Sara MODANESI

Research personnel
Researcher
+39 075 5014405
+39 075 5014420
sara.modanesi (at) irpi.cnr.it
Via della Madonna Alta, 126 06128, Perugia
Perugia


hydrology

Submitted in peer reviewed journals:

  1. De Lannoy, G. et al. Contributions of irrigation modeling, soil moisture and snow data assimilation to the skill of high-resolution digital replicas of the Po basin water budget. ESS Open Archive, 10.22541/essoar.171535793.33881670/v1. May 10, 2024.

Published in peer reviewed journals:

  1. Zappa, L., Dari, J., Modanesi, S., Quast, R., Brocca, L., Lannoy, G.D., Massari, C., Seguí, P.Q., Ortiz, A.B., Dorigo, W. Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture. Agric. Water Manag. 2024, 295, 108773.
  2. Brocca, L., Barbetta, S., Camici, S., Ciabatta, L., Dari, J., Filippucci, P., Massari, C., Modanesi, S., Tarpanelli, A., Bonaccorsi, B., Mosaffa, H., Wagner, W., Vreugdenhil, M., Quast, R., Alfieri, L, Gabellani, S., Avanzi, F., Rains, D., Miralles, D.G., Mantovani, S., Briese, C., Domeneghetti, A., Jacob, A., Castelli, M., Camps-Valls, G., Volden, E., Fernandez, D. A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations Front. Sci., 1 (2024), 10.3389/fsci.2023.1190191
  3. Girotto, M., Formetta, G., Azimi, S., Bachand, C., Cowherd, M., De Lannoy, G., Lievens, H., Modanesi, S., Raleigh, M.S., Rigon, R., Massari, C. Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals, Science of The Total Environment, Volume 906,167312, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2023.167312, 2024.
  4. Azimi, S., Massari, C., Formetta, G., Barbetta, S., Tazioli, A., Fronzi, D., Modanesi, S., Tarpanelli, A., and Rigon, R.: On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions, Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, 2023.
  5. Bechtold, M., Modanesi, S., Lievens, H., Baguis, P., Brangers, I., Carrassi, A., ... & De Lannoy, G. (2023). Assimilation of Sentinel-1 backscatter into a land surface model with river routing and its impact on streamflow simulations in two Belgian catchments. Journal of Hydrometeorology, 24(12), 2389-2408, https://doi.org/10.1175/JHM-D-22-0198.1
  6. Natali, M., Modanesi, S., Massari, C., Brocca, L., De Lannoy, G., J. M. Maino, A., Mantovan, F. A simple framework to calibrate a soil water balance model with Sentinel-1 and Sentinel-2 observations over irrigated fields. IEEE International Workshop on Metrology for Agriculture and Forestry, 2023
  7. Massari, C., Pellet, V., Tramblay, Y., Crow, W. T., Gründemann, G. J., Hascoetf, T., Penna, D., Modanesi, S., Brocca, L., Camici, S., & Marra, F. On the relation between antecedent basin conditions and runoff coefficient for European floods. Journal of Hydrology, 625, 130012. https://doi.org/10.1016/j.jhydrol.2023.130012, 2024
  8. Kragh, S. J., Dari, J., Modanesi, S., Massari, C., Brocca, L., Fensholt, R., Stisen, S., and Koch, J.: An inter-comparison of approaches and frameworks to quantify irrigation from satellite data, Hydrol. Earth Syst. Sci., 28, 441–457, https://doi.org/10.5194/hess-28-441-2024, 2024.
  9. Dari, J., Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., and Volden, E.: Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space, Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023
  10. Le Page M, Nguyen T, Zribi M, Boone A, Dari J, Modanesi S, Zappa L, Ouaadi N, Jarlan L. Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe. Remote Sensing. 2023; 15(5):1449. https://doi.org/10.3390/rs15051449
  11. Baguis, P., Carrassi, A., Roulin, E., Vannitsem, S., Modanesi, S., Lievens, H., Bechtold, M., De Lannoy, G. Assimilation of Backscatter Observations into a Hydrological Model: A Case Study in Belgium Using ASCAT Data. Remote Sens. 2022, 14, 5740. https://doi.org/10.3390/rs14225740
  12. Modanesi, S., Massari, C., Bechtold, M., Lievens, H., Tarpanelli, A., Brocca, L., Zappa, L., De Lannoy, G.J.M. Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP. Hydrol. Earth Syst. Sci., 2022, 26, 4685-4706. https://doi.org/10.5194/hess-26-4685-2022
  13. Elwan, E., Le Page, M., Jarlan L., Baghdadi, N., Brocca, L., Modanesi, S., Dari, J., Quintana-Seguí, P., Zribi, M. Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data. Water, 2022, 14(5), 804. DOI: https://doi.org/10.3390/w14050804
  14. Modanesi, S., Massari, C., Gruber, A., Lievens, H., Tarpanelli, A., Morbidelli, R., and De Lannoy, G. J. M.: Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land, Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, 2021 
  15. Modanesi, S., Dari, J., Massari, C., Tarpanelli, A., Barbetta, S., De Lannoy, G., Gruber, A., Lievens, H., Bechtold, M., Quast, R., Vreugdenhil, M.; Zribi, M., Le Page, M.,Brocca, L. A comparison between satellite- and model-based approaches developed in the ESA Irrigation+ project framework to estimate irrigation quantities. 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 268-272. doi: 10.1109/MetroAgriFor52389.2021.9628453
  16. Massari, C.; Modanesi, S.; Dari, J.; Gruber, A.; De Lannoy, G.J.M.; Girotto, M.; Quintana-Seguí, P.; Le Page, M.; Jarlan, L.; Zribi, M.; Ouaadi, N.; Vreugdenhil, M.; Zappa, L.; Dorigo, W.; Wagner, W.; Brombacher, J.; Pelgrum, H.; Jaquot, P.; Freeman, V.; Volden, E.; Fernandez Prieto, D.; Tarpanelli, A.; Barbetta, S.; Brocca, L. A review of irrigation information retrievals from space and their utility for users. Remote Sens., 2021, 13(20), 4112. DOI: https://doi.org/10.3390/rs13204112
  17. De Santis, D.; Biondi, D.; Crow, W.T.; Camici, S.; Modanesi, S.; Brocca, L.; Massari, C. Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An Extensive Experiment in over 700 Catchments throughout Europe. Water Resources Research, 2021, 57, 6. https://doi.org/10.1029/2021WR029643
  18. Modanesi, S.; Massari, C.; Camici, S.; Brocca, L.; Amarnath, G. Do Satellite Surface Soil Moisture Observations Better Retain Information About Crop-Yield Variability in Drought Conditions? Water Resources Research, 2020, 56 ,2. https://doi.org/10.1029/2019WR025855
  19. Azimi, S., Dariane, AB., Modanesi, S., Bauer-Marschallinger, B., Bindlish, R., Wagner, W., Massari, C. Assimilation of Sentinel 1 and SMAP - based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins. J Hydrol (Amst). 2020, 581:124367. Epub 2019 Nov 22. PMID: 33154604; PMCID: PMC7608049. https://doi.org/10.1016/j.jhydrol.2019.124367
  20. Bauer-Marschallinger, B.; Naeimi, V.; Cao, S.; Paulik, C.; Schaufler, S.; Stachl, T.; Modanesi, S.; Massari, C.; Ciabatta, L.; Brocca, L.; Wagner, W. Towards Global Soil Moisture Monitoring with Sentinel-1: Harnessing Assets and Overcoming Obstacles. IEEE Trans. Geosci. Remote Sens, 2018. DOI: 10.1109/TGRS.2018.2858004
  21. Bauer-Marschallinger, B.; Paulik, C.; Hochstöger, S.; Mistelbauer, T.; Modanesi, S.; Ciabatta, L.; Massari, C.; Brocca, L.; Wagner, W. Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sens., 2018, 10(7), 1030. https://doi.org/10.3390/rs10071030

