ORCID Profile
0000-0002-1776-3173
Current Organisation
CSIRO
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Publisher: CSIRO
Date: 2018
Publisher: American Geophysical Union (AGU)
Date: 08-2020
DOI: 10.1029/2020WR027578
Abstract: Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modeling. Hypothesis testing is essential to increase system understanding by analyzing and refuting alternative conceptual models. We present a systematic approach to conceptual model testing aimed at finding an ensemble of conceptual understandings consistent with prior knowledge and observational data. This differs from the traditional approach of tuning the parameters of a single conceptual model to conform with the data through inversion. We apply this approach to a simplified hydrogeological characterization of the Wildman River area (Northern Territory, Australia) and evaluate the connectivity of sinkhole‐type depressions to groundwater. Alternative models are developed representing the process structure (i.e., different fluxes representing interactions between surface water and groundwater) and physical structure (i.e., different lithologies underlying the depressions) of the conceptual model of the depressions. Remote sensing data are used to test the process structure, while geophysical data are used to test the physical structure. Both data types are used to remove inconsistent models from an ensemble of 16 models and to update the probability of the remaining alternative conceptual models. Three out of five depressions that are used as a test case are conditionally confirmed to act as conduits for recharge, while for the last two depressions, the data are inconclusive. Although the framework is not directly prediction oriented, the testing of plausible conceptual models will ultimately lead to increased confidence of any groundwater model based on accepted posterior conceptualizations.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-6520
Abstract: & & Fault and fracture networks can govern fluid flow patterns in the subsurface and predicting fluid flow on a regional scale is of interest in a variety of fields like groundwater management, mining engineering, energy, and mineral resources. Especially the pore fluid pressure can have a strong impact on the strength of fault zones and might be one of the drivers for fault reactivation. Reliable simulations of the transient changes in fluid pressure need to account for the generic architecture of fault zones that comprises strong permeability contrast between the fault core and damage zone.& & & & Particularly, the distribution and connectivity of large-scale fault zones can have a strong impact on the flow field. Yet, modelling numerically such features in their full complexity remains challenging. Often faults zones are conceptualized as forming exclusively either barriers or conduits to fluid flow. However, a generic architecture of fault zones often comprises a discrete fault core surrounded by a diffuse damage zone and conceptualizing large scale discontinuities simply as a barrier or conduit is unlikely to capture the regional scale fluid flow dynamics. It is known that if the fault zone is hosted in low-permeability strata, such as clays or crystalline rocks, a transversal flow barrier can form along the fault core whereas the fracture-rich fault damage zone represents a longitudinal conduit. In more permeable host-rocks (i.e. sandstones or carbonates) the reverse situation can occur, and the permeability distributions in the damage zones can be governed by the abundance of low-permeability deformation features. A reliable numerical model needs to account for the difference and strong contrasts in fluid flow properties of the core and the damage zone, both transversally and longitudinally, in order to make prediction about the regional fluid flow pattern.& & & & Here, we present a numerical method that accounts for the generic fault zone architecture as lower dimensional interfaces in conforming meshes during fluid flow simulations in fault networks. With this method we aim to decipher the impact of fault zone architecture on subsurface flow pattern and fluid pressure evolution in fractures and faulted porous media. The method is implemented in a finite element framework for Multiphysics simulations. We demonstrate the impact of considering the more generic geological structure of in idual faults on the flow field by conceptualizing discontinuities either as barriers, conduits or as a conduit-barrier system and show were these conceptualizations are applicable in natural systems. We further show that a reliable regional scale fluid flow simulation in faulted porous media needs to account for the generic fault zone architecture. The approach is finally used to evaluate the fluid flow response of statistically parameterised faulted media, in order to investigate the impact and sensitivity of each variable parameter.& &
Publisher: CSIRO
Date: 2013
Publisher: California Digital Library (CDL)
Date: 19-07-2020
Publisher: Elsevier BV
Date: 11-2010
Publisher: Elsevier BV
Date: 12-2020
Publisher: Wiley
Date: 19-09-2014
DOI: 10.1111/GWAT.12118
Abstract: The combination of ternary diagrams of cations and anions with a central diamond graph make the Piper plot very useful in visualizing groundwater chemistry datasets. One of the major drawbacks is that it is hard to link spatial attributes of the dataset to the plot. In this study, we propose a background color scheme of the Piper plot so that spatial representations of these data can be colored according to their location in the Piper plot. The color scheme is chosen to have maximum resolution while still being perceptually uniform. The linking between Piper plot and maps through this color scheme allows the interpretation of the trends and processes deduced from the Piper plot in terms of the location in the aquifer, the geology, and the groundwater flow dynamics. The colored Piper plot is applied to a groundwater quality dataset of the Condamine Alluvium in Queensland, Australia.
