A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

One of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.Ingeniería, Industria y Construcció

Using SWAT and Fuzzy TOPSIS to Assess the Impact of Climate Change in the Headwaters of the Segura River Basin (SE Spain)

The Segura River Basin is one of the most water-stressed basins in Mediterranean Europe. If we add to the actual situation that most climate change projections forecast important decreases in water resource availability in the Mediterranean region, the situation will become totally unsustainable. This study assessed the impact of climate change in the headwaters of the Segura River Basin using the Soil and Water Assessment Tool (SWAT) with bias-corrected precipitation and temperature data from two Regional Climate Models (RCMs) for the medium term (2041–2070) and the long term (2071–2100) under two emission scenarios (RCP4.5 and RCP8.5). Bias correction was performed using the distribution mapping approach. The fuzzy TOPSIS technique was applied to rank a set of nine GCM–RCM combinations, choosing the climate models with a higher relative closeness. The study results show that the SWAT performed satisfactorily for both calibration (NSE = 0.80) and validation (NSE = 0.77) periods. Comparing the long-term and baseline (1971–2000) periods, precipitation showed a negative trend between 6% and 32%, whereas projected annual mean temperatures demonstrated an estimated increase of 1.5–3.3 °C. Water resources were estimated to experience a decrease of 2%–54%. These findings provide local water management authorities with very useful information in the face of climate change.Ingeniería, Industria y Construcció

