This paper investigates the spatiotemporal dynamics and their changes of the southern limit of latitudinal permafrost (SLLP) and the lower limit of mountain permafrost (LLMP) in Northeast China, emphasizing the roles of climate change and human activities. Permafrost in this region is primarily distributed in the northern parts of the Da and Xiao Xing'anling mountain ranges and in the upper parts of the Changbai Mountains and at the summits of the Huanggangliang Mountains in the southern part of the Da Xing'anling Mountain Range. Permafrost degradation, ongoing since at least the local Holocene Megathermal Period (8.5-6.0 ka BP), has intermittently reversed during cooler climatic intervals but continues to exert significant impacts on regional environments, infrastructure stability, and carbon storage. Notably, the northward retreats of the SLLP since the mid-19th century underscore the sustained nature of this degradation, especially in southern patchy permafrost zones increasingly sensitive to warming and anthropogenic influences. LLMP variability is similarly shaped by a combination of climatic, hydrometeorological, ecological, and topographic factors. The distributions of SLLP and LLMP are further complicated by the presence of relict and sporadic permafrost, as well as the hydrothermal effects of vegetation and snow cover. Addressing the challenges of mapping and modeling boreal permafrost in Northeast China requires comprehensive field investigations, long-term in situ monitoring via station networks, and advanced numerical modeling. Emerging technologies, including satellite and airborne remote sensing (RS), geographic information systems (GIS), unmanned aerial vehicles (UAVs), surface geophysical methods, and big data analytics, offer new possibilities for enhancing permafrost monitoring and mapping. Integrating these tools with conventional field studies can significantly improve our understanding of permafrost dynamics. Continued efforts in monitoring, technological innovation, multidisciplinary collaboration, and international cooperation are essential to meet the challenges posed by permafrost degradation in a changing climate.

Permafrost, a major component of the cryosphere, is undergoing rapid degradation due to climate change, human activities, and other external disturbances, profoundly impacting ecosystems, hydroclimate, engineering geological stability, and infrastructure. In Northeast China, the thermal dynamics of Xing'an permafrost (XAP) are particularly complex, complicating the accurate assessment of its spatial extent. Many earlier mapping efforts, despite significant progress, fall short in accounting for some key local geo-environmental factors. Thus, this study introduces a new approach that incorporates four key driving factors-biotic, climatic, physiographic, and anthropogenic-by integrating multisource datasets and in situ observations. Four machine learning (ML) models [random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGB)] are applied to simulate permafrost distribution and probability, as well as to evaluate their performance. The results indicate that models' accuracy, ranked from highest to lowest, is as follows: RF (area under the curve (AUC) =0.88 and accuracy =0.81), XGB (0.86 and 0.77), LR (0.81 and 0.73), and SVM (0.76 and 0.66), with RF emerging as the most effective model for permafrost mapping in Northeast China. Analysis of the relationships between predictors and permafrost occurrence probability (POP) indicates that vegetation and snow cover exert nonlinear effects on permafrost, while human activities significantly reduce POP. Additionally, finer soil textures and higher soil organic matter content are positively correlated with increased POP. The modeling results, combined with field survey data, also show that permafrost is more prevalent in lowlands than in uplands, confirming the symbiotic relationship between permafrost and wetlands in Northeast China. This spatial variation is influenced by local microclimates, runoff patterns, and soil thermal properties. The primary sources of model error are uncertainties in the accuracy of multisource datasets at different scales and the reliability of observational data. Overall, ML models demonstrate great potential for mapping permafrost in Northeast China.

Aerosols can alter atmospheric stability through radiative forcing, thereby changing mean and daily extreme precipitation on regional scales. However, it is unclear how extreme sub-daily precipitation responds to aerosol radiative effects. In this study, we use the regional climate model (RCM) Consortium for Small-scale Modeling (COSMO) to perform convection-permitting climate simulations at a kilometer-scale (0.04 degrees/similar to 4.4 km) resolution for the period 2001-2010. By evaluating against the observed hourly precipitation-gauge data, the COSMO model with explicit deep convection can effectively reproduce sub-daily and daily extreme precipitation events, as well as diurnal cycles of summer mean precipitation and wet hour frequency. Moreover, aerosol sensitivity simulations are conducted with sulfate and black carbon aerosol perturbations to assess the direct and semi-direct aerosol effects on extreme sub-daily precipitation in the COSMO model. The destabilizing effects associated with decreased sulfate aerosols intensify extreme sub-daily precipitation, while increased sulfate aerosols tend to induce an opposite change. In contrast, the response of extreme sub-daily precipitation to black carbon aerosol perturbations exhibits a nonlinear behavior and potentially relies on geographical location. Overall, the scaling rates of extreme precipitation intensities decrease and approach the Clausius-Clapeyron rate from hourly to daily time scales, and the responses to sulfate and black carbon aerosols vary with precipitation durations. This study improves the understanding of aerosol radiative effects on sub-daily extreme precipitation events in RCMs.

