Integration of biological, economic and sociological knowledge by Bayesian belief networks: the interdisciplinary evaluation of potential Baltic salmon management plan

There is a growing need to evaluate fisheries management plans in a comprehensive interdisciplinary context involving stakeholders. In this paper we demonstrate a probabilistic management model to evaluate potential management plans for Baltic salmon fisheries. The analysis is based on several studies carried out by scientists from respective disciplines. The main part consisted of biological and ecological stock assessment with integrated economic analysis of the commercial fisheries. Recreational fisheries were evaluated separately. Finally, a sociological study was conducted aimed at understanding stakeholder perspectives and potential commitment to alternative management plans. In order to synthesize the findings from these disparate studies a Bayesian Belief Network (BBN) methodology is used. The ranking of management options can depend on the stakeholder perspective. The trade-offs can be analysed quantitatively with the BBN model by combining, according to the decision maker’s set of priorities, utility functions that represent stakeholders’ views. We show how BBN can be used to evaluate robustness of management decisions to different priorities and various sources of uncertainty. In particular, the importance of sociological studies in quantifying uncertainty about the commitment of fishermen to management plans is highlighted by modelling the link between commitment and implementation success.Baltic salmon, bio-economic modelling, Bayesian Belief Network, expert knowledge, fisheries management, commitment and implementation uncertainty, management plan, recreational fisheries, stakeholders., Resource /Energy Economics and Policy,

The use of participatory object-oriented Bayesian networks and agro-economic models for groundwater management in Spain

This paper describes the development of a participatory decision support system for water management in the Upper Guadiana basin in central Spain where there has long been competition for groundwater resources between the agricultural sector and the environment. In the last few decades the rapid development of irrigation has led to the over-exploitation of the Mancha Occidental aquifer, the main water source in the area; this in turn has led to the loss of ecologically important wetlands. Against this background the River Basin Authority (RBA) has designed a new water management plan aimed at reducing water consumption. The objective of this paper is to evaluate the impact of these measures on both the environment and the agricultural sector. To this end stakeholders have been invited to actively participate in the development of a decision support system (DSS) based on the combination of an agro-economic model and an object-oriented Bayesian network. This DSS has been used to evaluate the trade-off between agriculture and the environment for different management options at different scales. Results indicate that achieving even a partial recovery of the aquifer water levels will require strict enforcement by the RBA of water restrictions on farmers combined with a high offer price for the purchase of water rights. However, compliance with water restrictions inevitably leads to losses in farm income, especially in small vineyard farms, unless additional measures are taken to compensate for those potential losses. The purchase of water rights alone is insufficient to ensure the recovery of water levels; accompanying measures included in the new regional management plan will also need to be undertaken

mWater prototype review

This document reviews our current water policy-making decision-support framework, build on top of a regulated open Multi-Agent System (MAS),mWater [BGG+10, GGG+11], that models a flexible water-rights market. Our simulator focuses on the effect of regulations on demand and thus provides means to explore the interplay of norms and conventions that regulate trading (like trader eligibility conditions, tradeable features of rights, trading periods and price-fixing conventions), the assumptions about agent behaviour (individual preferences and risk attitude, or population profile mixtures) and market scenarios (water availability and use restrictions). A policy-maker would then assess the effects of those interactions by observing the evolution of the performance indicators (efficiency of use, price dynamics, welfare functions) (s)he designs. 1.2 OurBotti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Noriega, P. (2013). mWater prototype review. http://hdl.handle.net/10251/3212

Application of bayesian networks to assess water poverty

The conventional approaches to water assessment are inappropriate for describing the increasing complexity of water issues. Instead, an integrated and holistic framework is required to capture the wide range of aspects which are influencing sustainable development of water resources. It is with this in mind that the Water Poverty Index (WPI) was created, as an interdisciplinary policy tool to assess water stress that links physical estimates of water availability with the socio-economic drivers of poverty. In parallel, in light of the investments envisaged for the next decade to reach the sector targets set by the Millennium Development Goals (MDGs), appropriate Decision Support Systems (DSS) are required to inform about the expected impacts to be achieved throughout these interventions. This would provide water managers with adequate information to define strategies that are efficient, effective, and sustainable. The paper explores the use of object oriented Bayesian networks (ooBn) as a valid approach for supporting decision making in water resource planning and management. On the basis of the WPI, a simple ooBn model has been designed and applied to reflect the main issues that determine access to safe water and improved sanitation. A pilot case study is presented for the Turkana district, in Kenya, where the Government has launched a national program to meet sector targets set out in the MDGs. Main impacts of this initiative are evaluated and compared with respect to the present condition. The study concludes that this new approach is able to accommodate local conditions and represent an accurate reflection of the complexities of water issues. Such a tool helps decision-makers to assess the effects of sector-related development policies on the variables of the index, as well as to analyse different future scenarios.Postprint (published version

