Tian et al. (2026) Development of a coupled hydro-economic model to support groundwater irrigation decisions

Identification

• Journal: PLOS Water
• Year: 2026
• Date: 2026-04-03
• Authors: Boyao Tian, Andrea E. Brookfield, Margaret Insley
• DOI: 10.1371/journal.pwat.0000452

Research Groups

• Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, Canada
• Department of Economics, University of Waterloo, Waterloo, Canada

Short Summary

This study develops a farm-level coupled hydro-economic model incorporating Conditional Value-at-Risk to evaluate groundwater irrigation strategies under uncertainty. It demonstrates that optimal strategies balance short-term profitability with long-term sustainability, showing that increased pumping does not always lead to greater profitability due to diminishing returns and aquifer depletion.

Objective

• To build an accessible farm-level hydro-economic model focusing on groundwater availability and expected land values.
• To introduce Conditional Value-at-Risk (CVaR) analysis to quantify economic risk associated with variability in precipitation and crop price.
• To evaluate how different irrigation decisions affect farm value, using Monte Carlo simulations to identify optimal strategies.

Study Configuration

• Spatial Scale: Farm-level, applied to a representative site within the High Plains Aquifer (HPA) in Kansas, USA. The model conceptualizes single-layer aquifers (confined and unconfined).
• Temporal Scale: Yearly time steps for groundwater equations, monthly for precipitation and price simulations. Planning horizons of 20 years and "time to depletion" (TD) were considered, with price simulations extending up to 150 years.

Methodology and Data

• Models used:
  • Coupled hydro-economic model integrating hydrologic and economic components.
  • Precipitation model: First-order Markov chain exponential model.
  • Groundwater model: Analytical solutions for drawdown (Cooper and Jacob) and recovery (Theis).
  • Crop yield model: FAO Penman-Monteith for evapotranspiration (ET) and a modified yield function (Martin et al., Klocke).
  • Economic model: Mean Reverting (MR) process for crop prices, cost functions (fixed, harvest, pumping), and expected land value calculation.
  • Risk assessment model: Conditional Value-at-Risk (CVaR) for quantifying downside risk.
  • Monte Carlo simulations (10,000 per scenario) were used to account for stochastic precipitation and crop prices.
• Data sources:
  • Real-world data from the High Plains Aquifer (HPA) in Kansas for model demonstration and parameterization (e.g., S1 Table).
  • Published literature for crop coefficients and regional recharge estimates.
  • Simulated 150-year time series of corn prices based on a Mean Reverting process.

Main Results

• The model demonstrates that higher pumping rates do not always guarantee greater profitability, as diminishing returns and aquifer depletion can undermine long-term benefits.
• Irrigation strategies that align with site-specific aquifer properties and regulatory thresholds improve both economic performance and sustainability.
• A constant regulatory pumping limit of approximately 3800 cubic meters per day (m³/day) generated the highest expected land values for most scenarios, effectively balancing crop water demand (3600 to 3900 m³/day) with sustainable extraction.
• Strategies that initially allow higher pumping limits or irrigation fractions and then gradually reduce them (front-loaded pumping) consistently yielded the highest expected land values, especially over longer planning horizons, due to the effect of discounting near-term revenues.
• Higher irrigation fractions generally increase land values by better meeting crop water demand, but marginal benefits diminish sharply when meeting between 90% and 100% of demand. Increasing irrigation from 80% to 100% of demand reduced operational years from 375 to 171, highlighting a trade-off between short-term profit and long-term resource availability.
• CVaR analysis revealed that strategies leading to higher expected land values were also associated with lower downside risk (higher CVaR), indicating that improvements in average returns often correspond to stronger performance under unfavorable conditions.
• Higher hydraulic conductivity significantly enhanced both land values and CVaR, reflecting reduced financial risk due to improved groundwater availability and recovery (e.g., K = 10 m/day supported 52 years of operation, while K = 40 m/day extended this to 164 years).
• Variations in specific yield had a less noticeable influence on land value or CVaR in the preliminary analysis.
• Relaxing sustainable extraction rules beyond a certain point did not always lead to proportionally higher land values due to diminishing returns, suggesting regulators may have flexibility to reduce allowable extraction without significantly compromising farm profitability.

Contributions

• Development of a novel, accessible farm-level coupled hydro-economic model that integrates groundwater dynamics with economic outcomes to support irrigation decisions.
• Introduction of Conditional Value-at-Risk (CVaR) as a novel application in agricultural groundwater management to assess economic tail risk, emphasizing potential extreme adverse outcomes rather than just average performance or overall variability.
• Provides a scalable framework to inform irrigation policy, support farmer decision-making, and promote sustainable groundwater use under growing uncertainty.
• Offers a flexible tool adaptable to site-specific conditions for farmers, policymakers, and regulators seeking to align irrigation decisions with long-term groundwater sustainability.

Funding

• University of Waterloo Water Institute Seed Grant
• University of Waterloo Trailblazer Award
• Natural Sciences and Engineering Research Council of Canada [Discovery Grant]
• All funding was awarded to Andrea Brookfield (PI) and Margaret Insley (co-PI).

Citation

@article{Tian2026Development,
  author = {Tian, Boyao and Brookfield, Andrea E. and Insley, Margaret},
  title = {Development of a coupled hydro-economic model to support groundwater irrigation decisions},
  journal = {PLOS Water},
  year = {2026},
  doi = {10.1371/journal.pwat.0000452},
  url = {https://doi.org/10.1371/journal.pwat.0000452}
}