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With increasing water and energy use in the residential sector, due to population growth, urbanization, and climate change, demand-side management (DSM) is essential to complement supply-side interventions to meet future demands and reduce costs. This paper explores how customer segmentation analysis can support customized water and electricity DSM. We contribute a three-phase customer segmentation analysis of over 1000 residential accounts in the Los Angeles County (Southern California) to explore the heterogeneity of residential water-electricity demand profiles and provide insights for coordinated water-energy DSM. Results show that, on the one hand, daily water and electricity consumption are correlated, thus groups of high consumers can be targeted with coordinated water-electricity DSM interventions. On the other hand, the absence of a relevant causal nexus between water and electricity daily load shapes suggests that DSM actions for water should be differentiated from those for electricity. Finally, both objective (e.g., presence of swimming pool) and subjective psychographic features (e.g., conservation attitude) are found to be relevant potential drivers of water-electricity demands. Based on these findings, we propose recommendations for designing a portfolio of mixed customized water-electricity DSM interventions to foster conservation or peak shifting objectives.

[EN] 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of the marginal opportunity cost of water at weekly or longer timescales in the river basin from which water is withdrawn.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 15million 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 10million pound for each initial percentage point in daily peak-hour price increase., The work was supported by the research project SmartH2O: an ICT Platform to leverage on Social Computing for the efficient management of Water Consumption, funded by the EU Seventh Framework Programme under Grant Agreement No. 619172, and by EPSRC Grant No. P/G060460/1. The authors wish to thank Anna Wallen for comments on this manuscript, as well as the editor, associate editor David Rosenberg, and three anonymous reviewers, for their constructive comments.

In river basin systems, power asymmetry is often responsible of inefficient and unbalanced water allocations. Climate change and anthropogenic pressure will possibly exacerbate such disparities as the dominant party controls an increasingly limited shared resource. In this context, the deployment of cooperation mechanisms giving greater consideration to a balanced distribution of the benefits, while improving system-wide efficiency, may be desirable. This often implies the intervention of a third party (e.g., the river basin water authority) imposing normative constraints (e.g., a minimum release) on the party in the dominant position. However, this imposition will be more acceptable to the dominant party if coupled with some form of compensation. For a public agency, compensation may be burdensome, especially when the allowance is triggered by natural events whose timing and magnitude are highly uncertain. In this context, index-based insurance contracts may represent a viable alternative and reduce the cost of achieving socially desirable outcomes. In this paper, we develop a hybrid cooperation mechanism composed of i) a direct normative constraint imposed by a regulator, and ii) an indirect financial tool, an index-based insurance contract, to be used as a compensation measure. The approach is developed for the Lake Como multi-purpose water system, Italy: a complex Alpine river basin, supporting several hydropower reservoirs and finally flowing into a regulated lake which supplies water to several downstream uses, mostly irrigated agriculture. The system is characterized by a manifest geographic power asymmetry: the upstream hydropower companies are free to release their stored water in time irrespective of the timing of the downstream demands. This situation can lead to financial losses by the downstream users and undesirable social outcomes. Results suggest that financial instruments may offer a reliable and relatively inexpensive alternative to other forms of compensation, and thereby favor more balanced management of multi-purpose water systems characterized by power asymmetry. This finding is especially relevant in times when granting of licenses to use/withdrawal water are often being reviewed with attention to environmental protection and equity issues.

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world. We review high resolution residential water demand modeling studies.We provide a classification of existing technologies and methodologies.We identify current trends, challenges and opportunities for future development.

Recent advances in weather and climate services (WCSs) are showing increasing forecast skills over seasonal and longer time scales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end-users, especially when forecasts do not reach their final users, when the provider is not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of WCSs for irrigated agriculture by complementing traditional forecast quality assessments with a Coupled Human-Natural System behavioral model which reproduces farmers’ decisions. This allows a more critical assessment of the forecast value mediated by the end-users’ perspective, including farmers’ risk attitudes and behavioral factors. Our results show that the quality of state- of-the-art WCSs is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting the most skillful product. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure that is strongly impacted by the behavioral attitudes of the farmers, which can produce rank reversals in the quantification of the WCSs operational value depending on the different perceptions of risk and uncertainty.

