High-Frequency in Situ Optical Measurements During a Storm Event: Assessing Relationships Between Dissolved Organic Matter, Sediment Concentrations, and Hydrologic Processes

Dissolved organic matter (DOM) dynamics during storm events has received considerable attention in forested watersheds, but the extent to which storms impart rapid changes in DOM concentration and composition in highly disturbed agricultural watersheds remains poorly understood. In this study, we used identical in situ optical sensors for DOM fluorescence (FDOM) with and without filtration to continuously evaluate surface water DOM dynamics in a 415 km(2) agricultural watershed over a 4 week period containing a short-duration rainfall event. Peak turbidity preceded peak discharge by 4 h and increased by over 2 orders of magnitude, while the peak filtered FDOM lagged behind peak turbidity by 15 h. FDOM values reported using the filtered in situ fluorometer increased nearly fourfold and were highly correlated with dissolved organic carbon (DOC) concentrations (r(2) = 0.97), providing a highly resolved proxy for DOC throughout the study period. Discrete optical properties including specific UV absorbance (SUVA(254)), spectral slope (S(290-350)), and fluorescence index (FI) were also strongly correlated with in situ FDOM and indicate a shift toward aromatic, high molecular weight DOM from terrestrially derived sources during the storm. The lag of the peak in FDOM behind peak discharge presumably reflects the draining of watershed soils from natural and agricultural landscapes. Field and experimental evidence showed that unfiltered FDOM measurements underestimated filtered FDOM concentrations by up to similar to 60% at particle concentrations typical of many riverine systems during hydrologic events. Together, laboratory and in situ data provide insights into the timing and magnitude of changes in DOM quantity and quality during storm events in an agricultural watershed, and indicate the need for sample filtration in systems with moderate to high suspended sediment loads.

Distributions and Variability of Particulate Organic Matter in a Coastal Upwelling System

In this study we examined the spatial and temporal variability of particulate organic material (POM) off Oregon during the upwelling season. High-resolution vertical profiling of beam attenuation was conducted along two cross-shelf transects. One transect was located in a region where the shelf is relatively uniform and narrow (off Cascade Head (CH)); the second transect was located in a region where the shelf is shallow and wide (off Cape Perpetua (CP)). In addition, water samples were collected for direct analysis of chlorophyll, particulate organic carbon (POC), and particulate organic nitrogen (PON). Beam attenuation was highly correlated with POC and PON. Striking differences in distribution patterns and characteristics of POM were observed between CH and CP. Off CH, elevated concentrations of chlorophyll and POC were restricted to the inner shelf and were highly variable in time. The magnitude of the observed short-term temporal variability was of the same order as that of the seasonal variability reported in previous studies. Elevated concentrations of nondegraded chlorophyll and POM were observed near the bottom. Downwelling and rapid sinking are two mechanisms by which phytoplankton cells can be delivered to the bottom before being degraded. POM may be then transported across the shelf via the benthic nepheloid layer. Along the CP transect, concentrations of POM were generally higher than they were along the CH transect and extended farther across the shelf. Characteristics of surface POM, namely, C: N ratios and carbon: chlorophyll ratios, differed between the two sites. These differences can be attributed to differences in shelf circulation.

The Value of Adding Optics to Ecosystem Models: A Case Study

Many ecosystem models have been developed to study the ocean's biogeochemical properties, but most of these models use simple formulations to describe light penetration and spectral quality. Here, an optical model is coupled with a previously published ecosystem model that explicitly represents two phytoplankton (picoplankton and diatoms) and two zooplankton functional groups, as well as multiple nutrients and detritus. Surface ocean color fields and subsurface light fields are calculated by coupling the ecosystem model with an optical model that relates biogeochemical standing stocks with inherent optical properties (absorption, scattering); this provides input to a commercially available radiative transfer model (Ecolight). We apply this bio-optical model to the equatorial Pacific upwelling region, and find the model to be capable of reproducing many measured optical properties and key biogeochemical processes in this region. Our model results suggest that non-algal particles largely contribute to the total scattering or attenuation (> 50% at 660 nm) but have a much smaller contribution to particulate absorption (< 20% at 440 nm), while picoplankton dominate the total phytoplankton absorption (> 95% at 440 nm). These results are consistent with the field observations. In order to achieve such good agreement between data and model results, however, key model parameters, for which no field data are available, have to be constrained. Sensitivity analysis of the model results to optical parameters reveals a significant role played by colored dissolved organic matter through its influence on the quantity and quality of the ambient light. Coupling explicit optics to an ecosystem model provides advantages in generating: (1) a more accurate subsurface light-field, which is important for light sensitive biogeochemical processes such as photosynthesis and photo-oxidation, (2) additional constraints on model parameters that help to reduce uncertainties in ecosystem model simulations, and (3) model output which is comparable to basic remotely-sensed properties. In addition, the coupling of biogeochemical models and optics paves the road for future assimilation of ocean color and in-situ measured optical properties into the models.