Abstract Evapotranspiration (ET) and photosynthesis are key processes in ecosystem functioning on which soil moisture (SM) has an important influence. Eddy covariance measurements and machine learning (ML) increasingly enable flux prediction in ungauged regions. With numerous SM estimation methods available, each representing different spatiotemporal scales, understanding how data choice influences ML predictions is important. Our study examines how different SM data affect ML predictions of ET and photosynthesis. At semi‐arid to arid sites, we found that in situ near‐surface SM enhances ML predictions of ET. For photosynthesis, SM memory, indicative of deeper SM control, shows the highest predictive power, improving predictions by up to 30% at the driest sites. These contrasting responses reveal that ET and Gross Primary Productivity (GPP) are likely governed by distinct SM mechanisms: spatial scale matching for ET and temporal depth for GPP. Our study demonstrates that process‐guided feature engineering can improve ML predictions where root‐zone SM observations are often unavailable.

Growing evidence has shown that, apart from local environmental factors, changes in landscape-level factors by accelerated land-use change can also shape soil pathogenic fungal diversity. However, the global representativeness of such patterns remains unclear. Here, we assess how pathogenic fungal diversity in 511 soil samples worldwide responds to landscape factors, including landscape complexity index based on eight landscape metrics and quantity of different land cover types across six spatial scales (i.e., surrounding landscape, 250 m to 10,000 m radii from the sampling coordinate). We find that while soil variables explain over half of the variance, pathogenic fungal alpha diversity increases with landscape complexity and crop cover proportion, but decreases with grass and tree cover proportion, together explaining 23.4% of the total variance. Landscape factors have weaker impacts on beta diversity, explaining 13.0% of the variance. Across spatial scales, grassland ecosystems exhibit increasingly stronger responses to landscape variables compared to forest ecosystems. Landscape factors have a higher relative contribution to root-associated fungi than leaf/fruit/seed-associated fungi. Our results emphasize the importance of local factors and the complementary role of landscape patterns in shaping global soil pathogenic fungal distributions, highlighting scale-dependent effects across ecosystems and fungal functional groups.

Global and regional hydrological databases, as well as domain-agnostic repositories, play a crucial role in advancing scientific research and decision-making processes. With new and existing data infrastructures such as TERENO and eLTER, as well as governmental monitoring initiatives, efforts to enhance the size, capabilities, and accessibility of these services are underway. However, a key challenge persists across large-scale data collections - the need for rigorous harmonization of diverse data from various sources. This challenge extends beyond obvious considerations like numerical precision and date formats, encompassing more nuanced aspects such as data quality and its representation. Hydrological time series data, often acquired from remote sensors and monitoring stations, are susceptible to errors arising from sensor malfunctions, anomalies, and environmental fluctuations. Unchecked, these inaccuracies can lead to erroneous results and compromise decision-making processes. Addressing this critical issue, the System for Automated Quality Control - SaQC emerges as a pioneering solution, offering a comprehensive tool/framework for automated and customizable quality control and processing of time series data. SaQC empowers researchers and practitioners in the hydrological sciences, providing a convenient and efficient means to identify and rectify data anomalies. In addition to a large body of built-in routines and algorithms, the framework's extensibility allows users to implement custom quality check routines and schemes, tailoring the quality control process to specific research objectives and the evolving needs of data services. This presentation delves into the core principles of SaQC, showcasing its flexibility in handling diverse data types and adapting to various hydrological monitoring scenarios. Through real-world examples of fully automatized quality control and data processing workflows, we highlight the benefits of SaQC in enhancing data integrity, reducing manual intervention, and expediting the analysis pipeline. SaQC not only identifies anomalies but also provides a systematic and transparent approach to data quality assurance, contributing to the overall reliability of hydrological datasets. Lennart Schmidt, David Schäfer, Juliane Geller, Peter Lünenschloss, Bert Palm, Karsten Rinke, Corinna Rebmann, Michael Rode, Jan Bumberger, System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science, Environmental Modelling & Software, Volume 169, 2023, 105809, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2023.105809.

Long-term, spatially representative soil-moisture records are critical for characterising ecosystem responses to water availability. We present a decade-long (2014-2024) dataset of continuous soil-moisture observations from distributed in situ networks and cosmic-ray neutron sensing (CRNS) across a 1 ha temperate deciduous forest in Germany. Spatial sensor coverage varied over time and challenged the derivation of a consistent spatial average due to the persistence of soil moisture patterns. We therefore implemented a semi-automatic workflow that (i) identifies reference periods via a bootstrap-based minimum required number of sensors (MRNS) and (ii) maps point measurements to the field-scale distribution using empirical CDF transformation. The resulting record provides a coherent long-term signal suitable for ecohydrological analyses and validation of remote-sensing products. Since any decade-scale monitoring will encounter sensor losses and replacements, we emphasise the critical role of robust data integration techniques to ensure the reliability of extended soil moisture datasets.

Background: Over half of global soil organic carbon (SOC) stocks are stored in subsoil. A larger fraction of carbon (C) input may be retained and stabilized in subsoil as compared to topsoil. This retained fraction determines C sequestration efficiency, which may also depend on the amount of C input. Aims: This study aimed to determine how litter decomposition and stabilization differ between topsoil and subsoil and how these processes respond to increasing amounts of added litter. Methods: A field incubation experiment was initiated at three forest sites (Braunschweig, Hohes Holz and Krofdorf). At each site, C-13-enriched pre-decomposed beech litter was added at doses of 2-64 g kg(-1) of soil and buried in mesocosms in topsoils and subsoils. After 1, 2 and 4 years, samples were excavated and size-fractionated (<20 m). Results: Different amounts of substrate addition did not affect the mineralization of this substrate or the formation efficiency of new fine fraction carbon (OCfine). Furthermore, soil depth had little impact, with 75% and 71% of the added substrate mineralized in the topsoils and subsoils, respectively, after 4 years. Substrate addition also did not significantly enhance the mineralization of native SOC. The lowest formation of new OCfine was observed at the Braunschweig site characterized by sandy soils, suggesting a potential link between soil texture and OCfine formation. Conclusions: The SOC formation efficiency is decoupled from the amount of C added; nevertheless, increasing the amount of C input will lead to a continuous linear rise in SOC stocks.

In the rapidly advancing domain of environmental research, the deployment of a comprehensive, state-of-the-art Research Data Management (RDM) framework is increasingly pivotal. Such a framework is key to ensure FAIR data, laying the groundwork for transparent and reproducible earth system sciences.Today, datasets associated with research articles are commonly published via prominent data repositories like Pangaea or Zenodo. Conversely, data used in actual day-to-day research and inter-institutional projects tends to be shared through basic cloud storage solutions or, even worse, via email. This practice, however, often conflicts with the FAIR principles, as much of this data ends up in private, restricted systems and local storage, limiting its broader accessibility and use.In response to this challenge, our research project Cat4KIT aims to establish a cross-institutional catalog and Research Data Management framework. The Cat4KIT framework is, hence, an important building block towards the FAIRification of environmental data. It not only streamlines the process of ensuring availability and accessibility of large-scale environmental datasets but also significantly enhances their value for interdisciplinary research and informed decision-making in environmental policy.The Cat4KIT system comprises four essential elements: data service provision, meta(data) harvesting, catalogue service, and user-friendly data presentation. The data service provision module is tailored to facilitate access to data within typical storage systems by using well-defined and standardized community interfaces via tools like the Thredds data server, Intake Catalogues, and the OGC SensorThings API. By this, we ensure seamless data retrieval and management for typical use-casers in environmental sciences.(Meta)data harvesting via our so-called DS2STAC-package entails collecting metadata from various data services, followed by creating STAC-metadata and integrating it into our STAC-API-based catalog service.This catalog service module synergizes diverse datasets into a cohesive, searchable spatial catalog, enhancing data discoverability and utility via our Cat4KIT UI.Finally, our framework's data portal is tailored to elevate data accessibility and comprehensibility for a wide audience, including researchers, enabling them to efficiently search, filter, and navigate through data from decentralized research data infrastructures.One notable characteristic of Cat4KIT is its dependence on open-source solutions and strict adherence to community standards. This guarantees not just the framework's ability to function well with current data systems but also its simple adaption and expansion to meet future needs. Our presentation demonstrates the technical structure of Cat4KIT, examining the development and integration of each module to adhere to the FAIR principles. Additionally, it showcases examples to illustrate the practical use of the framework in real-life situations, emphasizing its efficacy in enhancing data management practices within KIT and its potential relevance in other research organizations.

