Extreme climate occurred frequently in subtropical region, which seriously affects carbon and water fluxes such as evapotranspiration (ET) and gross primary productivity (GPP) of terrestrial ecosystems. The process -based biome biogeochemical cycles (Biome-BGC) model is widely used for simulating carbon and water fluxes of forest ecosystems. However, the lack of the interaction information of climate, vegetation and soil, such as the hysteresis effect, canopy stratification on photosynthesis, impedes better simulations of the ecohydrological processes. Here, we tended to improve the simulation accuracy of Biome-BGC model at a subtropical forest on the Xin'an River in Southeastern China by reconstructing the precipitation series, modifying the ET and canopy multilayers modules, and optimizing the parameters. The spatiotemporal patterns of GPP, ET, water use efficiency (WUE) and their response to environmental factors across the Xin'an River Basin from 1982 to 2018 were further explored. The results showed that the improved model performed well, with the determination coefficient, root means square error and mean absolute error being 0.730, 1.522 gC/m2/d and 1.218 gC/m2/d for GPP, 0.857, 1.082 mm/d and 0.838 mm/d for ET, respectively. Basin -averaged GPP, ET and WUE increased during 1982-2018 and these increasing trends were more pronounced during 1999-2018. Significant positive trends of WUE occurred in the northeast corner. The increasing air temperature and precipitation respectively dominated the increase in GPP and ET, the increasing CO2 concentration and NDVI mitigated the negative effect of extreme precipitation on WUE. Given that human activities such as afforestation have effectively reduced the extent of damage to forest ecosystems from extreme precipitation, we highlight an urgent need to formulate adaptation strategies aimed at reducing the risk of extreme climate in humid regions.

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.