ORCID Profile
0000-0002-8820-2808
Current Organisations
Aegis School of Data Science
,
VK Consulting and Training
,
Indian Institute of Technology Bombay
,
University of Lausanne
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Publications
When Should We Use Multiple-Point Geostatistics?
Publisher: Springer International Publishing
Date: 2018
Estimation of deep infiltration in unsaturated limestone environments using cave lidar and drip count data
Publisher: Copernicus GmbH
Date: 20-01-2016
Abstract: Abstract. Limestone aeolianites constitute karstic aquifers covering much of the western and southern Australian coastal fringe. They are a key groundwater resource for a range of industries such as winery and tourism, and provide important ecosystem services such as habitat for stygofauna. Moreover, recharge estimation is important for understanding the water cycle, for contaminant transport, for water management, and for stalagmite-based paleoclimate reconstructions. Caves offer a natural inception point to observe both the long-term groundwater recharge and the preferential movement of water through the unsaturated zone of such limestone. With the availability of automated drip rate logging systems and remote sensing techniques, it is now possible to deploy the combination of these methods for larger-scale studies of infiltration processes within a cave. In this study, we utilize a spatial survey of automated cave drip monitoring in two large chambers of Golgotha Cave, south-western Western Australia (SWWA), with the aim of better understanding infiltration water movement and the relationship between infiltration, stalactite morphology, and unsaturated zone recharge. By applying morphological analysis of ceiling features from Terrestrial LiDAR (T-LiDAR) data, coupled with drip time series and climate data from 2012 to 2014, we demonstrate the nature of the relationships between infiltration through fractures in the limestone and unsaturated zone recharge. Similarities between drip rate time series are interpreted in terms of flow patterns, cave chamber morphology, and lithology. Moreover, we develop a new technique to estimate recharge in large-scale caves, engaging flow classification to determine the cave ceiling area covered by each flow category and drip data for the entire observation period, to calculate the total volume of cave discharge. This new technique can be applied to other cave sites to identify highly focussed areas of recharge and can help to better estimate the total recharge volume.
Stalagmite evidence for Early Holocene multidecadal hydroclimate variability in Ethiopia
Publisher: California Digital Library (CDL)
Date: 22-09-2021
DOI: 10.31223/X5SW5D
Abstract: A multiproxy oxygen and carbon isotope (d13C and d18O), growth rate and trace element stalagmite paleoenvironmental record is presented for the Early Holocene from Achere Cave, Ethiopia. The annually laminated stalagmite grew from 10.6 to 10.4 ka, and from 9.7 to 9.0 ka with a short hiatus at ~9.25 ka. Using oxygen and carbon isotopic, and cave monitoring data, we demonstrate that the stalagmite deposition is out of isotopic equilibrium, yet trace element and isotope geochemistry is sensitive to hydroclimate variability. Variogram analysis of annual growth rate data suggests that this proxy can only contain hydroclimate information over less than 28-year timescales. Statistically significant and coherent spectral frequencies in d13C and d18O are observed at 15-25 and 19-23 years respectively. Combined with compelling evidence for deposition out of isotope equilibrium, the observed ~1 ‰ litude variability in stalagmite d18O is likely forced by non-equilibrium deposition, likely due to kinetic effects during the progressive degassing of CO2 from the water film during stalagmite formation. These frequencies are similar to the periodicity reported for Holocene stalagmite records from Ethiopian caves, suggesting that multidecadal variability in stalagmite d18O is typical. We hypothesise that a hydroclimate forcing, such as runs of one or more years of low annual rainfall, is likely to be the primary control on the extent of the partial evaporation of soil and shallow epikarst water, which is subsequently modulated by karst hydrology, and the extent of in-cave non-equilibrium stalagmite deposition. Combined with possible recharge-biases in drip water d18O, modulated by karst hydrology, these processes can generate multidecadal d18O variability which can operate with opposite signs. Comparison of Early Holocene d18O stalagmite records from the monsoon regions of Ethiopia, Oman and central China show different multi-decadal d18O signals, implying regional difference in climate forcing. Seismic activities due to the active tectonics in the region control the frequency of growth gaps (hiatuses) by changing the water flow paths to the stalagmite.
Quantifying the value of laminated stalagmites for paleoclimate reconstructions
Publisher: American Geophysical Union (AGU)
Date: 03-2012
DOI: 10.1029/2012GL050986
Mapping the hydraulic connection between a coalbed and adjacent aquifer: example of the coal-seam gas resource area, north Galilee Basin, Australia
Publisher: Springer Science and Business Media LLC
Date: 26-07-2016
Extrapolating the Fractal Characteristics of an Image Using Scale-Invariant Multiple-Point Statistics
Publisher: Springer Science and Business Media LLC
Date: 10-2011
Special Issue on 20 Years of Multiple-Point Statistics: Part 1
Publisher: Springer Science and Business Media LLC
Date: 02-2014
Simulating Fully‐Integrated Hydrological Dynamics in Complex Alpine Headwaters: Potential and Challenges
Publisher: American Geophysical Union (AGU)
Date: 29-03-2022
DOI: 10.1029/2020WR029390
Abstract: Highly simplified approaches continue to underpin hydrological climate change impact assessments across the Earth's mountainous regions. Fully‐integrated surface‐subsurface models may hold far greater potential to represent the distinctive regimes of steep, geologically‐complex headwater catchments. However, their utility has not yet been tested across a wide range of mountainous settings. Here, an integrated model of two adjacent calcareous Alpine headwaters that accounts for two‐dimensional surface flow, three‐dimensional (3D) variably‐saturated groundwater flow, and evapotranspiration is presented. An energy balance‐based representation of snow dynamics contributed to the model's high‐resolution forcing data, and a sophisticated 3D geological model helped to define and parameterize its subsurface structure. In the first known attempt to calibrate a catchment‐scale integrated model of a mountainous region automatically, numerous uncertain model parameters were estimated. The salient features of the hydrological regime could ultimately be satisfactorily reproduced – over an 11‐month evaluation period, the Nash‐Sutcliffe efficiency of simulated streamflow at the main gauging station was 0.76. Spatio‐temporal visualization of the forcing data and simulated responses further confirmed the model's broad coherence. Presumably due to unresolved local subsurface heterogeneity, closely replicating the somewhat contrasting groundwater level signals observed near to one another proved more elusive. Finally, we assessed the impacts of various simplifications and assumptions that are commonly employed in physically‐based modeling – including the use of spatially uniform forcings, a vertically limited model domain, and global geological data products – on key simulated outputs, finding strongly affected model performance in many cases. Although certain outstanding challenges must be overcome if the uptake of integrated models in mountain regions around the world is to increase, our work demonstrates the feasibility and benefits of their application in such complex systems.
Nurturing a growing field: Computers & Geosciences
Publisher: Elsevier BV
Date: 10-2017
Truncated Plurigaussian Simulations to Characterize Aquifer Heterogeneity
Publisher: Wiley
Date: 2009
DOI: 10.1111/J.1745-6584.2008.00489.X
Abstract: Integrating geological concepts, such as relative positions and proportions of the different lithofacies, is of highest importance in order to render realistic geological patterns. The truncated plurigaussian simulation method provides a way of using both local and conceptual geological information to infer the distributions of the facies and then those of hydraulic parameters. The method (Le Loc'h and Galli 1994) is based on the idea of truncating at least two underlying multi-Gaussian simulations in order to create maps of categorical variable. In this article, we show how this technique can be used to assess contaminant migration in highly heterogeneous media. We illustrate its application on the biggest contaminated site of Switzerland. It consists of a contaminant plume located in the lower fresh water Molasse on the western Swiss Plateau. The highly heterogeneous character of this formation calls for efficient stochastic methods in order to characterize transport processes.