mWater Prototype 3

This report concerns the application of a regulated open Multi-Agent System (MAS), mWater, that uses intelligent agents to simulate a flexible water-right market. Our simulator focuses on demands and, in particular, on the type of regulatory (in terms of norms selection and agents behaviour), and market mechanisms that foster an efficient use of water while also trying to prevent conflicts among parties. In this scenario, a MAS plays a vital role as it allows us to define different norms, agents behaviour and roles, and assess their impact in the market, thus enhancing the quality and applicability of its results as a decision support tool.Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Noriega, P.; Gimeno, J. (2013). mWater Prototype 3. http://hdl.handle.net/10251/3212

Hydrology-oriented forest management trade-offs. A modeling framework coupling field data, simulation results and Bayesian Networks

[EN] Hydrology-oriented forest management sets water as key factor of the forest management for adaptation due to water is the most limiting factor in the Mediterranean forest ecosystems. The aim of this study was to apply Bayesian Network modeling to assess potential indirect effects and trade-offs when hydrology-oriented forest management is applied to a real Mediterranean forest ecosystem. Water, carbon and nitrogen cycles, and forest fire risk were included in the modeling framework. Field data from experimental plots were employed to calibrate and validate the mechanistic Biome-BGCMuSo model that simulates the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere. Many other 50-year long scenarios with different conditions to the ones measured in the field experiment were simulated and the outcomes employed to build the Bayesian Network in a linked chain of models. Hydrology-oriented forest management was very positive insofar as more water was made available to the stand because of an interception reduction. This resource was made available to the stand, which increased the evapotranspiration and its components, the soil water content and a slightly increase of deep percolation. Conversely, Stemflow was drastically reduced. No effect was observed on Runof due to the thinning treatment. The soil organic carbon content was also increased which in turn caused a greater respiration. The long-term effect of the thinning treatment on the LAI was very positive. This was undoubtedly due to the increased vigor generated by the greater availability of water and nutrients for the stand and the reduction of competence between trees. This greater activity resulted in an increase in GPP and vegetation carbon, and therefore, we would expect a higher carbon sequestration. It is worth emphasizing that this extra amount of water and nutrients was taken up by the stand and did not entail any loss of nutrients.This study is a component of research projects: HYDROSIL (CGL2011-28776-C02-02), SILWAMED (CGL2014-58127-C3-2) and CEHYRFO-MED (CGL2017-86839-C3-2-R) funded by the Spanish Ministry of Science and Innovation and FEDER funds. The authors are grateful to the Valencia Regional Government (CMAAUV, Generalitat Valenciana), ACCIONA for their support in allowing the use of the experimental forest and for their assistance in carrying out the fieldwork.Garcia-Prats, A.; González Sanchis, MDC.; Campo García, ADD.; Lull, C. (2018). Hydrology-oriented forest management trade-offs. A modeling framework coupling field data, simulation results and Bayesian Networks. The Science of The Total Environment. 639:725-741. https://doi.org/10.1016/j.scitotenv.2018.05.134S72574163

A Bayesian network to predict coastal vulnerability to sea level rise

This paper is not subject to U.S. copyright. The definitive version was published in Journal of Geophysical Research 116 (2011): F02009, doi:10.1029/2010JF001891.Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems contributes to large uncertainties in predicting long-term sea level rise impacts. Here we explore and demonstrate the capabilities of a Bayesian network (BN) to predict long-term shoreline change associated with sea level rise and make quantitative assessments of prediction uncertainty. A BN is used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN is used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates. Results demonstrate that the probability of shoreline retreat increases with higher rates of sea level rise. Where more specific information is included, the probability of shoreline change increases in a number of cases, indicating more confident predictions. A hindcast evaluation of the BN indicates that the network correctly predicts 71% of the cases. Evaluation of the results using Brier skill and log likelihood ratio scores indicates that the network provides shoreline change predictions that are better than the prior probability. Shoreline change outcomes indicating stability (−1 1 m/yr) was not well predicted. We find that BNs can assimilate important factors contributing to coastal change in response to sea level rise and can make quantitative, probabilistic predictions that can be applied to coastal management decisions.Funding for this work was provided by the USGS Coastal and Marine Geology and Global Change Research programs

