Shallow groundwater interacts strongly with surface water across a quarter of global land area, affecting significantly the terrestrial eco‐hydrology and biogeochemistry. We examined groundwater behavior subjected to unimodal impulse and irregular surface water fluctuations, combining physical experiments, numerical simulations, and functional data analysis. Both the experiments and numerical simulations demonstrated a damped and delayed response of groundwater table to surface water fluctuations. To quantify this hysteretic shallow groundwater behavior, we developed a regression model with the Gamma distribution functions adopted to account for the dependence of groundwater behavior on antecedent surface water conditions. The regression model fits and predicts well the groundwater table oscillations resulting from propagation of irregular surface water fluctuations in both laboratory and large‐scale aquifers. The coefficients of the Gamma distribution function vary spatially, reflecting the hysteresis effect associated with increased amplitude damping and delay as the fluctuation propagates. The regression model, in a relatively simple functional form, has demonstrated its capacity of reproducing high‐order nonlinear effects that underpin the surface water and groundwater interactions. The finding has important implications for understanding and predicting shallow groundwater behavior and associated biogeochemical processes, and will contribute broadly to studies of groundwater‐dependent ecology and biogeochemistry.
Abstract. Forest plantations have been widely used as an effective measure for increasing soil carbon (C), and nitrogen (N) stocks and soil enzyme activities play a key role in soil C and N losses during decomposition of soil organic matter. However, few studies have been carried out to elucidate the mechanisms behind the differences in soil C and N cycling by different tree species in response to climate warming. Here, we measured the responses of soil's extracellular enzyme activity (EEA) to a gradient of temperatures using incubation methods in 78-year-old forest plantations with different tree species. Based on a soil enzyme kinetics model, we established a new statistical model to investigate the effects of temperature and tree species on soil EEA. In addition, we established a tree species-enzyme-C/N model to investigate how temperature and tree species influence soil C/N contents over time without considering plant C inputs. These extracellular enzymes included C acquisition enzymes (β-glucosidase, BG), N acquisition enzymes (Nacetylglucosaminidase, NAG; leucine aminopeptidase, LAP) and phosphorus acquisition enzymes (acid phosphatases). The results showed that incubation temperature and tree species significantly influenced all soil EEA and Eucalyptus had 1.01-2.86 times higher soil EEA than coniferous tree species. Modeling showed that Eucalyptus had larger soil C losses but had 0.99-2.38 times longer soil C residence time than the coniferous tree species over time. The differences in the residual soil C and N contents between Eucalyptus and coniferous tree species, as well as between slash pine (Pinus elliottii Engelm. var. elliottii) and hoop pine (Araucaria cunninghamii Ait.), increase with time. On the other hand, the modeling results help explain why exotic slash pine can grow faster, as it has 1.22-1.38 times longer residual soil N residence time for LAP, which mediate soil N cycling in the long term, than native coniferous tree species like hoop pine and kauri pine (Agathis robusta C. Moore). Our results will be helpful for understanding the mechanisms of soil C and N cycling by different tree species, which will have implications for forest management.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.