Components, drivers and temporal dynamics of ecosystem respiration in a Mediterranean pine forest

To investigate the climate impacts on the different components of ecosystem respiration, we combined soil efflux data from a tree-girdling experiment with eddy covariance CO2 fluxes in a Mediterranean maritime pine (Pinus pinaster) forest in Central Italy. 73 trees were stem girdled to stop the flux of photosynthates from the canopy to the roots, and weekly soil respiration surveys were carried out for one year. Heterotrophic respiration (RH) was estimated from the soil CO2 flux measured in girdled plots, and rhizosphere respiration (RAb) was calculated as the difference between respiration from controls (RS) and girdled plots (RH). Results show that the RS dynamics were clearly driven by RH (average RH/RS ratio 0.74). RH predictably responded to environmental variables, being predominantly controlled by soil water availability during the hot and dry growing season (MayeOctober) and by soil temperature during the wetter and colder months (NovembereMarch). High RS and RH peaks were recorded after rain pulses greater than 10 mm on dry soil, indicating that large soil carbon emissions were driven by the rapid microbial oxidation of labile carbon compounds. We also observed a time-lag of one week between water pulses and RAb peaks, which might be due to the delay in the translocation of recently assimilated photosynthates from the canopy to the root system. At the ecosystem scale, total autotrophic respiration (RAt, i.e. the sum of carbon respired by the rhizosphere and aboveground biomass) amounted to 60% of ecosystem respiration. RAt was predominantly controlled by photosynthesis, and showed high temperature sensitivity (Q10) only during the wet periods. Despite the fact that the study coincided with an anomalous dry year and results might therefore not represent a general pattern, these data highlight the complex climatic control of the respiratory processes responsible for ecosystem CO2 emissions. © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Damage assessment in single-nave churches and analysis of the most recurring mechanisms after the 2016–2017 central Italy earthquakes

Assessment of churches based on empirical data at a territorial scale is a suitable tool to have an overview of the seismic behaviour of this peculiar structural typology and to evaluate their current state of vulnerability. Fragility and vulnerability curves are also aimed to perform the analysis of different seismic scenarios. The paper presents a detailed typological analysis of 633 single-nave churches, as a selected subset of the database previously examined by the authors, with the aim of evaluating more in detail the influence of some parameters, such as masonry typology, church dimensions and presence of the bell tower, on the vulnerability of the overall church. Then, specific analyses are carried out to assess the influence played by single mechanisms on the definition of the overall damage index, with the focus of providing qualitative evaluations and explicit vulnerability and fragility curves related to the most recurring and significant collapse mechanisms. This is an original contribution of the paper in the field of the vulnerability assessment of churches, since nowadays little information is available in the literature about the damage levels related to specific mechanisms, while most attention is still focused on global damage

Vegetation-based climate mitigation in a warmer and greener World

The mitigation potential of vegetation-driven biophysical effects is strongly influenced by the background climate and will therefore be influenced by global warming. Based on an ensemble of remote sensing datasets, here we first estimate the temperature sensitivities to changes in leaf area over the period 2003–2014 as a function of key environmental drivers. These sensitivities are then used to predict temperature changes induced by future leaf area dynamics under four scenarios. Results show that by 2100, under high-emission scenario, greening will likely mitigate land warming by 0.71 ± 0.40 °C, and 83% of such effect (0.59 ± 0.41 °C) is driven by the increase in plant carbon sequestration, while the remaining cooling (0.12 ± 0.05 °C) is due to biophysical land-atmosphere interactions. In addition, our results show a large potential of vegetation to reduce future land warming in the very-stringent scenario (35 ± 20% of the overall warming signal), whereas this effect is limited to 11 ± 6% under the high-emission scenario

Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials.However, simultaneous retrieval of LAI and Chll fromspace observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data. A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in centralNebraska for the period 2001–2005, demonstrate Chll retrievalwith a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD = 8.42 μg cm−2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 = 0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy reflectance model (SAIL). Additional advances in the retrieval of canopy biophysical and leaf biochemical constituents will require innovative use of existing remote sensing data within physically realistic canopy reflectancemodels along with the ability to exploit the enhanced spectral and spatial capabilities of upcoming satellite systems

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate system through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. <br><br> Terrestrial biosphere models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we used the Harvard Forest phenology record to investigate and characterize sources of uncertainty in predicting phenology, and the subsequent impacts on model forecasts of carbon and water cycling. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species, with 12 leaf bud-burst models that varied in complexity. <br><br> Akaike's Information Criterion indicated support for spring warming models with photoperiod limitations and, to a lesser extent, models that included chilling requirements. <br><br> We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO<sub>2</sub> emissions vs. low CO<sub>2</sub> emissions scenario). Parameter uncertainty was the smallest (average 95% Confidence Interval – CI: 2.4 days century<sup>−1</sup> for scenario B1 and 4.5 days century<sup>−1</sup> for A1fi), whereas driver uncertainty was the largest (up to 8.4 days century<sup>−1</sup> in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied among models (±7.7 days century<sup>−1</sup> for A1fi, ±3.6 days century<sup>−1</sup> for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 days °C<sup>−1</sup> and 5.2 days °C<sup>−1</sup> depending on model structure. <br><br> We quantified the impact of uncertainties in bud-burst forecasts on simulated photosynthetic CO<sub>2</sub> uptake and evapotranspiration (ET) using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of forest seasonality, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and ET of 9.6% and 2.9%, respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0% for annual GPP and about ±2.0% for ET. <br><br> For phenology models, differences among future climate scenarios (i.e. driver) represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of ecosystem processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenological model parameterization

