<figure><img src="image_1.png" /></figure>

HAL
open science

# Downslope solifluction movements and permafrost degradation in the northeastern Qinghai-Tibetan Plateau revealed by InSAR

Hugo Watine, Simon Daout, Jérôme Lavé, Marie-Pierre Doin

► To cite this version:

Hugo Watine, Simon Daout, Jérôme Lavé, Marie-Pierre Doin. Downslope solifluction movements and permafrost degradation in the northeastern Qinghai-Tibetan Plateau revealed by InSAR. Remote Sensing of Environment, 2025, 329, pp.114926. 10.1016/j.rse.2025.114926. hal-05406955

HAL Id: hal-05406955

https://hal.science/hal-05406955v1

Submitted on 9 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# Downslope solifluction movements and permafrost degradation in the northeastern Qinghai-Tibetan Plateau revealed by InSAR

Hugo Watine<sup>a, ●</sup>,*, Simon Daout<sup>a</sup>, Jérôme Lavé<sup>a</sup>, Marie-Pierre Doin<sup>b</sup>

## ARTICLE INFO

Edited by Jing M. Chen

Dataset link: https://doi.org/10.5281/zenodo.15223170

**Keywords:**

Qinghai-Tibetan plateau

InSAR time series

Solifluction

Permafrost degradation

## ABSTRACT

InSAR measurements reveal widespread and high-amplitude permafrost-related solifluction movements across the Qinghai-Tibetan Plateau (QTP). This raises questions about the mechanisms driving these slope processes and, more specifically, the relationships between cyclic soil expansion during freezing and irreversible downslope motions during thawing. In this study, we develop and apply a methodology to map solifluction motions using multi-temporal Interferometric Synthetic Aperture Radar (InSAR) data and characterize their amplitudes and spatial distributions at a regional scale. This method decomposes line-of-sight InSAR time series into pluri-annual velocities and seasonally reversible surface displacements that are then inverted into motions parallel and normal to the local steepest slope. Pluri-annual downslope and subsidence velocities in the northeastern QTP are analyzed and cross-correlated with slope-normal seasonal cycles and the slope across unconsolidated sediments and indurated bedrock colluvium separately. Our analysis indicates significant downslope velocities (> 4 mm/yr), with varying sensitivities to slope. Overall, downslope velocities exceed those expected from seasonal frost creep alone and are likely triggered by basal soil shearing during the thawing of the ice-rich layers. Downslope velocities are particularly high for indurated bedrock colluvium, which exhibit low-amplitude slope-normal seasonal cycles compared to unconsolidated sediments. We explain these differences by variations in regolith layer thicknesses, which impact the downslope deformation depths. Measurements of subsidence and amplified seasonal cycles through time indicate ongoing permafrost degradation and ice loss within the QTP, directly impacting slope stability, primarily by intensifying seasonal basal solifluction processes.

## 1. Introduction

In permafrost environments, the freeze-thaw cycles of the active layer (AL), the uppermost soil layer that undergoes seasonal thawing in summer and autumn and freezes in winter and early spring (Dobinski, 2011), result in cyclic ground surface movements driven by expansion and contraction associated with water phase changes. The Qinghai-Tibetan Plateau (QTP) hosts the largest high-altitude permafrost region in the Northern Hemisphere (e.g., Brown et al., 1997; Gruber, 2012; Obu et al., 2019). With ongoing climate warming in both the QTP and boreal areas, permafrost is progressively degrading (Biskaborn et al., 2019; Cheng and Wu, 2007). This degradation manifests itself as changes in seasonal ground displacement amplitudes, that could result from increased thickness of the active layer (AL) and/or from variations in soil water content (Liu et al., 2017, 2024b), as well as pluri-annual subsidence trends linked to ground settlement, often referred to as ‘isotropic thaw subsidence’ (Shiklomanov et al., 2013;

Streletskiy et al., 2017). In the QTP, beyond ecological (Yang et al., 2018) and infrastructural (roads, railways) impacts (Wei et al., 2009), these changes have led to an increased occurrence of slope failures such as thermokarsts or active layer detachment slides (Jiang et al., 2022; Li et al., 2019; Luo et al., 2019; Niu et al., 2012). Warming may alter ground freezing conditions, affecting downslope velocities and maximum depth of soil movement (Matsuoka, 2001).

On slopes, regolith coverage can respond to freeze-thaw cycles, generating slow downslope mass movements (Ballantyne and Harris, 1994; French, 2018). These movements result from a range of processes driven by freeze-thaw action within the active layer (AL; Fig. 1). Shallow processes like needle-ice formation and diurnal frost creep are caused by diurnal freeze-thaw cycles and affect depths down to ~5–10 cm (Fig. S1). In contrast, seasonal freeze-thaw cycles cause deeper movements such as seasonal frost creep and/or gelifluction (depths shallower than ~60 cm) and plug-like flow (deeper than ~

<footnote><sup>a</sup> Univ. de Lorraine, CNRS, CRPG, F-54000, Nancy, France</footnote>

<footnote><sup>b</sup> Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, Univ. Gustave Eiffel, ISTERre, 38000, Grenoble, France</footnote>

<footnote>* Corresponding author.
E-mail address: hugo.watine@univ-lorraine.fr (H. Watine).</footnote>
60 cm) (Fig. 1, S1; Harris and Davies, 2000; Mackay, 1981; Matsuoka, 2001; Matsuoka et al., 2005; Sun et al., 2017; Harris et al., 2008). Plug-like flow typically occurs in permafrost regions where the active layer is subjected to two-sided freezing: from the permafrost table upwards and from the ground surface downwards. This creates ice lenses near the active layer's base, acting as a shear zone upon thawing (Fig. S1; Mackay, 1981). Because these processes are difficult to differentiate, the term 'solifluction' is often used to encompass all such contributions to slow (several millimeters to several centimeters per year) periglacial mass wasting (e.g., Ballantyne and Harris, 1994; French, 2018; Matsuoka, 2001). Solifluction is generally considered the most widespread slope deformation process along gentle to moderately steep hillslopes in periglacial environments and may constitute a dominant mechanism for mass transfer on a year-to-year basis in a given environment (Akerman, 1996; Matsuoka, 2001).

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Schematic representation of a regolith layer overlying bedrock on a slope of angle $\alpha$, experiencing frost creep (in red) and gelifluction (in green) movements. The regolith thickness, which overlies the bedrock, is distinct from the active layer thickness (ALT, in blue), the latter being controlled by the mean annual air temperature, the seasonal temperature amplitude, and the ground ice content. (For interpretation of the colours in the figure(s), the reader is referred to the web version of this article.)</figcaption></figure>

Source: Modified from Harris and Davies (2000) and Matsuoka (2001).

In contrast to active-layer detachment slides and retrogressive thaw slumps (e.g., Xia et al., 2022; Luo et al., 2022; Mu et al., 2020; Sun et al., 2017; Niu et al., 2012; Jiang et al., 2022; Peng et al., 2023), the distribution, prevalence, and variability of solifluction processes have been poorly studied along the QTP (Meng et al., 2022; Wei et al., 2006; Wang and French, 1995). There is a clear lack of ground-based quantitative data documenting solifluction amplitudes and velocities on QTP hillslopes, and the few existing studies have been conducted in situ, offering limited spatial coverage. Remote sensing methods provide a valuable and broad-scale complement to field-based observations and may help unravel the dynamics of permafrost evolution and downslope movements (Ridefelt et al., 2009; Daout et al., 2020; Rouyet et al., 2021).

Cyclic ground surface movements induced by freeze-thaw cycles can be measured using Interferometric Synthetic Aperture Radar (InSAR) techniques (e.g., Bartsch et al., 2019; Chen et al., 2022; Daout et al., 2017, 2020; Dini et al., 2019b; Liu et al., 2010, 2012, 2015; Rouyet et al., 2019, 2021; Wendt et al., 2024). These measurements are conducted along the radar's line-of-sight (LOS) and are thus inherently one-dimensional. Since 2014, the launch of Sentinel-1 satellites has enabled the systematic acquisition of radar images at high-temporal frequency

(acquisition every 12 days in the QTP) with broad coverage (250 km swath). This frequency ensures better coherence when constructing radar interferograms (minimal changes in ground surface conditions between two images) and allows for regular monitoring of subtle ground deformations over time. Thanks to these new data, multi-temporal InSAR (MT-InSAR) techniques have emerged in recent years as powerful geodetic tools for measuring and quantifying continuous ground deformations over large study areas with millimeter-scale precision and high spatial resolution (e.g., Dalaison and Jolivet, 2020; Lemrabet et al., 2023; Liu et al., 2024a; Mathey et al., 2022; Ou et al., 2022; Fang et al., 2024). InSAR measurements of cyclic permafrost-related ground deformation thus integrate the effects of expansion/contraction induced by water phase changes in the AL (related to the penetration of the freezing front), modulated by the role of soil porosity and hydric conditions (drained/undrained, saturated/unsaturated). Applications of these techniques in flat periglacial environments have revealed the control of lithology and topography on the amplitude of seasonal displacements. Strong seasonal amplitudes are concentrated in low areas within sedimentary basins or areas composed of fine-grained porous and unconsolidated sediments that are highly frost-susceptible, allowing, from capillarity action, the formation of excess ground ice through ice lenses (Bartsch et al., 2019; Daout et al., 2017, 2020; Rouyet et al., 2019, 2021; Wendt et al., 2024).

Recent InSAR studies in periglacial environment like the QTP (Daout et al., 2020; Thollard et al., 2021; Ou et al., 2022; Lemrabet, 2022; Reinosch et al., 2020) and Iceland (Cao et al., 2023) have documented millimetric to centimeter-scale annual LOS movements on slope with opposite signs on ascending and descending geometries (Fig. S2), which have been interpreted as widespread material transfer along periglacial slopes (Daout et al., 2020; Lemrabet, 2022). Questions remain about the mechanisms driving these downslope movements and, more specifically, the relationships between cyclic soil expansion during freezing and downslope motion during thawing for different lithologies.

Despite their utility, InSAR data are limited by the one-dimensional nature of LOS measurements, which, on slopes, are a complex combination of normal and parallel displacements. Furthermore, due to the near-polar orbits of the satellites, the north-south displacement component is typically poorly resolved. Various solutions have been proposed to compensate for this lack of north-south sensitivity and invert LOS measurements into horizontal and vertical surface displacements, such as using north-south velocity fields interpolated from global navigation satellite system (GNSS) data (e.g., Ou et al., 2022) or using an interpolated GNSS velocity field to fix the orientation of the horizontal component (Lemrabet et al., 2023). However, these approaches are only valid for long-wavelength deformation and are not useful for studying local slope processes. Several approaches have also been developed to investigate gravitational slope processes using InSAR (e.g., Cao et al., 2023; Daout et al., 2020; Dini et al., 2019a; Rouyet et al., 2019, 2021; Reinosch et al., 2020). Some studies have directly interpreted the LOS motion (Dini et al., 2019a), which, on slopes, is a complex combination of normal and parallel displacements. Reinosch et al. (2020) use the approach of Notti et al. (2014) to quantify sliding velocities within a periglacial setting using only a single LOS measurement, neglecting vertical motions and assuming that all displacements occur in the downslope direction. Other studies have assumed that the north-south contribution is zero due to the insensitivity of InSAR measurements to displacement in the along-track direction and only inverted for vertical and east-west movements (Cao et al., 2023; Daout et al., 2020; Rouyet et al., 2019, 2021). However, all those assumptions provide biased estimates of horizontal or vertical movements (Brouwer and Hanssen, 2023).

