Climate warming increases thermokarst landscapes and thus leads to land degradation in the Circum-Arctic regions. Thermokarst landscapes were estimated to cover ~ 20% of the northern permafrost region, and their development is related to ground ice content and topographic conditions. However, changes in thaw slump distribution and development in mid- to low-latitude permafrost areas largely remain unknown. Here, we selected the Qilian Mountains of the northern Qinghai-Tibetan Plateau (QTP) as the study area. Combined with field investigation to measure the boundary of thaw slump using Real Time Kinematic (RTK), we analyzed historical changes in thaw slumps using 1969 and 1997 aerial photographs, 2009 and 2015 satellite imagery, and 2017 unmanned aerial vehicle (UAV) aerial images. The results showed that there are 15 thaw slumps covering an area of 0.03 km2, which were mainly distributed in terrain with a slope of 2–8° and elevation of 3552–3611 m. For the time periods of 1997–2009, 2009–2015, and 2015–2017, the average increase rates of thaw slump areas were approximately 61.8, 60.0, and 156.8 m2/y, and annual headwall retreat rates were approximately 1.3, 1.6 and 2.0 m/y. Based on the slopes, aspects, and altitudes from the Digital Elevation Model generated by UAV imagery, it suggested that geomorphic factors have no significant effect on the growth rates of thaw slump due to high heterogeneities of alpine environments. Our results showed that thaw slumps with polycyclic and active characteristics covered at least 0.9% of northern QTP permafrost regions and are expected to rapidly accelerate with climate warming.

Acceleration of thaw slump during 1997–2017 in the Qilian Mountains of the northern Qinghai-Tibetan plateau

Mechanism of shear strength deterioration of soil-rock mixture after freeze–thaw cycles

Abstract. Retrogressive thaw slumps (RTSs) are spectacular landforms that occur due to the thawing of ice-rich permafrost or melting of massive ground ice, often in hillslope terrain. RTSs occur in the Arctic, the subarctic, and high mountain (Qinghai–Tibet Plateau) permafrost regions and are observed to expand in size and number due to climate warming. As the observation of RTSs is receiving more and more attention due to their important role in permafrost thaw; impacts on topography; mobilization of sediment, carbon, nutrients, and contaminants; and their effects on downstream hydrology and water quality, the thematic breadth of studies increases and scientists from different scientific backgrounds and perspectives contribute to new RTS research. At this point, a wide range of terminologies originating from different scientific schools is used, and we identified the need to provide an overview of variable characteristics of RTSs to clarify terminologies and ease the understanding of the literature related to RTS processes, dynamics, and feedbacks. We review the theoretical geomorphological background of RTS formation and landform characteristics to provide an up-to-date understanding of the current views on terminology and underlying processes. The presented overview can be used not only by the international permafrost community but also by scientists working on ecological, hydrological, and biogeochemical consequences of RTS occurrence and by remote-sensing specialists developing automated methods for mapping RTS dynamics. The review will foster a better understanding of the nature and diversity of RTS phenomena and provide a useful base for experts in the field but also ease the introduction to the topic of RTSs for scientists who are new to it. 

Review article: Retrogressive thaw slump characteristics and terminology

Abstract A retrogressive thaw slump (RTS) is a slope failure formed by slope thaw settlement and retrogressive slump following the thawing of ice‐rich permafrost or the melting of massive ice. Here, we review recent literature on RTSs, one of the main geomorphological landscapes developed in the process of permafrost degradation. The main topics are as follows: development and temporal evolution, mechanisms and processes, influencing factors, evaluation susceptibility and calculation, and assessment of engineering and environmental impacts. There has been a rapid increase in the number and distribution area of RTSs over permafrost in recent years. Climate warming events, extreme rainfall, forest fires, bank and coast erosion, and anthropogenic activity are the primary factors leading to RTSs in permafrost regions, disrupting the initial hydrothermal equilibrium of permafrost slopes. This causes a rise in ground temperature and the thaw of ice‐rich permafrost. Meltwater seeps down and collects on the ice surface, weakening freeze–thaw interface shear resistance and resulting in soil collapse. The development of RTSs may last several decades or longer. RTSs destabilize infrastructure, destroy vegetation, boost soil erosion and land desertification, alter the environment of nearby waters, and increase emissions of some major greenhouse gases. Numerous methods have been developed and adopted to explore RTSs, including geographic information systems (GIS) and equilibrium, numerical, and reliability analysis methods. However, research on formation mechanisms and processes, quantitative prediction, engineering and environmental influences, and mitigative measures of RTSs under a warming climate are still inadequate. Existing research methods, such as numerical simulations, remote sensing, airborne ground‐based geophysical surveys, investigations and mapping, and hydrothermal and deformation field monitoring, should be systematically integrated. Additionally, equipment for laboratory testing and numerical models for simulating RTSs may need to be timely introduced and better developed. 

