The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive areas following disasters. It is well documented that convolutional neural network methods have superior performance in earthquake building damage assessment compared with traditional machine learning methods. However, deep learning models require a large number of samples, and sufficient numbers of samples are usually not available in the newly earthquake-stricken areas rapidly enough. At the same time, the historical samples inevitably differ from the new earthquake-affected areas due to the discrepancy of regional building characteristics. For this purpose, this study proposes a data transfer algorithm for evaluating the impact of a single historical training sample on the model performance. Then, beneficial samples are selected to transfer knowledge from the historical data for facilitating the calibration of the new model. Four models are designed with two earthquake damage building datasets and the performance of the models is compared and evaluated. The results show that the data transfer algorithm proposed in this work improves the reliability of the building damage assessment model significantly by filtering samples from the historical data that are suitable for the new task. The performance of the model built based on the data transfer method on the test set of new earthquakes task is approximately 8% higher in overall accuracy compared with the model trained directly with the new earthquake samples when the training data for the new task is only 10% of the historical data and is operating under the objective of four classes of building damage. The proposed data transfer algorithm has effectively enhanced the precision of the seismic building damage assessment in a data-limited context. Thus, it could be applicable to the building damage assessment of new disasters.

At first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and population projections are available. However, the intersection of landslide hazard with socioeconomic elements may result in significant variation of estimated landslide exposure due to considerable variations in population projections. This study aims to investigate the effects of different sources of population data on the evaluation of landslide‐exposed population in China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 and six high‐resolution spatially explicit static and dynamic population data sets to drive available landslide models. The results indicate an overall rise in landslide hazard projections, with an increase in the potential impact area of 0.4%–2.7% and an increase in the landslide frequency of 4.7%–20.1%, depending on the SSPs scenarios and future periods. However, the likely changes in future landslide exposed population, as modeled by incorporating population data from different sources with landslide hazard, yield divergent outcomes depending on the population data source. Thus, some of the projections depict an increase in future landslide exposure, while others show a clear decrease. The nationwide divergence ranged from −64% to +48%. These divergent findings were mainly attributed to differences in population data and a lesser extent to variations in GCMs. The present findings highlight the need to pay closer attention to the dynamic evolution of the elements at risk and the associated data uncertainties.

The multi-model ensemble (MME) approaches are highly regarded in climate prediction and risk assessment for their capacity to integrate multiple global climate models (GCMs) and minimize uncertainties associated with individual models. However, the quantitative impacts of spatial scale, weighted ensemble, and bias correction on the spatiotemporal comprehensive performance of MME remain unknown. In this study, we comprehensively assessed the historical simulation capabilities of 41 CMIP6 GCMs at national, basin, and grid scales. Additionally, we investigated the impact of bias correction and weighted ensemble on enhancing climate simulation performance. The results indicate that CMIP6 models exhibit notable differences in simulating regional climate characteristics of China across different scales. Weighted multi-model ensemble schemes incorporating better-performing models consistently outperform equal-weight approaches, achieving an average 20.67% reduction in the DISO (distance between indices of simulation and observation) index, with temporal performance improvements being particularly pronounced. Bias correction played a critical role in the enhancement of MMEs, reducing DISO values by 41.60% on average, particularly in the spatial dimension. Among all MMEs, the grid-scale optimized ensemble (GBQ), combining bias correction, model selection, and performance-based weighting, demonstrated superior comprehensive performance, achieving the lowest DISO values across spatial and temporal dimensions. These findings provide new insights for enhancing regional climate simulation and evaluation, and they provide more reliable scientific information for investigating climate change and formulating adaptation strategies in China.

Landslides are one of the most prevalent environmental disasters in the Hengduan Mountain Region. Landslides lead to severe economic damage and property loss, as well as fatalities. Furthermore, they tend to increase in the context of climate change. The purpose of this study is to comprehensively assess landslide susceptibility across the Hengduan Mountain Region in southwest China. Specifically, the analysis is focused on the eastern boundary of the Tibetan Plateau within the context of future climate change scenarios, which are based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model ensemble. The Generalized Additive Model (GAM), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) were selected in order to map landslide susceptibility within the context of 1.5–4.0 °C warming scenarios. This was achieved by considering the changes in extreme rainfall that exceeded the landslide triggering thresholds. The results show that the frequency over extreme rainfall thresholds (FOERT) tend to increase in conjunction with warming targets, thereby ranging from 2.3/a (at a 1.5 °C warming) to 9.0/a (at a 4.0 °C warming) on average. Such elevated extreme precipitation events contribute to an increase in projected future zones of high landslide susceptibility when compared to the historical baseline period ranging from −1.2% (at a 1.5 °C warming) to 4.0% (at a 4.0 °C warming) using different machine learning models. Moreover, the extent of high susceptibility zones increases more significantly in the context of 4.0 °C warming when compared to the historical baseline results. These results indicate the importance of limiting the global temperature rise to 1.5 as well as 2 °C. The high landslide susceptibility zones estimated by the CMIP6 multi-models ensemble are mainly located in the central and southeastern regions of the Hengduan Mountain Region. The possible changes in terms of introducing extreme precipitation in order to assess landslide susceptibility in the context of climate change that is proposed in this study may be further applied to additional study areas. These projections under different targets can provide scientific guidelines for the purposes of the development of climate change adaptation strategies.

Extreme heatwaves are among the most important climate-related disasters affecting public health. Assessing heatwave-related population exposures under different warming scenarios is critical for climate change adaptation. Here, the Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model ensemble output results are applied over several warming periods in the Intergovernmental Panel on Climate Change AR6 report, to estimate China’s future heatwave population exposure under 1.5 °C and 2.0 °C warming scenarios. Our results show a significant increase in projected future annual heatwave days (HD) under both scenarios. With an additional temperature increase of 0.5 °C to 2.0 °C of warming, by mid-century an additional 20.15 percent increase in annual HD would occur, over 1.5 °C warming. If the climate warmed from 1.5 °C to 2.0 °C by mid-century, population exposure would increase by an additional 40.6 percent. Among the three influencing elements that cause the changes in population exposure related to heatwaves in China–climate, population, and interaction (e.g., as urbanization affects population redistribution)–climate plays the dominant role in different warming scenarios (relative contribution exceeds 70 percent). Therefore, considering the future heat risks, humanity benefits from a 0.5 °C reduction in warming, particularly in eastern China. This conclusion may provide helpful insights for developing mitigation strategies for climate change.