reduction, as well as to meet the need for different temporal and spatial scales of information in the process of disaster emergencies.

Linking of ‘consumption-industrial production-surface water deterioration’ is essential for industrialised economies to understand the mechanism of industrial water pollution. However, such a connection may mislead policy decisions if sectoral details are lacking. This study investigated excess pollution discharge from 11,094 industrial enterprises comprising 22 economic sectors through setting discharge thresholds on 1338 water function zones in Jiangsu Province, the most industrialised province in China. We further evaluated the contribution of final consumption in Chinese provinces to excess pollution discharge in Jiangsu via a national multi-region input–output table. Notably, despite typically heavy polluting sectors contributing the maximum excess pollution discharge, high-tech manufacturing sectors had a higher level of risk for excess pollution discharge. This was attributed to the spatial agglomeration of these sectors, with enterprises typically located in industrial parks. The increasing final consumption of specific sectors in both Jiangsu and other provinces may further drive excess pollution discharge in Jiangsu.