Teaching Numerical Groundwater Flow Modeling with Spreadsheets

[EN] The use of spreadsheets for numerical groundwater flow modeling is not a novelty; however, its potential in the classroom has not been emphasized enough. This Teachers Aid provides a step-by-step implementation of a steady-state, vertically integrated two-dimensional groundwater flow model in a confined irregular aquifer with boundary conditions of the three kinds and subject to pumping and recharge that will enhance the learning experience of students that are confronted for the first time with the numerical solution of the groundwater flow partial differential equation.The author acknowledges Grant PID2019-109131RB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and Project InTheMED, which is part of the PRIMA Programme supported by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 1923.Gómez-Hernández, JJ. (2022). Teaching Numerical Groundwater Flow Modeling with Spreadsheets. Mathematical Geosciences. 54(6):1121-1138. https://doi.org/10.1007/s11004-022-10002-41121113854

New Developments in Subsurface Flow and Transport

Gómez-Hernández, JJ. (2012). New Developments in Subsurface Flow and Transport. Mathematical Geosciences. 44(2):131-132. doi:10.1007/s11004-012-9390-9S13113244

Multiple-point Geostatistics: Stochastic Modeling with Training Images by G. Mariethoz and J. Caers, December 2014. Wiley-Blackwell, United States (2014)

This is a book reviewGómez-Hernández, JJ. (2015). Multiple-point Geostatistics: Stochastic Modeling with Training Images by G. Mariethoz and J. Caers, December 2014. Wiley-Blackwell, United States (2014). Computers and Geosciences. 83:231-235. doi:10.1016/j.cageo.2015.06.010S2312358

Contaminant Source Identification in Aquifers: A Critical View

[EN] Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers.The first author wishes to acknowledge the financial contribution of the Spanish Ministry of Science and Innovation through Project No. PID2019-109131RB-I00, and the second author acknowledges the financial support from the Fundamental Research Funds for the Central Universities (B200201015) and Jiangsu Specially-Appointed Professor Program from Jiangsu Provincial Department of Education (B19052). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Gómez-Hernández, JJ.; Xu, T. (2022). Contaminant Source Identification in Aquifers: A Critical View. Mathematical Geosciences. 54(2):437-458. https://doi.org/10.1007/s11004-021-09976-443745854

Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering

[EN] When a contaminant is detected in a drinking well, source location, initial contaminant release time, and initial contaminant concentration are, in many cases, unknown; the responsible party may have disappeared and the identification of when and where the contamination happened may become difficult. Although contaminant source identification has been studied extensively in the last decades, we proposeto our knowledge, for the first timethe use of the ensemble Kalman filter (EnKF), which has proven to be a powerful algorithm for inverse modeling. The EnKF is tested in a two-dimensional synthetic deterministic aquifer, identifying, satisfactorily, the source location, the release time, and the release concentration, together with an assessment of the uncertainty associated with this identification.Financial support to carry out this work was received from the Spanish Ministry of Economy and Competitiveness through project CGL2014-59841-P. All data used in this analysis are available from the authors.Xu, T.; Gómez-Hernández, JJ. (2016). Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering. Water Resources Research. 52(8):6587-6595. https://doi.org/10.1002/2016WR019111S65876595528Aral, M. M., Guan, J., & Maslia, M. L. (2001). Identification of Contaminant Source Location and Release History in Aquifers. Journal of Hydrologic Engineering, 6(3), 225-234. doi:10.1061/(asce)1084-0699(2001)6:3(225)Butera, I., Tanda, M. G., & Zanini, A. (2012). Simultaneous identification of the pollutant release history and the source location in groundwater by means of a geostatistical approach. Stochastic Environmental Research and Risk Assessment, 27(5), 1269-1280. doi:10.1007/s00477-012-0662-1Chen, Y., Oliver, D. S., & Zhang, D. (2009). Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization. Journal of Petroleum Science and Engineering, 66(1-2), 1-14. doi:10.1016/j.petrol.2008.12.002Cupola, F., Tanda, M. G., & Zanini, A. (2014). Laboratory sandbox validation of pollutant source location methods. Stochastic Environmental Research and Risk Assessment, 29(1), 169-182. doi:10.1007/s00477-014-0869-4Evensen, G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343-367. doi:10.1007/s10236-003-0036-9Gorelick, S. M., Evans, B., & Remson, I. (1983). Identifying sources of groundwater pollution: An optimization approach. Water Resources Research, 19(3), 779-790. doi:10.1029/wr019i003p00779Gzyl, G., Zanini, A., Frączek, R., & Kura, K. (2014). Contaminant source and release history identification in groundwater: A multi-step approach. Journal of Contaminant Hydrology, 157, 59-72. doi:10.1016/j.jconhyd.2013.11.006Franssen, H. J. H., & Kinzelbach, W. (2009). Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems. Journal of Hydrology, 365(3-4), 261-274. doi:10.1016/j.jhydrol.2008.11.033Ma, R., Zheng, C., Zachara, J. M., & Tonkin, M. (2012). Utility of bromide and heat tracers for aquifer characterization affected by highly transient flow conditions. Water Resources Research, 48(8). doi:10.1029/2011wr011281Mahar, P. S. (2000). Water Resources Management, 14(3), 209-227. doi:10.1023/a:1026527901213McDonald , M. A. Harbaugh 1988Michalak, A. M., & Kitanidis, P. K. (2003). A method for enforcing parameter nonnegativity in Bayesian inverse problems with an application to contaminant source identification. Water Resources Research, 39(2). doi:10.1029/2002wr001480Michalak, A. M., & Kitanidis, P. K. (2004). Application of geostatistical inverse modeling to contaminant source identification at Dover AFB, Delaware. Journal of Hydraulic Research, 42(sup1), 9-18. doi:10.1080/00221680409500042Neupauer, R. M., & Lin, R. (2006). Identifying sources of a conservative groundwater contaminant using backward probabilities conditioned on measured concentrations. Water Resources Research, 42(3). doi:10.1029/2005wr004115Neupauer, R. M., & Wilson, J. L. (1999). Adjoint method for obtaining backward-in-time location and travel time probabilities of a conservative groundwater contaminant. Water Resources Research, 35(11), 3389-3398. doi:10.1029/1999wr900190Woodbury, A., Sudicky, E., Ulrych, T. J., & Ludwig, R. (1998). Three-dimensional plume source reconstruction using minimum relative entropy inversion. Journal of Contaminant Hydrology, 32(1-2), 131-158. doi:10.1016/s0169-7722(97)00088-0Woodbury, A. D., & Ulrych, T. J. (1996). Minimum Relative Entropy Inversion: Theory and Application to Recovering the Release History of a Groundwater Contaminant. Water Resources Research, 32(9), 2671-2681. doi:10.1029/95wr03818Xu, T., & Gómez-Hernández, J. J. (2015). Probability fields revisited in the context of ensemble Kalman filtering. Journal of Hydrology, 531, 40-52. doi:10.1016/j.jhydrol.2015.06.062Xu, T., Jaime Gómez-Hernández, J., Zhou, H., & Li, L. (2013). The power of transient piezometric head data in inverse modeling: An application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. Advances in Water Resources, 54, 100-118. doi:10.1016/j.advwatres.2013.01.006Yeh, H.-D., Chang, T.-H., & Lin, Y.-C. (2007). Groundwater contaminant source identification by a hybrid heuristic approach. Water Resources Research, 43(9). doi:10.1029/2005wr004731Zheng , C. 2010 MT3DMS v5. 3 Supplemental User's Guide Technical Report to the US Army Engineer Research and Development CenterZhou, H., Gómez-Hernández, J. J., Hendricks Franssen, H.-J., & Li, L. (2011). An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering. Advances in Water Resources, 34(7), 844-864. doi:10.1016/j.advwatres.2011.04.01

Factorial kriging of a geochemical dataset for the heavy-metal spatial-pattern characterization The Wallonian Region

