Effective indicators to enable robust decision making in the Murray-Darling Basin

Decision-making in natural resource management is increasingly challenged by the complexity, uncertainty and volume of information available. This leads to information overload and reduces decision quality. Here, we aim to reduce the degree of redundant information in a highly complex model used to manage environmental outcomes in the Murray-Darling Basin, Australia's largest river system. We identified extremely high levels of redundancy with 6–8 hydrological indicators able to explain 98–100% of the variation in the full suite of >2500. Many sets of 6–8 had similar explanatory power, providing flexibility for managers to tailor the subset used, and sets were produced for individual management targets (e.g. native fish, waterbirds) and for individual catchments. These subsets maintained the ability to distinguish between scenarios of plausible future climates and different policy settings, while traditional aggregation techniques did not (particularly for policy settings). Aggregations using key flow categories (e.g. overbank, low flows) were also largely able to distinguish among scenarios. This study is novel in its use of decision science to reduce information overload associated with a commonly used ecological model, illustrating the value of explicitly considering whether complexity in such a model is necessary. Such assessments are rarely undertaken. We illustrated how subsets of complex models provide simple, parsimonious approaches for credible, salient and legitimate decision making under uncertainty, and increase the likelihood of good evidence-based decision making in water management. • Complex information can hinder effective natural resource decisions. • We evaluated redundancy in environmental water requirement indicators. • A small subset (6–8 indicators) explained over 95% of variation in >2500 indicators. • Subsets distinguished climate and policy scenarios better than other methods. • Streamlined indicators enable more credible and evidence-based management decisions.