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.

Long-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existing satellite SM data are only available at coarse scale (+/- 25 km) and suffer a lot from data gaps due to satellite orbit coverage and snow cover, especially on the QTP. Although substantial efforts have been devoted to downscale SM utilizing multiple soil moisture indices (SMIs) or diverse machine learning (ML) methods, the potentials of different SMIs and ML approaches in SM downscaling on the complex plateau remain unclear, and there is still a necessity to obtain an accurate, long-term, high-resolution and seamless SM data over the QTP. To address this issue, this study generated the long-term, high-accuracy and seamless soil moisture dataset (LHS-SM) over the QTP during 2001-2020 using a two-step downscaling method (first downscaling then merging). Firstly, the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1 km utilizing five ML approaches. Then, a dynamic data merging method that considers spatiotemporal nonstationary error was applied to derive the final LHS-SM data. The performance of fifteen SMIs was also assessed and the optimal indexes for downscaling were identified. Results indicated that the shortwave infrared band-based indices had better performance than the near infrared band-based and energy-based indices. The generated LHS-SM data exhibited satisfying accuracy (mean R = 0.52, ubRMSE = 0.047 m(3)/m(3)) and certain improvement to the ESA CCI SM data both at station and network scales. Compared with existing 1 km SM datasets, the LHS-SM data also showed the best performance (mean R = 0.62, ubRMSE = 0.047 m(3)/m(3)), while existing datasets either failed to fully characterize the spatial details or had some data gaps and unreasonable distributions. Strong spatial heterogeneity was observed in the SM dynamics during 2001-2020 with the southwest and northeast showing a dry gets wetter scheme and the southeast presenting a wet gets drier trend. Overall, the LHS-SM dataset gained its added values by compensating the drawbacks of existing 1 km SM products over the QTP and was much valuable for many regional applications.

Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m$3$3 m${-3}$-3) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the $\tau -\omega$tau-omega model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m$3$3 m${-3}$-3 and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.

Background and aimsUnderstanding of the influences of soil moisture changes on plant phenological shifts on the Qinghai-Tibetan Plateau (QTP) is insufficient mainly because previous studies focused on the climatic factors. We explored the role of soil moisture in regulating plant autumn phenology on the QTP.MethodsBased on long-term ground observations of soil moisture, plant phenology, and meteorology, temporal and spatial changes in soil moisture and leaf senescence dates (LSD) were analyzed using ordinary least squares regression and a meta-analysis procedure. Influences of soil moisture changes on the LSD shifts were assessed through correlation analysis and support vector machine, and also compared with those of air temperature and precipitation.ResultsNonsignificant interannual changes in soil moisture were observed, and LSD significantly delayed at a rate of 2.7 days/decade. Spatial changes of LSD were more correlated with site elevation and air temperature, and soil moisture and precipitation showed insignificant negative impacts. However, correlations between annual LSD and average soil moisture were mainly positive. Soil moisture and precipitation showed greater importance in regulating the LSD of sedges and grasses, whereas temperature exerted a larger influence on the LSD of forbs. Precipitation showed higher importance in regulating the interannual shifts in LSD, while temperature played a more important role in determining the spatial variations.ConclusionSoil moisture had divergent influences on the temporal and spatial shifts in LSD of different plant functional groups on the QTP. Overall, soil moisture was outweighed by temperature and precipitation in regulating autumn phenological shifts. However, soil moisture may become increasingly important in the future and forbs are expected to be more competitive if the QTP becomes warmer and drier, which will bring challenges in grassland management and utilization on the QTP.

Monitoring and modelling surface deformation are crucial components of understanding the freeze-thaw process and preventing disasters in permafrost regions. However, previous methods had limitations that inhibited the interpretation of freeze-thaw deformation, such as a lack of physical meaning, an inability to reflect the physical freeze-thaw process and consideration of only a single external factor's impact on permafrost deformation. This study proposes an improved degree-day model (IDM) for quantitatively isolating surface deformation using interferometric synthetic aperture radar (InSAR) technology over permafrost. We considered the effect of soil moisture variation on permafrost deformation and incorporated interannual variation in the freeze-thaw process due to climate change. By applying small baseline subset (SBAS) technology to Sentinel-1 InSAR measurements over the Wudaoliang permafrost region on the Qinghai-Tibet Plateau from 2018 to 2019, we estimated long-term and seasonal permafrost deformation. The reliability of InSAR results was validated using in situ measurements, with root mean square errors (RMSEs) less than 10 mm. The results showed that the average linear deformation rates in 2018 and 2019 were -3.8 mm a-1 and -11.0 mm a-1, respectively, and the maximum seasonal deformations were 15.7 mm and 13.2 mm, respectively. Compared with the original degree-day model (ODM), the method used in this study produced smaller residual deformations of 6.9 mm and 6.4 mm, highlighting its ability to improve a quantitative description of permafrost deformation.

