Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change
Florida Bay is a large, subtropical estuary whose salinity varies from yearly and seasonal changes in rainfall and freshwater inflows. Water management changes during the 20th century led to a long-term reduction in inflows that increased mean salinity, and the frequency and severity of hypersalinit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/14/21/3495 |
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| author | Anteneh Z. Abiy Ruscena P. Wiederholt Gareth L. Lagerwall Assefa M. Melesse Stephen E. Davis |
| author_facet | Anteneh Z. Abiy Ruscena P. Wiederholt Gareth L. Lagerwall Assefa M. Melesse Stephen E. Davis |
| author_sort | Anteneh Z. Abiy |
| collection | DOAJ |
| description | Florida Bay is a large, subtropical estuary whose salinity varies from yearly and seasonal changes in rainfall and freshwater inflows. Water management changes during the 20th century led to a long-term reduction in inflows that increased mean salinity, and the frequency and severity of hypersalinity. Climate change may exacerbate salinity conditions in Florida Bay; however, future salinity conditions have not been adequately evaluated. Here, we employed a Multilayer Feedforward Artificial Neural Network model to develop baseline salinity models for nearshore and offshore sites. Then, we examined the impacts of climate change on salinity using forecasted changes in various input variables under two climate change scenarios, representative concentration pathways (RCP) 4.5 and 8.5. Salinity could rise by 30% and 70% under the RCP4.5 and RCP8.5 forecasts, respectively. Climate change affected nearshore salinity significantly more, which rapidly fluctuated between mesohaline (5 to 18 PSU) and metahaline (40 to 55 PSU) to hypersaline conditions (>55 PSU). Offshore salinities ranged between euhaline (30 to 40 PSU) to metahaline (40 to 55 PSU) conditions. Our study suggests that increased freshwater flow would help maintain suitable estuarine conditions in Florida Bay during climate change, while our novel modeling approach can guide further Everglades restoration efforts. |
| first_indexed | 2024-03-09T18:33:13Z |
| format | Article |
| id | doaj.art-6a10c77c973d463f94986eca1af0d91b |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T18:33:13Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-6a10c77c973d463f94986eca1af0d91b2023-11-24T07:20:25ZengMDPI AGWater2073-44412022-11-011421349510.3390/w14213495Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate ChangeAnteneh Z. Abiy0Ruscena P. Wiederholt1Gareth L. Lagerwall2Assefa M. Melesse3Stephen E. Davis4The Everglades Foundation, 18001 Old Cutler Road, Suite 625, Palmetto Bay, FL 33157, USAThe Everglades Foundation, 18001 Old Cutler Road, Suite 625, Palmetto Bay, FL 33157, USAThe Everglades Foundation, 18001 Old Cutler Road, Suite 625, Palmetto Bay, FL 33157, USADepartment of Earth and Environment, Florida International University, Miami, FL 33199, USAThe Everglades Foundation, 18001 Old Cutler Road, Suite 625, Palmetto Bay, FL 33157, USAFlorida Bay is a large, subtropical estuary whose salinity varies from yearly and seasonal changes in rainfall and freshwater inflows. Water management changes during the 20th century led to a long-term reduction in inflows that increased mean salinity, and the frequency and severity of hypersalinity. Climate change may exacerbate salinity conditions in Florida Bay; however, future salinity conditions have not been adequately evaluated. Here, we employed a Multilayer Feedforward Artificial Neural Network model to develop baseline salinity models for nearshore and offshore sites. Then, we examined the impacts of climate change on salinity using forecasted changes in various input variables under two climate change scenarios, representative concentration pathways (RCP) 4.5 and 8.5. Salinity could rise by 30% and 70% under the RCP4.5 and RCP8.5 forecasts, respectively. Climate change affected nearshore salinity significantly more, which rapidly fluctuated between mesohaline (5 to 18 PSU) and metahaline (40 to 55 PSU) to hypersaline conditions (>55 PSU). Offshore salinities ranged between euhaline (30 to 40 PSU) to metahaline (40 to 55 PSU) conditions. Our study suggests that increased freshwater flow would help maintain suitable estuarine conditions in Florida Bay during climate change, while our novel modeling approach can guide further Everglades restoration efforts.https://www.mdpi.com/2073-4441/14/21/3495Florida Bayclimate changeEvergladesartificial neural networksdata modelingsalinity forecasting |
| spellingShingle | Anteneh Z. Abiy Ruscena P. Wiederholt Gareth L. Lagerwall Assefa M. Melesse Stephen E. Davis Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change Water Florida Bay climate change Everglades artificial neural networks data modeling salinity forecasting |
| title | Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change |
| title_full | Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change |
| title_fullStr | Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change |
| title_full_unstemmed | Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change |
| title_short | Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change |
| title_sort | multilayer feedforward artificial neural network model to forecast florida bay salinity with climate change |
| topic | Florida Bay climate change Everglades artificial neural networks data modeling salinity forecasting |
| url | https://www.mdpi.com/2073-4441/14/21/3495 |
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