Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-811-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-811-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Feb 2021
Research article |
| 19 Feb 2021
| 19 Feb 2021
Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning
Martin J. Wells
Biological Systems Engineering Department, College of Engineering, University of Nebraska – Lincoln, Lincoln, NE 68583, USA
currently at: Natural Resources Conservation Service Field Office, Redmond, OR 97756, USA
Conservation and Survey Division, School of Natural Resources, University of Nebraska – Lincoln, Lincoln, NE 68583, USA
Biological Systems Engineering Department, College of Engineering, University of Nebraska – Lincoln, Lincoln, NE 68583, USA
Natalie Nelson
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Aaron Mittelstet
Biological Systems Engineering Department, College of Engineering, University of Nebraska – Lincoln, Lincoln, NE 68583, USA
John K. Böhlke
U.S. Geological Survey, Reston, VA 20192, USA
Viewed
Total article views: 3,168 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 May 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,247 | 848 | 73 | 3,168 | 264 | 52 | 56 |
- HTML: 2,247
- PDF: 848
- XML: 73
- Total: 3,168
- Supplement: 264
- BibTeX: 52
- EndNote: 56
Total article views: 2,208 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 19 Feb 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,615 | 541 | 52 | 2,208 | 129 | 34 | 41 |
- HTML: 1,615
- PDF: 541
- XML: 52
- Total: 2,208
- Supplement: 129
- BibTeX: 34
- EndNote: 41
Total article views: 960 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 May 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 632 | 307 | 21 | 960 | 135 | 18 | 15 |
- HTML: 632
- PDF: 307
- XML: 21
- Total: 960
- Supplement: 135
- BibTeX: 18
- EndNote: 15
Viewed (geographical distribution)
Total article views: 3,168 (including HTML, PDF, and XML)
Thereof 2,877 with geography defined
and 291 with unknown origin.
Total article views: 2,208 (including HTML, PDF, and XML)
Thereof 1,999 with geography defined
and 209 with unknown origin.
Total article views: 960 (including HTML, PDF, and XML)
Thereof 878 with geography defined
and 82 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
17 citations as recorded by crossref.
- Interplay of legacy irrigation and nitrogen fertilizer inputs to spatial variability of arsenic and uranium within the deep vadose zone A. Malakar et al. 10.1016/j.scitotenv.2023.165299
- Effects of the Japanese Nitrate Directive Plan (NDP) to curb groundwater nitrate-nitrogen content in the Miyakonojo River basin Z. Yu et al. 10.1016/j.jhydrol.2022.128563
- Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing A. Cardenas-Martinez et al. 10.1016/j.jhydrol.2021.127092
- Unveiling the biogeochemical mechanism of nitrate in the vadose zone-groundwater system: Insights from integrated microbiology, isotope techniques, and hydrogeochemistry D. Wang et al. 10.1016/j.scitotenv.2023.167481
- Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed A. Elsayed et al. 10.1016/j.jenvman.2024.123305
- Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives X. Wu et al. 10.1016/j.jhazmat.2022.129487
- Seasonal nitrate variation as a tracer of preferential flow in bedrock aquifers S. Worthington 10.1016/j.jhydrol.2024.132015
- Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches K. Alkindi et al. 10.1007/s11356-021-17224-9
- Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed A. Elsayed et al. 10.1016/j.jenvman.2023.118924
- Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning Y. Deng et al. 10.1016/j.jhydrol.2023.129934
- Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: Insight from machine learning and spatial mapping S. Abba et al. 10.1016/j.psep.2024.02.041
- Distribution, sources and main controlling factors of nitrate in a typical intensive agricultural region, northwestern China: Vertical profile perspectives D. Wang et al. 10.1016/j.envres.2023.116911
- Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran V. Gholami & M. Booij 10.1016/j.jclepro.2022.131847
- Addressing Nitrate Contamination in Groundwater: The Importance of Spatial and Temporal Understandings and Interpolation Methods M. Zaresefat et al. 10.3390/w15244220
- Unveiling nitrate contamination and health risks: Insights from groundwater quality assessment and Monte Carlo simulation along the Southern Caspian Sea Coasts M. Zazouli et al. 10.1016/j.gsd.2024.101340
- Enhancing local-scale groundwater quality predictions using advanced machine learning approaches A. Yadav et al. 10.1016/j.jenvman.2024.122903
- Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning Y. Tian et al. 10.1016/j.scitotenv.2024.171312
17 citations as recorded by crossref.
- Interplay of legacy irrigation and nitrogen fertilizer inputs to spatial variability of arsenic and uranium within the deep vadose zone A. Malakar et al. 10.1016/j.scitotenv.2023.165299
- Effects of the Japanese Nitrate Directive Plan (NDP) to curb groundwater nitrate-nitrogen content in the Miyakonojo River basin Z. Yu et al. 10.1016/j.jhydrol.2022.128563
- Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing A. Cardenas-Martinez et al. 10.1016/j.jhydrol.2021.127092
- Unveiling the biogeochemical mechanism of nitrate in the vadose zone-groundwater system: Insights from integrated microbiology, isotope techniques, and hydrogeochemistry D. Wang et al. 10.1016/j.scitotenv.2023.167481
- Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed A. Elsayed et al. 10.1016/j.jenvman.2024.123305
- Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives X. Wu et al. 10.1016/j.jhazmat.2022.129487
- Seasonal nitrate variation as a tracer of preferential flow in bedrock aquifers S. Worthington 10.1016/j.jhydrol.2024.132015
- Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches K. Alkindi et al. 10.1007/s11356-021-17224-9
- Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed A. Elsayed et al. 10.1016/j.jenvman.2023.118924
- Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning Y. Deng et al. 10.1016/j.jhydrol.2023.129934
- Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: Insight from machine learning and spatial mapping S. Abba et al. 10.1016/j.psep.2024.02.041
- Distribution, sources and main controlling factors of nitrate in a typical intensive agricultural region, northwestern China: Vertical profile perspectives D. Wang et al. 10.1016/j.envres.2023.116911
- Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran V. Gholami & M. Booij 10.1016/j.jclepro.2022.131847
- Addressing Nitrate Contamination in Groundwater: The Importance of Spatial and Temporal Understandings and Interpolation Methods M. Zaresefat et al. 10.3390/w15244220
- Unveiling nitrate contamination and health risks: Insights from groundwater quality assessment and Monte Carlo simulation along the Southern Caspian Sea Coasts M. Zazouli et al. 10.1016/j.gsd.2024.101340
- Enhancing local-scale groundwater quality predictions using advanced machine learning approaches A. Yadav et al. 10.1016/j.jenvman.2024.122903
- Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning Y. Tian et al. 10.1016/j.scitotenv.2024.171312
Latest update: 25 Dec 2024
Short summary
Groundwater in many agricultural areas contains high levels of nitrate, which is a concern for drinking water supplies. The rate at which nitrate moves through the subsurface is a critical piece of information for predicting how quickly groundwater nitrate levels may improve after agricultural producers change their approach to managing crop water and fertilizers. In this study, we explored a new statistical modeling approach to determine rates at which nitrate moves into and through an aquifer.
Groundwater in many agricultural areas contains high levels of nitrate, which is a concern for...
