Optimal redesign of a salinity monitoring network in Barataria Basin,Louisiana, USA using discrete entropy theory |
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| Institution: | 1. College of New Energy and Environment, Jilin University, Jilin Province, China;2. Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA, USA;3. Retired, United States Geological Survey, Louisiana Water Science Center, Baton Rouge, LA, USA;1. ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India;2. ICAR- National Institute of Agricultural Economics and Policy Research, New Delhi, India;3. ICAR-Indian Agricultural Research Institute, New Delhi, India;1. Department of Environmental Management and Toxicology, Federal University of Petroleum Resources, Nigeria;2. Department of Geography, University of Lagos, Nigeria;3. Department of GIS and Data Analytics, eHealth Africa Kano, Nigeria;1. College of Economics and Management, Hunan Institute of Science and Technology, Yueyang 414000, Hunan, China;2. College of Geography and Tourism, Hunan University of Arts and Science, Changde 415000, Hunan, China;1. Fundación Alium Pacific, Cali, Colombia;2. Instituto Politécnico Nacional, Centro Interdisciplinario de Ciencias Marinas, C.P. 23096 La Paz, Baja California Sur, Mexico;3. Universidad Jorge Tadeo Lozano, Programa de Biología Marina, Facultad de Ciencias Naturales e Ingeniería, Santa Marta, Colombia;4. Coastal Marine Education and Research Academy, Clearwater, FL, USA;1. Laboratory of Ecology, Department of Earth and Marine Sciences, University of Palermo, Viale delle Scienze Edificio 16, I-90128 Palermo, Italy;2. NBFC, National Biodiversity Future Center, Piazza Marina 61, I-90133 Palermo, Italy;3. National Research Council of Italy - Institute for Biomedical Research and Innovation (IRIB-CNR), Via Ugo La Malfa 153, I-90146 Palermo, Italy;4. Department of earth and marine science (DiSTeM), University of Palermo, I-90128 Palermo, Italy;5. National Research Council of Italy - Institute for Systems Analysis and Computer Science “A. Ruberti” (IASI-CNR), I-00168 Rome, Italy, I-67100, L''Aquila, Italy;6. Center of Excellence for Research DEWS, University of L''Aquila, Via Vetoio Coppito 1, I-67100 L''Aquila, Italy;7. Dept. of Biomatics, Obuda University, Budapest, Hungary |
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| 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. |
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