Bayesian networks modelling in support to cross-cutting analysis of water supply and sanitation in developing countries

Despite the efforts made towards the Millennium Goals targets during the last decade, access to improved Water Supply or basic Sanitation remains still not accessible for millions of people across the World. This paper proposes a set of models that use 25 key variables from the WatSan4Dev dataset and country profiles involving Water Supply and Sanitation (Dondeynaz et al 2012). This paper proposes the use of Bayesian Network modelling methods because adapted to the management of non-normal distribution, and integrate a qualitative approach for data analysis. They also offer the advantage to integrate preliminary knowledge into the probabilistic models. The statistical performance of the proposed models ranges between 80 and 95% which is very satisfactory taking into account the strong heterogeneity of variables. Probabilistic scenarios run from the models allow a quantification of the relationships between human development, external support, governance aspects, economic activities and Water Supply and Sanitation (WSS) access. According to models proposed in this paper, a strong poverty reduction will induce an increment of the WSS access equal to 75-76% through: 1) the organisation of on-going urbanisation process to avoid slums development; and, 2) the improvement of health care for instance for children. On one side, improving governance, such as institutional efficiency, capacities to make and apply rules or control of corruption will also have a positive impact on WSS sustainable development. The first condition for an increment of the WSS access remains of course an improvement of the economic development with an increment of household income. Moreover, a significant country environmental commitment associated with civil society freedom of expression constitutes a favourable environment for sustainable WSS services delivery. Intensive agriculture through irrigation practises also appears as a mean for sustainable WSS thanks to multi-uses and complementarities. Strong and structured agriculture sector facilitates rural development in areas where WSS access often steps behind compared to urban areas . External financial support, named Official Development Aid (ODA), plays a role in WSS improvement but comes last in the sensitivity analyses of models. This aid supports first poor countries at 47%, and is associated to governance aspects: 1) political stability and 2) country environmental commitment and civil society degree of freedom. These governance aspects constitute a good framework for aid implementation in recipient countries. Modelling is run with the five groups of countries as defined in Dondeynaz et al 2012. Models for profile 4 (essential external support) and profile 5 (primary material consumption) are specifically detailed and analysed in this paper. For countries in profile 4, to fight against water scarcity and desertification pressure should be the priority. However, for countries in profile 5, efforts should first concentrate on political stability consolidation while supporting economic activity diversification. Nevertheless, for both profiles, reduction of poverty should remain the first priority as previously indicated.JRC.H.1-Water Resource

Bayesian networks in environmental modeling

Bayesian networks (BNs), also known as Bayesian belief networks or Bayes nets, are a kind of probabilistic graphical model that has become very popular to practitioners mainly due to the powerful probability theory involved, which makes them able to deal with a wide range of problems.The goal of this review is to show how BNs are being used in environmental modelling. We are interested in the application of BNs, from January 1990 to December 2010, in the areas of the ISI Web of Knowledge related to Environmental Sciences. It is noted that only the 4.2% of the papers have been published under this item. The different steps that configure modelling via BNs have been revised: aim of the model, data preprocessing, model learning, validation and software. Our literature review indicates that BNs have barely been used for Environmental Science and their potential is, as yet, largely unexploited

mWater Analysis

The mWater scenario requires the expression and use of regulations of different sorts: from actual laws and regulations issued by governments, to policies and local regulations issued by basin managers, to social norms that prevail in a given community of users. Some will be regimented as part of the electronic institutional framework specification, but others need to be expressed in a declarative form so that one may reason about them, both off- and on-line, both at design and at run time, and both from the institutional (or legislative) perspective and the agent's individual perspective. Issues that are relevant in this respect range from the choice of expressive formalism to the decision-making strategies that agents might use to comply or disobey regulations. Thus, structural aspects like governance, dynamics of norms (also from the legislative and execution perspectives) as well as criteria to evaluate the effectiveness of norms may and need to be explored in the demonstrator.Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Igual, F.; Noriega, P.; Igual (2013). mWater Analysis. http://hdl.handle.net/10251/3210