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The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data‐driven approach based on combined neural networks (NNC‐part). NNC‐partincorporates process knowledge by introducing a photosynthetic response based on the light‐use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R2>.94) and RECO (R2>.8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC‐partperformed more realistic than the traditional methods for predicting additional patterns of gross CO2fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC‐partis a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface-atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface-atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean±1 SD: 0.8±0.1PgCyr-1, positive numbers are sources to the atmosphere), Russia (0.1±0.4PgCyr-1), East Asia (1.6±0.3PgCyr-1), South Asia (0.3±0.1PgCyr-1), Australia (0.2±0.3PgCyr-1), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of -5.4±2.0PgCyr-1. By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001-2010 indicates that the true value of NCE is a net CO2 source of 4.3±0.1PgCyr-1. This mismatch of nearly 10PgCyr-1 highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO2 data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water-atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over- or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed., SCOPUS: re.j, info:eu-repo/semantics/published

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simula- tions by process-based land surface models. While a num- ber of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we per- formed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R 2 < 0.5), ecosystem respiration (R 2 > 0.6), gross primary production (R 2 > 0.7), latent heat (R 2 > 0.7), sensible heat (R 2 > 0.7), and net radiation (R 2 > 0.8). The ML meth- ods predicted the across-site variability and the mean sea- sonal cycle of the observed fluxes very well (R 2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R 2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the trop- ics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.

FunTeaching è un portale per l’apprendimento attivo. Il presente contributo ne descrive l’architettura e le caratteristiche per mostrare come la tecnologia possa adattarsi ad una metodologia costruttivista per generare insegnamento e apprendimento attivo. Le modalità e funzionalità del sistema web-based progettate e gradualmente implementate, vengono esplorate nelle loro applicazioni alle classi virtuali, mentre i principali aspetti dell’appredimento-insegnamento blended, i vantaggi e le potenzialità, vengono discussi sulla base delle esperienze professionali nelle scuole secondarie, nell’ambito della formazione degli adulti e dei docenti in servizio. Un’attenta analisi dei bisogni degli utenti è stata il punto di partenza per la progettazione di materiali multimediali come valore aggiunto all’interazione nella piattaforma., Journal of e-Learning and Knowledge Society, Vol 3, No 1 (2007): Journal of e-Learning and Knowledge Society - Focus on ICT, scuola, processi cognitivi

The main sources of uncertainty in CO2 and H2O fluxes up-scaling using empirical methods can be summarized in 1) degree of correlation between inputs and target variables, 2) uncertainty in inputs values and 3) method used in the extrapolation. In this work a fluxes up-scaling dedicated algorithm will be presented. It uses only remotely sensed input in order to estimate GPP fluxes in spatially explicit way. The study has been conducted at canopy level, ecosystem level and global scale. One of the most important factor that affect carbon dioxide fluxes is water stress. Primary, at canopy level correlation between multispectral spectrometer data and vegetation water status variables have been evaluated. Preliminary hypothesis has been carried out by simulations using a radiative transfer model (PROSAIL). A water stress experiment has been also carried out, inducing water status in plants and acquiring spectrometer data to check the degree of correlation between multispectral vegetation indices and water status variables. A library of satellite available vegetation indices has been evaluated. In addition, all available multispectral bands in the range 350-2500 nm at bandpass of 10 nm, have been used to calculate all available vegetation indices and correlation with vegetation water status variables in order to evaluate other band combination not acquired currently by satellite. At ecosystem scale, relationships between eddy-covariance tower latent heat (LE) and gross primary production (GPP) to MODIS satellite multispectral indices has been evaluated. Degrees of correlation have been analysed in order to evaluate the if different fitting can be put in relation to plant functional type (PFT). The effects of meteorological gradient and sites specifics characteristics on degree of correlation between fluxes and vegetation indices haves been also evaluated. In order to define the climatic conditions where relationships between fluxes and remotely sensed indices in water stress situations works better, period with high water stress level (no precipitation for at least 24 days) have been selected and the shape of the relation studied. These studies are useful in order to elaborate empirical model for GPP up-scaling. We choose a model tree algorithm in order to up-scale GPP fluxes. The reason was related to capability of model tree to generalize relationships for datasets characterized by high variability and interaction. Model tree is able to stratify dataset in homogeneous subset in order to maximize degree of fitting of multiple regression function to a target variable. Model was been evaluated by leave one out method. Only remotely sensed input are used reducing the level of uncertainty due to errors in the input dataset. The degree of accuracy was similar to other remotely sensed methods that, in addition use ancillary interpolated and modeled input (es. MOD 17). This results must be encourage the use of empirica methods based only on measured data (like radiations) that have the advantage of a reduction of others sources of uncertainty. Dottorato di ricerca in Ecologia forestale

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R20.6), gross primary production (R2>0.7), latent heat (R2>0.7), sensible heat (R2>0.7), and net radiation (R2>0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2>0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2, Biogeosciences, 13 (14), ISSN:1726-4170

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R20.6), gross primary production (R2>0.7), latent heat (R2>0.7), sensible heat (R2>0.7), and net radiation (R2>0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2>0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2, Biogeosciences Discussions, ISSN:1810-6277, ISSN:1810-6285