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1. Hydromechanical modeling of evolving post-wildfire regional-scale landslide susceptibility

   https://doi.org/10.1016/j.enggeo.2024.107538  

   Abdollahi, Masood; Vahedifard, Farshid; Leshchinsky, Ben A (June 2024, Engineering Geology)

   Post-wildfire mass wasting is a major problem throughout many regions worldwide. Recent dramatic increases in global wildfire activities coupled with a shift in wildfire-prone elevation to higher altitudes raise the need to better predict post-fire rainfall-triggered landslides. Despite its importance, only a limited number of studies have investigated landslide susceptibility in areas hit by wildfires using hydromechanical models. However, most of these studies follow either qualitative or semi-quantitative approaches without explicitly considering the fire’s effects on the impacted area’s physical behavior. This study aims to develop and employ a physics-based framework to generate susceptibility maps of rainfall-triggered shallow landslides in areas disturbed by wildfire. A coupled hydromechanical model considering unsaturated flow and root reinforcement is integrated into an infinite slope stability model to simulate the triggering of shallow landslides from rainfall. The impact of fire is considered through its effects on soil and land cover properties, near-surface processes, and canopy interception. The developed model is then integrated into a geographic information system (GIS) to characterize the regional distribution of landslide potential and its variability considering topography, geology, land cover, and burn severity. The proposed framework was tested for a study site in Southern California. The site was burned in the San Gabriel Complex Fire in June 2016 and experienced widespread landsliding almost three years later following an extreme rainstorm in January 2019. The proposed framework could successfully model the location of observed shallow landslides. The model also revealed a significantly higher likelihood for slope failure in areas burned at moderate to high severities as opposed to unburned and low-burn severity areas. The findings of this study can be employed to predict the timing and general locations of rainfall-triggered shallow landslides following wildfires. 

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2. A hydrologic signature approach to analysing wildfire impacts on overland flow

   Bolotin, Lauren A; McMillan, Hilary K (June 2024, Hydrological processes)

   Post-fire flooding and debris flows are often triggered by increased overland flow resulting from wildfire impacts on soil infiltration capacity and surface roughness. Increasing wildfire activity and intensification of precipitation with climate change make improving understanding of post-fire overland flow a particularly pertinent task. Hydrologic signatures, which are metrics that summarize the hydrologic regime of watersheds using rainfall and runoff time series, can be calculated for large samples of watersheds relatively easily to understand post-fire hydrologic processes. We demonstrate that signatures designed specifically for overland flow reflect changes to overland flow processes with wildfire that align with previous case studies on burned watersheds. For example, signatures suggest increases in infiltration-excess overland flow and decrease in saturation-excess overland flow in the first and second years after wildfire in the majority of watersheds examined. We show that climate, watershed and wildfire attributes can predict either post-fire signatures of overland flow or changes in signature values with wildfire using machine learning. Normalized difference vegetation index (NDVI), air temperature, amount of developed/undeveloped land, soil thickness and clay content were the most used predictors by well-performing machine learning models. Signatures of overland flow provide a streamlined approach for characterizing and understanding post-fire overland flow, which is beneficial for watershed managers who must rapidly assess and mitigate the risk of post-fire hydrologic hazards after wildfire occurs. 

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3. Dry sediment loading of headwater channels fuels post-wildfire debris flows in bedrock landscapes

   https://doi.org/10.1130/G46847.1  

   DiBiase, Roman A.; Lamb, Michael P. (December 2019, Geology)

   Abstract Landscapes following wildfire commonly have significant increases in sediment yield and debris flows that pose major hazards and are difficult to predict. Ultimately, post-wildfire sediment yield is governed by processes that deliver sediment from hillslopes to channels, but it is commonly unclear the degree to which hillslope sediment delivery is driven by wet versus dry processes, which limits the ability to predict debris-flow occurrence and response to climate change. Here we use repeat airborne lidar topography to track sediment movement following the 2009 CE Station Fire in southern California, USA, and show that post-wildfire debris flows initiated in channels filled by dry sediment transport, rather than on hillsides during rainfall as typically assumed. We found widespread patterns of 1–3 m of dry sediment loading in headwater channels immediately following wildfire and before rainfall, followed by sediment excavation during subsequent storms. In catchments where post-wildfire dry sediment loading was absent, possibly due to differences in lithology, channel scour during storms did not occur. Our results support a fire-flood model in bedrock landscapes whereby debris-flow occurrence depends on dry sediment loading rather than hillslope-runoff erosion, shallow landslides, or burn severity, indicating that sediment supply can limit debris-flow occurrence in bedrock landscapes with more-frequent fires. 

