An official website of the United States government
Abstract Predicting rainfall‐induced landslide motion is challenging because shallow groundwater flow is extremely sensitive to the preexisting moisture content in the ground. Here, we use groundwater hydrology theory and numerical modeling combined with five years of field monitoring to illustrate how unsaturated groundwater flow processes modulate the seasonal pore water pressure rise and therefore the onset of motion for slow‐moving landslides. The onset of landslide motion at Oak Ridge earthflow in California’s Diablo Range occurs after an abrupt water table rise to near the landslide surface 52–129 days after seasonal rainfall commences. Model results and theory suggest that this abrupt rise occurs from the advection of a nearly saturated wetting front, which marks the leading edge of the integrated downward flux of seasonal rainfall, to the water table. Prior to this abrupt rise, we observe little measured pore water pressure response within the landslide due to rainfall. However, once the wetting front reaches the water table, we observe nearly instantaneous pore water pressure transmission within the landslide body that is accompanied by landslide acceleration. We cast the timescale to reach a critical pore water pressure threshold using a simple mass balance model that considers variable moisture storage with depth and explains the onset of seasonal landslide motion with a rainfall intensity‐duration threshold. Our model shows that the seasonal response time of slow‐moving landslides is controlled by the dry season vadose zone depth rather than the total landslide thickness.
more »
« less
Deep Learning as a Tool to Forecast Hydrologic Response for Landslide‐Prone Hillslopes
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
- Award ID(s):
- 1831770
- PAR ID:
- 10378294
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 47
- Issue:
- 16
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Modelling seasonal landslide motion: Does it only depend on fluctuations in normal effective stress?Abstract Landslide motion is often simulated with interface‐like laws able to capture changes in frictional strength caused by the growth of the pore water pressure and the consequent reduction of the effective stress normal to the plane of sliding. Here it is argued that, although often neglected, the evolution of all the 3D stress components within the basal shear zone of landslides also contributes to changes in frictional strength and must be accounted for to predict changes in seasonal velocity. For this purpose, an augmented sliding‐consolidation model is proposed which allows for the computation of excess pore pressure development and downslope sliding with any constitutive law with 3D stress evolution. Simulations of idealised infinite slope models subjected to hydrologic forcing are used to study the role of in‐situ stress conditions and stress rate multiaxiality. Specifically, a Drucker‐Prager perfectly plastic model is used to replicate frictional failure and shear deformation at the base of landslides. The model reveals that conditions amenable to the shearing of a frictional interface are met only after numerous rainfall cycles, that is, when multiaxial stress rates are suppressed. In this case, the landslide is predicted to move through a seasonal ratcheting controlled only by the effective stress component normal to the plane of sliding. By contrast, in newly formed landslides, the multiaxial stress evolution is found to produce further regimes of motion, from plastic shakedown to cyclic failure, neither of which can be captured by interface‐like frictional laws. Notably, the model suggests that a transition across these regimes can emerge in response to an aggravation of the magnitude of forcing, implying that (i) fluctuations in climate may alter the seasonal trends of motion observed today; (ii) our ability to quantify landslide‐induced risks is impaired unless proper geomechanical models are used to examine their long‐term dynamics.more » « less
-
Abstract Soil moisture is a major driver of microbial activity and thus, of the release of carbon (C) into the Earth's atmosphere. Yet, there is no consensus on the relationship between soil moisture and microbial respiration, and as a result, moisture response functions are a poorly constrained aspect of C models. In addition, models assume that the response of microbial respiration to moisture is the same for all ecosystems, regardless of climate history, an assumption that many empirical studies have challenged. These gaps in understanding of the microbial respiration response to moisture contribute to uncertainty in model predictions.We review our understanding of what drives microbial moisture response, highlighting evidence that historical precipitation can influence both responses to moisture and sensitivity to drought. We present two hypotheses, the ‘climate history hypothesis’, where we predict that baseline moisture response functions change as a function of precipitation history, and the ‘drought legacy hypothesis’, in which we suggest that the intensity and frequency of historical drought have shaped microbial communities in ways that will control moisture responses to contemporary drought. Underlying mechanisms include biological selection and filtering of the microbial community by rainfall regimes, which result in microbial traits and trade‐offs that shape function.We present an integrated modelling and empirical approach for understanding microbial moisture responses and improving models. Standardized measures of moisture response (respiration rate across a range of moistures) and accompanying microbial properties are needed across sites. These data can be incorporated into trait‐based models to produce generalized moisture response functions, which can then be validated and incorporated into conventional and microbially explicit ecosystem models of soil C cycling. Future studies should strive to analyse realistic moisture conditions and consider the role of environmental factors and soil structure in microbial response.Microbes are the engines that drive C storage and are sensitive to changes in rainfall. A greater understanding of the factors that govern this sensitivity could be a key part of improving predictions of soil C dynamics, climate change and C‐climate feedbacks. Read the freePlain Language Summaryfor this article on the Journal blog.more » « less
-
Abstract Regional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km2) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.more » « less
-
Abstract Atmospheric dryness (i.e., high vapor pressure deficit, VPD), together with soil moisture stress, limits plant photosynthesis and threatens ecosystem functioning. Regions where rainfall and soil moisture are relatively sufficient, such as the rainfed part of the U.S. Corn Belt, are especially prone to high VPD stress. With globally projected rising VPD under climate change, it is crucial to understand, simulate, and manage its negative impacts on agricultural ecosystems. However, most existing models simulating crop response to VPD are highly empirical and insufficient in capturing plant response to high VPD, and improved modeling approaches are urgently required. In this study, by leveraging recent advances in plant hydraulic theory, we demonstrate that the VPD constraints in the widely used coupled photosynthesis‐stomatal conductance models alone are inadequate to fully capture VPD stress effects. Incorporating plant xylem hydraulic transport significantly improves the simulation of transpiration under high VPD, even when soil moisture is sufficient. Our results indicate that the limited water transport capability from the plant root to the leaf stoma could be a major mechanism of plant response to high VPD stress. We then introduce a Demand‐side Hydraulic Limitation Factor (DHLF) that simplifies the xylem and the leaf segments of the plant hydraulic model to only one parameter yet captures the effect of plant hydraulic transport on transpiration response to high VPD with similar accuracy. We expect the improved understanding and modeling of crop response to high VPD to help contribute to better management and adaptation of agricultural systems in a changing climate.more » « less
