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2023
Journal article
Open Access
Managing coastal aquifer salinity under sea level rise using rice cultivation recharge for sustainable land cover
Abdelaty I, Sallam Gah, Pugliese L, Negm Am, Straface S, Scozzari A, Ahmed A
Study region: The coastal aquifer of Nile Delta, Egypt is used to develop the current study. Study focus: Excess water from rice irrigation is a source of incidental recharge to mitigate seawater intrusion. This paper numerically explores the optimal location of rice cultivations by subdividing the delta domain into three distinct recharging regions (north, central and south). Additionally, SEAWAT code was simulated under a combination of rice cultivation relocation and sea level rise (SLR). New hydrological insights for the region: The study findings revealed significant variations in salt volume reduction depending on the location of rice cultivation in the delta. Placing rice cultivation in the northern region resulted in the highest reduction of salt volume (19 %). In contrast, locating the recharge in the central region yielded a salt volume reduction of 0.50 %, while rice cultivation in the southern region produced a 15 % increase. Considering the projected SLR of 61 cm by 2100, there was an overall salt volume increment of 3 %. However, when accounting for both SLR and rice cultivation recharge in the northern region, a substantial salt volume reduction of 17 % was observed. The results demonstrated that incidental recharge by rice cultivation in coastal aquifers is an effective method for enhancing saltwater intrusion control. Moreover, this study improves our understanding of hydrological processes and expected responses in the delta under future climate scenarios.Source: JOURNAL OF HYDROLOGY. REGIONAL STUDIES, vol. 48
DOI: 10.1016/j.ejrh.2023.101466
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Abdelaty I, Sallam Gah, Pugliese L, Negm Am, Straface S, Scozzari A, Ahmed A
Study region: The coastal aquifer of Nile Delta, Egypt is used to develop the current study. Study focus: Excess water from rice irrigation is a source of incidental recharge to mitigate seawater intrusion. This paper numerically explores the optimal location of rice cultivations by subdividing the delta domain into three distinct recharging regions (north, central and south). Additionally, SEAWAT code was simulated under a combination of rice cultivation relocation and sea level rise (SLR). New hydrological insights for the region: The study findings revealed significant variations in salt volume reduction depending on the location of rice cultivation in the delta. Placing rice cultivation in the northern region resulted in the highest reduction of salt volume (19 %). In contrast, locating the recharge in the central region yielded a salt volume reduction of 0.50 %, while rice cultivation in the southern region produced a 15 % increase. Considering the projected SLR of 61 cm by 2100, there was an overall salt volume increment of 3 %. However, when accounting for both SLR and rice cultivation recharge in the northern region, a substantial salt volume reduction of 17 % was observed. The results demonstrated that incidental recharge by rice cultivation in coastal aquifers is an effective method for enhancing saltwater intrusion control. Moreover, this study improves our understanding of hydrological processes and expected responses in the delta under future climate scenarios.Source: JOURNAL OF HYDROLOGY. REGIONAL STUDIES, vol. 48
DOI: 10.1016/j.ejrh.2023.101466
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2024
Conference article
Open Access
A WEF NEXUS tool for integrating soil moisture and meteorological IoT data
Straface Salvatore, Brunetti Guglielmo Federico Antonio, Maiolo Mario, Brunetti Giuseppe, Scozzari Andrea
According to the European Parliamentary Research Service, agriculture is a major user of ground and surface water in the Mediterranean region. Agriculture accounts for more than 40% of water use in the EU and most freshwater abstraction is for agricultural use. Water applied as irrigation enables crop production in arid regions and replenishes soil moisture in humid regions when rainfall during the growing season is insufficient. It helps to increase crop productivity, but it also poses a threat to the conservation of water resources. The issue of water scarcity therefore requires careful consideration of the trade-off between increased agricultural productivity and the degradation of water resources. Ensuring food security in the face of climate change requires improved water management capacity. Nowadays, the interest in estimating the average soil moisture content (SM) and its variability is a cross-cutting issue in many areas of scientific research in the natural sciences. The water contained and transiting in the vadose zone is involved in and plays a central role in many natural processes related to plant physiology and agriculture, soil microbial activity, groundwater pollution and, more generally, eco-hydrological and bio-geochemical processes. SM depends either on soil characteristics, i.e. hydraulic conductivity, porosity, soil texture, etc., or on meteorological forcing, i.e. precipitation, temperature, evapotranspiration, etc. Knowing the soil characteristics, a numerical model for unsaturated flow (Hydrus-1D) can be calibrated using time-lapse measurements of meteorological forcing and SMs obtained by IoT enabled sensors. After the calibration, the numerical model can generate a very large number of SMs for many meteorological forcings. With these data, a WEF Nexus tool, based on a machine learning approach, integrates the SM and meteorological IoT data to estimate crop water demand. This research aims to develop and test a building block for possible future water demand estimation tools. As a future perspective, further development and integration may lead to new tools with user-friendly interfaces.DOI: 10.5194/egusphere-egu24-8914
Metrics:
Straface Salvatore, Brunetti Guglielmo Federico Antonio, Maiolo Mario, Brunetti Giuseppe, Scozzari Andrea
According to the European Parliamentary Research Service, agriculture is a major user of ground and surface water in the Mediterranean region. Agriculture accounts for more than 40% of water use in the EU and most freshwater abstraction is for agricultural use. Water applied as irrigation enables crop production in arid regions and replenishes soil moisture in humid regions when rainfall during the growing season is insufficient. It helps to increase crop productivity, but it also poses a threat to the conservation of water resources. The issue of water scarcity therefore requires careful consideration of the trade-off between increased agricultural productivity and the degradation of water resources. Ensuring food security in the face of climate change requires improved water management capacity. Nowadays, the interest in estimating the average soil moisture content (SM) and its variability is a cross-cutting issue in many areas of scientific research in the natural sciences. The water contained and transiting in the vadose zone is involved in and plays a central role in many natural processes related to plant physiology and agriculture, soil microbial activity, groundwater pollution and, more generally, eco-hydrological and bio-geochemical processes. SM depends either on soil characteristics, i.e. hydraulic conductivity, porosity, soil texture, etc., or on meteorological forcing, i.e. precipitation, temperature, evapotranspiration, etc. Knowing the soil characteristics, a numerical model for unsaturated flow (Hydrus-1D) can be calibrated using time-lapse measurements of meteorological forcing and SMs obtained by IoT enabled sensors. After the calibration, the numerical model can generate a very large number of SMs for many meteorological forcings. With these data, a WEF Nexus tool, based on a machine learning approach, integrates the SM and meteorological IoT data to estimate crop water demand. This research aims to develop and test a building block for possible future water demand estimation tools. As a future perspective, further development and integration may lead to new tools with user-friendly interfaces.DOI: 10.5194/egusphere-egu24-8914
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2024
Conference article
Open Access
Advancing irrigation strategies: synergistic modeling of soil moisture using cosmic-ray neutron sensing, Hydrus-1D, and machine learning
Straface S., Brunetti G. F. A., Scozzari A.