Semi-parametric resampling with extremes
Publisher: Elsevier BV
Date: 04-2021
The Properties of Annually Laminated Stalagmites-A Global Synthesis
Publisher: American Geophysical Union (AGU)
Date: 19-04-2021
DOI: 10.1029/2020RG000722
Abstract: Annually laminated speleothems have the potential to provide information on high‐frequency climate variability and, simultaneously, provide good chronological constraints. However, there are distinct types of speleothem annual laminae, from physical to chemical, and a common mechanism that links their formation has yet to be found. Here, we analyzed annually laminated stalagmites from 23 caves and 6 continents with the aim to find if there are common mechanisms underlying their development. Annually laminated stalagmites are least common in arid and semiarid climates, and most common in regions with a seasonality of precipitation. At a global scale, we observe faster growth rates with increasing mean annual temperature and decreasing latitude. Changepoints in average growth rates are infrequent and age‐depth relationships demonstrate that growth rates can be approximated to be constant. In general, annually laminated stalagmites are characterized by centennial‐scale stability in calcite precipitation due to a sufficiently large and well‐mixed water source, a time series spectrum showing first‐order autoregression due to mixing of stored water and annual recharged water, and an inter‐annual flickering of growth acceleration, bringing growth rates back to the long‐term mean. Climate forcing of growth rate variations is observed where a multi‐year climate signal is strong enough to be the dominant control on calcite growth rate variability, such that it retains a climate imprint after smoothing of this signal by mixing of stored water. In contrast, long‐term constant growth rate of laminated stalagmites adds further robustness to their unparalleled capacity to improve accuracy of chronology building.
Chaos and irregularity in karst percolation
Publisher: American Geophysical Union (AGU)
Date: 14-12-2012
DOI: 10.1029/2012GL054270
Spatiotemporal reconstruction of gaps in multivariate fields using the direct sampling approach
Publisher: American Geophysical Union (AGU)
Date: 10-2012
DOI: 10.1029/2012WR012115
Abstract: The development of spatially continuous fields from sparse observing networks is an outstanding problem in the environmental and Earth sciences. Here we explore an approach to produce spatially continuous fields from discontinuous data that focuses on reconstructing gaps routinely present in satellite‐based Earth observations. To assess the utility of the approach, we use synthetic imagery derived from a regional climate model of southeastern Australia. Orbital tracks, scan geometry influences, and atmospheric artifacts are artificially imposed upon these model simulations to examine the techniques' capacity to reproduce realistic and representative retrievals. The imposed discontinuities are reconstructed using a direct s ling technique and are compared against the original continuous model data: a synthetic simulation experiment. Results indicate that the multipoint geostatistical gap‐filling approach produces texturally realistic spatially continuous fields from otherwise discontinuous data sets. Reconstruction results are assessed through comparison of spatial distributions, as well as through visual assessment of fine‐scale features. Complex spatial patterns and fine‐scale structure can be resolved within the reconstructions, illustrating that the often nonlinear dependencies between variables can be maintained. The stochastic nature of the methodology makes it possible to expand the approach within a Monte Carlo framework in order to estimate the uncertainty related to subsequent reconstructions. From a practical perspective, the reconstruction method is straightforward and requires minimum user intervention for parameter adjustment. As such, it can be automated to systematically process real time remote sensing measurements.
Merging radar and in situ rainfall measurements: An assessment of different combination algorithms
Publisher: American Geophysical Union (AGU)
Date: 10-2016
DOI: 10.1002/2015WR018441
Training Images from Process-Imitating Methods
Publisher: Springer Science and Business Media LLC
Date: 03-12-2013
Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion
Publisher: Oxford University Press (OUP)
Date: 30-12-2015
DOI: 10.1093/GJI/GGV517
WlCount: Geological lamination detection and counting using an image analysis approach
Publisher: Elsevier BV
Date: 03-2022
Modeling complex geological structures with elementary training images and transform‐invariant distances
Publisher: American Geophysical Union (AGU)
Date: 07-2011
DOI: 10.1029/2011WR010412
Conditioning multiple-point statistics simulations to block data
Publisher: Elsevier BV
Date: 05-2016
MPS-APO: a rapid and automatic parameter optimizer for multiple-point geostatistics
Publisher: Springer Science and Business Media LLC
Date: 29-10-2019
Application of 1D paleo-fluvial process modelling at a basin scale to augment sparse borehole data: Example of a Permian formation in the Galilee Basin, Australia
Publisher: Wiley
Date: 29-12-2016
DOI: 10.1002/HYP.10747
Quantifying year-round nocturnal bird migration with a fluid dynamics model
Publisher: Cold Spring Harbor Laboratory
Date: 13-10-2020
DOI: 10.1101/2020.10.13.321844
Abstract: The movements of migratory birds constitute huge biomass flows that influence ecosystems and human economy, agriculture and health through the transport of energy, nutrients, seeds, and parasites. To better understand the influence on ecosystems and the corresponding services and disservices, we need to characterize and quantify the migratory movements at various spatial and temporal scales. Representing the flow of birds in the air as a fluid, we applied a flow model to interpolated maps of bird density and velocity retrieved from the European weather radar network, covering almost a full year. Using this model, we quantified how many birds take-off, fly, and land across Western Europe, (1) to track waves of bird migration between nights, (2) cumulate the number of bird on the ground and (3) quantify the seasonal flow into and out of the study area through several regional transects. Our results show that up to 188 million (M) birds take-off over a single night. Exemplarily, we tracked a migration wave in spring, in which birds crossed the study area in 4 days with nocturnal flights of approximately 300 km. Over the course of a season, we estimated that 494 million (M) birds entered through the southern transects and, at the same time, 251 M left in the northern transects, creating a surplus of 243 M birds within the study area. Similarly, in autumn, 544 M more birds departed than arrived: 314 M birds entered through the northern transects while 858 M left through the southern transects. Our study show-cases the potential of combining interdisciplinary data and methods to elucidate the dynamics of avian migration from nightly to seasonal and yearly time-scales and from regional to continental spatial scales.
Downscaling Multispectral Satellite Images Without Colocated High-Resolution Data: A Stochastic Approach Based on Training Images
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research
Publisher: Springer Science and Business Media LLC
Date: 30-10-2018
Abstract: Certain applications, such as understanding the influence of bedrock geology on hydrology in complex mountainous settings, demand 3D geological models that are detailed, high-resolution, accurate, and spatially-extensive. However, developing models with these characteristics remains challenging. Here, we present a dataset corresponding to a renowned tectonic entity in the Swiss Alps - the Nappe de Morcles - that does achieve these criteria. Locations of lithological interfaces and formation orientations were first extracted from existing sources. Then, using state-of-the-art algorithms, the interfaces were interpolated. Finally, an iterative process of evaluation and re-interpolation was undertaken. The geology was satisfactorily reproduced modelled interfaces correspond well with the input data, and the estimated volumes seem plausible. Overall, 18 formations, including their associated secondary folds and selected faults, are represented at 10 m resolution. Numerous environmental investigations in the study area could benefit from the dataset indeed, it is already informing integrated hydrological (snow/surface-water/groundwater) simulations. Our work demonstrates the potential that now exists to develop complex, high-quality geological models in support of contemporary Alpine research, augmenting traditional geological information in the process.
