Mohammedi et al. (2026) From Diagnosis to Rehabilitation: A Stochastic Framework for Improving Pressurized Irrigation System Performance Under Water Scarcity

Identification

• Journal: Water
• Year: 2026
• Date: 2026-04-10
• Authors: Serine Mohammedi, Francesco Gentile, Nicola Lamaddalena
• DOI: 10.3390/w18080907

Research Groups

• Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Bari, Italy
• International Centre for Advanced Mediterranean Agronomic Studies (CIHEAM-Bari), Bari, Italy

Short Summary

This study developed an integrated stochastic-hydraulic framework to diagnose and rehabilitate large-scale pressurized irrigation systems, demonstrating its application to the Sinistra Ofanto scheme where targeted pipe upgrades costing €85,452 increased hydraulically satisfied configurations from 62% to 100% during peak demand.

Objective

• Develop an enhanced diagnostic framework integrating stochastic demand modeling with hydraulic simulation for large-scale pressurized irrigation systems.
• Validate this framework using a real-world irrigation system (Sinistra Ofanto scheme).
• Demonstrate cost-effective rehabilitation strategies to improve system performance and provide a decision-support tool for sustainable water management under water scarcity.

Study Configuration

• Spatial Scale: District 10 of the Sinistra Ofanto irrigation scheme, Foggia, Southern Italy. The district covers approximately 2000 hectares, with 1423 hectares currently irrigated, served by a network of 317 hydrants.
• Temporal Scale: The study focused on the 2020 irrigation season, with a specific 10-day peak demand period identified from 21 to 30 July. Hourly discharge data were analyzed.

Methodology and Data

• Models used:
  • MASSPRES (Mapping System and Services for Pressurized Irrigation) framework (extended).
  • Clement probabilistic model (First Clement formula) for stochastic demand generation.
  • COPAM software (Version 4.0) for hydraulic performance simulation and rehabilitation optimization.
  • ICARE (Indexed Characteristic Curve Model) for configuration-level performance assessment.
  • AKLA for hydrant-level performance analysis (Relative Pressure Deficit and Reliability indicators).
  • Labye iterative discontinuous optimization procedure for rehabilitation.
• Data sources:
  • Continuous hourly discharge data recorded at the inlet to District 10 during the 2020 irrigation season.
  • Field discharge data for model calibration.
  • Network layout and design parameters (e.g., pipe diameters, nominal hydrant discharge).

Main Results

• The 10-day peak demand period was identified as 21–30 July, with daily water volumes frequently exceeding 8000–10,000 cubic meters per day (m³ day⁻¹).
• The Clement model accurately captured the empirical mean discharge (414 L s⁻¹) but underestimated upper-tail variability, with an empirical standard deviation of 111 L s⁻¹ compared to a theoretical 60.6 L s⁻¹.
• Prior to rehabilitation, the system operated with only 62% of simulated configurations hydraulically satisfied under peak demand conditions (646 L s⁻¹ discharge and 100 meters above sea level (m a.s.l.) inlet head).
• Localized pressure deficits (Relative Pressure Deficit < 0) were identified at specific hydrants (e.g., 11, 57, 58, 59, 60, 61, 190, and 191), with some exhibiting very low reliability (e.g., hydrant 11 showed zero reliability).
• The rehabilitation strategy involved targeted pipe diameter upgrades over a cumulative length of 1042 meters (m).
• The total estimated investment for the rehabilitation was €85,452.
• Post-rehabilitation, full hydraulic feasibility was restored, increasing the proportion of hydraulically satisfied configurations to 100% and ensuring non-negative Relative Pressure Deficit at all 317 hydrants.

Contributions

• Developed an enhanced, integrated diagnostic and simulation framework for pressurized on-demand irrigation systems, combining stochastic demand modeling, hydraulic simulation, and performance-based optimization.
• Demonstrated a cost-effective rehabilitation strategy that effectively resolves hydraulic vulnerabilities through targeted pipe upgrades, avoiding large-scale infrastructure replacement and additional energy consumption.
• Provided a robust decision-support tool for system managers to prioritize investments, quantify hydraulic risk, and evaluate intervention effectiveness under stochastic demand conditions.
• The methodology's core components are not site-specific, suggesting potential applicability to other pressurized on-demand irrigation systems of varying sizes and operating regimes.

Funding

• PhD program support.
• Project of Excellence of the Department of Soil, Plant and Food Sciences (DiSSPA), University of Bari Aldo Moro, ‘Marginal Areas: Valorization of Ecosystem Resources for Fair and Sustainable Livelihood (MAR.V.E.L)’.

Citation

@article{Mohammedi2026From,
  author = {Mohammedi, Serine and Gentile, Francesco and Lamaddalena, Nicola},
  title = {From Diagnosis to Rehabilitation: A Stochastic Framework for Improving Pressurized Irrigation System Performance Under Water Scarcity},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18080907},
  url = {https://doi.org/10.3390/w18080907}
}