2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) 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 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) 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 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
2025
Conference article
Open Access
Enhancing hydrological models with remote sensing: a review of products, techniques, and uncertainties
Taia S., Ait Brahim Y., Hssaisoune M., Scozzari A., El Mansouri B.
Distributed hydrological models are crucial for flood prediction, drought analysis, and water resource monitoring. They are typically calibrated using streamflow observations at the watershed outflow to determine the best parameter values within their common ranges. These models are then applied to analyze management and climate scenarios. However, accurately representing hydrological complexities is challenging due to limited knowledge, data availability, and imprecise measurements. Uncertainties in these models arise from parameters, model structure, calibration processes, and data, especially in regions with scarce data. Consequently, hydrological models require extensive hydro-meteorological data for calibration and validation, which can be costly and time-consuming. Recently, remote sensing techniques advanced hydrological modeling by providing regular sampling of essential variables like precipitation, soil moisture, and evapotranspiration. However, thanks to technological advancements, numerous global and regional remote seeing products for the same variable have become freely available. These products vary in their algorithms, approaches, spatial and temporal resolutions, leading to diverse datasets for the same variable. Therefore, different products can perform differently in terms of parameter estimation, model robustness, and water balance predictions within the same area. However, each product may introduce biases or uncertainties, necessitating modelers to assess their performance and carefully choose the most suitable product for their study objectives. This research reviews commonly used remotely sensed products and the techniques and approaches for integrating them into distributed and semi-distributed hydrological models. Additionally, this review examines the uncertainties associated with different existing products and their performance within hydrological models.DOI: 10.5194/egusphere-egu25-19593
Metrics:
Taia S., Ait Brahim Y., Hssaisoune M., Scozzari A., El Mansouri B.
Distributed hydrological models are crucial for flood prediction, drought analysis, and water resource monitoring. They are typically calibrated using streamflow observations at the watershed outflow to determine the best parameter values within their common ranges. These models are then applied to analyze management and climate scenarios. However, accurately representing hydrological complexities is challenging due to limited knowledge, data availability, and imprecise measurements. Uncertainties in these models arise from parameters, model structure, calibration processes, and data, especially in regions with scarce data. Consequently, hydrological models require extensive hydro-meteorological data for calibration and validation, which can be costly and time-consuming. Recently, remote sensing techniques advanced hydrological modeling by providing regular sampling of essential variables like precipitation, soil moisture, and evapotranspiration. However, thanks to technological advancements, numerous global and regional remote seeing products for the same variable have become freely available. These products vary in their algorithms, approaches, spatial and temporal resolutions, leading to diverse datasets for the same variable. Therefore, different products can perform differently in terms of parameter estimation, model robustness, and water balance predictions within the same area. However, each product may introduce biases or uncertainties, necessitating modelers to assess their performance and carefully choose the most suitable product for their study objectives. This research reviews commonly used remotely sensed products and the techniques and approaches for integrating them into distributed and semi-distributed hydrological models. Additionally, this review examines the uncertainties associated with different existing products and their performance within hydrological models.DOI: 10.5194/egusphere-egu25-19593
Metrics:
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CNR IRIS 
| meetingorganizer.copernicus.org | doi.org 
| CNR IRIS
2025
Conference article
Open Access
Using remote sensing and geographic information system for delineating suitable sites for artificial groundwater recharge: a multi-criteria decision-making approach
Fri R., Scozzari A., Haida S., Kili M., Erraoui L., Chaou J., Mridekh A., Goumghar L., El Mansouri B.
The semi-arid region of Deraa Oued Noun in Morocco faces significant challenges related to water scarcity, which greatly affects the availability of groundwater resources. With recurring droughts and periods of water shortage, it is imperative to address these challenges and implement effective measures for sustainable groundwater resource management. Artificial groundwater recharge has proven to be a viable solution for alleviating water scarcity issues. By capturing and storing excess water during periods of heavy precipitation or surface water availability, artificial recharge can replenish depleted aquifers and provide a reliable water source during drought periods. However, the success of recharge projects depends on identifying suitable sites that meet specific criteria and maximize the efficiency of the recharge process. The identification of suitable sites for artificial groundwater recharge in Daraa Oued Noun, through the integration of remote sensing, GIS (Geographic Information System), and MCDM (Multi-Criteria Decision Making) techniques, offers a promising solution to address water scarcity challenges in the context of climate change. The proposed research project aims to provide valuable and spatially explicit information for strategic groundwater resource management. This study was conducted in the Deraa Oued Noun district, where water shortages have been observed over the years. The research utilized geology, soil, land use, stream data, and Sentinel-2 and DEM images to develop thematic layers, including lithogeology, soil, slope, lineament density, land use, stream density, and water surface. Additionally, data on the vadose zone thickness were incorporated to enhance the analysis. By integrating GIS and image processing techniques, these thematic layers were utilized to prepare groundwater recharge maps of the area through a weighted overlay method on a GIS platform. The results revealed that artificial recharge potential was high in the northern and western parts of the study area. By following a systematic and rigorous methodology, including data collection, remote sensing analysis, MCDM evaluation, and site validation, this project aims to contribute to the successful implementation of artificial recharge projects in the region. By maximizing the efficiency of the recharge method, these projects will help ensure sustainable water supply, mitigate the impacts of drought, and promote long-term water security in Derâa Oued Noun and similar semi-arid regions.DOI: 10.5194/egusphere-egu25-20324
Metrics:
Fri R., Scozzari A., Haida S., Kili M., Erraoui L., Chaou J., Mridekh A., Goumghar L., El Mansouri B.
