Land surface models intended for large‐scale applications are often executed at coarse resolutions, and the sub‐grid heterogeneity is usually ignored. Here, a spatial scaling algorithm that integrates the information of vegetation heterogeneity (land cover type and leaf area index) and surface topography (elevation, slope, relative azimuth (Raz) between the sun and the slope background, sky‐view factor, and topographic wetness index), was proposed to correct errors in gross primary productivity (GPP) estimates at a coarse spatial resolution. An eco‐hydrological model named BEPS‐TerrainLab was used to simulate GPP at 30 and 480 m resolutions for 16 mountainous watersheds selected globally. Results showed that an obvious improvement on GPP estimates at 480 m resolution was achieved after the correction in comparison with GPP modeled at 30 m resolution, with the determination coefficient increased by 0.38 and mean bias error reduced by 203gCm⁻² yr⁻¹. The combination of all the seven factors made the largest improvement for GPP estimation at 480 m resolution, suggesting that a larger improvement would be achieved when more factors of surface heterogeneity are considered. More specifically, our results indicated that five factors, including land cover type and leaf area index regarded as integrated outcomes of all the environmental conditions, Raz and sky‐view factor associated with radiation redistribution, and slope related to soil water redistribution, were especially important in the spatial scaling procedure. This study suggests that incorporating the information of surface heterogeneity into the spatial scaling algorithm is useful for improving coarse resolution GPP estimates over mountainous areas.

中文翻译：

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利用植被异质性和表面地形学对十六个山区流域的总初级生产力进行空间缩放

用于大规模应用的地表模型通常以较高分辨率执行，并且通常忽略子网格异质性。在这里，一种空间缩放算法将植被异质性（土地覆盖类型和叶面积指数）和表面地形（海拔，坡度，太阳和坡度背景之间的相对方位角（Raz），太阳视因子和地形）的信息集成在一起提出了用湿度指数来校正粗略的空间分辨率下的总初级生产力（GPP）估计中的误差。一个名为BEPS-TerrainLab的生态水文模型被用来模拟全球范围内选择的16个山区流域在30和480 m分辨率下的GPP。⁻² 年− ^1年。所有这七个因素的组合在480 m分辨率下为GPP估计带来了最大的改善，表明当考虑更多表面非均质性因素时，将实现更大的改善。更具体而言，我们的结果表明，包括土地覆盖类型和叶面积指数在内的所有环境条件，与辐射重新分布相关的Raz和天空视野因子以及与土壤水分重新分布相关的坡度等五个因素特别重要。在空间缩放过程中。这项研究表明，将表面异质性信息整合到空间缩放算法中对于改善山区的粗分辨率GPP估计很有用。