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

[EN] The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R-CV(2) (cross-validated coefficient of determination) for the best-fit models.This research was partially funded by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 and RTC-2017-6389-5.Jimeno-Sáez, P.; Senent-Aparicio, J.; Cecilia-Canales, JM.; Pérez-Sánchez, J. (2020). Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental research and Public Health (Online). 17(4):1-14. https://doi.org/10.3390/ijerph17041189S114174Pérez-Ruzafa, A., Pérez-Ruzafa, I. M., Newton, A., & Marcos, C. (2019). Coastal Lagoons: Environmental Variability, Ecosystem Complexity, and Goods and Services Uniformity. Coasts and Estuaries, 253-276. doi:10.1016/b978-0-12-814003-1.00015-0Kennish, M. J. (2015). Coastal Lagoons. Encyclopedia of Earth Sciences Series, 140-143. doi:10.1007/978-94-017-8801-4_47García-Ayllón, S. (2019). New Strategies to Improve Co-Management in Enclosed Coastal Seas and Wetlands Subjected to Complex Environments: Socio-Economic Analysis Applied to an International Recovery Success Case Study after an Environmental Crisis. Sustainability, 11(4), 1039. doi:10.3390/su11041039Le Moal, M., Gascuel-Odoux, C., Ménesguen, A., Souchon, Y., Étrillard, C., Levain, A., … Pinay, G. (2019). Eutrophication: A new wine in an old bottle? Science of The Total Environment, 651, 1-11. doi:10.1016/j.scitotenv.2018.09.139Alcolea, A., Contreras, S., Hunink, J. E., García-Aróstegui, J. L., & Jiménez-Martínez, J. (2019). Hydrogeological modelling for the watershed management of the Mar Menor coastal lagoon (Spain). Science of The Total Environment, 663, 901-914. doi:10.1016/j.scitotenv.2019.01.375Nixon, S. W. (1995). Coastal marine eutrophication: A definition, social causes, and future concerns. Ophelia, 41(1), 199-219. doi:10.1080/00785236.1995.10422044Huang, J., Gao, J., & Zhang, Y. (2015). Combination of artificial neural network and clustering techniques for predicting phytoplankton biomass of Lake Poyang, China. Limnology, 16(3), 179-191. doi:10.1007/s10201-015-0454-7Canfield, D. E. (1983). PREDICTION OF CHLOROPHYLL A CONCENTRATIONS IN FLORIDA LAKES: THE IMPORTANCE OF PHOSPHORUS AND NITROGEN. Journal of the American Water Resources Association, 19(2), 255-262. doi:10.1111/j.1752-1688.1983.tb05323.xPhillips, G., Pietiläinen, O.-P., Carvalho, L., Solimini, A., Lyche Solheim, A., & Cardoso, A. C. (2008). Chlorophyll–nutrient relationships of different lake types using a large European dataset. Aquatic Ecology, 42(2), 213-226. doi:10.1007/s10452-008-9180-0EL PAÍS https://elpais.com/elpais/2019/10/22/inenglish/1571743580_215496.htmlGarcía-Ayllón, S. (2017). Integrated management in coastal lagoons of highly complexity environments: Resilience comparative analysis for three case-studies. Ocean & Coastal Management, 143, 16-25. doi:10.1016/j.ocecoaman.2016.10.007Garcia-Ayllon, S. (2018). The Integrated Territorial Investment (ITI) of the Mar Menor as a model for the future in the comprehensive management of enclosed coastal seas. Ocean & Coastal Management, 166, 82-97. doi:10.1016/j.ocecoaman.2018.05.004Pérez-Ruzafa, A., Campillo, S., Fernández-Palacios, J. M., García-Lacunza, A., García-Oliva, M., Ibañez, H., … Marcos, C. (2019). Long-Term Dynamic in Nutrients, Chlorophyll a, and Water Quality Parameters in a Coastal Lagoon During a Process of Eutrophication for Decades, a Sudden Break and a Relatively Rapid Recovery. Frontiers in Marine Science, 6. doi:10.3389/fmars.2019.00026Iglesias, C., Martínez Torres, J., García Nieto, P. J., Alonso Fernández, J. R., Díaz Muñiz, C., Piñeiro, J. I., & Taboada, J. (2013). Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain. Water Resources Management, 28(2), 319-331. doi:10.1007/s11269-013-0487-9Najah, A., El-Shafie, A., Karim, O. A., & El-Shafie, A. H. (2012). Application of artificial neural networks for water quality prediction. Neural Computing and Applications, 22(S1), 187-201. doi:10.1007/s00521-012-0940-3Li, X., Cheng, Z., Yu, Q., Bai, Y., & Li, C. (2017). Water-Quality Prediction Using Multimodal Support Vector Regression: Case Study of Jialing River, China. Journal of Environmental Engineering, 143(10), 04017070. doi:10.1061/(asce)ee.1943-7870.0001272Su, J., Wang, X., Zhao, S., Chen, B., Li, C., & Yang, Z. (2015). A Structurally Simplified Hybrid Model of Genetic Algorithm and Support Vector Machine for Prediction of Chlorophyll a in Reservoirs. Water, 7(12), 1610-1627. doi:10.3390/w7041610Abba, S. I., Hadi, S. J., & Abdullahi, J. (2017). River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, 120, 75-82. doi:10.1016/j.procs.2017.11.212Juntunen, P., Liukkonen, M., Pelo, M., Lehtola, M. J., & Hiltunen, Y. (2012). Modelling of Water Quality: An Application to a Water Treatment Process. Applied Computational Intelligence and Soft Computing, 2012, 1-9. doi:10.1155/2012/846321Li, X., Sha, J., & Wang, Z. (2016). A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen. Hydrology Research, 48(5), 1214-1225. doi:10.2166/nh.2016.149Charulatha, G., Srinivasalu, S., Uma Maheswari, O., Venugopal, T., & Giridharan, L. (2017). Evaluation of ground water quality contaminants using linear regression and artificial neural network models. Arabian Journal of Geosciences, 10(6). doi:10.1007/s12517-017-2867-6Keller, S., Maier, P., Riese, F., Norra, S., Holbach, A., Börsig, N., … Hinz, S. (2018). Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. International Journal of Environmental Research and Public Health, 15(9), 1881. doi:10.3390/ijerph15091881Li, X., Sha, J., & Wang, Z.-L. (2017). Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks. Water, 9(7), 524. doi:10.3390/w9070524Yi, H.-S., Park, S., An, K.-G., & Kwak, K.-C. (2018). Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea. International Journal of Environmental Research and Public Health, 15(10), 2078. doi:10.3390/ijerph15102078Nazeer, M., Bilal, M., Alsahli, M., Shahzad, M., & Waqas, A. (2017). Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters. ISPRS International Journal of Geo-Information, 6(11), 360. doi:10.3390/ijgi6110360Erena, Domínguez, Aguado, Soria, & García-Galiano. (2019). Monitoring Coastal Lagoon Water Quality Through Remote Sensing: The Mar Menor as a Case Study. Water, 11(7), 1468. doi:10.3390/w11071468García-Oliva, M., Marcos, C., Umgiesser, G., McKiver, W., Ghezzo, M., De Pascalis, F., & Pérez-Ruzafa, A. (2019). Modelling the impact of dredging inlets on the salinity and temperature regimes in coastal lagoons. Ocean & Coastal Management, 180, 104913. doi:10.1016/j.ocecoaman.2019.104913López-Ballesteros, A., Senent-Aparicio, J., Srinivasan, R., & Pérez-Sánchez, J. (2019). Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain). Agronomy, 9(10), 576. doi:10.3390/agronomy9100576Senent-Aparicio, J., Pérez-Sánchez, J., García-Aróstegui, J., Bielsa-Artero, A., & Domingo-Pinillos, J. (2015). Evaluating Groundwater Management Sustainability under Limited Data Availability in Semiarid Zones. Water, 7(12), 4305-4322. doi:10.3390/w7084305Navarro, M. C., Pérez-Sirvent, C., Martínez-Sánchez, M. J., Vidal, J., Tovar, P. J., & Bech, J. (2008). Abandoned mine sites as a source of contamination by heavy metals: A case study in a semi-arid zone. Journal of Geochemical Exploration, 96(2-3), 183-193. doi:10.1016/j.gexplo.2007.04.011Conesa, H. M., & Jiménez-Cárceles, F. J. (2007). The Mar Menor lagoon (SE Spain): A singular natural ecosystem threatened by human activities. Marine Pollution Bulletin, 54(7), 839-849. doi:10.1016/j.marpolbul.2007.05.007Domingo-Pinillos, J., Senent-Aparicio, J., García-Aróstegui, J., & Baudron, P. (2018). Long Term Hydrodynamic Effects in a Semi-Arid Mediterranean Multilayer Aquifer: Campo de Cartagena in South-Eastern Spain. Water, 10(10), 1320. doi:10.3390/w10101320Stefanova, A., Hesse, C., & Krysanova, V. (2015). Combined Impacts of Medium Term Socio-Economic Changes and Climate Change on Water Resources in a Managed Mediterranean Catchment. Water, 7(12), 1538-1567. doi:10.3390/w7041538Velasco, J., Lloret, J., Millan, A., Marin, A., Barahona, J., Abellan, P., & Sanchez-Fernandez, D. (2006). Nutrient And Particulate Inputs Into The Mar Menor Lagoon (Se Spain) From An Intensive Agricultural Watershed. Water, Air, and Soil Pollution, 176(1-4), 37-56. doi:10.1007/s11270-006-2859-8García-Oliva, M., Pérez-Ruzafa, Á., Umgiesser, G., McKiver, W., Ghezzo, M., De Pascalis, F., & Marcos, C. (2018). Assessing the Hydrodynamic Response of the Mar Menor Lagoon to Dredging Inlets Interventions through Numerical Modelling. Water, 10(7), 959. doi:10.3390/w10070959Wei, B., Sugiura, N., & Maekawa, T. (2001). Use of artificial neural network in the prediction of algal blooms. Water Research, 35(8), 2022-2028. doi:10.1016/s0043-1354(00)00464-4(2000). 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)Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., & Pulido-Velazquez, D. (2018). A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain. Water, 10(2), 192. doi:10.3390/w10020192Nguyen, V. D., Tan, R. R., Brondial, Y., & Fuchino, T. (2007). Prediction of vapor–liquid equilibrium data for ternary systems using artificial neural networks. Fluid Phase Equilibria, 254(1-2), 188-197. doi:10.1016/j.fluid.2007.03.014Bekkari, N., & Zeddouri, A. (2019). Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant. Management of Environmental Quality: An International Journal, 30(3), 593-608. doi:10.1108/meq-04-2018-0084Zhang, Y., Gao, X., Smith, K., Inial, G., Liu, S., Conil, L. B., & Pan, B. (2019). Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Research, 164, 114888. doi:10.1016/j.watres.2019.114888Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017). Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping. Water Resources Management, 31(9), 2761-2775. doi:10.1007/s11269-017-1660-3Kuhn, M. (2008). Building Predictive Models inRUsing thecaretPackage. Journal of Statistical Software, 28(5). doi:10.18637/jss.v028.i05Caret: Classification and Regression Training, R Package Version 6.0-84 https://CRAN.R-project.org/package=caretMaier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25(8), 891-909. doi:10.1016/j.envsoft.2010.02.003Kumar, S., & Bucher, P. (2016). Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features. BMC Bioinformatics, 17(S1). doi:10.1186/s12859-015-0846-zMjalli, F. S., Al-Asheh, S., & Alfadala, H. E. (2007). Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. Journal of Environmental Management, 83(3), 329-338. doi:10.1016/j.jenvman.2006.03.004Palani, S., Liong, S.-Y., & Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin, 56(9), 1586-1597. doi:10.1016/j.marpolbul.2008.05.021Kuo, J.-T., Hsieh, M.-H., Lung, W.-S., & She, N. (2007). Using artificial neural network for reservoir eutrophication prediction. Ecological Modelling, 200(1-2), 171-177. doi:10.1016/j.ecolmodel.2006.06.018Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., Pulido-Velazquez, D., & Cecilia, J. (2017). Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain. Water, 9(5), 347. doi:10.3390/w905034

