Analyzing the effects of geological and parameter uncertainty on prediction of groundwater head and travel time

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Abstract. Projections of climate change impact are associated with a cascade of uncertainties including in CO 2 emission scenarios, climate models, downscaling and impact models. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM-RCM model combination (from the ENSEMBLES project) forced by the same CO 2 scenario (A1B). The changes from the reference period (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) to the future period (2081-2100) in projected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context-dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty due to the climate models is more important for groundwater hydraulic heads and stream flow.

Section: Discussionmentioning

confidence: 99%

Climate model uncertainty versus conceptual geological uncertainty in hydrological modeling

Sonnenborg

Seifert²,

Refsgaard

2015

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“…The comparison of simulations of groundwater head and capture zone indicates that in cases where knowledge of the overall geological structure is available, an object-based training image may be just as good as a training image based on geological interpretations of the actual site. Although this conclusion was based on groundwater heads and capture zones, it is expected that similar conclusions would be obtained from simulations of groundwater age or solute transport as these variables are, similar to capture zones, very much dependent on path lines and velocities, which are sensitive to geological heterogeneity (He et al, 2013). This study was based on a highly heterogeneous two-facie geological system where the exact shape and location of the clay lenses may not affect the results from the groundwater model significantly.…”

Section: Discussionmentioning

confidence: 91%

“…Soft conditioning was applied in S4 but not in S3. According to He et al (2013) the standard deviation on simulated hydraulic head, based on multiple realizations of the geology, converges to a fixed value as more models are accumulated. A stable value is approached after 30 realizations and in this study 50 realizations were therefore generated in each scenario and subsequently anchored to the steady state groundwater model.…”

Section: Ensemble Analysismentioning

confidence: 99%

“…In fact, S3 has a more concentrated probabilistic capture zone than that of S4 for Well 2 and Well 3. In the study of He et al (2013), the simulation of particle travel time was applied on 100 groundwater models with different geological architecture, in combination with a parameter uncertainty analysis of each model. One discovery was that the geological architecture is the most critical factor regarding particle travel time, because it affects the particle travel path.…”

Section: Capture Zonesmentioning

confidence: 99%

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The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling

Sonnenborg

Jørgensen

et al. 2014

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Abstract. Multiple-point geostatistical simulation (MPS)has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS simulation was applied to different scenarios regarding the TI and soft conditioning. By comparing their output from simulations of groundwater flow and probabilistic capture zone, TI from both sources (directly converted from high-resolution geophysical data and generated by TiGenerator) yields comparable results, even for the probabilistic capture zones, which are highly sensitive to the geological architecture. This study also suggests that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided.

“…The above mentioned MPS studies conduct mostly 2-D simulations, partly on synthetic data. The training image is the backbone of the MPS method and it has been acknowledged by dell 'Arciprete et al (2012) and He et al (2013) that reliable 3-D training images are difficult to acquire.…”

Section: Introductionmentioning

confidence: 99%

Challenges in conditioning a stochastic geological model of a heterogeneous glacial aquifer to a comprehensive soft data set

Koch

Jensen

et al. 2014

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Abstract. In traditional hydrogeological investigations, one geological model is often used based on subjective interpretations and sparse data availability. This deterministic approach usually does not account for any uncertainties. Stochastic simulation methods address this problem and can capture the geological structure uncertainty. In this study the geostatistical software TProGS is utilized to simulate an ensemble of realizations for a binary (sand/clay) hydrofacies model in the Norsminde catchment, Denmark. TProGS can incorporate soft data, which represent the associated level of uncertainty. High-density (20 m × 20 m × 2 m) airborne geophysical data (SkyTEM) and categorized borehole data are utilized to define the model of spatial variability in horizontal and vertical direction, respectively, and both are used for soft conditioning of the TProGS simulations. The category probabilities for the SkyTEM data set are derived from a histogram probability matching method, where resistivity is paired with the corresponding lithology from the categorized borehole data. This study integrates two distinct data sources into the stochastic modeling process that represent two extremes of the conditioning density spectrum: sparse borehole data and abundant SkyTEM data. In the latter the data have a strong spatial correlation caused by its high data density, which triggers the problem of overconditioning. This problem is addressed by a work-around utilizing a sampling/decimation of the data set, with the aim to reduce the spatial correlation of the conditioning data set. In the case of abundant conditioning data, it is shown that TProGS is capable of reproducing non-stationary trends. The stochastic realizations are validated by five performance criteria: (1) sand proportion, (2) mean length, (3) geobody connectivity, (4) facies probability distribution and (5) facies probability-resistivity bias.In conclusion, a stochastically generated set of realizations soft-conditioned to 200 m moving sampling of geophysical data performs most satisfactorily when balancing the five performance criteria. The ensemble can be used in subsequent hydrogeological flow modeling to address the predictive uncertainty originating from the geological structure uncertainty.