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The main purpose of this paper was to study the effect of spatial variability of soil hydraulic properties (HP) and vegetation parameters (VP) (e.g., leaf-area index, LAI, and crop coefficient, Kc) on modelling agro-hydrological processes and optimising irrigation volumes at large scale. Based on this analysis, the effect of partly overlooking the spatial variability of soil HP and/or VP inputs was verified on a 140 ha irrigation sector in "Sinistra Ofanto" irrigation system in Apulia Region, Southern Italy. Five soil profiles were excavated and the HP were measured in all the soil horizons. Additionally, measurements of soil HP were taken in the surface soil layer in ninety sites distributed over the whole irrigation sector. All the HP measurements were carried out using tension infiltrometer. Remote sensing applications were used to obtain LAI and Kc using European Space Agency's (ESA) Sentinel-2 images with 10 m resolutions. First, distributed (on ninety polygons with an average area of about 1.5 ha) optimal irrigation volumes and related deep percolation volumes at a depth of 80 cm, were computed using an agro-hydrological model and accounting for the actual observed variability of soil HP and VP inputs. The sector scale irrigation and deep percolation volumes were obtained by aggregating the distributed irrigation volumes. This was considered as the reference scenario (hereafter DVS—Detailed Variability Scenario). Then, reduced variability scenarios (hereafter RVS—Reduced Variability Scenario) were considered, where the information on the actual spatial variability of the soil HP and VP was gradually overlooked to find the minimum data set needed to still have sector scale irrigation volumes and related deep percolation volumes comparable to those obtained under the DVS. Results showed that overlooking VP (RVS-VP) variability did not significantly change the optimal irrigation volumes and the deep percolation fluxes. By contrast, neglecting the HP variability (RVS-HP) showed significant effects on both the irrigation and percolation volumes compared to the DVS. The main practical finding was that, at least for the area investigated in this study, hydraulic characterization of one soil profile in an area of approximately 30 ha provides sector scale irrigation volumes and percolation fluxes comparable to those obtained under the DVS, thus by accounting for all the observed local variability. [ABSTRACT FROM AUTHOR]

A deterministic-stochastic combined model named HydroGEN was developed, as described in a companion paper (Part I: Model development), to enable the simulation of demanded daily volumes and hourly flow rates during peak periods in pressurized irrigation delivery networks. The model was applied to a pilot large-scale irrigation system located in southern Italy for calibration and for testing its reliability in analyzing the operation of large-scale pressurized delivery systems through the simulated flow configurations. Daily input data on rainfall, temperature, solar radiation, wind speed and relative humidity were gathered from a meteorological station located within the study area, whereas information on local irrigation management practices were collected through interviews with farmers and from extension specialists. The model was tested at different management levels, from district to sector and hydrants. The model testing was supported by the use of high-resolution remote-sensing imagery acquired on a single overpass date in 2006 and then classified and recoded following a ground-truthing campaign conducted during the same year. Simulations were performed to identify the 10-day peak-demand period and to generate the hydrographs of daily volumes and of hourly flow rates. Results from the different simulations were compared with historical datasets of irrigation volumes and discharges recorded during the 2008 and 2009 seasons at the upstream end of the irrigation network under study, at a sector level during the 2007 season and at selected delivery hydrants during the 2005 season. Some discrepancies between simulated and recorded data were noted that can be related to small errors in estimating crop and soil parameters, application efficiency at field level, as well as to large variability in irrigation management practices followed by local farmers. Overall, the results from testing showed that the model is capable of forecasting with good accuracy the timing of peak-demand periods, the irrigation volumes demanded during the season, as well as the hydrographs of daily volumes and hourly flow rates withdrawn by farmers during these peak-demand periods, especially when it is applied to large multi-cropped command areas. [ABSTRACT FROM AUTHOR]

The variability of flow regimes in on demand pressurized irrigation systems leads to uncertainty in head at the nodes affecting the system performance, and thus it should be considered when designing and/or rehabilitating water distribution systems. Based on these considerations a new approach for the optimization of on demand pressurized irrigation systems is presented combining the minimization of cost with the maximization of reliability taking into account the stochastic variability of the flows into each section of the network. The new model, Clément and the cumulated random generated discharges model (FAO model) were applied to three pressurized irrigation networks of different dimensions (large, medium and small) operating on demand in Southern Italy. The optimization algorithm used in all the cases is the Labye iterative discontinuous method, a formulation of the dynamic programming. The results of the different models were compared showing that the cost of the optimal network calculated using the new model was reduced by more than 20%, without any significant decrease of the system reliability or reduction of the network capacity. [ABSTRACT FROM AUTHOR]

