Integrating economic values and catchment modelling

Integrated catchment policies are widely used to manage natural resources in Australian catchments. Decision support tools available to aid integrated catchment management are often limited in their integration of environmental processes with socio-economic systems. Fully integrated models are required to support assessments of the environmental and economic trade-offs of catchment management changes. A Bayesian Network (BN) model is demonstrated to provide a suitable approach to integrate environmental modelling with economic valuation. The model incorporates hydrological, ecological and economic models for the George catchment in Tasmania. Information about the non-market costs and benefits of environmental changes is elicited using Choice Experiments, allowing an assessment of the efficiency of alternative management scenarios.Integrated catchment modelling, Bayesian networks, Uncertainty, Environmental values, Non-market valuation, Choice Modelling.,

Integrated Hydro-Economic Modelling: Challenges and Experiences in an Australian Catchment

Integrated catchment policies are widely used to manage natural resources in Australian catchments. Integration of environmental processes with socio-economic systems is often difficult due to the limitations of decision support tools. To support assessments of the environmental and economic trade-offs of changes in catchment management, fully integrated models are needed. This research demonstrates a Bayesian Network (BN) approach to integrating environmental modelling with economic valuation. The model incorporates hydrological, ecological and economic models for the George catchment in Tasmania. Choice experiments were used to elicit information about the non-market costs and benefits of environmental changes. This allows the efficiency of alternative management scenarios to be assessed.Hydro-economic modelling, Integrated catchment modelling, Ecological modelling, Valuation, Bayesian networks, Water quality, Community/Rural/Urban Development, Environmental Economics and Policy, Land Economics/Use,

Transferring and extrapolating estimates of cost-effectiveness for water quality outcomes: Challenges and lessons from the Great Barrier Reef

In recent decades the declining health of the Great Barrier Reef has led to a number of government policies being implemented to reduce pollutant loads from the adjacent agricultural-based catchments. There is increasing use of cost-effectiveness measures to help prioritise between different programs and actions to reduce pollutants, given limited resources and the scale of the issues. However there are a small number of primary studies available, and the consistency of cost-effectiveness measures and their application is limited, particularly given the various uncertainties that underlie the measures. Unlike Europe and the United States of America water policy or benefit transfer approaches, there are no procedural guidance studies that must be followed in the context of the Great Barrier Reef catchments. In this study we review the use of cost effectiveness estimates for pollutant reduction into the Great Barrier Reef in the context of a benefit transfer framework, where estimates of costs from a particular case study are transferred to various scenarios within different catchments. The conclusions suggest a framework be developed for the Great Barrier Reef, which is consistent, transparent, and rigorous

Cost-effectiveness analysis in reducing nutrient loading in Baltic and Black Seas: A review

Eutrophication represents a global environmental pressure that necessitates international co-operation and the diffusion of information to avoid information asymmetries, the construction of an appropriate legislative framework, the development of monitoring technologies and scientific research to provide the evidence base for any policy interventions. The health condition of the Baltic and Black Seas has deteriorated over a long period due to increases in nutrient inputs from anthropogenic and non-anthropogenic sources. The current report aims at providing a review of the literature and defining the possible gaps concerning (1) the attempts at regulatory intervention to address the problem of eutrophication in the Baltic and Black Seas, (2) the methodological issues in constructing a cost-effectiveness analysis, (3) the available applications of cost-effectiveness studies conducted and (4) the uncertainties and risks entailed in the cost-effectiveness studies

Risk control in recycled water schemes

Recycled water is becoming one of the indispensable and reliable water resources at present. When it is introduced as an alternative source, risks on human health and the environment become major constraints driving the application and extension of recycled water. The authors examine the sources and associated risks of recycled water and introduce the practical risk control technologies on various end uses. They also review some existing risk assessment models by comparing their strengths and weaknesses toward the good approach of integrated modeling. Some critical suggestions on risk management and communication are made based on the given information. © 2013 Taylor and Francis Group, LLC

Cost-effectiveness of changing land management practices in sugarcane and grazing to obtain water quality improvements in the Great Barrier Reef: Evaluation and synthesises of existing knowledge

This report aims to shape future assessments of cost-effectiveness and profitability of practice change within the Paddock to Reef Program for improved Great Barrier Reef (GBR) outcomes. A framework is provided to ensure that costs are more reconcilable and comparative. This will assist with ensuring the best return on investment is received for future government funding programs designed to address GBR water quality. The report evaluates and synthesises peer reviewed and published research on cost-effectiveness and profitability of changing land management practices in sugarcane and grazing land production systems for water quality improvements in catchments adjacent to the Great Barrier Reef. Methodological approaches to cost-effectiveness, key determinants of cost, assumptions and limitations in bio-physical modelling, and profitability in the literature have all been examined. The scope of the literature search included all grey and published literature on international, national, and Great Barrier Reef studies on paddock/property, region/catchment, and country levels

