rainfallqc

RainfallQC - Quality control for rainfall data

https://img.shields.io/pypi/v/rainfallqc.svg

Provides methods for running rainfall quality control.

Features

33 rainfall QC methods (25 from IntenseQC, 6 from SubHourlyQC and 2 from pyPWSQC)
polars DataFrame support for fast data processing
modular structure so you can pick and choose which checks to run
support for single rain gauges or networks of rain gauges
editable parameters so you can tweak thresholds, streak or accumulation lengths, and distances to neighbouring gauges

Installation

RainfallQC can be installed from PyPi:

pip install rainfallqc

Examples

Example 1. - Running individual checks on a single rain gauge

Let’s say you have data for a single rain gauge stored in “hourly_rain_gauge_data.csv” which looks like this:

Example data 1. Single rain gauge
time	rain_mm
2020-01-01 00:00	0.0
2020-01-01 01:00	0.1
2020-01-01 02:00	0.0
2020-01-01 03:00	105.0
2020-01-01 04:00	0.6
…	…

For the majority of the checks in RainfallQC, you can load in your data using polars and run the checks directly. Below, we run 2 example QC checks:

1. check_intermittency - to flag years where there are periods of non-zero bounded by 0 (see Figure 1.),
1. daily_accumulations - to flag accumulations of hourly values into daily.

https://thomasjkeel.github.io/UK-Rain-Gauge-Network/example_images/intermittency.png — **Figure 1.** Example of an intermittency issue within the rainfall record

import polars as pl
from rainfallqc import gauge_checks, timeseries_checks

data = pl.read_csv("hourly_rain_gauge_data.csv")

intermittent_years = gauge_checks.check_intermittency(data, target_gauge_col="rain_mm")

daily_accumulation_flags = timeseries_checks.check_daily_accumulations(
    data,
    target_gauge_col="rain_mm",
    gauge_lat=52.0,
    gauge_lon=2.0,
    smallest_measurable_rainfall_amount=0.1,
)

Please note that some checks may require additional metadata, such as gauge location (latitude and longitude) or smallest measurable rainfall amount (e.g. 0.1 mm). This could look like:

Example metadata 1. Rain gauge metadata
station_id	latitude	longitude	start_datetime	end_datetime	path
rain_mm_gauge_1	53.0	2.0	2020-01-01 00:00	2024-01-01 00:00	path/to/gauge_1.csv
rain_mm_gauge_2	54.1	-0.5	2018-01-01 00:00	2023-01-01 00:00	path/to/gauge_2.csv
rain_mm_gauge_3	56.9	1.9	2015-01-01 00:00	2025-01-01 00:00	path/to/gauge_3.csv
…	…	…			…

You could then run checks that require metadata i.e. the check_hourly_exceedance_etccdi_rx1day QC check which flags rainfall values exceeding the hourly day rainfall 1-day record at a given location (see Figure 2):

https://thomasjkeel.github.io/UK-Rain-Gauge-Network/example_images/rx1day_check.png — **Figure 2.** Example of an Rx1day check from the IntenseQC framework

The code for that check looks like:

import polars as pl
from rainfallqc import comparison_checks

data = pl.read_csv("hourly_rain_gauge_data_gauge_1.csv")
metadata = pl.read_csv("rain_gauge_metadata.csv")

target_gauge_id = "rain_mm_gauge_1"
target_metadata = metadata.filter(pl.col("station_id") == target_gauge_id)

rx1day_check = comparison_checks.check_hourly_exceedance_etccdi_rx1day(
     data,
     target_gauge_col=target_gauge_col,
     gauge_lat=target_metadata["latitude"],
     gauge_lon=target_metadata["longitude"]
)

Output flags will then look like:

Example flag outputs for the Rx1day QC check
time	rx1day_check
2020-01-01 00:00	0
2020-01-01 01:00	0
2020-01-01 02:00	0
2020-01-01 03:00	1
2020-01-01 04:00	0
…	…

Example 2. - Running multiple QC checks on a single target gauge

To run multiple QC checks, you can use the apply_qc_framework() method to run QC methods from a given framework (e.g. IntenseQC).

For more information about how to run multiple checks in a framework see Example 4 in the docs

Other examples

Of course, your data may not be tabular, or may not be stored in a single file. Therefore, please see our other Tutorials.

