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Many catchments in Australia have experienced increased sediment and nutrient loadings and concomitant declines in water quality and increases in problematic blue–green algal blooms over the past several decades. This paper describes an integrated hydrologic, sediment and nutrient export modelling framework known as catchment-scale management of diffuse sources (CatchMODS). The modelling framework is designed to simulate and assess catchment-scale land and water management activities designed to reduce nutrient and sediment delivery to receiving waters. The Ben Chifley Dam Catchment in New South Wales, Australia is used as a case study for describing the development of the framework. Improving water quality through reduction of sediment and nutrient loads will reduce the probability of algal bloom occurrence in the dam. The innovation of the system is the integration of otherwise separate modelling approaches to enable biophysical and economic assessment of different management options. The outputs from the research are used to improve and focus on-ground remediation, targeted to specific stream reaches or subcatchment areas, as well as to encourage sustainable management practices more broadly in the catchment. With minimal modification, the methods developed are applicable elsewhere to address water quality decline. [Copyright &y& Elsevier]

To meet the challenges of sustainability and catchment management requires an approach that assesses resource usage options and environmental impacts integratively. Assessment must be able to integrate several dimensions: the consideration of multiple issues and stakeholders, the key disciplines within and between the human and natural sciences, multiple scales of system behaviour, cascading effects both spatially and temporally, models of the different system components, and multiple databases. Integrated assessment (IA) is an emerging discipline and process that attempts to address the demands of decision makers for management that has ecological, social and economic values and considerations. This paper summarises the features of IA and argues the role for models and information systems as a prime activity. Given the complex nature of IA problems, the broad objectives of IA modelling should be to understand the directions and magnitudes of change in relation to management interventions so as to be able to differentiate between associated outcome sets. Central to this broad objective is the need for improved techniques of uncertainty and sensitivity analysis that can provide a measure of confidence in the ability to differentiate between different decisions. Three examples of problems treated with an IA approach are presented. The variations in the way that the different dimensions are integrated in the modelling are discussed to highlight the sorts of choices that can be made in model construction. The conclusions stress the importance of IA as a process, not just as a set of outcomes, and define some of the deficiencies to be overcome. [Copyright &y& Elsevier]