Luque-Sánchez et al. (2025) Optimizing water management: Identifying strategies to enhance irrigation efficiency under drought conditions

Identification

• Journal: Journal of Environmental Management
• Year: 2025
• Date: 2025-09-27
• Authors: Álvaro Luque-Sánchez, Juan Manuel Díaz‐Cabrera, Adela P. Galvín, Juan Carlos Valenzuela Gámez, Isabel Luisa Castillejo-González
• DOI: 10.1016/j.jenvman.2025.127439

Research Groups

• Department of Electric and Automatic Engineering, University of Córdoba, Córdoba, Andalucía, 14071, Spain
• Department of Rural Engineering, Civil Constructions and Engineering Projects, University of Córdoba, Córdoba, Andalucía, 14071, Spain
• Department of Electronic and Computer Engineering, University of Córdoba, Córdoba, Andalucía, 14071, Spain
• Department Graphic Engineering and Geomatics, University of Córdoba, Córdoba, Andalucía, 14071, Spain

Short Summary

This study analyzed the water-energy nexus in a traditional irrigation community under drought conditions (2020–2024) to identify management strategies for enhanced irrigation efficiency, demonstrating the critical importance of adaptive water management and sensorization for adjusting practices to limited water availability.

Objective

• To analyze the water-energy nexus in a traditional irrigation community under drought conditions (2020–2024) to identify management strategies that enhance irrigation efficiency, specifically by analyzing energy use evolution, identifying recurring consumption patterns, assessing management strategy effectiveness, and evaluating efficiency improvements through reduced variability in daily consumption profiles.

Study Configuration

• Spatial Scale: El Villar Irrigation Community, an agricultural region encompassing 2726 hectares distributed across 692 fields in the provinces of Cordoba and Seville, Andalusia, southern Spain (37.63°N and 5.05°W, WGS84 Datum).
• Temporal Scale: Five-year period from 2020 to 2024, focusing on the summer irrigation campaigns (May 15 to October 15) each year.

Methodology and Data

• Models used: K-means clustering algorithm for identifying daily electrical demand profiles. Optimal cluster number determination using the elbow method, Silhouette coefficient, and Davies–Bouldin index. Statistical analysis included cluster centroids, coefficient of variation (CV), and Euclidean distance.
• Data sources:
  • Quarter-hourly electrical demand data (active power in kilowatts) for the entire irrigation network from the El Villar Irrigation Community (2020–2024).
  • Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) from the State Meteorological Agency (AEMET) for drought assessment.
  • Interviews with technical staff of the El Villar Irrigation Community.
  • Sensor data (pressure and flow) from river intake and irrigation pond outflow (implemented in 2024).
  • Geographic Information System (GIS) and a digital management platform (implemented in 2024).

Main Results

• K-means clustering consistently identified four distinct daily electrical load profiles (k=4) each year, reflecting different irrigation strategies based on temporal energy use.
• Drought conditions from 2020 to 2023 led to a significant shift in energy demand, with peak power reduced by approximately 40% due to water scarcity. In 2023, the driest year, 81.2% of days exhibited low and stable demand (Cluster 0), indicating severe irrigation restrictions.
• In 2024, following the implementation of new management strategies (sensorization, adjusted intake timing, new electricity tariff structure, GIS/digital platform), energy use became more efficient: peak demand decreased further, a secondary peak was introduced in Cluster 1 to align with increased river inflow and high solar energy generation, and daily variability (CV) was significantly reduced compared to 2020 (a year with comparable water allocation).
• Temporal distribution analysis confirmed a progressive reduction in water consumption during the drought, with 2023 showing the most severe impact, while 2024 demonstrated a more organized and efficient consumption pattern due to improved management.
• Crop selection adapted to water availability, with a marked decline in water-intensive crops (e.g., cotton, vegetables) during peak drought in 2023, and a partial recovery of irrigated crops in 2024 under more efficient planning.

Contributions

• The study quantifies the impact of drought on irrigation energy demand, revealing a 40% reduction in peak power and significant shifts in consumption patterns in a traditional irrigation community.
• It demonstrates the effectiveness of adaptive water management strategies, including sensorization, electricity tariff optimization, and digital platforms, in enhancing irrigation efficiency and resilience under extreme drought conditions.
• It identifies four distinct daily energy consumption patterns using K-means clustering on high-frequency electricity data, offering actionable insights for optimizing irrigation scheduling and minimizing energy waste.
• The methodology is replicable in other irrigation districts with similar data, particularly in Mediterranean and semi-arid regions, contributing to sustainable water and energy resource management and supporting multiple Sustainable Development Goals (SDGs).
• It highlights the interdependence between crop selection and irrigation behavior, showing how communities adapt both operationally and agronomically to changing resource availability.

Funding

• INSIGNIA Operative Group (GOPG-JA-23-0002)
• European Union through the European Agricultural Fund for Rural Development (EAFRD)
• Andalusian Government through the Regional Ministry of Agriculture, Livestock, Fisheries and Sustainable Development

Citation

@article{LuqueSánchez2025Optimizing,
  author = {Luque-Sánchez, Álvaro and Díaz‐Cabrera, Juan Manuel and Galvín, Adela P. and Gámez, Juan Carlos Valenzuela and Castillejo-González, Isabel Luisa},
  title = {Optimizing water management: Identifying strategies to enhance irrigation efficiency under drought conditions},
  journal = {Journal of Environmental Management},
  year = {2025},
  doi = {10.1016/j.jenvman.2025.127439},
  url = {https://doi.org/10.1016/j.jenvman.2025.127439}
}