Development of Remote Sensing Assisted Water Quality Nowcasting and Forecasting Models for Coastal Beaches

A remote sensing assisted water quality modeling framework is developed in this dissertation for nowcasting and forecasting recreational water quality of Holly Beach in Louisiana, USA. The modeling framework is composed of four models/systems: (1) an Artificial Neural Network (ANN) model (Model 1) and an US EPA Virtual Beach (VB) Program-based model for predicting early morning enterococci (ENT) levels in beach waters; (2) an ANN model (Model 2) and an VB model for predicting early morning Fecal Coliform (FC) levels in beach waters; (3) a remote sensing assisted modeling system (Model 3) for predicting near real time ENT levels during daytime; and (4) a hybrid probabilistic/deterministic modeling approach (Model 4) for predicting the probability of beach water quality violation. New findings from Model 1 include (1) the identification of 7 explanatory variables and various combinations of the 7 variables responsible for the ENT level in coastal beach waters; and (2) Model 1 with Linear Correlation Coefficient (LCC) of 0.857 performs consistently better than the VB model with LCC of 0.320. A major finding from Model 2 is that a total of 6 independent environmental variables along with 8 different combinations are capable of explaining about 76% of variation in FC levels for model training data and 44% for independent data. Major new contributions made in Model 3 include (1) development of remote sensing algorithms for turbidity using Terra and Aqua satellite data; (2) development of an enhanced ANN model for predicting ENT levels at sunrise time by taking into account the cumulative effect of solar radiation on ENT inactivation; (3) development of a real-time model for predicting ENT level during the daytime by considering the turbidity effect on ENT inactivation. A novel feature of Model 4 (hybrid model) is the combination of advantages of a deterministic ANN model and a probabilistic Bayesian model. The hybrid model is capable of reproducing 86.25% of historical beach water quality advisories with 6.39% of false positive predictions and 7.36% of false negative predictions over the past 7-years. Applications of the models will improve the management of recreational beaches and the protection of public health

Modelling tools for cost-effective water management

Vlaanderen als regio wordt geconfronteerd met een hele reeks van waterbeheerskwesties zoals een ontoereikende oppervlaktewater- en grondwaterkwaliteit, een toenemend risico op overstromingen, sedimentbeheer en een slechte ecologische kwaliteit. Het identificeren van kosten-effectieve maatregelenprogramma’s die in staat zijn om deze kwesties geheel of gedeeltelijk op te lossen tegen zo laag mogelijke kosten is hierbij een belangrijke stap. De doelstelling van dit onderzoek is modellen en instrumenten te ontwikkelen die beleidsmakers ondersteunen bij het samenstellen van kosten-effectieve maatregelenprogramma’s voor waterbeleid. Belangrijk hierbij is dat de modellen enerzijds geschikt zijn om besluitvorming te ondersteunen op nationale of regionale schaal (macro-schaal), maar anderzijds ook gedetailleerd genoeg zijn om inzichten te geven op het lokale project-niveau (micro-schaal). Toepassingen die o.a. aan bod komen zijn een kosten-effectiviteitsanalyse voor oppervlaktewaterkwaliteit, een kosten-batenanalyse voor risico-gebaseerd overstromingsbeheer en hoe door de waardering van ecosysteemdiensten win-win situaties kunnen geïdentificeerd worden voor diverse wateraspecten gelijktijdig

Supporting uncertain policy decisions for global catastrophic risks

The three articles in this dissertation explore the contested, multi-dimensional concept of uncertainty and how experts and decision makers collectively grapple with it at governance organizations tasked with addressing global catastrophic risks (GCRs). This project examines the foundational concept of uncertainty and then explores “decision support” dynamics at the National Aeronautics and Space Administration (NASA) and the Intergovernmental Panel on Climate Change (IPCC) – the primary knowledge brokers in the governance regimes addressing planetary defense and climate change respectively. Article #1 begins by examining the contested, multidimensional concept of uncertainty itself. The paper presents a critical analysis of the conceptual literature on uncertainty that has become increasingly standardized behind the tripartite distinction between uncertainty location, uncertainty level, and the nature of uncertainty. I argue that the epistemological foundation on which this framework is built is both vague and inconsistent. Perhaps most surprising is its exclusion of the term “confidence” – which has become the dominant perspective for characterizing and communicating uncertainty in many disciplines and policy contexts today. This article reinterprets the tripartite framework from a Bayesian epistemological perspective, which views uncertainty as a mental phenomenon arising from “confidence deficits” as opposed to the ill-defined notion of “knowledge deficits” that dominates the literature. I propose a more consistent set of rules for determining when uncertainty may or may not be quantified, a clarification of the terms “ignorance” and “recognized ignorance,” and an expansion of the “level” dimension to include levels of uncertainty reducibility. Lastly, I challenge the usefulness of the conventional distinction made between aleatory and epistemic uncertainty and propose a more useful distinction based on developments in the field of complexity science that highlights the unique properties of complex reflexive (i.e. human) systems. Article #2 explores the decision support process of uncertainty reduction. “Mission-oriented” public research organizations like NASA invest in R&D to improve decision-making around complex policy problems, thus producing “public value.” However, the estimation of benefits produced by such R&D projects is notoriously difficult to predict and measure – a challenge that is magnified for GCRs. This article explores how public research organizations systematically reduce key uncertainties associated with GCRs. Building off of recent literature highlighting the organizational and political factors that influence R&D priority-setting at public research organizations, this article develops an analytical framework for explaining R&D priority-setting outcomes that integrates the key stages of decision analysis with organizational and political dynamics identified in the literature. This framework is then illustrated with a case study of the NASA planetary defense mission, which addresses the GCR of near-Earth object (asteroid and comet) impacts. The case study reveals how organizational and political factors interact with every stage in the R&D priority-setting process – from initial problem definition to project selection. Lastly, the article discusses the extent to which the case study can inform R&D priority-setting at other mission-oriented organizations, particularly those addressing GCRs. Article #3 investigates the decision support process of uncertainty communication. The uncertainty language framework used by the IPCC is designed to encourage the consistent characterization and communication of uncertainty between chapters, working groups, and reports. However, the framework has not been updated since 2010, despite criticism that it was applied inconsistently in the Fifth Assessment Report (AR5) and that the distinctions between the framework’s three language scales remain unclear. This article presents a mixed methods analysis of the application – and underlying interpretation – of the uncertainty language framework by IPCC authors in the three special reports published since AR5. First, I present an analysis of uncertainty language term usage in three recent special reports: Global Warming of 1.5°C (SR15), Climate Change and Land (SRCCL), and The Ocean and Cryosphere in a Changing Climate (SROCC). The language usage analysis highlights how many of the trends identified in previous reports – like the significant increase in the use of confidence terms – have carried forward into recent assessments. These observed trends, along with ongoing debates in the literature on how to interpret the framework’s three language scales inform an analysis of IPCC author experiences interpreting and implementing the framework. This discussion is informed by interviews with lead authors from the SRCCL and SROCC. Lastly, I propose several recommendations for clarifying the IPCC uncertainty language framework to address persistent sources of confusion highlighted by the authors

Through a Model, Darkly: An Investigation of Modellers’ Conceptualisation of Uncertainty in Climate and Energy Systems Modelling and an Application to Epidemiology

Policy responses to climate change require the use of complex computer models to understand the physical dynamics driving change, to evaluate its impacts and to evaluate the efficacy and costs of different mitigation and adaptation options. These models are often complex and built by large teams of dedicated researchers. All modelling requires assumptions, approximations and analytic conveniences to be employed. No model is without uncertainty. Authors have attempted to understand these uncertainties over the years and have developed detailed typologies to deal with them. However, it remains unknown how modellers themselves conceptualise the uncertainty inherent in their work. The core of this thesis involves the interviews of 38 modellers from climate science, energy systems modelling and integrated assessment to understand how they conceptualise the uncertainty in their work. This study finds that there is diversity in how uncertainty is understood and that various concepts from the literature are selectively employed to organise uncertainties. Uncertainty analysis is conceived as consisting of different phases in the model development process. The interplay between the complexity of the model and the capacities of modellers to manipulate these models shapes the ways in which uncertainty can be conceptualised. How we can attempt to wrangle with uncertainty in the present is determined by the path-dependent decisions made in the past; decisions that are influenced by a variety of factors within the context of the model’s creation. Furthermore, this thesis examines the application of these concepts to another field, epidemiology, to examine their generalisability in other contexts. This thesis concludes that in a situation such as climate change, where the nature of the problem changes in a dynamic way, emphasis should be placed on reducing the grip of these path dependencies and the resource costs of adapting models to face new challenges and answer new policy questions