- -

Decision making under uncertainty in environmental projects using mathematical simulation modeling

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Decision making under uncertainty in environmental projects using mathematical simulation modeling

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Llopis Albert, Carlos es_ES
dc.contributor.author Palacios Marqués, Daniel es_ES
dc.contributor.author Merigó -Lindahl, José María es_ES
dc.date.accessioned 2017-05-02T14:13:48Z
dc.date.available 2017-05-02T14:13:48Z
dc.date.issued 2016-10
dc.identifier.issn 1866-6280
dc.identifier.uri http://hdl.handle.net/10251/80331
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-016-6135-y es_ES
dc.description.abstract In decision-making processes, reliability and risk aversion play a decisive role. The aim of this study is to perform an uncertainty assessment of the effects of future scenarios of sustainable groundwater pumping strategies on the quantitative and chemical status of an aquifer. The good status of the aquifer is defined according to the terms established by the EU Water Framework Directive (WFD). A decision support systems (DSS) is presented, which makes use of a stochastic inverse model (GC method) and geostatistical approaches to calibrate equally likely realizations of hydraulic conductivity (K) fields for a particular case study. These K fields are conditional to available field data, including hard and soft information. Then, different future scenarios of groundwater pumping strategies are generated, based on historical information and WFD standards, and simulated for each one of the equally likely K fields. The future scenarios lead to different environmental impacts and levels of socioeconomic development of the region and, hence, to a different degree of acceptance among stakeholders. We have identified the different stakeholders implied in the decision-making process, the objectives pursued and the alternative actions that should be considered by stakeholders in a public participation project (PPP). The MonteCarlo simulation provides a highly effective way for uncertainty assessment and allows presenting the results in a simple and understandable way even for non-experts stakeholders. The methodology has been successfully applied to a real case study and lays the foundations to performa PPP and stakeholders' involvement in a decisionmaking process as required by the WFD. The results of the methodology can help the decision-making process to come up with the best policies and regulations for a groundwater system under uncertainty in groundwater parameters and management strategies and involving stakeholders with conflicting interests. es_ES
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Environmental Earth Sciences es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Water resources management es_ES
dc.subject Uncertainty assessment es_ES
dc.subject Stakeholders es_ES
dc.subject Public participation projects es_ES
dc.subject Over-exploited aquifers es_ES
dc.subject EU Water Framework Directive es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.subject.classification INGENIERIA MECANICA es_ES
dc.title Decision making under uncertainty in environmental projects using mathematical simulation modeling es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s12665-016-6135-y
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny es_ES
dc.description.bibliographicCitation Llopis Albert, C.; Palacios Marqués, D.; Merigó -Lindahl, JM. (2016). Decision making under uncertainty in environmental projects using mathematical simulation modeling. Environmental Earth Sciences. 75(19):1-11. doi:10.1007/s12665-016-6135-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s12665-016-6135-y es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 11 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 75 es_ES
dc.description.issue 19 es_ES
dc.relation.senia 318196 es_ES
dc.identifier.eissn 1866-6299
dc.description.references Arhonditsis GB, Perhar G, Zhang W, Massos E, Shi M, Das A (2008) Addressing equifinality and uncertainty in eutrophication models. Water Resour Res 44:W01420. doi: 10.1029/2007WR005862 es_ES
dc.description.references Capilla JE, Llopis-Albert C (2009) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. J Hydrol 371:66–74. doi: 10.1016/j.jhydrol.2009.03.015 es_ES
dc.description.references CHJ (Júcar Water Agency) (2016) Júcar river basin authority. http://www.chj.es/ es_ES
dc.description.references CHS (Segura Water Agency) (2016) Segura river basin authority. http://www.chsegura.es/ es_ES
dc.description.references Custodio E (2002) Aquifer overexploitation: what does it mean? Hydrogeol J 10:254–277 es_ES
dc.description.references EC (2000). Directive 2000/60/EC of the European Parliament and of the Council of October 23 2000, establishing a framework for community action in the field of water policy. Official Journal of the European Communities L327/1eL327/72. 22.12.2000 es_ES
dc.description.references EC (2006) Directive 2006/118/EC of the European Parliament and of the Council of 12 December 2006 on the protection of groundwater against pollution and deterioration es_ES