To study slope processes and, more particularly, to investigate the relationship between normal expansion generated by frost heaving and downslope movement, we develop a methodology to decompose MT-InSAR time series products, provided in the LOS direction, into
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (a) Footprints of the two ascending (purple rectangles) and one descending (blue rectangle) Sentinel-1 tracks analyzed in this study (InSAR products from Daout et al. (2020)), overlaid on the Permafrost Zonation Index (PZI) from Gruber (2012), which indicates the probability of permafrost occurrence. The white dot marks the center of the reference zone. Black lines indicate active faults (Daout et al., 2019). Inset in top-right: Regional setting and location (red rectangle) of the study area in the northeastern QTP. (b) Enlarged view within the study area showing the main geological units derived from GLIM data (Hartmann and Moosdorf, 2012). Areas 1 and 2 (black and orange outlines, respectively) represent the two zones used in the following analysis: (1) moraines and (2) siliciclastic sedimentary rocks (Figs. 4–6). (c) and (d) show the mean annual air temperature (MAAT; Fick and Hijmans (2017)) and the SRTM 30 m DEM (Farr et al., 2007), respectively. In (d), the DEM is overlaid with the river network (Lehner and Grill, 2013). Maps are displayed on top of the SRTM 30 m hill-shaded DEM (Farr et al., 2007).</figcaption></figure>

reversible and irreversible displacements parallel and normal to the steepest slope. This approach enables the correlation of slope-normal seasonal cycles, pluri-annual displacements, and external factors such as lithology, surface characteristics, and slope angle, thereby providing a framework for analyzing solifluction processes.

## 2. Study area

The study area, located in the Qilian Mountains in the northern Qinghai province of the QTP, China (38°N, 97°E; Fig. 2a), spans elevations from 2760 to 5720 m a.s.l. around Hala Lake (Fig. 2b). This region lies within the tectonic framework of the Indo-Asian collision and is characterized by folded sedimentary sequences alternating with ancient plutonic rocks that form the main landforms. Between these mountainous structures, basins are filled with Quaternary sediments of fluvial, eolian, and glacial origins (Métivier et al., 1998). The Qilian Mountains have a predominantly cold, semi-arid climate characterized by annual precipitation of around 300 mm with a mean annual air temperature (MAAT) at 2 m height of -7.1 °C and an annual temperature range of 13.3 °C at the median elevation of ~4300 m a.s.l., according to ERA-5 atmospheric models from the European Center for Medium-Range Weather Forecast (ECMWF) (Hersbach et al., 2020). Sparse vegetation covers the mountainous terrain. Large-scale coarse models (Gruber, 2012; Obu et al., 2019; Qin et al., 2017) predict the presence of cold, continuous permafrost above approximately 3500 m a.s.l. (Fig. 2a). Assuming that MAATs are equal to surface temperatures (Lin et al., 2015) and applying a purely diffusive model, the AL is estimated to be ~2 m thick (Anderson and Anderson, 2010). These estimates are of the same order of magnitude as ALT in-situ measurements, taken approximately 100 km northeast of Hala Lake (Cao et al., 2017, 2018). Moreover, these measurements indicate that ground temperatures at depths of 16–18 m have warmed on average by up to 0.37 °C per decade since 2012, leading to significant permafrost degradation (Cao et al., 2018).

## 3. Materials and methods

### 3.1. InSAR data

We use InSAR products from Daout et al. (2020) (Fig. 2a) that comprise LOS time series covering five years (2014–2019) and an area of 80,000 km² (Fig. 2b). Descending track 004 and ascending tracks 099 and 172 of the Sentinel-1 satellite were processed using the New Small Baseline Subset (NSBAS) processing chain (Doin et al., 2011, 2015; Grandin, 2015). The time series were corrected for tropospheric delays using the ERA-5 atmospheric models provided by ECMWF (Doin et al., 2009; Jolivet et al., 2011) and corrected for long-wavelength cubic and quadratic ramps in the azimuthal and range directions, respectively (Daout et al., 2020). The ramps were estimated in bedrock areas over the full footprint by masking pixels with high (>2mm) seasonal amplitudes. Additionally, all time-series maps were referenced to a stable area of 200 x 200 pixels (centered at ~38°05'N, 98°05'E), highlighted in Fig. 2a. This procedure aims to reference all maps to the same location and remove the tectonic deformation signal, assuming that the studied region is primarily affected by crustal block translation (i.e., not subject to non-uniform displacement, as when cross-cut by a major active fault).

InSAR measurements of Daout et al. (2020) in this area have shown pervasive ground subsidence of up to ~2 cm/year, interpreted as decreasing ice volumes in the soils near the permafrost table. These measurements have also highlighted mass transfers, interpreted as solifluction on hillslopes, with displacement magnitudes ranging from millimeters to centimeters per year, as well as increasing seasonal amplitudes and linear trends over the past two decades. Those downslope motion measurements are observed on both linear velocity maps and long-baseline interferograms (Figs. S3f,i).

As mentioned in the introduction, to go further in LOS displacement interpretation and investigate motions related to gravity-driven processes, we combine ascending and descending viewing geometries to estimate slope-parallel and slope-normal displacements in two steps:
<figure><img src="image_6.png" /><figcaption>Fig. 3. Methodological developments applied to analyze solifluction processes using InSAR data. (a) Timeseries decomposition into four terms parameterized by four coefficients: a, b, c, and d. (b) Spatial decomposition of each coefficient in the directions parallel (u_⊥) and normal (u_⊥) to the steepest slope line, assuming no lateral displacements (i.e. u_⊥ = 0). Top and side views of a theoretical mountain, (u_⊥ is not displayed on top view). The red arrow represents the true displacement, while the blue and purple arrows represent the projection of the true displacement onto the ascending and descending geometries, respectively. The angle α represents the slope, and the angle Ω represents the aspect of the slope (determined clockwise from the east). (Right) Schematic view of the descending observation geometry. The mathematical angle Φ corresponds to the angle between the east and the LOS vector projected onto the horizontal plane at the target location, in the direction towards the satellite (positive in anti-clockwise direction). The angle θ represents the angle between the horizontal plane of the local ellipsoid and the satellite LOS vector towards the radar at the target location (i.e., 90° minus the incidence angle).</figcaption></figure>

(1) the temporal decomposition of each pixel into reversible and irreversible terms (Fig. 3a), then (2) the spatial decomposition of each variable into slope-parallel and slope-normal components (Fig. 3b). Because these decompositions are defined locally for a given pixel, we will demonstrate this decomposition for a single pixel in the next two subsections. Throughout the text, we use the terms ‘reversible’ and ‘seasonal’ interchangeably to refer to periodic cycles and ‘irreversible’ and ‘cumulative’ to refer to pluri-annual, progressive displacements.

## 3.2. Temporal decomposition

The analysis of ground displacement time series (LOS(t); Fig. S4) shows that they can be decomposed into three components for each pixel of each track geometry (ascending, ‘asc’, and descending, ‘dsc’): a linear trend representing irreversible movements (coefficient a, Fig. 3a, Eq. (1)), and a seasonal amplitude that can evolve, reflecting reversible movements caused by freeze-thaw cycles (coefficients c and d, Fig. 3a, Eq. (1)). Although the seasonal signal is not strictly sinusoidal, the use of a 1-year period sinusoidal model offers a convenient linearized decomposition which allows to map at large-scale the spatial variations of both seasonal amplitude and timing of maximum subsidence (e.g., Fig. S4; Daout et al., 2017). These cumulative displacements LOS(t) are fitted as follows for both tracks:

$$ \text{LOS}_{\text{asc/dsc}}(t) = a_{\text{asc/dsc}}t + b_{\text{asc/dsc}} + (c_{\text{asc/dsc}} + d_{\text{asc/dsc}}t) \cos(\omega t + \phi_{\text{asc/dsc}}) \quad (1) $$

The problem is linearized (Appendix A; Eq. (A.3)) and solved with an iterative approach to decrease the weight of the outliers due to atmospheric residual delays or unwrapping errors, and produce velocity (a; Eq. (1)), seasonal amplitude (c; Eq. (1)), temporal variations of seasonal amplitude (d; Eq. (1)), and phase (φ; Eq. (1)) maps. For each parameter, we also compute an error σ_m^i derived from Eq. (A.5).

## 3.3. Spatial decomposition

We perform a spatial decomposition of the temporal decomposition products (i.e., coefficients a, c, and d; see Eq. (1)) from both the ascending and descending LOSs into slope-parallel and slope-normal directions

(Fig. 3b). In the following equations, we present this decomposition, focusing on the linear trend component (i.e., a), but the same inversion applies to other components. It is achieved by inverting the following equation:

$$ d_{\text{LOS}} = \text{Proj} \cdot d_{P,L,N} \quad (2) $$

Here, d LOS (Eq. (2)) is the data vector containing the ascending and descending LOS coefficients (i.e., a, c, or d), and d P,L,N (Eq. (2)) represents the displacement in the slope reference frame, with components in the parallel (P), lateral (L), and normal (N) directions (Appendix B; Eq. (B.2)). Proj is a 2 × 3 matrix that projects d P,L,N onto the LOS, based on the acquisition angles φ (mathematical angle between the east and LOS in the horizontal plane and the direction towards the satellite) and θ (angle between the horizontal plane and the LOS towards the radar at the target location), which vary depending on the input geometry, and on the aspect (Ω) and slope (α) angles, (Fig. 3b, S5; Eq. (B.2)). The angles Ω and α are computed using the 30 m SRTM Digital Elevation Model (DEM) (Farr et al., 2007), resampled at the dataset resolution (~90 m) with bilinear interpolation and smoothed using a Gaussian filter (σ = 2 pixels) to remove potential noise. We compare the slopes derived from the SRTM DEM (resampled at ~90 m) with those derived from the 8 m High Mountain Asia (HMA) DEM (resampled at ~90 m) (Shean, 2017). As shown in Fig. S6, at the resolution of our InSAR dataset, the choice of a higher-resolution DEM has a limited impact on slope estimation.

To solve the inverse problem (made of two data and three unknowns), we assume no displacement in the lateral direction, performing the inversion only in the plane defined by u_⊥ and u_⊥ (see Appendix B). We use the uncertainties from the temporal decomposition σ_m^i as prior information to derive the posterior uncertainties for each parameter (Eq. (B.7)).

Applying this two-step temporal then spatial decomposition produces six output variables: a_⊥ and a_⊥, and, assuming identical φ values for both ascending and descending geometries, c_⊥ and c_⊥, and d_⊥ and d_⊥.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Sensitivity analysis of displacements parallel (top) and normal (bottom) to the steepest slope line to lateral displacements for variable angles Ω (aspect) and α (slope) (Fig. 3.b). The test is performed for a positive lateral displacement of 1 mm/yr and zero parallel and normal displacements. Gray-shaded areas correspond to aspect values masked prior to the analysis.</figcaption></figure>

### 3.4. Synthetic tests

To check the validity of the spatial decomposition and its uncertainties, we perform synthetic tests (Fig. 4, S7).

