Diseño y gestión óptimos de sistemas de impulsión y de almacenamiento de agua para riego

Los sistemas que se estudiarán serán los constituidos por un conjunto de impulsores de motor eléctrico que tomará el agua de una fuente de suministro y del que partirá una conducción hasta depósito o balsa de regulación desde el/las cuales abastecerá la red de distribución según las demandas de agua existentes. El depósito o balsa, además de cumplir de acumulación del recurso agua, permitirá la reducción de costes energéticos al poder permitir una adaptación entre las horas de bombeo y el tipo de discriminación horaria de la facturación eléctrica. El objetivo principal es conseguir el régimen de explotación integral óptimo que origine el menor coste teniendo en cuenta la capacidad hidráulica de la estación de bombeo, el volumen del depósito o balsa, el coste de elevación del metro cúbico de agua y el contrato del suministro de energía eléctrica, todo ello compatibilizado con la capacidad de satisfacer una demanda dada. De esta manera, se planifica el esquema de operación de los sistemas de impulsión y de almacenamiento de agua en un futuro próximo, que en este trabajo es la campaña de riesgos, teniendo en cuenta las implicaciones en el diseño de los elementos de regulació

Regionalization of droughts in Portugal

Comunicação apresentada na "6th International Conference on River Basin Management", Riverside, California, 2011Droughts are complex natural hazards that distress large worldwide areas every year with serious impacts on society, environment and economy. Despite their importance they are still among the least understood extreme weather events. This paper is focused on the identification of regional patterns of droughts in Mainland Portugal based on monthly precipitation data, from September 1910 to October 2004, in 144 rain gages distributed uniformly over the country. The drought events were described by means of the Standardized Precipitation Index (SPI) applied to different time scales. To assess the spatial and temporal patterns of droughts, a principal component analysis (PCA) and K-means clustering method (KMC) were applied to the SPI series. The study showed that, for the different times scales, both methods resulted in an equivalent areal zoning, with three regions with different behaviours: the north, the centre and the south of Portugal. These three regions are consistent with the precipitation spatial distribution in Portugal Mainland, which in general terms decrease from North to South, with the central mountainous region representing the transition between the wet north and the progressively dry south. As the mean annual precipitation decreases southwards the hydrological regime becomes more irregular and consequently more prone to droughts

Regional frequency analysis of droughts: portuguese case

Poster apresentado WCRP (World Climate Research programme)- Workshop on Drought Predictability and Prediction in a Changing Climate: Assessing Current Knowledge and Capabilities, User Requirements and Research Priorities, 2-4 Março 2011, Barcelona (Spain).[Poster introduction] A common problem in drought risk analysis relates to the assessment of the rarity of the events, such as long duration droughts or high magnitude droughts. Being a frequent phenomena in the Southern Europe and in others regions of the world drought constitute a primary natural hazard for human activities. For this reason, and for an improved drought risk management, the preparation of drought hazard maps is an important and urgent task. The drought definition based on deviations from normal conditions or from reference stages implies that droughts can occur in any hydroclimatological region and at any time of the year with the same probability. In order to due so, a large number of quantitative drought characteristics must been considered. Two common approaches to select extreme events from a drought index time series were analyzed: the annual maximum series (AMS) and the partial duration series (PDS) approaches

Influence of the electric energy non-regulated market in the intensive aquaculture plants associated to cooling effluents

En este trabajo se analiza el efecto que la liberalización del mercado eléctrico tiene sobre la variación de los regímenes de temperatura del agua en plantas de acuicultura intensiva que aprovechan los efluentes de refrigeración de centrales generadoras de electricidad. Para ello se han utilizado datos de una instalación dedicada al engorde de anguilas europeas, la cual toma el agua caliente del efluente de refrigeración de la Central Térmica de Puente Nuevo (Córdoba). Los resultados indican que la liberalización del mercado del sector eléctrico tiene una influencia significativa sobre la forma y cantidad de energía generada por la Central Térmica, y por consiguiente sobre el régimen termal del efluente de refrigeración. Los niveles de temperatura en el interior de la instalación son dependientes asimismo de la temperatura del agua en el efluente de refrigeración, estimándose la disminución de los índices de crecimiento debidos a este factor en un 5%.In this paper, the effect of the electric energy non-regulated market in the water thermal regimes variation of intensive fishfarms that use the heated water for cooling of power plants is analysed. This way, data of aneel intensive rearing system was used. In this fishfarm the heated water is drawn from the cooling effluent of the Puente Nuevo power plant (Córdoba). The results show that the non-regulated market has a significant effect on the form and amount of generated energy and the thermal regime of the cooling effluent. The temperature levels in the fishfarm depend of the water temperature of cooling effluent, being estimated the decrease of the growth index in 5%

