Remote sensing spatiotemporal patterns of frozen soil and the environmental controls over the Tibetan Plateau during 2002–2016
Introduction
Frozen soil, including the seasonally frozen ground and permafrost, occupies more than 50% of the exposed land surface in the Northern Hemisphere (Zhang et al., 1999). The freezing/thawing processes periodically change the hydrothermal properties of ground soil, which exert influences on the lower atmosphere (Slater et al., 1998; Cheng and Wu, 2007), hydrological cycle (Wang, 1990; Wang et al., 2009), and ecosystem functioning (Grosse et al., 2016). Over the past decades, climate change, characterized by persistent warming, has incurred large impacts on the global cryosphere, including the Tibetan Plateau (TP) (Biskaborn et al., 2019; Cheng and Wu, 2007; Ding et al., 2019). Observations have indicated considerable degradations of permafrost and seasonally frozen ground on the TP since the 1980s (Wu and Zhang, 2010; Wu et al., 2015). The degradation of frozen soil is further accompanied with other environmental issues, such as the decline of water table, the drying of near-surface soil, and the reduction in soil carbon and nitrogen stocks (Baumann et al., 2009; Zhang et al., 2004; Zhou et al., 2000). In this light, a better understanding of the spatial and temporal changes of frozen soil and the driving mechanisms is imperative to achieve sustainable water and ecosystem management over the plateau region (Karjalainen et al., 2019).
Due to the harsh environment and complex landscape, in situ observations of frozen soil on the TP are extremely limited, which precludes the understanding of frozen soil distributions and changes over the entire plateau (Wang et al., 2006; Wu and Zhang, 2010; Wu et al., 2013; Yang et al., 2008). In comparison, satellite remote sensing provides an unprecedented opportunity to monitor the spatially continuous land surface information across large geographic extents, which has been proven to be a useful tool for monitoring frozen soil across the globe (Brucker et al., 2014; Li et al., 2015; Kim et al., 2017; Obu et al., 2019; Roy et al., 2015; Rautiainen et al., 2016). Compared with Interferometric Synthetic-Aperture Radar (InSAR) that usually has a coarse temporal resolution (typically longer than 5 d; Bianchini et al., 2018) and passive microwave remote sensing that often contains a coarse spatial resolution (typically coarser than 10 km; Lyu et al., 2018), the thermal-band remote sensing data has both shorter temporal intervals and finer spatial resolutions (e.g., 1 km and 12 h for MODIS land surface temperature; LST) that are more appropriate for capturing fine-scale variations of frozen soil properties in mountainous regions and has hence attracted increasing research interests in recent years (Ran and Li, 2019; Zheng et al., 2019). For example, remotely-sensed LST has been incorporated into statistical/empirical and process-based models to retrieve frozen soil properties (e.g., ground temperature, permafrost area, seasonally frozen depth, and active layer thickness) in many previous studies and obtained reasonable accuracies (Langer et al., 2013; Obu et al., 2019; Shi et al., 2018; Westermann et al., 2015; Zou et al., 2017; Yi et al., 2018, Yi et al., 2019; Zheng et al., 2019). Compared with statistical/empirical methods, which are generally more computationally efficient, process-based models have a more solid physical base and are able to simulate key relevant processes (Karjalainen et al., 2019; Wu et al., 2018). Additionally, process-based models are less contingent on ground observations for model calibration and thus often have a better transferability to different regions (e.g., Peng et al. (2017) vs Gao et al. (2018)).
In a previous study, Zheng et al. (2019) proposed a fully remote sensing-driven, process-based model for frozen soil simulation (i.e., geomorphology based ecohydrological model-remote sensing, GBEHM-RS) and tested the model in a mountainous region (~105 km2) in the northeast TP. This study will extend the study of Zheng et al. (2019) to the entire TP (~106 km2), which features a strong elevation gradient and expands over multiple climatic (i.e., arid, semi-arid, sub-humid, and humid) and ecological (i.e., forest, shrubland, grassland, alpine meadow, and desert) zones. Compared with other remote sensing-based models developed for the pan-arctic region (e.g., Langer et al., 2013; Yi et al., 2018), GBEHM-RS is superior in its ability to couple soil water-heat dynamics (Zheng et al., 2019), which is essential for regions where soil moisture presents large variations both through time and across space (Westermann et al., 2016), and might be more suitable for regions with complex climate and landscape, such as the TP. Additionally, Zheng et al. (2019) mainly evaluated the performance of GBEHM-RS over seasonally frozen ground. However, the model performance in simulating permafrost and temporal frozen soil changes are still not validated.
