HS3.6

This study aims at the stochastic characterization of fractured rocks with a low-permeability matrix based on transient data from hydraulic tomography experiments. In such rocks, fractures function as main flowpaths. Therefore, adequate insight about distribution and properties of fractures is essential for many applications such as groundwater remediation, constructing nuclear waste repositories or developing enhanced geothermal systems. At the Grimsel test site in Switzerland, multiple hydraulic tests have been conducted to investigate the hydraulic properties and structure of the fracture network between two shear zones. We present results from combined stochastic inversion of these tests to infer the fracture network of the studied crystalline rock formation.

Data from geological mapping at Grimsel and the hydraulic tomography experiments that were undertaken as part of in-situ stimulation and circulation experiments provide the prior knowledge for the model inversion. This information is used for the setting-up of a site-specific conceptual model, to define the boundary and initial conditions of the groundwater flow model, and for the configuration of the inversion problem. The pressure signals we apply for the inversion stem from cross-borehole constant rate injection tests recorded at different depths, whereby the different intervals are isolated by packer systems.

In the forward model, the fractures are represented explicitly as three-dimensional (3D) discrete fracture network (DFN). The geometric and hydraulic properties of the DFN are described by the Bayesian equation. The properties are inferred by sampling iteratively from the posterior density function according to the reversible jump Markov chain Monte Carlo sampling strategy. The goal of this inversion is providing DFN realizations that minimize the error between the simulated and observed pressure signals and that meet the prior information. During the course of the inversion, the number of fractures is iteratively adjusted by adding or deleting a fracture. Furthermore, the parameters of the DFN are adapted by moving a fracture and by changing the fracture length or hydraulic properties. Thereby, the algorithm switches between updates that change the number of parameters and updates that keep the number of parameters but adjust their value. The inversion results reveal the main structural and hydraulic characteristics of the DFN, the preferential flowpaths, and the uncertainty of the estimated model parameters.

How to cite: Ringel, L. M., Jalali, M., and Bayer, P.: Inversion of Hydraulic Tomography Data from the Grimsel Test Site with a Discrete Fracture Network Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2220, https://doi.org/10.5194/egusphere-egu22-2220, 2022.

For strongly non-linear inverse problems, Markov chain Monte Carlo (MCMC) methods may fail to properly explore the posterior probability density function (PDF). Particle methods are very well suited for parallelization and offer an alternative approach whereby the posterior PDF is approximated using the states and weights of a population of evolving particles. In addition, it provides reliable estimates of the evidence (marginal likelihood) that is needed for Bayesian model selection at essentially no cost. We consider adaptive sequential Monte Carlo (ASMC), which is an extension of annealed importance sampling (AIS). In these methods, importance sampling is performed over a sequence of intermediate distributions, known as power posteriors, linking the prior to the posterior PDF. The main advantages of ASMC with respect to AIS are that it adaptively tunes the tempering between neighboring distributions and it performs resampling of particles when the variance of the particle weights becomes too large. We consider a challenging synthetic groundwater transport inverse problem with a categorical channelized 2D hydraulic conductivity field designed such that the posterior facies distribution includes two distinct modes with equal probability. The model proposals are obtained by iteratively re-simulating a fraction of the current model using conditional multi-point statistics (MPS) simulations. We focus here on the ability of ASMC to explore the posterior PDF and compare it with previously published results obtained with parallel tempering (PT), a state-of-the-art MCMC inversion approach that runs multiple interacting chains targeting different power posteriors. For a similar computational budget involving 24 particles for ASMC and 24 chains for PT, the ASMC implementation outperforms the results obtained by PT: the models fit the data better and the reference likelihood value is contained in the ASMC sampled likelihood range, while this is not the case for PT range. Moreover, we show that ASMC recovers both reference modes, while none of them is recovered by PT. However, with 24 particles there is one of the modes that has a higher weight than the other while the approximation is improved when moving to a larger number of particles. As a future development, we suggest that including fast surrogate modeling (e.g., polynomial chaos expansion) within ASMC for the MCMC steps used to evolve the particles in-between importance sampling steps would strongly reduce the computational cost while still ensuring results of similar quality as the importance sampling steps could still be performed using the regular more costly forward solver.

