The accuracy of landslide susceptibility mapping is influenced by the quality of sample data, factor systems, and assessment methods. This study aims to enhance the representativeness and overall quality of the sample dataset through an effective sample expansion strategy, achieving greater precision and reliability in the landslide susceptibility model. An integrated interpretative framework for landslide susceptibility assessment is developed using the XGBoost-SHAP-PDP algorithm to deeply investigate the key contributing factors of landslides in karst areas. Firstly, 17 conditioning factors (e.g., surface deformation rate, land surface temperature, slope, lithology, and NDVI) were introduced based on field surveys, satellite imagery, and literature reviews, to construct a landslide susceptibility conditioning factor system in line with karst geomorphology characteristics. Secondly, a sample expansion strategy combining the frequency ratio (FR) with SBAS-InSAR interpretation results was proposed to optimize the landslide susceptibility assessment dataset. The XGBoost algorithm was then utilized to build the assessment model. Finally, the SHAP and PDP algorithms were applied to interpret the model, examining the primary contributing factors and their influence on landslides in karst areas from both global and single-factor perspectives. Results showed a significant improvement in model accuracy after sample expansion, with AUC values of 0.9579 and 0.9790 for the training and testing sets, respectively. The top three important factors were distance from mining sites, lithology, and NDVI, while land surface temperature, soil erosion modulus, and surface deformation rate also significantly contributed to landslide susceptibility. In summary, this paper provides an in-depth discussion of the effectiveness of LSM in predicting landslide occurrence in complex terrain environments. The reliability and accuracy of the landslide susceptibility assessment model were significantly improved by optimizing the sample dataset within the karst landscape region. In addition, the research results not only provide an essential reference for landslide prevention and control in the karst region of Southwest China and regional central engineering construction planning but also provide a scientific basis for the prevention and control of geologic hazards globally, showing a wide range of application prospects and practical significance.

Extreme heat events can induce delayed responses in seasonally frozen ground (SFG) by altering soil heat storage and release processes, yet their lag characteristics and spatial variability remain poorly quantified. Here, we develop a distributed lag nonlinear model integrated with an explainable artificial intelligence framework (DLNM-XAI) to quantify the lagged responses of SFG to extreme heat events across the Qinghai-Tibet Plateau (QTP), based on in situ observations, multi-source remote sensing, and reanalysis datasets. Results show that extreme heat significantly modifies freeze–thaw dynamics. On average, each additional extreme heat day advances the thaw end date by 3.02 days, delays the freeze onset date by 1.52 days, and reduces maximum freezing depth by approximately 1.12 cm. The response of freezing depth exhibits a clear nonlinear lag pattern, with effects emerging within 5-10 days, peaking after approximately 15-20 days, and gradually weakening thereafter. Spatially, lagged responses also show pronounced spatial heterogeneity across the QTP. Regions with deeper snow cover, higher soil moisture, and stronger surface energy exchange generally exhibit longer lag durations. These factors, including extreme heat duration, snow depth, soil moisture, and surface energy fluxes, jointly regulate soil heat transfer and energy retention, thereby modulating the timing and persistence of SFG responses. Overall, this study provides a regional-scale characterization of the delayed thermal responses of SFG to extreme heat events and improves understanding of thermal memory and land-atmosphere interactions under short-term extreme climate forcing in cold regions.

Extreme heat events can induce delayed responses in seasonally frozen ground (SFG) by altering soil heat storage and release processes, yet their lag characteristics and spatial variability remain poorly quantified. Here, we develop a distributed lag nonlinear model integrated with an explainable artificial intelligence framework (DLNM-XAI) to quantify the lagged responses of SFG to extreme heat events across the Qinghai-Tibet Plateau (QTP), based on in situ observations, multi-source remote sensing, and reanalysis datasets. Results show that extreme heat significantly modifies freeze–thaw dynamics. On average, each additional extreme heat day advances the thaw end date by 3.02 days, delays the freeze onset date by 1.52 days, and reduces maximum freezing depth by approximately 1.12 cm. The response of freezing depth exhibits a clear nonlinear lag pattern, with effects emerging within 5-10 days, peaking after approximately 15-20 days, and gradually weakening thereafter. Spatially, lagged responses also show pronounced spatial heterogeneity across the QTP. Regions with deeper snow cover, higher soil moisture, and stronger surface energy exchange generally exhibit longer lag durations. These factors, including extreme heat duration, snow depth, soil moisture, and surface energy fluxes, jointly regulate soil heat transfer and energy retention, thereby modulating the timing and persistence of SFG responses. Overall, this study provides a regional-scale characterization of the delayed thermal responses of SFG to extreme heat events and improves understanding of thermal memory and land-atmosphere interactions under short-term extreme climate forcing in cold regions.

Extreme heat events can induce delayed responses in seasonally frozen ground (SFG) by altering soil heat storage and release processes, yet their lag characteristics and spatial variability remain poorly quantified. Here, we develop a distributed lag nonlinear model integrated with an explainable artificial intelligence framework (DLNM-XAI) to quantify the lagged responses of SFG to extreme heat events across the Qinghai-Tibet Plateau (QTP), based on in situ observations, multi-source remote sensing, and reanalysis datasets. Results show that extreme heat significantly modifies freeze–thaw dynamics. On average, each additional extreme heat day advances the thaw end date by 3.02 days, delays the freeze onset date by 1.52 days, and reduces maximum freezing depth by approximately 1.12 cm. The response of freezing depth exhibits a clear nonlinear lag pattern, with effects emerging within 5-10 days, peaking after approximately 15-20 days, and gradually weakening thereafter. Spatially, lagged responses also show pronounced spatial heterogeneity across the QTP. Regions with deeper snow cover, higher soil moisture, and stronger surface energy exchange generally exhibit longer lag durations. These factors, including extreme heat duration, snow depth, soil moisture, and surface energy fluxes, jointly regulate soil heat transfer and energy retention, thereby modulating the timing and persistence of SFG responses. Overall, this study provides a regional-scale characterization of the delayed thermal responses of SFG to extreme heat events and improves understanding of thermal memory and land-atmosphere interactions under short-term extreme climate forcing in cold regions.

Extreme heat events can induce delayed responses in seasonally frozen ground (SFG) by altering soil heat storage and release processes, yet their lag characteristics and spatial variability remain poorly quantified. Here, we develop a distributed lag nonlinear model integrated with an explainable artificial intelligence framework (DLNM-XAI) to quantify the lagged responses of SFG to extreme heat events across the Qinghai-Tibet Plateau (QTP), based on in situ observations, multi-source remote sensing, and reanalysis datasets. Results show that extreme heat significantly modifies freeze–thaw dynamics. On average, each additional extreme heat day advances the thaw end date by 3.02 days, delays the freeze onset date by 1.52 days, and reduces maximum freezing depth by approximately 1.12 cm. The response of freezing depth exhibits a clear nonlinear lag pattern, with effects emerging within 5-10 days, peaking after approximately 15-20 days, and gradually weakening thereafter. Spatially, lagged responses also show pronounced spatial heterogeneity across the QTP. Regions with deeper snow cover, higher soil moisture, and stronger surface energy exchange generally exhibit longer lag durations. These factors, including extreme heat duration, snow depth, soil moisture, and surface energy fluxes, jointly regulate soil heat transfer and energy retention, thereby modulating the timing and persistence of SFG responses. Overall, this study provides a regional-scale characterization of the delayed thermal responses of SFG to extreme heat events and improves understanding of thermal memory and land-atmosphere interactions under short-term extreme climate forcing in cold regions.