Experimental validation of footprint models for eddy covariance CO2 flux measurements above grassland by means of natural and artificial tracers

Footprint models, which simulate source area for scalar fluxes, are fundamental tools for a correct interpretation of micromoeteorological flux measurements and ecosystem exchange inferred from such data. Over the last two decades models of varying complexity have been developed, but all of them suffer from a significant lack of experimental validation. In this study two different experimental tests have been conducted with the aim of offering validation: a manipulation of the vegetation cover and an artificial tracer emission. In the first case the extension of the flux source has been changed progressively by successive cuts of vegetation, while in the second case by varying the distance of a tracer emission line respect to the measurement point. Results have been used to validate two analytical and a numerical footprint models. The experimental data show a good agreement with footprint models and indicate a limited extension of the flux source area, with approximately 75% of the sources confined within a range of 10-20 times the effective measurement height, i.e. the measurement height above the zero plane displacement. Another interesting result was the strong dependence on the surface roughness of both experimental estimates and numerical simulations of footprint. The effect of surface roughness on experimental results and models outputs was comparable to the effect of atmospheric stability. This indicates that surface roughness and turbulence conditions may play a significant role in source area location, in particular above inhomogeneous surfaces with change in roughness, as in the case of the manipulation experiment. Consequently a careful site specific quantification of these parameters seems to be fundamental to obtain realistic footprint estimates and significantly improve eddy covariance flux interpretation at complex sites.Peer reviewe

Modeling Surface Energy Fluxes over a Dehesa (Oak Savanna) Ecosystem Using a Thermal Based Two-Source Energy Balance Model (TSEB) I

Savannas are among the most variable, complex and extensive biomes on Earth, supporting livestock and rural livelihoods. These water-limited ecosystems are highly sensitive to changes in both climatic conditions, and land-use/management practices. The integration of Earth Observation (EO) data into process-based land models enables monitoring ecosystems status, improving its management and conservation. In this paper, the use of the Two-Source Energy Balance (TSEB) model for estimating surface energy fluxes is evaluated over a Mediterranean oak savanna (dehesa). A detailed analysis of TSEB formulation is conducted, evaluating how the vegetation architecture (multiple layers) affects the roughness parameters and wind profile, as well as the reliability of EO data to estimate the ecosystem parameters. The results suggest that the assumption of a constant oak leaf area index is acceptable for the purposes of the study and the use of spectral information to derive vegetation indices is sufficiently accurate, although green fraction index may not reflect phenological conditions during the dry period. Although the hypothesis for a separate wind speed extinction coefficient for each layer is partially addressed, the results show that taking a single oak coefficient is more precise than using bulk system coefficient. The accuracy of energy flux estimations, with an adjusted Priestley–Taylor coefficient (0.9) reflecting the conservative water-use tendencies of this semiarid vegetation and a roughness length formulation which integrates tree structure and the low fractional cover, is considered adequate for monitoring the ecosystem water use (RMSD ~40W m-2)

How do variations in the temporal distribution of rainfall events affect ecosystem fluxes in seasonally water-limited Northern Hemisphere shrublands and forests?

As a result of climate change, rainfall regimes became more extreme over the course of the 20th century, characterised by fewer and larger rainfall events. Such changes are expected to continue throughout the current century. The effect of changes in the 5 temporal distribution of rainfall on ecosystem carbon fluxes is poorly understood, with most available information coming from experimental studies of grassland ecosystems. Here, continuous measurements of ecosystem carbon fluxes and precipitation from the worldwide FLUXNET network of eddy-covariance sites are exploited to investigate the effects of differences in rainfall distribution on the carbon balance of seasonally water10 limited shrubland and forest sites. Once the strong dependence of ecosystem fluxes on total annual rainfall amount is accounted for, results show that sites with more extreme rainfall distributions have significantly lower gross productivity, slightly lower ecosystem respiration and consequently a smaller net ecosystem productivity.JRC.H.7-Climate Risk Managemen

Direct and selective synthesis of a wide range of carbon nanomaterials by CVD at CMOS compatible temperatures

Biosensors benefit from specific nano- to monitor human metabolites [1], [2]. Moreover, small structuration of the active bio-interface layer. In this electrodes allow us to design many sensing sites, each perspective, a wide range of carbon nanomaterials including multi-walled carbon nanotubes (MWCNTs), nanographite and carbon nanowalls (CNWs) have been directly synthesised by chemical vapor deposition (CVD) on Pt microelectrodes for the first time down to CMOS-compatible temperatures. This integration process, extremely useful to develop nanostructured multi-sensing site biodevices, has been validated by testing sensors for glucose with enhanced and competitive performance. Moreover it paves the way to the full integration of CMOS circuits, nanostructures and bioprobes

Radiation measurements at ICOS ecosystem stations

Solar radiation is a key driver of energy and carbon fluxes in natural ecosystems. Radiation measurements are essential for interpreting ecosystem scale greenhouse gases and energy fluxes as well as many other observations performed at ecosystem stations of the Integrated Carbon Observation System (ICOS). We describe and explain the relevance of the radiation variables that arc monitored continuously at ICOS ecosystems stations and define recommendations to perform these measurements with consistent and comparable accuracy. The measurement methodology and instruments are described including detailed technical specifications. Guidelines for instrumental set up as well as for operation, maintenance and data collection arc defined considering both ICOS scientific objectives and practical operational constraints. For measurements of short-wave (solar) and long wave (infrared) radiation components, requirements for the ICOS network are based on available well-defined state-of-the art standards (World Meteorological Organization, International Organization for Standardization). For photosynthetically active radiation measurements, some basic instrumental requirements are based on the performance of commercially available sensors. Since site specific conditions and practical constraints at individual ICOS ecosystem stations may hamper the applicability of standard requirements, we recommend that ICOS develops mid-tern coordinated actions to assess the effective level of uncertainties in radiation measurements at the network scale.Peer reviewe

Implications of carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval

We analyze the impacts of the steady state assumption on inverse model parameter retrieval from biogeochemical models. An inverse model parameterization study using eddy covariance CO2 flux data was performed with the Carnegie Ames Stanford Approach (CASA) model under conditions of strict and relaxed carbon cycle steady state assumption (CCSSA) in order to evaluate both the robustness of the model’s structure for the simulation of net ecosystem carbon fluxes and the assessment of the CCSSA effects on simulations and parameter estimation. Net ecosystem production (NEP) measurements from several eddy covariance sites were compared with NEP estimates from the CASA model driven by local weather station climate inputs as well as by remotely sensed fraction of photosynthetically active radiation absorbed by vegetation and leaf area index. The parameters considered for optimization are directly related to aboveground and belowground modeled responses to temperature and water availability, as well as a parameter (h) that relaxed the CCSSA in the model, allowing for site level simulations to be initialized either as net sinks or sources. A robust relationship was observed between NEP observations and predictions for most of the sites through the range of temporal scales considered (daily, weekly, biweekly, and monthly), supporting the conclusion that the model structure is able to capture the main processes explaining NEP variability. Overall, relaxing CCSSA increased model efficiency (21%) and decreased normalized average error ( 92%). Intersite variability was a major source of variance in model performance differences between fixed (CCSSAf) and relaxed (CCSSAr) CCSSA conditions. These differences were correlated with mean annual NEP observations, where an average increase in modeling efficiency of 0.06 per 100 g Cm 2 a 1 (where a is years) of NEP is observed (a < 0.003). The parameter h was found to be a key parameter in the optimization exercise, generating significant model efficiency losses when removed from the initial parameter set and parameter uncertainties were significantly lower under CCSSAr. Moreover, modeled soil carbon stocks were generally closer to observations once the steady state assumption was relaxed. Finally, we also show that estimates of individual parameters are affected by the steady state assumption. For example, estimates of radiation-use efficiency were strongly affected by the CCSSAf indicating compensation effects for the inadequate steady state assumption, leading to effective and thus biased parameters. Overall, the importance of model structural evaluation in data assimilation approaches is thus emphasize

Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem

Este estudio investiga las diferentes actuaciones de ópticas sobre los índices para estimar la producción primaria bruta (GPP) del estrato herbáceo de una sabana mediterránea con diferente disponibilidad de nitrógeno (N) y de fósforo (P). La fluorescencia de la clorofila inducida por el sol sobre el rendimiento calculado en 760 nm (FY760), escala de índice de reflectancia fotoquímica (sPRI), MERIS terrestre (índice de clorofila MTCI) y el índice de vegetación de diferencia normalizada (NDVI) fueron calculadas desde cerca de la superficie y las mediciones de espectroscopia de campo recolectados se hicieron utilizando espectrómetros de alta resolución espectral, que abarcan las regiones del infrarrojo cercano visible. La GPP fue medida utilizando cámaras de dosel en las mismas localidades muestreadas por los espectrómetros. Hemos probado si la eficiencia del uso de los modelos de luz (LUE) impulsados por cantidades de teledetección (RSMs) pueden hacer un mejor seguimiento de los cambios en la GPP causada por fuentes de nutrientes en comparación con aquellos impulsados exclusivamente por datos meteorológicos (MM). En particular, comparamos los espectáculos de diferentes formulaciones de RSM -basándose en la utilización de FY760 o sPRI como proxy para LUE y NDVI MTCI o como una fracción de la radiación fotosintéticamente activa absorbida (APAR f)- con las clásicas de MM. Los resultados mostraron mayor GPP en la N -parcelas experimentales fertilizadas durante el período de crecimiento. Estas diferencias en la GPP desaparecieron en el período de secado, cuando los efectos de la senescencia enmascarada contiene diferencias de potencial debido a la planta N. Por consiguiente, MTCI estaba estrechamente relacionada con la media de la planta N, contenida a través de tratamientos (r2 D 0:86, p < 0:01), porque estaba mal relacionados con GPP (r2 D 0:45, p < 0:05). Por el contrario sPRI y FY760 se correlacionaban bien con GPP durante todo el período de medición. Los resultados revelaron que la relación entre el GPP y FY760 no es única en los tratamientos, pero no se ve afectada por la disponibilidad de N. Los resultados de un análisis de validación cruzada mostró que el MM (AICcv D 127, MEcv D 0:879) superó a RSM (AICcv D 140, MEcv D 0:8737,) cuando la humedad del suelo fue utilizada para restringir la dinámica estacional de LUE. Sin embargo, el análisis residual demostró que las predicciones de GPP con MM son inexactas cuando no revela explícitamente unas variables climáticas en cambios relacionados con el parámetro de nutrientes LUE. Estos resultados sugieren que RSM es un medio valioso para diagnosticar los efectos inducidos por los nutrientes en la actividad fotosintética.This study investigates the performances of different optical indices to estimate gross primary production (GPP) of herbaceous stratum in a Mediterranean savanna with different nitrogen (N) and phosphorous (P) availability. Sun-induced chlorophyll fluorescence yield computed at 760 nm (Fy760), scaled photochemical reflectance index (sPRI), MERIS terrestrial-chlorophyll index (MTCI) and normalized difference vegetation index (NDVI) were computed from near-surface field spectroscopy measurements collected using high spectral resolution spectrometers covering the visible near-infrared regions. GPP was measured using canopy chambers on the same locations sampled by the spectrometers. We tested whether light-use efficiency (LUE) models driven by remote-sensing quantities (RSMs) can better track changes in GPP caused by nutrient supplies compared to those driven exclusively by meteorological data (MM). Particularly, we compared the performances of different RSM formulations – relying on the use of Fy760 or sPRI as a proxy for LUE and NDVI or MTCI as a fraction of absorbed photosynthetically active radiation (f APAR) – with those of classical MM. Results showed higher GPP in the N-fertilized experimental plots during the growing period. These differences in GPP disappeared in the drying period when senescence effects masked out potential differences due to plant N content. Consequently, although MTCI was closely related to the mean of plant N content across treatments (r2 D 0:86, p < 0:01), it was poorly related to GPP (r2 D 0:45, p < 0:05). On the contrary sPRI and Fy760 correlated well with GPP during the whole measurement period. Results revealed that the relationship between GPP and Fy760 is not unique across treatments, but it is affected by N availability. Results from a cross-validation analysis showed that MM (AICcv D 127, MEcv D 0:879) outperformed RSM (AICcv D 140, MEcv D 0:8737) when soil moisture was used to constrain the seasonal dynamic of LUE. However, residual analyses demonstrated that GPP predictions with MM are inaccurate whenever no climatic variable explicitly reveals nutrient-related changes in the LUE parameter. These results suggest that RSM is a valuable means to diagnose nutrient-induced effects on the photosynthetic activity.Trabajo financiado por: Alexander von Humboldt Foundation y la Max Planck Research AwardpeerReviewe