This study was conducted on an irrigated area of southern Italy to analyze the current operation of a large-scale irrigation delivery system and the effects of the operation procedures on crop irrigation management and aquifer salinity increase. The area is characterized by relatively high levels of groundwater salinity in the summer that are probably due to intensive groundwater pumping by farmers during periods of peak irrigation demand, with the resulting seawater intrusion. Two alternative delivery schedules, namely the rotation delivery schedule and the flexible delivery schedule, referred to as RDS and FDS, respectively, were simulated using a soil-water balance model under different combinations of crop, soil and climatic conditions. The first set of simulations concerned the farm irrigation management constrained by the rotational delivery used by the local water management organization. The second scenario simulated the farm irrigation schedule most commonly used by growers in the area for maximizing crop yields. Based on crop irrigation management under RDS and FDS, two alternative operational scenarios were also developed at the scheme level and then compared for evaluation. Winter and summer salinity maps of the aquifer were developed by interpolating salinity measurements of the groundwater samples collected during the 2006 irrigation season. From these maps, a close relationship can be inferred among delivery schedule, aquifer exploitation and salinity increase, which justifies the need for implementing FDS that might reduce the groundwater demand for irrigation. [ABSTRACT FROM AUTHOR]

Water scarcity is a mounting problem in arid and semi-arid regions such as the Mediterranean. Therefore, smarter and more effective water management is required, especially in irrigated agriculture. One of the most challenging uncertainties in the operation of on-demand collective Pressurized Irrigation Distribution Systems (PIDSs) is to know, a priori, the number and the position of hydrants in simultaneous operation. To this end, a model was developed to generate close to reality operating hydrants configurations, with 15, 30 or 60 min time steps, by estimating the irrigation scheduling for the entire irrigation season, using climatic, crop and soil data. The model is incorporated in an integrated DSS called Decision Support for Irrigation Distribution Systems (DESIDS) and links two of its modules, namely, the irrigation demand and scheduling module and the hydraulic analysis module. The latter is used to perform two types of analyses for the performance assessment and decision-making processes. The model was used in a real case study in Italy to generate hydrants' operation taking into consideration irrigation scheduling. The results show that during the peak period, hydrants simultaneity topped 62%. The latter created pressure deficit in some hydrants, thus reducing the volume of water supplied for irrigation by up to 87 m3 in a single hydrant during the peak demand day. The developed model proved to be an important tool for irrigation managers, as it provides vital information with great flexibility and the ability to assess and predict the operation of PIDSs at any period during the irrigation season. [ABSTRACT FROM AUTHOR]

The evapotranspiration (ET) represents the Crop Water Requirements that must be provided by rainfall and/or irrigation to ensure the crop yield. A critical need for farmers and water managers is to have a reliable, accurate and reasonably accessible estimation of the ET rates of crops to optimize their irrigation scheduling and ensuring long-term water sustainability. This is true especially for arid and semiarid areas characterized by limited availability of water resources and high competitiveness among users and different sectors. Remote sensing provides reliable information for the quantification of ET over large areas and the availability of free of charge high-resolution remotely sensed imagery provides the possibility of better understanding of seasonal climate and vegetation variability.In the present work were tested the capabilities of the "Analytical Approach" to estimate the seasonal ET at irrigation district scale over a complex agro-climatic study area characterized by an extremely heterogeneous, fragmented landscape and a semi-arid Mediterranean climate. The method is based on the logic of the Penman-Monteith equation and on the standard single crop coefficient (Kc) approach proposed by the Food and Agriculture Organization (FAO). It combines agrometeorological data measured in-situ (air temperature and humidity, solar radiation and wind speed) with surface reflectance satellite derived data: the albedo (α) of the crop-soil surface and the Leaf Area Index (LAI). The test was conducted over the Irrigation scheme "Sinistra Ofanto" (northern of the Apulia Region, Italy) using the Sentinel-2 derived biophysical vegetation's parameters and the agrometeorological data measured by the "Consorzio per la Bonifica della Capitanata" which manage the irrigation scheme. Results were compared at irrigation district scale using the global MODIS ET products (MOD16A2 and MYD16A2 Version 6) characterized by lower spatial resolution (500 meters) and daily temporal resolution. [ABSTRACT FROM AUTHOR]

The improvement of performance of irrigation systems plays a fundamental role in increasing their efficiency in order to reach a sound use of irrigation water. The COPAM (Combined Optimization and Performance Analysis Model) has proven its usefulness in performance evaluation of on-demand irrigation systems; however, in many cases, input data, such as water volumes delivered by hydrants, is not readily available. To support a wider application of the COPAM, we tested the possibility of using irrigation volumes estimated by means of space-borne remote sensing. The Sentinel-2 (S2) constellation provides high spatial resolution images with a frequency between 2 and 5 days, which is compatible with COPAM input requirements. In the present work, an irrigation sector in the Capitanata irrigation network (Foggia Province, no. 6 of District 10) in Italy was chosen to assess its performance by using COPAM with volumes estimated from Sentinel-2 data. As an input of COPAM, the upstream discharge was determined after a proper transformation of the estimated irrigation water requirement volumes and the recorded volumes into flowrates. The estimation of the irrigation water requirement volumes was accomplished through the estimation of crop evapotranspiration, Etcrop, and effective precipitation, Pn, by combining crop parameters (leaf area index - LAI, fractional vegetation cover - fc, and Albedo) derived from S2 images and the meteorological data from the ERA5 single levels reanalysis dataset collected for the whole study period, from June 1st to September 30th, 2019. The study comprised a comparison of the estimated irrigation water volumes and the corresponding recorded volumes. The results showed a good agreement between the estimated and the registered volumes in a large time scale for 10 days and a one-month period, while a large difference was observed in a daily time scale. The performance analysis was carried out for the overall system and at hydrant level. The estimated discharge was lower than the registered discharge, indicating better performance. Last but not least, some recommendations were proposed for improving performance in critical zones. [ABSTRACT FROM AUTHOR]