Salt-alkaline stress is one of the most stressful occurrences, causing negative effects on plant development and agricultural yield. Identifying and utilizing genes that affect alkaline tolerance is an excellent approach to accelerate breeding processes and meet the needs for remediating saline-alkaline soil. Here, we employed a mapping population of 176 recombinant inbred lines (RILs) produced from a cross between alkali-tolerant Longdao5 and alkali-sensitive Zhongyouzao8 to identify the quantitative trait loci (QTLs) determining alkali tolerance at the seedling stage. For the evaluation of alkali tolerance, the recovered seedling's average alkali tolerance index (ATI), root number (RN), root length (RL), seedling dry weight (SW), root dry weight (RW), and seedling height (SH) were assessed, together with their relative alkaline damage rate. Under alkaline stress, the ATI was substantially negative connected with the root number, seedling height, seedling dry weight, and root dry weight; however, it was considerably positive correlated with the relative alkaline damage rate of the root number and root dry weight. A total of 13 QTLs for the root number, root length, seedling height, seedling dry weight, root dry weight, and alkali tolerance index under alkaline stress were identified, which were distributed across chromosomes 1, 2, 3, 4, 5, 7, and 8. All of these QTLs formed two QTL clusters for alkali tolerance on chromosome 5 and chromosome 7, designated AT5 and AT7, respectively. Nine QTLs were identified for the relative alkaline damage rate of the root number, root length, seedling height, seedling dry weight, and root dry weight under alkali stress. These QTLs were located on chromosome 2, 4, 6, 7, 8, 9, and 12. In conclusion, these findings further strengthen our knowledge about rice's genetic mechanisms for alkaline tolerance. This research offers clues to accelerate breeding programs for new alkaline-tolerance rice varieties.

Ecosystems at the southern edge of the permafrost distribution are highly sensitive to global warming. Changes in soil freeze-thaw cycles can influence vegetation growth in permafrost regions. Extant studies mainly focused on analyzing the differences of vegetation dynamics in different permafrost regions. However, the intrinsic drivers of permafrost degradation on vegetation growth remain elusive yet. Based on the top temperature of permafrost (TTOP) model, we simulated the spatial distribution of permafrost in Northeast China (NEC) from 2001 to 2020. Using the data of the vegetation Net Primary Productivity (NPP), vegetation phenology, climate and permafrost phenology, and analytical methods including partial correlation, multiple linear regression, and path analysis, we explored the response of vegetation growth and phenology to soil freeze-thaw changes and climate change under different degrees of permafrost degradation. Overall, the start date of the growing season (SOS) was very sensitive to the start date of soil thaw (SOT) changes, and multiple regression analyses showed that SOT was the main factor influencing SOS in 41.8% of the NEC region. Climatic factors remain the main factors affecting vegetation NPP in NEC, and the results of partial correlation analysis showed that only 9.7% of the regional duration of soil thaw (DOT) had a strong correlation with vegetation NPP. Therefore, we determined the mechanism responsible for the soil freeze-thaw changes and vegetation growth relationship using the path analysis. The results indicated that there is a potential inhibitory effect of persistent permafrost degradation on vegetation growth. Our findings would contribute to the improvement of process-based models of forest dynamics in the boreal region, which would help to plan sustainable development and conservation strategies in permafrost areas.

Identifying the changes in terrestrial water storage is essential for a comprehensive understanding of the regional hydrological mass balance under global climate change. This study used a partial least square regression model to fill the observation gaps between GRACE and GRACE-FO and obtained a complete series of terrestrial water storage anomaly data from April 2002 to December 2020 from southeast China. We investigated the variations in terrestrial water storage anomalies in the region and the influencing factors. The study revealed that terrestrial water storage (TWS) anomalies have been increasing in the region, with an average increase of 0.33 cm/yr (p < 0.01). The intra-annual variation showed a positive anomaly from March to September and a negative anomaly in other months. Terrestrial water storage anomalies increased in most regions (especially in the central and northern parts), whereas they decreased in the southern parts. In terms of the components, the soil moisture storage (SMS) contributes 58.3 % and the surface water storage (SWS, especially reservoirs water storage) contributes 41.4 % to the TWS. The study also found that changes in the precipitation explain approximately 71.7 % of the terrestrial water storage variation, and reservoirs contributes to the remaining 28.3 %. These results are essential for understanding the changes in the hydrological cycle and developing strategies for water management in Southeast China.