Main research interests include:

1. land surface modelling and hydrological modelling;

2. data assimilation of high resolution remote sensing observations in land surface and hydrological models to improve the water cycle description and track the effects of the human impacts on it (i.e., detection and quantification of irrigation);

3. drought monitoring through the use of remote sensing observations and land surface/hydrological models;

4. validation and application of innovative satellite products to improve hydrological/land surface modelling.

Sara Modanesi was born in Tarquinia, Italy, in 1985. She received a M.S. degree in Applied Geology in 2012 and a Master degree in Analysis and Management of Hydrological Hazard in 2016, from Sapienza University of Rome (Italy) both with excellence. In 2022 she received her PhD in Civil and Environmental Engineering jointly beween the University of Florence (Italy) and KU Leuven (Belgium).

From October 2017 to March 2018, she carried out an Internship with the Research Institute for Geo-Hydrological Protection (IRPI) of the National Research Council (CNR) of Perugia (Italy) within the project “New technologies for Hydrogeological Risk Assessment”. 

From May 2018 to May 2019 she worked with a research scholarship at the CNR-IRPI of Perugia.

From May 2019 to April 2023 she carried out research activities as a research fellow at the CNR-IRPI of Perugia.

From April 2023 to date she is working as a Researcher (III Level) at CNR-IRPI of Perugia.

Her main research interests include: 1) land surface and hydrological modelling; 2) data assimilation of remote sensing observations into land surface models for improving the water cycle description and tracking the effects of the human impacts (i.e., irrigation); 3) drought monitoring through the use of land surface/hydrological models and remote sensing observations; and 4) validation and application of innovative satellite products for the improvement of hydrological modelling.