Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

中文翻译：

---

21世纪中亚地表反照率时空变化及其影响因素及验证路径分析


地表反照率 （SA） 对于理解地表过程和气候模拟至关重要。本研究分析了 2001—2020 年中亚地区 SA 变化及其影响因素，并预测了 2025—2100 年。分析的因素包括积雪覆盖率、植被覆盖率、土壤湿度、平均状态气候指数（温度和降水）和极端气候指数（热浪指数和极端降水指数）。采用 Pearson 相关系数、地理收敛交叉映射和地理检测器量化 SA 与影响因素之间的相关性、因果关系强度和影响程度。为了解决多重共线性问题，将山脊回归 （RR） 、地理加权山脊回归 （GWRR） 和分段结构方程模型 （pSEM） 相结合，构建了 RR-pSEM 和 GWRR-pSEM 模型。结果表明，中亚的 SA 从 2001 年到 2010 年增加，从 2011 年到 2020 年下降，预计未来会下降。SA 与每个因素之间存在很强的相关性和显著的因果关系。积雪覆盖率被确定为影响 SA 的最关键因素。平均气温和降水对 SA 的影响大于极端气候指数，温度升高 1 °C 对应于 SA 降低 0.004。本研究增强了对气候变化下 SA 变化的理解，并为分析具有多重共线性的复杂系统提供了方法框架。所提出的模型为研究地球系统科学中相互关联的因素提供了有价值的工具。