Abstract 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.
more »
« less
Integrating Precipitation and Soil Moisture Measurements to Understand Landslide Movements along Alabama Highways
Landslides along Alabama highways are a relatively common occurrence in many regions of the state. These landslides can lead to damage to transportation infrastructure and significant traffic disruptions. The current practice identifies landslide locations primarily through maintenance personnel reports or motorist complaints. Once an unstable region is identified, the suspected slide area is commonly instrumented with inclinometers, which are then read at regular intervals to understand the slide plane location and identify changes in behavior. This inclinometer data has been collected at unstable sites across the state for many years and provides a unique dataset to understand how precipitation events influence landslide behavior along highways. Previously developed precipitation thresholds considering storm magnitude and duration were consistent with landslide events observed around the state, but there are many non-triggering events that fall above the thresholds (false positives). Approximately 70% of false positive storm events occurred during drier than average periods based on normalized soil moisture data from NASA’s SMAP instrument, while large movements occurred primarily during periods of average or above average soil moisture. This suggests that adding soil moisture data to landslide threshold predictions may help to reduce false positive events and to assess the likelihood of large movements occurring. These findings are now being used to develop improved warning thresholds that can highlight when landslides are likely to occur, allowing inspections and preventative maintenance to be prioritized.
more »
« less
- Award ID(s):
- 2047402
- PAR ID:
- 10519286
- Publisher / Repository:
- American Society of Civil Engineers
- Date Published:
- ISBN:
- 9780784485316
- Page Range / eLocation ID:
- 613 to 622
- Format(s):
- Medium: X
- Location:
- Vancouver, British Columbia, Canada
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
An inventory of landslides along Alabama highways was created using both inclinometer readings collected by the Alabama Department of Transportation and based on Detailed Damage Inspection Reports (DDIRs) submitted to the Federal Highway Administration (FHWA) for emergency assistance. For the inclinometer-based dataset, we have processed the readings to extract the change in displacement from the previous reading at the top of the slide plane. For the emergency relief landslides, we have extracted key information from the submitted DDIRs. These datasets can be used for landslide susceptibility modeling, testing remote sensing-based monitoring or warning systems, or to better understand landslide patterns in the southeastern United States.more » « less
-
Abstract Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts.more » « less
-
Abstract Landslides influence the global carbon (C) cycle by facilitating transfer of terrestrial C in biomass and soils to offshore depocenters and redistributing C within the landscape, affecting the terrestrial C reservoir itself. How landslides affect terrestrial C stocks is rarely quantified, so we derive a model that couples stochastic landslides with terrestrial C dynamics, calibrated to temperate rainforests in southeast Alaska, United States. Modeled landslides episodically transfer C from scars to deposits and destroy living biomass. After a landslide, total C stocks on the scar recover, while those on the deposit either increase (in the case of living biomass) or decrease while remaining higher than if no landslide had occurred (in the case of dead biomass and soil C). Specifically, modeling landslides in a 29.9 km2watershed at the observed rate of 0.004 landslides km−2 yr−1decreases average living biomass C density by 0.9 tC ha−1(a relative amount of 0.4%), increases dead biomass C by 0.3 tC ha−1(0.6%), and increases soil C by 3.4 tC ha−1(0.8%) relative to a base case with no landslides. The net effect is a small increase in total terrestrial C stocks of 2.8 tC ha−1(0.4%). The size of this boost increases with landslide frequency, reaching 6.5% at a frequency of 0.1 landslides km−2 yr−1. If similar dynamics occur in other landslide‐prone regions of the globe, landslides should be a net C sink and a natural buffer against increasing atmospheric CO2levels, which are forecast to increase landslide‐triggering precipitation events.more » « less
-
Abstract During December 2022–January 2023, nine atmospheric rivers (ARs) struck California consecutively, causing catastrophic flooding and 600+ landslides. The extensive footprints of landslide‐triggering storms and their diverse hydrometeorological forcings highlight the urgent need to incorporate regional‐scale hydrometeorology into landslide research. Here, using a meteorologically‐informed hydrologic model, we simulate the time‐evolving water budget during the nine‐AR event and identify hydrometeorological conditions that contributed to widespread landslide occurrences across California. Our analysis reveals that 89% of observed landslides occurred under excessively wet conditions, driven by precipitation exceeding the capacities of infiltration, storage, evapotranspiration, and soil drainage. Using K‐means clustering, we identify three distinct hydrometeorological pathways that increased landslide potential: intense precipitation‐induced runoff (∼32% of reported landslides), rain on pre‐wetted soils (∼53%), and snowmelt and soil ice thawing (∼15%). Our findings highlight the importance of constraining the compounding factors that influence slope stability over spatial scales consistent with landslide‐triggering weather systems.more » « less
An official website of the United States government