The decomposition assumes no displacements lateral to the slope direction, which implies that gravitational movements follow the line of maximum slope. However, like other tectonically active regions, the QTP is subject to long-term seismic and post-seismic tectonic displacements. Even if the regional displacement component of the underlying tectonic block has been empirically removed with ramps estimated on the bedrock (see Section 3.1), there may still exist some residual horizontal movement (e.g., associated with a local fault passing through the area of interest). Therefore, we explore the impact of lateral displacements on the quality of the spatial decomposition (Fig. 4), whether the displacements are due to random noise in the interferometric data or uncorrected tectonic displacement of the study area. We simulate a purely lateral displacement of 1 mm/yr for different slopes and aspects. Our results indicate that lateral displacements cause minimal biases in inverted displacements in the slope-parallel and slope-normal directions for slopes facing east or west. However, as slopes steepen and approach more northern or southern orientations, the biases become more pronounced, impacting both parallel and normal directions relative to the steepest slope line. We found errors exceeding 1 mm/yr

on steep slopes when the aspect is within about 30° of due north or due south, and these errors primarily impact parallel displacements. Therefore, if our assumption of zero lateral displacement is invalid, steep slopes with northerly or southerly aspects could have significant errors.

We perform a second test to evaluate our decomposition process when applied to a synthetic DEM shaped like a pyramid (with slopes facing north, south, east, and west) for two displacement scenarios: purely vertical uplift and purely horizontal displacement (Fig. S7). The inverted displacements in both the slope-parallel and slope-normal directions were consistent with the simulated displacements. However, as expected, due to the near-polar orbits of the satellites, the LOS displacements were nearly insensitive to north-south displacements, leading to unconstrained inversions for north- or south-oriented slopes.

### 3.5. Automatic masking and mapping

InSAR displacement data are the sums of different processes related to numerous factors. To focus only on permafrost-related displacements associated with slopes and ensure robust automatic analysis at the scale of our study area, we apply several masks before analysis. First, areas below 3500 m a.s.l. are masked because they are characterized by
MAATs above 0 °C and permafrost is presumed absent in those areas (Gruber, 2012; Obu et al., 2019; Qin et al., 2017). Second, because solifluction typically occurs on slopes ranging from 2° to 41° (Matsuoka, 2001), we mask all areas with slopes shallower than 2°. Third, based on our synthetic tests, we mask all pixels with aspects within 30° of due north and due south (Fig. 4). Fourth, for each pixel and each coefficient *k*, poorly-constrained pixels are masked when $\sigma_{P,N}^k$ (the posterior uncertainties after spatial decomposition; Eq. (B.7)) exceeded $3 \times |\text{coef}_k|$, where $\text{coef}_k$ denotes the six output variables from the two-step decomposition. Fifth, when examining seasonal amplitudes, we focus on the average amplitude between 2013 and 2019, analyzing the mean $c_{\perp} + d_{\perp}t$ for $t = 3$ years. To ensure that we analyze displacements resulting from the same phased processes, we mask seasonal amplitude pixels for which the phase values of the ascending and descending tracks, $\phi_{\text{asc}}$ and $\phi_{\text{dsc}}$ (Eq. (A.4)), differ by more than $\frac{\pi}{8}$ (≈23 days). When focusing on seasonal cycle, pixels with $c_{\perp} + 3d_{\perp} < 3$ mm are masked. For each parameter map, outlier pixels (beyond the 1st and 99th percentiles) are finally automatically masked.

# 4. Results

The downslope velocity map ($a_\parallel$, Fig. 5a) provides a continuous and automated mapping of gravitational movements at a regional scale. The data reveal the spatial distribution of widespread active slope processes that are primarily associated with surface mass movements occurring on the hillsides (Fig. 6). These movements exhibit millimetric to centimetric magnitudes, with median values of 1.4 mm/yr over the entire study area. Although the data show some spatial variation (e.g., between zone 1 and the plutonic rocks to the southwest; Figs. 2b, 5a), the downslope velocities are laterally continuous in areas subject to mass movements (e.g., Fig. 5b). Satellite images overlaid on topography in Fig. 6b highlight the correlation between downslope velocities and solifluction patterns.

Average seasonal amplitudes (i.e., the seasonal amplitude plus the increased seasonal amplitude at 3 years, $c_\perp + 3d_\perp$; Fig. 5e) range from 0 to 11 mm, showing strong spatial heterogeneity primarily controlled by the presence of unconsolidated Quaternary sediments. For instance, indurated bedrock areas are less sensitive to freeze-thaw cycle deformation.

Irreversible movements normal to the steepest slope line ($a_\perp$, Fig. 5c), reveal local subsidence (positive values) and uplift (negative values). Values range from -3.5 to 10.3 mm/yr, with a median of 0.6 mm/yr. 78% of the area with seasonal amplitudes >3 mm experience subsidence.

These maps reveal areas that exhibit significant downslope movement but display different behaviors in terms of seasonal amplitude, suggesting differences in downslope processes (Fig. 1). To further investigate these observations, two specific landscapes with distinct lithologies are selected for the following analysis (outlined areas in Figs. 2b, 5). By focusing on small areas with coherent lithologies and relief, we expect to be able to isolate specific processes. The first area (black outlines in Figs. 2b, 5) consists of Upper Pleistocene unconsolidated sediments, corresponding to moraines deposited by former glaciers flowing from the small mountain range to the north (Fig. 6a). Situated between 3874 and 4705 m a.s.l. (Fig. 2d), these moraines present relatively smooth crests and gentle slopes between 0 and 16° (Fig. 6a). We refine the shapefile delineating the moraine area using Landsat-8 multispectral data. The second area (orange lines in Figs. 2b, 5a,c,e) is characterized by indurated lower Silurian siliciclastic sedimentary rocks (Fig. 2b). This area corresponds to a small mountain range between 4238 and 5102 m a.s.l. (Fig. 2d), with valleys also carved by former glaciers (a few small glaciers remain on the northern slope of the 5000 m high peaks). Despite presenting steeper topography than area 1, the slopes are limited to 32° and bedrock cliffs are almost absent. Instead, the hillslopes are relatively smooth and covered by regolith, debris slopes, and colluvium that seem to move and deform

downslope (Fig. 6c,d,e). However, the layer of regolith or colluvium is relatively thin because the traces of the sub-vertical and E-W oriented Paleozoic siliciclastic strata can be recognized in satellite images in many places (Fig. 6c). Compared with the morainic landscape, this regolith is assumed to present a much smaller thickness of cohesionless material, and therefore a distinct behavior of the AL dynamics. In the following text, we use the terms ‘Unconsolidated sediments’ and ‘Indurated bedrock colluvium’ to refer to moraines and siliciclastic sedimentary rocks, respectively

## 4.1. Downslope displacements in the two selected areas

We observe marked differences between unconsolidated sediments and indurated bedrock colluvium areas (Table 1). While downslope velocities remain in the order of a few millimeters per year in both environments, subsidence is much more pronounced in unconsolidated sediments, both in terms of median rate and the proportion of affected area. Furthermore, the seasonal amplitude and its increase over time are significantly higher in these sediments compared to colluvium overlying indurated bedrock (Figs. 5e, 7e, S8).

## 4.2. Interrelations between normal and downslope velocities

In these periglacial environments, solifluction, which is assumed to be the main source of downslope velocities, is expected to be locally influenced by two factors: slope (Akerman, 1996; Ridefelt et al., 2011) and seasonal cycles (Matsuoka, 2001; Williams and Smith, 1989). An increase in either factor may lead to higher solifluction velocities: steeper slopes have a greater gravitational component driving downward movements, whereas larger seasonal amplitudes induce more pronounced soil heaving and subsequent settling (Fig. 1, S1).

Our measurements show a direct correlation between downslope velocities and terrain slope (Fig. 7a), with velocity increasing with increasing slope. Unconsolidated sediments exhibit a steeper correlation (black line in Fig. 7a, $0.64 \pm 0.07$ mm/yr/%) compared to indurated bedrock colluvium (orange line in Fig. 7a, $0.15 \pm 0.01$ mm/yr/%).

In indurated bedrock colluvium, we find no direct correlation between downslope velocities and seasonal amplitude ($c_\perp + 3d_\perp > 3$ mm; Fig. 7b). In contrast, in unconsolidated sediments, there is a clear positive correlation at lower seasonal amplitudes, then a clear negative correlation at seasonal amplitudes > 7 mm. Lower slopes in the western part of this region could potentially obscure the role of seasonal amplitude.

The potential frost creep (Fig. 1) is equal to $H \times \tan(\alpha)$, where H is the expansion height normal to the slope surface due to frost-heave (i.e the measured peak-to-peak seasonal amplitude normal to the slope $2\times(c_\perp + 3d_\perp)$), and α is the slope angle (Williams and Smith, 1989). We map the potential seasonal frost creep for each pixel and compare it to the measured downslope velocity for both lithologies (Fig. 7c). In unconsolidated sediments, we obtain a linear relation of $4.8 \pm 0.1$ yr⁻¹ and an improved R² coefficient compared to Fig. 7a,b. In indurated bedrock colluvium, the data follow a linear trend of $4.5 \pm 0.7$ yr⁻¹ until 1 mm of potential seasonal frost creep then plateau around a downslope velocity of ~7.3 mm/yr despite further increase in potential seasonal frost creep. Both lithologies exhibit linear coefficients much larger than unity, suggesting that potential seasonal frost creep is not the dominant factor driving these solifluction movements. This indicates that other processes play a significant role in enhancing the measured downslope displacement rates.
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Output maps from the two-step decomposition after automatic masking. Areas 1 and 2 correspond to the moraines (black outline) and siliciclastic sedimentary rocks (orange outline), respectively. (a) Ground displacement velocities parallel to the slope [mm/yr]. (b) 3D northward view (i.e., facing the northern massif) of the moraines, overlaid by slope-parallel displacements. (c) Ground displacement velocities normal to the slope, with positive and negative values representing subsidence (blue) and uplift (red) [mm/yr]. (d) 3D view northward from the southern portion of the siliciclastic sedimentary rock massif. (e) Map of slope-normal average seasonal amplitude ($c_{\perp} + 3d_{\perp}$) [mm].</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Geomorphological analysis of study areas using Google Earth images. Thin white dashed lines indicate solifluction landforms, and thicker white dashed arrows show the direction of movement. Moraine: (a) Overview of the area, with relatively smoothed crests and gentle slopes. No clear solifluction landforms are observed. (b) Close-up of a slope at the junction between moraines and plutonic rocks, showing a correlation between solifluction lobes and downslope velocities. Siliciclastic sedimentary rocks: (c) East-west color alternations (highlighted by yellow lines) associated with strata intersected by slow downslope movements, suggesting a relatively thin layer of regolith or colluvium. (d) Solifluction lobes and material diffusion following the line of maximum slope. (e) Ice polygons, which deform and elongate as they move downslope.</figcaption></figure>

<caption>**Table 1**</caption>

Summary of slope movement characteristics for unconsolidated sediments and indurated bedrock colluvium areas. For each parameter, the median and (1σ) standard deviation are displayed. Note that the seasonal amplitude indicated here represents the mathematical amplitude, which is half of the peak-to-peak seasonal amplitude.

