Improved modelling of the freshwater provisioning ecosystem service in water scarce river basins

Freshwater provisioning by the landscape contributes to human well-being through water use for drinking, irrigation and other purposes. The assessment of this ecosystem service involves the quantification of water resources and the valuation of water use benefits. Models especially designed to assess ecosystem services can be used. However, they have limitations in representing the delivery of the service in water scarce river basins where water management and the temporal variability of water resource and its use are key aspects to consider. Integrating water resources management tools represents a good alternative to ecosystem services models in these river basins. We propose a modelling framework that links a rainfall-runoff model and a water allocation model which allow accounting for the specific requirements of water scarce river basins. Moreover, we develop a water tracer which rebounds the value of the service from beneficiaries to water sources, allowing the spatial mapping of the service

Optimization of the Scarcity State Indicator in the Jucar river basin

[ES] Los indicadores de estado de escasez ayudan a evitar pérdidas económicas, sociales y ambientales que causan las sequías en las cuencas mediterráneas como la cuenca del río Júcar. El presente trabajo pretende optimizar la obtención del Indicador de Estado de Escasez (IEE) para reproducir las situaciones de escasez acontecidas en un sistema de recursos hídricos (RRHH). La metodología consiste en utilizar un modelo de gestión RRHH, Aquatool-Simges, para definir los períodos de escasez y su magnitud. A continuación, se calculan las variables del IEE y se aplica un algoritmo evolutivo para optimizar su ponderación. Los resultados muestran un incremento del 13.7% y del 78.8% del peso de las variables VE07 y EA03 respectivamente. Además, se obtiene un 62% de acierto del método para predecir estados de normalidad en la cuenca. Se puede concluir que esta propuesta de optimización del IEE presenta buenos resultados, aunque muestran una anticipación a los escenarios de escasez y falsos positivos que se solventarán en futuros estudios.[EN] Scarcity status indicators help to avoid economic, social and environmental losses caused by droughts in Mediterranean basins such as the Jucar River basin. The aim of this work is to optimize the Scarcity State Indicator (SSI) in order to reproduce the scarcity periods occurring in a water resource system (WRS). The methodology used consists of using an WRS model, Aquatool-Simges, to define the scarcity periods and their magnitude. Then we computed the SSI variables and applied an evolutionary algorithm to optimize their weighting. The results show an increase of 13.7% and 78.8% in the weight of variables VE07 and EA03 respectively. In addition, a 62% success rate is obtained from the method for predicting states of normality in the basin. It can be concluded that this proposal for optimization of the EEI presents good results, although they show an anticipation of the scenarios of scarcity and false positives that will be solved in future studies.Los autores agradecen a la Agencia Española de Investigación (MINECO) el apoyo económico al proyecto ERAS (CTM2016-77804-P, incluyendo fondos EU-FEDER). Además, también valoramos el apoyo de la Comunidad Europea que ha financiado el proyecto IMPREX (H2020-WATER-2014–2015, 641811).Palop-Donat, C.; Paredes-Arquiola, J.; Andreu, J. (2020). Optimización del indicador de escasez en la cuenca del río Júcar. Ingeniería del agua. 24(2):129-140. https://doi.org/10.4995/ia.2020.12275OJS129140242Alcamo, J., Acosta-Michlik, L., Carius, A., Eierdanz, F., Klein, R., Krömker, D., Tänzler, D. 2008. A new approach to quantifying and comparing vulnerability to drought. Regional Environmental Change, 8(4), 137-149. https://doi.org/10.1007/s10113-008-0065-5Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., Siebert, S. 2003. Global estimates of water withdrawals and availability under current and future "business-as-usual" conditions. Hydrological Sciences Journal, 48(3), 339-348. https://doi.org/10.1623/hysj.48.3.339.45278Andreu, J., Capilla, J., Sanchís, E. 1996. AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3-4), 269-291. https://doi.org/10.1016/0022-1694(95)02963-XBoletín Oficial del Estado (BOE). Instrucción de Planificación Hidrológica., 2008.CEDEX. 2017. Evaluación del impacto del Cambio Climático en los recursos hídricos y sequías en España. Recuperado de http://publicacionesoficiales.boe.es/CHJ. 2015. Plan Hidrológico del Júcar 2015-2021. Recuperado de https://www.chj.es/Descargas/ProyectosOPH/Consulta publica/PHC-2015-2021/PHJ1521_Memoria_151126.pdfCHJ. 2018. Plan Especial de Sequía Demarcación Hidrográfica del Júcar.EC, European Commission. 2012. Communication from the commission to the European parliament, the council, the European economic and social committee and the committee of the regions a Blueprint to Safeguard Europe's Water Resources. Recuperado de https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52012DC0673EC, European Commission. 2007. Addressing the challenge of water scarcity and droughts in the European Union. Official Journal of the European Union, (COM/2007), 1-14. https://doi.org/10.1017/CBO9781107415324.004Estrela, T., Sancho, T. A. 2016. Drought management policies in Spain and the european union: From traditional emergency actions to drought management plans. Water Policy, 18, 153-176. https://doi.org/10.2166/wp.2016.018Estrela, T., Vargas, E. 2012. Drought Management Plans in the European Union. The Case of Spain. Water Resources Management, 26(6), 1537-1553. https://doi.org/10.1007/s11269-011-9971-2EU. 2000. Directive 2000/60/EC of the European Parliament and of the Council establishing a framework for the Community action in the field of water policy. Recuperado de http://ec.europa.eu/environment/water/water-framework/index_en.htmlFalkenmark, M. 1989. The Massive Water Scarcity Now Threatening Africa: Why Isn't It Being Addressed? Ambio, 18, 112-118. https://www.jstor.org/stable/4313541Guttman, N. B. 1998. Comparing the palmer drought index and the standardized precipitation index. Journal of the American Water Resources Association, 34(1), 113-121. https://doi.org/10.1111/j.1752-1688.1998.tb05964.xIPCC. 2007. AR4 Climate Change 2007: Impacts, Adaptation, and Vulnerability - IPCC. Recuperado el 2 de Mayo, 2019, de https://www.ipcc.ch/report/ar4/wg2/IPCC. 2014. AR5 Climate Change 2014: Impacts, Adaptation, and Vulnerability - IPCC. Recuperado el 2 de Mayo, 2019 dehttps://www.ipcc.ch/report/ar5/wg2/Kampragou, E., Apostolaki, S., Manoli, E., Froebrich, J., Assimacopoulos, D. 2011. Towards the harmonization of water-related policies for managing drought risks across the EU. Environmental Science and Policy, 14(7), 815-824. https://doi.org/10.1016/j.envsci.2011.04.001Lerma, N., Paredes-Arquiola, J., Andreu, J., Solera, A. 2013. Development of operating rules for a complex multireservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58(4), 797-812. https://doi.org/10.1080/02626667.2013.779777Liu, J., Yang, H., Gosling, S. N., Kummu, M., Flörke, M., Hanasaki, N., Zheng, C. 2017. Water scarcity assessments in the past, present, and future. Earth's Future, 5(6), 545-559. https://doi.org/10.1002/2016EF000518MAAM. 2014. Plan Nacional De Adaptación al Cambio Climático. Madrid, España.Mckee, T. B., Doesken, N. J., Kleist, J. 1993. The relationship of drought frequency and duration to time scales. In Eighth Conference on Applied Climatology. Recuperado de https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdfOrtega-Gómez, T., Pérez-Martín, M. A., Estrela, T. 2018. Improvement of the drought indicators system in the Júcar River Basin, Spain. Science of the Total Environment, 610-611, 276-290. https://doi.org/10.1016/j.scitotenv.2017.07.250Pedro-Monzonís, M., Solera, A., Ferrer, J., Estrela, T., Paredes-Arquiola, J. 2015. A review of water scarcity and drought indexes in water resources planning and management. Journal of Hydrology, 527, 482-493. https://doi.org/10.1016/j.jhydrol.2015.05.003Pérez-Blanco, C. D., Gómez, C. M. 2014. Drought management plans and water availability in agriculture: A risk assessment model for a Southern European basin. Weather and Climate Extremes, 4, 11-18. https://doi.org/10.1016/j.wace.2014.02.003Quiring, S. M., Papakryiakou, T. N. 2003. An evaluation of agricultural drought indices for the Canadian prairies. Agricultural and Forest Meteorology, 118, 49-62. https://doi.org/10.1016/S0168-1923(03)00072-8Rijsberman, F. R. 2006. Water scarcity: Fact or fiction? Agricultural Water Management, 80(1-3 SPEC. ISS.), 5-22.https://doi.org/10.1016/j.agwat.2005.07.001Sánchez-Quispe, S. 2000. Gestión de Recursos Hídricos con decisiones basadas en Estimación del Riesgo. Universitat Politécnica de Valéncia.Zaniolo, M., Giuliani, M., Castelletti, A. F., Pulido-Velazquez, M. 2018. Automatic design of basin-specific drought indexes for highly regulated water systems. Hydrology and Earth System Sciences, 22(4), 2409-2424. https://doi.org/10.5194/hess-22-2409-201

Climate change effect on water quality in the Júcar River Basin

[EN] This study analyses the effect of climate change on water quality in the Júcar River Basin from future estimations of hydrological inputs and water temperature (WT). For this purpose, a large-scale water quality model was used to estimate the ecological status of all the water bodies, based on the concentrations of BDO5, P, NH4+ and NO3– for the future horizons 2020, 2050 and 2080. In this study, a greater number of water bodies with higher pollution levels (80-100% failures) were obtained in the horizons 2050 and 2080, which are located in the middle and lower parts of the basin. In addition, the degradation of BDO5 and the NH4+ is highly dependent on WT, highlighting the importance of considering this variable in the model.[ES] En este estudio se analiza el efecto del cambio climático en la calidad del agua de la cuenca del Júcar a partir de estimaciones futuras de aportaciones hidrológicas y temperatura del agua (Ta). Para ello, se utilizó un modelo de calidad de aguas a escala de cuenca con el que se estimó el estado ecológico de todas las masas de agua, basándose en las concentraciones de DBO5, P, NH4+ y NO3- para los horizontes futuros 2020, 2050 y 2080. De este análisis se obtuvo un incremento del número de masas con altos niveles de contaminación (80-100% incumplimientos) en los horizontes 2050 y 2080, localizadas sobre todo en la parte media y baja de la cuenca. Además, la degradación de la DBO5 y el NH4+ es muy dependiente de la temperatura del agua, poniendo de manifiesto la importancia de considerar esta variable en el modelo.Los autores agradecen al Ministerio de Ciencia e Innovación (MICINN) por financiar el proyecto RESPHIRA (PID2019106322RB-100) y a la Agencia Estatal de Investigación (MINECO) por financiar el proyecto ERAS (CTM2016-77804P), cofinanciado con Fondos FEDER. Además, también valoramos el apoyo de la Comunidad Europea en la financiación de los proyectos IMPREX (H2020-WATER-2014–2015, 641811) y SWICCA (ECMRWF-Copernicus-FA 2015/ C3S_441-LOT1/SMHI). Por último, agradecer al Programa de Apoyo para la Investigación y Desarrollo (PAID-01-17) de la Universitat Politècnica de València por fomentar y facilitar los contratos de formación de personal investigador.Suárez-Almiñana, S.; Paredes-Arquiola, J.; Andreu, J.; Solera, A. (2021). Efecto del cambio climático en la calidad del agua de la Cuenca del Júcar. Ingeniería del agua. 25(2):75-95. https://doi.org/10.4995/ia.2021.146447595252Ahmad, J.I., Dignum, M., Liu, G., Medema, G., van der Hoek, J.P. 2021. Changes in biofilm composition and microbial water quality in drinking water distribution systems by temperature increase induced through thermal energy recovery. Environmental Research, 194, 110648. https://doi.org/10.1016/j.envres.2020.110648Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R. 1998. Large area hydrologic modeling and assessment Part I: Model development. Journal of the American Water Resources Association, 34, 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.xBarranco, L., Dimas, M., Jiménez, A., Estrada, F. 2018. Nueva evaluación del impacto futuro del cambio climático en los recursos hídricos en España. Ingeniería Civil, 191, 34-55.BOE. 2015. Real Decreto 817/2015, de 11 de septiembre, por el que se establecen los criterios de seguimiento y evaluación del estado de las aguas superficiales y las normas de calidad ambiental, Actualidad Jurídica Ambiental.Bowie, G.L., Mills, W.B., Porcella, D.B., Campbell, C.L., Pagenkopf, J.R., Rupp, G.L., Johnson, K.M., Chan, P.W.H., Gherini, S.A., Chamberlin, C.E. 1985. Rates, Constants, and Kinetics Formulations in Surface Water Quality Modeling. EPA/600/3-. Athens, Georgia: U.S. Environmental Protection Agency.CEDEX. 2017. Evaluación Del Impacto Del Cambio Climático En Los Recursos Hídricos y Sequías de España. Informe técnico para el Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente. Madrid, España.CHJ. 2015. Plan Hidrológico de la Demarcación Hidrográfica del Júcar. Memoria ciclo de planificación hidrológica 2015-2021. Ministerio de Agricultura, Alimentación y Medio Ambiente. Valencia, España.European Parliament. 2000. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 Establishing a Framework for Community Action in the Field of Water Policy. Brussels: Official Journal.Ferrer, J., Seco, A. 2008. Tratamientos Biológicos de Aguas Residuales. Valencia, España: Universidad Politécnica de Valencia: Alfaomega Grupo Editor.Flynn, K.F., Suplee, M.W., Chapra, S.C., Tao, H. 2015. Model-Based Nitrogen and Phosphorus (Nutrient) Criteria for Large Temperate Rivers: 1. Model Development and Application. Journal of the American Water Resources Association, 51(2), 421-446. https://doi.org/10.1111/jawr.12253Gutiérrez, B., de Jalón, D.G., 1999. Modelización térmica de los ríos Cea y Manzanares. Limnetica, 17, 1-12.Hunink, J., Simons, G., Suárez-Almiñana, S., Solera, A., Andreu, J., Giuliani, M., Zamberletti, P., Grillakis, M., Koutroulis, A., Tsanis, I., Schasfoort, F., Contreras, S., Ercin, E., Bastiaanssen, W. 2019. A Simplified Water Accounting Procedure to Assess Climate Change Impact on Water Resources for Agriculture across Different European River Basins. Water, 11, 1976. https://doi.org/10.3390/w11101976IPCC, 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. https://doi.org/10.1017/CBO9781107415324Jin, L., Whitehead, P.G., Rodda, H., Macadam, I., Sarkar, S. 2018. Simulating climate change and socio-economic change impacts on flows and water quality in the Mahanadi River system, India. Science of the Total Environment, 637-638, 907-17. https://doi.org/10.1016/j.scitotenv.2018.04.349Jong-Suk, K., Shaleen, J., Joo-Heon, L., Hua, C., Seo-Yeon, P. 2019. Quantitative vulnerability assessment of water quality to extreme drought in a changing climate. Ecological Indicators, 103(March), 688-97. https://doi.org/10.1016/j.ecolind.2019.04.052Lemos, M.C., Rood, R.B. 2010. Climate Projections and Their Impact on Policy and Practice. Wiley Interdisciplinary Reviews: Climate Change, 1(5), 670-82. https://doi.org/10.1002/wcc.71Marcos-Garcia, P., Pulido-Velazquez, M. 2017. Cambio Climático y Planificación Hidrológica: ¿es adecuado asumir un porcentaje único de reducción de aportaciones para toda la demarcación? Ingeniería del Agua, 21(1), 35. https://doi.org/10.4995/ia.2017.6361Naustdalslid, J. 2011. Climate change - The challenge of translating scientific knowledge into action. International Journal of Sustainable Development and World Ecology, 18(3), 243-52. https://doi.org/10.1080/13504509.2011.572303Paredes-Arquiola, J. 2021. Manual Técnico Del Modelo Respuesta Rápida Del Estado Ambiental (RREA) de Masas de Agua Superficiales Continentales. Universitat Politècnica de València. Valencia, España. https://aquatool.webs.upv.es/files/manuales/rrea/ManualT%C3%A9cnicoModeloRREA_V3.pdfPellicer-Martínez, F., Martínez-Paz, J.M. 2016. The Water Footprint as an Indicator of Environmental Sustainability in Water Use at the River Basin Level. Science of the Total Environment, 571, 561-74. https://doi.org/10.1016/j.scitotenv.2016.07.022Pérez-Martín, M. 2005. Modelo distribuido de simulación del ciclo hidrológico con calidad de aguas integrado en sistemas de información geográfica para grandes cuencas. Aportación al análisis de presiones e impactos de la Directiva Marco Europea del Agua. Universidad Politécnica de Valencia. Valencia, España.Rocha, J., Carvalho-Santos, C., Diogo, P., Beça, P., Keizer, J.J., Nunes, J.P., 2020. Impacts of climate change on reservoir water availability, quality and irrigation needs in a water scarce Mediterranean region (southern Portugal). Science of the Total Environment 736. https://doi.org/10.1016/j.scitotenv.2020.139477Serpa, D., Nunes, J.P., Keizer, J.J., Abrantes, N. 2017. Impacts of climate and land use changes on the water quality of a small Mediterranean catchment with intensive viticulture. Environmental Pollution, 224, 454-65. https://doi.org/10.1016/j.envpol.2017.02.026Shrestha, S., Bhatta, B., Shrestha, M., Shrestha, P.K. 2018. Integrated assessment of the climate and landuse change impact on hydrology and water quality in the Songkhram River Basin, Thailand. Science of the Total Environment, 643, 1610-22. https://doi.org/10.1016/j.scitotenv.2018.06.306Suárez-Almiñana, S., Pedro-Monzonís, M., Paredes-Arquiola, J., Andreu, J., Solera, A. 2017. Linking Pan-European data to the local scale for decision making for global change and water scarcity within water resources planning and management. Science of the Total Environment, 603-604, 126-39. https://doi.org/10.1016/j.scitotenv.2017.05.259Suárez-Almiñana, S., Solera, A., Andreu, J., García-Romero, L. 2020a. Análisis de incertidumbre de las proyecciones climáticas en relación a las aportaciones históricas en la Cuenca del Júcar. Ingeniería del Agua, 24(2), 1-12. https://doi.org/10.4995/ia.2020.12149Suárez-Almiñana, S., Solera, A., Madrigal, J., Andreu, J., Paredes-arquiola, J. 2020b. Risk assessment in water resources planning under climate change at the Júcar River Basin. Hydrology and Earth System Science, 24(11), 5297-5315. https://doi.org/10.5194/hess-24-5297-2020Témez, J.R. 1977. Modelo matemático de transformación precipitación-aportación. ASINEL.Trewin, B.C. 2007. Función de las normales climatológicas en un clima cambiante. Edited by O. Baddour and H. Kontongomde. Organización Meteorológica Mundial. Vol. 43. Ginebra.Wang, Y., Zhang, N., Wang, D., Wu, J. 2020. Impacts of cascade reservoirs on Yangtze River water temperature: Assessment and ecological implications. Journal of Hydrology, 590, 125240. http://doi.org/10.1016/j.jhydrol.2020.125240Whitehead, P.G., Wilson, E.J., Butterfield, D., Seed, K. 1998. A semi-distributed integrated flow and nitrogen model for multiple source assessment in catchments (INCA): part II - application to large river basins in south Wales and eastern England. Science of the Total Environment, 210, 559-583. https://doi.org/10.1016/S0048-9697(98)00038-2Xu, L., Li, H., Liang, X., Yao, Y., Zhou, L., Cui, X. 2012. Water quality parameters response to temperature change in small shallow lakes. Physics and Chemistry of the Earth, 47-48, 128-134. https://doi.org/10.1016/j.pce.2010.11.005Zlatanović, L., van der Hoek, J.P., Vreeburg, J.H.G. 2017. An experimental study on the influence of water stagnation and temperature change on water quality in a full-scale domestic drinking water system. Water Research, 123, 761-772. https://doi.org/10.1016/j.watres.2017.07.01

Improving Indicators of Hydrological Alteration in Regulated and Complex Water Resources Systems: A Case Study in the Duero River Basin

[EN] Assessing the health of hydrological systems is vital for the conservation of river ecosystems. The indicators of hydrologic alteration are among the most widely used parameters. They have been traditionally assessed at the scale of river reaches. However, the use of such indicators at the basin scale is relevant for water resource management since there is an urgent need to meet environmental objectives to mitigate the effects of present and future climatic conditions. This work proposes a methodology to estimate the indicators of hydrological alteration at the basin scale in regulated systems based on simulations with a water allocation model. The methodology is illustrated through a case study in the Iberian Peninsula (the Duero River basin), where different minimum flow scenarios were defined, assessing their effects on both the hydrological alteration and the demand guarantees. The results indicate that it is possible to improve the hydrological status of some subsystems of the basin without affecting the water demand supplies. Thus, the methodology presented in this work will help decision makers to optimize water management while improving the hydrological status of the river basins.This research was funded by the Spanish Research Agency (AEI), grant number PID2019-106322RB-100; AEI/10.13039/501100011033. R.J.B. was partly funded by the Spanish Ministry of Science and Innovation through the research contract IJC2019-038848-I.Pardo-Loaiza, J.; Solera Solera, A.; Bergillos, RJ.; Paredes Arquiola, J.; Andreu Álvarez, J. (2021). Improving Indicators of Hydrological Alteration in Regulated and Complex Water Resources Systems: A Case Study in the Duero River Basin. Water. 13(19):1-18. https://doi.org/10.3390/w13192676118131

Integrating water management, habitat modelling and water quality at basin scale environmental flow assessment - Tormes River (Spain)

