HS3.6

EDI
Advances in diagnostics, inversion, sensitivity, uncertainty analysis, and hypothesis testing of Earth and Environmental Systems models

Proper characterization of uncertainty remains a major research and operational challenge in Environmental Sciences, and is inherent to many aspects of modelling impacting model structure development; parameter estimation; an adequate representation of the data (inputs data and data used to evaluate the models); initial and boundary conditions; and hypothesis testing. To address this challenge, methods for a) uncertainty analysis (UA) that seek to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models), have proved to be very helpful.

This session invites contributions that discuss advances, both in theory and/or application, in methods for SA/UA applicable to all Earth and Environmental Systems Models (EESMs), which embraces all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science.

Topics of interest include (but are not limited to):
1) Novel methods for effective characterization of sensitivity and uncertainty
2) Analyses of over-parameterised models enabled by AI/ML techniques
3) Single- versus multi-criteria SA/UA
4) Novel approaches for parameter estimation, data inversion and data assimilation
5) Novel methods for spatial and temporal evaluation/analysis of models
6) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data etc.)
7) The role of SA in evaluating model consistency and reliability
8) Novel approaches and benchmarking efforts for parameter estimation
9) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)

Co-organized by BG9/ESSI1/NP8
Convener: Juliane Mai | Co-conveners: Cristina PrietoECSECS, Hoshin Gupta, Thomas Wöhling, Anneli GuthkeECSECS, Saman Razavi, Wolfgang Nowak
Presentations
| Fri, 27 May, 08:30–11:50 (CEST)
 
Room 3.29/30

Session assets

Session materials

Presentations: Fri, 27 May | Room 3.29/30

Chairpersons: Juliane Mai, Cristina Prieto, Thomas Wöhling
08:30–08:35
Modeling, parameter inference, and model calibration
08:35–08:41
|
EGU22-1459
|
ECS
|
On-site presentation
Janneke Remmers, Ryan Teuling, and Lieke Melsen

Scientific hydrological modellers make multiple decisions during the modelling process, e.g. related to the calibration period and temporal resolution. These decisions affect the model results. Modelling decisions can refer to several steps in the modelling process. In this study, modelling decisions refer to the decisions made during the whole modelling process, beyond the definition of the model structure. This study is based on an analysis of interviews with scientific hydrological modellers, thus taking actual practices into account. Six modelling decisions were identified from the interviews, which are mainly motivated by personal and team experience (calibration method, calibration period, parameters to calibrate, pre-processing of input data, spin-up period, and temporal resolution). Different options for these six decisions, as encountered in the interviews, were implemented and evaluated in a controlled modelling environment, in our case the modular modelling framework Raven, to quantify their impact on model output. The variation in the results is analysed using three hydrological signatures to determine which decisions affect the results and how they affect the results. Each model output is a hypothesis of the reality; it is an interpretation of the real system underpinned by scientific reasoning and/or expert knowledge. Currently, there is a lack of knowledge and understanding about which modelling decisions are taken and why they are taken. Consequently, the influence of modelling decisions is unknown. Quantifying this influence, which was done in this study, can raise awareness among scientists. This study pinpoints what aspects are important to consider in studying modelling decisions, and can be an incentive to clarify and improve modelling procedures.

How to cite: Remmers, J., Teuling, R., and Melsen, L.: Modelling decisions: a quantification of their influence on model results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1459, https://doi.org/10.5194/egusphere-egu22-1459, 2022.

08:41–08:47
|
EGU22-11844
|
ECS
|
On-site presentation
Diana Spieler, Kan Lei, and Niels Schütze

Recent studies have introduced methods to simultaneously calibrate model structure choices and parameter values to identify an appropriate (conceptual) model structure for a given catchment. This can be done through mixed-integer optimization to identify the graph structure that links dominant flow processes (Spieler et al., 2020) or, likewise, by continuous optimization of weights when blending multiple flux equations to describe flow processes within a model (Chlumsky et al., 2021). Here, we use the combination of the mixed-integer optimization algorithm DDS and the modular modelling framework RAVEN and refer to it as Automatic Model Structure Identification (AMSI) framework.