Advances in modeling and control have always played an important role in supporting water resources systems planning and management. Changes in climate and society are now introducing additional challenges for controlling these systems, motivating the emergence of complex, integrated simulation models to explore key causal relationships and dependences related to uncontrolled sources of variability. In this brief, we contribute a massively parallel implementation of the evolutionary multiobjective direct policy search method for controlling large-scale water resources systems under uncertainty. The method combines direct policy search with nonlinear approximating networks and a hierarchical parallelization of the Borg multiobjective evolutionary algorithm. This computational framework successfully identifies control policies that address both the presence of multidimensional tradeoffs and severe uncertainties in the system dynamics and policy performance. We demonstrate the approach on a challenging real-world application, represented by the optimal control of a network of four multipurpose water reservoirs in the Red River basin in Northern Vietnam, under observed and synthetically generated hydrologic conditions. Results show that the reliability of the computational framework in finding near-optimal solutions increases with the number of islands in the adopted hierarchical parallelization scheme. This setting reduces the vulnerabilities of the designed solutions to the system’s uncertainty and improves the discovery of robust control policies addressing key system performance tradeoffs.

Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

Fluvial geomorphology has been increasingly recognized as a key component of modern river basin management. River morphological processes resulting from the interaction of natural and anthropogenic forces shape physical habitats, affect river infrastructures, and drive freshwater ecological processes. Nevertheless, geomorphic information or process-based assessments of fluvial systems on large scales (catchment, regional) are still scarce. This is especially the case in less developed countries where fluvial geomorphic assessments and data collection are not common routine due to a lack of location-specific expertise and of the resources necessary to collect, store and process hydromorphological information. In this paper we propose a new, scalable and globally applicable framework to analyse and classify fluvial systems in data--scarce environments using freely available remote--sensing information and several in--situ hydrological time--series. Key component of the framework is a fuzzy classifier through which individual river reaches are characterized by different fuzzy signatures of hydromorphological drivers. We demonstrate the framework on the Red River Basin, Vietnam, where human--induced alterations in hydro-morphological processes acutely endanger local livelihoods, while hydromorphological information is very limited at present. The classification obtained from our framework is then used to interpret time--series of high--resolution satellite images where it successfully identifies breakpoints as well as continuous downstream change in channel patterns and morphology. For the case study, we also provide evidence that the fuzzy hydromorphologic signatures can be applied for predictive modelling of fluvial forms and dynamics –- potentially a first step towards quantitative predictive models of HYMO dynamics to inform large—scale decision—-making., JRC.H.1-Water Resources

This study contributes a decision analytic framework to overcome policy inertia and myopia in complex river basin management contexts. The framework combines reservoir policy identification, many-objective optimization under uncertainty, and visual analytics to characterize current operations and discover key trade-offs between alternative policies for balancing competing demands and system uncertainties. The approach is demonstrated on the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. We have identified a baseline operating policy for the Conowingo Dam that closely reproduces the dynamics of current releases and flows for the Lower Susquehanna and thus can be used to represent the preferences structure guiding current operations. Starting from this baseline policy, our proposed decision analytic framework then combines evolutionary many-objective optimization with visual analytics to discover new operating policies that better balance the trade-offs within the Lower Susquehanna. Our results confirm that the baseline operating policy, which only considers deterministic historical inflows, significantly overestimates the system's reliability in meeting the reservoir's competing demands. Our proposed framework removes this bias by successfully identifying alternative reservoir policies that are more robust to hydroclimatic uncertainties while also better addressing the trade-offs across the Conowingo Dam's multisector services.

The authors acknowledge the publisher in granting permission for making post-print version available in open access institutional repository. The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

The SmartH2O project aims to provide water utilities, municipalities and citizens with an ICT enabled platform to design, develop and implement better water management policies using innovative metering, social media and pricing mechanisms. This project has as a working hypothesis that high data quality obtained from smart meters and communicable through social media and other forms of interaction could be used to design and implement innovative and effective water pricing policies. Planned case studies in the UK and Switzerland are introduced. We anticipate that SmartH20 research outcomes will be of use to those interested in linking smart metering, social media and smart pricing approaches to achieve more sustainable water management outcomes.

Water management problems generally involve conflicting and non-commensurable objectives. Assuming a centralized perspective at the system-level, the set of Pareto-optimal alternatives represents the ideal solution of most of the problems. Yet, in typical real-world applications, only a few primary objectives are explicitly considered, taking precedence over all other concerns. These remaining concerns are then internalized as static constraints within the problem's formulation. This approach yields to solutions that fail to explore the full set of objectives tradeoffs. In this paper, we propose a novel method, called direct policy conditioning (DPC), that combines direct policy search, multi-objective evolutionary algorithms, and input variable selection to design dynamic constraints that change according to the current system conditions. The method is demonstrated for the management problem of the Conowingo Dam, located within the Lower Susquehanna River, USA. The DPC method is used to identify environmental protection mechanisms and is contrasted with traditional static constraints defining minimum environmental flow requirements. Results show that the DPC method identifies a set of dynamically constrained control policies that overcome the current alternatives based on the minimum environmental flow constraint, in terms of environmental protection but also of the primary objectives.

[1] Input variable selection is an important issue associated with the development of several hydrological applications. Determining the optimal input vector from a large set of candidates to characterize a preselected output might result in a more accurate, parsimonious, and, possibly, physically interpretable model of the natural process. In the hydrological context, the modeled system often exhibits nonlinear dynamics and multiple interrelated variables. Moreover, the number of candidate inputs can be very large and redundant, especially when the model reproduces the spatial variability of the physical process. The ideal input selection algorithm should therefore provide modeling flexibility, computational efficiency in dealing with high dimension data set, scalability with respect to input dimensionality and minimum redundancy. In this paper, we propose the tree-based iterative input variable selection algorithm, a novel hybrid model-based/model-free approach specifically designed to fulfill these four requirements. The algorithm structure provides robustness against redundancy, while the tree-based nature of the underlying model ensures the other key properties. The approach is first tested on a well-known benchmark case study to validate its accuracy and subsequently applied to a real-world streamflow prediction problem in the upper Ticino River Basin (Switzerland). Results indicate that the algorithm is capable of selecting the most significant and nonredundant inputs in different testing conditions, including the real-world large data set characterized by the presence of several redundant variables. This permits one to identify a compact representation of the observational data set, which is key to improving the model performance and assisting with the interpretation of the underlying physical processes.

Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies – Marina catchment (Singapore) and Canning River (Western Australia) – representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

[1] Many large-scale water resources systems, especially in transboundary contexts, are characterized by the presence of several and conflicting interests and managed by multiple, institutionally independent decision makers. These systems are often studied adopting a centralized approach based on the assumption of full cooperation and information exchange among the involved parties. Such a perspective is conceptually interesting to quantify the best achievable performance but might have little practical impact given the real political and institutional setting. In this work, we propose a novel decision-analytic framework based on multiagent systems to model and analyze different levels of cooperation and information exchange among multiple decision makers. The Zambezi River basin is used as a case study. According to the proposed agent-based optimization approach, each agent represents a decision maker, whose decisions are defined by an explicit optimization problem considering only the agent's local interests. The economic value of information exchange is estimated comparing a noncooperative setting, where agents act independently, with the first basic level of cooperation, i.e., coordination, characterized by full information exchange. The economic value of cooperation is also estimated by comparison with the ideal, fully cooperative management of the system. Results show that coordination, obtained with complete information exchange, allows the downstream agents to better adapt to the upstream behaviors. The impact of information exchange depends on the objective considered, and we show coordination to be particularly beneficial to environmental interests.

[1] The operation of large-scale water resources systems often involves several conflicting and noncommensurable objectives. The full characterization of tradeoffs among them is a necessary step to inform and support decisions in the absence of a unique optimal solution. In this context, the common approach is to consider many single objective problems, resulting from different combinations of the original problem objectives, each one solved using standard optimization methods based on mathematical programming. This scalarization process is computationally very demanding as it requires one optimization run for each trade-off and often results in very sparse and poorly informative representations of the Pareto frontier. More recently, bio-inspired methods have been applied to compute an approximation of the Pareto frontier in one single run. These methods allow to acceptably cover the full extent of the Pareto frontier with a reasonable computational effort. Yet, the quality of the policy obtained might be strongly dependent on the algorithm tuning and preconditioning. In this paper we propose a novel multiobjective Reinforcement Learning algorithm that combines the advantages of the above two approaches and alleviates some of their drawbacks. The proposed algorithm is an extension of fitted Q-iteration (FQI) that enables to learn the operating policies for all the linear combinations of preferences (weights) assigned to the objectives in a single training process. The key idea of multiobjective FQI (MOFQI) is to enlarge the continuous approximation of the value function, that is performed by single objective FQI over the state-decision space, also to the weight space. The approach is demonstrated on a real-world case study concerning the optimal operation of the HoaBinh reservoir on the Da river, Vietnam. MOFQI is compared with the reiterated use of FQI and a multiobjective parameterization-simulation-optimization (MOPSO) approach. Results show that MOFQI provides a continuous approximation of the Pareto front with comparable accuracy as the reiterated use of FQI. MOFQI outperforms MOPSO when no a priori knowledge on the operating policy shape is available, while produces slightly less accurate solutions when MOPSO can exploit such knowledge.

