HS3.8

When modelling groundwater systems in Quaternary formation, one of the first steps is to construct a geological and petrophysical model. This  is often  repetitive because it relies on multiple manual steps which include geophysical interpretation, construction of a structural model, identification of geostatistical model parameters, facies and property simulations. Those steps are often carried out in different softwares, which makes the automation intractable or very difficult. A non automated approach requires a lot of time and is critical to update the model for integrating new available data or when some geological interpretations are modified, and to conduct a cross-validation procedure to assess the overall quality of the models. Moreover, it renders the quantification of the joint structural and parametric uncertainty tedious. 

To address these issues, we propose a new approach and a Python module to automatically generate realistics geological and parameter models. One of its main features is that the modelling operates in a hierarchical manner. The input data consists of a set of borehole data, surface geology, and a stratigraphic pile. The stratigraphic pile describes formally and in a compact manner how the model should be constructed. It contains the list of the different stratigraphic units and their order in the pile, their conformability (eroded or onlap), the surface interpolation method (e.g. kriging, SGS, MPS, etc.) or also the filling method (e.g. MPS, SIS, etc.). Then, the procedure is automatic. In a first step the stratigraphic unit boundaries are simulated. Secondly, they are filled with lithologies and finally the petrophysical property models are simulated inside the lithologies. All these steps are straightforward and automated once the stratigraphical pile and its related parameters have been defined. Hence, this approach is extremely flexible. It is illustrated using data from an alpine quaternary aquifer in the Upper Aare plain (south-east of Bern, Switzerland).

How to cite: Schorpp, L., Straubhaar, J., and Renard, P.: ArchPy, automated hierarchical modelling of Quaternary aquifers: an example from the Upper Aare Valley, Switzerland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2978, https://doi.org/10.5194/egusphere-egu22-2978, 2022.

Model structural error, arising from the inevitable simplification and abstraction modelers must make of complex real-world system and processes, has been shown to produce biased predictions as a direct result of parameter compensation during data assimilation. The specter of structural error especially plagues groundwater decision support modeling, since the inverse methods underpinning calibration and uncertainty analysis favor a computationally efficient and numerically stable model, or in other words, a simpler model. This work explores sequential data assimilation (DA) as a potential coping mechanism for the structural error encountered by simpler models. Unlike traditional batch methods, sequential methods assimilate data in discrete time intervals or ‘cycles’, and simultaneously estimate model state along with model parameters in order to advance the model forward in time. We hypothesize that the estimable model states in sequential DA afford more flexible and appropriate receptacles for the noise introduced into observations from model structural error. Using a paired complex-simple model approach, we empirically evaluate the predictive outcomes of batch and sequential DA in two model error scenarios: first where error arises from coarser resolution in the simple model, and second where error arises from both coarser resolution and fixed pumping rates in the simple model. Overall, we find that both formulations perform well in both history matching and forecasting when employing a high-dimensional parameterization stance, that is, treating all properties and stresses as uncertain and adjustable during the inversion process. When uncertain parameters are removed from the inversion process, however, the data assimilation process is degraded in different ways for batch and sequential formulations. These results have implications for groundwater decision support modeling as they underscore the pitfalls of fixing parameters a priori, such as with pumping, and present a proof of concept for using adjustable model states to cope with model error in decision support modeling contexts.

How to cite: Markovich, K., White, J., and Knowling, M.: Rapid, reproducible, and wrong? Exploring sequential data assimilation as a coping mechanism for model structural error in groundwater decision support modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10074, https://doi.org/10.5194/egusphere-egu22-10074, 2022.

Environmental water management often benefits from a risk-based approach where information on the area of interest is characterized, assembled, and incorporated into a decision model considering uncertainty. This includes prior information from literature, field measurements, professional interpretation, and data assimilation resulting in a decision tool with a posterior uncertainty assessment accounting for prior understanding and what is learned through model development and data assimilation. Model construction and data assimilation are time consuming and prone to errors, which motivates a repeatable workflow where revisions resulting from new interpretations or discovery of errors can be addressed and the analyses repeated efficiently and rigorously. In this work, motivated by the real-world application of delineating risk-based (probabilistic) sources of water to abstraction wells in a humid temperate climate, a scripted workflow was generated for groundwater model construction, data assimilation, particle-tracking, and post-processing. The workflow leverages existing datasets describing hydrogeology, hydrography, water use, recharge, and lateral boundaries to build the model. The workflow performs ensemble-based history matching and uses a posterior Monte Carlo approach to provide probabilistic capture zones describing areas that contribute recharge to wells in a risk-based framework. The water managers can then select areas of varying levels of protection based on their tolerance for risk of potential wrongness of the underlying models. All the tools in this workflow are open-source and free, which facilitates testing of this repeatable and transparent approach to other environmental problems. The specific data are available in the United States but the tools can be applied to similar datasets worldwide.

How to cite: Fienen, M., Corson-Dosch, N., White, J., Leaf, A., and Hunt, R.: From datasets to decisions – a repeatable workflow for groundwater decision support, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10686, https://doi.org/10.5194/egusphere-egu22-10686, 2022.

Groundwater models are simplified and simulated depictions of aquifer systems that represent flow and/or transport of groundwater. For decades, practitioners would construct a model with the help of a graphical user interface (GUI) for a simulator such as MODFLOW, a finite-difference groundwater flow modeling program written by the United States Geological Survey (USGS). Due to the nature of GUIs, most of the time (and therefore cost) went in to the creation of model input datasets, and the “calibration” of the model would be hastily rushed at the end of the project time window. Unfortunately, the calibration process will almost certainly reveal issues derived from early stages of the project, and in the GUI framework it can take significant time and effort to manually address issues with the upstream workflow elements.

More recently, script-driven modeling workflow tools have been made available for practitioners to use to mitigate the time and cost associated with undertaking complex groundwater modeling analyses. Tools like FloPy and pyEMU python packages for interfacing with MODFLOW and the parameter estimation software PEST and PEST++.  When used together, these tools allow for all workflow steps to be automated, including the creation of the model input datasets as well as deployment analyses like data assimilation and uncertainty analysis.  More importantly, a script-driven approach allows issues (which statistically will always occur) to be addressed cleanly and efficiently, with minimal effort and little to no loss of practitioner time.

In this presentation, we present the development and implementation of a decision-support workflow for the history matching of 1,000 geostatistical realizations of transmissivity, storage, anisotropy, and recharge using multiple simulations. This work is part of a larger risk-analysis for the Waste Isolation Pilot Plant (WIPP) project in southeastern New Mexico. The predictive focus is on estimating particle travel times of long-lived radionuclides. 

Using script-driven approaches and recent iterative ensemble smoother techniques, our team was able undertake an advanced data assimilation analysis in a few hours one afternoon using a single workstation.  Previously, a similar analysis for the WIPP project took months of practitioner time on a massively parallel super-computer.

How to cite: Kushnereit, R.: Calibrating an ensemble of 1,000 realizations for estimating the uncertainty of aquifer properties in the vicinity of a long‐lived radioactive waste repository using a script-driven approach (on a Friday afternoon), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10403, https://doi.org/10.5194/egusphere-egu22-10403, 2022.

The relationships between flow components and stream channel features during flash floods are theoretically well known and proven under controlled environments but are rarely explored and quantified for case studies in different geographic contexts. This research is focused on the spatial relationships between water depths and flow velocities for several return periods (R_T), and how these are influenced by stream channel features. Spatial analyses were performed in Geographic Information Systems (GIS) using the hydraulic modelling results (HEC-RAS/HEC-GeoRAS) in a section of a small watershed in Portugal, which is frequently affected by flash floods. This section comprises about 1000 meters of the main watercourse (Barcarena stream) and the last 350 meters of one of its main tributaries (Massamá stream).

