Abstract
With respect to model parameterization and sensitivity analysis, this work uses a practical example to suggest that methods that start with simple models and use computationally frugal model analysis methods remain valuable in any toolbox of model development methods. In this work, groundwater model calibration starts with a simple parameterization that evolves into a moderately complex model. The model is developed for a water management study of the Tivoli-Guidonia basin (Rome, Italy) where surface mining has been conducted in conjunction with substantial dewatering. The approach to model development used in this work employs repeated analysis using sensitivity and inverse methods, including use of a new observation-stacked parameter importance graph. The methods are highly parallelizable and require few model runs, which make the repeated analyses and attendant insights possible. The success of a model development design can be measured by insights attained and demonstrated model accuracy relevant to predictions. Example insights were obtained: (1) A long-held belief that, except for a few distinct fractures, the travertine is homogeneous was found to be inadequate, and (2) The dewatering pumping rate is more critical to model accuracy than expected. The latter insight motivated additional data collection and improved pumpage estimates. Validation tests using three other recharge and pumpage conditions suggest good accuracy for the predictions considered. The model was used to evaluate management scenarios and showed that similar dewatering results could be achieved using 20 % less pumped water, but would require installing newly positioned wells and cooperation between mine owners.
Résumé
En ce qui concerne le paramétrage des modèles et l’analyse de sensibilité, ce travail utilise un exemple pratique pour suggérer que les méthodes qui débutent avec des modèles simples et utilisent des méthodes d’analyses de modèle économe en calcul restent précieuses dans toute boîte à outils de méthodes de développement de modèles. Dans ce travail, l’étalonnage du modèle d’écoulement d’eaux souterraines commence par un paramétrage simple qui évolue vers un modèle de complexité moyenne. Le modèle est développé pour une étude de gestion des ressources en eau du bassin de Tivoli-Guidonia (Rome, Italie) où l’exploitation du sous-sol a conduit à un dénoyage important. L’approche pour le développement du modèle utilisée dans ce travail emploie des analyses répétées à l’aide de méthodes inverses et de sensibilité, y compris l’utilisation d’un nouveau graphique de l’importance des paramètres d’observation cumulée. Les méthodes sont fortement parallélisables et nécessitent peu d’exécution des modèles, ce qui rend possible des analyses répétées et des aperçus spécifiques. Le succès d’une conception de l’élaboration d’un modèle peut être mesuré par des aperçus des résultats et de la pertinence de la précision du modèle par rapport aux prévisions. Exemples d’informations obtenues : (1) Une croyance de longue date que, à l’exception de quelques fractures distinctes, le travertin est homogène, a été jugée comme inadéquate, et (2) le débit de pompage de dénoyage est plus critique que la précision du modèle, par rapport à ce qui était attendu. Cette dernière information a motivé la collecte de données supplémentaires et l’amélioration des estimations des pompages. Les essais de validation utilisant trois autres conditions de recharge et de pompage suggèrent une bonne précision pour les prévisions considérées. Le modèle a été utilisé pour évaluer des scenarios de gestion et a montré que des résultats similaires de dénoyage pourraient être obtenus en utilisant 20 % de moins d’eau pompée, mais nécessiterait l’installation de nouveaux puits et la coopération entre les propriétaires exploitant les ressources minérales du sous-sol.
Resumen
Con respecto a la parametrización del modelo y análisis de sensibilidad, este trabajo utiliza un ejemplo práctico para sugerir que los métodos que comienzan con modelos simples y utilizan métodos de análisis de modelos computacionalmente frugales siguen siendo valiosos en cualquier caja de herramientas de métodos de desarrollo para modelación. En este trabajo, la calibración del modelo del agua subterránea se inicia con una parametrización simple que evoluciona en un modelo de moderada complejidad. El modelo se desarrolla para un estudio sobre la gestión del agua de la cuenca del Tivoli-Guidonia (Roma, Italia), donde la minería de superficie se ha llevado a cabo en conjunción con una eliminación sustancial de agua. El enfoque de desarrollo del modelo utilizado en este trabajo emplea el análisis de sensibilidad y métodos inversos, incluso el uso de un gráfico de importancia de un nuevo parámetro de observación acumulado. Los métodos son altamente paralelizables y requieren unas pocas corridas del modelo, lo que hace posibles análisis repetidos y las interpretaciones. El éxito del diseño del desarrollo del modelo puede ser medido por las observaciones obtenidas y la exactitud demostrada por el modelo en relación a las predicciones. Se obtuvieron observaciones por ejemplo: (1) En relación con una creencia largamente sostenida de que, a excepción de una pocas fracturas claras, el travertino es homogéneo, se encontró que puede ser inadecuada, y (2) el ritmo de bombeo de la extracción de agua es más crítico para la precisión del modelo que lo esperado. Esta última explicación motivó una recolección adicional de datos y mejorá las estimaciones de volumen bombeado. Las pruebas de validación utilizando otras tres condiciones de recarga y bombeo sugieren una buena exactitud para las predicciones consideradas. El modelo se utilizó para evaluar los escenarios de gestión y mostró que los resultados de la eliminación del agua podrían alcanzarse usando 20 % menos de agua bombeada, pero requeriría instalar nuevos sitios para pozos y la cooperación entre los propietarios de minas.
摘要
针对模型参数化和灵敏度分析,本项研究利用实例提出从简单模型入手、采用计算简便的模型分析方法在任何模型开发方法中依然有价值。在本研究中,地下水模型校正从简单的参数化入手,然后进展到中等复杂的模型中。(意大利罗马市)Tivoli-Guidonia盆地露天开采,伴随着大量的排水,为当地的水管理研究开发了模型。本研究中的模型开发方法依靠灵敏度和反演法包括采用了新观测数据构成的参数重要性曲线进行重复分析。方法高度平行,需要很少运行模型,就可以进行重复分析并伴随得到认识结果。模型开发设计的成功可以通过获得的认识结果及展示的与预测相关的模型精确度来衡量。实例获取的认识结果有:1)除了几个明显的特点,石灰华是均质的这一长期持有的观念是不充分的,2)排水抽水速度对模型精确度来说比预想的更重要。后者的认识促使收集额外的资料,提高抽水量估算值精度。采用三个其他补给和抽水条件进行的校正试验对预测具有很好的精确度。模型用于评估各种管理方案,模型还表明,采用少于20%的抽水量可以获取类似的排水结果,但这需要打新定位的井及矿主之间的合作。
Resumo
Com respeito a parametrização de modelo e análise de sensibilidade, esse trabalho usa um exemplo prático para sugerir que métodos que se iniciam com modelos simples e usam métodos de análise do modelo computacionalmente frugal continuam a ser valiosos em qualquer caixa de ferramentas de métodos de desenvolvimento do modelo. Neste trabalho, calibração do modelo de águas subterrâneas começa com uma parametrização simples que evolui para um modelo de complexidade moderada. O modelo é desenvolvido para um estudo de gestão dos recursos hídricos da bacia do Tivoli-Guidonia (Roma, Itália) onde a mineração de superfície tem sido conduzida em conjunto com uma drenagem substancial. A abordagem para desenvolvimento do modelo utilizada nesse trabalho aplica análises repetidas utilizando análise de sensibilidade e métodos de inversão, incluindo o uso de um novo gráfico de importância das observações empilhadas. Os métodos são altamente paralelizável e exigem algumas realizações de modelo, que fazem as análises repetidas e compreensão de atendimento possíveis. O sucesso de um esquema de desenvolvimento de modelo pode ser medido por percepções alcançadas e demonstrou a precisão do modelo referente às previsões. Foram obtidos entendimentos, como por exemplo: (1) A antiga crença de que, com exceção de algumas fraturas distintas, o travertino é homogêneo foi considerada inadequada, e (2) A taxa de bombeamento de rebaixamento do lençol é mais crítica para modelar precisão do que o esperado. Este último entendimento motivou a coleta de dados adicionais e melhores estimativas de bombeamento. Testes de validação com três outras condições de recarga e de bombeamento sugerem boa precisão para as previsões consideradas. O modelo foi utilizado para avaliar cenários de gestão e que mostrou que resultados similares de drenagem podem ser alcançados utilizando 20 % menos água bombeada, mas pode requerer a instalação de novos poços posicionados e cooperação entre os donos de minas.
