Rainfall - Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)
Products used: rainfall_chirps_monthly, rainfall_chirps_daily
Keywords: datasets; CHIRPS, climate, rainfall, monthly
Background
This notebook demonstrates how to access and use the Monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) from the DE Africa Open Data Cube.
For offical information on this dataset, see CHIRPS. The abstract from this documentation is copied below:
Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. An evolving drier-than-normal season must be placed in a historical context so that the severity of rainfall deficits can be quickly evaluated. However, estimates derived from satellite data provide areal averages that suffer from biases due to complex terrain, which often underestimate the intensity of extreme precipitation events. Conversely, precipitation grids produced from station data suffer in more rural regions where there are less rain-gauge stations. CHIRPS was created in collaboration with scientists at the USGS Earth Resources Observation and Science (EROS) Center in order to deliver complete, reliable, up-to-date data sets for a number of early warning objectives, like trend analysis and seasonal drought monitoring.
The current CHIRPS datasets that are accessible from DE Africa’s platforms are the CHIRPS-2.0 Africa Monthly dataset, copied from here and the CHIRPS-2.0 Africa Daily dataset, copied from here. They have been converted to cloud-opitmized geotiffs, and indexed into DE Africa’s Open-Data-Cube.
Important specifications:
Datacube product name:
rainfall_chirps_monthlyMeasurement Type: Monthly Atmospheric Precipitation
Precipitation Units: Total mm/month
Date-range:
1981-01to presentSpatial resolution: 0.05 degrees, approximately 5.55 km
Datacube product name:
rainfall_chirps_dailyMeasurement Type: Monthly Atmospheric Precipitation
Precipitation Units: Total mm/day
Date-range:
1981-01to presentSpatial resolution: 0.05 degrees, approximately 5.55 km
Description
In this notebook we will load CHIRPS data using dc.load() to return a time series of datasets.
Topics covered include: 1. Inspecting the monthly CHIRPS product and measurements available in the datacube 2. Using the native dc.load() function to load CHIRPS data 3. Facet plotting the CHIRPS datasets 4. Conduct a simple analysis workflow: finding the long-term monthly mean rainfall
Getting started
To run this analysis, run all the cells in the notebook, starting with the “Load packages” cell.
Load packages
Import Python packages that are used for the analysis.
[1]:
%matplotlib inline
import datacube
import numpy as np
from matplotlib import pyplot as plt
from deafrica_tools.plotting import display_map
Connect to the datacube
[2]:
dc = datacube.Datacube(app="rainfall_chirps")
Analysis parameters
This section defines the analysis parameters, including
lat, lon, buffer: center lat/lon and analysis window size for the area of interesttime_period: time period to be investigatedoutput_crs: projection for loading data; output resolution is not defined so different resolutions can be used for Landsat and Sentinel-2
The default location cover all of Nigeria
[3]:
lat, lon = 9.4707, 8.3899
buffer_lat, buffer_lon = 6, 6
time_period = ('2020')
output_crs = 'epsg:6933'
#join lat,lon,buffer to get bounding box
lon_range = (lon - buffer_lon, lon + buffer_lon)
lat_range = (lat + buffer_lat, lat - buffer_lat)
View the selected location
The next cell will display the selected area on an interactive map. Feel free to zoom in and out to get a better understanding of the area you’ll be analysing. Clicking on any point of the map will reveal the latitude and longitude coordinates of that point.
[4]:
display_map(lon_range, lat_range)
[4]:
Available products and measurements
List products
We can use datacube’s list_products functionality to inspect the CHIRPS rainfall datasets available in the datacube. The table below shows the product names that we will use to load the data and a brief description of the data
[5]:
dc.list_products().loc[dc.list_products()['name'].str.contains('chirps')]
[5]:
| name | description | license | default_crs | default_resolution | |
|---|---|---|---|---|---|
| name | |||||
| rainfall_chirps_daily | rainfall_chirps_daily | Rainfall Estimates from Rain Gauge and Satelli... | None | None | None |
| rainfall_chirps_monthly | rainfall_chirps_monthly | Rainfall Estimates from Rain Gauge and Satelli... | None | None | None |
List measurements
We can further inspect the data available for CHIRPS using datacube’s list_measurements functionality. The table below lists each of the measurements available in the data.
[6]:
measurements = dc.list_measurements()
measurements.loc["rainfall_chirps_monthly"]
[6]:
| name | dtype | units | nodata | aliases | flags_definition | |
|---|---|---|---|---|---|---|
| measurement | ||||||
| rainfall | rainfall | float32 | mm | -9999.0 | NaN | NaN |
Load CHIRPS data using dc.load()
Now that we know what products and measurements are available for the products, we can load data from the datacube using dc.load.
In the first example below, we will load CHIRPS data for region covering Nigeria
Note: For a more general discussion of how to load data using the datacube, refer to the Introduction to loading data notebook.
[7]:
ds_rf_month = dc.load(product='rainfall_chirps_monthly',
time='2020',
y = lat_range,
x = lon_range,
resolution=(-5000, 5000),
output_crs=output_crs)
print(ds_rf_month)
<xarray.Dataset>
Dimensions: (time: 12, y: 303, x: 232)
Coordinates:
* time (time) datetime64[ns] 2020-01-16T11:59:59.500000 ... 2020-12...
