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.

Advancements in large language models (LLMs) provide opportunities to accelerate progress towards the attainment of the Sustainable Development Goals (SDGs). Current research largely overlooks the nuanced benefits and dangers LLMs introduce to sustainability research and communication, as well as broader challenges that need to be addressed in the longer term. This paper overcomes these shortcomings by introducing and discussing a framework that highlights how LLMs can benefit knowledge production, mobilization, and communication in the sustainability sciences, as well as any associated dangers. In addition, it outlines potential long-term challenges that must be acknowledged and addressed to ensure the responsible use of LLMs in advancing sustainability science. A key to the development and use of LLMs for sustainability science is the development of regulatory measures. These measures should be guided by what is needed for expanding sustainability science on the one hand and a holistic view to ensure its responsible use on the other. Failure to reflect and act on this might result in unintended consequences or misuse, making the technology another roadblock to progress towards the SDGs.