Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

Alpine vegetation, cold deserts, and glacial landscapes significantly impact runoff generation and convergence in cold and alpine regions. The presence of existing mountain permafrost complicates these impacts further. To better understand the specific regulation of runoff by alpine landscapes, we analyzed the spatiotemporal capacity for runoff generation and the contributions of water from different landscape types within a typical alpine permafrost watershed: the upper reaches of the Shule River (USR) basin in the Qinghai-Tibet Plateau. The analysis was informed by both field observations and simulations using the VIC model, which incorporated a new glacier module. We identified that glaciers, alpine meadows, cold deserts, and barren landscape zones as the four major runoff generation regions, collectively accounting for approximately 95 % of the USR runoff. The runoff depth in each landscape zone was calculated to express its runoff generation capacity, with an order of: glacier > cold desert > barren > alpine grassland > alpine meadow > shrub > swamp meadow. The alpine regions above 4000 m in altitude are the primary runoff generation areas, and the runoff generation capacity gradually decreases from high to low altitudes in the alpine basin. Due to seasonal variations in rainfall distribution, glacier melting, and permafrost thawing-freezing, the dominant landscape types contributing to runoff varied monthly. The simulated results indicate that permafrost plays an important role in runoff generation. Although permafrost degradation had a slight impact on the annual total runoff generated from each landscape zone (not taking into account of ground ice), seasonal runoff generated in each landscape exhibited significant changes in response to permafrost thawing. After permafrost completely thawed in each landscape zone, generated flood flow decreased, while low flow conversely increased, implying an enhanced water retention capacity of alpine landscapes following permafrost degradation. Additionally, the responses of runoff to permafrost changes varied across different alpine landscapes. These findings enhance our understanding of the mechanisms underlying runoff generation and convergence in cold and alpine watersheds of the Northern Hemisphere.

Iron (Fe) minerals possess a huge specific surface area and high adsorption affinity, usually considered as rust tanks of organic carbon (OC), playing an important role in global carbon storage. Microorganisms can change the chemical form of Fe by producing Fe-chelating agents such as side chains and form a stable complex with Fe(III), which makes it easier for microorganisms to use. However, in seasonal frozen soil thawing, the succession of soil Fe-cycling microbial communities and their coupling relationship with Fe oxides and Fe-bound organic carbon (Fe-OC) remains unclear. We characterized changes in the Fe phase, Fe-OC, Fe-oxidizing bacteria (FeOB), and Fe-reducing bacteria (FeRB) in the subsoil and analyzed the microbial mechanism underlying Fe-OC changes in alpine grassland by constructing a composite structural equation model (SEM). We found that the Fe(III) content consistently exceeded that of Fe(II). Among the three types of Fe oxides, organically complex Fe (Fe-p) decreased from 2.54 to 2.30 gkg(-1), whereas the opposite trend was observed for poorly crystalline Fe (Fe-o). The Fe-OC content also decreased (from 10.31 to 9.47 gkg(-1); p < 0.05). Fe-cycling microorganisms were markedly affected by the thawing of frozen soil (except FeRB). Fe-p and Feo directly affected changes in Fe-OC. Soil moisture (SM) and FeOB were significant indirect factors affecting Fe-OC changes. Freeze-thaw changes in the subsoil of alpine grassland in Central Asia significantly affected FeOB and Fe oxides, thus affecting the Fe-OC content. To the best of our knowledge, this was the first study to examine the influence of Fe-cycling microorganisms on the Fe phase and Fe-OC in the soil of alpine grassland in Central Asia. Overall, our findings provide scientific clues for exploring the biogeochemical cycle process in future climate change.

Palsas and peat plateaus occur in various environmental conditions, but their driving environmental factors have not been examined across the Northern Hemisphere with harmonized datasets. Such comparisons can deepen our understanding of these landforms and their response to climate change. We conducted a comparative study between four regions: Hudson Bay, Iceland, Northern Fennoscandia, and Western Siberia by integrating landform observations and geospatial data into a MaxEnt model. Climate and hydrological conditions were identified as primary, yet regionally divergent, factors affecting palsa and peat plateau occurrence. Suitable conditions for these landforms entail specific temperature ranges (500-1500 thawing degree days, 500-4000 freezing degree days), around 300 mm of rainfall, and high soil moisture accumulation potential. Iceland's conditions, in particular, differ due to higher precipitation, a narrower temperature range, and the significance of soil organic carbon content. The annual thermal balance is a critical factor in understanding the occurrence of permafrost peatlands and should be considered when comparing different regions. We conclude that palsas and peat plateaus share similar topographic conditions but occupy varying soil conditions and climatic niches across the Northern Hemisphere. These findings have implications for understanding the climatic sensitivity of permafrost peatlands and identifying potential greenhouse gas emitters.

