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Water scarcity is an increasingly recurring problem for irrigated agriculture in Mediterranean regions. It is, therefore, necessary to establish technical and financial measures to enable irrigators to deal with this problem. This study presents a new index-based drought insurance scheme in an irrigation district in the Jucar river basin in Spain, a highly regulated water system. Three insurance scheme options were evaluated and, the values of the fair risk premiums, the maximum compensation, and the deductible franchise were established. These insurance schemes were designed in agreement with the preexisting drought system operating rules to reduce moral hazard and adverse selection. Risk-reducing and effective evaluation methods were used to determine the insurance coverage's viability for irrigators: standard deviation gross margin, minimum gross margin, and RMSL. The proposed insurances were also evaluated using synthetic hydrological time series generated with a stochastic ARMA model through a basin-wide water resource simulation model developed in the DSS Shell AQUATOOL. Financial indicators, such as the basis risk and claim ratio were applied to analyze the economic feasibility for insurance companies. The results show that a suitable and efficient option is an early-bird contract combined with a trigger of emergency or alert state in a multi-year contract. This type of specialized insurance helps to fill the existing gap in traditional insurance schemes for irrigated crops and offered additional coverage to farmers under drought and water scarcity conditions. [ABSTRACT FROM AUTHOR]

Although many recent studies have quantified the potential effects of climate change on water resource systems, the scientific community faces now the challenge of developing methods for assessing and selecting climate change adaptation options. This paper presents a method for assessing impacts and adaptation strategies to global change in a river basin system at different temporal horizons using a hydroeconomic model. First, a multiobjective analysis selects climate change projections based on the fitting of the climate models to the historical conditions for the historical period. Inflows for climate change scenarios are generated using calibrated rainfall-runoff models, perturbing observed meteorological time series according to the projected anomalies in mean and standard deviation. Demands are projected for the different scenarios and characterized using economic demand curves. With the new water resource and demand scenarios, the impact of global change on system performance is assessed using a hydro-economic model with reliability and economic indices. A new economic loss index is defined to assess the economic equity of the system. Selected adaptation strategies are simulated to compare performance with the business-as-usual scenario. The approach is applied to the Jucar River water resource system, in eastern Spain, using climate projections from the European Union (EU) ENSEMBLES project. Results show that the system is vulnerable to global change, especially over the long term, and that adaptation actions can save €3-65 million=year. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative framework to couple historical records with expert knowledge and criteria to define a decision support system (DSS) that supports the seasonal operation of the reservoirs of the Jucar River system. The framework relies on the codevelopment of a DSS tool that is able to explicitly reproduce the decision-making processes and criteria considered by the system operators. Fuzzy logic is used to derive the implicit operating rules followed by the managers, and is based on historical decisions and expert knowledge obtained in the codevelopment process, combining both sources of information. Fuzzy regression is used to forecast future inflows based on the meteorological and hydrological variables considered by the system operators in their decisions on reservoir operation. The DSS was validated against historical records. The developed framework and tools offer the system operators a way to predefine a set of feasible ex ante management decisions, and to explore the consequences associated with any single choice. In contrast with other approaches, the fuzzy-based method used in this study is able to embed inflow uncertainty and its effects in the definition of the decisions on the system operation. Furthermore, the method is flexible enough to be applied to other water resource systems. [ABSTRACT FROM AUTHOR]

Economic theory suggests that water pricing can contribute to efficient management of water scarcity. The European Union (EU) Water Framework Directive (WFD) is a major legislative effort to introduce the use of economic instruments to encourage efficient water use and achieve environmental management objectives. However, the design and implementation of economic instruments for water management, including water pricing, has emerged as a challenging aspect of WFD implementation. This study demonstrates the use of a systems analysis approach to designing and comparing two economic approaches to efficient management of groundwater and surface water given EU WFD ecological flow requirements. Under the first approach, all wholesale water users in a river basin face the same volumetric price for water. This water price does not vary in space or in time, and surface water and groundwater are priced at the same rate. Under the second approach, surface water is priced using a volumetric price, while groundwater use is controlled through adjustments to the price of energy, which is assumed to control the cost of groundwater pumping. For both pricing policies, optimization is used to identify optimal prices, with the objective of maximizing welfare while reducing human water use in order to meet constraints associated with EU WFD ecological and groundwater sustainability objectives. The systems analysis approach demonstrates the successful integration of economic, hydrologic, and environmental components into an integrated framework for the design and testing of water pricing policies. In comparison to the first pricing policy, the second pricing policy, in which the energy price is used as a surrogate for a groundwater price, shifts a portion of costs imposed by higher water prices from low-value crops to high-value crops and from small urban/domestic locations to larger locations. Because growers of low-value crops will suffer the most from water price increases, the use of energy costs to control groundwater use offers the advantage of reducing this burden. [ABSTRACT FROM AUTHOR]

Summary: Future water management will shift from building new water supply systems to better operating existing ones. The variation of water values in time and space will increasingly motivate efforts to address water scarcity and reduce water conflicts. Hydro-economic models represent spatially distributed water resource systems, infrastructure, management options and economic values in an integrated manner. In these tools water allocations and management are either driven by the economic value of water or economically evaluated to provide policy insights and reveal opportunities for better management. A central concept is that water demands are not fixed requirements but rather functions where quantities of water use at different times have varying total and marginal economic values. This paper reviews techniques to characterize the economic value of water use and include such values in mathematical models. We identify the key steps in model design and diverse problems, formulations, levels of integration, spatial and temporal scales, and solution techniques addressed and used by over 80 hydro-economic modeling efforts dating back 45-years from 23 countries. We list current limitations of the approach, suggest directions for future work, and recommend ways to improve policy relevance. [Copyright &y& Elsevier]

Summary: 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. [Copyright &y& Elsevier]

Abstract: Incentive pricing programs have potential to promote economically efficient water use patterns and provide a revenue source to compensate for environmental damages. However, incentive pricing may impose disproportionate costs and aggravate poverty where high prices are levied for basic human needs. This paper presents an analysis of a two-tiered water pricing system that sets a low price for subsistence needs, while charging a price equal to marginal cost, including environmental cost, for discretionary uses. This pricing arrangement can promote efficient and sustainable water use patterns, goals set by the European Water Framework Directive, while meeting subsistence needs of poor households. Using data from the Rio Grande Basin of North America, a dynamic nonlinear program, maximizes the basin''s total net economic and environmental benefits subject to several hydrological and institutional constraints. Supply costs, environmental costs, and resource costs are integrated in a model of a river basin''s hydrology, economics, and institutions. Three programs are compared: (1) Law of the River, in which water allocations and prices are determined by rules governing water transfers; (2) marginal cost pricing, in which households pay the full marginal cost of supplying treated water; (3) two-tiered pricing, in which households'' subsistence water needs are priced cheaply, while discretionary uses are priced at efficient levels. Compared to the Law of the River and marginal cost pricing, two-tiered pricing performs well for efficiency and adequately for sustainability and equity. Findings provide a general framework for formulating water pricing programs that promote economically and environmentally efficient water use programs while also addressing other policy goals. [Copyright &y& Elsevier]

Abstract: Combined hydro-economic models of river basins are fundamental tools for assessing management and infrastructure strategies to improve the economic efficiency of water use in a context of competition over scarce water resources. Integrated hydro-economic models have to be capable to properly reproduce the physical behavior of the system, with a realistic representation of the different surface and groundwater resources, including their interaction, and the spatial and temporal variability of resource availability. On the other hand, such models must incorporate the value of water for different urban, agricultural and industrial uses and users. Economic values for water use are defined according to the marginal residual value of water for production (for agricultural and industrial uses) or the aggregated willingness-to-pay (WTP) for urban supply and other final water uses. In this paper, we present a systematic approach to estimate the marginal economic value of surface and groundwater resources at different locations within a complex water resources system. Based on a holistic conjunctive optimization model applied to the Adra river system in Spain we asses the total and marginal opportunity costs of capacity and operation constraints, including the opportunity cost of imposing environmental constraints on water use as foreseen in future Spanish water policy following the implementation of the European Water Framework Directive. The resulting opportunity costs provide important information to water managers about economic inefficiencies of current water allocation policy or infrastructure design, and about the resource opportunity costs to be considered in the design of efficient pricing policies in regions with water scarcity issues. [Copyright &y& Elsevier]