[1] In this paper, we use empirical modeling to predict and understand phytoplankton dynamics in a reservoir affected by water transfers. Prediction of phytoplankton biovolume is central to the management of water resources, particularly given the significant impacts on quality of the water-quantity oriented management of transfers between reservoirs. A novel tree-based iterative input variable selection algorithm is applied for the first time in an ecological context, and identifies a maximum of eight driving factors out of 77 candidates to explain the biovolume of chlorophytes, cyanobacteria and diatoms. The stepwise forward-selection to iteratively identify the most important inputs leads to a physically interpretable model able to infer the physical processes controlling phytoplankton biovolume. Reservoir inflows and outflows are found to exert a strong control over diatom and chlorophyte dynamics while water temperature, nitrate and phosphorus determine the biovolume of cyanobacteria. Following the selection of the most relevant inputs, the 1 week ahead predictions of four different data-driven model classes, i.e., neural networks, extra trees (ETs), model trees and linear regressions, are compared based on performance indices and statistical tests. ETs are found to outperform the other models by providing accurate predictions of cyanobacteria, chlorophyte and diatom biovolume by explaining 66.6%, 66.9%, and 80.5% of the variance, respectively. The methodology is applicable to different environmental studies and combines the strength of empirical modeling, i.e., compact models and accurate predictions, with a good understanding of the physical processes involved.

Despite human is an increasingly significant component of the hydrologic cycle in many river basins, most hydrologic models are still developed to accurately reproduce the natural processes and ignore the effect of human activities on the watershed response. This results in non-stationary model forecast errors and poor predicting performance every time these models are used in non-pristine watersheds. In the last decade, the representation of human activities in hydrological models has been extensively studied. However, mathematical models integrating the human and the natural dimension are not very common in hydrological applications and nearly unknown in the day-to-day practice. In this paper, we propose a new simple data-driven flow forecast correction method that can be used to simultaneously tackle forecast errors from structural, parameter and input uncertainty, and errors that arise from neglecting human-induced alterations in conceptual rainfall–runoff models. The correction system is composed of two layers: (i) a classification system that, based on the current flow condition, detects whether the source of error is natural or human induced and (ii) a set of error correction models that are alternatively activated, each tailored to the specific source of errors. As a case study, we consider the highly anthropized Aniene river basin in Italy, where a flow forecasting system is being established to support the operation of a hydropower dam. Results show that, even by using very basic methods, namely if-then classification rules and linear correction models, the proposed methodology considerably improves the forecasting capability of the original hydrological model. Copyright © 2013 John Wiley & Sons, Ltd.

This paper merges multimedia and environmental research to verify the utility of public web images for improving water management in periods of water scarcity, an increasingly critical event due to climate change. A multimedia processing pipeline fetches mountain images from multiple sources and extracts virtual snow indexes correlated to the amount of water accumulated in the snow pack. Such indexes are used to predict water availability and design the operating policy of Lake Como, Italy. The performance of this informed policy is contrasted, via simulation, with the current operation, which depends only on lake water level and day of the year, and with a policy that exploits official Snow Water Equivalent (SWE) estimated from ground stations data and satellite imagery. Virtual snow indexes allow improving the system performance by 11.6% w.r.t. the baseline operation, and yield further improvement when coupled with official SWE information, showing that the two data sources are complementary. The proposed approach exemplifies the opportunities and challenges of applying multimedia content analysis methods to complex environmental problems.

Robust decision-making is being increasingly used to support environmental resources decisions and policy analysis under changing climate and society. In this context, a robust decision is a decision that is as much as possible insensitive to a large degree of uncertainty and ensures certain performance across multiple plausible futures. Yet, the concept of robustness is neither unique nor static. Multiple robustness metrics, such as maximin, optimism-pessimism, max regret, have been proposed in the literature, reflecting diverse optimistic/pessimistic attitudes by the decision maker. Further, these attitudes can evolve in time as a response to sequences of favorable (or adverse) events, inducing possible dynamic changes in the robustness metrics. In this paper, we explore the impact of alternative definitions of robustness and their evolution in time for a case of water resources system management under changing climate. We study the decisions of the Lake Como operator, who is called to regulate the lake by balancing irrigation supply and flood control, under an ensemble of climate change scenarios. Results show a considerable variability in the system performance across multiple robustness metrics. In fact, the mis-definition of the actual decision maker’s attitude biases the simulation of its future decisions and produces a general underestimation of the system performance. The analysis of the dynamic evolution of the decision maker’s preferences further confirms the potentially strong impact of changing robustness definition on the decision-making outcomes. Climate change impact assessment studies should therefore include the definition of robustness among the uncertain parameters of the problem in order to analyze future human decisions under uncertainty.

Robust decision-making is being increasingly used to support environmental resources decisions and policy analysis under changing climate and society. In this context, a robust decision is a decision that is as much as possible insensitive to a large degree of uncertainty and ensures certain performance across multiple plausible futures. Yet, the concept of robustness is neither unique nor static. Multiple robustness metrics, such as maximin, optimism-pessimism, max regret, have been proposed in the literature, reflecting diverse optimistic/pessimistic attitudes by the decision maker. Further, these attitudes can evolve in time as a response to sequences of favorable (or adverse) events, inducing possible dynamic changes in the robustness metrics. In this paper, we explore the impact of alternative definitions of robustness and their evolution in time for a case of water resources system management under changing climate. We study the decisions of the Lake Como operator, who is called to regulate the lake by balancing irrigation supply and flood control, under an ensemble of climate change scenarios. Results show a considerable variability in the system performance across multiple robustness metrics. In fact, the mis-definition of the actual decision maker’s attitude biases the simulation of its future decisions and produces a general underestimation of the system performance. The analysis of the dynamic evolution of the decision maker’s preferences further confirms the potentially strong impact of changing robustness definition on the decision-making outcomes. Climate change impact assessment studies should therefore include the definition of robustness among the uncertain parameters of the problem in order to analyze future human decisions under uncertainty.

The operations of water resources infrastructures, such as dams and diversions, often involve multiple conflicting interests and stakeholders. Among the approaches that have been proposed to design optimal operating policies for these systems, those based on agents have recently attracted an increasing attention. The different stakeholders are represented as different agents and their interactions are usually modeled as distributed constraint optimization problems. Those few works that have attempted to model the interactions between stakeholders as negotiations present some significant limitations, like the necessity for each agent to know the preferences of all other agents. To overcome this drawback, in this paper we contribute a general monotonic concession protocol that allows the stakeholders-agents of a regulated lake to periodically reach agreements on the amount of water to release daily, trying to control lake floods and to supply water to agricultural districts downstream. In particular, we study two specific instances of the general protocol according to their ability to converge, reach Pareto optimal agreements, limit complexity, and show good experimental performance.

In a changing climate and society, large storage systems can play a key role for securing water, energy, and food, and rebalancing their cross-dependencies. In this letter, we study the role of large storage operations as flexible means of adaptation to climate change. In particular, we explore the impacts of different climate projections for different future time horizons on the multi-purpose operations of the existing system of large dams in the Red River basin (China–Laos–Vietnam). We identify the main vulnerabilities of current system operations, understand the risk of failure across sectors by exploring the evolution of the system tradeoffs, quantify how the uncertainty associated to climate scenarios is expanded by the storage operations, and assess the expected costs if no adaptation is implemented. Results show that, depending on the climate scenario and the time horizon considered, the existing operations are predicted to change on average from -7 to +5% in hydropower production, +35 to +520% in flood damages, and +15 to +160% in water supply deficit. These negative impacts can be partially mitigated by adapting the existing operations to future climate, reducing the loss of hydropower to 5%, potentially saving around 34.4 million US$ year-1 at the national scale. Since the Red River is paradigmatic of many river basins across south east Asia, where new large dams are under construction or are planned to support fast growing economies, our results can support policy makers in prioritizing responses and adaptation strategies to the changing climate., Environmental Research Letters, 11 (3), ISSN:1748-9326, ISSN:1748-9318

Retrieving the household electricity consumption at individual appliance level is an essential requirement to assess the contribution of different end uses to the total household consumption, and thus to design energy saving policies and user-tailored feedback for reducing household electricity usage. This has led to the development of nonintrusive appliance load monitoring (NIALM), or energy disaggregation, algorithms, which aim to decompose the aggregate energy consumption data collected from a single measurement point into device-level consumption estimations. Existing NIALM algorithms are able to provide accurate estimate of the fraction of energy consumed by each appliance. Yet, in the authors’ experience, they provide poor performance in reconstructing the power consumption trajectories overtime. In this brief, a new NIALM algorithm is presented, which, besides providing very accurate estimates of the aggregated consumption by appliance, also accurately characterizes the appliance power consumption profiles overtime. The proposed algorithm is based on the assumption that the unknown appliance power consumption profiles are piecewise constant overtime (as it is typical for power use patterns of household appliances) and it exploits the information on the time-of-day probability in which a specific appliance might be used. The disaggregation problem is formulated as a least-square error minimization problem, with an additional (convex) penalty term aiming at enforcing the disaggregate signals to be piecewise constant overtime. Testing on household electricity data available in the literature is reported.

The advent of online services, social networks, crowdsourcing, and serious Web games has promoted the emergence of a novel computation paradigm, where complex tasks are solved by exploiting the capacity of human beings and computer platforms in an integrated way. Water Resources Management systems can take advantage of human and social computation in several ways: collecting and validating data, complementing the analytic knowledge embodied in models with tacit knowledge from individuals and communities, using human sensors to monitor the variation of conditions at a fine grain and in real time, activating human networks to perform search tasks or actuate management actions. This exploratory paper overviews different forms of human and social computation and analyzes how they can be exploited to enhance the effectiveness of ICT-based Water Resources Management.

Flow routing models are an essential tool for water systems planning and management. In the last few years, advances in modelling capability, enhanced data availability, and increasing computational power have made the use of large, process-based, flow-routing models more common. However, the computational burden of such models still prevents their systematic application for operational purposes. This paper presents an alternative, data-driven approach to the identification of a flow-routing model both in one-step-ahead prediction and simulation mode, and demonstrates it by application to the Red River network, in Northern Vietnam. In particular, MIMO time-invariant models for the network are considered, their parameters are estimated using subspace model identification techniques and their performance is assessed with respect to the available data. Results show that the non-linear process of flow propagation is accurately reproduced both in prediction and simulation using very low order models.

The optimal management of large environmental systems is often limited by the high computational burden associated to the process-based models commonly adopted to describe such systems. In this paper we propose a novel data-driven Dynamic Emulation Modelling approach for the construction of small, computationally efficient models that accurately emulate the main dynamics of the original process-based model, but with less computational requirements. The approach combines the many advantages of data-based modelling in representing complex, non-linear relationships, but preserves the state-space representation, which is both particularly effective in several applications (e.g. optimal management and data assimilation) and facilitates the ex-post physical interpretation of the emulator structure, thus enhancing the credibility of the model to stakeholders and decision-makers. The core mechanism is a novel variable selection procedure that is recursively applied to a data-set of input, state and output variables generated via simulation of the process-based model. The approach is demonstrated on a real-world case study concerning the optimal operation of a selective withdrawal reservoir (Tono Dam, Japan) suffering from downstream water quality problems. The emulator is identified on a data-set generated with a 1D coupled hydrodynamic-ecological model and subsequently used to design the optimal operating policy for the dam. Preliminary results show that the proposed approach significantly simplifies the learning of good operating policies and can highlight interesting properties of the system to be controlled.

Emulation modelling is an effective way of overcoming the large computational burden associated with the process-based models traditionally adopted by the environmental modelling community. An emulator is a low-order, computationally efficient model identified from the original large model and then used to replace it for computationally intensive applications. As the number and forms of the problem that benefit from the identification and subsequent use of an emulator is very large, emulation modelling has emerged in different sectors of science, engineering and social science. For this reason, a variety of different strategies and techniques have been proposed in the last few years. The main aim of the paper is to provide an introduction to emulation modelling, together with a unified strategy for its application, so that modellers from different disciplines can better appreciate how it may be applied in their area of expertise. Particular emphasis is devoted to Dynamic Emulation Modelling (DEMo), a methodological approach that preserves the dynamic nature of the original process-based model, with consequent advantages in a wide variety of problem areas. The different techniques and approaches to DEMo are considered in two macro categories: structure-based methods, where the mathematical structure of the original model is manipulated to a simpler, more computationally efficient form; and data-based approaches, where the emulator is identified and estimated from a data-set generated from planned experiments conducted on the large simulation model. The main contribution of the paper is a unified, six-step procedure that can be applied to most kinds of dynamic emulation problem.

SmartH2O is a software platform that creates a virtuous feedback cycle between water users and the utilities, providing users information on their consumption in quasi real time, and thus enabling water utilities to plan and implement strategies to reduce/reallocate water consumption. The SmartH2O platform adopts a gamification paradigm to motivate users to change their water use behaviour and different incentives (virtual, physical, and social) are used to stimulate the competition among users. The SmartH2O platform, developed during the EU-FP7 project that bears the same name, has been deployed in two test sites: in Terre di Pedemonte, a small municipality in Tessin, Switzerland, and in Valencia, a large urban centre, in Spain. Thanks to its use it has been observed an average reduction in consumption of 10% in Switzerland and of 20% in Spain among the platform adopters.

Optimal management of water resources systems is often limited by the high computational burden associated to the adoption of process-based models. In this paper we propose a procedural approach for the construction of simple, computationally efficient models that emulate the main dynamics of the original process-based model, but with reduced computational requirements, and that can thus be employed for the resolution of optimal control problems. The core of the procedure is the novel Iterative Feature Ranking algorithm, through which the most relevant variables in explaining the objective function of the control problem are selected among the large set of candidate variables associated with the original process-based model. The final emulation model is then identified using Extremely Randomized Trees. The approach is demonstrated on a real-world case study (Tono Dam, JP).

An inflow prediction model is developed to compute flow from temperature records, taking into consideration snow-melt contribution to the flow using a Data-Based Mechanistic (DBM) modeling approach. DBM is used in order to keep at a minimum all the a-priori assumptions on the physical mechanism driving the flow formation process and to provide an a-posteriori meaningful interpretation of the model structure. A simulation version of the model is also identified based on such interpretation. The two models have been applied on the Jakulsa river basin, Iceland.

Although great progress has been made in the last 40 years, efficient operation of water reservoir systems still remains a very active research area. The combination of multiple water uses, non-linearities in the model and in the objectives, strong uncertainties in inputs and high dimensional state make the problem challenging and intriguing. The purpose of this paper is to review, in a strict Control Theory perspective, recent and significant advances in designing management policies for water reservoir networks, under economic, social and environmental constraints. A general and thorough problem formulation is provided, along with a description of traditional solution techniques, their limitations and possible alternative approaches.

In this paper an approach for coupling real-time control and socio-economic issues in participatory river basin planning is presented through a case study. It relies on the use of Bayesian Networks (Bns) to describe in a probabilistic way the behaviour of farmers within an irrigation district in response to some planning actions. Bayesian Networks are coupled with classical stochastic hydrological models in a decision-making framework concerning the real-time control of a water reservoir network. The approach is embedded within the framework of the Participatory and Integrated Planning (PIP) procedure.

Stochastic dynamic programming (SDP) can improve the management of a multipurpose water reservoir by generating management policies which are efficient with respect to the management objectives (flood protection, water supply for irrigation, hydropower generation, etc.). The improvement in efficiency is even more remarkable for networks of reservoirs. Unfortunately, SDP is affected by the well-known ‘curse of dimensionality’, i.e. computational time and computer memory occupation increase exponentially with the dimension of the problem (number of reservoirs), and the problem rapidly becomes intractable. Neuro-dynamic programming (NDP) can sensibly mitigate this limitation by approximating Bellman functions with artificial neural networks (ANNs). In this paper the application of NDP to the problem of the management of reservoir networks is introduced. Results obtained in a real-world case study are finally presented.

Bayesian Networks (Bns) are emerging as a valid approach for modelling and supporting decision making in the field of water resource management. Based on the coupling of an interaction graph to a probabilistic model, they have the potential to improve participation and allow integration with other models. The wide availability of ready-to-use software with which Bn models can be easily designed and implemented on a PC is further contributing to their spread. Although a number of papers are available in which the application of Bns to water-related problems is investigated, the majority of these works use the Bn semantics to model the whole water system, and thus do not discuss their integration with other types of model. In this paper some pros and cons of adopting Bns for water resource planning and management are analyzed by framing their use within the context of a participatory and integrated planning procedure, and exploring how they can be integrated with other types of models.

Advances in Environmental monitoring systems are making a wide range of data available at increasingly higher temporal and spatial resolution. This creates an opportunity to enhance real-time understanding of water systems conditions and to improve prediction of their future evolution, ultimately increasing our ability to make better decisions. Yet, many water systems are still operated using very simple information systems, typically based on simple statistical analysis and the operator's experience. In this work, we propose a framework to automatically select the most valuable information to inform water systems operations supported by quantitative metrics to operationally and economically assess the value of this information. The Hoa Binh reservoir in Vietnam is used to demonstrate the proposed framework in a multiobjective context, accounting for hydropower production and flood control. First, we quantify the expected value of perfect information, meaning the potential space for improvement under the assumption of exact knowledge of the future system conditions. Second, we automatically select the most valuable information that could be actually used to improve the Hoa Binh operations. Finally, we assess the economic value of sample information on the basis of the resulting policy performance. Results show that our framework successfully select information to enhance the performance of the operating policies with respect to both the competing objectives, attaining a 40% improvement close to the target trade-off selected as potentially good compromise between hydropower production and flood control.

Many water resources systems include multiple, independent, and distributed decision makers representing different and conflicting interests. In much of the water resources literature, the operation of these systems is studied assuming a fully cooperative attitude by the parties involved and maximizing the global efficiency at the system-level. However, assuming the presence of a social planner might be questionable when multiple institutions are involved, particularly in transboundary systems. At the other extreme, totally uncoordinated strategies among institutionally independent decision makers, acting according to the principle of individual-rationality, are more often experienced in these contexts, yielding a decrease in the system-level performance. In this paper, a novel approach is proposed based on multiagent systems to support the design of regulatory mechanisms, which drive the originally fully independent decision makers towards a more coordinated and system-wide efficient situation. T...

