Abstract

Permafrost affects soil water and soil temperature regimes; however, its effects on net primary production (NPP) remain unknown. Here, we examined temporal-spatial changes in grassland NPP during 2000–2018 in permafrost and permafrost-free areas on the Qinghai–Tibetan Plateau using the random forest (RF) and radial basis function artificial neural network (RBF-ANN). Our results indicated that the areas that showed increasing, decreasing, and non-significant trends for NPP accounted for 13.88%, 1.90%, and 84.22% of the permafrost area, respectively. For the permafrost-free areas, these NPP trends accounted for 22.25%, 2.68%, and 75.07% of the permafrost-free area, respectively. The mean NPP in the permafrost regions showed a faster and steadier (1.520 g C/m2/yr, p < 0.05) increase than in non-permafrost regions (1.224 g C/m2/yr, p < 0.05). The Biome-BGC model confirmed that these spatial NPP patterns could be attributed to differences in soil water and soil temperature between permafrost and permafrost-free areas. Both the soil temperature and soil water content in permafrost sites exhibited relatively lower variance than in permafrost-free sites. Although many factors may be attributed to these patterns, our results suggest that there is a possibility that the relatively stable change in permafrost NPP can be explained by the fact that permafrost can regulate soil water and temperature regimes. Therefore, climate warming can increase NPP in cold regions, and permafrost degradation may destabilize the grassland ecosystem, which may cause NPP values to exhibit greater interannual changes in the future.

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