<table><thead><tr><th></th><th>Downslope velocity (mm/yr)</th><th>Subsidence velocity (mm/yr)</th><th>Subsidence (% of area)</th><th>Seasonal amplitude (mm)</th><th>Seasonal amplitude increase (mm/yr)</th></tr></thead><tbody><tr><td>Unconsolidated sediment</td><td>4.2 ± 4.6</td><td>6.8 ± 3.1</td><td>98.0%</td><td>7.4 ± 2.3</td><td>1.5 ± 1.8</td></tr><tr><td>Indurated bedrock colluvium</td><td>5.4 ± 5.6</td><td>2.3 ± 3.0</td><td>82.3%</td><td>2.4 ± 1.2</td><td>0.3 ± 0.6</td></tr></tbody></table>

## 4.3. Seasonal displacement patterns in the two selected areas

We analyze the de-trended and normalized LOS seasonal signal over a 12-month period for both lithologies (Fig. 7d). In unconsolidated sediments, a clear seasonal pattern is observed, with maximum subsidence (LOS displacements away from the satellite) occurring in November, which is approximately 2 months after air temperatures drop below 0°C in September/October and 4 months after temperature peaks in early July. Following this period, the surface begins to uplift (displacement towards the satellite). In contrast, in indurated bedrock colluvium, seasonal patterns are rather noisy and do not fit well with a sinusoidal model ($R^2 = 0.1$). In these areas, maximum subsidence occurs as early as September/October, approximately 2–3 months after the peak surface temperature.

# 5. Discussion

## 5.1. Monitoring surface displacements with InSAR and their interpretation in terms of permafrost-related deformation

From the beginning and following former interpretation of InSAR signals in the northeastern QTP, we have assumed that the observed surface deformation is driven by permafrost-related processes. However, this raises two important questions: (1) To what extent are the data reliable and (2) could alternative interpretations explain the observed signals?

To start with, we know that several potential biases can affect InSAR measurements. The InSAR processing strategy used to mitigate InSAR

limitations is detailed in Daout et al. (2020), which we summarize below. One potential source of error in InSAR measurements is the influence of tropospheric delays. In the study area, these delays introduce millimetric to centimetric differences between the mountain summits and the valley floors (Figs. S9a,b). Such topography-correlated delays are corrected using the ERA-5 atmospheric models, leaving predominantly turbulent residual delays with no apparent correlation between LOS velocity and topography (Fig. S9c).

In addition, although differential interferograms are corrected from topographic effects from the 30 m SRTM elevation model (Farr et al., 2007), a second possible source of errors is the influence of DEM inaccuracies. The impact of DEM errors on interferograms is proportional to the geometrical baseline variations. However, because Sentinel-1 perpendicular baselines are generally well-known and controlled, this effect is relatively limited (Fig. S10). DEM errors empirically estimated from the time series displacements indicate a term proportional to the perpendicular baseline, on the order of ~±3 mm per 100 m of baseline (Fig. S10c). This is significantly lower than the displacements analyzed in this study, which reach ~15 mm for a single interferogram (e.g. Fig. S3). Moreover, the spatial wavelength of this potential error differs from the few-kilometer-scale deformation observed across the valleys (Figs. S10b,c,d). Besides, long perpendicular baseline interferograms with short-temporal baselines do not show phase delays that look like the analyzed surface deformation (Fig. S10d).

Recent publications have also evidenced a fading signal in the InSAR time series measurements linked to changes in soil moisture and vegetation, combined with spatial filtering (Ansari et al., 2020;
Maghsoudi et al., 2022). Daout et al. (2020) explored the robustness of the velocity map after changing the interferometric network. They showed that removing interferograms with short temporal baselines before performing a time series inversion of the interferometric network mitigates biases and stabilizes the inversion (identical results with variable networks), with results consistent with the permanent scatterer measurements of Ansari et al. (2020) in the same geographic area. Besides, downslope and slope-normal deformations are observed on both linear velocity maps and long-baseline interferograms (Figs. S3f,i), indicating that they are not linked with biases from the time series analysis (e.g., De Zan et al., 2013; Maghsoudi et al., 2022; Ansari et al., 2020).

Regarding the interpretation of the surface displacements shown in Fig. 5, the hypothesis of tectonic deformation can be ruled out, as the study area is not intersected by major fault systems and the observed deformations are of a small scale. Moreover, the downslope velocities, shown in Fig. 5a, reveal highly spatially continuous movements, which are strongly correlated with hillslopes (Fig. 5a,b), suggesting a gravity-driven process. Analysis of ~1 m resolution satellite images and comparisons with established databases (Luo et al., 2022; Xia et al., 2022; Peng et al., 2023) indicate that these movements are not related to thaw collapses or landslides; rather, they are sometimes associated with lobate landforms, characteristic of solifluction (Fig. 6b), but are also present in areas where no such landforms are identified in satellite images (Figs. 5a, 6a). As noted by Ridefelt et al. (2011), measurable solifluction activity can indeed occur even in the absence of distinct landforms. In Svalbard, observations on slopes underlain by permafrost by Matsuoka and Hirakawa (2000) are consistent with these findings, as they reported plug-like flow processes (characterized by the movement of the entire AL downslope as a result of the thaw of ice lenses near its base, Fig. S1; Mackay (1981)) that are rarely accompanied by a specific surface feature. Additionally, our observed millimetric to centimetric downslope velocities are consistent with solifluction velocity reported in alpine environments (French, 2018) and elsewhere in the QTP (Matsuoka, 2001).

Based on these observations and the periglacial context, we primarily attribute the spatial distribution of the deformation signal to thaw-heave cycles normal to the slope and downslope displacement due to solifluction processes. We select two regions where ridges and valleys are oriented north-south, optimizing conditions for our inversion and minimizing the effect of north-south LOS displacement insensitivity.

We aim to interpret the observed deformation in terms of solifluction processes and provide quantitative assessments where possible. Even if our interpretation appears to be the only plausible one based on current knowledge, validation through ground-based measurements would be highly beneficial. However, such data documenting solifluction movements are largely unavailable at present for the QTP, and particularly for the studied area, preventing any direct validation so far.

## 5.2. Downslope deformation and solifluction processes

As shown in previous studies (Harris et al., 1997; Matsuoka, 2001; Rouyet et al., 2021), downslope velocities are correlated with slope for both studied lithologies. Similarly, when the slope and seasonal amplitude are combined to reflect potential seasonal frost creep, a correlation is observed for unconsolidated sediments. However, in indurated bedrock colluvium, two distinct behaviors emerge. For low values of potential seasonal frost creep, the dynamics are similar to those of unconsolidated sediments. Beyond a certain threshold, however, velocities reach a plateau (Fig. 7c), suggesting a limit to the influence of seasonal frost creep in these materials.

Those correlations indicate that the potential seasonal frost creep contributes only a limited fraction of the observed movements, approximately 20% for unconsolidated sediments. The higher correlation

coefficient in unconsolidated sediments reflects their greater frost susceptibility, which allows for more pronounced seasonal frost heave (Table 1). Indeed, although AL freezing and thawing are widespread, the magnitudes of frost heave and thaw subsidence depend on the porosity of the ground to allow, from capillary action, the formation of ice lens, and on the water availability. Thus, in this dry and arid climate, large seasonal amplitudes detected by InSAR indicate porous, fined-grain soils with high subsurface water contents and, therefore, higher excess ground ice. In contrast, steep areas in indurated bedrock covered by a thin layer of regolith are less sensitive to frost heaving. This reduced sensitivity can be attributed to potentially good drainage conditions and the fact that a significant portion of seasonal frost penetration occurs within the bedrock, directly impacting seasonal amplitude and thus potential seasonal frost creep (Fig. 8; Matsuoka (2001) and Anderson (2002)). These findings indicate that additional processes not captured by the seasonal frost creep model contribute significantly to the observed velocities, particularly in indurated bedrock colluvium.

Other processes contributing to the overall movement may include: (1) diurnal processes and (2) mechanisms associated with the seasonal thawing of frozen ground, such as gelifluction or plug-like flow. These latter processes are driven by the downward penetration of the warm front in summer, generating shear strain within the thawing zones, affecting respectively the upper part and the basal ice-rich zone of the active layer, typically found in permafrost areas. Diurnal processes are typically observed in regions where the ground experiences frequent freeze-thaw cycles throughout the year, a characteristic more common in areas without permafrost. However, in our study area, the cold permafrost environment undergoes only a very short autumn, a condition that is unfavorable for major signature of frost effects on surface downslope displacements. Consequently, seasonal processes may predominate. In support of this interpretation, analysis of the time lag between peak temperatures and peak subsidence offers insight into the dominant solifluction process. Indeed, the timing of maximum subsidence coincides with the maximum thawing of the active layer (Fig. 7d), which occurs when the temperature reaches a maximum at depth. Due to the time required for heat to diffuse downward, this maximum is delayed. Assuming a purely diffusive model, with equality between the MAAT of -7.1 °C (at a median elevation of ~4300 m a.s.l.) and ground temperatures, and a typical thermal amplitude ($T_{amp}$) of 13.3 °C, the expected lag is approximately 36 days (Anderson and Anderson, 2010). The observed lags in Fig. 7d are significantly longer, ranging from 2-3 months for indurated bedrock colluvium (although the seasonal signal is not well defined for these lithologies) to 4 months for unconsolidated sediments. Previous studies (Daout et al., 2017; Li et al., 2015) in the western and southern QTP also reported time lags of 1-3 months. These extended delays can be attributed to latent heat and the deepening of the seasonal thawing front into the ice-rich layers at the bottom of the active layer, as latent heat and pressurization at depth prolong zero-curtain effects and phase change. Several in-situ measurements confirm this hypothesis and highlight the presence of ice-rich layers in the Qinghai-Tibet ground, particularly ice segregation in silty clays and/or sandy gravels in valley-bottom (Qingbai et al., 2002; Wei et al., 2009). Therefore, InSAR measurements in the Tibetan plateau suggest that the thawing front currently penetrates into the ice-rich layers, which may elevate both water content and pore pressure at the base of the AL for unconsolidated sediments and at the base of the regolith layer overlaying indurated bedrock. This can reduce the effective stress and induce incremental shear deformation, leading to plug-like flow (or deep gelifluction) in which the entire AL or regolith layer moves coherently due to the thawing of ice lenses near its base (Harris et al., 2008; Mackay, 1981; Matsuoka, 2001) (Figs. S1, 8). Based on these observations and the presence of deep cold permafrost enabling two-sided freezing, we suggest that most of the movements can be attributed to deep solifluction processes.

By extrapolating these results to the entire study area, we can infer similar dynamics in other massifs composed of indurated bedrock
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Correlation between output variables and morphometric parameters for the unconsolidated sediments (black) and indurated bedrock colluvium (orange) study areas. The first three scatter plots show the relationships between downslope velocities and (a) slope, (b) seasonal amplitude normal to the hillslope surface, and (c) the product of the peak-to-peak seasonal amplitude and slope, which reflects potential seasonal frost creep. The red dashed line in (c) is the 1:1 correspondence line between potential seasonal frost creep and downslope velocities. (d) Normalized seasonal evolution at the times of data acquisition (filled circles are median values across all pixels, vertical bars are standard deviations across all pixels) from Sentinel-1 descending track 004, for pixels with seasonal amplitudes exceeding 3 mm. Seasonal models are represented by dashed lines. (e) The relationship between subsidence and seasonal amplitude normal to the hillslope surface, with a histogram showing the distribution of the temporal rate of seasonal amplitude increase normal to the hillslope surface, for seasonal amplitudes greater than 3 mm. (f) The relationship between downslope velocity (normalized by the slope) and subsidence. In all scatter plots, points represent median values within bins, and shaded areas indicate standard deviations.</figcaption></figure>

colluvium or debris slopes (plutonic rocks or other bedrock types; Fig. 5), which exhibit significant downslope velocities (> 4mm/year) but low seasonal amplitudes. This suggests that these landforms are predominantly covered with thin slope colluvium produced by frost weathering, deforming similarly regardless of the underlying lithology. This analysis allows us, in areas affected by solifluction, to distinguish lithologies that facilitate material transport over significant thicknesses, which may be constrained by the ALT (e.g., unconsolidated sediments),

from those where the underlying bedrock constrains the movement depth (e.g., indurated bedrock colluvium). While this large-scale interpretation is consistent with observed deformation patterns, it remains primarily based on remote sensing evidence. Ground-based observations on both lithologies, as well as experimental work conducted under conditions similar to those in the northeastern QTP, testing various depths of the bedrock interface, would help improve our quantitative understanding of the relationship illustrated in Fig. 8.
### 5.3. Permafrost degradation

Our analysis indicates significant pluri-annual thaw subsidence (Fig. 5b, S4). If this subsidence were related to an increase of ALT alone, we would expect a pervasive increase in the slope-normal seasonal amplitude, which is not the case (Fig. S8). Moreover, ground elevation would remain constant every winter (when the entire ALT is frozen), which is not observed in all analyzed time series (Fig. S4). In periglacial environments, Liu et al. (2010), Shiklomanov et al. (2013), and Streletskiy et al. (2017) attributed such an “irreversible” subsidence, also called isotropic subsidence (Shiklomanov et al., 2013), to the thawing of the permafrost table just beneath the AL. When sufficient heat is transferred through the AL, the ice within the AL and the permafrost table thaws, releasing liquid water, especially in ice-rich areas. This water then drains downslope towards lower elevations, resulting in subsidence. These findings align with the large slope-normal seasonal thaw-subsidence delayed at the end of the year (Fig. 7d) and observations of Wang et al. (2023) and others, in the Qinghai-Xizang Tibetan Plateau, where long-term subsidence was attributed to the thawing of ground ice, rather than to an increase in ALT, based on combined InSAR and in situ analyzes.

This pluri-annual subsidence is closely correlated with the seasonal amplitudes with correlation coefficients of 0.96±0.02 for unconsolidated sediments and 0.52 ± 0.04 for indurated bedrock colluvium (Fig. 7e). When the permafrost table thaws, frost-susceptible soils naturally

experience the most significant volumetric loss, leading to pronounced thaw subsidence (Fig. 8).

Additionally, seasonal amplitudes increase over time (Fig. 7e, S8), particularly in moraine regions with potential ice-rich layers. This suggests an increase in ALT and/or water availability for freeze-thaw cycles. As seasonal amplitudes increase, frost heaving becomes more pronounced, driving more substantial soil displacement. Over time, this could lead to accelerated downslope movements, especially in poorly drained areas of high frost susceptibility.

We also observe a positive correlation between downslope velocities, normalized by slope, and the amplitude of irreversible subsidence in unconsolidated sediments (Fig. 7f). Beyond the effect of the increased potential seasonal frost creep, which is not the dominant factor controlling downslope velocities (Fig. 7c), irreversible thawing of the permafrost table may amplify deep solifluction processes (Fig. 8). This amplification can be explained by the increase in pore-water pressures, which reduce shear strengths after thawing of ice-rich zones at the base of the active layer and in the permafrost table (Harris et al., 2008; Matsuoka, 2001). Conversely, in indurated bedrock colluvions that are less susceptible to frost, irreversible thawing of the permafrost table may not significantly increase the amount of water released (and therefore in turn deep solifluction processes) possibly due to the permafrost table being located in the bedrock, with little ice lenses, rather than in the regolith (Fig. 8). We can, therefore, anticipate that downslope movements will intensify as permafrost degrades, primarily

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Summary of the active layer deformation interpretation scheme proposed to explain our InSAR observations, and represented in a schematic cross-section comparing unconsolidated sediments (left) and indurated bedrock colluvium (right) for the same slope angle. Unconsolidated sediments, characterized by high frost susceptibility and moisture content, undergo high seasonal amplitude, which increases over time, and significant isotropic subsidence. Seasonal thawing releases groundwater, which induces soil shearing at the thaw interface, i.e., the base of the active layer (AL). In contrast, indurated bedrock have a thin regolith layer made of colluvium or debris slope and have a lower capacity to form ice lenses and contain lower moisture. Consequently, this results in low seasonal amplitude, minimal isotropic subsidence, with only the surface layers affected by soil shearing during seasonal thawing, while the underlying impermeable bedrock remains stable.</figcaption></figure>
impacting lithologies exhibiting dynamics similar to unconsolidated sediments.

## 5.4. The promise of InSAR for slope dynamics and solifluction monitoring

Previous studies investigating solifluction rates using remote sensing techniques have typically used either optical imagery (Ridefelt et al., 2009; Harkema et al., 2023) at meter to hundred-meter scales or InSAR data (Rouyet et al., 2019, 2021) at kilometers scales. Moreover, many previous InSAR-based analyses have inverted LOS displacements into an east-west/up reference frame. As noted in the introduction, such approaches fail to capture the relationship between normal expansion during freezing and downslope displacement during thawing and introduce biases in the measurements by neglecting the north-south displacement component. In field studies, a comprehensive analysis of both slope-parallel and slope-normal displacements is essential to characterize solifluction processes (Harris et al., 2007). Fig. S11 compares the impact of such an east-west/up decomposition with a decomposition parallel and normal to the slope, highlighting the biases introduced by east-west/up decompositions. These differences, on the order of millimeters, are comparable to the magnitudes of the velocities analyzed herein and increase on steeper slopes with orientations deviating from east-west. The proposed spatial decomposition approach, which has been partially applied to landslides (Cascini et al., 2010; Colesanti and Wasowski, 2006; Schlögel et al., 2015; Samsonov et al., 2020), ice sheets (Ford et al., 2003; Samsonov, 2019), and lava flows (Carrara et al., 2019), and was generalized by Brouwer and Hanssen (2024), shows, here, strong potential for quantifying the relationships between reversible and irreversible soil expansion, subsidence, and slope creep. The methodology does not overcome the inherent limitations of Sentinel-1 data, which are acquired in two near-polar, right-looking geometries. As a result, measurements over north-south oriented slopes remain poorly constrained (see Section 3.4).

This method is not limited to solifluction phenomena and can be extended to analyze other gravitational movements. However, one must consider the deformation wavelength to derive the downslope direction from the DEM. Suppose the movement originates from deep-seated, non-planar (e.g., rotational) detachments. In that case, one might observe downslope movements that are independent of the local slope, instead reflecting the geometry of the detachment surface. Nevertheless, as permafrost deformation and solifluction are shallow processes, this prudence is not required in our case study.

Recently, new platforms offering automatic InSAR processing have emerged, such as Lazecký et al. (2020), Crosetto et al. (2020) and Thollard et al. (2021). These platforms open new opportunities to study solifluction at large scales by applying this methodology across diverse environmental settings and climatic contexts (e.g., Iceland, Sweden, Norway, Svalbard, QTP, Alps Cao et al., 2023; Ridefelt et al., 2011; Rouyet et al., 2021, 2019; Lemrabet, 2022; Harkema et al., 2023). Such comparative analysis will enhance our ability to assess the influence of environmental variables and climate change on solifluction processes.

# 6. Conclusions

We developed and implemented a large-scale InSAR method to monitor solifluction processes by inverting both reversible and irreversible components of soil movement, parallel and normal to the steepest slope. Our method effectively isolated and quantified downslope processes, showing that solifluction movements are significant and widespread across different terrains in the QTP.

The median downslope movement rates and slope-normal seasonal amplitudes are 5.4 mm/yr and 2.4 mm for indurated bedrock colluvium, and 4.2 mm/yr and 7.4 mm for unconsolidated sediments. Furthermore, significant delays between peak temperatures and maximum thaw subsidence indicate the presence of ice-rich layers at depth, which may play a key role in solifluction processes for both lithologies

by generating seasonal basal soil shearing at the base of the active layer or at the interface between colluvial material and bedrock for unconsolidated sediments and indurated bedrock colluvium, respectively.

Moreover, we measured irreversible isotropic subsidence rates of 6.8 mm/yr for unconsolidated sediments, along with increased seasonal amplitude, indicating ongoing permafrost degradation. This degradation leads to greater seasonal amplitudes, promoting higher rates of seasonal frost creep and potentially intensifying seasonal basal solifluction processes.

Alongside recent publications (e.g., Cao et al., 2023; Rouyet et al., 2019, 2021), this study illustrates the potential of InSAR measurements for large-scale analyses of solifluction phenomena in remote areas. We hope that this study, like previous work, will stimulate further field data acquisition and ground-based observations of solifluction processes in the QTP, facilitating further validation of these findings, refining interpretations, and enabling the monitoring of potential changes driven by climate change.

## CRediT authorship contribution statement

**Hugo Watine:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. **Simon Daout:** Writing – review & editing, Supervision, Methodology, Investigation, Funding acquisition, Data curation. **Jérôme Lavé:** Writing – review & editing, Supervision, Methodology, Investigation. **Marie-Pierre Doin:** Writing – review & editing, Methodology, Investigation.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This research was supported by the CNES, France, the Programme National de Télédétection Spatiale, France (INSU PNTS 2023 and 2024, MUVE project) and the Observatoire Terre et Environnement de Lorraine (OTELo), France. We are grateful to Léo Martin for the fruitful discussions and to anonymous reviewers for their constructive comments.

## Appendix A. Temporal decomposition

The analysis of ground displacement time series (LOS(t); Eqs. (A.1) and (A.2)) shows that they can be decomposed into three components for each pixel of each track geometry (ascending, 'asc', and descending, 'dsc'): (1) a linear trend representing irreversible movements (coefficient a, Fig. 3a), and (2) a seasonal amplitude that can (3) evolve over time, reflecting reversible movements caused by freeze-thaw cycles (coefficients c and d, Fig. 3a). To a first approximation, assuming sinusoidal displacement, these cumulative displacements LOS(t) can be fitted as follows for both tracks:

$$ \text{LOS}_{\text{asc}}(t) = a_{\text{asc}}t + b_{\text{asc}} + (c_{\text{asc}} + d_{\text{asc}}t) \cos(\omega t + \phi_{\text{asc}}) \quad (A.1) $$

$$ \text{LOS}_{\text{dsc}}(t) = a_{\text{dsc}}t + b_{\text{dsc}} + (c_{\text{dsc}} + d_{\text{dsc}}t) \cos(\omega t + \phi_{\text{dsc}}) \quad (A.2) $$

with time t set to zero on the date of the first image. Subsequent steps are presented for the ascending tracks, but the same operations are performed on the descending track. Eq. (A.1) is linearized as:

$$ \text{LOS}_{\text{asc}}(t) = A_{\text{asc}}t + B_{\text{asc}} + C_{\text{asc}} \cos(\omega t) + D_{\text{asc}} \sin(\omega t) + E_{\text{asc}}t \cos(\omega t) + F_{\text{asc}}t \sin(\omega t) \quad (A.3) $$

The linear problem, $l = G \cdot m$, is then solved with an iterative and constrained least-square approach, where l is the data vector consisting
of the LOS(t) time series, **m** is the model vector consisting of the parameters A, B, C, D, E, and F, **G** is the matrix composed of the temporal basis functions, and $D_{asc}/C_{asc} = F_{asc}/E_{asc}$ the constraints. In the first iteration, data are weighted by the average RMS obtained from the time-series analysis. In a second iteration, the misfit between the reconstructed phase delays and the parametric evolution from the first iteration is used for weighting. This allows to decrease the weight of the outliers due to atmospheric residual delays or unwrapping errors and produces velocity (a; Eq. (A.1)), seasonal amplitude (c; Eq. (A.1)), temporal variations of seasonal amplitude (d; Eq. (A.1)), and phase (φ; Eq. (A.1)) maps such as:

$$ 
\begin{aligned}
a_{asc} &= A_{asc} \\
c_{asc} &= \sqrt{C_{asc}^2 + D_{asc}^2} \\
\phi_{asc} &= \arctan \left( \frac{D_{asc}}{C_{asc}} \right) = \arctan \left( \frac{F_{asc}}{E_{asc}} \right)
\end{aligned} 
\quad (A.4) $$

For each parameter i (i.e., A, B, C, D, E, F), we compute an error $\sigma_M^i$ equal to:

$$ \sigma_M^i = \sqrt{\frac{N}{N-M} (\sigma_L)^2 (G^T G)_i^{-1}} \quad (A.5) $$

where N is the number of data, M the number of parameters, $(\sigma_L)^2$ is the RMS phase residual, and $(G^T G)_i^{-1}$ is the ith diagonal value of $(G^T G)^{-1}$. The errors $\sigma_M^i$ are then used to derive the errors for each parameter j (i.e., a, c, and d), defined as $\sigma_M^j$.

## Appendix B. Spatial decomposition

We perform a spatial decomposition of the temporal decomposition products (i.e., coefficients a, c, and d; see Eq. (A.1)) from both the ascending and descending LOSs into slope-parallel and slope-normal directions (Fig. 3b). 3-dimensional displacements in the slope reference frame $d_{P,L,N}$, with components in the parallel (P), lateral (L), and normal (N) directions, are projected in the ascending and descending LOS geometries, such as:

$$ d_{LOS} = \mathbf{Proj} \cdot d_{P,L,N} \quad (B.1) $$

where, $d_{LOS}$ (Eq. (B.1)) is data vector containing the ascending and descending LOS coefficients (i.e, a, c or d), and **Proj** is a 2 × 3 projection matrix:

$$ 
\begin{aligned}
\mathbf{d}_{LOS} &= \begin{pmatrix} a_{asc} \\ a_{dsc} \end{pmatrix}, \quad \mathbf{d}_{P,L,N} = \begin{pmatrix} a_{\parallel} \\ a_{\text{lat}}, a_{\perp} \end{pmatrix}, \\
\mathbf{Proj} &= \begin{pmatrix} \mathbf{Proj}_{\parallel}^{\text{asc}} & \mathbf{Proj}_{\text{lat}}^{\text{asc}} & \mathbf{Proj}_{\perp}^{\text{asc}} \\ \mathbf{Proj}_{\parallel}^{\text{dsc}} & \mathbf{Proj}_{\text{lat}}^{\text{dsc}} & \mathbf{Proj}_{\perp}^{\text{dsc}} \end{pmatrix}
\end{aligned} 
\quad (B.2) $$

For each input geometry $\gamma$ (in this example, one ascending and one descending), $Proj_{\parallel}^{\gamma}$, $Proj_{\text{lat}}^{\gamma}$, and $Proj_{\perp}^{\gamma}$ can be defined as:

$$ (Proj_{\parallel}^{\gamma} \quad Proj_{\text{lat}}^{\gamma} \quad Proj_{\perp}^{\gamma}) = \mathbf{proj}_{E,N,up}^{\gamma} \cdot \mathbf{R}_{\text{aspect}} \cdot \mathbf{R}_{\text{slope}} \quad (B.3) $$

Here, $proj_{E,N,up}^{\gamma}$ is a vector projecting the Cartesian displacements (east, north, and vertical displacement) into the LOS, based on the acquisition angles $\phi$ (mathematical angle between the east and LOS in the horizontal plane and the direction towards the satellite) and $\theta$ (angle between the horizontal plane of the local ellipsoid and the LOS towards the radar at the target location), which vary depending on the pixel location. $R_{aspect}$ and $R_{slope}$ are rotation matrices based on the aspect ($\Omega$) and slope ($\alpha$) angles, respectively, and also differ for each location (Fig. 3b). These angles are computed using the 30 m SRTM Digital Elevation Model (DEM) (Farr et al., 2007), rescaled to the dataset resolution (~90 m) with bilinear resampling and smoothed using a Gaussian filter ($\sigma = 2$ pixels) to remove potential noise. The angle maps are displayed in Fig. S5.

For one geometry $\gamma$, these matrices are defined as:

$$ \mathbf{proj}_{E,N,up}^{\gamma} = (\cos(\phi) \cos(\theta) \quad \sin(\phi) \cos(\theta) \quad \sin(\theta)) \quad (B.4) $$

$$ 
\begin{aligned}
\mathbf{R}_{\text{aspect}} &= \begin{pmatrix} \cos(\Omega) & \sin(\Omega) & 0 \\ -\sin(\Omega) & \cos(\Omega) & 0 \\ 0 & 0 & 1 \end{pmatrix}, \quad \mathbf{R}_{\text{slope}} = \begin{pmatrix} \cos(\alpha) & 0 & \sin(\alpha) \\ 0 & 1 & 0 \\ -\sin(\alpha) & 0 & \cos(\alpha) \end{pmatrix} 
\end{aligned} 
\quad (B.5) $$

At this stage, the system in Eq. (B.1) is underdetermined, and **Proj** is not invertible. To solve this, we assume no displacement in the lateral direction, performing the inversion only in the plane defined by $\mathbf{u}_{\parallel}$ and $\mathbf{u}_{\perp}$. Consequently, the system is simplified to:

$$ \mathbf{d}_{LOS} = \mathbf{Proj}^{*} \cdot \mathbf{d}_{P,N}^{*} \quad (B.6) $$

where $Proj^*$ and $d_{P,N}^*$ are, respectively, an invertible 2 × 2 matrix and a 2 × 1 matrix amputated of their lateral components. Consequently, the Eq. (B.6) can be inverted as $d_{P,N}^* = (Proj^*)^{-1} \cdot d_{LOS}^*$.

We use the uncertainties from the temporal decomposition $\sigma_M^j$ as prior information to derive the posterior uncertainties for each parameter k (e.g., $a_{\parallel}$, $a_{\perp}$), such as:

$$ \sigma_{P,N}^{k} = \sqrt{(\mathbf{Proj}^{*T} \mathbf{C}_{\text{diag}}^{-1} \mathbf{Proj}^{*})^{-1}} \quad (B.7) $$

where $C_{diag}$ is the prior uncertainty matrix composed of diagonal terms equal to $\sigma_M^j$.

## Appendix C. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.rse.2025.114926.

### Data availability

InSAR products are available through a Zenodo repository (https://doi.org/10.5281/zenodo.15223170).

### References

Akerman, H., 1996. Slow mass movements and climatic relationships, 1972–1994, Kapp Linné, West Spitsbergen. Adv. Hillslope Process. 1219–1256.

Anderson, R.S., 2002. Modeling the tor-dotted crests, bedrock edges, and parabolic profiles of high alpine surfaces of the Wind River Range, Wyoming. Geomorphol. 46 (1–2), 35–58.

Anderson, R.S., Anderson, S.P., 2010. Geomorphology: The Mechanics and Chemistry of Landscapes. Cambridge University Press.

Ansari, H., De Zan, F., Parizzi, A., 2020. Study of systematic bias in measuring surface deformation with SAR interferometry. IEEE Trans. Geosci. Remote Sens. 59 (2), 1285–1301.

Ballantyne, C.K., Harris, C., 1994. The Periglaciation of Great Britain. CUP Archive.

Bartsch, A., Leibman, M., Strozzi, T., Khomutov, A., Widhalm, B., Babkina, E., Mullanurov, D., Ermokhina, K., Kroisleitner, C., Bergstedt, H., 2019. Seasonal progression of ground displacement identified with satellite radar interferometry and the impact of unusually warm conditions on permafrost at the Yamal Peninsula in 2016. Remote. Sens. 11 (16), 1865. http://dx.doi.org/10.3390/rs11161865, URL: https://www.mdpi.com/2072-4292/11/16/1865, Number: 16 Publisher: Multidisciplinary Digital Publishing Institute.

Biskaborn, B.K., Smith, S.L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D.A., Schoeneich, P., Romanovsky, V.E., Lewkowicz, A.G., Abramov, A., et al., 2019. Permafrost is warming at a global scale. Nat. Commun. 10 (1), 264.

Brouwer, W.S., Hanssen, R.F., 2023. A treatise on InSAR geometry and 3-D displacement estimation. IEEE Trans. Geosci. Remote Sens. 61, 1–11.

Brouwer, W.S., Hanssen, R.F., 2024. Estimating three-dimensional displacements with InSAR: the strapdown approach. J. Geod. 98 (12), 110.

Brown, J., Ferrians, O., Heginbottom, J., Melnikov, E., 1997. Circum-Arctic map of permafrost and ground ice conditions.

Cao, B., Gruber, S., Zhang, T., Li, L., Peng, X., Wang, K., Zheng, L., Shao, W., Guo, H., 2017. Spatial variability of active layer thickness detected by ground-penetrating radar in the Qilian Mountains, Western China. J. Geophys. Res.: Earth Surf. 122 (3), 574–591.
Cao, Y., Jónsson, S., Hreinsdóttir, S., 2023. Iceland kinematics from InSAR. JGR Solid Earth 128 (3), e2022JB025546. http://dx.doi.org/10.1029/2022JB025546, URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JB025546.

Cao, B., Zhang, T., Peng, X., Mu, C., Wang, Q., Zheng, L., Wang, K., Zhong, X., 2018. Thermal characteristics and recent changes of permafrost in the upper reaches of the Heihe River Basin, Western China. J. Geophys. Res.: Atmospheres 123 (15), 7935-7949. http://dx.doi.org/10.1029/2018JD028442, URL: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018JD028442, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018JD028442.

Carrara, A., Pinel, V., Bascou, P., Chaljub, E., De la Cruz-Reyna, S., 2019. Post-emplacement dynamics of andesitic lava flows at Volcán de Colima, Mexico, revealed by radar and optical remote sensing data. J. Volcanol. Geotherm. Res. 381, 1-15.

Cascini, L., Fornaro, G., Peduto, D., 2010. Advanced low-and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales. Eng. Geol. 112 (1-4), 29-42.

Chen, J., Wu, T., Zou, D., Liu, L., Wu, X., Gong, W., Zhu, X., Li, R., Hao, J., Hu, G., Pang, Q., Zhang, J., Yang, S., 2022. Magnitudes and patterns of large-scale permafrost ground deformation revealed by Sentinel-1 InSAR on the central Qinghai-Tibet Plateau. Remote Sens. Environ. 268, 112778. http://dx.doi.org/10.1016/j.rse.2021.112778, URL: https://linkinghub.elsevier.com/retrieve/pii/S0034425721004983.

Cheng, G., Wu, T., 2007. Responses of permafrost to climate change and their environmental significance, Qinghai-Tibet Plateau. J. Geophys. Res. 112 (F2), 2006JF000631. http://dx.doi.org/10.1029/2006JF000631, URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2006JF000631.

Colesanti, C., Wasowski, J., 2006. Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Eng. Geol. 88 (3-4), 173-199.

Crosetto, M., Solari, L., Mróz, M., Balasis-Levinsen, J., Casagli, N., Frei, M., Oyen, A., Moldestad, D.A., Bateson, L., Guerrieri, L., et al., 2020. The evolution of wide-area DInSAR: From regional and national services to the European ground motion service. Remote. Sens. 12 (12), 2043.

