# MESTSEM

## Marine Environment Sediment Transport and Sorting Estimation Model

This document introduces the key features of a computer model for calculating the sediment transport and resulting sorting under a combination of waves and current. The original purpose of the model is to evaluate various sorting algorithms, although it may also be used for predicting sediment transport and the resulting changes of geomorphology. The software is developed by Ulf Erlingsson.

### Processing steps

The software takes as input a number of maps, that together describe the bathymetry, sedimentology, wave climate, and current regime, of an area.
In each time step, the potential sediment transport is calculated as the integral of all wave heights and current velocities, for each grain size and each cell of the map.

The potential sediment transport is adjusted using the sorting algorithm, which uses as input also the availability of each fraction.

After each time step, the bathymetry is updated, as is the sedimentology. The potential sediment transport is recalculated only if the depth has changed significantly.

The sediment transport formula combines bedload transport and suspended transport. In principle both factors are calculated as concentration times velocity.

### Input

The following parameters are needed in map form:

Depth

Sediment thickness

Mean grain size

Standard deviation of grain size

(a mixture of up to 3 Gaussian populations may be specified)

Mean wave height

Standard deviation of wave height

Wave steepness (the period is calculated from this)

Mean current direction

Mean current velocity in the mean direction

Standard deviation of the current in the mean direction

Special conditions: Sediment sources, land, etc.

A header file specifies the number of rows and columns, and the cell resolution

### Output

After each time step a depth file is created. On demand, the sediment data is automatically converted back to the input format, and saved as Gaussian mean and standard deviation, with up to three populations (unimodal, bimodal, or trimodal distribution).