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Uneven and sparse surface air temperature (Ta) observations challenge the understanding of the atmosphere‐land interactions and global environmental changes. Lapse rate (γ) is thought of with great potential to obtain accurate long‐term and high‐resolution Ta dataset. However, previous studies on γ over China were solely based on observed data, which limited our understanding since few stations are distributed in western China. In this study, an improved procedure for estimating γ by combining sparse observations and newly released ERA5 reanalysis data was proposed. The spatial heterogeneity in γ over China and its influencing factors (e.g., topography, climate, and vegetation) were then explored. A re‐examination of long‐term spatiotemporal variations in γ and its potential driving forces over China was also performed at grid scales. The results demonstrated that our γ estimations reproduced similar patterns to those derived from observations. The subsequent analyses revealed that the annual and seasonal variability in γ varied substantially among different altitude gradients, land cover types, and climate zones. The vital role played by vegetation covers in regulating local γ was emphasized in this study, which was poorly assessed previously. Moreover, γ in low altitude zones showed lower values but strong temporal fluctuations and contributed more to the interannual variability in γ over China, which could be mainly attributed to human activities. The present findings can provide an alternative method for estimating γ and be further used to assess regional or global temperature changes. [ABSTRACT FROM AUTHOR]

Permafrost monitoring using remote sensing techniques is an effective approach at present. Permafrost mostly occurs below the land surface, which limits permafrost monitoring by optical remote sensing. Considering the specific hydrothermal relations between permafrost and its active layer, we developed a permafrost monitoring and classification method that integrated the ground surface soil freeze/thaw states determined by the dual-index algorithm (DIA) and the permafrost classification method based on thermal stability. The modified frost index was introduced into the method as a link between the DIA and the permafrost classification method. Northeastern China was selected to establish and verify the proposed method and to examine the changes in regional permafrost against the background of global warming from 2002 to 2017. The results showed that the ground surface soil freeze/thaw states were significantly correlated with the permafrost distribution. The spatial continuity of permafrost and its sensitivity to climate change could be effectively reflected by the modified frost index. The proposed method had a high accuracy with a classification error smaller than 3%, compared with static permafrost maps. Moreover, the proportion of permafrost decreased from 29% at the beginning of the 21st century to 22.5% at present in northeastern China over the study period. The southern permafrost boundary in the study area generally moved northward approximately 25–75 km. Additionally, the method was applied to the Northern Hemisphere (30° N – 90° N), which demonstrated its effectiveness and extended applicability. [ABSTRACT FROM AUTHOR]

Accurate delineation of spatiotemporal variations in ground surface soil freeze and thaw (F/T) states is essential to appraise many geoscience issues, such as the hydrological circulation and land surface-atmosphere feedbacks. Recently, an Improved Dual-index algorithm (DIA) method was proposed by accounting for the influence of soil moisture variations on the discrimination accuracy with passive microwave remote sensing (RS) data products. Compared with the original DIA, the Improved DIA method has proven to be a more practical approach on surface soil F/T states discrimination. However, the method has only been applied and verified in cold regions of high-altitude (e.g., Tibetan Plateau), it's applicability and effectiveness in the cold areas in mid-high latitudes, where the geographic and climatic conditions are quite different, yet remained to be further explored. The present study investigated the feasibility of using AMSR-E (the Advanced Microwave Scanning Radiometer-EOS) and AMSR2 (the second Advance Microwave Scanning Radiometer) passive microwave RS data products to discriminate the F/T states of the ground surface for a long period from 2002 to 2019 by means of the Improved DIA method over a typical mid-high latitude cold region of Northeastern China. Seasonal variation characteristics of soil moisture in mid-high latitude areas were similar with those in high-altitude areas, even though the spatial heterogeneity of soil water content was significant in different regions. Discriminating surface soil F/T states with the Improved DIA method derived overall discriminating accuracy of about 91.6% in the study area, which demonstrated excellent feasibility of the Improved DIA method in mid-high latitude cold regions. The mapping results shown surface soil F/T cycle in Northeastern China responding to climate change was examined from the perspective of regional average, both the proportion of frozen soil area and frozen days showed significant decreasing trends continuously with differed quite spatially. The discriminating accuracy of the Improved DIA method was found to be lower in plain areas with dense populations and large farmland areas compared to mountainous areas when human activities were not taken into consideration, as quantifying human activities can be challenging. The Improved DIA method has been well verified in both high-altitude and high-latitude regions; it has great potential in global scale research. • The Improved DIA method derived accuracy of about 91.6% in Northeastern China. • The accuracy of the Improved DIA method is greatly affected by human activities. • The frozen soil in Northeastern China has been serious degradation in past decades. [ABSTRACT FROM AUTHOR]