Abstract: The structure of the stream–aquifer flow exchange solution, derived from the linear groundwater flow equation, reveals that stream–aquifer interaction can be conceptualized as the drainage of an infinite number of independent linear reservoirs. Discharge coefficients are the eigenvalues of an eigenproblem. This applies to any aquifer with linear behavior (i.e. linear response of head to stress), regardless of its heterogeneity, geometry, or boundary conditions. The eigenproblem can be analytically solved for homogeneous aquifers with simple geometry and boundary conditions. Numerical methods with spatial discretization of the aquifer domain are required for more complex conditions. Eigenfunctions (in the analytical case) or eigenvector matrices (for the spatially discretized problem) are preprocessed, condensing the stream–aquifer system performance. In most practical problems, stream–aquifer flow exchange can be accurately reproduced with few linear reservoirs. Applying the principle of superposition and solving the corresponding boundary-value problem, analytical solutions are derived for assessing the streamflow depletion rate induced by pumping on an aquifer of finite (rectangular) dimension. Simple and operative explicit state equations are obtained for both perfect and partial stream–aquifer hydraulic connection, which allow straightforward simulation of stream–aquifer interaction within conjunctive use management models. The results are compared with other analytical solutions for semi-infinite aquifers and numerical simulation, revealing that the impact of the lateral boundary on streamflow depletion assessment can be significant, which is in agreement with findings of previous works. The model also yields the baseflow recession of a streamflow hydrograph provided by the drainage of a non-stressed aquifer as the sum of time-dependent decaying exponentials, which represents the discharge from the linear reservoirs of the conceptualization. [Copyright &y& Elsevier]

Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water‐stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation‐optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization–simulation–optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under:Engineering Water > Water, Health, and SanitationEngineering Water > Methods [ABSTRACT FROM AUTHOR]

The Iberian Peninsula is a climate change hotspot, where the temperature is increasing faster than the global annual mean surface temperature, with the largest reduction of precipitation. Consequently, freshwater availability is expected to decrease substantially. In this context, freshwater systems are especially vulnerable in terms of meeting the water demands and ecosystem requirements we know today. In this paper, we present an extension of the eco-engineering decision scaling (EEDS) method to explore trade-offs in agricultural and ecologic metrics at the catchment scale across a range of unknown future hydrological and climate states. The extended EEDS method evaluates current water resource management rules focusing on agricultural and ecologic objectives, identifies climate hazards that make the system fail and assesses climate risk in three time horizons for the design of adaptation measures. The case study is the Serpis River basin, Spain, where 72% of available water is used for agricultural purposes. [ABSTRACT FROM AUTHOR]

Abstract Integrating processes of water and energy interdependence in water systems can improve the understanding of the tradeoffs between water and energy in management and policy. This study presents a development of an integrated water resources management model that includes water-related energy use and GHG emissions. We apply the model to a simplified representation of California's water system. Accounting for water demands from cities, agriculture, environment and the energy sector, and combining a surface water management model with a simple groundwater model, the model optimizes water use across sectors during shortages from an economic perspective, calculating the associated energy use and electricity generation for each water demand. The results of California's water system show that urban end-uses account for most GHG emissions of the entire water cycle, but large water conveyance produces significant peaks over the summer season. Different policy scenarios show the significant tradeoffs between water, energy, and GHG emissions. Highlights • We develop an integrated water-energy-GHG emissions model. • Water-related energy use and/or production are included for each water demand. • Water allocation is modeled using a mixed simulation/optimization algorithm. • 13% of electricity and 5% of GHG emissions are related with water use in California. • Several scenarios show the tradeoffs between water, energy, and GHG emissions. [ABSTRACT FROM AUTHOR]

