Deconstructing causal linkages between place attributes and disaster outcomes at coarse scales like zip codes and counties is difficult because heterogeneous socio-economic characteristics operating at finer scales are masked. However, capturing detailed disaster outcomes about individuals and households for large areas can be equally complicated. This dichotomy highlights the need for a more nuanced and empirically-driven approach to understanding financial disaster recovery support. This study assessed how social characteristics influenced federal disaster recovery support following the 2015 South Carolina floods. Ordinary linear and spatial regression models provided a mechanism for pinpointing statistically significant links between individual/compound vulnerabilities and resource distribution from four federal disaster response and recovery programmes. The study makes two unique contributions. First, exploration of how social characteristics influence recovery support is a critical, yet understudied path toward fair and equitable disaster recovery. Second, finer scale inquiry across a large impact area is rare in quantitative case studies of US disasters. While we found flood recovery assistance to be strongly associated with physical damage overall the relationship wase more tenuous in places with higher social vulnerability. Results indicate that future disaster recovery programs focusing on both physical damage and social vulnerable would lead to a more equitable disaster recoveries. Findings provide new understanding of equity at the intersection of social vulnerability, impacts, and disaster recovery and showcase both best-practices and areas for programme improvements for future disasters.

Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS), a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008). Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is necessary to take the risk level, physical exposure, and growth stages of crops into consideration together for frost disaster risk prevention planning.

Precipitation associated with landfalling tropical cyclones (TCs) poses a significant flood risk to vast regions along and inland of the coasts. Quantifying spatial characteristics of tropical cyclone precipitation (TCP) and defining homogeneous rainfall regions can benefit forecasts and hazard mitigation of TCs. This work aims to evaluate the application of spatial metrics and satellite precipitation data in characterizing precipitation associated with landfalling TCs over the North Atlantic. This study applied an object-based Geographic Information System method to measure rainfall fields associated with North Atlantic landfalling TCs from the satellite-based rain rate estimates from 1998–2014. Eleven spatial metrics measuring the entire rainfall field and the largest rainfall polygon within the rain field were evaluated using a set of non-parametric tests to determine if they could distinguish rainfall patterns among four storm intensity categories. A multivariate clustering method with hotspot analysis was utilized to investigate spatial variations and regionalize the rainfall patterns into nine clusters using selected metrics. Five spatial metrics, namely area, solidity, dispersion, closure, and roundness, were selected since they meet three criteria: 1) having a relatively full range of 0– 1 (besides area); 2) having a significant increasing or decreasing trend with intensity, and 3) showing significant differences between any two storm categories. The clustering results indicate that TC rainfall patterns exhibit significant regional variations when storms are over land as well as over sub-basins, including the Caribbean Sea, Gulf of Mexico, and the North Atlantic. The clustering results reveal that intensity is one of the key factors in determining rainfall patterns and reflect the impact of other factors, such as wind shear, moisture content, and interaction with land.