Snow Resources Research Articles

Snow Resource Reutilization: Design of Snow Collection and Compression Equipment Based on Functional Analysis Method

High-latitude regions of the Earth are rich in natural snow resources; however, owing to their negative impact on daily life, they have not been effectively utilized for a long time and are instead viewed as obstacles that require substantial resources for clearance. This waste of resources contradicts the principles of sustainable development. With the rapid development of the ice and snow industry, the social and economic value of snow resources is gradually becoming apparent. Therefore, to promote sustainable social and economic development, this study explores new methods for processing snow resources to achieve their recycling and high-value transformation. This study employs the functional analysis method to analyze and solve the functions of the design system, utilizing the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation for the objective assessment and selection of schemes. The obtained relative optimal principles are utilized to guide the parameter setting and innovative design of the scheme in terms of functional structure. After the final scheme is output, its feasibility is verified through finite element simulation. Ultimately, to address the issue of snow resource clearing and recycling, this study designs a product scheme capable of collecting and compressing snow on road surfaces, proposing the use of snow in the form of standardized compressed snow blocks for large-scale snow sculpture construction and other fields. This method significantly reduces the cost of snow sculpture production, enhances efficiency, realizes the comprehensive utilization and high-value transformation of snow resources, and provides a reference for the sustainable development of the low-altitude ice and snow tourism industry.

Capturing Snowmelt Runoff Onset Date under Different Land Cover Types Using Synthetic Aperture Radar: Case Study of Sierra Nevada Mountains, USA

Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and the early warning of snow-related disasters. A synthetic aperture radar (SAR) offers an effective means for capturing snowmelt runoff onset dates (RODs) over large areas, but its accuracy under different land cover types remains unclear. This study focuses on the Sierra Nevada Mountains and surrounding areas in the western United States. Using a total of 3117 Sentinel-1 images from 2017 to 2023, we extracted the annual ROD based on the Google Earth Engine (GEE) platform. The satellite extraction results were validated using the ROD derived from the snow water equivalent (SWE) data from 125 stations within the study area. The mean absolute errors (MAEs) for the four land cover types—tree cover, shrubland, grassland, and bare land—are 24, 18, 18, and 16 d, respectively. It indicates that vegetation significantly influences the accuracy of the ROD captured from Sentinel-1 data. Furthermore, we analyze the variation trends in the ROD from 2017 to 2023. The average ROD captured by the stations shows an advancing trend under different land cover types, while that derived from Sentinel-1 data only exhibits an advancing trend in bare land areas. It indicates that vegetation leads to a delayed trend in the ROD captured by using Sentinel-1 data, opposite to the results from the stations. Meanwhile, the variation trends of the average ROD captured by both methods are not significant (p > 0.05) due to the impact of the extreme snowfall in 2023. Finally, we analyze the influence of the SWE on RODs under different land cover types. A significant correlation (p < 0.05) is observed between the SWE and ROD captured from both stations and Sentinel-1 data. An increase in the SWE causes a delay in the ROD, with a greater delay rate in vegetated areas. These findings will provide vital reference for the accurate acquisition of the ROD and water resources management in the study area.

Evaluating the Snow Cover Service Value on the Qinghai–Tibet Plateau

The Snow Cover Service Value (SCSV) is an important component of the ecological assets of the Qinghai–Tibet Plateau (QTP). Exploring the SCSVs on the QTP is the key to maintaining the functions of climate regulators and Asian water towers, and it is also an important theoretical basis for maintaining the ecological security of ecological barrier areas. Using multi-source data such as daily and monthly observation data sets and related statistical yearbooks, an evaluation framework for the SCSVs on the QTP was constructed for the first time. The results showed that the average annual SCSV of the QTP from 2001 to 2020 was 6.99 trillion yuan, and the average annual climate regulation value was 5.81 trillion yuan, which was the most important SCSV. The Inner Plateau Basin, the Brahmaputra Basin, and the Yangtze Basin had the highest SCSVs, while the Yellow Basin had the lowest unit SCSV, where it was the most vulnerable area of snow cover resources on the QTP, and the SCSV of the Yellow Basin was significantly correlated with rainfall. The correlation between the SCSV and the temperature in the Indus and Ganges Basins was significant. The freshwater supply value of the snow in the Indus Basin and Tarim Basin was found to be able to reach 30% of the whole year in June, where it would be necessary to pay attention to the risk of flooding caused by snowmelt during the melting period. Finally, this paper discussed the strategies for the protection and development of snow resources in each basin based on the spatial distribution characteristics, seasonal variation characteristics, and influencing factors of the SCSVs. The research can provide reference for the rational allocation of snow resources and ecological protection on the QTP.

