hydrosheds_acc
Flow accumulation at 3-second resolution
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/hydro.yaml")
ds = cat["hydrosheds_acc"].to_dask()
Working with requester pays data
Several of the datasets within the cloud data catalog are contained in requester pays storage buckets. This means that a user requesting data must provide their own billing project (created and authenticated through Google Cloud Platform) to be billed for the charges associated with accessing a dataset. To set up an GCP billing project and use it for authentication in applications:- Create a project on GCP; if this is the first time using GCP, a prompt will appear to choose a Google account to link to all GCP-related activities.
- Create a Cloud Billing account associated with the project and enable billing for the project through this account.
- Using Google Cloud IAM, add the Service Usage Consumer role to your account, which enables it to make billed requests on the behalf of the project.
- Through command line, install the Google Cloud SDK; this can be done using conda:
conda install -c conda-forge google-cloud-sdk
- Initialize the
gcloud
command line interface, logging into the account used to create the aforementioned project and selecting it as the default project; this will allow the project to be used for requester pays access through the command line:gcloud auth login gcloud init
- Finally, use
gcloud
to establish application default credentials; this will allow the project to be used for requester pays access through applications:gcloud auth application-default login
Metadata
url | https://www.hydrosheds.org |
tags | ['flow'] |
Dataset Contents
xarray.DataArray
- concat_dim: 1
- band: 1
- y: 144000
- x: 390000
- dask.array<chunksize=(1, 1, 6000, 6000), meta=np.ndarray>
Array Chunk Bytes 449.28 GB 288.00 MB Shape (1, 1, 144000, 390000) (1, 1, 6000, 6000) Count 3121 Tasks 1560 Chunks Type float64 numpy.ndarray - x(x)float64-145.0 -145.0 ... 180.0 180.0
array([-144.999583, -144.99875 , -144.997917, ..., 179.997917, 179.99875 , 179.999583])
- y(y)float6460.0 60.0 60.0 ... -60.0 -60.0
array([ 59.999583, 59.99875 , 59.997917, ..., -59.997917, -59.99875 , -59.999583])
- band(band)int641
array([1])
- transform :
- (0.0008333333333333438, 0.0, -145.0, 0.0, -0.0008333333333333438, 60.0)
- crs :
- +init=epsg:4326
- res :
- (0.0008333333333333438, 0.0008333333333333438)
- is_tiled :
- 1
- nodatavals :
- (0.0,)
- scales :
- (1.0,)
- offsets :
- (0.0,)