Linga et al. (2025) Global Irrigation Modeling Relies More on Pragmatic Than Empirical Assumptions

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water Resources Research
• Year: 2025
• Date: 2025-12-01
• Authors: Seth Nathaniel Linga, Carmen Aguiló‐Rivera, Joshua Larsen, Michela Massimi, Nanxin Wei, Arnald Puy
• DOI: 10.1029/2025wr040674

Research Groups

Not specified in the abstract.

Short Summary

This study analyzes 102 assumptions across nine global irrigation models (GIMs) to distinguish between empirically grounded and pragmatic assumptions, finding that 70% are pragmatic, which suggests a larger uncertainty space in GIMs than typically addressed.

Objective

• To identify and categorize assumptions in global irrigation models (GIMs) as either empirically grounded or pragmatic, and to assess the implications for model uncertainty and reliability.

Study Configuration

• Spatial Scale: Global (analysis of global models).
• Temporal Scale: Analysis of existing model structures and assumptions, not a time-series simulation.

Methodology and Data

• Models used: Nine global irrigation models (GIMs).
• Data sources: Analysis of 50 studies across the nine GIMs, identifying 102 model assumptions. The methodology involved sensitivity auditing and philosophy of science.

Main Results

• A total of 102 model assumptions were identified across the nine global irrigation models analyzed.
• 70% of these identified assumptions are pragmatic (based on practical considerations rather than observational evidence).
• 35% of the assumptions are shared by multiple models, and the majority of these shared assumptions are also pragmatic.
• These findings indicate that the uncertainty space of GIMs may be larger than currently addressed using traditional uncertainty analyses.

Contributions

• Provides the first systematic appraisal of model assumptions in global irrigation models using sensitivity auditing and philosophy of science.
• Quantifies the proportion of pragmatic versus empirically grounded assumptions in GIMs.
• Highlights a significant, potentially underestimated, source of uncertainty in GIMs.
• Underscores the critical need for systematic appraisal of model assumptions to enhance transparency and improve the robustness of GIMs for water resource management and policy.

Funding

Not specified in the abstract.

Citation

@article{Linga2025Global,
  author = {Linga, Seth Nathaniel and Aguiló‐Rivera, Carmen and Larsen, Joshua and Massimi, Michela and Wei, Nanxin and Puy, Arnald},
  title = {Global Irrigation Modeling Relies More on Pragmatic Than Empirical Assumptions},
  journal = {Water Resources Research},
  year = {2025},
  doi = {10.1029/2025wr040674},
  url = {https://doi.org/10.1029/2025wr040674}
}