Abstract
The future of major aquifer systems supporting irrigated agriculture is threatened due to unsustainable groundwater pumping. Metering of pumping is key for implementing robust groundwater management, but metering is limited in most aquifers. Although machine learning methods have been used to estimate pumping over certain regions, these studies have not fully demonstrated the data quantity and input parameter requirements to accurately estimate regional groundwater pumping. This study determined the data quantity required and identified relevant features to develop Random Forests-based annual groundwater pumping estimates (2008–2020) over the Kansas High Plains aquifer. We predicted pumping at two spatial scales, i.e., point (well) and grid (2 km). We evaluated a combination of different training splits against a constant test set to understand the performance of the models. Summing predicted pumping over a 2 km grid was made possible with knowledge of crop irrigation area. This knowledge also decreased the uncertainty observed in linking individual wells with irrigated areas and further improved the spatial and temporal pumping estimates. At the 2 km scale, we observed that a model trained on 10 % of the total available data had coefficient of determination (R2) values of 0.98 and 0.75 for training and testing, respectively. These results show reasonable estimates of irrigation pumping are possible at the 2 km scale when 10 % of irrigation wells are metered and if the irrigated area is known. This finding has significant implications for groundwater management in many heavily stressed aquifers.
Recommended Citation
D. Asfaw et al., "Predicting Groundwater Withdrawals using Machine Learning with Limited Metering Data: Assessment of Training Data Requirements," Agricultural Water Management, vol. 318, article no. 109691, Elsevier, Sep 2025.
The definitive version is available at https://doi.org/10.1016/j.agwat.2025.109691
Department(s)
Geosciences and Geological and Petroleum Engineering
Publication Status
Open Access
Keywords and Phrases
Field-scale; Groundwater pumping; High Plains aquifer; Irrigation; Machine learning
International Standard Serial Number (ISSN)
1873-2283; 0378-3774
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Sep 2025
Comments
Colorado State University, Grant 80NSSC21K0979