A Practical Algorithm for Correcting Topographical Effects on Global GPP Products
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abstract
AbstractVegetation in mountainous areas contributes about 36% to the global gross primary productivity (GPP). However, the influences of topography on radiation and water redistributions in mountain ecosystems are so far ignored in existing global GPP data sets. Here, an eco‐hydrological model was adopted to simulate 30 m resolution mountain and flat GPP over 16 watersheds. Then, a topographical correction index (TCI) was developed based on simulated soil water redistribution (TCIwater), radiation redistribution (TCIrad), and redistribution of climate factors (TCIclim). Finally, the proposed TCI was applied to four GPP data sets. The mean‐bias‐error (MBE), determination coefficient (R2), and Root‐Mean‐Square‐Error (RMSE) between mountain GPP and flat GPP (or GPP data sets) were used for evaluation. Results showed that the MBE of flat GPP before correction (194 g C m−2 yr−1) was reduced to 126, 94, and 2 g C m−2 yr−1 after the corrections of TCIwater, TCIrad, and TCIclim, highlighting the effectiveness of integrated redistribution information in correcting the topographical effect on GPP estimation. The relationship between mountain and flat GPP after the TCI correction was improved at the 30 m resolution (increasing R2 by 0.09 and reducing RMSE by 90 g C m−2 yr−1) and 480 m resolution (increasing R2 by 0.13 and reducing RMSE by 178 g C m−2 yr−1). Regarding the four GPP data sets after the TCI correction, the MBE of 183 g C m−2 yr−1 was averagely reduced to 17 g C m−2 yr−1, and RMSE was reduced by 118 g C m−2 yr−1 at 480 m resolution. This study suggests that integrating topography‐induced interactions into current GPP data sets is a feasible way to understand the carbon budget in mountain ecosystems.