"This is an Accepted Manuscript of an article published in Hydrological Sciences Journal on 2014, available online: http://dx.doi.org/10.1080/02626667.2013.821573"Multidisciplinary models are useful for integrating different disciplines when addressing water planning and management problems. We combine water resources management, water quality and habitat analysis tools that were developed with the Decision Support System AQUATOOL at a basin scale. The water management model solves the allocation problem through network flow optimisation and considers the environmental flows in some river stretches. Once volumes and flows are estimated, the water quality model is applied. Furthermore, the flows are evaluated from an ecological perspective by using time series of aquatic species habitat indicators. This approach was applied in the Tormes River Water System, where agricultural demands jeopardise the environmental needs of the river ecosystem. Additionally, water quality problems in the lower part of the river result from wastewater loading and agricultural pollution. Our methodological framework can be used to define water management rules that maintain water supply, aquatic ecosystem and water quality legal standards. The integration of ecological and water management criteria in a software platform with objective criteria and heuristic optimisation procedures allows for the realistic assessment and application of environmental flows. Here, we improve the general methodological framework by assessing the hydrological alteration of selected environmental flow regime scenarios.This study was partially funded by the Spanish Ministry of Economy and Competitiveness and the SCARCE project [Consolider-Ingenio 2010 CSD2009-00065].Paredes Arquiola, J.; Solera Solera, A.; Martinez-Capel, F.; Momblanch Benavent, A.; Andreu Álvarez, J. (2014). Integrating water management, habitat modelling and water quality at basin scale environmental flow assessment - Tormes River (Spain). Hydrological Sciences Journal. 59(3-4):878-889. https://doi.org/10.1080/02626667.2013.821573S878889593-4Acreman, M. (2005). Linking science and decision-making: features and experience from environmental river flow setting. Environmental Modelling & Software, 20(2), 99-109. doi:10.1016/j.envsoft.2003.08.019Andreu, J., Capilla, J., & Sanchís, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3-4), 269-291. doi:10.1016/0022-1694(95)02963-xBelmar, O., Velasco, J., & Martinez-Capel, F. (2011). Hydrological Classification of Natural Flow Regimes to Support Environmental Flow Assessments in Intensively Regulated Mediterranean Rivers, Segura River Basin (Spain). Environmental Management, 47(5), 992-1004. doi:10.1007/s00267-011-9661-0Bovee, K.D., 1982.A guide to stream habitat analysis using the Instream Flow Incremental Methodology.Washington, DC: U.S. Department of the Interior, Fish and Wildlife Service Instream Flow Information Paper #12.Garcia De Jalon, D. and Lurueña, J., 2000. Estudio para la determinación de caudales mínimos en varios tramos de la cuenca del Tormes y del Alberche (provincia de Ávila). Technical Report of the Universidad Politécnica de Madrid for Junta de Castilla y León. (In Spanish)Liu, Y., Gupta, H., Springer, E., & Wagener, T. (2008). Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management. Environmental Modelling & Software, 23(7), 846-858. doi:10.1016/j.envsoft.2007.10.007Martinez-Capel, F.et al. 2006. Validació biològica del règim de cabals de manteniment definits al pla sectorial de les conques internes de Catalunya en 10 trams fluvials. Technical report of the Universidad Politécnica de Valencia for the Agència Catalana de l’Aigua (Generalitat de Catalunya). (In Spanish)Olaya-Marín, E. J., Martínez-Capel, F., Soares Costa, R. M., & Alcaraz-Hernández, J. D. (2012). Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain). Science of The Total Environment, 440, 95-105. doi:10.1016/j.scitotenv.2012.07.093Olden, J. D., & Poff, N. L. (2003). Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Research and Applications, 19(2), 101-121. doi:10.1002/rra.700Bain, M. B., & Meixler, M. S. (2008). A target fish community to guide river restoration. River Research and Applications, 24(4), 453-458. doi:10.1002/rra.1065Paredes, J., Andreu, J., & Solera, A. (2010). A decision support system for water quality issues in the Manzanares River (Madrid, Spain). Science of The Total Environment, 408(12), 2576-2589. doi:10.1016/j.scitotenv.2010.02.037Paredes-Arquiola, J., Andreu-Álvarez, J., Martín-Monerris, M., & Solera, A. (2010). Water Quantity and Quality Models Applied to the Jucar River Basin, Spain. Water Resources Management, 24(11), 2759-2779. doi:10.1007/s11269-010-9578-zParedes-Arquiola, J.et al. 2011. Implementing environmental flows in complex water resources systems – case study: the Duero river basin, Spain.River Research and Applications, 29, 451–468. doi:10.1002/rra.1617Poff, N. L., Allan, J. D., Bain, M. B., Karr, J. R., Prestegaard, K. L., Richter, B. D., … Stromberg, J. C. (1997). The Natural Flow Regime. BioScience, 47(11), 769-784. doi:10.2307/1313099POFF, N. L., RICHTER, B. D., ARTHINGTON, A. H., BUNN, S. E., NAIMAN, R. J., KENDY, E., … WARNER, A. (2010). The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshwater Biology, 55(1), 147-170. doi:10.1111/j.1365-2427.2009.02204.xSolomon, S.et al. 2007.Climate change 2007: The physical science basis.Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press

Development of operating rules for a complex multireservoir system by coupling genetic algorithms and network optimization

This is an Accepted Manuscript of an article published in Hydrological Sciences Journal on MAY 1 2013, available online: http://dx.doi.org/10.1080/02626667.2013.779777[EN] An alternative procedure for assessment of reservoir Operation Rules (ORs) under drought situations is proposed. The definition of ORs for multi-reservoir water resources systems (WRSs) is a topic that has been widely studied by means of optimization and simulation techniques. A traditional approach is to link optimization methods with simulation models. Thus the objective here is to obtain drought ORs for a real and complex WRS: the Júcar River basin in Spain, in which one of the main issues is the resource allocation among agricultural demands in periods of drought. To deal with this problem, a method based on the combined use of genetic algorithms (GA) and network flow optimization (NFO) is presented. The GA used was PIKAIA, which has previously been used in other water resources related fields. This algorithm was linked to the SIMGES simulation model, a part of the AQUATOOL decision support system (DSS). Several tests were developed for defining the parameters of the GA. The optimization of various ORs was analysed with the objective of minimizing short-term and long-term water deficits. The results show that simple ORs produce similar results to more sophisticated ones. The usefulness of this approach in the assessment of ORs for complex multi-reservoir systems is demonstrated.The authors wish to thank the Confederacion Hidrogrofica del Jucar (Spanish Ministry of the Environment) for the data provided in developing this study and the Comision Interministerial de Ciencia y Tecnologia, CICYT (Spanish Ministry of Science and Innovation) for funding the projects INTEGRAME (contract CGL2009-11798) and SCARCE (programme Consolider-Ingenio 2010, project CSD2009-00065). The authors also thank the European Commission (Directorate-General for Research and Innovation) for funding the project DROUGHT-R&SPI (programme FP7-ENV-2011, project 282769) and the Seventh Framework Programme of the European Commission for funding the project SIRIUS (FP7-SPACE-2010-1, project 262902). We are grateful to the reviewers for their valuable comments, which have improved this paper.Lerma Elvira, N.; Paredes Arquiola, J.; Andreu Álvarez, J.; Solera Solera, A. (2013). Development of operating rules for a complex multireservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal. 58(4):797-812. https://doi.org/10.1080/02626667.2013.779777S79781258

Integrated methodological framework fos assesing the risk of failure in water supply incorporating drought forecast. Case study: Andean regulated river basin

[EN] Hydroclimatic drought conditions can affect the hydrological services offered by mountain river basins causing severe impacts on the population, becoming a challenge for water resource managers in Andean river basins. This study proposes an integrated methodological framework for assessing the risk of failure in water supply, incorporating probabilistic drought forecasts, which assists in making decisions regarding the satisfaction of consumptive, non-consumptive and environmental requirements under water scarcity conditions. Monte Carlo simulation was used to assess the risk of failure in multiple stochastic scenarios, which incorporate probabilistic forecasts of drought events based on a Markov chains (MC) model using a recently developed drought index (DI). This methodology was tested in the Machángara river basin located in the south of Ecuador. Results were grouped in integrated satisfaction indexes of the system (DSIG). They demonstrated that the incorporation of probabilistic drought forecasts could better target the projections of simulation scenarios, with a view of obtaining realistic situations instead of optimistic projections that would lead to riskier decisions. Moreover, they contribute to more effective results in order to propose multiple alternatives for prevention and/or mitigation under drought conditions.This study was part of the doctoral thesis of Aviles A. at the Technical University of Valencia. This research was funded by the University of Cuenca through its Research Department (DIUC) and the Municipal public enterprise of telecommunications, drinking water, sewage and sanitation of Cuenca (ETAPA) through the projects: BIdentificacion de los procesos hidrometeorologicos que desencadenan inundaciones en la ciudad de Cuenca usando un radar de precipitacion" and "Ciclos meteorologicos y evapotranspiracion a lo largo de una gradiente altitudinal del Parque Nacional Cajas". The authors also thank INAMHI and the CBRM for providing the information for this study. The authors wish to thank the Spanish Ministry of Economy and Competitiveness for its financial support through the ERAS project (CTM2016-77804-P). We thank Angel Vazquez, who helped in the programming of the multiple simulations. Also we thank to the TropiSeca project.Avilés-Añazco, A.; Solera Solera, A.; Paredes Arquiola, J.; Pedro Monzonís, M. (2018). Integrated methodological framework fos assesing the risk of failure in water supply incorporating drought forecast. Case study: Andean regulated river basin. Water Resources Management. 32(4):1209-1223. https://doi.org/10.1007/s11269-017-1863-7S12091223324Andreu J, Capilla J, Sanchís E (1996) AQUATOOL, a generalized decision-support system for water-resources planning and operational management. J Hydrol 177(3-4):269–291. https://doi.org/10.1016/0022-1694(95)02963-XAndreu J, Solera A, Capilla J, Ferrer J (2007) Modelo SIMGES para simulación de cuencas. Manual de usuario v3. 00. Universidad Politécnica de Valencia, ValenciaAndreu J, Ferrer J, Perez MA et al (2013) Drought planning and management in the Júcar River Basin, Spain. In: Schwabe K et al (eds) Drought in arid and semi-arid regions. Springer science, Dordrecht, pp 237–249. https://doi.org/10.1007/978-94-007-6636-5_13Avilés A, Solera A (2013) Análisis de sistemas de recursos hídricos de la cuenca del rio Tomebamba en Ecuador, mediante modelos estocásticos y de gestión. In: Solera A, Paredes J, Andreu J (eds) Aplicaciones de sistemas soporte a la decisión en planificación y gestión integradas de cuencas hidrográficas. Marcombo, Barcelona, España pp 51–61Avilés A, Célleri R, Paredes J, Solera A (2015) Evaluation of Markov chain based drought forecasts in an Andean Regulated River basin using the skill scores RPS and GMSS. Water Resour Manag 29(6):1949–1963. https://doi.org/10.1007/s11269-015-0921-2Avilés A, Célleri R, Solera A, Paredes J (2016) Probabilistic forecasting of drought events using Markov chain-and Bayesian network-based models: a case study of an Andean Regulated River Basin. Water 8:1–16Barua S, Ng A, Perera B (2012) Drought assessment and forecasting: a case study on the Yarra River catchment in Victoria, Australia. Aust J Water Resour 15(2):95–108. https://doi.org/10.7158/W10-848.2012.15.2Bazaraa MS, Jarvis JJ, Sherali HD (2011) Linear programming and network flows, fourth Edi. John Wiley & Sons, New JerseyBrown C, Baroang KM, Conrad E et al (2010) IRI technical report 10–15, managing climate risk in water supply systems. Palisades, NYCancelliere A, Di Mauro G, Bonaccorso B, Rossi G (2007) Drought forecasting using the standardized precipitation index. Water Resour Manag 21(5):801–819. https://doi.org/10.1007/s11269-006-9062-yCancelliere A, Nicolosi V, Rossi G (2009) Assessment of drought risk in water supply systems in coping with drought risk in agriculture and water supply systems. Advances in natural and technological hazards research 26. In: Coping with drought risk in agriculture. Springer, pp 93–109. https://doi.org/10.1007/978-1-4020-9045-5_8Chen YD, Zhang Q, Xiao M, Singh VP, Zhang S (2016) Probabilistic forecasting of seasonal droughts in the Pearl River basin, China. Stoch Environ Res Risk Assess 30(7):2031–2040. https://doi.org/10.1007/s00477-015-1174-6Gong G, Wang L, Condon L, Shearman A, Lall U (2010) A simple framework for incorporating seasonal Streamflow forecasts into existing water resource management practices. JAWRA J Am Water Resour Assoc 46(3):574–585. https://doi.org/10.1111/j.1752-1688.2010.00435.xHaro D, Solera A, Paredes J, Andreu J (2014) Methodology for drought risk assessment in within-year regulated reservoir systems. Application to the Orbigo River system (Spain). Water Resour Manag 28(11):3801–3814. https://doi.org/10.1007/s11269-014-0710-3Haro-Monteagudo D, Solera A, Andreu J (2017) Drought early warning based on optimal risk forecasts in regulated river systems: application to the Jucar River basin (Spain). J Hydrol 544:36–45. https://doi.org/10.1016/j.jhydrol.2016.11.022Hashimoto T, Loucks DP, Stedinger JR (1982) Reliability, resiliency, and vulnerability criteria. Water Resour Res 18(1):14–20. https://doi.org/10.1029/WR018i001p00014Hwang Y, Carbone GJ (2009) Ensemble forecasts of drought indices using a conditional residual resampling technique. J Appl Meteorol Climatol 48(7):1289–1301. https://doi.org/10.1175/2009JAMC2071.1Kao S-C, Govindaraju RS (2010) A copula-based joint deficit index for droughts. J Hydrol 380(1-2):121–134. https://doi.org/10.1016/j.jhydrol.2009.10.029Keyantash JA, Dracup JA (2004) An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resour Res 40(9):1–13. https://doi.org/10.1029/2003WR002610Khadr M (2016) Forecasting of meteorological drought using hidden Markov model (case study: the upper Blue Nile river basin, Ethiopia). Ain Shams Eng J 7(1):47–56. https://doi.org/10.1016/j.asej.2015.11.005Madadgar S, Moradkhani H (2013) A Bayesian framework for probabilistic seasonal drought forecasting. J Hydrometeorol 14(6):1685–1706. https://doi.org/10.1175/JHM-D-13-010.1Madadgar S, Moradkhani H (2014) Spatio-temporal drought forecasting within Bayesian networks. J Hydrol 512:134–146. https://doi.org/10.1016/j.jhydrol.2014.02.039Mahmoudzadeh H, Mahmoudzadeh H, Afshar M, Yousefi S (2016) Applying first-order Markov chains and SPI drought index to monitor and forecast drought in West Azerbaijan Province of Iran. Int J Geo Sci Environ Plan 1:44–53. 10.22034/ijgsep.2016.40669Mishra AK, Singh VP (2010) Review paper a review of drought concepts. J Hydrol 391(1-2):202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resour Manag 23(5):881–897. https://doi.org/10.1007/s11269-008-9305-1Ochola WO, Kerkides P (2003) A Markov chain simulation model for predicting critical wet and dry spells in Kenya: Analysing rainfall events in the kano plains. Irrig Drain 52(4):327–342. https://doi.org/10.1002/ird.094Paulo AA, Pereira LS (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21(10):1813–1827. https://doi.org/10.1007/s11269-006-9129-9Phan TD, Smart JCR, Capon SJ, Hadwen WL, Sahin O (2016) Applications of Bayesian belief networks in water resource management: a systematic review. Environ Model Softw 85:98–111. https://doi.org/10.1016/j.envsoft.2016.08.006Pouget L, Roldán T, Gómez M et al (2015) Use of seasonal climate predictions in the water sector—preliminary results from the EUPORIAS project. In: Andreu J, Solera A, Paredes J et al (eds) Drought: research and science-policy interfacing. Taylor & Francis Group, London, UK, p 247Rossi G, Cancelliere A (2013) Managing drought risk in water supply systems in Europe: a review. Int J Water Resour Dev 29(2):272–289. https://doi.org/10.1080/07900627.2012.713848Rossi G, Caporali E, Garrote L (2012) Definition of risk indicators for reservoirs management optimization. Water Resour Manag 26(4):981–996. https://doi.org/10.1007/s11269-011-9842-xSánchez S, Andreu J, Solera A (2001) Gestión de Recursos Hídricos con Decisiones Basadas en Estimación del Riesgo. Universidad Politécnica De Valencia, ValenciaSandoval-Solis S, McKinney DC, Loucks M (2011) Sustainability index for water resources planning and management. J Water Resour Plan Manag 137(5):381–390. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000134Sankarasubramanian A, Lall U, Devineni N, Espinueva S (2009) The role of monthly updated climate forecasts in improving intraseasonal water allocation. J Appl Meteorol Climatol 48(7):1464–1482. https://doi.org/10.1175/2009JAMC2122.1Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35(2):1–7. https://doi.org/10.1029/2007GL032487Staudinger M, Stahl K, Seibert J (2014) A drought index accounting for snow. Water Resour Res 50(10):7861–7872. https://doi.org/10.1002/2013WR015143Sveinsson O, Salas JD, Lane W, Frevert D (2007) Stochastic analysis, modeling, and simulation (SAMS) version 2007, user’s manual. Computing Hydrology Laboratory, Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, ColoradoSvoboda M, Hayes M, Wilhite D, Tadesse T (2004) Recent advances in drought monitoring. Drought Mitig Cent Fac Publ 6:6Vogel RM (2017) Stochastic watershed models for hydrologic risk management. Water Secur 1:28–35. https://doi.org/10.1016/j.wasec.2017.06.001Wilks DS (2011) Statistical methods in the atmospheric sciences, third edit. Academic Press, USAWorld Meteorological Organization (2012) Standardized precipitation index user Guide (M. Svoboda, M. Hayes and D. Wood). (WMO - No. 1090), Geneva

Machine learning models to predict nitrate concentration in a river basin

Aquifer-stream interactions affect the water quality in Mediterranean areas; therefore, the coupling of surface water and groundwater models is generally used to solve water-planning and pollution problems in river basins. However, their use is limited because model inputs and outputs are not spatially and temporally linked, and the data update and fitting are laborious tasks. Machine learning models have shown great potential in water quality simulation, as they can identify the statistical relationship between input and output data without the explicit requirement of knowing the physical processes. This allows the ecological, hydrological, and environmental variables that influence water quality to be analysed with a holistic approach. In this research, feature selection (FS) methods and algorithms of artificial intelligence—random forest (RF) and eXtreme Gradient Boosting (XGBoost) trees—are used to simulate nitrate concentration and determine the main drivers related to nitrate pollution in Mediterranean streams. The developed models included 19 inputs and sampling of nitrate concentration in 159 surface water quality-gauging stations as explanatory variables. The models were trained on 70 percent data, with 30 percent used to validate the predictions. Results showed that the combination of FS method with local knowledge about the dataset is the best option to improve the model's performance, while RF and XGBoost simulate the nitrate concentration with high performance (r = 0.93 and r = 0.92, respectively). The final ranking, based on the relative importance of the variables in the RF and XGBoost models, showed that, regarding nitrogen and phosphorus concentration, the location explained 87 percent of the nitrate variability. RF and XGBoost predicted nitrate concentration in surface water with high accuracy without using conditions or parameters of entry and enabled the observation of different relationships between drivers. Thus, it is possible to identify and delimit zones with a spatial risk of pollution and approaches to implementing solutions

Habitat alteration assessment for the management of environmental flows in regulated basins

[EN] The management of environmental flows is of paramount importance in regulated water resources systems to preserve river ecosystems. This work proposes a methodology to assess habitat alteration in river basins altered by management activities. The methodology is based on the joint application of a basin management model (SIMGES, AQUATOOL) and a model to estimate habitat time series (CAUDECO). CAUDECO is based on the weighted useable areas of the species in their different vital stages that, in turn, depend on the flows in each river stretch and the biological periods of the species. The final output is an indicator of habitat alteration, which is defined ad hoc for this work to relate the habitat suitability under regulated and natural regimes. The methodology was applied to a case study in north-western Spain: the Órbigo River basin. The results in the current management scenario highlight that the ecological flows improve the habitat suitability of several species with respect to natural regime conditions. For instance, the mean values of the habitat time series in the Órbigo River for the brown trout and bermejuela under regulated conditions are 69.6% and 88%; whereas in natural regime they are equal to 55.1% and 72.9%, respectively. Based on these results, eight additional scenarios of ecological flows were tested and their effects on both habitat alteration and water demand reliability were quantified and discussed. It was found that increases in the ecological flows up to 30% do not affect the reliability of water demands and reduce habitat alteration (i.e., lead to values of the habitat alteration indicator closer to 1) for all species present in the river basin. These results highlight that the methodology and indicator of habitat alteration proposed in this paper are useful to support the management of regulated river basins, since they allow assessing the implications of ecological flows on both habitat suitability and reliability of water demands.The authors thank the Spanish Ministry of Science and Innovation (MCIN) and Spanish Research Agency (AEI) for the financial support MCIN/AEI/10.13039/501100011033 to RESPHIRA project (PID2019-106322RB-I00) . RB was partly funded by MCIN/AEI/10.13039/501100011033 through Juan de la Cierva program (IJC2019-038848-I) . Funding for open access charge: CRUE-Universitat Politecnica de Valencia. The authors also thank three anonymous reviewers for their suggestions to improve this work.Pardo-Loaiza, J.; Bergillos, RJ.; Solera Solera, A.; Paredes-Arquiola, J.; Andreu Álvarez, J. (2022). Habitat alteration assessment for the management of environmental flows in regulated basins. Journal of Environmental Management. 319:1-11. https://doi.org/10.1016/j.jenvman.2022.11565311131

Effects of environmental flows on hydrological alteration and reliability of water demands

[EN] This paper presents a methodology to assess the effects of management strategies of environmental flows on the hydrological alteration of river basins on a daily scale. It comprises the collection and analysis of data, the implementation and calibration of a water allocation model; the computation of the natural flow regime; and the estimation, normalization, and aggregation of hydrological alteration indicators to obtain a global indicator of the hydrological alteration. The methodology was applied to a case study in the Iberian Peninsula: The Orbigo River basin, which belongs to the Duero River basin district. For that, three management scenarios were defined: the current scenario, a scenario without any environmental flow and the scenario with the environmental flows initially projected for the period 2022-2027. These scenarios were modelled with the SIMGES water allocation model, which is calibrated in the study site, and the hydrological alterations in four river stretches with different locations and characteristics were assessed. The implications of each environmental flow scenario on the demand reliabilities were also analysed. The global indicator of hydrological alteration obtained in the projected scenario was greater (better) than those of the other two scenarios, but the reliabilities of the water demands were worse. The methodology proposed in this work can be helpful to design environmental flow regimes considering both the effects on the hydrological alteration and the implication on the water demand reliabilities. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).The authors thank the Spanish Ministry of Science and Innovation (MCIN) and Spanish State Research Agency (AEI) for the financial support MCIN/AEI/10.13039/501100011033 to RESPHIRA project (PID2019-106322RB-100). RB was partly funded by MCIN/AEI/10.13039/501100011033 through Juan de la Cierva program (IJC2019038848-I). Funding for open access charge: CRUE-Universitat Politecnica de Valencia. We thank three anonymous reviewers for their suggestions to improve this paper.Monico, V.; Solera Solera, A.; Bergillos, RJ.; Paredes Arquiola, J.; Andreu Álvarez, J. (2022). Effects of environmental flows on hydrological alteration and reliability of water demands. Science of The Total Environment. 810:1-11. https://doi.org/10.1016/j.scitotenv.2021.15163011181