Advances in retrogressive thaw slump research in permafrost regions

Water planning and management is very crucial for all computing sectors for water uses to maximize the scarce and allocated water uses based on their demands sustainably in Ethiopia's upper Bilate watershed. The shortage of surface water, especially during dry months, has become a major point of contention between upstream and downstream water users in the upper Bilate River. Therefore, the key objectives of the study are to evaluate the surface water resources and determine the best distribution for a range of requirements in the watershed. The historical climatic and stream flow daily data for the period of 2010-2019 have been used for the analysis. Hydrologic Engineering Center Hydrologic Modeling System-Geospatial Hydrologic Modeling Extension with the Hydrologic Engineering Center Hydrologic Modeling System was used for rainfall-runoff analysis. The model output further represents that the yearly overall surface water of the watershed is 502 MC M. Estimated annual environmental requirement is 75.32MCM which is 15% of the average annual available flow in watershed. Current annual irrigation, livestock, and domestic water demand were estimated to be 19.34 MC M, 12.39 MC M, and 79.4 MC M, respectively. The net amount of water delivered was 19.25 MC M, 79.28 MC M, and 12.36 MC M for irrigation, domestic, and Livestock demands, respectively, in the reference scenario. In the currently available (reference) scenario, 99.8% of the water supply need had been fulfilled, yet only 0.2% of the requirements for water were unmet. Average annual water demand of 111.13 MC M in the current scenario growth to 176.08 MC M when the future growth scenario. In contrast, for the future irrigation development and population projections scenario, 69.8% of the supply-demand became acquainted, and 30.2% of the demand for water remained unfulfilled in 2035. Therefore, to realize good water availability for productive use and allocate water optimal manner constructing a hydraulic structure (dam) upstream of the watershed was recommended. 

Assessment of surface water resource and allocation optimization for diverse demands in Ethiopia's upper Bilate Watershed

Thaw slumps are well-developed within a 10 km wide zone along the Qinghai-Tibet engineering corridor, especially along the Qinghai-Tibet highway and railway. Previous studies have focused on thaw slump instability such as its origin development, headwall retrogression rate, failure scale, and thermal regime, yet the intrinsic dynamic process of surface movement is relatively less known. In this study, we used InSAR based on the L-band ALOS PALSAR images acquired from January 2007 to October 2010 to investigate the distribution of thaw-induced slope failures containing retrogressive thaw slumps and active layer detachment failures along the Qinghai-Tibet highway (QTH). Our InSAR analysis reveals that the maximum annual average sedimentation rates are even up to -35 mm·yr−1 in the slope direction to the K3035 thaw slump, and the K3035W active layer detachment failure developed on the west side of K3035. The distribution, failure extent, and stability of the slope failures obtained by our InSAR analysis all agree well with the field investigations. Our study illustrates that InSAR is an effective tool for studying the distribution and processes of the thaw slump-derived thermokarst and provides useful references for evaluating permafrost degradation in response to climate warming and external disturbance on the Qinghai-Tibet plateau.

Monitoring the Thaw Slump-Derived Thermokarst in the Qinghai-Tibet Plateau Using Satellite SAR Interferometry

Densely distributed Global Navigation Satellite System (GNSS) stations can invert the terrestrial water storage anomaly (TWSA) with high precision. However, the uneven distribution of GNSS stations greatly limits the application of TWSA inversion. The purpose of this study was to compensate for the spatial coverage of GNSS stations by simulating the vertical deformation in unobserved grids. First, a new deep learning weight loading inversion model (DWLIM) was constructed by combining the long short-term memory (LSTM) algorithm, inverse distance weight, and the crustal load model. DWLIM is beneficial for improving the inversion accuracy of TWSA based on the GNSS vertical displacement. Second, the DWLIM-based and traditional GNSS-derived TWSA methods were utilized to derive TWSA over mainland China. Furthermore, the TWSA results were compared with the TWSA solutions of the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) model. The results indicate that the maximum Pearson’s correlation coefficient (PCC), Nash–Sutcliffe efficiency (NSE) coefficient, and root mean square error (RMSE) equal 0.81, 0.61, and 2.18 cm, respectively. The accuracy of DWLIM was higher than that of the traditional GNSS inversion method according to PCC, NSE, and RMSE, which were increased by 67.11, 128.15, and 22.75%. The inversion strategy of DWLIM can effectively improve the accuracy of TWSA inversion in regions with unevenly distributed GNSS stations. Third, this study investigated the variation characteristics of TWSA based on DWLIM in 10 river basins over mainland China. The analysis shows that the TWSA amplitudes of Songhua and Liaohe River basins are significantly higher than those of the other basins. Moreover, TWSA sequences in each river basin contain annual seasonal signals, and the wave peaks of TWSA estimates emerge between June and July. Overall, DWLIM provides a useful measure to derive TWSA in regions where GNSS stations are uneven or sparse.

