The Heilongjiang Province is the region in China with the highest grain production and the most significant ecological security barrier in the northern part of the country. In recent years, the growing necessity for water in human production and life has intensified water resource conflicts. In order to identify a solution to this situation, the InVEST model’s water yield module is employed to simulate the water yield in Heilongjiang Province in 2000, 2010, and 2020. The water yield in Heilongjiang Province from 2000 to 2020 is evaluated, and the scenario simulation method is employed to investigate the impact of climate change and land use type changes on water yield. The results indicate that from 2000 to 2020, the annual precipitation in Heilongjiang Province increased from 490 mm in 2000 to 764 mm in 2020. The spatial distribution shows a west–high and east–low pattern, with Jixi City having the highest average annual precipitation and the Greater Khingan region having the lowest. During the period of 2000–2020, woodland had the highest proportion, followed by arable land, while built-up land had the smallest proportion. The conversion of unused land and woodland represents the primary driver of the expansion in farmland areas, while the conversion of water bodies accounts for the majority of the growth in the area of unused land. The areas of woodland and water bodies exhibited a slight decrease. The order of water yield of land use types, from the greatest to the least, is as follows: built-up land, unused land, farmland, grassland, woodland, and water bodies. The main factor affecting changes in precipitation levels in the province is climate change, which contributed up to 99.58% during the period 2000–2020. In contrast, changes in land use types contributed a mere 0.42%. The sustained expansion of the urban population in Heilongjiang province has resulted in an augmented water yield in select regions.

Jilin Province, an important commodity grain base in China, relies on groundwater resources for its agricultural development. The implementation of a series of policies, including agricultural subsidies and food security policies, has led to a rapid expansion of the sowing area in recent decades, resulting in an increase in agricultural water demand. This has had a significant impact on the groundwater system. It is therefore imperative to understand the dynamics of the groundwater to ensure the security of water resources, ecological security, and food security. An evaluation of the sustainability of groundwater resources in Jilin Province was conducted through a quantitative analysis of the reliability, resilience, and vulnerability of groundwater. This analysis was informed by the inversion of changes in groundwater reserves over a period of 249 months, commencing from 2002-04 to 2022-12. The inversion process utilized data from the Gravity Recovery and Climate Experiment (GRACE) gravity satellite and Global Land Data Assimilation System (GLDAS), offering a comprehensive view of the temporal dynamics of groundwater reserves in the region. The results indicated the following: (1) Groundwater storage (total amount of water below the surface) in Jilin Province exhibited an overall decreasing trend, with the highest groundwater level recorded in June and the lowest in September on a monthly basis. (2) Prior to September 2010, groundwater reserves were in surplus most of the time. From October 2010 to August 2018, however, they began to fluctuate between surplus and deficit states. Since September 2018, the reserves have been in a long-term deficit, showing an overall downward trend. (3) Prior to 2005, the groundwater system was at a high/extremely high level of sustainability. However, following 2011, it fell to a very low level of sustainability and has continued to deteriorate. (4) The maximum information coefficient and correlation analysis indicate that the sown area is the most significant factor contributing to the decline in the sustainability of the groundwater system. This study reveals the spatial and temporal distribution pattern and evolution trend of groundwater resources sustainability in Jilin Province, and provides theoretical and data support for regional groundwater resources protection and management.

This study analyzed frost formation data provided by the Harbin Meteorological Bureau and considered geographic factors, temperature, and population density. Various analytical methods, including linear fitting, the Mann–Kendall mutation test, the Pettitt method, and the sliding t-test, were employed to identify the temporal and spatial changes as well as the effects of these factors on frost dates in Harbin. The study shows that the first FSD occurred on 18 August, in both 1966 and 1967, which was the 255th day. The latest FSD was observed on 10 October 2006, which was the 283rd day. The earliest occurrence of an FED was on 24 April 2015, which was the 114th day, and the latest was on 21 April 1974, which was the 141st day. The highest number of frost days occurred in 2012, with 161 days, whereas the shortest year was 1966, with only 123 frost days. Throughout the study period, the FSD increased by 7.8 days at a rate of −1.27d/10a, the FED increased by 10.9 days at a rate of 1.77d/10a, and the FFS increased by 18.9 days at a rate of 3.05d/10a. The propensity rates of the FSD and FFS at each location in Harbin indicate an upward trend, while for the FED, certain locations display an upward trend. In general, the FSD has exhibited a delayed trend, the FED has shown an earlier trend, and the FFS has experienced an extended trend. With one-way linear regression, the FSD exhibited an increasing trend at each site, while the FFS also indicated a similar trend, and the FED showed an overall decreasing trend. Throughout the study period, a change was observed in the FSD in 2000, resulting in an average arrival time of the 265th day, or 22 September, of that year. Subsequently, post mutation, the average arrival time of the FSD in the study area was the 272nd day, or 29 September, of that year. In 2006, the FED also underwent a change, with the average arrival time in the study area being the 128th day, or 4 April, of that year. After the change, the average arrival time of the FED in the study area was the 121st day, i.e., 8 April. In 1 April 2004 of that year saw a change in the FFS. Prior to the change, the FFS in the study area averaged the 137th day, whilst following the change, the FFS in the study area averaged the 150th day. The FSD and FFS within Harbin exhibit a negative correlation with latitude and a positive correlation with temperature. Additionally, the FED displays a positive correlation with latitude and a negative correlation with temperature. As the FSD, FED, and FFS in central Harbin are the earliest, latest, and longest, the Pearson correlation coefficient method and multiple regression cannot adequately reflect the effect of longitude.