The semi-arid region of Deraa Oued Noun in Morocco faces significant challenges related to water scarcity, which greatly affects the availability of groundwater resources. With recurring droughts and periods of water shortage, it is imperative to address these challenges and implement effective measures for sustainable groundwater resource management. Artificial groundwater recharge has proven to be a viable solution for alleviating water scarcity issues. By capturing and storing excess water during periods of heavy precipitation or surface water availability, artificial recharge can replenish depleted aquifers and provide a reliable water source during drought periods. However, the success of recharge projects depends on identifying suitable sites that meet specific criteria and maximize the efficiency of the recharge process. The identification of suitable sites for artificial groundwater recharge in Daraa Oued Noun, through the integration of remote sensing, GIS (Geographic Information System), and MCDM (Multi-Criteria Decision Making) techniques, offers a promising solution to address water scarcity challenges in the context of climate change. The proposed research project aims to provide valuable and spatially explicit information for strategic groundwater resource management. This study was conducted in the Deraa Oued Noun district, where water shortages have been observed over the years. The research utilized geology, soil, land use, stream data, and Sentinel-2 and DEM images to develop thematic layers, including lithogeology, soil, slope, lineament density, land use, stream density, and water surface. Additionally, data on the vadose zone thickness were incorporated to enhance the analysis. By integrating GIS and image processing techniques, these thematic layers were utilized to prepare groundwater recharge maps of the area through a weighted overlay method on a GIS platform. The results revealed that artificial recharge potential was high in the northern and western parts of the study area. By following a systematic and rigorous methodology, including data collection, remote sensing analysis, MCDM evaluation, and site validation, this project aims to contribute to the successful implementation of artificial recharge projects in the region. By maximizing the efficiency of the recharge method, these projects will help ensure sustainable water supply, mitigate the impacts of drought, and promote long-term water security in Derâa Oued Noun and similar semi-arid regions.DOI: 10.5194/egusphere-egu25-20324
Metrics:
See at:
CNR IRIS | meetingorganizer.copernicus.org | doi.org | CNR IRIS
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
Metrics:
2024
Conference article
Open Access
Advancing sustainability: research initiatives at the Signals and Images Lab
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
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ceur-ws.org | CNR IRIS | CNR IRIS
2024
Other
Open Access
A comprehensive system supporting sustainable agricultural production from farm to fork
Carboni A., Galesi G., Ignesti G., Leone G. R., Magrini M., Martinelli M., Martino G., Moroni D., Pardini F., Scozzari A.
Poster presented at ISTI Day 2023-2024 edition on June 14 2024.DOI: 10.5281/zenodo.12168200
Metrics:
Carboni A., Galesi G., Ignesti G., Leone G. R., Magrini M., Martinelli M., Martino G., Moroni D., Pardini F., Scozzari A.
Poster presented at ISTI Day 2023-2024 edition on June 14 2024.DOI: 10.5281/zenodo.12168200
Metrics:
2024
Conference article
Open Access
Leveraging remotely sensed evapotranspiration and soil moisture data for enhanced watershed modelling with the SWAT model
Taia S., Scozzari A., Erraioui L., Chao J., El Mansouri B.
In data-scarce watersheds, hydrological models are often calibrated by using only streamflow observations. This approach may overlook crucial landscape characteristics, which, instead, may significantly affect the runoff. This study explores the integration of parameters derived from remotely sensed data, focusing on evapotranspiration, soil moisture, and runoff, to enhance the overall accuracy of the Soil and Water Assessment Tool (SWAT) model. Four calibration scenarios were implemented: S1 (streamflow only), S2 (streamflow and evapotranspiration), S3 (streamflow and soil moisture), and S4 (all variables). Results showed that S2 achieved high scores for streamflow, outperforming S1, with slight improvements observed in some cases. However, scenarios incorporating root zone soil moisture (S3 and S4) negatively impacted the streamflow estimates. Nevertheless, S2 exhibited slightly better evapotranspiration simulation, while S3 and S4 improved soil moisture representation. Hydrograph comparisons highlighted satisfactory streamflow simulations in S1 and S2, while S3 and S4 overestimated flow peaks. The results of this investigation show that embedding remotely sensed data in the SWAT model, particularly evapotranspiration and soil moisture, may not necessarily improve runoff estimations, thus a careful analysis is required to determine the role of these parameters. In fact, these parameters play a pivotal role in enabling hydrological models to achieve a more comprehensive and accurate representation of the water balance within a watershed.DOI: 10.5194/egusphere-egu24-9363
Metrics:
Taia S., Scozzari A., Erraioui L., Chao J., El Mansouri B.