This study validates the AMSI framework by comparing the performance of the identified AMSI model structures to two different benchmark ensembles. The first ensemble consists of the best model structures from the brute force calibration of all possible structures included in the AMSI model space (7488+). The second ensemble consists of 35+ MARRMoT structures representing a structurally more divers set of models than currently implemented in the AMSI framework. These structures stem from the MARRMoT Toolbox introduced by Knoben et al. (2019) providing established conceptual model structures based on hydrologic literature.

We analyze if the model structure(s) AMSI identifies are identical to the best performing structures of the brute force calibration and comparable in their performance to the MARRMoT ensemble. We can conclude that model structures identified with the AMSI framework can compete with the structurally more divers MARRMoT ensemble. In fact, we were surprised to see how well we do with a simple two storage structure over the 12 tested MOPEX catchments (Duan et al.,2006). We aim to discuss several emerging questions, such as the selection of a robust model structure, Equifinality in model structures, and the role of structural complexity.

 

Spieler et al. (2020). https://doi.org/10.1029/2019WR027009

Chlumsky et al. (2021). https://doi.org/10.1029/2020WR029229

Knoben et al. (2019). https://doi.org/10.5194/gmd-12-2463-2019

Duan et al. (2006). https://doi.org/10.1016/j.jhydrol.2005.07.031

How to cite: Spieler, D., Lei, K., and Schütze, N.: Benchmarking Automatically Identified Model Structures with a Large Model Ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11844, https://doi.org/10.5194/egusphere-egu22-11844, 2022.

08:47–08:53
|
EGU22-9902
|
ECS
|
On-site presentation
Rodrigo Marinao-Rivas and Mauricio Zambrano-Bigiarini

In this work we introduce hydroMOPSO, a novel multi-objective R package that combines two search mechanisms to maintain diversity of the population and accelerate its convergence towards the Pareto-optimal set: Particle Swarm Optimisation (PSO) and genetic operations. hydroMOPSO is model-independent, which allows to interface any model code with the calibration engine, including models available in R (e.g., TUWmodel, airGR, topmodel), but also any other complex models that can be run from the system console (e.g. SWAT+, Raven, WEAP). In addition, hydroMOPSO is platform-independent, which allows it to run on GNU/Linux, Mac OSX and Windows systems, among others.

Considering the long execution time of some real-world models, we used three benchmark functions to search for a configuration that allows to reach the Pareto-optimal front with a low number of model evaluations, analysing different combinations of: i) the swarm size in PSO, ii) the maximum number of particles in the external archive, and iii) the maximum number of genetic operations in the external archive. In addition, the previous configuration was then evaluated against other state-of-the-art multi-objective optimisation algorithms (MMOPSO, NSGA-II, NSGA-III). Finally, hydroMOPSO was used to calibrate a GR4J-CemaNeige hydrological model implemented in the Raven modelling framework (http://raven.uwaterloo.ca), using two goodness-of-fit functions: i) the modified Kling-Gupta efficiency (KGE') and ii) the Nash-Sutcliffe efficiency with inverted flows (iNSE).

Our results showed that the configuration selected for hydroMOPSO makes it very competitive or even superior against MMOPSO, NSGA-II and NSGA- III in terms of the number of function evaluations required to achieve stabilisation in the Pareto front, and also showed some advantages of using a compromise solution instead of a single-objective one for the estimation of hydrological model parameters.

How to cite: Marinao-Rivas, R. and Zambrano-Bigiarini, M.: hydroMOPSO: A versatile Particle Swarm Optimization R package for multi-objective calibration of environmental and hydrological models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9902, https://doi.org/10.5194/egusphere-egu22-9902, 2022.

