Articles | Volume 28, issue 12
https://doi.org/10.5194/hess-28-2661-2024
© 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-28-2661-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A comprehensive framework for stochastic calibration and sensitivity analysis of large-scale groundwater models
Andrea Manzoni
Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, 20133 Milano, Italy
Giovanni Michele Porta
Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, 20133 Milano, Italy
Laura Guadagnini
Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, 20133 Milano, Italy
Alberto Guadagnini
Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, 20133 Milano, Italy
Monica Riva
CORRESPONDING AUTHOR
Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, 20133 Milano, Italy
Related authors
No articles found.
Chuan-An Xia, Jiayun Li, Bill X. Hu, Alberto Guadagnini, and Monica Riva
EGUsphere, https://doi.org/10.5194/egusphere-2025-5320, https://doi.org/10.5194/egusphere-2025-5320, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Pumping wells may not be officially registered or documented. We develop a new framework to jointly estimate spatially variable conductivity and identify unknown pumping well locations and rates. Our results support the ability of the new approach to accurately estimate conductivity and identify well location and rates under diverse configurations, attaining a quality of performance similar to its traditional counterpart while computational time is reduced by nearly an order of magnitude.
Stefano Conversi, Daniela Carrion, and Monica Riva
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 315–321, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-315-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-315-2025, 2025
Stefano Conversi, Daniela Carrion, Francesco Gioia, Alessandra Norcini, and Monica Riva
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 19–27, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-19-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-19-2024, 2024
David Luttenauer, Aronne Dell'Oca, Alberto Guadagnini, Sylvain Weill, and Philippe Ackerer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-73, https://doi.org/10.5194/hess-2024-73, 2024
Revised manuscript not accepted
Short summary
Short summary
Land Surface Model outputs (evaporation, transpiration, groundwater recharge) are influenced by uncertain parameters. Global sensitivity metrics provide a ranking of the importance of uncertain factors. Evaporation is directly influenced by the net radiation and by the parameters associated with the top litter layer. Transpiration appears as mainly influenced by the vegetation characteristics and by albedo. Groundwater recharge is influenced mainly by soil-related parameters.
S. Conversi, D. Carrion, A. Norcini, and M. Riva
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1363–1371, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1363-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1363-2023, 2023
Yaniv Edery, Martin Stolar, Giovanni Porta, and Alberto Guadagnini
Hydrol. Earth Syst. Sci., 25, 5905–5915, https://doi.org/10.5194/hess-25-5905-2021, https://doi.org/10.5194/hess-25-5905-2021, 2021
Short summary
Short summary
The interplay between dissolution, precipitation and transport is widely encountered in porous media, from CO2 storage to cave formation in carbonate rocks. We show that dissolution occurs along preferential flow paths with high hydraulic conductivity, while precipitation occurs at locations close to yet separated from these flow paths, thus further funneling the flow and changing the probability density function of the transport, as measured on the altered conductivity field at various times.
Giulia Ceriotti, Claudio Geloni, Matilde Dalla Rosa, Alberto Guadagnini, and Giovanni Porta
Hydrol. Earth Syst. Sci., 25, 3539–3553, https://doi.org/10.5194/hess-25-3539-2021, https://doi.org/10.5194/hess-25-3539-2021, 2021
Short summary
Short summary
Understanding the natural generation of CO2 in sedimentary basins is key to optimizing exploitation of natural resources and exploring feasibility of carbon capture and storage. We present a novel modeling approach to estimate the probability of CO2 generation caused by geochemical reactions at high temperatures and pressure in realistic sedimentary basins. Our model allows estimation of the most probable CO2 source depth and generation rate as a function of the composition of the source rock.
Chuan-An Xia, Xiaodong Luo, Bill X. Hu, Monica Riva, and Alberto Guadagnini
Hydrol. Earth Syst. Sci., 25, 1689–1709, https://doi.org/10.5194/hess-25-1689-2021, https://doi.org/10.5194/hess-25-1689-2021, 2021
Short summary
Short summary
Our study shows that (i) monitoring wells installed with packers provide the (overall) best conductivity estimates; (ii) conductivity estimates anchored on information from partially and fully screened wells are of similar quality; (iii) inflation of the measurement-error covariance matrix can improve conductivity estimates when a simplified flow model is adopted; and (iv) when compared to the MC-based EnKF, the MEs-based EnKF can efficiently and accurately estimate conductivity and head fields.