Spring drought forecasting in mainland Portugal based on large-scale climatic indices

[EN] The success of a strategy of mitigation of the effects of the droughts requires the implementation of an effective monitoring and forecasting system, able to identify drought events and follow their spatiotemporal evolution. This article demonstrates the capability of the artificial neural networks in predicting the spring standardized precipitation index, SPI, for Portugal. The validation of the models used the hindcasting, which is a technique by which a given model is tested through its application to historical data followed by the comparison of the results thus achieved with the data. The SPI index was calculated at the timescale of six months and the climate indices used as external predictors in the hindcasting were the North Atlantic Oscillation and temperatures of the sea surface. The study showed the added value of the inclusion of previous predictors in the model. Maps of the probabilities of the drought occurrences which may be very important for integrated planning and management of water resources were also developed.[PT] O sucesso de uma estratégia de mitigação dos efeitos da seca passa pela implementação de um sistema de monitorização e previsão eficaz, capaz de identificar os eventos de seca e de seguir a sua evolução espácio-temporal. Neste artigo demonstrase a eficiência de redes neuronais artificiais na previsão, para Portugal, do índice de precipitação padronizada, SPI, relativo à primavera. A validação dos modelos recorreu ao hindcasting, designando-se, por tal, a técnica através da qual um dado modelo é testado mediante a sua aplicação a períodos temporais históricos, com comparação dos resultados obtidos com as respectivas observações. O índice SPI foi calculado à escala temporal de 6 meses tendo o hindcast utilizado como indicadores climáticos a oscilação do Atlântico Norte e temperaturas da superfície do mar. O estudo evidenciou a mais valia da inclusão dos anteriores predictores externos no modelo de previsão. Elaboraram-se, ainda, mapas de probabilidade de ocorrência de seca os quais constituem importantes ferramentas no planeamento integrado e na gestão de recursos hídricosSantos, J.; Portela, M.; Pulido-Calvo, I. (2015). Previsão de secas na primavera em Portugal Continental com base em indicadores climáticos de larga escala. Ingeniería del Agua. 19(4):211-227. doi:10.4995/ia.2015.4109.SWORD211227194Agnew, C.T. (2000). Using the SPI to identify drought. Drought Network News, 12, 6-12.ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). Artificial neural networks in hydrology. I. Preliminary concepts. Journal of Hydrologic Engineering, 5(2), 115-123. doi:10.1061/(ASCE)1084-0699(2000)5:2(115)ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). Artificial neural networks in hydrology. II. Hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124-137. doi:10.1061/(ASCE)1084-0699(2000)5:2(124)Bordi, I., Fraedrich, K., Petitta, M., Sutera, A. (2005). Methods for predicting drought occurrences. In Proceedings of the 6th International Conference of the European Water Resources Association, Menton, France.Bowden, G.J., Dandy, G.C., Maier, H.R. (2005). Input determination for neural network models in water resources applications. Part 1-background and methodology. Journal of Hydrology, 301(1-4), 75-92. doi:10.1016/j.jhydrol.2004.06.021Campolo, M., Andreusi, P., Soldati, A. (1999). River flood forecasting with a neural network model. Water Resources Research, 35(4), 1191-1197. doi:10.1029/1998WR900086Cancelliere, A., Di Mauro, G., Bonaccorso, B., Rossi, G. (2005). Stochastic forecasting of Standardized Precipitation Index. In Proceedings of XXXI IAHR Congress Water Engineering for the future: Choice and Challenges, Seoul, Korea, 3252-3260.Cancelliere, A., Di Mauro, G., Bonaccorso, B., Rossi, G. (2007). Drought forecasting using the Standardized Precipitation Index. Water Resources Management, 21(5), 801-819. doi:10.1007/s11269-006-9062-yCordery, I., McCall, M. (2000). A model for forecasting drought from teleconnections. Water Resources Research, 36(3), 763-768. doi:10.1029/1999WR900318Dastorani, M.T., Afkhami, H. (2011). Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert, 16, 39-48.Dawson, D.W., Wilby, R. (1998). An artificial neural network approach to precipitation-runoff modeling. Hydrological Sciences Journal, 43(1), 47-66. doi:10.1080/02626669809492102Demyanov, V., Kanevsky, M., Chernov, S., Savelieva, E., Timonin, V. (1998). Neural network residual kriging application for climatic data. Journal of Geographic Information and Decision Analysis, 2(2), 215-232.Di Mauro, G., Bonaccorso, G.B., Cancelliere, A., Rossi, G. (2008). Use of NAO index to improve drought forecasting in the Mediterranean area: Application to Sicily region. Options Méditerranéennes. Série A: Séminaires Méditerranéens, No. 80.Fernando, M.K.G., Maier, H.R., Dandy, G.C. (2009). Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach. Journal of Hydrology, 367(3-4), 165-176. doi:10.1016/j.jhydrol.2008.10.019Gámiz-Fortis, S., Esteban-Parra, M.J., Trigo, R.M., Castro-Díez, Y. (2010). Potential predictability of Iberian river flow based on its relationship with previous winter global SST. Journal of Hydrology, 385, 143-149. doi:10.1016/j.jhydrol.2010.02.010Gámiz-Fortis, S., Pozo-Vázquez, D., Trigo, R.M., Castro-Díez, Y. (2008a). Quantifying the predictability of winter river flow in Iberia. Part I: Interannual predictability. Journal of Climate, 21, 2484-2502. doi:10.1175/2007JCLI1774.1Gámiz-Fortis, S., Pozo-Vázquez, D., Trigo, R.M., Castro-Díez, Y. (2008b). Quantifying the predictability of winter river flow in Iberia. Part II: Seasonal predictability. Journal of Climate, 21, 2503-2518. doi:10.1175/2007JCLI1775.1Hoerling, M., Kumar, A. (2003). The perfect ocean for drought. Science, 299(5607), 691-694. Geophysical Research Abstracts, 12, EGU2010-8454, EGU General Assembly 2010, Viena, Austria. doi:10.1126/science.1079053Hurrell, J.W. (1995). Decadal trends in North Atlantic Oscillation: regional temperatures and precipitation. Science, 269(5224), 676-679. doi:10.1126/science.269.5224.676Hurrell, J.W., Kushnir, Y., Visbeck, M. (2001). The North Atlantic Oscillation. Science, 291(5504), 603-605. doi:10.1126/science.1058761Hurrell, J.W., Kushnir, Y., Ottersen, G., Visbeck, M. (2003). The North Atlantic Oscillation: climatic significance and environmental impact. Geophysical Monograph Series, 134, American Geophysical Union, Washington, DC, USA.Ionita, M., Lhomann, G., Rimbu, N. (2008). Prediction of spring Elbe discharge based on stable teleconnections with winter global temperature and precipitation. Journal of Climate, 21(23), 6215-6226. doi:10.1175/2008JCLI2248.1Ionita, M., Lohmann, G., Rimbu, N., Chelcea, S., Dima, M. (2012). Interannual to decadal summer drought variability over Europe and its relationship to global sea surface temperature. Climate Dynamics, 38(1), 363-377. doi:10.1007/s00382-011-1028-yIyer, M.S., Rhinehart, R.R. (1999). A method to determine the required number of neural-network training repetitions. IEEE Transactions on Neural Networks, 10(2), 427-432. doi:10.1109/72.750573Jain, A., Kumar, A.M. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7(2), 585-592. doi:10.1016/j.asoc.2006.03.002Jones, P.D., Jonsson, T., Wheeler, D. (1997). Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and South-West Iceland. International Journal of Climatology, 17(13), 1433-1450. doi:10.1002/(SICI)1097-0088(19971115)17:133.0.CO;2-PJones, P.D., Osborn, T.J., Briffa, K.R. (2003). Pressure-based measures of the North Atlantic oscillation (NAO): a comparison and an assessment of changes in the strength of the NAO and in its influence on surface climate parameters in The North Atlantic Oscillation: climate significance and environmental impact. Geophysics Monogram 134, 51-62, American Geophysical Union.Karunanithi, N., Grenney, W.J., Whitely, D., Bovee, K. (1994). Neural networks for river flow prediction. Journal of Computing Civil Engineering, 8(2), 201-219. doi:10.1061/(ASCE)0887-3801(1994)8:2(201)Kim T. e Juan B. Valdés, (2003). Nonlinear Model for Drought Forecasting Based on a Conjunction of Wavelet Transforms and Neural Networks. Journal of Hydrologic Engineering, 8(6), 319-328. doi:10.1061/(ASCE)1084-0699(2003)8:6(319)Kitanidis, P.K., Bras, R.L. (1980). Real time forecasting with a conceptual hydrological model. 2. Applications and results. Water Resources Research, 16(6), 1034-1044. doi:10.1029/WR016i006p01034Kurnik, B. (2009). DESERT Action JRC, Drought forecasting methods. Ljubljana on 24 September 2009 - 1st DMCSEE - JRC Workshop on Drought Monitoring.Legates, D.R., McCabe Jr., G.J. (1999). Evaluating the use of 'goodness-of-fit' measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241. doi:10.1029/1998WR900018Lloyd-Hughes, B. (2002). The long range predictability of European drought. PhD Thesis, Department of Space and Climate Physics, University of London, University College London, UK.López-Moreno, J.I., Vicente-Serrano, S.M. (2008). Extreme phases of the wintertime North Atlantic Oscillation and drought occurrence over Europe: a multi-temporal-scale approach. Journal of Climate, 21(6), 1220-1243. doi:10.1175/2007JCLI1739.1López-Moreno, J.I., Beguería, S., Vicente-Serrano, S.M., García-Ruiz, J.M. (2007). The influence of the NAO on water resources in central Iberia: precipitation, streamflow anomalies and reservoir management strategies. Water Resources Research, 43,W09411, doi:10.1029/2007WR005864Martín, M.L., Luna, M.Y., Morata, A., Valero, F. (2004). North Atlantic teleconnection patterns of low-frequency variability and their links with springtime precipitation in the western Mediterranean. International Journal of Climatology, 24(2), 213-230. doi:10.1002/joc.993Martín-Vide, J., Fernández, D. (2001). El índice NAO y la precipitación mensual en la España peninsular. Investigaciones Geográficas, 26, 41-58. doi:10.14198/INGEO2001.26.07May, R.J., Maier, H.R., Dandy, G.C., Fernando, T.M.K.G. (2008). Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling and Software, 23(10-11), 1312-1326. doi:10.1016/j.envsoft.2008.03.007McKee, T.B., Doesken, N.J., Kleist, J. (1993).The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society, Boston, USA, 179-184.Mishra, A.K., Desai, V.R. (2006). Drought forecasting using feed-forward recursive neural network. Ecological Modelling, 198(1-2), 127-138. doi:10.1016/j.ecolmodel.2006.04.017Mo, K.C., Jae-Kyung, E., Schemm, E., Yoo, S.-H. (2009). Influence of ENSO and the Atlantic multi-decadal Oscillation on drought over the United States. Journal of Climate, 22, 5962-5982. doi:10.1175/2009JCLI2966.1Mutlu, E., Chaubey, I., Hexmoor, H., Bajwa, S.G. (2008). Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrological Processes, 22(26), 5097-5106. doi:10.1002/hyp.7136Michie, D., Spiegelhalter, D.J., Taylor, C.C. (1994). Machine learning, neural and statistical classification. Project StatLog, Department of Statistics, University of Leeds, UK.Ochoa-Rivera, J.C., García-Bartual, R., Andreu, J. (2002). Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks. Journal of Hydrology and Earth System Sciences, 6(4), 641-654. doi:10.5194/hess-6-641-2002Ochoa-Rivera, J.C., García-Bartual, R., Andreu, J. (2007). Influence of Inflows Modeling on Management Simulation of Water Resources System. Journal of Water Resources Planning and Management, ASCE, 133(2), 106-116. doi:10.1061/(ASCE)0733-9496(2007)133:2(106)Portela, M.M., Quintela, A.C. (2006). Estimação em Portugal Continental de escoamento e de capacidades úteis de albufeiras de regularização na ausência de informação. Recursos Hídricos, 27(2), 7-18.Pulido-Calvo, I., Portela, M.M. (2007). Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. Journal of Hydrology, 332(1-2), 1-15. doi:10.1016/j.jhydrol.2006.06.015Pulido-Calvo, I., Gutiérrez-Estrada, J.C., Savic, D. (2012). Heuristic modelling of the water resources management in the Guadalquivir River Basin, Southern Spain. Water Resources Management, 26(1), 185-209. doi:10.1007/s11269-011-9912-0Qian, B., Corte-Real, J.M., Xu, H. (2000a). Is the North Atlantic Oscillation the most important atmospheric pattern for precipitation in Europe? Journal of Geophysical Research, 105(D9), 901-910. doi:10.1029/2000JD900102Qian, B., Xu, H., Corte-Real, J.M. (2000b). Spatial-temporal structures of the quasi-periodic oscillations in precipitation over Europe. International Journal of Climatology, 20(13), 1583-1598. doi:10.1002/1097-0088(20001115)20:133.0.CO;2-YRodwell, M.J. (2003). On the predictability of the North Atlantic climate. The North Atlantic