In data-scarce watersheds, hydrological models are often calibrated by using only streamflow observations. This approach may overlook crucial landscape characteristics, which, instead, may significantly affect the runoff. This study explores the integration of parameters derived from remotely sensed data, focusing on evapotranspiration, soil moisture, and runoff, to enhance the overall accuracy of the Soil and Water Assessment Tool (SWAT) model. Four calibration scenarios were implemented: S1 (streamflow only), S2 (streamflow and evapotranspiration), S3 (streamflow and soil moisture), and S4 (all variables). Results showed that S2 achieved high scores for streamflow, outperforming S1, with slight improvements observed in some cases. However, scenarios incorporating root zone soil moisture (S3 and S4) negatively impacted the streamflow estimates. Nevertheless, S2 exhibited slightly better evapotranspiration simulation, while S3 and S4 improved soil moisture representation. Hydrograph comparisons highlighted satisfactory streamflow simulations in S1 and S2, while S3 and S4 overestimated flow peaks. The results of this investigation show that embedding remotely sensed data in the SWAT model, particularly evapotranspiration and soil moisture, may not necessarily improve runoff estimations, thus a careful analysis is required to determine the role of these parameters. In fact, these parameters play a pivotal role in enabling hydrological models to achieve a more comprehensive and accurate representation of the water balance within a watershed.DOI: 10.5194/egusphere-egu24-9363
Metrics:
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
Metrics:
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
Metrics:
2023
Journal article
Restricted
Shallow portion of an active geothermal system revealed by multidisciplinary studies: the case of Le Biancane (Larderello, Italy)
Granieri D, Mazzarini F, Cerminara M, Calusi B, Scozzari A, Menichini M, Lelli M
The natural park of Le Biancane is located in the southern sector of the Larderello-Travale geothermal field (LTGF). It extends over an approximately 100,000 m2 area where the impermeable caprock is locally absent and deep fluids may directly reach the surface. Through a multidisciplinary approach including measurements of soil CO2 flux (total output of 11.5 t day-1), soil temperature (average 34.4 °C), stable isotope and chemical data on fluids from fumaroles (dominated by a mixture of geothermal gases and air or gases from air-saturated meteoric water), and structural analysis of the formation outcropping, we found that anomalous CO2 emissions are positively correlated with shallow temperature anomalies. These are in restricted locations adjacent to vents and fumaroles, where a network of well-connected fractures (preferentially NW-SE and NE-SW orientated and with steep dips) drains efficiently allowing upward migration of the deep fluids and the energy toward the surface.Source: GEOTHERMICS, vol. 108
DOI: 10.1016/j.geothermics.2022.102616
Metrics:
Granieri D, Mazzarini F, Cerminara M, Calusi B, Scozzari A, Menichini M, Lelli M
The natural park of Le Biancane is located in the southern sector of the Larderello-Travale geothermal field (LTGF). It extends over an approximately 100,000 m2 area where the impermeable caprock is locally absent and deep fluids may directly reach the surface. Through a multidisciplinary approach including measurements of soil CO2 flux (total output of 11.5 t day-1), soil temperature (average 34.4 °C), stable isotope and chemical data on fluids from fumaroles (dominated by a mixture of geothermal gases and air or gases from air-saturated meteoric water), and structural analysis of the formation outcropping, we found that anomalous CO2 emissions are positively correlated with shallow temperature anomalies. These are in restricted locations adjacent to vents and fumaroles, where a network of well-connected fractures (preferentially NW-SE and NE-SW orientated and with steep dips) drains efficiently allowing upward migration of the deep fluids and the energy toward the surface.Source: GEOTHERMICS, vol. 108
DOI: 10.1016/j.geothermics.2022.102616
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CNR IRIS | CNR IRIS | CNR IRIS | www.sciencedirect.com
2023
Journal article
Open Access
The application of SWAT model and remotely sensed products to characterize the dynamic of streamflow and snow in a mountainous watershed in the High Atlas
Taia S, Erraioui L, Arjdal Y, Chao J, El Mansouri B, Scozzari A
Snowfall, snowpack, and snowmelt are among the processes with the greatest influence on the water cycle in mountainous watersheds. Hydrological models may be significantly biased if snow estimations are inaccurate. However, the unavailability of in situ snow data with enough spatiotemporal resolution limits the application of spatially distributed models in snow-fed watersheds. This obliges numerous modellers to reduce their attention to the snowpack and its effect on water distribution, particularly when a portion of the watershed is predominately covered by snow. This research demonstrates the added value of remotely sensed snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) in evaluating the performance of hydrological models to estimate seasonal snow dynamics and discharge. The Soil and Water Assessment Tool (SWAT) model was used in this work to simulate discharge and snow processes in the Oued El Abid snow-dominated watershed. The model was calibrated and validated on a daily basis, for a long period (1981-2015), using four discharge-gauging stations. A spatially varied approach (snow parameters are varied spatially) and a lumped approach (snow parameters are unique across the whole watershed) have been compared. Remote sensing data provided by MODIS enabled the evaluation of the snow processes simulated by the SWAT model. Results illustrate that SWAT model discharge simulations were satisfactory to good according to the statistical criteria. In addition, the model was able to reasonably estimate the snow-covered area when comparing it to the MODIS daily snow cover product. When allowing snow parameters to vary spatially, SWAT model results were more consistent with the observed streamflow and the MODIS snow-covered area (MODIS-SCA). This paper provides an example of how hydrological modelling using SWAT and snow coverage products by remote sensing may be used together to examine seasonal snow cover and snow dynamics in the High Atlas watershed.Source: SENSORS (BASEL), vol. 23 (issue 3)
DOI: 10.3390/s23031246
Metrics:
Taia S, Erraioui L, Arjdal Y, Chao J, El Mansouri B, Scozzari A
