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"source": [
"Trajectories Module\n",
"==================="
]
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"source": [
"The objective of the trajectory module is to ease the handling of multiple risk assessment at different point in time, and enable interpolation in time."
]
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"source": [
"## Quickstart example"
]
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"source": [
"In the following example we use the DEMO data to showcase the basic workflow to use this module.\n",
"\n",
"As usual, a thourough read of this tutorial is highly recommended!"
]
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"text": [
"2026-03-27 14:39:25,551 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/demo/data/tc_fl_1990_2004.h5\n",
"2026-03-27 14:39:25,587 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/demo/data/tc_fl_1990_2004.h5\n",
"2026-03-27 14:39:25,752 - climada.trajectories.calc_risk_metrics - WARNING - No group id defined in at least one of the Exposures object. Per group aai will be empty.\n"
]
},
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" date group measure metric unit risk\n",
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"(
"
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"source": [
"from climada.hazard import Hazard\n",
"from climada.util import HAZ_DEMO_H5\n",
"from climada.entity import Entity\n",
"from climada.util.constants import ENT_DEMO_FUTURE, ENT_DEMO_TODAY\n",
"from climada.trajectories.snapshot import Snapshot\n",
"from climada.entity import ImpactFuncSet, ImpfTropCyclone\n",
"from climada.trajectories import StaticRiskTrajectory, InterpolatedRiskTrajectory\n",
"import warnings\n",
"\n",
"warnings.simplefilter(\"ignore\")\n",
"\n",
"# Load exposure\n",
"exp_present = Entity.from_excel(ENT_DEMO_TODAY).exposures\n",
"exp_future = Entity.from_excel(ENT_DEMO_FUTURE).exposures\n",
"\n",
"# Load Hazard\n",
"haz_present = Hazard.from_hdf5(HAZ_DEMO_H5)\n",
"haz_future = Hazard.from_hdf5(HAZ_DEMO_H5)\n",
"haz_future.frequency *= 1.2\n",
"\n",
"# Load impact function set\n",
"impf_tc_present = ImpfTropCyclone.from_emanuel_usa()\n",
"impf_tc_future = ImpfTropCyclone.from_emanuel_usa(v_half=70.0)\n",
"impf_set_present = ImpactFuncSet([impf_tc_present])\n",
"impf_set_future = ImpactFuncSet([impf_tc_future])\n",
"\n",
"# Create the snapshots\n",
"snap1 = Snapshot(\n",
" exposure=exp_present, hazard=haz_present, impfset=impf_set_present, date=\"2018\"\n",
")\n",
"\n",
"snap2 = Snapshot(\n",
" exposure=exp_future, hazard=haz_future, impfset=impf_set_future, date=\"2050\"\n",
")\n",
"\n",
"# Create the trajectory\n",
"interpolated_risk_traj = InterpolatedRiskTrajectory([snap1, snap2])\n",
"\n",
"# Observe the results\n",
"display(interpolated_risk_traj.per_date_risk_metrics())\n",
"\n",
"interpolated_risk_traj.plot_waterfall()\n",
"interpolated_risk_traj.plot_time_waterfall()"
]
},
{
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"source": [
"## Important disclaimers"
]
},
{
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"id": "f7d4fdab-8662-4848-bb87-9b6045447957",
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"source": [
"### Interpolation of risk can be... risky"
]
},
{
"cell_type": "markdown",
"id": "8f9531a7-9a1a-400f-8c82-3a51fdc6671a",
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"source": [
"One purpose of this module is to improve the evaluation of risk in between two \"known\" points in time.\n",
"\n",
"This part relies on interpolation (linear by default) of impacts and risk metrics in between the different specified points, \n",
"which may lead to incoherent results in cases where this simplification drifts too far from reality.\n",
"\n",
"For instance if you are using different historical events as you points in time, a static comparison of the different risk\n",
"estimates may be interesting, but interpolating in between makes very little sense."
]
},
{
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"source": [
"### Memory and computation requirements\n",
"\n",
"This module adds a new dimension (time) to the risk, as such, it **multiplies** the memory and computation requirement along that dimension (although we avoid running a full-fledge impact computation for each \"interpolated\" point, we still have to define an impact matrix for each of those). \n",
"\n",
"This can of course (very) quickly increase the memory and computation requirements for bigger data. We encourage you to first try on small examples before running big computations.\n"
]
},
{
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"id": "b53b1da2-7be1-4507-96bb-2efd8dd3e910",
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"source": [
"## Using the `trajectories` module"
]
},
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"id": "4e0f3261-f443-4cc6-b85b-c6a3d90b73e3",
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"The fundamental idea behing the `trajectories` module is to enable a better assessment of the evolution of risk over time, both by facilitating point by point comparison, and the \"evolution\" or risk.\n",
"\n",
"This module aims at facilitating answering questions such as:\n",
"\n",
"- How does future hazards (probabilistic event set), exposure and vulnerability change impacts with respect to present?\n",
"- How would the impacts compare if a past event were to happen again with present / future exposure?\n",
"- How will risk evolve in the future under different assumptions on the evolution of hazard, exposure, vulnerability and discount rate?\n",
"- *etc*.\n",
"\n",
"To achieve this, this module introduces two concepts:\n",
"\n",
"- Snapshots of risk, a fixed representation of risk (via its three components Exposure, Hazard and Vulnerability) for a given date. This concept is intended to be generic, as such the given date can be something else than a year, a month or a day for instance, but keep in mind that we will not check that the data you provide makes sense for it!\n",
"- Trajectories of risk, a collection of snapshots,for which risk metrics can be computed and regrouped to ease their evaluation."
]
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"source": [
"## `Snapshot`: A snapshot of risk at a specific year"
]
},
{
"cell_type": "markdown",
"id": "274a342f-54c0-4590-9110-5e297010955e",
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"source": [
"We use `Snapshot` objects to define a point in time for risk. This object acts as a wrapper of the classic risk framework composed of Exposure, Hazard and Vulnerability. As such it is defined for a specific date (usually a year), and contains an `Exposures`, a `Hazard`, and an `ImpactFuncSet` object.\n",
"\n",
"Instantiating such a `Snapshot` is done simply with:\n",
"\n",
"```python\n",
"snap = Snapshot(\n",
" exposure=your_exposure,\n",
" hazard=your_hazard,\n",
" impfset=your_impfset,\n",
" date=your_date\n",
" )\n",
"```\n",
"\n",
"Note that to avoid any ambiguity, you need to write explicitly `exposure=your_exposure`.\n",
"\n",
"Think of `Snapshot` as a representation of risk at, or around, a specific date. Your hazard should be a probabilistic set of events that are representative for the designated date.\n",
"\n",
"To be consistent with the intuitive idea of a snapshot, by default `Snapshot` objects make a \"deep copy\" of the risk triplet and are immutable.\n",
"This means that they do not change once created (notably even if you change one of the component, e.g. the Hazard object, outside of the `Snapshot`).\n",
"If you want a `Snapshot` with a different `Hazard`, you need to create a new one.\n",
"\n",
"In that spirit, you cannot directly instantiate a Snapshot with an adaptation measure. To include adaptation, you need to first create the snapshot without adaptation, and then use `apply_measure()`, which\n",
"will return a new `Snapshot`, with the changed (Exposure, Hazard, ImpactFuncSet) according to the given measure.\n",
"\n",
"You can supply a measure object to `Snapshot(<...>, measure=measure)`, but **it will not be applied** to the triplet (Exposure, Hazard, ImpactFuncSet) and assume the triplet already include the change.\n",
"Only advanced users with a good understanding of what they are doing should supply a measure parameter directly to the `Snapshot` constructor, else, stick to the `apply_measure()` method.\n",
"\n",
"Below is an concrete example of how to create a Snapshot using data from the data API for tropical cyclones in Haiti:"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"2026-03-27 14:39:27,625 - climada.entity.exposures.base - INFO - Reading /home/sjuhel/climada/data/exposures/litpop/LitPop_150arcsec_HTI/v3/LitPop_150arcsec_HTI.hdf5\n",
