Responses of Terrestrial GPP to Extreme Compound Heatwave and Drought Events of Different Intensities in the Yangtze River Basin - Jiawen Zhu; Haokun Guo; Yankun Sun (Remote Sensing)
In 2022, a record-breaking extreme compound heatwave–drought (CHD) event occurred in China’s Yangtze River Basin (YRB), which significantly reduced terrestrial ecosystem Gross Primary Production (GPP), as many previous studies have shown. However, it remains uncertain how GPP responds to extreme CHD events of varying intensities, as well as the differences in GPP responses among different vegetation types in the YRB. This study used two independent GPP products (GPPGOSIF and GPPFluxSat) and Enhanced Vegetation Index (EVIMODIS) data to investigate these uncertainties during three identified CHD years: 2003, 2013, and 2022. In 2022, when the CHD event intensity was the strongest, the YRB experienced the most significant GPP reductions, with amplitudes of −0.37 gC m−2 day−1 (−3.05 standard deviation, hereafter STD) and −0.57 gC m−2 day−1 (−5.97 STD) for GPPGOSIF and GPPFluxSat, respectively. In the less intense years, the year 2003 had less than one-third of GPP reductions in 2022, while the year 2013 even showed an overall slight GPP increase. Moreover, the year 2022 also showed the widest extent with significant GPP reductions exceeding one STD, which was more than twice the grid points in 2003 and 2013. This study also revealed significant differences in GPP responses across different vegetation types. In 2022, GPP reductions were the most pronounced in shrub-dominated areas, followed by evergreen forests, while deciduous forests and mixed forests experienced relatively smaller decreases. However, there were no significant differences among vegetation types in 2003 and 2013. These findings enhance our understanding of the variability in the GPP responses to extreme CHD events of varying intensities in the YRB terrestrial ecosystems, and this enhancement provides an important scientific basis for optimizing related vegetation and carbon cycle models.