Biology subjects

Novick, K. A.

Publications and source records attributed to Novick, K. A..

2 recordsLinked to original sources

bioRxiv2023

The global distribution of paired eddy covariance towers

The eddy covariance technique has revolutionized our understanding of ecosystem-atmosphere interactions. Eddy covariance studies often use a "paired" tower design in which observations from nearby towers are used to understand how different vegetation, soils, hydrology, or experimental treatment shape ecosystem function and surface-atmosphere exchange. Paired towers have never been formally defined and their global distribution has not been quantified. We compiled eddy covariance tower information to find towers that could be considered paired. Of 1233 global eddy covariance towers, 692 (56%) were identified as paired by our criteria. Paired towers had cooler mean annual temperature (mean = 9.9 {degrees}C) than the entire eddy covariance network (10.5 {degrees}C) but warmer than the terrestrial surface (8.9 {degrees}C) from WorldClim 2.1, on average. The paired and entire tower networks had greater average soil nitrogen (0.57-0.58 g/kg) and more silt (36.0-36.4%) than terrestrial ecosystems (0.38 g/kg and 30.5%), suggesting that eddy covariance towers sample richer soils than the terrestrial surface as a whole. Paired towers existed in a climatic space that was more different from the global climate distribution sampled by the entire eddy covariance network, as revealed by an analysis of the Kullback-Leibler divergence, but the edaphic space sampled by the entire network and paired towers was similar. The lack of paired towers with available data across much of Africa, northern, central, southern, and western Asia, and Latin America with few towers in savannas, shrublands, and evergreen broadleaf forests point to key regions, ecosystems, and ecosystem transitions in need of additional research. Few if any paired towers study the flux of ozone and other atmospherically active trace gases at the present. By studying what paired towers measure - and what they do not - we can make infrastructural investments to further enhance the value of FLUXNET as it moves toward its fourth decade.

ecology↗

bioRxiv2021

Capturing site-to-site variability through Hierarchical Bayesian calibration of a process-based dynamic vegetation model

Process-based ecosystem models help us understand and predict ecosystem processes, but using them has long involved a difficult choice between performing data- and labor-intensive site-level calibrations or relying on general parameters that may not reflect local conditions. Hierarchical Bayesian (HB) calibration provides a third option that frees modelers from assuming model parameters to be completely generic or completely site-specific and allows a formal distinction between prediction at known calibration sites and "out-of-sample" prediction to new sites. Here, we compare calibrations of a process-based dynamic vegetation model to eddy-covariance data across 12 temperate deciduous Ameriflux sites fit using either site-specific, joint cross-site, or HB approaches. To be able to apply HB to computationally demanding process-based models we introduce a novel emulator-based HB calibration tool, which we make available through the PEcAn community cyberinfrastructure. Using these calibrations to make predictions at held-out tower sites, we show that the joint cross-site calibration is falsely over-confident because it neglects parameter variability across sites and therefore underestimates variance in parameter distributions. By showing which parameters show high site-to-site variability, HB calibration also formally gives us a structure that can detect which process representations are missing from the models and prioritize errors based on the magnitude of the associated uncertainty. For example, in our case-study, we were able to identify large site-to-site variability in the parameters related to the temperature responses of respiration and photosynthesis, associated with a lack of thermal acclimation and adaptation in the model. Moving forward, HB approaches present important new opportunities for statistical modeling of the spatiotemporal variability in modeled parameters and processes that yields both new insights and improved predictions.

ecology↗