Articles | Volume 24, issue 2
https://doi.org/10.5194/nhess-24-501-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-24-501-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Feb 2024
Research article |
| 14 Feb 2024
| 14 Feb 2024
CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment): a new model for geo-hydrological hazard assessment at the basin scale
Andrea Abbate
CORRESPONDING AUTHOR
Ricerca sul Sistema Energetico – RSE, Via Rubattino 54, Milan, Italy
Leonardo Mancusi
Ricerca sul Sistema Energetico – RSE, Via Rubattino 54, Milan, Italy
Francesco Apadula
Ricerca sul Sistema Energetico – RSE, Via Rubattino 54, Milan, Italy
Antonella Frigerio
Ricerca sul Sistema Energetico – RSE, Via Rubattino 54, Milan, Italy
Monica Papini
Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy
Laura Longoni
CORRESPONDING AUTHOR
Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy
Related authors
No articles found.
Federica Gaspari, Marco Gentile, Daniela Carrion, Riccardo Barzaghi, Lorenzo Panzeri, and Laura Longoni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B4-2026, 485–491, https://doi.org/10.5194/isprs-archives-XLIX-B4-2026-485-2026, https://doi.org/10.5194/isprs-archives-XLIX-B4-2026-485-2026, 2026
Marco Luppichini, Francesco Comiti, Andrea Andreoli, Barbara Aldighieri, Paolo Billi, Irene Maria Bollati, Andrea Brenna, Diletta Chirici, Paolo Ciavola, Silvia Cilli, Marco Donnini, Simona Francalanci, Lorenzo Innocenti, Laura Longoni, Luca Mao, Ivan Marchesini, Ilenia Murgia, Giacomo Pellegrini, Daniele Penna, Lorenzo Picco, Riccardo Rainato, Leonardo Schippa, Luca Solari, Gianluca Tronti, Bruno Testa, Matteo Verdone, and Monica Bini
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-157, https://doi.org/10.5194/essd-2026-157, 2026
Preprint under review for ESSD
Short summary
Short summary
Rivers carry sediment that shapes landscapes and coasts, but in Italy these data are scattered and often incomplete. We created a national database and online platform to gather and organize past and present sediment measurements in one place. By linking data with clear information about where, when, and how they were collected, we made them easier to explore and compare over time.
V. Yordanov, X. Q. Truong, M. Corti, L. Longoni, and M. A. Brovelli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1089–1096, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1089-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1089-2023, 2023
Sourish Basu, Xin Lan, Edward Dlugokencky, Sylvia Michel, Stefan Schwietzke, John B. Miller, Lori Bruhwiler, Youmi Oh, Pieter P. Tans, Francesco Apadula, Luciana V. Gatti, Armin Jordan, Jaroslaw Necki, Motoki Sasakawa, Shinji Morimoto, Tatiana Di Iorio, Haeyoung Lee, Jgor Arduini, and Giovanni Manca
Atmos. Chem. Phys., 22, 15351–15377, https://doi.org/10.5194/acp-22-15351-2022, https://doi.org/10.5194/acp-22-15351-2022, 2022
Short summary
Short summary
Atmospheric methane (CH4) has been growing steadily since 2007 for reasons that are not well understood. Here we determine sources of methane using a technique informed by atmospheric measurements of CH4 and its isotopologue 13CH4. Measurements of 13CH4 provide for better separation of microbial, fossil, and fire sources of methane than CH4 measurements alone. Compared to previous assessments such as the Global Carbon Project, we find a larger microbial contribution to the post-2007 increase.
V. A. Tran, X. Q. Truong, D. A. Nguyen, L. Longoni, and V. Yordanov
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W2-2021, 197–203, https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-197-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-197-2021, 2021
Cited articles
Abbate, A. and Mancusi, L.: Manuale del modello CRHyME (Climate Rainfall Hydrogeological Modelling Experiment), RSE Report RdS 21012462, Milano, RSE , https://www.rse-web.it/rapporti/manuale-del-modello-crhyme-climatic-rainfall-hydrogeological (last access: 1 November 3)202, 2021a.
Abbate, A. and Mancusi, L.: Strumenti per la mappatura delle minacce idrogeologiche per il sistema energetico e incidenza dei cambiamenti climatici, RSE Report RdS 21010317, Milano, RSE, https://www.rse-web.it/rapporti/strumenti-per-la-mappatura-delle-minacce-idrogeologiche-per (last access: 1 November 2023), 2021b.
Abbate, A., Longoni, L., Ivanov, V. I., and Papini, M.: Wildfire impacts on slope stability triggering in mountain areas, MDPI Geosci., 9, 1–15, https://doi.org/10.3390/geosciences9100417, 2019.
Abbate, A., Papini, M., and Longoni, L.: Analysis of meteorological parameters triggering rainfall-induced landslide: a review of 70 years in Valtellina, Nat. Hazards Earth Syst. Sci., 21, 2041–2058, https://doi.org/10.5194/nhess-21-2041-2021, 2021a.
Abbate, A., Papini, M., and Longoni, L.: Extreme Rainfall over Complex Terrain: An Application of the Linear Model of Orographic Precipitation to a Case Study in the Italian Pre-Alps, Geosciences, 11, 18, https://doi.org/10.3390/geosciences11010018, 2021b.
Abeshu, G. W., Li, H.-Y., Zhu, Z., Tan, Z., and Leung, L. R.: Median bed-material sediment particle size across rivers in the contiguous US, Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, 2022.
Allan, R., Pereira, L., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements – FAO Irrigation and drainage paper 56, FAO, https://www.fao.org/3/X0490E/x0490e00.htm (last access: 1 November 2023) 1998.
Alvioli, M., Melillo, M., Guzzetti, F., Rossi, M., Palazzi, E., von Hardenberg, J., Brunetti, M. T., and Peruccacci, S.: Implications of climate change on landslide hazard in Central Italy, Sci. Total Environ., 630, 1528–1543, https://doi.org/10.1016/j.scitotenv.2018.02.315, 2018.
