10,289 research outputs found

    Adaptation of WASH Services Delivery to Climate Change and Other Sources of Risk and Uncertainty

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    This report urges WASH sector practitioners to take more seriously the threat of climate change and the consequences it could have on their work. By considering climate change within a risk and uncertainty framework, the field can use the multitude of approaches laid out here to adequately protect itself against a range of direct and indirect impacts. Eleven methods and tools for this specific type of risk management are described, including practical advice on how to implement them successfully

    Time-dependent methods to evaluate the effects of urban sprawl on groundwater quality: a synthesis

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    Freshwater resources are threatened worldwide with unknown and unpredictable fate, due to non-stationarity and change of water cycle dynamics, and increasing demand resulting from population growth and economic expansion. Thus, practical actions, strategies and solutions are necessary to ensure the short-term and long-term provision of adequate, affordable, accessible and safe freshwater supply to meet the needs of the growing human population and ecosystems. Since the mid-1950s, Europe is experiencing the phenomenon of urban sprawl, characterized by an unplanned incremental urban development, no more tied with population growth (EEA 2006). Impacts of urban sprawl threaten both the natural and rural environments and the quality of life for people living in cities, with worsening of air quality, and surface- and groundwater quality and quantity. For the protection of groundwater, the European Union issued a series of Directives (Water Framework Directive, 2000/60/EC; Groundwater Directive, 2006/118/EC) that require member states to achieve a good chemical status of their groundwater bodies and the identification of areas where groundwater suffers increasing trends in contaminant concentrations. In order to cope with EU Directives, a time-dependent approach for groundwater vulnerability assessment is developed to account for both the recent status of groundwater contamination and its evolution in the Po Plain area of Lombardy Region (northern Italy). Such approach takes the advantages of a Bayesian spatial statistical method to assess groundwater vulnerability and satellite scatterometer data to delineate urban areas and monitor their evolution. The proposed approach can determine potential impacts of contamination events on groundwater quality, if policies are maintained at the status quo or if new measures are implemented for safeguarding groundwater resources

    Contributions to predicting contaminant leaching from secondary materials used in roads

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    Slags, coal ashes, and other secondary materials can be used in road construction. Both traditional and secondary materials used in roads may contain contaminants that may leach and pollute the groundwater. The goal of this research was to further the understanding of leaching and transport of contaminants from pavement materials. Towards this goal, a new probabilistic framework was introduced which provided a structured guidance for selecting the appropriate model, incorporating uncertainty, variability, and expert opinion, and interpreting results for decision making. In addition to the framework, specific contributions were made in pavement and embankment hydrology and reactive transport, Bayesian statistics, and aqueous geochemistry of leaching. Contributions on water movement and reactive transport in highways included probabilistic prediction of leaching in an embankment, and scenario analyses of leaching and transport in pavements using HYDRUS2D, a contaminant fate and transport model. Water flow in a Minnesota highway embankment was replicated by Bayesian calibration of hydrological parameters against water content data. Extent of leaching of Cd from a coal fly ash was estimated. Two dimensional simulations of various scenarios showed that salts in the base layer of pavements are depleted within the first year whereas the metals may never reach the groundwater if the pavement is built on adsorbing soils. Aqueous concentrations immediately above the groundwater estimated for intact and damaged pavements can be used for regulators to determine the acceptability of various recycled materials. Contributions in the aqueous geochemistry of leaching included a new modeling approach for leaching of anions and cations from complex matrices such as weathered steel slag. The novelty of the method was its simultaneous inclusion of sorption and solubility controls for multiple analytes. The developed model showed that leaching of SO4, Cr, As, Si, Ca, Mg, and V were controlled by corresponding soluble solids. Leaching of Pb was controlled by Pb(VO4)3 solubility at low pHs and by surface precipitation reactions at high pHs. Leaching of Cd and Zn were controlled by surface complexation and surface precipitation, respectively

    Robust decision analysis for environmental management of groundwater contamination sites

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    In contrast to many other engineering fields, the uncertainties in subsurface processes (e.g., fluid flow and contaminant transport in aquifers) and their parameters are notoriously difficult to observe, measure, and characterize. This causes severe uncertainties that need to be addressed in any decision analysis related to optimal management and remediation of groundwater contamination sites. Furthermore, decision analyses typically rely heavily on complex data analyses and/or model predictions, which are often poorly constrained as well. Recently, we have developed a model-driven decision-support framework (called MADS; http://mads.lanl.gov) for the management and remediation of subsurface contamination sites in which severe uncertainties and complex physics-based models are coupled to perform scientifically defensible decision analyses. The decision analyses are based on Information Gap Decision Theory (IGDT). We demonstrate the MADS capabilities by solving a decision problem related to optimal monitoring network design.Comment: This paper has been withdrawn by the author due to a crucial sign error in equations 7 and

    A Manifesto for the Equifinality Thesis.

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    This essay discusses some of the issues involved in the identification and predictions of hydrological models given some calibration data. The reasons for the incompleteness of traditional calibration methods are discussed. The argument is made that the potential for multiple acceptable models as representations of hydrological and other environmental systems (the equifinality thesis) should be given more serious consideration than hitherto. It proposes some techniques for an extended GLUE methodology to make it more rigorous and outlines some of the research issues still to be resolved

    Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring

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    Fitting statistical models to spatiotemporal data requires finding the right balance between imposing smoothness and following the data. In the context of P-splines, we propose a Bayesian framework for choosing the smoothing parameter which allows the construction of fully-automatic data-driven methods for fitting flexible models to spatiotemporal data. An implementation, which is highly computationally efficient and which exploits the sparsity of the design and penalty matrices, is proposed. The findings are illustrated using a simulation study and two examples, all concerned with the modelling of contaminants in groundwater. This suggests that the proposed strategy is more stable that competing methods based on the use of criteria such as GCV and AIC

    Stochastic hydro-economic model for groundwater quality management using Bayesian networks

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    A strong normative development in Europe, including the Nitrate Directive (1991) and the Water Framework Directive (WFD) (2000), has been promulgated. The WFD states that all water bodies have to reach a good quantitative and chemical status by 2015. It is necessary to consider different objectives, often in conflict, for tackling a suitable assessment of the impacts generated by water policies aimed to reduce nitrate pollution in groundwater. For that, an annual lumped probabilistic model based on Bayesian networks (BNs) has been designed for hydro-economic modelling of groundwater quality control under uncertain conditions. The information introduced in the BN model comes from different sources such as previous groundwater flow and mass transport simulations, hydro-economic models, stakeholders and expert opinion, etc. The methodology was applied to the El Salobral-Los Llanos aquifer unit within the 'Easter Mancha' groundwater body, which is one of the largest aquifers in Spain (7,400 km(2)), included in the Júcar River Basin. Over the past 30 years, socioeconomic development within the region has been mainly depending on intensive use of groundwater resources for irrigating crops. This has provoked a continuous groundwater level fall in the last two decades and significant streamflow depletion in the connected Júcar River. This BN model has proved to be a robust Decision Support System for helping water managers in the decision making process.The authors gratefully acknowledge the contributions of the following people and organizations. The study has been partially supported by the European Community 7th Framework Project GENESIS (226536) on groundwater systems and from the subprogram Juan de la Cierva (2010, 2011) of the Spanish Ministry of Science and Innovation as well as from the Plan Nacional I + D + i 2008-2011 of the Spanish Ministry of Science and Innovation (subprojects CGL2009-13238-C02-01 and CGL2009-13238-C02-02). Finally, thanks to the Jucar River Basin Authority (CHJ), IDR of Univ. of Castilla-La Mancha, the Junta Central de Regantes de la Mancha Oriental, and all the different stakeholders who have collaborated on the data and information provided in this research.Molina, J.; Pulido-Velazquez, M.; Llopis Albert, C.; Peña Haro, S. (2013). Stochastic hydro-economic model for groundwater quality management using Bayesian networks. Water Science and Technology. 67(3):579-586. https://doi.org/10.2166/wst.2012.598S57958667
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