Snow Cover Days Research Articles

Effects of Temperature, Precipitation, and Sunshine on Cold-Tolerant Wheat Yield Under Warming Trends: A 20-Year Study in Hokkaido, Japan

To clarify the adaptation strategies of cold-tolerant wheat against global warming, this study examined the effects of daily temperature, precipitation, and sunshine duration on wheat yield in Hokkaido, Japan, over 13 years (2011–2023). Yield components were also analyzed over 20 years (2004–2023). The number of snow-cover days decreased by about 24 days over the 20-year period. As a result, the growth of overwintered wheat accelerated, with the heading and maturity of plants advancing by about 8 and 5 days, respectively, and the grain-filling period extending from about 44 to about 48 days. Multiple regression analysis was conducted using wheat yield as the objective variable and weather conditions as explanatory variables. Three weather conditions were selected: precipitation for 8 days from 27 March, sunshine hours for 8 days from 21 March, and sunshine hours for 12 days from 13 June, which yielded a coefficient of determination of 0.953. Despite the highest mean summer temperatures on record being registered in 2023, high yields were ensured by the number of sunshine hours, which were approximately 1.5 times the normally recorded hours. This highlights the importance of this parameter in mitigating the impact of high summer temperatures.

Investigating climatic drivers of snow phenology by considering key-substage heterogeneity

Investigating the main climatic drivers responsible for changes in snow cover phenology (SCP) is crucial for making scientific countermeasures to ensure water resources security in global mountainous regions. However, most studies have explored drivers of SCP changes using a fixed substage division scheme and correlation analysis (referred to as the traditional method), potentially limiting reliability and accuracy in mountainous areas with complex terrain and climate. Here, a novel method is developed to efficiently identify main climatic drivers of SCP changes. This method employs a flexible scheme to account for the spatial heterogeneity of the dominant sub-period (the sub-period with the major climatic effect) in combination with regression analysis. Using the arid region of China as a case study, the new method was applied to three SCP parameters including snow cover days, snow start date, and snow end date, based on a seamless snow cover dataset from 2002 to 2019. The method’s effectiveness was evaluated by comparing it with the traditional method. The results indicate significant spatial heterogeneity in the dominant sub-period(s), closely associated with local temperature and elevation. The traditional method failed to accurately identify the main drivers of SCP changes, as evidenced by sub-region and elevation zone analyses showing adjusted coefficient of determination (R2) of < 0.5 in most cases. This inadequacy is attributed to its fixed and inappropriate scheme of substage division. In contrast, the new method, with its flexible scheme, achieved much higher adjusted R2 values (mostly > 0.5) and exhibited better performances in predicting SCP changes. Thanks to the new method, climatic causes of SCP changes were successfully identified in 12 hotspots (regions with significant SCP changes), all with adjusted R2 > 0.5. The climatic causes were found to vary significantly across different hotspot regions and SCP parameters. The proposed method holds significant potential to enhance the reliability of analyses concerning main climatic drivers of SCP changes in mountainous regions globally.

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

Climate Teleconnections Influencing Historical Variations, Trends, and Shifts in Snow Cover Days in Finland

Climate Teleconnections Influencing Historical Variations, Trends, and Shifts in Snow Cover Days in Finland

Improved snow depth estimation on the Tibetan Plateau using AMSR2 and ensemble learning models

Snow depth (SD) is essential for studying climate change and hydrological cycle on the Tibetan Plateau (TP). Despite the effectiveness of passive microwave remote sensing for large-scale SD measurement, its low spatial resolution and scanning gaps limit its application, particularly in the TP region where the terrain is complex and snow distribution exhibits obvious heterogeneity. This study developed Advanced Microwave Scanning Radiometer 2 (AMSR2) SD downscaling models for the TP using ensemble learning methods and AMSR2 brightness temperature data from October 1, 2012, to April 30, 2021. We employed five ensemble methods—AdaBoost, GBDT, XGBoost, LightGBM, and Random Forest—with LightGBM achieving the highest accuracy (RMSE=2.66 cm). Recursive feature elimination (RFE) was applied to the LightGBM model, optimizing factor selection and maintaining high accuracy. The models excelled in estimating shallow snow areas (SD<5 cm) with an RMSE of 1.60 cm. SHapley Additive exPlanations (SHAP) values were used to quantify global and local contributions of each factor in the modeling process. Key factors included snow cover days, meteorological influences, and brightness temperature (BT) at 89 GHz with horizontal polarization, although their contributions varied significantly across the TP due to environmental gradients. The resulting 500 m SD estimates offer detailed and accurate snow distribution information in complex mountainous regions. Our results help to improve water resource management and climate change analysis on the TP.

