Download or read book Data Model Assimilation at the FACE and AmeriFlux Sites Toward Predictive Understanding of Carbon Sequestration at Ecosystem and Regional Scales written by and published by . This book was released on 2013 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: The project was conducted during the period from 9/1/2007 to 8/31/2011 with three major tasks: (1) development of data assimilation (DA) techniques for terrestrial carbon research; (2) applications of DA techniques to analysis of carbon cycle at Duke and other FACE sites; and (3) inverse analysis at AmeriFlux sites. During this period, we have developed a variety of techniques, including (1) ensemble Kalman filter to estimate model parameters or state variables (Gao et al. 2011), (2) Conditional inversion to estimate parameters of a carbon cycle model (Wu et al. 2009), and (3) various methods to quantify uncertainty of estimated parameters and predicted C sinks (e.g., Weng et al. 2011), and (4) information theory to evaluate information content of different model structures and data sets (Weng and Luo 2011). We applied the DA techniques to and did modeling at the Duke FACE and other global change experimental sites. We addressed the following issues: (1) interactive effects of CO2, warming and precipitation on ecosystem processes (e.g., Luo et al. 2008, Weng and Luo 2008, Zhou et al. 2008), (2) effects of warming on estimated parameters related to photosynthesis and residence times (Zhou et al. 2010); and (3) uncertainty in estimated parameters and predicted C sequestration (Gao et al. 2011, Weng and Luo 2011). In addition, we have done data assimilation to estimate carbon residence and carbon sequestration in US continent (Zhou and Luo 2008) and temperature sensitivity at the global scale (Zhou et al. 2009).

Download or read book Final Report On DOE Project DE FG02 06ER64319 Data Model Assimilation at the FACE and AmeriFlux Sites Toward Predictive Understanding of Carbon Sequestration at Ecosystem and Regional Scales written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Land Carbon Cycle Modeling written by Yiqi Luo and published by CRC Press. This book was released on 2022-08-18 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Carbon moves through the atmosphere, through the oceans, onto land, and into ecosystems. This cycling has a large effect on climate – changing geographic patterns of rainfall and the frequency of extreme weather – and is altered as the use of fossil fuels adds carbon to the cycle. The dynamics of this global carbon cycling are largely predicted over broad spatial scales and long periods of time by Earth system models. This book addresses the crucial question of how to assess, evaluate, and estimate the potential impact of the additional carbon to the land carbon cycle. The contributors describe a set of new approaches to land carbon cycle modeling for better exploring ecological questions regarding changes in carbon cycling; employing data assimilation techniques for model improvement; and doing real- or near-time ecological forecasting for decision support. This book strives to balance theoretical considerations, technical details, and applications of ecosystem modeling for research, assessment, and crucial decision making. Key Features Helps readers understand, implement, and criticize land carbon cycle models Offers a new theoretical framework to understand transient dynamics of land carbon cycle Describes a suite of modeling skills – matrix approach to represent land carbon, nitrogen, and phosphorus cycles; data assimilation and machine learning to improve parameterization; and workflow systems to facilitate ecological forecasting Introduces a new set of techniques, such as semi-analytic spin-up (SASU), unified diagnostic system with a 1-3-5 scheme, traceability analysis, and benchmark analysis, for model evaluation and improvement Related Titles Isabel Ferrera, ed. Climate Change and the Oceanic Carbon Cycle: Variables and Consequences (ISBN 978-1-774-63669-5) Lal, R. et al., eds. Soil Processes and the Carbon Cycle (ISBN 978-0-8493-7441-8) Windham-Myers, L., et al., eds. A Blue Carbon Primer: The State of Coastal Wetland Carbon Science, Practice and Policy (ISBN 978-0-367-89352-1)