/pdf/improving-the-inversion-accuracy-of-terrestrial-water-dq8xn6id.pdf

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

Obtaining high-resolution products that can accurately estimate the spatiotemporal changes in regional water resources is essential for the rational utilization of water resources. The temporal gravity field model derived from the Gravity Recovery and Climate Experiment (GRACE) satellite can effectively monitor the regional terrestrial water storage (TWS) changes. Nevertheless, the main of GRACE products is their coarse spatial resolution, which limits their applicability to small-scale regions. Herein, we propose a geographically weighted regression downscaled model (GWRDM) to improve the spatial resolution of TWS from 0.5° to 0.1° and validate it against the downscaled results provided by the spatial global regression downscaled model (SGRDM) and temporal grid regression downscaled model (TGRDM). The results show that the downscaled products based on GWRDM outperform those based on SGRDM and TGRDM. The GWRDM not only effectively improves the spatial resolution of GRACE products but also maintains the intensity of the original signal. Similarly, the GWRDM is further used to downscale groundwater storage (GWS) and validated against the in situ observations. The downscaled GWS based on GWRDM agrees well with in situ observations. On the monthly scale, the average correlation coefficient (CC) for both is 0.53 and above 0.70 for some in situ observations. On the annual scale, 81.25% and 37.50% of the in situ observations have CC values larger than 0.60 and 0.90, respectively, which further verifies the reliability of GWRDM. Consequently, the GWRDM provides a practical algorithm for obtaining high-resolution water storage estimates, which is expected to provide a reference for small-scale regional water resources management.

https://doi.org/10.1109/jstars.2023.3272916

Improving Spatial Resolution of GRACE-Derived Water Storage Changes Based on Geographically Weighted Regression Downscaled Model

ABSTRACT Terrestrial water storage (TWS) is a fundamental component of the global hydrological cycle. Understanding changes in TWS is essential for assessing the characteristics of drought and flood events in a region. However, the discontinuity between Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) severely affects and limits their applicability in long-term hydrological studies. In this study, a new variable filter reconstruction model (NVFRM) is proposed to reconstruct the TWS anomaly (TWSA) by combining the Gamma test (GT) and Long short-term memory (LSTM) neural network. The research findings reveal that the TWSA reconstructed by the NVFRM is consistent with the GRACE-FO during the testing period (June 2018–December 2020). It shows that the Pearson correlation coefficient (PR), Nash-Sutcliffe efficiency coefficient (NSE), and root mean square error (RMSE) are 0.91, 0.81, and 4.90 cm, respectively, which demonstrates the reliability of the NVFRM. More importantly, four drought events are identified by the drought severity index (DSI) derived from the reconstructed TWSA, including August 2003–April 20 April 200420 April 200411–September 20 June 2011,20 June 201118–September 2018, and September 2019–December 2019, respectively. Flood events are monitored based on the flood potential index (FPI), which is calculated by using the reconstructed TWSA and precipitation. Flood events in the Poyang Lake Basin are characterized by a high FPI index of 0.58, 0.52 and 0.32 in summer (June–August), indicating a greater probability of flooding. The NVFRM model proposed in this study provides an effective measure for filling the missing TWSA in regional hydrological research, which is of great significance for monitoring extreme hydrological events and facilitating disaster prevention and mitigation.

Improving the accuracy of reconstructed terrestrial water storage during the GRACE/GRACE-FO gap based on a new variable filter reconstruction model

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Papers

Monitoring the Thaw Slump-Derived Thermokarst in the Qinghai-Tibet Plateau Using Satellite SAR Interferometry

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

Improving Spatial Resolution of GRACE-Derived Water Storage Changes Based on Geographically Weighted Regression Downscaled Model

Improving the accuracy of reconstructed terrestrial water storage during the GRACE/GRACE-FO gap based on a new variable filter reconstruction model