Lisa Victoria Luna

Metrics that approximate hillslope hydrologic response to rainfall are fundamental for informing landslide risk reduction efforts, such as early warning systems and hazard models. Notwithstanding the numerous publications using different wetness metrics that underpin and largely control the accuracy of landslide risk reduction products, a robust comparison of a broad array of different wetness metrics for regional landslide analysis is currently lacking in the literature. In this study, we statistically compare common wetness metrics for predicting the temporal occurrence of rainfall-triggered landslides at regional scales (> 1000 km²) using a landslide inventory covering the contiguous United States. We find that representations of hillslope wetting and drainage using parsimonious leaky bucket models that only require rainfall input and an estimated drainage factor can identify landslide-triggering hydrologic conditions across disparate ecological regions more accurately than unmodified precipitation metrics or more complex hydrological models. Due to the proliferation of global precipitation datasets and the limited input data needed for the parsimonious leaky bucket model, this model could be used to improve tools for landslide risk reduction.

Metrics that approximate hillslope hydrologic response to rainfall are fundamental for informing landslide risk reduction efforts, such as early warning systems and hazard models. Notwithstanding the numerous publications using different wetness metrics that underpin and largely control the accuracy of landslide risk reduction products, a robust comparison of a broad array of different wetness metrics for regional landslide analysis is currently lacking in the literature. In this study, we statistically compare common wetness metrics for predicting the temporal occurrence of rainfall-triggered landslides at regional scales (> 1000 km2) using a landslide inventory covering the contiguous United States. We find that representations of hillslope wetting and drainage using parsimonious leaky bucket models that only require rainfall input and an estimated drainage factor can identify landslide-triggering hydrologic conditions across disparate ecological regions more accurately than unmodified precipitation metrics or more complex hydrological models. Due to the proliferation of global precipitation datasets and the limited input data needed for the parsimonious leaky bucket model, this model could be used to improve tools for landslide risk reduction.

Mid‐range landslide outlooks can facilitate weather‐related landslide preparedness and disaster response planning, but seasonal landslide activity remains poorly quantified at continental scales. Leveraging >55,000 reported landslides from across the United States (U.S.), we used circular statistics to quantify landslide seasonality in 67 National Weather Service County Warning Areas (CWAs). We found regional differences in landslide season timing and duration, with transitions between domains variably corresponding to climate class or river basin. We assessed differences in seasonality by movement type for slides, flows, and falls, detecting apparent, but uncertain, differences between slide and fall seasonalities in 27 of 35 (77%) of CWAs with both types reported. In the Pacific Northwest, where long records exist, we found a credible shift toward a later mean landslide season in western Washington from 1990 to 2020, but no trend in western Oregon. Our results can provide emergency planners a resource to assess seasonal landslide probability nationwide.

Atmospheric rivers (ARs) are long, narrow, and transient corridors of intense water vapor transport in the lower atmosphere. By driving precipitation in the mid-latitudes, ARs sustain freshwater supply but also cause precipitation-induced disasters such as floods and landslides. In western North America, where precipitation-induced landslides (PILs) are a major geological hazard, ARs have been identified as key drivers of the precipitation regime and frequent precursors of landslide activity. Yet their value as predictors of PILs remains unknown.In this study, we assess whether AR conditions can inform landslide early-warning efforts in western North America. We employ PIKART—a state-of-the-art AR catalogue at 0.25° and 6-hourly resolution—and a compilation of landslide catalogues across the region—the USGS Landslide Inventories Across the United States, the NASA Cooperative Open Online Landslide Repository, and the Preliminary Canadian Landslide Database—to investigate the association between ARs and PILs from 1996 to 2018. Based on their intensity and persistence, we classify ARs on an AR-strength scale from AR1 to AR5 and analyze how PIL occurrence varies across these strength ranks.We find that AR conditions preceded more than 80% of days with reported PILs along the West Coast, yet most landslides were associated with weak, primarily beneficial ARs. Both isolated ARs and multi-event AR families contributed comparably to PIL occurrence. Despite this high co-occurrence, ARs exhibit little predictive power because most ARs do not trigger landslides: forecast skill is below 4% across most landslide-prone locations and does not exceed 15% even in regions with dense reporting, such as Portland, Oregon. Although neither the most frequent nor the most hazardous, moderate ARs of rank AR3 show the highest predictive skill. These results reveal a fundamental disconnect between the prevalence of ARs before landslides and their ability to predict them, highlighting both the challenges and opportunities of AR-based landslide forecasting in western North America.