Oscillation: climate significance and environmental impact, Geophysical Monograph, 134, 173-192, Amer. Geophys. Union. doi:10.1029/134GM08Rossi, G. (2003). Requisites for a drought watch system. In: G. Rossi et al. (eds), Tools for Drought Mitigation in Mediterranean Regions, pp. 147-157. Kluwer Academic Publishing: Dordrecht. doi:10.1007/978-94-010-0129-8_9Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536. doi:10.1038/323533a0Santos, J.A., Corte-Real, J., Leite, S.M. (2005). Weather regimes and their connection to the winter precipitation in Portugal. International Journal of Climatology, 25(1), 33-50. doi:10.1002/joc.1101Santos, J.F., Portela, M.M., Pulido-Calvo, I. (2011). Regional frequency analysis of droughts in Portugal. Water Resources Management, 25(14), 3537-3558. doi:10.1007/s11269-011-9869-zSantos, J.F., Portela, M.M., Pulido-Calvo, I. (2013). Dimensionality reduction in drought modelling. Hydrological Processes, 27(10), 1399-1410. doi:10.1002/hyp.9300Santos, J.F., Portela, M.M., Pulido-Calvo, I., (2014). Spring drought prediction based on winter NAO and global SST in Portugal, Hydrological Processes, 28(3), 1009-1024. doi:10.1002/hyp.9641Santos, J.F., Pulido-Calvo, I., Portela, M.M. (2010). Spatial and temporal variability of droughts in Portugal. Water Resources Research, 46(3). DOI: 10.1029/2009WR008071. doi:10.1029/2009WR008071Senthil-Kumar, A.R., Sudheer, K.P., Jain, S.K., Agarwal, P.K. (2005). Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrological Processes, 19(6), 1277-1291. doi:10.1002/hyp.5581Silva, A.T., Portela, M.M., Naghettini, M. (2012), Nonstationarities in the occurrence rates of flood events in Portuguese watersheds. Journal of Hydrology and Earth System Sciences, 16, 241-254. doi:10.5194/hess-16-241-2012Smith, T.M., Reynolds, R.W., Peterson, T.C. Lawrimore, J. (2008). Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006). Journal of Climate, 21, 2283-2296. doi:10.1175/2007JCLI2100.1Snedecor, G.W., Cochran, W.G. (1989). Statistical methods, Ames, Iowa State University Press (8th edition), Iowa, USA.Trigo, R.M., Osborn, T.J., Corte-Real, J.M. (2002). The North Atlantic Oscillation influence on Europe. Climate impacts and associated physical mechanisms. Climate Research, 20, 9-17. doi:10.3354/cr020009Trigo, R.M., Pozo-Vázquez, D., Osborn, T.J., Castro-Díez, Y., Gámiz-Fortis, S., Esteban-Parra, M.J. (2004). North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula. International Journal of Climatology, 24(8), 925-944. doi:10.1002/joc.1048Trigo, R., Xoplaki, E., Zorita, E., Luterbacher, J., Krichak, S.O., Alpert, P., Jacobeit, J., Sáenz, J., Fernández, J., González-Rouco, F., García-Herrera, R., Rodo, X., Brunetti, M., Nanni, T., Maugeri, M., Trkes, M., Gimeno, L., Ribera, P., Brunet, M., Trigo, I.F., Crepon, M., Mariotti, A. (2006). Relations between Variability in the Mediterranean region and mid-latitude variability. In: Mediterranean Climate Variability, edited by: Lionello P., Malanotte-Rizzoli P., e R. Boscolo. Amsterdam, Elsevier, 179-226. doi:10.1016/s1571-9197(06)80006-6Vicente-Serrano, S.M., López-Moreno, J.I., Lorenzo-Lacruz, J., El Kenawy, A., Azorin-Molina, C., Morán-Tejeda, E., Pasho, E., Zabalza, J., Beguería, S., Angulo-Martínez, M. (2011). The NAO impact on droughts in the Mediterranean region. In: VicenteSerrano S.M. e Trigo R. (Eds.), Hydrological, socioeconomic and ecological impacts of the North Atlantic Oscillation in the Mediterranean region. Advances in Global Research (AGLO) series, Springer-Verlag. doi:10.1007/978-94-007-1372-7_3Vinther, B.M., Andersen, K.K., Hansen, A.W., Schmith, T., Jones, P.D. (2003). Improving the Gibraltar/Reykjavik NAO Index. Geophysical Research Letters, 30(23), 2222. doi:10.1029/2003GL018220Xoplaki E., González-Rouco J.F., Luterbacher J. e H. Wanner, (2004). Wet season Mediterranean precipitation variability: influence of large-scale dynamics and predictability. Climate Dynamiques 23, 63-78.Xue, Y., Smith, T.M., Reynolds, R.W. (2003). Interdecadal changes of 30-yr SST normals during 1871-2000. Journal of Climate, 16, 1601-1612. doi:10.1175/1520-0442-16.10.1601Yevjevich, V. (1972). Stochastic Processes in Hydrology. Water Resources Publications, Fort Collins, Co

Variabilidade temporal e espacial das secas em Portugal Continental

Comunicação apresentada no 10º Congresso da Água, Associação Portuguesa dos recursos Hídricos, APRH, 21-24 Março 2010, Alvor (Portugal)É apresentada uma análise de secas em Portugal Continental baseada em séries de precipitação mensal, de Setembro de 1910 a Outubro de 2004, em 144 postos udométricos uniformemente distribuídos pelo País. Os eventos de seca foram caracterizados pelo índice de precipitação padronizada, SPI (Standardized Precipitation Index), aplicado a diferentes escalas temporais, designadamente 1, 6 e 12 meses consecutivos, 6 meses de Abril a Setembro e 12 meses de Outubro a Setembro. Para o estudo dos padrões temporal e espacial das secas aplicou-se às séries de SPI a análise de componentes principais (PCA) e a análise de clusters não-hierárquica, algoritmo de k-médias (KMC). Desta forma obtiveram-se três regiões diferentes e espacialmente bem definidas com diferentes padrões temporais de seca: norte, centro e sul de Portugal. Os padrões dos SPI obtidos com base na análise de componentes principais e na análise de clusters foram testados recorrendo a análise espectral, utilizando o algoritmo da transformada rápida de Fourier, tendo-se obtido um ciclo de 3.6 anos no padrão representativo do sul de Portugal e ciclos de 2.4 e 13.4 anos, no do norte do Pais. Conclui-se, assim, que a análise dos períodos de seca suporta a ocorrência de ciclos mais frequentes de seca no sul (secas moderadas a extremas aproximadamente cada 3.6 anos) do que no norte (secas de severas a extremas aproximadamente cada 13.4 anos). Contudo, é necessário prosseguir com a investigação de modo a avaliar a origem desses ciclos

