The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science ...today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future.
Plain Language Summary
This paper discusses the importance of creating comprehensive environmental observation systems to better understand and address global and regional environmental changes. In 2008, a German research infrastructure named Terrestrial Environmental Observatories (TERENO) was established to build and maintain a network of observatories. The goal is to conduct interdisciplinary, long‐term research on the impacts of global environmental changes on terrestrial ecosystems and their socio‐economic effects. The TERENO network employs advanced methods from environmental monitoring, geophysics, remote sensing, and modeling to study various environmental aspects. Over the past 15 years, four observatories have been part of this network, contributing to valuable experience in overcoming challenges and exceeding expectations. Today, TERENO is a crucial component for environmental modeling and forecasting in Germany, serving as an information hub for practitioners and policymakers. It also fosters international collaboration, supports large‐scale experiments, and drives methodological and technological advancements. The article highlights key lessons learned from this 15‐year effort and emphasizes the importance of continuing such integrated environmental monitoring programs in the future.
Key Points
Integrated observatories ensure a holistic Earth Systems perspective, offering data for current and future ecological challenges
The scientific and societal value of observatories is invaluable, but their design, construction and operation require considerable effort
For assured long‐term data collection, research infrastructure must have flexible design for adapting to changing research needs
As length and timing of the growing season are major factors explaining differences in carbon exchange of ecosystems, we analyzed seasonal patterns of net ecosystem carbon exchange (
F
NEE) using ...eddy covariance data of the FLUXNET data base (
http://www-eosdis.ornl.gov/FLUXNET). The study included boreal and temperate, deciduous and coniferous forests, Mediterranean evergreen systems, rainforest, native and managed temperate grasslands, tundra, and C
3 and C
4 crops. Generalization of seasonal patterns are useful for identifying functional vegetation types for global dynamic vegetation models, as well as for global inversion studies, and can help improve phenological modules in SVAT or biogeochemical models. The results of this study have important validation potential for global carbon cycle modeling.
The phasing of respiratory and assimilatory capacity differed within forest types: for temperate coniferous forests seasonal uptake and release capacities are in phase, for temperate deciduous and boreal coniferous forests, release was delayed compared to uptake. According to seasonal pattern of maximum nighttime release (evaluated over 15-day periods,
F
max) the study sites can be grouped in four classes: (1) boreal and high altitude conifers and grasslands; (2) temperate deciduous and temperate conifers; (3) tundra and crops; (4) evergreen Mediterranean and tropical forests. Similar results are found for maximum daytime uptake (
F
min) and the integral net carbon flux, but temperate deciduous forests fall into class 1.
For forests, seasonal amplitudes of
F
max and
F
min increased in the order tropical<Mediterranean and temperate coniferous<temperate deciduous and boreal forests, and the pattern seems relatively stable for these groups. The seasonal amplitudes of
F
max and
F
min are largest for managed grasslands and crops. Largest observed values of
F
min varied between −48 and −2
μmol
m
−2
s
−1, decreasing in the order C
4-crops>C
3-crops>temperate deciduous forests>temperate conifers>boreal conifers>tundra ecosystems.
Due to data restrictions, our analysis centered mainly on Northern Hemisphere temperate and boreal forest ecosystems. Grasslands, crops, Mediterranean ecosystems, and rainforests are under-represented, as are savanna systems, wooded grassland, shrubland, or year-round measurements in tundra systems. For regional or global estimates of carbon sequestration potentials, future investigations of eddy covariance should expand in these systems.
•Spatial survey of leaf inclination for most common broadleaf tree species in Europe.•Leaf angle distribution might be considered a species-specific trait for most cases.•Spherical approximation not ...a valid assumption for European broadleaf tree species.•Leaf angle distribution for 50 common broadleaf tree species in Europe also reported.•We strongly encourage the collection of actual in situ measurements where possible.
Leaf angle distribution influences forest canopy processes like radiation balance, photosynthesis, and evapotranspiration. Indeed, a strong sensitivity to variability in the leaf angle distribution is reported for many models. Difficulties in conducting leaf angle distribution measurements limit data availability and explanations of its species-specific phenology and variation across environmental gradients. This leads to the situation that leaf angle distribution is often the most poorly constrained model parameter. Here, we report a spatial survey of leaf angle distributions and their seasonal and vertical changes for five most abundant forest broadleaf tree species in Europe according to the spatially representative Level I ICP Forests monitoring network. Although we found evidence that leaf angle distribution might be considered a species-specific trait for all studied species except Betula pendula, we advocate the use of leaf angle distributions obtained from actual leaf inclination measurements whenever possible. Data on leaf angle distributions for 50 widespread forest broadleaf tree species in Europe is also reported.
