Stochastic hydro-economic modeling for optimal management of agricultural groundwater nitrate pollution under hydraulic conductivity uncertainty

[EN] In decision-making processes, reliability and risk aversion play a decisive role. This paper presents a framework for stochastic optimization of control strategies for groundwater nitrate pollution from agriculture under hydraulic conductivity uncertainty. The main goal is to analyze the influence of uncertainty in the physical parameters of a heterogeneous groundwater diffuse pollution problem on the results of management strategies, and to introduce methods that integrate uncertainty and reliability in order to obtain strategies of spatial allocation of fertilizer use in agriculture. A hydro-economic modeling approach is used for obtaining the allocation of fertilizer reduction that complies with the maximum permissible concentration in groundwater while minimizes agricultural income losses. The model is based upon nonlinear programming and groundwater flow and mass transport numerical simulation, condensed on a pollutant concentration response matrix. The effects of the hydraulic conductivity uncertainty on the allocation of nitrogen reduction among agriculture pollution sources are analyzed using four formulations: Monte Carlo simulation with pre-assumed parameter field, Monte Carlo optimization, stacking management, and mixed-integer stochastic model with predefined reliability. The formulations were tested in an illustrative example for 100 hydraulic conductivity realizations with different variance. The results show a high probability of not meeting the groundwater quality standards when deriving a policy from just a deterministic analysis. To increase the reliability several realizations can be optimized at the same time. By using a mixed-integer stochastic formulation, the desired reliability level of the strategy can be fixed in advance. The approach allows deriving the trade-offs between the reliability of meeting the standard and the net benefits from agricultural production. In a risk-averse decision making, not only the reliability of meeting the standards counts, but also the probability distribution of the maximum pollutant concentrations. A sensitivity analysis was carried out to assess the influence of the variance of the hydraulic conductivity fields on the strategies. The results show that the larger the variance, the greater the range of maximum nitrate concentrations and the worst case (or maximum value) that could be reached for the same level of reliability. © 2011 Elsevier Ltd.The study has been partially supported by the European Community 7th Framework Project GENESIS (226536) on groundwater systems and 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). The authors thank the anonymous reviewers for their suggestions for improving the paper.Peña Haro, S.; Pulido-Velazquez, M.; Llopis Albert, C. (2011). Stochastic hydro-economic modeling for optimal management of agricultural groundwater nitrate pollution under hydraulic conductivity uncertainty. Environmental Modelling and Software. 26(8):999-1008. https://doi.org/10.1016/j.envsoft.2011.02.010S999100826

A hydro-economic modeling framework for optimal management of groundwater nitrate pollution from agriculture

La contaminación difusa por nitratos de las aguas subterráneas, la cual es principalmente originada por la agricultura, es una creciente preocupación en casi cualquier parte del mundo. Esto ha provocado el desarrollado de normativas; en Europa, en 1991 se estableció la Directiva de Nitratos y el año 2000 la Directiva Europea Marco del Agua (DMA). La DMA establece que las masas de agua deben alcanzar el buen estado en el año 2015, además reconoce el rol que la economía puede tener en alcanzar los objetivos ecológicos y ambientales. En este trabajo se presenta un modelo hidro-económico que sugiere la gestión óptima de fertilizantes para controlar la contaminación por nitratos de las agua subterráneas. El modelo holístico de optimización determina la distribución espacio-temporal de la tasa de aplicación de fertilizantes que maximiza los beneficios netos en la agricultura, limitada por los requerimientos de calidad en el agua subterránea en diferentes puntos de control. El modelo relaciona la aplicación de fertilizantes con las concentraciones de nitratos en el agua subterránea mediante el uso de modelos agronómicos, de simulación del flujo y transporte en el agua subterránea, con los cuales se generan soluciones unitarias que son integradas en matrices de respuesta (RM). Las RM dentro del modelo de gestión permiten simular la evolución de la concentración de nitratos en el agua subterránea mediante superposición en diferentes puntos de control a largo del tiempo, debido a la emisión de contaminantes en diferentes zonas distribuidas en el espacio y variables en el tiempo. Los beneficios de la agricultura se determinan a través de funciones de producción y el precio de los cultivos. El modelo desarrollado se aplicó a un acuífero sintético. Se obtuvo la aplicación óptima de fertilizantes para problemas con diferentes condiciones iniciales, horizontes de planeación y tiempos de recuperación.Peña Haro, S. (2010). A hydro-economic modeling framework for optimal management of groundwater nitrate pollution from agriculture [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7483Palanci

