Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-3251 Conference proceedings 2022 Gutierrez et al. Fostering the digitalization in urban water systems with low-cost monitoring of combined sewer overflows ICA automation conference 10.5281/zenodo.7565845 Uncategorized Uncategorized No abstract available 820954
publications-3252 Conference proceedings 2021 José M Cecilia, Pietro Manzoni, Dennis Trolle, Anders Nielsen, Pablo Blanco, Catia Prandi, Salvador Peña-Haro, Line Barkved, Don Pierson, Javier Senent SMARTLAGOON: Innovative modelling approaches for predicting socio-environmental evolution in highly anthropized coastal lagoons Proceedings of the Conference on Information Technology for Social Good 10.1145/3462203.3475925 AI & Machine Learning Water Distribution Networks No abstract available 101017861
publications-3253 Other 2016 Water JPI (under WaterWorks2014) WaterJPI SRIA 2.0 Uncategorized Uncategorized No abstract available 641715
publications-3254 Other 2020 Water JPI (under WaterWorks2014) Water JPI SRIA 2025 Control Systems Water Distribution Networks No abstract available 641715
publications-3255 Other 2020 Water JPI (under WaterWorks2014) Water JPI Vision 2030 Uncategorized Uncategorized No abstract available 641715
publications-3256 Other 2020 Water JPI (under WaterWorks2014) Water JPI documemy on synergies with other initiatives Data Management & Analytics Water Distribution Networks No abstract available 641715
publications-3257 Conference proceedings 2023 Francesco Vuolo, Jekaterina Aleksandrova, Mateusz Żółtak and Carlo De Michele The COALA enabling platform for low impact agriculture in Australia: focus on products for irrigation management Digital twin Uncategorized No abstract available 870518
publications-3258 Conference proceedings 2023 Belfiore, O. R., Kustas, W. P., D'Urso, G., Knipper, K., Bambach-Ortiz, N., McElrone, A. J., Ryu, D., Castro, S., Prueger, J. H., Bhattarai, N., Alfieri, J. G., Hipps, L. E., Alsina, M. M., De Michele, C., Vuolo, F., and Alali, Q Estimating evapotranspiration by using canopy conductance models with Sentinel-2 data in irrigated crops in California and Australia 10.5194/egusphere-gc8-hydro-58 IoT & Sensors Water Distribution Networks <p>Deriving evapotranspiration is crucial for determining the water requirements of crops and for efficiently allocating water resources for irrigation. Various experiments and methods have proven that earth observation (EO) is a useful tool for estimating evapotranspiration and supporting irrigation and water resource management at different scales.</p> <p>This study presents a framework based on the Penman-Monteith big leaf model and Shuttleworth-Wallace sparse canopy model for estimating the evapotranspiration in irrigated crops with partial and full-canopy conditions.</p> <p>The approach fully utilizes the high-resolution and multi-spectral capabilities of the Sentinel-2 (S2) sensors for the derivation of surface parameters such as hemispherical shortwave albedo(α), Leaf Area Index (LAI), and the water status of the soil-canopy ensemble by using the OPTRAM model. Proposed by Sadeghi [1], the OPTRAM model uses the pixel distribution in the Shortwave Infrared Transformed Reflectance (STR)-NDVI space, where the water content of the soil-canopy system is linearly correlated to the STR index.</p> <p>In detail, the proposed approach estimates the contributions of soil and canopy to the total evapotranspiration by incorporating the OPRAM model to assess the water status of the surface and adjust the resistance terms in the combination equation [2]</p> <p>The results are validated by using Eddy Covariance data collected during the GRAPEX (Grape Remote Sensing Atmospheric Profile Evapotranspiration eXperiment) project [3], T-REX (Tree crop Remote sensing of Evapotranspiration eXperiment) project, and COALA (COpernicus Applications and services for Low impact agriculture in Australia) project [4]. These projects are conducted respectively in irrigated vineyards and almond orchards in California, and in irrigated maize and alfalfa in Australia.</p> <p>[1] Sadeghi, Morteza, Scott B. Jones, and William D. Philpot.: A linear physically-based model for remote sensing of soil moisture using short wave infrared bands. Remote Sensing of Environment 164, 66-76 (2015).</p> <p>[2] D’Urso, G., Bolognesi, S. F., Kustas, W. P., Knipper, K. R., Anderson, M. C., Alsina, M. M., ... & Belfiore, O. R.: Determining evapotranspiration by using combination equation models with sentinel-2 data and comparison with thermal-based energy balance in a California irrigated Vineyard. Remote Sensing, 13(18), 3720 (2021).</p> <p>[3] Kustas, W.P., Anderson, M.C., Alfieri, J.G., Knipper, K., Torres-Rua, A., Parry, C.K., Nieto, H., Agam, N., White, W.A., Gao, F. The grape remote sensing atmospheric profile and evapotranspiration experiment. Bulletin of the American Meteorological Society 2018, 99, 1791-1812.</p> <p>[4] COALA project. https://www.coalaproject.eu/</p> 870518
publications-3259 Book chapters 2023 Calera, Alfonso & Carrilero, Julio & Campoy, Jaime & Calera, María & Osann, A. & Finger, K. & Teece, Bonnie & Metternicht, Graciela. Mapping Gilgai Micro-relief and Its Impact on Dryland Agricultural Landscapes Using Time Series of NDVI Derived from Sentinel-2 Imagery Geopedology 10.1007/978-3-031-20667-2_18 IoT & Sensors Water Distribution Networks No abstract available 870518
publications-3260 Conference proceedings 2021 Andreas Kvas, Torsten Mayer-Guerr Regularization of the Gravity Field Inversion Process with High-Dimensional Vector Autoregressive Models Physical Science Forum 3 10.3390/psf2021003007 Data Management & Analytics Uncategorized No abstract available 870353