DOI: 10.6078/d18h32
title: Salmon Creek Organic Geochemistry Chemometric Data

description: This datafile contains fluorescence indices and the results of a parallel
 factor analysis (PARAFAC) performed on samples collected within the Salmon
 Creek Watershed in Sonoma County, Califonia. Please see Woelfle-Erskine et
 al. (2017) for site location details. The present dataset is provided in
 support of Larsen and Woelfle-Erskine (in review). The purpose of this
 dataset was to develop a fluorescent fingerprint for purposes of
 differentiating between surface water, groundwater, and hyporheic water.
description: Samples were collected with a peristaltic pump (Geotech Inc.,
 Denver, CO). Both piezometer and surface-water samples collected for DOC
 analyses were filtered in the field through 0.2 micron polyethersulfone
 filters, into acid-washed, 40 mL amber vials, and kept on ice during
 transport to the lab. Samples were analyzed for fluorescence within 3 days
 of collection. Isotope samples were collected in the same manner, but were
 not filtered and were frozen until analysis. Radon samples were collected
 in pre-washed 2-L plastic bottles.DOC fluorescence was
 analyzed on a Horiba Jobin Yvon Aqualog fluorometer, which obtains
 simultaneous absorbance and fluorescence information, allowing for
 rigorous correction for the inner-filter effect. Samples were
 blank-subtracted, corrected for the inner filter effect, and Raman
 normalized within the Aqualog instrument software. First- and second-order
 Rayleigh scattering regions were removed, and a triangle of zeros was
 inserted in the region where the excitation wavelength is greater than the
 emission wavelength.The entire library of samples
 collected from 2013 to 2015 (see distributional characteristics in Table
 1) was used to develop a PARAFAC model for the study area. PARAFAC was
 implemented using the drEEM toolbox in Matlab. EEMs were trimmed to
 excitation wavelengths 260-375 nm and emission wavelengths 350-620 nm to
 remove regions that exhibited excessively high leverage on model fits and
 to avoid fitting noise. Subsequently, they were normalized by their
 highest fluorescence value. Four outlier samples were identified based on
 their excessively high loadings and their EEMs and removed from subsequent
 analysis. Two of the outliers had no apparent organic matter fluorescence,
 whereas the other two had anomalous spectral features. Models with between
 two and eight components were evaluated. Residuals appeared to take on the
 appearance of unstructured noise with the five-component model, with
 typically less than ten percent of the magnitude of the sample
 fluorescence. Models with up to five components met the Tucker’s
 convergence criterion for spectral equivalence in split halves for two
 alternative splits, whereas models with up to seven components met the
 criterion for one of two splits. Repeated model solutions with different
 intializations converged upon the same local minimum in eight out of 10
 trials with the five-component model, whereas the six-component model
 converged upon a more diverse set of local minima, for which at least two
 of the spectral loadings were noticeably different.For
 reasons of stability of the minimum, reasonable appearance of the spectral
 loadings, and unstructured and low-fluorescence nature of the residuals,
 the five-component model was selected for further analysis. Component
 loadings were converted to Fmax values (which represent the fluorescence
 attributable to each component, in Raman units) through multiplication by
 the maximum excitation and emission loadings of the component.
 In addition to the development of the original PARAFAC
 model (hereafter referred to as the OP model), the PARAFAC model of Cory
 and McKnight (2005) (hereafter referred to as the CM model) was also
 applied to the set of collected EEMs. The CM model was developed from a
 library of samples collected from diverse environments, ranging from those
 with terrestrially dominated DOC to those with microbially dominated DOC.
 We interpreted the PARAFAC results through further
 multivariate analyses. We performed a principal components analysis (PCA)
 to evaluate those fluorescent components and indices that covaried with
 each other and were most strongly associated with different classes of
 samples (i.e., well, spring, piezometer, surface water). We also used a
 hierarchical clustering analysis to determine which groups of samples were
 most similar to each other with respect to the input fluorescent
 components and indices. The Fmax values from the CM and OP PARAFAC models,
 together with the FI, HIX, and BIX, constituted the dependent variables
 for the PCA and hierarchical clustering analysis, performed in JMP
 statistical software (SAS Institute, Inc., Cary, NC). The PCA was
 performed on the variable correlation matrix. The hierarchical clustering
 analysis was performed on standardized variables, using Ward’s minimum
 variance method.
description: Please see accompanying metadata.