RESUMEN
Solute concentration time series reflect hydrological and biological drivers through various frequencies, phases, and amplitudes of change. Untangling these signals facilitates the understanding of dynamic ecosystem conditions and transient water quality issues. A case in point is the inference of biogeochemical processes from diel solute concentration variations. This analysis requires approaches capable of isolating subtle diel signals from background variability at other scales. Conventional time series analyses typically assume stationary or deterministic background variability; however, most rivers do not respect such niceties. We developed a time-series filtering method that uses empirical mode decomposition to decompose a measured solute concentration time series into intrinsic mode frequencies. Based on externally supplied mechanistic knowledge, we then filter these modes by periodicity, phase, and coherence with neighboring days. This method is tested on three synthetic series that incorporate environmental variability and sensor noise and on a year of 15 min sampled concentration time series from three hydrologically and ecologically distinct rivers in the eastern United States. The proposed method successfully isolated signals in the measured data sets that corresponded with variability in gross primary productivity. The strength the diel signal isolated through this method was smaller compared to the true signal in the synthetic series; however, uncertainty analysis showed that the process-model-based estimates derived from these signals were similar to other inference methods. This signal decomposition method retains information that can be used for further process modeling while making different assumptions about the data than Fourier and wavelet analyses.
Asunto(s)
Ecosistema , Ríos , Hidrología , Estados UnidosRESUMEN
Diel variability in nutrient concentrations is common but not universal in aquatic ecosystems. Theoretical models of photoautotrophic systems attribute the absence of diel uptake variation to nutrient scarcity, such that diel variability in nutrient uptake disappears as nutrients become limiting. We tested this prediction in a mesocosm experiment, by exposing benthic algal communities to a range of nitrogen (N) and phosphorus concentrations and recording the rates of uptake during both day and night. We found that higher concentrations of N produced diel variability in uptake and that the difference between the day and night total mass uptakes approximately equaled N demand for observed primary production as seen in other studies. At lower concentrations of N, uptake rates during the day and night were indistinguishable. These results are the first empirical evidence to imply that diel nitrate patterns in streams and rivers indicate a release from N limitation and offer a new way to assess nutrient limitation.