Snow amounts and duration are susceptible to climate change and may significantly affect plant diversity and biomass in grassland ecosystems. Yet, the combined effects of grassland use (type and intensity) and snow depth on plant diversity and productivity remain poorly understood. We established two complementary field experiments to explore the mechanisms driving the effects of grassland use (type and intensity) and snow manipulation on plant diversity and productivity in the meadow steppe. An experiment on grassland use type and snow manipulation showed that lower snow cover in winter reduced soil moisture in the snowmelt period, significantly increased the abundance of ammonia-oxidizing archaea and ammonia-oxidizing bacteria, and initiated nitrification earlier, resulting in the loss of soil available nitrogen, and then reduced the aboveground biomass of early grasses. An experiment on grassland mowing intensity and snow manipulation showed that moderate mowing intensity can restrain the loss of grass biomass and soil nutrients and maintain grassland sustainability in winters with less snow. Stochasticity has played a more important role in plant community assembly in higher intensity of grassland use. Based on our results, we recommend that optimal defoliation height can restrain the loss of grass biomass and soil nutrients and maintain grassland sustainability in winters with less snow. This study has potential benefits for optimizing sustainable production and maintaining ecosystem function under winter snowfall changes in the future across large regions of arid and semiarid grasslands. (c) 2024 The Society for Range Management. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Due to the impact of climate change, significant alterations in snowmelt have already occurred, which have been demonstrated to play a crucial role in photosynthetic carbon sequestration processes in vegetation. However, the effect of changes in snowmelt on light use efficiency (LUE) of grassland remain largely unknown in the permafrost region of Qinghai-Tibetan Plateau (QTP). By utilizing remote sensing data from 2000 to 2017, we conducted an analysis on the spatial and temporal patterns of LUE for various types of permafrost and grassland on the QTP. The LUE of the growing season was 1.1588 g CMJ(-1), displaying variations among different ecosystems: alpine steppe of seasonally frozen ground (ASS) > alpine meadow of seasonally frozen ground (AMS) > alpine meadow of permafrost (AMP) > alpine steppe of permafrost (ASP). Furthermore, our study demonstrated that decreasing snowmelt during the growing season had a negative impact on LUE through meteorological factors, elucidating its influence on LUE for approximately 40.65%, 34.06%, 41.05%, and 32.68% of ecosystems studied. Reduced snowmelt indirectly affects LUE by lowering air temperatures, vapor pressure deficit and solar radiation, while replenishing soil moisture. Additionally, changes in snowmelt can directly affect LUE by reducing the insulating properties of snow cover. Therefore, when estimating gross primary productivity (GPP) using remote sensing data based on LUE, it is essential to consider the impact of snowmelt. This will better represent vegetation phenology's response to climate change.

Siberia occupies vast areas underlain by permafrost, and understanding its land cover changes is important for ecological environmental protection in a warming climate. Based on the land cover and climate datasets, we analyzed the land cover changes and their drivers in Siberia from 1992 to 2020. The results show that (1) From 1992 to 2020, the areas of evergreen needleleaf trees and deciduous needleleaf trees in Siberia decreased by 9% and 2.5%, and the areas of grassland, shrub, cropland, and construction land increased by 1.5%, 14.2%, 2.8%, and 39.2%, respectively. Cropland expansion had the fastest rate of 1.85% in the continuous permafrost zone, and construction land expansion had the fastest rate of 3.07% in the non-permafrost zone. (2) The center of gravity of agricultural land continues to migrate to the northeast, and the center of gravity of construction land continues to migrate to the southwest. (3) The primary drivers for the land cover changes were temperature and precipitation, and active layer thickness also affected grassland, cropland, and deciduous needleleaf trees. The correlation coefficient between active layer thickness and cropland area is 0.74 in the continuous permafrost zone. The interaction between factors is mostly manifested as a two-factor enhancement, with the highest q-value of the interaction of temperature and precipitation for explanatory power. Our results suggest that climate change and permafrost degradation significantly changed land cover in Siberia. This finding deepens our understanding of the mechanisms of land cover change under the influence of permafrost degradation and provides a new perspective on the land cover changes in permafrost regions.

Hydrologic-land surface models (H-LSMs) offer a physically-based framework for representing and predicting the present and future states of the extensive high-latitude permafrost areas worldwide. Their primary challenge, however, is that soil temperature data are severely limited, and traditional model validation, based only on streamflow, can show the right fit to these data for the wrong reasons. Here, we address this challenge by (1) collecting existing data in various forms including in-situ borehole data and different large-scale permafrost maps in addition to streamflow data, (2) comprehensively evaluating the performance of an H-LSM with a wide range of possible process parametrizations and initializations, and (3) assessing possible trade-offs in model performance in concurrently representing hydrologic and permafrost dynamics, thereby pointing to the possible model deficiencies that require improvement. As a case study, we focus on the sub-arctic Liard River Basin in Canada, which typifies vast northern sporadic and discontinuous permafrost regions. Our findings reveal that different process parameterizations tend to align with different data sources or variables, which largely exhibit inconsistencies among themselves. We further observe that a model may fail to represent permafrost occurrence yet seemingly fit streamflows adequately. Nonetheless, we demonstrate that accurately representing essential permafrost dynamics, including the active soil layer and insulation effects from snow cover and soil organic matter, is crucial for developing high-fidelity models in these regions. Given the complexity of processes and the incompatibility among different data sources/variables, we conclude that employing an ensemble of carefully designed model parameterizations is essential to provide a reliable picture of the current conditions and future spatio-temporal co-evolution of hydrology and permafrost.

By analyzing the last 50-60 years of climate changes in Arctic and Subarctic Yakutia, we have identified three distinct periods of climate development. The cold (1965-1987), pre-warming (1988-2004), and modern warming (2005-2023) periods are clearly identifiable. Yakutia's Arctic and Subarctic regions have experienced mean annual air temperature increases of 2.5 degrees C and 2.2 degrees C, respectively, compared to the cold period. The thawing index rose by an average of 171-214 degrees C-days, while the freezing index dropped by an average of 564-702 degrees C-days. During the pre-warming period, all three characteristics show a minor increase in warmth. Global warming intensified between 2005 and 2023, resulting in elevated permafrost temperatures and a deeper active layer. Monitoring data from the Tiksi site show that warming has been increasing at different depths since the mid-2000s. As a result, the permafrost temperature increased by 1.7 degrees C at a depth of 10 m and by 1.1 degrees C at a depth of 30 m. Soil temperature measurements at meteorological stations and observations at CALM sites both confirm the warming of the permafrost. A permafrost-climatic zoning study was conducted in Arctic and Subarctic Yakutia. Analysis identified seven regions characterized by similar responses to modern global warming. These study results form the foundation for future research on global warming's effects on permafrost and on how northern Yakutia's environment and economy adapt to the changing climate.

Globally, land subsidence (LS) often adversely impacts infrastructure, humans, and the environment. As climate change intensifies the terrestrial hydrologic cycle and severity of climate extremes, the interplay among extremes (e.g., floods, droughts, wildfires, etc.), LS, and their effects must be better understood since LS can alter the impacts of extreme events, and extreme events can drive LS. Furthermore, several processes causing subsidence (e.g., ice-rich permafrost degradation, oxidation of organic matter) have been shown to also release greenhouse gases, accelerating climate change. Our review aims to synthesize these complex relationships, including human activities contributing to LS, and to identify the causes and rates of subsidence across diverse landscapes. We primarily focus on the era of synthetic aperture radar (SAR), which has significantly contributed to advancements in our understanding of ground deformations around the world. Ultimately, we identify gaps and opportunities to aid LS monitoring, mitigation, and adaptation strategies and guide interdisciplinary efforts to further our process-based understanding of subsidence and associated climate feedbacks. We highlight the need to incorporate the interplay of extreme events, LS, and human activities into models, risk and vulnerability assessments, and management practices to develop improved mitigation and adaptation strategies as the global climate warms. Without consideration of such interplay and/or feedback loops, we may underestimate the enhancement of climate change and acceleration of LS across many regions, leaving communities unprepared for their ramifications. Proactive and interdisciplinary efforts should be leveraged to develop strategies and policies that mitigate or reverse anthropogenic LS and climate change impacts.