Plain Language Summary
Rock glaciers are debris-ice landforms and indicators of the status of perennially frozen ground, as known as permafrost, which is warming and thawing under climate change. The West Kunlun is among the driest mountain ranges in Asia where permafrost has been changing over the past decades and the information of rock glaciers is completely lacking. In this paper, we developed an effective workflow for mapping rock glaciers in a semi-automated manner and characterized their geomorphology and kinematics. The compiled dataset allows further investigation on rock glaciers for multiple scientific motivations such as geohazard management, water resource assessment, and permafrost change monitoring. The documented geomorphic characteristics provide insights into the genesis and evolution of rock glaciers in the arid mountains.
1 Introduction
Rock glaciers are debris-ice landforms widely distributed in areas of mountain permafrost globally (Ballantyne 2018). Rock glaciers have drawn a lot of research interest since their first identification at the beginning of the 20th century (Capps 1910), because they serve as visible indicators for alpine permafrost which is defined by its underground temperature and has been warming and undergoing degradation (Barsch 1996; Biskaborn et al. 2019). Inventorying rock glaciers is therefore motivated by producing baseline knowledge for addressing various scientific questions associated with alpine permafrost, such as indicating permafrost occurrence through the rock glacier distribution, characterizing permafrost changes in the warming climate, and assessing the future hydrological significance of rock glaciers. Several studies have revealed that multi-annual acceleration of rock glaciers is synchronous with the rise of air and ground temperatures (Haeberli et al. 2006; Delaloye et al. 2010; Delaloye et al. 2013; Sorg et al. 2015; Marcer et al. 2021), and their short-term velocity variations are sensitive to the pore pressure in the shear horizon which is adjusted by the precipitation and snow melt conditions (Ikeda et al. 2008; Müller et al. 2016; Wirz et al. 2016; Cicoira et al. 2019a; Cicoira et al. 2019b; Kenner et al. 2019). Hence rock glacier inventories are valuable databases for studying how climatic factors cause permafrost changes manifesting in landform kinematics which can be quantified continuously and remotely. Moreover, rock glaciers can contain massive amounts of ground ice and contribute significantly to hydrological systems in some catchments, such as the Andes, Himalayas, and Sierra Nevada (Azócar and Brenning 2010; Millar et al. 2013; Geiger et al. 2014; Jones et al. 2018; Schaffer et al. 2019; Jones et al. 2021). A comprehensive inventory of rock glaciers lays the foundation for estimating the potential water storage and evaluating their future role in maintaining water supplies.
Numerous efforts have been put into inventorying rock glaciers in various mountain ranges worldwide in the past several decades, such as in Central Europe (Chueca 1992; Roer and Nyenhuis 2007; Scotti et al. 2013; Onaca et al. 2017), South America (Brenning 2005; Falaschi et al. 2014; Rangecroft et al. 2014; Villarroel et al. 2018), and North America (Ellis and Calkin 1979; Janke 2007; Millar and Westfall 2008; Liu et al. 2013). Rock glaciers are abundant in mountainous western China where a vast area of alpine permafrost is underlying and undergoing accelerated degradation in response to the warming climate (Yang et al. 2010; Cheng et al. 2019; Yang et al. 2019; Yao et al. 2019; Zhao and Sheng 2019; Ni et al. 2020; Zhao et al. 2020; IPCC 2021). However, few regional-scale inventories of rock glaciers have been compiled until recently (Schmid et al. 2015; Wang et al. 2017; Ran and Liu 2018), which hinders rock glaciers functioning as a permafrost indicator. Such lack of knowledge is attributed to the following reasons: (1) rock glaciers in western China are mostly situated in remote and harsh environment where early in situ investigations are scarce and limited to case studies or small catchment-scale research (e.g., Cui 1985; Cui and Zhu 1988; Zhu et al. 1996; Harris et al. 1998); (2) mapping rock glaciers conventionally relies on manually detecting and outlining the landforms from optical images (Schmid et al. 2015), which is labor-intensive to apply to large permafrost region (e.g., West Kunlun Mountains) following an exhaustive strategy; (3) contentious opinions of identifying rock glaciers exist due to the complexity of the landforms (Harris et al. 1998; Berthling 2011; Hu et al. 2021), which obscures the definition of rock glaciers and makes it challenging to recognize the landforms.
