Visualizing Surge for Regional Risks

Integration of QGIS and Python Scripts to Model and Visualize Storm Impacts on Distributed Infrastructure Systems

Catalina González-Dueñas and Jamie E. Padgett - Rice University
Miku Fukatsu - Tokyo University of Science

This use case study shows how to automate the extraction of storm intensity parameters at the structure level to support regional risk assessment studies. This example leverages QGIS and python scripts to obtain the surge elevation and significant wave height from multiple storms at specific building locations. The case study also shows how to visualize the outputs in QGIS and export them as a web map. The following DesignSafe resources are leveraged in this example:

Geospatial data analysis and Visualization on DS - QGIS
Jupyter notebooks on DS Jupyterhub

Background

Citation and Licensing

Description

This case study aims to support pre-data processing workflows for machine learning applications and regional risk analysis. When developing predictive or surrogate models for the response of distributed infrastructure and structural systems, intensity measures (IMs) need to be associated with each component of the system (e.g., buildings, bridges, roads) under varying hazard intensity or different hazard scenarios. To accomplish this and given the different resolutions of the hazard and infrastructure data, geographical tools need to be used to associate the intensity measures with the distributed infrastructure or portfolio components. In this case study, python codes were developed to automate geospatial analysis and visualization tasks using QGIS.

This case study is divided into four basic components:

  1. Introduction and workflow of analysis
  2. Storm data analysis using Jupyter notebooks
  3. Geospatial analysis via QGIS
  4. Visualization of the outputs

Introduction and workflow of analysis

In this example, the automated procedure to extract intensity measures is leveraged to obtain the maximum surge elevation and significant wave height at specific house locations for different storm scenarios. The surge elevation and the significant wave height are important parameters when evaluating the structural performance of houses under hurricane loads, and have been used to formulate different building fragility functions (e.g., Tomiczek, Kennedy, and Rogers (2014); Nofal et al. (2021)){target=_blank}. As a proof of concept, the intensity measures (i.e., surge elevation and significant wave height) will be extracted for 3 different storms using the building portfolio of Galveston Island, Texas. The storms correspond to synthetic variations of storm FEMA 33, a probabilistic storm approximately equivalent to a 100-year return period storm in the Houston-Galveston region. The storms are simulated using ADCIC+SWAN numerical models of storm FEMA33, with varying forward storm velocity and sea-level rise. For more details on the storm definition, the user can refer to Ebad et al. (2020) and González-Dueñas and Padgett (2021).

In order to relate the storm data to the building portfolio data, it is necessary to convert the storm outputs to a surface data and then extract at the locations of interest. First, the output files from the ADCIRC+SWAN simulation corresponding to the surge elevation (file fort.63.nc) and significant wave height (file swan_HS.63.nc) need to be converted to a format that can be exported to a GIS (Geographical Information System) software. This pre-processing of the storm data provides the surge elevation and significant wave height in each of the grid points used to define the computational domain of the simulation in a vector data format. Since these points (i.e., ADCIRC+SWAN grid) have a different spatial resolution than the infrastructure system under analysis (i.e., building locations), the storm outputs are converted to a surface data format and then the value at each building location is extracted from it. This is repeated for each one of the storms under analysis and then the ouput data (IMs at each building location) is exported as a csv file. This file is used to support further analysis in the context of risk assessment or machine learning applications, as predictors or response of a system. The workflow of analysis is as follows:

caption

Storm data analysis using Jupyter notebooks

To read the ADCIRC+SWAN storm simulation outputs, two Jupyter notebooks are provided, which can extract the maximum surge elevation and significant wave height values within a particular region. The Read_Surge Jupyter notebook takes as an input the fort.63.nc ADCIRC+SWAN output file and provides a csv file with the maximum surge elevation value at each of the points within the region specified by the user. Specifying a region helps to reduce the computational time and to provide the outputs only on the region of interest for the user. Similarly, the Read_WaveHS Jupyter notebook, reads the swan_HS.63.nc file and provides the maximum significant wave height in the grid points of the specified area.

