Guadagnini, Alberto

Porta, Giovanni

Riva, Monica

English

Hydraulic Engineering Repository

Article, Published VersionGuadagnini, Alberto; Porta, Giovanni; Riva, MonicaUncertainty quantification across scales in the subsurfaceworldHydrolinkVerfügbar unter/Available at: https://hdl.handle.net/20.500.11970/109451Vorgeschlagene Zitierweise/Suggested citation:Guadagnini, Alberto; Porta, Giovanni; Riva, Monica (2019): Uncertainty quantification acrossscales in the subsurface world. In: Hydrolink 2019/3. Madrid: International Association forHydro-Environment Engineering and Research (IAHR). S. 84-86. http://iahr.oss-accelerate.aliyuncs.com/library/HydroLink/HydroLink2019_03_s09ds0a9s8d7sd.pdf.Standardnutzungsbedingungen/Terms of Use:Die Dokumente in HENRY stehen unter der Creative Commons Lizenz CC BY 4.0, sofern keine abweichendenNutzungsbedingungen getroffen wurden. Damit ist sowohl die kommerzielle Nutzung als auch das Teilen, dieWeiterbearbeitung und Speicherung erlaubt. Das Verwenden und das Bearbeiten stehen unter der Bedingung derNamensnennung. Im Einzelfall kann eine restriktivere Lizenz gelten; dann gelten abweichend von den obigenNutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Documents in HENRY are made available under the Creative Commons License CC BY 4.0, if no other license isapplicable. Under CC BY 4.0 commercial use and sharing, remixing, transforming, and building upon the materialof the work is permitted. In some cases a different, more restrictive license may apply; if applicable the terms ofthe restrictive license will be binding.84 hydrolink number 3/2019The Earth’s subsurface hosts key resources forthe development of society. Aquifers provideinvaluable freshwater reserves across severalregions worldwide, water demand being largelysatisfied by renewable or non-renewableresources hosted by subsurface reservoirs.Similar to all natural systems, geological mediaexhibit an intrinsic variability of properties, whichis the result of a variety of processes that haveshaped their formation and current internalmake-up. Sedimentary systems, for instance,are the result of the sedimentation of variousmaterials deposited over millions of years. Theproperties of such geological bodies displayremarkable variability across a variety of (spaceand/or time) scales, associated with physical,chemical or biological heterogeneities (seeFigure 1).Proper and sustainable management of thesesystems in the complex and ever changingmodern environment requires solid scientificunderstanding and handling of uncertainty toaddress system functioning and feedbacksamongst its multiple components. Acquiring thisknowledge often benefits from comprehensiveand scientifically sound conceptual andtheoretical frameworks, which are then trans-lated in mathematical and numerical models.The need for modeling is especially compellingwhen dealing with the Earth’s subsurfacebecause of limited direct access to it. Therefore,efforts aimed at managing subsurfaceresources ubiquitously rely on a (model or)representation of reality. The hydraulic conduc-tivity, a key parameter for the characterization ofsubsurface materials, is arguably the mostheterogeneous parameter in Earth sciences,with laboratory data spanning more than tenorders of magnitude. Paucity of observationsand the documented marked spatial hetero-geneity in the properties of natural subsurfacesystems require devising strategies and toolsthat take into account uncertainty inmanagement and engineering studies anddecisions. In this context, stochasticapproaches have been developed, motivatedby recognizing both the importance of spatialvariability and the impossibility of describing inan exhaustive manner the spatial distribution ofvariables of interest. As such, a non-determin-istic framework of analysis is required. In thefollowing we briefly review some of the mainelements that we faced in our researchconcerning the characterization of subsurfacesystems under uncertainty.Our research group is based in the Metropolitanarea of Milano (Italy), residing in the Po riverValley where the vast majority of drinking watersupply is associated with groundwaterresources. Yet, the quality and the quantity ofthis invaluable resource are threatened byanthropogenic activities, including pollutionresulting from industrial or agriculture activities,as well as by geogenic sources of hazardoussubstances (e.g., arsenic or chromium [1], [2]).Improved scientific understanding of the flowand transport processes and accounting for theuncertainty in the description of the system maybe the only realistic approach to handling (qualitatively and quantitatively) integratedhydrological problems with sparse data. Originalresults in this sense have been recently attainedthrough scientific outputs of the project UNCERTAINTY QUANTIFICATIONACROSS SCALES IN THE SUBSURFACE WORLDBY ALBERTO GUADAGNINI, GIOVANNI PORTA & MONICA RIVAThe research team at Politecnico di Milano (Italy) tackles conceptual, theoretical and numerical approaches to studyflow, transport and chemical/biological reactions in natural subsurface porous and fractured media under uncertainty.Our approach is to recognize the importance of tracking and quantifying the impact of uncertainty across scales toultimately identify uncertainty controls to constrain predictions. This article discusses some key aspects of ourresearch approach and vision on current challenges associated with groundwater quality and quantity.Figure 1. Flow and transport processes in subsurface porous media across scales (images modified from [7], [5], [4], [11])GROUNDWATER         SPECIAL85hydrolink number 3/2019WE-NEED (Water Needs, Availability, Quality andSustainability; http://www.we-need.polimi.it [3]),funded by the European Union and the ItalianMinistry for Education, University and Researchunder the 2015 Joint Activities developed by theWater Challenges for a Changing World JointProgramme Initiative (Water JPI). This majorproject was recently completed under thecoordination of Prof. M. Riva and with partici-pation of international partners from Israel(Weizmann Institute of Science), Portugal(University of Aveiro), and Spain (PolytechnicalUniversity of Catalunya). A significant challengeunderpinning the set-up of a model of an aquifersystem is how to address optimal data acqui-sition, system monitoring, and use of theensuing information content. This importantissue is addressed in detail by WE-NEED uponconsidering two major aquifer systems in Italy,associated with the areas of Bologna andCremona, respectively. While the former is a keysource of water for the metropolitan area ofBologna (the lower aquifer provides 80% of allgroundwater used for drinking and industrialpurposes), the strategic importance of the latteris related to the presence of a high number ofnatural high-quality water springs constitutingthe main supply to agriculture and key environ-mental drivers, with significant social, historicaland touristic value. As an example, Figure 2depicts the location of the study area and someaspects of the available dataset, which consti-tutes a unique source of information for thecharacterization of the system functioning.These data have enabled us to assess the effecton groundwater flow simulations of alternativeconceptual models used to represent the spatialarrangement of geomaterials characterizing theinternal architecture of the aquifer [4], a resultwhich constitutes one of the major outputs ofWE-NEED and is available to local watercompanies.IAHRFigure 3. Groundwater quality under uncertain environmental conditions: conceptual and geochemical modeling of hexavalent chromium (Cr(VI)) at hillslopescale. Sensitivity of Cr(VI) to oxygen fugacity exhibits highly nonlinear patterns (modified from [2])Assessing quality of groundwater resourcesrequires joint analysis of fluid flow and transportof chemicals dissolved in the water phase. Thecomplexity of the problem is exacerbated by theobservation that chemically- and biologically-driven reactive processes typically affecttransport in subsurface environments due to avariety of processes, including, e.g., mineral-water interactions or processes such assorption-desorption or microbial activity. As theability to fully describe  these processes in thenatural environment is still very limited, there is aneed for approaches that can assist to improvethe understanding of system behavior andquantify the implications of the uncertaintyassociated with model structure and the ensuingparameters. Some of our recent work showsthat relying on appropriate sensitivity analysistools is critical to enhance the knowledge ofthese complex processes. As an example, thegeochemical response of groundwater toenvironmental factors, such as temperature orredox conditions, may display a stronglynonlinear behavior (see Figure 3). In such casesthe sensitivity of the model can be onlymeasured by combining local sensitivity analysis(that can detect local nonlinearities) with globalsensitivity analysis approaches (enabling us tomeasure the overall impact of uncertain modelparameters on key statistics of modeling goals).When considering a simplified geochemicalmodel of hexavalent chromium (Cr(IV)) geogenicrelease [2], we show that combining varioustechniques is key to obtain a proper assessmentof parameter uncertainty and the way it propa-gates to modeling targets as well as to identifywhich model inputs should be further subject toimproved observation to enhance the ability tomeet given modeling goals.An increase of the complexity of the modeledprocesses may yield more pronouncedFigure 2. Integration of multiple sources of information and modeling approaches for field scale numericalsimulation of groundwater flow in the Cremona area (Italy) (modified from [4]) GROUNDWATER         SPECIAL86 hydrolink number 3/2019couplings and nonlinearities, as suggested byour recent analysis of atrazine biodegradation inagricultural soils [5]. This issue was consideredthrough the implementation of a coupledreaction network of biogeochemical processes,including aerobic and anaerobic processesactivated by various functional microbial groups.The model requires specifying 74 biochemicalparameters to describe the kinetic response ofthe system. The results show that uncertaintypropagation across these processes takingplace in natural soils and under transient condi-tions yields multimodal distributions forprescribed modeling goals. This implies thatdiverse and mutually exclusive final systemstates are likely to occur. Available prior infor-mation may not be strongly informative to allowdiscriminating amongst such states. In thiscontext, sensitivity analysis tools such as thosedeveloped in [6] can be used to improveprocess understanding through modeldiagnosis, as well as guide environmentalmonitoring investments, enabling one to prior-itize the acquisition of data that can potentiallyassist to identify parameters that are actuallyinformative on the system response.While characterizing state-of-the-art modelsunder uncertainty is of utmost importance,existing models and tools need to be improvedto resolve the limitations that hamper state-of-the-art modeling strategies. A key researchquestion in this context concerns the ability totransfer information and uncertainties acrossscales. Even seemingly homogeneous porousmedia may display a relatively complexgeometry and structure at the microscale. Thisimplies, for instance, that fluid velocity at thescale of the pore space is characterized byrelevant spatial variability, with spatial distribu-tions entailing preferential flow regions (fastchannels) and stagnant areas. These featuresare well known and explored in the recent liter-ature which documents pore-scale data setsand modeling efforts. In this context, there is stillthe need for approaches that are capable totransfer such richness of pore-scale informationto larger scales, where it could be used tostrengthen the interpretation of laboratory orfield scale observations. Local fluctuations andgradients, due to pore-scale features such aschanneling or segregated stagnant regions [7],[8], may heavily impact nonlinear reaction rates.In general, these features may be criticallyimportant for the parameterization of processesthat take place unevenly in space (e.g., surfacereactions such as adsorption-desorption). Ourwork in this context provides original operationalframeworks and quantitative tools to transferand upscale available information.With reference to scaling aspects, our researchteam has developed and applied a theoreticalframework and model enabling us to transferinformation on statistics of system parametersacross diverse spatial scales in real scenarios,and to quantify the way they impact the statisticsof the system states (e.g., fluxes and concentra-tions).Relying on the mounting evidence that manyspatially varying quantities exhibit non-Gaussianbehavior over a multiplicity of scales, we havedeveloped a theoretical model that capturesdocumented scalable non-Gaussian geosta-tistics of Earth and environmental variables [9],[10]. Such a model allows blending observationsof hydraulic parameter distributions into a newframework and is adaptable to diversespatial/temporal scales. In this context, a keyinnovative aspect is the development ofmethodologies and algorithms to generaterandom fields, exploiting available data to beemployed in computational analyses of ground-water flow and transport, with the aim of quanti-fying risk in realistic (non-Gaussian)environments.In summary, the core activity of the team isfocused on a variety of scientific/application-oriented objectives with the aim of providingquantitative understanding and process-basedmodels of hydrogeological systems and thegeochemical behavior of reactive chemicalspecies in environmentally and industriallyrelevant subsurface settings under model andparametric uncertainty. Our studies aim at identi-fying and developing methods to incorporateuncertainty quantification and its propagationacross observation scales, as grounded ondirect observations at diverse scales of interest.The research emphasizes the consequences ofhuman actions on groundwater resources bycoupling field and laboratory evidence withoriginal theoretical and modeling concepts. Thiscan support the evaluation of the sustainabilityof current and future plans for aquifer devel-opment with the aim of providing an effectivegovernance perspective while maximizingecosystem preservation. nReferences[1] Molinari, A., Guadagnini, L., Marcaccio, M., Guadagnini, A.(2019) Geostatistical multimodel approach for the assessmentof the spatial distribution of natural background concentrationsin large-scale groundwater bodies Water Research, 149, 522-532. doi: 10.1016/j.watres.2018.09.049 [2] Ceriotti, G.,Guadagnini, L., Porta, G., Guadagnini, A. (2018) Local andGlobal Sensitivity Analysis of Cr (VI) Geogenic Leakage UnderUncertain Environmental Conditions Water Resources Re-search, 54 (8), 5785-5802. [3] http://www.we-need.polimi.it[4] Bianchi Janetti E., L. Guadagnini, M. Riva, A. Guadagnini (2019)Global sensitivity analyses of multiple conceptual models withuncertain parameters driving groundwater flow in a regional-scale sedimentary aquifer, J. of Hydrology, 574, 544-556, doi:10.1016/j.jhydrol.2019.04.035[5] Porta, G., la Cecilia, D., Guadagnini, A., Maggi, F. (2018) Impli-cations of uncertain bioreactive parameters on a complex reac-tion network of atrazine biodegradation in soil Advances inWater Resources, 121, 263-276, doi: 10.1016/j.advwa-tres.2018.08.002.[6] Dell’Oca A., M. Riva, A. Guadagnini (2017) Moment-based Met-rics for Global Sensitivity Analysis of Hydrological Systems, Hy-drol. Earth Syst. Sci., 21, 6219–6234,doi:10.5194/hess-21-6219-2017.[7] Ceriotti, G., Russian, A., Bolster, D., Porta, G. (2019) A double-continuum transport model for segregated porous media: Deri-vation and sensitivity analysis-driven calibration Advances inWater Resources, 128, 206-217, doi: 10.1016/j.advwa-tres.2019.04.003[8] Siena M., O. Iliev, T. Prill, M. Riva, A. Guadagnini (2019) Identifi-cation of Channeling in Pore-Scale Flows, Geophysical Re-search Letters, 46(6), 3270-3278, doi: 10.1029/2018GL081697.[9] Riva M., M. Panzeri, A. Guadagnini, S. P. Neuman (2015), Simu-lation and analysis of scalable non-Gaussian statisticallyanisotropic random functions, J. Hydrol., 531 Part 1, 88-95,doi:10.1016/j.jhydrol.2015.06.066.[10] Guadagnini A., M. Riva, S.P. Neuman (2018) Recent Advancesin Scalable Non-Gaussian Geostatistics: The Generalized Sub-Gaussian Model, J. of Hydrology, 562, 685-691.[11] Cerotti G., G.M. Porta, C. Geloni, M. Dalla Rosa, A. Guadagnini(2017) Quantification of CO2 generation in sedimentary basinsthrough Carbonate/Clays Reactions with uncertain thermody-namic parameters, Geochimica et Cosmochimica Acta, 213,198-215.Alberto Guadagnini is Professorof Hydraulic Engineering andHead of the Department of Civiland Environmental Engineering atPolitecnico di Milano (Italy).Chair of the CommunicationCommittee of the InternationalSociety for Porous Media(Interpore). Vice Chair of the Committee onGroundwater Hydraulics and Management (IAHR).Executive Editor of Hydrology and Earth SystemSciences. Main research interests include statisticalscaling of hydrological quantities, stochasticgroundwater hydrology, multiscale flow and transportin porous media, multiphase flows, enhanced oilrecovery and environmental footprint of shale gasdevelopment.Giovanni Porta is currentlyassociate professor atPolitecnico di Milano (Italy). Thecore of his recent and ongoingresearch is focused ondeveloping models and methodsto improve the currentunderstanding of reactivetransport processes in porous media. Targetapplications span across a wide range of disciplines,processes and scales from micron/seconds scales togeological settings involving km-scale domainsevolving across millions of years. He is 2019 JuniorMarchi Lecturer (award by the Italian HydraulicEngineering Society (GII)).Monica Riva is Professor ofGroundwater and FluidMechanics at Politecnico diMilano (Italy). She has managedseveral research projectsfocusing on groundwaterresources and stochasticapproaches. She is currently thecoordinator of the Water JPI project WE-NEED. Chair ofthe EGU Groundwater Division and Vice-chair of theInterpore Council (International Society for PorousMedia). Her key areas of expertise include dataassimilation, stochastic inverse modeling, uncertaintyquantification, and multiphase flows.

Uncertainty quantification across scales in the subsurface world

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Uncertainty quantification across scales in the subsurface world

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