Quantifying the drivers of terrestrial drought and water stress impacts on carbon uptake in China

Journal article

Droughts are one of the most damaging weather-related hazards and can have detrimental impacts on ecosystem carbon uptake. However, little is known about the physical mechanisms underlying drought evolution as well as their potential effects on the carbon budget under historical and future climates. Here, we first quantify the impacts of water stress on carbon uptake under climate change in China. While heat and water stress play a crucial role in carbon uptake, the effect of ecosystem complexity is also significant. Then, we employ a machine learning model to explore the driving mechanisms of droughts, which are identified through the depletion of terrestrial water storage (TWS). Our results indicate that TWS droughts tend to be governed by atmospheric dryness, with precipitation, relative humidity (RH) and temperature playing dominant roles in drought evolution across most land areas. Precipitation and RH control moisture supply and demand, while rising temperature signifies increasing evaporative demand and enhanced evapotranspiration, leading to soil moisture depletion and reduced surface runoff, thereby intensifying drought. Further, by combining satellite data, field measurements, six global hydrological models, a global land surface model and a dynamic vegetation model, we find that water and heat stress have negative impacts on gross primary productivity (GPP), total ecosystem respiration (TER) and net ecosystem productivity (NEP), under both current and future climates. By the end of the 21st century (2071–2100), drought occurrence is projected to increase by sixfold over more than 60 % of land areas, leading to disproportionate negative impacts on carbon assimilation. Negative anomalies of NEP under drought stress are projected to decline from -0.09 g m_-2 day_-1 (historical period) to −0.16 -0.09 g m_-2 day_-1 (future period) under SSP370, with even more severe effects on future carbon assimilation under higher emission pathways. Our results suggest that more severe drought conditions might challenge ecosystem sustainability, and highlight the necessity of improving ecosystem resilience to climate warming.

Authors: Yang, Y; Yin, J; Kang, S; Slater, L; Gu, X

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Agricultural and Forest Meteorology
• Volume: 344
• Article number: 109817
• Publication date: 2023-11-17
• Acceptance date: 2023-11-13
• DOI: 10.1016/j.agrformet.2023.109817
• ISSN: 0168-1923
• Language: English
• Keywords: carbon uptake; water stress; China; machine learning; drought