GStatSim V1.0: a Python package for geostatistical interpolation and simulation

MacKie, Emma J.; Field, Michael E.; Wang, Lijing; Yin, Zhen Yu; Schoedl, Nathan; Hibbs, Matthew A.; Zhang, Allan

doi:10.5194/egusphere-2022-1224

Cited by 3 publications

(3 citation statements)

References 44 publications

(40 reference statements)

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“…Sequential Gaussian Simulation (SGS) is a stochastic method (Deutsch & Journel, 1997) that can be applied to simulate values between measurements based on the statistical characteristics of nearby data (e.g., Graham et al., 2017; Law et al., 2023; MacKie et al., 2020, 2021, 2023). Ice‐thickness data were decimated to 100 m spacing using a median reduction filter, and an SGS algorithm from the python package GStatSim (Mackie et al., 2022) was implemented to simulate values for cells without measurements using 40 conditioning data points selected using a nearest‐neighbor octant function (Mackie et al., 2023) with a search radius of 30 km. We empirically determined the optimum parameters for an exponential statistical model based on the experimental semivariogram (Text S2 in Supporting Information S1).…”

Section: Methodsmentioning

confidence: 99%

“…All radar‐derived ice thickness data sets accessed and used in this analysis are publicly available: JARE33‐60 (Tsutaki et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f, 2021g, 2021h, 2021i); AWI GEA (Eagles et al., 2021); AWI OIR (Eisen et al., 2020); and CReSIS data (CReSIS, 2021) (see Text S1.1 in Supporting Information S1). Code used for geospatial analysis and stochastic simulations was adapted from open‐source python packages: Verde (Uieda, 2018); GeoStatsPy (Pyrcz et al., 2021); SciKit Gstat (Mälicke et al., 2021); and GStatSim (Mackie et al., 2022, 2023) (see Text S2.1 in Supporting Information S1).…”

Section: Data Availability Statementmentioning

confidence: 99%

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Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage Near Dome Fuji, East Antarctica

Shackleton,

Matsuoka,

Moholdt

et al. 2023

JGR Earth Surface

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Topographic variability beneath ice sheets regulates ice flow, basal melting, refreezing processes, and meltwater drainage. The preservation of old ice layers and basal ice stratigraphy is sensitive to these subglacial processes, and Dome Fuji, inland East Antarctica, is one of the few regions where 1.5‐Ma old ice can be preserved for investigating a major climatic change that occurred in the mid‐Pleistocene. We used stochastic simulation methods and radar data to generate an ensemble of simulated bed topography with the continuous and realistic roughness necessary to assess basal conditions. Ensemble analysis reveals the magnitude and spatial distribution of topographic uncertainty, facilitating uncertainty‐constrained assessments of subglacial drainage and topographic adjustments to geothermal heat flow (GHF). We find that topographic variability can lead to widespread local GHF variations of ±20% of the background value, which aggregate to raise the regional value and suggest previously underestimated distributions and rates of basal melting. We also find that survey profile spacing has an increasing influence on topographic uncertainty for rougher bed, deriving an empirical relationship that could guide future survey planning based on uncertainty tolerance.

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Section: Methodsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage Near Dome Fuji, East Antarctica

Shackleton,

Matsuoka,

Moholdt

et al. 2023

JGR Earth Surface

View full text Add to dashboard Cite

show abstract

“…We used Python 3.9.7 to conduct the analysis, with packages for data manipulation, raster processing and analysis (numpy, rasterio, pandas, rioxarray, rasterstats, xarray, pyproj, cartopy, scipy). We also used and adapted tools for geospatial/ geostatistical analysis and stochastic methods from the following open-source python packages: Verde (Uieda, 2018), GeoStatsPy (Pyrcz et al, 2021), SciKit GStat (Mälicke et al, 2021), and GStatSim (Mackie et al, 2022). Specifically, the Verde 'BlockReduce' function was used for data decimation with median filter.…”

Section: S21 Software and Toolsmentioning

confidence: 99%

Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage near Dome Fuji, East Antarctica

Shackleton

Matsuoka

Moholdt

et al. 2023

Preprint

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Dome Fuji, inland East Antarctica is one of only few regions where 1.5-Ma old ice can be preserved for investigating the mid-Pleistocene Transition. We used stochastic simulation and various radar datasets to generate a bed topography ensemble with the continuous, realistic roughness necessary to assess basal conditions. Ensemble analysis reveals the magnitude and spatial distribution of topographic uncertainty, facilitating uncertainty-constrained assessments of subglacial drainage and topographic adjustments to geothermal heat flow. We find that topographic variability can lead to widespread local geothermal heat flow variations of ± 20% the background value, which aggregate to raise the regional value and suggest previously underestimated distributions and rates of basal melting. We also find that survey profile spacing has an increasing influence on topographic uncertainty for rougher bed, deriving an empirical relationship that could guide future survey planning based on uncertainty tolerance.

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GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications

Tang,

Wood,

Newman

et al. 2024

Geosci. Model Dev.

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Abstract. Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.

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GStatSim V1.0: a Python package for geostatistical interpolation and simulation

Cited by 3 publications

References 44 publications

Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage Near Dome Fuji, East Antarctica

Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage Near Dome Fuji, East Antarctica

Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage near Dome Fuji, East Antarctica

GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications

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