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

Effect of temporally heterogeneous light on photosynthetic light use efficiency, plant acclimation and growth in Abatia parviflora

Individual leaves have a unique instantaneous photosynthetic photon flux density (PPFD) at which net photosynthetic light use efficiency (?L, the ratio between net photosynthesis and PPFD) is maximised (PPFD?max). When PPFD is above or below PPFD?max, efficiency declines. Thus, we hypothesised that heterogeneous PPFD conditions should increase the amount of time leaves photosynthesise at a PPFD different than PPFD?max and result in reduced growth. To date, this prediction has not been rigorously tested. Here, we exposed seedlings of Abatia parviflora Ruiz and amp; Pav to light regimes of equal total daily irradiance but with three different daily time courses of PPFD: constant PPFD (NoH), low heterogeneity (Low-H) and high heterogeneity (High-H). Mean ?L, leaf daily photosynthesis and plant growth were all significantly higher in No-H and Low-H plants than in High-H plants, supporting our hypothesis. In addition, mean ?L was positively related to final plant biomass. Unexpectedly, High-H plants had more etiolated stems and more horizontal leaves than No-H and Low-H plants, possibly due to exposure to low PPFD in the morning and afternoon. In conclusion, PPFD heterogeneity had an important effect on average ?L, photosynthesis and growth, but also on allocation and plant morphology. © 2019 CSIRO

Effect of temporally heterogeneous light on photosynthetic light use efficiency, plant acclimation and growth in Abatia parviflora

Individual leaves have a unique instantaneous photosynthetic photon flux density (PPFD) at which net photosynthetic light use efficiency (?L, the ratio between net photosynthesis and PPFD) is maximised (PPFD?max). When PPFD is above or below PPFD?max, efficiency declines. Thus, we hypothesised that heterogeneous PPFD conditions should increase the amount of time leaves photosynthesise at a PPFD different than PPFD?max and result in reduced growth. To date, this prediction has not been rigorously tested. Here, we exposed seedlings of Abatia parviflora Ruiz and amp; Pav to light regimes of equal total daily irradiance but with three different daily time courses of PPFD: constant PPFD (NoH), low heterogeneity (Low-H) and high heterogeneity (High-H). Mean ?L, leaf daily photosynthesis and plant growth were all significantly higher in No-H and Low-H plants than in High-H plants, supporting our hypothesis. In addition, mean ?L was positively related to final plant biomass. Unexpectedly, High-H plants had more etiolated stems and more horizontal leaves than No-H and Low-H plants, possibly due to exposure to low PPFD in the morning and afternoon. In conclusion, PPFD heterogeneity had an important effect on average ?L, photosynthesis and growth, but also on allocation and plant morphology. © 2019 CSIRO

Detecting Hot Spots of Methane Flux Using Footprint-Weighted Flux Maps.

In this study, we propose a new technique for mapping the spatial heterogeneity in gas exchange around flux towers using flux footprint modeling and focusing on detecting hot spots of methane (CH4) flux. In the first part of the study, we used a CH4 release experiment to evaluate three common flux footprint models: the Hsieh model (Hsieh et al., 2000), the Kljun model (Kljun et al., 2015), and the K & M model (Kormann and Meixner, 2001), finding that the K & M model was the most accurate under these conditions. In the second part of the study, we introduce the Footprint-Weighted Flux Map, a new technique to map spatial heterogeneity in fluxes. Using artificial CH4 release experiments, natural tracer approaches and flux chambers we mapped the spatial flux heterogeneity, and detected and validated a hot spot of CH4 flux in a oligohaline restored marsh. Through chamber measurements during the months of April and May, we found that fluxes at the hot spot were on average as high as 6589 ± 7889 nmol m-2 s-1 whereas background flux from the open water were on average 15.2 ± 7.5 nmol m-2 s-1. This study provides a novel tool to evaluate the spatial heterogeneity of fluxes around eddy-covariance towers and creates important insights for the interpretation of hot spots of CH4 flux, paving the way for future studies aiming to understand subsurface biogeochemical processes and the microbiological conditions that lead to the occurrence of hot spots and hot moments of CH4 flux

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