Huanglong Scenic and Historic Interest Area

World Heritage Research

Discover scientific research and academic studies that deepen our understanding of Huanglong Scenic and Historic Interest Area from its history and significance to its conservation, management, and contemporary challenges.

• Future land use intensifies trade-offs between development and conservation in the Yellow River Basin's critical ecological areas

  Future land use intensifies trade-offs between development and conservation in the Yellow River Basin's critical ecological areas (CEAs). This study reveals that while expanding ecological land can partially mitigate the adverse effects of construction land on ecosystem multifunctionality and stability, it is insufficient to counteract the negative impacts on ecosystem integrity. The research employs the Ecosystem Multifunctionality–Stability–Integrity (MSI) framework to assess future CEA dynamics under four land use scenarios projected for 2030 and 2050: Historical Baseline (HIS), Economic Priority (EN), Ecological Priority (EL), and Ecological-Economic Coordination (EE). The findings indicate that the EL scenario yields the most favorable outcomes, preserving the largest extent of CEA retained regions (CRRs) and the smallest risk areas. Risk areas exhibit significantly lower levels of ecosystem stability and integrity compared to CEA averages. This study provides insights for balancing ecological risk prevention and sustainable development in vulnerable river basins.

  Wang, Y., Wang, H., Zhang, Y., Zhao, Z., Yao, S., Fu, B., Lü, Y., & Wu, X. (2026). Future land use intensifies trade-offs between development and conservation in the Yellow River Basin’s critical ecological areas. Resources, Environment and Sustainability, 23, 100301. https://doi.org/10.1016/j.resenv.2026.100301

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• Hydrogeochemical formation mechanism of springs in Huanglong, a world natural heritage, Sichuan, China

  A study of Huanglong World Natural Heritage site reveals that the unique alpine karst landscape, featuring calcareous pools and travertine formations, is primarily shaped by water–rock interactions. The hydrochemical composition of its springs, dominated by Ca²⁺ and HCO₃⁻ ions, originates from carbonate mineral weathering and dissolution. Using mathematical statistics, cluster analysis, and the Gibbs model, researchers determined that while tourism has some impact, alternating cation adsorption does not significantly alter the hydrochemistry. This research provides critical insights for the sustainable management of Huanglong's water resources, supporting conservation efforts to preserve its extraordinary geological features.

  Liu, X., Shen, T., Gao, W., Tang, J., Zhang, Q., Yang, T., & Luo, X. (2026). Hydrogeochemical formation mechanism of springs in Huanglong, a world natural heritage, Sichuan, China. Water Practice & Technology, 21(5), 1904–1914. https://doi.org/10.2166/wpt.2026.286

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• Lilium huanglongense (Liliaceae): a newly-discovered species in north-western Sichuan, China

  Researchers have identified Lilium huanglongense, a newly-discovered lily species within the section Lophophora, found in the Huanglong National Nature Reserve, Sichuan, China. This discovery fills a distribution gap between broad-leaved forest and alpine scrub species in the Hengduan Mountains, one of the world's biodiversity hotspots. Morphological similarities to Lilium fargesii and molecular phylogenetic analyses using nuclear ITS and chloroplast genomes confirm its independent status. The study enhances understanding of Lophophora diversity and distribution history, contributing to the region's rich botanical heritage.

  Wang, T., Yuan, Y., Zhou, T.-H., & Gao, Y. (2025). Lilium huanglongense (Liliaceae): a newly-discovered species in north-western Sichuan, China. PhytoKeys, 252, 9–24. https://doi.org/10.3897/phytokeys.252.135155

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• Simulation of Control Process of Fluid Boundary Layer on Deposition of Travertine Particles in Huanglong Landscape Water Based on Computational Fluid Dynamics Software (CFD)

  Research reveals that the deposition of calcium carbonate particles in Huanglong's colorful pools is significantly influenced by fluid boundary layers and low-velocity zones, particularly behind baffles. Under horizontal flow conditions, particle concentration peaked at 1.06%, with substantial aggregation and settling observed at the bottom right of the channel, where boundary layer effects are strongest. Inclined surfaces with baffles further enhanced deposition in low-speed regions, with calcium carbonate deposition rates ranging from 4.06 mm/a to 81.7 mm/a, closely aligning with field observations. The study employed Particle Image Velocimetry (PIV) for real-time flow measurements and computational fluid dynamics (CFD) simulations, including the Discrete Phase Model (DPM), to track particle trajectories and deposition rates, providing critical insights into sedimentation dynamics in aquatic environments.

