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https://github.com/cheginit/si_2019_coastal
Process-based assessment of US Gulf and East coast
https://github.com/cheginit/si_2019_coastal
coastal-modelling national-water-model numerical-analysis
Last synced: about 2 months ago
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Process-based assessment of US Gulf and East coast
- Host: GitHub
- URL: https://github.com/cheginit/si_2019_coastal
- Owner: cheginit
- License: mit
- Created: 2019-06-18T23:02:32.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-07-17T03:59:43.000Z (over 5 years ago)
- Last Synced: 2024-01-31T14:43:17.244Z (11 months ago)
- Topics: coastal-modelling, national-water-model, numerical-analysis
- Language: Python
- Homepage:
- Size: 232 MB
- Stars: 2
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Coastal Group: Roll Models
As part of summer institute 2019 held in National Water Center, Coastal Group works towards systematic analysis of large-scale modeling of coastal areas.
## Project Management
## Objectives
Identify contributions of relevant physical processes to total water prediction by modeling idealized scenarios in coastal transition zones to provide a framework for efficient forecasting.
## Idealized domains
The idealized domains were inspired by rivers and bays geometries over the Gulf and East Coast.
A few metrics were chosen to compare different rivers and bays and scale the idealized domains.
Bay/Estuary geometry:
- **Wt** Bay width at river end (upstream) - Wt = Wr in triagular geometry
- **Wb** Bay width at ocean end (downstream)
- **Lb** Bay length
- Parameters:
* Rbr = Wb / Wr
* Rbt = Wb / Wt
* Rlb = Lb / Wb
River geometry:
- **SI**nuosity = Curvilinear Length / Straight Line Length
- **Wr** River width
Three main **Classes** determined for this analysis:
1) River discharge directly in the ocean
2) River discharge in triangular bay
3) River discharge in trapezoidal/rectangular bay
For **Class 1**, two subdivisions were created to evaluate river sinuosity contribution in comparison with a straight line river:
A) SI = 1
B) SI = 1.45
For **Classes 2** and **3**, three subdivisions were created to include the analysis of a barrier island between the bay and the ocean.
A) SI = 1
B) SI = 1.45
C) SI = 1 with barrier island
Idealized models domains
## Modeling configuration
A set of scenarios was created to evaluate water levels under tides forcing, storm surge, and discharge and roughness variation.
Simulation Scenarios:
- **R**oughness Manning's (-)
- **D**ischarge (cms)
- **T**ides: **P**redicted, **S**torm Surge
| | Simulation Name | R | D | T | status |
|:----:|:---------------:|:-----:|:----:|:-------:|:----------------------------:|
| S1 | **Ref** | 0.025 | 0 | P |
- - [x]
| S2 | R20 | 0.020 | 0 | P |
- - [x]
| S3 | R30 | 0.030 | 0 | P |
- - [x]
| S4 | D1000 | 0.025 | 1000 | P |
- - [x]
| S5 | TS | 0.025 | 0 | S |
- - [x]
| S6 | D100 | 0.025 | 100 | P |
- - [x]
| S7 | D200 | 0.025 | 200 | P |
- - [x]
| S8 | D500 | 0.025 | 500 | P |
- - [x]
## Results
- Identification of tidal signal in the river upstream for under different geometries and scenarios
- Identification of river and bay geometry contribution, as well as roughness, discharge, tides and storm surge in total water prediction
## Deliverable
- Final Report and Presentation
## Instructions
The following steps should be taken for using the plotting scripts:
1. Install docker.
2. Change directory to `src/docker` and create an image from the `Dockerfile`:
```bash
docker build -t plot .
```
3. Copy the plotting scripts to the folder that contains the D-Flow outputs and run one of the script e.g., `cross_section.py`, as follows:
```bash
docker run -v "$PWD":/home/plot plot python cross_section.py C2_A1_S1_R25_D0_TPG
```
The following steps should be taken for using `tide_constituents.py`:
1. Install Anaconda and load it in a command line.
2. Run the following command to create a new environment called `tides`:
```bash
conda create -n tides pip requests shapely beautifulsoup4 pandas scipy
```
then activate the environment ```conda activate tides```.
3. Install some extra packages with `pip`:
```bash
pip install py_noaa baker astronomia filelike pyparsing
```
4. Clone the tappy repository to a location and install it:
```bash
git clone -b py3 https://github.com/taataam/tappy.git
cd tappy
python setup.py install
```
An example showing how to use the code is provided in `src/tide_constituents/mobile_bay.py`
The following steps should be taken for using `gantt.py`:
1. Install Anaconda and load it in a command line.
2. Run the following command to create a new environment called `gantt`:
```bash
conda create -n gantt plotly pandas psutil
```
then activate the environment ```conda activate gantt```.
3. Install an extra packages:
```bash
conda install -c plotly plotly-orca
```
4. Then go the script's directory and run it:
```bash
cd src/gantt
python gantt_chart.py
```