Ancey, C.: Bedload transport: a walk between randomness and determinism. Part 1. The state of the art, J. Hydraul. Res., 58, 1–17, https://doi.org/10.1080/00221686.2019.1702594, 2020.
Anderson, E. I.: Modeling groundwater–surface water interactions using the Dupuit approximation, Adv. Water Resour., 28, 315–327, https://doi.org/10.1016/j.advwatres.2004.11.007, 2005.
Angeli, M. G., Buma, J., Gasparetto, P., and Pasuto, A.: A combined hill slope hydrology/stability model for low-gradient slopes in the Italian Dolomites, Eng. Geol., 49, 1–13, https://doi.org/10.1016/S0013-7952(97)00033-1, 1998.
Autorità di Bacino Distrettuale del Fiume Po: Linee Generali di Assetto Idrogeologico e Quadro degli Interventi, https://pai.adbpo.it/index.php/documentazione-pai/ (last access: 1 November 2023), 2022.
Ballio, F., Brambilla, D., Giorgetti, E., Longoni, L., Papini, M., and Radice, A.: Evaluation of sediment yield from valley slopes: A case study, WIT Trans. Eng. Sci., 67, 149–160, https://doi.org/10.2495/DEB100131, 2010.
Bancheri, M., Rigon, R., and Manfreda, S.: The GEOframe-NewAge Modelling System Applied in a Data Scarce Environment, Water, 12, 86, https://doi.org/10.3390/w12010086, 2020.
Barnes, R.: Parallel Priority-Flood depression filling for trillion cell digital elevation models on desktops or clusters, Comput. Geosci., 96, 56–68, https://doi.org/10.1016/j.cageo.2016.07.001, 2016.
Barnes, R.: Parallel non-divergent flow accumulation for trillion cell digital elevation models on desktops or clusters, Environ. Model. Softw., 92, 202–212, https://doi.org/10.1016/j.envsoft.2017.02.022, 2017.
Bemporad, G. A., Alterach, J., Amighetti, F. F., Peviani, M., and Saccardo, I.: A distributed approach for sediment yield evaluation in Alpine regions, J. Hydrol., 197, 370–392, https://doi.org/10.1016/0022-1694(95)02978-8, 1997.
Berg, J. H.: Prediction of Alluvial Channel Pattern of Perennial Rivers, Geomorphology, 12, 259–279, https://doi.org/10.1016/0169-555X(95)00014-V, 1995.
Bonanno, R., Lacavalla, M., and Sperati, S.: A new high-resolution Meteorological Reanalysis Italian Dataset: MERIDA, Q. J. Roy. Meteorol. Soc., 145, 1756–1779, https://doi.org/10.1002/qj.3530, 2019.
Bordoni, M., Meisina, C., Valentino, R., Lu, N., Bittelli, M., and Chersich, S.: Hydrological factors affecting rainfall-induced shallow landslides: From the field monitoring to a simplified slope stability analysis, Eng. Geol., 193, 19–37, https://doi.org/10.1016/j.enggeo.2015.04.006, 2015.
Bovolo, C. I. and Bathurst, J. C.: Modelling catchment-scale shallow landslide occurrence and sediment yield as a function of rainfall return period, Hydrol. Process., 26, 579–596, https://doi.org/10.1002/hyp.8158, 2012.
Bovy, B., Braun, J., Cordonnier, G., Lange, R., and Yuan, X.: The FastScape software stack: reusable tools for landscape evolution modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9474, https://doi.org/10.5194/egusphere-egu2020-9474, 2020.
Bozzolan, E., Holcombe, E., Pianosi, F., and Wagener, T.: Including informal housing in slope stability analysis – an application to a data-scarce location in the humid tropics, Nat. Hazards Earth Syst. Sci., 20, 3161–3177, https://doi.org/10.5194/nhess-20-3161-2020, 2020.
Brambilla, D., Papini, M., Ivanov, V. I., Bonaventura, L., Abbate, A., and Longoni, L.: Sediment Yield in Mountain Basins, Analysis, and Management: The SMART-SED Project, in: Applied Geology: Approaches to Future Resource Management, edited by: De Maio, M. and Tiwari, A. K., Springer International Publishing, Cham, 43–59, https://doi.org/10.1007/978-3-030-43953-8_3, 2020.
Bresciani, E., Davy, P., and de Dreuzy, J.-R.: Is the Dupuit assumption suitable for predicting the groundwater seepage area in hillslopes?, Water Resour. Res., 50, 2394–2406, https://doi.org/10.1002/2013WR014284, 2014.
Campforts, B., Shobe, C. M., Steer, P., Vanmaercke, M., Lague, D., and Braun, J.: HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution, Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, 2020.
Cazorzi, F. and Dalla Fontana, G.: Snowmelt modelling by combining air temperature and a distributed radiation index, J. Hydrol., 181, 169–187, https://doi.org/10.1016/0022-1694(95)02913-3, 1996.
Ceriani, M., Lauzi, S., and Padovan, M.: Rainfall thresholds triggering debris-flow in the alpine area of Lombardia Region, central Alps – Italy, in: Proceedings of the Man and Mountain'94, First International Congress for the Protection and Development of Mountain Environmen, 20–24 June 1994, Ponte di Legno, BS, Italy, 1994.
Chen, L. and Young, M. H.: Green-Ampt infiltration model for sloping surfaces, Water Resour. Res., 42, W07420, https://doi.org/10.1029/2005WR004468, 2006.
Chiarelli, D. D., Galizzi, M., Bocchiola, D., Rosso, R., and Rulli, M. C.: Modeling snowmelt influence on shallow landslides in Tartano valley, Italian Alps, Sci. Total Environ., 856, 158772, https://doi.org/10.1016/j.scitotenv.2022.158772, 2023.