Profound knowledge of soil moisture and its variability plays a crucial role in hydrological modelling to support agricultural management, flood and drought monitoring and forecasting, and groundwater recharge estimation. Cosmic-ray neutron sensing (CRNS) has been recognised as a promising tool for soil moisture monitoring due to its hectare-scale footprint and decimetre-scale measurement depth. But since CRNS provides an integral measurement over several soil horizons, a direct comparison of observed and simulated soil moisture products is not possible. This study establishes a framework to assess the accuracy of soil moisture simulated by the mesoscale Hydrologic Model (mHM) by generating simulated neutron counts and comparing these with observed neutron measurements for the first time. We included three different approaches to estimate CRNS neutron counts in the mHM as a function of the simulated soil moisture profiles: two methods based on the Desilets equation and one based on the forward operator COSMIC (COsmic-ray Soil Moisture Interaction Code). For the Desilets method, we tested two different approaches to average the vertical soil moisture profiles: a uniform vs. a non-uniform weighting scheme depending on the CRNS measurement depth. The methods were tested at two agricultural sites, namely one pasture site and one forest site, in Germany. To explore the prior and posterior distributions of the mHM parameters when constrained by CRNS observations, we used a Monte Carlo method based on Latin hypercube sampling with a large sample size (S = 100 000). We found that all three methods performed well, with a Kling-Gupta efficiency > 0.75 and a percent bias < +/- 10 % across the majority of investigated sites and for the best 1 % of parameter sets. The performance of the neutron forward models varied slightly across different land cover types. The non-uniform approach generally showed good performance, particularly at the agricultural sites. On the other hand, the COSMIC method performed slightly better at the forest site. The uniform approach showed slightly better results at the grassland site. We also demonstrated for the first time that the incorporation of CRNS measurements into the mHM could improve both the soil moisture and the evapotranspiration products of the mHM. This suggests that CRNS is capable of improving the model parameter space in general and adds a broader perspective on the potential of CRNS to support large-scale hydrological and land surface models.

Abstract 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.

Aim To quantify the intra-community variability of leaf-out (ICVLo) among dominant trees in temperate deciduous forests, assess its links with specific and phylogenetic diversity, identify its environmental drivers, and deduce its ecological consequences with regard to radiation received and exposure to late frost. Location Eastern North America (ENA) and Europe (EUR). Time period 2009-2022 Major taxa studied Temperate deciduous forest trees. Methods We developed an approach to quantify ICVLo through the analysis of RGB images taken from phenological cameras. We related ICVLo to species richness, phylogenetic diversity and environmental conditions. We quantified the intra-community variability of the amount of radiation received and of exposure to late frost. Results Leaf-out occurred over a longer time interval in ENA than in EUR. The sensitivity of leaf-out to temperature was identical in both regions (-3.4 days per °C). The distributions of ICVLo were similar in EUR and ENA forests, despite the latter being more species-rich and phylogenetically diverse. In both regions, cooler conditions and an earlier occurrence of leaf-out resulted in higher ICVLo. ICVLo resulted in a ca. 8% difference of radiation absorption over spring among individual trees. Forest communities in ENA had shorter safety margins as regards the exposure to late frosts, and were actually more frequently exposed to late frosts. Main conclusions We conducted the first intercontinental analysis of the variability of leaf-out at the scale of tree communities. North American and European forests showed similar ICVLo, in spite of their differences in terms of species richness and phylogenetic diversity, highlighting the relevance of environmental controls on ICVLo. We quantified two ecological implications of ICVLo (difference in terms of radiation absorption and exposure to late frost), which should be explored in the context of ongoing climate change, which affects trees differently according to their phenological niche.

The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large-scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass-loss rates and stabilization factors of plant-derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy-to-degrade components accumulate during early-stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass-loss rates and stabilization, notably in colder locations. Using TBI improved mass-loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early-stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models.

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.