Cited articles
AdB-Po: Piano di Gestione del distretto idrografico del fiume Po al 2021, https://www.adbpo.it/PianoAcque2021/PdGPo2021_22dic21/, 2021 (in Italian).
Agenzia Regionale per la Protezione Ambientale Piemonte: Portale acque, Agenzia Regionale per la Protezione Ambientale Piemonte [data set], https://www.arpa.piemonte.it/rischinaturali/accesso-ai-dati, 2020 (in Italian).
Agrawala, S.: Climate change in the European Alps: adapting winter tourism and natural hazards management, OECD (Organisation for Economic Co-operation and Development, https://doi.org/10.1787/9789264031692-en, 2007.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration: Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper No. 56, Food and Agriculture Organization of the United Nations, ISBN 92-5-104219-5, 1998.
Amanambu, A. C., Obarein, O. A., Mossa, J., Li, L., Ayeni, S. S., Balogun, O., Oyebamiji, A., and Ochege, F. U.: Groundwater system and climate change: Present status and future considerations, J. Hydrol., 589, 125163, https://doi.org/10.1016/j.jhydrol.2020.125163, 2020.
Antonellini, M., Mollema, P., Giambastiani, B., Bishop, K., Caruso, L., Minchio, A., Pellegrini, L., Sabia, M., Ulazzi, E., and Gabbianelli, G.: Salt water intrusion in the coastal aquifer of the southern Po Plain, Italy, Hydrogeol. J., 16, 1541–1556, https://doi.org/10.1007/s10040-008-0319-9, 2008.
Balestrini, R., Delconte, C. A., Sacchi, E., and Buffagni, A.: Groundwater-dependent ecosystems as transfer vectors of nitrogen from the aquifer to surface waters in agricultural basins: The fontanili of the Po Plain (Italy), Sci. Total Environ., 753, 141995, https://doi.org/10.1016/j.scitotenv.2020.141995, 2021.
Bianchi Janetti, E., Guadagnini, L., Riva, M., and Guadagnini, A.: Global sensitivity analyses of multiple conceptual models with uncertain parameters driving groundwater flow in a regional-scale sedimentary aquifer, J. Hydrol., 574, 544–556, https://doi.org/10.1016/j.jhydrol.2019.04.035, 2019.
Bianchi Janetti, E., Riva, M., and Guadagnini, A.: Natural springs protection and probabilistic risk assessment under uncertain conditions, Sci. Total Environ., 751, 141430, https://doi.org/10.1016/j.scitotenv.2020.141430, 2021.
Bilke, L., Fischer, T., Naumov, D., Lehmann, C., Wang, W., Lu, R., Meng, B., Rink, K., Grunwald, N., Buchwald, J., Silbermann, C., Habel, R., Günther, L., Mollaali, M., Meisel, T., Randow, J., Einspänner, S., Shao, H., Kurgyis, K., Kolditz, O., and Garibay, J.: OpenGeoSys, Version 6.4.3, Zenodo [code], https://doi.org/10.5281/zenodo.7092676, 2022.
Bonafè, G., Morgillo, A., and Minguzzi, E.: Weather types and wind patterns classification in the Po Valley, during the PEGASOS field campaign (summer 2012), in: EGU General Assembly Conference Abstracts, p. 11939, 2014.
Bozzola, M. and Swanson, T.: Policy implications of climate variability on agriculture: Water management in the Po river basin, Italy, Environ. Sci. Policy, 43, 26–38, https://doi.org/10.1016/j.envsci.2013.12.002, 2014.
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Modell. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007.
Carcano, C. and Piccin, A.: Geologia degli acquiferi Padani della Regione Lombardia Regione Lombardia, Eni Divisione Agip, https://www.cartografia.regione.lombardia.it/metadata/acquiferi/doc/ (last access: 1 October 2022), 2001.
Carrera, J. and Neuman, S. P.: Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information, Water Resour. Res., 22, 199–210, https://doi.org/10.1029/WR022i002p00199, 1986.
Colombani, N., Volta, G., Osti, A., and Mastrocicco, M.: Misleading reconstruction of seawater intrusion via integral depth sampling, J. Hydrol., 536, 320–326, https://doi.org/10.1016/j.jhydrol.2016.03.011, 2016.
Dagdia, Z. C. and Mirchev, M.: When Evolutionary Computing Meets Astro- and Geoinformatics, in: Knowledge Discovery in Big Data from Astronomy and Earth Observation, edited by: Škoda, P. and Adam, F., Elsevier, 283–306, https://doi.org/10.1016/B978-0-12-819154-5.00026-6, 2020.
d'Andrimont, R., Verhegghen, A., Lemoine, G., Kempeneers, P., Meroni, M., and van der Velde, M.: From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations, Remote Sens. Environ., 266, 112708, https://doi.org/10.1016/j.rse.2021.112708, 2021.
De Caro, M., Perico, R., Crosta, G. B., Frattini, P., and Volpi, G.: A regional-scale conceptual and numerical groundwater flow model in fluvio-glacial sediments for the Milan Metropolitan area (Northern Italy), Journal of Hydrology: Regional Studies, 29, 100683, https://doi.org/10.1016/j.ejrh.2020.100683, 2020.
de Graaf, I., Condon, L., and Maxwell, R.: Hyper-Resolution Continental-Scale 3-D Aquifer Parameterization for Groundwater Modeling, Water Resour. Res., 56, e2019WR026004, https://doi.org/10.1029/2019WR026004, 2020.
De Lange, W. J., Prinsen, G. F., Hoogewoud, J. C., Veldhuizen, A. A., Verkaik, J., Oude Essink, G. H. P., Van Walsum, P. E. V., Delsman, J. R., Hunink, J. C., Massop, H. T. L., and Kroon, T.: An operational, multi-scale, multi-model system for consensus-based, integrated water management and policy analysis: The Netherlands Hydrological Instrument, Environ. Modell. Softw., 59, 98–108, https://doi.org/10.1016/j.envsoft.2014.05.009, 2014.
Dell'Oca, A., Riva, M., and Guadagnini, A.: Moment-based metrics for global sensitivity analysis of hydrological systems, Hydrol. Earth Syst. Sci., 21, 6219–6234, https://doi.org/10.5194/hess-21-6219-2017, 2017.
Dell'Oca, A., Riva, M., and Guadagnini, A.: Global Sensitivity Analysis for Multiple Interpretive Models With Uncertain Parameters, Water Resour. Res., 56, e2019WR025754, https://doi.org/10.1029/2019WR025754, 2020.
Dell'Oca, A., Manzoni, A., Siena, M., Bona, N. G., Moghadasi, L., Miarelli, M., Renna, D., and Guadagnini, A.: Stochastic inverse modeling of transient laboratory-scale three-dimensional two-phase core flooding scenarios, Int. J. Heat Mass. Tran., 202, 123716, https://doi.org/10.1016/j.ijheatmasstransfer.2022.123716, 2023.
Dripps, W. R. and Bradbury, K. R.: A simple daily soil-water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas, Hydrogeol. J., 15, 433–444, https://doi.org/10.1007/s10040-007-0160-6, 2007.
Elsasser, H. and Bürki, R.: Climate change as a threat to tourism in the Alps, Clim. Res., 20, 253–257, https://doi.org/10.3354/cr020253, 2002.
ESA: Copernicus DEM, ESA [data set], https://doi.org/10.5270/ESA-c5d3d65, 2019.
Éupolis Lombardia: Piano di Tutela delle Aque - revisione dei corpi idrici lombardia, 2016.
European Environment Agency (EEA): CORINE Land Cover 2018, EEA [data set], https://doi.org/10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0, 2018.
Farinotti, D., Pistocchi, A., and Huss, M.: From dwindling ice to headwater lakes: Could dams replace glaciers in the European Alps?, Environ. Res. Lett., 11, 054022, https://doi.org/10.1088/1748-9326/11/5/054022, 2016.
Fratianni, S. and Acquaotta, F.: The Climate of Italy, Landscapes and Landforms of Italy, Springer International Publishing, 29–38, https://doi.org/10.1007/978-3-319-26194-2_4, 2017.
Geuzaine, C. and Remacle, J.-F.: Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities, Int. J. Numer. Meth. Eng., 79, 1309–1331, https://doi.org/10.1002/nme.2579, 2009.
Giuliano, G.: Ground water in the PO basin: some problems relating to its use and protection, Sci. Total Environ., 171, 17–27, 1995.
Grimm, M., Jones, R. J. A., Rusco, E., and Montanarella, L.: Soil Erosion Risk in Italy: a revised USLE approach, European Soil Bureau Research Report No. 11, EUR 20677 EN, 28 pp., Office for Official Publications of the European Communities, Luxembourg, 2023.
Guadagnini, L., Menafoglio, A., Sanchez-Vila, X., and Guadagnini, A.: Probabilistic assessment of spatial heterogeneity of natural background concentrations in large-scale groundwater bodies through Functional Geostatistics, Sci. Total Environ., 740, 140139, https://doi.org/10.1016/j.scitotenv.2020.140139, 2020.
Hargreaves, G. H. and Samani, Z. A.: Reference Crop Evapotranspiration from Temperature, Appl. Eng. Agric., 1, 96–99, https://doi.org/10.13031/2013.26773, 1985.
Hendricks Franssen, H. J., Alcolea, A., Riva, M., Bakr, M., van der Wiel, N., Stauffer, F., and Guadagnini, A.: A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments, Adv. Water Resour., 32, 851–872, https://doi.org/10.1016/j.advwatres.2009.02.011, 2009.
Højberg, A. L., Troldborg, L., Stisen, S., Christensen, B. B. S., and Henriksen, H. J.: Stakeholder driven update and improvement of a national water resources model, Environ. Modell. Softw., 40, 202–213, https://doi.org/10.1016/j.envsoft.2012.09.010, 2013.
ISPRA: Reticolo Idrografico Nazionale, ISPRA [data set], https://geodati.gov.it/resource/id/ispra_rm:01Idro250N_DT (last access: 1 October 2022), 2010.
ISTAT: Public water supply use, ISTAT [data set], http://dati.istat.it/, 2020.
Kazakis, N., Busico, G., Colombani, N., Mastrocicco, M., Pavlou, A., and Voudouris, K.: GALDIT-SUSI a modified method to account for surface water bodies in the assessment of aquifer vulnerability to seawater intrusion, J. Environ. Manage., 235, 257–265, https://doi.org/10.1016/j.jenvman.2019.01.069, 2019.
Khan, S., Grudniewski, P., Muhammad, Y. S., and Sobey, A. J.: The benefits of co-evolutionary Genetic Algorithms in voyage optimisation, Ocean Eng., 245, 110261, https://doi.org/10.1016/j.oceaneng.2021.110261, 2022.
Kim, K. B., Kwon, H.-H., and Han, D.: Exploration of warm-up period in conceptual hydrological modelling, J. Hydrol., 556, 194–210, https://doi.org/10.1016/j.jhydrol.2017.11.015, 2018.
Manzoni, A.: manzoniandrea/Large_Basin_Scale_Recarge_Rate: Large_Basin_Scale_Recarge_Ratev1.0.0, Version Large_Basin_Scale_Recarge_Ratev1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.10013442, 2023.
Manzoni, A.: manzoniandrea/Large-scaleGWFlow: largeScale, Version v1.0.6, Zenodo [code], https://doi.org/10.5281/zenodo.10664413, 2024.
Manzoni, A., Porta, G. M., Guadagnini, L., Guadagnini, A., and Riva, M.: Probabilistic reconstruction via machine-learning of the Po watershed aquifer system (Italy), Hydrogeol. J., 31, 1547–1563, https://doi.org/10.1007/s10040-023-02677-8, 2023.
Mather, B., Müller, R. D., O'Neill, C., Beall, A., Vervoort, R. W., and Moresi, L.: Constraining the response of continental-scale groundwater flow to climate change, Sci. Rep.-UK, 12, 4539, https://doi.org/10.1038/s41598-022-08384-w, 2022.