Chow, V. T., Maidment, D. R., and Mays, L. W.: Applied hydrology, McGraw-Hill, New York, ISBN 007070242X, ISBN 9780070702424, 1988.
Ciampalini, A., Raspini, F., Lagomarsino, D., Catani, F., and Casagli, N.: Landslide susceptibility map refinement using PSInSAR data, Remote Sens. Environ., 184, 302–315, https://doi.org/10.1016/j.rse.2016.07.018, 2016.
Ciccarese, G., Mulas, M., Alberoni, P. P., Truffelli, G., and Corsini, A.: Debris flows rainfall thresholds in the Apennines of Emilia-Romagna (Italy) derived by the analysis of recent severe rainstorms events and regional meteorological data, Geomorphology, 358, 107097, https://doi.org/10.1016/j.geomorph.2020.107097, 2020.
Ciccarese, G., Mulas, M., and Alessandro, C.: Combining spatial modelling and regionalization of rainfall thresholds for debris flows hazard mapping in the Emilia-Romagna Apennines (Italy), Landslides, 18, 1–17, https://doi.org/10.1007/s10346-021-01739-w, 2021.
Cislaghi, A., Chiaradia, E. A., and Bischetti, G. B.: Including root reinforcement variability in a probabilistic 3D stability model, Earth Surf. Proc. Land., 42, 1789–1806, https://doi.org/10.1002/esp.4127, 2017.
CNR and IRPI: Rapporto Periodico sul Rischio posto alla Popolazione italiana da Frane e Inondazioni, Anno 2020, 19 pp., https://doi.org/10.30437/report2020, 2021.
Collischonn, W., Fleischmann, A., Paiva, R. C. D., and Mejia, A.: Hydraulic Causes for Basin Hydrograph Skewness, Water Resour. Res., 53, 10603–10618, https://doi.org/10.1002/2017WR021543, 2017.
Crosta, G. B. and Frattini, P.: Distributed modelling of shallow landslides triggered by intense rainfall, Nat. Hazards Earth Syst. Sci., 3, 81–93, https://doi.org/10.5194/nhess-3-81-2003, 2003.
Crosta, G. B., Imposimato, S., and Roddeman, D. G.: Numerical modelling of large landslides stability and runout, Nat. Hazards Earth Syst. Sci., 3, 523–538, https://doi.org/10.5194/nhess-3-523-2003, 2003.
Dade, W. B. and Friend, P. F.: Grain-Size, Sediment-Transport Regime, and Channel Slope in Alluvial Rivers, J. Geol., 106, 661–676, https://doi.org/10.1086/516052, 1998.
D'Agostino, V. and Marchi, L.: Debris flow magnitude in the Eastern Italian Alps: Data collection and analysis, Phys. Chem. Earth Pt. C, 26, 657–663, https://doi.org/10.1016/S1464-1917(01)00064-2, 2001.
Daly, C., Taylor, G., and Gibson, W.: The PRISM Approach to Mapping Precipitation and Temperature, https://api.semanticscholar.org/CorpusID:17141237 (last access: 1 November 2023), 1997.
Daly, C., Slater, M. E., Roberti, J. A., Laseter, S. H., and Swift Jr., L. W.: High-resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset, Int. J. Climatol., 37, 124–137, https://doi.org/10.1002/joc.4986, 2017.
Davolio, S., Della Fera, S., Laviola, S., Miglietta, M. M., and Levizzani, V.: Heavy precipitation over Italy from the Mediterranean storm “Vaia” in October 2018: Assessing the role of an atmospheric river, Mon. Weather Rev., 148, 3571–3588, 2020.
Davy, P. and Lague, D.: Fluvial erosion/transport equation of landscape evolution models revisited, J. Geophys. Res.-Earth, 114, F03007, https://doi.org/10.1029/2008JF001146, 2009.
de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., and Bierkens, M. F. P.: A high-resolution global-scale groundwater model, Hydrol. Earth Syst. Sci., 19, 823–837, https://doi.org/10.5194/hess-19-823-2015, 2015.
de Vente, J. and Poesen, J.: Predicting soil erosion and sediment yield at the basin scale: Scale issues and semi-quantitative models, Earth-Sci. Rev., 71, 95–125, https://doi.org/10.1016/j.earscirev.2005.02.002, 2005.
Devia, G. K., Ganasri, B. P., and Dwarakish, G. S.: A Review on Hydrological Models, Aquat. Proced., 4, 1001–1007, https://doi.org/10.1016/j.aqpro.2015.02.126, 2015.
De Vita, P., Fusco, F., Tufano, R., and Cusano, D.: Seasonal and Event-Based Hydrological and Slope Stability Modeling of Pyroclastic Fall Deposits Covering Slopes in Campania (Southern Italy), Water, 10, 1140, https://doi.org/10.3390/w10091140, 2018.
D'Odorico, P. and Fagherazzi, S.: A probabilistic model of rainfall-triggered shallow landslides in hollows: A long-term analysis, Water Resour. Res., 39, 1262, https://doi.org/10.1029/2002WR001595, 2003.
Erskine, R. H., Green, T. R., Ramirez, J. A., and MacDonald, L. H.: Comparison of grid-based algorithms for computing upslope contributing area, Water Resour. Res., 42, W09416, https://doi.org/10.1029/2005WR004648, 2006.
Fan, Y., Miguez-Macho, G., Weaver, C. P., Walko, R., and Robock, A.: Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations, J. Geophys. Res.-Atmos., 112, D10125, https://doi.org/10.1029/2006JD008111, 2007.
Fawcett, T.: An introduction to ROC analysis, Pattern Recog. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Formetta, G., Capparelli, G., and Versace, P.: Evaluating performance of simplified physically based models for shallow landslide susceptibility, Hydrol. Earth Syst. Sci., 20, 4585–4603, https://doi.org/10.5194/hess-20-4585-2016, 2016.
Gao, L., Zhang, L. M., and Cheung, R. W. M.: Relationships between natural terrain landslide magnitudes and triggering rainfall based on a large landslide inventory in Hong Kong, Landslides, 15, 727–740, https://doi.org/10.1007/s10346-017-0904-x, 2018.
Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate, Earth-Sci. Rev., 162, 227–252, https://doi.org/10.1016/j.earscirev.2016.08.011, 2016.
GDAL/OGR contributors: GDAL/OGR Geospatial Data Abstraction software Library, Open Source Geospatial Foundation, https://gdal.org/index.html (last access: 1 November 2023), 2020.
Girard, M.-C., Girard, C., Dominique, C., Gilliot, J.-M., Loubersac, L., Meyer-Roux, J., Monget, J.-M., Seguin, B., and Rao, N.: Corine Land Cover, Routledge, 331–344, https://doi.org/10.1201/9780203741917-19, 2018.
Gleick, P. H.: Climate change, hydrology, and water resources, Rev. Geophys., 27, 329–344, https://doi.org/10.1029/RG027i003p00329, 1989.
Globevnik, L., Holjević, D., Petkovšek, G., and Rubinić, J.: 145. Applicability of the Gavrilo vic Method in Erosion Calculation Using Spatial Data Manipulation Techniques, Tunnelling and Underground Space Technology, 14 pp. https://api.semanticscholar.org/CorpusID:54749986 (last access: 1 November 2023), 2003.
Govers, G.: Empirical relationships for the transport capacity of overland flow, https://iahs.info/uploads/dms/8088.45-63-189-Govers.pdf (last access: 1 November 2023), 1989.
Govers, G., Wallings, D. E., Yair, A., and Berkowicz, S.: Empirical relationships for the transport capacity of overland flow, International Association of Hydrological Sciences, 189 pp., 1990.
Groenendyk, D. G., Ferré, T. P. A., Thorp, K. R., and Rice, A. K.: Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function., PLoS One, 10, e0131299, https://doi.org/10.1371/journal.pone.0131299, 2015.
Guadagno, M., Guzzetti, I., Reichenbach, I., and Tonelli, I.: SICI – Sistema Informativo Catastrofi Idrogeologiche, Istituto di Ricerca per la Protezione Idrogeologica (IRPI) del Consiglio Nazionale delle Ricerche e Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche (GNDCI) del Consiglio Nazionale delle Ricerche, https://sici.irpi.cnr.it/ (last access: 1 November 2023), 2003.
Gudiyangada Nachappa, T., Tavakkoli Piralilou, S., Ghorbanzadeh, O., Shahabi, H., and Blaschke, T.: Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory, Appl. Sci., 9, 5393, https://doi.org/10.3390/app9245393, 2019.
Guzzetti, F. and Tonelli, G.: Information system on hydrological and geomorphological catastrophes in Italy (SICI): a tool for managing landslide and flood hazards, Nat. Hazards Earth Syst. Sci., 4, 213–232, https://doi.org/10.5194/nhess-4-213-2004, 2004.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., and Ardizzone, F.: Probabilistic landslide hazard assessment at the basin scale, Geomorphology, 72, 272–299, https://doi.org/10.1016/j.geomorph.2005.06.002, 2005.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: Rainfall thresholds for the initiation of landslides in central and southern Europe, Meteorol. Atmos. Phys., 98, 239–267, https://doi.org/10.1007/s00703-007-0262-7, 2007.
Harp, E. L., Michael, J. A., and Laprade, W. T.: Shallow-landslide hazard map of Seattle, USGS, Washington, Reston, VA, https://doi.org/10.3133/ofr20061139, 2006.
Hayashi, M.: Alpine Hydrogeology: The Critical Role of Groundwater in Sourcing the Headwaters of the World, Groundwater, 58, 498–510, https://doi.org/10.1111/gwat.12965, 2020.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Herrera, M.: Landslide Detection using Random Forest Classifier, Delft University of Technology, Delft, https://doi.org/10.13140/RG.2.2.31365.91369, 2019.
Huscroft, J., Gleeson, T., Hartmann, J., and Börker, J.: Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0), Geophys. Res. Lett., 45, 1897–1904, https://doi.org/10.1002/2017GL075860, 2018.
Iida, T.: A stochastic hydro-geomorphological model for shallow landsliding due to rainstorm, Catena, 34, 293–313, https://doi.org/10.1016/S0341-8162(98)00093-9, 1999.
ISPRA: Dissesto idrogeologico in Italia: pericolosità e indicatori di rischio, Ispra, https://www.isprambiente.gov.it/it/attivita/suolo-e-territorio/dissesto-idrogeologico (last access: 1 November 2023), 2018.
ITCOLD: La gestione dell'interrimento dei serbatoi artificiali italiani, Comitato Nazionale Italiano delle Grandi Dighe, https://www.itcold.it/wpsysfiles/wp-content/uploads/2016/07/RAPPFIN-GdLInterrimento-Fase1-20091.pdf (last access: 1 November 2023), 2009.
ITCOLD: La gestione dell'interrimento dei serbatoi artificiali italiani situazione attuale e prospettive, Comitato Nazionale Italiano delle Grandi Dighe, https://www.itcold.it/wpsysfiles/wp-content/uploads/2016/07/RAPPFIN-GdLInterrimento-Fase2-20092.pdf (last access: 1 November 2023), 2016.
Ivanov, V., Radice, A., Papini, M., and Longoni, L.: Event-scale pebble mobility observed by RFID tracking in a pre-Alpine stream: a field laboratory, Earth Surf. Proc. Land., 45, 535–547, https://doi.org/10.1002/esp.4752, 2020a.
Ivanov, V., Arosio, D., Tresoldi, G., Hojat, A., Zanzi, L., Papini, M., and Longoni, L.: Investigation on the Role of Water for the Stability of Shallow Landslides-Insights from Experimental Tests, Water, 12, 1203, https://doi.org/10.3390/w12041203, 2020b.
Iverson, R., Reid, M., and Lahusen, R.: Debris-flow mobilization from landslides. Annu Rev Earth Planet Sci, Annu. Rev. Earth Planet. Sci., 25, 85–138, https://doi.org/10.1146/annurev.earth.25.1.85, 1997.
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour. Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090, 2000.
Jackson, C. R., Bitew, M., and Du, E.: When interflow also percolates: downslope travel distances and hillslope process zones, Hydrol. Process., 28, 3195–3200, https://doi.org/10.1002/hyp.10158, 2014.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014.
Jakob, M. and Hungr, O.: Debris-Flow Hazards and Related Phenomena, Springer, ISBN 978-3-540-20726-9, 2005.
Jakob, M. and Jordan, P.: Design flood estimates in mountain streams – the need for a geomorphic approach, Can. J. Civ. Eng., 28, 425–439, https://doi.org/10.1139/l01-010, 2001.
Kadavi, P., Lee, C.-W., and Lee, S.: Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping, Remote Sens., 10, 1252, https://doi.org/10.3390/rs10081252, 2018.
Karssenberg, D., Schmitz, O., Salamon, P., de Jong, K., and Bierkens, M. F. P.: A software framework for construction of process-based stochastic spatio-temporal models and data assimilation, Environ. Model. Softw., 25, 489–502, https://doi.org/10.1016/j.envsoft.2009.10.004, 2010.
Kim, K.-S., Kim, M.-I., Lee, M.-S., and Hwang, E.-S.: Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics, Appl. Sci., 10, 3560, https://doi.org/10.3390/app10103560, 2020.
Klaus, J. and Jackson, C. R.: Interflow Is Not Binary: A Continuous Shallow Perched Layer Does Not Imply Continuous Connectivity, Water Resour. Res., 54, 5921–5932, https://doi.org/10.1029/2018WR022920, 2018.
Kobierska, F., Jonas, T., Kirchner, J. W., and Bernasconi, S. M.: Linking baseflow separation and groundwater storage dynamics in an alpine basin (Dammagletscher, Switzerland), Hydrol. Earth Syst. Sci., 19, 3681–3693, https://doi.org/10.5194/hess-19-3681-2015, 2015.
Kondolf, G. M.: Hungry Water: Effects of Dams and Gravel Mining on River Channels, Environ. Manage., 21, 533–551, https://doi.org/10.1007/s002679900048, 1997.
Lamb, M. P. and Venditti, J. G.: The grain size gap and abrupt gravel-sand transitions in rivers due to suspension fallout, Geophys. Res. Lett., 43, 3777–3785, https://doi.org/10.1002/2016GL068713, 2016.
Langland, M. J.: Bathymetry and Sediment-Storage Capacity Change in Three Reservoirs on the Lower Susquehanna River, 1996–2008, USGS, https://doi.org/10.3133/sir20095110, 2009.
Lazzari, M., Piccarreta, M., and Manfreda, S.: The role of antecedent soil moisture conditions on rainfall-triggered shallow landslides, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2018-371, 2018.
Lee, K. and Pin Chun, H.: Evaluating the adequateness of kinematic-wave routing for flood forecasting in midstream channel reaches of Taiwan, J. Hydroinform., 14, 1075, https://doi.org/10.2166/hydro.2012.093, 2012.
Legorreta Paulin, G., Bursik, M., Lugo-Hubp, J., and Zamorano Orozco, J. J.: Effect of pixel size on cartographic representation of shallow and deep-seated landslide, and its collateral effects on the forecasting of landslides by SINMAP and Multiple Logistic Regression landslide models, Phys. Chem. Earth Pt. A/B/C, 35, 137–148, https://doi.org/10.1016/j.pce.2010.04.008, 2010.
Lehner, B., Verdin, K., and Jarvis, A.: New Global Hydrography Derived From Spaceborne Elevation Data, Eos Trans. Am. Geophys. Union, 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Li, X., Xiao, Q., Niu, J., Dymond, S., McPherson, E. G., van Doorn, N., Yu, X., Xie, B., Zhang, K., and Li, J.: Rainfall interception by tree crown and leaf litter: An interactive process, Hydrol. Process., 31, 3533–3542, https://doi.org/10.1002/hyp.11275, 2017.
Longoni, L., Ivanov, V. I., Brambilla, D., Radice, A., and Papini, M.: Analysis of the temporal and spatial scales of soil erosion and transport in a Mountain Basin, Ital. J. Eng. Geol. Environ., 16, 17–30, https://doi.org/10.4408/IJEGE.2016-02.O-02, 2016.
López Vicente, M., Pérez-Bielsa, C., López-Montero, T., Lambán, L. J., and Navas, A.: Runoff simulation with eight different flow accumulation algorithms: Recommendations using a spatially distributed and open-source model, Environ. Model. Softw., 62, 11–21, 2014.
Luino, F.: Sequence of instability processes triggered by heavy rainfall in the Northern Italy, Geomorphology, 66, 13–39, https://doi.org/10.1016/j.geomorph.2004.09.010, 2005.
Ly, S., Charles, C., and Degre, A.: Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale. A review, Biotechnol. Agron. Soc. Environ., 17, 392–406, 2013.
Marnezy, A.: Alpine dams. From hydroelectric power to artificial snow, Revue De Geographie Alpine – Journal of Alpine Research, 96, 103–112, 2008.
Meisina, C., Zizioli, D., and Zucca, F.: Methods for shallow landslides susceptibility mapping: an example in Oltrepo Pavese (Northern Italy), Landslides Science and Practice, in: Volume 1: Landslide Inventory and Susceptibility and Hazard, edited by: Zoning Margottini, C., Canuti, P., and Sassa, K., Springer, 451–458, ISBN 978-3-642-31324-0, https://doi.org/10.1007/978-3-642-31325-7, 2013.
Merritt, W. S., Letcher, R. A., and Jakeman, A. J.: A review of erosion and sediment transport models, Environ. Model. Softw., 18, 761–799, https://doi.org/10.1016/S1364-8152(03)00078-1, 2003.
Michel, G. P., Kobiyama, M., and Goerl, R. F.: Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, southern Brazil, J. Soils Sediments, 14, 1266–1277, https://doi.org/10.1007/s11368-014-0886-4, 2014.
Milanesi, L., Pilotti, M., Clerici, A., and Gavrilovic, Z.: Application of an improved version of the Erosion Potential Method in Alpine areas, Ital. J. Eng. Geol. Environ., 1, 17–30, https://doi.org/10.4408/IJEGE.2015-01.O-02, 2015.
Milledge, D. G., Bellugi, D., McKean, J. A., Densmore, A. L., and Dietrich, W. E.: A multidimensional stability model for predicting shallow landslide size and shape across landscapes, J. Geophys. Res.-Earth, 119, 2481–2504, https://doi.org/10.1002/2014JF003135, 2014.
Mishra, S. K., Tyagi, J. V., and Singh, V. P.: Comparison of infiltration models, Hydrol. Process., 17, 2629–2652, https://doi.org/10.1002/hyp.1257, 2003.
Moges, E., Demissie, Y., Larsen, L., and Yassin, F.: Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis, Water, 13, 28, https://doi.org/10.3390/w13010028, 2021.
Montrasio, L.: Stability of soil-slip, Risk Analysis II, WIT Press, https://www.witpress.com/Secure/elibrary/papers/RISK00/RISK00033FU.pdf (last access: 1 November 2023), 2008.
Montrasio, L. and Valentino, R.: Modelling Rainfall-induced Shallow Landslides at Different Scales Using SLIP – Part II, Proced. Eng., 158, 482–486, https://doi.org/10.1016/j.proeng.2016.08.476, 2016.
Morbidelli, R., Corradini, C., Saltalippi, C., Flammini, A., Dari, J., and Govindaraju, R. S.: Rainfall Infiltration Modeling: A Review, Water, 10, 1873, https://doi.org/10.3390/w10121873, 2018.
Morgan, R. P. C. and Nearing, M. A. (Eds.): Handbook of erosion modelling, Blackwell Publishing Ltd, ISBN 9781405190107, ISBN 9781444328455, https://doi.org/10.1002/9781444328455, 2011.
Munich Re: Natural disasters caused overall losses of US $ 210bn Relevant natural catastrophe loss events worldwide 2020, https://www.munichre.com/content/dam/munichre/mrwebsiteslaunches/natcat-2021/2020_Jan-Dec_Weltkarte_e.pdf/_jcr_content/renditions/original./2020_Jan-Dec_Weltkarte_e.pdf (last access: 1 November 2023), 2021.
Nazari, M., Sadeghi, S. M. M., Van Stan, J., and Chaichi, M.: Rainfall interception and redistribution by maize farmland in central Iran, J. Hydrol.: Reg. Stud., 27, 100656, https://doi.org/10.1016/j.ejrh.2019.100656, 2019.
Nino, Y.: Simple Model for Downstream Variation of Median Sediment Size in Chilean Rivers, J. Hydraul. Eng., 128, 934–941, 2002.
Oguz, E. A., Depina, I., and Thakur, V.: Effects of soil heterogeneity on susceptibility of shallow landslides, Landslides, 19, 67–83, https://doi.org/10.1007/s10346-021-01738-x, 2022.
Pacina, J., Lenďáková, Z., Štojdl, J., Matys Grygar, T., and Dolejš, M.: Dynamics of Sediments in Reservoir Inflows: A Case Study of the Skalka and Nechranice Reservoirs, Czech Republic, ISPRS Int. J. Geo-Inform., 9, 258, https://doi.org/10.3390/ijgi9040258, 2020.
Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K., Montanarella, L., and Alewell, C.: The new assessment of soil loss by water erosion in Europe, Environ. Sci. Policy, 54, 438–447, https://doi.org/10.1016/j.envsci.2015.08.012, 2015.
Papini, M., Ivanov, V., Brambilla, D., Arosio, D., and Longoni, L.: Monitoring bedload sediment transport in a pre-Alpine river: An experimental method, Rendiconti Online della Società Geologica Italiana, 43, 57–63, https://doi.org/10.3301/ROL.2017.35, 2017.
Parenti, C., Rossi, P., Mancini, F., Scorpio, V., Grassi, F., Ciccarese, G., Lugli, F., and Soldati, M.: Multitemporal Analysis of Slow-Moving Landslides and Channel Dynamics through Integrated Remote Sensing and In Situ Techniques, Remote Sens., 15, 3563, https://doi.org/10.3390/rs15143563, 2023.
Pearson, E., Smith, M. W., Klaar, M. J., and Brown, L. E.: Can high resolution 3D topographic surveys provide reliable grain size estimates in gravel bed rivers?, Geomorphology, 293, 143–155, https://doi.org/10.1016/j.geomorph.2017.05.015, 2017.
Pebesma, E. J., de Jong, K., and Briggs, D.: Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: an air quality example, Int. J. Geogr. Inform. Sci., 21, 515–527, https://doi.org/10.1080/13658810601064009, 2007.
Peirce, S., Ashmore, P., and Leduc, P.: Evolution of grain size distributions and bed mobility during hydrographs in gravel-bed braided rivers, Earth Surf. Proc. Land., 44, 304–316, https://doi.org/10.1002/esp.4511, 2019.
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Syst., 8, 41–65, https://doi.org/10.1002/2015MS000526, 2016.
Pereira, S., Garcia, R., Zêzere, J., Oliveira, S., and Silva, M.: Landslide quantitative risk analysis of buildings at the municipal scale based on a rainfall triggering scenario, Geomat. Nat. Hazards Risk, 8, 624–648, https://doi.org/10.1080/19475705.2016.1250116, 2016.
