On the tenth anniversary of the 2008 Wenchuan Earthquake, investigating the evolution of disaster science is worthwhile and can be used to improve the future execution of disaster risk management. Based on more than 55,786 articles on the relative topic of “Disaster” derived from the Web of Science Core Collection from 1999–2017, this study employs CiteSpace and Google Earth to identify and visualize the spatial distribution of publications, bursts of keywords and categories, highly cited references, and interdisciplinary levels and then identify the emerging trends of disaster research over the past 20 years. The results show that the earthquake indeed jumpstarted a massive wave of disaster research around the world and increased international cooperation over the last decade. However, in terms of both the quantity and quality of publications in disaster research fields, China is lagging behind the U.S. and European countries. Moreover, although designing disaster prevention and mitigation strategies is a new popular field of disaster science, geological environment changes and geologic hazards triggered by earthquakes are more popular research topics than disaster emergency and recovery. In addition, the transdisciplinary level of disaster science increased after the earthquake. This interdisciplinary characteristic of disaster science gradually increased in popularity, which demonstrates that people can learn from catastrophes. These emerging trends could serve as a scientific basis to clearly understand disaster science progress over the last 20 years and provide a reference for rapidly identifying frontier issues in disaster science.

Earthquake-induced landslides (EQILs) represent a serious secondary disaster of earthquakes, and conducting an effective assessment of earthquake-induced landslide susceptibility (ELSA) post-earthquake is helpful in reducing risk. In light of the diverse demands for ELSA across different time periods following an earthquake and the growing availability of data, this paper proposes using remote sensing data to dynamically update the ELSA model. By studying the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023, rapid assessment results were derived from 12 pre-trained ELSA models combined with the spatial distribution of historical earthquake-related landslides immediately after the earthquake for early warning. Throughout the entire emergency response stage, the ELSA model was dynamically updated by integrating the EQILs points interpreted from remote sensing images as new training data to enhance assessment accuracy. After the emergency phase, the remote sensing interpretation results were compiled to create the new EQILs inventory. A high landslide potential area was identified using a re-trained model based on the updated inventory, offering a valuable reference for risk management during the recovery phase. The study highlights the importance of integrating remote sensing into ELSA model updates and recommends utilizing time-dependent remote sensing data for sampling to enhance the effectiveness of ELSA.

Tropical cyclone (TC) damage function (DF) is widely used to model TC‐event level damage and thus assess the TC risk for a country or region. The scalability of these DFs at more localized scales, such as the province scale, has not been systematically explored. We use a unique Chinese data set to examine the damage at the TC‐event scale and province scale. Our results show that the parameters and performance of TC DF are spatially dependent. For a sigmoidal DF, the parameter dependence is manifested by a flatter curve calibrated on the TC‐event scale compared to the province scale. In the case of a power‐law DF, the dependence of its parameters is evident in the statistically more significant coefficients of the explanatory variables that are aggregated to the TC‐event scale, compared to the province scale. Performance comparison results further reveal that the scale dependence of performance is related to the type of DF. Integrating hazard, exposure, and vulnerability, the power‐law DF complements the typical sigmoidal DF, producing more accurate estimates of direct economic loss and annual average damage at both the TC‐event and province scales. However, its performance, compared to that of the sigmoidal DF, is more influenced by the scale at which it is calibrated. Our findings elucidate scale‐related research questions in TC risk assessment, offer insights into the selection of DFs, and inspire the future prospect of using multiple DFs to reduce the functional uncertainty.

Tropical cyclones (TCs) and their economic cost risk under climate change are significant concerns globally. Previous studies on TC damage functions and risk assessment are mostly performed based on modeling TC-level damage and thus obtaining the annual average loss for a country or region. The scalability of these damage functions at finer scales has been less systematically explored. In this study, we examine how the model structure, estimated parameters, and model performance of TC damage functions vary with spatial scale. The comparisons are illustrated by fitting two types of damage functions based on reported damage data at the county, province, and TC scales. We find that the newly proposed precipitation-calibrated sigmoidal damage function significantly outperforms the wind-calibrated sigmoidal damage function at three scales of county, province and TC event. Another type of power-law damage function that integrates hazard, exposure, and vulnerability complements the typical sigmoidal damage function because it yields a better fit when estimating direct economic loss above the province scale. Our work provides an empirical assessment of the role of spatial scale and damage function in TC economic impact evaluation and demonstrates the importance of spatially scale-specific policy-making in TC risk management and climate adaptation strategies.

Tropical cyclones (TC) and their economic cost risk under climate change are significant concerns globally. Previous studies on TC damage functions and risk assessment are mostly performed based on modelling TC-level damage and thus obtaining annual average loss (AAL) for a country or region. In this study, we used officially released data on reported damage in China to examine patterns of TC damage functions ranging in form and inclusion of risk components, from the county and provincial to TC scale. We find that the robustness of simulated damage increases with spatial scale. Further, TC-induced precipitation tended to be more significant in determining exposure and therefore economic cost of TC, especially at larger scales. Our work provides an empirical assessment of the role of observation scale and damage function in TC economic consequence evaluation and demonstrates that future TC risk at a larger scale may depend on the impact of socioeconomic change.