• Mapping perturbation in pressurised irrigation systems. • Replacing directly programmed software with machine learning models for real time decision making. • Providing practical recommendations for pressurised irrigation networks management. Nowadays, the management of pressurised irrigation networks requires plenty of information to provide an efficient and reliable service to farmers. Perturbation is the propagation of pressure waves through the networks pipes which could expose the network to a serious risk that could cause components damaging. Several computational codes were developed to simulate such phenomenon. The most recent ones are efficient enough to provide a good image of the perturbation occurrence through different indicators, but they are time and computationally expensive. For real time decision making and more flexible management, there is a need for faster models to be developed. In this study the directly programmed models were used as big data generators to train a developed deep learning model. This approach was applied on a pressurised on-demand irrigation system located in south of Italy that consists of 19 hydrants (service outlets) and covers 57 ha. Two thousand configurations (operational scenarios) were simulated using a predeveloped directly programmed model and fed to train a deep learning model with the objective of forecasting the maximum pressure occurred due the perturbation at each section. The occurred pressure is represented as classes according to the case sensitivity and the required precision. In the present work, scenarios for 1, 2 and 3 bars steps were simulated. The model proved to be significantly time saving compared to previous approaches as the results are produced instantaneously with a forecasting accuracy of 85 %. Furthermore, from the called confusion matrix, the error committed by the model is of one class lower or higher that may be considered tolerable according to the system sensitivity. Thus, modelling the perturbation in the on-demand pressurised irrigation networks would add a significant contribution to provide practical recommendations for real-time decision-making processes. [ABSTRACT FROM AUTHOR]

On-demand pressurized irrigation distribution networks (IDN) operating by gravity may face pressure failures especially during the peak period. Several methods have been developed for irrigation sectoring where farmers are organized in turns. In general, the optimization methods used for pressurized systems consider, in the formulation of the problem, energy saving as objective function, which is not suitable for gravity-fed networks. In this study, an optimization model for pressurized IDN fed by gravity was developed to provide an optimal operating strategy based on irrigation periods. The model uses genetic algorithm to assign an irrigation period to each hydrant taking into account the minimization of pressure deficit at the most unfavorable hydrant of the network. The method is applied to a large scale irrigation distribution network in Italy. It shows to significantly improve the hydraulic performance of the network by insuring a satisfactory pressure at all hydrants, under the actual peak demand as well as higher water demands. [ABSTRACT FROM AUTHOR]

• Bimodal Arya&Paris hydraulic properties were developed based on soil structure. • The bimodal PTF provides better prediction of un/saturated hydraulic conductivity. • The bimodal PTF predicts well the spatial variability of the hydraulic properties. The main purpose of this paper is to develop a bimodal pedotransfer function to obtain soil water retention (WRC) and hydraulic conductivity (HCC) curves. The proposed pedo-transfer function (PTF) extends the Arya and Paris (AP) approach, which is based on particle size distribution (PSD), by incorporating aggregate-size distribution (ASD) into the PTF to obtain the bimodal WRC. A bimodal porosity approach was developed to quantify the fraction of each of the porous systems (matrix and macropores) in overall soil porosity. Saturated hydraulic conductivity, K 0 , was obtained from WRC using the Kozeny-Carman equation, whose parameters were inferred from the behaviour of the bimodal WRC close to saturation. Finally, the Mualem model was applied to obtain the HCC. In order to calibrate the PTF, measured soil physical and hydraulic properties data were used, coming from field infiltration experiments from an irrigation sector of 140 ha area in the "Sinistra Ofanto" irrigation system in Apulia, southern Italy. The infiltration data were fitted by using both bimodal and unimodal hydraulic properties by an inverse solution of the Richards equation. The bimodal "measured" hydraulic properties were then used to calibrate the scaling parameter (α AP) of the proposed bimodal AP (bimAP) PTF. Similarly, for the sake of comparison with the bimodal results, the unimodal hydraulic properties were used to calibrate the α AP of the classical unimodal AP (unimAP) PTF. Compared to the unimAP PTF, the proposed bimAP significantly improves the predictions of the mean WRC parameters and K 0 , as well as the prediction of the shape of the whole HCC. Moreover, compared to the unimodal approach, it also allows to reproduce statistics of the hydraulic parameters (for example, variance) similar to those observed in the calibration dataset. Multiple linear regression (MLR) was also applied to analyse the sensitivity of the bimodal α AP parameter to textural and structural features, confirming significant predictive effects of soil structure. [ABSTRACT FROM AUTHOR]