Drought is a major natural disaster worldwide. Understanding the correlation between meteorological drought (MD) and agricultural drought (AD) is essential for relevant policymaking. In this paper, standardized precipi-tation evapotranspiration index and standardized soil moisture index were used to estimate the MD and AD in the North China Plain (NCP) to identify the correlation between MD and AD during the growth period of winter wheat. In addition, we investigated the contributions of climate change (CC) and human activity (HA) to AD and the factors influencing the loss of winter wheat net primary production (NPP). Drought propagation time (PT) increased spatially from the southern to northern NCP (from 3 to 11 months). PT first increased and then decreased during the phenological period of winter wheat, and the decreasing trend was delayed with an increasing latitude. In general, the relative contribution of CC to AD was higher than that of HA; the correlation between MD and AD exhibited a weakening trend, particularly during the middle and late phenological stages of winter wheat. Precipitation was the main driver of the effects of HA on AD; the effects were stronger in areas with less precipitation. However, because of the improved irrigation conditions and scarce rainfall during the growth period of winter wheat in the study area, the effects of precipitation on AD were nonsignificant. Instead, tem-perature, wind, and total solar radiation, which are highly correlated with evapotranspiration, were identified as the primary drivers of AD; spatiotemporal variations were noted in these correlations. Prolonged drought PT reduced NPP; the sensitivity of winter wheat NPP to AD was higher in humid areas than in semiarid or semi-humid areas. NPP loss occurred primarily due to HA. Our findings revealed a correlation between MD and AD in agroecosystems and may facilitate policymaking related to drought mitigation and food security.

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 perma-frost 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/m(2)/yr, p < 0.05) increase than in non-permafrost regions (1.224 g C/m(2)/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.

The Biome-BGC (biome biogeochemical cycles) model is widely used for modeling the net primary productivity (NPP) of ecosystems. However, this model ignores soil water changes during the freeze-thaw process in permafrost regions, which may lead to considerable errors in the NPP estimations. In this study, we proposed a numerical simulation method for improving soil water during the freeze-thaw process based on the field observation data of soil water and temperature. This approach does not require new parameters and has no impact on other modules. The improvement of soil water content during the freeze-thaw process was then incorporated in the Biome-BGC model for NPP in an alpine meadow in the central Qinghai-Tibetan Plateau (QTP). The results indicated that this method effectively reduced the RMSEs (root mean square errors) of the simulated soil moisture, leaf area index, and NPP, indicating that this approach performs well in the Biome-BGC model. This study suggested that the improvement of soil water content during the freeze-thaw process is also applicable to other models and, thus, could be a useful method to reduce the uncertainty of NPP estimations in permafrost regions.

The Tibetan Plateau has the largest expanse of high-elevation permafrost in the world, and it is experiencing climate warming that may jeopardize the functioning of its alpine ecosystems. Many studies have focused on the effects of climate warming on vegetation production and diversity on the Plateau, but their disparate results have hindered a comprehensive, regional understanding. From a synthesis of twelve warming experiments across the Plateau, we found that warming increased aboveground net primary production (ANPP) and vegetation height at sites with permafrost, but ANPP decreased with warming at non-permafrost sites. Aboveground net primary production responded more negatively to warming under drier conditions, due to both annual drought conditions and warming-induced soil moisture loss. Decreases in species diversity with warming were also larger at sites with permafrost. These results support the emerging understanding that water plays a central role in the functioning of cold environments and suggest that as ecosystems cross a threshold from permafrost to non-permafrost systems, ANPP will decrease across a greater proportion of the Tibetan Plateau. This study also highlights the future convergence of challenges from permafrost degradation and grassland desertification, requiring new collaborations among these currently distinct research and stakeholder groups.