ALBERT

Description: Dissolved organic carbon (DOC) in soil solution is connected to DOC in surface waters through hydrological flows. Therefore, it is expected that long-term dynamics of DOC in surface waters reflect DOC trends in soil solution. However, a multitude of site-studies has failed so far to establish consistent trends in soil solution DOC, whereas increasing concentrations in European surface waters over the past decades appear to be the norm, possibly as a result from acidification recovery. The objectives of this study were therefore to understand the long-term trends of soil solution DOC from a large number of European forests (ICP Forests Level II plots) and determine their main physico-chemical and biological controls. We applied trend analys is at two levels: 1) to the entire European dataset and 2) to the individual time series and related trends with plot characteristics, i.e., soil and vegetation properties, soil solution chemistry and atmospheric deposition loads. Analyses of the entire dataset showed an overall increasing trend in DOC concentrations in the organic layers, but, at individual plots and depths, there was no clear overall trend in soil solution DOC across Europe with temporal slopes of soil solution DOC ranging between −16.8 % yr−1 and +23 % yr−1 (median= +0.4 % yr−1). The non-significant trends (40 %) outnumbered the increasing (35 %) and decreasing trends (25 %) across the 97 ICP Forests Level II sites. By means of multivariate statistics, we found increasing DOC concentrations with increasing mean nitrate (NO3−) deposition and decreasing DOC concentrations with decreasing me an sulphate (SO42−) deposition, with the magnitude of these relationships depending on plot deposition history. While the attribution of increasing trends in DOC to the reduct ion of SO42− deposition could be confirmed in N-poorer forests, in agreement with observations in surface waters, this was not the case in N-richer forests. In conclusion, long-term trends of soil solution DOC reflected the interactions between controls acting at local (soil and vegetation properties) and regional (atmospheric deposition of SO42− and inorganic N) scales.

Description: Plant phenological development is orchestrated through subtle changes in photoperiod, temperature, soil moisture and nutrient availability. Presently, the exact timing of plant development stages and their response to climate and management practices are crudely represented in land surface models. As visual observations of phenology are laborious, there is a need to supplement long-term observations with automated techniques such as those provided by digital repeat photography at high temporal and spatial resolution. We present the first synthesis from a growing observational network of digital cameras installed on towers across Europe above deciduous and evergreen forests, grasslands and croplands, where vegetation and atmosphere CO2 fluxes are measured continuously. Using colour indices from digital images and using piecewise regression analysis of time-series, we explored whether key changes in canopy phenology could be detected automatically across different land use types in the network. The piecewise regression approach could capture the start and end of the growing season, in addition to identifying striking changes in colour signals caused by flowering and management practices such as mowing. Exploring the dates of green up and senescence of deciduous forests extracted by the piecewise regression approach against dates estimated from visual observations we found that these phenological events could be detected adequately (RMSE 〈 8 and 11 days for leaf out and leaf fall respectively). We also investigated whether the seasonal patterns of red, green and blue colour fractions derived from digital images could be modelled mechanistically using the PROSAIL model parameterised with information of seasonal changes in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From a model sensitivity analysis we found that variations in colour fractions, and in particular the late spring "green hump" observed repeatedly in deciduous broadleaf canopies across the network, are essentially dominated by changes in the respective pigment concentrations. Using the model we were able to explain why this spring maximum in green signal is often observed out of phase with the maximum period of canopy photosynthesis in ecosystems across Europe. Coupling such quasi-continuous digital records of canopy colours with co-located CO2 flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO2 in the future.

Description: Plant phenological development is orchestrated through subtle changes in photoperiod, temperature, soil moisture and nutrient availability. Presently, the exact timing of plant development stages and their response to climate and management practices are crudely represented in land surface models. As visual observations of phenology are laborious, there is a need to supplement long-term observations with automated techniques such as those provided by digital repeat photography at high temporal and spatial resolution. We present the first synthesis from a growing observational network of digital cameras installed on towers across Europe above deciduous and evergreen forests, grasslands and croplands, where vegetation and atmosphere CO2 fluxes are measured continuously. Using colour indices from digital images and using piecewise regression analysis of time series, we explored whether key changes in canopy phenology could be detected automatically across different land use types in the network. The piecewise regression approach could capture the start and end of the growing season, in addition to identifying striking changes in colour signals caused by flowering and management practices such as mowing. Exploring the dates of green-up and senescence of deciduous forests extracted by the piecewise regression approach against dates estimated from visual observations, we found that these phenological events could be detected adequately (RMSE 〈 8 and 11 days for leaf out and leaf fall, respectively). We also investigated whether the seasonal patterns of red, green and blue colour fractions derived from digital images could be modelled mechanistically using the PROSAIL model parameterised with information of seasonal changes in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From a model sensitivity analysis we found that variations in colour fractions, and in particular the late spring `green hump' observed repeatedly in deciduous broadleaf canopies across the network, are essentially dominated by changes in the respective pigment concentrations. Using the model we were able to explain why this spring maximum in green signal is often observed out of phase with the maximum period of canopy photosynthesis in ecosystems across Europe. Coupling such quasi-continuous digital records of canopy colours with co-located CO2 flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO2 in the future.

Description: This study evaluates the performances of the new version (v.5.1) of 3D-CMCC Forest Ecosystem Model (FEM) in simulating gross primary production (GPP), against eddy covariance GPP data for ten FLUXNET forest sites across Europe. A new carbon allocation module, coupled with new both phenological and autotrophic respiration schemes, was implemented in this new version. Model ability in reproducing timing and magnitude of daily and monthly GPP fluctuations is validated at intra-annual and inter-annual scale, including extreme anomalous seasons. With the purpose to test the 3D-CMCC FEM applicability over Europe without a site-related calibration, the model has been deliberately parameterized with a single set of species-specific parameterizations for each forest ecosystem. The model consistently reproduces both in timing and in magnitude daily and monthly GPP variability across all sites, with the exception of the two Mediterranean sites. We find that 3D-CMCC FEM tends to better simulate the timing of inter-annual anomalies than their magnitude within measurements uncertainty. In six of eight sites where data were available the model well reproduces the 2003 summer drought event. Finally, for three sites we evaluate if a more accurate representation of forest structural characteristics (i.e. cohorts, forest layers) and species composition can improve model results. In two of the three sites results reveal that model slightly increases its performances, although, statistically speaking, not in a relevant way.

Description: This study evaluates the performances of the new version (v.5.1) of 3D-CMCC Forest Ecosystem Model (FEM) in simulating gross primary productivity (GPP), against eddy covariance GPP data for 10 FLUXNET forest sites across Europe. A new carbon allocation module, coupled with new both phenological and autotrophic respiration schemes, was implemented in this new daily version. Model ability in reproducing timing and magnitude of daily and monthly GPP fluctuations is validated at intra-annual and inter-annual scale, including extreme anomalous seasons. With the purpose to test the 3D-CMCC FEM applicability over Europe without a site-related calibration, the model has been deliberately parametrized with a single set of species-specific parametrizations for each forest ecosystem. The model consistently reproduces both in timing and in magnitude daily and monthly GPP variability across all sites, with the exception of the two Mediterranean sites. We find that 3D-CMCC FEM tends to better simulate the timing of inter-annual anomalies than their magnitude within measurements' uncertainty. In six of eight sites where data are available, the model well reproduces the 2003 summer drought event. Finally, for three sites we evaluate whether a more accurate representation of forest structural characteristics (i.e. cohorts, forest layers) and species composition can improve model results. In two of the three sites results reveal that model slightly increases its performances although, statistically speaking, not in a relevant way.