Snowfall, snowpack, and snowmelt are among the processes with the greatest influence on the water cycle in mountainous watersheds. Hydrological models may be significantly biased if snow estimations are inaccurate. However, the unavailability of in situ snow data with enough spatiotemporal resolution limits the application of spatially distributed models in snow-fed watersheds. This obliges numerous modellers to reduce their attention to the snowpack and its effect on water distribution, particularly when a portion of the watershed is predominately covered by snow. This research demonstrates the added value of remotely sensed snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) in evaluating the performance of hydrological models to estimate seasonal snow dynamics and discharge. The Soil and Water Assessment Tool (SWAT) model was used in this work to simulate discharge and snow processes in the Oued El Abid snow-dominated watershed. The model was calibrated and validated on a daily basis, for a long period (1981-2015), using four discharge-gauging stations. A spatially varied approach (snow parameters are varied spatially) and a lumped approach (snow parameters are unique across the whole watershed) have been compared. Remote sensing data provided by MODIS enabled the evaluation of the snow processes simulated by the SWAT model. Results illustrate that SWAT model discharge simulations were satisfactory to good according to the statistical criteria. In addition, the model was able to reasonably estimate the snow-covered area when comparing it to the MODIS daily snow cover product. When allowing snow parameters to vary spatially, SWAT model results were more consistent with the observed streamflow and the MODIS snow-covered area (MODIS-SCA). This paper provides an example of how hydrological modelling using SWAT and snow coverage products by remote sensing may be used together to examine seasonal snow cover and snow dynamics in the High Atlas watershed.Source: SENSORS (BASEL), vol. 23 (issue 3)
DOI: 10.3390/s23031246
Metrics:
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CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2023
Conference article
Open Access
Radar altimetry for the detection of ship traffic: an improved byproduct of satellite radar altimetry
Scozzari A, Grasso R
The detection of vessels is considered an attractive byproduct of satellite radar altimetry, because it may complement the conventional tracking systems with the possibility to build long-term global statistics of ship traffic based on relatively small and manageable datasets of freely available data. Satellite radar altimetry was initially conceived and applied to the observation of ocean topography, being later extended to the coastal zone and to the observation of inland water. The potentiality of SAR altimetry for the detection of ships has already been demonstrated with Cryosat2, and today Sentinel-3 is the first operational mission offering global SAR coverage with a constellation of two satellites. Thanks to the enhanced azimuth (along-track) resolution available in the synthetic aperture radar (SAR) mode, the radar altimeter on board the Sentinel-3 satellite could be beneficial to other applications than ocean topography. In particular, this work studies the performance of algorithms for the automatic detection of ship targets from SAR mode data. In addition, the pre-processing of altimeter data by reliable detection algorithms, filtering out signal outliers from the sea surface response, largely contributes to enhance geophysical products that are typical in ocean topography studies (e.g. mean sea level). Thus, altimeter data of today could be regarded as an additional non-cooperative source for vessel traffic monitoring or to map global traffic patterns over long periods of time. This work proposes a processing chain based on mathematical morphology filtering and robust statistics to estimate the structured background and detect target signatures from radargrams. The detection stage is followed by an additional binary morphological filtering phase that is useful to estimate target characteristics, such as the height. The study shows that robust statistics outperform non-robust ones, in terms of target signal to background ratio and of rejection of false alarms. The study finally provides a first attempt to validate the analysis comparing detected target contacts with automatic identification system (AIS) data.DOI: 10.5194/egusphere-egu23-13618
Metrics:
Scozzari A, Grasso R
The detection of vessels is considered an attractive byproduct of satellite radar altimetry, because it may complement the conventional tracking systems with the possibility to build long-term global statistics of ship traffic based on relatively small and manageable datasets of freely available data. Satellite radar altimetry was initially conceived and applied to the observation of ocean topography, being later extended to the coastal zone and to the observation of inland water. The potentiality of SAR altimetry for the detection of ships has already been demonstrated with Cryosat2, and today Sentinel-3 is the first operational mission offering global SAR coverage with a constellation of two satellites. Thanks to the enhanced azimuth (along-track) resolution available in the synthetic aperture radar (SAR) mode, the radar altimeter on board the Sentinel-3 satellite could be beneficial to other applications than ocean topography. In particular, this work studies the performance of algorithms for the automatic detection of ship targets from SAR mode data. In addition, the pre-processing of altimeter data by reliable detection algorithms, filtering out signal outliers from the sea surface response, largely contributes to enhance geophysical products that are typical in ocean topography studies (e.g. mean sea level). Thus, altimeter data of today could be regarded as an additional non-cooperative source for vessel traffic monitoring or to map global traffic patterns over long periods of time. This work proposes a processing chain based on mathematical morphology filtering and robust statistics to estimate the structured background and detect target signatures from radargrams. The detection stage is followed by an additional binary morphological filtering phase that is useful to estimate target characteristics, such as the height. The study shows that robust statistics outperform non-robust ones, in terms of target signal to background ratio and of rejection of false alarms. The study finally provides a first attempt to validate the analysis comparing detected target contacts with automatic identification system (AIS) data.DOI: 10.5194/egusphere-egu23-13618
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2023
Conference article
Open Access
Applicability of remote sensing evapotranspiration products in reducing uncertainty and equifinality in hydrological model calibration of Oued El Abid watershed
Taia S, Erraioui L, Chao J, El Mansouri B, Scozzari A
Typically, hydrological models are calibrated using observed streamflow at the outlet of the watershed. This approach may fail to mimic landscape characteristics, which significantly impact runoff generation because the streamflow incorporates contributions from several hydrological components. However, remotely sensed evapotranspiration (AET) products are commonly used as additional data with streamflow to better constrain model parameters. Several researchers demonstrated the efficacy of AET products in reducing the degree of equifinality and predictive uncertainty, resulting in a significant enhancement in hydrological modelling. Due to the variety of publicly available AET datasets, which vary in their methods, parameterization, and spatiotemporal resolution, selecting an appropriate AET for hydrological modelling is of great importance. The purpose of this study is to investigate the difference in simulated hydrologic responses resulting from the inclusion of different remotely sensed AET products in a single and multi-objective calibration with observed streamflow data. The GLEAM_3.6a, GLEAM_3.6b, MOD16A2, GLDAS, PML_V2, TerraClimate, FLDAS, and SSEBop datasets were downloaded and incorporated into the calibration of the SWAT hydrological model. The findings indicate that the incorporation of remotely sensed AET data in multi-objective calibration tends to improve model performance and decrease predictive uncertainty, as well as significantly improves parameter identification. Furthermore, AET single-variable calibration results show that the model would have performed well in simulating streamflow even without streamflow data. Moreover, each dataset included in this investigation responded differently. GLEAM_3.6b and GLEAM_3.6a performed the best, followed by FLDAS and PML_V2, while MOD16A2 was the least performing dataset. Thus, this research supports the use of remotely sensed AET in the calibration of hydrological models as a best practice.DOI: 10.5194/egusphere-egu23-9701
Metrics:
Taia S, Erraioui L, Chao J, El Mansouri B, Scozzari A
Typically, hydrological models are calibrated using observed streamflow at the outlet of the watershed. This approach may fail to mimic landscape characteristics, which significantly impact runoff generation because the streamflow incorporates contributions from several hydrological components. However, remotely sensed evapotranspiration (AET) products are commonly used as additional data with streamflow to better constrain model parameters. Several researchers demonstrated the efficacy of AET products in reducing the degree of equifinality and predictive uncertainty, resulting in a significant enhancement in hydrological modelling. Due to the variety of publicly available AET datasets, which vary in their methods, parameterization, and spatiotemporal resolution, selecting an appropriate AET for hydrological modelling is of great importance. The purpose of this study is to investigate the difference in simulated hydrologic responses resulting from the inclusion of different remotely sensed AET products in a single and multi-objective calibration with observed streamflow data. The GLEAM_3.6a, GLEAM_3.6b, MOD16A2, GLDAS, PML_V2, TerraClimate, FLDAS, and SSEBop datasets were downloaded and incorporated into the calibration of the SWAT hydrological model. The findings indicate that the incorporation of remotely sensed AET data in multi-objective calibration tends to improve model performance and decrease predictive uncertainty, as well as significantly improves parameter identification. Furthermore, AET single-variable calibration results show that the model would have performed well in simulating streamflow even without streamflow data. Moreover, each dataset included in this investigation responded differently. GLEAM_3.6b and GLEAM_3.6a performed the best, followed by FLDAS and PML_V2, while MOD16A2 was the least performing dataset. Thus, this research supports the use of remotely sensed AET in the calibration of hydrological models as a best practice.DOI: 10.5194/egusphere-egu23-9701
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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
Metrics:
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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2023
Journal article
Restricted
Reply to the comment by Bolognesi
Granieri D, Lelli M, Mazzarini F, Cerminara M, Calusi B, Scozzari A, Menichini M
Abstract not availableSource: GEOTHERMICS, vol. 113 (issue 102754)
DOI: 10.1016/j.geothermics.2023.102754
Metrics:
Granieri D, Lelli M, Mazzarini F, Cerminara M, Calusi B, Scozzari A, Menichini M
Abstract not availableSource: GEOTHERMICS, vol. 113 (issue 102754)
DOI: 10.1016/j.geothermics.2023.102754
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2023
Journal article
Open Access
Comparing the ability of different remotely sensed evapotranspiration products in enhancing hydrological model performance and reducing prediction uncertainty
Taia S, Scozzari A, Erraioui L, Kili M, Mridekh A, Haida S, Chao J, El Mansouri B
The mitigation of uncertainties in the identification of natural systems is a fundamental aspect in the development of hydrological models, and represents a major challenge for the improvement of modelling techniques. In particular, the calibration of hydrological models based on streamflow measurements at the outlet of a catchment is exposed to significant sources of uncertainty, such as the impact of landscape features on runoff generation. Remote sensing-based actual evapotranspiration (AET) data can be incorporated with streamflow to improve model accuracy and reduce the uncertainty in hydrological modelling, resulting in a significant enhancement of the model performance. The selection of the right AET dataset for hydrological modelling is a crucial task, in front of the availability of multi-source datasets that differ in methods, parameters, and spatiotemporal resolution. Despite the existence of a few studies proposing the usage of remote sensing-based AET data, there is a lack of systematic comparisons between different products, in terms of performance for hydrological modelling. This paper aims to compare the efficacy of different remote sensing-based AET products in improving the simulation of hydrological responses, both in single and in multi-variable scenarios. In this investigation, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated with observed streamflow data by experimenting with eight different AET datasets. The findings of our study suggest that the incorporation of remote sensing-based AET data in the calibration process of a hydrological model can significantly enhance the accuracy and reliability of model predictions. Thus, the proposed approach can contribute to improving the effectiveness of hydrological modelling as a quantitative tool for the management of water resources. Another finding of this study is that the calibration of the model based solely on AET yields reasonable simulation results of the streamflow, which is an advantageous and promising feature for ungauged basins.Source: ECOLOGICAL INFORMATICS, vol. 78 (issue 102352)
