⚠️This is the first operational version of the handbook, but it is still a work in progress and will be heavily updated during 2024!⚠️

Risk assessment for river flooding#

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

In this workflow we will visualize risks to built infrastructure presented by river flooding. The damages will be calculated using the methodology described in the risk workflow description section\(^{1}\). 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.

\(^{1}\)see Risk_workflow_description_FLOOD_RIVER.md

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 = 'Barcelona_Spain' 

Load libraries#

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

Download and explore the data#

Hazard data - river flood maps#

As the default option, we use the potential river flood depth maps from the Joint Research Centre, that we have downloaded using the hazard assessment workflow for river floods.
Below we load the flood maps and visualize them to check the contents.

floodmaps_path = os.path.join(hazard_data_dir,f'floodmaps_all_JRC_PresentScenario_{areaname}.nc')
floodmaps = xr.open_dataset(floodmaps_path)
Dimensions:      (x: 334, y: 311, band: 1)
  * x            (x) float64 3.648e+06 3.648e+06 ... 3.674e+06 3.674e+06
  * y            (y) float64 2.074e+06 2.074e+06 2.074e+06 ... 2.05e+06 2.05e+06
  * band         (band) int32 1
    spatial_ref  int32 ...
Data variables:
    RP10         (band, y, x) float64 ...
    RP20         (band, y, x) float64 ...
    RP30         (band, y, x) float64 ...
    RP40         (band, y, x) float64 ...
    RP50         (band, y, x) float64 ...
    RP75         (band, y, x) float64 ...
    RP100        (band, y, x) float64 ...
    RP200        (band, y, x) float64 ...
    RP500        (band, y, x) float64 ...
    AREA_OR_POINT:  Area
    scale_factor:   1.0
    add_offset:     0.0
    _FillValue:     -9999.0
# make a plot of one map in the dataset
fig, ax = plt.subplots(figsize=(7, 6))
ctx.add_basemap(ax=ax,crs=floodmaps.rio.crs,source=ctx.providers.CartoDB.Positron, attribution_size=6)
plt.title(f'Example of a floodmap retrieved from JRC \n for the area of {areaname}',fontsize=10);
fig.colorbar(bs, ax=ax, orientation="vertical", label='Inundation depth [m]');

Based on the hazard map extent we will define the coordinates of the area of interest (in projected coordinates, EPSG:3035).

bbox = [np.min(floodmaps.x.values),np.min(floodmaps.y.values),np.max(floodmaps.x.values),np.max(floodmaps.y.values)]
[3647766.4362665974, 2050063.9665709694, 3673779.775457935, 2074280.5886409834]

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

We will use coarse-resolution dataset from Aqueduct Floods to assess the impact of climate change on the projected extreme river flooding. These flood maps were already pre-processed in the hazard assessment workflow (see Hazard_assessment_FLOOD_RIVER.ipynb for more details). If you have not yet ran the Hazard assessment script fully, you can do that now to retrieve the flood maps for climate scenarios.

data_dir_aqueduct = os.path.join(hazard_data_dir,'aqueduct_floods')
aq_floodmap_base = xr.open_dataset(os.path.join(data_dir_aqueduct,f'inunriver_historical_000000000WATCH_1980_rp{return_period:05}_{areaname}.nc'))
aq_floodmaps = xr.open_dataset(os.path.join(data_dir_aqueduct,f'inunriver_AllScenarios_AllModels_AllYears_rp{return_period:05}_{areaname}.nc'))
# define discrete colormaps
cmap_flood = plt.get_cmap('Blues', 10) 
cmap_diff = plt.get_cmap('PuOr', 16) 
# Plot baseline flood map
cmap_flood = plt.get_cmap('Blues', 10) 
fig, ax = plt.subplots(figsize=(7, 5))
bs=aq_floodmap_base['inun'].plot(ax=ax,cmap=cmap_flood,alpha=0.7,vmin=0,vmax=np.ceil(np.nanmax(aq_floodmap_base['inun'].values)), cbar_kwargs={'label':'Inundation depth [m]'})
ctx.add_basemap(ax=ax,crs=aq_floodmap_base.rio.crs,source=ctx.providers.CartoDB.Positron, attribution_size=6)
plt.title(f'Flood map for the baseline scenario (ca. 1980) \n 1 in {return_period} years return period',fontsize=14);
# choose scenario to visualize
scenario = 'rcp8p5' # 'rcp8p5' or 'rcp4p5'
[2030, 2050, 2080]
# make plot

# open dataset containing flood maps for all years and models in this scenario
lims = [0,np.ceil(np.nanmax(aq_floodmaps['inun'].isel(year=-1,scenario=-1).values))]
lims_diff = [-1, 1]

# make figure
fig, axs = plt.subplots(figsize=(11, 7),nrows=2,ncols=3,sharex=True,sharey=True, constrained_layout=True)
fig.suptitle(f'Flood maps for scenario {scenario.upper().replace("P5",".5")}, 1 in {return_period} years return period',fontsize=14);

years_all = aq_floodmaps['year'].values.tolist()

for ii,year in enumerate(years_all):
    # plot inundation depth for the given scenario and year (mean of all models)
    if ii == len(years_all)-1:
        aq_floodmaps['inun'].sel(year=year,scenario=scenario).plot(ax=axs[0,ii],alpha=0.7, cmap=cmap_flood,vmin=lims[0],vmax=lims[1],cbar_kwargs={'label': "Inundation depth [m]",'pad':0.01,'aspect':20})
        aq_floodmaps['inun'].sel(year=year,scenario=scenario).plot(ax=axs[0,ii],alpha=0.7, cmap=cmap_flood,vmin=lims[0],vmax=lims[1],add_colorbar=False)
    ctx.add_basemap(axs[0,ii], crs=aq_floodmaps.rio.crs.to_string(), source=ctx.providers.CartoDB.Positron)
    axs[0,ii].set_title(f'ca. {year}',fontsize=12);
    # Plot difference against baseline scenario
    aq_floodmaps_diff = aq_floodmaps['inun'].sel(year=year,scenario=scenario) - aq_floodmap_base['inun']
    if ii == len(years_all)-1:
        aq_floodmaps_diff.plot(ax=axs[1,ii], cmap=cmap_diff,alpha=0.7, vmin=lims_diff[0],vmax=lims_diff[1], cbar_kwargs={'label': "Inundation depth difference [m]",'pad':0.01,'aspect':20})
        aq_floodmaps_diff.plot(ax=axs[1,ii], cmap=cmap_diff,alpha=0.7, vmin=lims_diff[0],vmax=lims_diff[1],add_colorbar=False)
    ctx.add_basemap(axs[1,ii], crs=aq_floodmaps.rio.crs.to_string(), source=ctx.providers.CartoDB.Positron)
    axs[1,ii].set_title(f'{year} flood map vs. 1980 baseline',fontsize=12);

    if ii>0:

The figure above provides a comparison between the projected inundation depths in the future under climate scenarios and the baseline maps (1980 climate). Where negative values are shown in the comparison, it means that the inundation depth is expected to decrease, possibly due to reduced river flow. In this case we can assume that the further analysis using the JRC floodmaps for the present climate are representative (or concervatively-representative) of the future risks as well.

In case a significant increase in inundation depth is seen in the comparison maps above, it is expected that the river flood hazard at this location will increase with climate change. While not quantifying the future risk directly in this workflow, we need to keep this in mind when interpreting the results.

Exposure - land-use data#

Next we need the information on land use. We will download the land use dataset from the JRC data portal, a copy of the dataset will be saved locally for ease of access. In this notebook we use the land use maps with 100 m resolution. The land use maps can also be downloaded manually from the JRC portal.

landuse_res = 100 # choose resolution (options: 50 or 100 m)
luisa_filename = f'LUISA_basemap_020321_{landuse_res}m.tif'

# Check if land use dataset has not yet been downloaded
if  not os.path.isfile(os.path.join(data_general_dir,luisa_filename)):
    # , define the URL for the LUISA basemap and download it
    url = f'http://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/LUISA/EUROPE/Basemaps/2018/VER2021-03-24/{luisa_filename}'
        known_hash=None,  # Hash value is not provided
        path=data_general_dir,    # Save the file to the specified data directory
        fname=luisa_filename  # Save the file with a specific name
    print(f'Land use dataset already downloaded at {data_general_dir}/{luisa_filename}')
Land use dataset already downloaded at FLOOD_RIVER_risk\general_data/LUISA_basemap_020321_100m.tif

The Land use data is saved into the local data directory. The data shows on a 100 by 100 meter resolution what the land use is for Europe in 2018. The land use encompasses various types of urban areas, natural land, agricultural fields, infrastructure and waterbodies. This will be used as the first exposure layer in the risk assessment.

# Define the filename for the land use map based on the specified data directory
filename_land_use = f'{data_general_dir}/{luisa_filename}'

# Open the land use map raster 
land_use = rxr.open_rasterio(filename_land_use)

# Display the opened land use map
<xarray.DataArray (band: 1, y: 46000, x: 65000)>
[2990000000 values with dtype=int32]
  * band         (band) int32 1
  * x            (x) float64 9e+05 9.002e+05 9.002e+05 ... 7.4e+06 7.4e+06
  * y            (y) float64 5.5e+06 5.5e+06 5.5e+06 ... 9.002e+05 9e+05
    spatial_ref  int32 0
    AREA_OR_POINT:  Area
    _FillValue:     0
    scale_factor:   1.0
    add_offset:     0.0

The land use dataset needs to be clipped to the area of interest. For visualization purposes, each land use type is then assigned a color. Land use plot shows us the variation in land use over the area of interest.

# Set the coordinate reference system (CRS) for the land use map to EPSG:3035
land_use.rio.write_crs(3035, inplace=True)

# Clip the land use map to the specified bounding box and CRS
land_use_local = land_use.rio.clip_box(*bbox, crs=floodmaps.rio.crs)

# File to store the local land use map
landuse_map = os.path.join(data_dir, f'land_use_{areaname}.tif') 

# Save the clipped land use map
with rasterio.open(
) as dst:
    # Write the data array values to the rasterio dataset
# Plotting

# Define values and colors for different land use classes
LUISA_values = [1111, 1121, 1122, 1123, 1130,
                1210, 1221, 1222, 1230, 1241,
                1242, 1310, 1320, 1330, 1410,
                1421, 1422, 2110, 2120, 2130,
                2210, 2220, 2230, 2310, 2410,
                2420, 2430, 2440, 3110, 3120,
                3130, 3210, 3220, 3230, 3240,
                3310, 3320, 3330, 3340, 3350,
                4000, 5110, 5120, 5210, 5220,

LUISA_colors = ["#8c0000", "#dc0000", "#ff6969", "#ffa0a0", "#14451a",
                "#cc10dc", "#646464", "#464646", "#9c9c9c", "#828282",
                "#4e4e4e", "#895a44", "#a64d00", "#cd8966", "#55ff00",
                "#aaff00", "#ccb4b4", "#ffffa8", "#ffff00", "#e6e600",
                "#e68000", "#f2a64d", "#e6a600", "#e6e64d", "#c3cd73",
                "#ffe64d", "#e6cc4d", "#f2cca6", "#38a800", "#267300",
                "#388a00", "#d3ffbe", "#cdf57a", "#a5f57a", "#89cd66",
                "#e6e6e6", "#cccccc", "#ccffcc", "#000000", "#ffffff",
                "#7a7aff", "#00b4f0", "#50dcf0", "#00ffa6", "#a6ffe6",

# Plot the land use map using custom levels and colors
land_use_local.plot(levels=LUISA_values, colors=LUISA_colors, figsize=(10, 8))

# Set the title for the plot
plt.title('LUISA Land Cover for the defined area');

Vulnerability - damage curves for land use#

We will use damage curve files from the JRC that are already available in the GitHub repository folder.

# Import damage curves of the JRC from a CSV file into a pandas DataFrame
JRC_curves = pd.read_csv('JRC_damage_curves.csv', index_col=0)

# Plot the JRC depth-damage curves

# Set the title and labels for the plot
plt.title('JRC depth-damage curves for different damage classes');
plt.xlabel('Flood depth [m]');
plt.ylabel('Damage ratio [%]');

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.

Combining datasets with different resolution#

Before we can calculate risk indices, we will prepare the data by aligning the spatial resolution of the datasets and by calculating the vulnerability curves for economic damages based on specified information.

The flood and land use datasets have different spatial resolutions. Flood extent maps are at a resolution of 30-75 m (resolution varies with latitude), while land use data is at a constant 100 m (or 50 m) resolution. We can bring them to the same resolution. It is preferable to interpolate the flood map onto the land use grid (and not the other way around), because land use is defined in terms of discrete values and on a more convenient regularly spaced grid. We will interpolate the flood data onto the land use map grid in order to be able to calculate the damages.

# Reproject the flood map to match the resolution and extent of the land use map
rps = list(floodmaps.data_vars) # available return periods

for rp in rps:
    ori_map = floodmaps[rp]
    new_map = ori_map.rio.reproject_match(land_use_local, resampling=Resampling.bilinear); del ori_map
    ds = new_map.to_dataset(); del new_map

    if (rp==rps[0]):
        floodmaps_resampled = ds
        floodmaps_resampled = floodmaps_resampled.merge(ds)
# check the new resolution of the floodmap (should be equivalent to the land use map resolution)
(100.0, -100.0)

We will save the resampled flood maps as raster files locally, so that we can more easily use them as input to calculate economic damages.

# Create GeoTIFF files for the resampled flood maps
tif_dir = os.path.join(data_dir,f'floodmaps_resampled')
if not os.path.isdir(tif_dir): os.makedirs(tif_dir)
<xarray.DataArray 'RP500' (y: 243, x: 261)>
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]])
    band         int32 1
    spatial_ref  int32 0
  * x            (x) float64 3.648e+06 3.648e+06 ... 3.674e+06 3.674e+06
  * y            (y) float64 2.074e+06 2.074e+06 2.074e+06 ... 2.05e+06 2.05e+06
    AREA_OR_POINT:  Area
['RP10', 'RP20', 'RP30', 'RP40', 'RP50', 'RP75', 'RP100', 'RP200', 'RP500']
for rp in rps:
    data_tif = floodmaps_resampled[rp][0]
    with rasterio.open(
    ) as dst:
        # Write the data array values to the rasterio dataset

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')

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('LUISA_damage_info_curves_template.xlsx', 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')

# Display the first 10 rows of the resulting DataFrame to view the result
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')

# 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)');

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,
                            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
        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)
Description RP10 RP20 RP30 RP40 RP50 RP75 RP100 RP200 RP500
1241 Airport areas 1158.999454 1404.808512 1533.091788 1612.795044 1674.352622 1781.027108 1852.157625 2018.832642 2249.584334
1210 Industrial or commercial units 747.289533 909.355639 1003.929579 1070.351325 1116.525195 1191.857739 1236.298380 1341.366077 1486.201279
2120 Permanently irrigated land 251.731494 282.366487 299.444448 310.603467 319.101484 332.570061 341.191409 359.757366 381.971369
2310 Pastures 171.193104 189.452325 199.515125 205.814118 210.051861 217.161695 222.094508 232.447148 243.953597
1330 Construction sites 90.479176 104.579858 110.944218 115.613433 117.424785 121.745790 124.754523 129.298313 137.979422
1221 Road and rail networks and associated land 86.861444 96.575525 102.151150 105.381311 108.800794 114.198418 117.427451 124.198874 133.844707
4000 Wetlands 71.605904 78.776840 82.347885 84.504523 86.341640 89.084468 90.954231 94.580496 98.686262
1230 Port areas 38.324046 42.472286 44.494384 47.167067 48.413815 51.637520 52.932449 56.585847 68.495454
3230 Sclerophyllous vegetation 45.564182 49.993778 52.378177 54.041834 55.150119 56.864291 58.501882 61.724303 65.771418
2220 Fruit trees and berry plantations 47.669309 52.698227 54.587242 55.625153 56.649320 57.937258 58.972930 61.432277 64.341207

Plot the results#

Now we can plot the damages to get a spatial view of what places can potentially be most affected economically. To do this, first the damage maps for all scenarios will be loaded into memory and formatted:

# load all damage maps and merge into one dataset
for rp in rps:
    damagemap = rxr.open_rasterio('{}/flood_{}_{}_damagemap.tif'.format(results_dir,areaname,rp)).squeeze()
    damagemap = damagemap.where(damagemap > 0)/10**6
    # prepare for merging
    damagemap.name = 'damages'
    damagemap = damagemap.assign_coords(return_period=rp); 
    ds = damagemap.to_dataset(); del damagemap
    ds = ds.expand_dims(dim={'return_period':1})

    # merge
    if (rp==rps[0]):
        damagemap_all = ds
        damagemap_all = damagemap_all.merge(ds)
damagemap_all.x.attrs['long_name'] = 'X coordinate'; damagemap_all.x.attrs['units'] = 'm'
damagemap_all.y.attrs['long_name'] = 'Y coordinate'; damagemap_all.y.attrs['units'] = 'm'
Dimensions:        (x: 261, y: 243, return_period: 9)
  * x              (x) float64 3.648e+06 3.648e+06 ... 3.674e+06 3.674e+06
  * y              (y) float64 2.074e+06 2.074e+06 ... 2.05e+06 2.05e+06
  * return_period  (return_period) <U5 'RP10' 'RP100' 'RP20' ... 'RP500' 'RP75'
    band           int32 1
    spatial_ref    int32 0
Data variables:
    damages        (return_period, y, x) float64 nan nan nan nan ... nan nan nan

Now we can plot the damage maps to compare:

# select return periods to plot
rps_sel = [10,50,100]

# Plot damage maps for different scenarios and return periods
fig,axs = plt.subplots(figsize=(10, 4),nrows=1,ncols=len(rps_sel),constrained_layout=True,sharex=True,sharey=True)

# define limits for the damage axis based on the map with highest damages
vrange = [0,np.nanmax(damagemap_all.isel(return_period=-1)['damages'].values)]

for rr,rp in enumerate(rps_sel):
    # Plot the damagemap with a color map representing damages and a color bar
    bs=damagemap_all.sel(return_period=f'RP{rps_sel[rr]}')['damages'].plot(ax=axs[rr], vmin=vrange[0], vmax=vrange[1], cmap='Reds', add_colorbar=False)
    ctx.add_basemap(axs[rr],crs=damagemap_all.rio.crs.to_string(),source=ctx.providers.CartoDB.Positron, attribution_size=6) # add basemap
    axs[rr].set_title(f'1 in {rps_sel[rr]} years event',fontsize=12)
    if rr>0:
fig.colorbar(bs,ax=axs[:],orientation="vertical",pad=0.01,shrink=0.9,aspect=30).set_label(label=f'Damage [mln. €]',size=14)  
fig.suptitle('River flood damages for extreme river flow scenarios in current day climate',fontsize=12);

fileout = os.path.join(plot_dir,'Result_map_{}_damages_overview.png'.format(areaname))

To get a better indication of why certain areas are damaged more than others, we can also plot the floodmap and land use maps in one figure for a given return period.

# Select year and return period to plot:
year = 2050
rp = 500

# load damage map
damagemap = rxr.open_rasterio('{}/flood_{}_RP{}_damagemap.tif'.format(results_dir,areaname,rp))
damagemap = damagemap.where(damagemap > 0)/10**6
fig, ([ax1, ax2, ax3]) = plt.subplots(figsize=(13, 4),nrows=1,ncols=3,sharex=True,sharey=True,layout='constrained')

# Plot flood damages on the first plot
damagemap.plot(ax=ax1, cmap='Reds', cbar_kwargs={'label': "Damage [mln. €]",'pad':0.01,'shrink':0.95,'aspect':30})
ax1.set_title(f'Flood damages')
ax1.set_xlabel('X coordinate in the projection'); ax1.set_ylabel('Y coordinate in the projection')

# Plot inundation depth on the second plot
max_inun=5 # set max depth to include in the colorbar
floodmaps_resampled[f'RP{rp}'].plot(ax=ax2, cmap='Blues', vmax=max_inun, cbar_kwargs={'label': "Inundation depth [m]",'pad':0.01,'shrink':0.95,'aspect':30})
ax2.set_title(f'Flood depth')
ax2.set_xlabel('X coordinate in the projection'); ax2.set_ylabel('Y coordinate in the projection')

# Plot land use on the third plot with custom colors
land_use_local.plot(ax=ax3, levels=LUISA_values, colors=LUISA_colors, cbar_kwargs={'label': "Land use class [-]",'pad':0.01,'shrink':0.95,'aspect':30})
ax3.set_title('LUISA land cover')
ax3.set_xlabel('X coordinate in the projection'); ax3.set_ylabel('Y coordinate in the projection')

plt.suptitle(f'Maps of flood and associated damages for extreme river water level scenarios in current climate \n 1 in {rp} year extreme event',fontsize=12)

# Add a map background to each plot using Contextily
ctx.add_basemap(ax1, crs=damagemap.rio.crs.to_string(), source=ctx.providers.CartoDB.Positron)
ctx.add_basemap(ax2, crs=floodmaps_resampled.rio.crs.to_string(), source=ctx.providers.CartoDB.Positron)
ctx.add_basemap(ax3, crs=land_use_local.rio.crs.to_string(), source=ctx.providers.CartoDB.Positron)

# Display the plot

fileout = os.path.join(plot_dir,'Result_map_{}_rp{}.png'.format(areaname,rp))

Here we see both the the potential flood depths and the associated economic damages. This overview helps to see which areas carry the most economic risk under the flooding scenarios.

Make sure to check the results and try to explain why high damages do or do not occur in case of high innundation.


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?


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.


Applied research institute Deltares (The Netherlands).

Authors of the workflow:
Natalia Aleksandrova
Ted Buskop