Similar content being viewed by others
References
Ahlfeld DP, Mulligan AE (2000) Optimal management of flow in groundwater systems. Academic Press, London
Anderman ER, Hill MC, Poeter EP (1996) Two-dimensional advective transport in ground-water flow parameter estimation. Ground Water 34(6):1001–1009
Anderson MP, Woessner WW, Hunt RJ (2015) Applied groundwater modeling: simulation of flow and advective transport, 2nd edn. Academic, San Diego
Aster RC, Borchers B, Thurber CH (2013) Parameter estimation and inverse problems. Academic, Amsterdam
Baiocchi V, Cazzella R, Giannone F, Liso L, Vecchio M (2010) Metodologie topografiche integrate per il rilievo di dettaglio del pozzo del Merro (Sant’Angelo Romano) [Topographical integrated methodologies for the detailed survey of the Pozzo del Merro -Sant’Angelo Romano]. Atti della XIV Conferenza Nazionale ASITA, Brescia, Italy, November 2010, pp 107–112
Barlebo HC, Hill MC, Rosbjerg D (2004) Identification of groundwater parameters at Columbus, Mississippi, using three-dimensional inverse flow and transport model. Water Resour Res 40(4):W04211. doi:10.1029/2002WR001935
Barth GR, Hill MC, Illangasekare TH, Rajaram H (2001) Predictive modeling of flow and transport in a two-dimensional intermediate-scale, heterogeneous porous media. Water Resour Res 37(10):2503–2512
Best MJ, Abramowitz G, Johnson HR, Pitman AJ, Balsamo G, Boone A, Cuntz M, Decharme B, Dirmeyer PA, Dong J, Guo M, Ek Z, Haverd V, van den Hurk BJJ, Nearing GS, Pak B, Peters-Lidard C, Santanello JA Jr, Stevens L, Vuichard N (2015) The plumbing of land surface models: benchmarking model performance. J Hydrometeor 16:1425–1442. doi:10.1175/JHM-D-14-0158.1
Beven K (2009) Environmental modeling: an uncertain future? Rutledge, Abingdon, UK
Borgonovo E (2006) Measuring uncertainty importance: investigation and comparison of alternative approaches. Risk Anal 26(5):1349–1361. doi:10.1111/j.1539-6924.2006.00806.x
Borgonovo E (2007) A new uncertainty importance measure. Reliab Eng Syst Safe 92(6):771–784. doi:10.1016/j.ress.2006.04.015
Capelli G, Cosentino D, Messina P, Raffi R, Ventura G (1987) Modalità di ricarica e assetto strutturale dell’acquifero delle sorgenti Capore – S.Angelo (Monti Lucretili – Sabina Meridionale) [Recharge and structural setting of the Capore aquifer sources: S. Angelo (Lucretili Mounts – Southern Sabina)]. Geol Romana 26:419–447
Capelli G, Mazza R, Gazzetti C (2005) Strumenti e strategie per la tutela e l’uso compatibile della risorsa idrica nel Lazio: gli acquiferi vulcanici—quaderni di tecniche di protezione ambientale—protezione delle acque sotterranee [Tools and strategies for the protection and sustainable use of water resources in Lazio Region: volcanic aquifers—notes on environmental protection techniques—groundwater protection]. Pitagora, Bologna, Italy
Capelli G, Mastrorillo L, Mazza R, Petitta M, Baldoni T, Banzato F, Cascone D, Di Salvo C, La Vigna F, Taviani S, Teoli P (2013) Carta idrogeologica del territorio della Regione Lazio scala 1:100.000 – 4 fogli [Hydrogeological Map of Latium Region 1:100.000 scale – 4 maps]. Regione Lazio, Rome
Carrera J, Alcolea A, Medina A, Hidalgo J, Slooten LJ (2005) Inverse problem in hydrogeology. Hydrogeol J 13:206–222
Carter RW, Anderson IE (1964) Accuracy of current meter measurements. Am Soc Civil Eng J 89(HV4):105–115
Carucci V, Petitta M, Aravena R (2011) Interaction between shallow and deep aquifers in the Tivoli Plain (central Italy) enhanced by groundwater extraction: a multi-isotope approach and geochemical modeling. Applied Geochem. doi:10.1016/j.apgeochem.2011.11.007
Clark MP, Slater AG, Rupp DE, Woods RA, Vrugt JA, Gupta HV, Wagener T, Hay LE (2008) Framework for understanding structural errors (FUSE): a modular framework to diagnose differences between hydrological models. Water Resour Res 44:W00B02. doi:10.1029/2007WR006735
Clement TP (2011) Complexities in hindcasting models: when should we say enough is enough? Ground Water 49(5):620–629. doi:10.1111/j.1745-6584.2010.00765.x
Cook RD, Weisberg S (1982) Residuals and influence in regression. Chapman and Hall, New York
Dausman AM, Doherty J, Langevin CD, Dixon J (2010) Hypothesis testing of buoyant plume migration using a highly parameterized variable density groundwater model at a site in Florida, USA. Hydrogeol J 18(1):147–160
de Marsily G, Delay F, Gonçalvés J, Renard P, Teles V, Violette S (2005) Dealing with spatial heterogeneity. Hydrogeol J 13:161–183
Di Baldassarre G, Montanari A (2009) Uncertainty in river discharge observations: a quantitative analysis. Hydrol Earth System Sci Discuss 6:39–61
Doherty J, Christensen S (2011) Use of paired simple and complex models to reduce predictive bias and quantify uncertainty. Water Resour Res 47(12):W12534. doi:10.1029/2011WR010763
Doherty J, Hunt RJ (2010) Response to Comment on: Two statistics for evaluating parameter identifiability and error reduction. Journal of Hydrology. doi:10.1016/j.jhydrol.2009.10.012
Doherty J, Welter D (2010) A short exploration of structural noise. Water Resour Res 46:W05525. doi:10.1029/2009WR008377
Faccenna C, Funiciello R, Montone P, Parotto M, Voltaggio M (1994) Late Pleistocene strike-slip tectonics in the Acque Albule basin (Tivoli - Latium). Memorie descrittive della Carta Geologica d’Italia, 49. Istituto Poligrafico e Zecca dello Stato, Rome
Faccenna C, Soligo M, Billi A, De Filippis L, Funiciello R, Rossetti C, Tuccimei P (2008) Late Pleistocene depositional cycles of the Lapis Tiburtinus travertine (Tivoli - central Italy). Glob Planet Change. doi:10.1016/j.gloplacha.2008.06.006
Fienen MR, Krabbenhoft HD, Clemo T (2009) Obtaining parsimonious hydraulic conductivity fields using head and transport observations: a Bayesian geostatistical parameter estimation approach. Water Resour Res 45:W08405. doi:10.1029/2008WR007431
Finsterle S (2015) Practical notes on local data worth analysis. Water Resour Res 51(12):9904–9924. doi:10.1002/2015WR017445
Finsterle S, Preuss P (1995) Solving the estimation-identification problem in two-phase flow modeling. Water Resour Res 31(4):913–924
Foglia L, Mehl SW, Hill MC, Perona P, Burlando P (2007) Testing alternative ground water models using cross validation and other methods. Ground Water 45(5):627–641. doi:10.1111/j.1745-6584.2007.00341.x
Foglia L, Hill MC, Mehl SW, Burlando P (2009) Sensitivity analysis, calibration, and testing of a distributed hydrological model using error-based weighting and one objective function. Water Resour Res 45:W06427. doi:10.1029/2008WR007255
Foglia L, Mehl SW, Hill MC, Burlando P (2013) Evaluating model structure adequacy: the case of the Maggia Valley groundwater system, southern Switzerland. Water Resour Res 49. doi:10.1029/2011WR011779
Funtowicz S, Ravetz J (1990) Post-normal science: a new science for new times. Scientific European 169:20–22