This research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a "proof of concept" that assesses the predictive capacity of the approach. The analytical power of CR revealed the logical temporal structure that defines the general behaviour of inflows, which was latent in the historical records. This allowed identifying/quantifying, through a dependence matrix, two temporal runoff fractions, one due to time and the other not. Finally, a predictive model for each temporal fraction was implemented, evaluating its forecasting skills through mean absolute error and root mean square error. This was applied to the reservoirs that supply water to the city of Ávila (Spain), whose watersheds present strong independent temporal behaviour. These results open new possibilities for developing predictive hydrological models within a CR modelling framework. [ABSTRACT FROM AUTHOR]

Shaping global change adaptation strategy in water resource systems requires an interdisciplinary approach to deal with the multiple dimensions of the problem. The modelling framework presented integrates climate, economic, agronomic and hydrological scenarios to design a programme of adaptation measures at the river basin scale. Future demand scenarios, combined with a down-scaled climate scenario, provide the basis to estimate the demand and water resources in 2030. A least-cost river basin optimisation model is then applied to select adaptation measures ensuring that environmental and supply management goals are achieved. In the Orb river basin (France), the least-cost portfolio selected suggests mixing demand and supply side measures to adapt to global change. Trade-offs among the cost of the programme of measures, the deficit in agricultural water supply and the level of environmental flows are investigated. The challenges to implement such interdisciplinary approaches in the definition of adaptation strategies are finally discussed. [ABSTRACT FROM AUTHOR]

Economic instruments can be used to control groundwater nitrate pollution due to the intensive use of fertilizers in agriculture. In order to test their efficiency on the reduction of nitrate leaching, we propose an approach based on the combined use of production and pollution functions to derive the impacts on the expected farmer response of these instruments. Some of the most important factors influencing nitrate leaching and crop yield are the type of soil and the climatic conditions. Crop yield and nitrate leaching responses to different soil and climatic conditions were classified by means of a cluster analysis, and crops located in different areas but with similar response were grouped for the analysis. We use a spatial economic optimization model to evaluate the potential of taxes on nitrogen fertilizers, water prices, and taxes on nitrate emissions to reduce nitrate pollution, as well as their economic impact in terms of social welfare and farmers' net benefits. The method was applied to the Mancha Oriental System (MOS) in Spain, a large area with different soil types and climatic conditions. We divided the study area into zones of homogeneous crop production and nitrate leaching properties. Results show spatially different responses of crop growth and nitrate leaching, proving how the cost-effectiveness of pollution control instruments is contingent upon the spatial heterogeneities of the problem. [ABSTRACT FROM AUTHOR]

• Water resources and habitat for eels will decrease in every scenario. • Increasing irrigation efficiency will fail in the pessimistic scenario. • Adaptation and agricultural redistribution will be similar in other scenarios. • Invasive bleak habitat and competition will increase in the pessimistic scenario. Damming and flow regulation ensure water availability during dearth periods, but they cause impacts on inhabiting biota and facilitate the establishment of Invasive Alien Species (IAS). Setting environmental flows (e-flows) has proven fundamental to ensure good ecological status of rivers, battle IAS, and sustain ecosystem services. However, ongoing climate change is further straining water resources availability and the fulfilment of e-flows in Spain, which has compelled River Basin Management Plans (RBMPs) to include specific adaptation measures. In this study, we assessed adaptation strategies addressing land use and climate change in the Serpis River (Eastern Spain), based on increasing irrigation efficiency (i.e. moving from gravity to drip irrigation). The evaluation considered two CMIP6 climate change scenarios (Shared Socioeconomic Pathways SSP1-2.6 and SSP5-8.5) for two time horizons (short term 2015–2040 and mid-term 2041–2070), together with three land use change scenarios (changes in irrigated areas and technologies). Future estimations included reservoir flow releases, e-flows fulfilment, agricultural benefits, and changes in the suitable habitat and potential competition for two native (Eastern Iberian chub Squalius valentinus and European eel Anguilla anguilla) and two invasive (common bleak Alburnus alburnus and pumpkinseed Lepomis gibbosus) fish species. Predictions indicated a decrease in the suitable habitat for eel and Eastern Iberian chub, but the established e-flows will be ensured. Although the maximum benefits will rarely be achieved, on average the modernisation expected in the RBMP would sustain agricultural incomes in the optimistic scenario (SSP1-2.6) and for the short term of the pessimistic scenario (SSP5-8.5), showing similar effects than the ongoing redistribution of agricultural plots. Habitat and competition with invasive species will experience little changes due to the strong flow regulation, although the invasive bleak would prevail in the mid-term of the most pessimistic scenario. These results highlight the necessity of proactive adaptation measures beyond those described in the RBMPs (e.g. forest management or crop substitution) to increase water availability and sustain agricultural benefits, in particular for the most pessimistic scenario. They also underscore the difficulties to counteract invasive species through streamflow management under water scarcity scenarios. [ABSTRACT FROM AUTHOR]

• CIRFs are useful for exploring risks of system failure under climate change. • Synthetic streamflow series are generated using SA and linked to climate conditions. • Logistic regression relates system responses to climate drivers. • System failure defined according to performance (reliability) and threshold. • LR model detects climate conditions which would require adaptation. Climate Impact Response Functions (CIRFs) can be useful for exploring potential risks of system failure under climate change. The performance of a water resource system can be synthesized through a CIRF that relates climate conditions to system behavior in terms of a specified threshold of deliveries to demands or environmental flow requirements. However, in highly regulated water resource systems this relationship may be quite complex, depending on storage capacity and system operation. In this paper we define a CIRF for these types of systems through a multivariable logistic regression (LR) model where a binary variable (system response) is explained by two continuous variables or predictors (precipitation and temperature). The approach involves generating multivariate synthetic inflow time series and relating them to specific climate conditions. Next, these inflows are used as inputs in a water management model, and the outcome is coded as a binary variable (failure or its absence) depending on selected vulnerability criteria. To identify the time span before the failure event in which climate variables are relevant, we characterized drought development stages through relative standardized indices. Mean values of precipitation and temperature for the selected time span are computed and used as explanatory variables through a LR model, which is validated using data from several climate models and scenarios. Results show that the predictive capacity of LR models is acceptable, so that they could be used as screening tools to detect challenging climate conditions for the system which would require adaption actions. [ABSTRACT FROM AUTHOR]

Streamflow forecasting services driven by seasonal meteorological forecasts from dynamic prediction systems deliver valuable information for decision-making in the water sector. Moving beyond the traditional river basin boundaries, large-scale hydrological models enable a coordinated, efficient, and harmonized anticipation and management of water-related risks (droughts, floods). However, the use of forecasts from such models at the river basin scale remains a challenge, depending on how the model reproduces the hydrological features of each particular river basin. Consequently, postprocessing of forecasts is a crucial step to ensure usefulness at the river basin scale. In this paper we present a methodology to postprocess seasonal streamflow forecasts from large-scale hydrological models and advance their quality for local applications. It consists of fuzzy logic systems that bias-adjust seasonal forecasts from a large-scale hydrological model by comparing its modeled streamflows with local observations. The methodology is demonstrated using forecasts from the pan-European hydrological model E-HYPE at the Jucar River basin (Spain). Fuzzy postprocessed forecasts are compared to postprocessed forecasts derived from a quantile mapping approach as a benchmark. Fuzzy postprocessing was able to provide skillful streamflow forecasts for the Jucar River basin, keeping most of the skill of raw E-HYPE forecasts and also outperforming quantile-mapping-based forecasts. The proposed methodology offers an efficient one-to-one mapping between large-scale modeled streamflows and basin-scale observations preserving its temporal dependence structure and can adapt its input set to increase the skill of postprocessed forecasts. [ABSTRACT FROM AUTHOR]

Highlights • Literature-based and data-driven fuzzy models can be combined. • Hydraulic spawning preferences proved stable across its distribution range. • The suitable spawning habitat will be reduced between 15.4–48.7%. • Brown trout will probably be extirpated from the lower Cabriel River. Abstract A fuzzy rule-based system combining empirical data on hydraulic preferences and literature information on temperature requirements was used to foresee the brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change. The climatic scenarios for the Cabriel River (Eastern Iberian Peninsula) corresponded to two Representative Concentration Pathways (4.5 and 8.5) for the short (2011–2040) and mid (2041–2070) term horizons. The hydraulic and hydrologic modelling were undertaken with process-based numerical models (i.e., River2D© and HBV-light) while the water temperature was modelled by assembling the predictions of three machine learning techniques (M5, Multi-Adaptive Regression Splines and Support Vector Regression). The predicted rise in the water temperature will not be compensated by the more benign lower flows. Consequently, the suitable spawning habitat will be reduced between 15.4–48.7%. The entire population shall suffer the effects of climate change and will probably be extirpated from the downstream segments of the river. [ABSTRACT FROM AUTHOR]

The advent of smart metering is set to revolutionize many aspects of the relationship between water utilities and their customers, and this includes the possibility of using time-varying water prices as a demand management strategy. These dynamic tariffs could promote water use efficiency by reflecting the variations of water demand, availability, and delivery costs over time. This paper relates the potential benefits of dynamic water tariffs, at the utility and basin scale, to their design across a range of timescales. On one end of the spectrum, subdaily peak pricing shifts use away from peak hours to lower a utility's operational and capital expenses. On the other end, scarcity pricing factors in the variations of the marginal opportunity cost of water at weekly or longer timescales in the river basin from which water is withdrawn. Dynamic pricing schemes that act across timescales can be devised to yield both types of benefits. The analysis estimates these benefits separately for Greater London (United Kingdom) and its 15 million inhabitants. Scarcity pricing implemented on a weekly timescale equates the marginal cost of residential water with estimates of the marginal economic values of environmental-recreational flows derived from tourism, property values, etc. Scarcity pricing during droughts could result in a 22–63% average reduction in environmental flow shortage while residential price increases would be capped at 150% of base levels. Yet, its ability to protect environmental flows could decrease in extreme shortage situations. The net present value of savings from peak pricing is conservatively evaluated at approximately £10 million for each initial percentage point in daily peak-hour price increase. [ABSTRACT FROM AUTHOR]

Meta-analyses synthesise available data on a phenomenon to get a broader understanding of its determinants. This work proposes a two-step methodology. 1) Based on a broad dataset of residential water demand studies, it builds a meta-regression model to estimate mean and standard deviation of price elasticity of residential water demand. 2) The resulting meta-model serves as a basis for implementing an approach that directly simulates the range of price elasticities resulting from policy-relevant combinations of its determinants. This simulation approach is validated using the available dataset. Despite evidence of low average price elasticity, the scenarios simulated using our meta-regression estimates show that increasing block rate tariffs are associated with higher price elasticity, and stresses the importance of using state-of-the-art methodologies when evaluating the price response. This completes other methodological insights obtained from the meta-analysis itself. Policy implications on the use of pricing to bring about water savings are discussed. [ABSTRACT FROM AUTHOR]

The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulation models provide excellent instruments for researchers to gain more insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessment was conducted by testing six detailed state-of-the-art models for simulation of nitrate leaching ( ARMOSA , COUPMODEL , DAISY , EPIC , SIMWASER/STOTRASIM , SWAP/ANIMO ) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of the models: 1) a blind test for 2005–2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009–2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement, model efficiency, root relative squared error, Pearson's linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates. [ABSTRACT FROM AUTHOR]