Combined Use of Multiple Cloud‐Free Snow Cover Products in China and Its High‐Mountain Region: Implications From Snow Cover Identification to Snow Phenology Detection

AbstractAccurate 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.

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

Unraveling the effects of snow cover change on vegetation productivity: Insights from underlying surface types

AbstractChanges in snow cover have an important effect on vegetation gross primary productivity (GPP), but the role of underlying surface types in this effect still remains unclear. The underlying surface refers to the surface of the Earth and is located at the bottom of the snow cover during the snow season. Here, we focus on the influence of snow cover change on GPP in Northeast China from 1982 to 2015, revealing the various dependencies of the association between snow cover and GPP on forest, grassland, and dryland. The results show that the increases in GPP in dryland and grassland are primarily influenced by the increase in snow water equivalent, while the variations in GPP in forest are primarily influenced by snow phenological indicators, with snow cover end date and snow cover days dominating GPP in the early and peak growing seasons, respectively. Additionally, snow cover can have a rather strong time‐lagged effect on GPP by changing the soil temperature in dryland. Our findings emphasize the importance of considering underlying surface types in the snow–GPP relation and provide insight into how climate change may affect ecosystems in regions with comparable underlying surface types.

An Optimized Variational Processing Method Based on Satellite-Station Data on Snow Cover Days on the Qinghai–Tibet Plateau

An Optimized Variational Processing Method Based on Satellite-Station Data on Snow Cover Days on the Qinghai–Tibet Plateau

A 20-Year Ecotone Study of Pacific Northwest Mountain Forest Vulnerability to Changing Snow Conditions

(1) Background: Global climate change is expected to significantly alter growing conditions along mountain gradients. Landscape ecological patterns are likely to shift significantly as species attempt to adapt to these changes. We evaluated the extent to which spatial (elevation and canopy cover) and temporal (decadal trend and El Niño–Southern Oscillation/Pacific Decadal Oscillation) factors impact seasonal snowmelt and forest community dynamics in the Western Hemlock–True Fir ecotone region of the Oregon Western Cascades, USA. (2) Methods: Tsuga heterophylla and Abies amabilis seedling locations were mapped three times over 20 years (2002–2022) on five sample transects strategically placed to cross the ecotone. Additionally, daily ground temperature readings were collected over 10 years for the five transects using 123 data loggers to estimate below-canopy snow metrics. (3) Results: Based on validation using time-lapse cameras, the data loggers proved highly reliable for estimating snow cover. The method reported fewer days of snow cover as compared to meteorological station-based snow products for the region, emphasizing the importance of direct under-canopy field observations of snow. Snow season variability was most significantly impacted temporally by cyclical ENSO/PDO climate patterns and spatially by differences in canopy cover within the ecotone. The associated seedling analysis identified clear sorting of species by elevation within the ecotone but reflected a lack of a long-term trend, as species dominance in the seedling strata did not significantly shift along the elevation gradient over the 20-year study. (4) Conclusions: The data logger-based approach provided estimates of snow cover at ecologically significant locations and fine enough spatial resolutions to allow for the study of forest regeneration dynamics. The results highlight the importance of long-term, understory snow measurements and the influence of climatic oscillations in understanding the vulnerability of mountain areas to the changing climate.

Long-Term Variations and Its Driving Factors of the TOA Albedo over the Loess Plateau