QC checks in RainfallQC

QC checks and appropriate time-resolution
Long name	Sub-module	QC Framework	<15-min	15-min	hourly	daily	monthly
Percentiles	Gauge checks	IntenseQC	✓	✓	✓	✓	✓
K-largest	Gauge checks	IntenseQC	✓	✓	✓	✓	✓
Days of week	Gauge checks	IntenseQC	✓	✓	✓	✓	☓
Hours of day	Gauge checks	IntenseQC	✓	✓	✓	☓	☓
Intermittency	Gauge checks	IntenseQC	✓	✓	✓	✓	✓
Breakpoints	Gauge checks	IntenseQC	✓	✓	✓	✓	☓
Minimum value change	Gauge checks	IntenseQC	✓	✓	✓	✓	✓
R99p	Comparison checks	IntenseQC	✓	✓	✓	✓	☓
PRCPTOT	Comparison checks	IntenseQC	✓	✓	✓	✓	☓
World Record	Comparison checks	IntenseQC	✓	✓	✓	✓	☓
Rx1day	Comparison checks	IntenseQC	✓	✓	✓	☓	☓
CDD (Dry spells)	Timeseries checks	IntenseQC	✓	✓	✓	✓	☓
Daily accumulations	Timeseries checks	IntenseQC	agg	agg	✓	✓	☓
Monthly accumulations	Timeseries checks	IntenseQC	✓	✓	✓	✓	☓
Streaks	Timeseries checks	IntenseQC	agg	agg	✓	✓	☓
Daily neighbours (wet)	Neighbourhood checks	IntenseQC	✓	✓	✓	✓	☓
Hourly neighbours (wet)	Neighbourhood checks	IntenseQC	✓	✓	✓	☓	☓
Daily neighbours (dry)	Neighbourhood checks	IntenseQC	✓	✓	✓	✓	☓
Hourly neighbours (dry)	Neighbourhood checks	IntenseQC	✓	✓	✓	☓	☓
Monthly neighbours	Neighbourhood checks	IntenseQC	agg	agg	agg	agg	✓
Timing offset	Neighbourhood checks	IntenseQC	✓	✓	✓	✓	☓
Pre-QC affinity index	Neighbourhood checks	IntenseQC	✓	✓	✓	✓	✓
Pre-QC pearson correlation	Neighbourhood checks	IntenseQC	✓	✓	✓	✓	✓
Daily factor	Neighbourhood checks	IntenseQC	agg	agg	agg	✓	☓
Monthly factor	Neighbourhood checks	IntenseQC	agg	agg	agg	agg	✓
Check exceedance of UK 1h record	Sub-hourly thresholds	SubHourlyQC	✓	✓	☓	☓	☓
Check exceedance of UK 24h record	Sub-hourly thresholds	SubHourlyQC	✓	✓	☓	☓	☓
Check 24h-sum exceedance of UK 24h record	Sub-hourly thresholds	SubHourlyQC	✓	✓	☓	☓	☓
Check streaks (20 mm min)	Sub-hourly thresholds	SubHourlyQC	✓	✓	☓	☓	☓
Check data has sub-hourly frequency	Sub-hourly thresholds	SubHourlyQC	✓	✓	☓	☓	☓
Check sub-hourly rainfall thresholds	Sub-hourly thresholds	SubHourlyQC	✓	✓	☓	☓	☓
Faulty Zeros	pyPWSQC filters	pyPWSQC	✓	✓	✓	✓	☓
Station Outliers	pyPWSQC filters	pyPWSQC	✓	✓	✓	☓	☓

Documentation and License

RainfallQC is developed and maintained by UKCEH.
Free software: GNU General Public License v3
Documentation: https://nerc-ceh.github.io/RainfallQC/

Note on time aggregation

Hourly data is aggregated with ‘label=right’, so 07:00:01 to 08:00 is labelled 08:00. For daily and monthly aggregation, label is left, so n-hour on D to n-hour on D+1 is D.

How to cite this package

To cite a specific version of RainfallQC, please see Zenodo DOI. For v1.0.2: https://doi.org/10.5281/zenodo.21030756

Credits

Builds upon IntenseQC, SubHourlyQC and (is compatible with) pyPWSQC:
Please email tomkee@ceh.ac.uk if you have any questions.
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Metadata

Release files for rainfallqc 1.1.0

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Source distribution for rainfallqc 1.1.0
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Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/NERC-CEH/RainfallQC
Publishing commit: github.com/NERC-CEH/RainfallQC/tree/ed7eb050be6aaeda84ed56a81d84702e4faaee88

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Publishing configuration: github.com/NERC-CEH/RainfallQC/blob/ed7eb050be6aaeda84ed56a81d84702e4faaee88/.github/workflows/publish-to-pypi.yml
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