The notion of convergent and transdisciplinary integration, which is about braiding together different knowledge systems, is becoming the mantra of numerous initiatives aimed at tackling pressing water challenges. Yet, the transition from rhetoric to actual implementation is impeded by incongruence in semantics, methodologies, and discourse among disciplinary scientists and societal actors. This paper confronts these disciplinary barriers by advocating a synthesis of existing and missing links across the frontiers distinguishing hydrology from engineering, the social sciences and economics, Indigenous and place-based knowledge, and studies of other interconnected natural systems such as the atmosphere, cryosphere, and ecosphere. Specifically, we embrace ‘integrated modeling’, in both quantitative and qualitative senses, as a vital exploratory instrument to advance such integration, providing a means to navigate complexity and manage the uncertainty associated with understanding, diagnosing, predicting, and governing human-water systems. While there are, arguably, no bounds to the pursuit of inclusivity in representing the spectrum of natural and human processes around water resources, we advocate that integrated modeling can provide a focused approach to delineating the scope of integration, through the lens of three fundamental questions: a) What is the modeling ‘purpose’? b) What constitutes a sound ‘boundary judgment’? and c) What are the ‘critical uncertainties’ and how do they propagate through interconnected subsystems? More broadly, we call for investigating what constitutes warranted ‘systems complexity’, as opposed to unjustified ‘computational complexity’ when representing complex natural and human-natural systems, with particular attention to interdependencies and feedbacks, nonlinear dynamics and thresholds, hysteresis, time lags, and legacy effects.

The future uncertainty and complexity of alternative socioeconomic and climatic scenarios challenge the model-based analysis of sustainable development. Obtaining robust insights requires a systematic processing of uncertainty and complexity not only in input assumptions, but also in the diversity of model structures that simulates the multisectoral dynamics of human and Earth system interactions. Here, we implement the global change scenarios, i.e., the Shared Socioeconomic Pathways and the Representative Concentration Pathways, in a feedback-rich, integrated assessment model of human-Earth system dynamics, called FeliX, to serve two aims: (1) to provide modellers with well-defined steps for the adoption of established scenarios in new integrated assessment models; (2) to explore the impacts of model uncertainty and its structural complexity on the projection of these scenarios for sustainable development. Our modelling shows internally consistent scenario storylines across sectors, yet with quantitatively different realisations of these scenarios compared to other integrated assessment models due to the new model’s structural complexity. The results highlight the importance of enumerating global change scenarios and their uncertainty exploration with a diversity of models of different input assumptions and structures to capture a wider variety of future possibilities and sustainability indicators.

The United Nations 2030 Agenda brings a holistic and multi-sectoral view on sustainability via the Sustainable Development Goals (SDGs). However, a successful implementation of this agenda is contingent on understanding the multiple, complex interactions among SDGs, including both synergies and trade-offs, for informing planning for sustainability at the local level. Using a case study in the Goulburn-Murray region in Victoria, Australia, we prioritised global goals and targets for the local context, characterised the interactions between them, analysed the main synergies and trade-offs, and identified potential policy solutions to achieve local sustainability. We identified the five highest priority SDGs for the region as clean water and sanitation (SDG 6), agricultural activities (SDG 2), economic growth (SDG 8), climate action (SDG 13), and life on land (SDG 15). Across these five priority SDGs and their 45 targets, we found 307 potential interactions, of which 126 (41%) were synergistic, 19 (6%) were trade-offs, and 162 (53%) were benign. We highlight the most salient trade-offs, particularly how unsustainable agricultural practices could negatively affect water resources, the environment, and sustainable economic growth. Also, critical ongoing uncertainties like climate change, local policies on environmental water recovery, international markets, and emerging new technologies could pose risks for the future of agriculture and the economy. Our results provide important insights for local and regional sustainability policy and planning across multiple sectors. Our methodology is also broadly applicable for prioritising SDGs and assessing their interactions at local scales, thereby supporting evidence-based policy-making for the SDGs.

We developed a machine learning based meta-model to identify sustainability pathways through rapid scenario generation and defined the safe operating space for achieving them via scenario discovery. We trained a meta-model to replicate the Land-Use Trade-Offs integrated model of the Australian land system. Latin hypercube sampling was used to create many scenarios exploring the impact of uncertainties in key drivers including future socio-economic development, climate change mitigation, and agricultural productivity at a granular level. Economic and environmental impacts were evaluated against nationally downscaled SDG targets. Scenario discovery revealed new pathways to achieving five SDG targets for 2050 which required crop yield increases above 1.78 times, a carbon price above 100 AU$ tCO2-1, a > 9% biodiversity levy on carbon plantings, and carefully regulated land-use policy. Machine learning based meta-modelling teamed with scenario discovery revealed the policy and scenario settings required for a sustainable future for the Australian land sector.