Assessing land–ocean connectivity via submarine groundwater discharge (SGD) in the Ria Formosa Lagoon (Portugal): combining radon measurements and stable isotope hydrology

Natural radioactive tracer-based assessments of basin-scale submarine groundwater discharge (SGD) are well developed. However, SGD takes place in different modes and the flow and discharge mechanisms involved occur over a wide range of spatial and temporal scales. Quantifying SGD while discriminating its source functions therefore remains a major challenge. However, correctly identifying both the fluid source and composition is critical. When multiple sources of the tracer of interest are present, failure to adequately discriminate between them leads to inaccurate attribution and the resulting uncertainties will affect the reliability of SGD solute loading estimates. This lack of reliability then extends to the closure of local biogeochemical budgets, confusing measures aiming to mitigate pollution. Here, we report a multi-tracer study to identify the sources of SGD, distinguish its component parts and elucidate the mechanisms of their dispersion throughout the Ria Formosa – a seasonally hypersaline lagoon in Portugal. We combine radon budgets that determine the total SGD (meteoric + recirculated seawater) in the system with stable isotopes in water (δ2H, δ18O), to specifically identify SGD source functions and characterize active hydrological pathways in the catchment. Using this approach, SGD in the Ria Formosa could be separated into two modes, a net meteoric water input and another involving no net water transfer, i.e., originating in lagoon water re-circulated through permeable sediments. The former SGD mode is present occasionally on a multi-annual timescale, while the latter is a dominant feature of the system. In the absence of meteoric SGD inputs, seawater recirculation through beach sediments occurs at a rate of  ∼  1.4  ×  106 m3 day−1. This implies that the entire tidal-averaged volume of the lagoon is filtered through local sandy sediments within 100 days ( ∼  3.5 times a year), driving an estimated nitrogen (N) load of  ∼  350 Ton N yr−1 into the system as NO3−. Land-borne SGD could add a further  ∼  61 Ton N yr−1 to the lagoon. The former source is autochthonous, continuous and responsible for a large fraction (59 %) of the estimated total N inputs into the system via non-point sources, while the latter is an occasional allochthonous source capable of driving new production in the system.

A time frequency approach to blind deconvolution in multipath underwater channels

Blind deconvolution is studied in the underwater acoustic channel context, by time-frequency (TF) processing. The acoustic propagation environment is modelled by ray tracing and mathematically described by a multipath propagation channel. Representation of the received signal by means of a signal-dependent TF distribution (radially Gaussian kernel distribution) allowed to visualize the resolved replicas of the emitted signal, while signi cantly attenuating the inherent interferences of classic quadratic TF distributions. The source signal instantaneous frequency estimation was the starting point for both source and channel estimation. Source signature estimation was performed by either TF inversion, based on the Wigner-Ville distribution of the received signal, or a subspace- -based method. The channel estimate was obtained either via a TF formulation of the conventional matched- lter, or via matched- - ltering with the previously obtained source estimate. A shallow water realistic scenario is considered, comprising a 135-m depth water column and an acoustic source located at 90-m depth and 5.6-km range from the receiver. For the corresponding noiseless simulated data, the quality of the best estimates was 0.856 for the source signal, and 0.9664 and 0.9996 for the amplitudes and time-delays of the impulse response, respectively. Application of the proposed deconvolution method to real data of the INTIMATE '96 sea trial conduced to source and channel estimates with the quality of 0.530 and 0.843, respectively. TF processing has proved to remove the typical ill-conditioning of single sensor deterministic deconvolution techniques.