Outgoing Near‐Infrared Radiation From Vegetation Scales With Canopy Photosynthesis Across a Spectrum of Function, Structure, Physiological Capacity, and Weather

We test the relationship between canopy photosynthesis and reflected near-infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near-infrared and red radiation from vegetation, with light-emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near-infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near-infrared radiation were highly correlated with measurements of canopy photosynthesis on half-hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near-infrared radiation were greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient-rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient-poor, mineral soils. Reflected near-infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near-infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Water level changes in Lake Erie drive 21st century CO2 and CH4 fluxes from a coastal temperate wetland.

Wetland water depth influences microbial and plant communities, which can alter the above- and below-ground carbon cycling of a wetland. Wetland water depths are likely to change due to shifting precipitation patterns, which will affect projections of greenhouse gas emissions; however, these effects are rarely incorporated into wetland greenhouse gas models. Seeking to address this gap, we used a mechanistic model, ecosys, to simulate a range of water depth scenarios in a temperate wetland, and analyzed simulated predictions of carbon dioxide (CO2) and methane (CH4) fluxes over the 21st century. We tested our model using eddy covariance measurements of CO2 and CH4 fluxes collected at the Old Woman Creek National Estuarine Research Reserve (OWC) during 2015 and 2016. OWC is a lacustrine, estuarine, freshwater, mineral-soil marsh. An empirical model found that the wetland water depth is highly dependent on the water depth of the nearby Lake Erie. Future wetland surface water depths were modeled based on projection of Lake Erie's water depth using four separate NOAA projections, resulting in four wetland water-depth scenarios. Two of the four 21st century projections for Lake Erie water depths used in this study indicated that the water depth of the wetland would remain nearly steady; however, the other two indicated decreases in the wetland water depth. In our scenario where the wetland dries out, we project the wetland's climatological warming effect will decrease due to smaller CH4 fluxes to the atmosphere and larger CO2 uptake by the wetland. We also found that increased water level can lower emissions by shifting the site towards more open water areas, which have lower CH4 emissions. We found that decreased water depths would cause more widespread colonization of the wetland by macrophyte vegetation. Using an empirical relationship, we also found that further drying could result in other, non-wetland vegetation to emerge, dramatically altering soil carbon cycling. In three of our four projections, we found that in general the magnitude of CO2 and CH4 fluxes steadily increase over the next 100 years in response to higher temperatures. However, in our driest simulations, we projected a different response due to increased oxidation of soil carbon, with CH4 emissions decreasing substantially from an annual cumulative peak of 224.6 to a minimum of 104.7 gC m-2 year-1. In that same simulation, net cumulative flux of CO2 changed from being a sink of 56.5 gC m-2 year-1 to a source of 369.6 gC m-2 year-1 over the same period, despite a temperature increase from 13.7 °C to 14.2 °C. This temperature shift in our other three cases with greater water depths increased the source strength of CH4 and the sink strength of CO2. We conclude that the magnitude of wetland greenhouse-gas fluxes depended on the water depth primarily as it affected the areal percentage of the wetland available for plant colonization, but dramatic decreases in water depths could cause significant reductions in the wetland CH4 fluxes, while simultaneously altering the wetland vegetation

Methanogenesis in oxygenated soils is a substantial fraction of wetland methane emissions.

The current paradigm, widely incorporated in soil biogeochemical models, is that microbial methanogenesis can only occur in anoxic habitats. In contrast, here we show clear geochemical and biological evidence for methane production in well-oxygenated soils of a freshwater wetland. A comparison of oxic to anoxic soils reveal up to ten times greater methane production and nine times more methanogenesis activity in oxygenated soils. Metagenomic and metatranscriptomic sequencing recover the first near-complete genomes for a novel methanogen species, and show acetoclastic production from this organism was the dominant methanogenesis pathway in oxygenated soils. This organism, Candidatus Methanothrix paradoxum, is prevalent across methane emitting ecosystems, suggesting a global significance. Moreover, in this wetland, we estimate that up to 80% of methane fluxes could be attributed to methanogenesis in oxygenated soils. Together, our findings challenge a widely held assumption about methanogenesis, with significant ramifications for global methane estimates and Earth system modeling

Methanogenesis in oxygenated soils is a substantial fraction of wetland methane emissions.

The current paradigm, widely incorporated in soil biogeochemical models, is that microbial methanogenesis can only occur in anoxic habitats. In contrast, here we show clear geochemical and biological evidence for methane production in well-oxygenated soils of a freshwater wetland. A comparison of oxic to anoxic soils reveal up to ten times greater methane production and nine times more methanogenesis activity in oxygenated soils. Metagenomic and metatranscriptomic sequencing recover the first near-complete genomes for a novel methanogen species, and show acetoclastic production from this organism was the dominant methanogenesis pathway in oxygenated soils. This organism, Candidatus Methanothrix paradoxum, is prevalent across methane emitting ecosystems, suggesting a global significance. Moreover, in this wetland, we estimate that up to 80% of methane fluxes could be attributed to methanogenesis in oxygenated soils. Together, our findings challenge a widely held assumption about methanogenesis, with significant ramifications for global methane estimates and Earth system modeling

Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions

The atmospheric boundary layer mediates the exchange of energy, matter, and momentum between the land surface and the free troposphere, integrating a range of physical, chemical, and biological processes and is defined as the lowest layer of the atmosphere (ranging from a few meters to 3 km). In this review, we investigate how continuous, automated observations of the atmospheric boundary layer can enhance the scientific value of co-located eddy covariance measurements of land-atmosphere fluxes of carbon, water, and energy, as are being made at FLUXNET sites worldwide. We highlight four key opportunities to integrate tower-based flux measurements with continuous, long-term atmospheric boundary layer measurements: (1) to interpret surface flux and atmospheric boundary layer exchange dynamics and feedbacks at flux tower sites, (2) to support flux footprint modelling, the interpretation of surface fluxes in heterogeneous and mountainous terrain, and quality control of eddy covariance flux measurements, (3) to support regional-scale modeling and upscaling of surface fluxes to continental scales, and (4) to quantify land-atmosphere coupling and validate its representation in Earth system models. Adding a suite of atmospheric boundary layer measurements to eddy covariance flux tower sites, and supporting the sharing of these data to tower networks, would allow the Earth science community to address new emerging research questions, better interpret ongoing flux tower measurements, and would present novel opportunities for collaborations between FLUXNET scientists and atmospheric and remote sensing scientists

FLUXNET-CH4: A global, multi-ecosystem dataset and analysis

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions, including their seasonality, due to quasi-continuous and high temporal resolution of flux measurements, coincident measurements of carbon, water, and energy fluxes, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we 1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4- community-product/). FLUXNET-CH4 includes half-hourly and daily gap-filled and non gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we 2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally, because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands and because freshwater wetlands are a substantial source of total atmospheric CH4 emissions; and 3) provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions, but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20° S to 20° N) the spring onset of elevated CH4 emissions starts three days earlier, and the CH4 emission season lasts 4 days longer, for each degree C increase in mean annual air temperature. On average, the onset of increasing CH4 emissions lags soil warming by one month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling, and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). The FLUXNET-CH4 dataset provides an open-access resource for CH4 flux synthesis, has a range of applications, and is unique in that it includes coupled measurements of important CH4 drivers such as GPP and temperature. Although FLUXNET-CH4 could certainly be improved by adding more sites in tropical ecosystems and by increasing the number of site-years at existing sites, it is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4408468. Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/, and a complete list of the 79 individual site data DOIs is provided in Table 2 in the Data Availability section of this document.ISSN:1866-359