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Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

机译:植被异质性和表面形貌对净初级生产率的空间缩放的影响

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Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modelled at the 1 km resolution is reduced from 14.8 g C m?2 yr?1 to 4.8 g C m?2 yr?1 in comparison with NPP modelled at 30 m resolution, where the mean NPP is 668 g C m?2 yr?1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI) and elevation have small and additive effects on improving the spatial scaling between these two resolutions.
机译:由于土地表面的异质性,空间缩放是与低分辨率地球系统模型(ESMS)相结合的陆地模型的不可避免的问题,用于预测土地气氛相互作用和碳气候反馈。在该研究中,开发了一种简单的空间缩放算法,以校正基于植被异质性和表面形貌的子像素信息在粗糙空间分辨率下进行的净初级生产率(NPP)估计的误差。一种生态水文模型Beps-Terrainlab,其考虑垂直和横向水流和碳循环的植被和地形效果,用于在30米和1公里的分辨率下模拟NPP,为5700平方公司的水域,高度范围中国山西省秦岭山518米至3767米。假设在30米分辨率下模拟的NPP表示现实,并且在1km分辨率上由于子像素异质性而受到错误,则开发空间缩放索引(SSI)以校正像素的粗略分辨率NPP值像素。在纠正后,这两项分辨率下的NPP值与r2 = 0.884之间的达成符合条件之间的协议。在1公里分辨率下建模的NPP中的平均偏差误差(MBE)从14.8g c m?2yr?1至4.8g cm≤2yr≤1,与在30米的分辨率下建模的NPP相比,其中NPP是668克C m?2 YR?1。 NPP的空间变化范围为30米分辨率大于1公里的分辨率。陆地盖分数是NPP空间缩放中最重要的植被因素,坡度是NPP空间缩放的最重要的地形因素,特别是在山区,由于其对横向水再分配,影响水位,土壤水分的影响。和植物生长。其他因素包括叶面积指数(赖)和高程,对改善这两种分辨率之间的空间缩放具有较小的和附加效果。

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