* y (y) float64 1.952e+06 1.948e+06 ... 4.475e+05 4.425e+05
* x (x) float64 2.325e+05 2.375e+05 ... 1.382e+06 1.388e+06
spatial_ref int32 6933
Data variables:
rainfall (time, y, x) float32 0.0 0.0 0.0 0.0 ... 79.19 76.53 79.77
Attributes:
crs: EPSG:6933
grid_mapping: spatial_ref
Plotting CHIRPS Monthly Rainfall
Let’s facet plot the time-series to see the total rainfall each month during 2020 over Nigeria.
[8]:
# set -9999 no-data values to NaN
ds_rf_month = ds_rf_month.where(ds_rf_month !=-9999.)
#facet plot rainfall
ds_rf_month['rainfall'].plot.imshow(col='time', col_wrap=6, cmap='YlGnBu', label=False);
Loading and plotting daily rainfall
In the above plot we can see that a lot of rain fell in July 2020. We’ll load the daily rainfall data for this month, aggregrate it across the region, and plot the daily totals to see how this rainfall was distributed within the month.
[9]:
ds_rf_daily = dc.load(product='rainfall_chirps_daily',
time='2020-07',
y = lat_range,
x = lon_range,
resolution=(-5000, 5000),
output_crs=output_crs)
print(ds_rf_daily)
<xarray.Dataset>
Dimensions: (time: 31, y: 303, x: 232)
Coordinates:
* time (time) datetime64[ns] 2020-07-01T11:59:59.500000 ... 2020-07...
* y (y) float64 1.952e+06 1.948e+06 ... 4.475e+05 4.425e+05
* x (x) float64 2.325e+05 2.375e+05 ... 1.382e+06 1.388e+06
spatial_ref int32 6933
Data variables:
rainfall (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
Attributes:
crs: EPSG:6933
grid_mapping: spatial_ref
[10]:
# set -9999 no-data values to NaN
ds_rf_daily = ds_rf_daily.where(ds_rf_daily !=-9999.)
#find the mean
ds_rf_daily_mean = ds_rf_daily.mean(['x', 'y']).drop('spatial_ref').to_dataframe()
[11]:
ds_rf_daily_mean.plot.bar(figsize=(17,4))
plt.title('Daily rainfall July 2020')
plt.ylabel('Rainfall (mm/day)')
plt.xlabel('Day of month')
plt.xticks(np.arange(0,31,1), np.arange(1,32,1));
Example application: finding the monthly mean rainfall over a region
The following section will demonstrate a simple analysis workflow based on CHIRPS rainfall. We will use a 10-year time-series of rainfall over Nigeria to find the long-term monthly mean rainfall total.
First we will load the data, the parameters here are the same as the example above only we’ve increased to time-range from one year to 10 years.
[12]:
ds_rf = dc.load(
product="rainfall_chirps_monthly",
time=('2010', '2020'),
y=lat_range,
x=lon_range,
resolution=(-5000, 5000),
output_crs=output_crs,
)
print(ds_rf)
<xarray.Dataset>
Dimensions: (time: 132, y: 303, x: 232)
Coordinates:
* time (time) datetime64[ns] 2010-01-16T11:59:59.500000 ... 2020-12...
* y (y) float64 1.952e+06 1.948e+06 ... 4.475e+05 4.425e+05
* x (x) float64 2.325e+05 2.375e+05 ... 1.382e+06 1.388e+06
spatial_ref int32 6933
Data variables:
rainfall (time, y, x) float32 0.0 0.0 0.0 0.0 ... 79.19 76.53 79.77
Attributes:
crs: EPSG:6933
grid_mapping: spatial_ref
Find the long-term monthly mean rainfall
We find the mean rainfall across the region (ds_rf.mean(['x', 'y'])), then we group all the same months together and find the mean of the all the January’s, February’s etc. (groupby('time.month').mean()). Lastly we convert the result to a pandas dataframe (.drop('spatial_ref').to_dataframe()) to faciliate the plotting of a bar-chart
[13]:
# set -9999 no-data values to NaN
ds_rf = ds_rf.where(ds_rf !=-9999.)
#find the mean
ds_rf_mean = ds_rf.mean(['x', 'y']).groupby('time.month').mean().drop('spatial_ref').to_dataframe()
ds_rf_mean.head()
[13]:
| rainfall | |
|---|---|
| month | |
| 1 | 3.673821 |
| 2 | 11.921728 |
| 3 | 36.385017 |
| 4 | 59.494869 |
| 5 | 113.811226 |
Plot the result
[14]:
ds_rf_mean.plot.bar(figsize=(17,5))
plt.title('Average monthly rainfall 2010-2020')
plt.ylabel('Rainfall (mm/month)');
Additional information
License: The code in this notebook is licensed under the Apache License, Version 2.0. Digital Earth Africa data is licensed under the Creative Commons by Attribution 4.0 license.
Contact: If you need assistance, please post a question on the Open Data Cube Slack channel or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with this notebook, you can file one on
Github.
Compatible datacube version:
[15]:
print(datacube.__version__)
1.8.15
Last Tested:
[16]:
from datetime import datetime
datetime.today().strftime('%Y-%m-%d')
[16]:
'2023-08-11'