## 2 Materials and Methods
### Forecasting Models
#### 2.3.3. Weather Forcings and Forecasting Windows  
The SPHY model was forced using weather forecasts generated with and without assimilation. Forecasts without data assimilation (OL) do not include MAGDA system data from in situ sensors, GNSS, and/or Meteodrones. This configuration is considered in this study as the benchmark for comparison purposes. DA forecasts assimilate diverse data sources, depending on availability: radar data, GNSS data, in situ temperature measurements, and data retrieved from Meteorrones.  
The impact of assimilation is expected to be more relevant during the first 12 to 24 h after the assimilation of in situ data, and progressively decreases as the forecast window extends. The magnitude of this impact may vary across test cases, depending on factors such as the quantity and type of observations available, atmospheric conditions at assimilation time, and event typology. Differences in IWR forecasts between OL and DA products highlight the potential advantages of using data assimilation in terms of water savings and more accurate irrigation quota adjustments.  
To account for spatial variability and uncertainty in weather forecasts, SPHY was forced with an ensemble of 49 forecast paired-value vectors of precipitation and reference evapotranspiration. Each member represents forecast trajectories generated within a bounding box of 21 km \(\times\) 21 km area (7 \(\times\) 7 grid points in the forecast mesh). The final recommendation of irrigation at each pilot site is delivered as the mean value of all IWR figures simulated in the ensemble.  
The demonstrator was run on the SCDAB (Romania) Tetto-Bernardo (Italy) pilot sites during different testing periods throughout the summer and autumn of 2024. In selecting the dates, priority was given to the occurrence of rainfall events and to periods with the highest availability of MAGDA data from GNSS, Meteorone flights, and/or field-scale weather and soil moisture sensors. The final selected dates are reported in Table 3. In order to comply with the user requirements previously surveyed among farmers and  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Site Name** & **SCADB** & **Tetto-Bernardo** \\ \hline
**Lat, Lon (EPSG:4326)** & (45.21, 27.92) & (44.41, 7.52) \\
**Altitude (m a.s.l.)** & 13.5 & 527 \\
**Crop type** & Maize-grain & Apple \\
**Planting date** & 01-apr & 01-jul \\
**Root depth (m)** & 0.5 & 1 \\
**Soil texture** & Loam & Loam \\
**Total porosity (\%)** & 0.85 & 0.85 \\
**Soil wilting point (vol/vol)** & 0.15 & 0.15 \\
**Soil field capacity (vol/vol)** & 0.35 & 0.35 \\
**Soil diffusivity coefficient, k (mm\({}^{2}\)/day)** & 0.2 & 0.2 \\
**Soil moisture diffusivity exponent, p (dimensionless)** & 1.5 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Site parameters used in the irrigation advisory service.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**SCADB, Maize-Grain**} \\  & **Initial** & **Develop.** & **Middle** & **End** \\ \hline
**Length crop stage (days)** & 30 & 40 & 50 & 50 \\
**Crop coefficient, K\({}_{\text{c}}\)** & 0.10 & 0.30 & 1.20 & 0.50 \\ \hline \hline  & \multicolumn{4}{c}{**Tetto-Bernardo, Apples**} \\  & **Initial** & **Develop.** & **Middle** & **End** \\ \hline
**Length crop stage (days)** & 35 & 45 & 40 & 15 \\
**Crop coefficient, K\({}_{\text{c}}\)** & 0.15 & 0.60 & 0.95 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Duration of crop development stages and crop coefficients for maize-grain (SCDAB) and apple (Tetto-Bernardo) crops.  
stakeholders, IWR forecasts were generated/updated with a frequency of 2 days, starting in our analysis from the beginning of each testing period. For comparison purposes between OL and DA, and short-term and medium-term forecast outcomes, results in Section 3 are delivered as the average of all the IWR retrieved in a common period of 5-6 days.