Spatial partitioning of terrestrial precipitation reveals varying dataset agreement across different environments

Yannis Markonis; Mijael Rodrigo Vargas Godoy; Rajani Kumar Pradhan; Shailendra Pratap; Johanna Ruth Thomson; Martin Hanel; Athanasios Paschalis; Efthymios Nikolopoulos; Simon Michael Papalexiou

doi:10.1038/s43247-024-01377-9

Spatial partitioning of terrestrial precipitation reveals varying dataset agreement across different environments

Yannis Markonis, Mijael Rodrigo Vargas Godoy, Rajani Kumar Pradhan, Shailendra Pratap, Johanna Ruth Thomson, Martin Hanel, Athanasios Paschalis, Efthymios Nikolopoulos, Simon Michael Papalexiou

Research output: Contribution to journal › Article › peer-review

Abstract

The study of the water cycle at planetary scale is crucial for our understanding of large-scale climatic processes. However, very little is known about how terrestrial precipitation is distributed across different environments. In this study, we address this gap by employing a 17-dataset ensemble to provide, for the first time, precipitation estimates over a suite of land cover types, biomes, elevation zones, and precipitation intensity classes. We estimate annual terrestrial precipitation at approximately 114,000 ± 9400 km³, with about 70% falling over tropical, subtropical and temperate regions. Our results highlight substantial inconsistencies, mainly, over the arid and the mountainous areas. To quantify the overall discrepancies, we utilize the concept of dataset agreement and then explore the pairwise relationships among the datasets in terms of “genealogy”, concurrency, and distance. The resulting uncertainty-based partitioning demonstrates how precipitation is distributed over a wide range of environments and improves our understanding on how their conditions influence observational fidelity.

Original language	English (US)
Article number	217
Journal	Communications Earth and Environment
Volume	5
Issue number	1
DOIs	https://doi.org/10.1038/s43247-024-01377-9
State	Published - Dec 2024
Externally published	Yes

ASJC Scopus subject areas

General Environmental Science
General Earth and Planetary Sciences

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10.1038/s43247-024-01377-9

Cite this

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title = "Spatial partitioning of terrestrial precipitation reveals varying dataset agreement across different environments",

abstract = "The study of the water cycle at planetary scale is crucial for our understanding of large-scale climatic processes. However, very little is known about how terrestrial precipitation is distributed across different environments. In this study, we address this gap by employing a 17-dataset ensemble to provide, for the first time, precipitation estimates over a suite of land cover types, biomes, elevation zones, and precipitation intensity classes. We estimate annual terrestrial precipitation at approximately 114,000 ± 9400 km3, with about 70% falling over tropical, subtropical and temperate regions. Our results highlight substantial inconsistencies, mainly, over the arid and the mountainous areas. To quantify the overall discrepancies, we utilize the concept of dataset agreement and then explore the pairwise relationships among the datasets in terms of “genealogy”, concurrency, and distance. The resulting uncertainty-based partitioning demonstrates how precipitation is distributed over a wide range of environments and improves our understanding on how their conditions influence observational fidelity.",

author = "Yannis Markonis and {Vargas Godoy}, {Mijael Rodrigo} and Pradhan, {Rajani Kumar} and Shailendra Pratap and Thomson, {Johanna Ruth} and Martin Hanel and Athanasios Paschalis and Efthymios Nikolopoulos and Papalexiou, {Simon Michael}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

month = dec,

doi = "10.1038/s43247-024-01377-9",

language = "English (US)",

volume = "5",

journal = "Communications Earth and Environment",

issn = "2662-4435",

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AU - Markonis, Yannis

AU - Vargas Godoy, Mijael Rodrigo

AU - Pradhan, Rajani Kumar

AU - Pratap, Shailendra

AU - Thomson, Johanna Ruth

AU - Hanel, Martin

AU - Paschalis, Athanasios

AU - Nikolopoulos, Efthymios

AU - Papalexiou, Simon Michael

PY - 2024/12

Y1 - 2024/12

N2 - The study of the water cycle at planetary scale is crucial for our understanding of large-scale climatic processes. However, very little is known about how terrestrial precipitation is distributed across different environments. In this study, we address this gap by employing a 17-dataset ensemble to provide, for the first time, precipitation estimates over a suite of land cover types, biomes, elevation zones, and precipitation intensity classes. We estimate annual terrestrial precipitation at approximately 114,000 ± 9400 km3, with about 70% falling over tropical, subtropical and temperate regions. Our results highlight substantial inconsistencies, mainly, over the arid and the mountainous areas. To quantify the overall discrepancies, we utilize the concept of dataset agreement and then explore the pairwise relationships among the datasets in terms of “genealogy”, concurrency, and distance. The resulting uncertainty-based partitioning demonstrates how precipitation is distributed over a wide range of environments and improves our understanding on how their conditions influence observational fidelity.

AB - The study of the water cycle at planetary scale is crucial for our understanding of large-scale climatic processes. However, very little is known about how terrestrial precipitation is distributed across different environments. In this study, we address this gap by employing a 17-dataset ensemble to provide, for the first time, precipitation estimates over a suite of land cover types, biomes, elevation zones, and precipitation intensity classes. We estimate annual terrestrial precipitation at approximately 114,000 ± 9400 km3, with about 70% falling over tropical, subtropical and temperate regions. Our results highlight substantial inconsistencies, mainly, over the arid and the mountainous areas. To quantify the overall discrepancies, we utilize the concept of dataset agreement and then explore the pairwise relationships among the datasets in terms of “genealogy”, concurrency, and distance. The resulting uncertainty-based partitioning demonstrates how precipitation is distributed over a wide range of environments and improves our understanding on how their conditions influence observational fidelity.

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Spatial partitioning of terrestrial precipitation reveals varying dataset agreement across different environments

Abstract

ASJC Scopus subject areas

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