KnowledgeMAGDA Irrigation Advisory System Shows Promising Results After Demonstration Phase
Knowledge

MAGDA Irrigation Advisory System Shows Promising Results After Demonstration Phase

 


The MAGDA Irrigation Advisory Service is a cutting-edge system designed to optimize irrigation management at the farm level by integrating advanced weather forecasts, hydrological modeling, and in-situ observations. The system leverages the IrriSPHY model, which simulates soil moisture dynamics and determines the Irrigation Water Requirements (IWR) based on meteorological conditions and crop-specific parameters.

This article summarizes the key results and validation process of the MAGDA irrigation advisory system, focusing on pilot implementations in Romania and Italy.

Irrigation Water Requirements (IWR)

The IrriSPHY model estimates daily IWR for short-term (2-day ahead) and medium-term (5-day ahead) forecast windows. These forecasts were tested at the Tetto-Bernardo pilot site (Italy) and two Romanian pilot sites (SCDAB and ISMB). Key findings include:

  • IWR derived from weather forecasts incorporating data assimilation (WRF-CIMA-DA) was 10-15% higher than those based on forecasts without data assimilation (WRF-CIMA-OL).
  • Accounting for spatial variability of weather forecasts reduced IWR by approximately 5% compared to single-point forecasts.
Figure 1. Examples or irrigation recommendations issued between 20-24 June 2024 (IWR, m³/ha/day) for the Tetto-Bernardo and SCDAB sites.

Irrigation Scheduling and Water Savings

The advisory system provides dynamic irrigation recommendations based on real-time soil moisture simulations. Findings include:

  • Improved forecasts with data assimilation significantly reduce water wastage, as they better account for localized meteorological conditions.
  • Short-term forecasts (2-day ahead) consistently outperform medium-term forecasts (5-day ahead), demonstrating higher accuracy in irrigation planning.

In some cases, the potential for water savings was substantial, particularly when real-time soil moisture and precipitation forecasts were included.

Validation

The validation process compared MAGDA simulation outputs with in-situ sensor measurements and satellite-based data. The primary validation metrics included:

Actual Evapotranspiration (ETa)

  • Satellite-based actual evapotranspiration (TSEB model) was compared to IrriSPHY estimates.
  • Satellite ETa values were higher than model simulations, with absolute differences ranging from 1.0 to 2.3 mm/day.
  • Differences in ETa estimates were within the expected uncertainty range (20%), demonstrating model reliability.

Soil Moisture Comparisons

Simulated soil moisture values were compared to in-situ sensor data. Main highlights:

  • Forecasts incorporating data assimilation showed slightly better agreement with observed soil moisture values compared to open-loop forecasts.
  • Absolute differences between modeled and observed soil moisture were minimal (~3% average deviation).

Key Findings from Validation

  • Forecasts with data assimilation provided more accurate soil moisture estimates and IWR recommendations.
  • Spatial uncertainty matters: incorporating ensemble forecasts improved reliability and reduced irrigation recommendations by 5%.
  • Satellite and in-situ validation confirmed model performance within expected error margins, reinforcing its applicability in farm-level decision-making.

 

Dissemination through the Farm Management System (FMS)

MAGDA’s irrigation advisory outputs are displayed through a user-friendly Farm Management System (FMS), ensuring accessibility for farmers. The key features include:

  • Forecasted accumulated precipitation for 2-day and 5-day windows.
  • Forecasted evapotranspiration estimates based on crop water needs.
  • Daily irrigation recommendations based on real-time soil moisture data.
  • Comparison of WRF-DA and WRF-OL forecasts to provide reliable advisory outputs.
Figure 2. Irrigation advice into MAGDA’s Farm Management System dashboard

Conclusion

The MAGDA irrigation advisory system effectively integrates hydrological modeling, weather forecasting, and real-time observations to enhance farm-level irrigation decisions. The validation results confirm that data assimilation improves irrigation recommendations and optimizes water use. Future efforts should focus on:

  • Improving long-term site-specific calibration for enhanced accuracy.
  • Expanding data assimilation methods to improve 5-day forecast reliability.
  • Enhancing user engagement through farmer training and feedback mechanisms.

With its proven ability to improve irrigation efficiency, MAGDA represents a significant advancement in precision agriculture, promoting sustainable water use and improved crop productivity.

Keywords

Climate change, irrigation, efficiency, hydrological models