{"id":{"repo_id":"aachen","oai_identifier":"oai:publications.rwth-aachen.de:59073"},"canonical_url":"https://search.dev.ndltd.org/etd/aachen/oai:publications.rwth-aachen.de:59073","repository":{"repo_id":"aachen","name":"RWTH Aachen University","base_url":"https://publications.rwth-aachen.de/oai2d"},"display":{"title":"Risk assessment and uncertainty analysis in groundwater modelling","abstract":"Aquifer properties are subject to uncertainty due to randomness nature of geologic and hydraulic environmental systems. Therefore, parameter uncertainty in groundwater models casts big doubts in the accuracy of the model output. The failure in determination and taken into consideration the affect of uncertainty in model parameters could considerably reduce the possibility of success of any management or remediation scheme. Stochastic approaches in groundwater modelling are usually used to quantify the uncertainty in model parameters. The first order reliability method (FORM) has been recently used in probabilistic modelling of structural reliability applications to estimate the occurrence of low probability events. Recently, this approach was extended and used in risk and uncertainty analysis in groundwater and contaminant transport modelling. The advantage of this approach that it does not require a large number of computations in compare with other methods (e.g. Monte Carlo simulation) when applied to simple problems and it produces reasonable accurate results. However, it has been found that the computations of (FORM) can equal or exceed that of Monte Carlo in case of large number of variables. The primary difficulty in (FORM) that it requires solving of optimisation problem to locate the failure point. This optimisation procedure requires solving for the first order derivative of the limit state function at each iteration in the problem of concern. The second difficulty is the large number of variables when solving contaminant transport problems. To eliminate the limitations of (FORM), a new approach was proposed with less computation effort. The problem of optimisation approach was solved by using of automatic differentiation to obtain the Jacobian matrix of the limit state function. Therefore, the derivative code of the limit state function was coupled with reliability code and the first order derivative was obtained with a very good accuracy. The problem of large number of variables was solved by introducing a zonation approach. In this approach, the spatial variables of aquifer parameters were zoned into sub-areas based on hydrogeological properties of the aquifer and thus, the number of variables was reduced. Since the probabilistic modelling requires the best estimate of parameters and their statistical descriptors, the first challenge in implementation of probabilistic model is the determination of input data estimates and their uncertainty. The input parameters and their statistical descriptors were estimated based on the measured data in the field (i.e. pumping test results) and the historical data. However, the most difficult parameter to estimate was the groundwater recharge. This parameter was estimated using two different models for groundwater recharge estimation and the results were compared with results in literature. Statistical analysis were done finally and the mean and standard deviation of each variable were obtained besides the probability distribution. In this research, the developed probabilistic model was applied on two case studies. In the first case, (FORM) model was coupled with a three dimensional finite difference groundwater flow model. The derivative code was obtained using automatic differentiation of Fortran (ADIFOR). The results of probabilistic groundwater flow model were compared with Monte Carlo. Besides the probability of failure, sensitivity results were obtained for the given limit state function. In the second case study, FORM was coupled with a two-dimensional finite element groundwater flow and contaminant transport model. FORM-Contaminant transport model was coupled with the derivative code as in the case of groundwater flow model. In both cases, hydraulic conductivity and groundwater recharge were treated as random variables. The results of the proposed probabilistic method were compared with Monte Carlo simulation and other methods. Based on the obtained results, it is found that the use of FORM is a very good tool for probabilistic risk assessment in groundwater and contaminant transport modelling. The developed FORM approach is shown to produce results that are comparable with those obtained by other methods but with less computational effort and more accurate results. Moreover, reliability approach produces sensitivity results without any further computations.","abstract_html":"Aquifer properties are subject to uncertainty due to randomness nature of geologic and hydraulic environmental systems. Therefore, parameter uncertainty in groundwater models casts big doubts in the accuracy of the model output. The failure in determination and taken into consideration the affect of uncertainty in model parameters could considerably reduce the possibility of success of any management or remediation scheme. Stochastic approaches in groundwater modelling are usually used to quantify the uncertainty in model parameters. The first order reliability method (FORM) has been recently used in probabilistic