Yiyang Zhao

The study examines the evolution, knowledge structure, and trends in snowmelt runoff prediction models. It identifies research hotspots, future directions, and offers a theoretical basis for accurate simulation and prediction. Utilizing CiteSpace software, 556 core Chinese and English publications from 2010 to 2025 were visually analyzed. Research on snowmelt runoff simulation shows: (1) Chinese publications are prominent in core journals like “Journal of Glaciology and Geocryology,” while English publications appear in high-impact journals like “Water Resources Research.” (2) Institutions like the University of Chinese Academy of Sciences, the Northwest Institute of Eco-Environment and Resources, and the University of California have formed a cross-regional research network. (3) International collaboration involves 42 countries, with a focus on China, the United States, and India. However, domestic institutional cooperation needs improvement. (4) Research trends in snowmelt runoff simulation have progressed from empirical statistics to remote sensing and model-driven physical mechanisms, and now to the integration of artificial intelligence with physical models. (5) The Chinese literature focuses on cold regions, while the English literature emphasizes intelligent modeling. This shift indicates a move towards “physical–intelligent” hybrid modeling. Future research should address challenges like model applicability in data-scarce areas, improving interpretability of complex models, quantifying uncertainties, and developing physically constrained deep learning models. Collaboration among institutions is crucial for enhancing water resource management and disaster warning systems in cold regions.

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China.

Under the national strategy of ecological protection and high-quality development in the Yellow River Basin, Ordos, a major coal production base in China contributing ~ 17.1% of China’s annual raw coal output, faces prominent water shortage and ecological fragility. This study constructed a “Game Theory-TOPSIS-Grey Prediction” coupled model: it integrated subjective and objective weights via game theory (reducing weight coefficient of variation by 35%), evaluated water resources carrying capacity (WRCC) during 2000–2023 (the WRCC index increased from 0.3285 to 0.6373, with an average annual growth rate of 2.92%; growth slowed to 1.69% during 2016–2023, showing a “strong east-weak west” spatial pattern), and predicted WRCC trends for 2025–2040 using the GM(1,1) model. The GM(1,1) model achieved first-level accuracy (average relative error 6.35%, posterior error ratio 0.327, small error probability 0.962), with predicted WRCC indices of 0.6261 (2025), 0.7676 (2030), and 0.9736 (2040). This study provides a dynamic tool for “water-energy-ecology” coordinated governance of resource-based cities in China’s Yellow River Basin.