Simulation of gross primary productivity and impact of drought in Liulin watershed of Taihang mountains over 2000-2020

As the largest flux in the global terrestrial carbon cycle, Gross Primary Productivity (GPP) is a key factor for the terrestrial carbon budget. Based on the eddy flux tower data, remote sensing data and the meteorological precipitation data, we calibrated three parameters of maximum light use efficiency, optimum temperature, and maximum temperature in the Vegetation Photosynthesis Model (VPM) to reproduce the GPP spatiotemporal characteristic in Liulin watershed of Taihang Mountains. We further analyzed the time lag effect and cumulative effect of the standard precipitation index (SPI) on GPP by the Pearson correlation coefficients. Results show that VPM captures the GPP characteristics of eddy flux tower observation in Liulin watershed. During the calibration period (R-2 = 0.94, RMSE = 0.47 g C m(-2)<middle dot>d(-1)) and validation period (R-2 = 0.91, RMSE = 0.60 g C m(-2) d(-1)), the adjusted VPM model successfully reproduced the temporal dynamics of site-observed GPP. From 2000 to 2020, VPM model can effectively reproduce the long-term variations in GPP (R-2 = 0.69 vs. NIRv_GPP), with the GPP ranging from 446.4 to 828.1 g C m(-2) y(-1) and an average value of 545.6 g C m(-2) y(-1) in Liulin watershed. The GPP exhibited an increasing trend from 2000 to 2020. A mutation in the GPP occurred in 2010, with an average annual increase of 13.9 g C m(-2) y(-1) from 2000 to 2010 (p < 0.05), followed by an average annual increase of 10.22 g C m(-2) y(-1) from 2011 to 2020 (p > 0.05). The SPI effectively monitored drought events in the Liulin watershed. With a cumulative effect of 9 months and a lag effect of 6-7 months, the time lag and cumulative effects of drought had a long-term effect on GPP. It is imperative to have long-term eddy flux tower data and meteorological data to better evaluate the spatiotemporal dynamics of GPP and the impact of drought.