Agriculture is an important NO emissions source. Water cycle and nitrogen cycles have important effects on NO in farmland ecosystems. The changes in the groundwater table can lead to changes in farmland the water and nitrogen cycle processes. However, how this such changes will affect N2O emissions from farmland remains unclear. In this study, a two-year volume lysimeter experiment (2019-2020), including four controlled groundwater tables (i.e., 40, 70, 110, and 150 cm), was performed to monitor the variations in the NO and NO concentrations in shallow groundwater as well as the direct NO emissions due to surface soil and groundwater evaporation. Our results showed that NO emissions during fertilization accounted for 80%-90% of the total NO emissions throughout the maize growing period. Direct NO emissions increase with the increase in the groundwater table. The total NO emissions in 2020 were 96.44, 9.75, 6.46, and 6.22 kg ha y at a groundwater table of 40, 70, 110, and 150 cm, respectively. The high water-filled pore space (WFPS) value resulting from the elevated groundwater table increased the groundwater-atmosphere connectivity, leading to significantly increased NO emissions after fertilization. Increased precipitation (454.90 mm in 2020 vs. 180.30 mm in 2019) accelerated the hydrological processes in agroecology, reducing the retention time of NO (6 weeks in 2020 vs. 7.5 weeks in 2019) and NO (6.75 weeks in 2020 vs. 7.25 weeks in 2019) in shallow groundwater. Studying the influence of shallow groundwater tables on direct NO emissions will provide insights into the interaction between the water and nitrogen cycles in agroecosystems. The results of this study suggest that direct NO emissions can be effectively reduced by controlling the groundwater table in agricultural fields in the North China Plain.
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http://dx.doi.org/10.1016/j.scitotenv.2021.149495 | DOI Listing |
PLoS One
January 2025
North China University of Water Resources and Electric Power, Zhengzhou City, Henan Province, P.R. China.
This study employs electrical resistivity tomography (ERT) to experimentally investigate the migration characteristics of light non-aqueous phase liquids (LNAPL) under various groundwater conditions. Through cross-hole measurements and time-lapse inversion, the migration process of LNAPL under three scenarios-unsaturated conditions, constant groundwater levels, and declining water levels-was systematically analyzed. The results indicate that LNAPL migration behavior exhibits significant differences under different conditions.
View Article and Find Full Text PDFSci Data
January 2025
Department of Hydraulic Engineering, Delft University of Technology, Delft, Netherlands.
Beach groundwater and nearshore hydrodynamic data were collected during a field experiment along two dissipative beach transects on Galveston Island, Texas, in the fall of 2023. The monitored beaches serve as nesting habitat for the critically endangered Kemp's ridley sea turtle. Conditions ranged from calm to stormy, with two storms occurring during the experiment, inundating the entire beach up to the dune toe.
View Article and Find Full Text PDFACS ES T Water
January 2025
Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
Russia's invasion of Ukraine continues to have a devastating effect on the well-being of Ukrainians and their environment. We evaluated a major environmental hazard caused by the war: the potential for groundwater contamination in proximity to the Zaporizhzhia Nuclear Power Plant (NPP). We quantified groundwater vulnerability with the DRASTIC index, which was originally developed by the United States Environmental Protection Agency and has been used at various locations worldwide to assess relative pollution potential.
View Article and Find Full Text PDFSci Total Environ
January 2025
Geological Survey of Denmark and Greenland (GEUS), Department of Hydrology, Copenhagen, Denmark.
Machine learning (ML) methods continue to gain traction in hydrological sciences for predicting variables at large scales. Yet, the spatial transferability of these ML methods remains a critical yet underexamined aspect. We present a metamodel approach to obtain large-scale estimates of drain fraction at 10 m spatial resolution, using a ML algorithm (Gradient Boost Decision Tree).
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Chinese-Israeli International Center for Research and Training in Agriculture, China Agricultural University, Beijing, People's Republic of China.
Specific yield (S) is an essential hydrogeological parameter in groundwater-related modeling and estimation. In this study, we proposed several new analytical expressions of S to characterize the nonlinear variations of S under shallow groundwater environments, encompassing S for three-layered soil, transition zone S, and flux-dependent S (in Boussinesq-type equation). The proposed S expression for three-layered soils expanded the applicability of previous expressions for homogeneous soil.
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