In water-scarce regions, effective water resource management is crucial for performing sustainable agriculture. Scientists and decision-makers increasingly exploit the coupled use of hydrological and crop models to address resource conservation and productivity challenges. While emphasizing crop system performance and environmental impact assessment, equal attention is needed for ensuring sustainable crop production and effective water management. In response to these needs, the Italian Space Agency (ASI), within the framework of the I4DP-SCIENCE programme, supports the collaborative THETIS project (EarTH Observation for the Early forecasT of Irrigation needS; Agreement n. 2023-52-HH.0). THETIS aims at developing a spatial decision support system that integrates hydrologic and crop growth models with advanced Earth observation (EO) products, Artificial Intelligence, and a WEBGIS interface. Starting activities focus on processing tomatoes, which are highly water-demanding. A synergic data exchange between models is established to provide estimates of water needs before, at the beginning, and during the irrigation season with periodic updates. An essential component of the project entails producing estimated time series of root zone soil moisture, crucial for initializing the AquaCrop model and evaluating the water balance within the designated fields. To accomplish this task, in the THETIS project a joint use of the daily basin-scale hydrological model DREAM and the physically based Soil Moisture Analytical Relationship (SMAR) model forming a Soil Water Balance (SWB) module is proposed. This paper provides a brief contextualization of the THETIS project focusing on the need for reliable estimations of soil moisture dynamics. To this end a review of the existing application of SMAR model is presented. It helped to draw remarks about the main challenges and perspectives of use of the model within a context where soil moisture may be strongly influenced by irrigation volumes and agricultural practices.
Advancing Sustainable Water Management in Southern Italy Through Integrated Hydrological Modeling and Earth Observation / Iacobellis, V.; Gioia, A.; Totaro, V.; Lombardo, M.; Izzaddin, A. B.; Manfreda, S.; Zhuang, R.; Satalino, G.; Balenzano, A.; Albertini, C.; Mattia, F.; Lovergine, F.; Palmisano, D.; Rinaldi, M.; Ruggieri, S.; Garofalo, P.; Impedovo, D.; Noviello, N.; Nardella, L.; Di Cataldo, M.; Guarini, R.; Virelli, M.; Sacco, P.; Tapete, D.. - 14819:(2024), pp. 217-229. (Intervento presentato al convegno 24th International Conference on Computational Science and Its Applications, ICCSA 2024 tenutosi a vnm nel 2024) [10.1007/978-3-031-65282-0_14].
Advancing Sustainable Water Management in Southern Italy Through Integrated Hydrological Modeling and Earth Observation
Iacobellis V.;Gioia A.;Totaro V.;Lombardo M.;Albertini C.;
2024-01-01
Abstract
In water-scarce regions, effective water resource management is crucial for performing sustainable agriculture. Scientists and decision-makers increasingly exploit the coupled use of hydrological and crop models to address resource conservation and productivity challenges. While emphasizing crop system performance and environmental impact assessment, equal attention is needed for ensuring sustainable crop production and effective water management. In response to these needs, the Italian Space Agency (ASI), within the framework of the I4DP-SCIENCE programme, supports the collaborative THETIS project (EarTH Observation for the Early forecasT of Irrigation needS; Agreement n. 2023-52-HH.0). THETIS aims at developing a spatial decision support system that integrates hydrologic and crop growth models with advanced Earth observation (EO) products, Artificial Intelligence, and a WEBGIS interface. Starting activities focus on processing tomatoes, which are highly water-demanding. A synergic data exchange between models is established to provide estimates of water needs before, at the beginning, and during the irrigation season with periodic updates. An essential component of the project entails producing estimated time series of root zone soil moisture, crucial for initializing the AquaCrop model and evaluating the water balance within the designated fields. To accomplish this task, in the THETIS project a joint use of the daily basin-scale hydrological model DREAM and the physically based Soil Moisture Analytical Relationship (SMAR) model forming a Soil Water Balance (SWB) module is proposed. This paper provides a brief contextualization of the THETIS project focusing on the need for reliable estimations of soil moisture dynamics. To this end a review of the existing application of SMAR model is presented. It helped to draw remarks about the main challenges and perspectives of use of the model within a context where soil moisture may be strongly influenced by irrigation volumes and agricultural practices.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
