Hello,

In testing the sensitivity of SLAMM to the salt elevation parameter I came across a situation that I'm having trouble understanding. At our tropical site, we had been using a salt elevation of 0.325 m above MTL and a GDT of 0.05 m. To test the sensitivity of the model to salt elevation, we reduced it by 25% to 0.244 and ran the model again to compare our results in 2100.  As expected, as compared to the base run, we saw increases in most fresh habitats and much less mangrove creation, as fresh habitats were now allowed to persist to a lower elevation.  However, the majority of the decrease in area of mangroves created was offset by a large increase in the amount of estuarine water created. I cannot understand how this would have happened.  (It was not because more inland open water was turning into estuarine water, and it clearly appears to be an issue of area once converting to mangroves now instead converting to estuarine water).

Any ideas?

Thanks!

Hi Jonathan,

As you know, I'm modeling a tropical site, and I'm trying to understand the conversions that are occurring with inundation and erosion.  I attempted to go through the technical documentation to "Map out" the successive conversions that happen (e.g., using the table on p. 32 and information within the habitat category descriptions). However, I still have some holes.  Most critically, I'm trying to understand what habitat type converts to Regularly flooded marsh in a tropical system.  My understanding what that the "default" for a tropical system would be a conversion to mangrove when a fresh or dry habitat goes below the salt elevation.  Could this just be my irregularly flooded marshes converting to regularly flooded?  Is it the case in a tropical system that irregularly flooded marshes are never "created" (because the default is mangrove) and that regularly flooded marshes are only created as a result of inundation of existing irregularly flooded marshes?

I don't suppose anyone has attempted to draw out a "decision tree" with the conversions that you could share?

Thanks! 

In follow-up to an older discussion that began here: http://warrenpinnacle.com/SLAMMFORUM/index.php?topic=31.0

I've read in several places that the ideal change in a habitat type between the initial condition and time 0 would be less than 5% or so.  I've got a situation here where the transition for all but one category is less than or just a hair over 5%.  However, all of the land converting FROM those categories that are losing land area in T=0 are converting to mangroves, causing the difference in mangrove area from initial state to T=0 to be +115%.  (Note that the conceptual model is fitting very well to our habitat types, with the lower bound of each category matching the 5th percentile fairly well).  In a decision tree in which pretty much everything below salt elevation turns into mangrove, I'm not sure how this could be avoided.  For example, even though we only lose 2% of our swamps to mangrove from initial to T=0, that is 105 ha, which is a lot of new mangroves in T=0 considering we only began with 600 ha of mangrove.

I'm not sure how concerned I should be about this.  The total cover of mangrove in the initial state is only 1%, and the 115% increase in mangroves still leaves total mangrove coverage fairly low, at 2%.  As you are aware, we've checked and double checked our parameterization.  My next step would be to lower the elevations in the conceptual model for some of the fresh habitats... Thoughts?

Thanks for your continued help on this!

I am using SLAMM to model an estuary that exists at the very northern range of mangrove habitat.  The northern part of my study area can be mangrove dominated or salt marsh dominated depending on the occurrence and frequency of hard freezes.  Thus, SLAMM's decision rule to convert almost every wetland habitat type into mangrove upon inundation leaves us with nearly all lands converted to mangrove by 2100 - a highly unlikely outcome.  Is there any way to override the mangrove succession such that we can run a simulation that assumes this area to NOT be tropical (i.e., remove "mangroves" from the succession tree)?  This would allow me to compare my original results to results from a scenario in which the area is salt marsh-dominant (which might simulate what the area would like like if several hard freezes occurred over the course of the simulation).

Thanks,
Jen

I'm trying to understand SLAMM's use and interpretation of erosion rates and have not found a clear explanation in the documentation. It appears to me from the technical documentation that erosion is modeled as "none", "Heavy" or "severe" depending on the fetch.  Since we input the erosion rates for marshes, swamps and tidal flats specifically into the model, what does it mean for SLAMM to model severity of erosion?  Does that mean it only uses some "portion" of the rate we gave it depending on the situation (e.g.., 100% for severe, 50% for heavy"), or does it change something else?

In simpler terms, my question is, does rising sea level affect the rate of erosion for these habitat types?

Jen

Hello,

We are having an issue with the elevation analysis where several of the "5th percentile" results are identical to one another. Specifically, inland fresh marsh, tidal fresh marsh, mangrove, inland open water, ad irreg. flooded marsh all have a value of 8.2744. Additionally, the "min" for inland fresh marsh and tidal fresh marsh are also 8.2744. I looked more closely at elevation analyses from previous runs at other subsites, and this issue actually came up once before. It's unclear why it only occurs some of the time.

I am wondering if there's possibly an issue in the elevation analysis code. I did consider the possibility that this issue was a result of error in our input files. But thinking conceptually, that doesn't seem possible. The inputs are processed separately from one another; the values within the habitat dataset and the elevation dataset wouldn't influence one another during the input creation. Even if there were errors in our elevation data or the habitat data, it still wouldn't lead to identical values for the 5th percentiles. To confirm, I did take the final input text files and convert them back to raster, and compared them to the original rasters from which we make the input text files, and they're identical.

Any thoughts on what might be going wrong here?  I'd like to use the elevation analysis to see if we need to make any revision to the conceptual model ranges, but I'm not feeling confident at this point that I can rely on the current analysis for that purpose.

Thanks!

