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- Methodologies for water accounting at the collective irrigation system scale aiming at optimizing water productivityPublication . Ferreira, Antónia; Rolim, João; Paredes, Paula; Cameira, Maria do RosárioTo improve water use efficiency and productivity, particularly in irrigated areas, reliable water accounting methodologies are essential, as they provide information on the status and trends in irrigation water availability/supply and consumption/demand. At the collective irrigation system level, irrigation water accounting (IWA) relies on the quantification of water fluxes from the diversion point to the plants, at both the conveyance and distribution network and the irrigated field level. Direct measurement is the most accurate method for IWA, but in most cases, there is limited metering of irrigation water despite the increasing pressure on both groundwater and surface water resources, hindering the water accounting procedures. However, various methodologies, tools, and indicators have been developed to estimate the IWA components, depending on the scale and the level of detail being considered. Another setback for the wide implementation of IWA is the vast terminology used in the literature for different scales and levels of application. Thus, the main objectives of this review, which focuses on IWA for collective irrigation services, are to (i) demonstrate the importance of IWA by showing its relationship with water productivity and water use efficiency; (ii) clarify the concepts and terminology related to IWA; and (iii) provide an overview of various approaches to obtain reliable data for the IWA, on the demand side, both at the distribution network and on-farm systems. From the review, it can be concluded that there is a need for reliable IWA, which provides a common information base for all stakeholders. Future work could include the development of user-friendly tools and methodologies to reduce the bridge between the technology available to collect and process the information on the various water accounting components and its effective use by stakeholders.
- Assessing spatio-temporal dynamics of deep percolation using crop evapotranspiration derived from Earth observations through Google Earth enginePublication . Ferreira, Antónia; Rolim, João; Paredes, Paula; Cameira, MariaExcess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often result in high deep percolation, requiring the separate identification of the two sources of deep percolated water. An integrated methodology was developed to estimate the spatio-temporal dynamics of deep percolation, with the actual crop evapotranspiration (ETc act) being derived from satellite images data and processed on the Google Earth Engine (GEE) platform. GEE allowed to extract time series of vegetation indices derived from Sentinel-2 enabling to define the actual crop coefficient (Kc act) curves based on the observed lengths of crop growth stages. The crop growth stage lengths were then used to feed the soil water balance model ISAREG, and the standard Kc values were derived from the literature; thus, allowing the estimation of irrigation water requirements and deep drainage for independent Homogeneous Units of Analysis (HUA) at the Irrigation Scheme. The HUA are defined according to crop, soil type, and irrigation system. The ISAREG model was previously validated for diverse crops at plot level showing a good accuracy using soil water measurements and farmers’ irrigation calendars. Results show that during the crop season, irrigation caused 11 ± 3% of the total deep percolation. When the hotspots associated with the irrigation events corresponded to soils with low suitability for irrigation, the cultivated crop had no influence. However, maize and spring vegetables stood out when the hotspots corresponded to soils with high suitability for irrigation. On average, during the off-season period, deep percolation averaged 54 ± 6% of the annual precipitation. The spatial aggregation into the Irrigation Scheme scale provided a method for earth-observation-based accounting of the irrigation water requirements, with interest for the water user’s association manager, and at the same time for the detection of water losses by deep percolation and of hotspots within the irrigation scheme.
- Linking participatory approach and rapid appraisal methods to select potential innovations in collective irrigation systemsPublication . Cameira, Maria do Rosário; Rodrigo, Isabel; Garção, Andreia; Neves, Manuela; Ferreira, Antónia; Paredes, PaulaThis paper presents a novel approach that integrates participatory methods and a rapid appraisal process to identify constraints and select potentially innovative solutions aimed at improving water and energy use effi- ciency at different levels in collective irrigation systems. First, a set of quantitative performance indicators is calculated, allowing the identification of the main problems. The study then adopts a bottom-up approach and emphasizes the need for active involvement of stakeholders from different backgrounds to identify potential opportunities to improve the sustainability of the collective irrigation system. From this collaborative and integrative approach, an innovation basket is developed, that includes a variety of techniques, technologies and management practices tailored to the specific challenges of the Lucefecit Collective Irrigation System. The results show that high-energy consumption is the main problem, with high values for energy consumption per hectare (2919.9 kWh) and per m3 of water delivered at the hydrants (0.43 kWh). Another problem is the lack of support for farmers in irrigation scheduling, which leads to large variations in irrigation water productivity across farms. The results of this study provide valuable insights into the effectiveness of the participatory approach and the feasibility of implementing innovative solutions in collective irrigation systems.