Reconstructing the climatic niche breadth of land use for animal production during the African Holocene
Publisher: Wiley
Date: 23-10-2019
DOI: 10.1111/GEB.13015
Efficient multi-objective calibration and uncertainty analysis of distributed snow simulations in rugged alpine terrain
Publisher: Elsevier BV
Date: 07-2021
Simulation of rainfall time series from different climatic regions using the direct sampling technique
Publisher: Copernicus GmbH
Date: 14-08-2014
DOI: 10.5194/HESS-18-3015-2014
Abstract: Abstract. The direct s ling technique, belonging to the family of multiple-point statistics, is proposed as a nonparametric alternative to the classical autoregressive and Markov-chain-based models for daily rainfall time-series simulation. The algorithm makes use of the patterns contained inside the training image (the past rainfall record) to reproduce the complexity of the signal without inferring its prior statistical model: the time series is simulated by s ling the training data set where a sufficiently similar neighborhood exists. The advantage of this approach is the capability of simulating complex statistical relations by respecting the similarity of the patterns at different scales. The technique is applied to daily rainfall records from different climate settings, using a standard setup and without performing any optimization of the parameters. The results show that the overall statistics as well as the dry/wet spells patterns are simulated accurately. Also the extremes at the higher temporal scale are reproduced adequately, reducing the well known problem of overdispersion.
Multiple-point geostatistical simulation using the bunch-pasting direct sampling method
Publisher: Elsevier BV
Date: 04-2013
The imaginary carrot: no correlation between raising funds and research productivity in geosciences
Publisher: Springer Science and Business Media LLC
Date: 05-02-2021
DOI: 10.1007/S11192-020-03855-1
Abstract: The ability of researchers to raise funding is central to academic achievement. However, whether success in obtaining research funds correlates with the productivity, quality or impact of a researcher is debated. Here we analyse 10 years of grant funding by the Swiss National Science Foundation in Earth and Environmental Sciences, and compare it to the publication record of the researchers who were awarded the funds. No significant statistical correlation can be established between the publication or citation record of a researcher and the amount of money this researcher obtains in grant funding. These results imply that researchers successful in raising funds are not necessarily in a position to be more productive or produce more impactful publications. Those results should be considered for deciding whether to use grant funding as a criterion for career advancement procedures.
Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research
Publisher: Elsevier BV
Date: 05-2014
Smart pilot points using reversible‐jump Markov‐chain Monte Carlo
Publisher: American Geophysical Union (AGU)
Date: 05-2016
DOI: 10.1002/2015WR017922
Determination of vertical hydraulic conductivity of aquitards in a multilayered leaky system using water-level signals in adjacent aquifers
Publisher: Elsevier BV
Date: 09-2013
A geostatistical approach to estimate high resolution nocturnal bird migration densities from a weather radar network
Publisher: Cold Spring Harbor Laboratory
Date: 02-07-2019
DOI: 10.1101/690065
Abstract: Quantifying nocturnal bird migration at high resolution is essential for (1) understanding the phenology of migration and its drivers, (2) identifying critical spatio-temporal protection zones for migratory birds, and (3) assessing the risk of collision with man-made structures. We propose a tailored geostatistical model to interpolate migration intensity monitored by a network of weather radars. The model is applied to data collected in autumn 2016 from 69 European weather radars. To cross-validate the model, we compared our results with independent measurements of two bird radars. Our model estimated bird densities at high resolution (0.2°latitude-longitude, 15min) and assessed the associated uncertainty. Within the area covered by the radar network, we estimated that around 120 million birds were simultaneously in flight [10-90 quantiles: 107-134]. Local estimations can be easily visualized and retrieved from a dedicated interactive website: birdmigrationmap.vogelwarte.ch . This proof-of-concept study demonstrates that a network of weather radar is able to quantify bird migration at high resolution and accuracy. The model presented has the ability to monitor population of migratory birds at scales ranging from regional to continental in space and daily to yearly in time. Near-real-time estimation should soon be possible with an update of the infrastructure and processing software.
Publisher Correction: A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research
Publisher: Springer Science and Business Media LLC
Date: 13-12-2019
DOI: 10.1038/S41597-019-0298-9
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
A new global database to improve predictions of permeability distribution in crystalline rocks at site scale
Publisher: American Geophysical Union (AGU)
Date: 05-2017
DOI: 10.1002/2017JB014106
Analogue-based colorization of remote sensing images using textural information
Publisher: Elsevier BV
Date: 2019
Feature-preserving interpolation and filtering of environmental time series
Publisher: Elsevier BV
Date: 10-2015
Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets
Publisher: American Geophysical Union (AGU)
Date: 2020
DOI: 10.1029/2019WR026085
Abstract: Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow‐only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias‐insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (−7%) and terrestrial water storage (−6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme.
Handbook of Engineering Hydrology
Publisher: CRC Press
Date: 21-03-2014
DOI: 10.1201/B16683
Bayesian inverse problem and optimization with iterative spatial resampling
Publisher: American Geophysical Union (AGU)
Date: 11-2010
DOI: 10.1029/2010WR009274
Integrating collocated auxiliary parameters in geostatistical simulations using joint probability distributions and probability aggregation
Publisher: American Geophysical Union (AGU)
Date: 08-2009
DOI: 10.1029/2008WR007408
Influence of Alluvial Morphology on Upscaled Hydraulic Conductivity
Publisher: Wiley
Date: 19-10-2015
DOI: 10.1111/GWAT.12378
Abstract: The hydraulic conductivity of aquifers is a key parameter controlling the interactions between resource exploitation activities, such as unconventional gas production and natural groundwater systems. Furthermore, this parameter is often poorly constrained by typical data used for regional groundwater modeling and calibration studies performed as part of impact assessments. In this study, a systematic investigation is performed to understand the correspondence between the lithological descriptions of channel-type formation and the bulk effective hydraulic conductivities at a larger scale (Kxeff , Kyeff , and Kzeff in the direction of channel cross section, along the channel and in the vertical directions, respectively). This will inform decisions on what additional data gathering and modeling of the geological system can be performed to allow the critical bulk properties to be more accurately predicted. The systems studied are conceptualized as stacked meandering channels formed in an alluvial plain, and are represented as two facies. Such systems are often studied using very detailed numerical models. The main factors that may influence Kxeff , Kyeff , and Kzeff are the proportion of the facies representing connected channels, the aspect ratio of the channels, and the difference in hydraulic conductivity between facies. Our results show that in most cases, Kzeff is only weakly dependent on the orientations of channelized structures, with the main effects coming from channel aspect ratio and facies proportion.
3D fluid flow in fault zones of crystalline basement rocks (Poehla‐Tellerhaeuser Ore Field, Ore Mountains, Germany)
Publisher: Wiley
Date: 17-09-2016
DOI: 10.1111/GFL.12192
Spatial Sensitivity Analysis of Simulated Land Surface Patterns in a Catchment Model Using a Set of Innovative Spatial Performance Metrics
Publisher: American Meteorological Society
Date: 04-2017
Abstract: Distributed hydrological models simulate states and fluxes of water and energy in the terrestrial hydrosphere at each cell. The predicted spatial patterns result from complex nonlinear relationships and feedbacks. Spatial patterns are often neglected during the modeling process, and therefore a spatial sensitivity analysis framework that highlights their importance is proposed. This study features a comprehensive analysis of spatial patterns of actual evapotranspiration (ET) and land surface temperature (LST), with the aim of quantifying the extent to which forcing data and model parameters drive these patterns. This framework is applied on a distributed model [MIKE Système Hydrologique Européen (MIKE SHE)] coupled to a land surface model [Shuttleworth and Wallace–Evapotranspiration (SW-ET)] of a catchment in Denmark. Twenty-two scenarios are defined, each having a simplified representation of a potential driver of spatial variability. A baseline model that incorporates full spatial detail is used to assess sensitivity. High sensitivity can be attested in scenarios where the simulated spatial patterns differ significantly from the baseline. The core novelty of this study is that the analysis is based on a set of innovative spatial performance metrics that enable a reliable spatial pattern comparison. Overall, LST is very sensitive to air temperature and wind speed whereas ET is rather driven by vegetation. Both are sensitive to groundwater coupling and precipitation. The conclusions may be limited to the selected catchment and to the applied modeling system, but the suggested framework is generically relevant for the modeling community. While the applied metrics focus on specific spatial information, they partly exhibit redundant information. Thus, a combination of metrics is the ideal approach to evaluate spatial patterns in models outputs.