How to cite: Amaya, M., Linde, N., and Laloy, E.: Hydrogeological inference by adaptive sequential Monte Carlo with geostatistical resampling model proposals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3691, https://doi.org/10.5194/egusphere-egu22-3691, 2022.

Transient measurements from lysimeters are frequently coupled with Richards-based solvers to inversely estimate soil hydraulic parameters (SHPs) and numerically describe vadose zone water fluxes, such as recharge. To reduce model predictive uncertainty, the lysimeter experiment should be designed to maximize the information content of observations. However, in practice, this is generally done by relying on the a priori expertise of the scientist/user, without exploiting the advantages of model-based experimental design. Thus, the main aim of this study is to demonstrate how model-based experimental design can be used to maximize the information content of observations in multiple scenarios encompassing different soil textural compositions and climatic conditions. The hydrological model HYDRUS is coupled with a Nested Sampling estimator to calculate the parameters’ posterior distributions and the Kullback-Leibler divergences. Results indicate that the combination of seepage flow, soil water content, and soil matric potential measurements generally leads to highly informative designs, especially for fine textured soils, while results from coarse soils are generally affected by higher uncertainty. Furthermore, soil matric potential proves to be more informative than soil water content measurements. Additionally, the propagation of parameter uncertainties in a contrasting (dry) climate scenario strongly increased prediction uncertainties for sandy soil, not only in terms of the cumulative amount and magnitude of the peak, but also in the temporal variability of the seepage flow. 

How to cite: Schübl, M., Stumpp, C., and Brunetti, G.: Uncertainty assessment and data-worth evaluation for estimating soil hydraulic parameters and recharge fluxes from lysimeter data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3782, https://doi.org/10.5194/egusphere-egu22-3782, 2022.

Numerical dispersion is a well-known problem that affects solute transport in groundwater simulations and can lead to wrong results, in terms of plume path overestimation and overprediction of contaminant dispersion. Numerical dispersion is generally introduced through stabilization techniques aimed at preventing oscillations, with the side effect of increasing mass spreading. Even though this issue has long been investigated in subsurface hydrology, little is known about its possible impacts on integrated surface–subsurface hydrological models (ISSHMs). In this study, we analyze numerical dispersion in the CATchment HYdrology (CATHY) model. In CATHY, a robust and computationally efficient time-splitting technique is implemented for the solution of the subsurface transport equation, whereby the advective part is solved on elements with an explicit finite volume scheme and the dispersive part is solved on nodes with an implicit finite element scheme. Taken alone, the advection and dispersion solvers provide accurate results. However, when coupled, the continuous transfer of concentration from elements to nodes, and vice versa, gives rise to a particular form of numerical dispersion. We assess the nature and impact of this artificial spreading through two sets of synthetic experiments. In the first set, the subsurface transport of a nonreactive tracer in two soil column test cases is simulated and compared with known analytical solutions. Different input dispersion coefficients and mesh discretizations are tested, in order to quantify the numerical error and define a criterion for its containment. In the second set of experiments, fully coupled surface–subsurface processes are simulated using two idealized hillslopes, one concave and one convex, and we examine how the additional subsurface dispersion affects the representation of pre-event water contribution to the streamflow hydrograph. Overall, we show that the numerical dispersion in CATHY that is caused by the transfer of information between elements and nodes can be kept under control if the grid Péclet number is less than 1. It is also suggested that the test cases used in this study can be useful benchmarks for integrated surface–subsurface hydrological models, for which thus far only flow benchmarks have been proposed.

How to cite: Gatto, B., Paniconi, C., Salandin, P., and Camporese, M.: Quantifying solute transport numerical dispersion in integrated surface-subsurface hydrological modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-986, https://doi.org/10.5194/egusphere-egu22-986, 2022.

For more than a century, the strong advice in geology has been to rely on multiple working hypotheses. However, in groundwater research, as supported by modelling, often a stepwise approach with respect to complexity is promoted and preferred by many. Defining a hypothesis, let alone multiple hypotheses, and testing these via groundwater models is rarely applied. The so-called ‘conceptual model’ is generally considered the starting point of our beloved modelling method. A conceptual model summarises our current knowledge about a groundwater system, describing the hydrogeology and the dominating processes. Conceptual model development should involve formulating hypotheses and leading to choices in the modelling that steer the model predictions. As many conceptual models can explain the available data, multiple hypotheses allow assessing the conceptual or structural uncertainty.

This presentation aims to review some of the key ideas of 125 years of research on (not) handling conceptual hydrogeological uncertainty, identify current approaches, unify scattered insights, and develop a systematic methodology of hydrogeological conceptual model development and testing. We advocate for a systematic model development approach based on mutually exclusive, collectively exhaustive range of hypotheses, although this is not fully achievable. We provide examples of this approach and the consequential model testing. It is argued that following this scientific recipe of refuting alternative models; we will increase the learnings of our research, reduce the risk of conceptual surprises and improve the robustness of the groundwater assessments. We conclude that acknowledging and explicitly accounting for conceptual uncertainty goes a long way in producing more reproducible groundwater research. Hypothesis testing is essential to increase system understanding by analyzing and refuting alternative conceptual models. It also provides more confidence in groundwater model predictions leading to improved groundwater management, which is more important than ever.

How to cite: Batelaan, O., Enemark, T., Peeters, L., and Mallants, D.: A review of conceptual model uncertainty in groundwater research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6882, https://doi.org/10.5194/egusphere-egu22-6882, 2022.

Hydraulic tomography showed great potential on estimating the spatial distribution of heterogeneous aquifer properties in the last decade.  Though this method is highly performant on synthetic studies, the transition from an application to synthetic models to real field applications is often associated to numerical instabilities. Inversion techniques can also suffer from ill-posedness and non-uniqueness of the estimates since several solutions might correctly mimic the observed hydraulic data. In this work, we investigate the origin of the instabilities observed when trying to perform HT using real field drawdown data. We firstly identify the cause of these instabilities. We then use different approaches, where one is proposed, in order to regain inverse model stability, which also allows to estimate different hydraulic property fields at local and regional scales. Results show that ill-posed models can lead into inversion instability while different approaches that limit these instabilities may lead into different estimates. The study also shows that the late time hydraulic responses are strongly linked to the boundary conditions and thus to the regional heterogeneity. Accordingly, the use on these late-time data in inversion might require a larger dimension of the inverted domain, so that it is recommended to position the boundary conditions of the forward model far away from the wells. Also, the use of the proposed technique might provide a performant tool to obtain a satisfying fitting of observation, but also to assess both the site scale heterogeneity and the surrounding variabilities.

How to cite: Jourde, H., Aliouache, M., Fischer, P., Wang, X., and Massonnat, G.: Effect of regional heterogeneities on inversion stability and estimated hydraulic properties field, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11794, https://doi.org/10.5194/egusphere-egu22-11794, 2022.

In the last years, there has been much interest in uncertainty quantification involving trajectories in ocean data sets. As more and more oceanic data become available the assessing quality of ocean models to address transport problems like oil spills, chemical or plastic transportation becomes of vital importance. In our work we are using two types of ocean models: the hindcast and the forecast in a specific domain in the North Atlantic, where drifter trajectory data were available. The hindcast approach requires running ocean (or atmospheric) models for a past period the duration of which is usually for several decades. On the other hand forecast approach is to predict future stages. Both ocean products are provided by CMEMS. Hindcast data includes extra observational data that was time-delayed and therefore to the original forecast run. This means that in principle, hindcast data are more accurate than archived forecast data. In this work, we focus on the comparison of the transport capacity between hindcast and forecast products in the Gulf stream and the Atlantic Ocean, based on the dynamical structures of the dynamical systems describing the underlying transport problem, in the spirit of [1]. In this work, we go a step forwards, by quantifying the transport performance of each model against observed drifters using tools developed in [2].

Acknowledgments

MA acknowledges support from the grant CEX2019-000904-S and IJC2019-040168-I funded by: MCIN/AEI/ 10.13039/501100011033, AMM and GGS acknowledge support from CSIC PIE grant Ref. 202250E001.

References

[1] C. Mendoza, A. M. Mancho, and S. Wiggins, Lagrangian descriptors and the assessment of the predictive capacity of oceanic data sets, Nonlin. Processes Geophys., 21, 677–689, 2014, doi:10.5194/npg-21-677-2014

[2] G.García-Sánchez, A.M.Mancho, and S.Wiggins, A bridge between invariant dynamical structures and uncertainty quantification, Commun Nonlinear Sci Numer Simulat 104, 106016, 2022, doi:10.1016/j.cnsns.2021.106016 

How to cite: Agaoglou, M., García-Sánchez, G., Marcano Larrinaga, A., Mouttapa, G., and Mancho, A. M.: Quantifying transport ability of hindcast and forecast ocean models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8583, https://doi.org/10.5194/egusphere-egu22-8583, 2022.

Model parameters are implemented in hydrological models to represent hydrological processes as accurate as possible under different catchment conditions. In the case of the mesoscale Hydrological Model (mHM), its parameters are estimated via transfer functions and scaling rules using the Multiscale Parameter Regionalization (MPR) approach [1]. Hereby, one consistent parameter set is selected for the entire model domain. To understand the impact of model parameters on simulated variables under different hydrological conditions, the spatio-temporal variability of parameter dominance and its relationship to the corresponding processes needs to be investigated.

In this study, mHM is applied to more than hundred German basins including the headwater areas in neighboring countries. To analyze the relevance of model parameters, a temporally resolved parameter sensitivity analysis using the FAST algorithm [2] is applied to derive dominant model parameters for each day. The temporal scale was further aggregated to monthly and seasonal averaged sensitivities. In analyzing a large number of basins, not only the temporal but also the spatial variability in the parameter relevance could be assessed. Four hydrological variables were used as target variable for the sensitivity analysis, i.e. runoff, actual evapotranspiration, soil moisture and groundwater recharge.

The analysis of the temporal parameter sensitivity shows that the dominant parameters vary in space and time and in using different target variables. Soil material parameters are most dominant on runoff and recharge. A switch in parameter dominance between different seasons was detected for an infiltration and an evapotranspiration parameter that are dominant on soil moisture in winter and summer, respectively. The opposite seasonal dominance pattern of these two parameters was identified on actual evapotranspiration. Further, each parameter shows high sensitivities to either high or low values of one or more hydrological variable(s). The parameter estimation approach leads to spatial consistent patterns of parameter dominances. Spatial differences and similarities in parameter sensitivities could be explained by catchment variability.

The results improve the understanding of how model parameter controls the simulated processes in mHM. This information could be useful for more efficient parameter identification, model calibration and improved MPR transfer functions.

References

[1] Samaniego et al. (2010, WRR), https://doi.org/10.1029/2008WR007327

[2] Reusser et al. (2011, WRR), https://doi.org/10.1029/2010WR009947

How to cite: Guse, B., Lüdtke, S., Rakovec, O., Thober, S., Wagener, T., and Samaniego, L.: Consistency and variability of spatial and temporal patterns of parameter dominance on four simulated hydrological variables in mHM in a large basin study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10431, https://doi.org/10.5194/egusphere-egu22-10431, 2022.

An integrated parallel hydrologic model (ParFlow-CLM) was constructed to predict water and energy transport between subsurface, land surface, and atmosphere for a synthetic study using basic physical properties of the Stettbach headwater catchment, Germany. Based on this model, a global sensitivity analysis was performed using the Latin-Hypercube (LH) sampling strategy followed by the One-factor-At-a-Time (OAT) method to identify the most influential and interactive parameters affecting the main hydrologic processes. In addition, the sensitivity analysis was also carried out for assumptions of different slopes and meteorological conditions to show the transferability of the results to regions with other topographies and climates. Our results show that the simulated energy fluxes, i.e. latent heat flux, sensible heat flux and soil heat flux, are more sensitive to the parameters of wilting point, leaf area index, and stem area index, especially for steep slope and subarctic climate conditions. The simulated water fluxes, i.e. evaporation, transpiration, infiltration, and runoff, are most sensitive to soil porosity, van-Genuchen parameter n, wilting point, and leaf area index. The subsurface water storage and groundwater storage were most sensitive to soil porosity, while the surface water storage is most sensitive to the Manning’s n parameter. For the different slope and climate conditions, the rank order of in input parameter sensitivity was consistent, but the magnitude of parameter sensitivity was very different. The strongest deviation in parameter sensitivity occurred for sensible heat flux under different slope conditions and for transpiration under different climate conditions. This study provides an efficient method of the identification of the most important input parameters of the model and how the variation in the output of a numerical model can be attributed to variations of its input factors. The results help to better understand process representation of the model and reduce the computational cost of running high numbers of simulations. 

