Reference : On the value of conditioning data to reduce conceptual model uncertainty in groundwat...
Scientific journals : Article
Engineering, computing & technology : Geological, petroleum & mining engineering
http://hdl.handle.net/2268/59998
On the value of conditioning data to reduce conceptual model uncertainty in groundwater modeling
English
Rojas, Rodrigo [Joint Research Centre (JRC), European Commission (EC) > Institute for Environment and Sustainability (IES) > Land management and natural hazards unit > >]
Feyen, Luc [Joint Research Centre (JRC), European Commission (EC) > Institute for Environment and Sustainability (IES) > Land management and natural hazards unit > >]
Batelaan, Okke [Katholieke Universiteit Leuven - KUL > Department of Earth and Environmental Sciences > Applied geology and mineralogy > >]
Dassargues, Alain mailto [Université de Liège - ULg > Département Argenco : Secteur GEO3 > Hydrogéologie & Géologie de l'environnement >]
Aug-2010
Water Resources Research
American Geophysical Union
46
8
W08520
Yes (verified by ORBi)
International
0043-1397
Washington
DC
[en] groundwater modelling ; uncertainty ; GLUE methodology ; conditioning ; conceptual model ; Bayesian model averaging
[en] Recent applications of multi-model methods have demonstrated their potential in quantifying conceptual model uncertainty in groundwater modeling applications. To date, however, little is known about the value of conditioning to constrain the ensemble of conceptualizations, to differentiate among retained alternative conceptualizations, and to reduce conceptual model uncertainty. We address these questions by conditioning multi-model simulations on measurements of hydraulic conductivity and observations of system-state variables and evaluating the e ffects on (i) the posterior multi-model statistics and (ii) the contribution of conceptual model uncertainty to the predictive uncertainty. Multi-model aggregation and conditioning is performed by combining the generalized likelihood uncertainty estimation (GLUE) method and Bayesian model averaging (BMA). As an illustrative example we employ a 3-dimensional hypothetical system under steady-state conditions, for which uncertainty about the conceptualization is expressed by an ensemble (M) of 7 models with varying complexity. Results show that conditioning on heads allowed for the exclusion of the two simplest models, but that their information content is limited to further diff erentiate among the retained conceptualizations. Conditioning on increasing numbers of conductivity measurements allowed for a further reffinement of the ensemble M and resulted in an increased precision and accuracy of the multi-model predictions. For some groundwater flow components not included as conditioning data, however, the gain in accuracy and precision was partially o ffset by strongly deviating predictions of a single conceptualization. Identifying the conceptualization producing the most deviating predictions may guide data collection campaigns aimed at acquiring data to further eliminate such conceptualizations. Including groundwater flow and river discharge observations
further allowed for a better diff erentiation among alternative conceptualizations and drastic reductions of the predictive variances. Results strongly advocate the use of observations less commonly available than groundwater heads to reduce conceptual model uncertainty in groundwater modeling.
Aquapôle - AQUAPOLE
Researchers ; Professionals
http://hdl.handle.net/2268/59998
10.1029/2009WR008822
http://www.agu.org/journals/wr/wr1008/2009WR008822/2009WR008822.pdf

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