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Energy system planning has evolved from a narrow focus on engineering and supply works towards addressing more complex, multifactorial challenges. Increasingly challenged by climate change, extreme events, economic shocks, and altered supply demand patterns, the analysis of energy systems requires holistic approaches based on data-driven models, taking into account key socio-economic factors. We draw insights from reviewing the literature, indicating the need to cover the following major gaps: the shift to transdisciplinary approaches, incorporating environmental system analysis; resilient and sustainable energy designs based on flexible portfolios of renewable mixes; the integration of socio-economic aspects, economic analyses and behavioural models to ensure energy systems are not only technically sound but socially acceptable and viable; the need for stakeholder engagement considering the human angle in energy security and behavioural shifts. Responding to these pressing challenges and emerging needs, the Global Climate Hub (GCH) initiative, operating under the UN Sustainable Development Solutions Network, offers a conceptual framework, leveraging transdisciplinary approaches. In this Concept Paper, we present for the first time the idea of the GCH as a framework that we believe has the potential to address the modern holistic needs for energy system analysis and policymaking. By setting the conceptual/theoretical ground of our suggested approach, we aim to provide guidance for innovative combinations of cutting-edge models, socio-economic narratives, and inclusive interaction with relevant stakeholders for the development and the long-term implementation of sustainable pathways. [ABSTRACT FROM AUTHOR]

Impacts of air pollution are not limited to human health and ecosystems, but are also important for building materials. The main objective of this study is the quantification and mapping of air pollution effects on the materials, namely the soiling effect of modern glass. An integrated modelling approach was implemented to quantify and analyze the spatial distribution of glass soiling due to exposure to air pollution. The methodology is based on an integrated modelling approach (transportation-emissions-dispersion modelling) applied with high spatial resolution for Coimbra (Portugal) urban area and compared with national scale modelling, showing the important contribution of local pollution sources affecting spatial variability in the soiling effect. Air quality data from CAMS (Copernicus Atmosphere Monitoring Service) were used to quantify the soiling effect at national scale. The results are presented and analyzed in terms of haze. The results obtained at national scale suggest that the average time to reach a 1% haze is 320 days, and this time is reduced to 180 days in the most affected areas. However, urban scale modelling applied with a detailed characterization of local pollution sources in Coimbra provides significantly different results and reveals that in the most affected areas, near road traffic, 1% haze could be reached in approximately 80 days. The methodology proposed and implemented in this study provides relevant information for the maintenance and preservation of building materials and highlights the importance of integrated modelling with high spatial resolution for the assessment of the soiling effect in the built environment. [ABSTRACT FROM AUTHOR]

Warming and drying trends in the Mediterranean Basin exacerbate regional water scarcity and threaten agricultural production, putting global food security at risk. This study aimed to review the most significant research on future water availability for the Mediterranean agricultural sector under climate change (CC) scenarios published during 2009–2024. Two searches were performed in the Scopus and Web of Science databases, to which previously identified significant studies from different periods were also added. By applying a methodology duly protocoled in the PRISMA2020-based guideline, a final number of 44 particularly relevant studies was selected for review. A bibliometric analysis has shown that most of the published research was focused on Southwestern European countries (i.e., Spain, Italy, Portugal) and grapevine and olive tree crops. Overall, the reviewed studies state that future Mediterranean water reserves may not meet agricultural water demands, due to reduced reservoir inflows and higher irrigation demands under future CC and socioeconomic scenarios. Regarding adaptation measures to improve water-use management in agriculture, the majority of the reviewed studies indicate that the use of integrated modelling platforms and decision–support systems can significantly contribute to the development and implementation of improved water/land-management practices. [ABSTRACT FROM AUTHOR]

In order to explore the influence of the infrared stealth technology on the performance of the integrated helicopter/engine system, an integrated modeling method of helicopter/engine/infrared suppressor is proposed. Firstly, based on the power calculation model of the helicopter, combined with the high-precision turboshaft engine component-level model, an integrated simulation platform is built, which takes into account the nonlinear characteristics. Then, the aerodynamic characteristics of infrared suppressors under different engine operation states are studied by CFD numerical computation method, and the infrared radiation characteristics are obtained through combining the positive and negative ray tracing method and narrow band model method. Ultimately, the utilization of the power turbine outlet stagnation pressure is employed as the pivotal interface linking the turboshaft engine and the infrared suppressor in order to formulate an integrated model encompassing the helicopter, engine, and infrared suppressor subsystems. The simulation results demonstrate that compared with the conventional exhaust system, the application of the infrared suppressor greatly enhances the stealth performance of the helicopter, but also results in the unexpected decrease in engine output power. Moreover, the specific fuel consumption of the turboshaft engine increase and the compressor surge margin decreases in case of the consistent flight condition. [ABSTRACT FROM AUTHOR]

The development of long-term scenarios to outline pathways for achieving carbon neutrality by 2050 has become a standard practice in climate change policy planning. In Argentina, a modelling process was initiated in 2019 in the agriculture, forestry and other land use (AFOLU) sector utilizing three tools: FABLE calculator, Dinamica EGO and Nature Map. In order to generate technical inputs for the modelling exercise a stakeholder dialogue was launched. A 2050 Carbon Neutrality scenario was developed, alongside several intermediate scenarios based on stakeholders´ visions of the future. The modelling results demonstrated the biophysical feasibility of achieving carbon neutrality in the Argentinean AFOLU sector by 2050. However, alignment with current sectoral priorities was identified as a challenge, leading stakeholders to propose less ambitious scenarios as more attainable targets. This experience underscored the significance of constructing multiple policy scenarios, facilitating the evaluation of diverse potential future trajectories for policymaking. These different pathways provided contrasting perspectives between political objectives, such as achieving carbon neutrality, and the practical feasibility of local implementation. Moreover, the process highlighted the vital role of integrating the private sector and environmental non-governmental organizations (NGOs) in long-term climate planning, emphasizing the need for inclusive collaboration to address climate challenges effectively. Stakeholder-based modelling approaches serve as valuable discussion tools to initiate and accompany political discussions concerning the efforts required to achieve carbon neutrality. Modelling outputs provide quantitative demonstrations of the challenges involved, while stakeholder dialogues facilitate the identification of barriers and enabling conditions required to promote a successful transition. It is necessary to enhance institutionalized public-private dialogue spaces that extend beyond specific projects and government changes, given their importance in facilitating participatory long-term thinking. Nevertheless, collective scenario construction alone seems insufficient to drive the profound changes that are required. [ABSTRACT FROM AUTHOR]

For mitigating the unintended environmental impacts associated with intensive farming across the world, it is crucial to understand the complex impacts of potential reductions in fertiliser use on multiple ecosystem services, including crop production, GHG emissions and changes in soil organic carbon (SOC) stocks. Using site specific spatial data and information, a novel integrated modelling approach using established agroecosystem models (SPACSYS and RothC) was implemented to evaluate the impacts of various fertiliser reductions (10 %, 30 % and 50 %) under current / baseline and projected (RCP2.6, RCP4.5 and RCP8.5) climate scenarios in a study catchment in southwest England. 48 unique combinations of soil types, climate conditions and fertiliser inputs were evaluated for five major arable crops (winter wheat, maize, winter barley, spring barley, winter oilseed rape) plus ryegrass. Modelled annual estimates of crop yields and biomass, emissions of gases with warming potentials (nitrous oxide, methane, carbon) and SOC stocks in the topsoil (0–30 cm) were tabulated for all combinations considered. These simulated data series could be further analysed to evaluate inter-annual variations and their implications for climate resilience and combined with additional data to quantify nutrient use efficiency and undertake cost- benefit analysis, and to contribute to inter-regional comparisons of fertiliser management at broad scale.

Study region: French part of the Moselle catchmentStudy focus: By relying on hydrological simulations forced by climate change scenarios, stakeholders can assess the magnitude of future changes in the rainfall–runoff relationship. The inclusion of human influences in water resources modelling in a non-stationary context is a way to improve the accuracy and usefulness of climate change impact studies. Here, we propose a modelling approach that explicitly considers water uses to evaluate adaptation measures for water management at the French Moselle catchment scale.New hydrological insights for the region: The results highlight the decrease in future low flows but also the change in the balance between demand and supply. Over the Moselle catchment, whatever the water use scenario considered, climate change induces lower water availability both for environmental flows and for human uses. This leads to a potential increase in the duration of water restriction of up to 8 weeks for RCP 8.5 in the long term (2070–2099) compared to 1976–2005. This study could provide water managers with more appropriate climate impact results and potentially help them to design adequate adaptation measures.

A recent trend in hydroinformatics has been the growing number of data, models, and cyber tools, which are web accessible, each aiming to improve common research tasks in hydrology through web technologies. Coupling web-based models and tools holds great promise for an integrated environment that can facilitate community participation, collaboration, and scientific replication. There are many examples of server-side, hydroinformatics resource coupling, where a common standard serves as an interface. Yet, there are few, if any, examples of client-side resource coupling, particularly cases where a common specification is employed. Toward this end, we implemented the basic model interface (BMI) specification in the JavaScript programing language, the most widely used programing language on the web. By using BMI, we coupled two client-side hydrological applications (HydroLang and HLM-Web) to perform rainfall–runoff simulations of historical events with rainfall data and a client-side hydrological model as a case study demonstration. Through this process, we present how a common and often tedious task – the coupling of two independent web resources – can be made easier through the adoption of a common standard. Furthermore, applying the standard has facilitated a step toward the possibility of client-side ‘Model as a Service’ for hydrological models. HIGHLIGHTS We present the basic model interface (BMI) specification for the JavaScript programming language.; We present a comprehensive example of how BMI may be used as a common standard to couple client-side, hydroinformatics web resources.; For developers, BMI for JavaScript simplifies the effort needed to implement an Application Programing Interface for their resource.; For users, BMI for JavaScript accelerates learning and working with a new resource.;

Abstract Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal drivers of migratory species' abundances is difficult because of extensive gaps in monitoring data. The collection of unstructured opportunistic data by volunteer (citizen science) networks provides a solution to address data gaps for locations and time periods during which structured, design‐based data are difficult or impossible to collect. To estimate population abundance and distribution at broad spatiotemporal extents, we developed an integrated model that incorporates unstructured data during time periods and spatial locations when structured data are unavailable. We validated our approach through simulations and then applied the framework to the eastern North American migratory population of monarch butterflies during their spring breeding period in eastern Texas. Spring climate conditions have been identified as a key driver of monarch population sizes during subsequent summer and winter periods. However, low monarch densities during the spring combined with very few design‐based surveys in the region have limited the ability to isolate effects of spring weather variables on monarchs. Simulation results confirmed the ability of our integrated model to accurately and precisely estimate abundance indices and the effects of covariates during locations and time periods in which structured sampling are lacking. In our case study, we combined opportunistic monarch observations during the spring migration and breeding period with structured data from the summer Midwestern breeding grounds. Our model revealed a nonstationary relationship between weather conditions and local monarch abundance during the spring, driven by spatially varying vegetation and temperature conditions. Data for widespread and migratory species are often fragmented across multiple monitoring programs, potentially requiring the use of both structured and unstructured data sources to obtain complete geographic coverage. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data.

Energy system planning has evolved from a narrow focus on engineering and supply works towards addressing more complex, multifactorial challenges. Increasingly challenged by climate change, extreme events, economic shocks, and altered supply demand patterns, the analysis of energy systems requires holistic approaches based on data-driven models, taking into account key socio-economic factors. We draw insights from reviewing the literature, indicating the need to cover the following major gaps: the shift to transdisciplinary approaches, incorporating environmental system analysis; resilient and sustainable energy designs based on flexible portfolios of renewable mixes; the integration of socio-economic aspects, economic analyses and behavioural models to ensure energy systems are not only technically sound but socially acceptable and viable; the need for stakeholder engagement considering the human angle in energy security and behavioural shifts. Responding to these pressing challenges and emerging needs, the Global Climate Hub (GCH) initiative, operating under the UN Sustainable Development Solutions Network, offers a conceptual framework, leveraging transdisciplinary approaches. In this Concept Paper, we present for the first time the idea of the GCH as a framework that we believe has the potential to address the modern holistic needs for energy system analysis and policymaking. By setting the conceptual/theoretical ground of our suggested approach, we aim to provide guidance for innovative combinations of cutting-edge models, socio-economic narratives, and inclusive interaction with relevant stakeholders for the development and the long-term implementation of sustainable pathways.

Warming and drying trends in the Mediterranean Basin exacerbate regional water scarcity and threaten agricultural production, putting global food security at risk. This study aimed to review the most significant research on future water availability for the Mediterranean agricultural sector under climate change (CC) scenarios published during 2009–2024. Two searches were performed in the Scopus and Web of Science databases, to which previously identified significant studies from different periods were also added. By applying a methodology duly protocoled in the PRISMA2020-based guideline, a final number of 44 particularly relevant studies was selected for review. A bibliometric analysis has shown that most of the published research was focused on Southwestern European countries (i.e., Spain, Italy, Portugal) and grapevine and olive tree crops. Overall, the reviewed studies state that future Mediterranean water reserves may not meet agricultural water demands, due to reduced reservoir inflows and higher irrigation demands under future CC and socioeconomic scenarios. Regarding adaptation measures to improve water-use management in agriculture, the majority of the reviewed studies indicate that the use of integrated modelling platforms and decision–support systems can significantly contribute to the development and implementation of improved water/land-management practices.

Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal drivers of migratory species' abundances is difficult because of extensive gaps in monitoring data. The collection of unstructured opportunistic data by volunteer (citizen science) networks provides a solution to address data gaps for locations and time periods during which structured, design‐based data are difficult or impossible to collect.To estimate population abundance and distribution at broad spatiotemporal extents, we developed an integrated model that incorporates unstructured data during time periods and spatial locations when structured data are unavailable. We validated our approach through simulations and then applied the framework to the eastern North American migratory population of monarch butterflies during their spring breeding period in eastern Texas. Spring climate conditions have been identified as a key driver of monarch population sizes during subsequent summer and winter periods. However, low monarch densities during the spring combined with very few design‐based surveys in the region have limited the ability to isolate effects of spring weather variables on monarchs.Simulation results confirmed the ability of our integrated model to accurately and precisely estimate abundance indices and the effects of covariates during locations and time periods in which structured sampling are lacking. In our case study, we combined opportunistic monarch observations during the spring migration and breeding period with structured data from the summer Midwestern breeding grounds. Our model revealed a nonstationary relationship between weather conditions and local monarch abundance during the spring, driven by spatially varying vegetation and temperature conditions.Data for widespread and migratory species are often fragmented across multiple monitoring programs, potentially requiring the use of both structured and unstructured data sources to obtain complete geographic coverage. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data. [ABSTRACT FROM AUTHOR]

Self-healing liquid metal divertors (LMDs) based on the Capillary Porous Structure (CPS) concept are currently being considered among the possible solutions to the power exhaust problem in future fusion reactors. Indeed, the passive replenishment of the plasma-facing surface by capillary forces and the self-shielding of the target via vapor emission can potentially improve the divertor lifetime and its resilience to transient loads. On the other hand, the LMD target erosion can be significant due to evaporation and thermal sputtering, on top of physical sputtering, possibly leading to unacceptable core plasma dilution/power losses (for a low-Z/high-Z metal such as Li and Sn, respectively). For this reason, it is necessary to assess whether an LMD is compatible with an European DEMO (EU-DEMO) plasma scenario. This requires a self-consistent model of the impurity emission from the target, the plasma in both the scrape-off layer (SOL) and the core regions and the transport of impurities therein. In this paper, an an integrated modelling approach is proposed, which is based on SOLPS-ITER and includes its coupling with a target erosion model written in FreeFem++ and a core plasma model (ASTRA/STRAHL). An application of the coupled SOL-target model to simulate experiments performed in the Magnum-PSI linear plasma device with a CPS target filled with Li is also included to provide a first demonstration of the capabilities of the approach. Results are promising, being in good agreement (within a few degrees) with the measured target temperature distribution. In perspective, the modelling framework presented here will be applied to the EU-DEMO with an Sn divertor. [ABSTRACT FROM AUTHOR]

Abstract Migration movements connect breeding and non-breeding bird populations over the year. Such links, referred to as migratory connectivity, have important implications for migratory population dynamics as they dictate the consequences of localised events for the whole population network. This calls for concerted efforts to understand migration processes for large-scale conservation. Over the last 20 years, the toolbox to investigate connectivity patterns has expanded and studies now consider migratory connectivity over a broader range of species and contexts. Here, we summarise recent developments in analysing migratory connectivity, focusing on strategies and challenges to pooling various types of data to both optimise and broaden the scope of connectivity studies. We find that the different approaches used to investigate migratory connectivity still have complementary strengths and weaknesses, whether in terms of cost, spatial and temporal resolution, or challenges in obtaining large sample sizes or connectivity estimates. Certain recent developments offer particularly promising prospects: robust quantitative models for banding data, improved precision of geolocators and accessibility of telemetry tracking systems, and increasingly precise probabilistic assignments based on genomic markers or large-scale isoscapes. In parallel, studies have proposed various ways to combine the information of different datasets, from simply comparing the connectivity patterns they draw to formally integrating their analyses. Such data combinations have proven to be more accurate in estimating connectivity patterns, particularly for integrated approaches that offer promising flexibility. Given the diversity of available tools, future studies would benefit from a rigorous comparative evaluation of the different methodologies to guide data collection to complete migration atlases: where and when should data be collected during the migratory cycle to best describe connectivity patterns? Which data are most favourable to combine, and under what conditions? Are there methods for combining data that are better than others? Can combination methods be improved by adjusting the contribution of the various data in the models? How can we fully integrate connectivity with demographic and environmental data? Data integration shows strong potential to deepen our understanding of migratory connectivity as a dynamic ecological process, especially if the gaps can be bridged between connectivity, population and environmental models.

Migration movements connect breeding and non-breeding bird populations over the year. Such links, referred to as migratory connectivity, have important implications for migratory population dynamics as they dictate the consequences of localised events for the whole population network. This calls for concerted efforts to understand migration processes for large-scale conservation. Over the last 20 years, the toolbox to investigate connectivity patterns has expanded and studies now consider migratory connectivity over a broader range of species and contexts. Here, we summarise recent developments in analysing migratory connectivity, focusing on strategies and challenges to pooling various types of data to both optimise and broaden the scope of connectivity studies. We find that the different approaches used to investigate migratory connectivity still have complementary strengths and weaknesses, whether in terms of cost, spatial and temporal resolution, or challenges in obtaining large sample sizes or connectivity estimates. Certain recent developments offer particularly promising prospects: robust quantitative models for banding data, improved precision of geolocators and accessibility of telemetry tracking systems, and increasingly precise probabilistic assignments based on genomic markers or large-scale isoscapes. In parallel, studies have proposed various ways to combine the information of different datasets, from simply comparing the connectivity patterns they draw to formally integrating their analyses. Such data combinations have proven to be more accurate in estimating connectivity patterns, particularly for integrated approaches that offer promising flexibility. Given the diversity of available tools, future studies would benefit from a rigorous comparative evaluation of the different methodologies to guide data collection to complete migration atlases: where and when should data be collected during the migratory cycle to best describe connectivity patterns? Which data are most favourable to combine, and under what conditions? Are there methods for combining data that are better than others? Can combination methods be improved by adjusting the contribution of the various data in the models? How can we fully integrate connectivity with demographic and environmental data? Data integration shows strong potential to deepen our understanding of migratory connectivity as a dynamic ecological process, especially if the gaps can be bridged between connectivity, population and environmental models. [ABSTRACT FROM AUTHOR]

Changeable manufacturing and platform-based co-development of products and manufacturing systems are becoming increasingly relevant for industrial manufacturing in order to respond to volatile markets, increased product variety and shorter product life cycles. However, in order to conduct platform-based co-development, it is necessary to manage the relations between the product domain and the manufacturing domain. This paper proposes an approach and meta-model for developing company-specific ontologies, which can be used for modelling the portfolio of components, processes and equipment and their interrelations as a basis for co-evolution. Developing such a model has implications for developing and utilising changeable manufacturing, which is also analysed in this paper. [ABSTRACT FROM AUTHOR]

Climate and hydrologic hazards pose a threat to the distribution of watersheds’ water resources in time and space, necessitating planning for sustainable resilience and adaptation. Hydrologic modelling has emerged as a potential solution for understanding watershed responses to projected climate change, and a prediction model that can deliver actionable information is necessary, although it requires basin-scale observations to calibrate the model to reliably predict basin-scale water resources hazards. Such luxury is not always tenable in watersheds with inadequate ground-based observation. However, satellite-based evapotranspiration (ET) data coupled with a machine learning feature selection as a data refinement process has made integrated water balance modelling widely regarded as a viable alternative for improving the capability of watershed modelling processes in data-sparse regions. This study developed a convincing hydrologic model framework to sufficiently calibrate and provide accurate behavioural solutions for all model responses. The framework was applied to four sub-basins that form the larger Lake Chad basin. The model results were applied to assess the dynamic changes in projected blue and green water resource sustainability in response to climate change in one of the sub-basins. Study findings indicate that hydrologic fluxes can be simulated accurately with varying degrees of acceptability, with R2 and NSE values in the range of 0.69–0.88 and 0.45–0.77 for calibration and 0.69–0.79 and 0.34–0.63 for validation, respectively, and captured within a satisfactory uncertainty range of P-factor and R-factor values of 0.68–0.93 and 0.73–1.31, respectively, in 83%, 67%, 85.7%, and 81.3% of the sub-watersheds based on multi-site simulation despite distinct watershed morphology, although there are significant trade-offs in parameter sensitivity. Whilst green water is the dominant freshwater component across the basin relative to blue water, climate change may be a significant factor influencing changes in the projected green water sustainability status, and the combination of socioeconomic drivers and climate change may significantly impact the projected blue water sustainability status across the basin. Projected changes in the green and blue water sustainability status have shown that more than 50% of the watershed will become ecologically fragile. In addition, the identified freshwater geographic sustainability hotspots may be beyond restoration without adequate long-term river basin water resource plans.

Designing a robust termination scenario for a burning ITER plasma is a challenge that requires extensive core plasma and divertor modelling. The presented work consists of coupled core/edge/SOL/divertor simulations, performed with the JINTRAC code, to study the Q = 10 flat-top phase and exit phase of the ITER 15 MA/5.3 T DT scenario. The modelling utilizes the recently implemented option to treat deuterium and tritium separately in the SOL/divertor, enabling a consistent treatment of deuterium and tritium in the whole plasma volume, which is a unique capability of JINTRAC. In addition, these are the first JINTRAC simulations of this scenario that use a first-principles transport model to self-consistently model the ECRH power deposition and to include tungsten while keeping track of tungsten sputtering and accumulation. The flat-top simulations demonstrate the possibility of sustaining a steady state fusion Q of 10 using pure deuterium gas puffs together with DT mixed pellets, which is an option to make a more effective use of tritium. Simulations of the exit phase are set up sequentially, with each phase providing initial conditions for the next, starting with a density decay at full current and auxiliary power, and demonstrate the possibility of reducing the density robustly within a few seconds. Following the density decay, a subsequent auxiliary power ramp-down in H-mode is performed with a late H–L transition at low auxiliary power, which may provide an option for the optimization of the plasma termination. The final ramp-down phase consists of a current ramp-down in L-mode to 3.75 MA.

An integrated framework that demonstrates multi-species, multi-channel modelling capabilities for the prediction of impurity density profiles and their feedback on the main plasma through radiative cooling and fuel dilution is presented. It combines all presently known theoretical elements in the local description of quasilinear turbulent and neoclassical impurity transport, using the models TGLF-SAT2 and FACIT. These are coupled to the STRAHL code for impurity sources and radiation inside the ASTRA transport solver. The workflow is shown to reproduce experimental results in full-radius L-mode modelling. In particular, a set of ASDEX Upgrade L-modes with differing heating power mixtures and plasma currents are simulated, including boron (B) and tungsten (W) as intrinsic impurities. The increase of predicted confinement with higher current and the reduction of core W peaking with higher central wave heating are demonstrated. Furthermore, a highly radiative L-mode scenario featuring an X-point radiator (XPR) with two intrinsic (B, W) and one seeded argon (Ar) species is simulated, and its measured radiated power and high confinement are recovered by the modelling. The stabilizing effect of impurities on turbulence is analysed and a simple model for the peripheral X-point radiation is introduced. A preliminary full-radius simulation of an H-mode phase of this same discharge, leveraging recent work on the role of the E×B shearing at the edge, shows promising results.

STEP is a spherical tokamak prototype power plant that is being designed to demonstrate net electric power. The design phase involves the exploitation of plasma models to optimise fusion performance subject to satisfying various physics and engineering constraints. A modelling workflow, including integrated core plasma modelling, MHD stability analysis, SOL and pedestal modelling, coil set and free boundary equilibrium solvers, and whole plant design, has been developed to specify the design parameters and to develop viable scenarios. The integrated core plasma model JETTO is used to develop individual flat-top operating points that satisfy imposed criteria for fusion power performance within operational constraints. Key plasma parameters such as normalised beta, Greenwald density fraction, auxiliary power and radiated power have been scanned to scope the operational space and to derive a collection of candidate non-inductive flat-top points. The assumed auxiliary heating and current drive is either from electron cyclotron (EC) systems only or a combination of EC and electron Bernstein waves. At present stages of transport modelling, there is a large uncertainty in overall confinement for relevant parameter regimes. For each of the two auxiliary heating and current drive systems scenarios, two candidate flat-top points have been developed based on different confinement assumptions, totalling to four operating points. A lower confinement assumption generally suggests operating points in high-density, high auxiliary power regimes, whereas higher confinement would allow access to a broader parameter regime in density and power while maintaining target fusion power performance.

In present-day fusion devices, central wave heating is crucial to avoid core tungsten (W) accumulation. In this work, we present an integrated modelling framework that reproduces the reduction of core W peaking in ASDEX Upgrade experiments when multiple transport channels are self-consistently evolved, emphasizing the effects of wave heating on turbulent and neoclassical W transport. Predictions for the ITER 15 MA baseline are then provided. We show that the core of a reactor is in a different transport regime for W as compared to present-day tokamaks. The challenges introduced by W arise from global radiation losses that can hinder operation in H-mode, instead of local central accumulation.

We explore and develop an integrated model to enhance understanding and evaluation of the economic and environmental sustainability performance of alternative agricultural cropping production systems. More specifically, our developed Environmentally Sustainable Residual Income Decomposition Modelling (ESRIDM) integrates accounting decomposition analysis, Water and Economic Sustainability Performance Measurement (WESM), system thinking, modular thinking, Environmentally Sustainable Residual Income (ESRI) theory, into a holistic modelling system influenced by Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) approaches. The ESRIDM is theorised and applied in the context of nitrogen management in Australian Cotton production through a detailed case study and comparative analysis of three nitrogen fertiliser options. We find the ESRIDM is able to: (i) provide an overall assessment of the economic and environmental sustainability performance of each scenario modelled; (ii) increase the transparency of the production system to enable the identification of key issues which influence the likely suitability of each scenario; (iii) enable an analysis of probable outcomes under various alternatives; (iv) evaluate the feasibility of regulatory option modelled. While this paper offers a novel perspective to a specific context, ESRIDM is designed to be adapted to a variety of contexts with a view to contributing to the evolution of more sustainable systems of production more generally. [Display omitted] [ABSTRACT FROM AUTHOR]

This study investigates the influence of window system configurations on the energy use/cost, access to daylight and view, visual comfort and environmental performance in a one-bed hospital patient room located in a temperate climate. The methodology combines dynamic energy simulations, visual comfort and daylight analysis in a life cycle assessment study of window systems, covering a wide range of environmental indicators. In this study, multi-criteria evaluation approach is used to analyse the different design alternatives. A graphical optimization method was elaborated to filter the solutions and select the most appropriate window design based on project objectives and architectural preferences. The results show that glazing with '0.30 < g-values≤0.50' and '0.50 < Tvis < 0.75' have lower energy use and/or cost, lower environmental impacts while maintaining sufficient daylight levels (sDA > 55%) and showing higher quality for visual comfort. These type of glazing allows for using bigger windows which leads to a higher percentage of the outside view. [ABSTRACT FROM AUTHOR]

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This work presents new data on the life cycle impact assessment of various lignocellulosic biomass types in Mexico. A comparative life cycle assessment model of biomass densification systems was conducted. An integrated approach that incorporated various process variables, such as technology and variations in feed properties, within the analysis was employed to evaluate the environmental impact of producing 1 MJ of energy-containing densified fuel. The results show that the densification unit and curing (fuel drying) have the highest impact on the life cycle's operational energy and the total life cycle energy, respectively. Of all the 33 biomass types from the 17 species sources considered in this study, sweet sorghum and sandbur grass have the highest global warming potential, 0.26 and 0.24 (kg CO2-eq), and human toxicity 0.58 and 0.53 (kg 1,4-dichlorobenzene-eq), respectively, while coffee pulp and cooperi pine wood have the least impact in both categories, with values of 0.08 and 0.09 (kg CO2-eq), and 0.17 and 0.16 (kg 1,4-dichlorobenzene-eq), respectively. Chichicaxtla sawmill slabs also have a low environmental impact, and cooperi pine and Ceiba wood have the lowest ozone depletion and ecotoxicity potential. A sensitivity analysis indicated the effects of the transportation system and energy source on the life cycle's environmental impact. Adequate feed preparation, the blending of multiple feeds in the optimum ratio, and the careful selection of densification technology could improve the environmental performance of densifying some of the low-bulk-density feed biomass types. [ABSTRACT FROM AUTHOR]

The properties of L-mode confinement have been investigated with a set of dedicated experiments in ASDEX Upgrade and with a related modelling activity with the transport code ASTRA and the quasi-linear turbulent transport model TGLFâ€"SAT2, with boundary conditions at the separatrix. The values at the boundary have been set by the two-point model for the electron temperature, with the ion temperature proportional to the electron temperature by a constant factor, and the electron density set by a constant fraction of the volume averaged density. The influx of neutrals has been set through a feedback procedure which ensures that in the simulation the same particle content as in the experiment is obtained. The sensitivity of the results under considerable variations in the choice of the boundary conditions has been investigated and found to be limited. The predictions of this full-radius modelling set-up have been compared to experimental results covering a scan in electron cyclotron resonance heating (ECRH) power in both hydrogen and deuterium plasmas, a plasma current scan with fixed magnetic field, under both ECRH and neutral beam injection heating, an increase in plasma density with constant ECRH power in hydrogen plasmas, as well as variations of the fraction of electron and ion heating at approximately constant total heating power, as well as a change of main ion from deuterium to hydrogen. The ASTRA-TGLF predictions have been found to reproduce all of the experimentally explored dependences with relatively good accuracy, providing evidence, for the first time to our knowledge, that the main properties of L-mode confinement can be reproduced by conventional full-radius transport modelling with a quasi-linear turbulent transport model. Evidences of largest disagreement, although usually not exceeding the 20%, have been found at high electron heating power, where TGLF underpredicts the electron and particularly the ion thermal stored energies, and in the current dependence of confinement, which, in electron heated conditions, is predicted to be weaker than in the experiment. [ABSTRACT FROM AUTHOR]

Successfully navigating a complex social environment can influence fitness (i.e. survival and reproductive success), which can cascade to outcomes in demography and population dynamics. Extensive research has focused on the mechanisms and drivers of fitness variation that result from differences in social structure and interactions, and separately, the effects of demography on population change. However, there have been few attempts to address the effects of social structure on population dynamics through demography. Here, we first review the effects of social structure and individual social position on fitness. We then address knowledge gaps, including the relationship between social structure and population dynamics, the carryover effects of social conditions, and the differential effects of social variables on specific demographic rates. We also review statistical tools and data requirements for the analysis of social networks and demography. We then propose that knowledge gaps could be filled by using joint modelling approaches. We used a simulation study to highlight the potential use of social networks to inform survival, a key demographic parameter. We developed a model that combines social network and survival (Cormack–Jolly–Seber) analyses and evaluated its performance to place inferences under different group structures and sampling scenarios using simulated data. Our results show that valid inferences on social and survival parameters and their connections can be achieved with realistic sample sizes, but precision is improved with more complete information (i.e. fewer missing individuals). Based on our review and simulation study, we suggest that further development of integrative modelling approaches can yield greater understanding and improved power to make predictions about the effects of social environments on populations and the feedback of population dynamics on social structure. • Social structure influences demography through fitness. • We conducted a simulation combining social network analysis and survival modelling. • Models showed minimal bias and good precision but incomplete data reduced precision. • We suggest further work to overcome limitations associated with data integration. [ABSTRACT FROM AUTHOR]

In the dry zone of Sri Lanka experiencing hydrological and weather extremes, non-availability of validated scientific data on stream-flows and groundwater stocks for sufficient time periods, and the presence of large numbers of small and large tanks that divert the water from the streams, make water resource planning a very complex challenge. An integrated modelling exercise was carried out: first using a hydrological simulation model to assess the annual and seasonal water flows of Yan Oya basin of Anuradhapura district and its seven sub-watersheds for 30 years and their dependability; then using a water balance model to analyze the extent to which the current water demands of five selected cascades are being met from the supplies available; and assessing the scope for augmenting the existing storage capacity of the tanks to reduce the irrigation deficits. This article presents the results of the study with regard to: the basin flows and their variability; and water balance scenarios for five selected cascades, with and without modifications in tank storage capacity. The article also discusses how the results from such integrated modelling studies should be used by irrigation planners for taking water management decisions for the basin with decentralized storages like tanks. HIGHLIGHTS Water resources planning around small systems is complex.; Non-availability of scientific data on stream-flows, and the presence of large number of small water systems are major challenges.; SWAT modelling was done to assess the yield of Yan Oya basin.; The rainfall-runoff model estimated for the basin showed linear relationship.; Analysis of five cascades with 30 tanks showed that in 16 cases, supply deficit existed.;

The European Commission's Farm to Fork strategy aims at reducing nutrient losses and fertilizer use, but has been criticized for its expected negative impacts on European economy, agriculture, and food supply. We apply an integrated modelling framework to analyze potential effects of fertilizer reductions on land use, nitrogen losses, and agricultural output of two fertilizer and four climate change scenarios. The fertilizer scenarios comprise a uniform 20 % reduction of mineral N fertilizer (f20) and a combination of several fertilizer restrictions (fcm). The model results indicate that the restrictions in fertilization lead to decreases in crop production of 6 to 9 %, whereas intensive and extensive grassland production increases. N losses to air, water, and soil are substantially reduced by 9 % (f20) and 20 % (fcm), yet fall short of the intended 50 % reduction. The regional heterogeneity of the model results shows that tailored measures need to be elaborated by taking climate change developments, the regional heterogeneity of prevalent farming systems, and bio-physical conditions into account. Uniform measures applied to the national policy context fall short to attain policy targets cost-effectively. N emission capping, taxes or managerial measures such as crop rotational N balancing are options to be explored in future research. [ABSTRACT FROM AUTHOR]

The dynamics for the transition from L-mode to a stationary high Q DT H-mode regime in ITER is expected to be qualitatively different to present experiments. Differences may be caused by a low fuelling efficiency of recycling neutrals, that influence the post transition plasma density evolution on the one hand. On the other hand, the effect of the plasma density evolution itself both on the alpha heating power and the edge power flow required to sustain the H-mode confinement itself needs to be considered. This paper presents results of modelling studies of the transition to stationary high Q DT H-mode regime in ITER with the JINTRAC suite of codes, which include optimisation of the plasma density evolution to ensure a robust achievement of high Q DT regimes in ITER on the one hand and the avoidance of tungsten accumulation in this transient phase on the other hand. As a first step, the JINTRAC integrated models have been validated in fully predictive simulations (excluding core momentum transport which is prescribed) against core, pedestal and divertor plasma measurements in JET C-wall experiments for the transition from L-mode to stationary H-mode in partially ITER relevant conditions (highest achievable current and power, H 98,y ∼ 1.0, low collisionality, comparable evolution in P net/P L-H, but different ρ ∗, T i/T e, Mach number and plasma composition compared to ITER expectations). The selection of transport models (core: NCLASS + Bohm/gyroBohm in L-mode/GLF23 in H-mode) was determined by a trade-off between model complexity and efficiency. Good agreement between code predictions and measured plasma parameters is obtained if anomalous heat and particle transport in the edge transport barrier are assumed to be reduced at different rates with increasing edge power flow normalised to the H-mode threshold; in particular the increase in edge plasma density is dominated by this edge transport reduction as the calculated neutral influx across the separatrix remains unchanged (or even slightly decreases) following the H-mode transition. JINTRAC modelling of H-mode transitions for the ITER 15 MA / 5.3 T high Q DT scenarios with the same modelling assumptions as those being derived from JET experiments has been carried out. The modelling finds that it is possible to access high Q DT conditions robustly for additional heating power levels of P AUX 53 MW by optimising core and edge plasma fuelling in the transition from L-mode to high Q DT H-mode. An initial period of low plasma density, in which the plasma accesses the H-mode regime and the alpha heating power increases, needs to be considered after the start of the additional heating, which is then followed by a slow density ramp. Both the duration of the low density phase and the density ramp-rate depend on boundary and operational conditions and can be optimised to minimise the resistive flux consumption in this transition phase. The modelling also shows that fuelling schemes optimised for a robust access to high Q DT H-mode in ITER are also optimum for the prevention of the contamination of the core plasma by tungsten during this phase.

The development of greenhouses equipped with photovoltaic (PV) panels to simultaneously produce food and energy is gaining attention, driven by the concept of agrivoltaics. In this work, an east-west oriented, uneven structured greenhouse equipped with photovoltaic panels was investigated through developing a mathematical model. The required cooling and heating loads, mature tomato production, and electricity generation were all calculated by integrating energy, growth, and solar photovoltaic panel models. To meet the cooling demand in cold months, the ratio of ventilator area to greenhouse floor area was determined through natural ventilation analysis. The effect of changing the number of PV panels on energy consumption, inside photosynthetic active radiation, and tomato and electricity production was evaluated. Assessments of the PV greenhouse in a location near Tehran revealed that a south roof angle of 25° and a PV cover ratio of 25 % provided better outcomes. Energy consumption per 1.0 kg of tomato production at a 25 % PV cover ratio was 14.88 kWh, which was 15.06 % less than in a no-panel scenario. In this case, over a 9-month growing period, the total tomato yield and power production were 15.52 tons and 46.4 MWh, respectively. To achieve uniform natural sunlight distribution, various configurations of the photovoltaic panels on the greenhouse roof were examined. The findings indicated that the most suitable arrangement was an asymmetric checkerboard layout. This proposed model provided a valuable opportunity for decision-makers to analyze the benefits and drawbacks of PV greenhouses in preferred locations. [ABSTRACT FROM AUTHOR]

• An integrated water system model with a reduced-complexity groundwater module. • The model performs well in simulating river and groundwater temporal dynamics. • A novel flux tracking approach to reveal groundwater and river baseflow origins. • Assessment of aquifer bodies' contributions to baseflow and groundwater abstraction. • Strategies informed by flux tracking results improve baseflow more efficiently. In systems where surface water and groundwater interact, management of the water resource often involves conflicting objectives between water supply and baseflow maintenance. Balancing such objectives requires understanding of the role of groundwater in integrated water systems to inform the design of an efficient strategy to minimise abstraction impacts. This study first develops a reduced-complexity, processed-based groundwater model within the water systems integration modelling framework (WSIMOD). This model is applied to the Lea catchment, UK, as a case study and evaluated against monitored groundwater level and river flow data. A flux tracking approach is developed to reveal the origins of both river baseflow at a critical assessment point and abstracted groundwater across the systems. The insights obtained are used to design two strategies for groundwater abstraction reduction. Results show that the model has good performance in simulating the groundwater and river flow dynamics. Three aquifer bodies that contribute the most to the river baseflow in the dry season at the assessment point are identified; contributions being 17 %, 15 %, and 5 %. The spatial distribution of abstracted groundwater originating from these aquifer bodies is illustrated. Compared to the default equal-ratio reduction, the strategy prioritising abstraction reduction in these three aquifer bodies increases a similar amount of baseflow (13 %) by reducing much less abstraction (23 %). The other strategy, which further decreases abstraction in the adjacent aquifer bodies, increases more baseflow (16 %) with a similar abstraction reduction (30 %). Both strategies can more efficiently improve the baseflow. The flux tracking approach can be further implemented to trace water from other origins, including runoff, stormwater, and wastewater, to enable coordinated management for better systems-level performance. [ABSTRACT FROM AUTHOR]

• This paper emphasizes on the necessity for a robust connection between science and practice in socio-environmental systems research. • Adopting DSR could bridge theory and practice gaps in SES research. • DSR application in case studies shows tailored solutions for timely implementation. • DSR, complementing traditional methods, enhances integration of practical studies and local knowledge into academia. A strong and close connection between science and practice in socio-environmental systems (SES) research and modelling is warranted to face complex and interdisciplinary socio-environmental challenges around issues such as sustainability and climate change. However, significant gaps and inadequate knowledge flow between the scientific and practical aspects of SES exist. This paper highlights several areas that require improvement, including reducing the lag time between scientific solutions and practical implementation, making academic research more relevant to practitioners and decision makers, enhancing the transfer and translation of scientific outputs into practice, improving the integration of practical studies and local knowledge into academia, and addressing the complexity of real-world problems more effectively. To bridge these gaps, we advocate for adopting a design science research (DSR) approach in socio-environmental research. DSR is a problem-solving paradigm that creates applied artifacts such as models, methods, and design theories to enrich knowledge and provide practical solutions. By applying DSR, we can reduce the time between identifying a problem and implementing a solution while facilitating the transfer and translation of research findings into practical applications. We demonstrate the value of artifacts in extracting solutions from practical studies and local knowledge and making them applicable to a broader range of socio-environmental research. DSR emphasises the design of solutions and learning about complex problems through the process of solving them. We demonstrate the merits of the DSR approach application in SES through two case studies to provide a first trial of DSR's practicality, value and challenges. Although DSR is not yet widely recognised or applied in socio-environmental research, this paper encourages its adoption as an overarching approach that complements traditional methods in SES research. By strengthening the connection between practice and science, DSR has the potential to address the existing gaps and improve the effectiveness of socio-environmental research and modelling. The paper therefore advocates that, for complex SES problems, DSR offers ways to not only strengthen but also escalate the overall value of SES modelling and practice to addressing earth's grand challenge problems. It contributes directly to the Joint Special Issue on good modelling practices, including developing Findable, Accessible, Interoperable and Reusable (FAIR) artifacts. [ABSTRACT FROM AUTHOR]

Agricultural systems have entered a period of significant disruption due to impacts from change drivers, increasingly stringent environmental regulations and the need to reduce unwanted discharges, and emerging technologies and biotechnologies. Governments and industries are developing strategies to respond to the risks and opportunities associated with these disruptors. Modelling is a useful tool for system conceptualisation, understanding, and scenario testing. Today, New Zealand and other nations need integrated modelling tools at the national scale to help industries and stakeholders plan for future disruptive changes. In this paper, following a scoping review process, we analyse modelling approaches and available agricultural systems' model examples per thematic applications at the regional to national scale to define the best options for the national policy development. Each modelling approach has specificities, such as stakeholder engagement capacity, complex systems reproduction, predictive or prospective scenario testing, and users should consider coupling approaches for greater added value. The efficiency of spatial decision support tools working with a system dynamics approach can help holistically in stakeholders' participation and understanding, and for improving land planning and policy. This model combination appears to be the most appropriate for the New Zealand national context. [ABSTRACT FROM AUTHOR]

Separating decision modelling from the processes modelling concern recently gained significant support in literature, as incorporating both concerns into a single model impairs the scalability, maintainability, flexibility, and understandability of both processes and decisions. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group provides a suitable solution for externalising decisions from processes and automating decision enactments for processes. This article introduces a systematic way of tackling the separation of the decision modelling concern from process modelling by providing a Decision as a Service (DaaS) layered Service-Oriented Architecture (SOA) which approaches decisions as automated and externalised services that processes need to invoke on demand to obtain the decision outcome. The DaaS mechanism is elucidated by a formalisation of DMN constructs and the relevant layer elements. Furthermore, DaaS is evaluated against the fundamental characteristics of the SOA paradigm, proving its contribution in terms of abstraction, reusability, loose coupling, and other pertinent SOA principles. Additionally, the benefits of the DaaS design on process-decision modelling and mining are discussed. Finally, the DaaS design is illustrated on a real-life event log of a bank loan application and approval process, and the SOA maturity of DaaS is assessed. [ABSTRACT FROM AUTHOR]

Recent JET experiments have been dedicated to the studies of fusion reactions between deuterium (D) and Helium-3 (3He) ions using neutral beam injection (NBI) in synergy with third harmonic ion cyclotron radio-frequency heating (ICRH) of the beam. This scenario generates a fast ion deuterium tail enhancing DD and D3He fusion reactions. Modelling and measuring the fast deuterium tail accurately is essential for quantifying the fusion products. This paper presents the modelling of the D distribution function resulting from the NBI+ICRF heating scheme, reinforced by a comparison with dedicated JET fast ion diagnostics, showing an overall good agreement. Finally, a sawtooth activity for these experiments has been observed and interpreted using SPOT/RFOF simulations in the framework of Porcelli's theoretical model, where NBI+ICRH accelerated ions are found to have a strong stabilizing effect, leading to monster sawteeth.