[Display omitted] • A fully distributed frozen soil hydrothermal processes model was developed. • The hydrological impacts of surface soil freeze-thaw cycle were quantified. • Soil ice meltwater contributing to watershed reached about 35% in thawing period. The freeze–thaw cycle over the surface seasonally frozen soil is an important soil hydrothermal dynamic process linking land surface processes and climatic changes in the cold regions. With the advancement of frozen soil hydrothermal dynamic studies and remote sensing technology, the simulation of frozen soil hydrological processes based on the distributed numerical model has become a hotspot to better understand the impact of frozen soil hydrothermal dynamics on the watershed hydrological processes in the cold regions over a large spatial scale. However, the quantitative analysis of the impact of seasonally frozen soil hydrothermal processes on watershed runoff at long-term time scales remained an unsolved issue in the field of frozen soil hydrology. Under the framework of the watershed distributed eco-hydrological model ESSI-3, a fully distributed frozen soil hydro-thermal processes integrated modeling system (FFIMS model) was established based on the coupled water and heat transferring mechanism for frozen soil hydro-thermal process simulations in the frozen surface or at a certain depth of a watershed. By coupling the FFIMS model with the distributed eco-hydrological model ESSI-3, the impacts of seasonally frozen soil hydrothermal processes on hydrological processes were investigated from the perspective of temporal-spatial domain with the simulated hydrothermal and hydrological processes for a long-term period from 2008 to 2016 over a watershed located in the cold region of Northeastern China. The results suggested that the soil freeze–thaw cycling posed different impacts with limited significance throughout the whole hydrological processes of the watershed in different seasons. Significant impacts on the hydrological processes were particularly observed in the thawing period of a year, when soil ice meltwater contributing to the discharge of the study watershed reached to about 35% in average in this period. ESSI-3 coupled with the FFIMS modelling system obviously improved the performance of the original ESSI-3 in cold region watershed simulations, and the averaged Nash efficiency coefficients obtained increased from almost 0 to 0.77 in the thawing period of a year. The study demonstrated the importance of application of spatially distributed numerical model with physical mechanism for seasonally frozen soil water and heat transfer process simulations. [ABSTRACT FROM AUTHOR]

Discriminating between surface soil freeze/thaw states with the use of passive microwave brightness temperature has been an effective approach so far. However, soil moisture has a direct impact on the brightness temperature of passive microwave remote sensing, which may result in uncertainties in the widely used dual-index algorithm (DIA). In this study, an improved algorithm is proposed to identify the surface soil freeze/thaw states based on the original DIA in association with the AMSR-E and AMSR2 soil moisture products to avoid the impact of soil moisture on the brightness temperature derived from passive microwave remotely-sensed soil moisture products. The local variance of soil moisture (LVSM) with a 25-day interval was introduced into this algorithm as an effective indicator for selecting a threshold to update and modify the original DIA to identify surface soil freeze/thaw states. The improved algorithm was validated against in-situ observations of the Soil Moisture/Temperature Monitoring Network (SMTMN). The results suggest that the temporal and spatial variation characteristics of LVSM can significantly discriminate between surface soil freeze/thaw states. The overall discrimination accuracy of the improved algorithm was approximately 89% over a remote area near the town of Naqu on the East-Central Tibetan Plateau, which demonstrated an obvious improvement compared with the accuracy of 82% derived with the original DIA. More importantly, the correct classification rate for the modified pixels was over 96%. [ABSTRACT FROM AUTHOR]

The Dasuji giant porphyry molybdenum deposit is one of the largest ore deposits recently discovered along the Yinshan–Yanshan–Liaoning molybdenum belt in China. Using this deposit along the Yinshan–Yanshan–Liaoning molybdenum belt as the study area, the present study proposed a two-stage approach aimed at marking out the hydrothermally altered anomalies in the study area for the guidance of future prospecting in other regions. First of all, the Principal Component Analysis (PCA) and specific Band Ratio methods were applied to the ASTER images from different acquisition dates to extract ferric oxides and hydroxyl alterations related to the porphyry molybdenum deposit. Then, the Fractal-Aided Anomaly-Overlaying Selection model was adopted to recognize two ferric and hydroxyl alteration layers for separating anomalies from the interferences caused by geology and random noise from the data. Furthermore, for lithological differentiation in the previously marked off area, the Random Forest Classifier (RFC) was applied to the composite data obtained via the ASTER, ETM, and DEM, and it is demonstrated that the DEM can significantly improve lithological mapping in areas with complex vegetation cover and topography. Based on field verification and comparison with geological maps, the research revealed that the suggested two-stage approach may effectively reduce erroneously recognized anomalies produced during the first stage while retaining ore-related anomalies for gigantic porphyry molybdenum deposit prospecting in the Dasuji area, which showed the good application potential of the proposed model to extract actual hydrothermally altered anomalies adopted for lithological discrimination and mapping. [ABSTRACT FROM AUTHOR]

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various "possible future" scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China's climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China's moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study's findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation. [ABSTRACT FROM AUTHOR]

Influences of the increasing pressure of climate change and anthropogenic activities on wetlands ecosystems and agriculture are significant around the world. This paper assessed the spatiotemporal land use and land cover changes (LULCC), especially for conversion from marshland to other LULC types (e.g., croplands) over the Songnen and Sanjiang Plain (SNP and SJP), northeast China, during the past 35 years (1980-2015). The relative role of human activities and climatic changes in terms of their impacts on wetlands and agriculture dynamics were quantitatively distinguished and evaluated in different periods based on a seven-stage LULC dataset. Our results indicated that human activities, such as population expansion and socioeconomic development, and institutional policies related to wetlands and agriculture were the main driving forces for LULCC of the SJP and SNP during the past decades, while increasing contributions of climatic changes were also found. Furthermore, as few studies have identified which geographic regions are most at risk, how the future climate changes will spatially and temporally impact wetlands and agriculture, i.e., the suitability of wetlands and agriculture distributions under different future climate change scenarios, were predicted and analyzed using a habitat distribution model (Maxent) at the pixel-scale. The present findings can provide valuable references for policy makers on regional sustainability for food security, water resource rational management, agricultural planning and wetland protection as well as restoration of the region. [ABSTRACT FROM AUTHOR]

The increasing number of landslide hazards worldwide has placed greater demands on the production and updating of landslide inventory maps. As an important data source for landslide detection, interferometric synthetic aperture radar (InSAR) data processing is time-consuming and also requires specialized knowledge, which severely hinders its widespread application. At present, a new cloud-based online platform, i.e., Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (ASF HyP3) was developed for massive SAR data processing. In this study, combining the HyP3 online platform and Stacking-InSAR method, we constructed a new easy-to-use processing chain for rapidly identifying slow-moving landslides over large areas. With this processing chain, a total of 923 interferometric pairs covering an area of over 1800 km2 were processed within a few hours (about 4 to 5 h). A total of 81 slow-moving landslides were immediately detected and mapped using Stacking-InSAR method, of which 65 landslides were confirmed by previous studies and 16 landslides were newly detected. Results show that the new processing chain can greatly improve the efficiency of wide-area landslide mapping and is expected to serve as an effective tool for rapid updating the existing landslide inventories and contribute to the prevention and management of geological hazards. [ABSTRACT FROM AUTHOR]

