Abstract
Grasslands in the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and anthropogenic activities. Quantifying the impacts of climate change and anthropogenic activities on grassland growth is an essential step for developing sustainable grassland ecosystem management strategies under the background of climate change and increasing anthropogenic activities occurring in the plateau. Net primary productivity (NPP) is one of the key components in the carbon cycle of terrestrial ecosystems, and can serve an important role in the assessment of vegetation growth. In this study, a modified Carnegie–Ames–Stanford Approach (CASA) model, which considers remote sensing information for the estimation of the water stress coefficient and time-lag effects of climatic factors on NPP simulation, was applied to simulate NPP in the Tibetan Plateau from 2001 to 2015. Then, the spatiotemporal variations of NPP and its correlation with climatic factors and anthropogenic activities were analyzed. The results showed that the mean values of NPP were 0.18 kg∙C∙m−2∙a−1 and 0.16 kg∙C∙m−2∙a−1 for the original CASA model and modified CASA model, respectively. The modified CASA model performed well in estimating NPP compared with field-observed data, with root mean square error (RMSE) and mean absolute error (MAE) of 0.13 kg∙C∙m−2∙a−1 and 0.10 kg∙C∙m−2∙a−1, respectively. Relative RMSE and MAE decreased by 45.8% and 44.4%, respectively, compared to the original CASA model. The variation of NPP showed gradients decreasing from southeast to northwest spatially, and displayed an overall decreasing trend for the study area temporally, with a mean value of −0.02 × 10−2 kg∙C∙m−2∙a−1 due to climate change and increasing anthropogenic activities (i.e., land use and land cover change). Generally, 54% and 89% of the total pixels displayed a negative relationship between NPP and mean annual temperature, as well as annual cumulative precipitation, respectively, with average values of –0.0003 (kg∙C∙m−2 a−1)/°C and −0.254 (g∙C∙m−2∙a−1)/mm for mean annual temperature and annual cumulative precipitation, respectively. Additionally, about 68% of the total pixels displayed a positive relationship between annual cumulative solar radiation and NPP, with a mean value of 0.038 (g∙C∙m−2·a−1)/(MJ m−2). Anthropogenic activities had a negative effect on NPP variation, and it was larger than that of climate change, implying that human intervention plays a critical role in mitigating the degenerating ecosystem. In terms of human intervention, ecological destruction has a significantly negative effect on the NPP trend, and the absolute value was larger than that of ecological restoration, which has a significantly positive effect on NPP the trend. Our results indicate that ecological destruction should be paid more attention, and ecological restoration should be conducted to mitigate the overall decreasing trend of NPP in the plateau.
Highlights
Net primary productivity (NPP) is the amount of net accumulation of organic matter by plants in a given period [1]
Due to ignorance of the time-lag effect of climatic factors in the Carnegie–Ames–Stanford Approach (CASA) model, and the uncertainness of the CASA model applied in certain specific ecoregions mentioned above, as well as the numerous soil parameters needed for the complex calculation of potential and estimated evapotranspiration to estimate the water stress coefficient in the CASA model, the aim of this study is to apply a modified CASA model—which considers time-lag effects of the climatic factors and remote sensing information, which were easy to obtain with large spatial and temporal scales, included in the estimation of the water stress coefficient—to estimate NPP over the Tibetan Plateau during 2001–2015, and to explore the spatiotemporal variations of NPP
The mean absolute error (MAE) and root mean square error (RMSE) for the original CASA model were 0.18 kg·C·m−2·a−1 and 0.24 kg·C·m−2·a−1, respectively, which decreased to 0.10 kg·C·m−2·a−1 and 0.13 kg·C·m−2·a−1, respectively, for the modified CASA model
Summary
Net primary productivity (NPP) is the amount of net accumulation of organic matter by plants in a given period [1] It is a critical component in a terrestrial ecosystem’s carbon cycle [2], and serves as a sensitive indicator of an ecosystem’s health and ecological balance at both the local and global scale [3]. Many previous studies have been conducted to simulate NPP at both the local and global scale through field measurements [6,7], the eddy covariance technique [8], and remote sensing [9,10]. Remote sensing techniques provide a powerful and integrative tool for simulating vegetation NPP to obtain explicit and detailed information about carbon exchange at a larger spatial scale [11], and they have been applied widely [10,14,15]. It is currently difficult to effectively separate them [27]
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