Assessing the Potential of Embedding Vegetation Dynamics into a Fire Behaviour Model : LPJ-GUESS-FARSITE

Disturbances such as wildfires are key players involved in the shape, structure and function of the ecosystems. Fire is rarely included in Dynamic global vegetation models due to their difficulty in implementing its processes and impacts associated. Therefore, it is essential to understand the variables and processes involved in fire, and to evaluate the strengths and weaknesses before going forward in global fire modelling. LPJ-GUESS-SPITFIRE allows the calculation of vegetation in a daily-time-step manner. However, the fire module has revealed some flaws in performance. For this reason, an alternative fire area simulator (FARSITE), a robust and semi-empirical model widely used worldwide, has been taken into account. The aim of this study is to assess a potential embedment of vegetation dynamic (LPJ-GUESS-SPITFIRE) into spatial-explicit fire behaviour modelling (FARSITE): LPJ-GUESS-FARSITE. The study includes: (1) a comparison between simulated vegetation and observed vegetation in Mediterranean regions and, to what extent to fire recurrence affects vegetation; (2) the evaluation and comparison of fuel- and tree-related variables from the observed data, and (3) the comparison of fire behaviour performed by each model. Simulations have shown that Quercus coccifera and C3 grasses are dominant at 25 years fire return interval. Besides, the fire return interval influences largely the successional stage of the vegetation. Biomass tends to increase whereas leaf area index and net primary production decrease from short to long fire recurrence periods. Dead fuel loading, fuel depth, fuel moisture 1hr and live grass, simulated in LPJ-GUESS-SPITFIRE, tend to underestimate field measurements. On contrary fuel moisture 10hr and 100hr are overestimated. Fire behaviour results from both models have underestimated field experimental results. FARSITE results, followed by LPJ-GUESS-FARSITE, have been closer related to field data than LPJ-GUESS-SPITFIRE. The results also showed evidence of more intense fires in LPJ-GUESS-FARSITE than in LPJ-GUESS-SPITFIRE, with identical input data. This thesis concludes that both FARSITE and LPJ-GUESS-FARSITE fire behaviour’s outputs are expected to be more realistic than LPJ-GUESS-SPITFIRE. Even though results do still underestimate real observations, there is enough evidence to say that the LPJ-GUESS framework could be improved. The substitution of the SPITFIRE module by FARSITE model, together with an increase of litter and fuel loading and a decrease of fuel moisture, reflects the promising advantages in creating the meta-model LPJ-GUESS-FARSITE.Can a simple algorithm save our planet? There is an increasing awareness nowadays about global warming. Ongoing researches alert about an excessive and fast rise of greenhouse gases emissions. Perhaps the estimations and future projections from scientist were not that misguided and we are already matching the worst scenarios possible. In this general perspective, many investigation projects are trying to find answers to a growing number of questions related with environmental issues. How does our planet work? How do complex mechanisms and processes perform at multiple inter-related spatio-temporal scales? How and how much does greenhouse gasses emissions affect the natural balance? One way of contributing into a better understanding of these processes are the forest fires’ modelling. Fires are important natural sources in terms of carbon dioxide emissions and fire has a worldwide effect in the climate. It is not fully understood how fires behave in many conditions and different locations. There are many parameters behind forest fires. Fuel, dryness and oxygen are the key cornerstones although there are more factors. How fast can a fire spread? Thus how much intensity can be reach by a fire? Hence how much burning emissions can be released? How do a specific type of vegetation, wind speed or terrain affect fire spread? People started to think about this issues in the early 70s. The answer to some of these questions can be explained by a combination of basic energy conservation physics, mathematical models and computer machines ready to calculate plenty of processes at the same time! These models were developed long time ago but still play an important role these days. These algorithms are the basis of current top research areas, which at the same time are fundamental tools predicting and modelling the rate at which the head of a specific fire will spread. Rate of spread equations can be compute in order to calculate how much emissions can be released hence the significance of fire emissions in global scale could be analysed in depth

Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.Peer reviewe

Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties

The regional variability in tundra and boreal carbon dioxide (CO2) fluxes can be high, complicating efforts to quantify sink-source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990-2015 from 148 terrestrial high-latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO2 fluxes and test the accuracy and uncertainty of different statistical models. CO2 fluxes were upscaled at relatively high spatial resolution (1 km(2)) across the high-latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE-focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO2 sink strength was larger in the boreal biome (observed and predicted average annual NEE -46 and -29 g C m(-2) yr(-1), respectively) compared to tundra (average annual NEE +10 and -2 g C m(-2) yr(-1)). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO2 budget, estimated using the annual NEE ensemble prediction, suggests the high-latitude region was on average an annual CO2 sink during 1990-2015, although uncertainty remains high

The ABCflux database : Arctic-boreal CO2 flux observations and ancillary information aggregated to monthly time steps across terrestrial ecosystems

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic-boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic-boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June-August; 32 %), and fewer observations were available for autumn (September-October; 25 %), winter (December-February; 18 %), and spring (March-May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).Peer reviewe

Pan-Arctic soil moisture control on tundra carbon sequestration and plant productivity

Long-term atmospheric CO2 concentration records have suggested a reduction in the positive effect of warming on high-latitude carbon uptake since the 1990s. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. However, the lack of consistent long-term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. In this study, we used a record of nearly 100 site-years of eddy covariance data from 11 continuous permafrost tundra sites distributed across the circumpolar Arctic to test the temperature (expressed as growing degree days, GDD) responses of gross primary production (GPP), net ecosystem exchange (NEE), and ecosystem respiration (ER) at different periods of the summer (early, peak, and late summer) including dominant tundra vegetation classes (graminoids and mosses, and shrubs). We further tested GPP, NEE, and ER relationships with soil moisture and vapor pressure deficit to identify potential moisture limitations on plant productivity and net carbon exchange. Our results show a decrease in GPP with rising GDD during the peak summer (July) for both vegetation classes, and a significant relationship between the peak summer GPP and soil moisture after statistically controlling for GDD in a partial correlation analysis. These results suggest that tundra ecosystems might not benefit from increased temperature as much as suggested by several terrestrial biosphere models, if decreased soil moisture limits the peak summer plant productivity, reducing the ability of these ecosystems to sequester carbon during the summer.Peer reviewe

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible

Search for new resonances decaying to pairs of merged diphotons in proton-proton collisions at s\sqrt{s} = 13 TeV

International audienceA search is presented for an extended Higgs sector with two new particles, X and $\phi$, in the process X $\to$$\phi\phi$$\to$$(\gamma\gamma)(\gamma\gamma)$. Novel neural networks classify events with diphotons that are merged and determine the diphoton masses. The search uses LHC proton-proton collision data at $\sqrt{s}$ = 13 TeV collected with the CMS detector, corresponding to an integrated luminosity of 138 fb$^{-1}$. No evidence of such resonances is seen. Upper limits are set on the production cross section versus the resonance masses, representing the most sensitive search in this channel

Enriching the physics program of the CMS experiment via data scouting and data parking

International audienceSpecialized data-taking and data-processing techniques were introduced by the CMS experiment in Run 1 of the CERN LHC to enhance the sensitivity of searches for new physics and the precision of standard model measurements. These techniques, termed data scouting and data parking, extend the data-taking capabilities of CMS beyond the original design specifications. The novel data-scouting strategy trades complete event information for higher event rates, while keeping the data bandwidth within limits. Data parking involves storing a large amount of raw detector data collected by algorithms with low trigger thresholds to be processed when sufficient computational power is available to handle such data. The research program of the CMS Collaboration is greatly expanded with these techniques. The implementation, performance, and physics results obtained with data scouting and data parking in CMS over the last decade are discussed in this Report, along with new developments aimed at further improving low-mass physics sensitivity over the next years of data taking