Risk assessment for river flooding

Risk assessment for river flooding#

Click to open this workflow on Binder.
Click to go to this workflow’s GitHub repository.

In this workflow we will visualize risks to built infrastructure presented by river flooding (fluvial flooding). Risk is expressed in this workflow in the form of economic damages. The damages are calculated using the methodology described in the risk workflow description (can also be found on GitHub). We will use pre-processed river flood maps and combine these with land use maps, as well as information on economic vulnerability (damage curves) to quantify the order of the damages in economic terms.

Note: Country-specific information is required to calculate the economic damages. As a minimum, GDP should be adjusted in the accompanying Excel sheet. A copy of the Excel sheet will be made as part of this workflow and stored in the dedicated folder for regional assessment LUISA_damage_info_curves_[area name].xlsx. In this file you will need to make adjustments.

Preparation work#

Select area of interest#

Before accessing the data we will define the area of interest. Before starting with this workflow, you have already prepared by downloading the river flood hazard map to your local directory (using the hazard assessment workflow for river flooding or using your own data). Please specify below the area name for the river flood maps.

areaname = 'Bremen_Germany' 

Load libraries#

Find out about the Python libraries we will use in this notebook.

In this notebook we will use the following Python libraries:

os - Provides a way to interact with the operating system, allowing the creation of directories and file manipulation.
pooch - A data retrieval and storage utility that simplifies downloading and managing datasets.
shutil - package for file operation (e.g. copying of files).
numpy - A powerful library for numerical computations in Python, widely used for array operations and mathematical functions.
pandas - A data manipulation and analysis library, essential for working with structured data in tabular form.
rasterio - A library for reading and writing geospatial raster data, providing functionalities to explore and manipulate raster datasets.
rioxarray - An extension of the xarray library that simplifies working with geospatial raster data in GeoTIFF format.
damagescanner - A library designed for calculating flood damages based on geospatial data, particularly suited for analyzing flood impact.
matplotlib - A versatile plotting library in Python, commonly used for creating static, animated, and interactive visualizations.
contextily A library for adding basemaps to plots, enhancing geospatial visualizations.

These libraries collectively enable the download, processing, analysis, and visualization of geospatial and numerical data, making them crucial for this risk workflow.

# Package for downloading data and managing files
import os
import pooch
import shutil

# Packages for working with numerical data and tables
import numpy as np
import pandas as pd

# Packages for handling geospatial maps and data
import xarray as xr
import rioxarray as rxr
import rasterio
from rasterio.enums import Resampling

# Package for calculating flood damages
from damagescanner.core import RasterScanner

# Ppackages used for plotting maps
import matplotlib.pyplot as plt
import contextily as ctx

Create the directory structure#

For this workflow to work, even if you download and use just this notebook, we need to have the directory structure for accessing and storing data. If you have already executed the hazard assessment workflow for river flooding, you would already have created the workflow folder ‘FLOOD_RIVER_hazard’ where the hazard data is stored. We create an additional folder for the risk workflow, called ‘FLOOD_RIVER_risk’.

# Define folder containing hazard data
hazard_folder = 'FLOOD_RIVER_hazard'
hazard_data_dir = os.path.join(hazard_folder, f'data_{areaname}')

# Define the folder for the risk workflow
workflow_folder = 'FLOOD_RIVER_risk'

# Check if the workflow folder exists, if not, create it
if not os.path.exists(workflow_folder):
    os.makedirs(workflow_folder)

# Define directories for data and results within the previously defined workflow folder
data_general_dir = os.path.join(workflow_folder,f'general_data')
data_dir = os.path.join(workflow_folder,f'data_{areaname}')
results_dir = os.path.join(workflow_folder, f'results_{areaname}')
plot_dir = os.path.join(workflow_folder, f'plot_{areaname}')

if not os.path.exists(data_general_dir):
    os.makedirs(data_general_dir)
if not os.path.exists(data_dir):
    os.makedirs(data_dir)
if not os.path.exists(results_dir):
    os.makedirs(results_dir)
if not os.path.exists(plot_dir):
    os.makedirs(plot_dir)

Processing data#

The maps of flooding, land use and infrastructure can be combined to assess multipe types of risk from river flooding in a region. This way we can estimate the exposure of population, infrastructure and economic assets to river floods. In this section we will align the resolutions of the datasets, prepare vulnerability curvers and calculate the damage maps.