Innovative monitoring techniques today facilitate advanced and reliable measurements in the vadose zone. This, coupled with the predictive capabilities of machine learning, has an ever-growing impact on the management of agricultural and irrigation practices. The vadose zone, particularly the root zone, plays a pivotal role in hydrological processes by regulating water and energy fluxes across the soil surface. Additionally, it influences nutrient transport, groundwater recharge, groundwater pollution, microbial activity, and plant physiology, as it links the atmosphere, soil, and groundwater. Among various monitoring techniques, Cosmic-Ray Neutron Sensing (CRNS) stands out as a ground-based remote sensing technique capable of measuring soil moisture within the root zone at relevant scales (up to 240 m) with a high level of reliability. It is based on nuclear interactions between incoming cosmic rays and elements in the Earth’s atmosphere, such as hydrogen. By employing the Hydrus-1D Cosmic module, effective soil moisture values can be derived based on the neutron intensity detected by Cosmic-Ray Neutron Probes (CRNPs). On the other hand, machine learning methods and neural networks (NN) hold enormous potential despite inherent limitations, notably the requirement for extensive datasets and their lack of a physical foundation in reproducing soil processes. In this study, we propose a synergistic approach to overcome these limitations. The physically-based Hydrus-1D model was utilized to train a single-layer NN for the direct prediction of soil moisture and irrigation water demand, relying exclusively on atmospheric forcings (temperature and precipitation) as input. In a proof-of-concept aimed at assessing the validity and robustness of our approach, a time series of synthetic data replicating soil characteristics, atmospheric forcings, and field measurements conducted through CRNPs was generated. These data were employed in the Hydrus-1D Cosmic module to calibrate a physically-based model, facilitating the generation of a continuous and extensive spatiotemporal soil moisture output dataset for the simulated synthetic field. The single-layer NN, trained with this synthetic soil moisture and atmospheric forcing data, demonstrated the potential to accurately predict soil moisture and irrigation needs of the terrain straightforwardly, using only atmospheric variables as input. The proposed synergistic approach has exhibited significant potential, and future developments in this research will involve the incorporation of real data.DOI: 10.5194/egusphere-egu24-8793
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Straface S., Brunetti G. F. A., Scozzari A.
Innovative monitoring techniques today facilitate advanced and reliable measurements in the vadose zone. This, coupled with the predictive capabilities of machine learning, has an ever-growing impact on the management of agricultural and irrigation practices. The vadose zone, particularly the root zone, plays a pivotal role in hydrological processes by regulating water and energy fluxes across the soil surface. Additionally, it influences nutrient transport, groundwater recharge, groundwater pollution, microbial activity, and plant physiology, as it links the atmosphere, soil, and groundwater. Among various monitoring techniques, Cosmic-Ray Neutron Sensing (CRNS) stands out as a ground-based remote sensing technique capable of measuring soil moisture within the root zone at relevant scales (up to 240 m) with a high level of reliability. It is based on nuclear interactions between incoming cosmic rays and elements in the Earth’s atmosphere, such as hydrogen. By employing the Hydrus-1D Cosmic module, effective soil moisture values can be derived based on the neutron intensity detected by Cosmic-Ray Neutron Probes (CRNPs). On the other hand, machine learning methods and neural networks (NN) hold enormous potential despite inherent limitations, notably the requirement for extensive datasets and their lack of a physical foundation in reproducing soil processes. In this study, we propose a synergistic approach to overcome these limitations. The physically-based Hydrus-1D model was utilized to train a single-layer NN for the direct prediction of soil moisture and irrigation water demand, relying exclusively on atmospheric forcings (temperature and precipitation) as input. In a proof-of-concept aimed at assessing the validity and robustness of our approach, a time series of synthetic data replicating soil characteristics, atmospheric forcings, and field measurements conducted through CRNPs was generated. These data were employed in the Hydrus-1D Cosmic module to calibrate a physically-based model, facilitating the generation of a continuous and extensive spatiotemporal soil moisture output dataset for the simulated synthetic field. The single-layer NN, trained with this synthetic soil moisture and atmospheric forcing data, demonstrated the potential to accurately predict soil moisture and irrigation needs of the terrain straightforwardly, using only atmospheric variables as input. The proposed synergistic approach has exhibited significant potential, and future developments in this research will involve the incorporation of real data.DOI: 10.5194/egusphere-egu24-8793
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2022
Other
Open Access
SI-Lab annual research report 2021
Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
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Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
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CNR IRIS | ISTI Repository | CNR IRIS