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

Abstract

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 (size < 2 km2/, relative to models based on map information alone. We use the concentration of total organic carbon (TOC), an important stream ecosystem parameter, as the target for our study. Between 2000 and 2008, we carried out 17 synoptic surveys in 9 mesoscale catchments (size 32-235 km2/. Over 900 water samples were collected in total, primarily from headwater streams but also including each catchment's river outlet during every survey. First we used partial least square regression (PLS) to model the distribution (median, interquartile range (IQR)) of headwater stream TOC for a given catchment, based on a large number of candidate variables including sub-catchment characteristics from GIS, and measured river 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) river outlet TOC (positively correlated with headwater stream TOC). Including river outlet TOC improved predictions, with 515% lower prediction errors than when using map information alone. Thus, data on water chemistry measured at river outlets offer information which can complement GIS-based modelling of headwater stream chemistry.