Bedrock weathering and soil production set the rate that hillslope colluvium is produced, while material properties determine whether sediment can fail under a given set of conditions. Together these factors generally control both the frequency and magnitude of landslides. In 2017, Puerto Rico (USA) experienced widespread landsliding across a range of lithologies due to Hurricane Maria, making it an ideal setting to explore the role of sediment generation in landslide response to storms. Based on an inventory of >70,000 landslides island-wide and detailed field mapping from a subset of source areas, we estimate that 0.01-0.1 km3 of material was evacuated from the hillslopes (approximately 1-10 mm of lowering). Focusing on the high-density landslide area of Utuado, we estimate an average lowering of 5-50 mm. From past inventories and records of storm events, a watershed is impacted by a hurricane every ~25 years with enough rain for widespread landslides every ~5 years. Assuming a similar density, the landslide contribution to hillslope lowering could be on the order of 1-10 mm/yr. In a humid-tropical environment, where weathering rates are likely high, can hillslopes continue to produce material at this pace? Elsewhere on the island, soil production rates are on the order of 0.1 mm/yr leading to soil residence times of approximately 10 ky. However, to keep pace with landslide events like Hurricane Maria, soil production likely needs to be at least an order of magnitude faster to maintain soil-mantled hillslopes in this study area. For our study area, we ask: has the large-magnitude rainfall from Hurricane Maria caused an abnormally high density of landsliding, resetting the clock on the material availability for areas like Utuado? Here we relate measures of material properties, bedrock weathering intensity, denudation, and land-use history to begin answering this question.

Matthew Thomas

and 5 more

Declining sea ice in the Arctic Ocean is exposing its coasts to more frequent and intense forms of wave energy and storm surge. As a result, erosion rates along some stretches of coastline in the Alaskan Arctic have doubled since the middle of the 20th century and now rank among the highest in the world. People concentrated near the coast are being heavily impacted by erosion, with some facing relocation. Coastal erosion is projected to increase the cost of maintaining infrastructure by billions of dollars in the coming decades. The financial impact of enhanced erosion will likely be further exacerbated by emerging geopolitical pressures, including the discovery of natural resources, opening of new shipping routes, and construction of support facilities in the Arctic. Scientific knowledge and engineering tools for predicting coastal erosion and guiding land-use decision are not well-suited for the ice-bonded bluffs of the Alaskan Arctic. Investigation of the oceanographic, thermal, and mechanical processes that are relevant to permafrost bluff failure along Arctic coastlines is thus needed. We introduce a geomechanical simulation framework, informed by field observation and laboratory testing, that focuses on the impact of bluff geometry and material variability on permafrost bluff stress states associated with a 9-km stretch of Alaskan Arctic coastline fronting the Beaufort Sea that is prone to toppling-mode block failure. Our approach is advantageous in that it is based on measurable physical properties (e.g., the bluff geometry, permafrost bulk density, Young’s Modulus, and Poisson’s Ratio) and does not require the potential failure to be defined a priori, but rather, the failure area can be interpreted from the multidimensional patterns of stress produced by the model. Our findings highlight how (1) block failure characteristics could be tied to variations in the intensity and duration of the storm energy that intersects the coastline and (2) how deformation processes that create non-uniform patterns of displacement may play a role in localizing block failure. We propose that this kind of physics-based simulation approach can facilitate hypothesis testing regarding the prediction of decadal-scale erosion rates for increasingly dynamic coastal permafrost systems.

Matthew Thomas

and 2 more

The devastating impacts of the widespread flooding and landsliding in Puerto Rico following the September 2017 landfall of Hurricane Maria highlight the enhanced hazard potential from increasingly extreme storms 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 antecedent soil wetness imposes a negligible effect on hazard potential. Our post-Maria in situ hillslope hydrologic observations indicate that while some slopes remain wet throughout the year, others exhibit appreciable seasonal and intra-storm subsurface drainage. Therefore, we used receiver-operating characteristic analysis and the Threat Score (TS) skill statistic to evaluate 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. We found that the hydro-meteorological thresholds outperformed intensity-duration thresholds for a seasonally wet, coarse-grained soil (TS = 0.8 vs. 0.6, respectively), although they did not outperform intensity-duration thresholds for a perennially wet, fine-grained soil (TS = 0.2 vs. 0.2, respectively). These 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 the humid-tropical climate zone, is the dominant factor that controls runoff generation and modulates the importance of antecedent soil wetness for our hillslope hydrologic response thresholds. Our findings encourage further deployment of continuous in situ hydrologic monitoring to facilitate the development of empirical hillslope hydrologic response and landslide thresholds for regional-scale hazard warning systems that must account for spatially variable soil types.

Elijah Orland

and 4 more

We apply deep learning to a synthetic near-surface hydrological response dataset of 4.4 million infiltration scenarios to determine conditions for the onset of positive pore-water pressures. This provides a rapid assessment of hydrologic conditions of potentially hazardous hillslopes where mass wasting is prevalent, and sidesteps the computationally expensive process of solving complex, highly non-linear equations. Each scenario considers antecedent soil moisture and storm depth with varying soil properties based on those measured at a USGS site in the East Bay Hills, CA, USA. Our model combines antecedent soil wetness and storm conditions with soil-hydraulic properties and predicts a binary output of whether or not positive pore pressures were generated. After parameterization, pore-water pressure conditions can be returned for any combination of antecedent soil moisture content and storm depth values. Similar to previous work, a deep learning model reduces computational cost: processing time is decreased by more than an order of magnitude for 1D simulated infiltration scenarios while maintaining high levels of accuracy. While the physical relevance and utility behind process-based numerical modeling cannot be replaced, the comparatively reduced computational cost of deep learning allows for rapid modeling of pore-water pressure conditions where solving complex, highly non-linear equations would otherwise be required. Furthermore, comparing the solution of a deep learning model with a hydrological model exemplifies how similar results can be produced through highly divergent mathematical relationships. This provides a unique opportunity to understand which variables are most relevant for the prediction of positive pore-water pressures on hillslopes, and can represent landslide-relevant hydrologic conditions for hillslopes where rapid analysis is imperative for informing potential hazard mitigation efforts. Ultimately, a calibrated deep learning model may reduce the need for computationally expensive physics-based modeling, which are often time and resource intensive, while providing critical statistical insight for the onset of hazardous conditions in landslide-prone areas.