Geology topics

Matthew A. Thomas

Publications and source records attributed to Matthew A. Thomas.

At least 19 recordsLinked to original sources

USGS2026

Regional models for postfire debris-flow likelihood and rainfall thresholds across the western United States

The U.S. Geological Survey (USGS) uses an empirical model developed with logistic regression (the ‘M1’ model) to rapidly assess debris-flow likelihood and to identify quantitative rainfall thresholds for debris flows after wildfire in the western United States. The M1 model was calibrated to a debris-flow inventory from southern California (United States) and has been applied throughout the western United States. Limited spatial coverage in the calibration dataset has motivated evaluation of M1 model accuracy outside the calibration region (e.g., the Sierra Nevada or the eastern Cascade Range, United States). Previous test cases showed that M1 overpredicts debris-flow likelihood and underpredicts rainfall thresholds for some locations (e.g., Arizona, northern California, Colorado, New Mexico, United States). We sought to improve the regional applicability of a debris-flow likelihood model by expanding the debris-flow inventory used for calibration, testing multiple potential models and generating an updated model framework. The updated inventory includes 3788 observations from 67 burned areas paired with short duration rainfall ratios. The updated model framework consists of a modified model structure and sets of coefficients calibrated separately to the entire updated inventory and to subsets of the inventory that intersect three Environmental Protection Agency (EPA) Level 2 ecoregions (Mediterranean California, Upper Gila Mountains and Western Cordillera). Comparisons of predictions from the updated models with observed rainfall and debris-flow activity show that the updated models outperform the M1 model by ~15%–60% and improve the uniformity of predictive performance across the western United States. The updated models also reduce false positive rates relative to M1 and generate rainfall thresholds that are better aligned with relative differences in regional climatology and debris-flow activity.

Arizona, California, Colorado, Idaho, Montana, Nev↗

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↗

USGS2026

Characterizing changes in postfire debris-flow hazard as burned areas recover

Emergency assessments of postfire debris-flow hazards that are performed by the U.S. Geological Survey (USGS) provide estimates of debris-flow likelihood and rainfall triggering conditions that are used for evaluating and managing runoff-generated debris-flow hazards in recently burned areas throughout the western United States. Although the immediate postfire period, within roughly one year after fire, is typically the most susceptible to runoff-generated debris flows, the hazard evolves in time and space as the burned area recovers. The recovery trajectory a given burned area will take depends on local climate and weather and can be difficult to predict. Some burned areas recover quickly, whereas others experience debris flows for multiple years after fire. As a result, extending our ability to update debris-flow likelihood estimates and rainfall thresholds based on observed recovery of the burned area would be beneficial. We present a method for multi-year runoff-generated debris-flow hazard assessment that leverages the USGS “M1” debris-flow likelihood model and integrates updated, satellite-derived, normalized burn ratio data to estimate vegetation recovery. We predict recovery-aware rainfall thresholds and validate them against a multi-year debris-flow hazard prediction and could be adapted for use with other debris-flow models that incorporate burn severity data.

Arizona, California, Colorado, New Mexico, Washing↗

USGS2025

Improved prediction of postfire debris flows through rainfall anomaly maps

Predicting where runoff-generated debris flows might occur during rainfall on steep, recently burned terrain is challenging. Studies of mass-movement processes in unburned areas indicate that event locations are well-predicted by rainfall anomaly, R* , in which peak observed rainfall is normalized by local rainfall climatology. Here, we use remote and field methods to map debris flows triggered within the 2020 Dolan Fire burn area in coastal California, demonstrate that a short-duration R* metric predicts debris-flow occurrence more effectively than absolute peak intensity or longer-duration rainfall metrics, and show that incorporating an R* criterion into an existing debris-flow likelihood model can reduce false positive predictions and improve accuracy. We test R * at three other climatically distinct fires in California, demonstrating its utility for mapping likely debris-flow locations in different climates. We also consider how R* can benefit postfire debris-flow prediction given recent increases in climatological variability within individual burn perimeters.

