Abstract.A primary goal of Earth system modelling is to improve understanding of the interactions and feedbacks between human decision making and biophysical processes. The nexus of land use and land cover change (LULCC) and the climate system is an important example. LULCC contributes to global and regional climate change, while climate affects the functioning of terrestrial ecosystems and LULCC. However, at present, LULCC is poorly represented in global circulation models (GCMs). LULCC models that are explicit about human behaviour and decision-making processes have been developed at local to regional scales, but the principles of these approaches have not yet been applied to the global scale level in ways that deal adequately with both direct and indirect feedbacks from the climate system. In this article, we explore current knowledge about LULCC modelling and the interactions between LULCC, GCMs and dynamic global vegetation models (DGVMs). In doing so, we propose new ways forward for improving LULCC representations in Earth system models. We conclude that LULCC models need to better conceptualise the alternatives for upscaling from the local to global scale. This involves better representation of human agency, including processes such as learning, adaptation and agent evolution, formalising the role and emergence of governance structures, institutional arrangements and policy as endogenous processes and better theorising about the role of teleconnections and connectivity across global networks. Our analysis underlines the importance of observational data in global-scale assessments and the need for coordination in synthesising and assimilating available data. Published by Copernicus

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Expertise in research integration and implementation is an essential but often overlooked component of tackling complex societal and environmental problems. We focus on expertise relevant to any complex problem, especially contributory expertise, divided into 'knowing-that' and 'knowing-how.' We also deal with interactional expertise and the fact that much expertise is tacit. We explore three questions. First, in examining 'when is expertise in research integration and implementation required?,' we review tasks essential (a) to developing more comprehensive understandings of complex problems, plus possible ways to address them, and (b) for supporting implementation of those understandings into government policy, community practice, business and social innovation, or other initiatives. Second, in considering 'where can expertise in research integration and implementation currently be found?,' we describe three realms: (a) specific approaches, including interdisciplinarity, transdisciplinarity, systems thinking and sustainability science; (b) case-based experience that is independent of these specific approaches; and (c) research examining elements of integration and implementation, specifically considering unknowns and fostering innovation. We highlight examples of expertise in each realm and demonstrate how fragmentation currently precludes clear identification of research integration and implementation expertise. Third, in exploring 'what is required to strengthen expertise in research integration and implementation?,' we propose building a knowledge bank. We delve into three key challenges: compiling existing expertise, indexing and organising the expertise to make it widely accessible, and understanding and overcoming the core reasons for the existing fragmentation. A growing knowledge bank of expertise in research integration and implementation on the one hand, and accumulating success in addressing complex societal and environmental problems on the other, will form a virtuous cycle so that each strengthens the other. Building a coalition of researchers and institutions will ensure this expertise and its application are valued and sustained.

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Peepers are multichambered equilibrium dialyzers placed within sediments to collect porewater samples. We developed computer models to study the equilibration dynamics of a vial peeper design. One model simulated the concentration distributions and dynamic behavior of solute within the peeper cell itself, and another simulated the equilibration of peepers placed within sediments. The first model, together with laboratory experiments, demonstrated that convection arising within peeper cells placed in saline sediments would ensure rapid mixing of the contents of the cells. The predictions of equilibration behavior made by the second model for four chemical species (K, Na, Ca, and Sr) generally compared well with the results of laboratory experiments. These results showed that peeper equilibration was limited almost wholly by solute diffusion through the sediments and demonstrated the importance of peeper dimensions and solute diffusivities for equilibration times.

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Abstract. A primary goal of Earth system modelling is to improve understanding of the interactions and feedbacks between human decision making and biophysical processes. The nexus of land use and land cover change (LULCC) and the climate system is an important example. LULCC contributes to global and regional climate change, while climate affects the functioning of terrestrial ecosystems and LULCC. However, at present, LULCC is poorly represented in Global Circulation Models (GCMs). LULCC models that are explicit about human behaviour and decision making processes have been developed at local to regional scales, but the principles of these approaches have not yet been applied to the global scale level in ways that deal adequately with both direct and indirect feedbacks from the climate system. In this article, we explore current knowledge about LULCC modelling and the interactions between LULCC, GCMs and Dynamic Global Vegetation Models (DGVMs). In doing so, we propose new ways forward for improving LULCC representations in Earth System Models. We conclude that LULCC models need to better conceptualise the alternatives for up-scaling from the local to global. This involves better representation of human agency, including processes such as learning, adaptation and agent evolution, formalising the role and emergence of governance structures, institutional arrangements and policy as endogenous processes and better theorising about the role of tele-connections and connectivity across global networks. Our analysis underlines the importance of observational data in global scale assessments and the need for coordination in synthesising and assimilating available data.

Numerous methods have been developed to support the assessment of environmental flow requirements for rivers. Most methods are based around models of hydrologic time series rather than models of the ecological endpoints of interest. Important limitations that arise from this include (1) an inability to represent the state dependency of response to future conditions (i.e. the effects of current ecosystem condition on future condition), (2) the inability to predict ecological states through time under alternative flow regimes and (3) limited sensitivity to compare the differences between flow regimes with similar return intervals of ecologically important events, but different sequencing of those events. Here we outline a simple state‐and‐transition modelling approach to assess differences in ecological responses to alternative sequences of floodplain inundation events in a lowland river system. Our approach explicitly incorporates the state dependency of biotic response to flooding, thereby representing the influences of both antecedent conditions and current condition (in this case population state; good > medium > poor > critical). Our approach thus captures the influence of the entire historical sequence of flow events via a first‐order Markov chain process. We use prior data and expert opinion to determine state transitions for a broad suite of ecological indicators. Despite being implemented with deterministic transitions, and drawing heavily on expert opinion, this approach greatly improves on existing methods used in environmental flows planning, particularly when comparing scenarios with the different sequencing of ecologically relevant flow events. The outputs from these models are testable, and the approach is readily extensible to incorporate probabilistic state transitions and uncertainty, mechanistic links (via increased model complexity) and quantitative measures of population state (e.g. measures of abundance or tree condition). Most importantly, the adoption of such a framework represents a fundamental shift to modelling ecological endpoints rather than relying on just quantifying hydrologic surrogates to compare environmental flow scenarios.