The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2704-5Characterizing the spatial patterns of variability is a fundamental aspect when investigating what could be the causes behind the spatial spreading of a set of variables. In this paper, a large multivariate dataset from the southeast of Belgium has been analyzed using factorial kriging. The purpose of the study is to explore and retrieve possible scales of spatial variability of heavy metals. This is achieved by decomposing the variance-covariance matrix of the multivariate sample into coregionalization matrices, which are, in turn, decomposed into transformation matrices, which serve to decompose each regionalized variable as a sum of independent factors. Then, factorial cokriging is used to produce maps of the factors explaining most of the variance, which can be compared with maps of the underlying lithology. For the dataset analyzes, this comparison identifies a few point scale concentrations that may reflect anthropogenic contamination, and it also identifies local and regional scale anomalies clearly correlated to the underlying geology and to known mineralizations. The results from this analysis could serve to guide the authorities in identifying those areas which need remediation.Benamghar, A.; Gómez-Hernández, JJ. (2014). Factorial kriging of a geochemical dataset for the heavy-metal spatial-pattern characterization The Wallonian Region. Environmental Earth Sciences. 71(7):3161-3170. doi:10.1007/s12665-013-2704-5S31613170717Bartholomé P et al (1977) Métallogénie de la Belgique, des Pays-Bas et du Luxembourg, Rapport nr 1, Belgium, pp 38Candeias C, Ferreira da Silva E, Salgueiro AR, Pereira HG, Reis AP, Patinha C, Matos JX, Avila PH (2011) The use of multivariate statistical analysis of geochemical data for assessing the spatial distribution of soil contamination by potentially toxic elements in the Aljustrel mining area (Iberian Pyrite Belt, Portugal)da Silva EF, Avila PF, Salgueiro AR, Candeias C, Pereira HG (2013) Quantitative spatial assessment of soil contamination in S. Francisco de Assis due to mining activity of the Panasqueira mine (Portugal). Environ Sci Pollut Res. doi: 10.1007/s11356-013-1495-2Delmer A (1912), La question du minerai de fer en Belgique (première partie et deuxiéme partie), Annales des mines de Belgique, Tome XVII", 4ème livraison, 853–940, (1912), and Tome XVIII", 2ème livraison, pp 325–448Goovaerts P (1997) Geostatistics for natural resources evaluation, 1st ed Oxford university press, Oxford, pp 483Goovaerts P (1998) Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biol Fertility Soils 27(4):315–334Goovaerts P (1993) Spatial orthogonality of the principal components computed from coregionalized variables. Mathematical Geolo 25:281–302Goovaerts P (1992) Factorial kriging analysis: a useful tool for exploring the structure of multivariate spatial soil information. J Soil Sci 43:597–619Goovaerts P (1991) Etude des relations entre propriétés physico-chimiques du sol par la géostatistique multivariable, Cahiers de Géostatistique, In:Compte-rendu des Journées de Géostatistique, Fontainebleau, France 1:247–261Goulard M, Voltz M (1992) Linear coregionalisation model: tools for estimation and choice of cross-variogram matrix. Math Geol 24(3):269–286Guagliardi I, Buttafuoco G, Cicchella D, De Rosa R (2013) A multivariate approach for anomaly separation of potentially toxic trace elements in urban and peri-urban soils: an application in a southern Italy area. J Soils Sediments 13(1):117–128Huang L-M, Deng C-B, Huang N, Huang X-J (2013) Multivariate statistical approach to identify heavy metal sources in agricultural soil around an abandoned PbZn mine in Guangxi Zhuang Autonomous Region, China. Environ Earth Sci 68(5):1331–1348Khedhiri S, Semhi Kh, Duplay J, Darragi F (2011) Comparison of sequential extraction and principal component analysis for determination of heavy metal partitioning in sediments: the case of protected Lagoon El Kelbia (Tunisia). Environ Earth Sci 62(5):1013–1025Keshav Krishna A, Rama Mohan K, Murthy NN, Periasamy V, Bipinkumar G, Manohar K, Srinivas Rao S (2013) Assessment of heavy metal contamination in soils around chromite mining areas, Nuggihalli, Karnataka, India. Environ Earth Sci. doi: 10.1007/s12665-012-2153-6Liebens J, Mohrherr C J, Ranga Rao K (2012) Trace metal assessment in soils in a small city and its rural surroundings, Pensacola, FL. Environ Earth Sci 65(6):1781–1793Maria Astel A, Chepanova L, Simeonov V (2011) Soil contamination interpretation by the use of monitoring data analysis. Water Air Soil Pollut. 216(1–4):375–390Matheron G (1982) Pour une analyse krigeante des données régionalisées, Note interne N-732, Centre de Géostatistique, Fontainbleau, FranceQueiroz JCB et al (2008) Geochemical characterization of heavy metal contaminated area using multivariate factorial kriging. Environ Geol 55:95-105Rodríguez Martín JA, Vázquez de la Cueva A, Grau Corbí JM, López Arias M (2007) Factors Controlling the Spatial Variability of Copper in Topsoils of the Northeastern Region of the Iberian Peninsula, Spain. Water Air Soil Pollut 186(1–4):311–321Sondag F, Martin H (1984) Inventaire géochimique des ressources métallifères de la Wallonie.In: Synthèse générale et rapport de fin de recherches, UCL, Projet Ministère de l’économie Wallonne, Belgique, pp 15Wackernagel H (1988) Geostatistical techniques for interpreting multivariate spatial information.In: Chung CF et al. (eds) Quantitative analysis of mineral and energy resources, Reidel publishing company, Dordrecht, pp 393–409Wackernagel H, Butenuth C (1989) Caractérisation d’anomalies géochimiques par la géostatistique multivariable. J Geochem Explor 32:437–444Wackernaegel H, Sanguinetti H (1992) Gold prospection with factorial cokriging in the Limousin, France. Document interne, Centre de géostatistique ENSMP, Paris, pp 1–11Xiao HY , Zhou WB, Zeng FP, Wu DS (2010) Water chemistry and heavy metal distribution in an AMD highly contaminated river. Environ Earth Sci 59(5):1023–1031Yeh M-S, Lin Y-P, Chang L-C (2006) Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms. Environ Geol 50(1):101–12