Callifornia↗

USGS2025

Landsliding follows signatures of wildfire history and vegetative regrowth in a steep coastal shrubland

Five years after the deadly and destructive 9 January 2018 Montecito debris flows (Santa Barbara County, California, USA), an atmospheric river storm on 9 January 2023 triggered widespread landsliding that affected many of the same drainages in the Santa Ynez Mountains. Using high-resolution aerial imagery, we identified >10,000 landslides over an ∼160 km 2 area. Most of the landslides were shallow (<1 m in depth) translational debris slides that initiated on steep (∼40°), south-facing hillslopes, with the highest incidence of landsliding in a sandstone-dominated bedrock unit. The landslides mobilized into debris flows and delivered substantial quantities of sediment downstream, which contributed to costly infrastructure impairments. We detected order-of-magnitude differences in landslide density across the study area that could not be attributed to variations in geomorphology (topographic aspect and slope), geology (bedrock type), or hydrology (seasonal antecedent rainfall, peak hourly storm rainfall intensity, total storm rainfall), which are usually considered relevant factors for shallow landsliding. Rather, we found that vegetation regrowth following wildfire was likely a relevant factor associated with the highly variable landslide densities. Hillslopes with less and different types of vegetation regrowth after fire appear to have been more susceptible to shallow landslides. We identify a possible vegetation control on postfire landsliding, which highlights an opportunity for hypothesis testing using more advanced techniques to track the evolution of vegetation cover and vegetation type in steep shrubland environments following wildfire.

California↗

USGS2025

River floods under wetter antecedent conditions deliver coarser sediment to the coast

Increasing hydrologic volatility—more extreme rain, and larger variations between wet and dry years—has become apparent in some regions, but few data exist to determine how intensifying hydrologic extremes affect sedimentary systems. Using uniquely high-resolution records of fluvial suspended sediment and coastal morphology, we quantify sedimentary responses from a steep, 357-km 2 watershed in California under extreme wet and dry hydrologic conditions. In years with multiple 2- to 10-year floods, fluvial sediment coarsened significantly as the wet season progressed, with late-season floods delivering dominantly sand-sized material to the coast. Greater and coarser sediment supply under wetter antecedent conditions affected nearshore geomorphic evolution for 4–5 years. The watershed and coastal changes we documented point to an increasing role of sediment-related hazards (flooding and hillslope erosion) and resources (nearshore accretion) as wet seasons intensify.

California↗

USGS2024

A robust quantitative method to distinguish runoff-generated debris flows from floods

Debris flows and floods generated by rainfall runoff occur in rocky mountainous landscapes and burned steeplands. Flow type is commonly identified post-event through interpretation of depositional structures, but these may be poorly preserved or misinterpreted. Prior research indicates that discharge magnitude is commonly amplified in debris flows relative to floods due to volumetric bulking and increased frictional resistance. Here, we use this flow amplification to develop a metric ( Q* ) to separate debris flows from floods based on the ratio of observed peak discharge to the theoretical maximum water discharge from rainfall runoff. We compile 642 observations of floods and debris flows and demonstrate that Q* distinguishes flow type to ∼92% accuracy. Q* allows for accurate identification of debris flows through simple channel cross-section surveys rather than through qualitative interpretation of deposits, and therefore should increase the performance of models and engineered structures that require accurate flow-type observations.

Geophysical Research Letters↗

USGS2024

Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA

As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1) rainfall intensity thresholds for postfire debris-flow initiation and (2) debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well these models performed over a 2-year period in the 2020 Grizzly Creek Fire burn perimeter in Glenwood Canyon, Colorado, USA, through the development of a debris-flow response inventory. The study area had the advantage of a network of 11 rain gauges for rainfall intensity measurements and repeat lidar data for volume estimates. Our observations showed that 89 % of observed debris flows in the first year postfire were triggered by rainfall rates higher than the fire-wide rainfall threshold produced by the current USGS operational model (M1). No debris flows were observed in the second year postfire, despite eight rainstorms with intensities higher than the modeled rainfall threshold. We found that the operational model for debris-flow initiation rainfall thresholds works well in this region during the first year but may be too conservative in year 2 due to vegetation recovery and sediment depletion. However, rainfall thresholds in the second year can be improved by using updated remote sensing imagery to recalculate the debris-flow initiation likelihood with the M1 model. The current volume model overestimates for this region by a median value of 4.4 times. However, the offset between the model estimates and observations is approximately linear, and the volumes from the Grizzly Creek debris flows had a similar magnitude to historic postfire debris flows in the region. Consequently, the current volume model could be adjusted with a regional correction factor.

