2022 CSDMS meeting-037: Difference between revisions

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{{CSDMS meeting personal information template-2022
{{CSDMS meeting personal information template-2022
|CSDMS meeting first name=chris
|CSDMS meeting first name=Chris
|CSDMS meeting last name=jenkins
|CSDMS meeting last name=Jenkins
|CSDMS meeting institute=University of Colorado Boulder
|CSDMS meeting institute=University of Colorado Boulder
|CSDMS meeting city=boulder
|CSDMS meeting city=boulder
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{{CSDMS meeting select clinics1 2022
{{CSDMS meeting select clinics1 2022
|CSDMS_meeting_select_clinics1_2022=5) Will not attend a clinic
|CSDMS_meeting_select_clinics1_2022=3) The Art of Modeling
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{{CSDMS meeting select clinics2 2022
{{CSDMS meeting select clinics2 2022
|CSDMS_meeting_select_clinics2_2022=2) Image Segmentation using Deep Learning and Human-In-the-Loop Machine Learning - Part I
|CSDMS_meeting_select_clinics2_2022=3) Introduction to Landlab
}}
}}
{{CSDMS meeting select clinics3 2022
{{CSDMS meeting select clinics3 2022
|CSDMS_meeting_select_clinics3_2022=3) Image Segmentation using Deep Learning and Human-In-the-Loop Machine Learning - Part II
|CSDMS_meeting_select_clinics3_2022=1) Component Creation with Landlab
}}
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{{CSDMS meeting abstract yes no 2022
{{CSDMS meeting abstract yes no 2022
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{{CSDMS meeting abstract template 2022
{{CSDMS meeting abstract template 2022
|CSDMS meeting abstract=This paper examines the behavior of an area of seabed in terms of temporal Extreme Value Analysis distributions for erosional/depositional events at the seabed. EVA provides an empirical quantitative basis for predicting the statistics of extreme events in areas. The study is thought to be the first to comprehensively derive EVA coefficients for an area of seabed.
|CSDMS meeting abstract=An Extreme Value Analysis (EVA) model is realized for seafloor elevation changes in an area of shallow continental shelf in the North Sea. Extreme events have practical application in this area of abundant Unexploded Ordinance at the seabed and also wind energy projects. The events being examined are from the motion of seabed sediment in megaripples, sand waves, sand bars and sand sheets, but driven by normal and extreme swell- and wind-waves, tides and human activities. Changes of seabed elevation up to 8m in one year are observed, but rare.


Using a large multidecadal (31 year) data-cube of hydrographic standard bathymetric soundings for the inner shelf of the German Bight, statistics of annual seabed elevation change were compiled. Time-domain exceedance analysis on the elevation changes showed that return times on >1m of change range from 1 to 20 years depending systematically on location in relation to tidal channels, ebb deltas and sand ridges.  
The observational dataset for the study is a large, publicly available compilation of 3-decades of annual, hydrographic-standard bathymetric soundings in the German Bight, provided in gridded form at a spatial resolution of 50m. Counts of annual seabed elevation changes by elapsed time were compiled and related to the seabed features, such as tidal channels (which have previously been well studied).


Of particular interest is that Weibull and Rayleigh distributions from EVA exhibited a close statistical fit to the change-frequency data – for the whole area and also sub-areas. Those distributions offer a new pathway for statistically modeling seabed deposition/erosion - especially for practical purposes in relation to human objects such as UXO, and in relation to episodic burial of epibenthic communities.
The change statistics were compared to forms of the Generalized Extreme Value and Generalized Pareto distributions, per pixel and also by small morphodynamically uniform subareas. The Generalized Pareto distribution with coefficient c ≈ -6.0 to -6.5 appears to be the appropriate model, but adjusted according to water depths and locations on features.
 
The result suggests a method to statistically model seabed behavior including extreme events.
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Latest revision as of 05:18, 18 April 2022



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Event Severity vs Frequency for a Patch of Seabed using Extreme Value Analysis (EVA)

Chris Jenkins, University of Colorado Boulder boulder Colorado, United States. jenkinsc0@gmail.com



An Extreme Value Analysis (EVA) model is realized for seafloor elevation changes in an area of shallow continental shelf in the North Sea. Extreme events have practical application in this area of abundant Unexploded Ordinance at the seabed and also wind energy projects. The events being examined are from the motion of seabed sediment in megaripples, sand waves, sand bars and sand sheets, but driven by normal and extreme swell- and wind-waves, tides and human activities. Changes of seabed elevation up to 8m in one year are observed, but rare.

The observational dataset for the study is a large, publicly available compilation of 3-decades of annual, hydrographic-standard bathymetric soundings in the German Bight, provided in gridded form at a spatial resolution of 50m. Counts of annual seabed elevation changes by elapsed time were compiled and related to the seabed features, such as tidal channels (which have previously been well studied).

The change statistics were compared to forms of the Generalized Extreme Value and Generalized Pareto distributions, per pixel and also by small morphodynamically uniform subareas. The Generalized Pareto distribution with coefficient c ≈ -6.0 to -6.5 appears to be the appropriate model, but adjusted according to water depths and locations on features.

The result suggests a method to statistically model seabed behavior including extreme events.