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Specifying accretion as a function of elevation

Started by slammdunk, October 01, 2019, 04:19:42 AM

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slammdunk

Hello Jonathon and Marco,

I am new to SLAMM and have had some difficulties understanding how to vary accretion rates as a function of cell elevation for different NWI wetland codes. I am hoping you can provide some more guidance on how to go about this.

I would like to use surface elevation change data from SETs that were placed in marsh and mangrove regions. Rather than breaking up the dataset for subsites around each SET, I would like to have accretion rates vary with elevation and tidal range for marsh and mangroves. I'm not certain about how to input this data into SLAMM though. Could you provide a step by step overview?

I have read through the user manual and technical guides, and have tried playing with the Excel SLAMM6_Accretion file included with the download. However, I'm still confused about how to go forward.  ???  Could you also provide an overview of the SLAMM6_Accretion file- bit of an introduction on how to utilize the spreadsheet?

Thank you ahead of time for all your help!   :)

Jonathan S. Clough

October 07, 2019, 08:33:52 AM #1 Last Edit: October 07, 2019, 01:51:43 PM by Jonathan S. Clough
First calculate the longest term average elevation-change rate for your SET tables that you have available.

Then calculate the elevation of the SET table relative to MTL in "half-tide units."  As a reminder, half-tide units are "elevation-in-meters / (0.5 * GTU-in-meters )"

Then, plot these data on a graph so you can understand the relationship between SET elevation and measured elevation changes.   

A line using the accretion spreadsheet data can then be fit through the data to the best of your capability.

e.g. Fig 4 from https://www.sciencedirect.com/science/article/pii/S1364815216302705#fig4

Often SET tables are not set at elevations throughout the tidal range and are focused up around MHW so you would need to estimate the other portions of the range or use a model such as MEM3 to help fill out the relationship between marsh platform elevation and predicted accretion rates.

This is a quick birds-eye view of the procedure, please ask specific questions about the spreadsheet and we will answer them ASAP.

Love the user name BTW.

Regards!  - -Jonathan

slammdunk

Hello Jonathan,

Thank you for in-depth and easy to follow instructions. Your response helped clear a lot of my confusion.

A follow-up question I have is, on the SLAMM accretion spreadsheet it states under the min and max "not parameters but reflective of elevation range of wetland type". Would it be better to have min and max across the whole estuary being modeled or would it be better to have the average min and max of vegetation types around SET tables?

Happy to hear that you like the username!


Jonathan S. Clough

There always will be outliers in terms of the elevation to wetland to tide range relationship.  (Influences of ground water can have fresh marsh occurring lower, or wind tides can have salt marsh occurring higher.  Also, elevation data can be uncertain especially in areas of high vertical relief.  Or wetland maps have horizontal error. So we very often have outliers outside of the modeled range for wetland categories.)

In general, you don't need to include these elevation outliers in your accretion to elevation relationship.  Those wetlands that fall below the modeled lower range (we often set this to approximately the fifth percentile of the wetland elevations) will be lost at "time zero."  You can then check to see if this is reasonable or not based on satellite imagery and make changes if required. 

Those wetlands that fall above the modeled higher range will be set to the accretion rate modeled at the highest range.  We usually do not model those wetlands as "drying" because they are too high, assuming some other local factor has those wetlands perched at that elevation.  So they will generally remain persistent unless the SLR becomes extreme. 

Overall, the accretion model should try to match the majority of the data (5th to 95th percent confidence interval?) and not worry about the outliers.

Hope this is helpful, thanks!

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