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USGS2024

Rainfall intensification amplifies exposure of American Southwest to conditions that trigger postfire debris flows

Short-duration, high-intensity rainfall can initiate deadly and destructive debris flows after wildfire. Methods to estimate the conditions that can trigger debris flows exist and guidance to determine how often those thresholds will be exceeded under the present climate are available. However, the limited spatiotemporal resolution of climate models has hampered efforts to characterize how rainfall intensification driven by global warming may affect debris-flow hazards. We use novel, dynamically downscaled (3.75-km), convection-permitting simulations of short-duration (15-min) rainfall to evaluate threshold exceedance for late 21st-century climate scenarios in the American Southwest. We observe significant increases in the frequency and magnitude of exceedances for regions dominated by cool- and warm-season rainfall. We also observe an increased frequency of exceedance in regions where postfire debris flows have not been documented, and communities are unaccustomed to the hazard. Our findings can inform planning efforts to increase resiliency to debris flows under a changing climate.

Arizona, California, Nevada, New Mexico, Utah↗
USGS2026

Spatially referenced watershed models for the binational Red–Assiniboine River Basin: Bayesian vs frequentist comparison

Excess nutrient loading remains a leading cause of declining water quality in lakes, estuaries, and coastal waters worldwide, with global economic costs of US$200 billion – US$2 trillion annually from impacts on fisheries, tourism, freshwater resources, and water treatment. Our study focuses on total phosphorus (TP) in Lake Winnipeg and its binational Red-Assiniboine River Basin, where nutrient inputs have degraded water quality and increased cyanobacterial blooms. These changes pose ecological, public health, and economic risks. We applied a spatially referenced watershed model with a hybrid statistical-mechanistic structure partitioning annual nutrient loads into land-use export, land-to-water delivery, and in-reservoir decay. Bayesian and traditional frequentist model calibrations were compared. In the frequentist model, coefficients for agricultural inputs, forests /wetlands, stream channels, precipitation, and reservoir losses were statistically significant, whereas coefficient for wastewater was not. In contrast, all variables were successfully calibrated using the Bayesian approach. Model results delineate TP-export hotspots across the basin, showing that 54–62% of TP originates from the U.S., with agricultural sources ranging 62–72%—highlighting the importance of agriculture-focused Best Management Practices. Given the global relevance of nutrient-driven water-quality challenges, our results highlight Bayesian calibration for robust risk assessment and adaptive nutrient management.

Red–Assiniboine River Basin↗
USGS2024

The projected exposure and response of a natural barrier island system to climate-driven coastal hazards

Accelerating sea level rise (SLR) and changing storm patterns will increasingly expose barrier islands to coastal hazards, including flooding, erosion, and rising groundwater tables. We assess the exposure of Cape Lookout National Seashore, a barrier island system in North Carolina (USA), to projected SLR and storm hazards over the twenty-first century. We estimate that with 0.5 m of SLR, 47% of current subaerial barrier island area would be flooded daily, and the 1-year return period storm would flood 74%. For 20-year return period storms, over 85% is projected to be flooded for any SLR. The modelled groundwater table is already shallow (< 2 m deep), and while projected to shoal to the land surface with SLR, marine flooding is projected to overtake areas with emergent groundwater. Projected shoreline retreat reaches an average of 178 m with 1 m of SLR and no interventions, which is over 60% of the current island width at narrower locations. Compounding these hazards is subsidence, with one-third of the study area currently lowering at > 2 mm/yr. Our results demonstrate the difficulty of managing natural barrier systems such as those managed by federal park systems tasked with maintaining natural ecosystems and protecting cultural resources.

North Carolina↗
USGS2024

Predicted exposure of communities in southeastern United States to climate-related coastal hazards

A rigorous analysis of 21st Century multi-hazard exposure for U.S. Southeast Atlantic coastal communities indicates that up to 70% of residents will be exposed daily to shallow and emerging groundwater by ~2100, 15 times higher than from surficial flooding alone. This threat further exacerbates other coastal stressors, such as flooding, subsidence, and beach erosion, that impact these vulnerable coastal communities and ecosystems under expected future climate change scenarios.

Florida, Georgia, North Carolina, South Carolina, ↗
USGS2024

Participatory engagement to reduce communication gaps

Underserved communities, especially those in coastal areas in Puerto Rico, face significant threats from natural hazards such as hurricanes and rising sea levels. Limited funding hinders the investment in costly mitigation measures, increasing exposure to natural disasters. Providing coastal resources and data products through effective communication mechanisms is fundamental to improving the well-being of these underserved coastal communities. The overall objectives of the pilot effort to engage and connect with underserved coastal communities in Puerto Rico were the following: (1) compile a comprehensive database of the projects and resources relevant to natural hazards in Puerto Rico; (2) foster connections with Puerto Rican interested parties to better understand their priorities regarding coastal hazards and provide them with pertinent U.S. Geological Survey (USGS) resources; and (3) identify knowledge gaps to guide future USGS projects in Puerto Rico. Here we outline our participatory engagement framework and process, along with two specific resources developed with the information collected from this effort. These resources are available in English and Spanish and consist of user-friendly, non-technical information products. Among them are: (1) a website where users can learn about USGS research on landslides, hurricanes, earthquakes, water resources, coastal hazards, tsunamis, and ecosystem hazards and environmental contaminants, and (2) a geonarrative highlighting shoreline changes in Puerto Rico with sections on historical shoreline trends, hurricane impacts, and potential solutions that could help protect communities and mitigate coastal hazards. Continuing participatory engagement in future projects could enhance the accessibility and usability of natural hazards resources within the community.

San Juan↗
USGS2026

Hazard potential of compound flooding from rainfall, storm surge, and groundwater in coastal New York and Connecticut

Compound flood events, the co-occurrence of multiple flood drivers, can result in flood hazard potential exceeding that of any single driver alone. To evaluate compound flooding in a semi-urbanized coastal area, historical records dating back to 1970 are used to study the co-occurrences of high precipitation, storm surge, and shallow groundwater conditions along the coastlines of New York and Connecticut. Joint return periods for coincident precipitation-surge events were computed using statistical dependence models and compared to the assumption of independence as a ratio, referred to here as a return period adjustment. Results indicate distinct seasonality where compound events in the area disproportionately occur in the cold season between October and April. Return period adjustments range from a factor of 1 to almost 9, demonstrating the range in precipitation-storm surge dependence across the study area. Across all 24 station triad locations, groundwater levels were elevated during times of precipitation- surge co-occurrence, reflecting the tendency for coastal storms and shallow groundwater conditions to co-occur seasonally. The result is a pseudo-trivariate compound flood hazard score and corresponding hazard map that integrates dependence between daily precipitation-surge events and overall monthly groundwater levels (as a precondition) into a relative compound hazard score. The location with the highest compound flood hazard score is on the south shore of Long Island, as well as locations across coastal Connecticut where groundwater levels compound the co-occurrence of heavy precipitation and storm surge.

Connecticut, New York↗
USGS2024

Projections of multiple climate-related coastal hazards for the US Southeast Atlantic

Faced with accelerating sea level rise and changing ocean storm conditions, coastal communities require comprehensive assessments of climate-driven hazard impacts to inform adaptation measures. Previous studies have focused on flooding but rarely on other climate-related coastal hazards, such as subsidence, beach erosion and groundwater. Here, we project societal exposure to multiple hazards along the Southeast Atlantic coast of the United States. Assuming 1 m of sea level rise, more than 70% of the coastal residents and US \ $ 1 trillion in property are in areas projected to experience shallow and emerging groundwater, 15 times higher than daily flooding. Storms increase flooding exposure by an order of magnitude over daily flooding, which could impact up to ~50% of all coastal residents and US \ $ 770 billion in property value. The loss of up to ~80% of present-day beaches and high subsidence rates that currently affect over 1 million residents will exacerbate flooding and groundwater hazard risks.

Florida, Georgia, North Carolina, South Carolina, ↗
USGS2025

Landslide-channel feedbacks amplify channel widening during floods

Channel widening is a major hazard during floods, particularly in confined mountainous catchments. However, channel widening during floods is not well understood and not always explained by hydraulic variables alone. Floods in mountainous regions often coincide with landslides triggered by heavy rainfall, yet landslide-channel interactions during a flood event are not well known or documented. Here we demonstrate with an example from the Great Colorado Flood in 2013, a 1000 year precipitation event, how landslide-channel feedbacks can substantially amplify channel widening and flood risk. We use a combination of DEM differencing, field analysis, and multiphase flow modeling to document landslide-channel interaction during the flood event in which sediment delivered by landslides temporarily dammed the channel before failing and generating substantial channel widening. We propose that such landslide-flood interactions will become increasingly important to account for in flood hazard assessment as flooding and landsliding both increase with extreme rainfall under climate change.

Colorado↗
USGS2026

The damability function: A probabilistic approach to regional landslide dam susceptibility analysis applied to the Oregon Coast Range, USA

Landslides can dam rivers and require rapid response to mitigate catastrophic outburst floods. Here, we present a workflow to map landslide dam formation susceptibility at a regional scale. We define a probabilistic function that combines river valley width and landslide volume to efficiently determine the likelihood of a landslide dam or “damability”. We combine damability values with landslide susceptibility to estimate landslide dam susceptibility. The valley width measurements are automated using a new elevation threshold-based algorithm. Landslide volume is represented as a statistical distribution from mapped landslides. We validate and apply our approach to the Oregon Coast Range, USA and find that 36 % of river stretches exceed a dam potential threshold; these are in river headwaters and steeper terrain, which in this case correlate with more resistant lithologies. We also estimate volumes of the potential dammed lakes and find that most rivers with high dam susceptibility are less likely to impound large lakes because they have low drainage areas. However, widespread susceptibility, and the potential impacts from exceptionally large landslides, suggest that this hazard should be considered in the Pacific Northwest. The damability function workflow can ingest new data and be applied more broadly to assess future landslide dam hazards.