Abstract Solar radiation balances significantly affect Earth’s surface energy balance and climate change. Studying top-of-the-atmosphere (TOA) albedo changes is of great significance for understanding Earth’s energy budget and atmospheric circulation. The Loess Plateau (LP), located in the middle reaches of the Yellow River in China, is one of the most severely eroded areas in the world. In this paper, long-term remote sensing data were used to analyze the changes in the TOA albedo in the LP from 1982 to 2016. The results showed that the TOA albedo, its atmospheric contribution (AC), and surface contribution (SC) exhibited decreasing trends: −0.0012, −0.0010, and −0.0003 a−1. The spatial pattern of the TOA albedo was similar to AC, which indicates that AC dominates the change in the TOA albedo. We detected driving factors for AC and SC and found that the cloud fraction (CF) was the main driving factor of the AC, whereas the soil moisture (SM) dominated the SC. The driving factors of two typical regions with a significantly decreasing trend in the TOA albedo were also detected. The Mu Us Desert, where vegetation improved significantly, showed a decreasing trend in the TOA albedo, and we found that NDVI was the main driving factor for the change in the SC of the TOA albedo. However, the Eastern Qilian Mountains, where snow cover decreased in recent years, also showed a significant decreasing trend in the TOA albedo; the SC here was mainly driven by the changes in snow cover days (SCD). These results indicate that changes in the surface environment alter the radiation balance. Significance Statement The Loess Plateau in China is one of the most severe cases of soil erosion in the world, and ecological restoration projects have been carried out to recover the fragile ecological environment. Our study was designed to explore changes in the top-of-the-atmosphere (TOA) albedo of the Loess Plateau between 1982 and 2016 using a long time series of multisource satellite products, and driving factors in the atmosphere and at the surface were analyzed. We concluded that the TOA albedo of the Loess Plateau decreased over 35 years, and its atmospheric contribution dominated the change in the TOA albedo. However, the significant ecological improvement in the Loess Plateau, especially in the central vegetation recovery region, such as the Mu Us Desert, was also strongly related to the regional changes in the surface contribution of the TOA albedo. The climate changes had a considerable impact on the eastern branch of the Qilian Mountains in the Qinghai region, where the decline in snow cover days affected the local Alpine meadow ecosystems; therefore, snow cover days also played a decisive role in the local variation of the surface contribution of the TOA albedo.

Global reduction of snow cover in ski areas under climate change.

Ongoing climate change substantially alters snowfall patterns with severe but diverging consequences for global ski areas. A global assessment as well as the investigation of potential implications for mountain ecosystems is currently lacking. We quantify future trends in natural snow cover days under different climate change scenarios until 2100 in seven major global skiing regions and discuss implications for mountainous biodiversity by analysing how natural snow cover days relate to regional human population density. Within all major skiing regions, snow cover days are projected to decrease substantially under every assessed climate change scenario. Thirteen percent of all current ski areas are projected to completely lose natural annual snow cover and one fifth will experience a reduction of more than 50% by 2071-2100 relative to historic baselines. Future skiable areas will concentrate in less populated areas, towards continental regions and inner parts of the mountain ranges. As skiable areas will be located at greater distances to highly populated areas in the future, we expect an expansion of infrastructure and increasing intervening actions (i.e., artificial snowmaking, slope grooming) to prolong snow duration. Our results are concerning for both the recreational and economic value of skiing as well as for mountain biodiversity since vulnerable high-altitude species might be threatened by space reductions with ski area expansion.

Contrasting Responses of Land Surface Temperature and Soil Temperature to Forest Expansion During the Dormant Season on the Qinghai‐Tibet Plateau

AbstractAs one of the important ecological responses of ecosystem to global climate change, forest expansion can alter land surface energy budget and local microclimate. Land surface temperature (LST) and soil temperature (ST) indicate the above‐ and below‐ground thermal state, respectively. However, the lack of studies on the relationships between LST and ST changes after forest expansion hinders our understanding of the vegetation‐microclimate interactions, especially during the dormant season. We quantified the change of LST (∆LST) and ST (∆ST) after forest expansion in the dormant season on the Qinghai‐Tibet Plateau (QTP), and then explored their differences and linkages. The results showed that forest expansion significantly decreased LST by 0.75 ± 0.20°C, while significantly increased ST in three layers (0–7, 7–28, and 28–100 cm) by 0.31 ± 0.06, 0.29 ± 0.05, and 0.20 ± 0.04°C, respectively. The decreased LST was conducive to the preservation of snow, leading to larger snow depth, larger fractional snow cover and more snow cover days in forests than grasslands, which further promoted the increased ST through enhanced thermal insulation. Furthermore, it is suggested that the cooling impact of forest expansion on LST and warming impact on ST would both be stronger at humid sites than at dry sites. These findings will contribute to understand the vegetation‐microclimate‐snow interactions and the decoupling phenomenon between LST and ST in the dormant season influenced by vegetation change.

Spatial stratified heterogeneity analysis of field scale permafrost in Northeast China based on optimal parameters-based geographical detector.

