Geostatistical interpolation using copulas

By András Bárdossy, Jing Li. 2008

285 citations recorded by the source corpus.

Subfields: Applied Mathematics, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/5d5dc0af9036ecd4752d0969e66a05cf1a2d588d

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In Applied Mathematics

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Copula based multisite model for daily precipitation simulation (2009, methodology): “We are particularly interested in the joint modelling of multisite daily rainfall and would eventually like to model the spatial behaviour, not as a conglomerate of bivariate relationships as has been done with the multinormal, but as jointly multidimensional copulas as was done by Bárdossy and Li (”
Trivariate Frequency Analyses of Peak Discharge, Hydrograph Volume and Suspended Sediment Concentration Data Using Copulas (2014, background): “…risk analyses (Chen et al. 2012), for rainfall analyses (Zhang and Singh 2007a; Balistrocchi and Bacchi 2011), for geostatistical interpolations (Bardossy and Li 2008; Bardossy 2011) and also for flood frequency analyses (Favre et al. 2004; Salvadori and De Michele 2004; Grimaldi and Serinaldi…”
Upper tail dependence in rainfall extremes: would we know it if we saw it? (2015, background): “2010) to spatial interpolation and simulation problems (e.g., Bárdossy 2006; Bárdossy and Li 2008; Bárdossy and Pegram 2013).”
A multivariate conditional model for streamflow prediction and spatial precipitation refinement (2015, background): “…analysis, identifying drought return periods, rainfall generator, spatial dependence modeling, and geostatistical interpolation [Favre et al., 2004; Salvadori and De Michele, 2004; Zhang and Singh, 2006; Kao and Govindaraju, 2007; Bárdossy and Li, 2008; Serinaldi, 2009; Hobæk Haff et al., 2015].”
Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design (2012, background): “, 1998; Gómez-Hernández and Wen, 1998; Bárdossy and Li, 2008]. It opens the path to consider various sources of uncertainty, e.g., heterogeneity, geostatistical assumptions, boundary conditions, measurement values, model structure uncertainty or uncertain model choice, and model errors with known pa”
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018, methodology): “Similarly, in geosciences, variogram-based [276] and parametric [277] approaches have also previously been used to model (spatial) correlations in data.”
Bivariate drought frequency analysis using the copula method (2012, background): “…Salvadori 2003; Grimaldi and Serinaldi 2006b; Zhang and Singh 2007; Singh and Zhang 2007), for estimating groundwater parameters (Bardossy 2006), geostatistical interpolation (Bardossy and Li 2008), and for remote sensing data processing (Gebremichael and Krajewski 2007; AghaKouchak et al. 2010).”
Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields (2012, methodology): “The underlying assumption of Gaussian behaviour is restricting the performace of standard approaches, as many studies showed that the interdependence in hydrological or meteorological data sets is usually more complex (e.g. Gomez-Hernandez and 5 Wen, 1998; Bárdossy, 2006; Bárdossy and Li, 2008).”
Modeling the spatial dependence of floods using the Fisher copula (2019, methodology): “However, the Gaussian copula does not have tail dependence (Malevergne and Sornette, 2003) and the dependence is symmetrical (Bárdossy and Li, 2008 2.”
Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin (2016, background): “Several researchers have adopted a copula function, initially proposed by Sklar (1959) for multivariate frequency analysis in hydrology (Kao and Govindaraju, 2007, 2010; Bárdossy and Li, 2008; AghaKouchak et al., 2010; Song and Singh, 2010; Chen et al., 2013).”