Articles | Volume 28, issue 16
https://doi.org/10.5194/hess-28-3777-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-3777-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Aug 2024
Research article |
| 22 Aug 2024
| 22 Aug 2024
On the combined use of rain gauges and GPM IMERG satellite rainfall products for hydrological modelling: impact assessment of the cellular-automata-based methodology in the Tanaro River basin in Italy
Annalina Lombardi
CORRESPONDING AUTHOR
CETEMPS, Centre of Excellence University of L'Aquila, 67100 L'Aquila, Italy
Department of Physical and Chemical Sciences, University of L'Aquila, 67100 L'Aquila, Italy
Barbara Tomassetti
CORRESPONDING AUTHOR
CETEMPS, Centre of Excellence University of L'Aquila, 67100 L'Aquila, Italy
Valentina Colaiuda
CETEMPS, Centre of Excellence University of L'Aquila, 67100 L'Aquila, Italy
Ludovico Di Antonio
Univ Paris Est Creteil, Université Paris Cité, CNRS, LISA, F-94010 Créteil, France
Paolo Tuccella
CETEMPS, Centre of Excellence University of L'Aquila, 67100 L'Aquila, Italy
Department of Physical and Chemical Sciences, University of L'Aquila, 67100 L'Aquila, Italy
Mario Montopoli
CETEMPS, Centre of Excellence University of L'Aquila, 67100 L'Aquila, Italy
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, Italy
Giovanni Ravazzani
Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, Italy
Frank Silvio Marzano
CETEMPS, Centre of Excellence University of L'Aquila, 67100 L'Aquila, Italy
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy
deceased, 8 May 2022
Raffaele Lidori
CETEMPS, Centre of Excellence University of L'Aquila, 67100 L'Aquila, Italy
Giulia Panegrossi
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, Italy
Related authors
No articles found.
Hannes Konrad, Rémy Roca, Anja Niedorf, Stephan Finkensieper, Marc Schröder, Sophie Cloché, Giulia Panegrossi, Paolo Sanò, Christopher Kidd, Rômulo Augusto Jucá Oliveira, Karsten Fennig, Thomas Sikorski, Madeleine Lemoine, and Rainer Hollmann
Earth Syst. Sci. Data, 17, 4097–4124, https://doi.org/10.5194/essd-17-4097-2025, https://doi.org/10.5194/essd-17-4097-2025, 2025
Short summary
Short summary
GIRAFE v1 is a global satellite-based precipitation dataset covering 2002 to 2022. It combines high-accuracy microwave and high-resolution infrared observations for retrieving daily precipitation, a respective sampling uncertainty for a more robust analysis, and monthly means. It is intended and suitable for climate monitoring and research and allows studies on water management, agriculture, and disaster risk reduction. A continuous extension from 2023 onwards will be implemented in 2025.
Chenjie Yu, Paola Formenti, Joel F. de Brito, Astrid Bauville, Antonin Bergé, Hichem Bouzidi, Mathieu Cazaunau, Manuela Cirtog, Claudia Di Biagio, Ludovico Di Antonio, Cécile Gaimoz, Franck Maisonneuve, Pascal Zapf, Tobias Seubert, Simone T. Andersen, Patrick Dewald, Gunther N. T. E. Türk, John N. Crowley, Alexandre Kukui, Chaoyang Xue, Cyrielle Denjean, Olivier Garrouste, Jean-Claude Etienne, Huihui Wu, James D. Allan, Dantong Liu, Yangzhou Wu, Christopher Cantrell, and Vincent Michoud
EGUsphere, https://doi.org/10.5194/egusphere-2025-2667, https://doi.org/10.5194/egusphere-2025-2667, 2025
Short summary
Short summary
We presented a field measurement in a Paris suburban forest region to characterise the impacts of photochemical aging process on aerosol physical chemical properties. Photochemical production of organic aerosols increased forest fine particle mass and significantly enhanced absorption of short-wavelength sunlight. This study highlights the critical need to incorporate light absorbing carbonaceous particles formation mechanisms into models to accurately simulate their direct radiative impacts.
Stefano Federico, Rosa Claudia Torcasio, Claudio Transerici, Mario Montopoli, Cinzia Cambiotti, Francesco Manconi, Alessandro Battaglia, and Maryam Pourshamsi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2095, https://doi.org/10.5194/egusphere-2025-2095, 2025
Short summary
Short summary
The Wind Velocity Radar Nephoscope (WIVERN) mission will be the first space-based mission to provide global in-cloud wind, cloud and precipitation measurements. The mission is proposed as a candidate for the ESA Earth Explorer 11. Its data could be beneficial to several sectors, including numerical weather prediction performance enhancement. This paper aims to contribute to the last point by analyzing the impact that WIVERN would have in the case of a Tropical-like cyclone event.
Ludovico Di Antonio, Matthias Beekmann, Guillaume Siour, Vincent Michoud, Christopher Cantrell, Astrid Bauville, Antonin Bergé, Mathieu Cazaunau, Servanne Chevaillier, Manuela Cirtog, Joel F. de Brito, Paola Formenti, Cecile Gaimoz, Olivier Garret, Aline Gratien, Valérie Gros, Martial Haeffelin, Lelia N. Hawkins, Simone Kotthaus, Gael Noyalet, Diana L. Pereira, Jean-Eudes Petit, Eva Drew Pronovost, Véronique Riffault, Chenjie Yu, Gilles Foret, Jean-François Doussin, and Claudia Di Biagio
Atmos. Chem. Phys., 25, 4803–4831, https://doi.org/10.5194/acp-25-4803-2025, https://doi.org/10.5194/acp-25-4803-2025, 2025
Short summary
Short summary
The summer of 2022 has been considered a proxy for future climate scenarios due to its hot and dry conditions. In this paper, we use the measurements from the Atmospheric Chemistry of the Suburban Forest (ACROSS) campaign, conducted in the Paris area in June–July 2022, along with observations from existing networks, to evaluate a 3D chemistry transport model (WRF–CHIMERE) simulation. Results are shown to be satisfactory, allowing us to explain the gas and aerosol variability at the campaign sites.
Diana L. Pereira, Chiara Giorio, Aline Gratien, Alexander Zherebker, Gael Noyalet, Servanne Chevaillier, Stéphanie Alage, Elie Almarj, Antonin Bergé, Thomas Bertin, Mathieu Cazaunau, Patrice Coll, Ludovico Di Antonio, Sergio Harb, Johannes Heuser, Cécile Gaimoz, Oscar Guillemant, Brigitte Language, Olivier Lauret, Camilo Macias, Franck Maisonneuve, Bénédicte Picquet-Varrault, Raquel Torres, Sylvain Triquet, Pascal Zapf, Lelia Hawkins, Drew Pronovost, Sydney Riley, Pierre-Marie Flaud, Emilie Perraudin, Pauline Pouyes, Eric Villenave, Alexandre Albinet, Olivier Favez, Robin Aujay-Plouzeau, Vincent Michoud, Christopher Cantrell, Manuela Cirtog, Claudia Di Biagio, Jean-François Doussin, and Paola Formenti
Atmos. Chem. Phys., 25, 4885–4905, https://doi.org/10.5194/acp-25-4885-2025, https://doi.org/10.5194/acp-25-4885-2025, 2025
Short summary
Short summary
In order to study aerosols in environments influenced by anthropogenic and biogenic emissions, we performed analyses of samples collected during the ACROSS (Atmospheric Chemistry Of the Suburban Forest) campaign in summer 2022 in the greater Paris area. After analysis of the chemical composition by means of total carbon determination and high-resolution mass spectrometry, this work highlights the influence of anthropogenic inputs on the chemical composition of both urban and forested areas.
Soo-Jin Park, Lya Lugon, Oscar Jacquot, Youngseob Kim, Alexia Baudic, Barbara D'Anna, Ludovico Di Antonio, Claudia Di Biagio, Fabrice Dugay, Olivier Favez, Véronique Ghersi, Aline Gratien, Julien Kammer, Jean-Eudes Petit, Olivier Sanchez, Myrto Valari, Jérémy Vigneron, and Karine Sartelet
Atmos. Chem. Phys., 25, 3363–3387, https://doi.org/10.5194/acp-25-3363-2025, https://doi.org/10.5194/acp-25-3363-2025, 2025
Short summary
Short summary
To accurately represent the population exposure to outdoor concentrations of pollutants of interest to health (NO2, PM2.5, black carbon, and ultrafine particles), multi-scale modelling down to the street scale is set up and evaluated using measurements from field campaigns. An exposure scaling factor is defined, allowing regional-scale simulations to be corrected to evaluate population exposure. Urban heterogeneities strongly influence NO2, black carbon, and ultrafine particles but less strongly PM2.5.
Ludovico Di Antonio, Claudia Di Biagio, Paola Formenti, Aline Gratien, Vincent Michoud, Christopher Cantrell, Astrid Bauville, Antonin Bergé, Mathieu Cazaunau, Servanne Chevaillier, Manuela Cirtog, Patrice Coll, Barbara D'Anna, Joel F. de Brito, David O. De Haan, Juliette R. Dignum, Shravan Deshmukh, Olivier Favez, Pierre-Marie Flaud, Cecile Gaimoz, Lelia N. Hawkins, Julien Kammer, Brigitte Language, Franck Maisonneuve, Griša Močnik, Emilie Perraudin, Jean-Eudes Petit, Prodip Acharja, Laurent Poulain, Pauline Pouyes, Eva Drew Pronovost, Véronique Riffault, Kanuri I. Roundtree, Marwa Shahin, Guillaume Siour, Eric Villenave, Pascal Zapf, Gilles Foret, Jean-François Doussin, and Matthias Beekmann
Atmos. Chem. Phys., 25, 3161–3189, https://doi.org/10.5194/acp-25-3161-2025, https://doi.org/10.5194/acp-25-3161-2025, 2025
Short summary
Short summary
The spectral complex refractive index (CRI) and single scattering albedo were retrieved from submicron aerosol measurements at three sites within the greater Paris area during the ACROSS field campaign (June–July 2022). Measurements revealed urban emission impact on surrounding areas. CRI full period averages at 520 nm were 1.41 – 0.037i (urban), 1.52 – 0.038i (peri-urban), and 1.50 – 0.025i (rural). Organic aerosols dominated the aerosol mass and contributed up to 22 % of absorption at 370 nm.
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech., 17, 2195–2217, https://doi.org/10.5194/amt-17-2195-2024, https://doi.org/10.5194/amt-17-2195-2024, 2024
Short summary
Short summary
The paper describes a new machine-learning-based snowfall retrieval algorithm for Advanced Technology Microwave Sounder observations developed to retrieve high-latitude snowfall events. The main novelty of the approach is the radiometric characterization of the background surface at the time of the overpass, which is ancillary to the retrieval process. The algorithm shows a unique capability to retrieve snowfall in the environmental conditions typical of high latitudes.
Adrien Deroubaix, Marco Vountas, Benjamin Gaubert, Maria Dolores Andrés Hernández, Stephan Borrmann, Guy Brasseur, Bruna Holanda, Yugo Kanaya, Katharina Kaiser, Flora Kluge, Ovid Oktavian Krüger, Inga Labuhn, Michael Lichtenstern, Klaus Pfeilsticker, Mira Pöhlker, Hans Schlager, Johannes Schneider, Guillaume Siour, Basudev Swain, Paolo Tuccella, Kameswara S. Vinjamuri, Mihalis Vrekoussis, Benjamin Weyland, and John P. Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2024-516, https://doi.org/10.5194/egusphere-2024-516, 2024
Preprint archived
Short summary
Short summary
This study assesses atmospheric composition using air quality models during aircraft campaigns in Europe and Asia, focusing on carbonaceous aerosols and trace gases. While carbon monoxide is well modeled, other pollutants have moderate to weak agreement with observations. Wind speed modeling is reliable for identifying pollution plumes, where models tend to overestimate concentrations. This highlights challenges in accurately modeling aerosol and trace gas composition, particularly in cities.
Adrien Deroubaix, Marco Vountas, Benjamin Gaubert, Maria Dolores Andrés Hernández, Stephan Borrmann, Guy Brasseur, Bruna Holanda, Yugo Kanaya, Katharina Kaiser, Flora Kluge, Ovid Oktavian Krüger, Inga Labuhn, Michael Lichtenstern, Klaus Pfeilsticker, Mira Pöhlker, Hans Schlager, Johannes Schneider, Guillaume Siour, Basudev Swain, Paolo Tuccella, Kameswara S. Vinjamuri, Mihalis Vrekoussis, Benjamin Weyland, and John P. Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2024-521, https://doi.org/10.5194/egusphere-2024-521, 2024
Preprint archived
Short summary
Short summary
This study explores the proportional relationships between carbonaceous aerosols (black and organic carbon) and trace gases using airborne measurements from two campaigns in Europe and East Asia. Differences between regions were found, but air quality models struggled to reproduce them accurately. We show that these proportional relationships can help to constrain models and can be used to infer aerosol concentrations from satellite observations of trace gases, especially in urban areas.
Ludovico Di Antonio, Claudia Di Biagio, Gilles Foret, Paola Formenti, Guillaume Siour, Jean-François Doussin, and Matthias Beekmann
Atmos. Chem. Phys., 23, 12455–12475, https://doi.org/10.5194/acp-23-12455-2023, https://doi.org/10.5194/acp-23-12455-2023, 2023
Short summary
Short summary
Long-term (2000–2021) 1 km resolution satellite data have been used to investigate the climatological aerosol optical depth (AOD) variability and trends at different scales in Europe. Average enhancements of the local-to-regional AOD ratio at 550 nm of 57 %, 55 %, 39 % and 32 % are found for large metropolitan areas such as Barcelona, Lisbon, Paris and Athens, respectively, suggesting a non-negligible enhancement of the aerosol burden through local emissions.
Edoardo Raparelli, Paolo Tuccella, Valentina Colaiuda, and Frank S. Marzano
The Cryosphere, 17, 519–538, https://doi.org/10.5194/tc-17-519-2023, https://doi.org/10.5194/tc-17-519-2023, 2023
Short summary
Short summary
We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced with the Weather Research and Forecasting model, to reproduce snowpack properties observed in the Italian central Apennines. We found that Alpine3D reproduces the observed snow height and snow water equivalent better than Noah, while no particular model differences emerge on snow cover extent. Finally, we observed that snow settlement is mainly due to densification in Alpine3D and to melting in Noah.
Adrien Deroubaix, Laurent Menut, Cyrille Flamant, Peter Knippertz, Andreas H. Fink, Anneke Batenburg, Joel Brito, Cyrielle Denjean, Cheikh Dione, Régis Dupuy, Valerian Hahn, Norbert Kalthoff, Fabienne Lohou, Alfons Schwarzenboeck, Guillaume Siour, Paolo Tuccella, and Christiane Voigt
Atmos. Chem. Phys., 22, 3251–3273, https://doi.org/10.5194/acp-22-3251-2022, https://doi.org/10.5194/acp-22-3251-2022, 2022
Short summary
Short summary
During the summer monsoon in West Africa, pollutants emitted in urbanized areas modify cloud cover and precipitation patterns. We analyze these patterns with the WRF-CHIMERE model, integrating the effects of aerosols on meteorology, based on the numerous observations provided by the Dynamics-Aerosol-Climate-Interactions campaign. This study adds evidence to recent findings that increased pollution levels in West Africa delay the breakup time of low-level clouds and reduce precipitation.
