A heuristic classification of woody plants based on contrasting shade and drought strategies.

Woody plants vary in their adaptations to drought and shade. For a better prediction of vegetation responses to drought and shade within dynamic global vegetation models, it is critical to group species into functional types with similar adaptations. One of the key challenges is that the adaptations are generally determined by a large number of plant traits that may not be available for a large number of species. In this study, we present two heuristic woody plant groups that were separated using cluster analysis in a three-dimensional trait-environment space based on three key metrics for each species: mean xylem embolism resistance, shade tolerance and habitat aridity. The two heuristic groups separate these species into tolerators and avoiders. The tolerators either rely on their high embolism resistance to tolerate drought in arid habitats (e.g., Juniperus and Prunus) or rely on high shade tolerance to withstand shaded conditions in wet habitats (e.g., Picea, Abies and Acer). In contrast, all avoiders have low embolism resistance and low shade tolerance. In arid habitats, avoiders tend to minimize catastrophic embolism (e.g., most Pinus species) while in wet habitats, they may survive despite low shade tolerance (e.g., Betula, Populus, Alnus and Salix). Because our approach links traits to the environmental conditions, we expect it could be a promising framework for predicting changes in species composition, and therefore ecosystem function, under changing environmental conditions.

The effect of warmer winters on the demography of an outbreak insect is hidden by intraspecific competition.

Warmer climates are predicted to increase bark beetle outbreak frequency, severity, and range. Even in favorable climates, however, outbreaks can decelerate due to resource limitation, which necessitates the inclusion of competition for limited resources in analyses of climatic effects on populations. We evaluated several hypotheses of how climate impacts mountain pine beetle reproduction using an extensive 9-year dataset, in which nearly 10,000 trees were sampled across a region of approximately 90,000 km2 , that was recently invaded by the mountain pine beetle in Alberta, Canada. Our analysis supports the hypothesis of a positive effect of warmer winter temperatures on mountain pine beetle overwinter survival and provides evidence that the increasing trend in minimum winter temperatures over time in North America is an important driver of increased mountain pine beetle reproduction across the region. Although we demonstrate a consistent effect of warmer minimum winter temperatures on mountain pine beetle reproductive rates that is evident at the landscape and regional scales, this effect is overwhelmed by the effect of competition for resources within trees at the site level. Our results suggest that detection of the effects of a warming climate on bark beetle populations at small spatial scales may be difficult without accounting for negative density dependence due to competition for resources.

Incorporating variability in simulations of seasonally forced phenology using integral projection models.

Phenology models are becoming increasingly important tools to accurately predict how climate change will impact the life histories of organisms. We propose a class of integral projection phenology models derived from stochastic individual-based models of insect development and demography. Our derivation, which is based on the rate summation concept, produces integral projection models that capture the effect of phenotypic rate variability on insect phenology, but which are typically more computationally frugal than equivalent individual-based phenology models. We demonstrate our approach using a temperature-dependent model of the demography of the mountain pine beetle (Dendroctonus ponderosae Hopkins), an insect that kills mature pine trees. This work illustrates how a wide range of stochastic phenology models can be reformulated as integral projection models. Due to their computational efficiency, these integral projection models are suitable for deployment in large-scale simulations, such as studies of altered pest distributions under climate change.

Predicting Chronic Climate-Driven Disturbances and Their Mitigation.

Society increasingly demands the stable provision of ecosystem resources to support our population. Resource risks from climate-driven disturbances, including drought, heat, insect outbreaks, and wildfire, are growing as a chronic state of disequilibrium results from increasing temperatures and a greater frequency of extreme events. This confluence of increased demand and risk may soon reach critical thresholds. We explain here why extreme chronic disequilibrium of ecosystem function is likely to increase dramatically across the globe, creating no-analog conditions that challenge adaptation. We also present novel mechanistic theory that combines models for disturbance mortality and metabolic scaling to link size-dependent plant mortality to changes in ecosystem stocks and fluxes. Efforts must anticipate and model chronic ecosystem disequilibrium to properly prepare for resilience planning.

GRACE satellite observations reveal the severity of recent water over-consumption in the United States.

Changes in the climate and population growth will critically impact the future supply and demand of water, leading to large uncertainties for sustainable resource management. In the absence of on-the-ground measurements to provide spatially continuous, high-resolution information on water supplies, satellite observations can provide essential insight. Here, we develop a technique using observations from the Gravity Recovery and Climate Experiment (GRACE) satellite to evaluate the sustainability of surface water and groundwater use over the continental United States. We determine the annual total water availability for 2003-2015 using the annual variability in GRACE-derived total water storage for 18 major watersheds. The long-term sustainable water quantity available to humans is calculated by subtracting an annual estimate of the water needed to maintain local ecosystems, and the resulting water volumes are compared to reported consumptive water use to determine a sustainability fraction. We find over-consumption is highest in the southwest US, where increasing stress trends were observed in all five basins and annual consumptive use exceeded 100% availability twice in the Lower Colorado basin during 2003-2015. By providing a coarse-scale evaluation of sustainable water use from satellite and ground observations, the established framework serves as a blueprint for future large-scale water resource monitoring.

CO2 deserts: implications of existing CO2 supply limitations for carbon management.

Efforts to mitigate the impacts of climate change will require deep reductions in anthropogenic CO2 emissions on the scale of gigatonnes per year. CO2 capture and utilization and/or storage technologies are a class of approaches that can substantially reduce CO2 emissions. Even though examples of this approach, such as CO2-enhanced oil recovery, are already being practiced on a scale >0.05 Gt/year, little attention has been focused on the supply of CO2 for these projects. Here, facility-scale data newly collected by the U.S. Environmental Protection Agency was processed to produce the first comprehensive map of CO2 sources from industrial sectors currently supplying CO2 in the United States. Collectively these sources produce 0.16 Gt/year, but the data reveal the presence of large areas without access to CO2 at an industrially relevant scale (>25 kt/year). Even though some facilities with the capability to capture CO2 are not doing so and in some regions pipeline networks are being built to link CO2 sources and sinks, much of the country exists in "CO2 deserts". A life cycle analysis of the sources reveals that the predominant source of CO2, dedicated wells, has the largest carbon footprint further confounding prospects for rational carbon management strategies.

Pre-site characterization risk analysis for commercial-scale carbon sequestration.

This study develops a probability framework to evaluate subsurface risks associated with commercial-scale carbon sequestration in the Kevin Dome, Montana. Limited knowledge of the spatial distribution of physical attributes of the storage reservoir and the confining rocks in the area requires using regional data to estimate project risks during the pre-site characterization analysis. A set of integrated Monte Carlo simulations are used to assess four risk proxies: the CO2 injectivity, area of review (AoR), migration rate into confining rocks, and a monitoring strategy prior to detailed site characterization. Results show a reasonable likelihood of reaching the project goal of injecting 1 Mt in 4 years with a single injection well (>58%), increasing to >70% if the project is allowed to run for 5 years. The mean radius of the AoR, based on a 0.1 MPa pressure change, is around 4.8 km. No leakage of CO2 through the confining units is seen in any simulations. The computed CO2 detection probability suggests that the monitoring wells should be located at less than 1.2 km away from the injection well so that CO2 is likely to be detected within the time frame of the project. The scientific results of this study will be used to inform the detailed site characterization process and to provide more insight for understanding operational and technical risks before injecting CO2.

Using infrastructure optimization to reduce greenhouse gas emissions from oil sands extraction and processing.

The Alberta oil sands are a significant source of oil production and greenhouse gas emissions, and their importance will grow as the region is poised for decades of growth. We present an integrated framework that simultaneously considers economic and engineering decisions for the capture, transport, and storage of oil sands CO(2) emissions. The model optimizes CO(2) management infrastructure at a variety of carbon prices for the oil sands industry. Our study reveals several key findings. We find that the oil sands industry lends itself well to development of CO(2) trunk lines due to geographic coincidence of sources and sinks. This reduces the relative importance of transport costs compared to nonintegrated transport systems. Also, the amount of managed oil sands CO(2) emissions, and therefore the CCS infrastructure, is very sensitive to the carbon price; significant capture and storage occurs only above 110$/tonne CO(2) in our simulations. Deployment of infrastructure is also sensitive to CO(2) capture decisions and technology, particularly the fraction of capturable CO(2) from oil sands upgrading and steam generation facilities. The framework will help stakeholders and policy makers understand how CCS infrastructure, including an extensive pipeline system, can be safely and cost-effectively deployed.

Greening coal: breakthroughs and challenges in carbon capture and storage.

Like it or not, coal is here to stay, for the next few decades at least. Continued use of coal in this age of growing greenhouse gas controls will require removing carbon dioxide from the coal waste stream. We already remove toxicants such as sulfur dioxide and mercury, and the removal of CO2 is the next step in reducing the environmental impacts of using coal as an energy source (i.e., greening coal). This paper outlines some of the complexities encountered in capturing CO2 from coal, transporting it large distances through pipelines, and storing it safely underground.

Mesoscale carbon sequestration site screening and CCS infrastructure analysis.

We explore carbon capture and sequestration (CCS) at the meso-scale, a level of study between regional carbon accounting and highly detailed reservoir models for individual sites. We develop an approach to CO(2) sequestration site screening for industries or energy development policies that involves identification of appropriate sequestration basin, analysis of geologic formations, definition of surface sites, design of infrastructure, and analysis of CO(2) transport and storage costs. Our case study involves carbon management for potential oil shale development in the Piceance-Uinta Basin, CO and UT. This study uses new capabilities of the CO(2)-PENS model for site screening, including reservoir capacity, injectivity, and cost calculations for simple reservoirs at multiple sites. We couple this with a model of optimized source-sink-network infrastructure (SimCCS) to design pipeline networks and minimize CCS cost for a given industry or region. The CLEAR(uff) dynamical assessment model calculates the CO(2) source term for various oil production levels. Nine sites in a 13,300 km(2) area have the capacity to store 6.5 GtCO(2), corresponding to shale-oil production of 1.3 Mbbl/day for 50 years (about 1/4 of U.S. crude oil production). Our results highlight the complex, nonlinear relationship between the spatial deployment of CCS infrastructure and the oil-shale production rate.