Data from: Dissolved organic carbon in streams within a subarctic catchment analysed using a GIS/remote sensing approach

• Main Authors: Mzobe, Pearl, Berggren, Martin, Pilesjö, Petter, Lundin, Erik, Olefeldt, David, Roulet, Nigel T., Persson, Andreas
• Language: unknown
• Published: 2018
• Subjects: Life sciences; medicine and health care; Stordalen; Subarctic
• Online Access: http://nbn-resolving.org/urn:nbn:nl:ui:13-n6-ad16; https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:109429

Climate change projections show that temperature and precipitation increases can alter the exchange of greenhouse gases between the atmosphere and high latitude landscapes, including their freshwaters. Dissolved organic carbon (DOC) plays an important role in greenhouse gas emissions, but the impact of catchment productivity on DOC release to subarctic waters remains poorly known, especially at regional scales. We test the hypothesis that increased terrestrial productivity, as indicated by the normalized difference vegetation index (NDVI), generates higher stream DOC concentrations in the Stordalen catchment in subarctic Sweden. Furthermore, we aimed to determine the degree to which other generic catchment properties (elevation, slope) the explain DOC concentration, and whether or not land cover variables representing the local vegetation type (e.g., mire, forest) need to be included to obtain adequate predictive models for DOC delivered into rivers. We show that the land cover type, especially the proportion of mire, played a dominant role in the catchment's release of DOC, while NDVI, slope, and elevation were supporting predictor variables. The NDVI as a single predictor showed weak and inconsistent relationships to DOC concentrations in recipient waters, yet NDVI was a significant positive regulator of DOC in multiple regression models that included land cover variables. Our study illustrates that vegetation type exerts primary control in DOC regulation in Stordalen, while productivity (NDVI) is of secondary importance. Thus, predictive multiple linear regression models for DOC can be utilized combining these different types of explanatory variables.