Dalaison, M., Jolivet, R., 2020. A Kalman filter time series analysis method for InSAR. J. Geophys. Res.: Solid Earth 125 (7), e2019JB019150. http://dx.doi.org/10.1029/2019JB019150, URL: https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JB019150, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019JB019150.

Daout, S., Dini, B., Haeberli, W., Doin, M.-P., Parsons, B., 2020. Ice loss in the Northeastern Tibetan Plateau permafrost as seen by 16 yr of ESA SAR missions. Earth Planet. Sci. Lett. 545, 116404. http://dx.doi.org/10.1016/j.epsl.2020.116404, URL: https://linkinghub.elsevier.com/retrieve/pii/S0012821X20303484.

Daout, S., Doin, M.-P., Peltzer, G., Socquet, A., Lasserre, C., 2017. Large-scale InSAR monitoring of permafrost freeze-thaw cycles on the Tibetan Plateau. Geophys. Res. Lett. 44 (2), 901-909. http://dx.doi.org/10.1002/2016GL070781, URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2016GL070781.

Daout, S., Sudhaus, H., Kausch, T., Steinberg, A., Dini, B., 2019. Interseismic and postseismic shallow creep of the North Qaidam Thrust faults detected with a multitemporal InSAR analysis. J. Geophys. Res.: Solid Earth 124 (7), 7259-7279.

De Zan, F., Parizzi, A., Prats-Iraola, P., López-Dekker, P., 2013. A SAR interferometric model for soil moisture. IEEE Trans. Geosci. Remote Sens. 52 (1), 418-425.

Dini, B., Daout, S., Manconi, A., Loew, S., 2019a. Classification of slope processes based on multitemporal DInSAR analyses in the Himalaya of NW Bhutan. Remote Sens. Environ. 233, 111408. http://dx.doi.org/10.1016/j.rse.2019.111408, URL: https://linkinghub.elsevier.com/retrieve/pii/S0034425719304274.

Dini, B., Manconi, A., Loew, S., 2019b. Investigation of slope instabilities in NW Bhutan as derived from systematic DInSAR analyses. Eng. Geol. 259, 105111. http://dx.doi.org/10.1016/j.enggeo.2019.04.008, URL: https://linkinghub.elsevier.com/retrieve/pii/S001379521831706X.

Dobinski, W., 2011. Permafrost. Earth-Sci. Rev. 108 (3-4), 158-169. http://dx.doi.org/10.1016/j.earscirev.2011.06.007, URL: https://linkinghub.elsevier.com/retrieve/pii/S0012825211000894.

Doin, M.-P., Lasserre, C., Peltzer, G., Cavalié, O., Doubre, C., 2009. Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models. J. Appl. Geophys. 69 (1), 35-50.

Doin, M.-P., Lodge, F., Guillaaso, S., Jolivet, R., Lasserre, C., Ducret, G., Grandin, R., Pathier, E., Pinel, V., 2011. Presentation of the small baseline NSBAS processing chain on a case example: The etna deformation monitoring from 2003 to 2010 using envisat data. In: Fringe Symposium.

Doin, M.-P., Twardzik, C., Ducret, G., Lasserre, C., Guillaaso, S., Jianbao, S., 2015. InSAR measurement of the deformation around Siling Co Lake: Inferences on the lower crust viscosity in central Tibet. J. Geophys. Res.: Solid Earth 120 (7), 5290-5310. http://dx.doi.org/10.1002/2014JB011768, URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/2014JB011768, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/2014JB011768.

Fang, J., Houseman, G.A., Wright, T.J., Evans, L.A., Craig, T.J., Elliott, J.R., Hooper, A., 2024. The dynamics of the India-Eurasia collision: Faulted viscous continuum models constrained by high-resolution sentinel-1 InSAR and GNSS velocities. J. Geophys. Res.: Solid Earth 129 (6), e2023JB028571.

Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., et al., 2007. The shuttle radar topography mission. Rev. Geophys. 45 (2).

Fick, S.E., Hijmans, R.J., 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37 (12), 4302-4315.

Ford, A.L., Forster, R.R., Bruhn, R.L., 2003. Ice surface velocity patterns on Seward Glacier, Alaska/Yukon, and their implications for regional tectonics in the Saint Elias Mountains. Ann. Glaciol. 36, 21-28.

French, H.M., 2018. The Periglacial Environment, fourth ed. J. Wiley, Hoboken.

Grandin, R., 2015. Interferometric processing of SLC Sentinel-1 TOPS data. In: FRINGE'15: Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop, Frascati, Italy, 23-27 March 2015.

Gruber, S., 2012. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6 (1), 221-233. http://dx.doi.org/10.5194/tc-6-221-2012, URL: https://tc.copernicus.org/articles/6/221/2012/tc-6-221-2012.html, Publisher: Copernicus GmbH.

Harkema, M.R., Nijland, W., de Jong, S.M., Kattenborn, T., Eichel, J., 2023. Monitoring solifluction movement in space and time: A semi-automated high-resolution approach. Geomorphol. 433, 108727.

Harris, C., Davies, M.C.R., 2000. Gelifluction: Observations from large-scale laboratory simulations. Arct. Antarct. Alp. Res. 32 (2), 202-207. http://dx.doi.org/10.1080/15230430.2000.12003356, URL: https://www.tandfonline.com/doi/full/10.1080/15230430.2000.12003356.

Harris, C., Davies, M.C., Coutard, J.-P., 1997. Rates and processes of periglacial solifluction: an experimental approach. Earth Surf. Process. Landforms: J. Br. Geomorphol. Group 22 (9), 849-868.

Harris, C., Kern-Luetschg, M., Smith, F., Isaksen, K., 2008. Solifluction processes in an area of seasonal ground freezing, Dovrefjell, Norway. Permafr. Periglac. Process. 19 (1), 31-47.

Harris, C., Luetschg, M., Davies, M.C., Smith, F., Christiansen, H.H., Isaksen, K., 2007. Field instrumentation for real-time monitoring of periglacial solifluction. Permafr. Periglac. Process. 18 (1), 105-114.

Hartmann, J., Moosdorf, N., 2012. The new global lithological map database GLiM: A representation of rock properties at the Earth surface. Geochem. Geophys. Geosystems 13 (12), http://dx.doi.org/10.1029/2012GC004370, URL: https://onlinelibrary.wiley.com/doi/abs/10.1029/2012GC004370, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2012GC004370.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al., 2020. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146 (730), 1999-2049.

Jiang, G., Gao, S., Lewkowicz, A.G., Zhao, H., Pang, S., Wu, Q., 2022. Development of a rapid active layer detachment slide in the Fenghuoshan Mountains, Qinghai-Tibet Plateau. Permafr. Periglac. Process. 33 (3), 298-309.

Jolivet, R., Grandin, R., Lasserre, C., Doin, M.-P., Peltzer, G., 2011. Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data. Geophys. Res. Lett. 38 (17).

Lazecký, M., Spaans, K., González, P.J., Maghsoudi, Y., Morishita, Y., Albino, F., Elliott, J., Greenlin, N., Hatton, E., Hooper, A., et al., 2020. LiCSAR: An automatic InSAR tool for measuring and monitoring tectonic and volcanic activity. Remote. Sens. 12 (15), 2430.

Lehner, B., Grill, G., 2013. Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrol. Process. 27 (15), 2171-2186.

Lemrabet, L., 2022. Apport Et Spécificités Des Mesures D'interférométrie Radar À L'échelle Continentale: Cas D'étude Des Déformations Actuelles Du Plateau Tibétain Oriental (Ph.D. thesis). Université Claude Bernard-Lyon I.

Lemrabet, L., Doin, M.-P., Lasserre, C., Durand, P., 2023. Referencing of continental-scale InSAR-derived velocity fields: Case study of the Eastern Tibetan Plateau. J. Geophys. Res.: Solid Earth 128 (7), e2022JB026251. http://dx.doi.org/10.1029/2022JB026251, URL: https://onlinelibrary.wiley.com/doi/abs/10.1029/2022JB026251, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022JB026251.

Li, D., Wen, Z., Luo, J., Zhang, M., Chen, B., 2019. Slope failure induced by cold snap and continuous precipitation in the seasonal frozen area of Qinghai-Tibet Plateau. Sci. Total Environ. 694, 133547.

Li, Z., Zhao, R., Hu, J., Wen, L., Feng, G., Zhang, Z., Wang, Q., 2015. InSAR analysis of surface deformation over permafrost to estimate active layer thickness based on one-dimensional heat transfer model of soils. Sci. Rep. 5 (1), 15542.

Lin, Z., Burn, C., Niu, F., Luo, J., Liu, M., Yin, G., 2015. The thermal regime, including a reversed thermal offset, of arid permafrost sites with variations in vegetation cover density, Wudaoliang Basin, Qinghai-Tibet plateau. Permafr. Periglac. Process. 26 (2), 142-159.

Liu, C., Ji, L., Zhu, L., Xu, C., Zhao, C., Lu, Z., Wang, Q., 2024a. Kilometer-resolution three-dimensional crustal deformation of Tibetan Plateau from InSAR and GNSS. Sci. China Earth Sci. 1-18.
Liu, Z., Kimball, J.S., Ballantyne, A., Watts, J.D., Natali, S.M., Rogers, B.M., Yi, Y., Klene, A.E., Moghaddam, M., Du, J., Zona, D., 2024b. Widespread deepening of the active layer in northern permafrost regions from 2003 to 2020. Env. Res. Lett. 19 (1), 014020. http://dx.doi.org/10.1088/1748-9326/ad0f73, URL: https://iopscience.iop.org/article/10.1088/1748-9326/ad0f73.

Liu, L., Schaefer, K.M., Chen, A.C., Gusmeroli, A., Zebker, H.A., Zhang, T., 2015. Remote sensing measurements of thermokarst subsidence using InSAR. J. Geophys. Res.: Earth Surf. 120 (9), 1935-1948. http://dx.doi.org/10.1002/2015JF003599, URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/2015JF003599, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/2015JF003599.

Liu, L., Schaefer, K., Zhang, T., Wahr, J., 2012. Estimating 1992-2000 average active layer thickness on the Alaskan North Slope from remotely sensed surface subsidence. J. Geophys. Res. 117 (F1), 2011JF002041. http://dx.doi.org/10.1029/2011JF002041, URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2011JF002041.

Liu, L., Zhang, T., Wahr, J., 2010. InSAR measurements of surface deformation over permafrost on the North Slope of Alaska. J. Geophys. Res. 115 (F3), 2009JF001547. http://dx.doi.org/10.1029/2009JF001547, URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2009JF001547.

Liu, G., Zhao, L., Li, R., Wu, T., Jiao, K., Ping, C., 2017. Permafrost warming in the context of step-wise climate change in the Tien Shan Mountains, China: Permafrost warming in the Tien Shan Mountains, China. Permafr. Periglac. Process. 28 (1), 130-139. http://dx.doi.org/10.1002/ppp.1885, URL: https://onlinelibrary.wiley.com/doi/10.1002/ppp.1885.

Luo, J., Niu, F., Lin, Z., Liu, M., Yin, G., 2019. Recent acceleration of thaw slumping in permafrost terrain of Qinghai-Tibet Plateau: An example from the Beiluhe Region. Geomorphol. 341, 79-85.

Luo, J., Niu, F., Lin, Z., Liu, M., Yin, G., Gao, Z., 2022. Inventory and frequency of retrogressive thaw slumps in permafrost region of the Qinghai-Tibet Plateau. Geophys. Res. Lett. 49 (23), e0222GL099829.

