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River discharges forced by EURO-CORDEX (mirror)

River discharges forced by EURO-CORDEX (mirror)#

General#

Dataset ID

river_discharges_mirror

Spatial coverage

Europe (E-HYPE domain with 8.8 million km², subdivided into 35408 subcatchments)

In workflows

../../_images/icon_s_floods.png River floods (discharge)

Original source

Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections (DOI 10.24381/cds.73237ad6)

Authors

Berg, P., Photiadou, C., Bartosova, A., Biermann, J., Capell, R., Chinyoka, S., Fahlesson, T., Franssen, W., Hundecha, Y., Isberg, K., Ludwig, F., Mook, R., Muzuusa, J., Nauta, L., Rosberg, J., Simonsson, L., Sjökvist, E., Thuresson, J., and van der Linden, E.

Year

2021

Attribution

Copernicus Climate Change Service information 2021. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

License

Creative Commons Attribution 4.0 International

Note

This mirror contains the original data with transposed coordinates (optimized for access to individual catchments) in a zarr dataset. See below for more information on the applied transformations.

Technical#

URLs

https://object-store.os-api.cci1.ecmwf.int/climaax/river_discharges_mirror/rdis_day_E-HYPEcatch_allmodels.zarr https://object-store.os-api.cci1.ecmwf.int/climaax/river_discharges_mirror/rdis_ymonmean_abs_E-HYPEcatch_allmodels.zarr https://object-store.os-api.cci1.ecmwf.int/climaax/river_discharges_mirror/rdis_extremes_abs_E-HYPEcatch_allmodels.zarr https://object-store.os-api.cci1.ecmwf.int/climaax/river_discharges_mirror/rdis_extremes_rel_E-HYPEcatch_allmodels.zarr

Dataset type

zarr

Warning

This mirror is provided for the convenice of CLIMAAX workflow users only. Please use the original data source for citations and general access. Availability of and URLs associated with this dataset may change without announcement.

Dataset transformation#

The following processing was applied to the datasets produced by this notebook to create the mirror:

import xarray as xr

(xr.open_dataset("rdis_day_E-HYPEcatch_allmodels.nc", chunks={"time": -1, "id": 100})
    .transpose("id", "gcm_rcm", "catchmodel", "time")
    .to_zarr("rdis_day_E-HYPEcatch_allmodels.zarr"))

(xr.open_dataset("rdis_ymonmean_abs_E-HYPEcatch_allmodels.nc", chunks={"time": -1, "id": 1000})
    .transpose("id", "scenario", "time_period", "gcm_rcm", "catchmodel", "time")
    .to_zarr("rdis_ymonmean_abs_E-HYPEcatch_allmodels.zarr"))

(xr.open_dataset("rdis_extremes_abs_E-HYPEcatch_allmodels.nc", chunks=-1 )
    .transpose("id", "scenario", "gcm_rcm", "time")
    .to_zarr("rdis_extremes_abs_E-HYPEcatch_allmodels.zarr"))

(xr.open_dataset("rdis_extremes_rel_E-HYPEcatch_allmodels.nc", chunks=-1)
    .transpose("id", "scenario", "gcm_rcm", "time")
    .to_zarr("rdis_extremes_rel_E-HYPEcatch_allmodels.zarr"))
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