Integrating local environmental data and information from non-driven citizen science to estimate jellyfish abundance in Costa del Sol (southern Spain)

Tourism, fishing and aquaculture are key economic sectors of Costa del Sol (southern Iberian Peninsula). The management of these activities is sometimes disturbed by the onshore arrival and stranding of jellyfish swarms. In the absence data on the occurrence of these organisms, it may be interesting to explore data from non-driven systems, such as social networks. The present study show how data in text format from a mobile app called Infomedusa can be processed and used to model the relationship between estimated abundance of jellyfish on the beaches and local environmental conditions. The data retrieved from this app using artificial intelligence procedures (transition network or TN algorithm), were used as input for GAM models to estimate the abundance of jellyfish based on wind speed and direction. The analysis of data provided by Infomedusa indicated that only 30.39% of messages provided by the users had information about absence/presence of jellyfishes in the beaches. On the other hand, the TN processing capacity showed an accuracy level to discriminate messages with information on absence/presence of jellyfish slightly higher than 80%. GAM models considering the wind direction and speed of previous day explained between 37% and 77% of the variance of jellyfish abundance estimate from Infomedusa data. In conclusion, this approach may contribute to the development of a system for predicting the onshore arrival of jellyfish in the Costa del Sol.Versión del edito

Lamellarin D bioconjugates II: synthesis and cellular internalization of dendrimer and nuclear location signal derivatives

The design and synthesis of Lamellarin D conjugates with a nuclear localization signal peptide and a poly(ethylene glycol)-based dendrimer are described. Conjugates 1-4 were obtained in 8-84% overall yields from the corresponding protected Lamellarin D. Conjugates 1 and 4 are 1.4 to 3.3-fold more cytotoxic than the parent compound against three human tumor cell lines(MDA-MB-231 breast, A-549 lung, and HT-29 colon). Besides, conjugates 3, 4 showed a decrease in activity potency in BJ skin fibroblasts, a normal cell culture. Cellular internalization was analyzed and nuclear distribution pattern was observed for 4, which contains a nuclear localization signalling sequence

Identification of TNF-α and MMP-9 as potential baseline predictive serum markers of sunitinib activity in patients with renal cell carcinoma using a human cytokine array

BACKGROUND: Several drugs are available to treat metastatic renal-cell carcinoma (MRCC), and predictive markers to identify the most adequate treatment for each patient are needed. Our objective was to identify potential predictive markers of sunitinib activity in MRCC. METHODS: We collected sequential serum samples from 31 patients treated with sunitinib. Sera of six patients with extreme phenotypes of either marked responses or clear progressions were analysed with a Human Cytokine Array which evaluates 174 cytokines before and after treatment. Variations in cytokine signal intensity were compared between both groups and the most relevant cytokines were assessed by ELISA in all the patients. RESULTS: Twenty-seven of the 174 cytokines varied significantly between both groups. Five of them (TNF-alpha, MMP-9, ICAM-1, BDNF and SDF-1) were assessed by ELISA in 21 evaluable patients. TNF-alpha and MMP-9 baseline levels were significantly increased in non-responders and significantly associated with reduced overall survival and time-to-progression, respectively. The area under the ROC curves for TNF-alpha and MMP-9 as predictive markers of sunitinib activity were 0.83 and 0.77. CONCLUSION: Baseline levels of TNF-alpha and MMP-9 warrant further study as predictive markers of sunitinib activity in MRCC. Selection of patients with extreme phenotypes seems a valid method to identify potential predictive factors of response