How to cite: Qu, W., Bogena, H., Schüth, C., Vereecken, H., and Schulz, S.: Global Sensitivity Analysis of an integrated parallel hydrologic model: ParFlow-CLM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2782, https://doi.org/10.5194/egusphere-egu22-2782, 2022.

Environmental models are central for advancing science by increasingly serving as a digital twin of the earth and its components. They allow us to conduct experiments to test hypotheses and understand dominant processes that are infeasible to do in the real world. To foster our knowledge, we build increasingly complex models hoping that they become more complete and realistic images of the real world. However, we believe that our scientific progress is slowed down as methods for the rigorous exploration of these models, in the face of unavoidable data- and epistemic-uncertainties, do not evolve in a similar manner.

Based on an extensive literature review, we show that even though methods for such rigorous exploration of model responses, e.g., global sensitivity analysis methods, are well established, there is an upper boundary to which level of model complexity they are applied today. Still, we claim that the potential for their utilization in a wider context is significant.

We argue here that a key issue to consider in this context is the framing of the sensitivity analysis problem. We show, using published examples, how problem framing defines the outcome of a sensitivity analysis in the context of scientific advancement. Without appropriate framing, sensitivity analysis of complex models reduces to a diagnostic analysis of the model, with only limited transferability of the conclusions to the real-world system.

How to cite: Reinecke, R., Pianosi, F., and Wagener, T.: Rigorous Exploration of Complex Environmental Models to Advance Scientific Understanding, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1639, https://doi.org/10.5194/egusphere-egu22-1639, 2022.

This study proposes a new approach for quantitively assessing the importance of precipitation features in space and time to predict streamflow discharge (and, hence, sensitivity). For this, we combine well-performing deep-learning (DL) models with interpretability tools.

The DL models are composed of convolutional neural networks (CNNs) and long-short term memory (LSTM) networks. Their input is precipitation data distributed over the watershed and taken back in time (other inputs, meteorological and watershed properties, can also be included). Its output is streamflow discharge at a present or future time. Interpretability tools allow learning about the modeled system. We used the Integrated Gradients method that provides a level of importance (IG value) for each space-time precipitation feature for a given streamflow prediction. We applied the models and interpretability tools to several watersheds in the US and India.

To understand the importance of precipitation features for flood generation, we compared spatial and temporal patterns of IG for high flows vs. low and medium flows. Our results so far indicate some similar patterns for the two categories of flows, but others are distinctly different. For example, common IG mods exist at short times before the discharge, but mods are substantially different when considered further back in time. Similarly, some spatial cores of high IG appear in both flow categories, but other watershed cores are featured only for high flows. These IG time and space pattern differences are presumably associated with slow and fast flow paths and threshold-runoff mechanisms.

There are several advantages to the proposed approach: 1) recent studies have shown DL models to outperform standard process-based hydrological models, 2) given data availability and quality, DL models are much easier to train and validate, compared to process-based hydrological models, and therefore many watersheds can be included in the analysis, 3) DL models do not explicitly represent hydrological processes, and thus sensitivities derived in this approach are assured to represent patterns arise from the data. The main disadvantage of the proposed approach is its limitation to gauged watersheds only; however, large data sets are publicly available to exploit sensitivities of gauged streamflow.

It should be stressed out that learning about hydrological sensitivities with DL models is proposed here as a complementary approach to analyzing process-based hydrological models. Even though DL is considered black-box models, together with interpretability tools, they can highlight hard or impossible sensitivities to resolve with standard models.

How to cite: Morin, E., Rojas, R., and Wiesel, A.: Quantifying space-time patterns of precipitation importance for flood generation via interpretability of deep-learning models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1907, https://doi.org/10.5194/egusphere-egu22-1907, 2022.