Geology topics

Colin Daniel

Publications and source records attributed to Colin Daniel.

4 recordsLinked to original sources

USGS2022

Operational assessment tool for forest carbon dynamics for the United States: A new spatially explicit approach linking the LUCAS and CBM-CFS3 models

Background Quantifying the carbon balance of forested ecosystems has been the subject of intense study involving the development of numerous methodological approaches. Forest inventories, processes-based biogeochemical models, and inversion methods have all been used to estimate the contribution of U.S. forests to the global terrestrial carbon sink. However, estimates have ranged widely, largely based on the approach used, and no single system is appropriate for operational carbon quantification and forecasting. We present estimates obtained using a new spatially explicit modeling framework utilizing a “gain–loss” approach, by linking the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). Results We estimated forest ecosystems in the conterminous United States stored 52.0 Pg C across all pools. Between 2001 and 2020, carbon storage increased by 2.4 Pg C at an annualized rate of 126 Tg C year −1 . Our results broadly agree with other studies using a variety of other methods to estimate the forest carbon sink. Climate variability and change was the primary driver of annual variability in the size of the net carbon sink, while land-use and land-cover change and disturbance were the primary drivers of the magnitude, reducing annual sink strength by 39%. Projections of carbon change under climate scenarios for the western U.S. find diverging estimates of carbon balance depending on the scenario. Under a moderate emissions scenario we estimated a 38% increase in the net sink of carbon, while under a high emissions scenario we estimated a reversal from a net sink to net source. Conclusions The new approach provides a fully coupled modeling framework capable of producing spatially explicit estimates of carbon stocks and fluxes under a range of historical and/or future socioeconomic, climate, and land management futures.

Continental United States↗

USGS2019

Effects of 21st century climate, land use, and disturbances on ecosystem carbon balance in California

Terrestrial ecosystems are an important sink for atmospheric carbon dioxide (CO 2 ), sequestering ~30% of annual anthropogenic emissions and slowing the rise of atmospheric CO 2 . However, the future direction and magnitude of the land sink is highly uncertain. We examined how historical and projected changes in climate, land use, and ecosystem disturbances affect the carbon balance of terrestrial ecosystems in California over the period 2001–2100. We modeled 32 unique scenarios, spanning 4 land use and 2 radiative forcing scenarios as simulated by four global climate models. Between 2001 and 2015, carbon storage in California's terrestrial ecosystems declined by −188.4 Tg C, with a mean annual flux ranging from a source of −89.8 Tg C/year to a sink of 60.1 Tg C/year. The large variability in the magnitude of the state's carbon source/sink was primarily attributable to interannual variability in weather and climate, which affected the rate of carbon uptake in vegetation and the rate of ecosystem respiration. Under nearly all future scenarios, carbon storage in terrestrial ecosystems was projected to decline, with an average loss of −9.4% (−432.3 Tg C) by the year 2100 from current stocks. However, uncertainty in the magnitude of carbon loss was high, with individual scenario projections ranging from −916.2 to 121.2 Tg C and was largely driven by differences in future climate conditions projected by climate models. Moving from a high to a low radiative forcing scenario reduced net ecosystem carbon loss by 21% and when combined with reductions in land‐use change (i.e., moving from a high to a low land‐use scenario), net carbon losses were reduced by 55% on average. However, reconciling large uncertainties associated with the effect of increasing atmospheric CO 2 is needed to better constrain models used to establish baseline conditions from which ecosystem‐based climate mitigation strategies can be evaluated.

California↗

USGS2016

State-and-transition simulation models: a framework for forecasting landscape change

Summary A wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features. We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states. We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years. State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of landscape dynamics.

Methods in Ecology and Evolution↗

USGS2015

An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California

Increased land-use intensity (e.g. clearing of forests for cultivation, urbanization), often results in the loss of ecosystem carbon storage, while changes in productivity resulting from climate change may either help offset or exacerbate losses. However, there are large uncertainties in how land and climate systems will evolve and interact to shape future ecosystem carbon dynamics. To address this we developed the Land Use and Carbon Scenario Simulator (LUCAS) to track changes in land use, land cover, land management, and disturbance, and their impact on ecosystem carbon storage and flux within a scenario-based framework. We have combined a state-and-transition simulation model (STSM) of land change with a stock and flow model of carbon dynamics. Land-change projections downscaled from the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emission Scenarios (SRES) were used to drive changes within the STSM, while the Integrated Biosphere Simulator (IBIS) ecosystem model was used to derive input parameters for the carbon stock and flow model. The model was applied to the Sierra Nevada Mountains ecoregion in California, USA, a region prone to large wildfires and a forestry sector projected to intensify over the next century. Three scenario simulations were conducted, including a calibration scenario, a climate-change scenario, and an integrated climate- and land-change scenario. Based on results from the calibration scenario, the LUCAS age-structured carbon accounting model was able to accurately reproduce results obtained from the process-based biogeochemical model. Under the climate-only scenario, the ecoregion was projected to be a reliable net sink of carbon, however, when land use and disturbance were introduced, the ecoregion switched to become a net source. This research demonstrates how an integrated approach to carbon accounting can be used to evaluate various drivers of ecosystem carbon change in a robust, yet transparent modeling environment.

California↗