Optimal redesign of a salinity monitoring network in Barataria Basin, Louisiana, USA using discrete entropy theory

Abstract

We use discrete entropy theory and the MaxT model to optimize a continuous salinity monitoring network in a coastal Louisiana estuary (71 stations, 5 station types). Four station types represent marsh zones based on plant tolerance to salinity: fresh, intermediate, brackish, and saline. The fifth station type include stations in open water spanning the entire estuarine salinity gradient. A dry year (2012) with reduced freshwater inputs, and a wet year (2019) were chosen to test the robustness of the model. Our analysis showed that thirty-one stations formed the core network for both water years, with an additional 12 (2012) and 14 (2019) stations, unique for each year, needed to capture the optimal information for salinity in all five habitats. Fourteen stations could be eliminated with minimal loss of information regardless of whether the estuary was under drought or excess freshwater conditions. The Nash-Sutcliffe coefficient (ENs), coefficient of determination (R2) and the relative error (RE) showed good to excellent agreement between salinity measured by the original network and the modeled salinity of the modified network. Discrete entropy theory and MaxT proved effective in identifying redundancies in information sufficient to allow a 20% reduction of stations. Considering the highly variable nature of salinity within an estuary over short- and long-term temporal scales within a particular geographic location and across the entire estuary, this result was unexpected. Further reductions in stations (and corresponding savings in resources and costs) will require approaches not focused on replicating the exact information content from station to station but rather on measuring those salinity patterns and thresholds that shape biotic resource response in an estuary and possibly a reformulation of monitoring objectives. Monitoring objectives should include sampling for parameters (e.g., suspended sediments and nutrients) that don't require continuous data and are less costly to sustain yet provide equally critical information for understanding coastal wetland habitat sustainability and restoration.