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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 C m - 2 ) and ∼ 25 % lower in 2019 ( ∼ 274 g C m - 2 ) compared to pre-drought years ( ∼ 367 g C m - 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.

Increasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast, non-destructive and cost-efficient method for soil parameter estimations. Microelectromechanical system devices (MEMS) have become available that are suitable for many application fields due to their low cost as well as their small size and weight. We investigated the performance of MEMS spectrometers in the visual and NIR range to estimate forest soil samples total C and N content of Ah and Oh horizons at the lab. The results were compared to a full-range device using PLSR and Cubist regression models at local (2.3 ha, n: Ah = 60, Oh = 50) and regional scale (State of Saxony, Germany, 184,000 km2, n: Ah = 186 and Oh = 176). For each sample, spectral reflectance was collected using MEMS spectrometer in the visual (Hamamatsu C12880MA) and NIR (NeoSpetrac SWS62231) range and using a conventional full range device (Veris Spectrophotometer). Both data sets were split into a calibration (70%) and a validation set (30%) to evaluate prediction power. Models were calibrated for Oh and Ah horizon separately for both data sets. Using the regional data, we also used a combination of both horizons. Our results show that MEMS devices are suitable for C and N prediction of forest topsoil on regional scale. On local scale, only models for the Ah horizon yielded sufficient results. We found moderate and good model results using MEMS devices for Ah horizons at local scale (R2≥ 0.71, RPIQ ≥ 2.41) using Cubist regression. At regional scale, we achieved moderate results for C and N content using data from MEMS devices in Oh (R2≥ 0.57, RPIQ ≥ 2.42) and Ah horizon (R2≥ 0.54, RPIQ ≥2.15). When combining Oh and Ah horizons, we achieved good prediction results using the MEMS sensors and Cubist (R2≥ 0.85, RPIQ ≥ 4.69). For the regional data, models using data derived by the Hamamatsu device in the visual range only were least precise. Combining visual and NIR data derived from MEMS spectrometers did in most cases improve the prediction accuracy. We directly compared our results to models based on data from a conventional full range device. Our results showed that the combination of both MEMS devices can compete with models based on full range spectrometers. MEMS approaches reached between 68% and 105% of the corresponding full ranges devices R2 values. Local models tended to be more accurate than regional approaches for the Ah horizon. Our results suggest that MEMS spectrometers are suitable for forest soil C and N content estimation. They can contribute to improved monitoring in the future as their small size and weight could make in situ measurements feasible.

Nitrification and denitrification are traditionally assumed to occur under aerobic and anoxic conditions, respectively. However, new and interesting alternatives challenge the traditional assumption. Along this line, we provide dual-nitrate isotopic and chemical evidence for the occurrence of denitrification linked with heterotrophic nitrification in an upper oxic region and nitrification in an underlying low-oxygen (sub-oxic/anoxic) layer of a stratified and channelized alluvial aquifer. Particularly significant is the source of the oxidant required for nitrification within the deeper low-oxygen layer. Combined with the existence of steep geochemical gradients, the introduction of a favorable manganese oxide oxidant from the upper oxic layer into the underlying low oxygen region during diffusive mixing resulted in the higher concentrations of nitrate observed (due to the anoxic reoxidation of nitrite to nitrate) as well as the strong positive correlation of nitrite/nitrate with manganese (II) ion concentrations (in the deeper anoxic layer compared with the shallower oxic layer). The observations and findings presented herein have implications for not only reconciling the discrepancies in such unconventional pathways of nitrogen metabolism between groundwater ecosystems and river/stream/-soil/marine systems (where such processes have most commonly been reported) but also for devising effective nitrogen nutrient remediation and complex nitrogen-cycling modeling strategies.

Recently, forest management faces new challenges resulting from increasing temperatures and drought occurrences. For sustainable, site-specific management strategies, the availability of up to date soil information is crucial. Proximal soil sensing techniques are a promising approach for rapid and inexpensive collection of data, and could facilitate the provision of the necessary information. This study evaluates the potential of visual and near-infrared spectroscopy (vis-NIRS) for estimating soil parameters relevant for humus mapping in Saxon forests. Therefore, soil samples from the organic layer are included. So far there is little knowledge about the applicability of vis-NIRS in the humus layer of forests. We investigate the spectral behaviour of samples from organic (Oh) and mineral (0–5 cm, Ah) horizons, pointing out differences in the occurring absorption features. Further, we identify and assess the accuracy of selected soil properties based on vis-NIRS for forest sites, compare the outcome of different regression methods, investigate the implications for forest soils due to the presence and different composition of the humus layer and organic horizons and interpret the results regarding their usefulness for soil mapping and monitoring purposes. For this, we used retained humus soil samples of forests from Saxony. Regression models were built with Partial Least Squares Regression, Support Vector Machine and Cubist. Investigated properties were carbon (C) and nitrogen (N) content, C/N ratio, pH value, cation exchange capacity (CEC) and base saturation (BS) due to their importance for assessing humus conditions in forests. In organic Oh horizons, prediction results for C and N content achieved R2 values between 0.44 and 0.58, with corresponding RPIQ ranging from 1.58 to 2.06 depending on the used algorithm. Estimations of C/N ratio were more precise with R2 = 0.65 and RMSE = 2.16. Best results were reported for pH value, with R2 = 0.90 and RMSE = 0.20. Regarding BS, the best model accuracy was R2 = 0.71, with RMSE = 13.97. In mineral topsoil, C and N content models achieved higher values of R2 = 0.59 to 0.72, with RPIQ values between 2.22 and 2.54. However, prediction accuracy was lower for C/N ratio (R2 = 0.50, RMSE = 3.52) and pH values (R2 = 0.62, RMSE = 0.29). Models for CEC achieved R2 = 0.65, with RPIQ = 2.81. In general, prediction precision varied dependent on the used algorithm, without showing clear tendencies. Classification into pH classes was exemplified since this offers a new perspective for humus mapping on forest soils. Balanced accuracy for the defined classes ranged from 0.50 to 0.87. We show that vis-NIR spectroscopy is suitable for assessing humus conditions in Saxon forests (Germany), in particular not only for mineral horizons but also for organic Oh horizons.

Managed Aquifer Recharge (MAR) sites suffer from the long-lasting problem of clogging. The causes of clogging are physical, biological, chemical and mechanical processes and their complex interaction, with physical clogging being recognised as the predominant process. The intrusion and deposition of particles during water recharge affect the hydraulic properties of the infiltration surface, resulting in a decline in the infiltration capacity of the site over the operating years. Cleaning operations are necessary to restore the original infiltration rates. For this purpose, assessing the risk of clogging can determine the site’s vulnerability and improve the scheme’s design. Numerical models are essential to replicate physical clogging processes and predict the decline in infiltration rates. So far, predictive tools for physical clogging assessment have been missing in MAR literature. Hence, the purpose of this study is to analyse and reorganise physical clogging models from applied engineering fields dealing with water infiltration in natural heterogeneous systems. The modelling approaches are illustrated, starting from the main assumptions and conceptualisation of the soil volume and intruding particles. The individual processes are untangled from the multiple studies and reorganised in a systematic comparison of mathematical equations relevant to MAR applications. The numerical models’ predictive power is evaluated for transferability, following limitations and recommendations for a process-based model applicable to surface spreading schemes. Finally, perspectives are given for clogging risk assessment at MAR sites from modelling and site characterisation. The predictive tool could assist decision-makers in planning the MAR site by implementing cost-effective strategies to lower the risk of physical clogging.

The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.

Data about the availability of nutrients in the humus layer of forest soils is vital information for sustainable forest management. For well-informed decision making, changes in humus layer conditions have to be monitored. The currently used schemes based on indicator plants are more and more overprinted by element input and forest management measures. Large and repeated field campaigns and laboratory analysis can provide the required information, but are expensive and time consuming. Techniques of Digital Soil Mapping (DSM) offer an alternative to gain the necessary information by spatially predicting selected soil properties, but their usage for mapping humus layer properties of forest soils is rare. Further, vis-NIR spectroscopy is a non-destructive approach that can provide physical and chemical soil properties via regression models. In this study, we present the application of an integrated framework as measuring system using DSM with the aim of providing spatial information about properties of the humus layer of forest soils. In order to reduce the amount of laboratory work, we applied the use of vis-NIR spectroscopy to determine the humus layer properties values within the framework. As input variables, we used available information based on soil forming factors and already existing information on mineral soils (soil form class, climate data, satellite data as proxy for vegetation, relief data and parent material and geographic location). Therefore, generally available data like satellite imagery and climate data can be used in combination with information that is already available in forest site mapping, e.g. on parent material, soil texture and pedogenetic soil type as well as hydromorphic features. Conditioned Latin Hypercube sampling was used to determine the locations of the field sampling points to collect the soil material, ensuring a valid representation of the humus layer properties in the test area. Probed layers were the Oh horizon and 0–5 cm depth. We tested the developed framework in a case study on a forest site in Saxony, investigating C/N ratio, pH value, cation exchange capacity and base saturation. Random Forest model calibration for spatial prediction achieved R2 > 0.9 for all investigated humus layer properties. Using the developed framework, we were able to create high resolution maps of humus layer properties on forest soils in the case study area. Especially for C/N ratio and pH value, the derived maps showed high spatial variation within the study area. For our test site, the framework revealed depleted humus conditions, which should be addressed by forest management measures. Vis-NIR predictions of humus layer properties were calculated using partial least squares regression. In Oh horizons, model results achieved R2 values between 0.17 and 0.69. In 0–5 cm, R2 values ranged from 0.43–0.62. RMSD values between produced maps based on chemical values and vis-NIR predictions were 0.7 and 0.96 for C/N, 0.06 for pH, 6.8 eq/g for CEC and 3.25% and 3.3% for BS. We conclude that the framework produced maps that can be used to assess humus conditions and thus support decision making in forest management. The use of vis-NIR spectroscopy offers the possibility to reduce the amount of laboratory work, but there is a trade-off in the accuracy of the results. In general, the framework can be used to fill the data gap by providing spatial information on humus layer properties at the forest stand scale.

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

Wetland environments, with their excellent conservation conditions, provide geoarchaeological archives of past human activities. However, the subsurface soil is difficult to access due to high groundwater tables, unstable sediments, and the high cost of excavation. In this study, we present a ground-based non- and minimal-invasive prospection concept adapted to the conditions of wetlands. We investigated the Fossa Carolina in South Germany, a canal that was intended in 792/793 AD by Charlemagne to bridge the Central European Watershed. Although the resulting Carolingian banks and the fairway with wooden revetments are very imposing, archaeological traces of off-site construction activities have not been identified hitherto. Based on a geophysically surveyed intensive linear magnetic anomaly parallel to the Carolingian canal, we aimed to prove potential off-site traces of Carolingian construction activities. In this context, we built up a high-resolution cross-section using highly depth-accurate direct push sensing and ground-truthing. Our results showed the exact geometry of the canal and the former banks. Thus, the magnetic mass anomaly could be clearly located between the buried organic-rich topsoil and the Carolingian banks. The thermoluminescence dating showed that the position of the magnetic mass anomaly reflected Carolingian activities during the construction phases, specifically due to heat exposure. Moreover, we found hints of the groundwater supply to the 5-metre wide navigable fairway.

The Early Medieval Fossa Carolina is the first hydro-engineering construction that bridges the Central European Watershed. The canal was built in 792/793 AD on order of Charlemagne and should connect the drainage systems of the Rhine-Main catchment and the Danube catchment. In this study, we show for the first time, the integration of Airborne LiDAR (Light Detection and Ranging) and geoarchaeological subsurface datasets with the aim to create a 3D-model of Charlemagne’s summit canal. We used a purged Digital Terrain Model that reflects the pre-modern topography. The geometries of buried canal cross-sections are derived from three archaeological excavations and four high-resolution direct push sensing transects. By means of extensive core data, we interpolate the trench bottom and adjacent edges along the entire canal course. As a result, we are able to create a 3D-model that reflects the maximum construction depth of the Carolingian canal and calculate an excavation volume of approx. 297,000 m3. Additionally, we compute the volume of the present dam remnants by Airborne LiDAR data. Surprisingly, the volume of the dam remnants reveals only 120,000 m3 and is much smaller than the computed Carolingian excavation volume. The difference reflects the erosion and anthropogenic overprint since the 8th century AD.