Publisher: Copernicus GmbH
Date: 21-01-2019
DOI: 10.5194/HESS-2019-16
Abstract: Abstract. Palaeovalleys are buried ancient river valleys that often form productive aquifers, especially in the semi-arid and arid areas of Australia. Delineating their extent and hydrostratigraphy is however a challenging task in groundwater system characterization. This study developed a methodology based on the deep learning super-resolution convolutional neural network (SRCNN) approach, to convert electrical conductivity (EC) estimates from an airborne electromagnetic (AEM) survey in South Australia to a high-resolution binary palaeovalley map. The SRCNN was trained and tested with a synthetic training dataset, where valleys were generated from readily available digital elevation model (DEM) data from the AEM survey area. Electrical conductivities typical of valley sediments were generated by Archie’s Law, and subsequently blurred by down-s ling and bicubic interpolation to represent noise from the AEM survey, inversion and interpolation. After a model training step, the SRCNN successfully removed such noise, and reclassified the low-resolution, unimodal but skewed EC values into a high-resolution palaeovalley index following a bimodal distribution. The latter allows distinguishing valley from non-valley pixels. Furthermore, a realistic spatial connectivity structure of the palaeovalley was predicted when compared with borehole lithology logs and valley bottom flatness indicator. Overall the methodology permitted to better constrain the three-dimensional palaeovalley geometry from AEM images that are becoming more widely available for groundwater prospecting.
Publisher: Elsevier BV
Date: 09-2018
Publisher: Copernicus GmbH
Date: 02-01-2020
Publisher: Wiley
Date: 20-05-2020
Publisher: Springer Science and Business Media LLC
Date: 30-12-2020
Publisher: Wiley
Date: 07-11-2019
DOI: 10.1002/HYP.13570
Publisher: Springer Science and Business Media LLC
Date: 14-07-2021
DOI: 10.1007/S11004-021-09946-W
Abstract: Spatial data mining helps to find hidden but potentially informative patterns from large and high-dimensional geoscience data. Non-spatial learners generally look at the observations based on their relationships in the feature space, which means that they cannot consider spatial relationships between regionalised variables. This study introduces a novel spatial random forests technique based on higher-order spatial statistics for analysis and modelling of spatial data. Unlike the classical random forests algorithm that uses pixelwise spectral information as predictors, the proposed spatial random forests algorithm uses the local spatial-spectral information (i.e., vectorised spatial patterns) to learn intrinsic heterogeneity, spatial dependencies, and complex spatial patterns. Algorithms for supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and clustering) learning are presented. Approaches to deal with big data, multi-resolution data, and missing values are discussed. The superior performance and usefulness of the proposed algorithm over the classical random forests method are illustrated via synthetic and real cases, where the remotely sensed geophysical covariates in North West Minerals Province of Queensland, Australia, are used as input spatial data for geology mapping, geochemical prediction, and process discovery analysis.
Publisher: Copernicus GmbH
Date: 02-01-2020
Abstract: Abstract. Identifying and quantifying recharge processes linked to ephemeral surface water features is challenging due to their episodic nature. We use a unique combination of well-established near-surface geophysical methods to provide evidence of a surface and groundwater connection under a small ephemeral recharge feature in a flat, semi-arid region near Adelaide, Australia. We use a seismic survey to obtain P-wave velocity through travel-time tomography and S-wave velocity through the multichannel analysis of surface waves. The ratios between P-wave and S-wave velocities allow us to infer the position of the water table. A separate survey was used to obtain electrical conductivity measurements from time-domain electromagnetics and water contents were acquired by downhole nuclear magnetic resonance. The combined geophysical observations provide evidence to support a groundwater mound underneath a subtle ephemeral feature. Our results suggest that recharge is localized and that small-scale ephemeral features play an important role in groundwater recharge. Furthermore, we show that a combined geophysical approach can provide a unique perspective that helps shape the hydrogeological conceptualization of a semi-arid region.
