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Adaptive policies have emerged as a valuable strategy for dealing with uncertainties by recognising the capacity of systems to adapt over time to new circumstances and surprises. The efficacy of adaptive policies hinges on detecting on-going change and ensuring that actions are indeed taken if and when necessary. This is operationalised by including a monitoring system composed of signposts and triggers in the design of the plan. A well-designed monitoring system is indispensable for the effective implementation of adaptive policies. Despite the importance of monitoring for adaptive policies, the present literature has not considered criteria enabling the a-priori evaluation of the efficacy of signposts. In this paper, we introduce criteria for the evaluation of individual signposts and the monitoring system as a whole. These criteria are relevance, observability, completeness, and parsimony. These criteria are intended to enhance the capacity to detect the need for adaptation in the presence of noisy and ambiguous observations of the real system. The criteria are identified from an analysis of the information chain, from system observations to policy success, focusing on how data becomes information. We illustrate how models, in particular, the combined use of stochastic and exploratory modelling can be used to assess individual signposts, and the whole monitoring system according to these criteria. This analysis provides significant insight into critical factors that may hinder learning from data. The proposed criteria are demonstrated using a hypothetical case, in which a monitoring system for a flood protection policy in the Niger River is designed and tested.

International audience; Manantali is a dam located on the Senegal River and is mainly used for hydropower production. Before the dam's construction, the annual river flood alimented the flood recession agriculture, a practice based on natural irrigation and fertilization of the flood plain, used traditionally by the local populations downstream. Analysis of the actual reservoir operation shows that annual floods have been largely reduced for the benefit of hydropower production. Moreover, the Senegal River Basin authority is evaluating the construction of different new dams, which could reduce even further the water available for flood support, given that the current operational focus is on satisfying hydropower demand. This study investigates the effects of an optimal reservoir operation strategy that maximizes hydropower production only, analyzing the results of this strategy in terms of effects on the two main objectives, i.e., hydropower production and flood support. The problem of finding optimal reservoir operation strategy is solved by applying the stochastic dual dynamic programming method. Results show the existence of a release strategy in which both objectives improve (+9% for hydropower and +7% for flood production) with respect to the historically observed operation. This solution, however, may require the electric system to compensate for the variability in energy supply along the year.

Uncertainty reduces reliability and performance of water system operations. A decision-maker can take action, accepting the present uncertainty and facing its risks, or reduce uncertainty by first obtaining additional information. Information, however, comes at a cost. The decision-maker must therefore efficiently use his/her resources, selecting the information that is most valuable. This can be done by solving the Optimal Design (OD) problem. The OD problem balances the cost of obtaining new information against its benefits, estimated as the added value of better informed actions. Despite its usefulness, the OD problem is not widely applied. Solving the OD problem requires a stochastic hydro-economic model, whereas most of hydro-economic models presently used are deterministic. In this paper we introduce an innovative methodology that makes deterministic hydro-economic models usable in the OD problem. In deterministic models, Least Squares Estimation (LSE) is often used to identify unknown parameters from data, making them, and hence the whole model, de facto stochastic. We advocate the explicit recognition of this uncertainty and propose to use it in risk-based decisions and formulation of the OD problem. The proposed methodology is illustrated on a flood warning scheme in the White Cart River, in Scotland, where a rating curve uncertainty can be reduced by extra gaugings. Gauging at higher flows is more informative but also more costly, and the LSE - OD formulation results in the economically optimal balance. We show the clear advantage of monitoring based on OD over monitoring uniquely based on uncertainty reduction.

Background Food security can benefit from the technology's transparency, relatively low transaction costs and instantaneous applications. A blockchain is a distributed database of records in the form of encrypted blocks, or a public ledger of all transactions or digital events that have been executed and shared among participating parties and can be verified at any time in the future. Generally, the robust and decentralized functionality of the blockchain is used for global financial systems, but it can easily be expanded to contracts and operations such as tracking of the global supply chain. In the precision agriculture context, Information and Communications Technology can be further implemented with a blockchain infrastructure to enable new farm systems and e-agriculture schemes. Results The purpose of this review is to show a panorama of the scientific studies (enriched by a terms mapping analysis) on the use of blockchain in the agri-food sector, from both an entirely computational and an applicative point of view. As evidenced by the network analysis, the reviewed studies mainly focused on software aspects (e.g. the architecture and smart contracts). However, some aspects regarding the different blockchain knots (computers always connected to the blockchain network) having the role to store and distribute an updated copy of each block in a food supply-chain, result of crucial importance. Conclusion These technologies appear very promising and rich of great potential showing a good flexibility for applications in several sectors but still immature and hard to apply due to their complexity. © 2019 Society of Chemical Industry.

International audience; The basin of the Seine River is an extremely important economic region for France and Europe. Four reservoirs are operated to reduce the natural variability of the Seine River, reducing both flood and drought risk. Presently, reservoir operation is not centrally coordinated, and release rules are based on empirical rule curves. This study presents the setting of an optimal and centralized solution to the problem of reservoir operation on the Upper Seine-Aube river system, found by applying the stochastic dual dynamic programming (SDDP) procedure. The novelty of this study lies on the combination of reservoir and hydraulic models in SDDP for flood and drought protection. Including the hydraulic process in SDDP is required for estimating flood and drought at different locations along the river, and for representing the delay between the release from the reservoirs and their effects downstream. The study case covers the Seine basin until the confluence with the Aube River: this system includes two reservoirs, the city of Troyes, France, and, at the confluence of the two rivers, the nuclear power plant at Nogent-Sur-Seine. Results shows that the SDDP solution can be effectively used to optimize the operation of a water system made of multiple reservoirs and multiple hydraulic transfer components, solving a relatively large stochastic dynamic programming problem in an acceptable time. The management obtained from SDDP rules exploits the centralized operation and, compared to the current operational rules, results in more frequent but shorter, less intense, and less severe flood and drought events at Nogent-Sur-Seine.

Climate change raises serious concerns for policymakers that want to ensure the success of long-term policies. To guarantee satisfactory decisions in the face of deep uncertainties, adaptive policy pathways might be used. Adaptive policy pathways are designed to take actions according to how the future will actually unfold. In adaptive pathways, a monitoring system collects the evidence required for activating the next adaptive action. This monitoring system is made of signposts and triggers. Signposts are indicators that track the performance of the pathway. When signposts reach pre-specified trigger values, the next action on the pathway is implemented. The effectiveness of the monitoring system is pivotal to the success of adaptive policy pathways, therefore the decision-makers would like to have sufficient confidence about the future capacity to adapt on time. &ldquo, On time&rdquo, means activating the next action on a pathway neither so early that it incurs unnecessary costs, nor so late that it incurs avoidable damages. In this paper, we show how mapping the relations between triggers and the probability of misclassification errors inform the level of confidence that a monitoring system for adaptive policy pathways can provide. Specifically, we present the &ldquo, trigger-probability&rdquo, mapping and the &ldquo, trigger-consequences&rdquo, mappings. The former mapping displays the interplay between trigger values for a given signpost and the level of confidence regarding whether change occurs and adaptation is needed. The latter mapping displays the interplay between trigger values for a given signpost and the consequences of misclassification errors for both adapting the policy or not. In a case study, we illustrate how these mappings can be used to test the effectiveness of a monitoring system, and how they can be integrated into the process of designing an adaptive policy.

This is the first work to introduce the use of blockchain technology for the electronic traceability of wood from standing tree to final user. Infotracing integrates the information related to the product quality with those related to the traceability [physical and digital documents (Radio Frequency IDentification&mdash, RFID&mdash, architecture)] within an online information system whose steps (transactions) can be made safe to evidence of alteration through the blockchain. This is a decentralized and distributed ledger that keeps records of digital transactions in such a way that makes them accessible and visible to multiple participants in a network while keeping them secure without the need of a centralized certification organism. This work implements a blockchain architecture within the wood chain electronic traceability. The infotracing system is based on RFID sensors and open source technology. The entire forest wood supply chain was simulated from standing trees to the final product passing through tree cutting and sawmill process. Different kinds of Internet of Things (IoT) open source devices and tags were used, and a specific app aiming the forest operations was engineered to collect and store in a centralized database information (e.g., species, date, position, dendrometric and commercial information).

[Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI; International audience; This article presents an innovative streamflow process model for use in reservoir operational rule design in stochastic dual dynamic programming (SDDP). Model features, which can be applied independently, are (1) a multiplicative process model for the forward phase and its linearized version for the backward phase; and (2) a nonuniform time-step length that is inversely proportional to seasonal variability. The advantages are (1) guaranteeing positive streamflow values still maintaining problem linearity, and (2) making system adaptation faster during high variable periods. Model identification is straightforward, as with the additive periodic autoregressive model generally used in SDDP. The proposed model is applied on the Senegal River system for the optimal operation of Manantali Reservoir and evaluated against the streamflow process model currently used in the water management literature.

[Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI; International audience; Model predictive control (MPC) can be employed for optimal operation of adjustable hydraulic structures. MPC selects the control to be applied to the system by solving an optimization problem over a finite horizon in real-time. The horizon finiteness is both the reason for MPC's success and its main limitation. MPC has in fact been successfully employed for short-term reservoir management. Short-term reservoir management deals effectively with fast processes, such as flood, but it is not capable of looking sufficiently ahead to handle long-term issues, such as drought. This study proposes an infinite horizon MPC solution that deals with both short and long-term objectives, tailored for reservoir management. In the proposed solution, the control signal is structured by the use of basis functions. Basis functions reduce the optimization argument to a small number of variables, making the control problem solvable in a reasonable time. The solution is tested for the operational management of Manantali reservoir, in the Senegal River. The long-term horizon offered by infinite horizon MPC is necessary to deal with the strongly seasonal climate of the region for both flood and drought prevention.

International audience; Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate.Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts' availability and accuracy raises the question on how to use them for operational management.The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance.TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.

Dams can produce electricity and ensure water security, but at the same time they radically alter the hydrological regime of rivers with significant consequences for the economic and environmental welfare of the region in which they are located. Cost-benefit analysis (CBA) is currently the most frequently used framework for the economic evaluations of dams. Changes at different time scales influence the economic appraisal of dams. However, change and adaptation at both the operational and the structural level are often not included in the CBA evaluation. Not including change and adaptation limits the realistic estimation of cost and benefits, and the appreciation of resilient solutions that offer satisfactory responses for a large set of future scenarios. In this paper we consider the specific features of large dams in an African context, and identify methods for an economic evaluation that takes into account for change and adaptation at both the operational and the structural scales, as well as their interplay. These methods are then applied to the ex-ante evaluation of a system of existing dams on the Senegal River Valley. Results indicate the economic potential of the dams under changing conditions, for both adaptive and non-adaptive reservoir operation strategies.

Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. The control of systems under uncertainty is, in general, a challenging problem. In this paper, we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. Each branch of the tree corresponds to a possible disturbance realization and has a certain probability associated to it. A model predictive controller is used to optimize the expected value of the system variables taking into account the disturbance tree. This technique, tree-based model predictive control (TBMPC), is solved in a distributed fashion. In particular, we apply dual decomposition to get an optimization problem that can be solved by different agents in parallel. In addition, different possibilities are considered in order to reduce the communicational burden of the distributed algorithm without reducing the performance of the controller significantly. Finally, the performance of this technique is compared with others such as minmax or multiple MPC.

Fig. 5. Performance of the MPC operation with perfect forecasts (PF), deterministic forecasts (DF), and ensemble forecasts (EF) over a flood event in 2007, averaged over monitoring stations: (a) loss in performance due to forecast uncertainty (with respect to perfect forecasts); (b) total number of days with violation of the (alert) thresholds; (c) mean duration of violation events; (d) maximum duration of violation events; (e) maximum flow exceedance with respect to the alert thresholds; (f) number of monitoring stations with at least one violation event over the simulation horizon

This paper presents a new methodology to generate a tree from an ensemble. The reason to generate a tree is to use the ensemble in multistage stochastic programming. A correct tree structure is of critical importance because it strongly affects the performance of the optimization. A tree, in contrast to an ensemble, specifies when its trajectories diverge from each other. A tree can be generated from the ensemble data by aggregating trajectories over time until the difference between them becomes such that they can no longer be assumed to be similar, at such a point, the tree branches. The proposed method models the information flow: it takes into account which observations will become available, at which moment, and their level of uncertainty, i.e. their probability distributions (pdf). No conditions are imposed on those distributions. The method is well suited to trajectories that are close to each other at the beginning of the forecasting horizon and spread out going on in time, as ensemble forecasts typically are. Copyright © 2012 John Wiley & Sons, Ltd.

The paper presents a nonlinear heteroscedastic model for flow forecasting. The model is composed of two submodels: the former provides the expected value of the flow, conditional on available information, e.g. past flow and precipitation records; the latter provides the variance of the prediction error as a function of past values of the prediction error itself and precipitation measures. The proposed model is tested on a real world case study, the inflow to Lake Verbano, Italy, where the inflow forecast is used for optimizing release decisions from the lake. Results are discussed and compared with those obtained with conventional modelling approach, where the flow is estimated based on a linear model of the flow logarithm, and the variance is not given a dynamical description but is assumed to be a time-varying parameter.

International audience; This article investigates the improvement of the operation of a four-reservoir system in the Seine River basin, France, by use of deterministic and ensemble weather forecasts and real-time control. In the current management, each reservoir is operated independently from the others and following prescribed rule-curves, designed to reduce floods and sustain low flows under the historical hydrological conditions. However, this management system is inefficient when inflows are significantly different from their seasonal average and may become even more inadequate to cope with the predicted increase in extreme events induced by climate change. In this work, a centralized real-time control system is developed to improve reservoirs operation by exploiting numerical weather forecasts that are becoming increasingly available. The proposed management system implements a well-established optimization technique, model predictive control (MPC), and its recently modified version that can incorporate uncertainties, tree-based model predictive control (TB-MPC), to account for deterministic and ensemble forecasts respectively. The management system is assessed by simulation over historical events and compared to the no-forecasts strategy based on rule-curves. Simulation results show that the proposed real-time control system largely outperforms the no-forecasts management strategy, and that explicitly considering forecast uncertainty through ensembles can compensate for the loss in performance due to forecast inaccuracy.

Uncertainty of hydrological forecasts represents valuable information for water managers and hydrologists. This explains the popularity of probabilistic models, which provide the entire distribution of the hydrological forecast. Nevertheless, many existing hydrological models are deterministic and provide point estimates of the variable of interest. Often, the model residual error is assumed to be homoscedastic; however, practical evidence shows that the hypothesis usually does not hold. In this paper we propose a simple and effective method to quantify predictive uncertainty of deterministic hydrological models affected by heteroscedastic residual errors. It considers the error variance as a hydrological process separate from that of the hydrological forecast and therefore predictable by an independent model. The variance model is built up using time series of model residuals, and under some conditions on the same residuals, it is applicable to any deterministic model. Tools for regression analysis applied to the time series of residual errors, or better their absolute values, combined with physical considerations of the hydrological features of the system can help to identify the most suitable input to the variance model and the most parsimonious model structure, including dynamic structure if needed. The approach has been called dynamic uncertainty modeling by regression on absolute errors and is demonstrated by application to two test cases, both affected by heteroscedasticity but with very different dynamics of uncertainty. Modeling results and comparison with other approaches, i.e., a constant, a cyclostationary, and a static model of the variance, confirm the validity of the proposed method.

Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. In this paper we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. A model predictive controller is used to optimize the expected value of the system variables taking into account the disturbance tree. This technique, tree-based model predictive control (TBMPC), is solved in a parallel fashion by means of dual decomposition. In addition, different possibilities are explored to reduce the communicational burden of the parallel algorithm. Finally, the performance of this technique is compared with others such as minmax or multiple model predictive control.