Mackay, J.R., 1981. Active layer slope movement in a continuous permafrost environment, Garry Island, Northwest Territories, Canada. Can. J. Earth Sci. 18 (11), 1666-1680. http://dx.doi.org/10.1139/e81-154, URL: http://www.nrcresearchpress.com/doi/10.1139/e81-154.

Maghsoudi, Y., Hooper, A.J., Wright, T.J., Lazecky, M., Ansari, H., 2022. Characterizing and correcting phase biases in short-term, multilooked interferograms. Remote Sens. Environ. 275, 113022.

Mathey, M., Doin, M.-P., André, P., Walpersdorf, A., Baize, S., Sue, C., 2022. Spatial heterogeneity of uplift pattern in the Western European alps revealed by InSAR time-series analysis. Geophys. Res. Lett. 49 (1), e2021GL095744. http://dx.doi.org/10.1029/2021GL095744, URL: https://onlinelibrary.wiley.com/doi/abs/10.1029/2021GL095744, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GL095744.

Matsuoka, N., 2001. Solifluction rates, processes and landforms: a global review. Earth-Sci. Rev. 55 (1-2), 107-134. http://dx.doi.org/10.1016/S0012-8252(01)00057-5, URL: https://linkinghub.elsevier.com/retrieve/pii/S0012825201000575.

Matsuoka, N., Hirakawa, K., 2000. Solifluction resulting from one-sided and two-sided freezing: field data from Svalbard. Polar Geosci. 13, 187-201.

Matsuoka, N., Ikeda, A., Date, T., 2005. Morphometric analysis of solifluction lobes and rock glaciers in the swiss alps. Permafr. Periglac. Process. 16 (1), 99-113.

Meng, Q., Intrieri, E., Raspini, F., Peng, Y., Liu, H., Casagli, N., 2022. Satellite-based interferometric monitoring of deformation characteristics and their relationship with internal hydrothermal structures of an earthflow in Zhimei, Yushu, Qinghai-Tibet Plateau. Remote Sens. Environ. 273, 112987.

Métivier, F., Gaudemer, Y., Tapponnier, P., Meyer, B., 1998. Northeastward growth of the Tibet plateau deduced from balanced reconstruction of two depositional areas: The Qaidam and Hexi Corridor basins, China. Tectonics 17 (6), 823-842.

Mu, C., Shang, J., Zhang, T., Fan, C., Wang, S., Peng, X., Zhong, W., Zhang, F., Mu, M., Jia, L., 2020. Acceleration of thaw slump during 1997-2017 in the Qilian Mountains of the northern Qinghai-Tibetan plateau. Landslides 17, 1051-1062.

Niu, F., Luo, J., Lin, Z., Ma, W., Lu, J., 2012. Development and thermal regime of a thaw slump in the Qinghai-Tibet plateau. Cold Reg. Sci. & Technol. 83, 131-138.

Notti, D., Herrera, G., Bianchini, S., Meisina, C., García-Davalillo, J.C., Zucca, F., 2014. A methodology for improving landslide PSI data analysis. Int. J. Remote Sens. 35 (6), 2186-2214.

Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H.H., Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov, A., Khomutov, A., Kääb, A., Leibman, M.O., Lewkowicz, A.G., Panda, S.K., Romanovsky, V., Way, R.G., Westergaard-Nielsen, A., Wu, T., Yamkhin, J., Zou, D., 2019. Northern Hemisphere permafrost map based on TTOP modelling for 2000-2016 at 1 km2 scale. Earth-Sci. Rev. 193, 299-316. http://dx.doi.org/10.1016/j.earscirev.2019.04.023, URL: https://linkinghub.elsevier.com/retrieve/pii/S0012825218305907.

Ou, Q., Daout, S., Weiss, J.R., Shen, L., Lazecký, M., Wright, T.J., Parsons, B.E., 2022. Large-scale interseismic strain mapping of the NE Tibetan Plateau from Sentinel-1 interferometry. J. Geophys. Res.: Solid Earth 127 (6), e2022JB024176. http://dx.doi.org/10.1029/2022JB024176, URL: https://onlinelibrary.wiley.com/doi/abs/10.1029/2022JB024176, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022JB024176.

Peng, X., Yang, G., Frauenfeld, O.W., Li, X., Tian, W., Chen, G., Huang, Y., Wei, G., Luo, J., Mu, C., et al., 2023. The first hillslope thermokarst inveritory for the permafrost region of the Qilian Mountains. Earth Syst. Sci. Data Discuss. 2023, 1-19.

Qin, Y., Wu, T., Zhao, L., Wu, X., Li, R., Xie, C., Pang, Q., Hu, G., Qiao, Y., Zhao, G., Liu, G., Zhu, X., Hao, J., 2017. Numerical modeling of the active layer thickness and permafrost thermal state across Qinghai-Tibetan Plateau. JGR Atmospheres 122 (21), http://dx.doi.org/10.1002/2017JD026858, URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2017JD026858.

Qingbai, W., Yongzhi, L., Jianming, Z., Changjiang, T., 2002. A review of recent frozen soil engineering in permafrost regions along Qinghai-Tibet Highway, China. Permafr. Periglac. Process. 13 (3), 199-205. http://dx.doi.org/10.1002/ppp.420, URL: https://onlinelibrary.wiley.com/doi/10.1002/ppp.420.

Reinosch, E., Buckel, J., Dong, J., Gerke, M., Baade, J., Riedel, B., 2020. InSAR time series analysis of seasonal surface displacement dynamics on the Tibetan Plateau. Cryosphere 14 (5), 1633-1650.

Ridefelt, H., Boelhouwers, J., Eiken, T., 2009. Measurement of solifluction rates using multi-temporal aerial photography. Earth Surf. Process. Landf. 34 (5), 725-737.

Ridefelt, H., Boelhouwers, J., Etzelmüller, B., 2011. Local variations of solifluction activity and environment in the Abisko Mountains, Northern Sweden. Earth Surf. Process. Landf. 36 (15), 2042-2053.

Rouyet, L., Karjalainen, O., Niittynen, P., Aalto, J., Luoto, M., Lauknes, T.R., Larsen, Y., Hjort, J., 2021. Environmental controls of InSAR-based periglacial ground dynamics in a sub-arctic landscape. J. Geophys. Res.: Earth Surf. 126 (7), e2021JF006175. http://dx.doi.org/10.1029/2021JF006175, URL: https://onlinelibrary.wiley.com/doi/abs/10.1029/2021JF006175, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021JF006175.

Rouyet, L., Lauknes, T.R., Christiansen, H.H., Strand, S.M., Larsen, Y., 2019. Seasonal dynamics of a permafrost landscape, Adventdalen, Svalbard, investigated by InSAR. Remote Sens. Environ. 231, 111236. http://dx.doi.org/10.1016/j.rse.2019.111236, URL: https://linkinghub.elsevier.com/retrieve/pii/S003442571930255X.

Samsonov, S., 2019. Three-dimensional deformation time series of glacier motion from multiple-aperture DInSAR observation. J. Geod. 93 (12), 2651-2660. http://dx.doi.org/10.1007/s00190-019-01325-y, URL: http://link.springer.com/10.1007/s00190-019-01325-y.

Samsonov, S., Dille, A., Dewitte, O., Kervyn, F., d'Oreye, N., 2020. Satellite interferometry for mapping surface deformation time series in one, two and three dimensions: A new method illustrated on a slow-moving landslide. Eng. Geol. 266, 105471.

Schlögel, R., Doubre, C., Malet, J.-P., Masson, F., 2015. Landslide deformation monitoring with ALOS/PALSAR imagery: A D-InSAR geomorphological interpretation method. Geomorphol. 231, 314-330.

Shean, D., 2017. High mountain Asia 8-meter DEM mosaics derived from optical imagery, version 1. URL: https://doi.org/10.5067/KXOVQ9L17SZ2, (Accessed 24 June 2025) [subset used: 290]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center.

Shiklomanov, N.I., Streletskiy, D.A., Little, J.D., Nelson, F.E., 2013. Isotropic thaw subsidence in undisturbed permafrost landscapes. Geophys. Res. Lett. 40 (24), 6356-6361. http://dx.doi.org/10.1002/2013GL058295, URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2013GL058295.

Streletskiy, D.A., Shiklomanov, N.I., Little, J.D., Nelson, F.E., Brown, J., Nyland, K.E., Klene, A.E., 2017. Thaw subsidence in undisturbed Tundra Landscapes, Barrow, Alaska, 1962-2015. Permafr. Periglac. 28 (3), 566-572. http://dx.doi.org/10.1002/ppp.1918, URL: https://onlinelibrary.wiley.com/doi/10.1002/ppp.1918.

Sun, Z., Wang, Y., Sun, Y., Niu, F., Gao, Z., et al., 2017. Creep characteristics and process analyses of a thaw slump in the permafrost region of the Qinghai-Tibet Plateau, China. Geomorphol. 293, 1-10.

Thollard, F., Clesse, D., Doin, M.-P., Donadieu, J., Durand, P., Grandin, R., Lasserre, C., Laurent, C., Deschamps-Ostanciaux, E., Pathier, E., et al., 2021. Flatsim: The form@ter large-scale multi-temporal sentinel-1 interferometry service. Remote. Sens. 13 (18), 3734.

Wang, B., French, H.M., 1995. In situ creep of frozen soil, Fenghuo Shan, Tibet Plateau, China. Can. Geotech. J. 32 (3), 545-552.

Wang, L., Zhao, L., Zhou, H., Liu, S., Hu, G., Li, Z., Wang, C., Zhao, J., 2023. Evidence of ground ice melting detected by InSAR and in situ monitoring over permafrost terrain on the Qinghai-Xizang (Tibet) Plateau. Permafr. Periglac. Process. 34 (1), 52-67.

Wei, M., Fujun, N., Satoshi, A., Dewu, J., 2006. Slope instability phenomena in permafrost regions of Qinghai-Tibet Plateau, China. Landslides 3, 260-264.
Wei, M., Guodong, C., Qingbai, W., 2009. Construction on permafrost foundations: Lessons learned from the Qinghai–Tibet railroad. Cold Reg. Sci. & Technol. 59 (1), 3–11. http://dx.doi.org/10.1016/j.coldregions.2009.07.007, URL: https://linkinghub.elsevier.com/retrieve/pii/S0165232X09001335.

Wendt, L., Rouyet, L., Christiansen, H.H., Lauknes, T.R., Westermann, S., 2024. InSAR sensitivity to active layer ground ice content in Adventdalen, Svalbard. EGU- sphere 2024, 1–26. http://dx.doi.org/10.5194/egusphere-2024-2972, URL: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2972/.

Williams, P.J., Smith, M.W., 1989. The frozen earth. p. 306, (No Title).

Xia, Z., Huang, L., Fan, C., Jia, S., Lin, Z., Liu, L., Luo, J., Niu, F., Zhang, T., 2022. Retrogressive thaw slumps along the Qinghai-Tibet Engineering Corridor: A comprehensive inventory and their distribution characteristics. Earth Syst. Sci. Data Discuss. 2022, 1–19.

Yang, Y., Hopping, K.A., Wang, G., Chen, J., Peng, A., Klein, J.A., 2018. Permafrost and drought regulate vulnerability of Tibetan Plateau grasslands to warming. Ecosphere 9 (5), e02233. http://dx.doi.org/10.1002/ecs2.2233, URL: https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.2233.