Publisher: Informa UK Limited
Date: 11-11-2019
Publisher: CSIRO
Date: 2018
Publisher: CSIRO
Date: 2018
Publisher: Society of Exploration Geophysicists
Date: 09-2021
Abstract: A hydrogeologic conceptualization is critical to understand, manage, protect, and sustain groundwater resources, particularly in regions where data are sparse and accessibility is difficult. We used airborne electromagnetics (AEM), shallow seismic reflection and refraction, and downhole nuclear magnetic resonance (NMR) logs to improve our understanding of an arid groundwater system influenced by palaeovalleys. We found that there is a limited connection between the palaeovalley and fractured bedrock aquifers because they are separated by a spatially variable layer of saprolite, which is the layer of chemically altered rock on top of the fractured bedrock. The AEM data provided an estimate of the top of the saprolite but failed to effectively image the bottom. In contrast, the seismic data provided an estimate of the bottom of the saprolite but failed to image the top. This geophysical combination of electrical and seismic data allowed us to map saprolite thickness in detail along a 1.7 km long transect that runs perpendicular the main trunk of a well-defined palaeovalley. These data indicate that the palaeovalley is lined with a heterogeneous layer of saprolite (approximately 3–120 m thick) that is thickest near its edges. Despite the observed variability, only a small percentage of the fractured bedrock aquifer (8%–17%) appears to be in contact with the palaeovalley aquifer. Furthermore, the lack of an elastic boundary at the top of saprolite suggests that the porosity of the saprolite is similar to the palaeovalley sediments — an observation that is supported by the downhole NMR-derived water contents. The electrical change at the top of saprolite is caused by a combination of a decrease in total dissolved solids of the groundwater in the saprolite and a change in pore structure associated weathering in situ versus transported weathered materials. The presence of saprolite, which commonly behaves as an aquitard, may limit the groundwater exchange between the palaeovalley and bedrock aquifers, with implications for the regional groundwater resource potential.
Publisher: Frontiers Media SA
Date: 10-09-2021
Abstract: Continental scale aquifers can store significant amounts of carbon as a result of immense water volumes, substantial concentrations of dissolved inorganic carbon (DIC) and its reactions with a matrix, thus contributing the global carbon storage and cycle. However, concentration of dissolved solutes may vary significantly over distances, which causes interpretative challenges and difficulties in process quantification. This occurs in the Guarani Aquifer System in South America, which is a subject of extensive research due to a significant strategic role in water supply. Dissolved CO 2 is expected to dissociate and undergo reactions with aluminosilicate minerals, but it is unknown how much DIC may get immobilised in the aquifer. To quantify the processes, we performed reactive transport modelling which combines hydrological and geochemical information followed by global sensitivity analysis. We show that more than a half of the infiltrated CO 2 may be consistently precipitated as CaCO 3 . The DIC concentrations across the aquifer depend primarily on the input carbon concentrations and the plagioclase hydrolysis rate, while other parameters including hydraulic conductivity, recharge rate and mineral stability are of the minor importance. We present how advanced modelling techniques may be used to interpret and quantify processes controlling water quality in continental scale groundwater systems.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-6577
Abstract: & & The topological and geometrical description of fault and fracture networks is an essential first step in any investigation of fractured or faulted media. The spatial arrangement, density, connectivity, and geometry of the discontinuities strongly impact the physical properties of the media such as resilience and permeability. Obtaining reliable metrics for characterizing fault and fracture networks is of interest for mining engineering, reservoir characterization, groundwater management, and studies on the regional fluid flow history. During large-scale studies, we mostly rely on two-dimensional lineaments obtained through structural mapping, outcrop analysis, or remote sensing. An efficient and widely applicable framework for discontinuity network characterization should therefore be based on the analysis of the frequently available two-dimensional data sets.& & & & Here, we present an automated framework for efficient and robust characterization of the geometric and topologic parameters of discontinuity networks. The geometry of the lineaments is characterised based on orientation, length, and sinuosity. The underlying distribution of these parameters are determined, and representative probability density functions are reported. The connection between the geometric parameters is validated, e.g. correlation between orientation and length. The spatial arrangement is determined by classical line- and window-s ling, by assessing the fractal dimension, and via graph-based topology analysis.& & & & In addition to the statistical analysis of lineament networks, we show how the graph data structure can be utilized for further characterization by linking it to raster data such as magnetic, gravimetric, or elevation. This procedure not only yields an additional means for lineament characterization but also allows users to assess dominant pathways based, for instance, on hydraulic gradients. We demonstrate the applicability of our algorithm on synthetic data sets and real-world case studies on mapped fault and fracture networks.& & & & We finally show how our framework can also be utilized to design detailed numerical studies on the fluid flow properties of analysed networks by conditioning mesh refinement on the type and number of intersections. In addition, due to known scaling relationships our framework can help to determine appropriate parameters for the simulations. We provide ex les of statistically parametrized fluid flow simulations in natural discontinuity networks and show the impact of conceptualizing the lineaments as conduits, barriers or conduit-barrier systems.& &
Publisher: Elsevier BV
Date: 05-2013
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 07-2021
Publisher: Springer Science and Business Media LLC
Date: 08-06-2020
DOI: 10.1007/S11004-020-09867-0
Abstract: The particularities of geosystems and geoscience data must be understood before any development or implementation of statistical learning algorithms. Without such knowledge, the predictions and inferences may not be accurate and physically consistent. Accuracy, transparency and interpretability, credibility, and physical realism are minimum criteria for statistical learning algorithms when applied to the geosciences. This study briefly reviews several characteristics of geoscience data and challenges for novel statistical learning algorithms. A novel spatial spectral clustering approach is introduced to illustrate how statistical learners can be adapted for modelling geoscience data. The spatial awareness and physical realism of the spectral clustering are improved by utilising a dissimilarity matrix based on nonparametric higher-order spatial statistics. The proposed model-free technique can identify meaningful spatial clusters (i.e. meaningful geographical subregions) from multivariate spatial data at different scales without the need to define a model of co-dependence. Several mixed (e.g. continuous and categorical) variables can be used as inputs to the proposed clustering technique. The proposed technique is illustrated using synthetic and real mining datasets. The results of the case studies confirm the usefulness of the proposed method for modelling spatial data.
Publisher: Copernicus GmbH
Date: 19-06-2020
Publisher: CSIRO
Date: 2016
Publisher: Australian Water Association
Date: 2018
Publisher: Elsevier BV
Date: 2022
Publisher: Elsevier BV
Date: 11-2013
Publisher: American Geophysical Union (AGU)
Date: 08-2010
DOI: 10.1029/2009WR008353
Publisher: American Geophysical Union (AGU)
Date: 03-2022
DOI: 10.1029/2021WR030963
Abstract: Sustainable management of groundwater relies on robust estimates of aquifer fluxes. As such, recharge can be estimated from groundwater age using analytical models assuming a homogeneous aquifer. We propose to assess how this assumption affects recharge estimates in fluvial deposits. First, we use CHILD, a landscape evolution model, to generate plausible deposits after tens of thousands of years of river evolution. Then, we use the fractional packing model to compute porosity and permeability of unconsolidated sediments assuming a mixture of a coarse and a fine fraction. Finally, we use PFLOTRAN to simulate groundwater flow and mean age over two thousand years of spatially uniform recharge. This reproduces the boundary conditions underlying Vogel's analytical model to estimate recharge rates. Our results are based on 20,000 realizations from varying inputs: grain sizes, aggradation rate of the river, porosity, recharge, etc. For most models, the mean estimate of the recharge rate remains below an absolute error of 25%. But fluvial heterogeneities can lead to local errors ranging from close to zero to several thousand percent, which means that estimates may vary drastically depending on s ling locations. A sensitivity analysis using the δ ‐importance measure and CUSUNORO curves exposes how fluvial processes influence the range and distribution of the error. For instance, a high bank erodibility and small coarse grain size under low aggradation rates favor the reworking of deposits, which increases heterogeneity and leads to high errors. This work paves the way for a more reliable estimation of recharge by considering the sedimentary context.