Knowledge of fluxes of water vapor and carbon at the land surface are paramount to our understanding of the Earth system. Large-scale network initiatives such as the Fluxnet allow us to better understand the environmental controls on the evapotranspiration and gross primary productivity. An important aspect of such initiatives is that its large number of sites allow for localized knowledge to be upscaled to a region or even globally. This can be either done by employing physics-based global land models or empirically, via data-driven approaches. Particularly, we have seen a significant increase of data-driven approaches with the use of machine learning techniques more recently. Here, we use a similar structure employed in the FLUXCOM initiative to focus particularly on the role of soil moisture information in predicting evapotranspiration and gross primary productivity at several flux sites encompassing a wide range of hydroclimates and biomes around the globe. Our analyses employ a machine learning method to a predictive model of evapotranspiration and gross primary productivity, while focusing primarily on how changes in the way soil moisture is incorporated into the methodology affects such predictions. First, we evaluate the predictive power of this model when soil moisture is directly estimated via observations against more indirect estimates via bucket-type models. Secondly, we evaluate the role of the spatial resolution of different soil moisture estimates in predicting both fluxes. We do this by using three sets of direct estimates covering distinct spatial footprints co-located at all flux sites: (1) point-scale time-domain reflectometers, (2) field-scale cosmic-ray neutron sensors, and (3) regional-scale satellite remote sensing products. In this talk, we summarize which hydroclimatic regions benefit from having direct estimate of soil moisture for evapotranspiration and gross primary productivity, while also providing some insights on the possible role of spatial scale mismatches between the fluxes and soil moisture.

Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site ’Hohes Holz’ (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was ∼ 16 % higher in 2018 ( ∼ 424 g_Cm^-2 ) and ∼ 25 % lower in 2019 ( ∼ 274 g_Cm^-2 ) compared to pre-drought years ( ∼ 367 g_Cm^-2 ). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly 50 % . We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.

Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Dario Papale, darpap@unitus.it

&lt;p&gt;Utilizing forest ecosystems to mitigate climate change effects and to preserve biodiversity requires detailed insights into the feedbacks between forest type, climatic and soil conditions, and in particular forest management history and practice. Analysis of long-term observations at the site level, remote sensing proxies and understanding relevant biogeochemical and biophysical processes are key to achieving these insights. In the recently started EU H2020 project &amp;#8220;CLimate Mitigation and Bioeconomy pathways for sustainable FORESTry&amp;#8221; (CLIMB-FOREST), we address these issues based on intensely monitored sites with flux measurements (ICOS, Fluxnet), other ecosystem research and observation networks (eLTER, National Forest Inventories), remotely sensed observations and process understanding. This presentation outlines the activities of CLIMB-FOREST regarding (1) carbon stocks and fluxes according to stand age, species distribution, management and disturbance history; (2) biophysical effects of forest structure; (3) effects and importance of short-lived climate forcers (e.g. BVOCs) and (4) management and extreme event (drought, fire) impact on SOC and N dynamics. We also outline how the gained knowledge informs scenario runs of the Vegetation and Earth System Model RCA-GUESS in the project.&lt;/p&gt;

Ambient atmospheric concentrations of isoprene and monoterpenes were measured at two forest sites, one deciduous and one coniferous, over the year 2022. Both sites in a regional area were sampled monthly between April and September. The samples were taken using sorbent tubes and analyzed with thermal desorption–gas chromatography–mass spectrometry. The highest concentrations were determined in August at both sites. While isoprene is the most abundant compound at the deciduous forest with an average concentration of 5.59 µg m−3 in August, α-pinene and β-pinene dominate throughout the year at the coniferous forest with the highest concentrations also in August (3.44 µg m−3 and 1.51 µg m−3). Because other monoterpenes (camphene, sabinene, 3-carene, p-cymene and limonene) are also emitted in significant amounts, the total concentration measured in the coniferous forest is higher (7.96 µg m−3 in August) in comparison to the deciduous forest (6.08 µg m−3). Regarding the detected monoterpenes in the deciduous forest, sabinene is the dominant monoterpene in addition to α-pinene and is sometimes present in higher (July) or equal (August) concentrations. The seasonal and diurnal concentrations of all monoterpenes correlate very well with each other at both sites. An exception is sabinene with a diurnal concentration profile similar to isoprene.

Data and Code for 'Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects' by Graf et al. (Communications Earth and Environment)