A practical algorithm for correcting topographical effects on global GPP products

Vegetation 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 datasets. Here, an eco-hydrological model was adopted to simulate 30 m resolution mountain and flat GPP over sixteen 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 datasets. The mean-bias-error (MBE), determination coefficient (R2), and Root-Mean-Square-Error (RMSE) between mountain GPP and flat GPP (or GPP datasets) were used for evaluation. Results showed that the MBE of flat GPP before correction (194 gCm−2yr−1) was reduced to 126, 94, and 2 gCm−2yr−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 gCm−2yr−1) and 480 m resolution (increasing R2 by 0.13 and reducing RMSE by 178 gCm−2yr−1). Regarding the four GPP datasets after the TCI correction, the MBE of 183 gCm−2yr−1 was averagely reduced to 17 gCm−2yr−1, and RMSE was reduced by 118 gCm−2yr−1 at 480 m resolution. This study suggests that integrating topography-induced interactions into current GPP datasets is a feasible way to understand the carbon budget in mountain ecosystems.