Laurent Menut, Bertrand Bessagnet, Régis Briant, Arineh Cholakian, Florian Couvidat, Sylvain Mailler, Romain Pennel, Guillaume Siour, Paolo Tuccella, Solène Turquety, and Myrto Valari
Geosci. Model Dev., 14, 6781–6811, https://doi.org/10.5194/gmd-14-6781-2021, https://doi.org/10.5194/gmd-14-6781-2021, 2021
Short summary
Short summary
The CHIMERE chemistry-transport model is presented in its new version, V2020r1. Many changes are proposed compared to the previous version. These include online modeling, new parameterizations for aerosols, new emissions schemes, a new parameter file format, the subgrid-scale variability of urban concentrations and new transport schemes.
Paolo Tuccella, Giovanni Pitari, Valentina Colaiuda, Edoardo Raparelli, and Gabriele Curci
Atmos. Chem. Phys., 21, 6875–6893, https://doi.org/10.5194/acp-21-6875-2021, https://doi.org/10.5194/acp-21-6875-2021, 2021
Short summary
Short summary
We calculate the radiation-absorbing aerosol quantity in snow with a global chemical and transport atmospheric model, validated with global observations. The perturbation to snow albedo and related climatic impact are assessed. The resulting average radiative flux change in snow is 0.068 W m−2. Black carbon is a major contributor (+0.033 W m−2), followed by dust (+0.012 W m−2) and brown carbon (+0.0066 W m−2). The impact is also characterized by significant seasonal and geographical variability.
Annalina Lombardi, Valentina Colaiuda, Marco Verdecchia, and Barbara Tomassetti
Hydrol. Earth Syst. Sci., 25, 1969–1992, https://doi.org/10.5194/hess-25-1969-2021, https://doi.org/10.5194/hess-25-1969-2021, 2021
Short summary
Short summary
The paper presents a modelling approach for the assessment of extremes in the hydrological cycle at a multi-catchment scale. It describes two new hydrological stress indices, innovative instruments that could be used by Civil Protection operators, for flood mapping in early warning systems. The main advantage in using the proposed indices is the possibility of displaying hydrological-stress information over any geographical domain.
Cited articles
Andiego, G., Waseem, M., Usman, M., and Mani, N.:The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model, Comput. Water Energ. Environ. Eng., 7, 27–50, https://doi.org/10.4236/cweee.2018.81002, 2018.
Benesty, J., Chen, J., and Huang, Y.: Time-delay estimation via linear interpolation and cross correlation, IEEE T. Speech Audio Process., 12, 509–519, https://doi.org/10.1109/TSA.2004.833008, 2004, 2004.
Berndt, D. J. and Clifford, J.: Using dynamic time warping to find patterns in time series. AAAIWS'94: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. AAAI Press, 359–370, Seattle WA 31 July–1 August, https://dl.acm.org/doi/proceedings/10.5555/3000850 (last access: 7 August 2024), 1994.
Bouttier, F. and Courtier, P.: Data Assimilation Concepts and Methods, https://www.ecmwf.int/en/elibrary/16928-data-assimilation-concepts-and-methods (last access: 6 August 2024), 1999.
Brocca, L., Massari, C., Pellarin, T., Filippucci, P., Ciabatta, L., Camici, S., Kerr, Y. H., and Fernández-Prieto, D.: River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa, Sci. Rep., 10, 12517, https://doi.org/10.1038/s41598-020-69343-x, 2020.
Camici, S., Massari, C., Ciabatta, L., Marchesini, I., and Brocca, L.: Which rainfall score is more informative about the performance in river discharge simulation? A comprehensive assessment on 1318 basins over Europe, Hydrol. Earth Syst. Sci., 24, 4869–4885, https://doi.org/10.5194/hess-24-4869-2020, 2020.
Camici, S., Giuliani, G., Brocca, L., Massari, C., Tarpanelli, A., Farahani, H. H., Sneeuw, N., Restano, M., and Benveniste, J.: Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation, Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, 2022.
Chacon-Hurtado, J. C., Alfonso, L., and Solomatine, D. P.: Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework, Hydrol. Earth Syst. Sci., 21, 3071–3091, https://doi.org/10.5194/hess-21-3071-2017, 2017.
Colaiuda, V., Lombardi, A., Verdecchia, M., Mazzarella, V., Ricchi, A., Ferretti, R. and Tomassetti, B.: Flood Prediction: 770 Operational Hydrological Forecast with the Cetemps Hydrological Model (CHyM), Int. J. Environ. Sci. Nat. Res., 24, 556137, https://doi.org/10.19080/IJESNR.2020.24.556137, 2020.
Collischonn, B., Collischonn, W., and Morelli Tucci, C. E.: Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates, J. Hydrol., 360, 207–216, https://doi.org/10.1016/j.jhydrol.2008.07.032, 2008.
Coppola, E., Tomassetti, B., Mariotti, L., Verdecchia, M., and Visconti, G.: Cellular automata algorithms for drainage network extraction and rainfall data assimilation, Hydrolog. Sci. J., 52, 579–592, https://doi.org/10.1623/hysj.52.3.579, 2007.
Coppola, E., Verdecchia, M., Giorgi, F., Colaiuda, V., Tomassetti, B., and Lombardi, A.: Changing hydrological conditions in the Po basin under global warming, Sci. Total Environ., 493, 1183–1196, https://doi.org/10.1016/j.scitotenv.2014.03.003, 2014.