Summary Aquifers and groundwater-dependent ecosystems (GDEs) are facing increasing pressure from water consumption, irrigation and climate change. These pressures modify groundwater levels and their temporal patterns and threaten vital ecosystem services such as arable land irrigation and ecosystem water requirements, especially during droughts. This review examines climate change effects on groundwater and dependent ecosystems. The mechanisms affecting natural variability in the global climate and the consequences of climate and land use changes due to anthropogenic influences are summarised based on studies from different hydrogeological strata and climate zones. The impacts on ecosystems are discussed based on current findings on factors influencing the biodiversity and functioning of aquatic and terrestrial ecosystems. The influence of changes to groundwater on GDE biodiversity and future threats posed by climate change is reviewed, using information mainly from surface water studies and knowledge of aquifer and groundwater ecosystems. Several gaps in research are identified. Due to lack of understanding of several key processes, the uncertainty associated with management techniques such as numerical modelling is high. The possibilities and roles of new methodologies such as indicators and modelling methods are discussed in the context of integrated groundwater resources management. Examples are provided of management impacts on groundwater, with recommendations on sustainable management of groundwater. [ABSTRACT FROM AUTHOR]

Agricultural economics Water Programming Models (WPM) has found that irrigators in water scarce areas have a rather inelastic response to water prices, making water pricing cost-ineffective towards water saving. We hypothesize that the predicted water saving performance of pricing is significantly underestimated by issues of model structure, due to the exclusion of deficit irrigation from the set of decision variables available to agents in conventional WPM. To test our hypothesis, we develop a model that integrates a continuous crop-water production function into a positive multi-attribute WPM, which allows us to assess agents' adaptive responses to pricing through deficit irrigation. The model is illustrated with an application to the El Salobral-Los Llanos irrigated area in Spain. Our results show that incorporating deficit irrigation as an adaptation option makes the water demand curve significantly more elastic as compared to an alternative model setting where deficit irrigation is precluded. We conclude that ignoring deficit irrigation can lead to a significant underestimation of the cost-effectiveness of water pricing towards water saving. • We integrate a crop-water production function into a mathematical programming model. • This makes possible to assess irrigators' responses through deficit irrigation. • Farmers' responses to pricing policy is assessed. • Ignoring deficit irrigation underestimates the effectiveness of water pricing towards water conservation. [ABSTRACT FROM AUTHOR]

Abstract: When linearity can be assumed (linear response of heads to stresses), stream–aquifer flow exchange can be simulated as the drainage of a number of independent linear reservoirs. This conceptual model, which can be mathematically deduced in a univocal way from an eigenvalue solution of the linear groundwater flow problem, facilitates the understanding of the physical phenomenon and the analysis of influencing factors. The number of reservoirs required to simulate stream depletion in some ideal homogeneous cases of stream–aquifer connection was analyzed in detail in a previous investigation using analytical eigenvalue solutions . However, most aquifers are heterogeneous in nature and numerical solutions must be employed to analyze whether they could also be simulated using few reservoirs. This paper presents a stochastic analysis of the influence of heterogeneity on the simulation of natural groundwater discharges in aquifers connected to rivers, as a series of linear reservoirs. A Monte-Carlo approach was employed to perform this study. The results show that, on a monthly time scale, many cases (even heterogeneous aquifers) can be simulated using just a few reservoirs with sufficient accuracy and at minimum computational cost. Therefore, this modeling technique can be useful to efficiently simulate the integrated management of complex water resources systems at the basin scale (with many aquifers, reservoirs, demands, etc.) that need to simultaneously consider surface and groundwater flow and stream–aquifer interaction. [Copyright &y& Elsevier]

