黄龙溪气候舒适度及其与戏水需求相关性研究

Climatic Comfortable Degree and Its Relationship with Water-Play Demand in Huanglongxi

摘要: 本研究聚焦成都黄龙溪古镇暑期戏水旅游人体舒适度研究，利用双流国家站(56288, 1990~2024)及4个区域站(2014~2024)气象资料，解析了区域人体舒适度的时空变化特征。通过百度指数搜索“黄龙溪 + 戏水”关键词在特定时段的热度，结合气象和假期因素，采用线性回归分析揭示了暑期戏水需求的时空特征及其与舒适度的相关性。结果表明：(1) 黄龙溪及周边区域人体舒适度以“舒服”(平均38%)为主，其次为“暖和”(19.70%)、“凉爽”(10.61%)，“热”等级最低(6.82%)；全年舒适日数(舒服至凉爽)约占70%；(2) 黄龙溪的网络关注度在暑期形成显著峰值，且其搜索热度与高温天气存在显著正相关，表明戏水需求是暑期旅游市场的主导驱动力。同时，研究识别出潜在的客流高峰时段，为景区管理部门开展客流实时预警、资源动态调配及目标客群精准营销提供了科学依据。基于网络搜索数据的旅游需求预测，可指导景区及相关部门优化运营管理流程、创新开发亲水旅游产品体系、实施有效的客流错峰引导措施，从而提升游客游览体验，保障景区可持续发展。

Abstract: This study centers on the assessment of human comfortable degrees during summer water-based tourism activities in Huanglongxi Ancient Town, Chengdu. Based on the meteorological data from Shuangliu National Station (Station ID: 56288, covering the period from 1990 to 2024) and four regional stations (spanning from 2014 to 2024), we analyzed the spatio-temporal characteristics of human comfort in the region. Through an examination of Baidu Index search trends for the keywords “Huanglongxi + water-playing” during specific periods, and by integrating meteorological conditions with holiday factors, we employed linear regression analysis to uncover the spatio-temporal characteristics of summer water-playing demand and its correlation with comfortable levels. The results were that: (1) In Huanglongxi and its environs, the predominant comfortable level was “comfortable,” averaging 38%, followed by “warm” at 19.70%, “cool” at 10.61%, and “hot” being the least prevalent at 6.82%. Comfortable days (ranging from comfortable to cool) constitute 70% of the annual total. (2) Online interesting in Huanglongxi peaks significantly during the summer, with search popularity showing a marked positive correlation with high-temperature conditions, underscoring water-playing as the primary driver of summer tourism. Additionally, the study pinpoints potential peak tourist periods, offering a scientific foundation for scenic management to implement real-time passenger flow alerts, dynamic resource allocation, and targeted marketing strategies. Forecasting tourism demand based on online search data could guide scenic areas and relevant authorities in optimizing operational management, innovating water-playing tourism product offerings, and adopting effective passenger flow distribution measures, thereby enhancing visitor experiences and ensuring the sustainable development of the scenic area.