Many contaminated unconfined aquifers are located in proximity to river systems. In groundwater studies, the physical presence of a river is commonly represented as a transient-head boundary that imposes hydrologic responses within the intersected unconfined aquifer. The periodic fluctuation of river-stage height at the boundary produces associated responses within the adjacent aquifer system, the magnitude of which is a function of the existing well, aquifer, boundary conditions, and characteristics of river-stage fluctuations. The presence of well responses induced by the river stage can significantly limit characterization and monitoring of remedial activities within the stress-impacted area. This article demonstrates the use of a time-domain, multiple-regression, convolution (superposition) method to develop well/aquifer river response function (RRF) relationships. Following RRF development, a multiple-regression deconvolution correction approach can be applied to remove river-stage effects from well water-level responses. Corrected well responses can then be analyzed to improve local aquifer characterization activities in support of optimizing remedial actions, assessing the area-of-influence of remediation activities, and determining mean groundwater flow and contaminant flux to the river system.
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http://dx.doi.org/10.1111/j.1745-6584.2010.00780.x | DOI Listing |
Sci Total Environ
December 2024
Department of Geosciences, University of Cincinnati, Cincinnati, OH, USA; Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH, USA. Electronic address:
Environ Sci Pollut Res Int
April 2024
College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China.
Urbanizations and industrializations may accelerate the contamination and deterioration of groundwater quality. This study aimed to evaluate the quality and potential human health risks associated with shallow groundwater in Shenzhen, China, a city characterized by high levels of urbanization and industrialization. The hydrochemistry characteristics, water quality levels, and human health risks of main ions, nutrient elements, and metals in 220 samples collected from Maozhou River Basin (MRB) located in the northwest of Shenzhen were investigated.
View Article and Find Full Text PDFData Brief
December 2023
Lincoln Agritech Limited, PO Box 69 133, Lincoln, Christchurch 7640, New Zealand.
Braided rivers play a significant role in replenishing groundwater, but our understanding of how these recharge rates fluctuate over time remains limited. Traditional techniques for gauging groundwater recharge are ineffective for studying complex braided river systems due to their insufficient spatiotemporal resolution. To address this gap, active-distributed temperature sensing (A-DTS) was used.
View Article and Find Full Text PDFJ Environ Manage
February 2023
College of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225009, China.
River stage fluctuation (RSF) induced by tides, dam releases, or storms may lead to enhanced nitrogen cycling (N cycling) in riparian zones (RZ). We conducted a laboratory water table manipulation experiment and applied a multiphase flow and transport model (TOUGHREACT) to investigate the role of RSF in N cycling in the RZ. Coupled nitrification and denitrification occur in the water table fluctuation zone under alternating aerobic and anaerobic conditions.
View Article and Find Full Text PDFSci Total Environ
August 2021
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, Jiangsu, China.
Developing models that can accurately simulate groundwater level is important for water resource management and aquifer protection. In particular, machine learning tools provide a new and promising approach to efficiently forecast long-term groundwater table fluctuations without the computational burden of building a detailed flow model. This study proposes a multistep modeling framework for simulating groundwater levels by combining the wavelet transform (WT) with the long short-term memory (LSTM) network; the framework is named the combined WT-multivariate LSTM (WT-MLSTM) method.
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