Influence of geographic location in concrete structures

Es muy importante considerar la localización geográfica de un edificio a la hora de realizar su chequeo estructural. Para ello, en este estudio se ha utilizado la conocida técnica de chequeo estructural basada en la correlación de resultados entre la velocidad de ultrasonidos (V) y la resistencia a compresión (R), obtenidas de las probetas testigo extraídas en elementos estructurales de hormigón armado. Para analizar la influencia de la localización geográfica de la estructura, se ha realizado el presente trabajo clasificando los edificios estudiados en función de su distancia a la costa y atendiendo las indicaciones de la instrucción EHE, es decir, utilizando una distancia de referencia de 500 m que permita ordenar los casos estudiados en función de dicha distancia. Como conclusión se puede afirmar que las estructuras más cercanas a las costas marinas están más influenciadas por el medio ambiente, lo que influye en gran manera en la calidad del hormigón de sus estructuras. Esta situación queda demostrada con el resultado de la investigación llevada a cabo, en el que se han analizado 185 casos reales. Para los más cercanos, se justifica estadísticamente la necesidad de su reparación-refuerzo en función de los resultados que arroje este chequeo y los resultados de correlación velocidad ultrasonidos-compresión en probetas testigo.It is very important to consider the geographic location of a building when performing structural check. Therefore, in this study we have used a technique called structural check based on the correlation of results between the ultrasound velocity (V) and compressive strength (R) obtained from the control samples taken in structural reinforced concrete elements. To analyze the influence of the geographic location of the structure, has made this work classifying the buildings studied in terms of their distance from the coast and following the instructions on the EHE instruction, ie, using a reference distance of 500 m order to allow the case studies based on this distance. In conclusion we can say that the closest structures to the shoreline are more influenced by the environment, which greatly influences the quality of concrete structures. This is demonstrated by the result of the research conducted, which used 185 real cases. To the nearest, is statistically justified the need for repair-reinforcement based on the results produced this check and the results of ultrasound-speed compression control specimens correlation

Evaluación de la sostenibilidad de cuencas mediterráneas semiáridas. Caso de estudio: cuenca del Segura, España