Local Responses to Climate Change Impact on Intangible Cultural Heritage in Poland

Tradition and intangible cultural heritage help local communities cope with the uncertainty through familiar and repetitive actions. What if it all depends on variable and unstable factors? This paper presents an anthropological approach to the impact of climate change on local practices towards intangible cultural heritage based on human-environment interactions. The author presents early results of an ongoing research project. By drawing on ethnographic data, this article explores the implications of declining snow resources, alterations in the vegetation cycle and hydrological drought on the traditions of winter horse-drawn carriage races, Corpus Christi flower carpets and wickerwork. Based on the notions of ethnoclimatology and anthropology of weather, the text draws attention to local perceptions of climate change and potential methods of safeguarding tradition in times of uncertainty. It serves as an outline of a possible way of thinking about the relationship between climate change and intangible cultural heritage.

Research on Creative Derivatives of Ice and Snow Culture -- Taking Jilin Province as an Example

In Jilin, a northern country with thousands of miles of ice and snow, the ice and snow culture has a long history, and Jilin Province also has the world s top ice and snow resources. Especially under the infuence of the 2022 Beijing Winter Olympics, Jilin Province has also vigorously promoted and promoted ice and snow culture, and a large number of creative derivatives of ice and snow culture have emerged. This article takes Jilin Province as an example to briefy study the creative derivative design and promotion strategies of ice and snow culture, hoping to maximize this natural advantage and help Jilin Province s ice and snow culture fourish, bringing greater economic and social ben-efts to the province.

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 < 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.

Analysis of spring drought in Northeast China from the perspective of atmosphere, snow cover, and soil

Northeast China is an important grain-producing area, and spring is the sowing time for crops. Drought hinders crop germination and leads to crop failure. Spring drought analysis helps to understand the characteristics of regional spring drought and provides a reference for formulating disaster prevention and reduction measures. Considering the characteristics of seasonally frozen soil regions in northeastern China, sampling at a resolution of 0.5 degrees, accounting for the snow cover factors in the early spring, as well as precipitation, temperature, evapotranspiration, and soil moisture during the spring planting period, and uses the projection pursuit information entropy model to analyze the spatial and temporal variability of spring drought in Northeast China. In the model, a new method was proposed to calculate the cutoff radius by adjusting the radius size with the parameter. The optimal parameters were ultimately determined using the Shapiro-Wilk test, Kolmogorov-Smirnov test, and Epps-Pulley test. The results are as follows: drought is relatively severe in the eastern parts of Inner Mongolia, as well as in the northwest of Liaoning. The central and northern parts of Inner Mongolia, the northern and northeastern parts of Heilongjiang, the southeastern parts of Jilin, and the eastern parts of Liaoning are relatively humid, while the drought situation in other regions is relatively moderate. Regional spring drought shows an increasing trend, and most regions show statistical significance. Areas with high temperatures and low precipitation, as well as low snow cover in the early spring, are prone to spring drought. Moist regions are characterized by high precipitation, low evapotranspiration, and abundant snow resources.

Comment on the current situation of northeast city image communication in the post-Winter Olympics era

This article analyzes the study of the spread of the image of the northeast city with unique ice and snow resources, Based on the analysis results of the questionnaire survey and the author's observation and thinking on the current situation of the image communication in Northeast China, Analyzing the effect of the Beijing Winter Olympics on the image dissemination of northeast China. At the same time, conduct in-depth research on the image of Northeast cities and characteristic festivals in Northeast China under traditional media, new media, and cultural products, deepen the culture of Northeast cities, explore the spiritual connotation of Northeast cities, and re-discover the new image of Northeast cities in the post-Winter Olympics era The way of shaping and the communication strategy, to build the Northeast of ice and snow and establish the overall communication view of the Northeast city image, fundamentally "reshape" and "new communication" the image of the Northeast city.

The Evaluation of Snow Depth Simulated by Different Land Surface Models in China Based on Station Observations

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Potential impacts of climate change on the spatial distribution of Chinese ski resorts

Chinese skiing tourism is rapidly developing, but it will be potentially affected by snow resources and climate conditions. This study systematically analysed the spatial characteristics of snow resources, climatic conditions and ski resorts in China; revealed the potential impacts of climatic environment on current ski resorts by using Regional Climate Model (RegCM4.4); and proposed suggestions for climate resource assessment and spatial planning of ski resorts. The mean snow depth and snow cover days are more than 4.50 cm and 150 d between the years 2000 and 2014, respectively, in high-latitude areas, where fewer ski resorts are located. In the middle-low latitude areas, the mean snow depth and snow cover days are less than 3.00 cm and 100 d, respectively, with ski resorts being intensively distributed. In most of these areas, the pronounced climate warming exceeded 0.50 °C during the ski period of 2009–2015 relative to 1961–2015, and mean annual precipitation was less than 800 mm, with some even less than 400 mm. In the 2030s and 2050s, the regions with drastic temperature increases are mainly in the Qinghai–Tibetan Plateau, Northeast China, and the Yangtze River Basin. The upper and middle reaches of the Yangtze River are expected to experience reduced snowfalland temperature increases with an almost 1–1.6 °C, respectively. In this context, some ski resorts will face major threats because of shortened skiing durations and rising management costs. Skiing tourism should not pursue a one-sided growth rate in the number of ski resorts in areas with short snowpack periods. Instead, it should rely highly on snow resources, climatic conditions, geographical environment, population and economic basis, and rationalise its spatial layout to promote the industry towards sustainable development.

Model of Deep Information Environmental Factors on the Development of ICE and Snow Sports

Abstract Driven by national policies and international competitions, China’s winter sports and industry development will enter a new course. How to release the enormous potential skiing demand in China is significant to drive 300 million people to participate in winter sports and promote the development of China’s ice and snow industry, and defining the restrictive factors is a necessary prerequisite to realise the effective transformation of potential demand to actual growth power. Based on the in-depth information, this paper further analyses the influence of environmental factors on the development of winter sports. The research shows that the number of skiers about 20 times is the highest, up to 75.5 %, followed by the number of skiers about five times, up to 67.4 %. Then about ten times accounted for 48.6 %, and finally, about 15 times accounted for 37.6 %. This frequency can fully satisfy skiing enthusiasts to enjoy leisure activities, experience modern life fashion, use skiing to increase social opportunities, delight body and mind, and improve health. Based on the in-depth information, this paper reveals the shortcomings and existing environmental factors of China’s winter sports development. It provides a reference for the rational utilisation and development of ice and snow resources and the scientific and practical development of China’s winter sports.

Seasonal forecasting of snow resources at Alpine sites

Abstract. Climate warming in mountain regions is resulting in glacier shrinking, seasonal snow cover reduction, and changes in the amount and seasonality of meltwater runoff, with consequences on water availability. Droughts are expected to become more severe in the future with economical and environmental losses both locally and downstream. Effective adaptation strategies involve multiple timescales, and seasonal forecasts can help in the optimization of the available snow and water resources with a lead time of several months. We developed a prototype to generate seasonal forecasts of snow depth and snow water equivalent with a starting date of 1 November and a lead time of 7 months, so up to 31 May of the following year. The prototype has been co-designed with end users in the field of water management, hydropower production and mountain ski tourism, meeting their needs in terms of indicators, time resolution of the forecasts and visualization of the forecast outputs. In this paper we present the modelling chain, based on the seasonal forecasts of the ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S) Climate Data Store. Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and relative humidity are bias-corrected and downscaled to three sites in the Western Italian Alps and finally used as input for the physically based multi-layer snow model SNOWPACK. Precipitation is bias-corrected with a quantile mapping method using ERA5 reanalysis as a reference and then downscaled with the RainFARM stochastic procedure in order to allow an estimate of uncertainties due to the downscaling method. The impacts of precipitation bias adjustment and downscaling on the forecast skill have been investigated. The skill of the prototype in predicting the deviation of monthly snow depth with respect to the normal conditions from November to May in each season of the hindcast period 1995–2015 is demonstrated using both deterministic and probabilistic metrics. Forecast skills are determined with respect to a simple forecasting method based on the climatology, and station measurements are used as reference data. The prototype shows good skills at predicting the tercile category, i.e. snow depth below and above normal, in the winter (lead times: 2–3–4 months) and spring (lead times: 5–6–7 months) ahead: snow depth is predicted with higher accuracy (Brier skill score) and higher discrimination (area under the relative operating characteristics (ROC) curve skill score) with respect to a simple forecasting method based on the climatology. Ensemble mean monthly snow depth forecasts are significantly correlated with observations not only at short lead times of 1 and 2 months (November and December) but also at lead times of 5 and 6 months (March and April) when employing the ECMWFS5 forcing. Moreover the prototype shows skill at predicting extremely dry seasons, i.e. seasons with snow depth below the 10th percentile, while skills at predicting snow depth above the 90th percentile are model-, station- and score-dependent. The bias correction of precipitation forecasts is essential in the case of large biases in the global seasonal forecast system (MFS6) to reconstruct a realistic snow depth climatology; however, no remarkable differences are found among the skill scores when the precipitation input is bias-corrected, downscaled, or bias-corrected and downscaled, compared to the case in which raw data are employed, suggesting that skill scores are weakly sensitive to the treatment of the precipitation input.

