Under the trend of climate warming, the high-latitude permafrost in Heilongjiang Province is becoming seriously degraded. The question of how to quantitatively analyze the spatial and temporal trends of multi-year permafrost has become fundamental for current permafrost research. In this study, the temporal and spatial variations of annual mean air temperature (MAAT), annual mean ground temperature (MAGST) and freezing/thawing index based on air and surface temperature data from 34 meteorological stations in Heilongjiang Province from 1971–2019, as well as the variation characteristics of permafrost distribution, were analyzed based on the freezing index model. The results showed that both MAAT and MAGST in Heilongjiang Province tended to decrease with the increase of altitude and latitude. For interannual variation, the MAAT and MAGST warming rates tended to be consistent across Heilongjiang Province, with multi-year variation from −8.64 to 5.60 °C and from −6.52 to 7.58 °C, respectively. From 1971–2019, the mean annual air freezing index (AFI) and ground surface freezing index (GFI) declined at −5.07 °C·d·a⁻¹ and −5.04 °C·d·a⁻¹, respectively, whereas the mean annual air thawing index (ATI) and ground surface thawing index (GTI) were elevated at 7.63 °C·d·a⁻¹ and 11.89 °C·d·a⁻¹, respectively. The spatial distribution of the multiyear mean AFI, ATI, GFI and GTI exhibited a latitudinal trend, whereas the effect of altitude in the northern mountainous areas was greater than that of latitude. Permafrost was primarily discovered in the Daxing’an and Xiaoxing’an Mountains in the north, and sporadically in the central mountainous regions. The southern boundary of permafrost shifted nearly 2° to the north from 1970 to 2010s, while the southern boundary of permafrost in Heilongjiang Province was stable at nearly 51° N. The total area of permafrost narrowed from 1.11 × 10⁵ km² in the 1970s to 6.53 × 10⁴ km² in the 2010s. The results of this study take on a critical significance for the analysis of the trend of perennial permafrost degradation at high latitudes in Heilongjiang Province and the whole northeastern China, as well as for mapping the distribution of large areas of permafrost using the freezing index model. This study provides a reference for natural cold resource development, ecological protection, climate change and engineering construction and maintenance in permafrost areas.

The study aimed to investigate the spatiotemporal variation of annual precipitation and extreme precipitation within the Songhua River Basin (SRB). It utilized precipitation data collected from 60 meteorological stations within the SRB during the period 1968–2019. Employing Empirical Orthogonal Function (EOF) analysis, it decomposed spatiotemporal characteristics of annual precipitation in the SRB. Through Pearson correlation analysis, application of the cross-wavelet transform, and wavelet coherence analysis, the current study explored the correlation between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation. The results indicated an increasing trend for annual precipitation and for most indices of extreme precipitation within the SRB, apart from the consecutive dry days (CDD). Spatially, a general pattern of “more in the east and less in the west” was observed. Annual precipitation types in the basin were resolved into two modes with the first mode showing a general tendency of more (or less) precipitation over the entire basin, while the second mode exhibited less (or more) precipitation in the western areas and more (or less) in the eastern areas. Longitude, latitude, and altitude significantly impacted annual precipitation and extreme precipitation. Local air temperature notably affected the consecutive wet days (CWD). The West Pacific Subtropical High (WPSH) exerts a strong influence on the annual precipitation and extreme precipitation within the basin.

Soil temperature is an important index of climate change, and the analysis of soil temperature change is of great significance for understanding climate change and ecohydrological processes. This study was based on the measured meteorological data of a meteorological station, combined with the soil temperature data of 0–10, 10–40, 40–100 and 100–200 cm from the Global Land Data Assimilation System (GLDAS-NOAH). The Mann–Kendall test, wavelet analysis, linear tendency estimation and other methods were used to analyze the variability, periodicity and trend of soil temperature in Lhasa from 2006 to 2022. The results showed that the soil temperature of different soil layers had abrupt changes in annual and seasonal time series, and all showed a warming phenomenon after abrupt changes. In terms of periodicity, the average annual soil temperature of different soil layers has similar periodic changes, and the periodic oscillation is strong around 10a, which is the main cycle of soil temperature change. The soil temperature in Lhasa showed a significant rising trend in the interannual and seasonal time series, and the average annual rising trend of soil temperature was greater than that of air temperature. The correlation between soil temperature and mean air temperature (MAT), maximum air temperature (Tmax), minimum air temperature (Tmin) and snow depth (SD) was investigated by Pearson correlation analysis. Soil temperature in spring, autumn and winter had a strong correlation with MAT, Tmax and Tmin, showing a significant positive correlation. The negative correlation between soil temperature and SD in 0–40 cm soil in spring and winter was more severe. The research results show that Lhasa has experienced a rise in air temperature and soil temperature in the past 17 years, and reveal the specific changes in soil temperature in Lhasa against the background of climate change. These findings have reference significance for understanding the impact of climate change on the natural environment.

Icings, a significant hydrogeological phenomenon in permafrost regions, form when groundwater flows to the surface or through river crevices and freezes under low temperatures. These formations pose serious threats to infrastructure, including roads, railways, and bridges, while also serving as vital freshwater resources. Despite their importance, the mechanisms governing icing formation and the quantitative relationships between groundwater-controlling factors—such as freeze–thaw processes and precipitation—and icing distribution remain poorly understood. This knowledge gap hinders disaster prevention efforts and the sustainable utilization of water resources in cold regions. This study investigates the development patterns and influencing factors of icings in Eruu, a high-latitude permafrost region, using Landsat 4–5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI imagery with a 30 m resolution (2005–2024) and meteorological and geothermal data. By combining NDSI and MDII, the differentiation accuracy of water bodies was improved, and the K-Means clustering algorithm was applied to extract the icing region. The results revealed that the annual icing surface area ranged from 208,800 to 459,000 m², with a minimum in 2009 and a maximum in 2011. The average annual increase was approximately 4304.5 m² (p = 0.0255). Icings began freezing in October, radiating outward from the center, and melted by late May or early June. The Pearson correlation analysis showed (1) a strong negative correlation between snowfall and icing area (r = −0.544); (2) a positive correlation between freezing duration and icing area (r = 0.471); and (3) over the study period, annual average temperature and total precipitation exhibited no obvious change trend, with weak positive correlations between icing area and total precipitation (r = 0.290) and annual average temperature (r = 0.248). The observations of icing areas will be further applied to disaster prevention efforts. Additionally, the source of icings is clean and can be extracted for drinking purposes. Therefore, these findings enhance the understanding of icing mechanisms, support the prediction of icing development, and inform disaster prevention and resource management in permafrost regions.

Baseflow is a crucial component of river flow in alpine inland basins, playing an essential role in watershed ecological health and water resource management. In high-latitude cold regions, seasonal freeze-thaw processes make baseflow formation mechanisms particularly complex. However, the dominant factors affecting baseflow and their relative contributions remain unclear, limiting the accuracy of flow estimation and effective water resource management. This study employed baseflow separation techniques and statistical methods, including the Mann-Kendall test, to investigate temporal trends and abrupt changes in baseflow and the baseflow index (BFI) at multiple time scales (annual, seasonal, and monthly) from 2005 to 2012. Additionally, the timing of snowmelt and its impact on baseflow were examined. Key findings include the following: (1) Baseflow and BFI showed distinct temporal variability with non-significant upward trends across all time scales. Annual BFI ranged from 0.48 to 0.61, contributing approximately 50% of total runoff. (2) At the seasonal scale, baseflow remained relatively stable in spring, increased in autumn, and showed non-significant decreases in summer and winter. Monthly baseflow exhibited an increasing trend. (3) The snowmelt period occurred between April and May, with baseflow during this period strongly correlated with climatic factors in the following order: winter precipitation > positive accumulated temperature > winter air temperature > negative accumulated temperature. The strongest positive correlation was observed between baseflow and winter precipitation (R = 0.724), while negative correlations were found with accumulated temperatures and winter air temperature. These findings offer valuable insights for predicting water resource availability and managing flood and ice-jam risks in cold regions.