The efficient management of irrigation networks is a pressing need, and it requires huge datasets to calibrate and simulate varying annual conditions. However, acquiring such datasets with the desired statistical properties might be challenging. This study presents a detailed methodology to generate synthetic years of flow rate data in irrigation networks. This approach is centred on the parameters of the best-adjusted distribution function, target volume, minimum and maximum constraints, and the number of values to generate, which is a crucial step towards more precise modelling and management of water resources.

The methodology was developed and implemented in MATLAB using the Statistics and Machine Learning Toolbox. First, the ideal distribution function was determined for each month of the dataset (e.g., Normal, Gamma, Lognormal). The input parameters werethe parameters of the best-adjusting distribution, the total volume for each month, the maximum and minimum flow values, and the total number of entries to generate. The function generates an initial set of random numbers following the specified distribution, then normalises and transforms the data. An iterative optimization process is carried out to adjust the values to match the desired monthly volume, ensuring the convergence criteria are met. Thus, the synthetic data represent the variations in the demands.

The methodology was tested with various distribution functions and target values to validate its performance. The generated synthetic years closely followed the input best-fitting distribution patterns and matched the target volume with minimal deviation. This study introduces a MATLAB-based methodology that effectively generates synthetic years of data tailored for irrigation networks. The approach facilitates realistic and reliable simulations of water usage patterns by ensuring adherence to the best-fitting distribution and the input constraints. This tool is valuable for planning, optimizing, and managing irrigation networks.