Changes in snowpack of the Changbai Mountain region over the last decade in reaction to global warming

As greenhouse gas emissions have been mounting in recent years, the issue of global warming has become more and more serious, sparking a great deal of concern. Among the effects of global warming, the melting of snowpack is particularly pronounced. However, most of the current researches focus on vegetation and biodiversity, and there are some gaps in studies of snow in the mountainous regions of China. This paper analyzes the Landsat8 satellite images of Changbai Mountain area in the past 10 years through QGIS and programming algorithms to study the trend of snowpack change, the correlation between snow melting and vegetation change and causes of snow melting. The analytical methods used in this paper include NDSI, NDVI, supervised classification and image binarization. From 2014 to 2023, the snow-covered areas in Changbai Nature Reserve have decreased dramatically, especially in northwestern part, middle part and edges of Changbai Mountain. The change rate of snowpack has reached a significant 13.979%. The vegetation area shows an increasing trend and is negatively correlated with snowpack change. In order to alleviate the challenge of snow melting, ecological engineering, new energy sources and rigorous regulation of human activities are all essentially necessary. In the future, it is expected that the research in this paper will have a profound positive impact on the ecological management of the Changbai Mountain region, the conservation of snow resources and the mitigation of global warming.

Analysis of the Reciprocal Feedback between Glacier Tourism Development and Regional Economic Growth in Hailuogou

Ice and snow tourism has yielded considerable socioeconomic benefits, especially for the countries and regions that are rich in ice and snow resources. Boosted by the Beijing Winter Olympics, China’s ice and snow tourism economy is likely to usher in a period of high-quality development. This study utilized Hailuogou Glacier Forest Park, a relatively well-developed glacier tourism area in China, as the study area. We used data on the regional GDP of Ganzi Prefecture and the comprehensive tourism revenue of Hailuogou scenic area from the period of 1990–2021 to quantitatively analyze the reciprocal feedback relationships between the regional economic growth of Ganzi Prefecture and the development of Hailuogou glacier tourism. The analyses were performed through the construction of a VAR model and through conducting robustness tests using the instrumental variable method. The results show that there is a long-term equilibrium cointegration relationship between regional economic growth in Ganzi Prefecture and tourism development in the Hailuogou scenic area, with each 1% increase in the former driving a corresponding growth of 2.01% in the latter. There is an asymmetric reciprocal feedback relationship between the economic growth of Ganzi Prefecture and the development of tourism in the Hailuogou scenic area. Economic growth in Ganzi Prefecture significantly contributes to the development of glacier tourism in Hailuogou over a longer period of time, with a contribution rate of 49%; meanwhile, the development of tourism in the Hailuogou scenic area has a limited effect on the quality and acceleration of economic development in Ganzi Prefecture, with a contribution rate of 16%. Based on the above results, relevant policy recommendations are proposed to provide a theoretical basis and decision-making reference for the high-quality development of the Hailuogou scenic area in the post-COVID-19 and post-Winter Olympics era.

Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China

AbstractSnow density is an essential property of snowpack. To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm−3) was generally greater than that in the stable (0.20 g cm−3) and accumulation periods (0.18 g cm−3), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p < 0.05) with elevation during the accumulation and stable periods. In addition to elevation, latitude and ground surface temperature also had critically impacted the spatial variability of snow density in the study area. In the current study, the machine learning models, especially RF, performed better than MLR for simulating snow density in the three periods. Based on the key environmental variables identified by the machine learning model and correlation analysis, this study also provides practical MLR equations to estimate the spatial variance of snow density during different snow periods in the middle Tianshan Mountains. This method can be used for regional snow mass and snow water equivalent prediction, leading to a better understanding of local snow resources.

A Review of the Impacts of Climate Change on Tourism in the Arid Areas: A Case Study of Xinjiang Uygur Autonomous Region in China

Tourism is more sensitive and susceptible in global arid regions to climate change than other sectors, and climate change mainly affects the behavior of tourists, selection of tourist destinations, tourism resources, and tourism safety. China’s Xinjiang Uygur Autonomous Region (XUAR) is a representative area of the global arid region. To review its comprehensive impacts of climate change on tourism has indicative significance for the global arid region tourism industry to cope with climate change impacts. On the whole, the impacts of climate change on tourism in the XUAR will coexist with opportunities and challenges both at present and in the future. The XUAR is experiencing or will experience climatic process of warming and wetting. For the tourism climate comfort and extension of suitable travel period, the opportunities far outweigh the risks (high reliability). However, future climate change is expected to have great negative effects on cultural heritages, glacier and snow resources, and agricultural landscapes in arid areas of northwest China (high reliability). The above impacts are potential and long-term, and the measures should be taken as soon as possible to mitigate and adapt to climate change challenges to tourism.

Multi-source Intelligent Management System of College Snow and Ice Teaching Based on Cloud Platform

The conventional college snow and ice teaching multi-source resource intelligent management system has the problem of incomplete software resource analysis function, which leads to high CPU usage of system. A cloud platform based college snow and ice teaching multi-source resource intelligent management system is designed. Hardware part: record the state of the pins when the register is reset, account for the data bit width and storage capacity of DDR2 SDRAM memory, and optimise the data storage module; Part of the software: obtain ice and snow sports course objectives in colleges and universities, rationally organise the course organization form, optimise the intelligent management mode of teaching multi-source resources by using the cloud platform, support online browsing of various text resources, set relevant parameters to construct various random modification operations, and design the software resource analysis function with the knowledge fusion algorithm. Experimental results: The average CPU usage of the multi-source intelligent resource management system for snow and ice teaching in colleges and universities in this paper and the other two systems is 34.257%, 47.458%, 53.578%. Experimental results show that the performance of multi-source intelligent resource management system for snow and ice teaching in colleges and universities has been significantly improved after making full use of cloud platform.

A conceptual model for simulating streamflow in a changing snow-covered catchment: Application to the data-sparse upper Brahmaputra River basin

Understanding the hydrological processes in the Tibetan Plateau (TP) is increasingly important as the snow resources feed the demand for freshwater for a vast downstream population. Given the limited information available, the task of formulating a hydrological model that characterises future streamflow at downstream locations is challenging. A flexible conceptual hydrological model is proposed relating snow cover fraction (SCF) and snow water equivalent (SWE) relationship based on remote sensing data with the aim of characterising flow in the region into the future where temperature profiles will be considerably warmer than present. The model’s novelty lies in its ability to simulate dynamically varying SCF, SWE, and streamflow using limited available data. Results show that NSE of modelled SCF, SWE and streamflow are 0.82, 0.66 and 0.75 respectively in the calibration period (1981 to 1992) whereas 0.78, 0.56 and 0.70 respectively in the validation period (1993 to 2000).

The Low-Carbon Effect Measurement of the Structural Adjustment of the Ice and Snow Industry Based on Artificial Intelligence

Ice and snow economy is an economy characterized by ice and snow, and its foundation is ice and snow resources. The ice and snow economy covers the tertiary industry of ice and snow activities, and its core power comes from ice and snow tourism, which promotes the common development of manufacturing, transportation, catering, retail, and other industries. This paper studies the low-carbon effect measurement analysis of the structural adjustment of the ice and snow industry based on artificial intelligence, which proves that the economic benefits and carbon emissions of the structural adjustment of the ice and snow industry will be greatly improved after the addition of artificial intelligence technology. For this research, a series of investigation experiments are conducted, and the ice and snow industry in Heilongjiang Province is selected for analysis. First, the important position of artificial intelligence technology in today’s information society and its practicality are fully analyzed, and then its feasibility in combination with the structural adjustment of the ice and snow industry is analyzed. Second, it analyzes the characteristics of the structural adjustment of the ice and snow industry while taking into account its requirements for measuring low-carbon effects. The mathematical model of industrial structure optimization based on low-carbon constraints is adopted in the application model of artificial intelligence technology in the adjustment of the ice and snow industry structure, and the effects of carbon emissions before and after structural adjustments in the ice and snow industry are calculated to achieve a better industrial structure adjustment plan. Experimental data shows that with the help of artificial intelligence in the process of structural adjustment of the ice and snow industry, the average income of residents has increased by nearly 7%, and the transformation of the ice and snow industry’s contribution to the reduction of carbon emissions has rapidly increased to 12.24%.

High-quality development of ice and snow resources in China: Theoretical review, practice turn and challenge response

High-quality development of ice and snow resources in China: Theoretical review, practice turn and challenge response