Affected by global warming, the permafrost in Northeast China (NEC) has been continuously degrading in recent years. Many researchers have focused on the spatial and temporal distribution characteristics of permafrost in NEC, however, few studies have delved into the field scale. In this study, based on the Optimal Parameters-based Geographical Detector (OPGD) model and Receiver Operating Characteristic (ROC) test, the spatial stratified heterogeneity of permafrost distribution and the indicating performance of environmental variables on permafrost in NEC at the field scale were analyzed. Permafrost spatial distribution data were obtained from the Engineering Geological Investigation Reports (EGIR) of six highways located in NEC and a total of 19 environmental variables related to heat transfer, vegetation, soil, topography, moisture, and ecology were selected. The H-factors (variables with the highest contribution in factor detector results and interaction detector results): slope position (γ), surface frost number (SFN), elevation (DEM), topographic diversity (TD), and annual snow cover days (ASCD) were found to be the major contributors to the distribution of permafrost at the field scale. Among them, γ has the highest contribution and is a special explanatory variable for permafrost. In most cases, interaction can improve the impact of variables, especially the interaction between H-factors. The risk of permafrost decreases with the increase of TD, RN, and SBD, and increases with the increase of SFN. The performance of SFN to indicate permafrost distribution was found to be the best among all variables (AUC = 0.7063). There is spatial heterogeneity in the distribution of permafrost on highways in different spatial locations. This study summarized the numerical and spatial location between permafrost and different environmental variables at the field scale, and many results were found to be informative for environmental studies and engineering construction in NEC.

Assessment of snow simulation using Noah-MP land surface model forced by various precipitation sources in the Central Tianshan Mountains, Central Asia

Accurate mountainous snow estimation is paramount for hydrological processes and water resources estimation in arid regions. Using Land surface models (LSMs) is a practical numerical approach for snow estimation in a complex orography region such as the Tianshan Mountains. However, the bias of precipitation forcing is a major source of uncertainty for snow simulation over scarce-data regions. This study evaluated the performance of 4 km snow simulations using the Noah-MP LSM driven by eight precipitation sources. They are retrieved from non-model products and climate model simulations during the 2018-2019 cold season in the Central Tianshan Mountains (CTS) region. Multi-source validation datasets (in-situ observation, field snow pits, and remote sensing products) have been used for evaluation and uncertainty estimation. The results showed that the difference in precipitation amount significantly affected the snowpack simulation performance. Compared with non-model products (Global Land Data Assimilation System (GLDAS), Global Precipitation Measurement (GPM), and Gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP)), the cold season precipitation from climate model simulations exhibited a better performance overall in the high-elevation regions (elevation > 1000 m) evaluated by in-situ observations. In addition, the 4 km convection-permitting modeling (CPM) precipitation (WRF-Morrison and WRF-WSM6) showed higher accuracy (RMSE: 23.48 mm/season and 18.94 mm/season, respectively) than gray-zone resolution simulations and ERA5-land driven runs in the high-elevation regions. The snow depth simulation driven by WRF-Morrison precipitation had the second smallest RMSE (4.67 cm/day) and lowest bias (-0.74 cm/day) value in the high-elevation regions compared with in-situ observation. Meanwhile, CPM precipitation-driven runs exhibited the smallest RMSE value based on the assessment of snow field pits. In addition, the CPM-driven simulation achieved the closest match to the elevation-based distribution of snow cover days in regions with elevation over 1000 m and duration over 60 days compared to the MODIS product. The findings of this study highlight that CPM with proper parameterization configurations has added values in producing realistic topographic precipitation for snow modeling using LSMs over data-scarcity mountainous areas.

Long‐Term Variability and Trends in Snow Depth and Cover Days Throughout Iranian Mountain Ranges

AbstractIn Iran, the mountain snow cover generally feeds major rivers and thereby largely provides water resources required for improving human lives and protecting nature. Hence, understanding historical variability and trends in mountainous snowpack water resources in Iran in response to global warming and climate change can play a critical role in the sustainable development of this country. Accordingly, this study investigated long‐term (1982–2018) snowpack climatology at 13 hydrometeorological measurement stations scattered throughout the Iranian mountain ranges, with a focus on Elburz, Azerbaijan, Zagros, and Khorasan mountainous regions. The non‐parametric Mann‐Kendall test was used to detect statistically significant (p &lt; 0.05) trends, the Pettitt test to identify possible abrupt shift years, the Pearson's correlation coefficient to measure relationships among different time series, and the partial correlation to determine the most important climate factor influencing snowpack dynamics The annual snow depth (maximum snow depth) significantly declined throughout Iranian mountain ranges during 1982–2018, with an average rate of 1.0 (3.4) cm decade−1. The annual snow cover days (SCDs) also showed significant decreasing trends, ranging from 3 to 15 days decade−1 during 1982–2018, in 69% of the stations studied. Such considerable reductions in snow depth and cover days were mainly related to the compound effects of substantial increases in temperature, sunshine, and wind speed as well as decreases in precipitation and cloudiness during the SCDs across the Iranian mountain ranges. However, precipitation was the most influential climate factor controlling snow resources throughout both the Elburz and Zagros mountains in Iran.

Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables

The mountain cryosphere is crucial for socio-economic processes, especially during the dry seasons. However, anthropogenic climate change has had a detrimental impact on the cryosphere due to its sensitivity. Over the past two decades, there has been a decline in precipitation and a temperature rise, leading to a substantial reduction in the timing and extent of snow cover. This increase in temperature also elevates the snow line elevation (SLE), further diminishing the volume of available freshwater in the snow-driven basins of the Andes. In this study, we use 22 years (2000–2021) of 8-day snow product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the annual and seasonal variability of snow cover area, SLE, and snow persistence (SP, an indicator of the duration of snow) in the Yeso River basin in Central Chile and the correlation of SP and SLE with hydrometeorological variables and climatic indices. We introduce a new approach called the Maximum Dissimilarity Method to obtain the SLE even on cloudy days. The results are as follows: (1) Snow cover area reductions of 34.0 km2 at low elevations in spring and 86.5 km2 at mid elevations in summer were found when comparing the period 2016–2021 to 2000–2004; (2) SP trends at the annual scale revealed a significant decrease in 89% of its area and an average of 3.6 fewer days of snow cover per year; (3) an upward and significant trend of 21 m‧year−1 in the annual SLE was found; and (4) annual SP and SLE were highly correlated with annual hydrometeorological variables, and spring and summer snow variables were significantly correlated with dry streamflow. This methodology can potentially serve as a valuable tool for detecting trends in snow-covered surfaces, and thereby associate these changes with climate change or other anthropogenic effects in future research.

Spatiotemporal variation of snow cover days and influencing factors on the Loess Plateau during 2000–2019

Identifying the large-scale teleconnection is of great practical significance to comprehend the formation mechanism of snow cover days (SCDs) anomalies and improve the disaster risk management of ecological construction on the Loess Plateau. Based on Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover days datasets from 2000 to 2020, we analyzed the spatiotemporal variation of SCDs on the Loess Plateau. Meanwhile, we identified the variability in large-scale atmospheric patterns associated with SCDs on the Loess Plateau. The results show that: in the past 20 years have revealed an interdecadal fluctuations of SCDs on the Loess Plateau. Spatially, the SCDs experienced a significant decreasing trend (p < 0.05) in only 13.1% of the regions, which were distributed in the Northern Earth-Rock Mountain and Hully and Gully Loess Region. The effects of climate-related factor variations on SCD dynamics showed obvious spatial and monthly differences. Specifically, SCDs dominated by precipitation were significantly positively correlated in the north-eastern part of the Loess Plateau. Temperature plays a decisive role in SCD dynamics in the southern part of the Loess Plateau. Among the influencing factors, the East Asian Trough Intensity (EATI), the Asian Polar Vortex Area index (APVA) and the sea surface temperature (SST) anomalies in the NINO W region are three effective indictors of SCDs anomaly. In January, with the deepening of East Asian Trough and low SST in the NINO W region (0°∼10°N, 140°E∼180°E), East Asian winter monsoon is relatively strengthening, temperature is lower on the Loess Plateau, leading to more SCDs in the southern study region. The bigger APVA, corresponding to more snow accumulation in December, could effectively prolong the SCDs dominated by precipitation on the Loess Plateau. The conclusions of this study have implications for excavating early warning information of snow cover anomalies on the Loess Plateau.

Unraveling Effect of Snow Cover on Spring Vegetation Phenology across Different Vegetation Types in Northeast China

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.

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

AbstractSnow cover is an important control element in the Tibetan Plateau (TP) ecosystem; however, the impact of snow cover changes on gross primary productivity (GPP) is largely unknown, particularly under complex geographical conditions. In this study, we investigated the impacts of snow cover changes on different seasonal GPP and their mechanisms in different geographical zones using multisource remote sensing data from 1982 to 2018. Snow cover significantly affected GPP by nearly 15% of the TP region, and snow cover days (SCDs) were the dominant snow cover indicators affecting GPP variations compared with snow water equivalent (SWE) and snow cover end date (SCED). In general, an increase in snow cover leads to a significant increase in GPP in regions with low precipitation and temperature, but limits the accumulation of GPP in humid and warm regions. Furthermore, from the humid to the arid zone, the “moisture effect” of snow cover (by altering soil moisture) plays an increasingly important role in regulating GPP variations with increasing drought levels. This study elucidates the importance of snow cover in regulating different seasonal GPP variations and significantly improves our insight into the response of vegetation carbon uptake to snow cover changes in the TP.