Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA)
In situ groundwater monitoring is critical for irrigated agroecosystems and informs land cover changes. Yet, such data can pose management challenges and confound agroecological relationships. Correspondingly, satellite-based approaches, including the GRACE-constellation, are increasing. Although in...
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2024-11-01
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| author | Lucas J. Heintzman Zahra Ghaffari Abdel R. Awawdeh Damien E. Barrett Lance D. Yarbrough Greg Easson Matthew T. Moore Martin A. Locke Hakan I. Yasarer |
| author_facet | Lucas J. Heintzman Zahra Ghaffari Abdel R. Awawdeh Damien E. Barrett Lance D. Yarbrough Greg Easson Matthew T. Moore Martin A. Locke Hakan I. Yasarer |
| author_sort | Lucas J. Heintzman |
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| description | In situ groundwater monitoring is critical for irrigated agroecosystems and informs land cover changes. Yet, such data can pose management challenges and confound agroecological relationships. Correspondingly, satellite-based approaches, including the GRACE-constellation, are increasing. Although in situ and GRACE-derived comparisons occur, limited research considers agroecological dependencies. Herein, we examined differences in groundwater monitoring approaches (observed [in situ, O] vs. predicted [GRACE-derived, P]) within the Yazoo–Mississippi Delta (YMD), an agroecosystem in the southeastern USA. We compared variations in modeled groundwater hydrology, land cover, and irrigation dynamics of the YMD within the upper-quartile (UQ) area of interest (AOI) (highest groundwater levels) and lower-quartile (LQ) AOI (lowest groundwater levels) every year from 2008 to 2020. Spatially, OUQ and PUQ were in northern portions of the YMD, with the OLQ and PLQ in southern portions. Groundwater levels between OUQ:PUQ and OLQ:PLQ each had correlations > 0.85. Regarding land cover, most categories varied within ±2.50% between model estimates over time. Relatedly, we documented 14 instances where correlations between land use category and groundwater level were inverted across models (OLQ:PLQ (5), OUQ:OLQ (6), PUQ:PLQ (3)). Irrigation results were not statistically different among all models. Overall, our results highlight the importance of quantifying model incongruences for groundwater and land cover management. |
| format | Article |
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| language | English |
| publishDate | 2024-11-01 |
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| series | Hydrology |
| spelling | doaj-art-b81c2b2cbccc463c94e68a1424655ae32025-08-20T02:04:57ZengMDPI AGHydrology2306-53382024-11-01111118610.3390/hydrology11110186Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA)Lucas J. Heintzman0Zahra Ghaffari1Abdel R. Awawdeh2Damien E. Barrett3Lance D. Yarbrough4Greg Easson5Matthew T. Moore6Martin A. Locke7Hakan I. Yasarer8Water Quality and Ecology Research Unit, National Sedimentation Laboratory, United States Department of Agriculture-Agricultural Research Service, Oxford, MS 38655, USADepartment of Geology and Geological Engineering, University of Mississippi, University, MS 38677, USADepartment of Civil Engineering, University of Mississippi, University, MS 38677, USAWater Quality and Ecology Research Unit, National Sedimentation Laboratory, United States Department of Agriculture-Agricultural Research Service, Oxford, MS 38655, USADepartment of Geology and Geological Engineering, University of Mississippi, University, MS 38677, USADepartment of Geology and Geological Engineering, University of Mississippi, University, MS 38677, USAWater Quality and Ecology Research Unit, National Sedimentation Laboratory, United States Department of Agriculture-Agricultural Research Service, Oxford, MS 38655, USANational Sedimentation Laboratory, United States Department of Agriculture-Agricultural Research Service, Oxford, MS 38655, USADepartment of Civil Engineering, University of Mississippi, University, MS 38677, USAIn situ groundwater monitoring is critical for irrigated agroecosystems and informs land cover changes. Yet, such data can pose management challenges and confound agroecological relationships. Correspondingly, satellite-based approaches, including the GRACE-constellation, are increasing. Although in situ and GRACE-derived comparisons occur, limited research considers agroecological dependencies. Herein, we examined differences in groundwater monitoring approaches (observed [in situ, O] vs. predicted [GRACE-derived, P]) within the Yazoo–Mississippi Delta (YMD), an agroecosystem in the southeastern USA. We compared variations in modeled groundwater hydrology, land cover, and irrigation dynamics of the YMD within the upper-quartile (UQ) area of interest (AOI) (highest groundwater levels) and lower-quartile (LQ) AOI (lowest groundwater levels) every year from 2008 to 2020. Spatially, OUQ and PUQ were in northern portions of the YMD, with the OLQ and PLQ in southern portions. Groundwater levels between OUQ:PUQ and OLQ:PLQ each had correlations > 0.85. Regarding land cover, most categories varied within ±2.50% between model estimates over time. Relatedly, we documented 14 instances where correlations between land use category and groundwater level were inverted across models (OLQ:PLQ (5), OUQ:OLQ (6), PUQ:PLQ (3)). Irrigation results were not statistically different among all models. Overall, our results highlight the importance of quantifying model incongruences for groundwater and land cover management.https://www.mdpi.com/2306-5338/11/11/186irrigationlandscape changeartificial intelligenceGRACE mascon |
| spellingShingle | Lucas J. Heintzman Zahra Ghaffari Abdel R. Awawdeh Damien E. Barrett Lance D. Yarbrough Greg Easson Matthew T. Moore Martin A. Locke Hakan I. Yasarer Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA) Hydrology irrigation landscape change artificial intelligence GRACE mascon |
| title | Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA) |
| title_full | Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA) |
| title_fullStr | Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA) |
| title_full_unstemmed | Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA) |
| title_short | Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA) |
| title_sort | assessing differences in groundwater hydrology dynamics between in situ measurements and grace derived estimates via machine learning a test case of consequences for agroecological relationships within the yazoo mississippi delta usa |
| topic | irrigation landscape change artificial intelligence GRACE mascon |
| url | https://www.mdpi.com/2306-5338/11/11/186 |
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