CGIAR Monthly Average Evapotranspiration (1970-2000)

Monthly Average Potential Evapostranspiration from CGIAR at a spatial resolution of 30 arc seconds (1km). This dataset is provided as 12 rasters (one per month) representing evapotranspiration (mm) averaged from 1970-2000. Potential Evapostranspiration was calculated using the FA0-56 Penman-Monteith Reference Evapotranspiration (ET0) equation. WorldClim 2.1 climate data was used to create this dataset. If you wish to use this dataset in an InVEST model, it is important to use it alongside the WorldClim 2.1 precipitation data. Layers were downloaded from CGIAR and converted to COGs with internal overviews by members of the Natural Capital Project team. For more information on the source data, please see the lineage section in the accompanying metadata (YML) file.

Download files

Field	Value
Author	Natural Capital Project, Stanford University
Tags	CGIAR EVAPOTRANSPIRATION MONTHLY EVAPOTRANSPIRATION
Places	GLOBAL
Last Updated	March 20, 2025, 23:54 (UTC)
Created	March 20, 2025, 23:53 (UTC)
Natcap Last Updated	2025-03-20T23:54:47.021807+00:00

Cite this dataset

Zomer, R.J.; Xu, J.; Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1

Embed this layer into your website

<div id="map" style="width: 100%; height: 400px;"></div>

<link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" integrity="sha256-p4NxAoJBhIIN+hmNHrzRCf9tD/miZyoHS5obTRR9BMY=" crossorigin="" />
<script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js" integrity="sha256-20nQCchB9co0qIjJZRGuk2/Z9VM+kNiyxNV1lvTlZBo=" crossorigin=""></script>
<script>
  var map = L.map('map').setView([0, 0], 4);

  L.tileLayer('https://tile.openstreetmap.org/{z}/{x}/{y}.png', {
    attribution: '&copy; <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors'
  }).addTo(map);

L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_05_cog.tif&rescale=0.0%2C702.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_06_cog.tif&rescale=0.0%2C370.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_07_cog.tif&rescale=0.0%2C377.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_08_cog.tif&rescale=0.0%2C352.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_09_cog.tif&rescale=0.0%2C313.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_01_cog.tif&rescale=0.0%2C288.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_02_cog.tif&rescale=0.0%2C258.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_03_cog.tif&rescale=0.0%2C305.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_04_cog.tif&rescale=0.0%2C339.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_10_cog.tif&rescale=0.0%2C296.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_11_cog.tif&rescale=0.0%2C274.0').addTo(map);
L.tileLayer('https://titiler-897938321824.us-west1.run.app/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@2x?colormap_name=viridis&bidx=1&url=https%3A%2F%2Fstorage.googleapis.com%2Fnatcap-data-cache%2Fglobal%2FCGIAR-ET0%2Fmonthly%2FCGIAR_ET0_v3_monthly_1970_2000%2Fet0_v3_12_cog.tif&rescale=0.0%2C294.0').addTo(map);
</script>

Use this data in Python

from osgeo import gdal

raster = gdal.OpenEx('/vsicurl/https://storage.googleapis.com/natcap-data-cache/global/CGIAR-ET0/monthly/CGIAR_ET0_v3_monthly_1970_2000/et0_v3_05_cog.tif')
band = raster.GetRasterBand(1)
array = band.ReadAsArray(
    xoff=my_window_x_offset,
    yoff=my_window_y_offset,
    win_xsize=my_window_width_in_pixels,
    win_ysize=my_window_height_in_pixels)