Download or read book Using a Regional Cluster of AmeriFlux Sites in Central California to Advance Our Knowledge on Decadal Scale Ecosystem Atmosphere Carbon Dioxide Exchange written by and published by . This book was released on 2015 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuous eddy convariance measurements of carbon dioxide, water vapor and heat were measured continuously between an oak savanna and an annual grassland in California over a 4 year period. These systems serve as representative sites for biomes in Mediterranean climates and experience much seasonal and inter-annual variability in temperature and precipitation. These sites hence serve as natural laboratories for how whole ecosystem will respond to warmer and drier conditions. The savanna proved to be a moderate sink of carbon, taking up about 150 gC m-2y-1 compared to the annual grassland, which tended to be carbon neutral and often a source during drier years. But this carbon sink by the savanna came at a cost. This ecosystem used about 100 mm more water per year than the grassland. And because the savanna was darker and rougher its air temperature was about 0.5 C warmer. In addition to our flux measurements, we collected vast amounts of ancillary data to interpret the site and fluxes, making this site a key site for model validation and parameterization. Datasets consist of terrestrial and airborne lidar for determining canopy structure, ground penetrating radar data on root distribution, phenology cameras monitoring leaf area index and its seasonality, predawn water potential, soil moisture, stem diameter and physiological capacity of photosynthesis.

Download or read book Land Carbon Cycle Modeling written by Yiqi Luo and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Carbon moves through the atmosphere, through the oceans, onto land, and into ecosystems. This cycling has a large effect on climate - changing geographic patterns of rainfall and the frequency of extreme weather - and is altered as the use of fossil fuels adds carbon to the cycle. The dynamics of this global carbon cycling are largely predicted over broad spatial scales and long periods of time by Earth system models. This book addresses the crucial question of how to assess, evaluate, and estimate the potential impact of the additional carbon to the land carbon cycle. The contributors describe a set of new approaches to land carbon cycle modeling for better exploring ecological questions regarding changes in carbon cycling; employing data assimilation techniques for model improvement; doing real- or near-time ecological forecasting for decision support; and combining newly available machine learning techniques with process-based models to improve prediction of the land carbon cycle under climate change. This new edition includes seven new chapters: machine learning and its applications to carbon cycle research (five chapters); principles underlying carbon dioxide removal from the atmosphere, contemporary active research and management issues (one chapter); and community infrastructure for ecological forecasting (one chapter). Key Features Helps readers understand, implement, and criticize land carbon cycle models Offers a new theoretical framework to understand transient dynamics of the land carbon cycle Describes a suite of modeling skills - matrix approach to represent land carbon, nitrogen, and phosphorus cycles; data assimilation and machine learning to improve parameterization; and workflow systems to facilitate ecological forecasting Introduces a new set of techniques, such as semi-analytic spin-up (SASU), unified diagnostic system with a 1-3-5 scheme, traceability analysis, and benchmark analysis, and PROcess-guided machine learning and DAta-driven modeling (PRODA) for model evaluation and improvement Reorganized from the first edition with seven new chapters added Strives to balance theoretical considerations, technical details, and applications of ecosystem modeling for research, assessment, and crucial decision-making

Download or read book Integrating Remote Sensing Field Observations and Models to Understand Disturbance and Climate Effects on the Carbon Balance of the West Coast U S written by and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: GOAL: To develop and apply an approach to quantify and understand the regional carbon balance of the west coast states for the North American Carbon Program. OBJECTIVE: As an element of NACP research, the proposed investigation is a two pronged approach that derives and evaluates a regional carbon (C) budget for Oregon, Washington, and California. Objectives are (1) Use multiple data sources, including AmeriFlux data, inventories, and multispectral remote sensing data to investigate trends in carbon storage and exchanges of CO2 and water with variation in climate and disturbance history; (2) Develop and apply regional modeling that relies on these multiple data sources to reduce uncertainty in spatial estimates of carbon storage and NEP, and relative contributions of terrestrial ecosystems and anthropogenic emissions to atmospheric CO2 in the region; (3) Model terrestrial carbon processes across the region, using the Biome-BGC terrestrial ecosystem model, and an atmospheric inverse modeling approach to estimate variation in rate and timing of terrestrial uptake and feedbacks to the atmosphere in response to climate and disturbance. APPROACH: In performing the regional analysis, the research plan for the bottom-up approach uses a nested hierarchy of observations that include AmeriFlux data (i.e., net ecosystem exchange (NEE) from eddy covariance and associated biometric data), intermediate intensity inventories from an extended plot array partially developed from the PI's previous research, Forest Service FIA and CVS inventory data, time since disturbance, disturbance type, and cover type from Landsat developed in this study, and productivity estimates from MODIS algorithms. The BIOME-BGC model is used to integrate information from these sources and quantify C balance across the region. The inverse modeling approach assimilates flux data from AmeriFlux sites, high precision CO2 concentration data from AmeriFlux towers and four new calibrated CO2 sites, reanalysis meteorology and various remote sensing products to generate statewide estimates of biosphere carbon exchange from the atmospheric point of view.

