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)

Abstract

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.

Keywords

• irrigation
• landscape change
• artificial intelligence
• GRACE mascon