Location: Southwest Watershed Research CenterTitle: Nonlinear carbon cycling responses to precipitation variability in a semiarid grassland
|LI, L.F. - Chinese Academy Of Sciences|
|KANG, X. - Chinese Academy Of Forestry|
|WANG, W. - Griffiths University|
|QIAN, R. - University Of Chinese Academy Of Sciences|
|ZHENG, Z. - University Of Chinese Academy Of Sciences|
|ZHANG, B. - University Of Chinese Academy Of Sciences|
|RAN, Q. - University Of Chinese Academy Of Sciences|
|XU, C. - Chinese Academy Of Sciences|
|LIU, W. - Yunnan University|
|CHE, R. - Yunnan University|
|XU, Z. - Griffiths University|
|CUI, X. - University Of Chinese Academy Of Sciences|
|HAO, Y. - University Of Chinese Academy Of Sciences|
|WANG, Y. - University Of Chinese Academy Of Sciences|
Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/7/2021
Publication Date: 8/10/2021
Citation: Li, L., Kang, X., Biederman, J.A., Wang, W., Qian, R., Zheng, Z., Zhang, B., Ran, Q., Xu, C., Liu, W., Che, R., Xu, Z., Cui, X., Hao, Y., Wang, Y. 2021. Nonlinear carbon cycling responses to precipitation variability in a semiarid grassland. Science of the Total Environment. 781. https://doi.org/10.1016/j.scitotenv.2021.147062.
Interpretive Summary: While it is well-known that precipitation amount is a strong control of carbon uptake and biomass production in grasslands, relatively little is understood about the impacts of temporal changes in precipitation, such as receiving the same annual rainfall amount packaged in fewer, larger storms. Here, we conducted a 3-year field experiment in which we applied the same amount of precipitation repackaged into many small events or few large events over plots in a semiarid grassland, and we measured the carbon uptake and productivity. We compared these results to those of an ecosystem model, which represents key tool in predicting the impacts of future precipitation variability on agroecosystem carbon cycling. We found that most metrics of grassland productivity were maximized at intermediate levels of precipitation variability. In contrast, the model incorrectly predicted a linear increase in productivity across with increasingly frequent (but also increasingly small) precipitation events. These results indicate that while agroecosystem models may correctly respond to changes in annual precipitation amounts, further work is needed to model the consequence of an increasingly variable hydrologic cycle.
Technical Abstract: Changes in precipitation amount and variability would profoundly affect carbon (C) cycling in arid and semiarid grasslands. However, compared to the effects of precipitation amounts, little is understood about the impacts of precipitation temporal variability on terrestrial C cycling. To explore relationships between precipitation variability and C cycling processes and the underlying mechanisms, we conducted a 3-year field experiment and a 12-year model simulation, in which the constant seasonal precipitation amount was temporally manipulated with four and six levels of precipitation variability, respectively, in a semiarid grassland. Based on the manipulative experiment, we found various nonlinear relationships between C cycling processes and the coefficient of precipitation variability (Pcv), including a trinomial relationship for soil respiration (tipping point: 3 and 4.3), convex relationships for gross and net ecosystem production (peak at 3.5) as well as belowground biomass (peak at 4) and a nonlinear negative relationship for ecosystem respiration (peak at 2.5). Such relationships were regulated by seasonal averaged soil water content (SWC), early-growing season precipitation amount, soil inorganic nitrogen availability (SIN), and both SWC and SIN, respectively. However, these results fromthemanipulative experiment did not match those from the model simulation, in which ecosystem C cycling processes, dominated only by SWC responses, showed positive linear responses to Pcv. Our results mirror that the nonlinear responses of grassland C cycling to precipitation variability as regulate by SWC and SIN should be incorporated into models to forecast future ecosystem shifts under climate change.