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often, which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency's National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. We present county-level landslide frequency (landslides per area per year) estimates for the 50 US states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide-susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred frequency model to inform the NRI based on information criteria and physically plausible parameter estimates. The model showed that average annual reported landslide frequencies vary by 5 orders of magnitude across US counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km⁻² yr⁻¹ in Kusilvak Census Area, Alaska, to 29 (19–46) landslides 1000 km⁻² yr⁻¹ in Lake County, California, reflecting the country's strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake-prone regions of the Midwest and Southeast, along the Appalachians, in southern and southeastern Alaska, and on some Hawaiian islands. By examining the number of landslides predicted in 99th percentile years for each county, we identified that 26 % of US counties likely have potential for widespread landsliding with more than 10 landslides 1000 km⁻² yr⁻¹, even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI, and our approach can inform other risk-reduction and loss-mitigation efforts across the United States and globally.

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often, which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency’s National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. In this study, we present county-level landslide frequency (landslides area^-1 y^-1) estimates for the 50 U.S. states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred model based on information criteria and physically plausible parameter estimates. Our preferred model showed that average annual reported landslide frequencies vary by five orders of magnitude across U.S. counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km^-2y^-1 in Kusilvak Census Area, Alaska to 29 (19–46) landslides 1000 km^-2 y^-1 in Lake County, California, reflecting the country’s strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake prone regions of the Midwest and Southeast, along the Appalachians, in southern Alaska, and on some Hawaiian Islands. By examining the number of landslides predicted in 99^th percentile years for each county, we identified that 26 % of U.S. counties likely have potential for widespread landsliding with more than 10 landslides 1000 km^-2 y^-1, even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI and can inform other risk reduction and loss mitigation efforts across the United States.

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often, which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency’s National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. In this study, we present county-level landslide frequency (landslides area^-1 y^-1) estimates for the 50 U.S. states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred model based on information criteria and physically plausible parameter estimates. Our preferred model showed that average annual reported landslide frequencies vary by five orders of magnitude across U.S. counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km^-2y^-1 in Kusilvak Census Area, Alaska to 29 (19–46) landslides 1000 km^-2 y^-1 in Lake County, California, reflecting the country’s strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake prone regions of the Midwest and Southeast, along the Appalachians, in southern Alaska, and on some Hawaiian Islands. By examining the number of landslides predicted in 99^th percentile years for each county, we identified that 26 % of U.S. counties likely have potential for widespread landsliding with more than 10 landslides 1000 km^-2 y^-1, even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI and can inform other risk reduction and loss mitigation efforts across the United States.

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often, which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency’s National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. In this study, we present county-level landslide frequency (landslides area^-1 y^-1) estimates for the 50 U.S. states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred model based on information criteria and physically plausible parameter estimates. Our preferred model showed that average annual reported landslide frequencies vary by five orders of magnitude across U.S. counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km^-2y^-1 in Kusilvak Census Area, Alaska to 29 (19–46) landslides 1000 km^-2 y^-1 in Lake County, California, reflecting the country’s strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake prone regions of the Midwest and Southeast, along the Appalachians, in southern Alaska, and on some Hawaiian Islands. By examining the number of landslides predicted in 99^th percentile years for each county, we identified that 26 % of U.S. counties likely have potential for widespread landsliding with more than 10 landslides 1000 km^-2 y^-1, even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI and can inform other risk reduction and loss mitigation efforts across the United States.

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often, which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency’s National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. In this study, we present county-level landslide frequency (landslides area^-1 y^-1) estimates for the 50 U.S. states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred model based on information criteria and physically plausible parameter estimates. Our preferred model showed that average annual reported landslide frequencies vary by five orders of magnitude across U.S. counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km^-2y^-1 in Kusilvak Census Area, Alaska to 29 (19–46) landslides 1000 km^-2 y^-1 in Lake County, California, reflecting the country’s strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake prone regions of the Midwest and Southeast, along the Appalachians, in southern Alaska, and on some Hawaiian Islands. By examining the number of landslides predicted in 99^th percentile years for each county, we identified that 26 % of U.S. counties likely have potential for widespread landsliding with more than 10 landslides 1000 km^-2 y^-1, even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI and can inform other risk reduction and loss mitigation efforts across the United States.