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4. High-Resolution Spatiotemporal Mapping of Surface Soil Moisture Using ConvLSTM Model and Sentinel-1 Data

   https://doi.org/10.3390/w17223300  

   Hosseinizadeh, Atieh; Sheng, Zhuping; Liu, Yi (November 2025, Water)

   Soil moisture plays a crucial role in hydrological processes and serves as a key driver of rainfall-induced landslides, especially in regions with steep terrain and intense precipitation. Traditional landslide risk models often oversimplify soil moisture and infiltration dynamics, which limits their predictive accuracy. This study presents a deep learning-based framework for generating high-resolution, spatiotemporal Surface Soil Moisture (SSM) maps for Prince George’s County, Maryland—a region highly susceptible to rainfall-triggered landslides—aimed at improving infiltration modeling and landslide prediction. A Convolutional Long Short-Term Memory (ConvLSTM) network integrates static spatial features (elevation, slope, soil type) with multi-temporal meteorological variables (precipitation, temperature, humidity, wind speed, evapotranspiration) and vegetation indices. The model is trained using dense SSM maps derived from Sentinel-1 SAR data processed through a change detection algorithm, providing a physically meaningful alternative to sparse in-situ observations. To address data imbalance, a two-pass patch extraction strategy was implemented to enhance representation of high-SSM conditions. The framework leverages high-performance computing resources to process large-scale, multi-temporal raster datasets efficiently. Evaluation results show strong predictive performance, with the two-day model achieving R2 = 0.72, correlation = 0.85, RMSE = 0.154, and MAE = 0.103. The results demonstrate the model’s capability to produce fine-resolution, wall-to-wall SSM maps that capture the spatial and temporal dynamics of surface soil moisture, supporting the development of early warning systems and landslide hazard mitigation strategies. 

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5. Wildfire and Extreme Rainfall Reduce Soil Carbon and Nitrogen Pools in a Semiarid Shrubland Ecosystem

   https://doi.org/10.1029/2025JG009183  

   Lipson, D A; Alvarez, A G; Brouwer, J N; Cleland, E E; Lively, J B; Parsons, D S; Rivas, Fortunato Vincente; Smith, K L; Varon, E L; Xu, X (February 2026, Journal of Geophysical Research: Biogeosciences)

   Abstract Due to a combination of anthropogenic causes, semi‐arid shrublands like those in California and other Mediterranean‐type ecosystems are increasingly subject to wildfires and variable rainfall, including drought and atmospheric rivers. Fire changes soil structure, leaving soils vulnerable to erosion and loss of organic matter (OM), especially when followed by intense rain events. However, interactions of fire and extreme rainfall are challenging to study due to their unpredictability and the complex interacting factors such as fire intensity, vegetation structure, topography, and soil type. This study took advantage of a wildfire, natural variation in rainfall, and a controlled rain‐shelter experiment to examine the interactions of fire and rain on soil structure and OM storage. Fire reduced organic carbon and nitrogen associated with particulate organic matter and water stable aggregates, and changed soil hydrology, slowing infiltration of water to deeper layers. The combination of rain shelters and the extreme rain fall year (2023) allowed us to evaluate threshold effects of rain in post‐fire soils: in 2023, there was a loss of total soil OM from burned relative to unburned plots; rain shelter effects on soil organic matter pools were only detectable in 2023; and the interactive effects of fire and rain shelters in this year reduced infiltration of rain into soil in burned areas exposed to full rain conditions. These results suggest a feedback mechanism in which fire causes loss of soil OM, damages soil structure, and decreases rainfall infiltration, increasing erosion through increased runoff and slowing recovery of soil structure. 

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