- Hub de inovação para sistemas de rega na agricultura mediterrânica: perspetiva da Engenharia Rural do ISA sobre um projeto de I&DPublication . Cameira, Maria do Rosário; Alves, Isabel; Ferreira, Antónia; Rolim, João; Paredes, PaulaA modernização da rega nos países mediterrânicos conduziu a um aumento do desempenho dos sistemas de rega, sensu lato. Novos desafios surgiram: (i) a lacuna no desempenho da rega continua maior do que o esperado; (ii) a intensificação da rega causou esgotamento de recursos hídricos; (iii) os modernos sistemas pressurizados consomem muita energia. O principal objetivo do projeto HubIS é promover, avaliar e impulsionar inovações que visem reduzir o défice de desempenho e melhorar a sustentabilidade dos sistemas de rega na região mediterrânica. O desenvolvimento das inovações baseou-se em processos participativos e na avaliação rápida de desempenho. Apresentam-se neste trabalho atividades desenvolvidas no caso de estudo português, Aproveitamento Hidroagrícola do Lucefecit, pela equipa de Engenharia Rural do ISA, parceiro do projeto. Com base no diagnóstico do desempenho, que indicou elevado consumo energético e variabilidade na produtividade da água entre parcelas, foram co-desenhadas, com os utilizadores finais, inovações, de entre as quais se destacam uma ferramenta para estimativa dos caudais distribuídos nos hidrantes e outra para avaliação do desempenho hidráulico e energético da rede de rega. Desta forma, o projeto PRIMA HubIS forneceu um estudo de caso elucidativo em Portugal sobre a forma como os projetos de transferência de inovação agrícola podem contribuir para aumentar a resiliência hídrica e energética dos aproveitamentos hidroagrícolas.
- Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth EnginePublication . Ferreira, Antónia; Rolim, João; Paredes, Paula; Cameira, Maria do RosárioExcess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often result in high deep percolation, requiring the separate identification of the two sources of deep percolated water. An integrated methodology was developed to estimate the spatio-temporal dynamics of deep percolation, with the actual crop evapotranspiration (ETc act) being derived from satellite images data and processed on the Google Earth Engine (GEE) platform. GEE allowed to extract time series of vegetation indices derived from Sentinel-2 enabling to define the actual crop coefficient (Kc act) curves based on the observed lengths of crop growth stages. The crop growth stage lengths were then used to feed the soil water balance model ISAREG, and the standard Kc values were derived from the literature; thus, allowing the estimation of irrigation water requirements and deep drainage for independent Homogeneous Units of Analysis (HUA) at the Irrigation Scheme. The HUA are defined according to crop, soil type, and irrigation system. The ISAREG model was previously validated for diverse crops at plot level showing a good accuracy using soil water measurements and farmers’ irrigation calendars. Results show that during the crop season, irrigation caused 11 3% of the total deep percolation. When the hotspots associated with the irrigation events corresponded to soils with low suitability for irrigation, the cultivated crop had no influence. However, maize and spring vegetables stood out when the hotspots corresponded to soils with high suitability for irrigation. On average, during the off-season period, deep percolation averaged 54 6% of the annual precipitation. The spatial aggregation into the Irrigation Scheme scale provided a method for earth-observation-based accounting of the irrigation water requirements, with interest for the water user’s association manager, and at the same time for the detection of water losses by deep percolation and of hotspots within the irrigation scheme
- Methodology for Obtaining ETo Data for Climate Change Studies: Quality Analysis and Calibration of the Hargreaves–Samani EquationPublication . Ferreira, Antónia; Cameira, Maria do Rosário; Rolim, JoãoReference evapotranspiration (ETo) is an important part of the water cycle, essential for cli- mate studies, water resource management, and agricultural planning. However, accurate estimation of ETo is challenging when meteorological data are insufficient or of low quality. Furthermore, in climate change studies where large amounts of data need to be managed, it is important to minimize the complexity of the ETo calculation. This study presents a comprehensive approach that integrates data quality analysis with two calibration methods—annual and cluster-based—to improve ETo estimates based solely on temperature data from a set of weather stations (WS). First, the quality and integrity of meteorological data from several WS were analyzed to reduce uncertainty. Second, the Hargreaves–Samani equation (HS) is site calibrated using two approaches: (a) annual calibration, where the radiation coefficient (kRs) is adjusted using a data set covering the entire year; (b) cluster- based calibration, where independent radiation coefficients are adjusted for clusters of years and months. The methodology was evaluated for the Alentejo region in Southern Portugal, using data from 1996 to 2023. When using the original HS equation with a kRs = 0.17 ◦C−0.5, ETo was estimated with errors from 14.9% to 22.9% with bias ranging from −9.0% to 8.8%. The annual calibration resulted in kRs values between 0.157 and 0.165 ◦C−0.5 with estimation errors between 13.3% and 20.6% and bias ranging from −1.5% to 1.0% across the different weather stations. Calibration based on clusters of months and years produced unclear results. Dry season months showed better results using cluster-based calibration, while wet season months performed poorly regardless of the calibra- tion approach. The results highlight the importance of meteorological data quality and site-specific calibration for refining temperature-based ETo estimation methods, and for the region studied, the gains do not justify the increased complexity of the cluster-based approach.