Oregon↗
USGS2026

An improved empirical model for predicting postfire debris-flow volume in the western United States

Reliable estimates of debris-flow volume can be used to help predict the magnitude of debris-flow hazards following wildfire in the western United States. In this study, we compiled and used a database of 227 postfire debris-flow volumes that were collected across the western United States to develop a multiple linear regression model for predicting postfire debris-flow volume. We explored 36 predictor variables related to rainfall, terrain, and fire characteristics, and selected the model with the combination of variables that yielded the most accurate predictions of debris-flow volume. We evaluated model performance against the entire volume database, as well as against four subsets of volume data from southern California, the Intermountain West, the Southwest, and regions with limited volume data, such as northern California and Washington. We also compared model performance against 3 existing postfire debris-flow volume models that were developed for use in southern California, the Intermountain West, and the Southwest. We demonstrate that the new volume model performs as well as the regional models in the regions for which they were developed and outperforms existing models when applied to volumes from data-limited regions in the western United States. These results indicate that the debris-flow volume model introduced in this study can be used to improve postfire hazard assessments across the western United States, especially outside of southern California.

Arizona, California, Colorado, New Mexico, Utah, W↗
USGS2024

Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling

Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and polygon), and better accommodating the varying data accuracy and precision in landslide inventories. However, the use of slope units in regional ( > 100 km 2 ) landslide studies remains limited due, in part, to the large computational costs and/or poor reproducibility with current delineation methods. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units that leverages tools from within TauDEM and GRASS, using an R interface. The algorithm uses geomorphic laws to define the appropriate scaling of the slope units representative of hillslope processes, avoiding the often ambiguous determination of slope unit size. We then demonstrate how slope units enable more robust regional-scale landslide susceptibility and event-specific landslide occurrence maps.

Natural Hazards and Earth Systems Sciences (NHESS)↗
USGS2026

Coastal and Marine Hazards and Resources Program decadal science strategy 2020–2030

Overview The decadal science strategy of the Coastal and Marine Hazards and Resources Program (CMHRP) of the U.S. Geological Survey for 2020 to 2030 describes the CMHRP's vision and mission and the strategic framework needed to support key program goals: Conduct research and develop science-based tools that lead to safer, more productive coastal communities and improved stewardship of natural resources.

Circular↗
USGS2025

Characterizing the scale of regional landslide triggering from storm hydrometeorology

Rainfall strongly affects landslide triggering; however, understanding how storm characteristics relate to the severity of landslides at the regional scale has thus far remained unclear, despite the societal benefits that would result from defining this relationship. As mapped landslide inventories typically cover a small region relative to a storm system, here we develop a dimensionless index for landslide-inducing rainfall, A * , based on extremes of modeled soil water relative to its local climatology. We calibrate A * using four landslide inventories, comprising over 11 000 individual landslides over four unique storm events, and find that a common threshold can be applied to estimate regional shallow-landslide-triggering potential across diverse climatic regimes in California (USA). We then use the spatial distribution of A * , along with topography, to calculate the landslide potential area (LPA) for nine landslide-inducing storm events over the past 20 years, and we test whether atmospheric metrics describing the strength of landfalling storms, such as integrated water vapor transport, correlate with the magnitude of hazardous landslide-inducing rainfall. We find that although the events with the largest LPA do occur during exceptional atmospheric river (AR) storms, the strength of landfalling atmospheric rivers does not scale neatly with landslide potential area, and even exceptionally strong ARs may yield minimal landslide impacts. Other factors, such as antecedent soil moisture driven by storm frequency and mesoscale precipitation features within storms, are instead more likely to dictate the patterns of landslide-generating rainfall throughout the state.

