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Snow density is of paramount importance in water resource management, snow avalanche warning, and climate change research. However, the lack of competent methods for long‐term and vast‐scale snow density mapping persists due to the intricate spatiotemporal dependencies inherent in snow density, resulting in scarce and inaccurate snow density products. To address this challenge, a spatiotemporal random forest (STRF) model is constructed by leveraging in‐situ measurements, multisource remote sensing, and reanalysis data. It tackles the spatiotemporal dependencies in snow density arising from its inherent heterogeneity and the relations involving snow density and nonlinearly connected meteorological, terrain, vegetation, and snow‐related factors. The effectiveness of the model is substantiated through rigorous validation methods, including random, temporal, and spatial block cross‐validations as well as independent validation, apparently surpassing ERA5‐Land snow density. The estimated snow density is also demonstrated to be able to improve existing snow water equivalent data set using fixed snow density. Utilizing the proposed model, a data set of daily 25‐km snow density from 1980 to 2018 is constructed for stable snow cover areas in China, which holds significant potential for research and applications in the realm of snow hydrology. Plain Language Summary: Snow density plays a crucial role in managing water resources and issuing snow avalanche warnings. Snow density exhibits great spatiotemporal dependent structure due to its spatiotemporal heterogeneity and its relations with various influencing factors, which pose challenges to mapping long‐term and large‐scale snow density. This leads to a scarcity of accurate snow density products. To tackle this issue, we develope a spatiotemporal random forest (STRF) model by combining ground measurements, remote sensing sources, and reanalysis data. Notably, our model shows impressive results apparently outperforming the ERA5‐Land snow density data set. Our estimated snow density can also enhance existing snow water equivalent data set that rely on fixed snow density. Using our model, we produce a data set of daily 25‐km snow density from 1980 to 2018 for stable snow cover areas in China. This data set holds significant potential for research and practical applications in the field of snow hydrology. Key Points: A spatiotemporal random forest (STRF) model is proposed for estimating large‐scale and long‐term snow densitySTRF depicts the spatiotemporal dependent structure of snow density and handles its nonlinear relations with various influencing factorsThe estimated snow density outperforms ERA5‐Land snow density and could improve snow water equivalent data set using fixed snow density [ABSTRACT FROM AUTHOR]

Accurate snow phenology detection, including snow cover days (SCD), snow start date (SSD), and snow end date (SED), is increasingly important for understanding mountain hydrology such as snow heterogeneity and snowmelt seasonality. Multiple cloud‐free daily snow cover products have recently been developed in China, employing diverse retrieval algorithms and cloud‐gap‐filling methods, resulting in varying accuracy levels. However, comprehensive analysis of differences among products and their impact on snow phenology detection is lacking. This study systematically evaluates eight state‐of‐the‐art snow cover products in China, focusing on the challenging Tibetan Plateau (TP). We introduce a novel metric, the consistency‐weighted correlation coefficient (CWR), customized for SSD and SED detection, and propose product‐combining schemes like "ensemble voting" and "sensor preference" to enhance reliability. Our findings highlight the prime influence of retrieval algorithms under clear‐sky conditions on accuracy, surpassing the importance of cloud‐gap‐filling methods. Specifically, a product optimizing normalized difference snow index thresholds for diverse landcover types consistently outperforms others in detecting all three snow phenology parameters, with correlation coefficients for SCD of 0.82 and 0.69, and CWR values for SSD of 0.54 and 0.40, and for SED of 0.53 and 0.37 in both China and the TP, respectively. Moreover, our proposed scheme combining three high‐accuracy products significantly enhances snow cover identification and SCD detection, especially when the best‐performing product alone faces substantial uncertainty. These findings provide immediate, crucial implications for optimizing the use of multiple cloud‐free products to enhance snow phenology detection, ultimately advancing the applicability of derived snow parameters in mountain hydrology research. Key Points: Performances of eight state‐of‐the‐art cloud‐free products and their combinations in detecting snow phenology parameters are evaluatedImpacts of retrieval algorithm, cloud‐gap‐filling method, and sensor differences are considered for analyzing products' accuracy differencesProposed scheme combining three high‐accuracy products notably enhances snow cover days detection but including less accurate ones degrades [ABSTRACT FROM AUTHOR]

Snow is a key component of the cryosphere and has significant impacts on surface energy balance, hydrology, atmospheric circulation, and etc. In addition, numerous studies have indicated that snow impurities, especially nitrate, are sensitive to sunlight and can be photolyzed to emit reactive species including NO2 and HONO, which serve as precursors of O3 and radicals and disturb the overlying atmospheric chemistry. This makes snow a reservoir of reactive species, and this reservoir is particularly important in remote and pristine regions with limited anthropogenic emissions. The magnitude of snow chemical emissions is also influenced by snow physical properties, including snow depth, density and concentrations of light-absorbing impurities (e.g., BC and dust). Exploring and elucidating the emissions and atmospheric consequences of the snow-sourced reactive species require a global or regional model with a snow module. Here, we parameterized atmospheric nitrate deposition and its distributions in snow using a regional chemical transport model, i.e., the WRF-Chem (the Weather Research and Forecasting Model coupled with Chemistry) model, and evaluated the performance of the WRF-Chem model in simulating snow cover, snow depth, and BC, dust and nitrate concentrations with field observations in northern China which is one of the regions with dense and prolong snow cover. The model reasonably reproduces the observed snow cover and depth in northeastern and northwestern China, and the observed snow dust and nitrate concentrations are also reasonably reproduced. These results illustrate the ability of WRF-Chem in simulating snow properties including concentrations of reservoir species in northern China, and in the future, we will incorporate snow nitrate photolysis in the model, exploring the emissions of snow NOx from nitrate photolysis and the impacts on local to regional atmospheric chemistry and air pollutant transformations. [ABSTRACT FROM AUTHOR]

Accurate representation of soil and snow state variables in the land surface model is crucial for good simulation results. In order to investigate the potential impacts of the overestimated snow cover over the Tibetan Plateau (TP) on precipitation over China, we designed comparative experiments over TP with and without updated soil texture data, soil hydrological parameters, and the fresh snow density scheme to address this issue with the Weather Research and Forecasting Model (WRF). The results show that snow cover fraction (SCF) and surface albedo in winter on the TP were overestimated in the WRF control experiment (CTL), with albedo bias exceeding 0.4 in the western TP. The SCF in the WRF improved experiment (WRFIM) is about 8% lower than that of the CTL, and the bias is decreased to 0.2–0.3 in the interior of the TP. The thermal and dynamic effects of the TP in winter have a significant impact on the weather and climate of East Asia during summer. Thus, the simulated summer rainfall bias is significantly reduced by WRFIM in North China, and the mid-lower reaches of the Yangtze River, along with the spatial correlation coefficient between the observed-based and WRF-simulated rainfall increased from 0.76 in CTL to 0.88 in WRFIM. The WRFIM-simulated reductions in snow cover and albedo cause a decrease in upward shortwave radiation and eventually increase net surface radiation. The horizontal temperature gradient of the atmosphere is increased in the WRFIM compared to the CTL, and the increased temperature in the lower and middle layers accommodates more water vapor, which is not conducive to precipitation formation. Correspondingly, the Western Pacific Subtropical High at 500 hPa in the middle troposphere weakened. The low-level anticyclonic circulation at 850 hPa also weakened to the east, affecting water vapor transport in eastern China. It leads to a decrease in the water vapor transported northward by southeasterly from the western Pacific Ocean and a decrease in the water vapor flux reaching North China. This work indicates that accurate characterization of soil properties and snow cover on the TP is crucial for improving the simulation bias of summer rainfall in China. [ABSTRACT FROM AUTHOR]

During the boreal spring, extratropical cyclones in East Asia exhibit the highest frequency of occurrence in northeastern China. These cyclones are characterized by a distinct cold core, which is commonly referred to as the northeastern China cold vortex (NCCV), and most of NCCV events are usually accompanied by complex and hazardous weather, significantly impacting the regional climate in northeastern China. In this study, an automatic algorithm was employed to identify spring NCCVs from 1979 to 2022 using ERA5 reanalysis dataset, and a notable annual periodicity of 3–4 years was observed based on the defined spring NCCV intensity index (NCCVI). Through the analysis of anomalous strong/weak NCCVI years, it was found that the consistent heating (cooling) in the middle and lower troposphere of central Asia (northeastern China), along with a corresponding negative anomaly of geopotential height, as well as the enhanced cyclonic circulation in northeastern China, all contribute to the intensification and maintenance of NCCV activities. Furthermore, the winter snow cover in central Asia plays a vital role in local atmospheric heating and is associated with anomalous quasi-stationary Rossby waves that propagate eastward into northeastern China. The thermal forcing caused by preceding (DJF) snow cover anomalies in central Asia was somewhat replicated in ECHAM 4.6 model simulations. However, the simulations underestimated the contribution of snow cover anomalies to the eastward propagating Rossby wave trains. [ABSTRACT FROM AUTHOR]