08:53–08:59
|
EGU22-2388
|
ECS
|
On-site presentation
Gillien Latour, Pierre Horgue, François Renard, Romain Guibert, and Gérald Debenest
Unsaturated water flows at watershed scale or Darcy-scale are generally described by the Richardson-Richards equation. This equation is highly non-linear and simulation domains are limited by computational costs. The porousMultiphaseFoam toolbox is a Finite Volume tool capable of modeling multiphase flows in porous media, including the solving of the Richardson-Richards equation. As it has been developed using the OpenFOAM environment, the software is natively fully parallelized and can be used on super computers. By using experimental data from real site with geographical informations and piezometrics values, an iterative algorithm is set up to solve an inverse problem in order to evaluate an adequate permeability field. This procedure is initially implemented using simplified aquifer model with a 2D saturated modeling approach. A similar procedure using a full 3D model of the actual site is performed (handling both saturated and unsaturated area). The results are compared between the two approaches (2D and 3D) for steady simulations and new post-processing tools are also introduced to spatialize the error between the two models and define the areas for which the behaviour of the models is different. In a second part, an optimization of the Van Genuchten parameters is performed to reproduce transient experimental data. The 3D numerical results at the watershed scale are also compared to the reference simulations using a 1D unsaturated + 2D satured modeling approach.

How to cite: Latour, G., Horgue, P., Renard, F., Guibert, R., and Debenest, G.: Estimation of simulation parameters for steady and transient 3D flow modeling at watershed scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2388, https://doi.org/10.5194/egusphere-egu22-2388, 2022.

08:59–09:05
|
EGU22-2220
|
ECS
|
On-site presentation
Lisa Maria Ringel, Mohammadreza Jalali, and Peter Bayer

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.

09:05–09:11
|
EGU22-1870
|
ECS
|
On-site presentation
Lea Friedli, Niklas Linde, David Ginsbourger, Alejandro Fernandez Visentini, and Arnaud Doucet

We consider non-linear Bayesian inversion problems to infer the (geostatistical) hyperparameters of a random field describing (hydro)geological or geophysical properties by inversion of hydrogeological or geophysical data. This problem is of particular importance in the non-ergodic setting as no analytical upscaling relationships exist linking the data (resulting from a specific field realization) to the hyperparameters specifying the spatial distribution of the underlying random field (e.g., mean, standard deviation, and integral scales). Jointly inferring the hyperparameters and the "true" realization of the field (typically involving many thousands of unknowns) brings important computational challenges, such that in practice, simplifying model assumptions (such as homogeneity or ergodicity) are made. To prevent the errors resulting from such simplified assumptions while circumventing the burden of high-dimensional full inversions, we use a pseudo-marginal Metropolis-Hastings algorithm that treats the random field as a latent variable. In this random effect model, the intractable likelihood of observing the hyperparameters given the data is estimated by Monte Carlo averaging over realizations of the random field. To increase the efficiency of the method, low-variance approximations of the likelihood ratio are ensured by correlating the samples used in the proposed and current steps of the Markov chain and by using importance sampling. We assess the performance of this correlated pseudo-marginal method to the problem of inferring the hyperparameters of fracture aperture fields using borehole ground-penetrating radar (GPR) reflection data. We demonstrate that the correlated pseudo-marginal method bypasses the computational challenges of a very high-dimensional target space while avoiding the strong bias and too low uncertainty ranges obtained when employing simplified model assumptions. These advantages also apply when using the posterior of the hyperparameters describing the aperture field to predict its effective hydraulic transmissivity.

How to cite: Friedli, L., Linde, N., Ginsbourger, D., Fernandez Visentini, A., and Doucet, A.: Inference of (geostatistical) hyperparameters with the correlated pseudo-marginal method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1870, https://doi.org/10.5194/egusphere-egu22-1870, 2022.