Generally, with increasing elevation, there is a corresponding decrease in annual mean air and soil temperatures, resulting in an overall decrease in ecosystem carbon dioxide (CO2) exchange. However, there is a lack of knowledge on the variations in CO2 exchange along elevation gradients in tundra ecosystems. Aiming to quantify CO2 exchange along elevation gradients in tundra ecosystems, we measured ecosystem CO2 exchange in the peak growing season along an elevation gradient (9-387 m above sea level, m.a.s.l) in an arctic heath tundra, West Greenland. We also performed an ex-situ incubation experiment based on soil samples collected along the elevation gradient, to assess the sensitivity of soil respiration to changes in temperature and soil moisture. There was no apparent temperature gradient along the elevation gradient, with the lowest air and soil temperatures at the second lowest elevation site (83 m). The lowest elevation site exhibited the highest net ecosystem exchange (NEE), ecosystem respiration (ER) and gross ecosystem production (GEP) rates, while the other three sites generally showed intercomparable CO2 exchange rates. Topography aspect-induced soil microclimate differences rather than the elevation were the primary drivers for the soil nutrient status and ecosystem CO2 exchange. The temperature sensitivity of soil respiration above 0 degrees C increased with elevation, while elevation did not regulate the temperature sensitivity below 0 degrees C or the moisture sensitivity. Soil total nitrogen, carbon, and ammonium contents were the controls of temperature sensitivity below 0 degrees C. Overall, our results emphasize the significance of considering elevation and microclimate when predicting the response of CO2 balance to climate change or upscaling to regional scales, particularly during the growing season. However, outside the growing season, other factors such as soil nutrient dynamics, play a more influential role in driving ecosystem CO2 fluxes. To accurately upscale or predict annual CO2 fluxes in arctic tundra regions, it is crucial to incorporate elevation-specific microclimate conditions into ecosystem models.

Driven by human activities and global climate change, the climate on the Qinghai-Xizang Plateau is experiencing a warming and humidifying trend. It significantly impacts the thermal-moisture dynamics in the active layer of the permafrost, which in turn affects the ecological environment of cold regions and the stability of cold region engineering. While the effect of air temperature on permafrost thaw has been well quantified, the processes and mechanisms behind the thermal-moisture response of the permafrost under the combined influence of increased rainfall and rising air temperature remain contentious and largely unknown. A coupled model was applied to quantify the impacts of increased rainfall, rising air temperature, and their compound effects on the thermal-moisture dynamics in the active layer, considering the sensible heat of rainwater in the ground surface energy balance and water balance process. The results indicate that the compound effect of warming and humidifying resulted in a significant increase in surface net radiation and evaporation latent heat, a more significant decrease in surface sensible heat, and a smaller impact of rainfall sensible heat, leading to an increase in surface soil heat flux. The compound effect of warming and humidifying leads to a significant increase in the liquid water flux with temperature gradient. The increase in liquid water flux due to the temperature gradient is larger than that of warming alone but smaller than the effect of humidifying alone. Warming and humidifying result in a smaller increase in soil moisture content during the warm season compared to rainfall increases alone. The thermal conductivity heat flux in the active layer increases significantly during the cold season but less than the effect of warming alone. The convective heat flux of liquid water flux increases noticeably during the warm season but less than the effect of rainfall increases alone. Increased rainfall significantly cools the soil during the warm season, while both warming and humidifying lead to a more pronounced warming effect on the soil during the cold season than during the warm season. An increase in the average annual temperature by 1.0 degrees C leads to a downward shift of the permafrost table by 10 cm, while an increase in rainfall by 100 mm causes an upward shift of the permafrost table by 8 cm. The combined effect of warming and humidifying results in a downward shift of the permafrost table by 6 cm. Under the influence of climate warming and humidifying, the cooling effect of increased rainfall on permafrost is relatively small, and the warming effect of increased temperature still dominates.

Introduction: Permafrost and seasonally frozen soil are widely distributed on the Qinghai-Tibetan Plateau, and the freezing-thawing cycle can lead to frequent phase changes in soil water, which can have important impacts on ecosystems.Methods: To understand the process of soil freezing-thawing and to lay the foundation for grassland ecosystems to cope with complex climate change, this study analyzed and investigated the hydrothermal data of Xainza Station on the Northern Tibet from November 2019 to October 2021.Results and Discussion: The results showed that the fluctuation of soil temperature showed a cyclical variation similar to a sine (cosine) curve; the deep soil temperature change was not as drastic as that of the shallow soil, and the shallow soil had the largest monthly mean temperature in September and the smallest monthly mean temperature in January. The soil water content curve was U-shaped; with increased soil depth, the maximum and minimum values of soil water content had a certain lag compared to that of the shallow soil. The daily freezing-thawing of the soil lasted 179 and 198 days and the freezing-thawing process can be roughly divided into the initial freezing period (November), the stable freezing period (December-early February), the early ablation period (mid-February to March), and the later ablation period (March-end of April), except for the latter period when the average temperature of the soil increased with the increase in depth. The trend of water content change with depth at all stages of freezing-thawing was consistent, and negative soil temperature was one of the key factors affecting soil moisture. This study is important for further understanding of hydrothermal coupling and the mechanism of the soil freezing-thawing process.

Satellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost-vegetation-climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near-surface air temperature in summer (June-August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude- and biome-dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (similar to 30% of climatic signal) on SSO with varying elevation- and canopy height-dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections. Climate change has caused substantial vegetation growth that was detected by satellite observations (greening) over northern permafrost regions. However, the consequences or feedbacks of vegetation greening remain largely unknown, hindering the understanding of near-surface thermal dynamics and bringing considerable uncertainty in model projections. Here we aimed to decipher the biophysical impact of permafrost greening on the summer surface offset (SSO), which is an indicator of permafrost degradation. We found latitude- and biome-dependent patterns of SSO changes and divergent responses of SSO to greening. Increases in satellite-observed leaf area index lead to higher SSO in relatively cold and wet areas but lower SSO in warm-dry regions. Biophysical mechanisms associated with surface albedo, evapotranspiration, and SM can help explain various effects of greening on SSO. Our results highlight greening feedbacks on the thermal dynamics of permafrost with climate warming, calling for the improvement of current projections. Vegetation greening impacts the thermal dynamics of permafrost surface Biophysical effects of greening on surface offset could be related to surface albedo, evapotranspiration, and soil moisture

Changes are projected for the boreal biome with complex and variable effects on forest vegetation including drought-induced tree mortality and forest loss. With soil and atmospheric conditions governing drought intensity, specific drivers of trees water stress can be difficult to disentangle across temporal scales. We used wavelet analysis and causality detection to identify potential environmental controls (evapotranspiration, soil moisture, rainfall, vapor pressure deficit, air temperature and photosynthetically active radiation) on daily tree water deficit and on longer periods of tree dehydration in black spruce and tamarack. Daily tree water deficit was controlled by photosynthetically active radiation, vapor pressure deficit, and air temperature, causing greater stand evapotranspiration. Prolonged periods of tree water deficit (multi-day) were regulated by photosynthetically active radiation and soil moisture. We provide empirical evidence that continued warming and drying will cause short-term increases in black spruce and tamarack transpiration, but greater drought stress with reduced soil water availability. This research explores how climate change could impact the water stress experienced by black spruce and tamarack trees in the western boreal forest of Canada. We focused on a key measure called tree water deficit to understand if the trees were under stress due to insufficient water. We examined how tree water deficit relates to environmental factors such as temperature, sunlight, and soil moisture. The findings revealed that, on a daily basis, factors like sunlight and temperature cause trees to release more water into the air. However, over longer periods (days to weeks), the amount of water in the soil becomes crucial, suggesting that trees might face water stress during dry spells. So, while trees could grow more on hotter, sunnier days, they could also experience water stress and reduced growth if the soil becomes too dry for an extended period. This study helps us grasp how various factors interact to influence tree water stress in the boreal forest, providing insights important for managing these ecosystems in a changing climate. A novel approach to determine environmental controls of tree water deficit across time scales with wavelet analysis and Granger causality Soil moisture emerges as a significant control of tree water deficit in boreal trees at longer scales (multi-days) Daily productivity gains with warming will be mitigated by decreased soil water availability in longer periods of tree water deficit