This paper contributes a novel High-Performance Integrated Control framework to support the real-time operation of urban water supply storages affected by water quality problems. We use a 3-D, high-fidelity simulation model to predict the main water quality dynamics and inform a real-time controller based on Model Predictive Control. The integration of the simulation model into the control scheme is performed by a model reduction process that identifies a low-order, dynamic emulator running 4 orders of magnitude faster. The model reduction, which relies on a semiautomatic procedural approach integrating time series clustering and variable selection algorithms, generates a compact and physically meaningful emulator that can be coupled with the controller. The framework is used to design the hourly operation of Marina Reservoir, a 3.2 Mm3 storm-water-fed reservoir located in the center of Singapore, operated for drinking water supply and flood control. Because of its recent formation from a former estuary, the reservoir suffers from high salinity levels, whose behavior is modeled with Delft3D-FLOW. Results show that our control framework reduces the minimum salinity levels by nearly 40% and cuts the average annual deficit of drinking water supply by about 2 times the active storage of the reservoir (about 4% of the total annual demand).

Integrated Water Resources Management (IWRM) is emerging as a worldwide agreed alternative to the reductionist and top-down approach that was central to the water resource management in the last century. It has been adopted by the Water Framework Directive, the most ambitious and influential water policy tool of these days, as the guiding principle for the development of River Basin Management Plans (RBMP) that will be the common tool for planning and managing water resource in Europe in the next years. Although the purpose and priorities of the RBMPS are clearly and unambiguously defined, the question of how integration and participation are to be promoted in their implementation has not been adequately addressed. This might be partially attributable to the central role generally assigned to the modelling issues with respect to the decision-making process, which is behind the definition of RBMPS and of which models are an essential – but not the only – element. This paper argues the need for a methodological approach to give a first answer to the question posed above and proposes a Participatory and Integrated Planning (PIP) procedure developed for that purpose. The PIP procedure is a 9 phases procedure that, starting from the identification of the goals of the planning activity, ends with a negotiation process among the stakeholders that produces a set of compromise alternatives to be submitted to the decision maker(s) for the final political decision. The procedure is presented both in its theoretical aspects and as an application to the planning of the Lake Maggiore, a transboundary water system between Italy and Switzerland. This application came out with a solution that will probably close a long-standing controversial between the two countries: it is strongly supported by the stakeholders of both the sides and the international agreement it requires is presently under consideration of the Foreign Offices of the two countries. The role for Multi Objective Decision Support Systems (MODSS) as prime tools to support and operationalize the procedure in practice is finally considered.

A precipitation-temperature-flow model is developed to compute flow from raw precipitation records, taking into consideration snow-melt contribution to the flow. The model does not require other measurements than flow, temperature and raw precipitation, thus resulting particularly useful in all those situations, the majority, where these are the only observed data. A Data-Based Mechanistic (DBM) modelling approach is used in order to keep at a minimum all the a-priori assumptions on the physical mechanism driving the flow formation process. The model has been applied on a classical set of data (the Jakulsa river basin, Iceland) which is well known in the non-linear modellers community.

The technologies and methods for integrated planning and management of water resource systems have matured considerably over the past decades. However, relatively few of them have been actually and regularly applied in real world decisional processes. We feel this is essentially due to a general lack of engagement of stakeholders and decision makers at every stage of the decisional process. Innovative methodologies and tools to improve participation are presented, with focus on water reservoir systems.

In this study we propose a novel approach to design Pareto-optimal operating policies via many-objective direct policy search in the Dez and Karoun multireservoir system (Iran), where the dimension of the system, the dry climate, and the presence of competing demands pose a number of challenges to water planners and managers. The three power plants connected to the main reservoirs in the modeled system (i.e., Dez, Karoun, and Masjed Soleyman) account for 20% of the national hydropower generation capacity. Irrigation and domestic supply, especially to the city of Ahwaz, are also strategic objectives, along with flood protection downstream of the dams. Given the complexity of the Dez and Karoun system and the multiple interests involved, the design of Pareto-optimal operating policies via many-objective direct policy search, combined with their a posteriori evaluation, represents an effective tool to support sustainable water reservoirs management in Iran. Our preliminary results show that the proposed decision analytic framework allowed the identification of the set of Pareto-optimal policies and discovered key tradeoffs to eventually aid the selection of few candidate compromise solutions that balance the competing objectives.

This study presents a novel approach which combines direct policy search and multi-objective evolutionary algorithms to solve high-dimensional state and control space water resources problems involving multiple, conflicting, and non-commensurable objectives. In such a multi-objective context, the use of universal function approximators is generally suggested to provide flexibility to the shape of the control policy. In this paper, we comparatively analyze Artificial Neural Networks (ANN) and Radial Basis Functions (RBF) under different sets of input to estimate their scalability to high-dimensional state space problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower production and flood control, is used as a case study. Results show that the RBF policy parametrization is more effective than the ANN one. In particular, the approximated Pareto front obtained with RBF control policies successfully explores the full tradeoff space between the two conflicting objectives, while the ANN solutions are often Pareto-dominated by the RBF ones.