•Understory, leaf angles and sun angle were the main drivers of near-infrared reflectance.•The influence of leaf albedo and leaf area on near-infrared reflectance was negligible.•Simulated seasonal ...forest canopy reflectances matched Sentinel 2 observations.•Corresponding sun angle effects depended on the viewing angle.•The link between GPP and NIRv may be weaker than expected.
The physical mechanisms behind correlations of earth observations and remote sensing products are of vital importance. The so-called ’near-infrared reflectance of vegetation’ (NIRV) and gross primary production (GPP) show high correlations among different ecosystems and temporal scales but the underlying relationship is still poorly understood. NIRV is defined as the product of normalized difference vegetation index (NDVI) and near-infrared (NIR) canopy reflectance (RNIR). We examined this relationship in the case of a temperate deciduous forest in Germany. GPP, RNIR and NIRV all exhibited a strong rise during leaf development in spring and a continual decline after the maximum in early summer. The decline of NIRV in late summer was mainly driven by the decline of RNIR, since NDVI remained saturated.
Here we tested the RNIR decline attributions to changes in leaf area index, leaf optical properties, canopy structure, sun-sensor geometry, or understory vegetation by measuring seasonal variations of those factors of the temperate deciduous forest. Leaf area was nearly constant between May and mid September, leaf albedo decreased slightly, leaf angles increased over time towards more vertical leaves, and understory reflectance decreased considerably.
We simulated the seasonal RNIR decline of the forest using the radiative transfer model FRT and quantified the sensitivity of the decline to variations in the measured parameters. FRT captured well the observed seasonal RNIR decline by Sentinel 2 using the measured optical and structural properties. Decreasing understory reflectance alone explained 43% of the simulated RNIR decrease, while leaf angle variations explained 31%, the solar zenith angle (SZA) 21%, leaf albedo 7%, and LAI 0%. The effect size of the SZA depended on the viewing angle and would hence be different for different satellites and for local instruments. The results may help to better understand and help to track seasonal changes in forest structure and leaf optical properties using remote sensing techniques. They also suggest that the proposed link between the seasonal evolution of GPP and NIRV may be weaker than expected.
The FLUXNET2015 dataset provides ecosystem-scale data on CO
, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites ...around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
Environmental sensor networks produce continuously increasing volumes of raw data that need to be transformed into usable data for monitoring ongoing environmental changes and decision-support. The ...crucial challenge is providing data in real-time, which requires the rigorous automation of quality control (QC) workflows using suitable software tools. We present the System for automated Quality Control (SaQC), a software package for the automated quality control of environmental time series data that is universal and that can be expanded in its set of domain-agnostic QC and processing functionalities, while at the same time being user-friendly in its low-code configuration environment. Two applications present the configuration of basic and advanced quality control applications using SaQC. We also elaborate on the explicit user controls over the handling of quality flags and how SaQC can be used to make QC-workflows traceable and reproducible, thus promoting FAIR data streams of high quality.
•We present the Python package System for automated Quality Control (SaQC).•SaQC facilitates the implementation of workflows for automated quality control•It is designed for domain scientists that manage environmental sensor networks.•It is universal, user-friendly and can be extended.•It addresses crucial challenges regarding quality control and the FAIRness of data streams.
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture ...data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
Germany's 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.
The insertion of thermal dissipation (TD) sensors on tree stems for sap flux density (SFD) measurements can lead to SFD underestimations due to a wound formation close to the drill hole. However, the ...wound effect has not been assessed experimentally for this method yet. Here, we propose an empirical approach to investigate the effect of the wound healing on measured sap flux with TD probes. The approach was performed for both, diffuse-porous (Fagus sylvatica (Linnaeus)) and ring-porous (Quercus petraea (Lieblein)) species. Thermal dissipation probes were installed on different dates along the growing season to document the effects of the dynamic wound formation. The trees were cut in autumn and additional sensors were installed in the cut stems, therefore, without potential effects of wound development. A range of water pressures was applied to the stem segments and SFDs were simultaneously measured by TD sensors as well as gravimetrically in the laboratory. The formation of wounds around sensors installed in living tree stems led to underestimation of SFD by 21.4 ± 3 and 47.5 ± 3.8% in beech and oak, respectively. The differences between SFD underestimations of diffuse-porous beech and ring-porous oak were, however, not statistically significant. Sensors with 5-, 11- and 22-week-old wounds also showed no significant differences, which implies that the influence of wound formation on SFD estimates was completed within the first few weeks after perforation. These results were confirmed by time courses of SFD measurements in the field. Field SFD values decreased immediately after sensor installation and reached stable values after ~2 weeks with similar underestimations to the ones observed in the laboratory. We therefore propose a feasible approach to correct directly field observations of SFD for potential underestimations due to the wound effect.
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