Climate change threatens water resources in snowmelt-dependent regions by altering the fraction of snow and rain and spurring an earlier snowmelt season. The bulk of hydrological research has focused on forecasting response in streamflow volumes and timing to a shrinking snowpack; however, the degree to which subsurface storage offsets the loss of snow storage in various alpine geologic settings, i.e. the hydrogeologic buffering capacity, is still largely unknown. We address this research need by assessing the affects of climate change on storage and runoff generation for two distinct hydrogeologic settings present in alpine systems: a low storage granitic and a greater storage volcanic hillslope. We use a physically based integrated hydrologic model fully coupled to a land surface model to run a base scenario and then three progressive warming scenarios, and account for the shifts in each component of the water budget. For hillslopes with greater water retention, the larger storage volcanic hillslope buffered streamflow volumes and timing, but at the cost of greater reductions in groundwater storage relative to the low storage granite hillslope. We found that the results were highly sensitive to the unsaturated zone retention parameters, which in the case of alpine systems can be a mix of matrix or fracture flow. The presence of fractures and thus less retention in the unsaturated zone significantly decreased the reduction in recharge and runoff for the volcanic hillslope in climate warming scenarios. This approach highlights the importance of incorporating physically based subsurface flow in to alpine hydrology models, and our findings provide ways forward to arrive at a conceptual model that is both consistent with geology and hydrologic principles. Copyright © 2016 John Wiley & Sons, Ltd.

Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing 'Big Data' and, more importantly, make the vital step from 'Big Data' to 'Big Information'. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example.

Curbing GHG emissions while preserving economic growth is one of the main challenges that developing countries are facing to meet the Paris Agreement commitments. Brazil's NDC target aims to reduce economy-wide absolute levels of GHG emissions by 37% in 2025 and 43% in 2030, compared to 2005 emissions. In this paper, we compare command-and-control and carbon pricing policies to induce the Brazilian economy to meet its NDC targets. We focus on analysing synergies and trade-offs in macroeconomic and social development, captured by economic growth and income distribution while reducing GHG emissions. By integrating a series of sectoral models and a computable general equilibrium (CGE) model, we develop and run different policy scenarios that simulate a set of carbon pricing schemes in Brazil. Our analysis shows that NDC implementation in Brazil under carbon pricing policies allows the country to meet its targets and improve economic and social indicators compared to a command-and-control policy. With about the same GHG emissions up to 2030, important macroeconomic and social co-benefits can be achieved under a carbon pricing policy in Brazil, allowing for reduced welfare losses against business-as-usual trends. Carbon pricing policies are more cost-effective to meet NDC targets in Brazil up to 2030, resulting in higher GDP and household income, in comparison to other individual policy instruments, including command-and-control and subsidies to investments. A carbon price of about 10 USD/tCO2e, combined importantly with deforestation rates under control, would allow Brazil to meet its NDC targets. Recycling carbon pricing revenues can help soften the burden on the labour market and protect low-income households from welfare losses. [ABSTRACT FROM AUTHOR]

Abstract Changes to Australia’s climate and land-use patterns could result in expanded spatial and temporal distributions of endemic mosquito vectors including Aedes and Culex species that transmit medically important arboviruses. Climate and land-use changes greatly influence the suitability of habitats for mosquitoes and their behaviors such as mating, feeding and oviposition. Changes in these behaviors in turn determine future species-specific mosquito diversity, distribution and abundance. In this review, we discuss climate and land-use change factors that influence shifts in mosquito distribution ranges. We also discuss the predictive and epidemiological merits of incorporating these factors into a novel integrated statistical (SSDM) and mechanistic species distribution modelling (MSDM) framework. One potentially significant merit of integrated modelling is an improvement in the future surveillance and control of medically relevant endemic mosquito vectors such as Aedes vigilax and Culex annulirostris, implicated in the transmission of many arboviruses such as Ross River virus and Barmah Forest virus, and exotic mosquito vectors such as Aedes aegypti and Aedes albopictus. We conducted a focused literature search to explore the merits of integrating SSDMs and MSDMs with biotic and environmental variables to better predict the future range of endemic mosquito vectors. We show that an integrated framework utilising both SSDMs and MSDMs can improve future mosquito-vector species distribution projections in Australia. We recommend consideration of climate and environmental change projections in the process of developing land-use plans as this directly impacts mosquito-vector distribution and larvae abundance. We also urge laboratory, field-based researchers and modellers to combine these modelling approaches. Having many different variations of integrated (SDM) modelling frameworks could help to enhance the management of endemic mosquitoes in Australia. Enhanced mosquito management measures could in turn lead to lower arbovirus spread and disease notification rates.

Participatory integrated assessment (PIA) emerged as a response to conventional integrated assessment methods in the mid-to-late 1990s. PIA is based on the tenet that more inclusive stakeholder involvement may lead to increased accountability and legitimacy in decision-making, greater levels of trust and social learning between participants, and improved quality and relevancy of knowledge outputs. In this paper, we conduct a systematic literature review to update and deepen our understanding of the approaches, methods, opportunities, and challenges associated with PIA as applied at the catchment scale. Of the total 278 studies identified in our literature search, only 37 catchment-level cases presented a clear PIA application. From our review, lessons learnt were drawn in relation to the integration of less-easily quantified areas of social science, entry and exit planning in PIA, boundary work on issue cycles and accounting for the human dimension. We conclude that PIA is a potentially useful approach for navigating the dual social-ecological dimensions of current environmental and resource management issues, especially when projects include tailored objectives and methods, user-friendly outputs, and early and consistent stakeholder involvement. However, we also highlight gaps in the field concerning the integrative reach of PIA, PIA's real-world impact, and the relationship between PIA processes and outcomes along stages of environmental issue cycles. We conclude that further work is therefore still needed to help advance the field of PIA in both research and boundary work practice.