The Laptev Sea, as a marginal sea and a key source of sea ice for the Arctic Ocean, has a profound influence on the dynamic processes of sea ice evolution. Under a 2 °C global warming scenario, the accelerated ablation of Arctic sea ice is projected to greatly impact Arctic warming. The ocean regulates global climate through its interactions with the atmosphere, where sea surface temperature (SST) serves as a crucial parameter in exchanging energy, momentum, and gases. SST is also a key driver of sea ice concentration (SIC). In this paper, we analyze the spatiotemporal variability of SST and SIC, along with their interrelationships in the Laptev Sea, using daily optimum interpolation SST datasets from NCEI and daily SIC datasets from the University of Bremen for the period 2004–2023. The results show that: (1) Seasonal variations are observed in the influence of SST on SIC. SIC exhibited a decreasing trend in both summer and fall with pronounced interannual variability as ice conditions shifted from heavy to light. (2) The highest monthly averages of SST and SIC were in July and September, respectively, while the lowest values occurred in August and November. (3) The most pronounced trends for SST and SIC appeared both in summer, with rates of +0.154 °C/year and −0.095%/year, respectively. Additionally, a pronounced inverse relationship was observed between SST and SIC across the majority of the Laptev Sea with correlation coefficients ranging from −1 to 0.83.

In this study, based on frost formation data provided by Harbin Meteorological Bureau, combined with geographical factors, temperature and population density, linear fitting, Mann-Kendall mutation test, pettitt method and sliding T test were used to analyze the spatio-temporal variation characteristics of frost date in Harbin Municipality and the effects of geographical factors, temperature and population density on frost regularity. The results showed that (1) The earliest year of FSD appeared was 1966 and 1967, on the 255th, i.e., August 18th, and the latest was 283 days, i.e., October 10th, 2006. The earliest year in which the FED appeared was April 24th of 2015, the 114th day of that year, and the latest was April 21st of 1974, the 141st day of that year. The year with the most frost was 2012, with 161 days, and the year with the shortest was 1966, with 123 days. (2) Throughout the study period, FSD increased by 7.8 days at a rate of -1.27d/10a, FED increased by 10.9 days at a rate of 1.77d/10a, and FFS increased by 18.9 days at a rate of 3.05d/10a. The tendency rate of FSD and FFS at each site in Harbin showed an increasing trend. For FED, the tendency rate of some sites showed an increasing trend. In general, FSD showed a delayed trend, FED showed an advanced trend, and FFS showed a prolonged trend. Using the method of unary linear regression, FSD of each site showed an increasing trend, FFS showed an increasing trend, and FED showed a decreasing trend in general. (3) Throughout the study period, FSD was mutated in 2000, and the arrival time of it in the study area averaged the 265th, i.e., September 22nd, and after that, the arrival time of it changed to the 272nd, i.e., September 29th of that year. FED was mutated in 2006, and the arrival time of it in the study area averaged 128th, i.e., April 8th. After the mutation, the arrival time of FED changed to 121st, i.e., April 1st of that year. FFS was mutated in 2004. Before the mutation, with an average arrival time of 137days in the study area .But after that, the time changed to 150 days.(4) FSD and FFS in Harbin Municipality were negatively correlated with latitude and positively correlated with temperature, while FED was positively correlated with latitude and negatively correlated with temperature. The FSD, FED and FFS in the central part of Harbin Municipality were the earliest, the latest and the longest, respectively, so the Pearson correlation coefficient method and the influence of longitude could not be reflected in the multiple regression. Several factors were judged by grey correlation analysis method. The longitude is significantly related to FSD, FED and FFS.

In the context of global warming and the intensification of human activities, the change in runoff is also increasing. It is very important to determine the change in runoff for the rational utilization of water resources. In order to determine the influencing factors of runoff change in Muling River, the SWAT model was used in this study to separate different coupling factors and calculate the contribution rate of a single factor to runoff change at the annual scale and quarterly scale, respectively. In the process of calibration, different single rate times were used to analyze the influence of different rate times on the calibration results. The results show that the runoff in the Muling River basin shows a downward trend, the quarterly temperature factor has the greatest influence on the runoff change, which is 50–60%, the annual precipitation has the greatest influence on the runoff change, which is 68%, and the maximum change in the runoff from the reservoir is 42.5% under the change in human activities. In the SWAT-CUP software, the optimal number of calibration for this basin is 500. This research provides a scientific basis for the flow analysis of the Muling River basin.