As an important source of soil moisture content during spring in high-latitude areas, snow cover affects the occurrence of spring drought and crop yield and quality. There has not been sufficient research on the effect of winter snow cover on spring soil moisture content. This paper focuses on the main agricultural areas of Northeast China—the Songnen Plain and the Sanjiang Plain. Using meteorological data of both spring soil moisture content and snow cover at 19 agricultural meteorological stations from 1983 to 2019, the effect of snow cover on spring soil moisture content in the Sanjiang Plain and Songnen Plain is studied by variance analysis, spatial analysis, and correlation analysis. The results show that: (1) Compared to the Sanjiang Plain, the Songnen Plain has a significantly lower content of soil moisture at the surface (0–10 cm) and deep layer (10–20 cm, 20–30 cm) during the entire spring and every month of spring (p < 0.05), and a greater interannual variation of soil moisture. (2) Snow cover has a significant effect on spring soil moisture in the Songnen Plain, but not as much as one in the Sanjiang Plain. For the Songnen Plain, snow-cover duration and the snow-cover onset date has a lasting influence on spring soil moisture until May, which can extend to as deep as 20–30 cm. As months go by, its influence on shallow-layer soil gradually wears off. Maximum snow depth and the snow-cover end date only influence the April surface soil. (3) Snow cover has a strong effect on soil moisture conservation in more arid areas. Delayed snow-cover onset date, earlier snow-cover end date, and significantly shortened snow-cover duration all contribute to a spring drought soil condition in the Songnen Plain.

Changes in net ecosystem productivity (NEP) in terrestrial ecosystems in response to climate warming and land cover changes have been of great concern. In this study, we applied the normalized difference vegetation index (NDVI), average temperature, and sunshine hours to drive the C-FIX model and to simulate the regional NEP in China from 2000 to 2019. We also analyzed the spatial patterns and the spatiotemporal variation characteristics of the NEP of terrestrial ecosystems and discussed their main influencing factors. The results showed that (1) the annual average NEP of terrestrial ecosystems in China from 2000 to 2019 was 1.08 PgC, exhibiting a highly significant increasing trend with a rate of change of 0.83 PgC/10 y. The terrestrial ecosystems in China remained as carbon sinks from 2000 to 2019, and the carbon sink capacity increased significantly. The NEP of the terrestrial ecosystem increased by 65% during 2015–2019 compared to 2000–2004 (2) There was spatial differences in the NEP distribution of the terrestrial ecosystems in China from 2000–2019. Taking the line along the Daxinganling-Yin Mountains-Helan Mountains-Transverse Range as the boundary, the NEP was significantly higher in the eastern part than in the western part. Among them, the NEP was positive (carbon sink) in northeastern, central, and southern China, and negative (carbon source) in parts of northwestern China and the Tibet Autonomous Region. The spatial variation of NEP in terrestrial ecosystems increased from 2000 to 2009. The areas with a significant increase accounted for 45.85% and were mainly located in the central and southwestern regions. (3) The simulation results revealed that vegetation changes and CO₂ concentration changes both contributed to the increase in the NEP in China, contributing 85.96% and 36.84%, respectively. The vegetation changes were the main factor causing the increase in the NEP. The main contribution of this study is to further quantify the NEP of terrestrial ecosystems in China and identify the influencing factors that caused these changes.

The accurate delineation of the spatial extent of cold regions provides the basis for the study of global environmental change. However, attention has been lacking on the temperature-sensitive spatial changes in the cold regions of the Earth in the context of climate warming. In this study, the mean temperature in the coldest month lower than − 3 °C, no more than 5 months over 10 °C, and an annual mean temperature no higher than 5 °C were selected to define cold regions. Based on the Climate Research Unit land surface air temperature (CRUTEM) of monthly mean surface climate elements, the spatiotemporal distribution and variation characteristics of the Northern Hemisphere (NH) continental cold regions from 1901 to 2019 are analyzed in this study, by adopting time trend and correlation analyses. The results show: (1) In the past 119 years, the cold regions of the NH covered on average about 4.074 × 10⁷ km², accounting for 37.82% of the total land area of the NH. The cold regions can be divided into the Mid-to-High latitude cold regions and the Qinghai-Tibetan Plateau cold regions, with spatial extents of 3.755 × 10⁷ km² and 3.127 × 10⁶ km², respectively. The Mid-to-High latitude cold regions in the NH are mainly distributed in northern North America, most of Iceland, the Alps, northern Eurasia, and the Great Caucasus with a mean southern boundary of 49.48° N. Except for the southwest, the entire region of the Qinghai-Tibetan Plateau, northern Pakistan, and most of Kyrgyzstan are cold regions. (2) In the past 119 years, the rates of change in the spatial extent of the cold regions in the NH, the Mid-to-High latitude, and the Qinghai-Tibetan Plateau were − 0.030 × 10⁷ km²/10 a, − 0.028 × 10⁷ km²/10 a, and − 0.013 × 10⁶ km²/10 a, respectively, showing an extremely significant decreasing trend. In the past 119 years, the mean southern boundary of the Mid-to-High latitude cold regions has been retreating northward at all longitudes. For instance, the mean southern boundary of the Eurasian cold regions moved 1.82° to the north and that of North America moved 0.98° to the north. The main contribution of the study lies in the accurate definition of the cold regions and documentation of the spatial variation of the cold regions in the NH, revealing the response trends of the cold regions to climate warming, and deepening the study of global change from a new perspective.

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 (SWE_max) increased in most areas. However, the proportion of the correlation between SWE_max 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.

Snowmelt water in spring is an important source of soil water, which is critical to supporting crop growth. Quantifying the contribution of snowmelt water to soil water and its dynamic changes is essential for evaluating soil moisture and allocating agricultural water resources. In this paper, through controlled outdoor experiments, different snow depths and soil depth gradients were set; and snow, precipitation, and soil samples were collected regularly. To analyze the contribution of snowmelt water to soil water and its dynamic changes, the MAT-253 stable isotope ratio mass spectrometer was adopted for hydrogen and oxygen isotope analyses. The results showed that the snowmelt water for snow depths of 10 cm, 30 cm, and 50 cm all contributed to the 0–30 cm soil layer. The contribution increased with soil depth, contributing 8.13%, 8.55%, and 11.24%, respectively. The contribution of the snow cover at the same depth to the soil moisture at different depths also varied, i.e., the contribution increased with increasing soil depth. The snowmelt water retention time at depths of 10 cm, 30 cm, and 50 cm was inconsistent, i.e., it was the longest at 0–10 cm (average of 69 days), followed by 20–30 cm (average of 59 days), and the shortest at 10–20 cm (average of 54 days). The greater the snow depth, the shorter the retention time of the snowmelt water in the different soil layers. For surface soil, the contribution of the snowmelt water at greater depths was significantly different; while for deep soil, the contribution was more sensitive to the snow depth. Regardless of snow depth, soil contributions at different depths were significantly different. Precipitation also affected the contribution of the snowmelt water to the soil water, exhibiting different effects at different depths.