Fast and Interactive Editing Tools for Spatial Models
Publisher: Springer Science and Business Media LLC
Date: 12-09-2018
Stochastic reconstruction of paleovalley bedrock morphology from sparse datasets
Publisher: Elsevier BV
Date: 03-2014
Downscaling Images with Trends Using Multiple-Point Statistics Simulation: An Application to Digital Elevation Models
Publisher: Springer Science and Business Media LLC
Date: 08-2019
Determining the evolution of an alpine glacier drainage system by solving inverse problems
Publisher: Cambridge University Press (CUP)
Date: 22-01-2021
DOI: 10.1017/JOG.2020.116
Abstract: Our understanding of the subglacial drainage system has improved markedly over the last decades due to field observations and numerical modelling. However, integrating data into increasingly complex numerical models remain challenging. Here we infer two-dimensional subglacial channel networks and hydraulic parameters for Gorner Glacier, Switzerland, based on available field data at five specific times (snapshots) across the melt season of 2005. The field dataset is one of the most complete available, including borehole water pressure, tracer experiments and meteorological variables. Yet, these observations are still too sparse to fully characterize the drainage system and thus, a unique solution is neither expected nor desirable. We use a geostatistical generator and a steady-state water flow model to produce a set of subglacial channel networks that are consistent with measured water pressure and tracer-transit times. Field data are used to infer hydraulic and morphological parameters of the channels under the assumption that the location of channels persists during the melt season. Results indicate that it is possible to identify locations where subglacial channels are more likely. In addition, we show that different network structures can equally satisfy the field data, which support the use of a stochastic approach to infer unobserved subglacial features.
Utilizing spatial association analysis to determine the number of multiple grids for multiple-point statistics
Publisher: Elsevier BV
Date: 08-2016
Analog-based meandering channel simulation
Publisher: American Geophysical Union (AGU)
Date: 02-2014
DOI: 10.1002/2013WR013730
Which Path to Choose in Sequential Gaussian Simulation
Publisher: Springer Science and Business Media LLC
Date: 09-08-2017
Improving in situ data acquisition using training images and a Bayesian mixture model
Publisher: Elsevier BV
Date: 06-2016
Accelerating geostatistical simulations using graphics processing units (GPU)
Publisher: Elsevier BV
Date: 09-2012
Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework
Publisher: Elsevier BV
Date: 11-2016
Investigating extreme scenarios with multiple-point geostatistics and variance maximization
Publisher: Springer Science and Business Media LLC
Date: 16-12-2019
Parameterization of training images for aquifer 3‐D facies modeling integrating geological interpretations and statistical inference
Publisher: American Geophysical Union (AGU)
Date: 10-2014
DOI: 10.1002/2013WR014949
Multiple‐Point Geostatistics
Publisher: Wiley
Date: 22-10-2014
EUPollMap: The European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach
Publisher: California Digital Library (CDL)
Date: 30-01-2023
DOI: 10.31223/X52650
Abstract: Modern and fossil pollen data are widely used in paleoenvironmental research to characterise past environmental changes in a given location. However, their discrete and discontinuous nature can limit the inferences that can be made from them. In contrasts, deriving continuous spatial maps of the pollen presence from point-based datasets would enable more robust regional characterization of such past changes. To address this problem, we propose a comprehensive collection of European pollen presence maps including 194 pollen taxa derived from the interpolation of pollen data from the Eurasian Modern Pollen Database (EMPD v2) restricted to the Euro-Mediterranean Basin. To do so, we developed an automatic Kriging-based interpolation workflow to select an optimal geostatistical model describing the spatial variability for each taxon. The output of the interpolation model consists in a series of multivariate predictive maps of Europe at 25-km scale, showing the occurrence probability of pollen taxa, the predicted presence based on erse probability thresholds, and the interpolation uncertainty for each taxon. Visual inspections of the maps and systematic cross-validation tests showed that the ensemble of predictions is reliable even in data-scarce regions, with a relatively low uncertainty, and robust to complex and non-stationary pollen distributions. The maps, freely distributed as GeoTIFF files, are proposed as a ready-to-use tool for spatial paleoenvironmental characterization. Since the interpolation model only uses the coordinates of the observation to spatialise the data, similar maps could also be derived for fossil pollen records, thus enabling the spatial characterization of past changes, and possibly, their subsequent use for quantitative paleoclimate reconstructions.
Simulating fully-integrated hydrological dynamics in complex Alpine headwaters: potential and challenges
Publisher: California Digital Library (CDL)
Date: 09-02-2022
DOI: 10.31223/X5RG7Q
Abstract: Highly simplified approaches continue to underpin hydrological climate change impact assessments across the Earth’s mountainous regions. Fully-integrated surface-subsurface models may hold far greater potential to represent the distinctive regimes of steep, geologically-complex headwater catchments. However, their utility has not yet been tested across a wide range of mountainous settings. Here, an integrated model of two adjacent calcareous Alpine headwaters that accounts for 2D surface flow, 3D variably-saturated groundwater flow, and evapotranspiration is presented. An energy balance-based representation of snow dynamics contributed to the model’s high-resolution forcing data, and a sophisticated 3D geological model helped to define and parameterize the subsurface structure. In the first known attempt to calibrate a catchment-scale integrated model of a mountainous region automatically, numerous uncertain model parameters were estimated. The salient features of the hydrological regime could ultimately be satisfactorily reproduced – over an 11-month evaluation period, the Nash-Sutcliffe efficiency of simulated streamflow at the main gauging station was 0.76. Spatio-temporal visualization of the forcing data and simulated responses further confirmed the model’s broad coherence. Presumably due to unresolved local subsurface heterogeneity, closely replicating the somewhat contrasting groundwater level signals observed near to one another proved more elusive. Finally, we assessed the impacts of various common model simplifications and assumptions on key simulated outputs, finding strongly affected model performance in many cases. Although certain outstanding challenges must be overcome if the global uptake of integrated models in mountain regions is to increase, our work demonstrates the feasibility and benefits of their application in such complex systems.
Probabilistic inversion with graph cuts: Application to the Boise Hydrogeophysical Research Site
Publisher: American Geophysical Union (AGU)
Date: 02-2017
DOI: 10.1002/2016WR019347
Abstract: Inversion methods that build on multiple‐point statistics tools offer the possibility to obtain model realizations that are not only in agreement with field data, but also with conceptual geological models that are represented by training images. A recent inversion approach based on patch‐based geostatistical resimulation using graph cuts outperforms state‐of‐the‐art multiple‐point statistics methods when applied to synthetic inversion ex les featuring continuous and discontinuous property fields. Applications of multiple‐point statistics tools to field data are challenging due to inevitable discrepancies between actual subsurface structure and the assumptions made in deriving the training image. We introduce several amendments to the original graph cut inversion algorithm and present a first‐ever field application by addressing porosity estimation at the Boise Hydrogeophysical Research Site, Boise, Idaho. We consider both a classical multi‐Gaussian and an outcrop‐based prior model (training image) that are in agreement with available porosity data. When conditioning to available crosshole ground‐penetrating radar data using Markov chain Monte Carlo, we find that the posterior realizations honor overall both the characteristics of the prior models and the geophysical data. The porosity field is inverted jointly with the measurement error and the petrophysical parameters that link dielectric permittivity to porosity. Even though the multi‐Gaussian prior model leads to posterior realizations with higher likelihoods, the outcrop‐based prior model shows better convergence. In addition, it offers geologically more realistic posterior realizations and it better preserves the full porosity range of the prior.
