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Abstract Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future.

Abstract Projection of land use and land‐cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and land cover. The land availability data include protected land and agricultural suitability and is incorporated into the Moirai land data system for initializing the Global Change Analysis Model. Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land‐use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, compared with uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling the current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts on agriculture but may not meet conservation goals. Rather than focusing on an area target, a more robust approach may be to carefully select newly protected land to meet well‐defined conservation goals while minimizing impacts to agriculture.

Abstract Land scarcity is increasing over time, driven by complex multisector dynamics. The impacts of land scarcity on the economy and environment are multi‐faceted and regional, so any action to convert land will contain inherent tradeoffs. These impacts are complicated by the deeply uncertain evolution of the various sectors influencing land scarcity. A need therefore exists to provide multi‐metric and multi‐sector assessments that are robust to myriad uncertainties. Land conservation effectively limits the supply of productive land, while biofuel consumption increases the demand and competition for that land, and how these dynamics individually and jointly propagate to economic and environmental impacts is an important open question. To address this, we adopt the Global Change Analysis Model (GCAM) that has representations of various important systems including the climate, macroeconomic, energy, agriculture and land, and water resources systems. Various scenarios of increased land demand (from biofuels) and decreased land supply (from conservation) under various socioeconomic scenarios drawn from the SSPs were simulated using GCAM. We find that while biofuel consumption and land conservation reduce carbon emissions, this comes at the cost of higher food prices, reduced crop production, and increased water withdrawals. Additionally, some regions experience these tradeoffs more severely than others and are more heavily impacted from the same biofuel mandate or by an additional percent of protected land. These and other findings highlight the importance of multisector modeling frameworks that capture many cross‐sector linkages, and acknowledge the important uncertainties confronting the human‐Earth system when making any analysis of land scarcity impacts.

The Moirai land data system, written in C and R, is designed to produce recent historical land inputs for an integrated human-Earth systems model. The primary function of Moirai is to combine spatially explicit input data (e.g., raster images) with tabular input data (e.g., crop price table) to generate spatially-referenced tabular data of crop production, crop harvested area, land value, irrigated and rainfed crop area, water footprint, soil and vegetation carbon density of unmanaged land, and historical land use/cover. These data are aggregated to user-defined geographic boundaries within 231 countries and the default boundaries are defined globally by 235 watersheds. The production, harvested area, and land value outputs reconstruct those available from the Global Trade Analysis Project, while the other outputs provide additional information for various applications, such as initializing or evaluating a land use change model or an economic general/partial equilibrium model. Furthermore, Moirai is a modular system that can be updated and customized through replacement and addition of source data.

Stabilizing climate requires reducing greenhouse gas (GHG) emissions and storing atmospheric carbon dioxide (CO _2 ) in land or ocean systems. Soil management practices can reduce GHG emissions or sequester atmospheric CO _2 into inorganic and organic forms. However, whether soil carbon strategies represent a viable and impactful climate mitigation pathway is uncertain. A specific question concerns the role that land-management practices and soil amendments can play in realizing California’s ambition for carbon neutrality by 2045. Here we examine the carbon flux impacts of soil conservation (i.e., compost, reduced tillage, cover crop) and enhanced silicate rock weathering (EW) practices at different areal extents of implementation in cropland, grassland, and savanna in California under two climate change cases. We show that with implementation areas of 15% or 50% of private cultivated land, grassland, and savanna in California, soil conservation practices alone can contribute $1.4_{0.7}^{2.1}$ % ( $ - 1.8_{ - 0.9}^{ - 2.7}$ Mt CO _2 eq y ^−1 ) and $4.6_{2.3}^{6.9}$ % ( $ - 6.0_{ - 3.0}^{ - 8.9}$ Mt CO _2 eq y ^−1 ) of the additional emissions reduction needed (beyond previous targets) to meet the 2045 net neutrality goal (−129.3 Mt CO _2 eq y ^−1 ), respectively, on an average annual basis, including climate uncertainty. Including EW in these scenarios increases the total contributions of management practices to $4.1_{2.5}^{5.6}$ % ( $ - 5.2_{ - 3.2}^{ - 7.3}$ Mt CO _2 eq y ^−1 ) and $13.5_{8.2}^{18.6}$ % ( $ - 17.5_{ - 10.7}^{ - 24.2}$ Mt CO _2 eq y ^−1 ), respectively, of this reduction. This highlights that the extent of implementation area is a major factor in determining benefits and that EW has the potential to make a real contribution to net reduction targets. Results are similar across climate cases, indicating that contemporary field data can be used to make future projections. With EW there remains mechanistic uncertainties, however, such as rock dissolution rate and environmental controls on weathering products, which require additional field research to improve understanding of the technological efficacy of this approach for California’s 2045 carbon neutrality goal.

The effectiveness of land-based climate mitigation strategies is generally estimated on a case-by-case basis without considering interactions with other strategies or influencing factors. Here we evaluate a new, comprehensive approach that incorporates interactions among multiple management strategies, land use/cover change, wildfire, and climate, although the potential effects of climate change are not evaluated in this study. The California natural and working lands carbon and greenhouse gas model (CALAND) indicates that summing individual practice estimates of greenhouse gas impacts may underestimate emission reduction benefits in comparison with an integrated estimate. Annual per-area estimates of the potential impact of specific management practices on landscape emissions can vary based on the estimation period, which can be problematic for extrapolating such estimates over space and time. Furthermore, the actual area of implementation is a primary factor in determining potential impacts of management on landscape emissions. Nonetheless, less intensive forest management, avoided conversion to urban land, and urban forest expansion generally create the largest annual per-area reductions, while meadow restoration and forest fuel reduction and harvest practices generally create the largest increases with respect to no management. CALAND also shows that data uncertainty is too high to determine whether California land is a source or a sink of carbon emissions, but that estimating effects of management with respect to a baseline provides valid results. Important sources of this uncertainty are initial carbon density, net ecosystem carbon accumulation rates, and land use/cover change data. The appropriate choice of baseline is critical for generating valid results.

Land-use and -cover change (LUCC) is globally important to climate change mitigation. However, using land-based strategies to support aggressive subnational greenhouse gas emissions reduction targets is challenging due to competing land use priorities and uncertainty in ecosystem carbon dynamics and climate change effects. We used the California natural and working lands carbon and greenhouse gas model to quantify the direct ecosystem carbon emissions (CO _2 and CH _4 ) impacts, trade-offs, and climate change interactions of two policy scenarios identified by the State of California for fulfilling multiple land use goals, including the competing goals of mitigating wildfire severity and landscape carbon emissions, among others. Here we show that emissions from desired forest management to reduce the amount of combustible biomass (fuel reduction) initially outweighed emissions reductions from other strategies (e.g. less intensive forest management, restoration, land conservation); however, avoided emissions and enhanced carbon sequestration from the other strategies gradually outweighed fuel reduction emissions. Thus, in jurisdictions with large-scale wildfire mitigation goals, practices that reduce emissions and/or increase carbon sequestration can simultaneously offset fuel reduction emissions. Our analysis highlights the complexities inherent in LUCC planning, underscoring the need for governments to begin the task now.

Debate continues over the adequacy of existing field plots to sufficiently capture Amazon forest dynamics to estimate regional forest carbon balance. Tree mortality dynamics are particularly uncertain due to the difficulty of observing large, infrequent disturbances. A recent paper (Chambers et al 2013 Proc. Natl Acad. Sci. 110 3949–54) reported that Central Amazon plots missed 9–17% of tree mortality, and here we address ‘why’ by elucidating two distinct mortality components: (1) variation in annual landscape-scale average mortality and (2) the frequency distribution of the size of clustered mortality events. Using a stochastic-empirical tree growth model we show that a power law distribution of event size (based on merged plot and satellite data) is required to generate spatial clustering of mortality that is consistent with forest gap observations. We conclude that existing plots do not sufficiently capture losses because their placement, size, and longevity assume spatially random mortality, while mortality is actually distributed among differently sized events (clusters of dead trees) that determine the spatial structure of forest canopies.