Fractures are the main reservoir space in basement weathering crusts and control the development of dissolution/alteration pores. A clear understanding of the main factors controlling fracture formation is needed to accurately predict reservoir characteristics. In this study, the reservoir characteristics along with the vertical zonation and thermal history of basement weathering crust were studied through lithology, mineral identification, porosity and permeability tests, nuclear magnetic resonance (T2), whole-rock analysis, and fission-track dating based on core samples, cuttings, and imaging logging data. Under the constraints of the Anderson model, the formation stages and timing of fractures were analyzed according to the regional stress field, fracture strike, fracture filling characteristics, and rock mechanical properties. The results revealed tensile structural fractures, shear structural fractures, weathering micro-fractures, alteration fractures, and intracrystalline alteration pores in the weathering crust of the Pre-Cenozoic basement in Lishui Sag. The reservoirs were characterized by low porosity, low permeability, and small pore diameter. The reservoir quality of granite was better than that of gneiss. The weathering crust could be divided into four zones: the soil layer, weathering dissolution zone, weathering fracture zone, and bedrock zone. The thickness of the soil layer and weathering dissolution zone were small. Four stages of fractures were identified: Yandang movement shear fractures, Paleocene tension structural fractures, Huagang movement shear fractures, and Longjing movement shear fractures. The main stage of basement fracture formation differed between the Lingfeng buried hill zone and Xianqiao structural zone. Considering the influence of the temperature and pressure environment on the rock's mechanical properties, the differential fracture formation is related to the lithology, the coupling between the uplifted and exposed basement histories, and the tectonic stress field. Combined with the thermal histories of the Lingfeng buried hill zone and Xianqiao structural zone, the results suggest that the Lingfeng buried hill granite is favorable for basement fractures in Lishui Sag. Overall, this paper provides a novel method for analyzing the stages of fracture formation.

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index eta with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index eta. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003-2007, 2008-2012, 2013-2017, and 2018-2021, and the permafrost area is, respectively, 28.35 x 10(4) km(2), 35.14 x 10(4) km(2), 28.96 x 10(4) km(2), and 25.21 x 10(4) km(2). The proportion of extremely stable permafrost (< -5.0 degrees C), stable permafrost (-3.0 similar to -5.0 degrees C), sub-stable permafrost (-1.5 similar to -3.0 degrees C), transitional permafrost (-0.5 similar to -1.5 degrees C), and unstable permafrost (0 similar to -0.5 degrees C) is 0.50-1.27%, 6.77-12.45%, 29.08-33.94%, 34.52-39.50%, and 19.87-26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of +/- 1 degrees C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 mu m, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 mu m in northeast China, 2001-2019. The mean value was 441.78 mu m, with an annual increase of 0.26 mu m/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 mu m, followed by January and February with 450.77 mu m and 417.78 mu m, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 mu m to 497.141 mu m. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow-ice albedo and their radiative forcing effect.

Brown carbon (BrC) represents not only a major component of haze pollution but also a non-negligible contributor to positive radiative forcing, making it a key species for coordinating air quality and climate policies. In China, field observations on BrC remain limited given the highly variable emission sources and meteorological conditions across different regions. Here we focused on the optical properties of BrC in a distinct but rarely studied megacity in Northeast China, which is within a major agricultural region and experiences extremely cold winter. Agricultural fires were evident in April of 2021 and the fall of 2020, although open burning was strictly prohibited. Such emissions enhanced BrC's mass absorption efficiency at 365 nm (MAE365), more efficiently by the fall fires which were inferred to have relatively high combustion efficiencies (CE). After taking CE into consideration, the relationships between MAE365 and the levoglucosan to organic carbon ratio (a measure of the significance of agricultural fire influence) roughly converged for the fire episodes in different seasons, including those identified in February and March of 2019 by a previous campaign. Agricultural fires also influenced the determination of absorption & ANGS;ngstrom exponent (AAE), by resulting in non-linearity for BrC's absorption spectra shown on ln-ln scale. Based on three indicators developed by this study, the non-linearity was inferred to be caused by similar chromophores although the fires were characterized by various CE levels in different seasons. In addition, for the samples without significant influence of open burning, coal combustion emissions were identified as the dominant influencing factor for MAE365, whereas none solid link was found between the solution-based AAE and aerosol source.