- -

Future trends of dissolved inorganic nitrogen concentrations in Northwestern Mediterranean coastal waters under climate change

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Future trends of dissolved inorganic nitrogen concentrations in Northwestern Mediterranean coastal waters under climate change

Mostrar el registro completo del ítem

Temino-Boes, R.; García-Bartual, R.; Romero Gil, I.; Romero-Lopez, R. (2021). Future trends of dissolved inorganic nitrogen concentrations in Northwestern Mediterranean coastal waters under climate change. Journal of Environmental Management. 282:1-12. https://doi.org/10.1016/j.jenvman.2020.111739

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/163190

Ficheros en el ítem

Metadatos del ítem

Título: Future trends of dissolved inorganic nitrogen concentrations in Northwestern Mediterranean coastal waters under climate change
Autor: Temino-Boes, Regina García-Bartual, Rafael Romero Gil, Inmaculada Romero-Lopez, Rabindranarth
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Fecha difusión:
Resumen:
[EN] Coastal ecosystems are amongst the most vulnerable to climate change, due to their location at the land-sea interface. In coastal waters, the nitrogen cycle can be significantly altered by rising temperatures and other ...[+]
Palabras clave: Artificial neural networks , Climate change , Coastal waters , Dissolved inorganic nitrogen , Mediterranean sea
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Journal of Environmental Management. (issn: 0301-4797 )
DOI: 10.1016/j.jenvman.2020.111739
Editorial:
Elsevier
Versión del editor: https://doi.org/10.1016/j.jenvman.2020.111739
Agradecimientos:
Field data collection was supported by the Valencian Ministry of the Environment, Water, Urban Planning and Housing. The work was partly supported by a Cotutelle PhD scholarship granted by Universitat Politècnica de València.[+]
Tipo: Artículo

References

Abdullahi, J., & Elkiran, G. (2017). Prediction of the future impact of climate change on reference evapotranspiration in Cyprus using artificial neural network. Procedia Computer Science, 120, 276-283. doi:10.1016/j.procs.2017.11.239

Aguilera, R., Marcé, R., & Sabater, S. (2015). Detection and attribution of global change effects on river nutrient dynamics in a large Mediterranean basin. Biogeosciences, 12(13), 4085-4098. doi:10.5194/bg-12-4085-2015

Al-Zahrani, M. A., & Abo-Monasar, A. (2015). Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models. Water Resources Management, 29(10), 3651-3662. doi:10.1007/s11269-015-1021-z [+]
Abdullahi, J., & Elkiran, G. (2017). Prediction of the future impact of climate change on reference evapotranspiration in Cyprus using artificial neural network. Procedia Computer Science, 120, 276-283. doi:10.1016/j.procs.2017.11.239

Aguilera, R., Marcé, R., & Sabater, S. (2015). Detection and attribution of global change effects on river nutrient dynamics in a large Mediterranean basin. Biogeosciences, 12(13), 4085-4098. doi:10.5194/bg-12-4085-2015

Al-Zahrani, M. A., & Abo-Monasar, A. (2015). Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models. Water Resources Management, 29(10), 3651-3662. doi:10.1007/s11269-015-1021-z

Alam, M. J., Goodall, J. L., Bowes, B. D., & Girvetz, E. H. (2017). The Impact of Projected Climate Change Scenarios on Nitrogen Yield at a Regional Scale for the Contiguous United States. JAWRA Journal of the American Water Resources Association, 53(4), 854-870. doi:10.1111/1752-1688.12537

Altunkaynak, A. (2006). Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks. Water Resources Management, 21(2), 399-408. doi:10.1007/s11269-006-9022-6

Barrera-Escoda, A., Gonçalves, M., Guerreiro, D., Cunillera, J., & Baldasano, J. M. (2013). Projections of temperature and precipitation extremes in the North Western Mediterranean Basin by dynamical downscaling of climate scenarios at high resolution (1971–2050). Climatic Change, 122(4), 567-582. doi:10.1007/s10584-013-1027-6

Basu, N. B., Destouni, G., Jawitz, J. W., Thompson, S. E., Loukinova, N. V., Darracq, A., … Rao, P. S. C. (2010). Nutrient loads exported from managed catchments reveal emergent biogeochemical stationarity. Geophysical Research Letters, 37(23), n/a-n/a. doi:10.1029/2010gl045168

Bi, W., Weng, B., Yuan, Z., Ye, M., Zhang, C., Zhao, Y., … Xu, T. (2018). Evolution Characteristics of Surface Water Quality Due to Climate Change and LUCC under Scenario Simulations: A Case Study in the Luanhe River Basin. International Journal of Environmental Research and Public Health, 15(8), 1724. doi:10.3390/ijerph15081724

Bittig, H. C., Steinhoff, T., Claustre, H., Fiedler, B., Williams, N. L., Sauzède, R., … Gattuso, J.-P. (2018). An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Frontiers in Marine Science, 5. doi:10.3389/fmars.2018.00328

Brito, A. C., Newton, A., Tett, P., & Fernandes, T. F. (2012). How will shallow coastal lagoons respond to climate change? A modelling investigation. Estuarine, Coastal and Shelf Science, 112, 98-104. doi:10.1016/j.ecss.2011.09.002

Modeling Dryness Severity Using Artificial Neural Network at the Okavango Delta, Botswana. (2016). Global NEST Journal, 18(3), 463-481. doi:10.30955/gnj.001731

Camargo, J. A., & Alonso, Á. (2006). Ecological and toxicological effects of inorganic nitrogen pollution in aquatic ecosystems: A global assessment. Environment International, 32(6), 831-849. doi:10.1016/j.envint.2006.05.002

Chaudhuri, S., & Dutta, D. (2014). Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models. Environmental Monitoring and Assessment, 186(8), 4719-4742. doi:10.1007/s10661-014-3733-6

Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean Ocean Colour Chlorophyll Trends. PLOS ONE, 11(6), e0155756. doi:10.1371/journal.pone.0155756

Coppola, E. A., Rana, A. J., Poulton, M. M., Szidarovszky, F., & Uhl, V. W. (2005). A neural network model for predicting aquifer water level elevations. Ground Water, 43(2), 231-241. doi:10.1111/j.1745-6584.2005.0003.x

Coutinho, F. H., Thompson, C. C., Cabral, A. S., Paranhos, R., Dutilh, B. E., & Thompson, F. L. (2019). Modelling the influence of environmental parameters over marine planktonic microbial communities using artificial neural networks. Science of The Total Environment, 677, 205-214. doi:10.1016/j.scitotenv.2019.04.009

Deng, J., Paerl, H. W., Qin, B., Zhang, Y., Zhu, G., Jeppesen, E., … Xu, H. (2018). Climatically-modulated decline in wind speed may strongly affect eutrophication in shallow lakes. Science of The Total Environment, 645, 1361-1370. doi:10.1016/j.scitotenv.2018.07.208

Doğan, E., Kocamaz, U. E., Utkucu, M., & Yıldırım, E. (2016). Modelling daily water level fluctuations of Lake Van (Eastern Turkey) using Artificial Neural Networks. Fundamental and Applied Limnology, 187(3), 177-189. doi:10.1127/fal/2015/0736

Du, J., Shen, J., Park, K., Wang, Y. P., & Yu, X. (2018). Worsened physical condition due to climate change contributes to the increasing hypoxia in Chesapeake Bay. Science of The Total Environment, 630, 707-717. doi:10.1016/j.scitotenv.2018.02.265

Elgaali, E., & Garcia, L. A. (2007). Using Neural Networks to Model the Impacts of Climate Change on Water Supplies. Journal of Water Resources Planning and Management, 133(3), 230-243. doi:10.1061/(asce)0733-9496(2007)133:3(230)

Flo, E., Garcés, E., Manzanera, M., & Camp, J. (2011). Coastal inshore waters in the NW Mediterranean: Physicochemical and biological characterization and management implications. Estuarine, Coastal and Shelf Science, 93(4), 279-289. doi:10.1016/j.ecss.2011.04.002

García-Ruiz, J. M., López-Moreno, J. I., Vicente-Serrano, S. M., Lasanta–Martínez, T., & Beguería, S. (2011). Mediterranean water resources in a global change scenario. Earth-Science Reviews, 105(3-4), 121-139. doi:10.1016/j.earscirev.2011.01.006

Garcia-Solsona, E., Garcia-Orellana, J., Masqué, P., Rodellas, V., Mejías, M., Ballesteros, B., & Domínguez, J. A. (2010). Groundwater and nutrient discharge through karstic coastal springs (<i>Castelló</i>, Spain). Biogeosciences, 7(9), 2625-2638. doi:10.5194/bg-7-2625-2010

Gardner, M. ., & Dorling, S. . (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14-15), 2627-2636. doi:10.1016/s1352-2310(97)00447-0

Genua-Olmedo, A., Alcaraz, C., Caiola, N., & Ibáñez, C. (2016). Sea level rise impacts on rice production: The Ebro Delta as an example. Science of The Total Environment, 571, 1200-1210. doi:10.1016/j.scitotenv.2016.07.136

He, J., Valeo, C., Chu, A., & Neumann, N. F. (2010). Stormwater quantity and quality response to climate change using artificial neural networks. Hydrological Processes, 25(8), 1298-1312. doi:10.1002/hyp.7904

Herrero, A., Gutiérrez-Cánovas, C., Vigiak, O., Lutz, S., Kumar, R., Gampe, D., … Sabater, S. (2018). Multiple stressor effects on biological quality elements in the Ebro River: Present diagnosis and predicted responses. Science of The Total Environment, 630, 1608-1618. doi:10.1016/j.scitotenv.2018.02.032

Herrmann, M., Estournel, C., Adloff, F., & Diaz, F. (2014). Impact of climate change on the northwestern Mediterranean Sea pelagic planktonic ecosystem and associated carbon cycle. Journal of Geophysical Research: Oceans, 119(9), 5815-5836. doi:10.1002/2014jc010016

Huo, S., Zhang, H., Ma, C., Xi, B., Zhang, J., He, Z., … Wu, F. (2019). Algae community response to climate change and nutrient loading recorded by sedimentary phytoplankton pigments in the Changtan Reservoir, China. Journal of Hydrology, 571, 311-321. doi:10.1016/j.jhydrol.2019.02.005

Jennerjahn, T. C. (2012). Biogeochemical response of tropical coastal systems to present and past environmental change. Earth-Science Reviews, 114(1-2), 19-41. doi:10.1016/j.earscirev.2012.04.005

Jiménez, J. A., Valdemoro, H. I., Bosom, E., Sánchez-Arcilla, A., & Nicholls, R. J. (2016). Impacts of sea-level rise-induced erosion on the Catalan coast. Regional Environmental Change, 17(2), 593-603. doi:10.1007/s10113-016-1052-x

Kitsiou, D., & Karydis, M. (2011). Coastal marine eutrophication assessment: A review on data analysis. Environment International, 37(4), 778-801. doi:10.1016/j.envint.2011.02.004

Kohonen, T. (1988). An introduction to neural computing. Neural Networks, 1(1), 3-16. doi:10.1016/0893-6080(88)90020-2

Kumar, S., Bhavya, P. S., Ramesh, R., Gupta, G. V. M., Chiriboga, F., Singh, A., … Godhe, A. (2018). Nitrogen uptake potential under different temperature-salinity conditions: Implications for nitrogen cycling under climate change scenarios. Marine Environmental Research, 141, 196-204. doi:10.1016/j.marenvres.2018.09.001

Lazzari, P., Mattia, G., Solidoro, C., Salon, S., Crise, A., Zavatarelli, M., … Vichi, M. (2014). The impacts of climate change and environmental management policies on the trophic regimes in the Mediterranean Sea: Scenario analyses. Journal of Marine Systems, 135, 137-149. doi:10.1016/j.jmarsys.2013.06.005

Lee, K. H., Jeong, H. J., Lee, K., Franks, P. J. S., Seong, K. A., Lee, S. Y., … Kim, K. Y. (2019). Effects of warming and eutrophication on coastal phytoplankton production. Harmful Algae, 81, 106-118. doi:10.1016/j.hal.2018.11.017

Lek, S., & Guégan, J. F. (1999). Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling, 120(2-3), 65-73. doi:10.1016/s0304-3800(99)00092-7

Liu, L., Ma, C., Huo, S., Xi, B., He, Z., Zhang, H., … Xia, X. (2018). Impacts of climate change and land use on the development of nutrient criteria. Journal of Hydrology, 563, 533-542. doi:10.1016/j.jhydrol.2018.06.039

Liu, Z., Peng, C., Xiang, W., Tian, D., Deng, X., & Zhao, M. (2010). Application of artificial neural networks in global climate change and ecological research: An overview. Chinese Science Bulletin, 55(34), 3853-3863. doi:10.1007/s11434-010-4183-3

Macias, D., Garcia-Gorriz, E., & Stips, A. (2018). Deep winter convection and phytoplankton dynamics in the NW Mediterranean Sea under present climate and future (horizon 2030) scenarios. Scientific Reports, 8(1). doi:10.1038/s41598-018-24965-0

Mattei, F., Franceschini, S., & Scardi, M. (2018). A depth-resolved artificial neural network model of marine phytoplankton primary production. Ecological Modelling, 382, 51-62. doi:10.1016/j.ecolmodel.2018.05.003

Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., … Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747-756. doi:10.1038/nature08823

Nawi, N. M., Atomi, W. H., & Rehman, M. Z. (2013). The Effect of Data Pre-processing on Optimized Training of Artificial Neural Networks. Procedia Technology, 11, 32-39. doi:10.1016/j.protcy.2013.12.159

Ontoria, Y., Gonzalez-Guedes, E., Sanmartí, N., Bernardeau-Esteller, J., Ruiz, J. M., Romero, J., & Pérez, M. (2019). Interactive effects of global warming and eutrophication on a fast-growing Mediterranean seagrass. Marine Environmental Research, 145, 27-38. doi:10.1016/j.marenvres.2019.02.002

Paches, M., Aguado, D., Martínez-Guijarro, R., & Romero, I. (2019). Long-term study of seasonal changes in phytoplankton community structure in the western Mediterranean (Valencian Community). Environmental Science and Pollution Research, 26(14), 14266-14276. doi:10.1007/s11356-019-04660-x

Paulmier, A., & Ruiz-Pino, D. (2009). Oxygen minimum zones (OMZs) in the modern ocean. Progress in Oceanography, 80(3-4), 113-128. doi:10.1016/j.pocean.2008.08.001

Pesce, M., Critto, A., Torresan, S., Giubilato, E., Santini, M., Zirino, A., … Marcomini, A. (2018). Modelling climate change impacts on nutrients and primary production in coastal waters. Science of The Total Environment, 628-629, 919-937. doi:10.1016/j.scitotenv.2018.02.131

Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214-232. doi:10.1016/j.envsoft.2016.02.008

Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., & Osuch, M. (2015). Comparing various artificial neural network types for water temperature prediction in rivers. Journal of Hydrology, 529, 302-315. doi:10.1016/j.jhydrol.2015.07.044

Rabalais, N. N., Turner, R. E., Díaz, R. J., & Justić, D. (2009). Global change and eutrophication of coastal waters. ICES Journal of Marine Science, 66(7), 1528-1537. doi:10.1093/icesjms/fsp047

Romanou, A., Tselioudis, G., Zerefos, C. S., Clayson, C.-A., Curry, J. A., & Andersson, A. (2010). Evaporation–Precipitation Variability over the Mediterranean and the Black Seas from Satellite and Reanalysis Estimates. Journal of Climate, 23(19), 5268-5287. doi:10.1175/2010jcli3525.1

Romero, I., Pachés, M., Martínez-Guijarro, R., & Ferrer, J. (2013). Glophymed: An index to establish the ecological status for the Water Framework Directive based on phytoplankton in coastal waters. Marine Pollution Bulletin, 75(1-2), 218-223. doi:10.1016/j.marpolbul.2013.07.028

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. doi:10.1038/323533a0

Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379-1389. doi:10.1080/01621459.1968.10480934

Severin, T., Kessouri, F., Rembauville, M., Sánchez-Pérez, E. D., Oriol, L., Caparros, J., … Conan, P. (2017). Open-ocean convection process: A driver of the winter nutrient supply and the spring phytoplankton distribution in the Northwestern Mediterranean Sea. Journal of Geophysical Research: Oceans, 122(6), 4587-4601. doi:10.1002/2016jc012664

Shinn, M. B. (1941). Colorimetric Method for Determination of Nitrate. Industrial & Engineering Chemistry Analytical Edition, 13(1), 33-35. doi:10.1021/i560089a010

Sinha, E., Michalak, A. M., Calvin, K. V., & Lawrence, P. J. (2019). Societal decisions about climate mitigation will have dramatic impacts on eutrophication in the 21st century. Nature Communications, 10(1). doi:10.1038/s41467-019-08884-w

Statham, P. J. (2012). Nutrients in estuaries — An overview and the potential impacts of climate change. Science of The Total Environment, 434, 213-227. doi:10.1016/j.scitotenv.2011.09.088

Temino-Boes, R., Romero, I., Pachés, M., Martinez-Guijarro, R., & Romero-Lopez, R. (2019). Anthropogenic impact on nitrification dynamics in coastal waters of the Mediterranean Sea. Marine Pollution Bulletin, 145, 14-22. doi:10.1016/j.marpolbul.2019.05.013

Vargas-Yáñez, M., García-Martínez, M. C., Moya, F., Balbín, R., López-Jurado, J. L., Serra, M., … Salat, J. (2017). Updating temperature and salinity mean values and trends in the Western Mediterranean: The RADMED project. Progress in Oceanography, 157, 27-46. doi:10.1016/j.pocean.2017.09.004

Voss, M., Bange, H. W., Dippner, J. W., Middelburg, J. J., Montoya, J. P., & Ward, B. (2013). The marine nitrogen cycle: recent discoveries, uncertainties and the potential relevance of climate change. Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1621), 20130121. doi:10.1098/rstb.2013.0121

Wagena, M. B., & Easton, Z. M. (2018). Agricultural conservation practices can help mitigate the impact of climate change. Science of The Total Environment, 635, 132-143. doi:10.1016/j.scitotenv.2018.04.110

Wang, F., & Polcher, J. (2019). Assessing the freshwater flux from the continents to the Mediterranean Sea. Scientific Reports, 9(1). doi:10.1038/s41598-019-44293-1

Wannicke, N., Frey, C., Law, C. S., & Voss, M. (2018). The response of the marine nitrogen cycle to ocean acidification. Global Change Biology, 24(11), 5031-5043. doi:10.1111/gcb.14424

Zarzuelo, C., D’Alpaos, A., Carniello, L., López-Ruiz, A., Díez-Minguito, M., & Ortega-Sánchez, M. (2019). Natural and Human-Induced Flow and Sediment Transport within Tidal Creek Networks Influenced by Ocean-Bay Tides. Water, 11(7), 1493. doi:10.3390/w11071493

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem