By Dr. Tim Sandle
EDITOR AT LARGE
DIGITAL JOURNAL
August 23, 2025
Nguyen Thi Kim sitting on a pillar of a destroyed house at the original site of Lang Nu village in Lao Cai province, after part of it was wiped away in a landslide triggered by Typhoon Yagi - Copyright AFP Nhac NGUYEN
Rainfall intensity, soil saturation and snowmelt drive widespread landslide pathways, according to a new scientific study.
Current methods to predict landslides rely primarily on rainfall intensity. In contrast, the research presents a new model that combines various water-related factors with machine learning. When the model was applied to more than 600 landslides in California, the model identified the conditions that caused 89% of the events.
Northwestern University and University of California, Los Angeles (UCLA) scientists have collaborated to develop a new process-based framework that provides a more accurate and dynamic approach to landslide prediction over large areas.
This new approach integrates various water-related processes with a machine-learning model. By accounting for diverse and sometimes compounding factors, the framework offers a more robust understanding of what drives these destructive events.
With further development, the new framework could help improve early warning systems, inform hazard planning and enhance strategies for climate resilience in regions vulnerable to landslides. Ultimately, these approaches could help save lives and prevent damage.
Different landslides can be caused by different hydrological processes. Hence, the researchers are trying to identify which landslides are caused by which processes.
Simulating a ‘parade’ of storms
Dangerous flows of water, mud and rocks, landslides can be difficult to predict — especially across large areas with varied landscapes and different climates. To better understand how and why widespread landslides occur, scientists looked to one month of extreme weather in California.
During the winter of 2022-23, California experienced an unprecedented “parade” of nine consecutive atmospheric rivers, which caused catastrophic flooding and more than 600 landslides. To understand the pathways that caused these landslides, the scientists adopted a community-developed computer model that simulates how water moves through the environment, including rain infiltrating into the ground, running off on the surface, evaporating, and freezing or melting of snow and ice.
To drive the model, the team used a diverse array of meteorological, geographical and historical data. This included information about terrain, soil depth, past wildfires, precipitation, and meteorological and climatic conditions.
Using model outputs, the team developed a metric, called “water balance status” (WBS), to assess when there is too much water in a particular area. A positive WBS means there’s more water than the ground can handle through absorption, storage, evaporation or drainage. This also means there’s higher potential for landslides.
Identifying main pathways
The scientists applied a machine-learning technique to group together similar landslides based on their sites’ specific conditions. Through this technique, they identified three main pathways that led to the California landslides: intense rainfall, rain on already saturated soils and melting snow or ice.
The research predicts that heavy, rapid downpours caused about 32% of the landslides. Roughly 53% of the landslides occurred after moderate rain fell on soils already saturated from previous storms. And about 15% of the landslides were linked to snow or ice, with rain accelerating the snowmelt or ice thaw.
When the scientists compared these events to their model, they found a significant majority (89%) of California’s landslides occurred in areas where the WBS was positive. This finding validated that the metric can accurately identify conditions ripe for landslides.
The research appears in the journal Geophysical Research Letters. The research paper is titled “Mixed hydrometeorological processes explain regional landslide potential.”
Nguyen Thi Kim sitting on a pillar of a destroyed house at the original site of Lang Nu village in Lao Cai province, after part of it was wiped away in a landslide triggered by Typhoon Yagi - Copyright AFP Nhac NGUYEN
Rainfall intensity, soil saturation and snowmelt drive widespread landslide pathways, according to a new scientific study.
Current methods to predict landslides rely primarily on rainfall intensity. In contrast, the research presents a new model that combines various water-related factors with machine learning. When the model was applied to more than 600 landslides in California, the model identified the conditions that caused 89% of the events.
Northwestern University and University of California, Los Angeles (UCLA) scientists have collaborated to develop a new process-based framework that provides a more accurate and dynamic approach to landslide prediction over large areas.
This new approach integrates various water-related processes with a machine-learning model. By accounting for diverse and sometimes compounding factors, the framework offers a more robust understanding of what drives these destructive events.
With further development, the new framework could help improve early warning systems, inform hazard planning and enhance strategies for climate resilience in regions vulnerable to landslides. Ultimately, these approaches could help save lives and prevent damage.
Different landslides can be caused by different hydrological processes. Hence, the researchers are trying to identify which landslides are caused by which processes.
Simulating a ‘parade’ of storms
Dangerous flows of water, mud and rocks, landslides can be difficult to predict — especially across large areas with varied landscapes and different climates. To better understand how and why widespread landslides occur, scientists looked to one month of extreme weather in California.
During the winter of 2022-23, California experienced an unprecedented “parade” of nine consecutive atmospheric rivers, which caused catastrophic flooding and more than 600 landslides. To understand the pathways that caused these landslides, the scientists adopted a community-developed computer model that simulates how water moves through the environment, including rain infiltrating into the ground, running off on the surface, evaporating, and freezing or melting of snow and ice.
To drive the model, the team used a diverse array of meteorological, geographical and historical data. This included information about terrain, soil depth, past wildfires, precipitation, and meteorological and climatic conditions.
Using model outputs, the team developed a metric, called “water balance status” (WBS), to assess when there is too much water in a particular area. A positive WBS means there’s more water than the ground can handle through absorption, storage, evaporation or drainage. This also means there’s higher potential for landslides.
Identifying main pathways
The scientists applied a machine-learning technique to group together similar landslides based on their sites’ specific conditions. Through this technique, they identified three main pathways that led to the California landslides: intense rainfall, rain on already saturated soils and melting snow or ice.
The research predicts that heavy, rapid downpours caused about 32% of the landslides. Roughly 53% of the landslides occurred after moderate rain fell on soils already saturated from previous storms. And about 15% of the landslides were linked to snow or ice, with rain accelerating the snowmelt or ice thaw.
When the scientists compared these events to their model, they found a significant majority (89%) of California’s landslides occurred in areas where the WBS was positive. This finding validated that the metric can accurately identify conditions ripe for landslides.
The research appears in the journal Geophysical Research Letters. The research paper is titled “Mixed hydrometeorological processes explain regional landslide potential.”
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