Gingerich SB, Voss CI (2005) Three-dimensional variable-density flow simulation of a coastal aquifer in southern Oahu, Hawaii, USA. Hydrogeol J 13:436–450
Giordano G, Mattei M, Funiciello R (2009) Geological map of the Colli Albani Volcano, scale 1:50.000, In: The Colli Colli Albani Volcano. Special Publication of IAVCEI, 3. Insert, Geological Society of London
Good PI (2001) Resampling methods: a practical guide to data analysis. Birkhauser, Boston
Gupta HV, Clark MP, Vrugt JA, Abramowitz G, Ye M (2012) Towards a comprehensive assessment of model structural adequacy. Water Resour Res 48. doi:10.1029/2011WR011044
Harbaugh AW (2005) MODFLOW-2005. The U.S. Geological Survey modular ground-water-model: the ground water flow process. US Geol Surv Techniques Methods 6 A16
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer Series in Statistics, Springer, New York, 745 pp
Helsel DR, Hirsch RM (2002) Statistical methods in water resources, chap A3. In: US Geological Survey Techniques of Water-Resources Investigations, book 4. Hydrologic Analysis and Interpretation, USGS, Reson, VA, 510 pp
Hill MC (2006) The practical use of simplicity in developing ground water models. Ground Water 44(6):775–781
Hill MC (2010) Comment on “Two statistics for evaluating parameter identifiability and error reduction” by John Doherty and Randall J. Hunt. Journal of Hydrology 380(3–4):481–488
Hill MC (2008) Data error and highly parameterized groundwater models. In: Refsgaard JC et al. (eds) Calibration and reliability in groundwater modeling. Credibility of modeling, Proceedings of the ModelCARE2007 Conference, Copenhagen, September 2007, IAHS Pub. 320, IAHS, Wallingford, UK, pp 316–321
Hill MC, Nolan BT (2011) Sensitivity analysis for inverse problems solved by singular value decomposition (SVD). In: Poeter EP, Hill MC, Zheng C, Maxwell R (eds) Proceedings of the 2011 MODFLOW and More Conference, Golden, CO, June 2011
Hill MC, Østerby O (2003) Determining extreme parameter correlation in ground-water models. Ground Water 41(4):420–430
Hill MC, Tiedeman CR (2007) Effective groundwater model calibration. Wiley, Hoboken, NJ
Hill MC, Faunt CC, Belcher WR, Sweetkind DS, Tiedeman CR, Davetski D (2013) Knowledge, transparency, and refutability in groundwater models, an example from the Death Valley regional groundwater system. Phys Chem Earth 64:105–116
Hill MC, Kavetski D, Clark M, Ye M, Arabi M, Lu D, Foglia L, Mehl S (2015) Practical use of computationally frugal model analysis methods. Groundwater. doi:10.1111/gwat.12330
Hunt RJ, Doherty J, Tonkin MJ (2007) Are models too simple? Arguments for increased parameterization. Ground Water 45(3):254–262
International Standard (2007) ISO 748:2007, Hydrometry: measurement of liquid flow in open channels using current-meters or floats. International Organization for Standardization, Geneva
Kavetski D, Clark MP (2010) Ancient numerical daemons of conceptual hydrological modeling, part 2: impact of time stepping schemes on model analysis and prediction. Water Resour Res 46:W10511. doi:10.1029/2009WR008896
Keating EH, Doherty J, Vrugt JA, Kang Q (2010) Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality. Water Resour Res 46:W10517. doi:10.1029/2009WR008584
Kirchner JW (2006) Getting the right answers for the wrong reasons: linking measurements, analyses, and models to advance the science of hydrology. Water Resour Res 42:W03S04. doi:10.1029/2005WR004362
