Groundwater level changes with a focus on agricultural areas in the Mid-Atlantic region of the United States, 2002–2016

Dong, Yuxuan; Jiang, Chengsheng; Suri, Mayhah; Pee, Daphne; Meng, Lingkui; Goldstein, Rachel E. Rosenberg

doi:10.1016/j.envres.2019.01.004

Cited by 23 publications

(13 citation statements)

References 45 publications

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“…Declines in annual precipitation in the western United States are expected to reduce water supplies for irrigation during the summer and fall growing seasons ( 11 ). While the mid-Atlantic is currently not experiencing water scarcity, this is predicted to change in the coming years ( 12 – 14 ). In this region, groundwater is the most commonly used water source for agricultural irrigation, accounting for 53% of irrigation water withdrawals ( 14 , 15 ), and increased demand has led to a steady decline in groundwater levels in this region ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

“…While the mid-Atlantic is currently not experiencing water scarcity, this is predicted to change in the coming years ( 12 – 14 ). In this region, groundwater is the most commonly used water source for agricultural irrigation, accounting for 53% of irrigation water withdrawals ( 14 , 15 ), and increased demand has led to a steady decline in groundwater levels in this region ( 14 ). In 2005, it was reported that the Coastal Plain region of Maryland, including ESM, had a 2-foot per year decline in groundwater levels between 1980 and 2005, potentially affecting the long-term sustainability of groundwater resources in this area ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

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Levels of Salmonella enterica and Listeria monocytogenes in Alternative Irrigation Water Vary Based on Water Source on the Eastern Shore of Maryland

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Levels of Salmonella enterica and Listeria monocytogenes in Alternative Irrigation Water Vary Based on Water Source on the Eastern Shore of Maryland

et al. 2021

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“…The increase in soil salinization is one of the most common environmental threats for crop yield and quality [ 33 , 34 ]. This problem has been regarded as one of the major hurdles in crop productivity in arid and semiarid regions [ 35 , 36 ]. Sodic and saline soil is reducing the cultivable area for agriculture by 1–2% every year, thereby reducing food production [ 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

Isolation and characterization of salt-tolerant bacteria with plant growth-promoting activities from saline agricultural fields of Haryana, India

Sharma

Dev

Sourirajan

et al. 2021

Journal of Genetic Engineering and Biotechnology

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Background Soil salinity has been one of the biggest hurdles in achieving better crop yield and quality. Plant growth-promoting rhizobacteria (PGPR) are the symbiotic heterogeneous bacteria that play an important role in the recycling of plant nutrients through phytostimulation and phytoremediation. In this study, bacterial isolates were isolated from salt-polluted soil of Jhajjar and Panipat districts of Haryana, India. The potential salt-tolerant bacteria were screened for their PGPR activities such as phosphate solubilization, hydrogen cyanide (HCN), indole acetic acid (IAA) and ammonia production. The molecular characterization of potent isolates with salt tolerance and PGPR activity was done by 16S rDNA sequencing. Results Eighteen soil samples from saline soils of Haryana state were screened for salt-tolerant bacteria. The bacterial isolates were analyzed for salt tolerance ranging from 2 to 10%. Thirteen isolates were found salt tolerant at varied salt concentrations. Isolates HB6P2 and HB6J2 showed maximum tolerance to salts at 10% followed by HB4A1, HB4N3 and HB8P1. All the salt-tolerant bacterial isolates showed HCN production with maximum production by HB6J2. Phosphate solubilization was demonstrated by three isolates viz., HB4N3, HB6P2 and HB6J2. IAA production was maximum in HB4A1 (15.89) and HB6P2 (14.01) and least in HB4N3 (8.91). Ammonia production was maximum in HB6P2 (12.3) and least in HB8P1 (6.2). Three isolates HB6J2, HB8P1 and HB4N3 with significant salt tolerance, and PGPR ability were identified through sequencing of amplified 16SrRNA gene and were found to be Bacillus paramycoides, Bacillus amyloliquefaciens and Bacillus pumilus, respectively. Conclusions The salt-tolerant plant growth-promoting rhizobacteria (PGPR) isolated from saline soil can be used to overcome the detrimental effects of salt stress on plants, with beneficial effects of physiological functions of plants such as growth and yield, and overcome disease resistance. Therefore, application of microbial inoculants to alleviate stresses and enhance yield in plants could be a low cost and environmental friendly option for the management of saline soil for better crop productivity.

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“…The water table of these sites have trended downward and are greatly affected by anthropogenic activities. In this regard, Dong et al (2019) concluded that the water table dropped most significantly in the place with the highest proportion of agricultural land.…”

Section: Importance Evaluation Of Each Variablementioning

confidence: 99%

Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

Liang

Liu

et al. 2021

Front. Environ. Sci.

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Accurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stationarity of water table depth data, the performance of NNs in areas of over-exploitation is challenging. Therefore, reducing data noise is an essential step before simulating the water table depth. This research proposed a novel method to model the non-stationary time series data of water table depth through combing the advantages of wavelet analysis and Long Short-Term Memory (LSTM) neural network (NN). A typical groundwater over-exploitation area, Baoding, North China Plain (NCP), was selected as a study area. To reflect the impact of anthropogenic activities, the variables harnessed to develop the model includes temperature, precipitation, evaporation, and some socio-economic data. The results show that decomposing the time series of the water table depth into three sub-temporal components by Meyer wavelets can significantly improve the simulation effect of LSTM on the water table depth. The average NSE (Nash-Sutcliffe efficiency coefficient) value of all the sites increased from 0.432 to 0.819. Additionally, a feedforward neural network (FNN) is used to compare forecasts over 12-months. As expected, wavelet-LSTM outperforms wavelet-FNN. As the prediction time increases, the advantages of wavelet-LSTM become more evident. The wavelet-LSTM is satisfactory for forecasting the water table depth at most in 6 months. Furthermore, the importance of input variables of wavelet-LSTM is analysed by the weights of the model. The results indicate that anthropogenic activities influence the water table depth significantly, especially in the sites close to the Baiyangdian Lake, the largest lake in the North China Plain. This study demonstrates that the wavelet-LSTM model provides an option for water table depth simulation and predicting areas of over-exploitation of groundwater.

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Groundwater level changes with a focus on agricultural areas in the Mid-Atlantic region of the United States, 2002–2016

Cited by 23 publications

References 45 publications

Levels of Salmonella enterica and Listeria monocytogenes in Alternative Irrigation Water Vary Based on Water Source on the Eastern Shore of Maryland

Levels of Salmonella enterica and Listeria monocytogenes in Alternative Irrigation Water Vary Based on Water Source on the Eastern Shore of Maryland

Isolation and characterization of salt-tolerant bacteria with plant growth-promoting activities from saline agricultural fields of Haryana, India

Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

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