Publisher: Copernicus GmbH
Date: 09-09-2020
DOI: 10.5194/HESS-24-4353-2020
Abstract: Abstract. Identifying and quantifying recharge processes linked to ephemeral surface water features is challenging due to their episodic nature. We use a combination of well-established near-surface geophysical methods to provide evidence of a surface and groundwater connection under a small ephemeral recharge feature in a flat, semi-arid region near Adelaide, Australia. We use a seismic survey to obtain P-wave velocity through travel-time tomography and S-wave velocity through the multichannel analysis of surface waves. The ratios between P-wave and S-wave velocities are used to calculate Poisson's ratio, which allow us to infer the position of the water table. Separate geophysical surveys were used to obtain electrical conductivity measurements from time-domain electromagnetics and water contents from downhole nuclear magnetic resonance. The geophysical observations provide evidence to support a groundwater mound underneath a subtle ephemeral surface water feature. Our results suggest that recharge is localized and that small-scale ephemeral features may play an important role in groundwater recharge. Furthermore, we show that a combined geophysical approach can provide a perspective that helps shape the hydrogeological conceptualization of a semi-arid region.
Publisher: Wiley
Date: 25-02-2022
DOI: 10.1111/GWAT.13185
Abstract: Graphical methods have been widely used for visualization, classification, and interpretation of aqueous geochemical data to obtain a better understanding of surface and subsurface hydrologic systems. This method note presents WQChartPy, an open-source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data. WQChartPy can handle various data formats including Microsoft Excel, comma-separated values (CSV), and general delimited text. The 12 diagrams include eight traditional diagrams (trilinear Piper, Durov, Stiff, Chernoff face, Schoeller, Gibbs, Chadha, and Gaillardet) and four recently proposed diagrams (rectangle Piper, color-coded Piper, contour-filled Piper, and HFE-D) that have not been implemented in existing graphing software. The diagrams generated by WQChartPy can be saved as portable network graphics (PNG), scalable vector graphics (SVG), or portable document format (PDF) files for scientific publications. Jupyter and Google Colab notebooks are available online to illustrate how to use WQChartPy with ex le datasets. The geochemical diagrams can be generated with several lines of Python codes. Source codes of WQChartPy are publicly available at GitHub (yangfsu/WQChartPy) and PyPI (roject/wqchartpy/).
Publisher: American Geophysical Union (AGU)
Date: 02-2014
DOI: 10.1002/2012WR013424
Publisher: American Society of Civil Engineers (ASCE)
Date: 2020
Publisher: Copernicus GmbH
Date: 03-04-2014
DOI: 10.5194/HESS-18-1273-2014
Abstract: Abstract. Accounting for groundwater recharge from overbank flooding is required to reduce uncertainty and error in river-loss terms and groundwater sustainable-yield calculations. However, continental- and global-scale models of surface water–groundwater interactions rarely include an explicit process to account for overbank flood recharge (OFR). This paper upscales previously derived analytical equations to a continental scale using national soil atlas data and satellite imagery of flood inundation, resulting in recharge maps for seven hydrologically distinct Australian catchments. Recharge for three of the catchments was validated against independent recharge estimates from bore hydrograph responses and one catchment was additionally validated against point-scale recharge modelling and catchment-scale change in groundwater storage. Flood recharge was predicted for four of the seven catchments modelled, but there was also unexplained recharge present from the satellite's flood inundation mapping data. At a catchment scale, recharge from overbank flooding was somewhat under-predicted using the analytical equations, but there was good confidence in the spatial patterns of flood recharge produced. Due to the scale of the input data, there were no significant relationships found when compared at a point scale. Satellite-derived flood inundation data and uncertainty in soil maps were the key limitations to the accuracy of the modelled recharge. Use of this method to model OFR was found to be appropriate at a catchment to continental scale, given appropriate data sources. The proportion of OFR was found to be at least 4% of total change in groundwater storage in one of the catchments for the period modelled, and at least 15% of the riparian recharge. Accounting for OFR is an important, but often overlooked, requirement for closing water balances in both the surface water and groundwater domains.