In addition to knowledge on the spatial and temporal patterns of frozen soil, it is also of great importance to understand the driving factors that lead to these patterns (Karjalainen et al., 2019; Smith and Riseborough, 2002). In theory, frozen soil conditions are primarily affected by large-scale climatic forcings (i.e., precipitation and temperature) and mediated by several local factors (e.g., vegetation, topography, and soil texture) (Ding et al., 2019; Shur and Jorgenson, 2007; Wu et al., 2015; Yang et al., 2008; Yin et al., 2017). In terms of the spatial patterns, temperature and precipitation dominantly control long-term heat and water distributions that determine the large-scale distributions of ground thermal regimes (e.g., Westermann et al., 2015), while local factors add further fine-scale heterogeneities to frozen soil distribution (Karjalainen et al., 2019). On the one hand, temperature is directly linked with ground thermal status; on the other hand, precipitation, both as rainfall (Pliquid) and snowfall (Psolid), impacts on the soil freezing/thawing processes through changing soil water movement (Kane et al., 2002; Luthin and Guymon, 1974), changing soil thermal properties (e.g., phase-change heat, heat capacity, and thermal conductivities; Hinkel et al., 2001; Wen et al., 2014), and mediating the heat exchange between the land and the atmosphere due to the insulation effect of the snow layer (Hardy et al., 2001; Stieglitz et al., 2003; Yang et al., 2008; Zhang, 2005), respectively. For temporal changes, changes in climate conditions are presumably the only factors that could lead to evident changes in frozen soil (Ran et al., 2018), as topography and soil texture are generally stable at the climatic time scale and vegetation often co-varies with climate (Zhong et al., 2010). Unfortunately, despite its importance for predicting future frozen soil conditions under climate change, the driving mechanisms underlying the spatial and temporal dynamics of frozen soil over the TP region is still largely unknown.
Therefore, the objectives of this study were to (i) apply GBEHM-RS to simulate frozen soil across the entire TP and comprehensively evaluate the model performance in the region, (ii) map the spatial patterns of frozen soil over the TP and identify the relative importance of relevant drivers leading to these patterns, and (iii) examine the temporal changes of frozen soil over the TP during 2002–2016 and quantify the contributions of the controlling factors.
Section snippets
Study area
The TP region with an elevation higher than 2000 m a.s.l (see Fig. 1) is our study area, which locates between 70°–105°E and 25°–40°N and covers a total area of 3.35 million km2 (including the glaciers and lakes). The TP has a typical cold and semiarid climate and a complex landscape (Supplementary Figs. S1 and S2; Lu et al., 2017). From southeast to northwest, the vegetation type changes from shrub, alpine meadow/grassland, to alpine steppe/desert (Feng et al., 2019), the mean annual LST
The GBEHM-RS
The GBEHM-RS is used to simulate the spatial patterns and temporal changes of frozen soil over the TP during 2002–2016. Here we provide a brief introduction of GEBHM-RS and more details on the model description and its parameterizations can be found in Zheng et al. (2019) and Gao et al. (2018). The GBEHM-RS is a remote sensing version of the geomorphology based ecohydrological model (GBEHM), which is a distributed ecohydrological model designed for simulating the coupled interactions between
Spatial distributions of seasonally frozen ground and permafrost
In this study, permafrost is defined as the area with Tsoil at any depth between 0 and 70 m deep remaining at or below the freezing point (0 °C) for at least two consecutive years, otherwise, the ground is identified as seasonally frozen ground (with annually freezing and thawing) or unfrozen ground (with Tsoil remaining positive for the entire period) (van Everdigen, 1998). Fig. 3 depicts the mean spatial distribution of permafrost and seasonally frozen ground during 2002–2016, with each
Discussion
The comprehensive validations against ground measurements of frozen ground types, MAGT, ALT, Tsoil, and Df approve the reliability of using process-based model driven by satellite data to reproduce the ground thermal regime of permafrost and seasonally frozen ground in the TP region (Fig. 3, Fig. 4, and Supplementary Figs. S7–S10). Using GBEHM-RS, we quantified the spatiotemporal changes of frozen soil over the TP since the beginning of the 21st century. Overall, our simulated spatial patterns
Conclusion
In this study, a process-based, satellite-driven model (GBEHM-RS) is employed for frozen soil simulation at an unprecedented high spatial resolution (1 × 1 km) over the TP region with complex climate, topography, and landscape conditions. Following comprehensive model validations, the spatial and temporal patterns of frozen soil during 2002–2016 are quantified and the driving mechanisms are investigated. Major conclusions are summarized below:
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Validated against observations at 608 boreholes,
Acknowledgments
This research was supported by grants from the National Natural Science Foundation of China (Grant No. 41630856 and 41890821). Y. Yang acknowledges support from the Department of Science and Technology, Qinghai, China (Grant No. 2019-SF-A4). D. Chen acknowledges supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060401) and the Swedish VR (2017-03780). We thank Jingfeng Wang at Georgia Institute of Technology, Hong-yi Li at the University of Houston, and
Declaration of interests
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.
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This work was done as a private venture and not in the author's capacity as an employee of the Jet Propulsion Laboratory, California Institute of Technology.