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

[摘要] In spite of the great abundance and ecological importance of headwaterstreams, managers are usually limited by a lack of information about waterchemistry in these headwaters. In this study we test whether river outletchemistry can be used as an additional source of information to improve theprediction of the chemistry of upstream headwaters (size < 2 km²),relative to models based on map information alone. We use theconcentration of total organic carbon (TOC), an important stream ecosystemparameter, as the target for our study. Between 2000 and 2008, we carriedout 17 synoptic surveys in 9 mesoscale catchments (size 32–235 km²).Over 900 water samples were collected in total, primarily from headwaterstreams but also including each catchment's river outlet during everysurvey. First we used partial least square regression (PLS) to model thedistribution (median, interquartile range (IQR)) of headwater stream TOC fora given catchment, based on a large number of candidate variables includingsub-catchment characteristics from GIS, and measured river chemistry at thecatchment outlet. The best candidate variables from the PLS models were thenused in hierarchical linear mixed models (MM) to model TOC in individualheadwater streams. Three predictor variables were consistently selected forthe MM calibration sets: (1) proportion of forested wetlands in thesub-catchment (positively correlated with headwater stream TOC), (2)proportion of lake surface cover in the sub-catchment (negatively correlatedwith headwater stream TOC), and (3) river outlet TOC (positively correlatedwith headwater stream TOC). Including river outlet TOC improved predictions,with 5–15 % lower prediction errors than when using map information alone.Thus, data on water chemistry measured at river outlets offer informationwhich can complement GIS-based modelling of headwater stream chemistry.

[学科分类] 地球化学与岩石