Traditional methods for assessing urban waterlogging hazards lack real-time efficiency. This study develops a rapid hazard assessment method for urban waterlogging events using social media data and large language models, with the urban waterlogging event in Guilin in 2024 as a case study. We constructed a keyword database for urban waterlogging, combined with web crawling techniques to collect related data from social media platforms. This study applied the BERT-BILSTM-CRF model, the GPT-3.5-turbo model, and the ERNIE-3.5-128K model to extract waterlogging locations, comparing them with the official waterlogging location data to validate and select the optimal model. The model was then used for urban waterlogging hazard assessment at the township scale. The results indicate that the establishment of a keyword database facilitates access to more social media data. The GPT-3.5-turbo model demonstrates superior performance in waterlogging location identification, with a coverage of 93% compared with the actual waterlogging locations, and an F1 score of 0.8066. During the Guilin 6.19 waterlogging, areas with high hazard intensities mainly concentrated in the city center; meanwhile, the hazard intensities of some townships in the suburbs intensified from 17 June to 21 June. This study validates the effectiveness and timeliness of using social media data and large language models for rapid hazard assessment of urban waterlogging events through practical cases, which can provide reliable support for future urban disaster prevention and reduction efforts.

Cirrus clouds play a significant role in the Earth’s energy balance and in the hydrological cycle of the atmosphere. Here, a high-performance Micro Pulse Lidar was continuously used to investigate cirrus cloud formation and characteristics at Ali (32.50°N, 80.08°E; 4279 m), in the western Tibetan Plateau from 25 July to 23 September 2016, a time frame that spanned the prevalence and degeneration period of the Asian summer monsoon (ASM). The cirrus clouds frequently occurred with sharp fluctuations in the vertical distribution from 8 to 14 km above ground level (AGL) during the ASM period. In contrast, cirrus clouds were remarkably reduced and consistently existed near 10 km in September, when the ASM began subsiding due to the lack of a driving force that triggers ice formation. Approximately half of the cirrus clouds were caused by deep convective activity during the ASM period, which held one-third of total cirrus clouds during the whole measurement period. These anvil cirrus clouds have a liquid origin and are characterised by optically thicker clouds with Cloud Optical Depth values greater than 0.2, high depolarisation ratios and high lidar ratios. These observations indicate that, in agreement with other studies at mid-latitudes and in the Arctic, liquid origin cirrus could be associated with thicker, larger and more complex nonspherical ice crystals in comparison to in situ formed cirrus. Cold perturbations were responsible for the formation and evolution of the remaining two-thirds of cirrus clouds. These clouds were mostly associated with in situ formation of ice crystals, in the slow updrafts in the tropical transition layer over the Tibetan Plateau.

With the acceleration of global climate change and urbanization, urban resilience has become a critical issue. This study, based on the Pressure-State-Response (PSR) model, constructs an urban resilience evaluation index system for Sanming City. Indicator weights are determined by combining the Analytic Hierarchy Process (AHP) and the entropy weight method. Spatial analysis methods, such as spatial autocorrelation, kernel density estimation, standard deviation ellipses, and geographic detectors, are employed to explore spatial–temporal analysis and driving factors of urban resilience. The results show the following: (1) from 2014 to 2022, Sanming’s urban resilience index initially increased and then declined; (2) the spatial distribution of urban resilience is uneven, with high-resilience areas concentrated in the city center and southeast, while the northwest is relatively low; (3) Local Moran’s I analysis confirms significant positive spatial autocorrelation, with regional differences gradually expanding; (4) geographic detector analysis reveals that NDVI, monthly maximum precipitation, nighttime light index, annual average PM2.5 concentration, and impervious surface ratio are key drivers of urban resilience; (5) factor interactions show nonlinear enhancement, with ecological and climatic–environmental factors interacting as key drivers of urban resilience changes.

Between the occurrence of a disaster and the initiation of formal investigation and assessment, the overall evolution of the disaster often remain unclear, which hinders the timely progress of investigative work. To address this challenge, this study proposes a framework for constructing disaster event chains from limited post-disaster information. The framework first defines event patterns based on disaster system theory, then employs the unified information extraction model to automatically extract event chain nodes and their relationships from news texts. DBSCAN clustering is further applied to achieve semantic normalization and entity standardization, enabling the reconstruction of the disaster event chain. Finally, a fuzzy comprehensive evaluation is conducted to assess the effectiveness of the proposed framework. The results demonstrate that the framework can provide valuable initial analytical insights and auxiliary support for disaster investigation efforts.

Loess landslide disasters are influenced by a multitude of factors, including slope conditions, triggering mechanisms, and spatial attributes. Extracting these factors from unstructured geological texts is challenging due to nested entities, semantic ambiguity, and rare domain-specific terms. This study proposes a joint extraction framework guided by a domain ontology that categorizes six types of loess landslide influencing factors, including spatial relationships. The ontology facilitates conceptual classification and semi-automatic nested entity annotation, enabling the construction of a high-quality corpus with eight tag types. The model integrates a Soft-Lexicon mechanism that enhances character-level GloVe embeddings with explicit lexical features, including domain terms, part-of-speech tags, and word boundary indicators derived from a domain-specific lexicon. The resulting hybrid character-level representations are then fed into a BiLSTM-CRF architecture to jointly extract entities, attributes, and multi-level spatial and causal relationships. Extracted results are structured using a content-knowledge model to build a spatially enriched knowledge graph, supporting semantic queries and intelligent reasoning. Experimental results demonstrate improved performance over baseline methods, showcasing the framework’s effectiveness in geohazard information extraction and disaster risk analysis.