A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty

In this paper a methodology for the stochastic management of groundwater quality problems is presented, which can be used to provide agricultural advisory services. A stochastic algorithm to solve the coupled flow and mass transport inverse problem is combined with a stochastic management approach to develop methods for integrating uncertainty; thus obtaining more reliable policies on groundwater nitrate pollution control from agriculture. The stochastic inverse model allows identifying non-Gaussian parameters and reducing uncertainty in heterogeneous aquifers by constraining stochastic simulations to data. The management model determines the spatial and temporal distribution of fertilizer application rates that maximizes net benefits in agriculture constrained by quality requirements in groundwater at various control sites. The quality constraints can be taken, for instance, by those given by water laws such as the EU Water Framework Directive (WFD). Furthermore, the methodology allows providing the trade-off between higher economic returns and reliability in meeting the environmental standards. Therefore, this new technology can help stakeholders in the decision-making process under an uncertainty environment. The methodology has been successfully applied to a 2D synthetic aquifer, where an uncertainty assessment has been carried out by means of Monte Carlo simulation techniques. (c) 2014 Elsevier B.V. All rights reserved.Llopis Albert, C.; Palacios Marqués, D.; Merigó, JM. (2014). A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty. Journal of Hydrology. 511:10-16. doi:10.1016/j.jhydrol.2014.01.021S101651

Decision making under uncertainty in environmental projects using mathematical simulation modeling

