Nonlinear Variations of Net Primary Productivity and Its Relationship with Climate and Vegetation Phenology, China

Jian Yang, Xin Zhang, Zhao Luo, Xi Yu
2017 Forests  
Net primary productivity (NPP) is an important component of the terrestrial carbon cycle. In this study, NPP was estimated based on two models and Moderate Resolution Imaging Spaectroradiometer (MODIS) data. The spatiotemporal patterns of NPP and the correlations with climate factors and vegetation phenology were then analyzed. Our results showed that NPP derived from MODIS performed well in China. Spatially, NPP decreased from the southeast toward the northwest. Temporally, NPP showed a
more » ... ar increasing trend at a national scale, but the magnitude became slow after 2004. At a regional scale, NPP in Northern China and the Tibetan Plateau showed a nonlinear increasing trend, while the NPP decreased in most areas of Southern China. The decreases in NPP were more than offset by the increases. At the biome level, all vegetation types displayed an increasing trend, except for shrub and evergreen broad forests (EBF). Moreover, a turning point year occurred for all vegetation types, except for EBF. Generally, climatic factors and Length of Season were all positively correlated with the NPP, while the relationships were much more diverse at a regional level. The direct effect of solar radiation on the NPP was larger (0.31) than precipitation (0.25) and temperature (0.07). Our results indicated that China could mitigate climate warming at a regional and/or global scale to some extent during the time period of 2001-2014. widely used [13,14]; however, uncertainties from inputs and the algorithm resulted in biases and restrained data use regionally or globally to some extent. For instance, 70-80% accuracy of MODIS land cover products (MOD12Q1) are an assimilated meteorological dataset, not observed data with coarse spatial resolution, the cloud-contaminated MODIS FPAR/LAI (MOD15A2), and weaknesses in the MOD17 algorithm [15, 16] . Therefore, it is essential to compare MODIS derived NPP to other models and/or field observed NPP, especially for China, which encompasses a wide range of ecosystems and climates. In addition, the responses of different ecosystems to different magnitudes of climate change are still far from clear, especially in China [17] . Moreover, few studies [12] have investigated the effect of solar radiation as an integrated surrogate for the effects of both day length and sunlight intensity [18] on vegetation NPP, especially when temperature and precipitation are considered simultaneously. In addition, previous studies have shown that vegetation phenology, an important factor that affects plant productivity, has changed dramatically due to climate changes and anthropogenic interference [19] , but only a few studies have explored the effect of phenophase variation on NPP [20] . At present, due to the diversity of the trends and magnitudes of vegetation phenophases at different scales, its effects on NPP are still unclear, especially in China, where the vegetation phenophases are diverse at both the regional and biome levels [19] . Furthermore, a linear regression method has been applied by most studies to analyze NPP trend [10,21] despite the trend always showing a non-linear trend, or has one or more turning points within the time period [22, 23] . These limitations and/or gaps have impeded our understanding of the dynamic relationship and consequently researchers may have underestimated future changes in plant productivity and/or the carbon cycle throughout China. China encompasses a variety of ecosystems and climates. The regional climate ranging from tropical to cold-temperate, and from humid in the south to extremely dry in the northwest [24] . Land cover types are diverse, including a broad range of tropical, temperate and boreal forests, grassland, cropland and desert [25] . In recent decades, China has experienced dramatic changes in climate such as remarkably strong El Niño events [26], the freezing low temperatures in early 2008 [24] , and frequent occurrences of severe droughts [27] . Meanwhile, land use and land cover changes have occurred at unprecedented rates due to quick economic development, dramatic urbanization [28] , and implementation of several large scale forest plantation programs [29] . These changes have resulted in large variations in China's terrestrial ecosystem productivity and have definitely adjusted the terrestrial carbon cycle in China [30] . However, whether the temporal trend of NPP continuously increased or decreased during the study period is still unclear given the possible changes of climate derivers and anthropogenic activities. Additionally, the correlations between NPP and climate derivers as well as vegetation phenophases in recent decade still remain unclear [31] . Due to climatic variability, topographic complexity, natural ecosystem diversity, and intensive human disturbance, China is becoming one of the most critical and sensitive regions in the global carbon cycle for determining the carbon budget at regional and global scales. Furthermore, it provides a good opportunity to identify the effects of climate change on NPP to forecast the potential biosphere feedback to nature in the climate system. Therefore, two simple models were applied to estimate NPP and the results accompanied by MODIS derived NPP were all compared with the field observed NPP. Then, the results that showed less biases with the field observed NPP were selected to analyze the vegetation NPP dynamics and its relationship with both climate and vegetation phenology in China. More specifically, the authors aim was to (1) explore which model's result was more accurate, and the quantity of uncertainty of MODIS derived NPP in China; (2) understand the spatial pattern of NPP, and investigate whether the temporal trend of NPP was continuously increasing or decreasing in China for the period 2001-2014 given climate change and anthropogenic activities; and (3) estimate the effects of climatic driving factors and vegetation phenology changes on vegetation NPP.
doi:10.3390/f8100361 fatcat:lyxswpnzcrdi5ou3c3xfupdx6m