Integrated Modeling Framework Research Articles

OBOMod - Integrated modelling framework for residents' exposure to pesticides

BackgroundPesticides can be transported from the site of application to homes via different routes and lead to exposure of residents, raising concerns regarding health effects. We built a deterministic model framework (OBOmod) to assess exposure of residents living near fields where pesticides are applied. MethodsOBOmod connects five independent models operating on an hourly timescale and high spatial resolution (meters). Models include descriptions of spray drift, volatilization, atmospheric transport and dispersion, exchange between outdoor and indoor air and exchange between indoor air and dust. Fourteen bulb field applications under different weather conditions and comprising 12 pesticides were simulated. Each simulation included the first seven days after the application. The concentrations computed with OBOmod were compared with those measured in outdoor and indoor air and the amounts measured in indoor dust samples. ResultsModel evaluation indicated suitability of the developed framework to estimate outdoor and indoor air concentrations. For most pesticides, model accuracy was good. The framework explained about 30% to 95% of the temporal and spatial variability of air concentrations. For 20% of the simulations, the framework explained more than 35% of spatial variability of concentrations in dust. In general, OBOmod estimates remained within one order of magnitude from measured levels. Calculations showed that in addition to spray drift during application, volatilization from the field after spraying and pesticides in house dust are important routes for residents' exposure to pesticides. ConclusionsOur framework covers many processes needed to calculate exposure of residents to pesticides. The evaluation phase shows that, with the exception of the dust model, the framework can be used in support of health and epidemiological studies, and can serve as a tool to support development of regulations and policy making regarding pesticide use.

Options for reforming agricultural subsidies from health, climate, and economic perspectives

Agricultural subsidies are an important factor for influencing food production and therefore part of a food system that is seen as neither healthy nor sustainable. Here we analyse options for reforming agricultural subsidies in line with health and climate-change objectives on one side, and economic objectives on the other. Using an integrated modelling framework including economic, environmental, and health assessments, we find that on a global scale several reform options could lead to reductions in greenhouse gas emissions and improvements in population health without reductions in economic welfare. Those include a repurposing of up to half of agricultural subsidies to support the production of foods with beneficial health and environmental characteristics, including fruits, vegetables, and other horticultural products, and combining such repurposing with a more equal distribution of subsidy payments globally. The findings suggest that reforming agricultural subsidy schemes based on health and climate-change objectives can be economically feasible and contribute to transitions towards healthy and sustainable food systems.

Hybrid Physics-Based Neural Network Models for Diesel Combustion Phasing Prediction

Conventional control-oriented physics-based models can be leveraged for combustion phasing control in modern diesel engines, but very accurate models often require significant computational power. On the other hand, Artificial Neural Network (ANN) models can have high predictive capabilities and lower computational costs but their heuristic nature may make them less desirable for complex and impactful parameters like combustion phasing. This paper addresses these issues by leveraging the advantages of both modeling methodologies in an integrated modeling framework which can be utilized for real-time combustion control for a modern diesel engine. The models are validated with data from a 2.0 L turbocharged diesel engine which is fitted with dual loop exhaust gas recirculation. The results of the modeling frameworks are compared and show that the proposed hybrid model framework can have a Pearson Product-Correlation Coefficient (PPMCC) of a 100% and an RMSE of 8.79 × 10-8 CAD; as compared to the physics-based model with a PPMCC of a 95% and an RMSE of 0.17 CAD.

A Reduced Complexity Model With Graph Partitioning for Rapid Hydraulic Assessment of Sewer Networks

AbstractExisting, high‐fidelity models for sewer network modeling are accurate but too slow and inflexible for modern applications such as optimization or scenario analysis. Reduced complexity surrogate modeling has been applied in response to this, however, current approaches are expensive to set up and still require high‐fidelity simulations to derive parameters. In this study, we compare and develop graph partitioning algorithms to automatically group sections of sewer networks into semi‐distributed compartments. These compartments can then be simulated using sewer network information only in the integrated modeling framework, CityWat‐SemiDistributed (CWSD), which has been developed for application to sewer network modeling in this study. We find that combining graph partitioning with CWSD can produce accurate simulations 100–1,000× faster than existing high‐fidelity modeling. Because we anticipate that many CWSD users will not have high‐fidelity models available, we demonstrate that the approach provides reasonable simulations even under significant parametric uncertainty through a sensitivity analysis. We compare multiple graph partitioning techniques enabling users to specify the spatial aggregation of the partitioned network, also enabling them to preserve key locations for simulation. We test the impact of temporal resolution, finding that accurate simulations can be produced with timesteps up to one hour. Our experiments show a log‐log relationship between temporal/spatial resolution and simulation time, enabling users to pre‐specify the efficiency and accuracy needed for their applications. We expect that the efficiency and flexibility of our approach may facilitate novel applications of sewer network models ranging from continuous simulations for long‐term planning to spatially optimizing the placement of network sensors.

A grid-based evolutionary spatial algorithm for airline service design in multi-airport systems

This paper proposes an integrated modeling framework to support airline frequency planning in a multi-airport system. First, a grid-based spatial model is developed to estimate passenger demand as a function of air-service characteristics (such as flight frequency and airfare) and ground accessibility. Second, an evolutionary algorithm is proposed to provide decision support for the allocation of flights to airports belonging to the multi-airport system, subject to fleet availability, flow balance and operational requirements. We apply the proposed approach to model frequent flyer passengers departing from one of the major multi-airport systems in Europe. Our results show that the degree of diversification of a multi-airport system—i.e., the presence of alternative airports—plays a significant role on both the demand and supply sides, providing passengers with more diversified and accessible services and providing airlines with higher operational flexibility and resilience in case of disruptions. Our modeling framework can be extended to capture additional managerial objectives or practical requirements.

Impacts of Long-Term Strategies for the Swedish Forest Sector: Analyses with the Bioframe Integrated Modelling Framework

Impacts of Long-Term Strategies for the Swedish Forest Sector: Analyses with the Bioframe Integrated Modelling Framework

Evaluating Distributed Policies for Conjunctive Surface Water‐Groundwater Management in Large River Basins: Water Uses Versus Hydrological Impacts

AbstractIt is imperative to understand the interconnectedness of water use and hydrological impacts for water policy design underlying varying hydrological conditions across space and over time. However, such analysis remains difficult, constrained by the lack of appropriate modeling tools that fully integrate water policies, water use, and hydrological processes with high spatiotemporal resolutions. To address this challenge, this study proposes a distributed policy design scheme featuring spatially variable and temporally dynamic policies for conjunctive surface water‐groundwater management in large river basins. A fully integrated modeling framework is developed to tightly couple (a) an agent‐based model for farmers' water use under distributed water policies and (b) a physically based hydrological model for surface water‐groundwater processes. The modeling framework is applied to the Heihe River Basin to assess water use and hydrological impacts under distributed water policies. By using the distributed policy scheme to adjust a water policy (e.g., groundwater tax) across space and over time, we found that hydrological outcomes can be improved without adversely reducing agricultural water supply. For example, by shifting the implementation of a high groundwater tax from dry to wet years, a rise of the water table by 0.28 m (0.03–0.95 m across different irrigation districts) can be achieved while the total water supply is maintained at a similar level. Furthermore, hydrological externality effects among nearby districts can be explicitly identified and quantified based on assessments of spatially varying water policies. This study highlights the need for water policy design to consider spatiotemporal variations in the physical hydrological system.

Identifying the dynamic evolution and feedback process of water resources nexus system considering socioeconomic development, ecological protection, and food security: A practical tool for sustainable water use

Abstract. The accelerated consumption of water resources caused by the rapid increase in population and urbanization is intensifying the complex interactions across water resources, socioeconomic development, ecological protection, and food security (WSEF), which causes not only the imbalance between water supply and demand but also the vulnerability of both food and ecological systems. Therefore, identifying the dynamic coevolution and feedback process is one of the most crucial ways of achieving the goal of sustainable water use. In this study, we developed an integrated modeling framework to better identify the dynamic interaction and coevolution process of the nexus across WSEF systems in the context of sustainable water uses by coupling system dynamic (SD) model and multi-objective optimization model. The SD model is used to simulate both the dynamic interaction of each agent and the coevolution process of the whole nexus system by positive/negative feedback loops. The multi-objective optimization model is used to quantify the negative feedback loops of the SD model by generating the optimal scheme of different water users. Finally, the model uncertainty considering different weighting factors is analyzed. The framework is applied to the upper reaches of the Guijiang River basin, China. Results show that (i) the rapid economic growth increases the conflict between the water uses for socioeconomic development and ecological protection, intensifying the ecological awareness and resulting in more water shortages of socioeconomic and food agents, which is unable to support such rapid development. (ii) Once the economic growth rate decreases, water resources are able to support economic development with a decreased overload index and stable crop yield, which further contributes to water sustainability. (iii) The river ecological agent is the critical factor that affects the robustness of the model. (iv) The equal consideration of each water usage is the most beneficial to sustainable development. These results highlight the importance of water resources management, considering the tradeoffs across multiple stakeholders, and give a strong reference to policymakers for comprehensive urban planning.

Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records

The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities based solely on available information at coarser time scales. The methodology is based on three key elements: (a) the analysis of statistics’ behaviour across multiple temporal scales; (b) the use of parametric functions to model this behaviour; and (c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to a 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements.

An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes.

To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The "source-to-outcome" local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.

Construction of intelligent integrated model framework for the workshop manufacturing system via digital twin

With the boosting development of the advanced manufacturing industry in the world, the original production pattern transformed from the traditional industries into the intelligence mode is completed with the least delay possible, which are still facing new challenges. The timeliness, stability, and reliability of them are significantly restricted due to the lack of real-time communication. Therefore, a model framework of intelligent workshop manufacturing system based on a digital twin is proposed in this paper, driving the deep information integration among the physical entity, data collection, and information decision-making. The traditional digital twin of conceptualization and fuzziness needs to be refined, optimized, and upgraded on the basis of the four-dimension collaborative model thinking. The model framework of a refined nine-layer intelligent digital twin is established. Firstly, the physical evaluation is refined into entity layer, auxiliary layer, and interface layer, scientific managing the physical resources and the instrument, and coordinating the overall system. Secondly, dividing the data evaluation into the data layer and the processing layer can greatly improve the flexible response-ability and ensure the synchronization of the real-time data. Finally, the system evaluation is subdivided into information layer, algorithm layer, scheduling layer, and functional layer, developing flexible manufacturing plan more reasonably, shortening the production cycle, and reducing logistics cost. Simultaneously, combining SLP and artificial bee colonies is applied to investigate the production system optimization of the textile workshop. The results indicate that the production efficiency of the optimized production system is increased by 34.46%.

Dynamics of Musical Success: A Machine Learning Approach for Multimedia Data Fusion

The success of creative products depends on the felt experience of consumers. Capturing such consumer reactions requires the fusing of different types of experiential covariates and perceptual data in an integrated modeling framework. In this article, the authors develop a novel multimodal machine learning framework that combines multimedia data (e.g., metadata, acoustic features, user-generated textual data) in creative product settings and apply it to predict the success of musical albums and playlists. The authors estimate the proposed model on a unique data set collected using different online sources. The model integrates different types of nonparametrics to flexibly accommodate diverse types of effects. It uses penalized splines to capture the nonlinear impact of acoustic features and a supervised hierarchical Dirichlet process to represent crowd sourced textual tags, and it captures dynamics via a state-space specification. The authors show the predictive superiority of the model with respect to several benchmarks. The results illuminate the dynamics of musical success over the past five decades. The authors then use the components of the model for marketing decisions such as forecasting the success of new albums, conducting album tuning and diagnostics, constructing playlists for different generations of music listeners, and providing contextual recommendations.

Development of a coupled simulation framework representing the lake and river continuum of mass and energy (TCHOIR v1.0)

Abstract. Terrestrial surface water temperature is a key variable affecting water quality and energy balance, and thermodynamics and fluid dynamics are tightly coupled in fluvial and lacustrine systems. Streamflow generally plays a role in the horizontal redistribution of heat, and thermal exchange in lakes predominantly occurs in a vertical direction. However, numerical models simulate the water temperature for uncoupled rivers and lakes, and the linkages between them on a global scale remain unclear. In this study, we proposed an integrated modeling framework: Tightly Coupled framework for Hydrology of Open water Interactions in River–lake network (TCHOIR, read as “tee quire”). The objective is to simulate terrestrial fluvial and thermodynamics as a continuum of mass and energy in solid and liquid phases redistributed among rivers and lakes. TCHOIR uses high-resolution geographical information harmonized over fluvial and lacustrine networks. The results have been validated through comparison with in situ observations and satellite-based data products, and the model sensitivity has been tested with multiple meteorological forcing datasets. It was observed that the “coupled” mode outperformed the “river-only” mode in terms of discharge and temperature downstream of lakes; moreover, it was observed that seasonal and interannual variation in lake water levels and temperature are also more reliable in the “coupled” mode. The inclusion of lakes in the coupled model resulted in an increase in river temperatures during winter at midlatitudes and a decrease in temperatures during summer at high latitudes, which reflects the role of lakes as a form of large heat storage. The river–lake coupling framework presented herein provides a basis for further elucidating the role of terrestrial surface water in Earth's energy cycle.

Systems Analysis of Coupled Natural and Human Processes in the Mekong River Basin

The Mekong River Basin is one of the world’s major transboundary basins. The hydrology, agriculture, ecology, and other watershed functions are constantly changing as a result of a variety of human activities carried out inside and by neighboring countries including China, Myanmar, Thailand, Laos, Cambodia, and Vietnam in order to meet increased food and water demands for an increasing population. The Mekong River, which provides irrigation and fishing for a population of over 60 million people, also has an estimated 88,000 MW of untapped hydropower potential. The construction of dams for energy supply has a wide-ranging impact on downstream reservoir regions, resulting in unprecedented changes in hydrologic functions, the environment, and people’s livelihoods. We present a holistic view of how external stressors such as climate change and variability, land cover, and land-use change affect supply and demand. We present an integrated modeling framework for analyzing the supply–demand scenarios and tradeoffs between different sectors. Specifically, we evaluated the impacts of future climate on irrigation, hydropower, and other needs in the basin through a feedback loop. We focused on hydrologic extremes to evaluate their impacts on the reservoir operations during flood and low flow events. The inflow is projected to change by +13% to −50% in the future, while a 0.25% (15.24 billion m3) reduction is projected for the net irrigation water requirement (NIWR). A unit percentage increase in irrigation demand will reduce energy generation by 0.15%, but climate change has a beneficial impact on dam performance with a predicted increase in energy generation and supply to all sectors. Flood events will cause excessive stress on reservoir operation to handle up to six times more flow volumes; however, the low-flow events will marginally affect the system. While the flow and storage rule curves consider both supply and demand, changing human water use comes second to changing climate or other biophysical considerations. This paper emphasizes the importance of considering feedback between climate–water–human society in the systems modeling framework in order to meet societal and ecological challenges. The findings will provide information on the risks and tradeoffs that exist in the water, energy, and food sectors of the basin.

Co-benefits of deep carbon reduction on air quality and health improvement in Sichuan Province of China

Facing the dual challenges of air pollution and climate change, China has set ambitious goals and made decisive efforts to reduce its carbon emission and win the ‘Battle for Blue Sky’. However, how the low-carbon transition and air quality targets could be simultaneously achieved at the sub-national levels remains unclear. The questions arise whether province-level climate change mitigation strategies could help ease the air pollution and close the air quality gap, and how these co-benefits can be compared with the cost of the green transition. Here, using an integrated modeling framework, we combined with local air pollutant emission inventories and issued policy documents to quantitatively evaluated the current situation and targets of the air quality and health co-benefits of deep carbon mitigation in Sichuan, a fast-developing inland province in China. We found that by 2035, without system-wide energy transformation induced by carbon mitigation policies, the improvement in air quality in Sichuan Province might be limited, even under stringent end-of-pipe emission control measures. On the contrary, the co-benefits of low-carbon policies would be significant. On top of stringent end-of-pipe controls, the implementation of carbon mitigation policy in line with China’s enhanced climate target could further reduce the average PM2.5 concentration in Sichuan by as much as 2.8 µg m−3, or the population-weighted PM2.5 concentration by 5.9 µg m−3 in 2035. The monetized health co-benefits in Sichuan Province would amount to 23 billion USD under the stringent carbon mitigation scenario, exceeding 1.7 billion USD of the mitigation cost by 2035. The results indicate that significant air quality and health benefits could both be achieved from carbon mitigation at the provincial level. Both air-pollution or carbon-reduction oriented policies would be important for improving environmental quality and public health.

Reconciling regional nitrogen boundaries with global food security.

While nitrogen inputs are crucial to agricultural production, excess nitrogen contributes to serious ecosystem damage and water pollution. Here, we investigate this trade-off using an integrated modelling framework. We quantify how different nitrogen mitigation options contribute to reconciling food security and compliance with regional nitrogen surplus boundaries. We find that even when respecting regional nitrogen surplus boundaries, hunger could be substantially alleviated with 590 million fewer people at risk of hunger from 2010 to 2050, if all nitrogen mitigation options were mobilized simultaneously. Our scenario experiments indicate that when introducing regional N targets, supply-side measures such as the nitrogen use efficiency improvement are more important than demand-side efforts for food security. International trade plays a key role in sustaining global food security under nitrogen boundary constraints if only a limited set of mitigation options is deployed. Policies that respect regional nitrogen surplus boundaries would yield a substantial reduction in non-CO2 GHG emissions of 2.3 GtCO2e yr-1 in 2050, which indicates a necessity for policy coordination.

Achieving sustainable development goals in agricultural energy-water-food nexus system: An integrated inexact multi-objective optimization approach

Sustainable Development Goals (SDGs) take the global challenges into a new phase, calling for reasonable resources management from holistic perspectives. This study develops a novel integrated modelling framework for sustainable agricultural energy-water-food nexus (EWFN) management, with the objectives of maximum social welfare of water resources allocation, maximum hydroelectric generation, maximum grain crop production, maximum positive farmland ecosystem service value, and minimum negative farmland ecosystem service value. The proposed framework is capable of: (1) balancing benefit efficiency and allocation equity using social welfare function; (2) reconciling conflicting targets among socio-economic, resource, and eco-environmental spheres; (3) generating sustainable water and land resources allocation strategies considering complex and uncertain environment. The proposed model was applied to the Zhanghe Reservoir irrigation area, central China. Flexible water and land resources allocation schemes among different sectors, crops, and periods were generated, as well as managerial insights into what efforts should be done were provided for decision-makers. After optimization, efficiency-equity tradeoff was balanced with social welfare index reaching [0.94, 0.99]. Optima results show that GHGs emission contributed majority of the total loss, which cannot be totally neutralized by carbon sequestration, causing negative eco-environmental impacts of [2.3, 3.4] × 108 CNY. The proposed model performs well on generating robust and coordinated solutions according to scenarios analysis and models comparison. The proposed approach has potential on achieving SDGs in agricultural EWFN system, and is portable to other agriculture-centered areas suffering from similar resources crisis.

Integrated model framework for the evaluation and prediction of the water environmental carrying capacity in the Guangdong-Hong Kong-Macao Greater Bay Area

Rapid economic development and continuous population expansion have placed increasing pressure on the regional water environment. Maintaining a good water environment is the foundation for the sustainable development of human society. Therefore, an overall understanding of the regional water environment carrying capacity (WECC) is of great significance to promote regional sustainable development. In this study, a novel integrated model framework is proposed to comprehensively evaluate and predict the WECCs of 9 cities in the Guangdong-Hong Kong-Macao Greater Bay Area by coupling the entropy weight method, multi-objective linear weighting function, cloud model and Markov chain. Furthermore, an obstacle degree model is used to explore the major factors affecting the WECC in the study area. The results indicate that the WECCs of 9 cities from 2013 to 2018 show an overall improving trend, and the highest average WECC appears in Shenzhen (0.59) with a good state, while those of the other eight cities are in a medium state. From 2021 to 2030, it is predicted that the WECCs of the 9 cities will present gradual and specific changes, with the WECC of Shenzhen developing from a good to an excellent state, the WECCs of Guangzhou, Zhuhai, Huizhou, Dongguan and Jiangmen developing from medium to good, and the WECCs of Foshan, Zhongshan and Zhaoqing maintaining medium states for a long time. The main factors obstructing the WECC in the Greater Bay Area are water resources per capita, ecological water use rate, wastewater discharge per million yuan GDP and proportion of the tertiary industry, which should give priority in future development. This framework may contribute significantly to providing an effective scientific reference for regional water environmental planning and management.

Modeling Cumulative Effects of Climate and Development on Moose, Wolf, and Caribou Populations

ABSTRACTWildlife models focused solely on a single strong influence (e.g., habitat components, wildlife harvest) are limited in their ability to detect key mechanisms influencing population change. Instead, we propose integrated modeling in the context of cumulative effects assessment using multispecies population dynamics models linked to landscape‐climate simulation at large spatial and temporal scales. We developed an integrated landscape and population simulation model using ALCES Online as the model‐building platform, and the model accounted for key ecological components and relationships among moose (Alces alces), grey wolves (Canis lupus nubilus), and woodland caribou (Rangifer tarandus caribou) in northern Ontario, Canada. We simulated multiple scenarios over 5 decades (beginning 2020) to explore sensitivity to climate change and land use and assessed effects at multiple scales. The magnitude of effect and the relative importance of key factors (climate change, roads, and habitat) differed depending on the scale of assessment. Across the full extent of the study area (654,311km2[ecozonal scale]), the caribou population declined by 26% largely because of climate change and associated predator‐prey response, which led to caribou range recession in the southern part of the study area. At the caribou range scale (108,378 km2), which focused on 2 herds in the northern part of the study area, climate change led to a 10% decline in the population and development led to an additional 7% decline. At the project scale (8,331 km2), which was focused more narrowly on the landscape surrounding 4 proposed mines, the caribou population declined by 29% largely in response to simulated development. Given that observed caribou population dynamics were sensitive to the cumulative effects of climate change, land use, interspecific interactions, and scale, insights from the analysis might not emerge under a less complex model. Our integrated modeling framework provides valuable support for broader regional assessments, including estimation of risk to caribou and Indigenous food security, and for developing and evaluating potential caribou recovery strategies. © 2021 The Authors.The Journal of Wildlife Managementpublished by Wiley Periodicals LLC on behalf of The Wildlife Society.

Climate-Induced Tradeoffs in Planning and Operating Costs of a Regional Electricity System.

Electricity grid planners design the system to supply electricity to end-users reliably and affordably. Climate change threatens both objectives through potentially compounding supply- and demand-side climate-induced impacts. Uncertainty surrounds each of these future potential impacts. Given long planning horizons, system planners must weigh investment costs against operational costs under this uncertainty. Here, we developed a comprehensive and coherent integrated modeling framework combining physically based models with cost-minimizing optimization models in the power system. We applied this modeling framework to analyze potential tradeoffs in planning and operating costs in the power grid due to climate change in the Southeast U.S. in 2050. We find that planning decisions that do not account for climate-induced impacts would result in a substantial increase in social costs associated with loss of load. These social costs are a result of under-investment in new capacity and capacity deratings of thermal generators when we included climate change impacts in the operation stage. These results highlight the importance of including climate change effects in the planning process.