09:11–09:17
|
EGU22-1210
|
ECS
|
On-site presentation
Michelle Viswanathan, Tobias K. D. Weber, and Anneli Guthke

Conveying uncertainty in model predictions is essential, especially when these predictions are used for decision-making. Models are not only expected to achieve the best possible fit to available calibration data but to also capture future observations within realistic uncertainty intervals. Model calibration using Bayesian inference facilitates the tuning of model parameters based on existing observations, while accounting for uncertainties. The model is tested against observed data through the likelihood function which defines the probability of the data being generated by the given model and its parameters. Inference of most plausible parameter values is influenced by the method used to combine likelihood values from different observation data sets. In the classical method of combining likelihood values, referred to here as the AND calibration strategy, it is inherently assumed that the given model is true (error-free), and that observations in different data sets are similarly informative for the inference problem. However, practically every model applied to real-world case studies suffers from model-structural errors that are typically dynamic, i.e., they vary over time. A requirement for the imperfect model to fit all data sets simultaneously will inevitably lead to an underestimation of uncertainty due to a collapse of the resulting posterior parameter distributions. Additionally, biased 'compromise solutions' to the parameter estimation problem result in large prediction errors that impair subsequent conclusions. 
    
We present an alternative AND/OR calibration strategy which provides a formal framework to relax posterior predictive intervals and minimize posterior collapse by incorporating knowledge about similarities and differences between data sets. As a case study, we applied this approach to calibrate a plant phenology model (SPASS) to observations of the silage maize crop grown at five sites in southwestern Germany between 2010 and 2016. We compared model predictions of phenology on using the classical AND calibration strategy with those from two scenarios (OR and ANDOR) in the AND/OR strategy of combining likelihoods from the different data sets. The OR scenario represents an extreme contrast to the AND strategy as all data sets are assumed to be distinct, and the model is allowed to find individual good fits to each period adjusting to the individual type and strength of model error. The ANDOR scenario acts as an intermediate solution between the two extremes by accounting for known similarities and differences between data sets, and hence grouping them according to anticipated type and strength of model error. 
    
We found that the OR scenario led to lower precision but higher accuracy of prediction results as compared to the classical AND calibration. The ANDOR scenario led to higher accuracy as compared to the AND strategy and higher precision as compared to the OR scenario. Our proposed approach has the potential to improve the prediction capability of dynamic models in general, by considering the effect of model error when calibrating to different data sets.

How to cite: Viswanathan, M., Weber, T. K. D., and Guthke, A.: An alternative strategy for combining likelihood values in Bayesian calibration to improve model predictions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1210, https://doi.org/10.5194/egusphere-egu22-1210, 2022.

09:17–09:23
|
EGU22-8729
|
On-site presentation
Simone Ulzega and Carlo Albert

Conceptual models are indispensable tools for hydrology. In order to use them for making probabilistic predictions, they need to be equipped with an adequate error model, which, for ease of inference, is traditionally formulated as an additive error on the output (discharge). However, the main sources of uncertainty in hydrological modelling are typically not to be found on the output, but on the input (rain) and in the model structure. Therefore, more reliable error models and probabilistic predictions can be obtained by incorporating those uncertainties directly where they arise, that is, into the model. This, however, leads us to stochastic models, which render traditional inference algorithms such as the Metropolis algorithm infeasible due to their expensive likelihood functions. However, thanks to recent advancements in algorithms and computing power, full-fledged Bayesian inference with stochastic models is no longer off-limit for hydrological applications. We demonstrate this with a case study from urban hydrology, for which we employ a highly efficient Hamiltonian Monte Carlo inference algorithm with a time-scale separation.

How to cite: Ulzega, S. and Albert, C.: Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8729, https://doi.org/10.5194/egusphere-egu22-8729, 2022.

09:23–09:29
|
EGU22-3691
|
ECS
|
On-site presentation
Macarena Amaya, Niklas Linde, and Eric Laloy

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.

09:29–09:35
|
EGU22-3782
|
ECS
|
On-site presentation
Marleen Schübl, Christine Stumpp, and Giuseppe Brunetti

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.