Vulnerability of European forests to natural disturbances

European forests provide a set of fundamental services that contribute to climate change mitigation and human well-being. At the same time, forests are vulnerable systems because the long life-span of trees limits the possibility of rapid adaptation to drastic environmental changes. Climate-driven disturbances in forests, such as fires, windstorms and insect outbreaks, are expected to rise drastically under global warming. As a result, key forest services, such as carbon sequestration and supply of wood materials, could be seriously affected in the near future. Despite the relevance and urgency of the issue, little is known about the vulnerability of European forests to multiple climate-related hazards and the adaptation benefits of alternative forest management strategies. To fill this knowledge gap we investigated the susceptibility of European forests when exposed to a given natural disturbance under different forest management scenarios. For this purpose, we assessed forest vulnerability by integrating in a data-driven framework satellite observations, national forest inventories, land surface climatic data and records of disturbances over the 2000-2017 period. The integration of these data streams is meant to capture the key drivers of vulnerability and to quantify, for the first time, the vulnerability of European forests to fires, windstorms and insect outbreaks in a systematic and spatially explicit manner. We point out that, the term vulnerability is used in this study to express to what degree a forest ecosystem is affected when exposed to a given disturbance. In order to derive risk estimates, vulnerability estimates should be integrated with hazard and exposure components, according to typical impact assessment frameworks. Results of this analyses show that in average at Europe level forest vulnerability to windstorms appears the disturbance with larger biomass loss both in relative and absolute terms (~38%, ~17 t ha-1) compared to fires (~24%, ~12.5 t ha-1) and insect outbreaks (~21%, ~9 t ha-1). Substantial spatial variations in vulnerability emerge and depict generally higher values in norther and Mediterranean regions. Overall, forest structural properties play a larger control on the vulnerability of European forests to natural disturbances compared to climate and landscape features. However, increases in temperature and changes in precipitation patterns occurred over the last two decades, have contributed substantially to make European forests more vulnerable to natural disturbances. We found that these changes in climate led to a limited increase in vulnerability at Europe for fires and windstorms and to a strong increase for insect outbreaks. However, contrasting regional trends emerging over Europe mask relevant temporal changes in vulnerability occurring at local scale. When analyses of single disturbances are combined together, results show that large part of the European forests are substantially vulnerable to at least one natural disturbance and that many of the areas more vulnerable have been subject to an amplification of vulnerability over the observational period due to changes in climate. Reducing tree age and tree density appear effective forest management strategies to reduce the vulnerability of European forests to climate-driven disturbances. The magnitude of the potential benefits appears strongly dependent on local environmental conditions. Previous assessments of future climate risks to European forests, based on catalogues of disturbances collected at country level, have showed that damage from fires, windstorms and insect outbreaks is likely to increase further in coming decades. Such intensification could offset the impact of land-based strategies aiming to increase the forest carbon sink. However, the country scale approach used in such studies do not allow to explore in detail the underlying physical processes and to elaborate adaptation strategies at appropriate local scales. It is therefore fundamental to elaborate new modelling approaches that address in explicit manner the high spatial and temporal variability of forest disturbances. In this respect, machine learning approaches and the increasing availability of multi-platform satellite observations of land surface in combination with high regional climate model simulations, represent valuable opportunities to appraise the impact of forest disturbances at a spatial and temporal resolution relevant for forest management strategies. This explorative study represents a first step towards such integrated framework.JRC.D.1-Bio-econom

Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al.

AbstractThe timely and accurate monitoring of forest resources is becoming of increasing importance in light of the multi-functionality of these ecosystems and their increasing vulnerability to climate change. Remote sensing observations of tree cover and systematic ground observations from National Forest Inventories (NFIs) represent the two major sources of information to assess forest area and use. The specificity of two methods is calling for an in-depth analysis of their strengths and weaknesses and for the design of novel methods emerging from the integration of satellite and surface data. On this specific debate, a recent paper by Breidenbach et al. published in this journal suggests that the detection of a recent increase in EU forest harvest rate—as reported in Nature by Ceccherini et al.—is largely due to technical limitations of satellite-based mapping. The article centers on the difficulty of the approaches to estimate wood harvest based on remote sensing. However, it does not discuss issues with the robustness of validation approaches solely based on NFIs. Here we discuss the use of plot data as a validation set for remote sensing products, discussing potentials and limitations of both NFIs and remote sensing, and how they can be used synergistically. Finally, we highlight the need to collect in situ data that is both relevant and compatible with remote sensing products within the European Union