dc.description.references Gómez-Hernández JJ, Srivastava RM (1990) ISIM3D: an ANSI-C three dimensional multiple indicator conditional simulation program. Comput Geosci 16(4):395–440 es_ES
dc.description.references Harbaugh AW, Banta ER, Hill MC and McDonald MG (2000) MODFLOW- 2000, The US geological survey modular groundwater model-user guide to modularization concepts and the groundwater flow process. US Geol. Surv. Open-File Rep 00–92, 12 es_ES
dc.description.references Hu LY (2000) Gradual deformation and iterative calibration of Gaussian related stochastic models. Math Geol 32(1):87–108 es_ES
dc.description.references Jagelke J, Barthel R (2005) Conceptualization and implementation of a regional groundwater model for the Neckar catchment in the framework of an integrated regional model. Adv Geosci 5:105–111 es_ES
dc.description.references Llopis-Albert C (2008) Stochastic inverse modeling conditional to flow, mass transport and secondary information. Universitat Politècnica de València, València. ISBN 978-84-691-9796-7 es_ES
dc.description.references Llopis-Albert C, Capilla JE (2009a) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. Demonstration on a synthetic aquifer. J Hydrol 371:53–55. doi: 10.1016/j.jhydrol.2009.03.014 es_ES
dc.description.references Llopis-Albert C, Capilla JE (2009b) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. Application to the macrodispersion experiment (MADE-2) site, on Columbus air force base in Mississippi (USA). J Hydrol 371:75–84. doi: 10.1016/j.jhydrol.2009.03.016 es_ES
dc.description.references Llopis-Albert C, Capilla JE (2010a) Stochastic simulation of non-gaussian 3D conductivity fields in a fractured medium with multiple statistical populations: a case study. J Hydrol Eng 15(7):554–566. doi: 10.1061/(ASCE)HE.1943-5584.0000214 es_ES
dc.description.references Llopis-Albert C, Capilla JE (2010b) Stochastic inverse modeling of hydraulic conductivity fields taking into account independent stochastic structures: a 3D case study. J Hydrol 391:277–288. doi: 10.1016/j.jhydrol.2010.07.028 es_ES
dc.description.references Llopis-Albert C, Pulido-Velazquez D (2014) Discussion about the validity of sharp-interface models to deal with seawater intrusion in coastal aquifers. Hydrol Process 28(10):3642–3654 es_ES
dc.description.references Llopis-Albert C, Pulido-Velazquez D (2015) Using MODFLOW code to approach transient hydraulic head with a sharp-interface solution. Hydrol Process 29(8):2052–2064. doi: 10.1002/hyp.10354 es_ES
dc.description.references Llopis-Albert C, Palacios-Marqués D, Merigó JM (2014) A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty. J Hydrol 511:10–16. doi: 10.1016/j.jhydrol.2014.01.021 es_ES
dc.description.references Llopis-Albert C, Merigó JM, Palacios-Marqués D (2015) Structure adaptation in stochastic inverse methods for integrating information. Water Resour Manage 29(1):95–107. doi: 10.1007/s11269-014-0829-2 es_ES
dc.description.references Llopis-Albert C, Merigó JM, Xu Y (2016) A coupled stochastic inverse/sharp interface seawater intrusion approach for coastal aquifers under groundwater parameter uncertainty. J Hydrol 540:774–783. doi: 10.1016/j.jhydrol.2016.06.065 es_ES
dc.description.references McDonald MG and Harbaugh AW (1988) A modular three-dimensional finite-difference groundwater flow model. US geological survey technical manual of water resources investigation, Book 6, US geological survey, Reston, Virginia, 586 es_ES
dc.description.references Molina JL, Pulido-Velazquez M, Llopis-Albert C, Peña-Haro S (2013) Stochastic hydro-economic model for groundwater quality management using Bayesian networks. Water Sci Technol 67(3):579–586. doi: 10.2166/wst.2012.598 es_ES
dc.description.references Peña-Haro S, Llopis-Albert C, Pulido-Velazquez M (2010) Fertilizer standards for controlling groundwater nitrate pollution from agriculture: El Salobral-Los Llanos case study, Spain. J Hydrol 392:174–187. doi: 10.1016/j.jhydrol.2010.08.006 es_ES
dc.description.references Peña-Haro S, Pulido-Velazquez M, Llopis-Albert C (2011) Stochastic hydro-economic modeling for optimal management of agricultural groundwater nitrate pollution under hydraulic conductivity uncertainty. Environ Model Softw 26(8):999–1008. doi: 10.1016/j.envsoft.2011.02.010 es_ES
dc.description.references Pulido-Velazquez D, Llopis-Albert C, Peña-Haro S, Pulido-Velazquez M (2011) Efficient conceptual model for simulating the effect of aquifer heterogeneity on natural groundwater discharge to rivers. Adv Water Resour 34(11):1377–1389. doi: 10.1016/j.advwatres.2011.07.010 es_ES
dc.description.references Reichert P, Borsuk M, Hostmann M, Schweizer S, Spörri C, Tockner K, Truffer B (2005) Concepts of decision support for river rehabilitation. Environ Model Softw 22:188–201 es_ES
dc.description.references Wright SAL, Fritsch O (2011) Operationalising active involvement in the EU water framework directive: why, when and how? Ecol Econ 70(12):2268–2274 es_ES
dc.description.references Zhou H, Gómez-Hernández JJ, Li L (2014) Inverse methods in hydrogeology: evolution and recent trends. Adv Water Resour 63:22–37. doi: 10.1016/j.advwatres.2013.10.014 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem