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The role of landscape properties, storage and evapotranspiration on variability in streamflow recessions in a boreal catchment
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.ORCID iD: 0000-0002-3367-5077
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Uppsala Centre for Sustainable Development, CSD Uppsala. Swedish Univ Agr Sci, Dept Aquat Sci & Assessment, Uppsala, Sweden.ORCID iD: 0000-0002-8057-1051
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.ORCID iD: 0000-0002-6537-0753
Swedish Univ Agr Sci, Dept Forest Ecol & Management, Umea, Sweden.
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2019 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 570, p. 315-328Article in journal (Refereed) Published
Abstract [en]

Streamflow recession analysis provides valuable insights into catchment functioning that can be related to runoff generation, storage retention and baseflow dynamics. As an integrated characteristic, recession analysis is particularly useful in catchment comparison studies to help explain drivers of spatial and temporal variability in hydrological behavior. Here, five years of hourly streamflow data from 14, partly nested, catchments within a 68 km(2) boreal forest landscape in Northern Sweden were used to explore spatiotemporal variation in hydrological processes through recession analysis. The aim of this study was to better understand spatial variation in runoff generation and storage-discharge dynamics across the landscape, as well as the relation to landscape properties. Due to high collinearity between variables, partial least square regression was used to quantify the associations between recession characteristics and catchment properties, as well as to identify key variables controlling recession behavior. We analyzed recession characteristics using both an aggregated approach including all recession data and individual recession events. The analyses based on individual recession events, indicated that catchment topography, quantified by indices such as mean slope or elevation above the stream network, is a primary control on the recession behavior during relatively high flows, whereas catchment area gains importance when flows are relatively low. The proportion of sediment and deep soils also controlled recession behavior. Furthermore, we found that recession characteristics are influenced by both evapotranspiration (ET) and proxies of antecedent catchment storage, but that the patterns were different depending on catchment properties. ET was less influential in catchments with deeper soils and larger catchment area. Shifts in recession rates were primarily related to variation in storage, with faster streamflow recessions occurring during periods with low storage. The results demonstrate the influence of catchment properties on recession behavior, and we found great value in analyzing individual recession events for an increased understanding of spatial and temporal recession characteristics. When recession properties were lumped together, the relationships to catchment characteristics were obscured. This indicates the value of more detailed analyses, at least under the strongly seasonal hydroclimatic conditions of this site.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2019. Vol. 570, p. 315-328
Keywords [en]
Recession curves, Spatio-temporal variability, Landscape analysis, Runoff, Krycklan
National Category
Oceanography, Hydrology and Water Resources
Identifiers
URN: urn:nbn:se:uu:diva-380487DOI: 10.1016/j.jhydrol.2018.12.065ISI: 000460709400025OAI: oai:DiVA.org:uu-380487DiVA, id: diva2:1301698
Funder
Swedish Research CouncilSwedish Research Council FormasThe Kempe FoundationsAvailable from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-04-02Bibliographically approved

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Karlsen, ReinertBishop, KevinGrabs, T.homas J.Seibert, Jan

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