Aligning evidence generation and use across health, development, and environment

Although health, development, and environment challenges are interconnected, evidence remains fractured across sectors due to methodological and conceptual differences in research and practice. Aligned methods are needed to support Sustainable Development Goal advances and similar agendas. The Bridge Collaborative, an emergent research-practice collaboration, presents principles and recommendations that help harmonize methods for evidence generation and use. Recommendations were generated in the context of designing and evaluating evidence of impact for interventions related to five global challenges (stabilizing the global climate, making food production sustainable, decreasing air pollution and respiratory disease, improving sanitation and water security, and solving hunger and malnutrition) and serve as a starting point for further iteration and testing in a broader set of contexts and disciplines. We adopted six principles and emphasize three methodological recommendations: (1) creation of compatible results chains, (2) consideration of all relevant types of evidence, and (3) evaluation of strength of evidence using a unified rubric. We provide detailed suggestions for how these recommendations can be applied in practice, streamlining efforts to apply multi-objective approaches and/or synthesize evidence in multidisciplinary or transdisciplinary teams. These recommendations advance the necessary process of reconciling existing evidence standards in health, development, and environment, and initiate a common basis for integrated evidence generation and use in research, practice, and policy design

Knowledge Priorities on Climate Change and Water in the Upper Indus Basin: A Horizon Scanning Exercise to Identify the Top 100 Research Questions in Social and Natural Sciences

River systems originating from the Upper Indus Basin (UIB) are dominated by runoff from snow and glacier melt and summer monsoonal rainfall. These water resources are highly stressed as huge populations of people living in this region depend on them, including for agriculture, domestic use, and energy production. Projections suggest that the UIB region will be affected by considerable (yet poorly quantified) changes to the seasonality and composition of runoff in the future, which are likely to have considerable impacts on these supplies. Given how directly and indirectly communities and ecosystems are dependent on these resources and the growing pressure on them due to ever-increasing demands, the impacts of climate change pose considerable adaptation challenges. The strong linkages between hydroclimate, cryosphere, water resources, and human activities within the UIB suggest that a multi- and inter-disciplinary research approach integrating the social and natural/environmental sciences is critical for successful adaptation to ongoing and future hydrological and climate change. Here we use a horizon scanning technique to identify the Top 100 questions related to the most pressing knowledge gaps and research priorities in social and natural sciences on climate change and water in the UIB. These questions are on the margins of current thinking and investigation and are clustered into 14 themes, covering three overarching topics of ‘governance, policy, and sustainable solutions’, ‘socioeconomic processes and livelihoods’, and ‘integrated Earth System processes’. Raising awareness of these cutting-edge knowledge gaps and opportunities will hopefully encourage researchers, funding bodies, practitioners, and policy makers to address them

Comparison of two modelling approaches for an integrated crop economic model

Integrated crop models with biophysical and economic component models were developed in order to support decisions on agricultural land productivity assessments in northern Thailand. Over the past few decades, efforts to produce higher crop yield focussed on extending crop land to increase crop yield per capita. This process included considerations of land and water quality, improved land and water use efficiency and greater involvement of farmer communities in the planning process. In order to support the planning process, decision makers needed a tool to assist in assessing the complex production system, focusing on the beginning of the cropping production process to leaving the farm. Such a tool should also support the dynamic assessment of economic land suitability for the 19 major economic crop types used in northern Thailand. The Land Development Department (LDD) in Thailand framework provides a guide to the suitability of different crop types to a range of land quality attributes. Two modelling approaches were used to develop an integrated crop-economic model based on the framework of the LDD. The first model type is a mechanistic model, which estimates crop yields using soil and climate information and estimates economic returns. To introduce uncertainty into the model, fuzzy sets were used. The second approach used a Bayesian network model. The Bayesian network was used to estimate the probability of achieving a crop yield given climate and soil input data. Economic returns in the Bayesian network were estimated using utilities. The LDD framework is used in the models to estimate crop yield and economic returns using available biophysical and economic information. This paper will introduce both models and assess the constraints that influenced the construction of the models. A set of criteria will be used to evaluate the models in order to examine their usefulness, representativeness and robustness. The comparison of models will focus on the: data type, technique, and model outcomes. The results of this comparison will help in evaluating the strengths and weaknesses of each of the modelling approaches, and based on these outcomes, recommendations on methods for building cropeconomic models will be made

Developing Bayesian network models within a Risk Assessment framework

The risk assessment framework is increasingly being applied to examine both human and non-human stressors on ecological systems. Risk-based decision-making aims to quantify the likelihood of a threat occurring, the consequences of this to an ecological system, process or value, and the associated uncertainty in the predictions. Until recently, the ability to predict changes in dynamic ecosystems due to stressors was limited by both the poor understanding of the drivers of ecological processes and structure, and the lack of modelling tools that could represent such complexity with associated uncertainties. However, the recent growth in the use of Bayesian network tools for ecological risk assessments has resulted in major advances in better understanding and managing ecosystems despite their inherent complexity. Bayesian networks have the advantage of being able to investigate the impacts of multiple stressors in complex environments, while explicitly acknowledging the associated uncertainties resulting from inherent variability and lack of knowledge of ecological systems within an adaptive framework. Bayesian networks have the flexibility to incorporate diverse knowledge systems, ranging from 'gut feel' to quantitative process-based or simulation models. In this paper, we discuss the relationships between the risk assessment framework and Bayesian network building process, and will illustrate the main concepts with a series of Bayesian network models

River restoration using simple decision support tools in the Lower Snowy River

Humans have extensively modified rivers throughout Australia. Such modifications can be direct, via changes to the natural flow regime, or indirect, such as removal of vegetation in the catchment, altering river geomorphology and changing sediment delivery to rivers. Past and ongoing human interventions have drastically changed the hydrology and geomorphology of the Snowy River, which has had a profound effect on the ecology of the system. Due to impoundments and water extraction, the Lower Snowy has seen a reduction of flows in the order of 55% (James 1989). However, environmental flows were introduced into the river in New South Wales at Jindabyne Dam in 2002. In the upper section, much attention has been given to the restoration of flows. In the Lower section, the Snowy has seen significant changes in geomorphology due to catchment and hydrology changes. Community concern at the observed environmental degradation resulted in the development of the Snowy Rehabilitation Project, a cooperative project involving Victorian state and regional bodies. One major outcome of this project was funding for rehabilitation works in the Lower Snowy. As a result, much effort was put into modelling work. The focus of this was on better understanding the behaviour of sediments within the river and defining the needs for restoring ecological functions, with a primary focus on providing fish passage. This type of information was intended to develop a way forward for rehabilitation works in the Lower Snowy. It is important to note that this section of the Snowy is surrounded by privately owned agricultural land. Any rehabilitation works also needs to consider how proposed changes in river behaviour will impact on surrounding land. From this work, a simple decision support system (DSS) was built. The DSS was designed to use modelled information and to assist in decision-making processes by linking management activities (or interventions) to outcomes. The tool uses a 'risk' approach to acknowledge the uncertainties that exist in our knowledge, including models, and the inherent variability of this natural system. The DSS ('The Snowy tool') user is the Victorian East Gippsland Catchment Management Authority (CMA). The purpose of the Snowy tool is to 'provide an assessment of the cumulative outcomes and risks due to different levels of management intervention'. The rehabilitation activities of interest are: riparian vegetation management; management of vegetation on in-channel benches; and installation of large wood in channel. The model aims to predict the likely outcomes of interventions to: scour holes for fish habitat and migration; occurrence of overbank inundation; avulsion likelihood; and bench and bank stability. The Snowy tool is a probabilistic model (Bayesian network) that incorporates data from the hydraulic model, HEC-RAS, as well as expert opinion, and a set of ecological response models developed previously. The model was evaluated within an expert workshop. The tool highlighted flaws in past studies, and could only partially address the needs of the decision-makers. The outcome of the project stresses the need for decision-making tools to be designed early in the project development, in order to better guide process modelling and data collection exercises. Without being designed upfront, much of the data collected can be well intentioned but poorly targeted at addressing the key needs for the river system

Development of Bayesian Network Decision Support Tools to Support River Rehabilitation Works in the Lower Snowy River

Community concern at the continual environmental decline of the Snowy River (New South Wales and Victoria, Australia) has resulted in a substantial investment in river rehabilitation works, referred to as the Snowy Rehabilitation Project. Much of the investment in the science component of the project went into developing physical models to better understand the behavior of sediments that have accumulated over time in the river channel. The outcomes of these models were intended to assist river managers in better controlling sediment impacts to restore instream ecological function. To synthesize the learnings from these studies for use by catchment managers, a simple decision support tool, referred to as the Snowy tool, was constructed. The Snowy tool was designed to link outcomes from the models with management activities (or interventions) to outcomes within the river channel. It took the form of a probabilistic model (Bayesian network) that incorporates data from a hydraulic model (HEC-RAS), combined with expert opinion, and riverine response models. This article overviews the Snowy tool, and stresses the importance of the use of Bayesian networks in adaptive management frameworks and in guiding investments in research and on-ground decision-making

Bayesian networks for modelling habitat suitability of an endangered species

Bayesian networks (BNs) are simple graphical causal models that have been applied in a diverse range of fields. They were first conceived in the 1980s at the interface between artificial intelligence, expert systems and statistics, to deal with problems of reasoning and decision making under uncertainty. They have served many purposes including diagnosis, prediction, simulation and analysis, and are particularly suited to problems involving causality with inherent uncertainty. In environmental modelling, one of their strengths is in their ability to integrate various forms of knowledge across disciplines into a single modelling framework. In this paper we introduce a BN that was developed to model the habitat suitability of Astacopsis gouldi, the endangered giant freshwater crayfish in Tasmania. The BN was linked to GIS, thereby placing the model inputs and outputs in a spatial context. The modelling work is based on research by the Tasmanian forestry practices industry that developed a set of habitat mapping rules for the species that were translated into a habitat suitability map. The BN is used to represent current knowledge of Astacopsis habitat, however, unlike the previous mapping work, all causal relations are made explicit and transparent to users. The BN also allows management strategies to be tested, which better promotes system understanding, and its modular architecture will enable it to be integrated into a larger model or Decision Support System, making it more useful in a decision making context