Characterization of surface water isotope spatial patterns of Scotland

Sánchez-Murillo, Ricardo (Ricardo Alonso Sánchez Murillo) The extended National Waters Inventory of Scotland (NWIS) monitoring network in combination with an extensive, supplementary low flow sampling campaign was used to create isoscapes of surface water for management purposes at high spatial reso...

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Bibliographic Details
Published in:Journal of Geochemical Exploration
Main Authors: Birkel, Christian, Helliwell, Rachel, Thornton, Barry, Gibbs, Sheila, Cooper, Pat, Soulsby, Chris, Tetzlaf, Doerthe, Spezia, Luigi, Esquivel-Hernández, Germain, Sánchez-Murillo, Ricardo, Midwood, Andrew J.
Format: Other/Unknown Material
Language:English
Published: Elsevier B.V. 2018
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Online Access:http://hdl.handle.net/11056/17255
https://doi.org/10.1016/j.gexplo.2018.07.011
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Summary:Sánchez-Murillo, Ricardo (Ricardo Alonso Sánchez Murillo) The extended National Waters Inventory of Scotland (NWIS) monitoring network in combination with an extensive, supplementary low flow sampling campaign was used to create isoscapes of surface water for management purposes at high spatial resolution (100 m grid) across Scotland. The δ2 H isoscape shows a strong isotopic separation along a north-south and east-west topographic (mountainous to the north and west and lowlands to the east) and climatic (wetter west, drier east) gradients. Isotopes were enriched in the western domain and depleted in the east and central Highland domains. The surface water d-excess isoscape show more complex spatial variability mainly related to contrasting moisture sources (sub-tropical North Atlantic Ocean, the North Sea, Polar Continental, and the Arctic) as well as secondary evaporation processes. The two-year NWIS isotope record exhibited a significant seasonal evaporative effect on surface water isotopes that progresses from winter through to a maximum in autumn as indicated by Local Evaporation Lines (LELs). The surface water isoscapes can be efficiently reproduced with geographically weighted regression (GWR) models using gridded annual precipitation, remotely sensed actual evapotranspiration, land cover, soil wetness, catchment area, and mean elevation. The GWR models showed potential to assess isotopic changes under future climate and land use change.