4 Discussion and Conclusion
Our study documents the first application of open-access standardized
InSAR products from JPL ARIA to identify and monitor landslides across
large regions. Although the coarse resolution of the ARIA InSAR product
limits our ability to detect smaller landslides, it still provides
valuable data that can be used to better understand landslide processes.
Due to the large volume of open-access InSAR data that is currently
available, and will continue to increase with time, especially with the
upcoming launch of the NASA-ISRO SAR (NISAR) satellite, standardized
InSAR products will become one of the primary ways to deliver InSAR data
to the broader scientific community. The JPL Observational Products for
End-Users from Remote Sensing Analysis (OPERA) project will soon be
generating an operational high resolution displacement timeseries from
Sentinel-1 and NISAR data over North America. With a spatial resolution
of 30 m or better this product will be well-suited for identifying and
monitoring landslides. Thus, it is important to continue to explore new
approaches to analyze these InSAR products for scientific research,
including use of automated or semi-automated detection and mapping
techniques (e.g., Amatya et al., 2021; Milillo et al., 2021) .
Active slow-moving landslides across California occur in both wet and
dry environments. Despite more than an order of magnitude difference in
mean annual rainfall and factor of ~4 difference in
estimated landslide thickness, these landslides exhibit similar first
order behaviors including 1) seasonal and annual changes in displacement
corresponding to local changes in rainfall, and 2) sensitivity to
seasonal, annual, and multi-annual changes in rainfall. One explanation
for climate- and size-independent sensitivity is that persistently
active landslides maintain sufficiently high groundwater levels that
keep them close to an acceleration threshold. Prior work has shown that
active slow-moving landslides typically have high groundwater levels
year round and become effectively saturated during the wet season
(Finnegan et al., 2021; Iverson & Major, 1987; Malet et al., 2002;
Schulz et al., 2009; 2018). The ability to maintain high groundwater
levels may be a consequence of rock type-controlled critical zone (i.e.,
ground surface to unweathered bedrock) structure (Murphy et al., 2022).
Hahm et al. (2019) showed that landslides in a wet region of northern
California (mean annual rainfall ~1800 mm/yr) in the
Franciscan mélange, the predominant bedrock of landslides in our
inventory, have a critical zone characterized by a thin (< 3
m) seasonally unsaturated zone with low hydraulic conductivity that
becomes effectively saturated after ~100 to 200 mm of
seasonal rainfall. Similarly, Finnegan et al. (2021), showed that the
Oak Ridge landslide, in an area of moderate rainfall in central
California (mean annual rainfall ~640 mm/yr), also
becomes effectively saturated after ~200 mm of seasonal
rainfall. While not all landslides in our study occur in the Franciscan
mélange, the other rock types are mostly marine and nonmarine
sedimentary rocks that may bear similarity and thus may have a similar
critical zone structure (Riebe et al., 2017). Additionally, the
landslides themselves may create environments that allow water retention
due to low permeability shear zones that inhibit water flow (Baum &
Reid, 2000; Nereson et al., 2018). Therefore, landslides with thin
seasonally unsaturated zones can often reach saturation in both the
wetter and drier parts of the state. Once saturation occurs, excess
precipitation should be shed as overland flow (e.g., Hahm et al., 2019),
which may explain why landslides exhibit a muted response to large
differences in annual rainfall across California (e.g., Murphy et al.,
2022).
Another consequence of limited annual groundwater fluctuations is that
slow-moving landslides display a relatively narrow range of velocities
and rarely fail catastrophically. Finnegan et al. (2021) proposed that
the narrow range of pore-water pressures (10-20 kPa) experienced by
slow-moving landslides provides a natural limit to the stress changes
that drive landslide acceleration. Yet, slow-moving landslides
occasionally fail catastrophically. One example is the 2017 Mud Creek
landslide in California (also in Franciscan mélange) that exhibited a
minimum of 8 years of slow motion prior to rapid acceleration and
collapse (Handwerger, Huang, et al., 2019). This transition from stable
to unstable sliding may result from additional mechanisms that cause
pore-water pressures to rise beyond the maximum from rainfall inputs.
Both shear-induced contraction and longitudinal compression of landslide
material can cause pore-water pressures to increase sharply (e.g.,
Iverson, 2005; Iverson et al., 2015), as can weakening mechanisms, such
as slip localization (e.g., Handwerger, Huang, et al., 2019; Viesca and
Rice, 2012). Although many mechanical-hydrological interactions
contribute to the behavior of landslides, our results agree with
Finnegan et al. (2021) and Murphy et al. (2022), which suggest that the
volume of material that can accommodate water input exerts a primary
control on the landslide sensitivity and response to precipitation.
Our study revealed that active slow-moving landslides moved seasonally
during both dry and wet years and in dry and wet climates, indicating
that even during dry periods and at dry landslides, there is often still
sufficient water input to maintain downslope motion for many landslides.
Climate models predict that rainfall in California is likely to become
more seasonal (i.e., a higher proportion of rainfall delivered in
December to March) (Swain, 2021) and dry to wet year extremes will
become more common (Dong et al., 2019; Persad et al., 2020; Polade et
al., 2017; Swain et al., 2018). Therefore, our study period may be
representative of future precipitation and landslide behavioral patterns
throughout California. While we currently cannot reliably predict
landslide motion due to complex nonlinear relationships between
precipitation, pore-water pressure, and velocity (e.g., Carey et al.,
2019; Malet et al., 2002; Murphy et al., 2022), we may be able to
predict relative changes in landslide velocity in response to relative
changes in precipitation. Therefore, it is necessary to continue to
document landslide behaviors during ‘normal’ years that may serve as
baselines for comparison and prediction of future landslide behaviors.