Publisher: Elsevier BV
Date: 06-2017
Publisher: MDPI AG
Date: 15-07-2019
DOI: 10.3390/W11071463
Abstract: Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modelling. In this regard, hypothesis testing is essential to increase system understanding by refuting alternative conceptual models. Often a stepwise approach, with respect to complexity, is promoted but hypothesis testing of simple groundwater models is rarely applied. We present an approach to model-based Bayesian hypothesis testing in a simple groundwater balance model, which involves optimization of a model in function of both parameter values and conceptual model through trans-dimensional s ling. We apply the methodology to the Wildman River area, Northern Territory, Australia, where we set up 32 different conceptual models. A factorial approach to conceptual model development allows for direct attribution of differences in performance to in idual uncertain components of the conceptual model. The method provides a screening tool for prioritizing research efforts while also giving more confidence to the predicted water balance compared to a deterministic water balance solution. We show that the testing of alternative conceptual models can be done efficiently with a simple additive and linear groundwater balance model and is best done relatively early in the groundwater modelling workflow.
Publisher: Elsevier BV
Date: 12-2020
Publisher: Copernicus GmbH
Date: 19-06-2020
DOI: 10.5194/GMD-2020-106
Abstract: Abstract. This study introduces an efficient deep learning approach based on convolutional neural networks with joint autoencoder and adversarial structures for 3D subsurface mapping from surface observations. The method was applied to delineate palaeovalleys in an Australian desert landscape. The neural network was trained on a 6,400 km2 domain by using a land surface tomography as 2D input and an airborne electromagnetic (AEM)-derived probability map of palaeovalley presence as 3D output. The trained neural network has a maximum square error
Publisher: CSIRO
Date: 2017
Publisher: MDPI AG
Date: 14-12-2021
DOI: 10.3390/W13243588
Abstract: The trend to a hotter and drier climate, with more extended droughts, has been observed in recent decades in southern Australia and is projected to continue under climate change. This paper reviews studies on the projected impacts of climate change on groundwater and associated environmental assets in southern Australia, and describes groundwater planning frameworks and management responses. High-risk areas are spatially patchy due to highly saline groundwater or low-transmissivity aquifers. The proportional reduction in rainfall is lified in the groundwater recharge and some groundwater discharge fluxes. This leads to issues of deteriorating groundwater-dependent ecosystems, streamflow depletion, reduced submarine discharge, groundwater inundation and intrusion in coastal regions and reduced groundwater supply for extraction. Recent water reforms in Australia support the mitigation of these impacts, but groundwater adaptation is still at its infancy. Risk management is being incorporated in regional water and groundwater management plans to support a shift to a more sustainable level of use and more climate-resilient water resources in affected areas. The emerging strategies of groundwater trade and managed aquifer recharge are described, as is the need for a national water-focused climate change planning process.
Publisher: CSIRO
Date: 2017
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-6882
Abstract: & & 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 & #8216 conceptual model& #8217 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 ex les 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.& &
Publisher: Elsevier BV
Date: 12-2013
Publisher: Springer Science and Business Media LLC
Date: 03-02-2022
Publisher: Elsevier BV
Date: 10-2008
Publisher: Springer Science and Business Media LLC
Date: 20-09-2017
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-11688
Abstract: & & Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modelling. In this regard, hypothesis testing is essential to increase system understanding by analysing and refuting alternative conceptual models. We present a systematic approach to conceptual model development and testing, which involves defining alternative models and then attempting to refute the alternative understandings using independent data. The method aims at finding an ensemble of conceptual understandings that are consistent with prior knowledge and observational data, rather than tuning the parameters of a single conceptual model to conform with the data through inversion.& & & & The alternative understandings we test relate to the hydrological functioning of enclosed depressions in the landscape of the Wildman River Area, Northern Territory, Australia. These depressions provide potential for time-dependent surface water-groundwater interactions. Alternative models are developed representing the process structure and physical structure of the conceptual model of the depressions. Remote sensing data is used to test the process structure, while geophysical data is used to test the physical structure of the conceptual models.& & & & The remote sensing and geophysical data are used twice in the applied workflow. First in a model rejection step, where models whose priors are inconsistent with the observations are rejected and removed from the ensemble. Then the data are used to update the probability of the accepted alternative conceptual models.& & & & The updated conceptual model probabilities of the combined physical and process structures revealed the data indicated that the depressions act as preferential groundwater recharge features for three out of five depressions used as test case. For the fourth depression, the data is indecisive, and more testing would be needed to discriminate between model structures. For the fifth depression, all physical structures were rejected indicating that the model structure is still an unknown unknown.& & & & This insight into system functioning gained from testing alternative conceptual models can be used in future modelling exercises. With more confidence in the conceptual model, confidence in the predictions of future modelling exercise increase, which can that underpin environmental management decisions.& &
Publisher: American Geophysical Union (AGU)
Date: 12-2008
DOI: 10.1029/2008WR006921
Publisher: CSIRO Water for a Healthy Country
Date: 2012
Publisher: CSIRO
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 26-03-2015
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-16122
Abstract: Reliable predictions of water systems& #8217 response to external pressures and ongoing changes are highly important to ensure informed decision-making to support sustainable water resources management for human use and the functioning of healthy ecosystems. Recent strong development of numerical models offers a potential to understand and forecast water systems under anthropogenic and climatic influences to provide information for decision-making, process understanding of the & #8216 unseen& #8217 part of the water cycle and hazard risk analysis. However, the reliability of numerical model predictions is strongly influenced by various sources of uncertainties, data qualities and assumptions, and often lacks stakeholders' point-of-view. A new, improved approach is needed and in this paper, we present six basic principles to improve the reliability and accuracy of numerical water model predictions considering explicitly stakeholders' needs and, thereby, better serving the society. Six highlighted principles are: (i) clearly defining the objectives and the purpose of the model, sustaining them during the entire modelling process (ii) incorporating expert and local community knowledge through stakeholders' feedback (iii) implementing a multi-model approach in which a range of conceptualizations are explored (iv) considering and representing the uncertainties arising from model inputs, parameters, conceptual model structure and measurement/information error (v) translating the results to concrete and understandable strategies that policymakers can use for their informed decision-making and (vi) long term capacity building and monitoring data collection to reduce knowledge gaps, test and improve predictions. We argue that implementing these six principles reduces uncertainties, improves the predictive capacity of the numerical water models, and ensures informed decision-making to support sustainable water resources management and thereby serve society better.
Publisher: Elsevier BV
Date: 06-2018
Publisher: Wiley
Date: 02-04-2019
DOI: 10.1111/GWAT.12880
Publisher: Elsevier BV
Date: 05-2021
Publisher: Wiley
Date: 17-07-2017
DOI: 10.1111/GWAT.12565
Publisher: CSIRO
Date: 2012
Publisher: American Geophysical Union (AGU)
Date: 09-2012
DOI: 10.1029/2011WR011441
Abstract: Overbank flood recharge is increasingly acknowledged as important for estimations of aquifer sustainable yield. The physics of this process in areas with shallow groundwater, however, is not well understood and typically is not included in river or groundwater models. Modeling of the overbank flood recharge process was undertaken using a fully coupled, surface‐subsurface flow model to compare the volume of infiltration through a floodplain with varying surface sediment, aquifer, and flood parameters. The infiltration volume was found to increase with the conductance of the clogging layer (represented by a thin veneer of sediments across the floodplain and river bed), flood wave height, peak duration, and aquifer transmissivity and to decrease with increasing water table gradient (positive toward the river). The influence of the flood wave and aquifer hydraulic parameters was more pronounced in systems with sand or loam clogging layers. Irregularities in floodplain elevation had a large effect on infiltration volume. A dimensionless analysis of the flood recharge process identified the factors that limited flood infiltration for each of the modeled scenarios: the clogging layer conductance, unsaturated aquifer storage, or aquifer transmissivity. A dimensionless number F * was used to predict the limiting factor in floodplain systems. An analytical equation was developed to estimate the infiltration volume for catchments where full numerical modeling is not warranted or applicable. Results from the analytical equation compared favorably with recharge modeled using a more complex numerical model.
Publisher: American Geophysical Union (AGU)
Date: 08-2015
DOI: 10.1002/2015WR016967
Publisher: CSIRO Publishing
Date: 2021
DOI: 10.1071/AJ20037
Abstract: The Geological and Bioregional Assessment (GBA) Program assesses the potential impacts of shale, tight and deep coal gas development on water and the environment. The cause-and-effect relationships that allow unconventional gas resource development activities to lead to an impact on environmental values are often interrelated, leading to both direct and indirect impacts. The GBA Program developed an impact assessment methodology based on causal networks that explicitly map out these cause-and-effect relationships, including all direct and indirect impacts. This network approach is coupled with a spatially explicit, systematic evaluation of the likelihood, consequence, control and mitigation strategy for each cause-and-effect relationship. Combining these evaluations based on the network structures identifies which activities could lead to potential impacts. The assessment identifies where more detailed local-scale assessments, management and monitoring should focus, as well as where impacts are unlikely to occur.
Publisher: CSIRO
Date: 2018
Publisher: Copernicus GmbH
Date: 29-09-2014
DOI: 10.5194/HESS-18-3777-2014
Abstract: Abstract. The simulation of routing and distribution of water through a regulated river system with a river management model will quickly result in complex and nonlinear model behaviour. A robust sensitivity analysis increases the transparency of the model and provides both the modeller and the system manager with a better understanding and insight on how the model simulates reality and management operations. In this study, a robust, density-based sensitivity analysis, developed by Plischke et al. (2013), is applied to an eWater Source river management model. This sensitivity analysis methodology is extended to not only account for main effects but also for interaction effects. The combination of sensitivity indices and scatter plots enables the identification of major linear effects as well as subtle minor and nonlinear effects. The case study is an idealized river management model representing typical conditions of the southern Murray–Darling Basin in Australia for which the sensitivity of a variety of model outcomes to variations in the driving forces, inflow to the system, rainfall and potential evapotranspiration, is examined. The model outcomes are most sensitive to the inflow to the system, but the sensitivity analysis identified minor effects of potential evapotranspiration and nonlinear interaction effects between inflow and potential evapotranspiration.
Publisher: Elsevier BV
Date: 11-2018
Publisher: Copernicus GmbH
Date: 08-06-2021
Abstract: Abstract. This study introduces an efficient deep-learning model based on convolutional neural networks with joint autoencoder and adversarial structures for 3D subsurface mapping from 2D surface observations. The method was applied to delineate paleovalleys in an Australian desert landscape. The neural network was trained on a 6400 km2 domain by using a land surface topography as 2D input and an airborne electromagnetic (AEM)-derived probability map of paleovalley presence as 3D output. The trained neural network has a squared error .10 across 99 % of the training domain and produces a squared error .10 across 93 % of the validation domain, demonstrating that it is reliable in reconstructing 3D paleovalley patterns beyond the training area. Due to its generic structure, the neural network structure designed in this study and the training algorithm have broad application potential to construct 3D geological features (e.g., ore bodies, aquifer) from 2D land surface observations.
Publisher: Copernicus GmbH
Date: 13-06-2019
DOI: 10.5194/HESS-23-2561-2019
Abstract: Abstract. Paleovalleys are buried ancient river valleys that often form productive aquifers, especially in the semiarid and arid areas of Australia. Delineating their extent and hydrostratigraphy is however a challenging task in groundwater system characterization. This study developed a methodology based on the deep learning super-resolution convolutional neural network (SRCNN) approach, to convert electrical conductivity (EC) estimates from an airborne electromagnetic (AEM) survey in South Australia to a high-resolution binary paleovalley map. The SRCNN was trained and tested with a synthetic training dataset, where valleys were generated from readily available digital elevation model (DEM) data from the AEM survey area. Electrical conductivities typical of valley sediments were generated by Archie's law, and subsequently blurred by down-s ling and bicubic interpolation to represent noise from the AEM survey, inversion and interpolation. After a model training step, the SRCNN successfully removed such noise, and reclassified the low-resolution, converted unimodal but skewed EC values into a high-resolution paleovalley index following a bimodal distribution. The latter allows us to distinguish valley from non-valley pixels. Furthermore, a realistic spatial connectivity structure of the paleovalley was predicted when compared with borehole lithology logs and a valley bottom flatness indicator. Overall the methodology permitted us to better constrain the three-dimensional paleovalley geometry from AEM images that are becoming more widely available for groundwater prospecting.
Publisher: Elsevier BV
Date: 06-2012
Publisher: CSIRO Land and Water
Date: 2012
Publisher: CSIRO
Date: 2021
DOI: 10.25919/4CBW-KK97
No related grants have been discovered for Luk Peeters.