Landslides can be caused by natural forcing and anthropogenic activities. Zhouqu County (China) on the eastern margin of Qinghai‐Tibet Plateau is set within the active Pingding‐Huama fault zone with ...evident fractures on the land surface. Frequent landslides and debris flows have occurred in this region due to river erosion, rainfall and deforestation. Here we quantified the slope movements using time‐series synthetic aperture radar interferometry (InSAR) based on the ascending and descending Sentinel‐1 satellite images acquired between October 2014 and August 2020. We observed distinct displacements in the highly fractured fault zone. The eastward and vertical displacement time series between February 2017 and July 2020 were constrained by the common‐day ascending and descending acquisitions. The eastward rates (461 mm/year) were greater than those in the vertical direction (−185 mm/year). We also note displacement discontinuities across the thrust faults beneath the Suoertou and Zhongpai landslides. Seasonal variations in the displacement time series suggest that the cyclic rainfall is the primary driver for the mass wasting processes rather than the tectonic loading. As a complement to in situ observations, our results demonstrate that InSAR is an effective tool to characterize the spatio‐temporal nature of landslide displacements in complicated geological environments.
Plain Language Summary
Zhouqu County in the Pingding‐Huama fault zone in the eastern margin of Qinghai‐Tibet Plateau is identified as a high priority site to research on clusters of landslides and debris flows in a mixed geodynamic setting of active tectonics, seasonal rainfall, river erosion and anthropogenic activities. However, our knowledge about landslide kinematics in this complicated region is still limited. We relied on remote sensing images from one ascending and one descending Sentinel‐1 satellite tracks to constrain the spatial–temporal displacement dynamics of active landslides from 2014 to 2020. The spatial patterns of displacements are determined by thrust faulting, river erosion, and anthropogenic activities. The temporal variations of landslide speed are mainly controlled by the seasonal rainfall rather than the tectonic loading.
Landslide kinematics in Pingding‐Huama fault zone in China are resolved by Sentinel‐1 satellite images during 2014–2020.
The 2D displacement time series are constrained by the common‐day ascending and descending InSAR measurements.
Spatially, some landslides are overlapped with or bound the active faults; temporally, seasonal rainfall regulates the landslide speed.
2-D phase unwrapping (PU) is one of the biggest challenges in synthetic aperture radar (SAR) interferometry (InSAR) processing. As an ill-posed problem, the performance of the traditional algorithmic ...model-based 2-D PU algorithms is not guaranteed to be correct with rapid ground deformation or topographic changes. An increasing number of remote sensing observations collected by different sensors (e.g., LiDAR and GPS) provides new opportunities to assist the traditional 2-D InSAR PU by reducing the nondeterminacy. In this article, we propose a novel knowledge-aided PU (KAPU) approach. KAPU compiles different prior knowledge from different sources with InSAR observations simultaneously through an integer programming model. More importantly, the mathematical proof demonstrates that the constraint of the optimization model of KAPU is totally unimodular, so KAPU can be efficiently solved without having to have the constraint that the ambiguity number is an integer. Theoretical analysis and extensive experimental results illustrate that KAPU outperforms the existing model-based 2-D InSAR PU algorithms on digital elevation model (DEM) generation and surface deformation estimation.
Abstract
Landslides modify the natural landscape and cause fatalities and property damage worldwide. Quantifying landslide dynamics is challenging due to the stochastic nature of the environment. ...With its large area of ~1 km
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and perennial motions at ~10–20 mm per day, the Slumgullion landslide in Colorado, USA, represents an ideal natural laboratory to better understand landslide behavior. Here, we use hybrid remote sensing data and methods to recover the four-dimensional surface motions during 2011–2018. We refine the boundaries of an area of ~0.35 km
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below the crest of the prehistoric landslide. We construct a mechanical framework to quantify the rheology, subsurface channel geometry, mass flow rate, and spatiotemporally dependent pore-water pressure feedback through a joint analysis of displacement and hydrometeorological measurements from ground, air and space. Our study demonstrates the importance of remotely characterizing often inaccessible, dangerous slopes to better understand landslides and other quasi-static mass fluxes in natural and industrial environments, which will ultimately help reduce associated hazards.
Hybrid graphene aerogels (HGA) consisting of graphene oxide (GO) and graphene nanoplatelets (GNP) were prepared and introduced into polyethylene glycol (PEG) via vacuum impregnation, aiming at ...obtaining composite phase change materials (PCMs) with high thermal conductivity, outstanding shape-stabilization, high energy storage density, commendable thermal repeatability and the ability to light-to-heat energy storage. GO nanosheets formed a three-dimensional supporting network to keep the shape of PEG stable during phase change and GNP dispersed uniformity along the network structure of GO and thus a thermal conductive pathway was constructed. The incorporation of HGA remarkably enhanced the thermal conductivity and shape-stabilization of the composite PCMs. The PEG/HGA composite PCM with only ca. 0.45 wt% GO and ca. 1.8 wt% GNP, showed an enhanced thermal conductivity of 1.43 W/mK from 0.31 W/mK of pure PEG and an improvement of 361%, much higher than the improvement that can be achieved by solution or melt blending. Moreover, an energy conversion from light to heat was realized with the composite PCMs. Thus, this work provides a simple, green and environmentally friendly way to achieve simultaneous enhancement of the thermal conductivity, energy storage density and shape-stabilization of PCMs and realize light-to-thermal energy conversion.
Expansive soils pose a significant challenge in geotechnical engineering, especially in coastal areas. While research has mainly focused on their elastic properties, this study explores the ...overlooked aspect of inelastic subsidence during prolonged droughts, utilizing decade‐long GPS datasets from the University of Houston Coastal Center. Our findings reveal substantial subsidence, approximately one to two dm, during the summer droughts of 2018, 2020, 2022, and 2023, due to compaction within the upper 4 m of expansive soils. Inelastic subsidence constitutes roughly 10% of the total subsidence, resulting in step‐like permanent land elevation loss over time. Notably, drought‐induced subsidence is prominent in open‐field areas with expansive soils but is minor in built‐up areas or in non‐expansive soil regions. The occurrence of inelastic subsidence challenges traditional assessments of relative sea‐level rise and coastal flooding, emphasizing the need to consider it in coastal infrastructure planning for enhanced resilience against climate uncertainties.
Plain Language Summary
Expansive soils, often found in coastal regions, are known for causing issues like land shifts and unstable buildings. Our research adds a new dimension: prolonged droughts can lead to significant, irreversible sinking of expansive soils, permanently lowering the elevation of open land areas. Using a decade of GPS data, we found that during droughts, these soils can sink considerably and will not fully recover. Interestingly, in developed regions where pavement covers the soils, thereby minimizing moisture loss, this sinking is observed to be minimal. In the Galveston coastal area, drought‐induced sinking can reach one to two dm, with irreversible subsidence making up about 10% of the total subsidence. This becomes a growing concern as droughts become more frequent due to climate change, especially in coastal areas. Additionally, our results suggest that current methods for estimating sea‐level rise may be missing a key factor: we have underestimated the speed at which uninhabited coastal lands are sinking because we did not account for this irreversible sinking due to droughts.
Key Points
Prolonged droughts cause up 2 dm of subsidence in shallow expansive soils (<4 m deep) in Galveston, TX, 10% of which is inelastic
Prolonged droughts lead to notable subsidence in open fields with expansive soils, but less in built‐up areas or non‐expansive soil areas
Recurring droughts lead to permanent land elevation losses in open‐field coast, possibly skewing sea‐level rise and flood risk projections
The combined application of continuous Global Positioning System data (high temporal resolution) with spaceborne interferometric synthetic aperture radar data (high spatial resolution) can reveal ...much more about the complexity of large landslide movement than is possible with geodetic measurements tied to only a few specific measurement sites. This approach is applied to an ~4 km2 reactivated translational landslide in the Columbia River Gorge (Washington State), which moves mainly during the winter rainy season. Results reveal the complex three‐dimensional shape of the landslide mass, how onset of sliding relates to cumulative rainfall, how surface velocity during sliding varies with location on the topographically complex landslide surface, and how the ground surface subsides slightly in weeks prior to downslope sliding.
Key Points
Sentinel‐1A InSAR and GPS results reveal rainfall‐triggered landslide‐body surface subsidence prior to downslope sliding
3D deformation measurements provide insights on the temporal and spatial complexity of landslide dynamics
Spaceborne InSAR‐derived displacement fields can be used to invert for active landslide thickness variation based on mass conservation
Landslide rheology governs the deformation and flow behavior of sliding masses. As rheology strongly varies as a function of the composition and environment of landslides, a wide range of viscosities ...have been suggested based on very limited experimental or observational constraints. Here, we introduce a novel method to quantify the landslide rheology from remote sensing data. We focus on an ideal natural laboratory, the Slumgullion landslide, Colorado, which has moved at tens of millimeters per day for centuries. A joint analysis of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometric Synthetic Aperture Radar (InSAR)‐derived surface displacements and Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM)‐derived landslide thickness at its frontal toe allows us to invert for the intrinsic viscosity (109–1011.5 Pa·s under different degrees of plasticity) based on the Bingham plastic model. Detailed displacement measurements also elucidate local variations in magnitude and orientation. Our method presents the capability of remote sensing data to understand the rheology of quasi‐static debris slides in general.
Plain Language Summary
Landslides are common geological hazards that may lead to casualties and damage. Landslides also present as a surface process that reshapes mountainous landscapes around the world. The soil and rock materials, water content, and the associated vegetation and organic matter contribute to a variety of landslide mechanical properties and rheology, which characterize the slide/flow approximation of the mass wasting process and thus the landslide speed. To date, the determination of the landslide rheology has mainly relied on analyzing samples in the lab. However, many landslides are inaccessible for sample collection, and the lab environment subject to isolated and small samples can hardly be compared to the intact landslide in nature. Taking advantage of high‐resolution airborne remote sensing data sets at the Slumgullion debris slide in Colorado, we extract the landslide surface displacements and topography. Incorporating our observations, we consider a classic model in fluid mechanism to infer the rheological parameters. We also identify the spatiotemporally variable landslide movements. A joint analysis and interdisciplinary approach incorporating remote sensing and basic physics can help us better determine the landslide dynamics and mitigate the risks to humans.
Key Points
Airborne UAVSAR interferometry elucidates local variations in magnitude and orientation of displacements at the Slumgullion landslide
High‐resolution LiDAR DEM reveals the landslide thickness at the emergent toe
InSAR‐measured landslide speed helps constrain the material viscosity (109–1011.5 Pa·s at the Slumgullion) based on Bingham plastic model
The frequency of extreme climate events has escalated since 1980. In February 2021, an unprecedented winter storm dumped the snow record in Texas. It claimed hundreds of lives and evolved into a ...national major disaster. However, we still lack a systematic approach to quantify large‐scale snow depth. Here, we use the differential coherence from Sentinel‐1 synthetic aperture radar (SAR) imagery to characterize the surface disturbance due to this winter storm. We further rely on machine‐learning algorithms to quantify Texas statewide snow depth using surface disturbance map, SAR amplitude, precipitation, temperature, surface topography, land cover, and population. Our approach can provide an independent snow depth estimation. Approximately 89% of Texas accumulated over 30‐mm snow depth. The SAR and machine‐learning integrated methods can also be applied to quantify other forms of surface disturbance and to ultimately help natural hazard mitigation.
Plain Language Summary
With the climate changes across the world, the U.S. has experienced more than 300 climate‐related disasters since the 1980s at a cost of more than two trillion U.S. dollars. In February 2021, the winter storm Uri, declared as a national major disaster, left ∼70% of the households without power for several days and hundreds of deaths in Texas. Precisely estimate state‐wide snow depth in time is essential for post‐disaster recovery. Satellite radar coherence quantifies the similarities between radar phases and elucidates land surface changes. We generate the surface disturbance map (SDM) by differentiating the coherence before and during/shortly after the event. We further use the machine learning methods to synergize the SDM, radar amplitude, precipitation, temperature, topography, land cover, and population, and to estimate the snow depth across Texas. Our approach can be transferred to map and quantify other extreme natural phenomena associated with surface disturbance.