09:35–09:41
|
EGU22-7774
|
ECS
|
Virtual presentation
Shiran Levy, Eric Laloy, and Niklas Linde

We propose an approach for solving geophysical inverse problems which significantly reduces computational costs as compared to Markov chain Monte Carlo (MCMC) methods while providing enhanced uncertainty quantification as compared to efficient gradient-based deterministic methods. The proposed approach relies on variational inference (VI), which seeks to approximate the unnormalized posterior distribution parametrically for a given family of distributions by solving an optimization problem. Although prone to bias if the family of distributions is too limited, VI provides a computationally-efficient approach that scales well to high-dimensional problems. To enhance the expressiveness of the parameterized posterior in the context of geophysical inverse problems, we use a combination of VI and inverse autoregressive flows (IAF), a type of normalizing flows that has been shown to be efficient for machine learning tasks. The IAF consists of invertible neural transport maps transforming an initial density of random variables into a target density, in which the mapping of each instance is conditioned on previous ones. In the combined VI-IAF routine, the approximate distribution is parameterized by the IAF, therefore, the potential expressiveness of the unnormalized posterior is determined by the architecture of the network. The parameters of the IAF are learned by minimizing the Kullback-Leibler divergence between the approximated posterior, which is obtained from samples drawn from a standard normal distribution that are pushed forward through the IAF, and the target posterior distribution. We test this approach on problems in which complex geostatistical priors are described by latent variables within a deep generative model (DGM) of the adversarial type. Previous results have concluded that inversion based on gradient-based optimization techniques perform poorly in this setting because of the high nonlinearity of the generator. Preliminary results involving linear physics suggest that the VI-IAF routine can recover the true model and provides high-quality uncertainty quantification at a low computational cost. As a next step, we will consider cases where the forward model is nonlinear and include comparison against standard MCMC sampling. As most of the inverse problem nonlinearity arises from the DGM generator, we do not expect significant differences in the quality of the approximations with respect to the linear physics case.

How to cite: Levy, S., Laloy, E., and Linde, N.: Efficient inversion with complex geostatistical priors using normalizing flows and variational inference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7774, https://doi.org/10.5194/egusphere-egu22-7774, 2022.

09:41–09:47
|
EGU22-986
|
ECS
|
On-site presentation
Beatrice Gatto, Claudio Paniconi, Paolo Salandin, and Matteo Camporese

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.

09:47–10:00
Coffee break
Chairpersons: Juliane Mai, Thomas Wöhling, Cristina Prieto
10:20–10:25
Uncertainty quantification
10:25–10:35
|
EGU22-6882
|
solicited
|
Highlight
|
On-site presentation
Okke Batelaan, Trine Enemark, Luk Peeters, and Dirk Mallants

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.

10:35–10:41
|
EGU22-10654
|
Highlight
|
On-site presentation
Alberto Montanari and Demetris Koutsoyiannis

We present a new method for simulating and predicting hydrologic variables and in particular river flows, which is rooted in the probability theory and conceived in order to provide a reliable quantification of its uncertainty for operational applications. In fact, recent practical experience during extreme events has shown that simulation and prediction uncertainty is essential information for decision makers and the public. A reliable and transparent uncertainty assessment has also been shown to be essential to gain public and institutional trust in real science. Our approach, that we term with the acronym "Bluecat", assumes that randomness is a fundamental component of physics and results from a theoretical and numerical development. Bluecat is conceived to make a transparent and intuitive use of uncertain observations which in turn mirror the observed reality. Therefore, Bluecat makes use of a rigorous theory while at the same time proofing the concept that environmental resources should be managed by making the best use of empirical evidence and experience and recognising randomness as an intrinsic property of hydrological systems. We provide an open and user friendly software to apply the method to the simulation and prediction of river flows and test Bluecat's reliability for operational applications.

How to cite: Montanari, A. and Koutsoyiannis, D.: Uncertainty assessment with Bluecat: Recognising randomness as a fundamental component of physics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10654, https://doi.org/10.5194/egusphere-egu22-10654, 2022.

10:41–10:47
|
EGU22-1742
|
On-site presentation
Uwe Ehret and Pankaj Dey

We propose a method to analyse, classify and compare dynamical systems of arbitrary dimension by the two key features uncertainty and complexity. It starts by subdividing the system’s time-trajectory into a number of time slices. For all values in a time slice, the Shannon information entropy is calculated, measuring within-slice variability. System uncertainty is then expressed by the mean entropy of all time slices. We define system complexity as “uncertainty about uncertainty”, and express it by the entropy of the entropies of all time slices. Calculating and plotting uncertainty u and complexity c for many different numbers of time slices yields the c-u-curve. Systems can be analysed, compared and classified by the c-u-curve in terms of i) its overall shape, ii) mean and maximum uncertainty, iii) mean and maximum complexity, and iv) its characteristic time scale expressed by the width of the time slice for which maximum complexity occurs. We demonstrate the method at the example of both synthetic and real-world time series (constant, random noise, Lorenz attractor, precipitation and streamflow) and show that conclusions drawn from the c-u-curve are in accordance with expectations. The method is based on unit-free probabilities and therefore permits application to and comparison of arbitrary data. It naturally expands from single- to multivariate systems, and from deterministic to probabilistic value representations, allowing e.g. application to ensemble model predictions. 

How to cite: Ehret, U. and Dey, P.: c-u-curve: A method to analyze, classify and compare dynamical systems by uncertainty and complexity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1742, https://doi.org/10.5194/egusphere-egu22-1742, 2022.

10:47–10:53
|
EGU22-11794
|
On-site presentation
Hervé Jourde, Mohammed Aliouache, Pierre Fischer, Xiaoguang Wang, and Gerard Massonnat

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.

10:53–10:59
|
EGU22-12691
|
ECS
|
Virtual presentation
Aleksandra Skakun and Dmitry Volobuev

Pearson’s correlation is usually used as a criterion for the presence or absence of a relationship between time series, but it is not always indicative for nonlinear systems like climate. Therefore, we implement one of the methods of nonlinear dynamics to detect connections in the Sun-climate system. Here we estimate the causal relationship between Total Solar Irradiance (TSI) and Ocean climate indices over the past few decades using the method of conditional dispersions (Cenys et al., 1991). We use a conceptual ocean-atmosphere model (Jin, 1997) with TSI added as a forcing to calibrate the method. We show that the method provides expected results for connection between TSI and the model temperature. Premixing of Gaussian noise to model data leads to decrease of detectable causality with increase of noise amplitude, and the similar effect occurs in empirical data. Moreover, in the case of the empirical data, we show that the method can be used to independently estimate uncertainties of Ocean climate indices.

How to cite: Skakun, A. and Volobuev, D.: Ocean climate indices and Total Solar Irradiance: causality over the past few decades and revision of indices uncertainties , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12691, https://doi.org/10.5194/egusphere-egu22-12691, 2022.

10:59–11:05
|
EGU22-8583
|
ECS
|
On-site presentation
Makrina Agaoglou, Guillermo García-Sánchez, Amaia Marcano Larrinaga, Gabriel Mouttapa, and Ana M. Mancho

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.

Sensitivity Analysis
11:05–11:11
|
EGU22-10431
|
Virtual presentation
Björn Guse, Stefan Lüdtke, Oldrich Rakovec, Stephan Thober, Thorsten Wagener, and Luis Samaniego

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.

11:11–11:17
|
EGU22-10370
|
ECS
|
Presentation form not yet defined
A Multi-Objective Regional Sensitivity Analysis of the Variable Infiltration Capacity Model in the North of the Iberian Peninsula
(withdrawn)
Patricio Yeste, Emilio Romero-Jiménez, Juan José Rosa-Cánovas, Matilde García-Valdecasas-Ojeda, Sonia Raquel Gámiz-Fortis, Yolanda Castro-Díez, and María Jesús Esteban-Parra
11:17–11:23
|
EGU22-2782
|
ECS
|
On-site presentation
Wei Qu, Heye Bogena, Christoph Schüth, Harry Vereecken, and Stephan Schulz

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.

11:23–11:29
|
EGU22-1639
|
ECS
|
On-site presentation
Robert Reinecke, Francesca Pianosi, and Thorsten Wagener

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.

11:29–11:35
|
EGU22-1907
|
On-site presentation
Efrat Morin, Ronen Rojas, and Ami Wiesel

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.

11:35–11:50