California↗
USGS2025

Factors influencing landslide occurrence in low-relief formerly glaciated landscapes: Landslide inventory and susceptibility analysis in Minnesota, USA

In landscapes recently impacted by continental glaciation, landslides may occur where topographic relief has been generated by the drainage of glacial lakes and ensuing post-glacial fluvial network development into unconsolidated glacially derived sediments and exhumed bedrock. To investigate linkages among environmental variables, post-glacial landscape development, and landslides, we created a landslide inventory of nearly 10,000 landslides in five regions of the formerly glaciated low-relief state of Minnesota, USA. Multivariate logistic regression indicates the importance of slope angle, lithology, and the development of stream valleys to landslide distribution. Areas underlain by fine-grained glaciolacustrine and nearshore deposits that are incised by streams are particularly prone to shallow (<1-2 m depth) landslides. Landslides also occur in a wide range of glacial and fluvial deposits, and as rockfall in layered Paleozoic sedimentary rocks in central and southern Minnesota and Precambrian igneous and sedimentary rocks in northeastern Minnesota. Although no more than 1-2% of the studied regions are susceptible to landslides, they can pose risk to life and safety, damage infrastructure, and impact water quality. The combination of recently generated low-relief steep slopes, extensive unconsolidated sediments, and layered sedimentary bedrock make this formerly glaciated landscape more susceptible to landslides than current national-scale models indicate.

Minnesota↗
USGS2024

Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico

Hurricane Maria induced about 70 000 landslides throughout Puerto Rico, USA, including thousands each in three municipalities situated in Puerto Rico's rugged Cordillera Central range. By combining a nonlinear soil-depth model, presumed wettest-case pore pressures, and quasi-three-dimensional (3D) slope-stability analysis, we developed a landslide susceptibility map that has very good performance and continuous susceptibility zones having smooth, buffered boundaries. Our landslide susceptibility map enables assessment of potential ground-failure locations and their use as landslide sources in a companion assessment of inundation and debris-flow runout. The quasi-3D factor of safety, F 3 , showed strong inverse correlation to landslide density (high density at low F 3 ). Area under the curve (AUC) of true positive rate (TPR) versus false positive rate (FPR) indicated success of F 3 in identifying head-scarp points (AUC = 0.84) and source-area polygons (0.85 ≤ AUC ≤ 0.88). The susceptibility zones enclose specific percentages of observed landslides. Thus, zone boundaries use successive F 3 levels for increasing TPR of landslide head-scarp points, with zones bounded by F 3 at TPR = 0.75, very high; F 3 at TPR = 0.90, high; and the remainder moderate to low. The very high susceptibility zone, with 118 landslides km −2 , covered 23 % of the three municipalities. The high zone (51 landslides km −2 ) covered another 10 %.

Puerto Rice↗
USGS2025

Engineering and risk research—A strategic vision for extending USGS earthquake science to risk assessment and mitigation

As a part of the U.S. Geological Survey (USGS) Natural Hazards Mission Area, the Engineering and Risk Project (ERP) of the Geologic Hazards Science Center conducts civil engineering research to extend natural hazards science to risk assessments. The ERP includes predominantly, but not exclusively, staff funded by the USGS Earthquake Hazards Program. A major focus of the ERP is to deliver actionable information to decisionmakers that helps promote the safety, security, and economic well-being of the Nation. The report discusses briefly the ERP’s evolution and scope, its organization structure, describes key products and deliverables, their efforts to engage with external partners, and its strategy and vision in conducting range of engineering and risk research. The research topics outlined in this report, as developed by the ERP, may be used to facilitate project planning activities during the next 10 years and serve as a longer-term planning resource for the USGS Earthquake Hazards Program and Geologic Hazards Science Center.

Circular↗
USGS2024

Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall

Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned areas based on forecast rainfall would provide decision-makers with information that directly addresses the potential for downstream impacts. We introduce a framework that integrates a 24 h lead-time ensemble precipitation forecast with debris-flow likelihood, volume, and runout models to produce probabilistic maps of debris-flow inundation. We applied this framework to simulate debris-flow inundation associated with the 9 January 2018 debris-flow event in Montecito, California, USA. When the observed debris-flow volumes were used to drive the probabilistic forecast model, analysis of the simulated inundation probabilities demonstrates that the model is both reliable and sharp. In the fully predictive model, however, in which debris-flow likelihood and volume were computed from the atmospheric model ensemble's predictions of peak 15 min rainfall intensity, I 15 , the model generally under-forecasted the inundation area. The observed peak I 15 lies in the upper tail of the atmospheric model ensemble spread; thus a large fraction of ensemble members forecast lower I 15 than observed. Using these I 15 values as input to the inundation model resulted in lower-than-observed flow volumes which translated into under-forecasting of the inundation area. Even so, approximately 94 % of the observed inundated area was forecast to have an inundation probability greater than 1 %, demonstrating that the observed extent of inundation was generally captured within the range of outcomes predicted by the model. Sensitivity analyses indicate that debris-flow volume and two parameters associated with debris-flow mobility exert significant influence on inundation predictions, but reducing uncertainty in postfire debris-flow volume predictions will have the largest impact on reducing inundation outcome uncertainty. This study represents a first step toward a near-real-time hazard assessment product that includes probabilistic estimates of debris-flow inundation and provides guidance for future improvements to this and similar model frameworks by identifying key sources of uncertainty.

California↗