Cressman, G. P.: An operational objective analysis system, Mon. Weather Rev., 87, 367–374, 1959.
Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017.
Darko, S., Adjei, K.A., Gyamfi, C., Nii Odai, S., and Osei-Wusuansa, H.: Evaluation of RFE Satellite Precipitation and its Use in Streamflow Simulation in Poorly Gauged Basins, Environ. Process., 8, 691–712, https://doi.org/10.1007/s40710-021-00495-2, 2021.
Degiorgis, M., Gnecco, G., Gorni, S., Roth, G., Sanguineti, M. and Taramasso, A. C.: Flood hazard assessment via threshold binary classifiers: the case study of the Tanaro River Basin, Irrig. Drain., 62, 1–10, https://doi.org/10.1002/ird.1806, 2013.
Dembélé, M., Hrachowitz, M., Savenije, H. H. G., and Mariéthoz, G.: Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite datasets, Water Resour. Res., 56, e2019WR026085, https://doi.org/10.1029/2019WR026085, 2020.
Di Baldassarre, G. and Claps, P.: A hydraulic study on the applicability of flood rating curves, Hydrol. Res., 42, 10–19, https://doi.org/10.2166/nh.2010.098, 2011.
Di Baldassarre, G. and Montanari, A.: Uncertainty in river discharge observations: a quantitative analysis, Hydrol. Earth Syst. Sci., 13, 913–921, https://doi.org/10.5194/hess-13-913-2009, 2009.
Di Muzio, E., Riemer, M., Fink, A. H., and Maier-Gerber, M.: Assessing the predictability of Medicanes in ECMWF ensemble forecasts using an object-based approach, Q. J. Roy. Meteorol. Soc., 145, 1202–1217, 2019.
Dinku, T., Ceccato, P., Grover-Kopec, E., Lemma, M., Connor, S. J., and Ropelewski, C. F.: Validation of satellite rainfall products over East Africa's complex topography, Int. J. Remote Sens., 28, 1503–1526, 2007.
Duque-Gardeazábal, N., Zamora, D., and Erasmo Rodríguez, E.: Analysis of the kernel bandwidth influence in the double smoothing merging algorithm to improve rainfall fields in poorly gauged basins. HIC 2018: 13th Int. Conf. on Hydroinformatics, vol. 3., 635–626, https://doi.org/10.29007/2xp6, 2018.
Ebert, E. E., Janowiak, J. E., and Kidd, C.: Comparison of nearreal-time precipitation estimates from satellite observations and numerical models, B. Am. Meteorol. Soc., 88, 47–64, 2007.
European Space Agency: Copernicus Digital Elevation Model (DEM), AWS [data set], https://registry.opendata.aws/copernicus-dem (last access: 6 August 2024), 2021.
Falck, A. S., Tomasella, J., Diniz, F. L. R., and Maggioni, V.: Applying a precipitation error model to numerical weather predictions for probabilistic flood forecasts, J. Hydrol., 598, 126374, https://doi.org/10.1016/j.jhydrol.2021.126374, 2021.
Ferretti, R., Lombardi, A., Tomassetti, B., Sangelantoni, L., Colaiuda, V., Mazzarella, V., Maiello, I., Verdecchia, M., and Redaelli, G.: A meteorological–hydrological regional ensemble forecast for an early-warning system over small Apennine catchments in Central Italy, Hydrol. Earth Syst. Sci., 24, 3135–3156, https://doi.org/10.5194/hess-24-3135-2020, 2020.
French, M. N. and Krajewski, W. F.: A model for real-time quantitative rainfall forecasting using remote sensing: 1. Formulation, Water Resour. Res., 30, 1075–1083, 1994.
Gandin, L. S.: The Planning of Meteorological Station Networks, Technical Note No. 111, WMO No. 265, Geneva, 1970.
Gebremichael, M. and Krajewski, W. F.: Characterization of the temporal sampling error in space-time-averaged rainfall estimates from satellites, J. Geophys. Res., 109, D11110, https://doi.org/10.1029/2004JD004509, 2004.
Ghulami, M., Babel, M. S., and Shrestha, M. S.: Evaluation of gridded precipitation datasets for the Kabul Basin, Afghanistan, Int. J. Remote Sens., 38, 3317–3332, 2017.
Goodrich, D. C., Lane, L. J., Shillito, R. M., Miller, S. N., Syed, K. H., and Woolhiser, D. A.: Linearity of basin response as a function of scale in a semiarid watershed, Water Resour. Res., 33, 2951–2965, https://doi.org/10.1029/97WR01422, 1997.
Guetter, A. K., Georgakakos, K. P., and Tsonis, A. A.: Hydrologic applications of satellite data: 2. Flow simulation and soil water estimates, J. Geophys. Res.-Atmos., 101, 26527–26538, 1996.
Guo, J., Su, T., Li, Z., Miao, Y., Li, J., Liu, H., Xu, H., Cribb, M., and Zhai, P.: Declining frequency of summertime local-scale precipitation over eastern China from 1970–2010 and its potential link to aerosols, Geophys. Res. Lett., 44, 5700–5708, 2017.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hallouin, T.: HydroEval: Streamflow Simulations Evaluator (Version 0.0.3), Zenodo [code], https://doi.org/10.5281/zenodo.2591217, 2019.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Hirpa, F. A., Gebremichael, M., and Hopson, T.: Evaluation of high-resolution satellite precipitation products over very complex terrain in Ethiopia, J. Appl. Meteorol. Climatol., 49, 1044–1051, 2010.
Hong, Y., Hsu, K.-L., Moradkhani, H., and Sorooshian, S.: Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response, Water Resour. Res., 42, W08421, https://doi.org/10.1029/2005WR004398, 2006.
Hong, Y., Gochis, D., Cheng, J., Hsu, K., and Sorooshian, S.: Evaluation of PERSIANN-CCS Rainfall Measurement Using the NAME Event Rain Gauge Network, J. Hydrometeorol., 8, 469–482, https://doi.org/10.1175/JHM574.1, 2007.
Hossain, F. and Anagnostou, E. N.: Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture, Adv. Water Resour., 28, 1336–1350, 2005.
Hossain, F. and Anagnostou, E. N.: A two-dimensional satellite rainfall error model, IEEE T. Geosci. Remote, 44, 1511–1522, https://doi.org/10.1109/TGRS.2005.863866, 2006.
Huffman, G. J., Bolvin, D., Braithwaite, D., Hsu, K.-L., Joyce, R., Kidd, C., Nelkin, E., Sorooshian, S., Tan, J., and Xie, P.: Integrated Multi-Satellite Retrievals for GPM (IMERG) Technical Documentation, NASA/GSFC, Greenbelt, MD, USA, https://gpm.nasa.gov/sites/default/files/202307/IMERG_TechnicalDocumentation_final_230713.pdf (last access: 6 August 2024),, 2018.
Hughes, D. A.: Comparison of satellite rainfall data with observations from gauging station networks, J. Hydrol., 327, 399–410, 2006.
Italian Civil Protection Department, CIMA Research Foundation: The Dewetra Platform: A Multi-perspective Architecture for Risk Management during Emergencies, in: Information Systems for Crisis Response and Management in Mediterranean Countries, edited by: Hanachi, C., Bénaben, F., and Charoy, F., ISCRAM-med 2014, Lecture Notes in Business Information Processing, vol 196, Springer, Cham, https://doi.org/10.1007/978-3-319-11818-5_15, 2014.
Jiang, D. and Wang, K.: The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review, Water, 11, 1615, https://doi.org/10.3390/w11081615, 2019.
Keogh, E. J. and Pazzani, M.: Derivative Dynamic Time Warping, Proceedings of the 2001 SIAM International Conference on Data Mining (SDM), pp. 1–11, ISBN 978-0-89871-495-1, 2001, https://doi.org/10.1137/1.9781611972719.1, 2001.
Keogh, E. J. and Ratanamahatana, C. A.: Exact indexing of dynamic time warping, Knowl. Inform. Syst., 7, 358–386, https://doi.org/10.1007/s10115-004-0154-9, 2005.
Kidd, C. and Levizzani, V.: Status of satellite precipitation retrievals, Hydrol. Earth Syst. Sci., 15, 1109–1116, https://doi.org/10.5194/hess-15-1109-2011, 2011.
Kirstetter P. E., Viltard N., and Gosset M.: An error model for instantaneous satellite rainfall estimates: evaluation of BRAIN-TMI over West Africa, Q. J. Roy. Meteor. Soc., 139, 894–911, https://doi.org/10.1002/qj.1964, 2013.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Kubota, T., Ushio, T., Shige, S., Kida, S., Kachi, M., and Okamoto, K.: Verification of high-resolution satellite-based rainfall estimates around Japan using gauge-calibrated ground radar dataset, J. Meteor. Soc. Japan, 87A, 203–222, https://doi.org/10.2151/jmsj.87A.203, 2009.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, Eos, Transactions, American Geophysical Union, 89, 93–94, https://doi.org/10.1029/2008eo100001, 2008 (data available at: https://www.hydrosheds.org/, last access: 7 August 2024).
Li, M. and Shao, Q.: An improved statistical approach to merge satellite rainfall estimates and raingauge data, J. Hydrol., 385, 51–64, https://doi.org/10.1016/j.jhydrol.2010.01.023, 2010.
Li, X., Chen, Y., Deng, X., Zhang, Y., and Chen, L.: Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China, Remote Sens., 13, 866, https://doi.org/10.3390/rs13050866, 2021.
Liang, S., Li, X., and Wang, J.: Advanced Remote Sensing, Academic Press, 533–556, ISBN 9780123859549, https://doi.org/10.1016/B978-0-12-385954-9.00017-4, 2012.
Lombardi, A., Colaiuda, V., Verdecchia, M., and Tomassetti, B.: User-oriented hydrological indices for early warning systems with validation using post-event surveys: flood case studies in the Central Apennine District, Hydrol. Earth Syst. Sci., 25, 1969–1992, https://doi.org/10.5194/hess-25-1969-2021, 2021.
Luino, F.: Flooding vulnerability of a town in the Tanaro basin: the case of Ceva (Piedmont-northwest Italy), Workshop, Barcelona, vol. 16, no. 19, 2002.
Maggioni, V. and Massari, C.: On the performance of satellite precipitation products in riverine flood modeling: A review, J. Hydrol., 558, 214–224, https://doi.org/10.1016/j.jhydrol.2018.01.039, 2018.
Maggioni, V. and Massari, C. (Eds.): Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment A Remote Sensing Approach, in: 1st Edn., Elsevier, Inc., Cambridge, MA, ISBN 9780128148990, 2019.
Maggioni, V., Reichle, R. H., and Anagnostou, E. N.: The Effect of Satellite Rainfall Error Modeling on Soil Moisture Prediction Uncertainty, J. Hydrometeorol., 12, 413–428, 2011.
Maggioni, V., Sapiano, M. R. P., Adler, R. F., Tian, Y., and Huffman, G. J.: An Error Model for Uncertainty Quantification in High-Time-Resolution Precipitation Products, J. Hydrometeorol., 15, 1274–1292, 2014.
Maggioni, V. and Massari, C. (Eds.): Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment. A Remote Sensing Approach, 1st edn., Elsevier, Inc., Cambridge, MA, ISBN 9780128148990, 2019.
Maier-Gerber, M., Riemer, M., Fink, A. H., Knippertz, P., Di Muzio, E., and McTaggart-Cowan, R.: Tropical transition of Hurricane Chris (2012) over the North Atlantic Ocean: a multiscale investigation of predictability, Mon. Weather Rev., 147, 951–970, https://doi.org/10.1175/MWR-D-18-0188.1, 2019.
Marchi, E., Roth, G., and Siccardi, F.: The November 1994 flood event on the Po River: structural and non-structural measures against inundations, Workshop on hydrometeorology, impacts, and Management of Extreme Floods, Perugia, Italy, 1995.
Mathevet, T., Michel, C., Andreassian, V., and Perrin, C.: A bounded version of the Nash–Sutcliffe criterion for better model assessment on large sets of basins, in: Large Sample Basin Experiment for Hydrological Model Parameterization: Results of the Model Parameter Experiment – MOPEX, edited by: Andréassian, V., Hall, A., Chahinian, N., and Schaake, J., IAHS Publ., 30 p. 567, https://www.researchgate.net/profile/Thibault-Mathevet/publication/282319260_A_bounded_version_of_the_ Nash-Sutcliffe_criterion_for_better_model_assessment_on_large_ sets_of_basins/links/62de9581aa5823729ee0b9dd/A-bounded-version-of-the-Nash-Sutcliffe-criterion-for-better-model-assessment-on-large-sets-of-basins.pdf (last access: 7 August 2024), 2006.
McCollum, J. R., Krajewski, W. F., Ferraro, R. R., and Ba, M. B.: Evaluation of biases of satellite rainfall estimation algorithms over the continental United States, J. Appl. Meteorol., 41, 1065–1080. 2002.
Mei, Y., Nikolopoulos, E. I., Anagnostou, E. N., and Borga, M.: Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous Basins, J. Hydrometeorol., 17, 1407–1423, 2016.
Mei, Y., Anagnostou, E., Shen, X., and Nikolopoulos, E.: Decomposing the Satellite Precipitation Error Propagation through the Rainfall-Runoff Processes, Adv. Water Resour., 109, 253–266, https://doi.org/10.1016/j.advwatres.2017.09.012, 2017.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual modelspart I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Nemec, J.: Examples of design of HFS in WMO-assisted projects, in: Hydrological Forecasting, Water Science and Technology Library, 5, Springer, Dordrecht, https://doi.org/10.1007/978-94-009-4680-4_6, 1986.
Nijssen, B. and Lettenmaier, D. P.: Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites, J. Geophys. Res., 109, D02103, https://doi.org/10.1029/2003JD003497, 2004.
Nikolopoulos, E. I., Anagnostou, E. N., Hossain, F., Gebremichael, M., and Borga, M.: Understanding the scale relationships of uncertainty propagation of satellite rainfall through a distributed hydrologic model, J. Hydrometeorol., 11, 520–532, https://doi.org/10.1175/2009JHM1169.1, 2010.
O, S., Foelsche, U., Kirchengast, G., Fuchsberger, J., Tan, J., and Petersen, W. A.: Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria, Hydrol. Earth Syst. Sci., 21, 6559–6572, https://doi.org/10.5194/hess-21-6559-2017, 2017.
Packard, N. H. and Wolfram, S.: Two-dimensional cellular automata, J. Stat. Phys., 38, 901–946, 1985.
Po River Basin Authority: Linee Generali Di Assetto Idrogeologico E Quadro Degli Interventi, Bacino del Tanaro, http://www.adbpo.it/PAI/3 - Linee generali di assetto idraulico e idrogeologico/3.3 - Elaborato Piemonte/Tanaro.pdf (last access: 14 June 2023), 2023.
Pool, S., Vis, M., and Seibert, J.: Evaluating model performance: towards a non-parametric variant of the Kling–Gupta efficiency, Hydrolog. Sci. J., 63, 1941–1953, https://doi.org/10.1080/02626667.2018.1552002, 2018.
Rabiner, L. R. and Gold, B.: Theory and Application of Digital Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, p. 401, ISBN 0139141014, 1975.
Rabiner, L. R. and Schafer, R. W.: Digital Processing of Speech Signals, in: Signal Processing Series, Prentice Hall, Upper Saddle River, NJ, 147–148, ISBN 0132136031, 1978.
Sakoe, H. and Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26, 43–49, https://doi.org/10.1109/TASSP.1978.1163055, 1978.
Sangelantoni, L., Tomassetti, B., Colaiuda, V., Lombardi, A., Verdecchia, M., Ferretti, R., and Redaelli, G.: On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin, Atmosphere, 10, 799, https://doi.org/10.3390/atmos10120799, 2019.
Saouabe, T., El Khalki, E. M., Saidi, M. E. M., Najmi, A., Hadri, A., Rachidi, S., Jadoud, M., and Tramblay, Y.: Evaluation of the GPM-IMERG Precipitation Product for Flood Modeling in a Semi-Arid Mountainous Basin in Morocco, Water, 12, 2516, https://doi.org/10.3390/w12092516, 2020.
Sevruk, B.: Snow cover measurements and areal assessment of precipitation and soil moisture, Operational Hydrology Report, 35, Publ. 749, World Meteorological Organisation, Geneva, 91 pp., https://www.cabidigitallibrary.org/doi/full/10.5555/19941904049 (last access: 7 August 2024), 1992.
Shi, H., Chen, J., Li, T., and Wang, G.: A new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data, J. Hydro-Environ. Res., 28, 1–14, https://doi.org/10.1016/j.jher.2017.10.006; 2020.
Singh, P. and Kumar, N.: Impact assessment of climate change on the hydrological response of a snow and glacier melt runoff dominated Himalayan River, J. Hydrol., 193, 316–350, https://doi.org/10.1016/S0022-1694(96)03142-3, 1997.
Solakian, J., Maggioni, V., and Godrej, A.: Investigating the Error Propagation from Satellite-Based Input Precipitation to Output Water Quality Indicators Simulated by a Hydrologic Model, Remote Sens., 12, 3728, https://doi.org/10.3390/rs12223728, 2020.
Su, F., Hong, Y., and Lettenmaier, D. P.: Evaluation of TRMM multisatellite precipitation analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin, J. Hydrometeorol., 9, 622–640, 2008.
Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., and De Roo, A.: Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin, J. Hydrol., 499, 324–338, https://doi.org/10.1016/j.jhydrol.2013.07.012, 2013.
Tian, Y. and Peters-Lidard, C. D.: A global map of uncertainties in satellite-based precipitation measurements, Geophys. Res. Lett., 37, L24407, https://doi.org/10.1029/2010GL046008, 2010.
Todini, E.: Influence of parameter estimation uncertainty in Kriging: Part 1 – Theoretical Development, Hydrol. Earth Syst. Sci., 5, 215–223, https://doi.org/10.5194/hess-5-215-2001, 2001.
Tomassetti, B., Coppola, E., Verdecchia, M., and Visconti, G.: Coupling a distributed grid based hydrological model and MM5 meteorological model for flooding alert mapping, Adv. Geosci., 2, 59–63, https://doi.org/10.5194/adgeo-2-59-2005, 2005.
Tramblay, Y., El Mahdi, E. K., Ciabatta, L., Camici, S., Hanich, L., El Mehdi Saidi, M., Ezzahouani, M., Benaabidate, L., Mahé, G., and Brocca, L.: River runoff estimation with satellite rainfall in Morocco, Hydrol. Sci. J., 68, 474–487, https://doi.org/10.1080/02626667.2023.2171295, 2023.
Tsintikidis, D., Georgakakos, K. P., Artan, G. A., Tsonis, A. A.: A feasibility study on mean areal rainfall estimation and hydrologic response in the Blue Nile region using METEOSAT images, J. Hydrol., 221, 97–116, https://doi.org/10.1016/S0022-1694(99)00071-2, 1999.
Verdecchia, M., Coppola, E., Tomassetti, B., and Visconti, G.: Cetemps Hydrological Model (CHyM), a Distributed Grid-Based Model Assimilating Different Rainfall Data Sources, in: Hydrological Modelling and the Water Cycle, edited by: Sorooshian, S., Hsu, K. L., Coppola, E., Tomassetti, B., Verdecchia, M., and Visconti, G., Water Sci. Technol. Library, vol 63, 165–201, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-77843-1_8, 2008
Wilk, J., Kniveton, D., Andersson, L., Layberry, R., Todd, M. C., Hughes, D., Ringrose, S., and Vanderpost, C.: Estimating rainfall and water balance over the Okavango River Basin for hydrological applications, J. Hydrol., 331, 18–29, https://doi.org/10.1016/j.jhydrol.2006.04.049, 2006.
WMO: Guide to Hydrological Practices, 5th edn., WMO no. 168, World Meteorological Organization, Geneva, Switzerland, ISBN 9789263151681, 1994.
Xu, H., Xu, C. Y., Chen, H., Zhang, Z., and Li, L.: Assessing theinfluence of rain gauge density and distribution on hydrologicalmodel performance in a humid region of China, J. Hydrol., 505, 1–12, 2013.
Yong, B., Ren, L. L., Hong, Y., Wang, J. H., Gourley, J. J.,Jiang, S. H., Chen, X., and Wang, W.: Hydrologic evaluation of Multisatellite Precipitation Analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe basin, China, Water Resour. Res., 46, W07542, https://doi.org/10.1029/2009wr008965, 2010.
Short summary
The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
The accurate estimation of precipitation and its spatial variability within a watershed is...