Pérez-Peña, J. V., Azañón, J. M., and Azor, A.: CalHypso: An ArcGIS extension to calculate hypsometric curves and their statistical moments. Applications to drainage basin analysis in SE Spain, Comput. Geosci., 35, 1214–1223, 2009.
Rahardjo, H., Satyanaga, A., Leong, E. C., Santoso, V. A., and Ng, Y. S.: Performance of an instrumented slope covered with shrubs and deep-rooted grass, Soils Foundat., 54, 417–425, https://doi.org/10.1016/j.sandf.2014.04.010, 2014.
Raj, P. P.: Comparison of True and Residual Friction Angles, Soils Foundat., 21, 99–103, https://doi.org/10.3208/sandf1972.21.3_99, 1981.
Ravi, V., Williams, J. R., and Ouyang, Y.: Estimation of infiltration rate in the vadose zone: compilation of simple mathematical models, https://api.semanticscholar.org/CorpusID:16241319 (last access: 1 November 2023), 1998.
Raziei, T. and Pereira, L.: Estimation of ETo with Hargreaves-Samani and FAO-PM temperature methods for a wide range of climates in Iran, Agr. Water Manage., 121, 1–18, https://doi.org/10.1016/j.agwat.2012.12.019, 2013.
Remondo, J., Bonachea, J., and Cendrero, A.: A statistical approach to landslide risk modelling at basin scale: From landslide susceptibility to quantitative risk assessment, Landslides, 2, 321–328, https://doi.org/10.1007/s10346-005-0016-x, 2005.
Rete Monitoraggio ARPA Emilia-Romagna: Dati di Monitoraggio Idro-Meteorologico, https://www.arpae.it/it/temi-ambientali/meteo/dati-e-osservazioni (last access: 1 November 2023), 2023.
Rete Monitoraggio ARPA Lombardia: Dati di Monitoraggio Idro-Meteorologico, https://www.arpalombardia.it/dati-e-indicatori/meteo-e-clima/ (last access: 1 November 2023), 2023.
Rickenmann, D.: Empirical Relationships for Debris Flows, Nat. Hazards, 19, 47–77, https://doi.org/10.1023/A:1008064220727, 1999.
Rocha, J., Duarte, A., Silva, M., Fabres, S., Vasques, J., Revilla-Romero, B., and Quintela, A.: The Importance of High Resolution Digital Elevation Models for Improved Hydrological Simulations of a Mediterranean Forested Catchment, Remote Sens., 12, 3287, https://doi.org/10.3390/rs12203287, 2020.
Ronchetti, F., Borgatti, L., Cervi, F., C, G., Piccinini, L., Vincenzi, V., and Alessandro, C.: Groundwater processes in a complex landslide, northern Apennines, Italy, Nat. Hazards Earth Syst. Sci., 9, 895–904, https://doi.org/10.5194/nhess-9-895-2009, 2009.
Roo, A., Wesseling, C. G., Jetten, V. G., and Ritsema, C.: LISEM: A physically-based hydrological and soil erosion model incorporated in a GIS, in: Application of geographic information systems in hydrology and water resources management, edited by: Kovar, K. and Nachtnebel, H. P., Wallingford, UK, IAHS Publ., 235, 395–403, 1996.
Ross, C. W., Prihodko, L., Anchang, J., Kumar, S., Ji, W., and Hanan, N. P.: HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling, Sci. Data, 5, 180091–180091, https://doi.org/10.1038/sdata.2018.91, 2018.
Salles, T.: eSCAPE: Regional to Global Scale Landscape Evolution Model v2.0, Geosci. Model Dev., 12, 4165–4184, https://doi.org/10.5194/gmd-12-4165-2019, 2019.
Sambrook Smith, G. H. and Ferguson, R. I.: The gravel-sand transition along river channels, J. Sediment. Res., 65, 423–430, https://doi.org/10.1306/D42680E0-2B26-11D7-8648000102C1865D, 1995.
Scheidl, C. and Rickenmann, D.: Topflowdf – a simple gis based model to simulate debris-flow runout on the fan, Ital. J. Eng. Geol. Environ., 253–262, https://doi.org/10.4408/IJEGE.2011-03.B-030, 2011.
Schellekens, J., van Verseveld, W., Visser, M., Winsemius, H., Euserand, T., Bouaziz, L. C. T., de Vriesand, S., Boisgontierand, H., Eilanderand, D., Tollenaarand, D., Weertsand, A., Baartand, F., Hazenbergand, P., Lutz, L., ten Velden, C., Jansen, M., and Benedict, M.: Wflow, openstreams/wflow: unstable-master. OpenStream wflow documentation release, doi: Zenodo. https://doi.org/10.5281/zenodo.4291730, 2020.
Schoener, G. and Stone, M. C.: Monitoring soil moisture at the catchment scale – A novel approach combining antecedent precipitation index and radar-derived rainfall data, J. Hydrol., 589, 125155, https://doi.org/10.1016/j.jhydrol.2020.125155, 2020.
Shobe, C. M., Tucker, G. E., and Barnhart, K. R.: The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution, Geosci. Model Dev., 10, 4577–4604, https://doi.org/10.5194/gmd-10-4577-2017, 2017.
Sklar, L. S., Riebe, C. S., Marshall, J. A., Genetti, J., Leclere, S., Lukens, C. L., and Merces, V.: The problem of predicting the size distribution of sediment supplied by hillslopes to rivers, Geomorphology, 277, 31–49, 2017.
Smith, R. E. and Parlange, J.-Y.: A parameter-efficient hydrologic infiltration model, Water Resour. Res., 14, 533–538, https://doi.org/10.1029/WR014i003p00533, 1978.
Strahler, A. N.: Dynamic basis of geomorphology, Geol. Soc. Am. Bull., 63, 923–938, 1952.
Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: A hydroclimatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, 2018.
Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018.
Takahashi, T.: A Review of Japanese Debris Flow Research, Int. J. Erosion Contr. Eng., 2, 1–14, https://doi.org/10.13101/ijece.2.1, 2009.
Tangi, M., Schmitt, R., Bizzi, S., and Castelletti, A.: The CASCADE toolbox for analyzing river sediment connectivity and management, Environ. Model. Softw., 119, 400–406, https://doi.org/10.1016/j.envsoft.2019.07.008, 2019.
Tanyaş, H., van Westen, C. J., Allstadt, K. E., and Jibson, R. W.: Factors controlling landslide frequency–area distributions, Earth Surf. Proc. Land., 44, 900–917, https://doi.org/10.1002/esp.4543, 2019.
Tavares da Costa, R., Mazzoli, P., and Bagli, S.: Limitations Posed by Free DEMs in Watershed Studies: The Case of River Tanaro in Italy, Front. Earth Sci., 7, 141, https://doi.org/10.3389/feart.2019.00141, 2019.
Terzago, S., Palazzi, E., and von Hardenberg, J.: Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology, Nat. Hazards Earth Syst. Sci., 18, 2825–2840, https://doi.org/10.5194/nhess-18-2825-2018, 2018.
Theule, J.: Geomorphic study of sediment dynamics in active debris-flow catchments (French Alps), Environmental Sciences, Doctorat de l'université de Grenoble, Science de la Terre, de l'Univers et de l'Environnement, Grenoble, https://hal.science/tel-02600319/ (last access: 1 November 2023), 2012.
Tian, J., Zhang, B., He, C., and Yang, L.: Variability In Soil Hydraulic Conductivity And Soil Hydrological Response Under Different Land Covers In The Mountainous Area Of The Heihe River Watershed, Northwest China, Land Degrad. Dev., 28, 1437–1449, https://doi.org/10.1002/ldr.2665, 2016.
Tóth, B., Weynants, M., Pásztor, L., and Hengl, T.: 3D soil hydraulic database of Europe at 250 m resolution, Hydrol. Process., 31, 2662–2666, https://doi.org/10.1002/hyp.11203, 2017.
Tramblay, Y., Bouvier, C., Martin, C., Didon-Lescot, J.-F., Todorovik, D., and Domergue, J.-M.: Assessment of initial soil moisture conditions for event-based rainfall–runoff modelling, J. Hydrol., 387, 176–187, https://doi.org/10.1016/j.jhydrol.2010.04.006, 2010.
Uber, M., Vandervaere, J.-P., Zin, I., Braud, I., Heistermann, M., Legoût, C., Molinié, G., and Nord, G.: How does initial soil moisture influence the hydrological response? A case study from southern France, Hydrol. Earth Syst. Sci., 22, 6127–6146, https://doi.org/10.5194/hess-22-6127-2018, 2018.
Vakhshoori, V. and Zare, M.: Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps?, Geomat. Nat. Hazards Risk, 9, 249–266, https://doi.org/10.1080/19475705.2018.1424043, 2018.
Van Der Knijff, J. M., Younis, J., and De Roo, A. P. J.: LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood simulation, Int. J. Geogr. Inform. Sci., 24, 189–212, https://doi.org/10.1080/13658810802549154, 2010.
Van Genuchten, M.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Vetsch, D., Siviglia, A., Caponi, F., Ehrbar, D., Gerke, E., Kammerer, S., Koch, A., Peter, S., Vanzo, D., Vonwiller, L., Facchini, M., Gerber, M., Volz, C., Farshi, D., Mueller, R., Rousselot, P., Veprek, R., and Faeh, R.: System Manuals of BASEMENT Version 2.8, https://people.ee.ethz.ch/~basement/baseweb/download/documentation/BMdoc_Reference_Manual_v2-8-2.pdf (last access: 1 November 2023), 2018.
Vitvar, T., Burns, D. A., Lawrence, G. B., McDonnell, J. J., and Wolock, D. M.: Estimation of baseflow residence times in watersheds from the runoff hydrograph recession: method and application in the Neversink watershed, Catskill Mountains, New York, Hydrol. Process., 16, 1871–1877, https://doi.org/10.1002/hyp.5027, 2002.
Yu, B., Xie, C., Cai, S., Chen, Y., Lv, Y., Mo, Z., Liu, T., and Yang, Z.: Effects of Tree Root Density on Soil Total Porosity and Non-Capillary Porosity Using a Ground-Penetrating Tree Radar Unit in Shanghai, China, Sustainability, 10, 4640, https://doi.org/10.3390/su10124640, 2018.
Zhang, H., Li, Z., Saifullah, M., Li, Q., and Li, X.: Impact of DEM Resolution and Spatial Scale: Analysis of Influence Factors and Parameters on Physically Based Distributed Model, Adv. Meteorol., 2016, 8582041, https://doi.org/10.1155/2016/8582041, 2016.
Zheng, S., Zhang, G., Yuan, X., Ye, F., and Fu, W.: Failure characteristics of shallow soil slope considering surface runoff and interstitial flow, Geomat. Nat. Hazards Risk, 11, 845–868, https://doi.org/10.1080/19475705.2020.1758222, 2020.
Zomlot, Z., Verbeiren, B., Huysmans, M., and Batelaan, O.: Spatial distribution of groundwater recharge and base flow: Assessment of controlling factors, J. Hydrol.: Reg. Stud., 4, 349–368, https://doi.org/10.1016/j.ejrh.2015.07.005, 2015.
Short summary
CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) is a new physically based and spatially distributed rainfall-runoff model. The main novelties consist of reproducing rainfall-induced geo-hydrological hazards such as shallow landslide, debris flow and watershed erosion through a multi-hazard approach. CRHyME was written in Python, works at a high spatial and temporal resolution, and is a tool suitable for quantifying extreme rainfall consequences at the basin scale.
CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) is a new physically based and...
Altmetrics
Final-revised paper
Preprint