Projection of net primary production under changing environment in Xinjiang using an improved wCASA model

Abstract

Net Primary Productivity (NPP) is an important component of the carbon cycle of terrestrial ecosystems and plays an important role in the evaluation of vegetation growth. Among all the factors, wind also plays a vital role in modeling vegetation productivity. However, none of the current models have considered the impact of wind on the process of productivity estimation. An in-depth study was conducted on the temporal and spatial distribution and internal relations between wind speed and NPP. By introducing the influence factor wind, the Carnegie-Ames-Stanford model (CASA) based on the land-surface water index was modified and named as wCASA model. The estimation and prediction results of wCASA model considering the influence factor wind were better than that of the CASA model. The modified wCASA model was applied in Xinjiang, China. The simulation results were improved with the coefficient of determination (R²) increased by 9.59%, the root mean square error (RMSE) decreased by 13.78%, and the residual of prediction deviation (RPD) improved by 12.08% as compared to CASA model. The optimal model was used to simulate and predict the NPP of cropland and grassland for 2022–2050 under three climate scenarios, SSP126, SSP245, and SSP585, respectively, based on the CMIP6 dataset. The wCASA model can accurately estimate and predict NPP, which provides scientific basis for efficient agricultural water use and food production.