The University of Southampton
University of Southampton Institutional Repository

Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments

Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments
Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments
We introduce a set of global high-resolution (0.05°) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the “true” long-term P using a Budyko curve, which is an empirical equation relating long-term P, Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-art P climatologies [the “WorldClim version 2” database (WorldClim V2); Climatologies at High Resolution for the Earth’s Land Surface Areas, version 1.2 (CHELSA V1.2 ); and Climate Hazards Group Precipitation Climatology, version 1 (CHPclim V1)], after which we used random-forest regression to produce global gap-free bias correction maps for the P climatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors on the basis of gauge catch efficiencies. We found that all three climatologies systematically underestimate P over parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. In addition, all climatologies underestimate P at latitudes >60°N, likely because of gauge undercatch. Exceptionally high long-term correction factors (>1.5) were obtained for all three P climatologies in Alaska, High Mountain Asia, and Chile—regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely used P datasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimate P over Chile, the Himalayas, and along the Pacific coast of North America. Mean P for the global land surface based on the bias-corrected WorldClim V2 is 862 mm yr−1 (a 9.4% increase over the original WorldClim V2). The annual and monthly bias-corrected P climatologies have been released as the Precipitation Bias Correction (PBCOR) dataset, which is available online
0894-8755
1299-1315
Beck, Hylke E.
edbdb027-f978-47dd-a9d3-43a1cce92e9a
Wood, Eric F.
0929fcf4-b795-4e99-9f98-371932bef995
Mcvicar, Tim R.
b2a90141-c4d0-4a24-b1a4-3a78c000a64b
Zambrano-bigiarini, Mauricio
060984a1-d15e-4b1a-a4ec-836276b394fc
Alvarez-garreton, Camila
7f2d8847-57c5-4295-bec9-214480c88a25
Baez-villanueva, Oscar M.
583dae72-1f84-40f4-b2c1-3caa612c8b54
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Karger, Dirk N.
8572c3be-0c67-4395-9ba3-50911cd94395
Beck, Hylke E.
edbdb027-f978-47dd-a9d3-43a1cce92e9a
Wood, Eric F.
0929fcf4-b795-4e99-9f98-371932bef995
Mcvicar, Tim R.
b2a90141-c4d0-4a24-b1a4-3a78c000a64b
Zambrano-bigiarini, Mauricio
060984a1-d15e-4b1a-a4ec-836276b394fc
Alvarez-garreton, Camila
7f2d8847-57c5-4295-bec9-214480c88a25
Baez-villanueva, Oscar M.
583dae72-1f84-40f4-b2c1-3caa612c8b54
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Karger, Dirk N.
8572c3be-0c67-4395-9ba3-50911cd94395

Beck, Hylke E., Wood, Eric F., Mcvicar, Tim R., Zambrano-bigiarini, Mauricio, Alvarez-garreton, Camila, Baez-villanueva, Oscar M., Sheffield, Justin and Karger, Dirk N. (2020) Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments. Journal of Climate, 33 (4), 1299-1315. (doi:10.1175/JCLI-D-19-0332.1).

Record type: Article

Abstract

We introduce a set of global high-resolution (0.05°) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the “true” long-term P using a Budyko curve, which is an empirical equation relating long-term P, Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-art P climatologies [the “WorldClim version 2” database (WorldClim V2); Climatologies at High Resolution for the Earth’s Land Surface Areas, version 1.2 (CHELSA V1.2 ); and Climate Hazards Group Precipitation Climatology, version 1 (CHPclim V1)], after which we used random-forest regression to produce global gap-free bias correction maps for the P climatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors on the basis of gauge catch efficiencies. We found that all three climatologies systematically underestimate P over parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. In addition, all climatologies underestimate P at latitudes >60°N, likely because of gauge undercatch. Exceptionally high long-term correction factors (>1.5) were obtained for all three P climatologies in Alaska, High Mountain Asia, and Chile—regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely used P datasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimate P over Chile, the Himalayas, and along the Pacific coast of North America. Mean P for the global land surface based on the bias-corrected WorldClim V2 is 862 mm yr−1 (a 9.4% increase over the original WorldClim V2). The annual and monthly bias-corrected P climatologies have been released as the Precipitation Bias Correction (PBCOR) dataset, which is available online

Text
jcli-d-19-0332.1 - Version of Record
Available under License Other.
Download (2MB)
Text
JCLI-D-19-0332_R2_manuscript
Restricted to Repository staff only
Request a copy

More information

e-pub ahead of print date: 15 January 2020
Published date: 15 February 2020
Additional Information: Publisher Copyright: © 2020 American Meteorological Society.

Identifiers

Local EPrints ID: 438350
URI: http://eprints.soton.ac.uk/id/eprint/438350
ISSN: 0894-8755
PURE UUID: e157f597-66ea-4680-851a-9150e1ae9484
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

Catalogue record

Date deposited: 06 Mar 2020 17:33
Last modified: 09 Nov 2022 02:48

Export record

Altmetrics

Contributors

Author: Hylke E. Beck
Author: Eric F. Wood
Author: Tim R. Mcvicar
Author: Mauricio Zambrano-bigiarini
Author: Camila Alvarez-garreton
Author: Oscar M. Baez-villanueva
Author: Dirk N. Karger

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×