The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-016-6135-yIn decision-making processes, reliability and risk aversion play a decisive role. The aim of this study is to perform an uncertainty assessment of the effects of future scenarios of sustainable groundwater pumping strategies on the quantitative and chemical status of an aquifer. The good status of the aquifer is defined according to the terms established by the EU Water Framework Directive (WFD). A decision support systems (DSS) is presented, which makes use of a stochastic inverse model (GC method) and geostatistical approaches to calibrate equally likely realizations of hydraulic conductivity (K) fields for a particular case study. These K fields are conditional to available field data, including hard and soft information. Then, different future scenarios of groundwater pumping strategies are generated, based on historical information and WFD standards, and simulated for each one of the equally likely K fields. The future scenarios lead to different environmental impacts and levels of socioeconomic development of the region and, hence, to a different degree of acceptance among stakeholders. We have identified the different stakeholders implied in the decision-making process, the objectives pursued and the alternative actions that should be considered by stakeholders in a public participation project (PPP). The MonteCarlo simulation provides a highly effective way for uncertainty assessment and allows presenting the results in a simple and understandable way even for non-experts stakeholders. The methodology has been successfully applied to a real case study and lays the foundations to performa PPP and stakeholders' involvement in a decisionmaking process as required by the WFD. The results of the methodology can help the decision-making process to come up with the best policies and regulations for a groundwater system under uncertainty in groundwater parameters and management strategies and involving stakeholders with conflicting interests.Llopis Albert, C.; Palacios Marqués, D.; Merigó -Lindahl, JM. (2016). Decision making under uncertainty in environmental projects using mathematical simulation modeling. Environmental Earth Sciences. 75(19):1-11. doi:10.1007/s12665-016-6135-yS1117519Arhonditsis GB, Perhar G, Zhang W, Massos E, Shi M, Das A (2008) Addressing equifinality and uncertainty in eutrophication models. Water Resour Res 44:W01420. doi: 10.1029/2007WR005862Capilla JE, Llopis-Albert C (2009) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. 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Comput Geosci 16(4):395–440Harbaugh AW, Banta ER, Hill MC and McDonald MG (2000) MODFLOW- 2000, The US geological survey modular groundwater model-user guide to modularization concepts and the groundwater flow process. US Geol. Surv. Open-File Rep 00–92, 12Hu LY (2000) Gradual deformation and iterative calibration of Gaussian related stochastic models. Math Geol 32(1):87–108Jagelke J, Barthel R (2005) Conceptualization and implementation of a regional groundwater model for the Neckar catchment in the framework of an integrated regional model. Adv Geosci 5:105–111Llopis-Albert C (2008) Stochastic inverse modeling conditional to flow, mass transport and secondary information. Universitat Politècnica de València, València. ISBN 978-84-691-9796-7Llopis-Albert C, Capilla JE (2009a) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. Demonstration on a synthetic aquifer. J Hydrol 371:53–55. doi: 10.1016/j.jhydrol.2009.03.014Llopis-Albert C, Capilla JE (2009b) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. Application to the macrodispersion experiment (MADE-2) site, on Columbus air force base in Mississippi (USA). J Hydrol 371:75–84. doi: 10.1016/j.jhydrol.2009.03.016Llopis-Albert C, Capilla JE (2010a) Stochastic simulation of non-gaussian 3D conductivity fields in a fractured medium with multiple statistical populations: a case study. J Hydrol Eng 15(7):554–566. doi: 10.1061/(ASCE)HE.1943-5584.0000214Llopis-Albert C, Capilla JE (2010b) Stochastic inverse modeling of hydraulic conductivity fields taking into account independent stochastic structures: a 3D case study. J Hydrol 391:277–288. doi: 10.1016/j.jhydrol.2010.07.028Llopis-Albert C, Pulido-Velazquez D (2014) Discussion about the validity of sharp-interface models to deal with seawater intrusion in coastal aquifers. Hydrol Process 28(10):3642–3654Llopis-Albert C, Pulido-Velazquez D (2015) Using MODFLOW code to approach transient hydraulic head with a sharp-interface solution. Hydrol Process 29(8):2052–2064. doi: 10.1002/hyp.10354Llopis-Albert C, Palacios-Marqués D, Merigó JM (2014) A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty. J Hydrol 511:10–16. doi: 10.1016/j.jhydrol.2014.01.021Llopis-Albert C, Merigó JM, Palacios-Marqués D (2015) Structure adaptation in stochastic inverse methods for integrating information. Water Resour Manage 29(1):95–107. doi: 10.1007/s11269-014-0829-2Llopis-Albert C, Merigó JM, Xu Y (2016) A coupled stochastic inverse/sharp interface seawater intrusion approach for coastal aquifers under groundwater parameter uncertainty. J Hydrol 540:774–783. doi: 10.1016/j.jhydrol.2016.06.065McDonald MG and Harbaugh AW (1988) A modular three-dimensional finite-difference groundwater flow model. US geological survey technical manual of water resources investigation, Book 6, US geological survey, Reston, Virginia, 586Molina JL, Pulido-Velazquez M, Llopis-Albert C, Peña-Haro S (2013) Stochastic hydro-economic model for groundwater quality management using Bayesian networks. Water Sci Technol 67(3):579–586. doi: 10.2166/wst.2012.598Peña-Haro S, Llopis-Albert C, Pulido-Velazquez M (2010) Fertilizer standards for controlling groundwater nitrate pollution from agriculture: El Salobral-Los Llanos case study, Spain. J Hydrol 392:174–187. doi: 10.1016/j.jhydrol.2010.08.006Peña-Haro S, Pulido-Velazquez M, Llopis-Albert C (2011) Stochastic hydro-economic modeling for optimal management of agricultural groundwater nitrate pollution under hydraulic conductivity uncertainty. 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Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators

[EN] There is a growing interest in environmental policies about how to implement public participation engagement in the context of water resources management. This paper presents a robust methodology, based on ordered weighted averaging (OWA) operators, to conflict resolution decision-making problems under uncertain environments due to both information and stakeholders' preferences. The methodology allows integrating heterogeneous interests of the general public and stakeholders on account of their different degree of acceptance or preference and level of influence or power regarding the measures and policies to be adopted, and also of their level of involvement (i.e., information supply, consultation and active involvement). These considerations lead to different environmental and socio-economic outcomes, and levels of stakeholders' satisfaction. The methodology establishes a prioritization relationship over the stakeholders. The individual stakeholders' preferences are aggregated through their associated weights, which depend on the satisfaction of the higher priority decision maker. The methodology ranks the optimal management strategies to maximize the stakeholders' satisfaction. It has been successfully applied to a real case study, providing greater fairness, transparency, social equity and consensus among actors. Furthermore, it provides support to environmental policies, such as the EU Water Framework Directive (WFD), improving integrated water management while covering a wide range of objectives, management alternatives and stakeholders.Llopis Albert, C.; Merigó-Lindahl, JM.; Liao, H.; Xu, Y.; Grima-Olmedo, J.; Grima-Olmedo, C. (2018). Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators. 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Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNNEC methods

Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85–0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R2 = 0.71) that were relatively similar to RF (RMSE = 10.41, R2 = 0.72) and higher than SVM (RMSE = 13.28, R2 = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions

Geochemical reactivity of subsurface sediments as potential buffer to anthropogenic inputs: a strategy for regional characterization in the Netherlands

Geochemical reactivity of subsurface sediments as potential buffer to anthropogenic inputs: a strategy for regional characterization in the Netherland

A hydro-economic modelling framework for optimal management of groundwater nitrate pollution from agriculture

A hydro-economic modelling framework is developed for determining optimal management of groundwater nitrate pollution from agriculture. A holistic optimization model determines the spatial and temporal fertilizer application rate that maximizes the net benefits in agriculture constrained by the quality requirements in groundwater at various control sites. Since emissions (nitrogen loading rates) are what can be controlled, but the concentrations are the policy targets, we need to relate both. Agronomic simulations are used to obtain the nitrate leached, while numerical groundwater flow and solute transport simulation models were used to develop unit source solutions that were assembled into a pollutant concentration response matrix. The integration of the response matrix in the constraints of the management model allows simulating by superposition the evolution of groundwater nitrate concentration over time at different points of interest throughout the aquifer resulting from multiple pollutant sources distributed over time and space. In this way, the modelling framework relates the fertilizer loads with the nitrate concentration at the control sites. The benefits in agriculture were determined through crop prices and crop production functions. This research aims to contribute to the ongoing policy process in the Europe Union (the Water Framework Directive) providing a tool for analyzing the opportunity cost of measures for reducing nitrogen loadings and assessing their effectiveness for maintaining groundwater nitrate concentration within the target levels. The management model was applied to a hypothetical groundwater system. Optimal solutions of fertilizer use to problems with different initial conditions, planning horizons, and recovery times were determined. The illustrative example shows the importance of the location of the pollution sources in relation to the control sites, and how both the selected planning horizon and the target recovery time can strongly influence the limitation of fertilizer use and the economic opportunity cost for meeting the environmental standards. There is clearly a trade-off between the time horizon to reach the standards (recovery time) and the economic losses from nitrogen use reductions. (C) 2009 Elsevier B.V. All rights reserved.The authors thank the Editor, Geoff Syme, and two anonymous reviewers for their detailed and helpful comments on improving the paper. Support for this research was provided by the Mexican Ministry of Science and Technology (CONACyT).Peña Haro, S.; Pulido-Velazquez, M.; Sahuquillo Herráiz, A. (2009). A hydro-economic modelling framework for optimal management of groundwater nitrate pollution from agriculture. Journal of Hydrology. 373(1-2):193-203. doi:10.1016/j.jhydrol.2009.04.024S1932033731-