Due to various factors such as urban development, climate change, and tectonic movements, landslides are a common geological phenomenon in the Qinghai–Tibet Plateau region, especially on both sides of a road, where large landslide hazards often result in traffic disruptions and casualties. Identifying the spatial distribution of landslides and monitoring their stability are essential for predicting landslide occurrence and implementing prevention measures. In this study, taking the Kangding-Batang section of Shanghai-Nyalam Road as the study area, we adopted a semi-automated time-series interferometric synthetic aperture radar (InSAR) method to identify landslides and monitor their activity. A total of 446 Sentinel-1 ascending and descending SAR images from January 2018 to December 2021 were thus collected and processed by using open-source InSAR processing software. After a series of error corrections, we obtained surface deformation maps covering the study area, and a total of 236 potential landslides were subsequently identified and classified into three categories, namely slow-sliding rockslides, debris flows, and debris avalanches, by combining deformation maps, optical images, and a digital elevation model (DEM). For a typical landslide, we performed deformation decomposition and analyzed the relationship between its deformation and rainfall, revealing the contribution of rainfall to the landslide. In addition, we discussed the effect of SAR geometric distortion on landslide detection, highlighting the importance of joint ascending and descending observations in mountainous areas. We analyzed the controlling factors of landslide distribution and found that topographic conditions are still the dominant factor. Our results may be beneficial for road maintenance and disaster mitigation. Moreover, the entire processing is semi-automated based on open-source tools or software, which provides a paradigm for landslide-related studies in other mountainous regions of the world. [ABSTRACT FROM AUTHOR]

In the context of global warming, the general increase in temperature has led to an increase in heatwave events, as well as a dramatic intensification of economic losses and social risks. This study employs the latest intensity–area–duration (IAD) framework that takes into account the temporal continuity and spatial dynamics of extreme events to identify regional heatwave events, and extracts key parameters of heatwave events to study the associated changes in frequency, intensity, influence area, and duration in seven geographic subregions of China in the 1979–2018 period. Heatwaves of all durations increased in frequency and intensity during the research period, with shorter heatwaves increasing in frequency and intensity at a faster rate than longer heatwaves. Among the seven geographic subregions, Xinjiang (XJ) and Southern China (SC) are the regions with the most frequent heatwave occurrence, while the Southwest (SW) and SC have the highest increase in heatwave frequency. In terms of regional distributions, XJ has the strongest heatwave event intensity and the largest affected area, while SC has the longest duration. However, in terms of spatial trends, SC, XJ, and the SW have the highest rates of intensity growth, influence area, and duration, respectively. In addition, heatwaves with extended durations and vast influence areas are more likely to occur in SC, and their frequency is on the rise. During the study period, the intensity, influence area, and length of heatwave occurrences in China exhibited an upward tendency, and it was shown that the longer the duration, the greater the intensity and the broader the influence area. In addition, the evolutionary characteristics of heatwave events with the longest duration indicate a certain consistency in their intensity and influence. These findings can contribute to the development of strategies to prepare for and mitigate the adverse effects of heatwave occurrences. [ABSTRACT FROM AUTHOR]

Accurate estimation of precipitation at local to global scales can considerably enhance our understanding of climate system dynamics. While numerous precipitation products are available as indispensable tools for investigating precipitation and its associated processes, none can consistently provide the lowest estimation error across environmental conditions. The multiple source precipitation ensemble (MSPE) methods have been considered a vital solution. A new MSPE framework is proposed here, which simultaneously uses machine learning (ML) classification and regression techniques within an automatic workflow (MSPE aml). Six precipitation products and their ensembles based on different MSPE strategies were evaluated at 2365 gauged and 800 randomly selected ungauged sites over China. Results revealed significant precision inconsistencies among the products primarily due to their different data sources and retrieval algorithms; while MSPE aml can effectively reduce the random and classification errors of estimated precipitation according to the Kling-Gupta efficiency and Heidke skill score. The improvements demonstrated the unique features of MSPE aml , particularly the necessity of the joint use of ML classifiers and regressors and assigning spatiotemporal dynamic weights for merging precipitation data. Moreover, MSPE aml can substantially improve its generalizability through a simple binning procedure, making it applicable under more complex conditions. The varying contributions of predictor variables (indicated by Shapely values) in different ML models identified the complexity of the MSPE issue and further the importance of designing proper ML models according to specific targets. The proposed MSPE framework is expected to be a suitable solution for assembling multiple precipitation data sources with different time periods and scales. • A machine learning-based framework for merging multi-source daily precipitation datasets is proposed. • It is effective in producing high-precision precipitation estimates in both gauged and ungauged regions. • The new method provides enhanced generalizability through a simple binning procedure. • It is necessary to combine machine learning classification and regression for an ensemble. • Spatiotemporal dynamically weighted ensembles based on machine learning classification have great potential. [ABSTRACT FROM AUTHOR]