DOI: 10.1016/j.ecoinf.2023.102352
Metrics:
Taia S, Scozzari A, Erraioui L, Kili M, Mridekh A, Haida S, Chao J, El Mansouri B
The mitigation of uncertainties in the identification of natural systems is a fundamental aspect in the development of hydrological models, and represents a major challenge for the improvement of modelling techniques. In particular, the calibration of hydrological models based on streamflow measurements at the outlet of a catchment is exposed to significant sources of uncertainty, such as the impact of landscape features on runoff generation. Remote sensing-based actual evapotranspiration (AET) data can be incorporated with streamflow to improve model accuracy and reduce the uncertainty in hydrological modelling, resulting in a significant enhancement of the model performance. The selection of the right AET dataset for hydrological modelling is a crucial task, in front of the availability of multi-source datasets that differ in methods, parameters, and spatiotemporal resolution. Despite the existence of a few studies proposing the usage of remote sensing-based AET data, there is a lack of systematic comparisons between different products, in terms of performance for hydrological modelling. This paper aims to compare the efficacy of different remote sensing-based AET products in improving the simulation of hydrological responses, both in single and in multi-variable scenarios. In this investigation, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated with observed streamflow data by experimenting with eight different AET datasets. The findings of our study suggest that the incorporation of remote sensing-based AET data in the calibration process of a hydrological model can significantly enhance the accuracy and reliability of model predictions. Thus, the proposed approach can contribute to improving the effectiveness of hydrological modelling as a quantitative tool for the management of water resources. Another finding of this study is that the calibration of the model based solely on AET yields reasonable simulation results of the streamflow, which is an advantageous and promising feature for ungauged basins.Source: ECOLOGICAL INFORMATICS, vol. 78 (issue 102352)
DOI: 10.1016/j.ecoinf.2023.102352
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2022
Journal article
Open Access
Groundwater modeling with process-based and data-driven approaches in the context of climate change
Menichini M, Franceschi L, Raco B, Masetti G, Scozzari A, Doveri M
In the context of climate change, the correct management of groundwater, which is strategic for meeting water needs, becomes essential. Groundwater modeling is particularly crucial for the sustainable and efficient management of groundwater. This manuscript provides different types of modeling according to data availability and features of three porous aquifer systems in Italy (Empoli, Magra, and Brenta systems). The models calibrated on robust time series enabled the performing of forecast simulations capable of representing the quantitative and qualitative response to expected climate regimes. For the Empoli aquifer, the process-based models highlighted the system's ability to mitigate the effects of dry climate conditions thanks to its storage capability. The data-driven models concerning the Brenta foothill aquifer pointed out the high sensitivity of the system to climate extremes, thus suggesting the need for specific water management actions. The integrated datadriven/process-based approach developed for the Magra Valley aquifer remarked that the water quantity and quality effects are tied to certain boundary conditions over dry climate periods. This work shows that, for groundwater modeling, the choice of the suitable approach is mandatory, and it mainly depends on the specific aquifer features that result in different ways to be sensitive to climate. This manuscript also provides a novel outcome involving the integrated approach wherein it is a very efficient tool for forecasting modeling when boundary conditions, which significantly affect the behavior of such systems, are subjected to evolve under expected climate scenarios.Source: WATER, vol. 14 (issue 23)
DOI: 10.3390/w14233956
Metrics:
Menichini M, Franceschi L, Raco B, Masetti G, Scozzari A, Doveri M
In the context of climate change, the correct management of groundwater, which is strategic for meeting water needs, becomes essential. Groundwater modeling is particularly crucial for the sustainable and efficient management of groundwater. This manuscript provides different types of modeling according to data availability and features of three porous aquifer systems in Italy (Empoli, Magra, and Brenta systems). The models calibrated on robust time series enabled the performing of forecast simulations capable of representing the quantitative and qualitative response to expected climate regimes. For the Empoli aquifer, the process-based models highlighted the system's ability to mitigate the effects of dry climate conditions thanks to its storage capability. The data-driven models concerning the Brenta foothill aquifer pointed out the high sensitivity of the system to climate extremes, thus suggesting the need for specific water management actions. The integrated datadriven/process-based approach developed for the Magra Valley aquifer remarked that the water quantity and quality effects are tied to certain boundary conditions over dry climate periods. This work shows that, for groundwater modeling, the choice of the suitable approach is mandatory, and it mainly depends on the specific aquifer features that result in different ways to be sensitive to climate. This manuscript also provides a novel outcome involving the integrated approach wherein it is a very efficient tool for forecasting modeling when boundary conditions, which significantly affect the behavior of such systems, are subjected to evolve under expected climate scenarios.Source: WATER, vol. 14 (issue 23)
DOI: 10.3390/w14233956
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2022
Journal article
Open Access
Bathymetric and capacity relationships based on Sentinel-3 mission data for Aswan High Dam Lake, Egypt
Hossen H, Khairy M, Ghaly S, Scozzari A, Negm A, Elsahabi M