To address these problems, recent research progress in compiling rock glacier inventories includes (1) integrating InSAR techniques to facilitate active rock glacier identification and kinematics quantification (e.g., Liu et al. 2013; Barboux et al. 2014; Wang et al. 2017; Cai et al. 2021; Reinosch et al. 2021; Zhang et al. 2021); (2) implementing Convolutional Neural Networks (CNN) to demonstrate the feasibility of automating rock glacier delineation (Robson et al. 2020) or to improve the consistency of existing rock glacier inventories (Erharter et al. 2022); and (3) establishing widely accepted inventorying guidelines by the international rock glacier research community (RGIK, 2021).
Here we combine the InSAR technique and a state-of-the-art deep learning network, namely DeepLabv3+ (Chen et al. 2018), to map rock glaciers across the West Kunlun Mountains of China where widespread permafrost is warming (Li 1986; Cheng et al. 2019), and knowledge of rock glaciers is completely lacking. Manual delineation of rock glaciers based on InSAR and high-resolution optical imagery in this study is guided by the baseline concepts proposed by the International Permafrost Association (IPA) Action Group on rock glaciers to ensure a standard high-quality dataset utilized to train the deep learning network, and thus, the final mapping results (RGIK, 2021). We adopted the deep learning method to improve the mapping efficiency by automating the identification and delineation tasks, and more importantly, to generate a more comprehensive geodatabase by overcoming the limitations of InSAR-based method (Cai et al., 2021).
This study aims to develop an automated approach to map rock glaciers on a regional scale in western China, i.e., the West Kunlun Mountains. By producing the first automatically mapped inventory at the mountain-range scale, we demonstrate the effectiveness of using a deep-learning-based method to delineate rock glaciers in a consistent manner across the vast study area. We provide essential attributes to the mapped landforms according to the inventorying guidelines. We also conduct statistical analyses to summarize the spatial distribution and geomorphologic characteristics of the mapped rock glaciers. The compiled inventory will provide baseline knowledge for conducting long-term studies of rock glaciers and permafrost in a changing climate.
2 Study area
The West Kunlun is a major mountain range situated in the northwest of Tibetan Plateau, extending ~800 km from the eastern margin of Pamir Plateau to the Keriya Pass of Kunlun Mountains, with a total study area of ~124,000 km2(74–81.5°E, 35–39.5°N) (Figure 1). The elevation of the study region ranges between 3,000 m and 7,500 m.
Across the vast study area, a cold desert climate (Köppen climate classification BWk) is dominant (Peel et al. 2007). Climatic conditions of the western part are revealed by the record of the nearest meteorological station in Tashikurgan (75.23°E, 37.77°N; 3090 m a.s.l.) during 1957–2017: the mean annual air temperature (MAAT) and mean annual accumulated precipitation are 4.2°C and 51 mm, respectively (data source: China Meteorological Administration,http://data.cma.cn/ ). The study area has been warming at a rate of ~0.033°C/yr during the past six decades, similar to the average warming rate (0.031°C/yr) across the entire plateau (Zhang et al. 2020). In the eastern part, the MAAT is -6 °C and the annual precipitation is 103.3 mm, as reported by the Tianshuihai meteorological station (79.55°E, 35.36°N; 4844 m a.s.l) from 2015 to 2018 (Zhao et al. 2021).
The easternmost part of the study region is overlapped with the West Kunlun permafrost survey area (78.8–81.4°E, 34.5–36.0°N; 4,200–6,100 m a.s.l.) established by the Cryosphere Research Station (CRS) on the Qinghai-Tibet Plateau, Chinese Academy of Sciences, where in situ observations are available to represent the state of permafrost in the West Kunlun. Ice-rich permafrost is widely distributed in the survey area (Zhao and Sheng, 2019). The mean annual ground temperature (MAGT) is higher than -2.7°C as revealed by borehole measurements and permafrost was warming at an average rate of 0.11°C/10 yr from 2010 to 2017 (Cheng et al. 2019; Zhao and Sheng, 2019). The lowest altitudinal limit of permafrost occurrence is between 4,650 m and 4,800 m depending on different slope aspects according to previous field surveys focusing on a subregion of the West Kunlun (Li et al. 2012).