· @commonchaffinch
4 followers · 47 posts · Server qoto.org
· @commonchaffinch
4 followers · 47 posts · Server qoto.org


Name: GRACE-REC

Spatial coverage: global

Spatial resolution: 0.5 degrees

Temporal coverage: MSWEP-based reconstruction 1979-2016, ERA5-based 1979-present, GSWP3-based 1901-2014

Temporal resolution: Monthly (100 ensemble members for each combination of the three meteorological datasets and two GRACE satellite mascons), daily (ensemble mean only)

Gap-free: Yes

Year of publication: 2019

Algorithm: (1) linear water storage model, driven by temperature and precipitation, calibrated against de-seasonalized, de-trendedd GRACE satellite data (2002-2017), (2) spatial autoregressive model was used to generate an ensemble of spatially autocorrelated residuals, to facilitate uncertainty propagation from grid-level to regional or global averages. This method to propagate uncertainty is noteworthy.

Pros: outperforms hydrological and land surface models when evaluated against de-seasonalized, de-trended GRACE terrestrial water data, sea-level budget, basin-scale water balance, and streamflow.

Cons: The trends in GRACE-REC is purely driven by precipitation changes, therefore missing a ton of relevant factors (evapotranspiration change, dams, human water withdrawal, ice melt...). The seasonality is constant and not particularly reliable, either.

essd.copernicus.org/articles/1

#data_products #terrestrial_water_storage

Last updated 2 years ago

· @commonchaffinch
4 followers · 47 posts · Server qoto.org


Spatial coverage: Global

Spatial resolution: country x sector x fuel

Temporal coverage: 1750-most recent year

Temporal resolution: annual

Gap-free: Yes

Year of publication: continually updated

Algorithm: N/A (not examined)

globalchange.umd.edu/ceds/

#Carbon_emissions #data_products

Last updated 2 years ago

· @commonchaffinch
4 followers · 47 posts · Server qoto.org


Name: The Global LAnd Surface Satellite evapotranspiration product version 5.0 (GLASS ET)

Spatial coverage: Global

Spatial resolution: 1 km

Temporal coverage: 2001-2015

Temporal resolution: 8 day

Gap-free: Yes

Year of publication: 2022

Algorithm: machine learning, integrating five satellite-derived ET products

sciencedirect.com/science/arti

#data_products #evapotranspiration

Last updated 2 years ago