Key Points
Surface disturbance map derived from synthetic aperture radar coherence characterizes the statewide snow depth (>30 mm for 89% of Texas)
Machine‐learning methods synergize the surface disturbance, topography, meteorological data, and land cover to derive large‐scale snow depth
Precipitation, surface elevation, and surface disturbance play the primary roles in snow depth estimation
Stereocomplex (SC) crystallites, formed between enantiomeric poly(l-lactide) (PLLA) and poly(d-lactide) (PDLA), show a melting point 50 °C higher than that of PLLA or PDLA homocrystallites, which ...makes it possible for SC crystallites to be reserved in the melt of PLLA in asymmetric PLLA/PDLA blends and to act as a rheological modifier and a nucleation agent for PLLA. Herein, by a rheological approach, a transition from the liquid-like to solid-like viscoelastic behavior was observed for the SC crystallites reserved melt, and a frequency-independent loss tangent at low frequencies appeared at a PDLA concentration of 2.0 wt %, revealing the formation of SC crystallite network. By a delicately designed dissolution experiment, the structure of the formed network was explored. The results indicate that the network are not formed by SC crystallites connected directly with each other or by bridging molecules, but by the interparticle polymer chains which are significantly restrained by the cross-linking effect of SC crystallites. Nonisothermal and isothermal crystallization show that the reserved SC crystallites can accelerate remarkably the crystallization rate of PLLA due to heterogeneous nucleation effect. Besides, a special PDLA concentration dependence, e.g., the overall crystallization rate is almost independent of PDLA content for the blends with PDLA content higher than PDLA percolation concentration (2.0 wt %), was also observed. The increase of nuclei density for the blends containing PDLA from 2 to 5 wt % was estimated from POM observations. The result of an enhanced nucleation but an unchanged overall crystallization rate reveals the confining effect of the SC crystallite network on PLLA crystallization. This confining effect can be ascribed to the restrained diffusion ability of PLLA chains owing to the SC crystallite network.
Cryospheric responses to climate warming include glacier retreat, altitude‐dependent thermal instability, and abundant meltwater, which increase the frequency of catastrophic glacier hazard chain ...(CGHC) events. Here we investigated the formation mechanism of a special CGHC event in 2018, in the Sedongpu Glacier, Eastern Himalayas, China. Based on the multi‐source remote sensing, seismic signal analysis, and numerical simulation, we conducted long‐term retrospective analysis and co‐event process reconstruction. The results show that the event could be divided into two phases. First, the hanging glacier with a volume of 8.5 × 106 m3 collapsed onto the downstream trunk glacier. Next, ∼1.17 × 108 m3 eroded materials from the impacted glacier transformed into debris flow and traveled downstream 8 km. During the cascading process, ice‐rock avalanche momentum and glacier velocity are key factors in determining CGHC formation and eventual volume. Our study helps better understand the domino effects of the CGHC disaster.
Plain Language Summary
Glacier instability in cryosphere is significantly influenced by global warming. The glacial lake outburst floods are the most widely studied and catastrophic events resulting from this instability, but they are not the only ones. In recent years, increasing catastrophic glacier hazard chain (CGHC) events have reminded us of disasters during climate warming, especially in the Himalayas region. Here, we report a typical CGHC event in the Eastern Himalayas, where the retreated glaciers are widely distributed. Based on our observations and simulations, we completed the retrospective analysis of CGHC through its formation conditions, evolution process, and specific kinematic process. These analyses help us understand the driving mechanism, the volume‐increasing effect, and the super‐fluidity of CGHC. Our findings help improve such hazard chain identification and mitigation in the Eastern Himalayas and glacierized environments elsewhere in the world.
Key Points
New insights of Ice‐rock avalanche hazard chain on retreated hanging glacier in Eastern Himalaya
Erosion process on the trunk glacier led to a volume‐increasing and super‐fluidity of catastrophic glacier hazard chain (CGHC)
The pre‐evolution and formation of CGHC were closely related to climate warming
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