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Abstract: Increasing worldwide water withdrawal for irrigation purposes requires a more efficient management of water resources and an accurate description of irrigation water demand. This paper, with the aim of thinking ‘in blue and green water terms’, proposes a new approach for the design of release policies in reservoir systems serving irrigation districts. It is based on the solution of an optimal control problem, where the dynamics of the irrigation demand is modelled through a metamodel, i.e. a simple model identified on the basis of the data produced by a distributed-parameter, conceptual model. The metamodel inherits the physical description of the distributed-parameter model and, at same time, is sufficiently simple to allow the solution of the optimal control problem with stochastic dynamic programming. The proposed approach is tested on a real-world case study, the management of the Lake Como system, for which it provides satisfactory results. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

Abstract: The operation of complex environmental systems usually accounts for multiple, conflicting objectives, whose presence imposes to explicitly consider the preference structure of the parties involved. Multi-objective Markov Decision Processes are a useful mathematical framework for the resolution of such sequential, decision-making problems. However, the computational requirements of the available optimization techniques limit their application to problems involving few objectives. In real-world applications it is therefore common practice to select few, representative objectives with respect to which the problem is solved. This paper proposes a dimensionality reduction approach, based on the Non-negative Principal Component Analysis (NPCA), to aggregate the original objectives into a reduced number of principal components, with respect to which the optimization problem is solved. The approach is evaluated on the daily operation of a multi-purpose water reservoir (Tono Dam, Japan) with 10 operating objectives, and compared against a 5-objectives formulation of the same problem. Results show that the NPCA-based approach provides a better representation of the Pareto front, especially in terms of consistency and solution diversity. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: This study presents a new interactive procedure for supporting Decision Makers (DMs) in environmental planning problems involving large, process-based, dynamic models and many (more than two) conflicting objectives. Because of such features of the model, computationally-onerous simulations are the only feasible way of analysis, while the multi-objective nature of the problem entails the combined use of optimization techniques and appropriate tools for the visualization of the associated Pareto frontier. The procedure proposed is based on the iterative improvement of the current best compromise alternative based on interactions with the DM. At each iteration, the DM is informed about the Pareto frontier of a local multi-objective optimization problem, which is generated by linearizing the response surfaces that describe the objectives and constraints of the original planning problem. Interactive visualization of the multi-dimensional Pareto frontier is used to support the DM in choosing the new best compromise alternative. The procedure terminates when the DM is fully satisfied with the current best compromise alternative. The approach is demonstrated in Googong Reservoir (Australia), which is periodically affected by high concentrations of Manganese and Cyanobacteria. Results indicate that substantial improvements could be observed by simply changing the location of the two mixers installed in 2007 and adding another pair of mixers. [Copyright &y& Elsevier]

Abstract: Complex decision-making problems, related to planning and management of irrigation water resources, generally preclude the use of large, distributed-parameter models, which are then commonly substituted by lumped-parameter models. This paper, with the aim of improving the quality of these latter, introduces a new approach for their design. This approach is based on metamodelling, which proposes to identify a simple, lumped-parameter model on the basis of the data produced via simulation with a distributed-parameter model. The approach proposed is tested on a real-world case study, namely the identification of a metamodel describing the water demand of the Muzza–Bassa Lodigiana irrigation district (Italy). The metamodel, which inherits the physical description of the original distributed-parameter model, is sufficiently simple to permit the resolution of an optimal control problem, i.e. the design, via stochastic dynamic programming, of the release policy of Lake Como, serving the Muzza irrigation district. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]