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015.
Mishra, S. K. and Singh, V. P.: Soil Conservation Service Curve Number (SCS-CN) Methodology, Springer Science & Business Media, https://doi.org/10.1007/978-94-017-0147-1, 2003.
Molnau, M. and Bissell, V.: A continuous frozen ground index for flood forecasting, in: Proceedings 51st Annual Meeting Western Snow Conference, Vancouver, 19–21 April 1983, 109–119, 1983.
Morgan Jr., G. M.: A General Description of the Hail Problem in the Po Valley of Northern Italy, J. Appl. Meteorol., 12, 338–353, https://doi.org/10.1175/1520-0450(1973)012<0338:AGDOTH>2.0.CO;2, 1973.
Morris, M. D.: Factorial Sampling Plans for Preliminary Computational Experiments, Technometrics, 33, 161–174, https://doi.org/10.2307/1269043, 1991.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Naz, B. S., Sharples, W., Ma, Y., Goergen, K., and Kollet, S.: Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe, Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-202, 2023.
Nespoli, M., Cenni, N., Belardinelli, M. E., and Marcaccio, M.: The interaction between displacements and water level changes due to natural and anthropogenic effects in the Po Plain (Italy): The different point of view of GNSS and piezometers, J. Hydrol., 596, 126112, https://doi.org/10.1016/j.jhydrol.2021.126112, 2021.
Neuman, S. P.: Maximum likelihood Bayesian averaging of uncertain model predictions, Stoch. Env. Res. Risk A., 17, 291–305, https://doi.org/10.1007/s00477-003-0151-7, 2003.
OpenStreetMap: OpenStreetMap database [PostgreSQL], OpenStreetMap Foundation, Cambridge, UK, https://gisdata.mapog.com/italy/Municipality%20level%204 (last access: 1 October 2022), 2021.
Panzeri, M., Riva, M., Guadagnini, A., and Neuman, S. P.: EnKF coupled with groundwater flow moment equations applied to Lauswiesen aquifer, Germany, J. Hydrol., 521, 205–216, https://doi.org/10.1016/j.jhydrol.2014.11.057, 2015.
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., and Wagener, T.: Sensitivity analysis of environmental models: A systematic review with practical workflow, Environ. Modell. Softw., 79, 214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 1 May 2016.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Razavi, S. and Gupta, H. V.: What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models, Water Resour. Res., 51, 3070–3092, https://doi.org/10.1002/2014WR016527, 2015.
Regione Emilia-Romagna and ENI-AGIP: Riserve idriche sotterranee della Regione Emilia-Romagna. S.EL.CA, Firenze, 1998 (in Italian).
Regione Emilia-Romagna: Piezometrie e qualità delle acque sotterranee nella pianura emiliano-romagnola, Regione Emilia-Romagna [data set], https://ambiente.regione.emilia-romagna.it/it/geologia/cartografia/webgis-banchedati/piezometrie-qualita-acque-sotterranee (last access: 1 October 2022), 2020.
Regione Emilia-Romagna: Riserve idriche sotterranee della Regione Emilia-Romagna, 1998 (in Italian).
Regione Lombardia: Banca dati geologica sottosuolo, Regione Lombardia [data set], https://www.geoportale.regione.lombardia.it/metadati?p_p_id=detailSheetMetadata_WAR_gptmetadataportlet&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&_detailSheetMetadata_WAR_gptmetadataportlet_identifier=r_lombar%3Aad085d2a-519f-4ed6-a84f-b81de6cec1db&_jsfBridgeRedirect=true (last access: 1 August 2022), 2016.
Regione Lombardia and ENI-AGIP: Geologia degli acquiferi padani della Regione Lombardia. S.EL.CA, Firenze, 2002 (in Italian).
Regione Lombardia: Geoportale della Lombardia, Regione Lombardia [data set], https://www.geoportale.regione.lombardia.it/ (last access: 1 March 2022), 2021.
Regione Piemonte: Geoportale Piemonte, Regione Piemonte [data set], https://www.geoportale.piemonte.it/geonetwork/srv/eng/catalog.search#/metadata/r_piemon:023ef6df-b781-4751-b5d2-a442427916d0 (last access: 1 June 2022), 2022.
Ricci Lucchi, F., Colalongo, M. L., Cremonini, G., Gasperi, G. F., Iaccarino, S., Papani, G., Raffi, S., and Rio, D.: Evoluzione sedimentaria e paleogeografica del margine appenninico (Sedimentary and palaeogeographic evolution of the Apenninic margin), Guida alla geologia del margine appenninico padano, Guide geologiche regionali, Soc. Geol. Ital., 17–46, 1982.
Rink, K., Bilke, L., and Kolditz, O.: Visualisation Strategies for Modelling and Simulation Using Geoscientific Data, in: 1st Workshop on Visualisation in Environmental Sciences (EnvirVis), EuroVis 2013, Leipzig, Germany, 17–18 June 2013, The Eurographics Association, 47–51, https://doi.org/10.2312/PE.EnvirVis.EnvirVis13.047-051, 2013.
Riva, M., Guadagnini, A., Neuman, S. P., Janetti, E. B., and Malama, B.: Inverse analysis of stochastic moment equations for transient flow in randomly heterogeneous media, Adv. Water Resour., 32, 1495–1507, https://doi.org/10.1016/j.advwatres.2009.07.003, 2009.
Roland, C. J., Zoet, L. K., Rawling, J. E., and Cardiff, M.: Seasonality in cold coast bluff erosion processes, Geomorphology, 374, 107520, https://doi.org/10.1016/j.geomorph.2020.107520, 2021.
Rossi, M., Donnini, M., and Beddini, G.: Nationwide groundwater recharge evaluation for a sustainable water withdrawal over Italy, Journal of Hydrology: Regional Studies, 43, 101172, https://doi.org/10.1016/j.ejrh.2022.101172, 2022.
Schaap, M. G., Leij, F. J., and van Genuchten, M. T.: ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions, J. Hydrol., 251, 163–176, https://doi.org/10.1016/S0022-1694(01)00466-8, 2001.
Schroeder, W., Martin, K., and Lorensen, B.: The Visualization Toolkit, 4th edn., edited by: Squillacote, A., Kitware, 528 pp., ISBN-10: 193093419X, ISBN-13: 978-1930934191, 2006.
SEDAC: Gridded Population of the World (GPWv4), Version 4: Population Density, Revision 11, SEDAC [data set], https://doi.org/10.7927/H49C6VHW, 2018.
Shrestha, P., Sulis, M., Masbou, M., Kollet, S., and Simmer, C.: A scale-consistent terrestrial systems modeling platform based on COSMO, CLM, and ParFlow, Mon. Weather Rev., 142, 3466–3483, https://doi.org/10.1175/MWR-D-14-00029.1, 2014.
Shuler, C., Brewington, L., and El-Kadi, A. I.: A participatory approach to assessing groundwater recharge under future climate and land-cover scenarios, Tutuila, American Samoa, Journal of Hydrology: Regional Studies, 34, 100785, https://doi.org/10.1016/j.ejrh.2021.100785, 2021.
Siena, M. and Riva, M.: Impact of geostatistical reconstruction approaches on model calibration for flow in highly heterogeneous aquifers, Stoch. Env. Res. Risk A., 34, 1591–1606, https://doi.org/10.1007/s00477-020-01865-2, 2020.
Simoncini, D. and Zhang, K. Y. J.: Population-Based Sampling and Fragment-Based De Novo Protein Structure Prediction, in: Encyclopedia of Bioinformatics and Computational Biology, edited by: Ranganathan, S., Gribskov, M., Nakai, K., and Schönbach, C., Elsevier, 774–784, https://doi.org/10.1016/B978-0-12-809633-8.20507-4, 2019.
Soltani, S. S., Fahs, M., Bitar, A. A., and Ataie-Ashtiani, B.: Improvement of soil moisture and groundwater level estimations using a scale-consistent river parameterization for the coupled ParFlow-CLM hydrological model: A case study of the Upper Rhine Basin, J. Hydrol., 610, 127991, https://doi.org/10.1016/j.jhydrol.2022.127991, 2022.
Sophocleous, M. and Perkins, S. P.: Methodology and application of combined watershed and ground-water models in Kansas, J. Hydrol., 236, 185–201, https://doi.org/10.1016/S0022-1694(00)00293-6, 2000.
Storn, R. and Price, K.: A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, J. Global Optim., 11, 341–359, https://doi.org/10.1023/A:1008202821328, 1997.
Thornthwaite, C. W.: An Approach toward a Rational Classification of Climate, Geogr. Rev., 38, 55–94, https://doi.org/10.2307/210739, 1948.
Thornthwaite, C. W. and Mather, J. R.: The water balance, Publications in Climatology, Drexel Institute of Technology, Laboratory of Climatology, 1955.
Thornthwaite, C. W. and Matter, J. R.: Instructions and Tables for Computing Potential Evaporation and the Water Balance, Climatology, Drexel Institute of Technology, Laboratory of Climatology, 1957.
Trunfio, G. A.: A Cooperative Coevolutionary Differential Evolution Algorithm with Adaptive Subcomponents, Procedia Comput. Sci., 51, 834–844, https://doi.org/10.1016/j.procs.2015.05.209, 2015.
Tusar, T. and Filipic, B.: Differential evolution versus genetic algorithms in multiobjective optimization, Evolutionary Multi-Criterion Optimization, Springer Berlin Heidelberg, 257–271, https://doi.org/10.1007/978-3-540-70928-2_22, 2007.
Vrugt, J. A., Stauffer, P. H., Wöhling, Th., Robinson, B. A., and Vesselinov, V. V.: Inverse Modeling of Subsurface Flow and Transport Properties: A Review with New Developments, Vadose Zone J., 7, 843–864, https://doi.org/10.2136/vzj2007.0078, 2008.
Westenbroek, S. M., Engott, J. A., Kelson, V. A., and Hunt, R. J.: SWB Version 2.0 – A soil-water-balance code for estimating net infiltration and other water-budget components, U.S. Geological Survey, Techniques and Methods 6–A59, https://doi.org/10.3133/tm6A59, 2018.
Wriedt, G., Van der Velde, M., Aloe, A., and Bouraoui, F.: Estimating irrigation water requirements in Europe, J. Hydrol., 373, 527–544, https://doi.org/10.1016/j.jhydrol.2009.05.018, 2009.
Yang, Z., Tang, K., and Yao X.: Large scale evolutionary optimization using cooperative coevolution, Inform. Sciences, 178, 2985–2999, https://doi.org/10.1016/j.ins.2008.02.017, 2008.
Ye, M., Pohlmann, K. F., Chapman, J. B., Pohll, G. M., and Reeves, D. M.: A model-averaging method for assessing groundwater conceptual model uncertainty, Ground Water, 48, 716–728, https://doi.org/10.1111/j.1745-6584.2009.00633.x, 2010.
Zhang, J., Felzer, B. S., and Troy, T. J.: Extreme precipitation drives groundwater recharge: the Northern High Plains Aquifer, central United States, 1950–2010, Hydrol. Process., 30, 2533–2545, https://doi.org/10.1002/hyp.10809, 2016.
Zhou, H., Gómez-Hernández, J. J., and Li, L.: Inverse methods in hydrogeology: Evolution and recent trends, Adv. Water Resour., 63, 22–37, https://doi.org/10.1016/j.advwatres.2013.10.014, 2014.
Zhou, Y. and Li, W.: A review of regional groundwater flow modeling, Geosci. Front., 2, 205–214, https://doi.org/10.1016/j.gsf.2011.03.003, 2011.
Short summary
We introduce a comprehensive methodology that combines multi-objective optimization, global sensitivity analysis (GSA) and 3D groundwater modeling to analyze subsurface flow dynamics across large-scale domains. In this way, we effectively consider the inherent uncertainty associated with subsurface system characterizations and their interactions with surface waterbodies. We demonstrate the effectiveness of our proposed approach by applying it to the largest groundwater system in Italy.
We introduce a comprehensive methodology that combines multi-objective optimization, global...