To use the Jupyter notebooks, the user must:

  1. Create a new folder in My data and copy the Jupyter notebooks from the Community Data folder
  2. Ensure that the fort.63.nc and swan_HS.63.nc are located in the same folder as the Jupyer notebooks
  3. Change the coordinates of the area of interest in [6]:
### Example of a polygon that contains Galveston Island, TX (The coordinates can be obtained from Google maps)
polygon = Polygon([(-95.20669, 29.12035), (-95.14008, 29.04294), (-94.67968, 29.35433), (-94.75487, 29.41924), (-95.20669, 29.12035)])
  1. Modify the name of the output csv file in [8] (if desired):
### In this example, the output name of the csv surge elevation file is "surge_max"
with open('surge_max.csv','w') as f1:

Once the Jupyter notebooks run, two output csv files containing the maximum surge elevation and significant wave height in the area of interest will be generated and saved in your folder. The first two columns of the csv file correspond to the geographical coordinates (i.e., longitud and latitude) of the grid point, and the third one corresponds to the respective intensity measure (maximum surge elevation or significant wave height).

Geospatial analysis via QGIS

Opening a QGIS session in DesignSafe

To access QGIS via DesignSafe go to Workspace -> Tools & Applications -> Visualization -> QGIS Desktop 3.16. You will be prompted the following window:

Fig2

Change the desktop resolution according to your screen size preferences, provide a name for your job, and hit Launch when you finish. After a couple of minutes your interactive session will start, click Connect:

Fig3

You will be directed to an interactive QGIS session, create a new project by clicking the New Project icon or press Ctrl+N:

Fig4

Modify user inputs and run the python script

A python script called IM_Extract is provided to extract the desired IMs at specific locations. Follow these steps to use this code:

  1. Create a folder to store the outputs of the analysis in your My data folder in DS
  2. Provide a csv file that specifies the points for which you wish to obtain the intensity measures. This file should be in the following format (see the Complete_Building_Data file for an example of the building stock of Galveston Island, TX): a. The first column should contain an ID (e.g., number of the row) b. The second column corresponds to the longitude of each location c. The third column corresponds to the latitude of each location
  3. Create a folder named Storms in which you will store the data fromt the different storms
  4. Within the Storms folder, create a folder for each one of the storms you wish to analyze. Each folder should contain the output csv files from the ADCIRC+SWAN simulations (e.g., surge_max.csv, wave_H_max.csv). In our case study, we will use three different storms.

Once the folder of analysis is created in your Data Depot, we can proceed to perform the geospatial analysis in QGIS. Open the python console within QGIS, click the Show Editor button, and then click Open Script:

Fig5

In the file explorer, go to your data folder and open the IM_Extract script:

Fig6

Modify the path of the folder for your own data folder in line 17:

path= r"/home/ubuntu/mydata/**name of your folder**"

If desired, you can change the interpolation method (line 75 of the script) and the cell size used for the interpolation (line 22 of the script):

### Line 22
    # 2. Change cell size (defalt is 0.001)
    cell_size = 0.001
### Line 75
    # Interpolation method
      alg = "qgis:tininterpolation"

Once you finish the modifications, click Run Script.

Visualization of the outputs

Once the script finish running, the time taken to run the script will appear in the python console and the layers created in the analysis will be displayed in the Layers section (left-bottom window) in QGIS:

Fig7

Right click one of the layers, and go to Properties -> Symbology to modify the appearance of the layer (e.g., color, size of the symbol):

Fig8

Click OK when you finish the modifications. You will be directed to the main window again, go to the the toolbar and click Plugins -> Manage and Install Plugins. In the search tab type qgis2web, select the plugin, and click Install Plugin:

Fig11

Go to Web -> qgis2web -> Create web map:

Fig10

In the new window, select the layer(s) that you wish to export in the Layers and Groups tab, and modify the appearance of the map in the Appearance tab. Then go to the Export tab and click in the icon next to Export to folder and select your working data folder:

Fig14

Once you finish, a new web explorer window will open in your interactive session with the exported QGIS map:

FigMap

Go to your working folder in the Data Depot, a new folder containing the web map will be created. You can download the folder and double click Index, the web map you created will be displayed in the web explorer of your local computer:

Fig15