Variability and Changes in Climate, Phenology, and Gross Primary Production of an Alpine Wetland Ecosystem

Abstract

Abstract:Quantifying the variability and changes in phenology and gross primary production (GPP) of alpine wetlands in the Qinghai-Tibetan Plateau under climate change is essential for assessing carbon (C) balance dynamics at regional and global scales. In this study, in situ eddy covariance (EC) flux tower observations and remote sensing data were integrated with a modified, satellite-based vegetation photosynthesis model (VPM) to investigate the variability in climate change, phenology, and GPP of an alpine wetland ecosystem, located in Zoige, southwestern China. Two-year EC data and remote sensing vegetation indices showed that warmer temperatures corresponded to an earlier start date of the growing season, increased GPP, and ecosystem respiration, and hence increased the C sink strength of the alpine wetlands. Twelve-year long-term simulations (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) showed that: (1) there were significantly increasing trends for the mean annual enhanced vegetation index (EVI), land surface water index (LSWI), and growing season GPP (R 2 ě 0.59, p < 0.01) at rates of 0.002, 0.11 year´1 and 16.32 g¨C¨m´2¨year´1, respectively, which was in line with the observed warming trend (R 2 = 0.54, p = 0.006); (2) the start and end of the vegetation growing season (SOS and EOS) experienced a continuous advancing trend at a rate of 1.61 days¨year´1 and a delaying trend at a rate of 1.57 days¨year´1 from 2000 to 2011 (p ď 0.04), respectively; and (3) with increasing temperature, the advanced SOS and delayed EOS prolonged the wetland's phenological and photosynthetically active period and, thereby, increased wetland productivity by about 3.7-4.2 g¨C¨m´2¨year´1 per day. Furthermore, our results indicated that warming and the extension of the growing season had positive effects on carbon uptake in this alpine wetland ecosystem.

Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

“…Despite these challenges, the four different, NDVI-based estimators predicted biomass production with reasonable quality (39-65% of variance explained during calibration). However, the two NDVI biomass estimators derived from TIMESAT models of LSP performed significantly better than those based on a single image per year only-reinforcing previous suggestions that LSP may improve biomass determination in complex ecosystems [20,57]. The improved performance of LSP estimators is probably caused by the higher sensitivity of single-image estimators to several sources of error and noise, such as sensor resolution and calibration, digital quantization errors, ground and atmospheric conditions, or orbital and sensor degradation [7]; and to the rapid changes in the NDVI signal in heterogeneous ecosystems-which may bias such estimators, for example, if an image is taken after a rainfall episode [38].…”

Investigation of Urbanization Effects on Land Surface Phenology in Northeast China during 2001–2015

“…We used the same method mentioned in the above section to reproject and mosaic the MODIS LST data. We assumed that the mean of MODIS LST nighttime temperature and MODIS LST daytime temperature can represent daily average temperature, the mean temperature during March to May can represent spring temperature, and the mean temperature during September to November can represent autumn temperature, according to a previous study [10].…”

Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

“…Wetlands act as one of the most important types of ecosystems and perform many vital functions, including water storage and purification, flood and erosion control, shoreline protection, conservation of biological diversity, and as a habitat for wildlife and fishery resources for human communities [1][2][3]. As one of the most important abiotic factors, the wetland inundation extent greatly dominates the function of the wetland ecosystem and its consequent effects on the interactions between the land and atmosphere system [4].…”