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The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , 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. Measurement(s) net ecosystem exchange • carbon dioxide • water • energy Technology Type(s) eddy covariance • measurement device Sample Characteristic - Environment terrestrial biome • atmosphere Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12295910

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the five MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties, and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in-situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models, and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice-ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

Torrential rainfall predisposes hills to catastrophic landslides resulting in serious damage to life and property. Landslide inventory maps are therefore essential for rapid response and developing disaster mitigation strategies. Manual mapping techniques are laborious and time-consuming, and thus not ideal in rapid response situations. Automated landslide mapping from optical satellite imagery using deep neural networks (DNNs) is becoming popular. However, distinguishing landslides from other changed objects in optical imagery using backbone DNNs alone is difficult. Attention modules have been introduced recently into the architecture of DNNs to address this problem by improving the discriminative ability of DNNs and suppressing noisy backgrounds. This study compares two state-of-the-art attention-boosted deep Siamese neural networks in mapping rainfall-induced landslides in the mountainous Himalayan region of Nepal using Planetscope (PS) satellite imagery. Our findings confirm that attention networks improve the performance of DNNs as they can extract more discriminative features. The Siamese Nested U-Net (SNUNet) produced the best and most coherent landslide inventory map among the methods in the test area, achieving an F1-score of 0.73, which is comparable to other similar studies. Our findings demonstrate a prospect for application of the attention-based DNNs in rapid landslide mapping and disaster mitigation not only for rainfall-triggered landslides but also for earthquake-triggered landslides.

The major objectives of this paper are: (1) to review the pros and cons of the scenarios of past anthropogenic land cover change (ALCC) developed during the last ten years, (2) to discuss issues related to pollen-based reconstruction of the past land-cover and introduce a new method, REVEALS (Regional Estimates of VEgetation Abundance from Large Sites), to infer long-term records of past land-cover from pollen data, (3) to present a new project (LANDCLIM: LAND cover - CLIMate interactions in NW Europe during the Holocene) currently underway, and show preliminary results of REVEALS reconstructions of the regional land-cover in the Czech Republic for five selected time windows of the Holocene, and (4) to discuss the implications and future directions in climate and vegetation/land-cover modeling, and in the assessment of the effects of human-induced changes in land-cover on the regional climate through altered feedbacks. The existing ALCC scenarios show large discrepancies between them, and few cover time periods older than AD 800. When these scenarios are used to assess the impact of human land-use on climate, contrasting results are obtained. It emphasizes the need for methods such as the REVEALS model-based land-cover reconstructions. They might help to fine-tune descriptions of past land-cover and lead to a better understanding of how long-term changes in ALCC might have influenced climate. The REVEALS model is demonstrated to provide better estimates of the regional vegetation/land-cover changes than the traditional use of pollen percentages. This will achieve a robust assessment of land cover at regional- to continental-spatial scale throughout the Holocene. We present maps of REVEALS estimates for the percentage cover of 10 plant functional types (PFTs) at 200 BP and 6000 BP, and of the two open-land PFTs \"grassland\" and \"agricultural land\" at five time-windows from 6000 BP to recent time. The LANDCLIM results are expected to provide crucial data to reassess ALCC estimates for a better understanding of the land suface-atmosphere interactions.

Drought is a significant natural hazard that severely challenges water resource management and agricultural sustainability. This study aims to propose a novel approach for predicting streamflow drought indices (SDI-3, SDI-6, and SDI-12) in humid continental (Stockholm) and semi-arid (ELdiem) climates at different time-steps. The approach utilizes a Quantile Regression Neural Network (QRNN) coupled with wavelet decomposition (WD) techniques. Six mother wavelets (haar, sym8, coif5, bior6.8, demy, and db10) were used to decompose the SDI time series into different frequency bands, helping to identify patterns and trends in drought signals. The QRNN was compared with a tree-based machine learning (ML) model and two deep learning models: Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Results from stand-alone models showed that the LSTM model outperformed others in predicting SDI-3, while the QRNN model performed best in predicting SDI-6 and SDI-12 in both study regions. In the Stockholm station, the hybrid models achieved acceptable accuracy with bior6.8-LSTM2 (Nash–Sutcliffe efficiency (NSE) = 0.927), bior6.8-QRNN2 (NSE = 0.962), and demy-QRNN2 (NSE = 0.984) performing best for SDI-3, SDI-6, and SDI-12 predictions during the test phase, respectively. For the ELdiem station, the db10-QRNN3 (NSE = 0.926), demy-QRNN3 (NSE = 0.934), and demy-QRNN2 (NSE = 0.981) models demonstrated superior performance during the test phase in predicting SDI-3, SDI-6, and SDI-12, highlighting the robust capability of hybrid models across two case studies. The results indicate that combining WD with ML models can produce more accurate hydrological drought predictions than traditional models.

