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

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Freshwater ecosystem services in mining regions : modelling options for policy development support

The ecosystem services (ES) approach offers an integrated perspective of social-ecological systems, suitable for holistic assessments of mining impacts. Yet for ES models to be policy-relevant, methodological consensus in mining contexts is needed. We review articles assessing ES in mining areas focusing on freshwater components and policy support potential. Twenty-six articles were analysed concerning (i) methodological complexity (data types, number of parameters, processes and ecosystem-human integration level) and (ii) potential applicability for policy development (communication of uncertainties, scenario simulation, stakeholder participation and management recommendations). Articles illustrate mining impacts on ES through valuation exercises mostly. However, the lack of ground-and surface-water measurements, as well as insufficient representation of the connectivity among soil, water and humans, leave room for improvements. Inclusion of mining-specific environmental stressors models, increasing resolution of topographies, determination of baseline ES patterns and inclusion of multi-stakeholder perspectives are advantageous for policy support. We argue that achieving more holistic assessments exhorts practitioners to aim for high social-ecological connectivity using mechanistic models where possible and using inductive methods only where necessary. Due to data constraints, cause-effect networks might be the most feasible and best solution. Thus, a policy-oriented framework is proposed, in which data science is directed to environmental modelling for analysis of mining impacts on water ES

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous