Diverse Roles of Previous Years’ Water Conditions in Gross Primary Productivity in China

Abstract

Gross primary productivity is one of the most important indicators of ecosystem function, which is related to water conditions and shown high interannual variation. Due to the time-lag effect, not only the current water condition but also the previous water conditions (e.g., one year before) impact the gross primary productivity (GPP). Revealing the impacts of current and previous years’ water status is currently a hot topic. In this study, we designed a series of water deficit scenarios based on the meteorological dataset of the Climatic Research Unit (CRU) and then analysed the responses of the remote sensing-based moderate resolution imaging spectroradiometer (MODIS) gross primary productivity (GPP) in China, from which the role of water deficit in time periods was evaluated. The results indicate that the impact of climate factors (i.e., water, temperature and radiation) on GPP has a high spatial heterogeneity and that water-limited regions that are primarily distributed in North and Northwestern China show a stronger water-GPP relationship than water-unlimited regions. The water deficit that occurred in different periods had a variable impact on GPP. Specifically, GPP was primarily controlled by the current year’s water conditions in the water-limited regions, with the importance value of 52.8% (the percentage of Increased Mean Square Error, %IncMSE) and 3.8 (the mean decrease in node impurity, IncNodePurity), but at the same time, it was conditionally affected by the water status in the previous year, with the importance value of 20.4% (%IncMSE) and 0.6 (IncNodePurity). The role of water in previous years is multifarious, which depends on the water conditions of the current year. The results revealed by the scenarios indicate that the influence of water conditions in the previous year was not statistically significant when the water conditions of the current year were in a drought. In contrast, when the current year’s water conditions were normal or wetter, the water conditions in the previous year (i.e., one-year time lag) were also important and the increase of GPP significantly depended on the water condition (p < 0.05). The diverse roles of water conditions in previous years on GPP and its non-ignorable time-lag effect revealed in this study imply that not only the current year’s water condition but also its dynamic changes in previous years should be considered when predicting changes in GPP caused by climate change.