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Precipitation

Mean annual precipitation (1970-1999)

Description​

A region-wide, simulated precipitation dataset was provided by the University of Washington Climate Impacts Group. Methodology used to develop this dataset is documented in (Mauger et al., 2018). This dataset contains modeled hourly precipitation using the GFDL CM3 global climate model and the Representative Concentration Pathways (RCP) 8.5 scenario.

The GFDL model was chosen by CIG to due to its ability to accurately model winter storm drivers, important for stormwater applications. Combined with the higher emissions scenario, this modeling scenario represents the upper end of expected future climate changes effects.

CIG downscaled GCM results using a statistical-dynamical approach to capture the anticipated changes in extreme events as well as the different drivers of rainfall that affect the Puget Sound Region. Regional simulations were performed using the Weather Research and Forecasting community mesoscale model. This resulted in hourly rainfall predictions at an approximately 12 km grid size across Puget Sound. Predictions were bias-corrected on a quantile-mapping basis.

Mean annual precipitation was calcuated on a per-grid basis for the period between 1970 and 1999. Grid cells were resampled using bicubic interpolation.

Layer Access in Earth Engine​

The javascript commands below can be used to access this layer within the Google Earth Engine Code Editor. A Google Earth Engine account is required.

// Import the layer data dictionary
var data = require('users/stormwaterheatmap/apps:data/public')

// To view data dictionary, print to the console:
print('Data:', data)

//Get this layer from the layer data dictionary:
var layer_name = data.rasters["Precipitation"]

Viewing​

Individual objects contain all the info used in the stormwater heatmap. To add it to the map, add the layer object.

var display_image = layer_name.layer
Map.addLayer(display_image)

Analysis​

To get the raw image data for analysis, access the eeObject key.

var raw_image = layer_name.layer.eeObject
Map.addLayer(raw_image,{},'Precipitation')

Visualization​

Palette​

Colors
ffffffffffff
fdef9afdef9a
aad85caad85c
5ab9785ab978
3c93873c9387
206e8b206e8b
14439c14439c
2a186c2a186c
4a14864a1486
6a51a36a51a3
807dba807dba
9e9ac89e9ac8
bcbddcbcbddc
dadaebdadaeb

Minimum: 500 mm/year

Maximum: 3800 mm/year

Source​

SalathΓ© et al 2019

https://cig.uw.edu/our-work/applied-research/heavy-precip-and-stormwater/