Median Polish Kriging and Sequential Gaussian Simulation for the Spatial Analysis of Source Rock Data

Varouchakis, Emmanouil A.

doi:10.3390/jmse9070717

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Berke [37] also developed the modified median polish kriging method to generate more robust spatial predictions for Wolfcamp-Aquifer. Varouchakis [38] applied median polish kriging and sequential Gaussian simulation to explore the spatial distribution of source rock data in terms of total organic carbon weight concentration. In regression-based methods, they incorporate additional factors, such as sociodemographic variables, into the modeling process.…”

Section: Review Of Methods For Spatial Analysis and Modelingmentioning

confidence: 99%

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

Yamaguchi

Torriti

et al. 2023

Energy and Buildings

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Section: Review Of Methods For Spatial Analysis and Modelingmentioning

confidence: 99%

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

Yamaguchi

Torriti

et al. 2023

Energy and Buildings

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“…The stochastic simulations present a trade-off: on one side they provide more spatial variable fields than kriging (which is known for its smoothening properties), and on the other side, because the goal is to maintain the global statistics, may suffer from larger errors at the local scale. Examples of different stochastic simulations are the sequential Gaussian simulations (SGS) (Cinnirella et al, 2005;Emery, 2010;Ersoy and Yünsel, 2009;Gyasi-Agyei and Pegram, 2014;Jang, 2015;Jang and Huang, 2017;Liao et al, 2016;Poggio et al, 2010;Ribeiro and Pereira, 2018;Szatmári and Pásztor, 2019;Varouchakis, 2021;Yang et al, 2018), sequential indicator simulations (SIS) (Bastante et al, 2008;Goovaerts, 1999aGoovaerts, , 2001Luca et al, 2007), simulated annealing (SA) (Goovaerts, 2000;Hofmann et al, 2010;Lin and Chang, 2000), turning bands (TB) (Namysłowska-Wilczyńska, 2015) etc. As seen, the most preferred stochastic simulation in the literature is the SGS due to its simplicity, followed by the SIS and then by SA.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty estimation of regionalised depth–duration–frequency curves in Germany

Shehu

Haberlandt

2022

Preprint

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Abstract. The estimation of rainfall depth-duration-frequency (DDF) curves is necessary for the design of several water systems and protection works. These curves are typically estimated from observed locations, but due to different sources of uncertainties, the risk may be underestimated. Therefore, it becomes crucial to quantify the uncertainty ranges of such curves. For this purpose, the propagation of different uncertainty sources in the regionalisation of the DDF curves for Germany is investigated. Annual extremes are extracted at each location for different durations (from 5mins up to 7days), and local extreme value analysis is performed according to Koutsoyiannis et al. (1998). Following this analysis, five parameters are obtained for each station, from which four are interpolated using external drift kriging, while one is kept constant over the whole region. Finally, quantiles are derived for each location, duration and given return period. Through a non-parametric bootstrap and geostatistical spatial simulations, the uncertainty is estimated in terms of precision (width of 95 % confidence interval) and accuracy (expected error) for three different components of the regionalisation: i) local estimation of parameters, ii) variogram estimation and iii) spatial estimation of parameters. First two methods were tested for their suitability in generating multiple equiprobable spatial simulations: sequential Gaussian simulations (SGS) and simulated annealing (SA) simulations. Between the two, SGS proved to be more accurate and was chosen for the uncertainty estimation from spatial simulations. Next, 100 realisations were run at each component of the regionalisation procedure to investigate their impact on the final regionalisation of parameters and DDFs curves, and later combined simulations were performed to propagate the uncertainty from the main components to the final DDFs curves. It was found that spatial estimation is the major uncertainty component in the chosen regionalisation procedure, followed by the local estimation of rainfall extremes. In particular, the variogram uncertainty had very little effect in the overall estimation of DDFs curves. We conclude that the best way to estimate the total uncertainty consisted of a combination between local resampling and spatial simulations, which resulted in more precise estimation at long observation locations, and a decline in precision at un-observed locations according to the distance and density of the observations in the vicinity. Through this combination, the total uncertainty was simulated by 10,000 runs in Germany, and indicated, that depending on the location and duration level, tolerance ranges from ±10–30 % for low return periods (lower than 10 years), and from ±15–60 % for high return periods (higher than 10 years) should be expected, with the very short durations (5min) being more uncertain than long durations.

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Uncertainty estimation of regionalised depth–duration–frequency curves in Germany

Shehu¹,

Haberlandt²

2023

Hydrol. Earth Syst. Sci.

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Abstract. The estimation of rainfall depth–duration–frequency (DDF) curves is necessary for the design of several water systems and protection works. These curves are typically estimated from observed locations, but due to different sources of uncertainties, the risk may be underestimated. Therefore, it becomes crucial to quantify the uncertainty ranges of such curves. For this purpose, the propagation of different uncertainty sources in the regionalisation of the DDF curves for Germany is investigated. Annual extremes are extracted at each location for different durations (from 5 min up to 7 d), and local extreme value analysis is performed according to Koutsoyiannis et al. (1998). Following this analysis, five parameters are obtained for each station, from which four are interpolated using external drift kriging, while one is kept constant over the whole region. Finally, quantiles are derived for each location, duration and given return period. Through a non-parametric bootstrap and geostatistical spatial simulations, the uncertainty is estimated in terms of precision (width of 95 % confidence interval) and accuracy (expected error) for three different components of the regionalisation: (i) local estimation of parameters, (ii) variogram estimation and (iii) spatial estimation of parameters. First, two methods were tested for their suitability in generating multiple equiprobable spatial simulations: sequential Gaussian simulations (SGSs) and simulated annealing (SA) simulations. Between the two, SGS proved to be more accurate and was chosen for the uncertainty estimation from spatial simulations. Next, 100 realisations were run at each component of the regionalisation procedure to investigate their impact on the final regionalisation of parameters and DDF curves, and later combined simulations were performed to propagate the uncertainty from the main components to the final DDF curves. It was found that spatial estimation is the major uncertainty component in the chosen regionalisation procedure, followed by the local estimation of rainfall extremes. In particular, the variogram uncertainty had very little effect on the overall estimation of DDF curves. We conclude that the best way to estimate the total uncertainty consisted of a combination between local resampling and spatial simulations, which resulted in more precise estimation at long observation locations and a decline in precision at unobserved locations according to the distance and density of the observations in the vicinity. Through this combination, the total uncertainty was simulated by 10 000 runs in Germany, and it indicated that, depending on the location and duration level, tolerance ranges from ± 10 %–30 % for low-return periods (lower than 10 years) and from ± 15 %–60 % for high-return periods (higher than 10 years) should be expected, with the very short durations (5 min) being more uncertain than long durations.

show abstract

Median Polish Kriging and Sequential Gaussian Simulation for the Spatial Analysis of Source Rock Data

Cited by 3 publications

References 28 publications

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

Uncertainty estimation of regionalised depth–duration–frequency curves in Germany

Uncertainty estimation of regionalised depth–duration–frequency curves in Germany

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