Using CDOM spectral shape information to improve the estimation of DOC concentration in inland waters: A case study of Andean Patagonian Lakes.

For the last two decades different scientific disciplines have focused on lacustrine dissolved organic matter (DOM) given its importance in the biogeochemistry of carbon and in ecosystem functioning. New satellites supply the appropriate resolutions to evaluate chromophoric dissolved organic matter (CDOM) in inland waters, opening the possibility to estimate DOM at appropriate spatiotemporal scales. This requires, however, a robust relationship between CDOM and dissolved organic carbon (DOC). In this work, we evaluated the use of CDOM as a proxy of DOC in 7 Andean Patagonian lakes. Considering the entire data set, CDOM absorption coefficients (a355 and a440) were linearly related with DOC. Shallow lakes, however, drove this relationship showing a moderate relationship, whereas, deep lakes with lower colour presented a weaker relationship. Therefore, we assessed the use of CDOM spectral shape information to improve DOC estimates regardless of observed DOM differences due to climatic seasonality and lakes' morphometry. The use of well-known CDOM spectral shape metrics (i.e., S275-295 and a250:a365 ratio) significantly improved DOC estimation. Particularly, using a Gaussian decomposition approach we found that much of the variation in the spectral shape, associated with the variability of CDOM:DOC ratio, was explained by differences in two dynamic regions centred at 270 and 320 nm. A strong nonlinear relationship was found between the a270:a320 ratio and the DOC-specific absorption coefficients a*355 and a*440. This was translated into a further improvement in DOC estimation yielding the higher R2 and lower mean absolute differences (MAPD < 16%), either considering the entire data set or shallow and deep lakes separately. Our results highlight that incorporating the CDOM spectral shape information improves the characterization of the DOC pool of inland waters, which is particularly relevant for remote and/or inaccessible sites and has significant implications for the environmental management, biogeochemical studies and future remote sensing applications.

Fluorescence components of natural dissolved organic matter (DOM) from aquatic systems of an Andean Patagonian catchment: Applying different data restriction criteria for PARAFAC modelling.

Fluorescence spectroscopy is a widely applied technique to characterize the composition of the fluorescent fraction of dissolved organic matter (DOM), allowing to infer sources and diagenetic state of soil, marine and freshwater DOM samples. The analysis of fluorescent DOM (FDOM) is often carried out by multi-way models such as parallel factor analysis (PARAFAC), which allows decomposing excitation-emission matrices (EEMs) obtained from DOM samples into their underlying chemical components. Some aspects of the performance/accuracy of the EEM-PARAFAC technique regarding the use of selective vs. non-selective EEM data are still in discussion. In this investigation, we evaluated the outcome of two different approaches (non-selective and selective) applied to study the composition of DOM from four headwater streams (Case 1) and from two neighboring shallow lakes (Case 2), all belonging to the same Andean watershed within Nahuel Huapi National Park in North Patagonia (Argentina). In both cases, the outcome of the PARAFAC performed to non-selective data (EEM datasets from all the streams and the two lakes) vs. selective datasets (EEMs from each stream and each lake treated separately) is compared on the basis of modelled fluorescent components. Regardless of the restriction criteria applied for the analyses, the results obtained indicated similar component loadings in the four streams and in the two lakes. The similarity of the outcomes likely relates to the low internal variation of the EEMs, since these are located in the same catchment, influenced by similar soils and vegetation which are the main DOM sources. Therefore, we conclude that the use of a small selective EEM dataset may not condition the validation of the FDOM components and their temporal dynamics.