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Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa

2022-09-09Artigo científico Acesso aberto
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dc.contributor.authorKüçük, Çağlar
dc.contributor.authorKoirala, Sujan
dc.contributor.authorCarvalhais, Nuno
dc.contributor.authorMiralles, Diego G.
dc.contributor.authorReichstein, Markus
dc.contributor.authorJung, Martin
dc.contributor.institutionCENSE - Centro de Investigação em Ambiente e Sustentabilidade
dc.contributor.institutionDCEA - Departamento de Ciências e Engenharia do Ambiente
dc.contributor.pblFrontiers Media
dc.date.accessioned2023-03-16T22:36:12Z
dc.date.available2023-03-16T22:36:12Z
dc.date.issued2022-09-09
dc.descriptionFunding Information: ÇK acknowledges funding from the International Max Planck Research School for Global Biogeochemical Cycles. SK acknowledges the support of the Erdsystemforschung: Afrikanische Grundwasserressourcen im Zuge des globalen Wandels (Earth System Research: Groundwater Resources in Africa under Global Change) project of the Max Planck Society. Publisher Copyright: Copyright © 2022 Küçük, Koirala, Carvalhais, Miralles, Reichstein and Jung.
dc.description.abstractLocal studies and modeling experiments suggest that shallow groundwater and lateral redistribution of soil moisture, together with soil properties, can be highly important secondary water sources for vegetation in water-limited ecosystems. However, there is a lack of observation-based studies of these terrain-associated secondary water effects on vegetation over large spatial domains. Here, we quantify the role of terrain properties on the spatial variations of dry season vegetation decay rate across Africa obtained from geostationary satellite acquisitions to assess the large-scale relevance of secondary water effects. We use machine learning based attribution to identify where and under which conditions terrain properties related to topography, water table depth, and soil hydraulic properties influence the rate of vegetation decay. Over the study domain, the machine learning model attributes about one-third of the spatial variations of vegetation decay rates to terrain properties, which is roughly equally split between direct terrain effects and interaction effects with climate and vegetation variables. The importance of secondary water effects increases with increasing topographic variability, shallower groundwater levels, and the propensity to capillary rise given by soil properties. In regions with favorable terrain properties, more than 60% of the variations in the decay rate of vegetation are attributed to terrain properties, highlighting the importance of secondary water effects on vegetation in Africa. Our findings provide an empirical assessment of the importance of local-scale secondary water effects on vegetation over Africa and help to improve hydrological and vegetation models for the challenge of bridging processes across spatial scales.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent2746141
dc.identifier.doi10.3389/fdata.2022.967477
dc.identifier.issn2624-909X
dc.identifier.otherPURE: 56032866
dc.identifier.otherPURE UUID: bd1f60cb-385e-4dc0-b472-5d9ea0b9025e
dc.identifier.otherScopus: 85138742263
dc.identifier.otherWOS: 000858628200001
dc.identifier.otherPubMed: 36156935
dc.identifier.otherPubMedCentral: PMC9500241
dc.identifier.urihttp://hdl.handle.net/10362/150707
dc.identifier.urlhttps://www.scopus.com/pages/publications/85138742263
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/715254/EU
dc.relationDo droughts self-propagate and self-intensify?
dc.relationDOWN2EARTH: Translation of climate information into multilevel decision support for social adaptation, policy development, and resilience to water scarcity in the Horn of Africa Drylands
dc.relationUnderstanding and Modelling the Earth System with Machine Learning
dc.subjectAfrica
dc.subjectdrylands
dc.subjectecohydrology
dc.subjectgroundwater
dc.subjectsecondary water resources
dc.subjecttopography
dc.subjectvegetation decay rate
dc.subjectwater limitation
dc.subjectComputer Science (miscellaneous)
dc.subjectInformation Systems
dc.subjectArtificial Intelligence
dc.titleObservation-based assessment of secondary water effects on seasonal vegetation decay across Africaen
dc.typejournal article
degois.publication.titleFrontiers in Big Data
degois.publication.volume5
dspace.entity.typePublication
oaire.awardNumber715254
oaire.awardNumber869550
oaire.awardNumber855187
oaire.awardTitleDo droughts self-propagate and self-intensify?
oaire.awardTitleDOWN2EARTH: Translation of climate information into multilevel decision support for social adaptation, policy development, and resilience to water scarcity in the Horn of Africa Drylands
oaire.awardTitleUnderstanding and Modelling the Earth System with Machine Learning
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/715254/EU
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/869550/EU
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/855187/EU
oaire.fundingStreamH2020
oaire.fundingStreamH2020
oaire.fundingStreamH2020
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
project.funder.nameEuropean Commission
project.funder.nameEuropean Commission
rcaap.rightsopenAccess
relation.isProjectOfPublicationf6340f64-1faf-48f9-b89a-19ed9de228e8
relation.isProjectOfPublicationad50641f-6d27-45bf-89d7-174ed2cdf53a
relation.isProjectOfPublication95d7cb9e-946e-4bfb-b3a4-e4ed4149cb66
relation.isProjectOfPublication.latestForDiscoveryad50641f-6d27-45bf-89d7-174ed2cdf53a

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