Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

Abstract

Soil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1-D diffusion-reaction model to calculate depth-resolved CO 2 production rates from soil CO 2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016-2018. While soil respiration correlated with temperature on diel timescales (p < 0.05), seasonal variability was dominated by soil moisture and plant phenology (p < 0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest that the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore-summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Finally, we observed significant late season CO 2 fluxes from the deep subsoil (>165 cm) that support growing evidence for the importance of subsoil processes in driving integrated respiration fluxes.

Depthwise Soil CO2 Production Is Controlled by Freeze‐Thaw Processes in a Tibetan Alpine Steppe

“…These results reflect those of Winnick et al. (2020), who also found the transformation of CO 2 source and sink in the deep soil layer. Considering that these deep soil layers almost have no root distribution and the organic carbon content is also extremely low, the CO 2 dynamic there might be attributed to non‐biological processes such as soil gas transport (Maier et al., 2010) and geochemical CO 2 production or consumption (Rey, 2015; Sánchez‐Cañete et al., 2018).…”

Improving Predictions of Stream CO2 Concentrations and Fluxes Using a Stream Network Model: A Case Study in the East River Watershed, CO, USA

“…The optimization of the model resulted in a C GW pCO 2 of 18,000 ppm, C wet pCO 2 of 44,000 ppm, F wc of 9*10 −11 mol L −1 s −1 , and a benthic hyporheic zone pCO 2 elevation (C hz -C) of 700 ppm. The best three optimization runs all had the same C GW and C wet values, with C GW values falling within the range of sub-soil (>30 cm) growing season pCO 2 values measured in a soil profile within the East River watershed (∼7,000-23,000 ppm; Winnick et al, 2020). Point measurements of standing waters within wetland areas were above the 25,000 ppm calibration of the EGM-5, though typically displayed values between 30,000-100,000 ppm supporting the elevated model optimization value.…”

“…During the sampled period, snow was present in the higher elevations and meltwater was contributing to the discharge ( Q ). The three major life zones within the basin are alpine, montane, and subalpine (Carroll et al., 2018; Hubbard et al., 2018) with the tree line at 3,500 m (Winnick et al., 2020). The modeled East River watershed comprises a range of land cover including conifer and aspen forests (32%), unvegetated rock (28%), grass and shrubland (23%), and wetlands (16%), with <1% of land as a mixture of human uses (U.S. Geological Survey, 2016) (Figure S1 in Supporting Information S1).…”

Soil Respiration Phenology Improves Modeled Phase of Terrestrial net Ecosystem Exchange in Northern Hemisphere

“…When the O 2 diffusion limit is combined with a linear or sublinear function that increases with soil moisture (i.e., representing greater substrate availability), the result is a triangular function with a fairly narrow range of optimum soil moisture, which agrees with the observation that SM is most limiting on R H when soils are relatively dry or approaching saturation (Reichstein et al., 2003). At high northern latitudes, these conditions may predominate during spring thaw (Oikawa et al., 2014; Winnick et al., 2020), which underscores the key role of SM in accurately modeling the corresponding carbon cycle transitions.…”

“…For instance, Järveoja et al found that the temperature sensitivity of R H in northern peatlands is enhanced in dry periods, possibly due to increased O 2 supply to heterotrophs. It has also been found to improve RECO estimation at wetland sites (Sulman et al, 2012) and where snowmelt also leads to an increase in soil water content in spring (Oikawa et al, 2014;Winnick et al, 2020). A vertically stratified soil column has been adopted in land models (dos Santos et al, 2021;Tao et al, 2017).…”