Download or read book Estimation of Net Ecosystem Carbon Exchange for the Conterminous UnitedStates by Combining MODIS and AmeriFlux Data written by and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board NASA's Terra satellite to scale up AmeriFlux NEE measurements to the continental scale. We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a regression tree approach. The predictive model was trained and validated using NEE data over the periods 2000-2004 and 2005-2006, respectively. We found that the model predicted NEE reasonably well at the site level. We then applied the model to the continental scale and estimated NEE for each 1 km x 1 km cell across the conterminous U.S. for each 8-day period in 2005 using spatially-explicit MODIS data. The model generally captured the expected spatial and seasonal patterns of NEE. Our study demonstrated that our empirical approach is effective for scaling up eddy flux NEE measurements to the continental scale and producing wall-to-wall NEE estimates across multiple biomes. Our estimates may provide an independent dataset from simulations with biogeochemical models and inverse modeling approaches for examining the spatiotemporal patterns of NEE and constraining terrestrial carbon budgets for large areas.

Download or read book Estimation of Net Ecosystem Carbon Exchange for the Conterminous United States by Combining MODIS and AmeriFlux Data written by and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the National Aeronautics and Space Administration's (NASA) Terra satellite to scale up AmeriFlux NEE measurements to the continental scale. We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a modified regression tree approach. The predictive model was trained and validated using eddy flux NEE data over the periods 2000-2004 and 2005-2006, respectively. We found that the model predicted NEE well (r = 0.73, p

Download or read book Scaling Up of Carbon Exchange Dynamics from AmeriFlux Sites to a Super Region in the Eastern United States written by and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary objective of this project was to evaluate carbon exchange dynamics across a region of North America between the Great Plains and the East Coast. This region contains about 40 active carbon cycle research (AmeriFlux) sites in a variety of climatic and landuse settings, from upland forest to urban development. The core research involved a scaling strategy that uses measured fluxes of CO2, energy, water, and other biophysical and biometric parameters to train and calibrate surface-vegetation-atmosphere models, in conjunction with satellite (MODIS) derived drivers. To achieve matching of measured and modeled fluxes, the ecosystem parameters of the models will be adjusted to the dynamically variable flux-tower footprints following Schmid (1997). High-resolution vegetation index variations around the flux sites have been derived from Landsat data for this purpose. The calibrated models are being used in conjunction with MODIS data, atmospheric re-analysis data, and digital land-cover databases to derive ecosystem exchange fluxes over the study domain.

Download or read book Global Sensitivity Analysis Probabilistic Calibration and Predictive Assessment for the Data Assimilation Linked Ecosystem Carbon Model written by and published by . This book was released on 2015 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.

Download or read book Improving Terrestrial Carbon Modeling written by Brett Raczka and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land-atmosphere CO2 fluxes, and have great potential to predict the terrestrial ecosystem response to changing climate. This dissertation evaluates ways to improve biosphere model performance by 1) evaluating short term (5 years) performance across a broad range of representation complexity, 2) identifying sources of parametric uncertainty for long term (~100 years) performance within a mechanistically detailed model (Ecosystem Demography) and 3) identifying observations that best constrain long term performance. Chapter 2 evaluates the performance of continental-scale carbon flux estimates from 17 models against carbon flux observations from 36 North American flux towers. On average the regional model runs overestimate the annual gross primary productivity (5%) and total respiration (15%), and significantly underestimate the annual net carbon uptake (64%) during the time period 2000-2005. Comparison with site-level simulations implicate choices specific to regional model simulations as contributors to the gross flux biases, but not the net carbon uptake bias. The models perform the best at simulating carbon exchange at deciduous broadleaf sites; likely because a number of models use prescribed phenology to simulate seasonal fluxes. In general, the models do not perform as well for crop, grass and evergreen sites in terms of bias, correlation and magnitude of variation. The regional models match the observations most closely in terms of seasonal correlation and seasonal magnitude of variation, but have very little skill at inter-annual correlation and minimal skill at inter-annual magnitude of variability. The comparison of site versus regional level model runs demonstrate that 1) the inter-annual correlation is higher for site-level model runs but the skill remains low, and 2) the underestimation of year-to-year variability for all fluxes is an inherent weakness of the models. The best performing regional models that do not use flux tower calibration are CLM-CN, CASA--GFEDv2 and SIB3. Two empirical models, calibrated with flux towers observations, EC-MOD and MOD17+, perform as well as the best process-based models. This suggests that 1) empirical, calibrated models can perform as well as complex, process-based models, and 2) combining process-based model structure with relevant constraining data could significantly improve model performance. Through a sensitivity analysis of the Ecosystem Demography model (version 2.1), Chapter 3 identifies quantum efficiency and leaf respiration rate parameters as the highest contributors to model uncertainty regardless of time frame (annual, decadal, centennial). This finding is sensitive to methodological choices within the meta-analysis process. Trait data provides relatively modest constraint upon the model simulation whereas integrative measurements of NEE and AGB provide strong constraints to the model and parameter uncertainty. Key actions for model improvement include 1) locating additional measurements related to quantum efficiency, leaf respiration rate and water fluxes (e.g. sap flux, soil moisture) and 2) implementing a more mechanistic representation of growth respiration within the model.

