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Map-based Prediction of Organic Carbon in Headwaters Streams Improved by Downstream Observations from the River Outlet : Volume 12, Issue 12 (16/06/2015)

By Temnerud, J.

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Book Id: WPLBN0003978431
Format Type: PDF Article :
File Size: Pages 37
Reproduction Date: 2015

Title: Map-based Prediction of Organic Carbon in Headwaters Streams Improved by Downstream Observations from the River Outlet : Volume 12, Issue 12 (16/06/2015)  
Author: Temnerud, J.
Volume: Vol. 12, Issue 12
Language: English
Subject: Science, Biogeosciences, Discussions
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Nyberg, L., Buffam, I., Fölster, J., Brömssen, C. V., Temnerud, J., Andersson, J., & Bishop, K. (2015). Map-based Prediction of Organic Carbon in Headwaters Streams Improved by Downstream Observations from the River Outlet : Volume 12, Issue 12 (16/06/2015). Retrieved from

Description: Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden. In spite of the great abundance and ecological importance of headwater streams, managers are usually limited by a lack of information about water chemistry in these headwaters. In this study we test whether river outlet chemistry can be used as an additional source of information to improve the prediction of the chemistry of upstream headwaters (size < 2 km2), relative to models based on map information alone. Between 2000 and 2008, we conducted 17 synoptic surveys of streams within 9 mesoscale catchments (size 32–235 km2). Over 900 water samples were collected from catchments ranging in size from 0.03 to 235 km2. First we used partial least square regression (PLS) to model headwater stream total organic carbon (TOC) median and interquartile values for a given catchment, based on a large number of candidate variables including catchment characteristics from GIS, and measured chemistry at the catchment outlet. The best candidate variables from the PLS models were then used in hierarchical linear mixed models (MM) to model TOC in individual headwater streams. Three predictor variables were consistently selected for the MM calibration sets: (1) proportion of forested wetlands in the sub-catchment (positively correlated with headwater stream TOC), (2) proportion of lake surface cover in the sub-catchment (negatively correlated with headwater stream TOC), and (3) whole-catchment river outlet TOC (positively correlated with headwater stream TOC). Including river outlet TOC as a predictor in the models gave 5–15% lower prediction errors than using map information alone. Thus, data on water chemistry measured at river outlets offers information which can complement GIS-based modelling of headwater stream chemistry.

Map-based prediction of organic carbon in headwaters streams improved by downstream observations from the river outlet

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