The relationships between water depths and flow velocities are not particularly evident in the floodable areas, although correlation coefficients increase with increasing return periods (0.39 for 5-year R_T; 0.50 for 100-year R_T). Water depths tend to grow with increasing flow velocities and vice versa. Nevertheless, this trend changes when high values of water depth or velocity are reached, preventing higher correlations. This inversion is explained by modifications in channel geometry, morphology or slope, the presence of confluences and obstacles, and flood width/overbank flooding. Unlike what happens with the entire floodable area, strong negative correlations between water depths and flow velocities were found along the stream centrelines. Correlation coefficients of -0.78 and -0.83 (2-year R_T), and -0.66 and -0.87 (100-year R_T) were determined for the Barcarena and Massamá streams, respectively. The more direct relationship in the tributary can be explained by the narrower channel when compared to the main watercourse and by the limited overbank flooding. Bed slope, channel and flood width, and roughness are highly relevant on the longitudinal variations of water depths and velocities and on the location of their maximum values. The relationships between water depths and velocities can also change in result of increasing peak discharges and return periods.

The 1D hydraulic model provided good results in the definition of floodable areas, water depths and longitudinal flow velocities. Lateral velocities are correctly represented in straight sections or in mildly curved bends, which are present in most of the study area, but there are errors in the sharp bends and at the confluence of the Barcarena and Massamá streams. The lack of hydrometric data compromises the calibration and validation of the velocity results. The non-existence of LiDAR elevation data in the floodplains and the lack of elevation data along the stream channels compromise the quality of the DSM. However, it was possible to overcome the lack of elevation data along the stream channels by including the position of the thalwegs in the DSM through the Topo to Raster tool of ArcMap. This guarantees the transversal and longitudinal variations of elevation, improving the hydrologic modelling results in areas with scarce or no elevation data along the channels. The obtained results demonstrate the usefulness of GIS to represent hydraulic modelling results and perform spatial analysis for flood events and other natural hazards.

How to cite: Leal, M., Reis, E., and Pinto Santos, P.: Flash flood spatial analysis using hydraulic modelling and Geographic Information Systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5095, https://doi.org/10.5194/egusphere-egu22-5095, 2022.

Setting groundwater allocation limits requires an understanding of recharge fluxes to the aquifer system. Very often rainfall percolation through the subsurface represents the critical recharge flux. In this groundwater limit setting context, recharge estimates are often established as a component of the groundwater flow model history matching process. Typically, there are many recharge models available, and the basis for selecting any particular model is often confusing.

Some of these recharge models are numerical solutions of variably saturated pressure head and flow and represent the full complexity of the soil-vegetation-atmosphere transfer of water. Such models require many parameters that may not be measured or verified, and/or are computationally expensive. This can make the history matching process and predictive uncertainty analyses difficult.

Simpler model representations of the recharge processes are also available, either through upscaling (e.g., by lumping together different soil profiles, with different vegetation) and/or by simplification of the recharge estimation method. Such simplifications may involve empirical equations to derive gross recharge, single bucket-type root zone water balance calculations, or solving net recharge with the help of analytical solutions of flow or pressure heads and linear approximations of gross recharge or evapotranspiration from groundwater as function of the groundwater head. These simpler models often have a greater utility (i.e., they are quicker to run and are more numerically stable) but may be accompanied by additional ‘simplification’ induced uncertainty.

Regardless of the method used, the uncertainty and bias of these recharge predictions can be high.  The uncertainty of groundwater model predictions underpinning the setting of allocation limits can also be high. However, the performance of a recharge model in terms of how it impacts the reliability of the predicted impacts relevant to the groundwater allocation limit, is currently not considered. This study addresses this issue, exploring the costs and benefits of recharge models of varying complexity, in the context of setting groundwater abstraction limits. This is demonstrated using a synthetic, but realistic case study in Western Australia.

We adopt a paired complex-simple model analysis workflow, and implement it using the Flopy-PyEMU Python-based scripting framework. This workflow is then used to explore the performance of more complex and simpler models within the groundwater allocation management context by measuring each model’s bias and uncertainty. We compare a cell-by-cell Richards’ equation-based recharge model, with a series of simpler recharge contender models. This scripted workflow supports the efficient deployment of the paired complex-simple model stochastic analysis and interpretation of its outputs.

How to cite: Schmid, W., Moore, C., Hall, J., Dawes, W., Silberstein, R., Guzman, S., Siade, A., and Nelson, R.: Recharge model performance in the context of setting groundwater allocation limits, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4195, https://doi.org/10.5194/egusphere-egu22-4195, 2022.

Numerical models and optimization algorithms can be valuable tools for decision-making in coastal and island aquifers, where pumping wells are threatened by salinization. Yet, the implementation of pumping optimization under uncertainty remains limited in practice, because of long simulation times and challenges associated with uncertainty propagation through series of models. A method was developed to optimize pumping rates in an island freshwater lens considering parameter, observation, and climate uncertainty. It was implemented in an island aquifer in the Magdalen Islands (Québec, Canada). A seawater intrusion model with rapid simulation times was developed using MODFLOW-SWI2. The iterative ensemble smoother algorithm implemented by PESTPP-IES allowed for history matching and nonlinear uncertainty quantification. The model predictive uncertainties were coupled with climate uncertainties, including recharge uncertainty (derived from various global circulation models and emission scenarios) and sea-level rise uncertainty. Using PESTPP-OPT, the pumping rates in the freshwater lens were then maximized while avoiding the risk of well salinization and considering parameter, observation, and climate uncertainty. Results of the pumping optimization were compared with estimates of water demand uncertainty. This study used widely available, model-independent software and could be used to support groundwater management decision-making in other insular or coastal areas.

How to cite: Coulon, C., Lemieux, J.-M., Pryet, A., and Gatel, L.: Optimization of pumping rates in an island freshwater lens considering parameter, observation, and climate uncertainty, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5826, https://doi.org/10.5194/egusphere-egu22-5826, 2022.

The assimilation and prediction of concentration data is often impeded by the computation time of groundwater transport models based on the resolution of the advective-dispersive equation. This is unfortunate because such data is often rich in information and the prediction of concentration values is of great interest for decision making.  Particle tracking may be used as an efficient alternative under a series of simplifying assumptions, which are often reasonable at groundwater sinks. A rapid transport model allows the use of assimilation and optimization methods requiring many model calls.  We developed a Python package to facilitate the use of the USGS MODFLOW6 and MODPATH7 models to simulate the transfer of tracer or contaminant concentrations to a groundwater sink (typically a pumping well or a drain). The approach requires the identification of one or a series of sources of tracer/contaminant such as a contaminated stream or area in the model domain. The package handles particle seeding around the sink and estimation of the concentration of water withdrawn from the sinks. Both “strong” and “weak” sources can be considered. Concentrations are computed with a mixing law from the particle endpoints and velocities. We investigated the best practice to obtain robust derivatives with this approach, which is essential for all methods based on the linearized version of the model. We provide a step-by-step workflow from model construction to parameter estimation, linear uncertainty analysis, and chance-constraints optimization with the PEST suite. The interest and practical details of the approach are illustrated on a well field vulnerable to a stream, and a parametric analysis is provided in order to evaluate the impact of key numerical parameters on the presented results.

How to cite: Pryet, A., Matran, P., Cousquer, Y., and Roubinet, D.: Particle tracking as a vulnerability assessment tool for drinking water production, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5966, https://doi.org/10.5194/egusphere-egu22-5966, 2022.

The Food and Agriculture Organization (FAO) AquaCrop model has demonstrated its ability to accurately simulate the growth of various crops. However, the quality of simulation results depends on the calibration of the model, which in turn requires field observations of model inputs and parameters. This limits the utility of AquaCrop in data-scarce regions, such as the Global South. A user-friendly method to analyze parameter sensitivities and model output uncertainties could facilitate the assessment of the model output reliability. The AquaCrop version provided by FAO, however, is run through a standalone graphical user interface (GUI) and therefore does not allow for systematic calibration. Besides, the user cannot customize parameter-specific features like irrigation scheduling.

This work presents a tool that enhances the MATLAB-based open-source application of AquaCrop, AquaCrop-OS (AOS), with the following functionalities: A Bayesian modeling feature is designed to calibrate the AOS model considering input data uncertainty, while the MATLAB toolbox Sensitivity Analysis For Everybody (SAFE) is integrated to automate sensitivity and uncertainty analysis. Irrigation schedules may now also be created dynamically and depending on different simulated parameters like the rooting depth. The user can distinguish between different environmental stresses, either by cause or affected variable. Every functionality is supplemented with intuitive graphics. The tool will be released under an open-source license on GitHub. A standalone executable version with a GUI will cater for non-MATLAB users.

The AOS model is calibrated on field data from an experimental agricultural plot in the Ganga River basin in Kanpur, India, for two wheat cropping seasons between 2018 and 2019. The proposed tool is used to quantify the uncertainty in the model input data and parameters and their effects on model outputs.

How to cite: Bruckmaier, F., Adla, S., Disse, M., and Tripathi, S.: Modified AquaCrop-OpenSource tool for data-scarce regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10694, https://doi.org/10.5194/egusphere-egu22-10694, 2022.

Water distribution network (WDN) is an essential infrastructure for conveying potable water to communities. Constituting different components, it has a complex structure involving significant financial investments for its design. During its life span, the failure of WDN partially or entirely is an inevitable consequence of the network's hydraulic or mechanical uncertainties. Therefore, the WDN design problem naturally involves a tradeoff between the reliability and cost aspects. The present study formulates a reliability-based hybrid metaheuristic optimization model for its robust design. Primarily, the proposed framework is composed of three components, an optimization algorithm, a simulation model, and a reliability assessment model. 
A novel hybrid technique in a combination of differential evolution (DE) and krill herd algorithm (KHA), DE-KHA, is used as the optimization algorithm. The DE-KHA is the computationally efficient algorithm for effortlessly tackling the WDN design problems by balancing the exploration and exploitation features. EPANET 2.0 hydraulic simulator that performs the hydraulic analysis of WDN is used as a simulation model. The hydraulic characteristics of the network, such as flow-through pipes, unit headloss, the actual and total pressure head at the demand nodes, and the demands delivered to the demand nodes, are assessed using EPANET 2.0. The reliability model evaluates the network's performance under mechanical uncertainties. The mechanical failures are the scenarios of networks component failure, which in the present study are considered network pipe outages. The reliability model is based on the minimum cut set method, where the pipe failure combinations that cause the failure of the network are found explicitly considering the minimum pressure head requirement at the demand nodes.
Search for the optimal solution is progressed by the optimization technique, where the constraints of continuity and energy balance equations are explicitly taken care of by the EPANET 2.0 simulation model. Then considering the hydraulic head at the demand node, the minimum cut sets are finalized, and the network’s performance under mechanical failure scenarios is assessed using the reliability model. The DE-KHA and reliability model code is written in MATLAB and linked to EPANEt 2.0 using MATLAB-EPANET toolkit.
The application of the developed framework is validated considering Two loop Network (TLN). It is a hypothetical network studied by many researchers for validating their optimization models. TLN is made up of eight pipes, connected by seven nodes with a single reservoir of 210 m total head that feeds the entire network. Thus, it is a gravity-fed network with no pumps operated. Considering this simple case study, yet a challenging problem with 14⁸ possible solutions in the search space, the reliability-based model is validated. The results present the computational efficiency of the model in yielding the optimal design cost of $ 419,000 with minimal computational effort. Furthermore, the algorithm proposed is efficient in exploring various alternate optimal solutions with considerable reliability, thus, presenting robust design options for TLN. Considering the efficiency of the proposed model, the study suggests it for the robust design of real-life WDNs.

Keywords: Water distribution network design; Reliability; Mechanical uncertainty; Metaheuristic algorithms 

How to cite: Naga Poojitha, S. and Jothiprakash, V.: Reliability-Based Hybrid Metaheuristic Optimization Model for the Design of Two Loop Network Under Mechanical Uncertain Scenarios, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12971, https://doi.org/10.5194/egusphere-egu22-12971, 2022.