Medeiros et al. (2026) Agricultural human-water systems

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Pedro Medeiros, Xi Chen, Christophe Cudennec, Mohammad Ghoreishi, Thushara Gunda, Suxia Liu, Landon Marston, Jimmy O’Keeffe, Julio Iván González Piedra, Mahendran Roobavannan, Murugesu Sivapalan, Pieter van Oel, Giulia Vico, Yi‐Chen E. Yang, Samuel C. Zipper
• DOI: 10.1016/b978-0-443-41736-8.00009-9

Research Groups

• Federal Institute of Education, Science and Technology of Ceará, Fortaleza, Brazil
• University of Cincinnati, Cincinnati, OH, United States
• Institut Agro, INRA, SAS, Rennes, France
• University of Saskatchewan, Saskatoon, SK, Canada
• Sandia National Laboratories, Albuquerque, NM, United States
• Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
• College of Resources and Environment/Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China
• Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
• Dublin City University, Dublin, Ireland
• University of Havana, Havana, Cuba
• WaterNSW, Parramatta, NSW, Australia
• University of Illinois at Urbana-Champaign, Champaign, IL, United States
• Wageningen University & Research, Wageningen, Netherlands
• Swedish University of Agricultural Sciences, Uppsala, Sweden
• Lehigh University, Bethlehem, PA, United States
• University of Kansas, Lawrence, KS, United States

Short Summary

This chapter synthesizes the understanding of agricultural human-water systems, demonstrating how bidirectional feedbacks between human decisions and water resources lead to emergent phenomena and unintended consequences across multiple spatial and temporal scales. It advocates for sociohydrological modeling and comprehensive data integration to achieve sustainable water management in agriculture.

Objective

• To synthesize the current understanding of agricultural human-water systems, focusing on the complex interactions and bidirectional feedbacks between human decisions and water resources.
• To identify and explain emergent phenomena (e.g., pendulum swing, irrigation efficiency paradox, reservoir effect, groundwater depletion lock-in) that arise from these interactions.
• To review sociohydrological modeling approaches and relevant datasets for analyzing and predicting the coevolutionary dynamics of agricultural human-water systems.
• To propose pathways for developing sustainable and resilient solutions for agricultural water management by incorporating human behavior and system-level interventions.

Study Configuration

• Spatial Scale: Farm, field, local, regional, catchment, basin, national, and global scales. Case studies include specific basins (e.g., Murrumbidgee, Jaguaribe, Tarim, Bow River, Mekong, Niger, High Plains Aquifer, Central Valley of California) and countries (e.g., India, Bangladesh, Cuba, China, United States, Australia).
• Temporal Scale: Short-term (annual or shorter), intermediate (interannual to decadal), and long-term (decades to centuries). Historical analyses span the 20th century (e.g., Murrumbidgee Basin) and global trends from 1960 to 2010.

Methodology and Data

• Models used:
  • Sociohydrological models (general framework)
  • System Dynamics models (e.g., stylized models for Murrumbidgee Basin, O’Keeffe et al. (2018) for North India)
  • Agent-Based Models (ABMs) (e.g., ABM-SWAT for Mekong/Niger, ABAD for Bow River Basin)
  • Hydrological models (e.g., bucket models, Soil & Water Assessment Tool - SWAT)
  • Agricultural models (e.g., empirical relations, semi-empirical relations, process-based models like AquaCrop, VIP)
  • Hydroeconomic models (e.g., NeStRes for non-strategic reservoirs)
• Data sources:
  • Ecological/Agricultural: FAO AQUASTAT, Spatial Production Allocation Model (SPAM), Global Yield Gap Atlas (GYGA), GEPIC, WATPRO, USGS irrigation water use data, Mekonnen and Hoekstra (2014) water footprint benchmarks, USDA Agricultural Resource Management Survey (ARMS), USDA Irrigation and Water Management Survey.
  • Hydrological: Global Precipitation Climatology Center (GPCC), Climatic Research Unit gridded Time Series (CRU TS), Tropical Rainfall Measuring Mission/Global Precipitation Measurement Mission (TRMM/GPM), Global Runoff Data Center (GRDC), Global Surface Water Explorer (GSWE), Gravity Recovery and Climate Experiment (GRACE), Global Reservoir and Dam (GRanD), Global Lakes and Wetlands Database (GLWD), Randolph Glacier Inventory (RGI), Snow Water Equivalent (SWE), Soil Moisture Active Passive (SMAP), International Soil Moisture Network (ISMN), European Space Agency-Climate Change Initiative (ESA-CCI).
  • Socio-economic: World Bank indicators (AgGDP, poverty, GDP, employment, education, population), national census bureaus (e.g., US Census Bureau, China Census Bureau, Brazilian Institute of Geography and Statistics - IBGE), newspaper articles, household surveys, community workshops.
  • Water Quality: United Nations Environment Program (UNEP) GEMStat, GLObal RIver CHemistry database (GLORICH), European Environment Agency Waterbase, US Water Quality Portal.
  • Remote sensing data, in-situ measurements.

Main Results

• Agricultural human-water systems are complex adaptive systems where bidirectional feedbacks between human decisions and water resources lead to emergent phenomena and unintended consequences.
• Key emergent phenomena identified and illustrated with case studies include:
  • Adaptation effect (pendulum swing): Societal values shift between prioritizing economic development (agriculture) and environmental health, leading to cyclical changes in water allocation (e.g., Murrumbidgee Basin, Australia).
  • Rebound effect (irrigation efficiency paradox): Improvements in irrigation efficiency at the farm scale can paradoxically lead to increased overall water consumption at the basin scale due to behavioral responses (e.g., Tarim River Basin, China; Lower Rio Grande, US).
  • Reservoir effect (safe development paradox): Increased water storage (e.g., through reservoir construction) initially mitigates water stress but can foster a false sense of security, leading to increased water demand and heightened vulnerability to droughts in the long term (e.g., Jaguaribe Basin, Brazil; global trends).
  • Groundwater depletion as a lock-in: Policies like subsidized electricity for groundwater pumping can lead to unsustainable depletion, creating a "lock-in" where reversing the policy is politically and economically challenging (e.g., Punjab, India).
• Collective governance and community-based management schemes can be effective interventions to avoid resource depletion and steer systems toward sustainability (e.g., Local Enhanced Management Areas (LEMAs) in the Kansas High Plains Aquifer, US; negotiated water allocation in the Jaguaribe Basin, Brazil).
• Socio-economic factors such as migration, employment, and income are intricately linked to agricultural water management decisions and their consequences (e.g., Murrumbidgee Basin, farmer suicides in India, rural-to-urban migration in China).
• Global markets and crop prices significantly influence local water use decisions, highlighting the interconnectedness of local and global human-water systems (e.g., California drought and global nut markets).

Contributions

• Provides a comprehensive synthesis of the complex, multiscale, and coevolutionary nature of agricultural human-water systems.
• Systematically identifies and explains key emergent sociohydrological phenomena (pendulum swing, irrigation efficiency paradox, reservoir effect, groundwater depletion lock-in) with real-world case studies, linking them to system archetypes.
• Emphasizes the critical role of human agency, evolving values, preferences, and cognitive biases in shaping system dynamics, advocating for their endogenous inclusion in models.
• Reviews and categorizes diverse modeling approaches (System Dynamics, Agent-Based Models) and extensive datasets (ecological/agricultural, hydrological, socio-economic, water quality) relevant for studying these systems.
• Highlights knowledge gaps and future research opportunities in sociohydrology theory, model development (especially human behavior representation), and data collection (e.g., social data, citizen science) to achieve sustainable agricultural water management.

Funding

Funding information for this specific chapter is not provided in the text.

Citation

@article{Medeiros2026Agricultural,
  author = {Medeiros, Pedro and Chen, Xi and Cudennec, Christophe and Ghoreishi, Mohammad and Gunda, Thushara and Liu, Suxia and Marston, Landon and O’Keeffe, Jimmy and Piedra, Julio Iván González and Roobavannan, Mahendran and Sivapalan, Murugesu and Oel, Pieter van and Vico, Giulia and Yang, Yi‐Chen E. and Zipper, Samuel C.},
  title = {Agricultural human-water systems},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-41736-8.00009-9},
  url = {https://doi.org/10.1016/b978-0-443-41736-8.00009-9}
}