scholarly journals Global Sensitivity Analysis to Optimize Basin-Scale Conductive Model Calibration - Insights on the Upper Rhine Graben

2020 ◽  
Author(s):  
Denise Degen ◽  
Karen Veroy ◽  
Jessica Freymark ◽  
Magdalena Scheck-Wenderoth ◽  
Florian Wellmann
Geothermics ◽  
2021 ◽  
Vol 95 ◽  
pp. 102143
Author(s):  
Denise Degen ◽  
Karen Veroy ◽  
Jessica Freymark ◽  
Magdalena Scheck-Wenderoth ◽  
Thomas Poulet ◽  
...  

2020 ◽  
Author(s):  
Monica Riva ◽  
Aronne Dell'Oca ◽  
Alberto Guadagnini

<p>Modern models of environmental and industrial systems have reached a relatively high level of complexity. The latter aspect could hamper an unambiguous understanding of the functioning of a model, i.e., how it drives relationships and dependencies among inputs and outputs of interest. Sensitivity Analysis tools can be employed to examine this issue.</p><p>Global sensitivity analysis (GSA) approaches rest on the evaluation of sensitivity across the entire support within which system model parameters are supposed to vary. In this broad context, it is important to note that the definition of a sensitivity metric must be linked to the nature of the question(s) the GSA is meant to address. These include, for example: (i) which are the most important model parameters with respect to given model output(s)?; (ii) could we set some parameter(s) (thus assisting model calibration) at prescribed value(s) without significantly affecting model results?; (iii) at which space/time locations can one expect the highest sensitivity of model output(s) to model parameters and/or knowledge of which parameter(s) could be most beneficial for model calibration?</p><p>The variance-based Sobol’ Indices (e.g., Sobol, 2001) represent one of the most widespread GSA metrics, quantifying the average reduction in the variance of a model output stemming from knowledge of the input. Amongst other techniques, Dell’Oca et al. [2017] proposed a moment-based GSA approach which enables one to quantify the influence of uncertain model parameters on the (statistical) moments of a target model output.</p><p>Here, we embed in these sensitivity indices the effect of uncertainties both in the system model conceptualization and in the ensuing model(s) parameters. The study is grounded on the observation that physical processes and natural systems within which they take place are complex, rendering target state variables amenable to multiple interpretations and mathematical descriptions. As such, predictions and uncertainty analyses based on a single model formulation can result in statistical bias and possible misrepresentation of the total uncertainty, thus justifying the assessment of multiple model system conceptualizations. We then introduce copula-based sensitivity metrics which allow characterizing the global (with respect to the input) value of the sensitivity and the degree of variability (across the whole range of the input values) of the sensitivity for each value that the prescribed model output can possibly undertake, as driven by a governing model. In this sense, such an approach to sensitivity is global with respect to model input(s) and local with respect to model output, thus enabling one to discriminate the relevance of an input across the entire range of values of the modeling goal of interest. The methodology is demonstrated in the context of flow and reactive transport scenarios.</p><p> </p><p><strong>References</strong></p><p>Sobol, I. M., 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Sim., 55, 271-280.</p><p>Dell’Oca, A., Riva, M., Guadagnini, A., 2017. Moment-based metrics for global sensitivity analysis of hydrological systems. Hydr. Earth Syst. Sci., 21, 6219-6234.</p>


2012 ◽  
Vol 17 (1) ◽  
pp. 25-42 ◽  
Author(s):  
Luca Formaggia ◽  
Alberto Guadagnini ◽  
Ilaria Imperiali ◽  
Valentina Lever ◽  
Giovanni Porta ◽  
...  

2020 ◽  
Author(s):  
Haifan Liu ◽  
Heng Dai ◽  
Jie Niu ◽  
Bill X. Hu ◽  
Han Qiu ◽  
...  

