Comparison of environmental forcings affecting suspended sediments variability in two macrotidal, highly-turbid estuaries

The relative contribution of environmental forcing frequencies on turbidity variability is, for the first time, quantified at seasonal and multiannual time scales in tidal estuarine systems. With a decade of high-frequency, multi-site turbidity monitoring, the two nearby, macrotidal and highly-turbid Gironde and Loire estuaries (west France) are excellent natural laboratories for this purpose. Singular Spectrum Analyses, combined with Lomb-Scargle periodograms and Wavelet Transforms, were applied to the continuous multiannual turbidity time series. Frequencies of the main environmental factors affecting turbidity were identified: hydrological regime (high versus low river discharges), river flow variability, tidal range, tidal cycles, and turbulence. Their relative influences show similar patterns in both estuaries and depend on the estuarine region (lower or upper estuary) and the time scale (multiannual or seasonal). On the multiannual time scale, the relative contribution of tidal frequencies (tidal cycles and range) to turbidity variability decreases up-estuary from 68% to 47%, while the influence of river flow frequencies increases from 3% to 42%. On the seasonal time scale, the relative influence of forcings frequencies remains almost constant in the lower estuary, dominated by tidal frequencies (60% and 30% for tidal cycles and tidal range, respectively); in the upper reaches, it is variable depending on hydrological regime, even if tidal frequencies are responsible for up 50% of turbidity variance. These quantifications show the potential of combined spectral analyses to compare the behavior of suspended sediment in tidal estuaries throughout the world and to evaluate long-term changes in environmental forcings, especially in a context of global change. The relevance of this approach to compare nearby and overseas systems and to support management strategies is discussed (e.g., selection of effective operation frequencies/regions, prediction of the most affected regions by the implementation of operational management plans). (C) 2017 Elsevier Ltd. All rights reserved.