Spatiotemporal Change of Marsh Vegetation and Its Response to Climate Change in China From 2000 to 2019

Abstract

China has the third largest area of marshes in the world. Understanding the change of marsh, vegetation and its response to climate change in China is important for the protection of wetland ecosystem. Based on the climate and normalized difference vegetation index (NDVI) data, we investigated the spatiotemporal variation in vegetation and its response to climate change for different types of marshes in China. The results indicated that growing season NDVI increased significantly (0.02/decade, P < 0.01) over the whole marshes of China from 2000 to 2019. The increased precipitation and minimum temperature during the growing season could promote the growth of marsh vegetation over China. This study found for the first time that the increase of temperature during the day and at night had asymmetric effects on vegetation growth in marshes of China, and the positive effect of night warming was more significant. Spatially, increasing growing season precipitation was beneficial to the vegetation growth of herbaceous marshes, marsh meadows, inland salt marshes, and seasonal saltwater marshes in eastern Inner Mongolia. Increased growing season minimum temperature could greatly promote the vegetation growth of marsh meadows, herbaceous marshes, inland salt marshes, forest swamp, and bush swamp in cold and high‐altitude regions of Northern Northeast China and the Tibet Plateau. In the context of global climate change, more attention should be paid to the different responses of vegetation to climate change for different marsh types, especially when we use the model to simulate the impact of climate change on vegetation in different marsh regions of China.

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

“…In this study, we used the trend analysis method to calculate the trends of AGB and meteorological factors in the marshes of the Sanjiang Plain. The formula is included below (Shen et al, 2021b ):…”

“…In this study, we interpolated the meteorological data using the Kriging interpolation method, and the grid images of precipitation, T min , T max , and T mean in the annual (or monthly) of marsh vegetation in the Sanjiang Plain with the same spatial resolution (250 m) as the AGB image were obtained (Shen et al, 2021b ). The correlation between meteorological factors in the annual vegetation (or month) and AGB was calculated by using the correlation analysis method.…”

“…However, these studies focused on analyzing changes in marsh AGB and their responses to climatic change at a species scale, and no research has been conducted over the entire Sanjiang Plain due to the limitations of field investigations. Climate change could induce different responses of different marsh vegetation in a region or the same marsh vegetation in different regions to climate changes (Shen et al, 2019a(Shen et al, , 2021b. Thus, studying the variations in marsh AGB over the entire Sanjiang Plain and the influences of climate change on marsh AGB is necessary.…”

“…The over-compensation effect refers to the phenomenon that warming nighttime temperatures can promote photosynthesis and increase organic matter consumption during the next day to exceed the organic matter consumed due to enhanced respiration (Belsky, 1986;Peng et al, 2011;Chen et al, 2014). According to previous studies, when vegetation grows in sufficient nutrient and water environments, it has a high compensation effect and sometimes even shows an overcompensation effect (Belsky, 1986;Shen et al, 2021b). In July, owing to good hydrothermal conditions and sufficient nutrients (Wang et al, 2003), marsh vegetation in the Sanjian Plain has a strong photosynthetic capacity and it is easy to have an overcompensation effect (Wang et al, 2003).…”

Quantitative Assessment of Climate Change Impacts On Forest Ecosystem

“…The results show that climate warming has a negative effect on forest NPP in southern China, but a positive effect on northern China (Li et al, 2017;Wang et al, 2017). There is a good correlation between Normalized Difference Vegetation Index (NDVI) and vegetation growth (Piao et al, 2015;Ghebrezgabhera et al, 2020;Dearborn et al, 2021;Shen et al, 2021;), and the time series of vegetation index extracted by remote sensing can be used to study the response of vegetation to climate change at a large regional scale. The size of the time window can affect the results of the vegetation cover change trend study.…”