Search Results (3)

The seasonal and interannual irregularity of temperature and precipitation is a feature of the Mediterranean climate that is intensified by climate change and constitutes a relevant driver of water and soil degradation. This study was developed during three years in a hydro-agricultural area of the Alqueva irrigation system (Portugal) with Mediterranean climate conditions. The sampling campaigns included collecting water samples from eight irrigation hydrants, analyzed four times yearly. The analysis incorporated meteorological data and indices (precipitation, temperature, and drought conditions) alongside chemical parameters, using multivariate statistics (factor analysis and cluster analysis) to identify key water quality drivers. Additionally, machine learning models (Random Forest regression and Gradient Boosting machine) were employed to predict electrical conductivity (ECw), sodium adsorption ratio (SAR), and pH based on chemical and climatic variables. Water quality evaluation showed a prevalence of a slight to moderate soil sodification risk. The factor analysis outcome was a three-factor model related to salinity, sodicity, and climate. The cluster analysis revealed a grouping pattern led by year and followed by stage, pointing to the influence of inter-annual climate irregularity. Variations in water quality from the reservoirs to the distribution network were not substantial. The Random Forest algorithm showed superior predictive accuracy, particularly for ECw and SAR, confirming its potential for the reliable forecasting of irrigation water quality. This research emphasizes the importance of integrating time-sensitive monitoring with data-driven predictions of water quality to support sustainable water resources management in agriculture. This integrated approach offers a promising framework for early warning and informed decision-making in the context of increasing drought vulnerability across Mediterranean agro-environments. Full article

The colonization of pressurized irrigation networks by zebra mussels (Dreissena polymorpha) poses a serious risk to water delivery, reducing pipeline capacity and potentially causing complete blockages. Despite the critical need for early detection and effective management, existing methods often rely on costly, time-consuming field inspections or indirect indicators with limited accuracy. To address this gap, we present SIMZEBRA, a cloud-based toolkit that assesses invasions using hydraulic monitoring and simulation. The tool employs the Normalized Pressure Method, comparing real-time pressure data from transducers with EPANET simulations of a mussel-free network. An optimization process adjusts friction coefficients in network segments until simulated and measured pressures align, enabling the generation of infestation maps over user-defined time periods. Compared to conventional approaches, SIMZEBRA enhances detection accuracy, reduces the reliance on physical inspections, and provides a scalable, automated solution for continuous monitoring. The tool also integrates experimental data to establish relationships between mussel density, pipeline diameter, and roughness. In the presented case study, roughness increases of up to 10 mm were detected in affected pipes, while local head losses at hydrants ranged between 9 and 11 m, depending on flow conditions. Developed in R with CPU parallelization, the toolkit operates remotely on a cloud server, ensuring fast, efficient, and cost-effective detection and management of zebra mussel infestations. This approach improves early warning capabilities and supports proactive invasive species management in pressurized irrigation networks. Full article

The perturbation in hydraulic networks for irrigation systems is often created when sudden changes in flow rates occur in the pipes. This is essentially due to the manipulation of hydrants and depends mainly on the gate closure time. Such a perturbation may lead to a significant pressure variation that may cause a pipe breakage. In a recent study, computer code simulating unsteady flow in pressurized irrigation systems—generated by the farmers’ behavior—was developed and the obtained results led to the introduction of an indicator called the relative pressure variation (RPV) to evaluate the pressure variation occurring into the system, with respect to the steady-state pressure. In the present study, two indicators have been set up: The hydrant risk indicator (HRI), defined as the ratio between the participation of the hydrant in the riskiest configurations and its total number of participations; and the relative pressure exceedance (RPE), which provides the variation of the unsteady state pressure with respect to the nominal pressure. The two indicators could help managers better understand the network behavior with respect to the perturbation by defining the riskiest hydrants and the potentially affected pipes. The present study was applied to an on-demand pressurized irrigation system in Southern Italy. Full article