The hydrant pressure head in an on-demand water distribution system can be subject to high fluctuation depending on the discharge flowing inside the pipes, with consequent impacts on the performance of on-farm irrigation systems. In this work, an Italian water distribution system was analyzed using the AKLA model at upstream discharges of 1,200 and 600 L⋅ s−1 to estimate the range of hydrant pressure variation. A computer model was developed, calibrated, and used to evaluate the performance of a drip irrigation system by relating the on-farm network with the hydrant characteristic curve at a certain operating status. The flow regulator within the hydrant played an important role in stabilizing the performance of the network at hydrant pressures higher than 27 m. At lower hydrant pressures, to apply the same amount of water, irrigation time must be extended by 17 and 95% for pressure heads of 20 and 12 m, respectively. These approaches described have great utility to ensure adequate irrigation management w...

This paper mainly aims to illustrate an irrigation management tool to simulate scheduling of district-level water needs over the course of an irrigation season. The tool is mostly based on a daily model for simulating flow of water (and solutes) in heterogeneous agri-environmental systems (called FLOWS-HAGES). The model produces information on the daily evolution of: soil water contents and pressure potentials in the soil profile; water uptake and actual evapotranspiration; stress periods for each crop; return fluxes to the groundwater and their quality in terms of solute concentrations (e.g., nitrates). FLOWS-HAGES provides a daily list of hydrants to be operated according to water or crop-based criteria. The daily optimal sequence of hydrant use may thus be established by passing the volumes to be delivered on to the model for simulating the hydraulics of the irrigation network, in order to ensure that the discharges flowing inside the network of distribution pipes are delivered under optimal pressure head distribution in the system. All the above evaluations can be carried out in a stochastic framework to account for soil heterogeneity and climate changes. To illustrate the potential of FLOWS-HAGES, a case study was considered for a selected sector of the Irrigation District 10 in the “Sinistra Ofanto” irrigation system (southern Italy, Apulia region). In a 139 ha area (Sector 6 of the Irrigation District), soil profiles were analyzed for characterization of hydraulic properties variability. Hydraulic properties were determined by a combination of field and laboratory measurements. Model simulations were validated by comparing soil water storage simulated and measured by a sensor based on electromagnetic induction technique. Irrigation water volumes and frequency calculated by the model were compared to the volumes actually supplied by the farmers. Compared to the farmers behavior, the model simulates more frequent irrigations with lower irrigation volumes. Finally, some indexes of irrigation performance were calculated for each farm under study. The resulting maps provide useful information on the spatial distribution of farmer behavior, indicating the abuse or underuse of water as well as the fraction of the water lost by drainage following the irrigation method applied.

The present study provides an assessment of the climate variability at a subnational scale, focusing on the case of the Apulia region, in Southeastern Italy. The variables considered for the purpose of a trend analysis were the minimum, maximum, and mean temperatures, and reference evapotranspiration. These are very important in an urban–rural planning context. The study was based on 38 monitoring stations and consisted in the application of the nonparametric Mann–Kendall test and a progressive trend analysis, both used to detect the changes. The 1950–2003 period was investigated on seasonal and annual scales. The results generally showed a warming process and an acceleration of the atmospheric evaporative demand which took place especially since the mid-1970s. The latter had a significant positive trend, while the period before the break point of the 70s had a cooling effect. Finally, the warming effect was more pronounced for minimum temperatures.