Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5251-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-5251-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Nov 2020
Research article |
| 12 Nov 2020
| 12 Nov 2020
Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling, and a satellite–model fusion approach
Oliver Miguel López Valencia
CORRESPONDING AUTHOR
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Kasper Johansen
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Bruno José Luis Aragón Solorio
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Rasmus Houborg
Planet, Analytics Engineering, San Francisco, CA 94107, USA
Yoann Malbeteau
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Samer AlMashharawi
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Muhammad Umer Altaf
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Essam Mohammed Fallatah
National Center for Water Research and Studies, Ministry of Environment Water and Agriculture, Riyadh, Saudi Arabia
Hari Prasad Dasari
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Ibrahim Hoteit
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Matthew Francis McCabe
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Cited
16 citations as recorded by crossref.
- Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture L. Zappa et al. 10.3390/rs13091727
- Towards SDG 15: Using Remote Sensing to Restore Our Lands, From the Coastal Fringe to the Deep Desert J. Blanco-Sacristán et al. 10.3389/frym.2024.1393515
- Model-based water accounting for integrated assessment of water resources systems at the basin scale M. Delavar et al. 10.1016/j.scitotenv.2022.154810
- Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains S. Wei et al. 10.3390/rs14133004
- Understanding the role of the radiometric indices in temporal evapotranspiration estimation in arid environments S. Hussain et al. 10.5004/dwt.2022.28359
- A Retrospective Analysis of National-Scale Agricultural Development in Saudi Arabia from 1990 to 2021 T. Li et al. 10.3390/rs15030731
- Satellite‐Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy T. Foster et al. 10.1029/2020WR028378
- Center Pivot Irrigation Systems and Where to Find Them: A Deep Learning Approach to Provide Inputs to Hydrologic and Economic Models D. Cooley et al. 10.3389/frwa.2021.786016
- Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region S. Filippelli et al. 10.1016/j.agwat.2022.107764
- Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future W. Jiao et al. 10.1016/j.rse.2021.112313
- A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al. 10.1016/j.earscirev.2023.104462
- Retrieving the irrigation actually applied at district scale: Assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model P. Laluet et al. 10.1016/j.agwat.2024.108704
- From coarse resolution to practical solution: GRACE as a science communication and policymaking tool for sustainable groundwater management L. Xu et al. 10.1016/j.jhydrol.2023.129845
- A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data T. Li et al. 10.1016/j.isprsjprs.2022.02.002
- Present‐Day Motion of the Arabian Plate R. Viltres et al. 10.1029/2021TC007013
- Sensitivity of Groundwater Recharge Assessment to Input Data in Arid Areas S. Ajjur & E. Di Lorenzo 10.3390/hydrology11020028
16 citations as recorded by crossref.
- Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture L. Zappa et al. 10.3390/rs13091727
- Towards SDG 15: Using Remote Sensing to Restore Our Lands, From the Coastal Fringe to the Deep Desert J. Blanco-Sacristán et al. 10.3389/frym.2024.1393515
- Model-based water accounting for integrated assessment of water resources systems at the basin scale M. Delavar et al. 10.1016/j.scitotenv.2022.154810
- Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains S. Wei et al. 10.3390/rs14133004
- Understanding the role of the radiometric indices in temporal evapotranspiration estimation in arid environments S. Hussain et al. 10.5004/dwt.2022.28359
- A Retrospective Analysis of National-Scale Agricultural Development in Saudi Arabia from 1990 to 2021 T. Li et al. 10.3390/rs15030731
- Satellite‐Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy T. Foster et al. 10.1029/2020WR028378
- Center Pivot Irrigation Systems and Where to Find Them: A Deep Learning Approach to Provide Inputs to Hydrologic and Economic Models D. Cooley et al. 10.3389/frwa.2021.786016
- Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region S. Filippelli et al. 10.1016/j.agwat.2022.107764
- Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future W. Jiao et al. 10.1016/j.rse.2021.112313
- A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al. 10.1016/j.earscirev.2023.104462
- Retrieving the irrigation actually applied at district scale: Assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model P. Laluet et al. 10.1016/j.agwat.2024.108704
- From coarse resolution to practical solution: GRACE as a science communication and policymaking tool for sustainable groundwater management L. Xu et al. 10.1016/j.jhydrol.2023.129845
- A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data T. Li et al. 10.1016/j.isprsjprs.2022.02.002
- Present‐Day Motion of the Arabian Plate R. Viltres et al. 10.1029/2021TC007013
- Sensitivity of Groundwater Recharge Assessment to Input Data in Arid Areas S. Ajjur & E. Di Lorenzo 10.3390/hydrology11020028
Latest update: 20 Nov 2024
Short summary
The agricultural sector in Saudi Arabia has expanded rapidly over the last few decades, supported by non-renewable groundwater abstraction. This study describes a novel data–model fusion approach to compile national-scale groundwater abstractions and demonstrates its use over 5000 individual center-pivot fields. This method will allow both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.
The agricultural sector in Saudi Arabia has expanded rapidly over the last few decades,...