Download or read book A National Strategy for Advancing Climate Modeling written by Division on Earth and Life Studies and published by National Academies Press. This book was released on 2013-01-24 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: As climate change has pushed climate patterns outside of historic norms, the need for detailed projections is growing across all sectors, including agriculture, insurance, and emergency preparedness planning. A National Strategy for Advancing Climate Modeling emphasizes the needs for climate models to evolve substantially in order to deliver climate projections at the scale and level of detail desired by decision makers, this report finds. Despite much recent progress in developing reliable climate models, there are still efficiencies to be gained across the large and diverse U.S. climate modeling community. Evolving to a more unified climate modeling enterprise-in particular by developing a common software infrastructure shared by all climate researchers and holding an annual climate modeling forum-could help speed progress. Throughout this report, several recommendations and guidelines are outlined to accelerate progress in climate modeling. The U.S. supports several climate models, each conceptually similar but with components assembled with slightly different software and data output standards. If all U.S. climate models employed a single software system, it could simplify testing and migration to new computing hardware, and allow scientists to compare and interchange climate model components, such as land surface or ocean models. A National Strategy for Advancing Climate Modeling recommends an annual U.S. climate modeling forum be held to help bring the nation's diverse modeling communities together with the users of climate data. This would provide climate model data users with an opportunity to learn more about the strengths and limitations of models and provide input to modelers on their needs and provide a venue for discussions of priorities for the national modeling enterprise, and bring disparate climate science communities together to design common modeling experiments. In addition, A National Strategy for Advancing Climate Modeling explains that U.S. climate modelers will need to address an expanding breadth of scientific problems while striving to make predictions and projections more accurate. Progress toward this goal can be made through a combination of increasing model resolution, advances in observations, improved model physics, and more complete representations of the Earth system. To address the computing needs of the climate modeling community, the report suggests a two-pronged approach that involves the continued use and upgrading of existing climate-dedicated computing resources at modeling centers, together with research on how to effectively exploit the more complex computer hardware systems expected over the next 10 to 20 years.

Download or read book Understanding the Representativeness of FLUXNET for Upscaling Carbon Flux from Eddy Covariance Measurements written by and published by . This book was released on 2016 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Carbon Dynamics and Land Use Choices written by Suzi Kerr and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Policy enabling tropical forests to approach their potential contribution to global-climate-change mitigation requires forecasts of land use and carbon storage on a large scale over long periods. In this paper, we present an integrated modeling methodology that addresses these needs. We model the dynamics of the human land-use system and of C pools contained in each ecosystem, as well as their interactions. The model is national scale, and is currently applied in a preliminary way to Costa Rica using data spanning a period of over fifty years. It combines an ecological process model, parameterized using field and other data, with an economic model, estimated using historical data to ensure a close link to actual behavior. These two models are linked so that ecological conditions affect land-use choices and vice versa. The integrated model predicts land use and its consequences for C storage for policy scenarios. These predictions can be used to create baselines, reward sequestration, and estimate the value in both environmental and economic terms of including C sequestration in tropical forests as part of the efforts to mitigate global climate change. The model can also be used to assess the benefits from costly activities to increase accuracy and thus reduce errors and their societal costs.