Kucherenko S, Rodriguez-Fernandez M, Pantelides C, Shah N (2009) Monte Carlo evaluation of derivative-based global sensitivity measures. Reliab Eng Syst Safe 94:1135–1148. doi:10.1016/j.ress.2008.05.006
Kucherenko S, Tarantola S, Annoni P (2012) Estimation of global sensitivity indices for models with dependent variables. Comput Phys Commun 183(4):937–946. doi:10.1016/j.cpc.2011.12.020
La Vigna F (2009) Modello numerico del flusso dell’unità idrogeologica termominerale delle Acque Albule (Roma) [Groundwater numerical flow model of the hydrothermal unit Acque Albule (Rome)]. PhD Thesis, Roma TRE University, Rome. http://dspace-roma3.caspur.it/handle/2307/434. Accessed January 2016
La Vigna F (2011) Modello numerico del sistema idrogeologico delle Acque Albule (Roma): test di simulazione in condizioni critiche di ricarica [Numerical model of the hydrogeological system of Acque Albule (Rome): simulation tests on critical recharge conditions]. In: Polemio M (ed) Le modificazioni climatiche e i rischi e risorse naturali: strategie e criteri d’intervento per l’adattamento e la mitigazione [The climatic changes, risks and natural resources: strategies and intervention criteria for adaptation and mitigation]. CNR-IRPI, Bari, Italy
La Vigna F, Gnoni A (2014) Groundwater–geothermal preliminary model of the Acque Albule Basin (Rome): future perspectives of geothermal resources exploitation. Acque Sotterranee. doi:10.7343/AS-000-13-0000
La Vigna F, Mazza R, Taviani S, Teoli P, Capelli G (2007) Development of a modern hydrogeological monitoring network in urban contest: the case of Acque Albule Plain, central Italy, Latium Region, Rome. Geophys Res Abstr 9:1124
La Vigna F, Rossetto R, Mazza R (2009a) Ground water model calibration using geology information along with sensitivity analysis and estimation methods (UCODE-2005), the Acque Albule model, Rome (Italy). In: Calibration and reliability in groundwater modeling: managing groundwater and the environment. China University of Geosciences Press, Wuhan, China, pp 79–82
La Vigna F, Teoli P, Rossetto R, Mazza R (2009b) Equivalent porous media approach in fractured hydrogeological systems: the Acque Albule Plain case, Rome - Italy. 3rd National Congress AIGA, Valdarno, Italy, February 2009
La Vigna F, Ciadamidaro S, Mazza R, Mancini L (2010) Water quality and relationship between superficial and ground water in Rome (Aniene River basin, central Italy). Environ Earth Sci. doi:10.1007/s12665-009-0267-2
La Vigna F, Rossetto R, Mazza R, Capelli G (2011) Can we calibrate a complex groundwater model just by running an automatic calibration code? A case study from Italy: the Acque Albule Plain model (Rome). IAHS Pub. 341, IAHS, Wallingford, UK
La Vigna F, Mazza R, Capelli G (2012a) Detecting the flow relationships between deep and shallow aquifers in an exploited groundwater system, using long-term monitoring data and quantitative hydrogeology: the Acque Albule basin case (Rome - Italy). Hydrol Process. doi:10.1002/hyp.9494
La Vigna F, Carucci V, Mariani I, Minelli L, Pascale F, Mattei M, Mazza R, Tallini M (2012b) Intermediate field hydrogeological response induced by L’Aquila earthquake: the Acque Albule hydrothermal system (central Italy). In: Pantosti D, Boncio P, Cavinato GP (eds) Understanding the April 6th L’Aquila earthquake: the geological contribution. Ital J Geosci. DOI: 10.3301/IJG.2012.05
