Impact of CO2 storage flux sampling uncertainty on net ecosystem exchange measured by eddy covariance

Complying with several assumption and simplifications, most of the carbon budget studies based on eddy covariance (EC) measurements quantify the net ecosystem exchange (NEE) by summing the flux obtained by EC ( FC ) and the storage flux ( SC ). SC is the rate of change of a scalar, CO 2 molar fraction in this case, within the control volume underneath the EC measurement level. It is given by the difference in the quasi-instantaneous profiles of concentration at the beginning and end of the EC averaging period, divided by the averaging period. The approaches used to estimate SC largely vary, from measurements based on a single sampling point usually located at the EC measurement height, to measurements based on profile sampling. Generally a single profile is used, although multiple profiles can be positioned within the control volume. Measurement accuracy reasonably increases with the spatial sampling intensity, however limited resources often prevent more elaborated measurement systems. In this study we use the experimental dataset collected during the ADVEX campaign in which turbulent and non-turbulent fluxes were measured in three forest sites by the simultaneous use of five towers/profiles. Our main objectives are to evaluate both the uncertainty of SC that derives from an insufficient sampling of CO 2 variability, and its impact on concurrent NEE estimates.Results show that different measurement methods may produce substantially different SC flux estimates which in some cases involve a significant underestimation of the actual SC at a half-hourly time scales. A proper measuring system, that uses a single vertical profile of which the CO 2 sampled at 3 points (the two closest to the ground and the one at the lower fringe of the canopy layer) is averaged with CO 2 sampled at a certain distance and at the same height, improves the horizontal representativeness and reduces this (proportional) bias to 2–10% in such ecosystems. While the effect of this error is minor on long term NEE estimates, it can produce significant uncertainty on half-hourly NEE fluxes

Effects of an extremely dry winter on net ecosystem carbon exchange and tree phenology at cork oak woodland

In seasonally dry climates, such as the Mediterranean, lack of rainfall in the usually wet winter may originate severe droughts which are a main cause of inter-annual variation in carbon sequestration. Leaf phenology variability may alter the seasonal pattern of photosynthetic uptake, which in turn is determined by leaf gas exchange limitations. The current study is based on the monitoring of an extremely dry winter in an evergreen cork oak woodland under the Mediterranean climate of central Portugal. Results are focused on net ecosystem CO2 exchange (NEE), phenology and tree growth measurements during two contrasting years: 2011, a wet year with a typical summer drought pattern and 2012, with an extremely unusual dry winter (only 10mmof total rainfall) that exacerbated the following summer drought effects. Main aims of this study were to assess the effects of an extreme dry winter in (1) annual and seasonal net ecosystem CO2 exchange, and in (2) cork oak phenology. The dry year 2012 was marked by a 45% lower carbon sequestration (−214 vs. −388gCm−2 year−1) and a 63% lower annual tree diameter growth but only a 9% lower leaf area index compared to the wet year 2011. A significant reduction of 15% in yearly carbon sequestration was associated with leaf phenological events of canopy renewal in the early spring. In contrast to male flower production, fruit setting was severely depressed by water stress with a 54% decrease during the dry year. Our results suggest that leaf growth and leaf area maintenance are resilient ecophysiological processes under winter drought and are a priority carbon sink for photoassimilates in contrast to tree diameter growth. Thus, carbon sequestration reductions under low water availabilities in cork oak woodland should be ascribed to stomatal regulation or photosynthetic limitations and to a lesser extent to leaf area reductionsinfo:eu-repo/semantics/publishedVersio

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

High-resolution drought simulations and comparison to soil moisture observations in Germany

Germany\u27s 2018–2020 consecutive drought events resulted in multiple sectors – including agriculture, forestry, water management, energy production, and transport – being impacted. High-resolution information systems are key to preparedness for such extreme drought events. This study evaluates the new setup of the one-kilometer German drought monitor (GDM), which is based on daily soil moisture (SM) simulations from the mesoscale hydrological model (mHM). The simulated SM is compared against a set of diverse observations from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations, and lysimeters at 40 sites in Germany. Our results show that the agreement of simulated and observed SM dynamics in the upper soil (0–25 cm) are especially high in the vegetative active period (0.84 median correlation R) and lower in winter (0.59 median R). The lower agreement in winter results from methodological uncertainties in both simulations and observations. Moderate but significant improvements between the coarser 4 km resolution setup and the ≈ 1.2 km resolution GDM in the agreement to observed SM dynamics is observed in autumn (+0.07 median R) and winter (+0.12 median R). Both model setups display similar correlations to observations in the dry anomaly spectrum, with higher overall agreement of simulations to observations with a larger spatial footprint. The higher resolution of the second GDM version allows for a more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality, observational soil moisture database

Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations

The eddy covariance is a powerful technique to estimate the surface-atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO2, water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in tenns of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in e.g. ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO2 and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-the-art processing methods.Peer reviewe

A pre-whitening with block-bootstrap cross-correlation procedure for temporal alignment of data sampled by eddy covariance systems

The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods have been proposed and are currently applied, the application of the EC method to trace gases measurements highlighted the difficulty of a correct time lag detection when the fluxes are small in magnitude. Failure to correctly synchronise the time series entails a systematic error on covariance estimates and can introduce large uncertainties and biases in the calculated fluxes. This work aims at overcoming these issues by introducing a new time lag detection procedure based on the assessment of the cross-correlation function (CCF) between variables subject to (i) a pre-whitening based on autoregressive filters and (ii) a resampling technique based on block-bootstrapping. Combining pre-whitening and block-bootstrapping facilitates the assessment of the CCF, enhancing the accuracy of time lag detection between variables with correlation of low order of magnitude (i.e. lower than -1) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite