Mechanistic models, grounded in the Richards and advection-dispersion equations, provide a comprehensive theoretical framework for describing hydrological processes and solute transport in the vadose zone. Due to the limited transferability of laboratory estimates to field conditions, model parameters are often inversely estimated from transient field observations, making calibration an increasingly common practice in vadose zone modeling. The inescapable necessity to include some form of uncertainty assessment has led to the rise of Bayesian inference as the preferred tool for probabilistic calibration. By combining prior information with observations and model predictions, Bayesian inference enables the estimation of parameter posterior distributions, verification of model adequacy, and assessment of the model’s predictive uncertainty (The Good). Nevertheless, its application to mechanistic vadose zone models poses multiple challenges, among which the curse of dimensionality is likely the most critical (The Bad). We demonstrate that the performance of state-of-the-art Markov Chain Monte Carlo (MCMC) methods deteriorates even for moderately high-dimensional inverse problems, due to the shrinking of the typical set and improper spatiotemporal discretizations of the vadose zone domain during Monte Carlo runs, with both issues being exacerbated under model misspecification. Although using the gradient of the posterior density could mitigate the former problem, it is often rendered impractical due to numerical challenges. While these issues are generally manageable in low-dimensional settings, Bayesian inference remains hindered when applied in combination with computationally intensive vadose zone models (e.g., 2D/3D models, reactive solute transport) (The Ugly). We demonstrate that surrogate-based models can alleviate this problem, though their training and validation are not without difficulties. Based on these findings, we draw some conclusions and propose possible future directions for uncertainty assessment in physically based vadose zone modeling.
How to cite: Brunetti, G. and Šimůnek, J.: Bayesian Inference in Physically-based Vadose Zone Modeling: The Good, The Bad and The Ugly, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2130, https://doi.org/10.5194/egusphere-egu25-2130, 2025.
Hydrogeological modeling is critical in effective water management, particularly in response to increasing water demands and climate variability. However, it is subject to significant uncertainty, especially in hydrological data-scarce regions such as mountainous areas. Reducing parameter and prediction uncertainty and efficiently quantifying and analyzing uncertainty are essential for optimizing water resource management. Time-lapse gravity (TLG) is an emerging hydrogeophysical technique that provides spatially-integrative information on water storage changes. It is a promising, non-invasive solution for filling hydrological data gaps, yet efficient assimilation into hydrogeological models has not yet been achieved.
To help address these challenges, we have developed a numerical framework for the coupled assimilation of TLG and traditional hydro(geo)logical data into groundwater models. The open-source, user-friendly python tool integrates coupled groundwater-gravity forward modelling and powerful inverse modeling procedures. It implements a highly accurate and computationally efficient forward 3-D gravity model. The framework accommodates varying levels of hydrological model complexity, as developed in FloPy (a python wrapper for MODFLOW-based models). Moreover, by integrating PyEMU (a python wrapper for PEST++), the framework employs First-Order, Second-Moment (FOSM)- based techniques, offering an efficient approach for estimating uncertainty. Our tool facilitates the assimilation of TLG data to constrain parameters, make predictions, and perform uncertainty analyses. Finally, we employ our framework to test the impacts of including TLG data in groundwater models. Our results show that TLG data can significantly reduce parameter and prediction uncertainty, as well as computational time.
How to cite: Mohammadi, N., Mohammadigheymasi, H., and J.S. Halloran, L.: Mitigating uncertainty in hydrogeological modeling by integrating time-lapse gravity data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7128, https://doi.org/10.5194/egusphere-egu25-7128, 2025.
Numerical groundwater models are essential tools for simulating and understanding subsurface hydrological processes, supporting water resource management and environmental decision-making. Global sensitivity analysis (GSA) and history matching (HM) are critical methods for evaluating the influence of uncertain model parameters and calibrating models to observed data. However, applying these methods to transient, computationally expensive, large-scale groundwater models presents significant challenges.
A key obstacle arises from the requirement to adapt initial conditions for every model input parameter set during GSA and HM. Unlike steady-state models, transient groundwater systems often lack equilibrium, requiring initialization that reflects the dynamic nature of the system. Traditional approaches, such as performing a warmup simulation for each parameter set, ensure accurate initialization but are computationally infeasible for highly parameterized models.
To address this limitation, we propose a novel method to approximate suitable initial conditions for each parameter set without the need for costly warmup simulations. Our approach utilizes the fact that, after a system-specific relaxation time, the simulation becomes independent of the initial condition. Using a toy model as a test case, we demonstrate that the approximated initial conditions are sufficiently accurate for practical applications, with minimal impact on the outcomes of GSA and HM. The computational savings achieved through this method are substantial, making it particularly advantageous for large-scale systems with many parameters.
We also provide an analysis of the trade-offs between accuracy and efficiency and show that the inaccuracy introduced by the approximation is negligible. Finally, we outline a roadmap for extending this method to real-world groundwater models, addressing the computational barriers that may currently limit the application of GSA and HM in transient systems.
How to cite: Jupe, T. and Class, H.: Efficient Approximation of Initial Conditions for Global Sensitivity Analysis and History Matching of Transient Numerical Groundwater Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11364, https://doi.org/10.5194/egusphere-egu25-11364, 2025.
Hydrological studies often depend on model simulations to analyze flood occurrences and frequency. A major challenge in this domain is quantifying and reducing uncertainty in simulations, particularly when dealing with complex models like WRF-Hydro, which involve extensive parameterization. To address this, we present a novel parameter estimation approach using Iterative Ensemble Smoothers (iES). While iES has been widely applied in calibrating parameters for general circulation models and groundwater models, its potential in improving surface water predictions remains underexplored. This study leverages iES to efficiently estimate and refine parameters, generating ensembles of equally plausible parameter sets. These ensembles yield streamflow predictions that incorporate parameter uncertainty. Unlike traditional sequential simulation methods, iES reduces computational costs by running ensembles of simulations (e.g., 100 members) parallelly refining the parameter space iteratively. Typically, only 3–4 iterations are sufficient to achieve convergence, resulting in reliable parameter sets with low wall clock times. We applied the iES-based calibration framework to the Carson River watershed in the mountainous western United States, focusing on 16 parameters spanning the land surface model, terrain routing, and channel routing components of WRF-Hydro. These parameters capture soil properties, runoff characteristics, groundwater dynamics, vegetation attributes, and snow processes. By refining these parameters, our approach improved the simulation of high-flow events, particularly by better representing snowmelt dynamics critical for flood modeling. Enhanced simulation of snow accumulation and melt processes led to more accurate streamflow predictions, providing valuable insights for flood risk management and water resource planning in snow-dominated regions. Specifically, the iES algorithm demonstrated convergence by the third iteration, with the KGE value improving from 0 in the initial run to 0.41 in the first iteration, 0.65 in the second, and 0.71 in the third Our results highlight significant advancements in computational efficiency, parameter precision, and uncertainty quantification.
How to cite: Pradhan, A., Wright, D., Peng, K., Fienen, M., and Alexander, G. A.: Iterative Ensemble Calibration of WRF-Hydro for Improved Hydrological Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16292, https://doi.org/10.5194/egusphere-egu25-16292, 2025.
This paper applies recent results of risk bounds for time series forecasting to identify optimal complexity of conceptual models and complexity regularised streamflow predictions based on Vapnik-Chervonenkis generalization theory. Earlier reported similar study with SAC-SMA and SIXPAR conceptual models but on two large regions from CAMELS data set is extended to more basins in CONUS to demonstrate the effect of regularizing hydrological model calibration on streamflow prediction over unseen data. SAC-SMA and SIXPAR (conceptual simplification of SAC-SMA) are used as model examples. Results show that the effect of complexity regularization more visible on SIXPAR than SAC-SMA. Results further suggest that when basins itself are complex, regularizing complexity of models does not help and depends on hydrological characteristics of the basins. The benefits of complexity regularization are more evident when assessed based on variance based performance metrices such as correlation coefficient and the slope of observed vs predicted fit than bias and mean absolute error metrices. The paper therefore offers a novel, though computationally intense, method to calibrate conceptual models while controlling for their model complexity.
How to cite: Pande, S., Moayeri, M., and Ponce-Pacheco, M.: Regularized calibration of conceptual hydrological models , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18346, https://doi.org/10.5194/egusphere-egu25-18346, 2025.
Global sensitivity analysis (GSA) is a crucial tool for identifying influential parameters in hydrologic models. GSA methods can guide the selection of calibration parameters, thereby reducing the dimensionality of the parameter space by discarding less influential parameters. This study will explore the use of Random Forest as a GSA method. In particular, feature importance in Random Forest can be interpreted as sensitivity measures of input parameters once the regression task is performed with respect to the output variable of interest. This work will be focused on a large-sample application of the Variable Infiltration Capacity (VIC) model across Europe. A substantial number of Monte Carlo simulations will be carried out for each catchment in order to explore the parameter space and generate a large dataset for the Random Forest regression. Parameters sensitivities will be quantified for the Kling-Gupta Efficiency (KGE) of daily streamflow based on feature importance, and results will be compared against traditional GSA techniques such as the Standardized Regression Coefficients (SRC) and the Regional Sensitivity Analysis (RSA) methods.
Acknowledgments: This study has been funded by a Humboldt Research Fellowship for Postdoctoral Researchers from the Alexander von Humboldt Foundation.
How to cite: Yeste, P. and Bronstert, A.: On the use of Random Forest as a Global Sensitivity Analysis method: a large-sample application across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10333, https://doi.org/10.5194/egusphere-egu25-10333, 2025.
Information-theoretical evaluation of probabilistic hydrological forecasts has several advantages. Firstly, forecasts in terms of probability put the onus for correct expression of uncertainty on the forecaster, as opposed to the recipient of the forecast. Secondly, formulating the evaluation of forecast quality in terms of information-measures enables consistency with the principle of minimum description length.
When applying the information-theoretical evaluation framework to forecasts of mixed-type variables, such as streamflow in rivers with intermittent flow regimes, subjectivity is introduced through the choice of units in which streamflow is measured. This can lead to preference reversals between forecasts when using certain information measures.
At the hand of some examples, we explore the origins of this subjectivity, possible interpretations, as well as avenues for its resolution. Among others, the role of observation uncertainty and the physical meaning of zero flow are discussed.
How to cite: Weijs, S.: Subjectivity in evaluation of forecasts for intermittent streamflow - an information-theoretical perpective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15081, https://doi.org/10.5194/egusphere-egu25-15081, 2025.
Global Water Models (GWMs) are essential for understanding and projecting global hydrological fluxes under changing climate conditions, yet their outputs often diverge from each other, limiting their utility for robust decision-making. We can evaluate GWM using functional relationships that capture the spatial co-variability of over 100 forcing variables, model parameters and key output variables (such as groundwater recharge). Uncovering and identifying relationships and interactions embedded in the high-dimensional and complex input-output datasets created by these simulation models requires measures of dependence that can capture a wide range of functional behaviors. In this study, we test the Maximal Information Coefficient (MIC), an information theory based, non-parametric measure of dependence, to systematically explore and characterize input-output relationships in the Community Water Model (CWatM) forced with ISIMIP3a observed climate data. Our results demonstrate that MIC not only recovers expected hydrological controls but also reveals previously unnoticed functional relationships that Pearson and Spearman correlation coefficients would have overlooked. Additional analysis steps enable us to isolate key explanatory factors from the model’s internal structure and domain-specific factors. This information theory based approach provides a systematic methodology to improve model diagnostic capabilities, guide targeted research directions, and ultimately strengthen the credibility and interpretability of large-scale hydrological simulations.
How to cite: Wagener, T., Serra Lasierra, M.-M., Wiesner, K., Höhne, M., Herbinger, J., Tang, T., and Wada, Y.: Using information theory to understand process controls in Global Water Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14758, https://doi.org/10.5194/egusphere-egu25-14758, 2025.
Water resource management is impossible without accurate information about stream discharge. However measuring stream discharge is difficult and therefore stream height measurements, which are easier to obtain, are used as proxies. The conversion of stream height to stream discharge relies on a stage-discharge relationship, which is unique to individual gauging stations and is known as a ‘rating curve’. Accurate assessment of this rating curve and accompanying predictions, is essential for effective water resources management. However, the stage-discharge relationship is often non-stationary, due to erosion or sediment deposition at gauging sites which results from natural processes such as flooding. As a result, the rating curve needs to be re-calibrated regularly. This paper present an approach to estimate the time varying stage-discharge relationships, which we call ‘AdaptRatin’. ‘AdaptRatin' first partitions the time-ordered data into an unknown yet finite number of segments, which are locally stationary. Within each segment, the stage-discharge relationship is modelled non-parametrically by placing a Gaussian process prior over the unknown relationship. We take a Bayesian approach and inference regarding the number and location of locally stationary segments and the corresponding rating curve for each segment is made via the joint posterior distribution of these quantities. We use Reversible Jump Markov Chain Monte Carlo (RJMCMC) to obtain a sample based estimate of this joint posterior. We demonstrate the ability of ‘AdaptRatin’ to successfully capture the changes in the stage-discharge relationship over time and provide reliable estimates of the underlying stage-discharge relationship for each time period for both stationary and non-stationary processes.
How to cite: Athukorala, D. R. M., Vervoort, R. W., Afshar, H. M., and Cripps, S.: Adaptive Rating Curve Estimation: AdaptRatin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-378, https://doi.org/10.5194/egusphere-egu25-378, 2025.
Several factors inherently influence the accuracy of hydrological model simulations –including uncertainties in input data, model parameterization, and the (unavoidably simplified) representation of physical processes. Among these, precipitation data used as input play a crucial role as they directly influence the magnitude and timing of streamflows. This study aims to unravel the propagation of uncertainty in hydrological modelling from precipitation data to streamflow simulations.
To this end, we built a semi-distributed and process-based hydrological model using the Hydrological Predictions for the Environment (HYPE) code of the Reno river basin in Italy. Moreover, we used four gridded precipitation datasets —ERG5 (5 km), CHIRPS (5 km), E-OBS (0.1°), and ERA5 (0.1°)—to calculate mean annual and seasonal precipitation at the sub-basin scale for the period 2001–2022. Despite similarities in seasonal patterns, notable differences emerge during the wet season (especially in winter) and in annual averages, particularly in the small and mountain sub-basin. ERA5 and CHIRPS generally underestimate precipitation during the wet season, while E-OBS exhibits strong correlation with the observed ERG5 dataset.
Observed daily streamflow data were used to calibrate (2001–2010) and validate (2011–2022) the hydrological model. While the Kling-Gupta Efficiency (KGE) values were overall acceptable, we found larger uncertainties across all sub-basins. In the small and mountainous sub-basin, simulated streamflow shows greater variability and peak flows are often overestimated during the winter. This might be attributed to limitations in gridded datasets, such as the density of gauge stations and the capturing of snow precipitation. These uncertainties also propagate into the dry season, where the variability in simulated streamflow is relatively larger compared to the entire basin for the same season. These findings underscore the significant influence of uncertainty in precipitation data on hydrological simulations, especially in areas with complex orography. We also discuss the importance of addressing such uncertainties in hydrological modeling across different scales.
How to cite: Cenobio-Cruz, O. and Di Baldassarre, G.: Quantifying Precipitation-Driven Uncertainty in Streamflow Simulations: Application to the Reno River Basin (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2930, https://doi.org/10.5194/egusphere-egu25-2930, 2025.
Reliable estimates of the water availability and fluxes within the vadose zone and groundwater are important for numerous applications. Integrated numerical subsurface flow models can give comprehensive estimates of states and fluxes within both compartments (vadose zone and groundwater), accounting for two-way feedbacks between them. However, those estimates are highly uncertain. Using data assimilation (DA), one can reduce the forecast uncertainty of the numerical model by utilizing information obtained from measurements. The numerical model state is updated, determining its most likely value, given a certain observation. Despite what might be intuitively assumed, it is not necessarily the case in DA with integrated subsurface flow models that assimilating observations from one compartment improves estimates in the other one. In fact, updates often need to be limited to the respective compartment to avoid deteriorations by the DA due to spurious covariances. Considering the core idea of integrated modeling, there are incentives to work out strategies to i.) mitigate such deteriorations and ii.) utilize interactions between the subsurface compartments more extensively when conducting DA with such models.
We test DA strategies using the ensemble Kalman Filter, which is a common choice for data assimilation with subsurface flow models. We extract measurements from a numerical reference model that exhibits heterogeneous soil hydraulic parameter fields. We acknowledge that such heterogeneous structures are commonly not known in real catchments, so we use homogenized soil hydraulic parameters in the ensemble (forecast model). We conduct the experiments on the plot/hillslope scale but consider spatial averages of the estimates for transferability to larger spatial scales. The analyzed variables are the soil moisture near the land surface, the soil moisture within the root zone, groundwater recharge, and the groundwater table height. They are all highly relevant for applications and give a comprehensive overview of the whole subsurface.
Both soil moisture and groundwater table assimilation consistently improve estimates in their respective compartment but sometimes deteriorate estimates in the other compartment. We find both bias correction and vertical localization to be suitable measures to mitigate the deterioration of groundwater table height predictions by soil moisture assimilation. Estimates of groundwater recharge are generally deteriorated by the updates of DA since DA introduces artificial balancing fluxes between the compartments. Still, recharge estimates can be improved in a simulation without DA, which uses the states and soil hydraulic parameters estimated by DA. We find that applying information from the groundwater observations to both the groundwater and the deep vadose zone can dampen the artificial balancing fluxes between the compartments, which leads to improved estimates of the groundwater table height. Multivariate DA of both soil moisture and groundwater leads to similarly good estimates as univariate DA near the respective observations and better estimates between the observations (i.e., within the root zone).
How to cite: Waldowski, B. and Neuweiler, I.: Data Assimilation with the Ensemble Kalman Filter using Integrated Subsurface Flow Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6395, https://doi.org/10.5194/egusphere-egu25-6395, 2025.
In order to make generalised, quantitative statements and predictions about hydrological systems, numerically-estimated mathematical parameterisations are essential. Few-parameter models, optimised using measurements of physical/chemical tracers, are ubiquitous in the hydrological sciences. Examples include groundwater noble gas paleothermometry, determination of groundwater pollution sources from toluene measurements and determination of water mass fractions/transformation processes using hydrographic variables and gas/mineral solute concentrations. All of these applications involve minimising a cost function against a small (~10 or fewer) set of tracer observations for each sample. However, the computational implementation of any new tracer exchange model is time-consuming and often involves many redundant steps: a programmatically-represented mathematical model often must be retroactively suited to previous code, not necessarily written by the same person. With increasing project complexity, even small editions to a model may necessitate many changes propagating throughout a program, costing the programmer time and more easily introducing errors. With time-pressure and greater emphasis on the scientific results of a project rather than the code used to obtain them, accessibility and readability of software (i.e. its FAIRness) suffers.
We have developed PAGOS (Python Analysis of Groundwater and Ocean Samples), a Python package which serves to streamline the development and testing of hydrological models, reducing the “scientist-side” time and effort required while also providing an environment conducive to highly accessible code development. Any hydrographic variable/tracer-based model representable as a function in Python can be quickly implemented in as few as 3 lines of code, whereafter it can immediately be forward-run with known parameters, or those parameters can be estimated by inverse-modelling against a user-provided dataset. PAGOS also automatically handles units, sparing the user the task of writing and re-writing methods to account for different units, and avoiding unit-conversion errors (to which hydrological investigations are particularly prone). A plotting subroutine is also provided by the package.
The case study for which PAGOS was initially developed is an investigation of surface gas exchange processes in the Arctic Ocean, parameterising new models with respect to noble gas measurements. Additionally, parameter estimates for selected models in groundwater and ocean sciences literature have been reproduced by PAGOS. These applications all use 3–5 noble gas tracers, but any number of tracers of any kind may be employed by the user.
Collaborative extension of PAGOS’s scope to more areas in hydrology is encouraged through a public GitHub repository (https://github.com/TeamPAGOS/PAGOS).
How to cite: Scott, S., Hubner, C., Arck, Y., and Aeschbach, W.: PAGOS: A New Python Library for Fast Implementation of (Tracer-Based) Hydrological Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7347, https://doi.org/10.5194/egusphere-egu25-7347, 2025.
Highly parameterized numerical models of groundwater flow and contaminant transport play a central role in water resources management. Quantifying and analysing uncertainties associated with such models is a key challenge for decision-making, especially under the impacts of climate change. Furthermore, an important question often being overlooked in groundwater model applications is where the next observation point should be located and which state variable should be observed in order to reduce (predictive) uncertainty. We utilize the recently introduced Multilevel Generalized Likelihood Uncertainty Estimation methodology (MLGLUE; DOI: 10.1029/2024WR037735) to perform Bayesian inversion, accelerated by exploiting different spatial model resolutions. For a given model we consider two scenarios; in one scenario we utilize all available state observations while we remove environmental tracer observations from the dataset in a second scenario. We analyse the intrinsic data-worth of environmental tracer observations with respect to simulation uncertainty, especially regarding the estimates of quantities of interest derived from model outputs. Besides simulated observation equivalents we let the computational model also return potential future observations during inversion. We then use measures from information theory to select potential future observations which will result in the most substantial reduction of uncertainty regarding quantities of interest in both scenarios. We apply the combined methodology to a synthetic example as well as a previously developed steady-state regional groundwater flow and transport model. Our results demonstrate that the worth of environmental tracer observations is substantial to reduce model output uncertainty and to increase model accuracy. We show that future environmental tracer observations are especially relevant to better constrain estimates of quantities of interest when sampled at informative locations. The approach promises to improve the capabilities of groundwater models used for decision support and water resources management.
How to cite: Rudolph, M. G., Wöhling, T., Wagener, T., and Hartmann, A.: Where and What to Sample Next? Bayesian Data-Worth Analysis for Regional Groundwater Models Using Multilevel GLUE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9980, https://doi.org/10.5194/egusphere-egu25-9980, 2025.
Floodplain aquifers receive substantial water input from adjacent hillslopes, most likely concentrated in hillslope hollows. Other localized inputs are expected from tributary valleys and along surface-water bodies. However, quantifying and localizing subsurface water fluxes is inherently difficult. A key requirement for flux estimation is the knowledge of the hydraulic-conductivity distribution at the scale of interest. Conventional hydrogeological investigation techniques, such as pumping and slug tests, may fail in the presence of heterogeneity and complex structural boundaries. While advanced 2-D and 3-D hydraulic tomography may resolve small-scale heterogeneity, it is typically limited to small spatial scales and require complex and costly field installations. We choose a simplified tomographic approach using a limited number of pumping and observation wells targeting spatial ranges in the order of 100 m. To infer the spatially variable hydraulic-conductivity field with its uncertainty, we apply an iterative ensemble smoother and pilot-point parameterization for dimensionality reduction. Subsequently the resulting conditional realizations of the hydraulic-conductivity field are used to calculate water fluxes applying mean hydraulic gradients. We test the approach by a synthetic scenario mimicking the conditions in a hillslope hollow connected to a floodplain aquifer. We aim at applying the approach as field method in a local floodplain aquifer in Southwest Germany near the city of Tübingen to quantify the lateral inflow from an adjacent hillslope hollow.
How to cite: Drach, K., Leven, C., and Cirpka, O. A.: Localized subsurface water flux estimation by simplified hydraulic tomography: Synthetic test case and outlook for field application, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12069, https://doi.org/10.5194/egusphere-egu25-12069, 2025.
The covariance function is a powerful tool for characterizing random processes and is fully parameterized by (anisotropic) correlation lengths and angles of rotation. While correlation lengths have been successfully estimated, the periodicity of rotation has posed challenges in determining unique covariance function parameterizations. Good prior knowledge of the covariance function has been shown to greatly improve the results of parameter inference methods. However, knowledge of the full anisotropy of the covariance function is difficult to obtain. Therefore, we propose an extension to the pilot point ensemble Kalman filter (PPEnKF) that is capable of estimating the full anisotropy of the covariance function based on attainable, initially random knowledge. We address the periodicity of rotation by incorporating the unique elements of the covariance transformation matrix into the PPEnKF. Based on the estimates of the covariance function, we further modify the filter by generating conditional field realizations in each assimilation step, increasing the inherent ensemble variance and preventing filter inbreeding. We demonstrate the methodology by estimating the covariance function of a field of hydraulic conductivity in a synthetic study of a 2D groundwater model. The full anisotropy of the covariance function and the hydraulic conductivity at pilot points are estimated via the assimilation of hydraulic head data. The success of this method depends more on the configuration of pilot points than on the quality of prior knowledge, as ensembles initialized with faulty random knowledge successfully estimated the correct parameterization of the covariance function, as well as the corresponding parameter values at the pilot points. The resulting parameter fields enabled accurate predictions of groundwater head levels during a verification period, with normalized root mean square errors reduced by 77 - 97 % compared to ensembles excluded from the parameter update. The methodology presented in this study mitigates the importance of informative prior knowledge of the covariance function in parameter inference methods, showcasing the effectiveness of random processes in achieving robust parameter field estimations, especially in highly anisotropic settings.
How to cite: Geiger, J., Finkel, M., and Cirpka, O.: Estimating the Full Anisotropy of the Covariance Function in Geostatistical Inversion using the Pilot-Point Ensemble Kalman Filter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16262, https://doi.org/10.5194/egusphere-egu25-16262, 2025.
Estimates of aquitard hydraulic parameters are typically derived from pumping test drawdowns in aquifers. Analytical solutions for leaky aquifers and semi-analytical solutions for multiple aquifer systems assume homogeneity of hydraulic parameters in both aquifers and aquitards. In settings where the hydraulic parameters cannot be assumed to be homogeneous, the parameters estimated with these methods are generally considered spatial averages. In this study, we investigate the spatial sensitivity of aquitard hydraulic properties at various observation times in both the pumped and overlying aquifers using a synthetic pumping test model. Results show that the area around both the observation and pumping wells exhibits the highest sensitivity in both the overlying and pumped aquifers. A parabolic shape in sensitivity is observed between the wells. Over time, the sensitive area shifts from an approximate line between the wells to an expanding ellipse. However, if the transmissivity of the overlying aquifer is lower than that of the pumped aquifer, the observation in the overlying aquifer becomes increasingly sensitive to the hydraulic conductivity near the observation well. Conversely, if the transmissivity of the pumped aquifer is lower than that of the overlying aquifer, the sensitivity shifts towards the area close to the extraction well. Understanding these sensitivity patterns is essential for translating pumping test results into parameters for regional groundwater flow models and improving pumping test design.
How to cite: van Leer, M., Zaadnoordijk, W. J., Zech, A., Griffioen, J., and Bierkens, M.: Spatial sensitivity of aquitard hydraulic parameters derived from pumping tests in multi-aquifer systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16883, https://doi.org/10.5194/egusphere-egu25-16883, 2025.
Globally, the management of water resources is facing increasing challenges due to population growth, economic expansion, and climate change. These factors emphasize the need for sustainable and improved strategies for the allocation system. The current study uses the Water Assessment and Planning System, a decision model, for the Keritis basin, which is a vital water resource for the city of Chania and mainly provides water for domestic and agricultural use. The model incorporates hydrological and meteorological data to simulate surface runoff. Comparisons are made with previous studies on runoff in the area and with literature that has used other hydrological models to simulate other processes. Climate projections will also be incorporated into the model to assess projected precipitation patterns for the coming decades. Part of this work is also to simulate how the planned infrastructure, in particular two dams in a nearby catchment, can help improve water distribution and, most importantly, meet the large demands of agriculture.
This work was supported by OurMED PRIMA Program project funded by the European Union’s Horizon 2020 research and innovation under grant agreement No. 2222.
How to cite: Koroptsenko, I., Varouchakis, E., Karatzas, G., Vozinaki, I., and Lyronis, A.: Improved strategies to improve water allocation in a Mediterranean watershed., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20200, https://doi.org/10.5194/egusphere-egu25-20200, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot A
EGU25-860 | ECS | Posters virtual | VPS9
Addressing hydraulic parameter uncertainties for resilient irrigation canal modelling and controlTue, 29 Apr, 14:00–15:45 (CEST) | vPA.30
Efficient water management in irrigation canal systems demands accurate modelling and control of hydraulic dynamics, especially under conditions of uncertainty. This study investigates the effects of variations in hydraulic parameters, particularly the roughness coefficient, on the performance and reliability of canal models. The roughness coefficient, a critical factor influencing flow resistance, often varies along a canal's length and over time, leading to deviations in water level and discharge predictions. Such uncertainties can significantly impact control strategies and overall water management efficiency.
To address these challenges, we utilized a comprehensive canal model structure (Pandey et al., 2024) to develop models for three distinct canal types and analyzed their behavior under extreme operating conditions. The study simulated scenarios with both uniform and non-uniform flow conditions, incorporating a 30% variation in the roughness coefficient to create tuned and untuned configurations. Through detailed simulations, we evaluated the sensitivity of model parameters, including upstream and downstream water areas and delays, and assessed water level deviations arising from parameter uncertainties. Disturbances were introduced at the downstream end to observe the model's robustness across varying operating conditions.
The findings highlight the substantial influence of roughness coefficient variations on model behavior, particularly in terms of discharge accuracy and water level control. Comparative analysis revealed the limitations of untuned models in handling parameter uncertainties, emphasizing the need for adaptive and robust control strategies. Additionally, the results demonstrate the varying impacts of uncertainties across different canal configurations and flow conditions, providing insights into model reliability and the design of resilient irrigation systems.
By addressing parameter uncertainties and evaluating model responses under diverse conditions, this research contributes to the development of adaptive and reliable water management strategies. The outcomes are crucial for advancing sustainable irrigation infrastructure capable of coping with real-world complexities and variabilities.
How to cite: Pandey, R., Gobbalipur Ranganath, J., and Mohan Kumar, M. S.: Addressing hydraulic parameter uncertainties for resilient irrigation canal modelling and control, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-860, https://doi.org/10.5194/egusphere-egu25-860, 2025.
