Integrated Modelling for Understanding Watershed Development Impacts on Social and Biophysical Systems

The intention of watershed development (WD) programs in India is to improve the livelihoods of people and preserve the natural resource base, particularly in areas where water scarcity limits the development potential of rural communities. In practice, there are many complications to implementing WD programs in an effective and equitable way for all people within and between villages in a catchment. Our understanding of the potential implications of a program is often limited by the way in which we investigate the biophysical-social-economic system. Two common failings are (a) not properly considering the importance of the place, scope and scale of a problem and (b) using a disciplinary approach to make conclusions about the system as a whole. This paper discusses how we are addressing these issues as part of an integrated assessment project looking at WD in the state of Andhra Pradesh, India. The multi-disciplinary project team includes agronomists, economists, environmental modellers, groundwater and surface water hydrologists, and social scientists who together are aiming to develop a holistic understanding of the impacts of WD on biophysical, social and economic systems. Key to the project philosophy is the inclusion of government representatives, communities, and non-government organisations (NGOs) in developing the researchers\u27 understanding of the issues and complexities associated with WD and the critical questions that need addressing by the project. An integrated model is being developed that will incorporate crop production water use and hydrological (surface water and groundwater) models in addition to knowledge gained from extensive household surveys in villages in two case study catchments. The household surveys were developed based on discussions with NGOs working with the rural communities in Andhra Pradesh and are being used to examine economic and social outcomes (positive and negative) of WD for households. Measures of equity and resilience are being developed to measure differences in outcomes between villages (e.g. upstream, downstream) and within villages (e.g. income groups, gender, land ownership, etc)

Using Bayesian belief networks to support health risk assessment for sewer workers

Abstract The sanitary sewerage connection rate is an important indicator of advanced cities. Following the construction of sanitary sewerages, the maintenance and management systems are required for keeping pipelines and facilities functioning well. These maintenance tasks often require sewer workers to enter the manholes and the pipelines, which are confined spaces short of natural ventilation and have the potential for hazardous substances to be present. Working in sewers could be easily exposed to a risk of adverse health effects. This paper proposes the use of Bayesian belief networks as a higher level of noncarcinogenic health risk assessment of sewer workers. On the basis of the epidemiological studies, the actual hospital attendance records and expert experiences, the Bayesian belief networks is capable of capturing the probabilistic relationships between the hazardous substances in sewers and their adverse health effects, and accordingly inferring the morbidity and mortality of the adverse health effects. The provision of the morbidity and mortality rates of the related diseases is more informative and can alleviate the drawbacks of conventional methods

Factors influencing actors at the interface between the socio-technical and the ecological systems: The case of on-site sewage systems and eutrophication

Eutrophication, caused by nutrient loading, is a global environmental problem, particularly severe for the Baltic Sea. Long-term solutions rely on the ability of society to respond effectively. This involves, for example, instigating actors who have the capability to directly influence nutrient loads to take action. In Sweden, one of the substantial sources of nutrients is on-site sewage systems (OSS), that is, wastewater treatment systems for one or a few households. The aim of this thesis is to increase the knowledge about factors influencing actors that directly and indirectly influence nutrient loads from Swedish OSS by (1) Identify factors influencing homeowners’ to change OSS, and (2) Investigate the relative strength of these factors. Semi-structured interviews with homeowners and authority inspectors were used to elicit tentative conceptualizations of influencing factors. The results together with literature on pro-environmental and compliance behavior were used in the construction of a questionnaire, directed at Swedish homeowners with OSS. The analysis involved statistical methods, including principal components analysis to identify underlying motivational factors, and regression analysis to investigate the relative strength of these factors, while controlling for government and authority interventions. The result shows that authority interventions are needed to make homeowners change OSS. Informal means, such as information, will not likely lead to a large-scale transformation of OSS. They may, however, be effective in combination with more deterrent means of regulatory enforcement, such as injunctions. Such interventions are needed because of the high costs and no, or very low, benefits of changing OSS from the homeowner point of view. Furthermore, the awareness about the need to change OSS from an environmental point of view seems to be low. In this situation, homeowners are most inclined to defend their self-interest and do not act based on environmental concerns. Homeowners are more inclined to change OSS under the condition that other homeowners are cooperating

Using Bayesian Networks to complement conventional analyses to explore landholder management of native vegetation

Influencing the management of private landholders is a key element of improving the condition of Australia's natural resources. Despite substantial investment by governments, effecting behavioural change on a scale likely to stem biodiversity losses has proven difficult. Understanding landholder decision-making is now acknowledged as fundamental to achieving better policy outcomes. There is a large body of research examining landholder adoption of conservation practices. Social researchers are able to employ a suite of conventional techniques to analyse their survey data and assist in identifying significant and causal relationships between variables. However, these techniques can be limited by the type of data available, the breadth of issues that can be represented and the extent that causality can be explored. In this paper we discuss the findings of a unique study exploring the benefits of combining Bayesian Networks (BNs) with conventional statistical analysis to examine landholder adoption. Our research examined the landholder fencing of native bushland in the Wimmera region in south east Australia. Findings from this study suggest that BNs provided enhanced understanding of the presence and strength of causal relationships. There was also the additional benefit that a BN could be quickly developed and that this process helped the research team clarify and understand relationships between variables. However, a key finding was that the interpretation of the results of the BNs was complemented by the conventional data analysis and expert review. An additional benefit of the BNs is their capacity to present key findings in a format that is more easily interpreted by researchers and enables researchers to more easily communicate their findings to natural resource practitioners and policy makers

A framework for characterising and evaluating the effectiveness of environmental modelling

Environmental modelling is transitioning from the traditional paradigm that focuses on the model and its quantitative performance to a more holistic paradigm that recognises successful model-based outcomes are closely tied to undertaking modelling as a social process, not just as a technical procedure. This paper redefines evaluation as a multi-dimensional and multi-perspective concept, and proposes a more complete framework for identifying and measuring the effectiveness of modelling that serves the new paradigm. Under this framework, evaluation considers a broader set of success criteria, and emphasises the importance of contextual factors in determining the relevance and outcome of the criteria. These evaluation criteria are grouped into eight categories: project efficiency, model accessibility, credibility, saliency, legitimacy, satisfaction, application, and impact. Evaluation should be part of an iterative and adaptive process that attempts to improve model-based outcomes and foster pathways to better futures

A Framework and System for a Multi-Model Decision Aid for Sustainable Farming Practices

Decision support systems (DSS) for farmers address the need for modeling multiple processes and scenarios that affect farmer decision making. Existing DSS have various drawbacks that stop them from being deployed as decision support tools. This research proposes a multi-model simulation framework that can be used to analyze farm management practices at the crop level, individual farm level and at the community level to show the impact and alternatives for smallholder farming practices. A generic crop growth model is proposed, based on existing equations. We run sensitivity analysis on the model to identify important variables. The outputs from the crop model are utilized in a series of linear programming models to estimate the optimal scheduling of crops. In addition to these models we build a rule-based fuzzy system to replicate existing trends among farmers. Predicting these trends help us in identifying the decision patterns of farmers and help us in conducting scenario analysis to gauge the farmers reactions to external stimuli. The various limitations and assumptions of the models are described, and we conclude with suggestions for improving these models

A new integrated modeling approach to support management decisions of water resources systems under multiple uncertainties

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational framework. Such integrative research to link different knowledge domains faces several practical challenges. The complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. This thesis aims to overcome some of these challenges, and to demonstrate how new modeling approaches can provide successful integrative water resources research. It focuses on the development of new integrated modeling approaches which allow integration of not only physical processes but also socio-economic and environmental issues and uncertainties inherent in water resources systems. To achieve this goal, two new approaches are developed in this thesis. At first, a Bayesian network (BN)-based decision support tool is developed to conceptualize hydrological and socio-economic interaction for supporting management decisions of coupled groundwater-agricultural systems. The method demonstrates the value of combining different commonly used integrated modeling approaches. Coupled component models are applied to simulate the nonlinearity and feedbacks of strongly interacting groundwater-agricultural hydrosystems. Afterwards, a BN is used to integrate the coupled component model results with empirical knowledge and stakeholder inputs. In the second part of this thesis, a fuzzy-stochastic multiple criteria decision analysis tool is developed to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrates physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approaches are applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structures. The results show the effectiveness of the proposed methods. The first method can aid in the impact assessment of alternative management interventions on sustainability of aquifer systems while accounting for economic (agriculture) and societal interests (employment in agricultural sector) in the study area. Results from the second method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. The new approaches can be applied to address the complexities and uncertainties inherent in water resource systems to support management decisions, while serving as a platform for stakeholder participation

Integrated models, frameworks and decision support tools to guide management and planning in Northern Australia. Final report

[Extract] There is a lot of interest in developing northern Australia while also caring for the unique Australian landscape (Commonwealth of Australia 2015). However, trying to decide how to develop and protect at the same time can be a challenge. There are many modelling tools available to inform these decisions, including integrated models, frameworks, and decision support tools, but there are so many different kinds that it’s difficult to determine which might be best suited to inform different decisions. To support planning and development decisions across northern Australia, this project aimed to create resources to help end-users (practitioners) to assess: 1. the availability and suitability of particular modelling tools; and 2. the feasibility of using, developing, and maintaining different types of modelling tools

Behavior Analysis and Modeling of Stakeholders in Integrated Water Resource Management with a Focus on Irrigated Agriculture

The scarcity of freshwater resources in the Sultanate of Oman, makes it essential that both surface and groundwater resources are carefully managed. Introducing new water demand management tools is important, especially for the coastal agricultural areas (e. g. Al Batinah coastal region) which are affected by sea water intrusion. Based on a social survey performed during this work, the existing situation generates conflicts between different stakeholders (SHs) which have different interests regarding water availability, sustainable aquifer management, and profitable agricultural production. The current aim is to evaluate the implementation potential of several management interventions and their combinations by analysing opinions and responses of the relevant stakeholders in the region. Influencing the behavior and drivers affecting farmers’ decision-making manner, can be a valuable tool to improve water demand management. The work also introduces the use of a participatory process within the frame of an integrated water resources management (IWRM) to support decision makers in taking better informed decisions. Data were collected by questionnaires from different groups of stakeholders. These data were analysed statistically for each group separately as well as relations amongst groups by using the SPSS (Statistical Package for Social Science) software package. Differences were examined between opinions of farmers and decision makers (DM’s) regarding potential interventions. Farmers’ frequency curves showed differences in opinions in some interventions, while differences in opinions were not so high within the group of DM’s. Therefore, Cross Tabulation and Discriminant Analysis (DA) were performed to identify the drivers influencing farmers’ opinions regarding the intervention measures. As an advanced step, a Bayesian Networks (BNs) approach is used for mapping stakeholders’ behaviors and to show the strength of a relationship between dependent and predictor variables. By using BNs it is possible to analyse future scenarios for implementation and acceptance of interventions