气候变化领域集成服务门户

In aquatic systems, time series of dissolved oxygen (DO) have been used to compute estimates of ecosystem metabolism. Central to this open-water method is the assumption that the DO time series is a Lagrangian specification of the flow field. However, most DO time series are collected at fixed locations, such that changes in DO are assumed to reflect metabolism and that effects of advection or mixing are negligible. A weighted regression model was applied to remove variability in DO time series from tides, thereby helping to partially relax this assumption and improve metabolism estimates. The method offers a distinct advantage over traditional deconvulution methods by targeting the periodicity of the tidal component while preserving the true biological signal. The model was first applied to simulated DO time series with specified biological and physical characteristics, and then applied to 1 yr of continuous monitoring data from four stations within the National Estuarine Research Reserve System. The correlation of DO and metabolism estimates with tides was greatly reduced after using weighted regression. The model was especially effective when the magnitude of tidal influence was high and correlations between tidal change and the solar cycle were low at the time scales of interest. The model was less robust when tides and the solar cycle were correlated for protracted periods. By reducing the effects of physical transport on metabolism estimates, there may be increased potential to empirically relate metabolic rates to causal factors on timescales of several days to several weeks.