  Liu, X., Gao, W., Zuo, Y., Sun, D., Zhang, W., Zhang, Z., Liu, S., Dong, J., Wang, S., Xu, H., Chen, H., & Xu, M. (2025). Simulation of Control Process of Fluid Boundary Layer on Deposition of Travertine Particles in Huanglong Landscape Water Based on Computational Fluid Dynamics Software (CFD). Water, 17(5), 638. https://doi.org/10.3390/w17050638

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• Sedimentation differences and depositional model in slope‐controlled travertine precipitation stream in Huanglong, China

  A novel depositional model for travertine formation reveals how slope gradients and hydrodynamic conditions shape the growth of continental carbonate deposits. Research at Huanglong, China, demonstrates that steep slopes induce water jumps and jagged fluctuations in calcium carbonate concentrations, leading to distinct lamination structures characterized by alternating dense calcite and sparse calcite–microbial assemblages. The interplay between topography, hydrodynamics, and microbial activity regulates travertine aggradation and progradation, influencing the curvature of arc structures. This study integrates sedimentological analysis, hydrodynamic modeling, and regional hydrochemical variability to elucidate morphological evolution in slope-controlled travertine systems, offering insights applicable to other continental carbonate depositional areas.

  Zhang, T., Dai, Q., Guo, X., Jiang, R., Cai, J., Li, Q., Zhang, Q., Luo, X., Jiang, Z., & Dong, F. (2025). Sedimentation differences and depositional model in slope‐controlled travertine precipitation stream in Huanglong, China. Sedimentology, 72(6), 1786–1804. Portico. https://doi.org/10.1111/sed.70021

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• Complete Mitochondrial Genomes of Spotted Forest Musk Deer (Moschus berezovskii) from Huanglong Mountain, Shaanxi, China, and Phylogenetic Analysis of Moschidae

  Phylogenetic analysis confirms that the spotted forest musk deer (Moschus berezovskii) from Huanglong Mountain, Shaanxi, China, belongs to the forest musk deer species, resolving long-standing taxonomic uncertainty. The study sequenced and assembled complete mitochondrial genomes of two individuals, revealing an average length of 16,355 base pairs with 13 protein-coding genes, two ribosomal RNA genes, and 22 transfer RNA genes. By incorporating all available Moschidae mitogenomes into a robust phylogenetic framework, the research clarifies evolutionary relationships within the family and supports the monophyly of M. berezovskii. This work provides critical molecular evidence to distinguish between morphologically similar musk deer species and advances understanding of their genetic diversity.

  Sun, K., Tan, X., Zhang, L., Dai, Y., Bian, K., Li, F., Suo, L., Du, X., Yang, C., & Tang, J. (2025). Complete Mitochondrial Genomes of Spotted Forest Musk Deer (Moschus berezovskii) from Huanglong Mountain, Shaanxi, China, and Phylogenetic Analysis of Moschidae. Biology, 14(12), 1794. https://doi.org/10.3390/biology14121794

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• Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method

  A novel hyperspectral identification method achieved high accuracy in distinguishing the state of travertine formations at Huanglong. The PSO-BPNN classifier, combining particle swarm optimization with backpropagation neural networks, demonstrated exceptional performance with an overall accuracy of 93%, F1-score of 92%, and Kappa coefficient of 97%. This method effectively categorized healthy travertine, blackened formations, those affected by algae erosion, and bare ground using hyperspectral data. The approach involved correlation analysis, sensitive band extraction, and real-world application on Micro-Hyperspectral imaging data, outperforming traditional BPNN classifiers.

  Xu, M., Wang, W., Cai, J., Dai, Q., Fan, J., & Li, S. (2024). Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method. Geocarto International, 39(1). https://doi.org/10.1080/10106049.2024.2365886

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