Stream Nutrient Load and Concentration Estimation From Minimal Measurements
Abstract High‐resolution measurements of nutrients in rivers are vital to assess water quality and catchment material balances. Yet, such measurements are often cost‐prohibitive. To improve sampling efficiency, data‐driven sparse sensing (DSS) is proposed to recover high‐resolution nutrient time‐ser...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2025GL114935 |
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| _version_ | 1850038520784093184 |
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| author | Wasif Bin Mamoon Kun Zhang Mitul Luhar Anthony J. Parolari |
| author_facet | Wasif Bin Mamoon Kun Zhang Mitul Luhar Anthony J. Parolari |
| author_sort | Wasif Bin Mamoon |
| collection | DOAJ |
| description | Abstract High‐resolution measurements of nutrients in rivers are vital to assess water quality and catchment material balances. Yet, such measurements are often cost‐prohibitive. To improve sampling efficiency, data‐driven sparse sensing (DSS) is proposed to recover high‐resolution nutrient time‐series from sparse flow and concentration measurements. DSS leverages dimension‐reduction to identify basis functions that optimally represent available data, and analyzes these basis functions to identify optimal times and locations for future measurements. A model trained on high‐resolution flow and concentration measurements from few locations accurately reconstructed nutrient concentration time‐series and annual loads at target sites spanning the Midwest region of the US. Optimal sampling times occurred in spring, while sampling locations were distributed across catchment area and flow. Sparse measurements (20–80 per year) at optimal sampling times and locations were sufficient to accurately estimate nutrient concentrations and loads (error <±2% for NOx; <±9% for total phosphorus). DSS promises to enable cost‐effective water quality monitoring. |
| format | Article |
| id | doaj-art-283089c527bc4de08aadb7cfe35453e9 |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-283089c527bc4de08aadb7cfe35453e92025-08-20T02:56:34ZengWileyGeophysical Research Letters0094-82761944-80072025-04-01528n/an/a10.1029/2025GL114935Stream Nutrient Load and Concentration Estimation From Minimal MeasurementsWasif Bin Mamoon0Kun Zhang1Mitul Luhar2Anthony J. Parolari3Department of Civil, Construction, and Environmental Engineering Marquette University Milwaukee WI USADepartment of Civil Engineering University of Minnesota Duluth Duluth MN USADepartment of Aerospace and Mechanical Engineering University of Southern California Los Angeles CA USADepartment of Civil, Construction, and Environmental Engineering Marquette University Milwaukee WI USAAbstract High‐resolution measurements of nutrients in rivers are vital to assess water quality and catchment material balances. Yet, such measurements are often cost‐prohibitive. To improve sampling efficiency, data‐driven sparse sensing (DSS) is proposed to recover high‐resolution nutrient time‐series from sparse flow and concentration measurements. DSS leverages dimension‐reduction to identify basis functions that optimally represent available data, and analyzes these basis functions to identify optimal times and locations for future measurements. A model trained on high‐resolution flow and concentration measurements from few locations accurately reconstructed nutrient concentration time‐series and annual loads at target sites spanning the Midwest region of the US. Optimal sampling times occurred in spring, while sampling locations were distributed across catchment area and flow. Sparse measurements (20–80 per year) at optimal sampling times and locations were sufficient to accurately estimate nutrient concentrations and loads (error <±2% for NOx; <±9% for total phosphorus). DSS promises to enable cost‐effective water quality monitoring.https://doi.org/10.1029/2025GL114935 |
| spellingShingle | Wasif Bin Mamoon Kun Zhang Mitul Luhar Anthony J. Parolari Stream Nutrient Load and Concentration Estimation From Minimal Measurements Geophysical Research Letters |
| title | Stream Nutrient Load and Concentration Estimation From Minimal Measurements |
| title_full | Stream Nutrient Load and Concentration Estimation From Minimal Measurements |
| title_fullStr | Stream Nutrient Load and Concentration Estimation From Minimal Measurements |
| title_full_unstemmed | Stream Nutrient Load and Concentration Estimation From Minimal Measurements |
| title_short | Stream Nutrient Load and Concentration Estimation From Minimal Measurements |
| title_sort | stream nutrient load and concentration estimation from minimal measurements |
| url | https://doi.org/10.1029/2025GL114935 |
| work_keys_str_mv | AT wasifbinmamoon streamnutrientloadandconcentrationestimationfromminimalmeasurements AT kunzhang streamnutrientloadandconcentrationestimationfromminimalmeasurements AT mitulluhar streamnutrientloadandconcentrationestimationfromminimalmeasurements AT anthonyjparolari streamnutrientloadandconcentrationestimationfromminimalmeasurements |