Last post by azarcos - January 31, 2020, 02:25:34 AM
I am new with this software. I want to setup a model for portugal. I have Lidar data for the baseline DEM, but I'm not certain about how to input this data into SLAMM . Could you provide a step by step overview?
My DEM is an ascii file (x,y,z) with a local coordinate system ETRS89:TM06 Portugal. I need to change this coordinate system to NAVD88? how to input coordinates in squared arranged grid? In the manual it says that the "The model does not require NAVD88 data specifically, just that data be converted to an MTL basis. The user can either convert to a MTL basis prior to importing the data to SLAMM and set the "MTL-NAVD88" correction to zero, or use the other datum and interpret the "MTL-NAVD88" parameter to mean "MTL minus other datum." how to compute MTL-NAVD88 correction to zero? Is there any tutorial to setup a model?
The scale on which SLAMM can be applied is an interesting question.
Early versions of the model (mid 1980s) were designed to be run with very large cell sizes (500m x 500m for example). Within those cells there would be a classification of strips of wetlands and dry lands characterized as widths. Each wetland and dry land would have an elevation and slope among other characteristics.
Newer version of the model have kept some of that architecture, but with the advent of GIS mapping, smaller cell sizes were desired. In order to conserve memory the maximum number of classes per cell is now down to 3 in the latest version. So that would not support larger cell sizes. On the other hand, it exceeds the computational capacity and memory capacity of most machines to model the entire globe with cell sizes of 30 meters or less. And that is likely an understatement.
So I guess it would be possible to perform such a run but the source code would need to revert back to an older version that supports larger cell sizes and the processing of data for model inputs would be quite tricky.
Validation is often confounded by -- lack of adequate historical SLR to cause impacts to wetlands, lack of high-quality historic land-cover and especially elevation datasets, and other anthropogenic changes to wetland cover (non-SLR losses)
Last post by zhiliehui1 - January 09, 2020, 03:03:38 AM
Dear sir? In a study by my research group, it is necessary to predict the change of coastal wetland vegetation and the distribution of ground salinity in the future SLR scenario. We have successfully performed small area studies with SLAMM, and we do not know whether SLAMM is applicable to global scales.I remember the technical documentation mentioning that the coastal zone can be divided into subsites. Whether the global scale can divide multiple sub-areas according to the direction to the sea and set rough model parameters for research. If we want to carry on this research, do you have any good suggestions? Look forword to your reply.
Thank you for your question, I'm not sure this is adequately addressed in the technical documentation.
The way that the "include dikes" and "dike location raster" works is that the model uses the connectivity algorithm to see if there is a connection between the land behind the dike and a "salt water source." Specifically, the categories "riverine tidal," "estuarine water," "tidal creek," and "open ocean." must not appear behind the dike. Otherwise the dike location raster will not work and the results will be no different.
You may check the connectivity of your site using the connectivity map option when setting up your model.
With regards to Question 2:
The time-zero run is a very important step. To the extent possible, it should not be any different than the model's initial conditions. It tests that the conceptual model, the wetland coverage, the elevation data, and the tide range data are all consistent. In the results the initial condition is shown as "0" and the "time zero" result is shown as the first date of the simulation.
From the tech doc:
SLAMM can also simulate a "time zero" step, in which the conceptual model can be validated against the data inputs for your site. The time-zero model predicts the changes in the landscape given specified model tide ranges, elevation data, and land-cover data. Any discrepancy in time-zero results can provide a partial sense of the uncertainty of the model. There will almost always be some minor changes predicted at time zero due to horizontal off-sets between the land-cover and elevation data-sets, general data uncertainty, or other local conditions that make a portion of your site not conform perfectly to the conceptual model. However, large discrepancies could reflect an error in model parameterization with regards to tide ranges or dike locations, for example, and should be closely investigated.
Last post by zhiliehui1 - November 14, 2019, 01:40:26 AM
I am a student from China. I had several problems using slamm: 1\ I want to simulate the influence of the presence and absence of dikes on the coastal wetlands in the current and future situations. The data type of the dikes I used is the type of position and height. In the execution window, I made the Settings including and excluding dikes respectively, as shown in the screenshot below.However, the result is consistent with the fact that there is no dike, but obviously this is not consistent with the actual situation, please help me how to solve this problem.
2\In the process of model simulation, I noticed there are run model for NWI photo date (0) in execution window , refer to the technical manuals, but I still don't quite understand, why no matter whether the check this option, the result is 0 year which is not consistent with the starting year wetland distribution in simulation , please help me to solve, whether in the simulation of actual need to check this option.
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.