Verifying the high-order consistency of training images with data for multiple-point geostatistics
Publisher: Elsevier BV
Date: 09-2014
Adaptive spatial resampling as a Markov chain Monte Carlo method for stochastic seismic reservoir characterization
Publisher: Society of Exploration Geophysicists
Date: 09-2012
A general parallelization strategy for random path based geostatistical simulation methods
Publisher: Elsevier BV
Date: 07-2010
Integrating multiple scales of hydraulic conductivity measurements in training image-based stochastic models
Publisher: American Geophysical Union (AGU)
Date: 2015
DOI: 10.1002/2014WR016150
Evaporative cooling of speleothem drip water
Publisher: Springer Science and Business Media LLC
Date: 04-06-2014
DOI: 10.1038/SREP05162
Abstract: This study describes the first use of concurrent high-precision temperature and drip rate monitoring to explore what controls the temperature of speleothem forming drip water. Two contrasting sites, one with fast transient and one with slow constant dripping, in a temperate semi-arid location (Wellington, NSW, Australia), exhibit drip water temperatures which deviate significantly from the cave air temperature. We confirm the hypothesis that evaporative cooling is the dominant, but so far unattributed, control causing significant disequilibrium between drip water and host rock/air temperatures. The amount of cooling is dependent on the drip rate, relative humidity and ventilation. Our results have implications for the interpretation of temperature-sensitive, speleothem climate proxies such as δ 18 O, cave microecology and the use of heat as a tracer in karst. Understanding the processes controlling the temperature of speleothem-forming cave drip waters is vital for assessing the reliability of such deposits as archives of climate change.
Spatially dense drip hydrological monitoring and infiltration behaviour at the Wellington Caves, South East Australia
Publisher: University of South Florida Libraries
Date: 07-2012
High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data
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.
A fast edge-based two-stage direct sampling method
Publisher: Elsevier BV
Date: 05-2021
Effect of Tool Condition on Cutting Mechanism in Micromilling
Publisher: American Society of Mechanical Engineers
Date: 10-06-2019
Abstract: Cutting mechanism in micromilling is governed by the tool condition along with the machining parameters and workpiece material properties. A rapid tool wear in micromilling often deteriorates the surface quality, which could be due to the occurrence of plowing. The effects of tool condition on the transition in cutting mechanisms from shearing to plowing have not been adequately addressed in micro milling. In this work, we attempt to correlate cutting mechanism with tool conditions, so that their influence on force and surface profiles are investigated. Micro milling experiments are performed to investigate these correlations. A fluctuation parameter has been defined to quantify the fluctuation in force signal. It is evident that as the feed varies from 0.2 μm/teeth to 5 μm/teeth, the fluctuation reduces and similar fluctuations are reflected on the generated surface also. The surfaces corresponding to lower force fluctuations has an Ra value less than 350 nm. As cutting edge radius increases, surface finish decreases. However, with chipping, new sharper cutting edges are formed which may improve the surface finish locally but contribute to the overall variation in the surface profiles.
Correcting bias in radar Z – R relationships due to uncertainty in point rain gauge networks
Publisher: Elsevier BV
Date: 11-2014
Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings
Publisher: Elsevier BV
Date: 02-2019
Dealing with non-stationarity in sub-daily stochastic rainfall models
Publisher: Copernicus GmbH
Date: 19-11-2018
DOI: 10.5194/HESS-22-5919-2018
Abstract: Abstract. Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeller. It is therefore desirable to establish quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space–time–intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities. Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution.
Constraining distance-based multipoint simulations to proportions and trends
Publisher: Elsevier BV
Date: 10-2015
Merging parallel tempering with sequential geostatistical resampling for improved posterior exploration of high-dimensional subsurface categorical fields
Publisher: Elsevier BV
Date: 04-2016
Hydrological characterization of cave drip waters in a porous limestone: Golgotha Cave, Western Australia
Publisher: Copernicus GmbH
Date: 06-02-2018
Abstract: Abstract. Cave drip water response to surface meteorological conditions is complex due to the heterogeneity of water movement in the karst unsaturated zone. Previous studies have focused on the monitoring of fractured rock limestones that have little or no primary porosity. In this study, we aim to further understand infiltration water hydrology in the Tamala Limestone of SW Australia, which is Quaternary aeolianite with primary porosity. We build on our previous studies of the Golgotha Cave system and utilize the existing spatial survey of 29 automated cave drip loggers and a lidar-based flow classification scheme, conducted in the two main chambers of this cave. We find that a daily s ling frequency at our cave site optimizes the capture of drip variability with the least possible s ling artifacts. With the optimum s ling frequency, most of the drip sites show persistent autocorrelation for at least a month, typically much longer, indicating le storage of water feeding all stalactites investigated. Drip discharge histograms are highly variable, showing sometimes multimodal distributions. Histogram skewness is shown to relate to the wetter-than-average 2013 hydrological year and modality is affected by seasonality. The hydrological classification scheme with respect to mean discharge and the flow variation can distinguish between groundwater flow types in limestones with primary porosity, and the technique could be used to characterize different karst flow paths when high-frequency automated drip logger data are available. We observe little difference in the coefficient of variation (COV) between flow classification types, probably reflecting the le storage due to the dominance of primary porosity at this cave site. Moreover, we do not find any relationship between drip variability and discharge within similar flow type. Finally, a combination of multidimensional scaling (MDS) and clustering by k means is used to classify similar drip types based on time series analysis. This clustering reveals four unique drip regimes which agree with previous flow type classification for this site. It highlights a spatial homogeneity in drip types in one cave chamber, and spatial heterogeneity in the other, which is in agreement with our understanding of cave chamber morphology and lithology.
Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections
Publisher: Copernicus GmbH
Date: 20-12-2018
DOI: 10.5194/HESS-22-6547-2018
Abstract: Abstract. Multiple-point statistics (MPS) has shown promise in representing complicated subsurface structures. For a practical three-dimensional (3-D) application, however, one of the critical issues is the difficulty in obtaining a credible 3-D training image. However, bidimensional (2-D) training images are often available because established workflows exist to derive 2-D sections from scattered boreholes and/or other s les. In this work, we propose a locality-based MPS approach to reconstruct 3-D geological models on the basis of such 2-D cross sections (3DRCS), making 3-D training images unnecessary. Only several local training subsections closer to the central uninformed node are used in the MPS simulation. The main advantages of this partitioned search strategy are the high computational efficiency and a relaxation of the stationarity assumption. We embed this strategy into a standard MPS framework. Two probability aggregation formulas and their combinations are used to assemble the probability density functions (PDFs) from different subsections. Moreover, a novel strategy is adopted to capture more stable PDFs, where the distances between patterns and flexible neighborhoods are integrated on multiple grids. A series of sensitivity analyses demonstrate the stability of the proposed approach. Several hydrogeological 3-D application ex les illustrate the applicability of the 3DRCS approach in reproducing complex geological features. The results, in comparison with previous MPS methods, show better performance in portraying anisotropy characteristics and in CPU cost.
Social tipping points in global groundwater management
Publisher: Springer Science and Business Media LLC
Date: 21-08-2017
DOI: 10.1038/S41562-017-0181-7
Abstract: Groundwater is critical to global food security, environmental flows, and millions of rural livelihoods in the face of climate change
Reconstruction of Incomplete Data Sets or Images Using Direct Sampling
Publisher: Springer Science and Business Media LLC
Date: 05-03-2010
Permeability estimation conditioned to geophysical downhole log data in sandstones of the northern Galilee Basin, Queensland: Methods and application
Publisher: Elsevier BV
Date: 06-2013
A high-resolution image time series of the Gorner Glacier - Swiss Alps - derived from repeated unmanned aerial vehicle surveys
Publisher: No publisher found
Date: 2019
Simulating Small‐Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data‐Driven Approach Based on Rainfall Radar Images
Publisher: American Geophysical Union (AGU)
Date: 10-2017
DOI: 10.1002/2017WR020876
Missing data simulation inside flow rate time-series using multiple-point statistics
Publisher: Elsevier BV
Date: 12-2016
A Novel Image Reconstruction Strategy for ECT: Combining Two Algorithms With a Graph Cut Method
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Numerical investigation on the implications of spring temperature and discharge rate with respect to the geothermal background in a fault zone
Publisher: Springer Science and Business Media LLC
Date: 16-04-2018
Long-Term Spatiotemporal Variability of Whitings in Lake Geneva from Multispectral Remote Sensing and Machine Learning
Publisher: MDPI AG
Date: 06-12-2022
DOI: 10.3390/RS14236175
Abstract: Whiting events are massive calcite precipitation events turning hardwater lake waters to a milky turquoise color. Herein, we use a multispectral remote sensing approach to describe the spatial and temporal occurrences of whitings in Lake Geneva from 2013 to 2021. Landsat-8, Sentinel-2, and Sentinel-3 sensors are combined to derive the AreaBGR index and identify whitings using appropriate filters. 95% of the detected whitings are located in the northeastern part of the lake and occur in a highly reproducible environmental setting. An extended time series of whitings in the last 60 years is reconstructed from a random forest algorithm and analyzed through a Bayesian decomposition for annual and seasonal trends. The annual number of whiting days between 1958 and 2021 does not follow any particular monotonic trend. The inter-annual changes of whiting occurrences significantly correlate to the Western Mediterranean Oscillation Index. Spring whitings have increased since 2000 and significantly follow the Atlantic Multidecadal Oscillation index. Future climate change in the Mediterranean Sea and the Atlantic Ocean could induce more variable and earlier whiting events in Lake Geneva.
A fast two part direct sampling method based on interpolation
Publisher: Elsevier BV
Date: 06-2023
A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature
Publisher: American Geophysical Union (AGU)
Date: 08-2015
DOI: 10.1002/2014WR016729
Who cares about impact factor?
Publisher: Elsevier BV
Date: 06-2018
Long-term spatiotemporal variability of whitings in Lake Geneva from multispectral remote sensing and machine learning
Publisher: Wiley
Date: 29-09-2022
Using bivariate multiple-point statistics and proximal soil sensor data to map fossil ice-wedge polygons
Publisher: Elsevier BV
Date: 2014
Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data
Publisher: MDPI AG
Date: 09-01-2023
DOI: 10.3390/RS15020409
Abstract: Offshore wind is expected to play a key role in future energy systems. Wind energy resource studies often call for long-term and spatially consistent datasets to assess the wind potential. Despite the vast amount of available data sources, no current means can provide relevant sub-daily information at a fine spatial scale (~1 km). Synthetic aperture radar (SAR) delivers wind field estimates over the ocean at fine spatial resolution but suffers from partial coverage and irregular revisit times. Physical model outputs, which are the basis of reanalysis products, can be queried at any time step but lack fine-scale spatial variability. To combine the advantages of both, we use the framework of multiple-point geostatistics to realistically reconstruct wind speed patterns at time instances for which satellite information is absent. Synthetic fine-resolution wind speed images are generated conditioned to coregistered regional reanalysis information at a coarser scale. Available simultaneous data sources are used as training data to generate the synthetic image time series. The latter are then evaluated via cross validation and statistical comparison against reference satellite data. Multiple realizations are also generated to assess the uncertainty associated with the simulation outputs. Results show that the proposed methodology can realistically reproduce fine-scale spatiotemporal variability while honoring the wind speed patterns at the coarse scale and thus filling the satellite information gaps in space and time.
An agent-based platform for simulating complex human-aquifer interactions in managed groundwater systems
Publisher: Elsevier BV
Date: 11-2015
From white to green: Snow cover loss and increased vegetation productivity in the European Alps
Publisher: American Association for the Advancement of Science (AAAS)
Date: 03-06-2022
Abstract: Mountains are hotspots of bio ersity and ecosystem services, but they are warming about twice as fast as the global average. Climate change may reduce alpine snow cover and increase vegetation productivity, as in the Arctic. Here, we demonstrate that 77% of the European Alps above the tree line experienced greening (productivity gain) and % browning (productivity loss) over the past four decades. Snow cover declined significantly during this time, but in % of the area. These trends were only weakly correlated: Greening predominated in warmer areas, driven by climatic changes during summer, while snow cover recession peaked at colder temperatures, driven by precipitation changes. Greening could increase carbon sequestration, but this is unlikely to outweigh negative implications, including reduced albedo and water availability, thawing permafrost, and habitat loss.
Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method
Publisher: MDPI AG
Date: 27-12-2016
DOI: 10.3390/RS9010012
Erratum to: Which Path to Choose in Sequential Gaussian Simulation
Publisher: Springer Science and Business Media LLC
Date: 06-09-2017
Patch‐based iterative conditional geostatistical simulation using graph cuts
Publisher: American Geophysical Union (AGU)
Date: 08-2016
DOI: 10.1002/2015WR018378
Characterization of alluvial formation by stochastic modelling of paleo-fluvial processes: The concept and method
Publisher: Elsevier BV
Date: 05-2015
Hybrid parallel framework for multiple-point geostatistics on Tianhe-2: A robust solution for large-scale simulation
Publisher: Elsevier BV
Date: 12-2021
Quantifying year-round nocturnal bird migration with a fluid dynamics model
Publisher: The Royal Society
Date: 06-2021
Abstract: To understand the influence of biomass flows on ecosystems, we need to characterize and quantify migrations at various spatial and temporal scales. Representing the movements of migrating birds as a fluid, we applied a flow model to bird density and velocity maps retrieved from the European weather radar network, covering almost a year. We quantified how many birds take-off, fly, and land across Western Europe to (1) track bird migration waves between nights, (2) cumulate the number of birds on the ground and (3) quantify the seasonal flow into and out of the study area through several regional transects. Our results identified several migration waves that crossed the study area in 4 days only and included up to 188 million (M) birds that took-off in a single night. In spring, we estimated that 494 M birds entered the study area, 251 M left it, and 243 M birds remained within the study area. In autumn, 314 M birds entered the study area while 858 M left it. In addition to identifying fundamental quantities, our study highlights the potential of combining interdisciplinary data and methods to elucidate the dynamics of avian migration from nightly to yearly time scales and from regional to continental spatial scales.
Simulation of Earth textures by conditional image quilting
Publisher: American Geophysical Union (AGU)
Date: 04-2014
DOI: 10.1002/2013WR015069
Reducing the impact of a desalination plant using stochastic modeling and optimization techniques
Publisher: Elsevier BV
Date: 02-2009
Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations
Publisher: American Geophysical Union (AGU)
Date: 2013
DOI: 10.1029/2012WR012602
A Fast Approximation for Seismic Inverse Modeling: Adaptive Spatial Resampling
Publisher: Springer Science and Business Media LLC
Date: 07-07-2017
High resolution multi-facies realizations of sedimentary reservoir and aquifer analogs
Publisher: Springer Science and Business Media LLC
Date: 07-07-2015
Abstract: Geological structures are by nature inaccessible to direct observation. This can cause difficulties in applications where a spatially explicit representation of such structures is required, in particular when modelling fluid migration in geological formations. An increasing trend in recent years has been to use analogs to palliate this lack of knowledge, i.e., exploiting the spatial information from sites where the geology is accessible (outcrops, quarry sites) and transferring the observed properties to a study site deemed geologically similar. While this approach is appealing, it is difficult to put in place because of the lack of access to well-documented analog data. In this paper we present comprehensive analog data sets which characterize sedimentary structures from important groundwater hosting formations in Germany and Brazil. Multiple 2-D outcrop faces are described in terms of hydraulic, thermal and chemical properties and interpolated in 3-D using stochastic techniques. These unique data sets can be used by the wider community to implement analog approaches for characterizing reservoir and aquifer formations.
Efficient training image selection for multiple-point geostatistics via analysis of contours
Publisher: Elsevier BV
Date: 07-2019
Semi-arid zone caves: Evaporation and hydrological controls on δ18O drip water composition and implications for speleothem paleoclimate reconstructions
Publisher: Elsevier BV
Date: 2016
Stochastic Rainfall Modeling at Sub‐kilometer Scale
Publisher: American Geophysical Union (AGU)
Date: 06-2018
DOI: 10.1029/2018WR022817
Influence of microclimate and geomorphological factors on alpine vegetation in the Western Swiss Alps
Publisher: Wiley
Date: 23-10-2019
DOI: 10.1002/ESP.4715
Hydrogeophysical data integration through Bayesian Sequential Simulation with log-linear pooling
Publisher: Oxford University Press (OUP)
Date: 06-02-2020
DOI: 10.1093/GJI/GGAA072
Abstract: Bayesian sequential simulation (BSS) is a geostastistical technique, which uses a secondary variable to guide the stochastic simulation of a primary variable. As such, BSS has proven significant promise for the integration of disparate hydrogeophysical data sets characterized by vastly differing spatial coverage and resolution of the primary and secondary variables. An inherent limitation of BSS is its tendency to underestimate the variance of the simulated fields due to the smooth nature of the secondary variable. Indeed, in its classical form, the method is unable to account for this smoothness because it assumes independence of the secondary variable with regard to neighbouring values of the primary variable. To overcome this limitation, we have modified the Bayesian updating with a log-linear pooling approach, which allows us to account for the inherent interdependence between the primary and the secondary variables by adding exponential weights to the corresponding probabilities. The proposed method is tested on a pertinent synthetic hydrogeophysical data set consisting of surface-based electrical resistivity tomography (ERT) data and local borehole measurements of the hydraulic conductivity. Our results show that, compared to classical BSS, the proposed log-linear pooling method using equal constant weights for the primary and secondary variables enhances the reproduction of the spatial statistics of the stochastic realizations, while maintaining a faithful correspondence with the geophysical data. Significant additional improvements can be achieved by optimizing the choice of these constant weights. We also explore a dynamic adaptation of the weights during the course of the simulation process, which provides valuable insights into the optimal parametrization of the proposed log-linear pooling approach. The results corroborate the strategy of selectively emphasizing the probabilities of the secondary and primary variables at the very beginning and for the remainder of the simulation process, respectively.
Dripwater organic matter and trace element geochemistry in a semi-arid karst environment: Implications for speleothem paleoclimatology
Publisher: Elsevier BV
Date: 06-2014
Bayesian Inference of Subglacial Channel Structures From Water Pressure and Tracer‐Transit Time Data: A Numerical Study Based on a 2‐D Geostatistical Modeling Approach
Publisher: American Geophysical Union (AGU)
Date: 06-2019
DOI: 10.1029/2018JF004921
Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors
Publisher: Springer Science and Business Media LLC
Date: 08-11-2019
Conditioning Facies Simulations with Connectivity Data
Publisher: Springer Science and Business Media LLC
Date: 05-10-2011
Vitamin D deficiency and supplementation in critical illness—the known knowns and known unknowns
Publisher: Springer Science and Business Media LLC
Date: 29-10-2018
A comparison of gap-filling approaches for Landsat-7 satellite data
Publisher: Informa UK Limited
Date: 10-08-2017
Accelerating Sequential Gaussian Simulation with a constant path
Publisher: Elsevier BV
Date: 03-2018
A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm
Publisher: Elsevier BV
Date: 03-2013
Correction to: Downscaling Images with Trends Using Multiple-Point Statistics Simulation: An Application to Digital Elevation Models
Publisher: Springer Science and Business Media LLC
Date: 10-10-2019
WlCount: Geological lamination detection and counting using an image analysis approach
Publisher: California Digital Library (CDL)
Date: 31-01-2022
DOI: 10.31223/X5KS6W
Abstract: The manual identification and count of laminae in layered textures is a common practice in the study of geological records, which can be time consuming and carry large uncertainty for dense or disturbed lamina textures. We present here a novel image analysis approach to detect and count laminae in geoscientific imagery, called WlCount. Based on Dynamic Time Warping and Wavelet analysis, WlCount firstly aligns persistent vertical elements to increase the continuity of the lamina structure. Then, using a graphical interface, the user extracts the most significant signal frequencies and allows the automatic count of the laminae. The software, tested on a series of stalagmite cut images showing different types of laminations and a tree-ring image, provides an estimation of the laminae detection and count comparable to the manual one. WlCount presents as a useful open-source tool to help geoscientists, sensibly speeding up the lamination count process.
Bathymetry fusion using multiple-point geostatistics: Novelty and challenges in representing non-stationary bedforms
Publisher: Elsevier BV
Date: 12-2013
Water accounting under climate change in the transboundary Volta River Basin with a spatially calibrated hydrological model
Publisher: Elsevier BV
Date: 11-2223
Local curvature entropy-based 3D terrain representation using a comprehensive Quadtree
Publisher: Elsevier BV
Date: 05-2018
Simulation of fine-scale electrical conductivity fields using resolution-limited tomograms and area-to-point kriging
Publisher: Oxford University Press (OUP)
Date: 20-04-2019
DOI: 10.1093/GJI/GGZ185
Abstract: Deterministic geophysical inversion approaches yield tomographic images with strong imprints of the regularization terms required to solve otherwise ill-posed inverse problems. While such tomograms enable an adequate assessment of the larger-scale features of the probed subsurface, the finer-scale details tend to be unresolved. Yet, representing these fine-scale structural details is generally desirable and for some applications even mandatory. To address this problem, we have developed a two-step methodology based on area-to-point kriging to generate fine-scale multi-Gaussian realizations from smooth tomographic images. Specifically, we use a co-kriging system in which the smooth, low-resolution tomogram is related to the fine-scale heterogeneity through a linear mapping operation. This mapping is based on the model resolution and the posterior covariance matrices computed using a linearization around the final tomographic model. This, in turn, allows us for analytical computations of covariance and cross-covariance models. The methodology is tested on a heterogeneous synthetic 2-D distribution of electrical conductivity that is probed with a surface-based electrical resistivity tomography (ERT) survey. The results demonstrate the ability of this technique to reproduce a known geostatistical model characterizing the fine-scale structure, while simultaneously preserving the large-scale structures identified by the smoothness-constrained tomographic inversion. Small discrepancies between the geophysical forward responses of the realizations and the reference synthetic data are attributed to the underlying linearization. Overall, the method provides an effective and fast alternative to more comprehensive, but computationally more expensive approaches, such as, for ex le, Markov chain Monte Carlo techniques. Moreover, the proposed method can be used to generate fine-scale multivariate Gaussian realizations from virtually any smoothness-constrained inversion results given the corresponding resolution and posterior covariance matrices.
A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network
Publisher: MDPI AG
Date: 25-09-2019
DOI: 10.3390/RS11192233
Abstract: Quantifying nocturnal bird migration at high resolution is essential for (1) understanding the phenology of migration and its drivers, (2) identifying critical spatio-temporal protection zones for migratory birds, and (3) assessing the risk of collision with artificial structures. We propose a tailored geostatistical model to interpolate migration intensity monitored by a network of weather radars. The model is applied to data collected in autumn 2016 from 69 European weather radars. To validate the model, we performed a cross-validation and also compared our interpolation results with independent measurements of two bird radars. Our model estimated bird densities at high resolution (0.2 latitude–longitude, 15 min) and assessed the associated uncertainty. Within the area covered by the radar network, we estimated that around 120 million birds were simultaneously in flight (10–90 quantiles: 107–134). Local estimations can be easily visualized and retrieved from a dedicated interactive website. This proof-of-concept study demonstrates that a network of weather radar is able to quantify bird migration at high resolution and accuracy. The model presented has the ability to monitor population of migratory birds at scales ranging from regional to continental in space and daily to yearly in time. Near-real-time estimation should soon be possible with an update of the infrastructure and processing software.
Paleoclimate change in Ethiopia around the last interglacial derived from annually-resolved stalagmite evidence
Publisher: Elsevier BV
Date: 12-2018
Sustainable groundwater management: How long and what will it take?
Publisher: Elsevier BV
Date: 09-2019
Combining global climate models using graph cuts
Publisher: Springer Science and Business Media LLC
Date: 15-03-2022
DOI: 10.1007/S00382-022-06213-4
Abstract: Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine in idual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity.
An Improved Parallel Multiple-point Algorithm Using a List Approach
Publisher: Springer Science and Business Media LLC
Date: 16-03-2011
Estimation of deep infiltration in unsaturated limestone environments using cave LiDAR and drip count data
Publisher: Copernicus GmbH
Date: 02-09-2015
DOI: 10.5194/HESSD-12-8891-2015
Abstract: Abstract. Limestone aeolianites constitute karstic aquifers covering much of the western and southern Australian coastal fringe. They are a key groundwater resource for a range of industries such as winery and tourism, and provide important ecosystem services such as habitat for stygofauna. Moreover, recharge estimation is important for understanding the water cycle, for contaminant transport, for water management and for stalagmite-based paleoclimate reconstructions. Caves offer a natural inception point to observe both the long-term groundwater recharge and the preferential movement of water through the unsaturated zone of such limestone. With the availability of automated drip rate logging systems and remote sensing techniques, it is now possible to deploy the combination of these methods for larger scale studies of infiltration processes within a cave. In this study, we utilize a spatial survey of automated cave drip monitoring in two large chambers of the Golgotha Cave, South-West Western Australia (SWWA), with the aim of better understanding infiltration water movement and the relationship between infiltration, stalactite morphology and unsaturated zone recharge. By applying morphological analysis of ceiling features from Terrestrial LiDAR (T-LiDAR) data, coupled with drip time series and climate data from 2012–2014, we demonstrate the nature of the relationships between infiltration through fractures in the limestone and unsaturated zone recharge. Similarities between drip-rate time series are interpreted in terms of flow patterns, cave chamber morphology and lithology. Moreover, we develop a new technique to estimate recharge in large scale caves, engaging flow classification to determine the cave ceiling area covered by each flow category and drip data for the entire observation period, to calculate the total volume of cave discharge. This new technique can be applied to other cave sites to identify highly focused areas of recharge and can help to better estimate the total recharge volume.
Special Issue on 20 Years of Multiple-Point Statistics: Part 2
Publisher: Springer Science and Business Media LLC
Date: 12-06-2014
Snow cover persistence as a useful predictor of alpine plant distributions
Publisher: Wiley
Date: 18-07-2023
DOI: 10.1111/JBI.14689
Abstract: Snow cover persistence (SCP) has significant effects on plants in high‐elevation ecosystems. It determines the length of the growing season, provides insulation against low temperatures and influences water availability, thereby shaping the vegetation mosaic. Despite its importance, SCP is rarely used in plant species distribution modelling. In this study, we examine whether incorporating SCP in plant species distribution models (SDMs) improves their predictive power. We investigate the link between species' ecology and SDM improvements by the addition of various SCP predictors. Western Swiss Alps. 206 alpine flowering plants (angiosperms). We produced three maps of landsat satellite‐based SCP indices over an entire mountain region, one of them using an online open access platform allowing quick and easy replication and used them as a predictor in plant SDMs alongside commonly used predictors. We tested whether this improved the predictive performance of plant SDMs. All three SCP indices improved the overall SDM predictive accuracy, but the overall improvement was potentially limited by their correlation with other climatic predictors. Alpine plant species known for their dependence on snow benefited more from the additional snow information. SCP should be used for predicting at least the distribution of alpine, snow‐related plant species. Given that adding snow cover improves SDMs and that snow duration decreases as climate warms, future predictions of alpine plant distributions should account for both snow predictor and associated snow change scenarios.
A new methodology to train fracture network simulation using multiple-point statistics
Publisher: Copernicus GmbH
Date: 17-04-2019
Abstract: Abstract. Natural fracture network characteristics can be establishes from high-resolution outcrop images acquired from drone and photogrammetry. Such images might also be good analogues of subsurface naturally fractured reservoirs and can be used to make predictions of the fracture geometry and efficiency at depth. However, even when supplementing fractured reservoir models with outcrop data, gaps will remain in the model and fracture network extrapolation methods are required. In this paper we used fracture networks interpreted from two outcrops from the Apodi area, Brazil, to present a revised and innovative method of fracture network geometry prediction using the multiple-point statistics (MPS) method. The MPS method presented in this article uses a series of small synthetic training images (TIs) representing the geological variability of fracture parameters observed locally in the field. The TIs contain the statistical characteristics of the network (i.e. orientation, spacing, length/height and topology) and allow for the representation of a complex arrangement of fracture networks. These images are flexible, as they can be simply sketched by the user. We proposed to simultaneously use a set of training images in specific elementary zones of the Apodi outcrops in order to best replicate the non-stationarity of the reference network. A sensitivity analysis was conducted to emphasise the influence of the conditioning data, the simulation parameters and the training images used. Fracture density computations were performed on selected realisations and compared to the reference outcrop fracture interpretation to qualitatively evaluate the accuracy of our simulations. The method proposed here is adaptable in terms of training images and probability maps to ensure that the geological complexity in the simulation process is accounted for. It can be used on any type of rock containing natural fractures in any kind of tectonic context. This workflow can also be applied to the subsurface to predict the fracture arrangement and fluid flow efficiency in water, geothermal or hydrocarbon fractured reservoirs.
The Direct Sampling method to perform multiple‐point geostatistical simulations
Publisher: American Geophysical Union (AGU)
Date: 11-2010
DOI: 10.1029/2008WR007621
A composite annual-resolution stalagmite record of North Atlantic climate over the last three millennia
Publisher: Springer Science and Business Media LLC
Date: 11-06-2015
DOI: 10.1038/SREP10307
Abstract: Annually laminated stalagmites can be used to construct a precise chronology and variations in laminae thickness provide an annual growth-rate record that can be used as a proxy for past climate and environmental change. Here, we present and analyse the first composite speleothem annual growth-rate record based on five stalagmites from the same cave system in northwest Scotland, where precipitation is sensitive to North Atlantic climate variability and the winter North Atlantic Oscillation (NAO). Our 3000-year record confirms persistently low growth-rates, reflective of positive NAO states, during the Medieval Climate Anomaly (MCA). Another persistently low growth period occurring at 290-550 CE coincides with the European Migration Period and a subsequent period of sustained fast growth-rate (negative NAO) from 600-900 AD provides the climate context for the Viking Age in northern and western Europe.
Handling Soft Probabilities in Multiple Point Statistics Simulation
Publisher: Springer Berlin Heidelberg
Date: 08-10-2013
A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction
Publisher: American Geophysical Union (AGU)
Date: 05-2013
DOI: 10.1002/WRCR.20231
Simulating rainfall time-series: how to account for statistical variability at multiple scales?
Publisher: Springer Science and Business Media LLC
Date: 11-04-2017
Terrestrial LiDAR Survey and Morphological Analysis to Identify Infiltration Properties in the Tamala Limestone, Western Australia
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Conditioning Surface-Based Geological Models to Well and Thickness Data
Publisher: Springer Science and Business Media LLC
Date: 05-04-2013
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Stochastic Simulation Of Climatic Data With The Direct Sampling Method
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Funder: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
View Funded ActivityNational Centre For Groundwater Research And Training (NCGRT), Subprogram 1A
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Funder: Australian Research Council
View Funded ActivitySpecial Research Initiatives - Grant ID: NCGRT
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Amount: $14,999,996.00
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