Modeling dissolved organic carbon in subalpine and alpine lakes with GIS and remote sensing

Current global trends in lake dissolved organic carbon (DOC) concentrations suggest a need for tools to more broadly measure and predict variation in DOC at regional landscape scales. This is particularly true for more remote subalpine and alpine regions where access is difficult and the minimal levels of anthropogenic watershed disturbance allow these systems to serve as valuable reference sites for long-term climate change. Here geographic information system (GIS) and remote sensing tools are used to develop simple predictive models that define relationships between watershed variables known to influence lake DOC concentrations and lake water color in the Absaroka-Beartooth Wilderness in Montana and Wyoming, USA. Variables examined include watershed area, topography, and vegetation cover. The resulting GIS model predicts DOC concentrations at the lake watershed scale with a high degree of accuracy (R2 = 0.92; P ≤ 0.001) by including two variables: vegetation coverage (representing sites of organic carbon fixation) and areas of low slope (0–5%) within the watershed (wetland sites of DOC production). Importantly, this latter variable includes not only surficially visible wetlands, but “cryptic” subsurface wetlands. Modeling with Advanced Land Imager satellite remote sensing data provided a weaker relationship with water color and DOC concentrations (R2 = 0.725; P ≤ 0.001). Model extrapolation is limited by small sample sizes but these models show promise in predicting lake DOC in subalpine and alpine regions.