Towards observation-based gridded runoff estimates for Europe

Gudmundsson, L.; Seneviratne, S. I.

doi:10.5194/hess-19-2859-2015

Articles | Volume 19, issue 6

https://doi.org/10.5194/hess-19-2859-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-19-2859-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 19, issue 6

Research article

| Highlight paper

|

22 Jun 2015

Research article | Highlight paper |

| 22 Jun 2015

Towards observation-based gridded runoff estimates for Europe

L. Gudmundsson and S. I. Seneviratne

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Month	HTML	PDF	XML	Total
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Feb 2015	20	22	1	43
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Jul 2015	198	115	6	319
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Sep 2015	64	60	8	132
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Apr 2016	32	38	0	70
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Jun 2024	27	15	1	43
Jul 2024	38	8	3	49
Aug 2024	39	7	3	49
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Feb 2025	25	14	0	39
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Aug 2025	39	19	2	60
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Cumulative views and downloads (calculated since 18 Nov 2014)

Month	HTML	PDF	XML	Total
Nov 2014	119	101	9	229
Dec 2014	20	133	0	153
Jan 2015	23	263	1	287
Feb 2015	20	22	1	43
Mar 2015	13	3	0	16
Apr 2015	18	7	1	26
May 2015	7	4	0	11
Jun 2015	298	101	5	404
Jul 2015	198	115	6	319
Aug 2015	106	88	1	195
Sep 2015	64	60	8	132
Oct 2015	76	61	0	137
Nov 2015	62	64	2	128
Dec 2015	42	57	1	100
Jan 2016	82	59	1	142
Feb 2016	34	44	3	81
Mar 2016	42	66	15	123
Apr 2016	32	38	0	70
May 2016	32	30	0	62
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Jan 2023	72	33	5	110
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Aug 2025	39	19	2	60
Sep 2025	53	25	2	80
Oct 2025	40	23	4	67
Nov 2025	47	23	4	74

Latest update: 28 Nov 2025

Short summary

Water storages and fluxes on land are key variables in the Earth system. To provide context for local investigations and to understand phenomena that emerge at large spatial scales, information on continental freshwater dynamics is needed. This paper presents a methodology to estimate continental-scale runoff on a 0.5° spatial grid, which combines the advantages of in situ observations with the power of machine learning regression. The resulting runoff estimates compare well with observations.