One Step at a Time: The Origins of Sequential Simulation and Beyond

[EN] In the mid-1980s, still in his young 40s, Andre Journel was already recognized as one of the giants of geostatistics. Many of the contributions from his new research program at Stanford University had centered around the indicator methods that he developed: indicator kriging and multiple indicator kriging. But when his second crop of graduate students arrived at Stanford, indicator methods still lacked an approach to conditional simulation that was not tainted by what Andre called the 'Gaussian disease'; early indicator simulations went through the tortuous path of converting all indicators to Gaussian variables, running a turning bands simulation, and truncating the resulting multi-Gaussian realizations. When he conceived of sequential indicator simulation (SIS), even Andre likely did not recognize the generality of an approach to simulation that tackled the simulation task one step at a time. The early enthusiasm for SIS was its ability, in its multiple-indicator form, to cure the Gaussian disease and to build realizations in which spatial continuity did not deteriorate in the extreme values. Much of Stanford's work in the 1980s focused on petroleum geostatistics, where extreme values (the high-permeability fracture zones and the low-permeability shale barriers) have much stronger anisotropy, and much longer ranges of correlation in the maximum continuity direction, than mid-range values. With multi-Gaussian simulations necessarily imparting weaker continuity to the extremes, SIS was an important breakthrough. The generality of the sequential approach was soon recognized, first through its analogy with multi-variate unconditional simulation achieved using the lower triangular matrix of an LU decomposition of the covariance matrix as the multiplier of random normal deviates. Modifying LU simulation so that it became conditional gave rise to sequential Gaussian simulation (SGS), an algorithm that shared much in common with SIS. With nagging implementation details like the sequential path and the search neighborhood being common to both methods, improvements in either SIS or SGS often became improvements to the other. Almost half of the contributors to this Special Issue became students of Andre in the classes of 1984-1988, and several are the pioneers of SIS and SGS. Others who studied later with Andre explored and developed the first multipoint statistics simulation procedures, which are based on the same concept that underlies sequential simulation. Among his many significant intellectual accomplishments, one of the cornerstones of Andre Journel's legacy was sequential simulation, built one step at a time.The first author wishes to acknowledge the financial contribution of the Spanish Ministry of Science and Innovation through Project Number PID2019-109131RB-I00.Gómez-Hernández, JJ.; Srivastava, RM. (2021). One Step at a Time: The Origins of Sequential Simulation and Beyond. Mathematical Geosciences. 53(2):193-209. https://doi.org/10.1007/s11004-021-09926-0S19320953

Díaz Viera, M.A., Sahay, N., Coronado, M. and Ortiz Tapia, A. (eds.) : Mathematical and Numerical Modeling in Porous Media: Applications in Geosciences

“The final publication is available at Springer via http://dx.doi.org/10.1007/s11004-013-9498-6"Gómez-Hernández, JJ. (2014). Díaz Viera, M.A., Sahay, N., Coronado, M. and Ortiz Tapia, A. (eds.) : Mathematical and Numerical Modeling in Porous Media: Applications in Geosciences. Mathematical Geosciences. 46(3):377-380. doi:10.1007/s11004-013-9498-6S37738046

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer

Contaminant-Source Detection in a Water Distribution System Using the Ensemble Kalman Filter

[EN] Early detection of a contamination leach into a water distribution system, followed by the identification of the source and an evaluation of the total amount of the contaminant that has been injected into the system, is of paramount importance in order to protect a water user's health. The ensemble Kalman filter, which has been recently applied in hydrogeology to detect contaminant sources in aquifers, is extended to the identification of a contaminant source and its intensity in a water distribution system. The driving concept is the assimilation of contaminant observations at the nodes of the pipeline network at specified time intervals until enough information has been collected to allow the positioning of the source and the estimation of its intensity. Several scenarios are analyzed considering sources at different nodes, with different delays between the beginning of the pollution and the start of the measurements, different sampling time intervals, and different observation ending times. The scenarios are carried out in the benchmarking Anytown network, demonstrating the ability of the ensemble Kalman filter for contaminant-source detection in real water distribution systems. The use of the ensemble Kalman filter supposed a major breakthrough in the inverse modeling of subsurface flow and transport, and the successful results of its application to the synthetic Anytown network warrant further exploration of its capabilities in the realm of water distribution systems. (C) 2021 American Society of Civil Engineers.Butera, I.; Gómez-Hernández, JJ.; Nicotra, S. (2021). Contaminant-Source Detection in a Water Distribution System Using the Ensemble Kalman Filter. Journal of Water Resources Planning and Management. 147(7):1-11. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001383S111147