Aswan High Dam Lake (AHDL) is one of the most relevant hot spots at both local and global levels after construction of the Grand Ethiopian Renaissance Dam (GERD) was announced. The management of AHDL is a vital task, which requires the input of reliable information such as the lake bathymetry, water level, and the water surface area. Traditional, bathymetric methods are still very expensive and difficult to operate. Nowadays, satellite data and remote sensing techniques are easily accessible. In particular, datasets produced by operational missions are freely and globally available, and may provide efficient and inexpensive solutions for the retrieval of quantitative parameters concerning strategic water bodies, such as AHDL. This work identifies the performance of Sentinel-3A optical imagery data in the visible and NIR bands from the two optical instruments SLSTR and OLCI, and proposes the integration with Sentinel-3A radar altimetry from SRAL instrument applied to AHDL. This preliminary and first study investigated the relationship between the reflectance data and in situ data for water depth after a bathymetric campaign in the deep-water region using statistical regression models. These statistical models showed promising results in terms of correlation value (R > 0.8) and normalized root mean square errors (NRMSE < 0.4). Also, Heron's formula was applied to combine optical imagery and Sentinel-3 altimetry water level datasets to estimate water storage variations in AHDL. In addition, equations governing the relationship between water level, water surface area, and water volume were analyzed. The work is very useful for all authorities and stakeholders dealing with large water bodies.Source: WATER, vol. 14 (issue 711)
DOI: 10.3390/w14050711
Metrics:
Hossen H, Khairy M, Ghaly S, Scozzari A, Negm A, Elsahabi M
Aswan High Dam Lake (AHDL) is one of the most relevant hot spots at both local and global levels after construction of the Grand Ethiopian Renaissance Dam (GERD) was announced. The management of AHDL is a vital task, which requires the input of reliable information such as the lake bathymetry, water level, and the water surface area. Traditional, bathymetric methods are still very expensive and difficult to operate. Nowadays, satellite data and remote sensing techniques are easily accessible. In particular, datasets produced by operational missions are freely and globally available, and may provide efficient and inexpensive solutions for the retrieval of quantitative parameters concerning strategic water bodies, such as AHDL. This work identifies the performance of Sentinel-3A optical imagery data in the visible and NIR bands from the two optical instruments SLSTR and OLCI, and proposes the integration with Sentinel-3A radar altimetry from SRAL instrument applied to AHDL. This preliminary and first study investigated the relationship between the reflectance data and in situ data for water depth after a bathymetric campaign in the deep-water region using statistical regression models. These statistical models showed promising results in terms of correlation value (R > 0.8) and normalized root mean square errors (NRMSE < 0.4). Also, Heron's formula was applied to combine optical imagery and Sentinel-3 altimetry water level datasets to estimate water storage variations in AHDL. In addition, equations governing the relationship between water level, water surface area, and water volume were analyzed. The work is very useful for all authorities and stakeholders dealing with large water bodies.Source: WATER, vol. 14 (issue 711)
DOI: 10.3390/w14050711
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2022
Conference article
Open Access
Seepage loss from unlined, lined, and cracked-lined canals: a case study of Ismailia canal reach from 28.00-49.00 Km, Egypt
Elkamhawy E, Zelenakova M, Straface S, Vranayová Z, Negm Am, Scozzari A, Abdelaty I
Water resources face global and local challenges. In Egypt, for example, the negative impacts of climatic changes and the Grand Ethiopian Renaissance Dam (GERD), cause a shortage of water resources. Shortage of water resources is considered an urgent issue particularly in semiarid regions (like many MENA countries) and arid ones (like Egypt). Therefore, the Egyptian Ministry of Water Resources and Irrigation has launched the national project of canals rehabilitation and lining for effective water resource management and decreasing seepage losses. This study dealt with three different lining techniques, as well cracked-liner for the Ismailia canal, which is considered the largest end of the Nile in Egypt. A steady-state 2-D seep/w model was established for the Ismailia canal section, at the stretch from 28 to 49 km. The results showed that the amount of seepage was considerably depending on the hydraulic characteristics of the lining material. Pumping from aquifers through wells also has a significant influence on the seepage rate from the unlined canal. Nevertheless, a negligible effect was present in the lined canal case. The highest efficiency was obtained with the concrete liner, after that the geomembrane liner, and then the bentonite liner; with nearly 99%, 96%, and 54%, respectively, in the case of no pumping from aquifer via wells. The efficiency decreased by 4% for the bentonite and geomembrane liners during pumping from the aquifer, but the concrete liner efficiency did not change significantly. However, in the case of deterioration of the lining material through cracks, the efficiency strictly decreased to 25%, irrespective of the utilized lining technique. The dual effect of both cracked-liner material and extraction from the aquifer via pumping wells revealed an efficiency of 16%, regardless of the utilized liner type.DOI: 10.5194/egusphere-egu22-13134
DOI: https://doi.org/10.5194/egusphere-egu22-13134
Metrics:
Elkamhawy E, Zelenakova M, Straface S, Vranayová Z, Negm Am, Scozzari A, Abdelaty I
Water resources face global and local challenges. In Egypt, for example, the negative impacts of climatic changes and the Grand Ethiopian Renaissance Dam (GERD), cause a shortage of water resources. Shortage of water resources is considered an urgent issue particularly in semiarid regions (like many MENA countries) and arid ones (like Egypt). Therefore, the Egyptian Ministry of Water Resources and Irrigation has launched the national project of canals rehabilitation and lining for effective water resource management and decreasing seepage losses. This study dealt with three different lining techniques, as well cracked-liner for the Ismailia canal, which is considered the largest end of the Nile in Egypt. A steady-state 2-D seep/w model was established for the Ismailia canal section, at the stretch from 28 to 49 km. The results showed that the amount of seepage was considerably depending on the hydraulic characteristics of the lining material. Pumping from aquifers through wells also has a significant influence on the seepage rate from the unlined canal. Nevertheless, a negligible effect was present in the lined canal case. The highest efficiency was obtained with the concrete liner, after that the geomembrane liner, and then the bentonite liner; with nearly 99%, 96%, and 54%, respectively, in the case of no pumping from aquifer via wells. The efficiency decreased by 4% for the bentonite and geomembrane liners during pumping from the aquifer, but the concrete liner efficiency did not change significantly. However, in the case of deterioration of the lining material through cracks, the efficiency strictly decreased to 25%, irrespective of the utilized lining technique. The dual effect of both cracked-liner material and extraction from the aquifer via pumping wells revealed an efficiency of 16%, regardless of the utilized liner type.DOI: 10.5194/egusphere-egu22-13134
