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

Abstract

The Sanjiang Plain has the greatest concentration of freshwater marshes in China. Marshes in this area play a key role in adjusting the regional carbon cycle. As an important quality parameter of marsh ecosystems, vegetation aboveground biomass (AGB) is an important index for evaluating carbon stocks and carbon sequestration function. Due to a lack of in situ and long-term AGB records, the temporal and spatial changes in AGB and their contributing factors in the marsh of Sanjiang Plain remain unclear. Based on the measured AGB, normalized difference vegetation index (NDVI), and climate data, this study investigated the spatiotemporal changes in marsh AGB and the effects of climate variation on marsh AGB in the Sanjiang Plain from 2000 to 2020. Results showed that the marsh AGB density and annual maximum NDVI (NDVImax) had a strong correlation, and the AGB density could be accurately calculated from a power function equation between NDVImax and AGB density (AGB density = 643.57 × NDVImax4.2474). According to the function equation, we found that the AGB density significantly increased at a rate of 2.47 g·C/m2/a during 2000–2020 in marshes of Sanjiang Plain, with the long-term average AGB density of about 282.05 g·C/m2. Spatially, the largest increasing trends of AGB were located in the north of the Sanjiang Plain, and decreasing trends were mainly found in the southeast of the study area. Regarding climate impacts, the increase in precipitation in winter could decrease the marsh AGB, and increased temperatures in July contributed to the increase in the marsh AGB in the Sanjiang Plain. This study demonstrated an effective approach for accurately estimating the marsh AGB in the Sanjiang Plain using ground-measured AGB and NDVI data. Moreover, our results highlight the importance of including monthly climate properties in modeling AGB in the marshes of the Sanjiang Plain.

Effects of Temporal and Spatial Changes in Wetlands on Regional Carbon Storage in the Naoli River Basin, Sanjiang Plain, China

“…The results showed that the wetlands in the NLR basin showed a degradation trend, which was consistent with the results of previous studies, and the marsh wetlands in the NLR basin were severely degraded in the last 40 years due to the influences of various factors [41]. Natural and anthropogenic factors have contributed to the gradual loss of wetlands in the region [42]. Specifically, between 1993 and 2022, the wetland area decreased by 1507.18 hm 2 , with 416.42 hm 2 being converted to cultivated land.…”

Spatiotemporal variation in vegetation phenology and its response to climate change in marshes of Sanjiang Plain, China

“…These indicate that the later EOS and earlier SOS may explain longer LOS in the east of study area. This is because that the climate in Sanjiang Plain is a monsoon climate (Jin et al, 2016 ), and the hydrothermal conditions are better and more suitable for vegetation growth in the east of this region (Liu et al, 2022 ). Therefore, the SOS of marsh vegetation was early, and the EOS was delayed; thereby, extending the LOS in the east of Sanjiang Plain.…”

“…However, precipitation was not the main element influencing SOS in the marsh vegetation of Sanjiang Plain. It may be because that water is relatively abundant in marshes of Sanjiang Plain (Jin et al, 2016 ; Liu et al, 2022 ), and thus precipitation has no significant effects on marsh vegetation growth in Sanjiang Plain. By contrast, temperature plays an important role in many developmental biological processes of marsh vegetation (Badeck et al, 2004 ).…”

“…However, this result was different from a previous study (Liu et al, 2016 ), in which precipitation was the main determinant of grassland EOS and that increasing precipitation can alleviate water stress and delay EOS. The marsh ecosystem has sufficient water compared with the arid grassland ecosystem (Liu et al, 2022 ; Ma, Shen, et al, 2022 ; Ma, Xia, et al, 2022 ; Shen, Liu, Zhang, et al, 2022 ). Therefore, precipitation in autumn may have no significant impact on the marsh EOS.…”

“…We applied the ordinary Kriging approach to interpolate the climatic data of weather stations into the marsh distribution of Sanjiang Plain, and then unified the spatial resolution of interpolated climate dataset into the same as NDVI dataset (Liu et al, 2022 ). Monthly climatic data were used to calculate mean values of temperature and precipitation in winter (previous December–February), autumn (September–November), summer (June–August), and spring (March–May).…”