Meydani et al. (2025) Scenario-driven Decision Support System for assessing water balance to support agricultural and ecosystem sustainability

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-11-26
• Authors: Amirreza Meydani, Parviz Rezaei, Mostafa Javadian, Hamidreza Khodaei, Massoud Tajrishy, Delphis F. Levia
• DOI: 10.1016/j.jhydrol.2025.134679

Research Groups

• Department of Geography & Spatial Sciences, University of Delaware, Newark, DE, USA
• Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
• Center for Ecosystem Science and Society, Northern Arizona University, AZ, USA
• Department of Water Engineering, Faculty of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran
• Department of Plant & Soil Sciences, University of Delaware, Newark, DE, USA
• Department of Civil, Construction, and Environmental Engineering, University of Delaware, Newark, DE, USA

Short Summary

This study proposes a novel scenario-driven decision support system (DSS) that integrates hydrological modeling and multi-objective optimization to evaluate complex water allocation strategies in arid regions. Applied to Iran's Lake Urmia basin, the DSS identified optimal strategies capable of increasing environmental water supply by 31% while simultaneously boosting agricultural profit by 26%.

Objective

• To propose and apply a novel scenario-driven decision support system (DSS) for evaluating complex water allocation strategies under realistic hydrological conditions to support agricultural and ecosystem sustainability in arid and semi-arid regions.

Study Configuration

• Spatial Scale: Lake Urmia basin (LUB), Iran.
• Temporal Scale: Long-term planning based on current conditions and forecast-informed scenarios, with specific strategies like shifting Peak Environmental Supply (PES) to late winter–early spring.

Methodology and Data

• Models used:
  • Decision Support System (DSS) framework
  • Water Evaluation and Planning (WEAP) hydrological model (physically-based)
  • Soil Moisture (SM) module
  • MABIA (MAitrise des Besoins d’Irrigation en Agriculture) module
  • Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D)
• Data sources:
  • Forecast-informed inflow scenarios
  • Hydrological conditions
  • Agricultural data (crop patterns, deficit irrigation strategies)

Main Results

• Optimal water allocation strategies can increase Environmental Water Supply (EWS) to Lake Urmia by 31% while simultaneously boosting agricultural profit by 26%.
• Shifting the timing of Peak Environmental Supply (PES) to late winter–early spring provides a robust compromise, increasing EWS while keeping agricultural supply and unmet demand within acceptable limits.
• The developed DSS effectively ranks water allocation scenarios and clarifies how outcomes shift based on varying priorities (agricultural-driven, environmental-driven, or balanced).

Contributions

• Development of a novel scenario-driven DSS that uniquely integrates forecast-informed inflow scenarios with a physically-based WEAP hydrological model (refined by SM and MABIA modules) and a multi-objective calibration method.
• Provides a transferable and practical tool for identifying water-management strategies that enhance agricultural profitability while supporting the ecological recovery and long-term ecosystem function of shrinking lakes, particularly relevant under increasing water scarcity and variability due to climate change.
• Demonstrates the effectiveness of dynamic PES, crop pattern changes, and deficit irrigation strategies in achieving win-win outcomes for both agricultural and ecosystem needs.

Funding

• The provided text does not explicitly list funding projects, programs, or reference codes.

Citation

@article{Meydani2025Scenariodriven,
  author = {Meydani, Amirreza and Rezaei, Parviz and Javadian, Mostafa and Khodaei, Hamidreza and Tajrishy, Massoud and Levia, Delphis F.},
  title = {Scenario-driven Decision Support System for assessing water balance to support agricultural and ecosystem sustainability},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134679},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134679}
}