Google Earth Engine APP (GEE) - high-resolution gridded precipitation dataset for Peru and Ecuador watersheds (1981-2015)

High-Resolution Gridded Precipitation Dataset (1981-2015) for Peru and Ecuador Watersheds
RAIN4PE is a new daily gridded precipitation dataset that combines multi-source precipitation data (satellite-based Climate Hazards Group Infrared Precipitation, CHIRP (Funk et al., 2015), reanalysis ERA5 (Hersbach et al., 2020, and surface precipitation) were obtained by merging with topographic elevation. In addition, RAIN4PE uses stream data for hydrological correction in catchments where precipitation is underestimated by inverse hydrology. RAIN4PE is thus the only gridded precipitation product for Peru and Ecuador that benefits from the maximum available in situ observations, multiple precipitation sources, elevation data, supplemented by stream data to correct for the Paramós and upland watersheds The precipitation is underestimated. Preface – Bed Length Artificial Intelligence Tutorial

Currently included layers are:

Earth Engine Snippet: Annual mean¶

var rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/annual_mean')

/**
rain4pe: High-resolution gridded precipitation dataset for Peruvian and 
Ecuadorian watersheds (1981-2015)

Image properties: 
  - 'system:time_start' (Unix time)
Spatial resolution: 0.1° (or roughly 10km x 10km)


Google Earth Engine assets:
  - rain4pe monthly: "users/csaybar/rainpe/monthly"
  - rain4pe daily: "users/csaybar/rainpe/monthly" & "users/ryali93/rainpe/monthly"
  - rain4pe monthly climatology: "users/csaybar/rainpe/monthly_clim"
  - rain4pe annual mean: "users/csaybar/rainpe/annual"
  
More Information: https://dataservices.gfz-potsdam.de/pik/showshort.php?id=6f766e20-2d94-11eb-9603-497c92695674
*/

// 1. Load the pp ee.Image.
var rain4pe_ym = ee.Image("users/csaybar/rainpe/annual_mean")


// 2. Define an area of interest geometry.
var aoi = rain4pe_ym.geometry()
  
// 3. Display results.
var palette = ['000096','0064ff', '00b4ff', '33db80', '9beb4a', 'ffeb00', 'ffb300', 'ff6400', 'eb1e00', 'af0000'];
Map.centerObject(aoi)
Map.addLayer(rain4pe_ym, {min:0, max:5000, palette: palette})


// 4. Export ee.Image
Export.image.toDrive({
  image: rain4pe_ym,
  description: "rain4pe-pp-mean",
  fileNamePrefix: "rain4pe_pp_mean"
})

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-YEARLY

Earth Engine Snippet: Monthly climatology¶

var rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/monthly_clim')

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-MONTHLY-CLIM

Earth Engine Snippet: Monthly data¶

var rain4pe_clim = ee.ImageCollection('users/csaybar/rainpe/monthly')

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-MONTHLY

Earth Engine Snippet: Daily data¶

var rain4pe_daily = ee.ImageCollection('projects/sat-io/open-datasets/rainpe/daily')

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/RAIN4PE-GRIDDED-PRECIP-DAILY

Resolution: 0.1° (or roughly 10km x 10km)¶

citation¶

When using the data please cite:

Fernandez-Palomino, C. A.; Hattermann, F. F.; Krysanova, V.; Lobanova, A.; Vega-Jácome, F.; Lavado, W.;
Santini, W.; Aybar, C.; Bronstert, A. (2021). Rain for Peru and Ecuador (RAIN4PE). V. 1.0. GFZ Data
Services. https://doi.org/10.5880/pik.2020.010

The data are supplementary material to:

Fernandez-Palomino, C. A.; Hattermann, F. F.; Krysanova, V.; Lobanova, A.; Vega-Jácome, F.; Lavado, W.;
Santini, W.; Aybar, C.; Bronstert, A. (2021). A novel high-resolution gridded precipitation dataset for
Peruvian and Ecuadorian watersheds – development and hydrological evaluation. Journal of
Hydrometeorology. https://doi.org/10.1175/jhm-d-20-0285.1

 

 

License¶

This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.

Curated by: Cesar Aybar & Samapriya Roy

Keywords: precipitation, streamflow, Peru, Ecuador, random forest, SWAT, reverse hydrology, satellite data, Earth observation, GIS.