• Physicochemical soil properties nonlinearly explain the history of LUC in China. • An explainable machine-learning method provides both local and global explanations. • A new perspective on LUC change with reliance on soil properties as drivers. • Toward knowledge gap-filling regarding soil properties in explaining LUC history. Enhancing our comprehension of soil processes and their impact on Earth requires precise quantification of human-induced soil alterations, particularly those related to land use/cover (LUC) histories. Thoroughly validated LUC and soil maps specific to China, and an explainable machine-learning approach were applied to reveal how soil physicochemical properties, independently or in combination, explain LUC histories in the last sixty years. Validation using historical data showed that the proposed machine-learning models can adequately simulate the pattern of LUC histories. Nonlinear mappings of various soil properties in explaining LUC histories were demonstrated and critical thresholds for changes in the explanatory capacities of each soil property to specific LUC histories were identified. Specifically, physical soil properties, except for soil pH, particularly soil thickness, clay content, bulk density, and coarse fragments, play significant roles in explaining the historical trajectories of forests, croplands, and pasture/rangelands than chemical soil properties. Predicted LUC changes suggest more intensive dynamics that are characterized by expansions in forest and pasture/rangeland areas, coupled with a reduction in cropland areas. Overall, improving understanding of the bidirectional links between soil and LUC changes is a crucial and imperative step towards refining the representation of soils in Earth system models. [ABSTRACT FROM AUTHOR]

• Multi-water-variable validated SWAT model simulated blue (BW) and green water (GW). • We examined historical trends and persistence of BW and GW changes in the Yangtze. • We analysed responses of BW/GW to climate, land use, and ecosystem variations. • Asymmetry of variation characteristics of BW and GW was identified. • Land use change has significant impact on GW change under climate variability. Renewable freshwater is a fundamental natural resource for sustaining human social development and the health of terrestrial ecosystems. While blue water (BW), which can be directly utilized by human beings, has received significant attention, the importance of green water (GW), crucial for ecosystem safety, has often been overlooked in water resource assessments. To date, there has been a lack of integrated research incorporating the detection and attribution of changes in both BW and GW in the Yangtze River basin (YRB), China's largest river basin, resulting in a deficiency of comparative and interactive analyses between them. In this study, we examined the historical trends and long-term memory of BW and GW flows across 272 sub-basins of the YRB using a multi-water-variable calibrated SWAT model, which was validated by streamflow, evapotranspiration, and terrestrial water storage change. We determined the individual contributions of climate variability and land use changes to BW and GW changes. Additionally, we identified the responses characteristics of BW and GW changes in four different ecosystems primarily characterized by alpine grasslands, cropland, forests, and river–lake systems, respectively. The results indicate a non-significant decreasing trend in precipitation and BW in most regions, while an increasing trend is observed in GW, which is expected to persist in the future. Climate variability played a predominant role in BW and GW changes, while land use change also appeared to have significant impact on monthly GW changes. GW showed a positive correlation with precipitation/humidity index in relatively dry and moderately humid areas, but a negative correlation in highly humid areas. Moreover, a stronger correlation was found between the gross primary production (GPP) of terrestrial ecosystems and GW compared to GPP and BW. The findings contribute to guiding the harmonious utilization of water resources between humans and ecosystems in the YRB, as well as optimizing water allocation. [ABSTRACT FROM AUTHOR]

Carbon neutrality is a goal the world is striving to achieve in the context of global warming. Carbon capture and storage (CCS) has received extensive attention as an effective method to reduce carbon dioxide (CO2) in the atmosphere. What follows is the migration pathway and leakage monitoring after CO2 injection. Interferometric synthetic aperture radar (InSAR) technology, with its advantages of extensive coverage in surface deformation monitoring and all-weather traceability of the injection processes, has become one of the promising technologies frequently adopted in worldwide CCS projects. However, there is no mature evaluation system to determine whether InSAR technology is suitable for each CO2 sequestration area. In this study, a new evaluation model is proposed based on the eight factors that are selected from the principle of the InSAR technique and the unique characteristics of the CO2 sequestration area. According to the proposed model, the feasibility of InSAR monitoring is evaluated for the existing typical sequestration areas in the world. Finally, the challenges and prospects of InSAR in the CCS project are discussed. [ABSTRACT FROM AUTHOR]