Colorado↗

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↗

USGS2023

How long do runoff-generated debris-flow hazards persist after wildfire?

Runoff-generated debris flows are a potentially destructive and deadly response to wildfire until sufficient vegetation and soil-hydraulic recovery have reduced susceptibility to the hazard. Elevated debris-flow susceptibility may persist for several years, but the controls on the timespan of the susceptible period are poorly understood. To evaluate the connection between vegetation recovery and debris-flow occurrence, we calculated recovery for 25 fires in the western United States using satellite-derived leaf area index (LAI) and compared recovery estimates to the timing of 536 debris flows from the same fires. We found that the majority (>98%) of flows occurred when LAI was less than 2/3 of typical prefire values. Our results show that total vegetation recovery is not necessary to inhibit runoff-generated flows in a wide variety of regions in the western United States. Satellite-derived vegetation data show promise for estimating the timespan of debris-flow susceptibility.

Geophysical Research Letters↗

USGS2023

The spatial distribution of debris flows in relation to observed rainfall anomalies: Insights from the Dolan Fire, California

A range of hydrologic responses can be observed in steep, recently burned terrain, which makes predicting the spatial distribution of large debris flows challenging. Studies from rainfall-induced landslides in unburned areas show evidence of hydroclimatic tuning of landslide triggering, such that the spatial distribution of events is best predicted by the observed rainfall anomaly relative to climatic norms rather than by absolute rainfall. In this paper, we test whether the spatial distribution of debris flows in response to rainfall can be similarly predicted by rainfall anomaly. The 520 km 2 Dolan Fire burn scar in Monterey County, California, USA, spans a sharp hydroclimatic gradient and experienced a widespread storm in January 2021 that triggered floods and debris flows, providing a natural experiment in which to test this hypothesis. In this study, we use remote and field methods to map debris-flow response and examine its spatial heterogeneity. Together with rainfall data, our mapping reveals that the observed anomalies in peak 15-min rainfall intensity (I 15 ) relative to the intensity of the 1-yr return interval storm predict debris-flow occurrence better than the absolute peak I 15 . Our findings indicate that debris-flow processes and threshold rainfall required for debris-flow initiation may be tuned to local hydroclimate.

California↗

USGS2023

Postfire hydrologic response along the central California (USA) coast: Insights for the emergency assessment of postfire debris-flow hazards

The steep, tectonically active terrain along the Central California (USA) coast is well known to produce deadly and destructive debris flows. However, the extent to which fire affects debris-flow susceptibility in this region is an open question. We documented the occurrence of postfire debris floods and flows following the landfall of a storm that delivered intense rainfall across multiple burn areas. We used this inventory to evaluate the predictive performance of the US Geological Survey M1 likelihood model, a tool that presently underlies the emergency assessment of postfire debris-flow hazards in the western USA. To test model performance, we used the threat score skill statistic and found that the rainfall thresholds estimated by the M1 model for the Central California coast performed similarly to training (Southern California) and testing (Intermountain West) data associated with the original model calibration. Model performance decreased when differentiating between “minor” and “major” postfire hydrologic response types, which weigh effects on human life and infrastructure. Our results underscore that the problem of false positives is a major challenge for developing accurate rainfall thresholds for the occurrence of postfire debris flows. As wildfire activity increases throughout the western USA, so too will the demand for the assessment of postfire debris-flow hazards. We conclude that additional collection of field-verified inventories of postfire hydrologic response will be critical to prioritize which model variables may be suitable candidates for regional calibration or replacement.