Hourly automatic snow depth stations have enhanced insights into the dynamics and spatial variability of daily snowmelt. From 2021 to 2022, we gathered hourly snow depth measurements from six Hulun Buir grassland stations. Our analysis shed light on the dynamics of snowmelt and the key drivers in this northern region. We found that in northern China's mid-high latitude grasslands, winter snow cover persists for about 80 to 134 days. The transition to the melting phase in early March spans 5 to 12 days, with continuous and rapid phases. Snow under 3 cm quickly collapses. If the average temperature from 10:00 to 18:00 exceeds 0 °C, complete melting occurs within 36 h. Daily snow melting sees initial stability, swift decline, and gradual reduction, peaking between 11:00 and 14:00. Finally, thermal conditions primarily drive snow melt dynamics, with 14:00 ground temperature being pivotal. These findings shed light on snow dynamics and key factors in the mid-high latitude grasslands of northern China. [ABSTRACT FROM AUTHOR]

Surface freezing and thawing processes pose significant influences on surface water and energy balances, which, in turn, affect vegetation growth, soil moisture, carbon cycling, and terrestrial ecosystems. At present, the changes in surface freezing and thawing states are hotspots of ecological research, but the variations of surface frozen days (SFDs) are less studied, especially in the permafrost areas covered with boreal forest, and the influence of the environmental factors on the SFDs is not clear. Utilizing the Advanced Microwave Scanning Radiometer for EOS (AMSRE) and Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data, this study applies the Freeze–Thaw Discriminant Function Algorithm (DFA) to explore the spatiotemporal variability features of SFDs in the Northeast China Permafrost Zone (NCPZ) and the relationship between the permafrost distribution and the spatial variability characteristics of SFDs; additionally, the Optimal Parameters-based Geographical Detector is employed to determine the factors that affect SFDs. The results showed that the SFDs in the NCPZ decreased with a rate of −0.43 d/a from 2002 to 2021 and significantly decreased on the eastern and western slopes of the Greater Khingan Mountains. Meanwhile, the degree of spatial fluctuation of SFDs increased gradually with a decreasing continuity of permafrost. Snow cover and air temperature were the two most important factors influencing SFD variability in the NCPZ, accounting for 83.9% and 74.8% of the spatial variation, respectively, and SFDs increased gradually with increasing snow cover and decreasing air temperature. The strongest explanatory power of SFD spatial variability was found to be the combination of air temperature and precipitation, which had a coefficient of 94.2%. Moreover, the combination of any two environmental factors increased this power. The findings of this study can be used to design ecological environmental conservation and engineer construction policies in high-latitude permafrost zones with forest cover. [ABSTRACT FROM AUTHOR]

Oscillations in global modes of variability (MoVs) form global teleconnections that affect regional climate variability and modify the potential for severe and damaging weather conditions. Understanding the link between certain MoVs and regional climate can improve the ability to more accurately predict environmental conditions that impact human life and health. In this study, we explore the connection between different MoVs, including the Arctic oscillation (AO), Eurasian teleconnection, Indian Ocean dipole (IOD), North Atlantic oscillation (NAO), and El Niño southern oscillation (Nino34), with winter and summer climates in the High Mountain Asia (HMA) region, including geopotential height at 250 hPa (z250), 2 m air temperature (T2M), total precipitation (PRECTOT), and fractional snow cover area (fSCA). Relationships are explored for the same monthly period between the MoVs and the climate variables, and a lagged correlation analysis is used to investigate whether any relationship exists at different time lags. We find that T2M has a negative correlation with the Eurasian teleconnection in the Inner Tibetan Plateau and central China in both winter and summer and a positive correlation in western China in summer. PRECTOT has a positive correlation with all MoVs in most regions in winter, especially with the IOD, and a negative correlation in summer, especially with the Eurasian teleconnection. Snow cover in winter is positively correlated with most indices throughout many regions in HMA, likely due to wintertime precipitation also being positively correlated with most indices. Generally, the AO and NAO show similar correlation patterns with all climate variables, especially in the winter, possibly due to their oscillations being so similar. Furthermore, the AO and NAO are shown to be less significant in explaining the variation in HMA climate compared to other MoVs such as the Eurasian teleconnection. Overall, our results identify different time windows and specific regions within HMA that exhibit high correlations between climate and MoVs, which might offer additional predictability of the MoVs as well as of climate and weather patterns in HMA and throughout the globe. [ABSTRACT FROM AUTHOR]

Snowpacks are natural water reservoirs providing a considerable amount of water for humans and ecosystems. However, current global snow products (e.g., ESA GlobSnow v3.0), lack high spatial resolution and regional calibrations necessary to capture the high heterogeneity of snow water equivalents (SWEs) in complex Asian mountainous terrains. Therefore, our understanding of snow drought characteristics in China remains limited. Herein, we used an improved SWE product calibrated specifically for China to explore the characteristics of snow droughts, delineated by a standardized SWE index (SWEI) between 1993 and 2019. Our analysis was focused over three main snow‐covered regions of China: Qinghai–Tibet Plateau (QTP), northern Xinjiang, Northeast China. Especially during the period from 1993 to 2010, we found that the SWEI increased significantly at rates of 0.022/yr (Northeast China), 0.017/yr (northern Xinjiang), and 0.011/yr (QTP) (p < 0.01, Mann‐Kendall trend test). Increased SWEI contributed to decreasing snow drought events across China, with an obvious short‐term characteristic, whilst area proportion of the identified 1‐month snow droughts was above 46.5% across three regions. Furthermore, we found that the occurrence of snow droughts was likely mediated by large‐scale atmospheric circulation, since increased water vapor transport caused a significant vapor flux convergence in cold seasons over three regions, especially in northern Xinjiang and Northeast China. Plain Language Summary: Snow droughts are caused by snow water equivalent (SWE) deficits and pose a significant climatic risk to humans and natural systems, particularly in snow‐dependent watersheds. Hence, understanding their main characteristics and driving forces is crucial for diagnosing and predicting potential climate risks. However, current research is not clear in terms of the occurrence and characteristics of snow drought in China. Therefore, we aim to investigate snow droughts based on specially calibrated SWE data in China. Alongside the increase of SWE in the three main snow‐covered areas across China, we found that the intensity of snow drought in China showed a clear downward trend, whilst both the extent and number of snow droughts decreased rapidly. Overall, these changes were likely regulated by atmospheric circulation, with the southward water vapor flux clearly increasing, providing a reliable source of water vapor and promoting the increase of snowfall. Our findings provide an important basis for the management and regulation of spring water resources to snow droughts in snow‐dependent watersheds. Key Points: A reliable snow water equivalent product calibrated specifically for China was used to assess snow droughtsBetween 1993 and 2019, the occurrence of snow droughts decreased rapidly, alongside their intensity and extentThe occurrence of snow droughts was regulated by large‐scale water vapor transport [ABSTRACT FROM AUTHOR]

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east–west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions. [ABSTRACT FROM AUTHOR]

Snow cover is a sensitive indicator of global climate change, and optical images are an important means for monitoring its spatiotemporal changes. Due to the high reflectivity, rapid change, and intense spatial heterogeneity of mountainous snow cover, Sentinel-2 (S2) and Landsat 8 (L8) satellite imagery with both high spatial resolution and spectral resolution have become major data sources. However, optical sensors are more susceptible to cloud cover, and the two satellite images have significant spectral differences, making it challenging to obtain snow cover beneath clouds and cloud shadows (CCSs). Based on our previously published approach for snow reconstruction on S2 images using the Google Earth Engine (GEE), this study introduces two main innovations to reconstruct snow cover: (1) combining S2 and L8 images and choosing different CCS detection methods, and (2) improving the cloud shadow detection algorithm by considering land cover types, thus further improving the mountainous-snow-monitoring ability. The Babao River Basin of the Qilian Mountains in China is chosen as the study area; 399 scenes of S2 and 35 scenes of L8 are selected to analyze the spatiotemporal variations of snow cover from September 2019 to August 2022 in GEE. The results indicate that the snow reconstruction accuracies of both images are relatively high, and the overall accuracies for S2 and L8 are 80.74% and 88.81%, respectively. According to the time-series analysis of three hydrological years, it is found that there is a marked difference in the spatial distribution of snow cover in different hydrological years within the basin, with fluctuations observed overall. [ABSTRACT FROM AUTHOR]