Desde la introducción del concepto de desarrollo sostenible en el Informe Brundtland a finales del siglo pasado, numerosos científicos han trabajado en la medición objetiva de la sostenibilidad mediante índices. La aplicación de estos índices a la gestión de los recursos hídricos permite evaluar el estado actual de los mismos y servir como herramienta de ayuda a la toma de decisiones por parte de los organismos competentes. Uno de los índices más utilizados es el Índice de Sostenibilidad de Cuencas. Este índice ha sido aplicado por distintos investigadores en numerosas cuencas a lo largo de todo el mundo, principalmente América Central y Sudamérica. Sin embargo, no se han encontrado referencias sobre su aplicación en Europa. El objetivo de este estudio es la aplicación del Índice de Sostenibilidad de Cuencas en una cuenca mediterránea semiárida, como es la cuenca del Segura (España). Se han adaptado algunos de los indicadores a las características del caso de estudio. La cuenca del Segura se caracteriza por su alto déficit hídrico y por estar sometida a los requisitos exigidos por la Directiva Marco del Agua. Se ha obtenido un valor del índice de sostenibilidad para la cuenca del Segura durante el periodo 2006-2010 de 0.64, lo que equivale a un nivel intermedio de sostenibilidad. La metodología propuesta puede ser utilizada en numerosas cuencas mediterráneas europeas que presentan condiciones hidrológicas, ambientales, sociales y políticas muy similares a las del caso objeto de estudio

Impact Assessment of Gridded Precipitation Products on Streamflow Simulations over a Poorly Gauged Basin in El Salvador

[EN] In this study, five open access gridded precipitation (GP) products (CFSR, MSWEPv1.1, PERSIANN-CDR, CMORPH, and CHIRPSv2.0) and local climate data were evaluated over the Grande de San Miguel (GSM) River Basin in El Salvador. The main purpose was to identify optional data sources of precipitation for hydrological modelling given that ground-based precipitation gauges in El Salvador are scarce and their data includes important temporal and spatial gaps. Firstly, a direct comparison was made between the precipitation data from the five GP products and from the rain gauges. Secondly, the SWAT model was used to simulate the streamflow regimen based on the precipitation datasets. The analysis of results showed that the models produced correct predictions, and the accuracy increased as models were calibrated to each specific precipitation product. Overall, PERSIANN-CDR produced the best simulation results, including streamflow predictions in the GSM basin, and outperformed other GP products and also the results obtained from data precipitation gauges. The findings of this research support the hydrological modelling based on open-access GP products, particularly when the data from precipitation gauges are scarce and poor.This research was funded by Ministerio de Ciencia e Innovacion of Espana (grant numbers RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5) and by Ramon y Cajal Program (grant number RYC2018-025580-I).Jimeno-Sáez, P.; Blanco-Gómez, P.; Pérez-Sánchez, J.; Cecilia-Canales, JM.; Senent-Aparicio, J. (2021). Impact Assessment of Gridded Precipitation Products on Streamflow Simulations over a Poorly Gauged Basin in El Salvador. Water. 13(18):1-21. https://doi.org/10.3390/w13182497121131

Cost-Benefit Analysis of the Managed Aquifer Recharge System for Irrigation under Climate Change Conditions in Southern Spain

Droughts and climate change in regions with profitable irrigated agriculture will impact groundwater resources with associated direct and indirect impacts. In the integrated water resource management (IWRM), managed aquifer recharge (MAR) offers efficient solutions to protect, conserve, and ensure survival of aquifers and associated ecosystems, as the Water Framework Directive requires. The purpose of this paper is to analyse the socio-economic feasibility of the MAR system in the overexploited Boquerón aquifer in Hellín (Albacete, Spain) under climate change and varying irrigation demand conditions. To assess, in monetary terms, the profitability of the MAR system, a cost-benefit analysis (CBA) has been carried out. The results for the period 2020–2050 showed that the most favourable situations would be scenarios involving artificial recharge, in which future irrigation demand remains at the present level or falls below 10% of the current irrigation surface, as these scenarios generated an internal rate of return of between 53% and 57%. Additionally, the regeneration of the habitat will take between 5 and 9 years. Thus, the IWRM with artificial recharge will guarantee the sustainability of irrigation of the agricultural lands of Hellín and will achieve water balance even in severe climate change conditions.Administración y Dirección de Empresa

Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain

[EN] Study region: Peninsular Spain. Study focus: Weather data are the key drivers of hydrological modelling. However, available weather data can present gaps in data sequences and are often limited in their spatial coverage for use in such hydrological models as the Soil and Water Assessment Tool (SWAT). To overcome this limitation, SWAT includes a weather generator algorithm that can complete this data based on long-term weather statistics. This work presents a newly developed weather statistics dataset for Peninsular Spain (PSWG), calculated from national gridded datasets according to the SWAT model format. PSWG provides a higher resolution that stands as a compelling alternative to the statistics calculated from the Climate Forecast System Reanalysis (CFSR) that are available on the SWAT website. New hydrological insights for the region: The dataset has been evaluated using PSWG and CFSR datasets for different data availability scenarios to reconstruct weather series in three watersheds with contrasting weather climates. Results underscore the superiority of the PSWG dataset in reconstructing missing data for hydrological simulations. This approach provides a strong alternative for SWAT applications in Peninsular Spain and the applied methodology can be replicated in other countries that dispose of high-resolution gridded rainfall and temperature datasets.This research was funded by the Ministry of Science, Innovation and Universities of Spain under grants RTC-2017-6389-5 and RTI2018096384BI00. This work has also received funding from the European Union's Horizon 2020 research and innovation programme within the framework of the project SMARTLAGOON under grant agreement No. 101017861. Adrian Lopez Ballesteros was sponsored by the Ministry of Science, Innovation and Universities of Spain under an FPU grant (FPU17/00923) and Jose M. Cecilia under the Ramon y Cajal Program (Grant No. RYC2018025580I) . The authors would like to acknowledge AEMET for the precipitation and air temperature data provided for this work (AEMET grid dataset can be found at http:// www.aemet.es/es/serviciosclimaticos/cambio_climat) .Senent-Aparicio, J.; Jimeno-Sáez, P.; López-Ballesteros, A.; Ginés Giménez, J.; Pérez-Sánchez, J.; Cecilia-Canales, JM.; Srinivasan, R. (2021). Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain. Journal of Hydrology: Regional Studies. 35:1-15. https://doi.org/10.1016/j.ejrh.2021.100826S1153

Summarizing the impacts of future potential global change scenarios on seawater intrusion at the aquifer scale

[EN] Climate change affects rainfall and temperature producing a breakdown in the water balance and a variation in the dynamic of freshwater-seawater in coastal areas, exacerbating seawater intrusion (SWI) problems. The target of this paper is to propose a method to assess and analyze impacts of future global change (GC) scenarios on SWI at the aquifer scale in a coastal area. Some adaptation measures have been integrated in the definition of future GC scenarios incorporating complementary resources within the system in accordance with urban development planning. The proposed methodology summarizes the impacts of potential GC scenarios in terms of SWI status and vulnerability at the aquifer scale through steady pictures (maps and conceptual 2D cross sections for specific dates or statistics of a period) and time series for lumped indices. It is applied to the Plana de Oropesa-Torreblanca aquifer. The results summarize the influence of GC scenarios in the global status and vulnerability to SWI under some management scenarios. These GC scenarios would produce higher variability of SWI status and vulnerability.This work has been partially supported by the GeoE.171.008-TACTIC and GeoE.171.008-HOVER projects from GeoERA organization funded by European Union's Horizon 2020 research and innovation program; Plan de Garantia Juvenil from MINECO (Ministerio de Economia y Competitividad), co-inancing by BEI (Banco Europeo de Inversiones) and FSE (Fondo Social Europeo); and SIGLO-AN (RTI2018-101397-B-I00) project from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad). The authors also thank AEMET and UC for the data provided for this work (Spain02 dataset, https://www.meteo.unican.es/datasets/spain02).Baena-Ruiz, L.; Pulido-Velázquez, D.; Collados-Lara, A.; Renau-Pruñonosa, A.; Morell, I.; Senent-Aparicio, J.; Llopis-Albert, C. (2020). Summarizing the impacts of future potential global change scenarios on seawater intrusion at the aquifer scale. Environmental Earth Sciences. 79(5):1-13. https://doi.org/10.1007/s12665-020-8847-2S113795Baena-Ruiz L, Pulido-Velazquez D, Collados-Lara AJ, Renau-Pruñonosa A, Morell I (2018) Global assessment of seawater intrusion problems (status and vulnerability). Water Resour Manage 32:2681–2700. https://doi.org/10.1007/s11269-018-1952-2Benini L, Antonellini M, Laghi L (2016) Assessment of water resources availability and groundwater salinization in future climate and land use change scenarios: a case study from a coastal drainage basin in Italy. 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