California↗

USGS2023

The rainfall intensity-duration control of debris flows after wildfire

Increased wildfire activity in the western United States has exposed regional gaps in our understanding of postfire debris-flow generation. To address this problem, we characterized flows in an unstudied area to test the rainfall intensity-duration control of the hazard. Our rainfall measurements and field observations from the northern Sierra Nevada (California, USA) show that debris flows resulted from a short burst rainfall during a low-accumulation storm. In contrast, a much higher accumulation storm (∼10 times more rainfall) with lower short-duration rainfall rates only produced low-hazard flooding. We conclude that total storm rainfall is not an ideal metric for identifying the rainfall conditions that initiate runoff-generated debris flows in the first year after wildfire. Rather, a focus on short-duration (<1 hr), high-intensity rainfall that can occur during localized thunderstorms, or bands of intense rainfall during prolonged rainstorms, is more beneficial for the purposes of hazard assessment and warning.

California↗

USGS2021

Postwildfire soil‐hydraulic recovery and the persistence of debris flow hazards

Deadly and destructive debris flows often follow wildfire, but understanding of changes in the hazard potential with time since fire is poor. We develop a simulation‐based framework to quantify changes in the hydrologic triggering conditions for debris flows as postwildfire infiltration properties evolve through time. Our approach produces time‐varying rainfall intensity‐duration thresholds for runoff‐ and infiltration‐generated debris flows with physics‐based hydrologic simulations that are parameterized with widely available hydroclimatic, vegetation reflectance, and soil texture data. When we apply our thresholding protocol to a test case in the San Gabriel Mountains (California, USA), the results are consistent with existing regional empirical thresholds and rainstorms that caused runoff‐ and infiltration‐generated debris flows soon after and three years following a wildfire, respectively. We find that the hydrologic triggering mechanisms for the two observed debris flow types are coupled with the effects of fire on the soil saturated hydraulic conductivity. Specifically, the rainfall intensity needed to generate debris flows via runoff increases with time following wildfire while the rainfall duration needed to produce debris flows via subsurface pore‐water pressures decreases. We also find that variations in soil moisture, rainfall climatology, median grain size, and root reinforcement could impact the median annual probability of postwildfire debris flows. We conclude that a simulation‐based method for calculating rainfall thresholds is a tractable approach to improve situational awareness of debris flow hazard in the years following wildfire. Further development of our framework will be important to quantify postwildfire hazard levels in variable climates, vegetation types, and fire regimes.

Journal of Geophysical Research: Earth Surface↗

USGS2020

Hillslopes in humid-tropical climates aren’t always wet: Implications for hydrologic response and landslide initiation in Puerto Rico, USA

The devastating impacts of the widespread flooding and landsliding in Puerto Rico following the September 2017 landfall of Hurricane Maria highlight the increasingly extreme atmospheric disturbances and enhanced hazard potential in mountainous humid‐tropical climate zones. Long‐standing conceptual models for hydrologically driven hazards in Puerto Rico posit that hillslope soils remain wet throughout the year, and therefore, that antecedent soil wetness imposes a negligible effect on hazard potential. Our post‐Maria in situ hillslope hydrologic observations, however, indicate that while some slopes remain wet throughout the year, others exhibit appreciable seasonal and intra‐storm subsurface drainage. Therefore, we evaluated the performance of hydro‐meteorological (soil wetness and rainfall) versus intensity‐duration (rainfall only) hillslope hydrologic response thresholds that identify the onset of positive pore‐water pressure, a predisposing factor for widespread slope instability in this region. Our analyses also consider the role of soil‐water storage and infiltration rates on runoff generation, which are relevant factors for flooding hazards. We found that the hydro‐meteorological thresholds outperformed intensity‐duration thresholds for a seasonally wet, coarse‐grained soil, although they did not outperform intensity‐duration thresholds for a perennially wet, fine‐grained soil. These end‐member soils types may also produce radically different stormflow responses, with subsurface flow being more common for the coarse‐grained soils underlain by intrusive rocks versus infiltration excess and/or saturation excess for the fine‐grained soils underlain by volcaniclastic rocks. We conclude that variability in soil‐hydraulic properties, as opposed to climate zone, is the dominant factor that controls runoff generation mechanisms and modulates the relative importance of antecedent soil wetness for our hillslope hydrologic response thresholds.

Puerto Rico↗

USGS2020

Deep Learning as a tool to forecast hydrologic response for landslide-prone hillslopes

Empirical thresholds for landslide warning systems have benefitted from the incorporation of soil‐hydrologic monitoring data, but the mechanistic basis for their predictive capabilities is limited. Although physically based hydrologic models can accurately simulate changes in soil moisture and pore pressure that promote landslides, their utility is restricted by high computational costs and nonunique parameterization issues. We construct a deep learning model using soil moisture, pore pressure, and rainfall monitoring data acquired from landslide‐prone hillslopes in Oregon, USA, to predict the timing and magnitude of hydrologic response at multiple soil depths for 36‐hr intervals. We find that observation records as short as 6 months are sufficient for accurate predictions, and our model captures hydrologic response for high‐intensity rainfall events even when those storm types are excluded from model training. We conclude that machine learning can provide an accurate and computationally efficient alternative to empirical methods or physical modeling for landslide hazard warning.

Oregon↗

USGS2020

Geometric and material variability influences stress states relevant to coastal permafrost bluff failure

Scientific knowledge and engineering tools for predicting coastal erosion are largely confined to temperate climate zones that are dominated by non-cohesive sediments. The pattern of erosion exhibited by the ice-bonded permafrost bluffs in Arctic Alaska, however, is not well explained by these tools. Investigation of the oceanographic, thermal, and mechanical processes that are relevant to permafrost bluff failure along Arctic coastlines is needed. We conducted physics-based numerical simulations of mechanical response that focus on the impact of geometric and material variability on permafrost bluff stress states for a coastal setting in Arctic Alaska that is prone to toppling mode block failure. Our three-dimensional geomechanical boundary-value problems output static realizations of compressive and tensile stresses. We use these results to quantify variability in the loci of potential instability. We observe that niche dimension affects the location and magnitude of the simulated maximum tensile stress more strongly than the bluff height, ice wedge polygon size, ice wedge geometry, bulk density, Young’s Modulus, and Poisson’s Ratio. Our simulations indicate that variations in niche dimension can produce radically different potential failure areas and that even relatively shallow vertical cracks can concentrate displacement within ice-bonded permafrost bluffs. These findings suggest that stability assessment approaches, for which the geometry of the failure plane is delineated a priori, may not be ideal for coastlines similar to our study area and could hamper predictions of erosion rates and nearshore sediment/biogeochemical loading.

Frontiers in Earth Science↗

USGS2019

Assessing the feasibility of satellite-based thresholds for hydrologically driven landsliding

Elevated soil moisture and heavy precipitation contribute to landslides worldwide. These environmental variables are now being resolved with satellites at spatiotemporal scales that could offer new perspectives on the development of landslide warning systems. However, the application of these data to hydro-meteorological thresholds (which account for antecedent soil moisture and rainfall) first need to be evaluated with respect to proven, direct measurement-based thresholds that use rain gauges and in situ soil moisture sensors. Here, we compare ground-based hydrologic data to overlapping satellite-based data before, during, and after a recent season of widespread shallow landsliding in the San Francisco Bay Area (California, USA). We then explore how the remotely sensed information could be used to empirically define hypothetical thresholds for shallow landsliding. We find that the ground-based thresholds developed with a single monitoring station show superior performance because the in situ soil saturation data better reflect the gravity-dominated subsurface flow conditions that are characteristic of hillslopes during the rainy season. Although the satellite-based thresholds can identify most of the landslide days, they include a greater number of false alarms due to overestimates of soil moisture between major storm events. To avoid the type of false alarms that are characteristic of our satellite-based thresholds, further post-processing of the near-surface hydrologic response data to better reflect gravity-dominated drainage should be integrated into satellite-based model outputs. Our results encourage further deployment of ground stations in landslide-prone terrain and cautious exploration of satellite-based hydro-meteorological thresholds where in situ networks are nonexistent.

California↗