Summary: The computational cost of groundwater flow simulation can be crucial when analyzing complex conjunctive use water resources systems that need to simulate simultaneously surface and groundwater components. A general methodology for accurate simulation of unconfined groundwater flow with low computational cost is presented. It requires linearizing the unconfined groundwater flow problem governed by the Boussinesq equation. The technique is based on a change of variable and depends on the reference level adopted. Some recommendations have been provided to set the reference level to estimate the spatially variant parameters required to define the linearized problem. Using this linearization, more accurate results can be obtained than those derived with the classical assumption of invariant transmissivity. Solving the problem with eigenvalue techniques, the solution can be defined with a semi-explicit state equation with low computational cost. Some case studies have been analyzed in order to demonstrate that the methodology can be applied to any aquifer geometry (including non-horizontal bottoms), hydrodynamic properties and boundary conditions (even different prescribed head values). The results have been compared with those obtained with other linearization methods and MODFLOW [McDonald, M.G., Harbaugh, A.W., 1988. A modular three dimensional finite difference ground water flow model. Open – File Report 83-875, US Geological Survey, Washington DC] for unconfined aquifers. A case study defined from a previously calibrated finite-difference model of the “Delta Adra” aquifer, located in southern Spain, has been also analyzed. [Copyright &y& Elsevier]

Stochastic programming algorithms are able to combine inflow forecasting systems with water resources optimization models taking explicitly into account inflow uncertainty. In case of using a hydroeconomic approach, monetary gains associated with alternative forecasting systems can be compared to rank their suitability according to added economic value. In here we present an assessment of the economic value of several forecasting systems including tools and datasets at the pan-European scale, at the local scale and combinations of both. This analysis has been applied to the Jucar River System (Spain). The economic value is estimated using a hydroeconomic Stochastic Dual Dynamic Programming (SDDP) approach, combined with 1-month leadtime forecasts in a rolling horizon, in line with its frequency of update.Forecasting systems used are: 1) average monthly values of hydrological discharge (no-forecast situation); 2) the pan-European hydrological forecasting system developed by the Swedish Meteorological and Hydrological Institute (SMHI), based on the bias-adjusted System 4 meteorological forecasts from the European Centre of Medium-Range Weather Forecasts (ECMWF) used as input to the E-HYPE hydrological model, post-processed using fuzzy logic to adjust forecasts to the local hydrological conditions; 3) the meteorological forecasting service SEAS5 from ECMWF, bias-adjusted to the local meteorology using fuzzy logic and combined with locally-adjusted hydrological models; and 4) a locally-adjusted stochastic model ARMA 1,1. Additional alternatives considered are: 5) a scenario with a 1-month perfect forecast; and 6) a scenario with a perfect forecast of the whole analysis period (deterministic programming).Case study is the Jucar River System in Spain, characterized by Mediterranean hydrology, intensive use of water in agriculture, the existence of large reservoirs and complex decision-making processes. The SDDP problem is solved to obtain future benefit functions for each month of the year. Then, a forward-moving optimal control problem is solved, for the 1998-2012 period (including a drought period between 2005 and 2008), optimizing the current plus future benefits for each forecasting system.Results show that SEAS5 meteorological forecasts combined with local hydrological models offer the best performance among forecasting systems, overwhelming the no-forecast situation by 2.71 M€/year and the second best forecast, the ARMA 1,1; by 0.31 M€/year. The best forecasting system is able to fill 76% of the economic gap between no forecasts and 1-month perfect forecasts. E-HYPE forecasts, on the other hand, show a lower economic performance than the other two alternatives, being similar to climatology. These economic results do not agree with the comparison of forecasting skills, in which SEAS5 and ARMA 1,1 show similar scores while E-HYPE clearly outperforms climatology. SEAS5 outperforms ARMA 1,1 in benefit levels due to its better accuracy during the worst part of the drought period (year 2007), in which accurate forecasts are distinctly valuable. E-HYPE performance suffers from being less accurate for the same year.Acknowledgements:This study has been supported by the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811), by the European Research Area for Climate Services programme (ER4CS) under the INNOVA project (grant agreement: 690462), and by the postdoctoral program of Universitat Politècnica de València (UPV) [ABSTRACT FROM AUTHOR]