Hello,

I am interested in converting the results of the elevation analysis to true elevation in order to allow for easier consideration by local experts of the elevation ranges in which the various habitat types exist.  To do so, I would think that the following conversion equation would be appropriate:

[results in HTU] * [HTU in meters] = Result in meters adjusted to MTL = 0
[results in meters adjusted to MTL = 0] - [NAVD 88 correction factor] = Results in meters, NAVD 88

So for example:
the 5th percentile elevation for Transitional Salt Marsh is -1.54 HTU. 
HTU in this system is 0.11 meters.
the 5th percentile elevation in meters is thus -1.54*0.11 = -0.17 meters (relative to a system in which MTL = 0)
The NAVD 88 correction factor in this area is 0.205 m
-0.17 meters - 0.205 m = -0.37 m NAVD 88

So -0.37 m NAVD 88 is the 5th percentile of the elevation range for Transitional Salt Marsh.

I would be grateful is someone could check my math on this and let me know if this is the appropriate way to do this conversion.  I thought it was, but got confused about whether the results of the analysis were already in NAVD88.  This came up because I noticed that in the Conceptual model output it calculated the "Min in HTU" and "Max in HTU" values based on the salt elevation in NAVD 88, rather than on the salt elevation corrected such that MTL = 0.

Thank you!

I am modeling a large estuary with an "unconventional" tidal/sea level situation.  In the estuary, there is a relatively small daily tidal range, but a significant (approx. 1 ft) difference in sea level over the course of the year.  Specifically, sea level is about 1 foot higher in October than it is in May.  Thus, traditional mean tide level datapoints tend to mask the true extent of saline inundation/exposure in the area.   We are in the process of determining how best to accommodate this situation in SLAMM.

The most obvious issue that this creates in SLAMM is that the habitats in the region are not going to conform to SLAMM's predicted habitat ranges.  (see my previous post for an example of how this plays out in a model run).  It seems there are two options for dealing with this:
1. run the elevation analysis and revise the ranges to better reflect reality
2. rather than using the "sanctioned' calculations for the various tidal data, use proxies of other tidal statistics that would have the same operational meaning of the requested tidal data and would fit the habitat types better.  (e.g., define MLW as whatever the lower extent of the tidal flats are, rather than calculating it as you normally would).

Option 2 was suggested by individuals in the region prior to me becoming familiar with the elevation analysis.  It now seems that Option 1 would work well, and is a much more straightforward way to deal with this.

Other than providing a limit for some of the habitat types, how else does SLAMM utilize the user-provided tidal data?  Are there other tweaks that might need to be made to accommodate this situation?


Thanks,
Jen

I am running SLAMM in a large estuary using 2004 LULC data (converted to SLAMM categories) and circa 2006 LiDAR data (5 ft x 5 ft resolution).  We are running the model on a 10 m square resolution, and have resampled the LiDAR data using bilinear interpolation to get to that resolution.  We have run the model for 4 subsites and in each case the habitat conditions in T=0 (the input) are drastically different from what is being modeled for 2004 as the "initial" condition.  We're seeing things like an over 800% increase in mangroves, and conversion of large extents of irregularly flooded marsh being converted to regularly flooded marsh (there was no regularly flooded marsh identified in the input data), to name a few.

Just as a test, we ran one of the subsites with the Pre-processor on and it yielded a T=0 and 2004 initial condition that are nearly identical.  However, even with the resampling that we did, I wouldn't think we should be running the pre-processor since we have Lidar data.

What process is SLAMM going through from the input to the initial condition that could cause the extent of change that we are seeing?  My one thought is that it might have something to do with a unique tidal regime in the estuary (which I will ask about in detail in another post) which causes the elevation ranges of wetland habitats to be quite different from what SLAMM assumes.  In other words, perhaps at T=0 it is seeing habitat types at elevations it does not "agree" with and is revising them according to its assumptions about correct ranges? 

Any thoughts? 

Thanks!
Jen

I am modeling in an area that has been significantly altered by mosquito impoundments.  We have high quality LiDAR data for the region, so I'm wondering if there is any reason to use a separate dike file.  Based on our results, it appears that the dike is simply being overtopped after a sufficient sea level rise has occurred (I'm a bit confused, however, because the area behind the dike is becoming inundated but the dike itself, dry land which is not assumed to be protect in my run, remains as such).  It is not the intent to protect these diked areas from inundation, but rather to just let inundation occur naturally, and efforts are being made to re-establish connectivity.  Would the connectivity model allow water to simply flow into these diked areas, rather than inundation only occurring after the dike itself has been overtopped?

Thanks!
Jen

I'm modeling an area in which some "recoding" of the LiDAR data was done prior to its distribution and am wondering what effects this could potentially have on my results.  Specifically, all inland water bodies were recoded to a blanket elevation of -0.03 meters.  (The mean sea level in the region is about -0.21 meters).  Thus, in certain instances we have wetland habitats that are at a lower elevation than adjacent water bodies.

The model seemed to run without any trouble and I don't see anything jumping out as odd because of this issue.

On a somewhat related note, the LiDAR data have also been purged of any codes of the estuaries and open ocean (ie., those areas are all "no data").  It seemed critical to have at least a few cells of water adjacent to land coded as such, and I've assigned them all as the MSL elevation.

More generally, my question is, what role does the elevation of water play, if any, in habitat conversions or other model functions within SLAMM?  Would you suggest other strategies for getting around the issues described above?

Thanks,
Jen