"2026-03-27 14:39:32,846 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020/v2/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020.hdf5\n"
]
}
],
"source": [
"from climada.util.api_client import Client\n",
"from climada.entity import ImpactFuncSet, ImpfTropCyclone\n",
"from climada.trajectories.snapshot import Snapshot\n",
"\n",
"client = Client()\n",
"\n",
"exp_present = client.get_litpop(country=\"Haiti\")\n",
"\n",
"haz_present = client.get_hazard(\n",
" \"tropical_cyclone\",\n",
" properties={\n",
" \"country_name\": \"Haiti\",\n",
" \"climate_scenario\": \"historical\",\n",
" \"nb_synth_tracks\": \"10\",\n",
" },\n",
")\n",
"\n",
"impf_set = ImpactFuncSet([ImpfTropCyclone.from_emanuel_usa()])\n",
"exp_present.gdf.rename(columns={\"impf_\": \"impf_TC\"}, inplace=True)\n",
"exp_present.gdf[\"impf_TC\"] = 1\n",
"\n",
"snap1 = Snapshot(\n",
" exposure=exp_present, hazard=haz_present, impfset=impf_set, date=\"2018\"\n",
")"
]
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"source": [
"All risk dimensions are freely accessible from the snapshot:"
]
},
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"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"2026-03-27 14:39:32,894 - climada.util.coordinates - INFO - Raster from resolution 0.04166665999999708 to 0.04166665999999708.\n"
]
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""
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"snap1.exposure.plot_raster()\n",
"snap1.hazard.plot_intensity(0)\n",
"snap1.impfset.plot()"
]
},
{
"cell_type": "markdown",
"id": "d2e6daae-6345-41ac-a560-71040942db39",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"## Evaluating risk from multiple snapshots using trajectories"
]
},
{
"cell_type": "markdown",
"id": "8e8458c3-a3f9-4210-9de0-15293167f2f9",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"Trajectories facilitate the evaluation of risk of multiple snapshot. The module implements two kinds of trajectories:\n",
"\n",
"- `StaticRiskTrajectory`: which estimate the risk at each snaphot only, and regroups the results nicely.\n",
"- `InterpolatedRiskTrajectory`: which also includes the evolution of risk in between the snapshots using interpolation.\n",
"\n",
"So first, let us define `Snapshot` for a future point in time. We will increase the value of the exposure following a certain growth rate, and use future tropical\n",
"cyclone data for the hazard, we will also change the vulnerability to be slightly lower in the future:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c516c861-c5c1-475b-82e2-c867c5c08ec9",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2026-03-27 14:39:40,926 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2040/v2/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2040.hdf5\n"
]
}
],
"source": [
"import copy\n",
"\n",
"future_year = 2040\n",
"exp_future = copy.deepcopy(exp_present)\n",
"exp_future.ref_year = future_year\n",
"n_years = exp_future.ref_year - exp_present.ref_year + 1\n",
"growth_rate = 1.02\n",
"growth = growth_rate**n_years\n",
"exp_future.gdf[\"value\"] = exp_future.gdf[\"value\"] * growth\n",
"\n",
"haz_future = client.get_hazard(\n",
" \"tropical_cyclone\",\n",
" properties={\n",
" \"country_name\": \"Haiti\",\n",
" \"climate_scenario\": \"rcp60\",\n",
" \"ref_year\": str(future_year),\n",
" \"nb_synth_tracks\": \"10\",\n",
" },\n",
")\n",
"\n",
"impf_set = ImpactFuncSet(\n",
" [\n",
" ImpfTropCyclone.from_emanuel_usa(v_half=78.0),\n",
" ]\n",
")\n",
"exp_future.gdf.rename(columns={\"impf_\": \"impf_TC\"}, inplace=True)\n",
"exp_future.gdf[\"impf_TC\"] = 1\n",
"snap2 = Snapshot(\n",
" exposure=exp_future, hazard=haz_future, impfset=impf_set, date=str(future_year)\n",
")\n",
"\n",
"### Now we can define a list of two snapshots, present and future:\n",
"snapcol = [snap1, snap2]"
]
},
{
"cell_type": "markdown",
"id": "27ca72b1-b1fa-4cd2-8f74-a69dc6eb3c9c",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"Based on such a list of snapshots, we can then evaluate a risk trajectory using a `StaticRiskTrajectory` or a `InterpolatedRiskTrajectory` object."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e782ab8b",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"from climada.trajectories import StaticRiskTrajectory, InterpolatedRiskTrajectory\n",
"\n",
"static_risk_traj = StaticRiskTrajectory(snapcol)\n",
"interpolated_risk_traj = InterpolatedRiskTrajectory(snapcol)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "483767e7-9089-4b5e-a307-514ac302e773",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"remove-input"
]
},
"outputs": [
{
"data": {
"text/html": [
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
""
]
},
{
"cell_type": "markdown",
"id": "2d7e8653-4ef9-40f5-8f8a-ef0e8b3b8a8c",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### Tidy format\n",
"\n",
"We use the \"tidy\" format to output most of the results.\n",
"\n",
"A **tidy data** format is a standardized way to structure datasets, making them easier to analyze and visualize. It's based on three main principles:\n",
"\n",
"1. **Each variable forms a column.**\n",
"2. **Each observation forms a row.**\n",
"3. **Each type of observational unit forms a table.**\n",
"\n",
"Example:\n",
"\n",
"| group | date | metric | risk |\n",
"| :---: | :---: | :---: | :---: |\n",
"| All | 2018-01-01 | aai | $1.840432 \\times 10^{8}$ |\n",
"| All | 2040-01-01 | aai | $6.946753 \\times 10^{8}$ |\n",
"| All | 2018-01-01 | rp\\_20 | $1.420589 \\times 10^{8}$ |\n",
"\n",
"In this example, every descriptive quality (variable) of the risk evaluation is placed in its own column:\n",
"\n",
"* **`group`**: The exposure subgroup for the risk evalution point.\n",
"* **`date`**: The date for the risk evalution point.\n",
"* **`metric`**: The specific risk measure (e.g., 'aai', 'rp\\_20', 'rp\\_100').\n",
"* **`unit`**: The unit of the risk evaluation.\n",
"* **`risk`**: The actual value being measured.\n",
"\n",
"Each row represents a single, complete observation. For example, the very first row is a measurement of the **'aai' metric** for **group 'All'** on **'2018-01-01'**, with the resulting **risk** value of **$1.840432 \\times 10^{8}$ USD**."
]
},
{
"cell_type": "markdown",
"id": "ca8951cc-4a0a-4f3d-9c21-96dd6a835810",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### Static and Interpolated trajectories"
]
},
{
"cell_type": "markdown",
"id": "dc76cb91",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"`StaticRiskTrajectory` will compute and hold risk metrics for all the given snapshots without interpolation:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "14453563",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2026-03-27 14:39:41,093 - climada.trajectories.calc_risk_metrics - WARNING - No group id defined in the Exposures object. Per group aai will be empty.\n"
]
},
{
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"
no_measure
\n",
"
rp_50
\n",
"
USD
\n",
"
4.580720e+09
\n",
"
\n",
"
\n",
"
6
\n",
"
2018-01-01
\n",
"
All
\n",
"
no_measure
\n",
"
rp_100
\n",
"
USD
\n",
"
5.719050e+09
\n",
"
\n",
"
\n",
"
7
\n",
"
2040-01-01
\n",
"
All
\n",
"
no_measure
\n",
"
rp_100
\n",
"
USD
\n",
"
8.477125e+09
\n",
"
\n",
" \n",
"
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"
],
"text/plain": [
" date group measure metric unit risk\n",
"0 2018-01-01 All no_measure aai USD 1.840432e+08\n",
"1 2040-01-01 All no_measure aai USD 2.749295e+08\n",
"2 2018-01-01 All no_measure rp_20 USD 1.420589e+08\n",
"3 2040-01-01 All no_measure rp_20 USD 2.357976e+08\n",
"4 2018-01-01 All no_measure rp_50 USD 3.059112e+09\n",
"5 2040-01-01 All no_measure rp_50 USD 4.580720e+09\n",
"6 2018-01-01 All no_measure rp_100 USD 5.719050e+09\n",
"7 2040-01-01 All no_measure rp_100 USD 8.477125e+09"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"static_risk_traj.per_date_risk_metrics()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cd169d1b-741c-471c-b402-391096e20613",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
" The `InterpolatedRiskTrajectory` object goes further and computes the metrics for all the dates between the different snapshots in the given collection for a given time resolution (one year by default). In this example, from the snapshot in 2018 to the one in 2040. \n",
"\n",
"Note that this can require a bit of computation and memory, especially for large regions or extended range of time with high time resolution.\n",
"Also note, that most computations are only run and stored when needed, not at instantiation.\n",
"\n",
"From this object you can access different risk metrics:\n",
"\n",
"* Average Annual Impact (aai) both for all exposure points (group == \"All\") and specific groups of exposure points (defined by a \"group_id\" in the exposure).\n",
"* Estimated impact for different return periods (20, 50 and 100 by default)\n",
"\n",
"Both as average over the whole period:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9c485dc4-c009-46fb-aa4a-603bc9dcf5b4",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2026-03-27 14:39:42,146 - climada.trajectories.calc_risk_metrics - WARNING - No group id defined in at least one of the Exposures object. Per group aai will be empty.\n"
]
},
{
"data": {
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" period group measure metric unit risk\n",
"0 2018 to 2040 All no_measure aai USD 2.309016e+08\n",
"1 2018 to 2040 All no_measure rp_100 USD 7.148372e+09\n",
"2 2018 to 2040 All no_measure rp_20 USD 1.896739e+08\n",
"3 2018 to 2040 All no_measure rp_50 USD 3.847129e+09"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interpolated_risk_traj.per_period_risk_metrics()"
]
},
{
"cell_type": "markdown",
"id": "af53286d-ee62-44a5-907b-84103302663d",
"metadata": {
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"slideshow": {
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},
"tags": []
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"source": [
"Or on a per-date basis:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6b73a589-9ee4-41e8-90e0-910bfe4dd8fc",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2026-03-27 14:39:42,191 - climada.trajectories.calc_risk_metrics - WARNING - No group id defined in at least one of the Exposures object. Per group aai will be empty.\n"
]
},
{
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"text/plain": [
" date group measure metric unit risk\n",
"0 2018 All no_measure aai USD 1.840432e+08\n",
"1 2019 All no_measure aai USD 1.885312e+08\n",
"2 2020 All no_measure aai USD 1.929908e+08\n",
"3 2021 All no_measure aai USD 1.974211e+08\n",
"4 2022 All no_measure aai USD 2.018214e+08\n",
".. ... ... ... ... ... ...\n",
"87 2036 All no_measure rp_100 USD 8.025179e+09\n",
"88 2037 All no_measure rp_100 USD 8.140512e+09\n",
"89 2038 All no_measure rp_100 USD 8.254300e+09\n",
"90 2039 All no_measure rp_100 USD 8.366514e+09\n",
"91 2040 All no_measure rp_100 USD 8.477125e+09\n",
"\n",
"[92 rows x 6 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interpolated_risk_traj.per_date_risk_metrics()"
]
},
{
"cell_type": "markdown",
"id": "00e0a09b-9dd6-4378-81a1-cda5290f9aa4",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"You can also plot the \"contribution\" or \"components\" of the change in risk (Average ) via a waterfall graph:\n",
"\n",
" - The 'base risk', i.e., the risk without change in hazard or exposure, compared to trajectory's earliest date.\n",
" - The 'exposure contribution', i.e., the additional risks due to change in exposure (only)\n",
" - The 'hazard contribution', i.e., the additional risks due to change in hazard (only)\n",
" - The 'vulnerability contribution', i.e., the additional risks due to change in vulnerability (only)\n",
" - The 'interaction contribution', i.e., the additional risks due to the interaction term (between exposure, hazard and vulnerability)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "08c226a4-944b-4301-acfa-602adde980a5",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interpolated_risk_traj.plot_waterfall()"
]
},
{
"cell_type": "markdown",
"id": "7896af66-b0aa-4418-b22e-c64fd4d2cfe1",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"And as well on a per date basis (keep in mind this is an interpolation, thus should be interpreted with caution):"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6a15775f-af9e-4940-b18d-eb16bd0c8c85",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interpolated_risk_traj.plot_time_waterfall()"
]
},
{
"cell_type": "markdown",
"id": "501e455b-e7c6-4672-9191-d5fefe38d424",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### DiscRates"
]
},
{
"cell_type": "markdown",
"id": "0dba0218-55fe-423d-a520-61d3cb2a991c",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"To correctly assess the future risk, you may also want to apply a discount rate, in order to express future costs in net present value.\n",
"\n",
"This can easily be done providing an instance of the already existing `DiscRates` class when instantiating the trajectory.\n",
"\n",
"The discount rate is applied by assuming the year of the date of the first Snapshot is the baseline (no discounting).\n",
"\n",
"Note that when interpolating on a sub-yearly basis, the discount rate remains on a yearly basis: All dates "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "651e31cb-5a55-4a22-a7c3-b5f79b3a20ef",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"from climada.entity import DiscRates\n",
"import numpy as np\n",
"\n",
"year_range = np.arange(exp_present.ref_year, exp_future.ref_year + 1)\n",
"annual_discount_stern = np.ones(n_years) * 0.014\n",
"discount_stern = DiscRates(year_range, annual_discount_stern)\n",
"discounted_risk_traj = InterpolatedRiskTrajectory(\n",
" snapcol, risk_disc_rates=discount_stern\n",
")"
]
},
{
"cell_type": "markdown",
"id": "d86bedbb-6c0a-4f7d-a63e-5012510339d3",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"You can easily notice the difference with the previously defined trajectory without discount rate."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ee3b0217-fe14-44a9-98f5-e1fc7f45e613",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = interpolated_risk_traj.aai_metrics().plot(\n",
" x=\"date\", y=\"risk\", label=\"No discount rate\"\n",
")\n",
"discounted_risk_traj.aai_metrics().plot(\n",
" x=\"date\", y=\"risk\", label=\"Stern discount rate\", ax=ax\n",
")"
]
},
{
"cell_type": "markdown",
"id": "81a836a4-ef97-49ca-88a7-a9989e9ca4df",
"metadata": {},
"source": [
"## When to use Static vs Interpolated trajectories?\n",
"\n",
"`StaticTrajectory` objects do not bring new information compared to usual impact computation with `ImpactCalc`, they are just a way to ease computations and results handling over many different triplets at different dates.\n",
"\n",
"Conversely, `InterpolatedTrajectory` objects bring an interpolated estimate of risk (per change in risk component), in between the dates. This can be useful when estimates of the risk in between two dates matters, for instance:\n",
"- To evaluate when certain thresholds are reached.\n",
"- When stress-testing across a time horizon\n",
"- To integrate with other related time-series"
]
},
{
"cell_type": "markdown",
"id": "0152e9fa-55fa-4cf2-b187-59e6228af563",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"## Advanced usage\n",
"\n",
"In this section we present some more advanced features and use of this module."
]
},
{
"cell_type": "markdown",
"id": "dbf4b23d-d502-4c06-8e0d-eb832af8ebe4",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### Exposure sub groups"
]
},
{
"cell_type": "markdown",
"id": "86a58a22-63a5-42a9-9afb-cf8962156e36",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"It is often useful to look at sub-groups of your exposure (social groups of different social vulnerability, buildings of different type, etc.)\n",
"\n",
"The `trajectory` module facilitate looking at risk specifically for sub-groups of exposure points. In order to do so, you need to set a column \"group_id\" in the `GeoDataFrame` of your exposure.\n",
"\n",
"Here we create dummy groups for exposure points above and below the mean exposure value:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "566f0c34-b19b-403a-a905-92c51093c182",
"metadata": {
"editable": true,
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"source": [
"exp_present.gdf[\"group_id\"] = (\n",
" exp_present.gdf[\"value\"] > exp_present.gdf[\"value\"].mean()\n",
") * 1\n",
"exp_future.gdf[\"group_id\"] = (\n",
" exp_future.gdf[\"value\"] > exp_future.gdf[\"value\"].mean()\n",
") * 1\n",
"\n",
"snap1 = Snapshot(\n",
" exposure=exp_present, hazard=haz_present, impfset=impf_set, date=\"2018\"\n",
")\n",
"snap2 = Snapshot(exposure=exp_future, hazard=haz_future, impfset=impf_set, date=\"2040\")\n",
"static_risk_traj = StaticRiskTrajectory([snap1, snap2])\n",
"interpolated_risk_traj = InterpolatedRiskTrajectory([snap1, snap2])"
]
},
{
"cell_type": "markdown",
"id": "e0123f8f-ec7f-47ac-9f60-0f59808b9670",
"metadata": {
"editable": true,
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"slide_type": ""
},
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},
"source": [
"You can now access the `aii_per_group` metric, which will give you the average impact (for the frequency unit of you hazard) restricted to the exposure points of the corresponding group."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "344a84d7-275c-426d-80d1-5375696f5cc3",
"metadata": {
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"
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],
"source": [
"risk_traj.plot_time_waterfall()"
]
},
{
"cell_type": "markdown",
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"source": [
"### Non-default return periods"
]
},
{
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"source": [
"You can easily change the default return periods computed, either at initialisation time, or via the property `return_periods`.\n",
"Note that estimates of impacts for specific return periods are highly dependant on the data you provided.\n",
"\n",
"**We cannot check if the event set you provide is fit for computing impacts for a specific return period.** "
]
},
{
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"execution_count": 21,
"id": "0ade93f9-c43a-4e8a-8225-9343bbbb3615",
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"90 2039 All no_measure rp_30 USD 9.367770e+08\n",
"91 2040 All no_measure rp_30 USD 9.608368e+08\n",
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"[92 rows x 6 columns]"
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".. ... ... ... ... ... ...\n",
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],
"source": [
"snapcol = [snap1, snap2]\n",
"risk_traj = InterpolatedRiskTrajectory(snapcol, return_periods=[10, 15, 20, 30])\n",
"display(risk_traj.return_periods_metrics())\n",
"\n",
"risk_traj.return_periods = [150, 250, 500]\n",
"display(risk_traj.return_periods_metrics())"
]
},
{
"cell_type": "markdown",
"id": "39059ec5-9125-4cfc-b8c6-e6327d8b98cc",
"metadata": {
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"slide_type": ""
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"source": [
"### Non-yearly date index"
]
},
{
"cell_type": "markdown",
"id": "4f8f83d6-a45d-4d3b-b25d-d3294e6e1955",
"metadata": {
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"source": [
"You can use any valid pandas [frequency string for periods](https://pandas.pydata.org/docs/user_guide/timeseries.html#period-aliases) for the time resolution,\n",
"for instance \"5Y\" for every five years. This reduces the resolution of the interpolation, which can reduce the required computations at the cost of \"precision\".\n",
"Conversely you can also increase the time resolution to a monthly base for instance.\n",
"\n",
"Same as for the return periods, you can change that at initialisation or afterward via the property.\n",
"\n",
"Keep in mind that risk metrics are still computed the same way, so if you initialy had hazards with annual frequency values, you would still have \"Average Annual Impacts\" values for every months and not average monthly ones!\n",
"\n",
"Also note that `InterpolatedRiskTrajectory` uses `PeriodIndex` for the time dimension. These indexes are defined with the dates of the first and last snapshot, and the given time resolution.\n",
"\n",
"This means that an `InterpolatedRiskTrajectory` for a 2020 `Snapshot` and 2040 `Snapshot` with a yearly time resolution will include all years from 2020 to 2040 included (11 years in total).\n",
"\n",
"However, a trajectory with the same snapshots with a monthly resolution will have January 2040 as a last period if you only provided year 2040 for the last date. If you want to include the whole 2040 year, you need to explicitly give the date \"2040-12-31\" to the last snapshot."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "128fac77-e077-4241-a003-a60c4afcad74",
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"... ... ... ... ... ... ...\n",
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"\n",
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"execution_count": 23,
"metadata": {},
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],
"source": [
"## snapcol = [snap, snap2]\n",
"\n",
"## Here we use \"1MS\" to get a monthly basis\n",
"risk_traj.time_resolution = \"1M\"\n",
"\n",
"## We would have to divide results by 12 to get \"average monthly impacts\"\n",
"risk_traj.per_date_risk_metrics()"
]
},
{
"cell_type": "markdown",
"id": "f5d6b725-41ee-495b-bc72-5806db4cfdba",
"metadata": {
"editable": true,
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"slide_type": ""
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"tags": []
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"source": [
"### Non-linear interpolation"
]
},
{
"cell_type": "markdown",
"id": "a8065729-5d0b-4250-8324-2ce82cb0d644",
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"source": [
"The module allows you to define your own interpolation strategy. Thus you can decide how to interpolate along each dimension of risk (Exposure, Hazard and Vulnerability).\n",
"This is done via `InterpolationStrategy` objects, which simply require three functions stating how to interpolate along each dimensions.\n",
"\n",
"For convenience the module provides an `AllLinearStrategy` (the risk is linearly interpolated along all dimensions) and a `ExponentialExposureStrategy` (uses exponential interpolation along exposure, and linear for the two other dimensions).\n",
"\n",
"This can prove helpfull if you are interpolating between two distant dates with an exponential growth factor for the exposure value. On the example below, we show the difference in risk estimates using an the two different interpolation strategies for the exposure dimension:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "c97e768e-bd4c-47d7-bace-96645f8b3bc4",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2026-03-27 14:40:21,528 - climada.hazard.io - INFO - Reading /home/sjuhel/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2080/v2/tropical_cyclone_10synth_tracks_150arcsec_rcp60_HTI_2080.hdf5\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Comparison of average annual impact estimate for different interpolation approaches')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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