Abstract. Sensitivity analysis is an effective tool for identifying important uncertainty sources and improving model calibration and predictions, especially for integrated systems with heterogeneous parameter inputs and complex process coevolution. In this work, an advanced hierarchical global sensitivity analysis framework, which integrates the concept of variance-based global sensitivity analysis with a hierarchical uncertainty framework, was implemented to quantitatively analyse several uncertainties associated with a three-dimensional, process-based hydrologic model (PAWS). The uncertainty sources considered include model parameters (three vadose zone parameters, two groundwater parameters, and one overland flow parameter), model structure (different thicknesses to represent unconfined and confined aquifer layers) and climate scenarios. We apply the approach to an ~ 9,000 km2 Amazon catchment modeled at 1 km resolution to provide a demonstration of multiple uncertainty source quantification using a large-scale process-based hydrologic model. The sensitivity indices are assessed based on two important hydrologic outputs: evapotranspiration (ET) and groundwater contribution to streamflow (QG). It was found that, in general, parameters, especially the vadose zone parameters, are the most important uncertainty contributors for all sensitivity indices. In addition, the influence of climate scenarios on ET predictions is also nonignorable. Furthermore, the thickness of the aquifers along the river grid cells is important for both ET and QG. These results can assist in model calibration and provide modelers with a better understanding of the general sources of uncertainty in predictions associated with complex hydrological systems in Amazonia. We demonstrated a pilot example of comprehensive global sensitivity analysis of large-scale, complex hydrological and environmental models in this study. The hierarchical sensitivity analysis methodology used is mathematically rigorous and capable of being implemented in a variety of large-scale hydrological models with various sources of uncertainty.


2021 ◽  
Author(s):  
Denise Degen ◽  
Mauro Cacace ◽  
Cameron Spooner ◽  
Magdalena Scheck-Wenderoth ◽  
Florian Wellmann

<p>Geophysical process simulations pose several challenges including the determination of i) the rock properties, ii) the underlying physical process, and iii) the spatial and temporal domain that needs to be considered.</p><p>Often it is not feasible or impossible to include the entire complexity of the given application. Hence, we need to evaluate the consequences of neglecting certain processes, properties, etc. by using, for instance, sensitivity analyses. However, this evaluation is for basin-scale application non-trivial due to the high computational costs associated with them. These high costs arise from the high-dimensional character of basin-scale applications in the parameter, spatial, and temporal domain.</p><p>Therefore, this evaluation is often not performed or via computationally fast algorithms as, for example, the local sensitivity analysis. The problem with local sensitivity analyses is that they cannot account for parameter correlations. Thus, a global sensitivity analysis is preferential. Unfortunately, global sensitivity analyses are computationally demanding.</p><p>To allow the usage of global sensitivity analysis for a better evaluation of the changes in the influencing parameters, we construct in this work a surrogate model via the reduced basis method.</p><p>The reduced basis method is a model order reduction technique that is physics-preserving.  Hence, we are able to retrieve the entire state variable (i.e. temperature) instead of being restricted to the observation space.</p><p>To showcase the benefits of this methodology, we demonstrate with the Central European Basin System how the influences of the thermal rock properties change when moving from a steady-state to a transient system.</p><p>Furthermore, we use the case study of the Alpine Region to highlight the influences of the spatial distribution of measurements on the model response. This latter aspect is especially important since measurements are often used to calibrate and validate a given geological model. Thus, it is crucial to determine which amount of bias is introduced through our commonly unequal data distribution.</p>


Author(s):  
Claire Bossennec ◽  
Yves Géraud ◽  
Johannes Böcker ◽  
Bernd Klug ◽  
Luca Mattioni ◽  
...  

Deeply buried sandstone reservoirs are targeted in the Upper Rhine Graben (URG) for geothermal and hydrocarbon resources. These reservoirs, which are located at the top of the geothermal convective cells, have a complex diagenetic and structural history recorded by paragenesis. Here the focus is made on the characterization of carbonates and barite cementations which trace paleo geothermal circulations within the fracture network affecting the sandstones. These mineralizations are studied with a double approach on geochemistry and structural, faults and associated fracture network, to characterize fluid-flow episodes on different structural positions in the rift basin and its shoulders. Barite sulphur isotopic ratios suggest a common signature and source for all the locations. REE patterns, oxygen isotopic ratios, and fluid inclusion study suggest though two regimes of fluid flow forming barite, depending on their location. On the graben shoulders the barite have a higher content in total REE and contain non-saline fluids inclusions, suggesting that fluid circulations at the graben border faults interact with sulphate rich layers, and precipitate at high temperatures .In -deep-seated sandstones, fluid inclusions in barites show a wide range of salinities, suggesting a higher contribution of sedimentary brines, and precipitation at lower temperatures. These barite mineralizations are associated with carbonates and apatite with a diagenetic origin, according to their REE signature. These data are used to build a model for fluids circulation within the graben: Fast and deep down- and up-flows are taking place along the major border faults, which are leaching evaporitic horizons, and precipitates from geothermal fluid during fault activity. A part of these deep-down meteoric waters is reaching the centre of the basin. In this central part of the basin, fluid circulation is slower and restricted to the bottom of the basin, where fluid-mixing with sedimentary brines occurs. This new understanding of fluid pathways in the targeted reservoir brings new insights on the compartmentalization of geothermal circulations at the basin scale.


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