Extreme cold events exert enormous impacts on the ecosystems and human health. A quantitative attribution analysis of the surface temperature anomalies associated with extreme cold events in China is conducted using an updated version of the coupled atmosphere-surface climate feedback response analysis method. For both extreme warm and cold years, the effects of anomalous surface albedo, water vapor, atmospheric dynamics, and aerosols are the major positive contributors, and the processes associated with surface dynamics provide a major negative effect. Specifically, the effect of surface albedo (water vapor) is directly linked to the change in local snow cover (precipitable water) over China. More local snow cover (precipitable water) provides a cooling (warming) effect and vice versa. The process associated with atmospheric dynamics exhibits an asymmetric contribution for the extreme warm and cold years. It provides a warming effect over most of China in the extreme warm years, which is possibly related to the nation-wide warm air advection. On the other hand, the effect of atmospheric dynamics displays large regional and year-to-year variations for the cold cases. In addition, a considerable impact of anthropogenic aerosols is detected over central-southern China, which is possibly related to the emission reduction policies in China since 2013. Although clouds show only a moderate impact on the national-averaged temperature anomalies, they provide a considerable effect over southern China. [ABSTRACT FROM AUTHOR]

Climate warming exacerbates the degradation of the mountain cryosphere, including glacier retreat, reduction in snow cover area, and permafrost degradation. These changes dramatically alter the local and downstream hydrological regime, posing significant threats to basin-scale water resource management and sustainable development. However, there is still a lack of systematic research that evaluates the variation of cryospheric elements in mountainous catchments and their impacts on future hydrology and water resources. In this study, we developed an integrated cryospheric-hydrologic model, referred to as the FLEX-Cryo model. This model comprehensively considers glaciers, snow cover, frozen soil, and their dynamic impacts on hydrological processes in the mountainous Hulu catchment located in the Upper Heihe river of China. We utilized the state-of-the-art climate change projection data from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate the future changes in the mountainous cryosphere and their impacts on hydrology. Our findings showed that the two glaciers in the Hulu catchment will completely melt out around the years 2045–2051. By the end of the 21st century, the annual maximum snow water equivalent is projected to decrease by 41.4 % and 46.0 %, while the duration of snow cover will be reduced by approximately 45 and 70 days. The freeze onset of seasonal frozen soil is expected to be delayed by 10 and 22 days, while the thaw onset of permafrost is likely to advance by 19 and 32 days. Moreover, the maximum freeze depth of seasonal frozen soil is projected to decrease by 5.2 and 10.9 cm per decade, and the depth of the active layer will increase by 8.2 and 15.5 cm per decade. Regarding hydrology, runoff exhibits a decreasing trend until the complete melt-out of glaciers, resulting in a total runoff decrease of 15.6 % and 18.1 %. Subsequently, total runoff shows an increasing trend, primarily due to an increase in precipitation. Permafrost degradation causes the duration of low runoff in the early thawing season to decrease, and the discontinuous baseflow recession gradually transitions into linear recessions, leading to an increase in baseflow. Our results highlight the significant changes expected in the mountainous cryosphere and hydrology in the future. These findings enhance our understanding of cold-region hydrological processes and have the potential to assist local and downstream water resource management in addressing the challenges posed by climate change. [ABSTRACT FROM AUTHOR]

Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms of spring snow cover variability over NEC during 1983–2018 based on the satellite-derived snow cover data and atmospheric reanalysis products. The empirical orthogonal function (EOF) analysis showed that the first EOF mode (EOF1) explains about 50% of the total variances and characterizes a coherent snow cover variability pattern over NEC. Further analyses suggested that the formation of the EOF1 mode is jointly affected by the atmospheric internal variability and the sea surface temperature (SST) anomaly at the interannual timescale. Specifically, following a negative phase of the atmospheric teleconnection of the Polar–Eurasian pattern, a prominent cyclonic circulation appears over NEC, which increases the snowfall over the east of NEC by enhancing the water vapor transport and decreases the air temperature through reducing the solar radiation and intensifying the cold advection. As a result, the snow cover has increased over NEC. Additionally, the tripole structure of the North Atlantic spring SST anomaly could excite a wave-train-type anomalous circulation propagating to NEC that further regulates the snow cover variability by altering the atmospheric dynamic and thermodynamic conditions and the resultant air temperature and snowfall. Our results have important implications on the understanding of the spring snow cover anomaly over NEC and the formulation of the local agricultural production plan. [ABSTRACT FROM AUTHOR]

The snow cover in temperate areas is undergoing significant changes, which may affect soil respiration (Rs), the second largest carbon flux in global carbon cycling. However, currently, there are relatively few in situ field studies on the effects of altered snow cover on Rs in temperate areas during the non-growing season compared to the research on Rs during the growing season. Therefore, it limited the accurate prediction of the characteristics and magnitude of changes in soil carbon emissions in temperate areas under global change scenarios. Here, an in situ field experiment was conducted in a typical grassland in Inner Mongolia in China to explore the characteristics of Rs under three different snow cover treatments, i.e., increasing snow (IS), decreasing snow (DS), and ambient snow that was regarded as the control check treatment (CK). The results showed that the range of Rs flux and cumulative emission flux in all treatments in the non-growing season in the study area ranged from 5.87 ± 0.20 to 55.11 ± 6.42 mg CO2 m−2 h−1 and from 22.81 ± 0.68 to 26.36 ± 0.41 g C m−2, respectively. During the observation period, the depth of the largest snow cover for each treatment did not exceed 18 cm, and none of the snow treatments caused significant variations in Rs flux (p > 0.05). However, the cumulative flux of Rs in the whole non-growing season was only stimulated significantly by 15.6% by the IS treatment compared with that of CK. The relatively high Rs flux in the non-growing season was observed to mainly occur in the soil deeply frozen period (DFP) and the soil melting period (SMP). Further analysis revealed that Rs flux under different snow treatments were mainly positively correlated with soil temperature during SMP. The main factors controlling Rs varied with different sampling periods. Our findings suggest that the non-growing season is also an important period of non-negligible carbon emissions from typical grassland soils in temperate zones. [ABSTRACT FROM AUTHOR]

The systematic simulated cold biases in models for surface air temperature (SAT) over China have been under discussion for a long time, but the related attribution is still unclear. In this study, we investigate the main contributors and relevant physical processes of SAT biases over China based on 31 models participating in the sixth phase of the Coupled Model Intercomparison Project from a surface energy budget perspective. On annual and seasonal scales, less downward clear‐sky longwave radiation in the models, which is due to the underestimated tropospheric temperature and water vapor amount, is the main cause of cold biases over the Tibetan Plateau, Tarim Basin, Sichuan Basin, and most of eastern China. Meanwhile, the simulation biases in surface albedo and sensible heat flux induce cold biases depending on regions and seasons. In winter and spring, the overestimated surface albedo in association with more snow cover leads to prevalent cold biases over the western Tibetan Plateau and Northeast China. Excessive sensible heat release caused by the overestimated ground–air temperature difference facilitates SAT underestimation in the Sichuan Basin during summer and autumn. In addition, the cold biases in winter and spring are larger than those in summer and autumn, due to the stronger snow cover overestimation and tropospheric temperature underestimation. Plain Language Summary: The global climate model is a complex system used for describing the atmosphere, land surface, ocean, sea ice components, and their interactions. It has been widely applied to investigate climate change and its associated mechanisms. However, there are still deficiencies in current models for surface air temperature (SAT) simulations. The underlying cause for insufficient simulation is a hot topic of research. In past decades, many studies have shown prevalent SAT cold biases over China in most models, while the related mechanisms remain unknown. Based on the state‐of‐the‐art models, we investigate the causes of the cold biases over China from the perspective of a surface energy budget. The results indicate that the downward clear‐sky longwave radiation simulation error due to the bias of atmospheric states is the primary reason, and the cold biases induced by surface albedo and sensible heat flux vary among regions and seasons. Key Points: We investigate the reasons for the simulated surface air temperature cold biases in sixth phase of the Coupled Model Intercomparison Project models over ChinaLess downward clear‐sky longwave radiation due to underestimated tropospheric temperature and water vapor amount is the main causeCold biases induced by surface albedo and sensible heat flux vary among regions and seasons [ABSTRACT FROM AUTHOR]

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 μm, 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 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/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 μm, followed by January and February with 450.77 μm and 417.78 μm, 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 μm to 497.141 μm. 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. [ABSTRACT FROM AUTHOR]

This study reveals that South China precipitation (SCP) anomalies tend to persist well from winter to the following spring after the late 1990s, favoring long-lasting drought or flood events over South China. Mechanism analysis indicates that the interdecadal changes in El Niño-Southern Oscillation (ENSO) and the preceding November central Asian snow cover could contribute to the increased persistence of winter-to-spring SCP anomalies. ENSO has a stable impact on winter SCP, whereas its impact on spring SCP is significantly enhanced after the late 1990s. With a weakened intensity and faster decay rate in the recent two decades, the ENSO-related spring SST anomalies over the tropical Pacific are relatively weaker, inducing a weakened and more southward-located western North Pacific anticyclone. This further leads to an interdecadal migration of the spring rainfall belt anomaly, consequently favoring the persistence of winter-to-spring SCP anomalies after the late 1990s. Additionally, the impacts of November central Asian snow cover on winter and spring SCP are both strengthened after the late 1990s. In the most recent two decades, the snow-cover-related cooling effect has become stronger, which induces winter cyclonic anomalies over Lake Baikal, favoring increased winter SCP. In addition, increased snow cover excites upward-propagating waves from the troposphere to the stratosphere, consequently weakening the stratospheric polar vortex. In spring, the stratospheric polar vortex signals propagate downward and result in a negative Arctic Oscillation in the troposphere, favoring more spring SCP. Therefore, central Asian snow cover is also conductive to the persistence of winter-to-spring SCP anomalies after the late 1990s. [ABSTRACT FROM AUTHOR]

Snow cover has significantly changed due to global warming in recent decades, causing large changes in the vegetation ecosystem. However, the impact of snow cover changes on the spring phenology of different vegetation types in Northeast China remains unclear. In this study, we investigated the response of the start of the growing season (SOS) to different snow cover indicators using partial correlation analysis and stepwise regression analysis in Northeast China from 1982 to 2015 based on multiple remote sensing datasets. Furthermore, we revealed the underlying mechanisms using a structural equation model. The results show that decreased snow cover days (SCD) and an advanced snow cover end date (SCED) led to an advanced SOS in forests. Conversely, an increased SCD and a delayed SCED led to an advanced SOS in grasslands. The trends of SCD and SCED did not exhibit significant changes in rainfed cropland. The maximum snow water equivalent (SWEmax) increased in most areas. However, the proportion of the correlation between SWEmax and SOS was small. The impact of snow cover changes on the SOS varied across different vegetation types. Snow cover indicators mainly exhibited positive correlations with the SOS of forests, including deciduous broadleaf forests and deciduous coniferous forests, with positive and negative correlations of 18.61% and 2.58%, respectively. However, snow cover indicators mainly exhibited negative correlations in the SOS of grasslands and rainfed croplands, exhibiting positive and negative correlations of 4.87% and 13.06%, respectively. Snow cover impacted the SOS through the "temperature effect" in deciduous broadleaf forests, deciduous coniferous forests, and rainfed croplands, while it affected SOS through the "moisture effect" in grasslands. These results provide an enhanced understanding of the differences in snow cover changes affecting SOS in different vegetation types under climate change in Northeast China. [ABSTRACT FROM AUTHOR]

Climate and topography are pivotal factors influencing snow cover variation, highlighting the significance of investigating the altitudinal response of snow cover to climate change. This study adopted a new MODIS snow cover extent product over China, reanalysis climate data, and digital elevation model (DEM) data to analyze the variation characteristics of snow cover frequency (SCF) and climatic factors with elevation in the Keriya River Basin (KRB) during the hydrological years from 2000 to 2020. The Partial Least Squares Regression (PLSR) method was utilized to explore the elevation-based relationships between SCF and climatic factors. Our findings can be summarized as follows: (1) The SCF exhibited an "increasing–decreasing–increasing–decreasing" pattern intra-annually, with insignificant monthly inter-annual variations. Only November, January, April, and May demonstrated upward trends, whereas October and December remained relatively stable, and other months exhibited declines. (2) Vertical variations in SCF and climatic factors revealed fluctuating upward trends in SCF and wind speed. On the other hand, the air temperature consistently decreased at a lapse rate ranging from 0.60 to 0.85 °C/100 m. Precipitation demonstrated "rising–falling" or "rapidly rising–slowly rising" patterns, bounded by 3821 m (range 3474–4576 m). (3) A new decision scheme, which took into account the alteration of the primary SCF controlling factors and shifts between positive and negative impacts caused by these factors, was used to determine five threshold elevation zones: 2585 m (range 2426–2723 m), 3447 m (range 3125–3774 m), 4251 m (range 4126–4375 m), 5256 m (range 4975–5524 m), and 5992 m (range 5874–6425 m). These threshold elevation zones were evident in spring, with four of these appearing in autumn (excluding 4251 m) and summer (excluding 2585 m). Only two threshold elevation zones were observed in winter with elevation values of 3447 m and 5992 m, respectively. Our findings are crucial for a deeper understanding of snow cover variation patterns at different elevations and offer essential insights for the responsible management of regional water resources. [ABSTRACT FROM AUTHOR]

Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, there is a small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here, we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021/22. Compared to the open-loop experiment (without SCF assimilation), the root-mean-square error (RMSE) of SCF is reduced by 6% through the original EnSRF and is even lower (by 14%) in the combined DI and EnSRF (EnSRFDI) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling–Gupta efficiency (KGE) increasing at 60% and 56%–70% stations, respectively, particularly under conditions with near-freezing temperature, in which reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products. Significance Statement: Due to the small spread between the seasonal snowpack of ensemble simulations, ensemble snow cover fraction (SCF) data assimilation (DA) proves to be ineffective. Therefore, we apply a hybrid method that combines the direct insertion (DI) and ensemble square root filter (EnSRF) to assimilate the spaceborne SCF into a land surface model (LSM) driven by high-resolution climate forcings. Our results reveal the applicability of the EnSRFDI to further improve snow cover simulations over regions with high SCF. Furthermore, the DA experiments were validated through a large number of in situ observations from the China Meteorological Administration. The uncertainties of snow depth and soil temperature simulations are also slightly reduced by the SCF DAs, particularly over regions with a poor LSM performance. [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its spatial resolution is low. In this study, the Qinghai–Tibet Plateau, which has a harsh climate with few meteorological stations, was selected as the study area. We propose a deep learning model called the Dual-Branch Super-Resolution Semantic Segmentation Network (DSRSS-Net), in which one branch focuses with super resolution to obtain high-resolution snow distributions and the other branch carries out semantic segmentation to achieve accurate snow recognition. An edge enhancement module and coordinated attention mechanism were introduced into the network to improve the classification performance and edge segmentation effect for cloud versus snow. Multi-task loss is also used for optimization, including feature affinity loss and edge loss, to obtain fine structural information and improve edge segmentation. The 1 km resolution image obtained by coupling bands 1, 2, and 3; the 2 km resolution image obtained by coupling bands 4, 5, and 6; and the 500 m resolution image for a single channel, band 2, were inputted into the model for training. The accuracy of this model was verified using ground-based meteorological station data. Snow classification accuracy, false detection rate, and total classification accuracy were compared with the MOD10A1 snow product. The results show that, compared with MOD10A1, the snow classification accuracy and the average total accuracy of DSRSS-Net improved by 4.45% and 5.1%, respectively. The proposed method effectively reduces the misidentification of clouds and snow, has higher classification accuracy, and effectively improves the spatial resolution of FY-4A satellite snow cover products. [ABSTRACT FROM AUTHOR]

The northernmost margin of the East Asian summer monsoon (NMEASM) is the northernmost position that the East Asia summer monsoon (EASM) can reach. NMEASM has obvious multi-scale variability, and well reflects the wet/dry climate variability in northern China. Predicting the location change of the NMEASM is important for understanding future East Asian climate change. However, the variability of the NMEASM has not been studied extensively, and its underlying mechanisms have not been clarified. To explore the movement of the NMEASM and its causes, we use reanalysis datasets to evaluate the NMEASM index from 1979 to 2018. The NMEASM indicates a decreasing trend over 40 years and a significant abrupt point in 2000, which is positively correlated with the Tibetan Plateau snow cover before 2000 and the Siberian snow cover after 2000 in spring. The decreased Siberian snow cover increases the soil temperature and decreases the atmospheric baroclinicity over Mongolia and northern China after 2000. The decreased atmospheric baroclinicity induces the dipole mode of anticyclonic anomaly over Mongolia and northern China and the cyclonic anomaly over the Sea of Japan by modulating the wave activity flux (WAF). The WAF's southeastward propagation strengthens the anticyclonic anomaly over Mongolia and northern China and the cyclonic anomaly over the Sea of Japan, which weakens the upward movement and water vapor transport, respectively. Hence, the decreased Siberian snow cover in spring modulates the precipitation over Mongolia and northern China and the southward movement of NMEASM by turbulent westerly circulation. [ABSTRACT FROM AUTHOR]

Southern China is prone to floods during the wet season, and regional extreme precipitation is expected to become more frequent given the context of climate change. However, the prediction skills and predictability of the spatial distribution of extreme precipitation days (EPDs) over southern China remain unclear. To address this issue, through observational diagnosis and numerical experiment, the present study revealed the formation mechanisms of the two leading modes of pre-summer (May and June) EPDs in southern China, and established a physical-based empirical model (P-E model) to predict the distribution of EPDs in the region. The results revealed the following: (1) the first mode of pre-summer EPDs in southern China displayed a uniform pattern, while the second mode presented a meridional dipole pattern. The uniform pattern of the first mode is associated with the tropical western North Pacific anomalous anticyclone (TWPAC), whereas the meridional dipole pattern of the second mode is related to the subtropical western North Pacific anomalous anticyclone (SWPAC); (2) the spring North Atlantic tripole sea surface temperature (SST) pattern, the northern North America surface air temperature, and the snow cover depth over central Siberia contribute to the variation of the first mode and TWPAC, whereas the spring extratropical North Atlantic dipole SST pattern and the spring change of sea ice concentration over the Barents and Kara seas are physically linked with the variation of the second mode and SWPAC; (3) based on these predictors, the established P-E model demonstrated predictive skill with regard to the principal components of the first two modes, and the spatial distribution of EPDs in southern China was reconstructed. The areal mean temporal correlation coefficient skill for the independent prediction period (2011–2021) was 0.34, while for the pattern correlation coefficient skill averaged over the entire period was 0.28. In comparison with the maximum attainable skills, both metrics indicate potential for improvement. The findings of this study represent a reference for the prediction and predictability of the spatial distribution of pre-summer EPDs in southern China. [ABSTRACT FROM AUTHOR]

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As a significant component of the cryosphere, snow cover plays a crucial role in modulating atmospheric circulation and regional hydrological equilibrium. Therefore, studying the dynamics of snow cover and its response to climate change is of great significance for regional water resource management and disaster prevention. In this study, reanalysis climate datasets and a new MODIS snow cover extent product over China were used to analyze the characteristics of climate change and spatiotemporal variations in snow cover in the Keriya River Basin (KRB). Furthermore, the effects of climate factors on snow cover and their coupling effects on runoff were quantitatively evaluated by adopting partial least squares regression (PLSR) method and structural equation modeling (SEM), respectively. Our findings demonstrated the following: (1) Air temperature and precipitation of KRB showed a significant increase at rates of 0.24 °C/decade and 14.21 mm/decade, respectively, while the wind speed did not change significantly. (2) The snow cover frequency (SCF) in the KRB presented the distribution characteristics of "low in the north and high in the south". The intra-annual variation of snow cover percentage (SCP) of KRB displayed a single peak (in winter), double peaks (in spring and autumn), and stability (SCP > 75%), whose boundary elevations were 4000 m and 6000 m, respectively. The annual, summer, and winter SCP in the KRB declined, while the spring and autumn SCP experienced a trend showing an insignificant increase during the hydrological years of 2001–2020. Additionally, both the annual and seasonal SCF (except autumn) will be further increased in more than 50% of the KRB, according to estimates. (3) Annual and winter SCF were controlled by precipitation, of which the former showed a mainly negative response, while the latter showed a mainly positive response, accounting for 43.1% and 76.16% of the KRB, respectively. Air temperature controlled SCF changes in 45% of regions in spring, summer, and autumn, mainly showing negative effects. Wind speed contributed to SCF changes in the range of 11.23% to 26.54% across annual and seasonal scales. (4) Climate factors and snow cover mainly affect annual runoff through direct influences, and the total effect was as follows: precipitation (0.609) > air temperature (−0.122) > SCP (0.09). [ABSTRACT FROM AUTHOR]

The presence of litter and winter snow cover can affect the decomposition of organic matter in forest soils and changes in δ13C values of soil-respired carbon dioxide (CO2). However, limited information is available on the responses of CO2 emissions from forest soils and their δ13C values to snow cover and litter addition over the year. We experimentally manipulated snow cover to study the impacts of light and heavy artificial snow cover on soil heterotrophic respiration and its δ13C values, using undisturbed large soil columns collected from two typical temperate forests in Northeastern China. Based on the average temperatures of surface forest soils in four seasons of the year in this study region, the simulations of autumn freeze–thaw, winter freeze, spring freeze–thaw, and the growing season were sequentially carried out under laboratory conditions. A set of novel analysis systems, including automated chamber equipment and laser spectroscopy analysis with high-frequency measurements for CO2 concentrations and the 13C/12C isotopic ratios in CO2, was used to study the effects of artificial snow cover and the presence of litter on soil heterotrophic respiration and its δ13C values. During the autumn freeze–thaw simulation, there were larger CO2 emissions and less negative δ13C values of soil-respired CO2 upon heavy snow cover than upon light snow cover, indicating that the presence of increased snow cover prior to winter freeze can increase the decomposition of organic C in subsurface soils under temperate forests. The δ13C values of soil-respired CO2 in all treatments were, on average, less negative as the simulated spring freeze–thaw proceeded, which was contrary to the variations of the δ13C during the autumn freeze–thaw simulation. Soil heterotrophic respiration and its δ13C values during the spring freeze–thaw simulation were, on average smaller upon heavy snow cover than upon light snow cover, which differed from those during the autumn freeze–thaw and growing season simulations, respectively. Taken together, the results highlight that the effects of snow cover on soil heterotrophic respiration and its δ13C values under temperate forests may vary with different seasons of the year and the presence of litter. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

The features and causes of the leading intermonth modes of winter surface air temperature anomalies (SATA) over China are investigated, and associated prediction models are developed. The first three intermonth modes of winter SATA over China are obtained by extended empirical orthogonal function (i.e., EEOF1–3) analysis. The results show that EEOF1 represents consistent variations in the whole winter, with a variance contribution of 32.3%, whereas EEOF2 and EEOF3 show spatiotemporally inconsistent changes, and their variance contributions are 16.9% and 12.5%, respectively. EEOF2 has out-of-phase variations between December and January–February, and EEOF3 exhibits a temporal warm–cold alternating pattern, with spatially reversing changes over northwestern and southern China. However, the Climate Forecast System, version 2 (CFSv2) presents a limited prediction skill for winter SATA over China and their intermonth modes. Further investigations indicate that the September sea ice over the Barents–Laptev Seas, the November snow cover over western Europe and East Asia, and the November northern Atlantic sea surface temperature can be, respectively, adopted to develop prediction schemes for the consistent mode (EEOF1 scheme) and two inconsistent modes (EEOF2 and EEOF3 schemes) based on specific mechanisms. These schemes show effective performances in predicting both individual modes and the reconstruction field of SATA over China. The temporal correlation coefficients (TCCs) between cross-validation results and observations are 0.48, 0.51, and 0.31 for the EEOF1–3 modes, respectively (the 90% confidence level is 0.27). For the reconstruction field, the TCCs are 0.40, 0.27, and 0.45 in December, January, and February, respectively, which are much higher than those of the CFSv2 outputs (0.23, −0.16, and −0.09). [ABSTRACT FROM AUTHOR]

Light climate environment (LCE) has a significant impact on human health, behavioral characteristics, and the safety of life and property due to the high albedo of snow on the ground cover type, which in turn affects the regional climate and socio-economic development, but less relevant studies have been found. In this study, the effect of snow on daytime and nighttime light levels was quantified using comparative field observations and controlled experiments in artificial climate chambers, combined with analysis of variance and model fitting. The results of the study found that there was a significant difference between the presence and absence of snow on both daytime and nighttime light levels. During daytime, the ambient light level on the ground with snow is 5.68 times higher than without snow, an improvement of 12,711.06 Lux. At night, with moonlight, the nighttime illuminance with and without snow is 0.213 Lux and 0.01 Lux, respectively. When there is no moonlight, the snow has no significant effect on the light level. In addition, significant differences in LCE intensity with different snow depths, snow densities and black carbon (BC) pollution. At the same background light intensity, the LCE intensity varies significantly with increasing snow depth, snow density and BC pollution. The results reveals the quantitative impact of snow on LCE, providing scientific support for regional natural light energy use, human health and safety, urban environmental management and economic development. [ABSTRACT FROM AUTHOR]

Various climatic factors and their interactions affect China's snow depth alterations. However, a measure of the relative importance of climatic factors regarding the variance in snow depth is lacking. Here, we use a heretofore largest dataset, quality‐controlled daily observations from 1,665 meteorological stations, and statistical analyses to identify proxy drivers and their contributions regarding regional variability and trends in China's snow depth and snow depth‐climate relationships from 1951 to 2019. Results show that seasonal snow depth alterations are substantial in China and caused by various climatic drivers. In winter, observed positive trends in the northwest and northeast of China mainly respond to augmentation in solid precipitation and snow accumulation in fall. For the Tibetan Plateau, the negative snow depth anomaly seems to respond to the rising air temperature below the freezing point along with some secular changes in solid precipitation and shallower fall snowpacks. All these trends in winter snow depth over the northwest, northeast, and Tibetan Plateau of China account approximately for 30% of the total interannual variance in spring snow depth in these regions. In the milder central north and south of China, snow accumulation is not evident. Besides solid precipitation and rising air temperature below the freezing point, atmospheric humidity is also notable for its effect on the interannual variance in winter snow depth in these regions. In spring and fall, however, the negative effect of warming climate on snow depth becomes remarkably more important. It seems that about 13–33% (12–50%) of the total interannual variance in the widespread decrease (insignificant perturbation), except for some weak increases in the northeast region (decreases in the Tibetan Plateau), of China's spring (fall) snow depth are caused by the rising air temperature below the freezing point. [ABSTRACT FROM AUTHOR]

Fragile ecological environment and shortage of water resources in Northwest China have seriously affected agricultural and economic development. Among them, the precipitation form can be one of the most important factors in the water cycle in the northwest arid region. The changes in precipitation forms can be of great significance to determining the water resources change in the background of climate warming-wetting trend in northwest China. In this study, the wet bulb temperature dynamic threshold model was established to separate the snowfall and rainfall using the daily precipitation, air temperature, relative humidity, atmospheric pressure, and station altitude data observed by the National Meteorological Station in northwest China during 1961-2017. The accuracy of the separation was also validated. A systematic analysis was made on the temporal and spatial variation characteristics of precipitation, rainfall, snowfall, and S/R in the region. Furthermore, the regional differences of precipitation in different forms and the S/R ratio were determined on the spatial scale of the secondary basin. The snowfall was divided into four types, in order to analyze the change characteristics of different types of snowfall. An investigation was implemented to explore the changes in snowfall and rainfall separation threshold and the influencing factors of precipitation forms. The results show that the precipitation in different forms in Northwest China showed a significant increasing trend during 1961-2017. However, the increasing trend of rainfall was more outstanding than that of snowfall. The Snowfall/Rainfall ratio showed a non-significant decreasing trend, with a decrease of 0.07%/10a. The spatial variation trend shared the outstanding regional differences with an “increase in the west and decrease in the east”, and the distribution of regions with the outstanding increase was concentrated in the central region of the northern Xinjiang and Hexi Corridor. The Snowfall/Rainfall ratio is distributed with “an increase in the west and decrease in the east”, and “increase in the north and decrease in the south”. The significant increase of extreme snowfall in Northwest China led to a significant increase of snowfall, with an increase of 1.07 mm/10a. Specifically, the increased rate of extreme snowfall was 1.03 mm/10 a. On the contrary, there was a more outstandingly decreasing trend in light snowfall and moderate snowfall. The slight decreasing trend of snowfall days was mainly caused by the significant reduction of light snowfall days and the significant increase of extreme snowfall days. By contrast, there was no change in moderate snowfall days and heavy snowfall days. The threshold temperature of snowfall and rainfall separation fluctuated greatly on a seasonal scale, with the highest threshold temperature in autumn, the second in winter, and the lowest in spring. The change in relative humidity caused the change in threshold temperature. Furthermore, the higher the relative humidity was, the greater the threshold temperature difference was. On the interannual scale, the threshold temperature showed a non-significant decrease trend, with a mutation change in 2008. On the watershed scale, the change rate of precipitation in each phase increased with the increase of altitude, and the Snowfall/Rainfall ratio was the opposite, the change of snowfall was mainly affected by temperature and relative humidity, and the change of Snowfall/Rainfall ratio was mainly affected by temperature in Northwest China. This finding can greatly contribute to the scientific understanding of the changing of water resources in Northwest China. It is of great significance to the rational regulation of regional hydrology and water resources. [ABSTRACT FROM AUTHOR]

Snow depth is an important parameter to characterize the characteristics of snow cover, and it is also one of the most sensitive response factors to regional climate change. However, the extent of snow depth variability and its driving mechanisms are still unknown in China. Therefore, in this study, we used the regression analysis, root-mean-square error analysis, anomalous year analysis, and correlation analysis methods to explore the spatiotemporal variation characteristics of snow depth in China from 1979 to 2019 based on the reanalysis snow depth dataset. The results show that (1) the snow distribution in China is obviously spatially heterogeneous, and the southeastern, western, and southern regions of the Qinghai-Tibet Plateau, northern Xinjiang, and northeastern China have high values of snow depth; (2) the high-value regions are also the sensitive regions for anomalous variations in snow depth in China; (3) in the past 41 years, the interannual variability of snow depth in China has shown a significantly decreasing trend, and the linear tendency of snow depth is − 0.093 cm/10 a (p < 0.01) and the snow depth in four seasons showed a decreasing trend (p < 0.05); and (4) the driving factors of snow heterogeneity are dissimilar in different regions and seasons. In temperate zones, average air temperature is the main factor affecting snow depth in cold temperature, mid temperature, and warm temperature zones; the maximum air temperature is the main factor affecting snow depth in mid temperate and warm temperate zones. Both the minimum air temperature and the average land-surface temperature are important factors affecting the snow depth in the cold temperate, mid temperate and warm temperate zones, and all passed the significance test of 0.01. [ABSTRACT FROM AUTHOR]

Based on the digital elevation data, snow depth and snow cover remote sensing data, this paper divides six snow evolution areas and geographical partitions, extracts the geographical partitions of each evolution area and obtains the geographical characteristics of the evolution area for analysis. The results show that: (1) From 2003 to 2017, the average snow area decreased at a rate of − 0.004, and the average snow depth increased at a rate of 0.03. (2) The snow in the middle altitude hill with shady gentle slope area is the most obvious in the seasonal evolution, and the percentage of this region in the seasonal snow evolution area is 5.46%, the snow depth in the middle altitude hill with sunny and gentle slopes area increased and decreased significantly in the past 15 years, and the percentage of this region in the SD significant changes evolution area was 6.32%. The snow in the low relief middle altitude mountain with shady and moderate slope area not only shows obvious seasonal evolution, but also increases and decreases significantly in snow depth. And the percentage of this region in the seasonal snow significant evolution area is 5.82%. (3) The geographical partitions with the largest area in all evolution areas is the middle altitude hill with sunny and gentle slopes area (4.75%). (4) The geographical partition with the largest variation of snow depth in Tianshan region is the low relief middle altitude mountain with shady and moderate slope area (12.02 cm). (5) The snow accumulation and melting are obvious in the range of 1000–3500 m above altitude, different geomorphology types lead to obvious differences in snow characteristics. The snow melting is most obvious in the gentle slope area of the low topographic relief geomorphology types, and the snow accumulation is most obvious in the steep slope area of the middle relief geomorphology types. [ABSTRACT FROM AUTHOR]

Permafrost in northeast China, which is at the southern edge of the high-latitude permafrost belt in Eurasia, is extremely sensitive to climate warming. However, the distribution of permafrost in the region in recent years has been poorly studied, and there is a lack of understanding of the relative importance of environmental factors affecting the region. Based on observed ground surface temperature (GST) data, this study quantifies changes in the permafrost area in northeast China from 1982 to 2020 using a surface frost number model, and the influencing factors are identified based on dominance analysis and spatial correlation analysis. The results suggest that the permafrost in northeast China during the observation period underwent degradation with a degradation rate of 0.33 × 104 km2/a. In addition, the permafrost degradation also exhibited altitudinal and latitudinal zonality. Permafrost degradation under typical grassland, deciduous forest, and savannah cover was more significant than that under evergreen forest, mixed forest, and shrubbery cover. As revealed by the dominance analysis results, the annual average snow cover, annual average snow depth and annual average normalized difference vegetation index (NDVI) had the largest contributions to the variance of the permafrost area in northeast China, accounting for 88.3% of the total variance contribution of the six influencing factors. The spatial correlation results reveal that areas with a significantly increased NDVI and significantly reduced snow depth and snow cover were coincident with areas with significantly degraded permafrost. Hence, the snow cover, snow depth, and NDVI were found to have the greatest influence on the permafrost distribution in northeast China. The results of this study evidently increase the understanding of the changing permafrost in northeast China, providing important knowledge about permafrost for researchers and the related community. [ABSTRACT FROM AUTHOR]

Spatiotemporally continuous soil moisture (SM) data are increasingly in demand for ecological and hydrological research. Satellite remote sensing has potential for mapping SM, but the continuity of satellite-derived SM is hampered by data gaps resulting from inadequate satellite coverage, snow cover, frozen soil, radio-frequency interference, and so on. Therefore, we propose a new gap-filling approach to reconstruct daily SM time series using the European Space Agency Climate Change Initiative (ESA CCI). The developed approach integrates satellite observations, model-driven knowledge, and a machine learning algorithm that leverages both spatial and temporal domains. Taking SM in China as an example, the reconstructed SM showed high accuracy when validated against multiple sets of in situ measurements, with a root mean square error (RMSE) and a mean absolute error (MAE) of 0.09–0.14 and 0.07–0.13 cm 3 cm -3 , respectively. Further evaluation with a 10-fold cross-validation revealed median values of the coefficient of determination (R 2), RMSE, and MAE of 0.56, 0.025, and 0.019 cm 3 cm -3 , respectively. The reconstructive performance was noticeably reduced both when excluding one explanatory variable and keeping the other variables unchanged and when removing the spatiotemporal domain strategy or the residual calibration procedure. In comparison with gap-filled SM data based on a satellite-derived diurnal temperature range (DTR), the gap-filled SM data from bias-corrected model-derived DTRs exhibited relatively lower accuracy but higher spatial coverage. Application of our gap-filling approach to long-term SM datasets (2005–2015) produced a promising result (R2=0.72). A more accurate trend was achieved relative to that of the original CCI SM when assessed with in situ measurements (i.e., 0.49 versus 0.28, respectively, in terms of R2). Our findings indicate the feasibility of integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning to fill gaps in short- and long-term SM time series, thereby providing a potential avenue for applications to similar studies. [ABSTRACT FROM AUTHOR]

Vegetation change can alter surface energy balance and subsequently affect the local climate. This biophysical impact has been well studied for forestation cases, but the sign and magnitude for persistent earth greening remain controversial. Based on long-term remote sensing observations, we quantify the unidirectional impact of vegetation greening on radiometric surface temperature over 2001–2018. Here, we show a global negative temperature response with large spatial and seasonal variability. Snow cover, vegetation greenness, and shortwave radiation are the major driving factors of the temperature sensitivity by regulating the relative dominance of radiative and non-radiative processes. Combined with the observed greening trend, we find a global cooling of −0.018 K/decade, which slows down 4.6 ± 3.2% of the global warming. Regionally, this cooling effect can offset 39.4 ± 13.9% and 19.0 ± 8.2% of the corresponding warming in India and China. These results highlight the necessity of considering this vegetation-related biophysical climate effect when informing local climate adaptation strategies. Using satellite observations over the recent two decades, the authors quantify the biophysical impact of earth greening on land surface temperature and show a considerable cooling effect in India and China, important for climate mitigation. [ABSTRACT FROM AUTHOR]

Rock glaciers are typical periglacial landforms with tongue or lobate morphological shapes and characterized by the distinct front, lateral margins, and often by ridge-and-furrow surface topography textures as well as kinematic characteristics, widely distributed in alpine environments. Multitemporal Synthetic aperture radar interferometry (MT-InSAR), is a remote sensing technique with demonstrated effectiveness for detecting landform kinematics. However, its application to rock glaciers is challenged by temporal decorrelation and atmospheric phase noises due to complex topography and snow cover. We designed a quadtree segmentation and parallel computing-based MT-InSAR method to improve the quality and efficiency of deformation measurement of rock glaciers. We applied the method to a rock glacier inventory of the Nyainqêntanglha Range, China, derived from high-resolution Gaofen-2 images, to quantify the activity rate of each rock glacier. Results showed that 32.1% (6,389) of the identified rock glaciers exhibited slope-parallel deformation rates exceeding 100 mm/y. The activities of the rock glaciers exhibited strong correlations with their distance to glaciers, precipitation, freeze–thaw magnitude, and permafrost occurrence probability. The results demonstrate the effectiveness of the developed segmentation-parallel MT-InSAR method for monitoring rock glacier deformation over a large region. [ABSTRACT FROM AUTHOR]

Snow density plays a critical role in estimating water resources and predicting natural disasters such as floods, avalanches, and snowstorms. However, gridded products for snow density are lacking for understanding its spatiotemporal patterns. In this study, considering the strong spatiotemporal heterogeneity of snow density, as well as the weak and nonlinear relationship between snow density and the meteorological, topographic, vegetation, and snow variables, the geographically and temporally weighted neural network (GTWNN) model is constructed for estimating daily snow density in China from 2013 to 2020, with the support of satellite, ground, and reanalysis data. The leaf area index of high vegetation, total precipitation, snow depth, and topographic variables are found to be closely related to snow density among the 20 potentially influencing variables. The 10-fold cross-validation results show that the GTWNN model achieves an R2 of 0.531 and RMSE of 0.043 g cm -3 , outperforming the geographically and temporally weighted regression model (R2=0.271), geographically weighted neural network model (R2=0.124), and reanalysis snow density product (R2=0.095), which demonstrates the superiority of the GTWNN model in capturing the spatiotemporal heterogeneity of snow density and the nonlinear relationship to the influencing variables. The performance of the GTWNN model is closely related to the state and amount of snow, in which more stable and plentiful snow would result in higher snow density estimation accuracy. With the benefit of the daily snow density map, we are able to obtain knowledge of the spatiotemporal pattern and heterogeneity of snow density in China. The proposed GTWNN model holds the potential for large-scale daily snow density mapping, which will be beneficial for snow parameter estimation and water resource management. [ABSTRACT FROM AUTHOR]

In recent years, the photovoltaic (PV) systems of rural buildings have been demonstratively applied in severe cold areas of China. However, the lack of power generation performance and economic evaluation results is not conducive to the PV promotion. This study innovatively presented the performance and economic evaluation of the grid-connected rural building applied PV (BAPV) system in Hailar, China from the predicted and measured data. The measured monthly PV outputs were counted and analyzed from the year 2020 to 2021. The ambient temperature, solar radiation and predicted output were obtained by the software of PVSYST. The performance ratio (PR), efficiency and loss were investigated, and their relationship with temperature and sunshine was analyzed. According to the measured data and theoretical calculation, the payback periods and profits were calculated. The results indicate that the measured output yields are 14,123.70 kWh in 2020 and 13,265.10 kWh in 2021, which are 6.05%~11.76% lower than the predicted value due to snow cover in the cold months. The monthly predicted array output is about 3.4% higher than the energy output. The array capture loss (LC) is 14.99–34.80 kWh and the system loss (LS) is 29.6–5.81 kWh. The PR is 83.30%-99.80%. The average system efficiency and array efficiency are 16.15% and 15.62%. The lower PR and efficiency are caused by the high temperature. The measured and theoretical payback periods are 6.7 and 5.6 years, respectively. The theoretical annual rate of income is 4828.15 Chinese yuan (CNY). These results provide an important reference for the promotion of rural residential BAPV in severe cold areas. [ABSTRACT FROM AUTHOR]

Knowledge of the difference between soil and air temperatures (ΔT) is helpful to improve our understanding on the land‐atmosphere thermal interactions and temperature‐dependent soil processes. Based on 272 stations across China, this study investigated the spatiotemporal variations of the annual and seasonal ΔT (difference between soil temperature at a depth of 0.4 m and air temperature) from 1981 to 2014, and quantified the relative contributions of multiple environmental variables (snow cover, precipitation, vegetation, soil moisture, and solar radiation) to ΔT variation for the first time. Air temperature primarily controls soil temperature dynamics, but the asynchronous trends of soil and air temperatures may lead to the complexity of the land‐atmosphere relationship. Almost no apparent trends in ΔT were detected for the entire China (except in summer), but the spatial heterogeneity of trends was evident. Snow cover conditions greatly dominated the ΔT dynamics both annually and seasonally (except in summer). The relative contribution of snow cover duration to ΔT variation was significantly greater than that of mean snow depth for the entire China, but the regional differences in the contributions of the two variables were noticeable at different seasons. The greening of vegetation closely associated with the ΔT variation in annual, autumn and winter, and soil moisture exerted a great influence on summer ΔT, associated with sunshine duration (a proxy for surface solar radiation). The amount of precipitation made a slight impact on ΔT at either seasonal or annual scales. [ABSTRACT FROM AUTHOR]

A long-term high-resolution national dataset of precipitation (P), soil moisture (SM), and snow water equivalent (SWE) is necessary for predicting floods and droughts and assessing the impacts of climate change on streamflow in China. Current long-term daily or sub-daily datasets of P , SM, and SWE are limited by a coarse spatial resolution or the lack of local correction. Although SM and SWE data derived from hydrological simulations at a national scale have fine spatial resolutions and take advantage of local forcing data, hydrological models are not directly calibrated with SM and SWE data. In this study, we produced a daily 0.1 ∘ dataset of P , SM, and SWE in 1981–2017 across China, using global background data and local on-site data as forcing input and satellite-based data as reconstruction benchmarks. Global 0.1 ∘ and local 0.25 ∘P data in 1981–2017 are merged to reconstruct the historical P of the 0.1 ∘ China Merged Precipitation Analysis (CMPA) available in 2008–2017 using a stacking machine learning model. The reconstructed P data are used to drive the HBV hydrological model to simulate SM and SWE data in 1981–2017. The SM simulation is calibrated by Soil Moisture Active Passive Level 4 (SMAP-L4) data. The SWE simulation is calibrated by the national satellite-based snow depth dataset in China (Che and Dai, 2015) and the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data. Cross-validated by the spatial and temporal splitting of the CMPA data, the median Kling–Gupta efficiency (KGE) of the reconstructed P is 0.68 for all grids at a daily scale. The median KGE of SM in calibration is 0.61 for all grids at a daily scale. For grids in two snow-rich regions, the median KGEs of SWE in calibration are 0.55 and - 2.41 in the Songhua and Liaohe basins and the northwest continental basin respectively at a daily scale. Generally, the reconstruction dataset performs better in southern and eastern China than in northern and western China for P and SM and performs better in northeast China than in other regions for SWE. As the first long-term 0.1 ∘ daily dataset of P , SM, and SWE that combines information from local observations and satellite-based data benchmarks, this reconstruction product is valuable for future national investigations of hydrological processes. [ABSTRACT FROM AUTHOR]

Snow cover has significant impacts on the global water cycle, ecosystem, and climate change. At present, satellite remote sensing is regarded as the most efficient approach to detect long-term and multiscale observations of snow cover extent. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze and evaluate the consistency of the snow cover products retrieved from these two sensors. In this study, we performed comparisons and a consistency evaluation between the MODIS and VIIRS snow cover products in three major snow distribution regions in China: Northeast China (NE), Northwest China (NW) and the Qinghai–Tibet Plateau (QT). The results demonstrated that (1) the normalized difference snow index (NDSI)-derived snow cover products showed suitable consistency between VIIRS and MODIS under clear sky conditions, with a mean difference value of less than 5%; (2) the VIIRS snow cover product presented much more snow and fewer clouds than that of MODIS in the snow season due to the differences in cloud-masking algorithms; (3) cloud mask strongly affects the potential of snow cover observation, and presents seasonal pattern in the test regions; and (4) VIIRS is able to distinguish clouds from snow with greater accuracy. The comparisons indicated that the greater the difference in cloud cover, the poorer the agreement in snow cover. This evaluation implies that perfecting the cloud-masking algorithm of VIIRS to update the MODIS would be the best solution to achieve better consistency for long-term and high-quality snow cover products. [ABSTRACT FROM AUTHOR]

Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth. [ABSTRACT FROM AUTHOR]

The land surface model is extensively used to simulate turbulence fluxes and hydrological and momentum variables at the land–atmosphere interface. In this study, the Community Land Model, version 5 (CLM5), driven by the 0.1° × 0.1° Chinese Meteorological Forcing Dataset (CMFD) and the field-surveyed soil parameters, is used to simulate land surface processes during 1979–2018. Various high-quality land surface datasets are adopted to assess the model simulations. In general, the CLM5 well captures the monthly variations of 0–10-cm soil moisture in subregions, particularly in the Tibetan Plateau, with an anomaly correlation coefficient between 0.56 and 0.88. However, the simulated soil moisture shows overall wet biases in the whole country, resulting from several reasons. The model simulation is skillful in replicating both the magnitude and spatial pattern when they are compared with the MODIS snow cover dataset. Compared with in situ measured soil temperature in multiple soil layers within 320-cm soil depth from 1980 to 2018, the simulations accurately capture spatial patterns, vertical profiles, and long-term warming trends. For land surface energy components, the simulations have a highly temporal correlation with the observation of Chinese Flux Observation and Research Network (ChinaFLUX) cropland and grassland sites, except for four forest sites, where biases exist in both atmospheric forcing variables and surface vegetation phenology in the model default input dataset. In summary, this study reveals the overall capability of CLM5 in reproducing land surface energy fluxes and hydrological variables over conterminous China, and the validation results may also provide some references for future model improvement and application. Significance Statement: The offline Community Land Model, version 5 (CLM5), driven by a 0.1° × 0.1° (∼10 km) horizontal resolution atmospheric forcing dataset and a set of field-surveyed soil parameters, are used to simulate the land surface hydrological and heat fluxes in continental China for 1980–2018. The simulated hydrological variables and energy fluxes are validated with various sources of high-quality observation-based datasets. From our systematic evaluations, the current CLM5 high–resolution simulation accurately captures the spatial patterns and temporal variations in most of the water and energy balance components, although biases exist in some simulated variables. Overall, this study reveals the capability of the offline CLM5 simulation in conterminous China and provides the reference for future model improvement and application. [ABSTRACT FROM AUTHOR]

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Investigating the spatial and temporal changes in snow cover over mountain areas is significant for understanding the impact of regional climate variability. In this study, using cloud‐removed snow cover data, which are generated based on Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products, the spatiotemporal changes of snow cover fraction (SCF) and its relationship with temperature and precipitation changes from 2002 to 2020 were examined over Altai Mountains, China. The results demonstrate that the distribution and changes of SCF are highly spatiotemporally heterogeneous. Within a year, the maximum SCF occurs in January at 98.6%, and the minimum appears in July at 8.7%. The annual‐mean SCF shows an increasing trend at 0.09%·annum−1, owing to the significantly increasing SCF in the snow accumulation period at 0.5%·annum−1 and the decreasing SCF in the snow melting period at −0.2%·annum−1. The SCF distribution, as well as its interannual change, is greatly influenced by elevation. During the snow cover period, a positive linear correlation between SCF and elevation is found at 0.02%·m−1 (p <.01). The annual‐mean SCF decreases in the area below 1,200 m, whereas it increases in the area above 1,200 m. Accordingly, the elevation‐dependent SCF results in various SCF distributions on different slopes and watersheds. The SCF shows an apparent pattern in different aspects, with similar SCFs between the north and east aspects and between the west and south aspects but a difference between the northeast aspects and the westsouth aspects. The SCF is negatively correlated with air temperature (r = −0.74, p < 0.01) and positively correlated with precipitation (r = 0.74, p < 0.01). In addition, temperature shows a significant and larger correlation with SCF in both the snow accumulation and melting periods, indicating the major factor of temperature for the changes in SCF. [ABSTRACT FROM AUTHOR]

The application of the low impact development (LID) in a cold climate such as northeastern China is constrained by two unresolved research questions with regards to its infiltration potential through the winter and its varied runoff regimes between winters and summers. This study picked a typical residential district under construction in Changchun, China, and modeled the storm drainage system with and without LID facilities based on the Storm Water Management Model. The hydrological performance of LID was evaluated through various design storms and historic rain events in dry, average, and wet years. The influence of the Horton and the Green–Ampt infiltration methods on the seasonal water budgets was particularly compared since the former is universally adopted in China while the latter is more widely used in the U.S. and other countries. The results indicate that the Horton method tended to generate a higher infiltration volume than the Green–Ampt method. Consequently, when driven by the 100-year design storm, the Horton method led to a 17.4% higher outflow than the Green–Ampt method; when driven by the measured 3-year precipitation in the study area, the yearly runoff coefficients, with regards to the Horton method, were at least 1.3 times higher than those modeled by the Green–Ampt method. This finding challenged the interchangeable use of the Horton and Green–Ampt methods without tests. Furthermore, the formation of snow covers in winter also reduced the permeability of LID and its capacity of managing runoff compared to summer. However, LID still exhibited a decent potential of regulating the winter runoff in the cold region compared to the baseline, possibly owing to the presence of frequent freezing-thawing cycles. [ABSTRACT FROM AUTHOR]

Extreme snow loads can collapse roofs. This load is calculated based on the ground snow load (that is, the snow water equivalent on the ground). However, snow water equivalent (SWE) measurements are unavailable for most sites, while the ground snow depth is frequently measured and recorded. A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth, precipitation data, wind speed, and air temperature. For the evaluation, the precipitation was classified as snowfall or rainfall according to the air temperature, the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch, and the effect of snow water loss was considered. The developed algorithm was applied and validated using data from 57 meteorological stations located in the northeastern region of China. The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements. The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code. The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load. Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping. [ABSTRACT FROM AUTHOR]

Microwave Land Surface Emissivity (MLSE) over China under both clear and cloudy sky conditions was retrieved using measurements of recalibrated microwave brightness temperatures (Tbs) from Fengyun-3B Microwave Radiation Imager (FY-3B MWRI), combined with cloud properties derived from Himawari-8 Advanced Himawari Imager (AHI) observations. The contributions from cloud particles and atmospheric gases to the upwelling Tbs at the top of atmosphere were calculated and removed in radiative transfer. The MLSEs at horizontal polarizations at 10.65, 18.7, and 36.5 GHz during 7 July 2015 to 30 June 2019 over China showed high values in the southeast vegetated area and low values in the northwest barren, or sparsely vegetated, area. The maximum values were found in the belt area of the Qinling-Taihang Mountains and the eastern edge of the Qinghai-Tibet Plateau, which is highly consistent with MLSEs derived from AMSR-E. It demonstrates that the measurements of FY-3B MWRI Tbs, including its calibration and validation, are reliable, and the retrieval algorithm developed in this study works well. Seasonal variations of MLSE in China are mainly driven by the combined effects of vegetation, rainfall, and snow cover. In tropical and southern forest regions, the seasonal variation of MLSE is small due to the enhancement from vegetation and the suppression from rainfall. In the boreal area, snow causes a significant decrease of MLSE at 36.5 GHz in winter. Meanwhile, the MLSE at lower frequencies experiences less suppression. In the desert region in Xinjiang, increases of MLSEs at all frequencies are observed with increasing snow cover. [ABSTRACT FROM AUTHOR]