modelling of structural reliability applications to estimate the occurrence of low probability events. Recently, this approach was extended and used in risk and uncertainty analysis in groundwater and contaminant transport modelling. The advantage of this approach that it does not require a large number of computations in compare with other methods (e.g. Monte Carlo simulation) when applied to simple problems and it produces reasonable accurate results. However, it has been found that the computations of (FORM) can equal or exceed that of Monte Carlo in case of large number of variables. The primary difficulty in (FORM) that it requires solving of optimisation problem to locate the failure point. This optimisation procedure requires solving for the first order derivative of the limit state function at each iteration in the problem of concern. The second difficulty is the large number of variables when solving contaminant transport problems. To eliminate the limitations of (FORM), a new approach was proposed with less computation effort. The problem of optimisation approach was solved by using of automatic differentiation to obtain the Jacobian matrix of the limit state function. Therefore, the derivative code of the limit state function was coupled with reliability code and the first order derivative was obtained with a very good accuracy. The problem of large number of variables was solved by introducing a zonation approach. In this approach, the spatial variables of aquifer parameters were zoned into sub-areas based on hydrogeological properties of the aquifer and thus, the number of variables was reduced. Since the probabilistic modelling requires the best estimate of parameters and their statistical descriptors, the first challenge in implementation of probabilistic model is the determination of input data estimates and their uncertainty. The input parameters and their statistical descriptors were estimated based on the measured data in the field (i.e. pumping test results) and the historical data. However, the most difficult parameter to estimate was the groundwater recharge. This parameter was estimated using two different models for groundwater recharge estimation and the results were compared with results in literature. Statistical analysis were done finally and the mean and standard deviation of each variable were obtained besides the probability distribution. In this research, the developed probabilistic model was applied on two case studies. In the first case, (FORM) model was coupled with a three dimensional finite difference groundwater flow model. The derivative code was obtained using automatic differentiation of Fortran (ADIFOR). The results of probabilistic groundwater flow model were compared with Monte Carlo. Besides the probability of failure, sensitivity results were obtained for the given limit state function. In the second case study, FORM was coupled with a two-dimensional finite element groundwater flow and contaminant transport model. FORM-Contaminant transport model was coupled with the derivative code as in the case of groundwater flow model. In both cases, hydraulic conductivity and groundwater recharge were treated as random variables. The results of the proposed probabilistic method were compared with Monte Carlo simulation and other methods. Based on the obtained results, it is found that the use of FORM is a very good tool for probabilistic risk assessment in groundwater and contaminant transport modelling. The developed FORM approach is shown to produce results that are comparable with those obtained by other methods but with less computational effort and more accurate results. Moreover, reliability approach produces sensitivity results without any further computations.","abstract_has_math":false,"creators":["Baalousha, Husam Musa"],"institution":"Mainz [u.a.]","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Köngeter, Jürgen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2002,"date_issued":"2002","date_published":"2002","updated_at":"2026-07-30T19:42:31Z","subjects":["info:eu-repo/classification/ddc/500","Grundwasser","Stochastisches Modell","Risikoanalyse","Naturwissenschaften","groundwater","contaminant transport","uncertainty analysis"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-120889%22"],"render_values":[{"text":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-120889%22","href":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-120889%22","code":true}]}]},"links":{"outbound_url":"https://publications.rwth-aachen.de/record/59073","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Köngeter, Jürgen"]},{"key":"dc:creator","label":"Author","values":["Baalousha, Husam Musa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:coverage","label":"Dc Coverage","values":["DE"]},{"key":"dc:date","label":"Dc Date","values":["2002"]},{"key":"dc:publisher","label":"Institution","values":["Mainz [u.a.]"]},{"key":"dc:relation","label":"Dc Relation","values":["info:eu-repo/semantics/altIdentifier/issn/1437-8477","info:eu-repo/semantics/altIdentifier/urn/urn:nbn:de:hbz:82-opus-6690","info:eu-repo/semantics/altIdentifier/doi/10.18154/RWTH-CONV-120889"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis","info:eu-repo/semantics/publishedVersion"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["info:eu-repo/classification/ddc/500","Grundwasser","Stochastisches Modell","Risikoanalyse","Naturwissenschaften","groundwater","contaminant transport","uncertainty analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://publications.rwth-aachen.de/record/59073","https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-120889%22"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Aquifer properties are subject to uncertainty due to randomness nature of geologic and hydraulic environmental systems. Therefore, parameter uncertainty in groundwater models casts big doubts in the accuracy of the model output. The failure in determination and taken into consideration the affect of uncertainty in model parameters could considerably reduce the possibility of success of any management or remediation scheme. Stochastic approaches in groundwater modelling are usually used to quantify the uncertainty in model parameters. The first order reliability method (FORM) has been recently used in probabilistic modelling of structural reliability applications to estimate the occurrence of low probability events. Recently, this approach was extended and used in risk and uncertainty analysis in groundwater and contaminant transport modelling. The advantage of this approach that it does not require a large number of computations in compare with other methods (e.g. Monte Carlo simulation) when applied to simple problems and it produces reasonable accurate results. However, it has been found that the computations of (FORM) can equal or exceed that of Monte Carlo in case of large number of variables. The primary difficulty in (FORM) that it requires solving of optimisation problem to locate the failure point. This optimisation procedure requires solving for the first order derivative of the limit state function at each iteration in the problem of concern. The second difficulty is the large number of variables when solving contaminant transport problems. To eliminate the limitations of (FORM), a new approach was proposed with less computation effort. The problem of optimisation approach was solved by using of automatic differentiation to obtain the Jacobian matrix of the limit state function. Therefore, the derivative code of the limit state function was coupled with reliability code and the first order derivative was obtained with a very good accuracy. The problem of large number of variables was solved by introducing a zonation approach. In this approach, the spatial variables of aquifer parameters were zoned into sub-areas based on hydrogeological properties of the aquifer and thus, the number of variables was reduced. Since the probabilistic modelling requires the best estimate of parameters and their statistical descriptors, the first challenge in implementation of probabilistic model is the determination of input data estimates and their uncertainty. The input parameters and their statistical descriptors were estimated based on the measured data in the field (i.e. pumping test results) and the historical data. However, the most difficult parameter to estimate was the groundwater recharge. This parameter was estimated using two different models for groundwater recharge estimation and the results were compared with results in literature. Statistical analysis were done finally and the mean and standard deviation of each variable were obtained besides the probability distribution. In this research, the developed probabilistic model was applied on two case studies. In the first case, (FORM) model was coupled with a three dimensional finite difference groundwater flow model. The derivative code was obtained using automatic differentiation of Fortran (ADIFOR). The results of probabilistic groundwater flow model were compared with Monte Carlo. Besides the probability of failure, sensitivity results were obtained for the given limit state function. In the second case study, FORM was coupled with a two-dimensional finite element groundwater flow and contaminant transport model. FORM-Contaminant transport model was coupled with the derivative code as in the case of groundwater flow model. In both cases, hydraulic conductivity and groundwater recharge were treated as random variables. The results of the proposed probabilistic method were compared with Monte Carlo simulation and other methods. Based on the obtained results, it is found that the use of FORM is a very good tool for probabilistic risk assessment in groundwater and contaminant transport modelling. The developed FORM approach is shown to produce results that are comparable with those obtained by other methods but with less computational effort and more accurate results. Moreover, reliability approach produces sensitivity results without any further computations."]},{"key":"dc:source","label":"Dc Source","values":["Aachen : Mainz [u.a.], Mitteilungen / Lehrstuhl und Institut für Wasserbau und Wasserwirtschaft, Rheinisch-Westfälische Technische Hochschule Aachen 133, XIII, 153 S. : Ill., graph. Darst., Kt. (2002). doi:10.18154/RWTH-CONV-120889 = Zugl.: Aachen, Techn. Hochsch., Diss., 2003"]},{"key":"dc:title","label":"Title","values":["Risk assessment and uncertainty analysis in groundwater modelling"]}]}],"canonical_facts":{"dc:contributor":["Köngeter, Jürgen"],"dc:coverage":["DE"],"dc:creator":["Baalousha, Husam Musa"],"dc:date":["2002"],"dc:description":["Aquifer properties are subject to uncertainty due to randomness nature of geologic and hydraulic environmental systems. Therefore, parameter uncertainty in groundwater models casts big doubts in the accuracy of the model output. The failure in determination and taken into consideration the affect of uncertainty in model parameters could considerably reduce the possibility of success of any management or remediation scheme. Stochastic approaches in groundwater modelling are usually used to quantify the uncertainty in model parameters. The first order reliability method (FORM) has been recently used in probabilistic modelling of structural reliability applications to estimate the occurrence of low probability events. Recently, this approach was extended and used in risk and uncertainty analysis in groundwater and contaminant transport modelling. The advantage of this approach that it does not require a large number of computations in compare with other methods (e.g. Monte Carlo simulation) when applied to simple problems and it produces reasonable accurate results. However, it has been found that the computations of (FORM) can equal or exceed that of Monte Carlo in case of large number of variables. The primary difficulty in (FORM) that it requires solving of optimisation problem to locate the failure point. This optimisation procedure requires solving for the first order derivative of the limit state function at each iteration in the problem of concern. The second difficulty is the large number of variables when solving contaminant transport problems. To eliminate the limitations of (FORM), a new approach was proposed with less computation effort. The problem of optimisation approach was solved by using of automatic differentiation to obtain the Jacobian matrix of the limit state function. Therefore, the derivative code of the limit state function was coupled with reliability code and the first order derivative was obtained with a very good accuracy. The problem of large number of variables was solved by introducing a zonation approach. In this approach, the spatial variables of aquifer parameters were zoned into sub-areas based on hydrogeological properties of the aquifer and thus, the number of variables was reduced. Since the probabilistic modelling requires the best estimate of parameters and their statistical descriptors, the first challenge in implementation of probabilistic model is the determination of input data estimates and their uncertainty. The input parameters and their statistical descriptors were estimated based on the measured data in the field (i.e. pumping test results) and the historical data. However, the most difficult parameter to estimate was the groundwater recharge. This parameter was estimated using two different models for groundwater recharge estimation and the results were compared with results in literature. Statistical analysis were done finally and the mean and standard deviation of each variable were obtained besides the probability distribution. In this research, the developed probabilistic model was applied on two case studies. In the first case, (FORM) model was coupled with a three dimensional finite difference groundwater flow model. The derivative code was obtained using automatic differentiation of Fortran (ADIFOR). The results of probabilistic groundwater flow model were compared with Monte Carlo. Besides the probability of failure, sensitivity results were obtained for the given limit state function. In the second case study, FORM was coupled with a two-dimensional finite element groundwater flow and contaminant transport model. FORM-Contaminant transport model was coupled with the derivative code as in the case of groundwater flow model. In both cases, hydraulic conductivity and groundwater recharge were treated as random variables. The results of the proposed probabilistic method were compared with Monte Carlo simulation and other methods. Based on the obtained results, it is found that the use of FORM is a very good tool for probabilistic risk assessment in groundwater and contaminant transport modelling. The developed FORM approach is shown to produce results that are comparable with those obtained by other methods but with less computational effort and more accurate results. Moreover, reliability approach produces sensitivity results without any further computations."],"dc:identifier":["https://publications.rwth-aachen.de/record/59073","https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-120889%22"],"dc:language":["eng"],"dc:publisher":["Mainz [u.a.]"],"dc:relation":["info:eu-repo/semantics/altIdentifier/issn/1437-8477","info:eu-repo/semantics/altIdentifier/urn/urn:nbn:de:hbz:82-opus-6690","info:eu-repo/semantics/altIdentifier/doi/10.18154/RWTH-CONV-120889"],"dc:rights":["info:eu-repo/semantics/openAccess"],"dc:source":["Aachen : Mainz [u.a.], Mitteilungen / Lehrstuhl und Institut für Wasserbau und Wasserwirtschaft, Rheinisch-Westfälische Technische Hochschule Aachen 133, XIII, 153 S. : Ill., graph. Darst., Kt. (2002). doi:10.18154/RWTH-CONV-120889 = Zugl.: Aachen, Techn. Hochsch., Diss., 2003"],"dc:subject":["info:eu-repo/classification/ddc/500","Grundwasser","Stochastisches Modell","Risikoanalyse","Naturwissenschaften","groundwater","contaminant transport","uncertainty analysis"],"dc:title":["Risk assessment and uncertainty analysis in groundwater modelling"],"dc:type":["info:eu-repo/semantics/doctoralThesis","info:eu-repo/semantics/publishedVersion"]},"updated_at":"2026-07-30T19:42:31Z"}