Project Funding: 2017 - 2020

NRA: 2016 NASA: Carbon Cycle Science   

Funded by NASA

Abstract:
Land use, land cover changes, and ecosystem-specific management practices are increasingly recognized for their roles in mediating the climatic effects on ecosystem structure and function. A major challenge is that our understanding and forecasting of ecosystem C fluxes cannot rely solely on conventional biophysical regulations at any scale, from the local ecosystem to the globe. A second challenge is to quantify the magnitude of the C fluxes from managed ecosystems and landscapes over the lifetime of the C cycle, and to deduct the various energy inputs during management. Our overall objective, consequently, is to quantify the landscape-scale C footprint of both managed agricultural-forest landscapes and people, using the Kalamazoo watershed in southwestern Michigan as our testbed. The underlying mechanisms from both human activities and biophysical changes on ecosystem C dynamics at different temporal and spatial scales will be explored by modeling total net ecosystem C production (including physical and social C fluxes), exploring the complex relationships through Bayesian structural equation modeling (SEM), and performing a spatially-explicit LCA on the total C production within the contrasting landscapes and the entire watershed. We will take a bottom-up approach to quantify landscape C fluxes and a top-down scaling effort to characterize the contributions of climatic change, land use, and site- specific management practices at two spatial scales: landscapes with contrasting structure and composition and the entire Kalamazoo River watershed. Our overarching hypothesis is that social C flux is more responsible than physical C flux for the dynamics, and especially the uncertainty, of the cumulative CO2 equivalent (CO2eq) production of the landscapes. However, their proportions vary significantly among the landscapes and over history because of the great variations in land conversions, land use, site-specific management practices, and climatic changes/extremes. A combination of remote sensing technology, available geospatial data, historical records of management practices, survey of historical practices, a land surface model (Community Land Model, CLM), in situ measurements of C fluxes from seven permanent and two mobile eddy covariance flux towers, historical CO2eq import/export records, biophysical/empirical modeling of key C-cycle process, and LCA will be used to achieve our study objectives. Through our collaborations with the LTER/GLBRC at KBS, Planetek, the RS&GIS Center, MSU the three research tasks will provide an understanding of the changes and regulations of CO2eq production in the four contrasting landscapes and the entire watershed: (1) model the dynamics of the physical C fluxes of the watershed for 1978–2018 and of the four contrasting landscapes for 1938–2018; (2) estimate the social C fluxes for the same time period; and (3) diagnose the underlying mechanisms from land use, land cover changes, site-specific management practices, mean climatic change, and climatic extremes on the total CO2eq fluxes at the two spatial and temporal scales through Life Cycle Analysis. The physical C fluxes of the landscapes and watershed will be quantified through the Community Land Model after ecosystem-specific parameterization and independent validation. Back-of-the-envelope calculations will be applied to estimate the social C fluxes with precision land cover, create land ownership maps, and take extensive surveys of land owners for the same time period. We anticipate a reliable forecasting method will be developed through modeling alternative climate and management practices for the future. Our objectives will also be enhanced through the broad engagement of stakeholders and the wide distribution of our data and scientific products. This ambitious study could not be successful without the many relevant projects in the study region, rich historical data, existing infrastructure, as well as a collaborative research team.

Publications:

Chen, J., Sciusco, P., Ouyang, Z., Zhang, R., Henebry, G. M., John, R., Roy, D. P. 2019. Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions. Landscape Ecology. 34(12), 2917-2934. DOI: 10.1007/s10980-019-00928-2

Dahlin, K. M., Akanga, D., Lombardozzi, D. L., Reed, D. E., Shirkey, G., Lei, C., Abraha, M., Chen, J. 2020. Challenging a Global Land Surface Model in a Local Socio-Environmental System. Land. 9(10), 398. DOI: 10.3390/land9100398

Poe, J., Reed, D. E., Abraha, M., Chen, J., Dahlin, K. M., Desai, A. R. 2020. Geospatial coherence of surface-atmosphere fluxes in the upper Great Lakes region. Agricultural and Forest Meteorology. 295, 108188. DOI: 10.1016/j.agrformet.2020.108188

Reed, D. E., Chen, J., Abraha, M., Robertson, G. P., Dahlin, K. M. 2019. The Shifting Role of mRUE for Regulating Ecosystem Production. Ecosystems. 23(2), 359-369. DOI: 10.1007/s10021-019-00407-4

Reed, D. E., Poe, J., Abraha, M., Dahlin, K. M., Chen, J. 2021. Modeled Surface-Atmosphere Fluxes From Paired Sites in the Upper Great Lakes Region Using Neural Networks. Journal of Geophysical Research: Biogeosciences. 126(8). DOI: 10.1029/2021JG006363

Sciusco, P., Chen, J., Abraha, M., Lei, C., Robertson, G. P., Lafortezza, R., Shirkey, G., Ouyang, Z., Zhang, R., John, R. 2020. Spatiotemporal variations of albedo in managed agricultural landscapes: inferences to global warming impacts (GWI). Landscape Ecology. 35(6), 1385-1402. DOI: 10.1007/s10980-020-01022-8

More details may be found in the following project profile(s):

• North American Carbon Program (NACP) Project Profile