© Institute of Urban Water Management and Landscape Water Engineering, Graz University of Technology and Markus Pichler

Intensity duration frequency analysis (based on KOSTRA)

Heavy rainfall intensity as a function of duration and return period is defined according to DWA-A 531 (2012). This program reads rainfall measurement data and calculates the distribution of design rainfall as a function of both return period and duration, for durations up to 12 hours (and beyond) and return periods in the range 0.5 a ≤ Tₙ ≤ 100 a.

The guideline was also applied in the KOSTRA-DWD application.

Since version 0.4, the updated guidelines of DWA-A 531 (2025) have also been implemented.

Heavy rainfall data are among the most important planning parameters in water management and hydraulic engineering practice. In urban areas, for example, they are required as initial parameters for the design of rainwater drainage systems and in watercourses for the dimensioning of hydraulic structures. The accuracy of the target values of the corresponding calculation methods and models depends crucially on their accuracy. Their overestimation can lead to considerable additional costs in the structural implementation, their underestimation to an unacceptable, excessive residual risk of failure during the operation of water management and hydraulic engineering facilities. Despite the area-wide availability of heavy rainfall data through "Coordinated Heavy Rainfall Regionalisation Analyses" (KOSTRA), there is still a need for local station analyses, e.g. to evaluate the now extended data series, to evaluate recent developments or to classify local peculiarities in comparison to the KOSTRA data. However, this is only possible without restrictions if the methodological approach recommended in the worksheet is followed.

DWA-A 531 (2012) Translated with www.DeepL.com/Translator

An intensity-duration-frequency (IDF) curve is a mathematical function that relates the rainfall intensity with its duration and frequency of occurrence. These curves are commonly used in hydrology for flood forecasting and civil engineering for urban drainage design. However, the IDF curves are also analysed in hydrometeorology because of the interest in the time concentration or time-structure of the rainfall.

Wikipedia

This package was developed by Markus Pichler during his bachelor thesis and finalised it in the course of his employment at the Institute of Urban Water Management and Landscape Water Engineering.

Documentation

Read the docs here 📖.

Please cite as

Pichler, M. (2025). idf-analysis: Heavy rainfall intensity as a function of duration and return period. Journal of Open Source Software, 10(106), 7607. https://doi.org/10.21105/joss.07607

Installation

This package is written in Python3. (use a version > 3.5)

pip install idf-analysis

Add the following tags to the command for special options:

• --user: To install the package only for the local user account (no admin rights needed)
• --upgrade: To update the package

Windows

You have to install python first (i.e. the original python from the website).

To use the command-line-tool, it is advisable to add the path to your Python binary to the environment variables ^path1. There is also an option during the installation to add python to the PATH automatically. ^path2

Linux/Unix

Python is pre-installed on most operating systems (as you probably knew).

Dependencies

Packages required for this program will be installed with pip during the installation process and can be seen in the requirements.txt file.

mermaid flowchart TD contourpy["contourpy<br>1.3.1"] cycler["cycler<br>0.12.1"] fonttools["fonttools<br>4.54.1"] kiwisolver["kiwisolver<br>1.4.7"] matplotlib["matplotlib<br>3.9.2"] mpmath["mpmath<br>1.3.0"] numpy["numpy<br>2.1.3"] packaging["packaging<br>24.2"] pandas["pandas<br>2.2.3"] pillow["pillow<br>11.0.0"] pyparsing["pyparsing<br>3.2.0"] python-dateutil["python-dateutil<br>2.9.0.post0"] pytz["pytz<br>2024.2"] pyyaml["PyYAML<br>6.0.2"] scipy["scipy<br>1.14.1"] six["six<br>1.16.0"] sympy["sympy<br>1.13.3"] tqdm["tqdm<br>4.67.0"] tzdata["tzdata<br>2024.2"] contourpy -- "&ge;1.23" --> numpy matplotlib -- "&ge;0.10" --> cycler matplotlib -- "&ge;1.0.1" --> contourpy matplotlib -- "&ge;1.23" --> numpy matplotlib -- "&ge;1.3.1" --> kiwisolver matplotlib -- "&ge;2.3.1" --> pyparsing matplotlib -- "&ge;2.7" --> python-dateutil matplotlib -- "&ge;20.0" --> packaging matplotlib -- "&ge;4.22.0" --> fonttools matplotlib -- "&ge;8" --> pillow pandas -- "&ge;1.26.0" --> numpy pandas -- "&ge;2.8.2" --> python-dateutil pandas -- "&ge;2020.1" --> pytz pandas -- "&ge;2022.7" --> tzdata python-dateutil -- "&ge;1.5" --> six scipy -- "&ge;1.23.5,&lt;2.3" --> numpy sympy -- "&ge;1.1.0,&lt;1.4" --> mpmath

Usage

The documentation of the Python-package can be found here.

One basic usage could be:

```python import pandas as pd from idfanalysis import IntensityDurationFrequencyAnalyse from idfanalysis.definitions import *

initialize of the analysis class

idf = IntensityDurationFrequencyAnalyse(serieskind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extendeddurations=True)

series = pd.Series(index=pd.DatetimeIndex(...), data=...) # this is just a placeholder

setting the series for the analysis

idf.set_series(series)

auto-save the calculated parameter so save time for a later use, as the parameters doesn't have to be calculated again.

idf.autosaveparameters('idf_parameters.yaml') ```

If you only want to analyse an already existing IDF-table

```python import pandas as pd from idf_analysis import IntensityDurationFrequencyAnalyse

idf_table = pd.DataFrame(...) # this is just a placeholder

index: Duration Steps in minutes as int or float

columns: Return Periods in years as int or float

values: rainfall height in mm

idf = IntensityDurationFrequencyAnalyse.fromidftable(idf_table) ```

Commandline tool

The following commands show the usage for Linux/Unix systems. To use these features on Windows you have to add python -m before each command.

To start the script use following commands in the terminal/Prompt

idf_analysis

idf_analysis -h

``` usage: main.py [-h] -i INPUT [-ws {ATV-A121,KOSTRA,convectivevs_advective}] [-kind {partial,annual}] [-t {>= 0.5 a and <= 100 a}] [-d {>= 5 min and <= 8640 min}] [-r {>= 0 L/s*ha}] [-hN {>= 0 mm}] [--r7201] [--plot] [--exporttable]

heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012) All files will be saved in the same directory of the input file but in a subfolder called like the inputfile + "idfdata". Inside this folder a file called "idf_parameter.yaml"-file will be saved and contains interim- calculation-results and will be automatically reloaded on the next call.

optional arguments: -h, --help show this help message and exit -i INPUT, --input INPUT input file with the rain time-series (csv or parquet) -ws {ATV-A121,KOSTRA,convectivevsadvective}, --worksheet {ATV-A121,KOSTRA,convectivevsadvective} From which worksheet the recommendations for calculating the parameters should be taken. -kind {partial,annual}, --serieskind {partial,annual} The kind of series used for the calculation. (Calculation with partial series is more precise and recommended.) -t {>= 0.5 a and <= 100 a}, --returnperiod {>= 0.5 a and <= 100 a} return period in years (If two of the three variables (rainfall (height or flow-rate), duration, return period) are given, the third variable is calculated.) -d {>= 5 min and <= 8640 min}, --duration {>= 5 min and <= 8640 min} duration in minutes (If two of the three variables (rainfall (height or flow-rate), duration, return period) are given, the third variable is calculated.) -r {>= 0 L/(sha)}, --flowrateof_rainfall {>= 0 L/(sha)} rainfall in Liter/(s * ha) (If two of the three variables (rainfall (height or flow-rate), duration, return period) are given, the third variable is calculated.) -hN {>= 0 mm}, --heightofrainfall {>= 0 mm} rainfall in mm or Liter/m^2 (If two of the three variables (rainfall (height or flow-rate), duration, return period) are given, the third variable is calculated.) --r7201 design rainfall with a duration of 720 minutes (=12 h) and a return period of 1 year --plot get a plot of the idf relationship --exporttable get a table of the most frequent used values ```

Example

In these examples you can see the usage in a reproducible way. This examples uses open data provided from the Austrian government. You can also find a link to the script used to download this data.

Example Jupyter notebook for the commandline (or in the docs)

Example Jupyter notebook for the python api (or in the docs)

Example Jupyter notebook for the python api of the DWA 2025 methodology (or in the docs)

Example python skript

Example Files

Interim Results of the idf analysis

Example Plot using the DWA 2012 methodology

Example Plot using the DWA 2025 methodology

Example IDF table

IDF-Table

| return period in a
duration in min | 1 | 2 | 3 | 5 | 10 | 20 | 25 | 30 | 50 | 75 | 100 | |--------------------------------------:|------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:| | 5 | 9.39 | 10.97 | 11.89 | 13.04 | 14.61 | 16.19 | 16.69 | 17.11 | 18.26 | 19.18 | 19.83 | | 10 | 15.15 | 17.62 | 19.06 | 20.88 | 23.35 | 25.82 | 26.62 | 27.27 | 29.09 | 30.54 | 31.56 | | 15 | 19.03 | 22.25 | 24.13 | 26.51 | 29.72 | 32.94 | 33.98 | 34.83 | 37.20 | 39.08 | 40.42 | | 20 | 21.83 | 25.71 | 27.99 | 30.85 | 34.73 | 38.62 | 39.87 | 40.89 | 43.75 | 46.02 | 47.63 | | 30 | 25.60 | 30.66 | 33.62 | 37.35 | 42.41 | 47.47 | 49.10 | 50.43 | 54.16 | 57.12 | 59.22 | | 45 | 28.92 | 35.51 | 39.37 | 44.23 | 50.83 | 57.42 | 59.54 | 61.28 | 66.14 | 69.99 | 72.73 | | 60 | 30.93 | 38.89 | 43.54 | 49.40 | 57.36 | 65.31 | 67.88 | 69.97 | 75.83 | 80.49 | 83.79 | | 90 | 33.37 | 41.74 | 46.64 | 52.80 | 61.17 | 69.54 | 72.23 | 74.43 | 80.60 | 85.49 | 88.96 | | 180 | 38.01 | 47.13 | 52.46 | 59.18 | 68.30 | 77.42 | 80.36 | 82.76 | 89.48 | 94.81 | 98.60 | | 270 | 41.01 | 50.60 | 56.21 | 63.28 | 72.87 | 82.46 | 85.55 | 88.07 | 95.14 | 100.75 | 104.73 | | 360 | 43.29 | 53.23 | 59.04 | 66.37 | 76.31 | 86.25 | 89.45 | 92.06 | 99.39 | 105.20 | 109.33 | | 450 | 45.14 | 55.36 | 61.33 | 68.87 | 79.08 | 89.30 | 92.59 | 95.28 | 102.81 | 108.79 | 113.03 | | 600 | 47.64 | 58.23 | 64.43 | 72.23 | 82.82 | 93.41 | 96.82 | 99.61 | 107.42 | 113.61 | 118.01 | | 720 | 49.29 | 60.13 | 66.47 | 74.45 | 85.29 | 96.12 | 99.61 | 102.46 | 110.44 | 116.78 | 121.28 | | 1080 | 54.41 | 64.97 | 71.15 | 78.94 | 89.50 | 100.06 | 103.46 | 106.24 | 114.02 | 120.20 | 124.58 | | 1440 | 58.02 | 67.72 | 73.39 | 80.54 | 90.24 | 99.93 | 103.05 | 105.61 | 112.75 | 118.42 | 122.45 | | 2880 | 66.70 | 77.41 | 83.68 | 91.57 | 102.29 | 113.00 | 116.45 | 119.26 | 127.16 | 133.42 | 137.87 | | 4320 | 71.93 | 85.72 | 93.78 | 103.95 | 117.73 | 131.52 | 135.96 | 139.58 | 149.75 | 157.81 | 163.53 | | 5760 | 78.95 | 95.65 | 105.42 | 117.72 | 134.43 | 151.13 | 156.50 | 160.89 | 173.20 | 182.97 | 189.90 | | 7200 | 83.53 | 101.38 | 111.82 | 124.98 | 142.83 | 160.68 | 166.43 | 171.12 | 184.28 | 194.72 | 202.13 | | 8640 | 85.38 | 104.95 | 116.40 | 130.82 | 150.38 | 169.95 | 176.25 | 181.40 | 195.82 | 207.27 | 215.39 |

Contributing and Support

If you're interested in contributing or need help, check out the CONTRIBUTING.md file for guidelines.

Testing

The code is written in pure python so no compiling is needed.

Most of the code is (or at least will be) automatically tested using the test suite in the ./tests folder. To run the tests, use:

sh pytest tests

Background

Pseudocode for the parameter calculation.

``` For every duration step calculating maximum rainfall intensity for each event for the corresponding duration step

if using annual event series:  # only recommeded for measurements longer than 20 year
    converting max event rainfall intensities per year to a series
    calculating parameters u and w using the gumbel distribution

elif using partial event series:
    converting the n (approximatly 2.72 x measurement duration in years) biggest event rainfall intensities to a series
    calculating parameters u and w using the exponential distribution

Splitting IDF curve formulation in to several duration ranges For each duration range: For each parameter (u and w): balancing the parameter over all duation steps (in the range) using a given formulation (creating parameters a and b) # one-folded-logaritmic | two-folded-logarithmic | hyperbolic

u(D) = f(au, bu, D) w(D) = f(aw, bw, D)

h(D,Tn) = u(D) + w(D) * ln(Tn) ```