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Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities.

 

Salt marshes are highly productive ecosystems relevant for Blue Carbon assessments, but information for estimating gross primary productivity (GPP) from proximal remote sensing (PRS) is limited. Temperate salt marshes have seasonal canopy structure and metabolism changes, defining different canopy phenological phases, GPP rates, and spectral reflectance. We combined multi-annual PRS data (i.e., PhenoCam, discrete hyperspectral measurements, and automated spectral reflectance sensors) with GPP derived from eddy covariance. We tested the performance of empirical models to predict GPP from 12 common vegetation indices (VIs; e.g., NDVI, EVI, PSRI, GCC), Sun-Induced Fluorescence (SIF), and reflectance from different areas of the electromagnetic spectrum (i.e., VIS-IR, RedEdge, IR, and SIF) across the annual cycle and canopy phenological phases (i.e., Greenup, Maturity, Senescence, and Dormancy). Plant Senescence Reflectance Index (PSRI) from hyperspectral data and the Greenness Index (GCC) from PhenoCam, showed the strongest relationship with daily GPP across the annual cycle and within phenological phases (r2=0.30–0.92). Information from the visible-infrared electromagnetic region (VIS-IR) coupled with a partial least square approach (PLSR) showed the highest data-model agreement with GPP, mainly because of its relevance to respond to physiological and structural changes in the canopy, compared with indices (e.g., GCC) that particularly react to changes in the greenness of the canopy. The most relevant electromagnetic regions to model GPP were ∼550 nm and ∼710 nm. Canopy phenological phases impose challenges for modeling GPP with VIs and the PLSR approach, particularly during Maturity, Senescence, and Dormancy. As more eddy covariance sites are established in salt marshes, the application of PRS can be widely tested. Our results highlight the potential to use canopy reflectance from the visible spectrum region for modeling annual GPP in salt marshes as an example of advances within the AmeriFlux network. 

The frequency and persistence of tidal inundation varies along the coastal terrestrial-aquatic interface, from frequently inundated wetlands to rarely inundated upland forests. This inundation gradient controls soil and sediment biogeochemistry and influence the exchange of soils and sediments from terrestrial to aquatic domains. Although a rich literature exist on studies of the influence of tidal waters on the biogeochemistry of coastal ecosystem soils, few studies have experimentally addressed the reverse question: How do soils (or sediments) from different coastal ecosystems influence the biogeochemistry of the tidal waters that inundate them? To better understand initial responses of coastal waters that flood coastal wetlands and uplands, we conducted short-term laboratory experiments where seawater was amended with sediments and soils collected across regional gradients of inundation exposure (i.e., frequently to rarely inundated) for 14 sites across the Mid-Atlantic, USA. Measured changes in dissolved oxygen and greenhouse gas concentrations were used to calculate gas consumption or production rates occurring during seawater exposure to terrestrial materials. We also measured soil and water physical and chemical properties to explore potential drivers. We observed higher oxygen consumption rates for seawater incubated with soils/sediments from frequently inundated locations and higher carbon dioxide production for seawater incubated with soils from rarely inundated transect locations. Incubations with soil from rarely inundated sites produced the highest global warming potential, primarily driven by carbon dioxide and secondarily by nitrous oxide. We also found environmental drivers of gas rates varied notably between transect locations. Our findings indicate that seawater responses to soil and sediment inputs across coastal terrestrial-aquatic interfaces exhibit some consistent patterns and high intra- and inter-site variability, suggesting potential biogeochemical feedback loops as inundation regimes shift inland.

 

Abstract. Quantifying the role of soils in nature-based solutions requires accurate estimates of soil greenhouse gas (GHG) fluxes. Technological advancesallow us to measure multiple GHGs simultaneously, and now it is possible to provide complete GHG budgets from soils (i.e., CO2, CH4,and N2O fluxes). We propose that there is a conflict between the convenience of simultaneously measuring multiple soil GHG fluxes at fixedtime intervals (e.g., once or twice per month) and the intrinsic temporal variability in and patterns of different GHG fluxes. Information derived fromfixed time intervals – commonly done during manual field campaigns – had limitations to reproducing statistical properties, temporal dependence,annual budgets, and associated uncertainty when compared with information derived from continuous measurements (i.e., automated hourly measurements)for all soil GHG fluxes. We present a novel approach (i.e., temporal univariate Latin hypercube sampling) that can be applied to provide insightsand optimize monitoring efforts of GHG fluxes across time. We suggest that multiple GHG fluxes should not be simultaneously measured at a few fixedtime intervals (mainly when measurements are limited to once per month), but an optimized sampling approach can be used to reduce bias anduncertainty. These results have implications for assessing GHG fluxes from soils and consequently reduce uncertainty in the role of soils innature-based solutions.