Download or read book An Improved Analysis of Forest Carbon Dynamics Using Data Assimilation written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-06-24 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and onl

Download or read book Coupling Flow and Poromechanics Simulations for Geological Carbon Storage written by Xueying Lu (Ph. D.) and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Under the framework of the Paris Agreement, achieving carbon neutrality by the middle of the century is the fundamental solution to cope with the Climate Crisis. Carbon Capture, Storage, and Usage (CCUS) is a key group of technology to achieve a net-zero energy system. A high-fidelity model that depicts the multiphysics of the carbon storage processes over multiple temporal and spatial scales is essential to predict the fate of injected CO2 and the associated geological formation. In this dissertation, we address several computational challenges arising from high-fidelity simulations of coupling geomechanics models to the multiphase multicomponent fluid flow models for geological carbon sequestration. The necessity of the coupling is first demonstrated using field data from the Cranfield site. Numerical experiments demonstrate that coupling geomechanics enables more accurate estimation of storage volume by considering the geological formation deformation. The geomechanics simulations also depict the stress evolution in both the reservoir and caprock during the carbon storage processes, which is key to ensure caprock integrity for both short-term and long-term success of the project. However, geomechanics simulations are computationally expensive in field-scale simulations. We develop several multiscale adaptive algorithms that root on rigorous a posteriori error estimates of the Biot system solved with a fixed-stress split. Error indicators are developed using residual-based a posteriori error estimates, with theoretical guarantees. We validated the effectiveness of the error indicators with Mandel's problem and proposed novel adaptive algorithms leveraging these a posteriori error estimators. The efficiency of these error estimators to guide dynamic mesh refinement is demonstrated with a prototype unconventional reservoir model containing a fracture network. We further propose a novel stopping criterion for the fixed-stress iterations using the error indicators to balance the fixed-stress split error with the discretization errors. The new stopping criterion does not require hyperparameter tuning and demonstrates efficiency and accuracy in numerical experiments. We also formulate a three-way coupling algorithm for fluid flow models and poromechanics models. The three-way coupling uses an error indicator at each time step to determine if the mechanics equation must be solved and whether the fixed-stress iterative coupling is necessary; otherwise, only the flow equation is solved with an extrapolated mean stress. The convergence of three-way coupling is established for the single-phase flow and linear elasticity with numerical validations. We further extend the algorithm to the compositional flow model. Field scale simulations demonstrate the accuracy and efficiency of the three-way coupling algorithm in that the mechanics update time is reduced significantly compared to the standard fixed-stress split. Another attempt is to integrate Bayesian optimization into the high-fidelity simulations for carbon injection scheduling optimization. The proposed framework represents a first attempt at incorporating high-fidelity physical models and machine learning techniques for data assimilation and optimization for field-scale geological carbon sequestration applications. The high-fidelity multiphysics simulations strictly honor the physical processes during carbon sequestration, while the Bayesian optimization provides a rigorous statistical framework that balances the exploration-exploitation tradeoff, and effectively searches the surrogate solution space. A benchmark with other commonly used algorithms such as genetic algorithm and evolution strategy demonstrates a very high potential of further applications of Bayesian optimization

Download or read book Carbon water Cycling in the Critical Zone written by and published by . This book was released on 2016 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the largest knowledge gaps in environmental science is the ability to understand and predict how ecosystems will respond to future climate variability. The links between vegetation, hydrology, and climate that control carbon sequestration in plant biomass and soils remain poorly understood. Soil respiration is the second largest carbon flux of terrestrial ecosystems, yet there is no consensus on how respiration will change as water availability and temperature co-vary. To address this knowledge gap, we use the variation in soil development and topography across an elevation and climate gradient on the Front Range of Colorado to conduct a natural experiment that enables us to examine the co-evolution of soil carbon, vegetation, hydrology, and climate in an accessible field laboratory. The goal of this project is to further our ability to combine plant water availability, carbon flux and storage, and topographically driven hydrometrics into a watershed scale predictive model of carbon balance. We hypothesize: (i) landscape structure and hydrology are important controls on soil respiration as a result of spatial variability in both physical and biological drivers: (ii) variation in rates of soil respiration during the growing season is due to corresponding shifts in belowground carbon inputs from vegetation; and (iii) aboveground carbon storage (biomass) and species composition are directly correlated with soil moisture and therefore, can be directly related to subsurface drainage patterns.