DOI: https://doi.org/10.5194/egusphere-egu22-13134
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2022
Conference article
Open Access
Integration of multiple geoscientific investigation methods for a better understanding of a water system: the example of Chimborazo glaciers melting effects on the Chambo aquifer, Ecuador
Scozzari A, Catelan P, Chidichimo F, De Biase M, Mendoza Bg, Trujillo Pa, Carrettero P, Straface S
The identification of the processes underlining natural systems often requires the adoption of multiple investigation techniques for the assessment of the sites under study. In this work, the combination of information derived from non-invasive sensing techniques, such as geophysics, remote sensing and hydrogeochemistry, highlights the possible influence of global climate change on the future water availability related to an aquifer in a peculiar glacier context, located in central Ecuador. In particular, we show that the Chambo aquifer, which supplies potable water to the region, does not contain fossil water, and it's instead recharged over time. Indeed, the whole Chambo river basin is affected by the Chimborazo volcano, which is a glacerised mountain located in the inner tropics, one of the most critical places to be observed in the frame of climate impact on water resources. Thanks to the infomation gathered by the various surveying techniques, numerical modelling permitted an estimate of the recharge, which can be fully originated by the runoff from Chimborazo melting glaciers. Actually, the retreat of the glaciers on top of the Chimborazo is an ongoing process presumably related to global climate change.DOI: 10.5194/egusphere-egu22-13153
DOI: https://doi.org/10.5194/egusphere-egu22-13153
Metrics:
Scozzari A, Catelan P, Chidichimo F, De Biase M, Mendoza Bg, Trujillo Pa, Carrettero P, Straface S
The identification of the processes underlining natural systems often requires the adoption of multiple investigation techniques for the assessment of the sites under study. In this work, the combination of information derived from non-invasive sensing techniques, such as geophysics, remote sensing and hydrogeochemistry, highlights the possible influence of global climate change on the future water availability related to an aquifer in a peculiar glacier context, located in central Ecuador. In particular, we show that the Chambo aquifer, which supplies potable water to the region, does not contain fossil water, and it's instead recharged over time. Indeed, the whole Chambo river basin is affected by the Chimborazo volcano, which is a glacerised mountain located in the inner tropics, one of the most critical places to be observed in the frame of climate impact on water resources. Thanks to the infomation gathered by the various surveying techniques, numerical modelling permitted an estimate of the recharge, which can be fully originated by the runoff from Chimborazo melting glaciers. Actually, the retreat of the glaciers on top of the Chimborazo is an ongoing process presumably related to global climate change.DOI: 10.5194/egusphere-egu22-13153
DOI: https://doi.org/10.5194/egusphere-egu22-13153
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2022
Conference article
Open Access
Using SWAT model to evaluate the plausible changes in a karst snow-fed watershed in the Moroccan High Atlas
Taia S, Erraioui L, Chao J, Scozzari A, El Mansouri B
High Atlas is considered as one of the major reservoirs of freshwater for crop yield and hydropower production in the plains of central Morocco. Nevertheless, snowmelt and discharge in this region have been reported very vulnerable to climate variability, which threaten the sustainability and development of socio-economic activities in this region. Thus, there's a strong need to understand the spatio-temporal variability of water cycle in addition to the impact of the changing climate on the main hydrological components. This work focuses on the application of SWAT model in the mountainous watershed of Oued Al Abid river, which is potentially threatened by climate and anthropogenic forcings. The study is based on two main axes: (i) the implementation of SWAT to model the snowmelt discharge processes over this watershed taking into consideration the karst structure of this area, (ii) the projection of climate change has been also analyzed by forcing SWAT model using three simulations of Regional Climate Model RCA4. Results showed that SWAT model performed satisfactory to very good in reproducing discharge and reservoir inflow. According to the results, the hydrological components showed a significant variability, particularly in snowmelt, infiltration and surface runoff. Furthermore, negative variation and peak shift in the projected inflows to the dam have been demonstrated by this study.DOI: 10.5194/egusphere-egu22-13351
DOI: https://doi.org/10.5194/egusphere-egu22-13351
Metrics:
Taia S, Erraioui L, Chao J, Scozzari A, El Mansouri B
High Atlas is considered as one of the major reservoirs of freshwater for crop yield and hydropower production in the plains of central Morocco. Nevertheless, snowmelt and discharge in this region have been reported very vulnerable to climate variability, which threaten the sustainability and development of socio-economic activities in this region. Thus, there's a strong need to understand the spatio-temporal variability of water cycle in addition to the impact of the changing climate on the main hydrological components. This work focuses on the application of SWAT model in the mountainous watershed of Oued Al Abid river, which is potentially threatened by climate and anthropogenic forcings. The study is based on two main axes: (i) the implementation of SWAT to model the snowmelt discharge processes over this watershed taking into consideration the karst structure of this area, (ii) the projection of climate change has been also analyzed by forcing SWAT model using three simulations of Regional Climate Model RCA4. Results showed that SWAT model performed satisfactory to very good in reproducing discharge and reservoir inflow. According to the results, the hydrological components showed a significant variability, particularly in snowmelt, infiltration and surface runoff. Furthermore, negative variation and peak shift in the projected inflows to the dam have been demonstrated by this study.DOI: 10.5194/egusphere-egu22-13351
DOI: https://doi.org/10.5194/egusphere-egu22-13351
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