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• Crop water consumption is estimated under climate and land use change effects. • Global crop GWC and BWC increase by 12% and 70% by 2090s, respectively. • Climate change leads to 8.5% increase of global GWC and dominates its trend. • Expansion in irrigated area dominates the trend of global BWC. • Global crop blue water dependence will increase, especially in arid regions. Agriculture accounts for 90% of global freshwater consumption and it is expected to intensify in the future. Climate and land use changes are two major factors affecting crop green and blue water consumption, and in this study we explicitly consider the effects of both factors in a consistent modeling framework. Two important research questions are addressed: 1) How will global crop green and blue water consumption evolve over the 21st century under climate and land use changes; and 2) what are the individual and combined effects of climate and land use changes on future crop green and blue water consumption? To tackle these two questions, a crop water use module is developed based on the Global Change Assessment Model (GCAM) and its hydrology module (i.e., Xanthos). Crop specific green and blue water consumption are then calculated at global 0.5° × 0.5° grid scale. Results show that global crop green water consumption increases by 12% in 2090s when compared with that in 1971–2000, and climate change dominates over land use change in determining the trend of global crop green water consumption. However, expansion in global irrigated area dominates the changing trend of global crop blue water consumption which increases 70% by 2090s, especially in regions with significant irrigated land expansion (e.g. northern Africa, central Asia, China, Mexico, the Middle East, Russia, southern Asia, and Argentina). Furthermore, global crop blue water dependence will increase under climate and land use changes, especially in arid regions. [ABSTRACT FROM AUTHOR]

A coupled simulation-optimization with reanalysis-based short-term weather forecasts from a regional climate model (RCM) is proposed to optimize an irrigation scheduling problem. Using different physical configurations of the climate extension of a weather research and forecasting model (CWRF) that is driven by national atmospheric model project reanalysis data, five ensemble outlooks of 15 consecutive daily forecasts have been generated during five different crop-growing seasons. Six daily climatic variables are forecasted, namely, rainfall, minimum temperature, maximum temperature, humidity, wind speed, and solar radiation. To correct the forecasts for any inherent bias, the quantile mapping method is applied to all six daily climatic variables. After bias correction, a skill assessment of the reanalysis-based RCM forecasts indicate that only the first three climatic variables are predicted with reliable accuracy; thus, average climatic means are used to replace the remaining three variables (humidity, wind speed, and solar radiation). The framework is applied to the Havana Lowlands region, Illinois, as a case study, and the value of forecasts is assessed against two baseline scenarios: no-rain forecast (a pessimistic case) and average climatology (a normal case). Using reanalysis-based RCM forecasts to guide farmers' irrigation decisions could yield about 1-3% in expected profit gain and 4-6% in water reduction when compared to the no-rain forecast scenario, and 1-6% in expected profit gain when compared to the average climatology scenario. This study is a first preliminary attempt to use an ensemble of weather simulations in the optimization of irrigation scheduling, and the developed framework can be used to incorporate operational forecasting once the reanalysis boundary is replaced by global weather forecasts. [ABSTRACT FROM AUTHOR]

Projections of building electricity consumption are used in multiple fields and for a variety of purposes, from energy utility investment decisions to global climate assessments. Existing approaches to modeling building electricity consumption span a range of structural methodologies, spatial resolutions, and temporal scales, potentially leading to divergent projections. This paper compares how two models with different structures and resolutions respond to a common set of population and climate drivers in the western U.S. The BEND model simulates hourly residential and commercial building electricity consumption at the county scale by weighting the results of simulations of thousands of representative buildings. In contrast, the projected electricity demand in GCAM-USA is determined for each state, year, sector (residential/commercial), and service (e.g., heating, cooling, and others), based on population, income, technology, energy prices, and average annual climate. This paper aggregates the two models' results to a common resolution: annual residential and commercial building electricity consumption by state. Both models show similar responses to future projected population change and climate change, with population change having the larger impact between the two. Differences are primarily due to how the models capture changes in the aggregate energy efficiency of the building stock as it evolves over time. • Models of building electricity consumption are methodologically diverse. • Two models of building electricity using very different approaches are compared. • The models mostly agree in their projections of building electricity consumption. • How the efficiency of buildings evolves drives disagreement between the models. [ABSTRACT FROM AUTHOR]

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in 5-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region's demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use 11 scenarios, varying the socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios demonstrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model. [ABSTRACT FROM AUTHOR]

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in five-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region's demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use eleven scenarios, varying socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios illustrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model. [ABSTRACT FROM AUTHOR]

The Platform for Regional Integrated Modeling and Analysis (PRIMA) is an innovative modeling system developed at Pacific Northwest National Laboratory (PNNL) to simulate interactions among natural and human systems at scales relevant to regional decision making. PRIMA brings together state-of-the-art models of regional climate, hydrology, agriculture and land use, socioeconomics, and energy systems using a flexible coupling approach. Stakeholder decision support needs underpin the application of the platform to regional issues, and an uncertainty characterization process is used to identify robust decisions. The platform can be customized to inform a variety of complex questions, such as how a policy in one sector might affect the ability to meet climate mitigation targets or adaptation goals in another sector. Current numerical experiments focus on the eastern United States, but the framework is designed to be regionally flexible. This paper provides a high-level overview of PRIMA's functional capabilities and describes some key challenges and opportunities associated with integrated regional modeling. [ABSTRACT FROM AUTHOR]

Electricity demand is expected to grow over the coming decades creating a need for long-term power sector planning. Planning will require two key considerations. First, maintaining reliability requires enough capacity to supply electricity during hours of peak demand. This requires an understanding of the character of load profiles and the availability of dispatchable electricity. Second, understanding the resilience of the power sector to future stressors will require an assessment of the interactions of the power sector with co-evolving natural and human systems within a multi-sectoral context. For example, future temperature increases could lead to an increase in the demand for air conditioning resulting in increased peak demands and consequently the need for additional capacity.Many analytical tools are used for long-term power sector planning. Power sector focused models capture the complexities of electricity supply and demand but are limited in capturing the interactions of the sector with the broader system. In contrast, multi-sector human-Earth system models capture complex relationships across sectors and systems but represent power sector dynamics at fairly aggregate levels. We study the evolution of the electricity system by simultaneously accounting for power sector focused process dynamics and the interactions of the power sector with the broader system under a consistent integrated framework. To do so, we make improvements to GCAM-USA, a long-term multi-sector human-Earth system model. We incorporate i.) Information about sub-annual demand profiles combined with the ability to separate investment and operation decisions to track capacity requirements in response to changes in peak demands and ii.) Empirically derived relationships between temperature change and peak electricity demands in the building sector. Using the improved model, we study investments and operations in the US power sector under plausible scenarios of future temperature variability. [ABSTRACT FROM AUTHOR]