La Vigna F, Demiray Z, Mazza R (2013a) Exploring the use of alternative groundwater models to understand the hydrogeological flow processes in an alluvial context (Tiber River, Rome - Italy). Environ Earth Sci. doi:10.1007/s12665-013-2515-8
La Vigna F, Mazza R, Capelli G (2013b) Le risorse idriche nei travertini della piana di Tivoli-Guidonia: la modellazione numerica come strumento di gestione degli acquiferi [Water resources in the travertine plain of Tivoli-Guidonia: a numerical model as a management tool). Rend Online Soc Geol It 27:77–85. doi:10.3301/ROL.2013.21
La Vigna F, Mazza R, Amanti M, Di Salvo C, Petitta M, Pizzino L, Pietrosante A, Martarelli L, Bonfà I, Capelli G, Cinti D, Ciotoli F, Ciotoli G, Conte G, Del Bon A, Dimasi M, Falcetti S, Gafà RM, Lacchini A, Mancini M, Martelli S, Mastrorillo L, Monti GM, Procesi M, Roma M, Sciarra A, Silvi A, Stigliano F, Succhiarelli C (2016a) Groundwater of Rome. Journal of Maps. doi:10.1080/17445647.2016.1158669
La Vigna F, Mazza R, Amanti M, Di Salvo C, Petitta M, Pizzino L (2016b) The synthesis of decades of groundwater knowledge: the new Hydrogeological Map of Rome Acque Sotterranee - Italian Journalof Groundwater 4(142):9–17. doi:10.7343/AS-128-15-0155
Laloy E, Vrugt JA (2012) High-dimensional posterior exploration of hydrologic models using multiple-try DREAM (ZS) and high-performance computing. Water Resour Res 48:W01526. doi:10.1029/2011WR010608
Liu G, Zheng C, TickG R, Butler JJ, Gorelick SM (2010) Relative importance of dispersion and rate limited mass transfer in highly heterogeneous porous media: analysis of a new tracer test at the Macrodispersion Experiment (MADE) site. Water Resour Res 46:W03524. doi:10.1029/2009WR008430
Lu D, Ye M, Hill MC (2012) Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification. Water Resour Res 48:W09521. doi:10.1029/2011WR011289
Lu D, Ye M, Meyer PD, Curtis GP, Shi X, Niu X-F, Yabusaki SB (2013) Effects of error covariance structure on estimation of model averaging weights and predictive performance. Water Resour Res 49. doi:10.1002/wrcr.20441
Lu D, Ye M, Hill MC, Poeter EP, Curtis GP (2014) A computer program for uncertainty analysis integrating regression and Bayesian methods. Environ Modelling Software 60:45–56
Manfredini M (1949) Alcuni dati sulla falda idrica che alimenta le sorgenti delle Acque Albule [Some data on the aquifer feeding the Acque Albule springs]. Bolle Serv Geol Ital 71:113–119
Maxia C (1949) Studio geologico del bacino delle Acque Albule [Geological study of the Acque Albule catchment]. Ricerca Scientifica 19:20–27
Maxia C (1950) Il bacino delle Acque Albule [The Acque Albule catchment]. Contrib Sci Geol 1124:20–27
Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33(2):161–174
Oreskes N (2000) Why predict? Historical perspectives on prediction in Earth sciences. In: Sarawitz D, Pielke RA, Byerly R (eds) Prediction. Island, Washington, DC, pp 23–40
Petitta M, Primavera P, Tuccimei P, Aravena R (2010) Interaction between deep and shallow groundwater systems in areas affected by Quaternary tectonics (central Italy): a geochemical and isotope approach. Environ Earth Sci. doi:10.1007/s12665-010-0663-7
Poeter EP, Hill MC (1997) Inverse modeling, a necessary next step in ground-water modeling. Ground Water 35(2):250–260
Poeter EP, Hill MC, Lu D, Tiedeman CR, Mehl S (2014) UCODE_2014, with new capabilities to define parameters unique to predictions, calculate weights using simulated values, estimate parameters with SVD, evaluate uncertainty with MCMC, and more. Integrated Groundwater Modeling Center Report GWMI 2014-02, IGWMC, Golden, CO
Pollock DW (1989) Documentation of computer programs to compute and display pathlines using results from the U.S. Geological Survey modular three-dimensional finite difference groundwater model. US Geol Surv Open File Rep 89-381
Rakovec O, Hill MC, Clark MP, Weerts AH, Teuling AJ, Uijlenhoet R (2014) Distributed evaluation of local sensitivity analysis (DELSA), with application to hydrologic models. Water Resour Res 50. doi:10.1002/2013WR014063
Razavi S, Gupta HV (2015) What do we mean by sensitivity analysis? The need for comprehensive characterization of ‘Global’ sensitivity in Earth and environmental systems models. Water Resour Res. doi:10.1002/2014WR016527
Refsgaard JC (1997) Parameterization, calibration, and validation of distributed hydrological models. J Hydrol 198(1–4):69–97
Refsgaard JC, Christensen S, Sonnenborg TO, Seifert S, Holberg AL, Troldberg L (2012) Review of strategies for handling geological uncertainty in groundwater flow and transport modeling. Adv Water Resour 36:36–50
Renard B, Kavetski D, Leblois E, Thyer M, Kuczera G, Franks SW (2011) Toward a reliable decomposition of predictive uncertainty in hydrological modeling: characterizing rainfall errors using conditional simulation. Water Resour Res 47:W11516. doi:10.1029/2011WR010643
Saltelli A, Funtowicz S (2014) When all models are wrong. Issues Sci Technol 30(2):79–85
Saltelli A, Funtowicz S (2015) Evidence-based policy at the end of the Cartesian dream: the case of mathematical modeling. In: Pereira AG, Funtowicz S (eds) Science, philosophy and sustainability: the end of the Cartesian dream, Routledge Explorations in Sustainability and Governance. Routledge, New York, pp 147–162
Saltelli A, Tarantolla S, Chan KPS (1999) A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41(1):39–56
Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J (2008) Global sensitivity analysis: the primer. Wiley, New York
Sokal A (2009) Beyond the hoax: science, philosophy and culture. Oxford University Press, Oxford
Spitz K, Moreno J (1996) A practical guide to groundwater and solute transport modeling. Wiley, New York
Tonkin M, Doherty J (2009) Calibration-constrained Monte Carlo analysis of highly parameterized models using subspace techniques. Water Resour Res 45:W00B10. doi:10.1029/2007WR006678
Wellman TP, Shapiro AM, Hill MC (2009) Effects of simplifying fracture network representation on inert chemical migration in fracture-controlled aquifers. Water Resour Res 45:W01416. doi:10.1029/2008WR007025
Worthington RSH (2009) Diagnostic hydrogeologic characteristics of a karst aquifer (Kentucky, USA). Hydrogeol J 17:1665–1678. doi:10.1007/s10040-009-0489-0
Acknowledgements
The authors are grateful to the two anonymous reviewers whose thoughtful comments contributed to improving the paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Francesco La Vigna, the corresponding author, was affiliated with Roma Tre University at the time of the study.
Rights and permissions
About this article
Cite this article
La Vigna, F., Hill, M.C., Rossetto, R. et al. Parameterization, sensitivity analysis, and inversion: an investigation using groundwater modeling of the surface-mined Tivoli-Guidonia basin (Metropolitan City of Rome, Italy). Hydrogeol J 24, 1423–1441 (2016). https://doi.org/10.1007/s10040-016-1393-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10040-016-1393-z