Influence of extreme rainfall events on soil carbon release in the Loess Hilly Region, China

Abstract

Projected increases in the frequency and magnitude of extreme rainfall events can profoundly impact terrestrial ecosystems. However, the response of soil carbon release (RS) at various depths to these events and associated regulatory processes has not been well-documented in small watersheds. We measured the mean CO2 concentration, soil moisture (SM), and soil temperature (ST) at depths of 10, 50, and 100 cm in situ before and after extreme rainfall events between June and September 2019, in a typical small watershed of the Loess Hilly Region, China. RS, calculated using Fick’s first law in one dimension, revealed the relationships between RS and extreme rainfall events. During the study period, we screened four extreme rainfall events, of which three were used. The mean RS at 10, 50, and 100 cm soil depths after extreme rainfall (post-RS) were significantly higher than those before (pro-RS) (P < 0.05). Simultaneously, decreased ST at a depth of 10 cm and increased SM at a depth of 10 cm during periods of extreme rainfall contributed to decreased RS at a depth of 10 cm. By contrast, the RS at depths of 50 and 100 cm were enhanced during periods of extreme rainfall and peaked between days two and four after the extreme rainfall events due to the significant stimulating increase in SM (P < 0.05); the relative change in RS at 50 cm soil depth was the largest. Moreover, the RS values at three soil depths were changed by extreme rainfall events (with higher SM to RS), yet the R2 of the ST was lower after extreme rainfall. In addition, after extreme rainfall, the temperature sensitivity (Q10) increased at a depth of 10 cm, while it decreased at depths of 50 and 100 cm. These findings imply that more CO2 is released from deep soil in semi-arid shrubs under extreme rainfall events with sustained increase for 2–4 days. The results will help predict the impact of future climate change on regional carbon emissions and improve the accuracy of predictive model.