Estimation of Groundwater Abstractions from Irrigation Wells in Mediterranean Agriculture: An Ensemble Approach Integrating Remote Sensing, Soil Water Balance, and Spatial Analysis

Luís Catarino, João Rolim, Paula Paredes and Maria do Rosário Cameira ()
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Luís Catarino: Instituto Superior de Agronomia, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
João Rolim: A LEAF-Linking Landscape, Environment, Agriculture and Food-Research Center, Associated Laboratory TERRA, Instituto Superior de Agronomia, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
Paula Paredes: A LEAF-Linking Landscape, Environment, Agriculture and Food-Research Center, Associated Laboratory TERRA, Instituto Superior de Agronomia, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
Maria do Rosário Cameira: A LEAF-Linking Landscape, Environment, Agriculture and Food-Research Center, Associated Laboratory TERRA, Instituto Superior de Agronomia, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal

Sustainability, 2025, vol. 17, issue 12, 1-27

Abstract: This study presents a robust methodology for the indirect estimation of groundwater abstraction for irrigation at the scale of individual wells, addressing a key gap in data-scarce agricultural settings. The approach combines NDVI time series, crop water requirement modelling, and spatial analysis of irrigation systems within a GIS environment. A soil water balance model was applied to Homogeneous Units of Analysis, and irrigation requirements were estimated using an ensemble approach accounting for key sources of uncertainty related to phenology detection, soil moisture at sowing (%SAW), and irrigation system efficiency. A spatial linkage algorithm was developed to associate individual wells with the irrigated areas they supply. Sensitivity analysis demonstrated that 10% increases in %SAW resulted in abstraction reductions of up to 1.98%, while 10% increases in irrigation efficiency reduced abstractions by an average of 6.48%. These findings support the inclusion of both parameters in the ensemble, generating eight abstraction estimates per well. Values ranged from 33,000 to 115,000 m 3 for the 2023 season. Validation against flowmeter data confirmed the method’s reliability, with an R 2 of 0.918 and an RMSE equivalent to 9.3% of the mean observations. This approach offers an accurate, spatially explicit estimation of groundwater abstractions without requiring direct metering and offers a transferable, cost-effective tool to improve groundwater accounting and governance in regions with limited monitoring infrastructure.

Keywords: irrigation water accounting; geospatial decision support; sustainable groundwater management; data-scarce agricultural regions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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