Linking land use types to economic damages#

In order to assess the potential damage done by the flooding in a given scenario, we also need to assign a monetary value to the land use categories. We define this as the potential loss in €/m². The calculation of economic value for different land use types is made with help of an accompanying template (LUISA_damage_info_curves.xlsx).

damage_info_file = f'LUISA_damage_info_curves_{areaname}.xlsx'
template_file = os.path.join('..','00_Input_files_for_damage_analysis','LUISA_damage_info_curves_template.xlsx')
shutil.copyfile(template_file,os.path.join(data_dir,damage_info_file))

'FLOOD_RIVER_risk\\data_Bremen_Germany\\LUISA_damage_info_curves_Bremen_Germany.xlsx'

Important: Please go to the newly created file and adjust the information on the GDP per capita in the first line of the Excel sheet.

# Read damage curve information from an Excel file into a pandas DataFrame
LUISA_info_damage_curve = pd.read_excel(os.path.join(data_dir,damage_info_file), index_col=0)

# Extract the 'total €/m²' column to get the maximum damage for reconstruction
maxdam = pd.DataFrame(LUISA_info_damage_curve['total €/m²'])

# Save the maximum damage values to a CSV file
maxdam_path = os.path.join(data_dir, f'maxdam_luisa.csv')
maxdam.to_csv(maxdam_path)

# Display the first 10 rows of the resulting DataFrame to view the result
maxdam.head(10)

	total €/m²
Land use code
1111	600.269784
1121	414.499401
1122	245.022999
1123	69.919184
1130	0.000000
1210	405.238393
1221	40.417363
1222	565.843080
1230	242.504177
1241	565.843080

# Create a new DataFrame for damage_curves_luisa by copying JRC_curves
damage_curves_luisa = JRC_curves.copy()

# Drop all columns in the new DataFrame
damage_curves_luisa.drop(damage_curves_luisa.columns, axis=1, inplace=True)

# Define building types for consideration
building_types = ['residential', 'commercial', 'industrial']

# For each land use class in maxdamage, create a new damage curve
for landuse in maxdam.index:
    # Find the ratio of building types in the class
    ratio = LUISA_info_damage_curve.loc[landuse, building_types].values

    # Create a new curve based on the ratios and JRC_curves
    damage_curves_luisa[landuse] = ratio[0] * JRC_curves.iloc[:, 0] + \
                                   ratio[1] * JRC_curves.iloc[:, 1] + \
                                   ratio[2] * JRC_curves.iloc[:, 2]

# Save the resulting damage curves to a CSV file
curve_path = os.path.join(data_dir, 'curves.csv')
damage_curves_luisa.to_csv(curve_path)    

# Plot the vulnerability curves for the first 10 land cover types
damage_curves_luisa.iloc[:, 0:10].plot()
plt.title('Vulnerability curves for flood damages for the LUISA land cover types');
plt.ylabel('Damage (%)');
plt.xlabel('Inundation depth (m)');

../../../../_images/6fad51bfc5079a754190b678a41ef82252b90c4b4ee7cf61994fff00fecf9084.png

Calculate potential economic damage to infrastructure using DamageScanner#

Now that we have all pieces of the puzzle in place, we can perform the risk calculation. For this we are using the DamageScanner python library which allows for an easy damage calculation.

The DamageScanner takes the following data:

The clipped and resampled flood map
The clipped land use map
The vulnerability curves per land use category
A table of maximum damages per land use category

We can perform the damage calculations for all scenarios and return periods now:

for rp in rps:
    inun_map = os.path.join(tif_dir, f'floodmap_resampled_{areaname}_{rp}.tif') # Define file path for the flood map input 

    # Do the damage calculation and save the results
    loss_df = RasterScanner(landuse_map,
                            inun_map,
                            curve_path,
                            maxdam_path, 
                            save = True, 
                            nan_value = None, 
                            scenario_name= '{}/flood_{}_{}'.format(results_dir,areaname,rp),
                            dtype = np.int64)
    loss_df_renamed = loss_df[0].rename(columns={"damages": "{}".format(rp)})
    
    if (rp==rps[0]):
        loss_df_all = loss_df_renamed
    else:
        loss_df_all = pd.concat([loss_df_all, loss_df_renamed], axis=1)

Now the dataframe loss_df_all contains the results of damage calculations for all scenarios and return periods. We will format this dataframe for easier interpretation:

# Obtain the LUISA legend and add it to the table of damages
LUISA_legend = LUISA_info_damage_curve['Description']

# Convert the damages to million euros
loss_df_all_mln = loss_df_all / 10**6

# Combine loss_df with LUISA_legend
category_damage = pd.concat([LUISA_legend, (loss_df_all_mln)], axis=1)

# Sort the values by damage in descending order (based on the column with the highest damages)
category_damage.sort_values(by='RP500', ascending=False, inplace=True)

# Display the resulting DataFrame (top 10 rows)
category_damage.head(10)

	Description	RP10	RP50	RP100	RP200	RP500
2310	Pastures	5510.164812	6608.972878	7020.148574	7408.357227	7863.919402
1210	Industrial or commercial units	2426.271216	3325.541785	3659.194095	3961.576387	4443.388074
2110	Non irrigated arable land	1483.584796	1782.859264	1880.719380	1973.194827	2076.031137
1241	Airport areas	1093.486961	1259.036546	1318.359250	1361.272999	1410.228684
1122	Low density urban fabric	376.509682	654.591027	763.370864	893.685156	1126.544414
1121	Medium density urban fabric	252.754574	424.724633	521.509309	593.049691	693.697110
1230	Port areas	260.832564	359.139184	404.998644	440.924843	479.108497
1123	Isolated or very low density urban fabric	214.784752	306.400937	340.349626	375.936090	428.086005
1421	Sport and leisure green	166.023336	208.206996	222.028123	236.939494	268.308150
1410	Green urban areas	129.665912	162.021690	172.410424	181.675478	201.978592

Conclusions#

Now that you were able to calculate damage maps based on flood maps and view the results, it is time to revisit the information about the accuracy and applicability of European flood maps to local contexts.

Consider the following questions:

How accurate do you think this result is for your local context? Are there geographical and/or infrastructural factors that make this result less accurate?
What information are you missing that could make this assessment more accurate?
What can you already learn from these maps of river flood potential and maps of potential damages?
How do you expect the projected damages due to river flooding to change under climate change in this region?

Important

In this risk workflow we learned:

How to access use European-scale land use datasets.
How to assign each land use with a vulnerability curve and maximum damage.
Combining the flood (hazard), land use (exposure), and the vulnerability curves (vulnerability) to obtain an economic damage estimate.
Understand where damage comes from and how exposure and vulnerability are an important determinant of risk.

Contributors#

Applied research institute Deltares (The Netherlands)

Institute for Environmental Studies, VU Amsterdam (The Netherlands)

Authors of the workflow:
Natalia Aleksandrova (Deltares) Ted Buskop (Deltares, VU Amsterdam)

Risk assessment for river flooding

Contents

Risk assessment for river flooding#

Preparation work#

Select area of interest#

Load libraries#

Create the directory structure#

Download and explore the data#

Hazard data - river flood maps#

Impact of climate change on river discharges and estimated flood hazard#

Exposure - land-use data#

Vulnerability - damage curves for land use#

Processing data#

Combining datasets with different resolution#

Linking land use types to economic damages#

Calculate potential economic damage to infrastructure using DamageScanner#

Plot the results#

Conclusions#

Contributors#