The effects of single macrofauna taxa on benthic nitrogen (N) cycling have been extensively studied, whereas how macrofaunal communities affect N-related processes remains poorly explored. In this study, we characterized benthic N-cycling in bioturbated sediments of the oligotrophic Öre Estuary (northern Baltic Sea). Solute fluxes and N transformations (N₂ fixation, denitrification and dissimilative nitrate reduction to ammonium [DNRA]) were measured in sediments and macrofauna-associated microbes (holobionts) to partition the role of three dominant taxa (the filter feeder Limecola balthica, the deep deposit feeder Marenzelleria spp., and the surface deposit feeder Monoporeia affinis) in shaping N-cycling. In the studied area, benthic macrofauna comprised a low diversity community with dominance of the three taxa, which are widespread and dominant in the Baltic. The biomass of these taxa in macrofaunal community explained up to 30% of variation in measured biogeochemical processes, confirming their important role in ecosystem functioning. The results also show that these taxa significantly contributed to the benthic metabolism and N-cycling (direct effect) as well as to sediments bioturbation with positive feedback to dissimilative nitrate reduction (indirect effect). Taken together, these functions promoted a reuse of nutrients at the benthic level, limiting net losses (e.g. denitrification) and effluxes to bottom water. Finally, the detection of multiple N transformations in macrofauna holobionts suggested a community-associated versatile microbiome, however, its role was of minor importance as compared to the activity of sediment-associated microbial communities. The present study highlights hidden and interactive effects among microbes and macrofauna, which should be considered analysing benthic functioning.

During the past few years, the number of regional and national assessments of groundwater quality in regard to saltwater intrusion in coastal aquifers has increased steadily. However, most of the international literature on saltwater intrusion is focused on coastal plains with aquifers in unconsolidated material. Case studies, modelling approaches and parameter studies dealing with saltwater intrusion in those systems are abundant. While the hydrogeology of fractured rock has been intensively studied with both modelling approaches and parameter studies—mainly in relation to deep-laying fractured crystalline bedrock as potential waste repositories—case studies on saltwater intrusion in shallow fractured rocks are still an exception. This review summarizes the actual knowledge on saltwater intrusion in fractured crystalline rock. In combination with short overviews of the processes of saltwater intrusion, flow in fractured systems and the genesis of these systems, the review highlights the importance of the fracture systems and its specific characteristics. Fracture properties are a direct consequence of the geological history as well as the current situation of the coastal area. A holistic assessment of water quality in coastal areas hosting fractured crystalline bedrock therefore requires the combination of different approaches in order to investigate the impact of saltwater intrusion through the fractured system.

There is an urgent need for primary data collection on food waste to obtain solid quantification data that can be used as an indicator in the goal of halving food waste by 2030. This study examined how quality baselines for food waste can be achieved within the different segments of the hospitality sector, encompassing establishments such as canteens, elderly care units, hospitals, hotels, preschools, primary schools, restaurants, and upper secondary schools. The empirical material comprised food-waste quantification data measured in 1189 kitchens in Sweden, Norway, Finland, and Germany for 58,812 quantification days and 23 million portions. All the data were converted to a common format for analysis. According to the findings, around 20% of food served became waste. Waste per portion varied widely between establishments, ranging from 50.1 ± 9.4 g/portion for canteens to 192 ± 30 g/portion for restaurants. To identify the measurement precision needed for tracking changes over time, we suggest statistical measures that could be used in future studies or in different food-waste tracking initiatives.

Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.

Recent studies have recognised highly oxygenated organic molecules (HOMs) in the atmosphere as important in the formation of secondary organic aerosol (SOA). A large number of studies have focused on HOM formation from oxidation of biogenically emitted monoterpenes. However, HOM formation from anthropogenic vapours has so far received much less attention. Previous studies have identified the importance of aromatic volatile organic compounds (VOCs) for SOA formation. In this study, we investigated several aromatic compounds, benzene (C6H6), toluene (C7H8), and naphthalene (C10H8), for their potential to form HOMs upon reaction with hydroxyl radicals (OH). We performed flow tube experiments with all three VOCs and focused in detail on benzene HOM formation in the Jülich Plant Atmosphere Chamber (JPAC). In JPAC, we also investigated the response of HOMs to NOx and seed aerosol. Using a nitrate-based chemical ionisation mass spectrometer (CI-APi-TOF), we observed the formation of HOMs in the flow reactor oxidation of benzene from the first OH attack. However, in the oxidation of toluene and naphthalene, which were injected at lower concentrations, multi-generation OH oxidation seemed to impact the HOM composition. We tested this in more detail for the benzene system in the JPAC, which allowed for studying longer residence times. The results showed that the apparent molar benzene HOM yield under our experimental conditions varied from 4.1 % to 14.0 %, with a strong dependence on the OH concentration, indicating that the majority of observed HOMs formed through multiple OH-oxidation steps. The composition of the identified HOMs in the mass spectrum also supported this hypothesis. By injecting only phenol into the chamber, we found that phenol oxidation cannot be solely responsible for the observed HOMs in benzene experiments. When NOx was added to the chamber, HOM composition changed and many oxygenated nitrogen-containing products were observed in CI-APi-TOF. Upon seed aerosol injection, the HOM loss rate was higher than predicted by irreversible condensation, suggesting that some undetected oxygenated intermediates also condensed onto seed aerosol, which is in line with the hypothesis that some of the HOMs were formed in multi-generation OH oxidation. Based on our results, we conclude that HOM yield and composition in aromatic systems strongly depend on OH and VOC concentration and more studies are needed to fully understand this effect on the formation of HOMs and, consequently, SOA. We also suggest that the dependence of HOM yield on chamber conditions may explain part of the variability in SOA yields reported in the literature and strongly advise monitoring HOMs in future SOA studies.