Groundwater recharge indicates the existence of renewable groundwater resources and is therefore an important component in sustainability studies. However, recharge is also one of the least understood, largely because it varies in space and time and is difficult to measure directly. For most studies, only a relatively small number of measurements is available, which hampers a comprehensive understanding of processes driving recharge and the validation of hydrogeological model formulations for small- and large-scale applications. We present a new global recharge dataset encompassing >5000 locations. In order to gain insights into recharge processes, we provide a systematic analysis between the dataset and other global-scale datasets, such as climatic or soil-related parameters. Precipitation rates and seasonality in temperature and precipitation were identified as the most important variables in predicting recharge. The high dependency of recharge on climate indicates its sensitivity to climate change. We also show that vegetation and soil structure have an explanatory power for recharge. Since these conditions can be highly variable, recharge estimates based only on climatic parameters may be misleading. The freely available dataset offers diverse possibilities to study recharge processes from a variety of perspectives. By noting the existing gaps in understanding, we hope to encourage the community to initiate new research into recharge processes and subsequently make recharge data available to improve recharge predictions.

This is the collection associated with list S66 EAWAGTPS - Parent-Transformation Product Pairs from Eawag on the NORMAN Suspect List Exchange.

https://www.norman-network.com/nds/SLE/

Parent-Transformation Product Pairs of various micropollutants from Eawag: Swiss Federal Institute for Aquatic Science and Technology (https://www.eawag.ch/en/), described in Schollee et al 2017 DOI:10.1007/s13361-017-1797-6 . Dataset DOI: 10.5281/zenodo.3754448

Update 23/04/2020: fixed names, added synonym columns and classification information.Update 15/05/2020: adjusted classification information following feedback from Juliane.Update 13/01/2023: adjusted selected BT entries based on feedback from Emma Palm.Update 07/02/2023: adjusted selected BT entries to fix 4-Me-BT issues.

Lib4RI analyzed publication data in the institutional repository of the Swiss Federal Institute of Aquatic Science and Technology (DORA Eawag, https://www.dora.lib4ri.ch/eawag/) covering the years 2018 to 2021. This analysis aims to produce absolute figures on the proportion of open and closed scientific publications authored by researchers affiliated with the Swiss Federal Institute of Aquatic Science and Technology as part of the annual survey 2022 conducted by the Swiss Open Access Monitor (Repository Monitor, https://oamonitor.ch/charts-data/repository-monitor/).
The analysis covered four resource types: journal articles, books, book chapters, and conference papers. Only basic Open Access status is considered, based on the availability of full text (open | closed) in DORA Eawag. Publications under embargo at the time of data collection are classified as closed.

The download package of the Eawag AGS model. Content: 1. Eawag AGS Model files 2. User guide 3. Sumo project examples 4. Data extraction files 5. R tool 6. Python runner More information on the model can be found here: https://www.eawag.ch/en/department/eng/projects/abwasser/ags-aerobic-granular-slugde-model/

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) contains information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. The UM-BBD Pathway Prediction System (PPS) predicts microbial catabolic reactions using substructure searching, a rule-base, and atom-to-atom mapping. The PPS recognizes organic functional groups found in a compound and predicts transformations based on biotransformation rules. These rules are based on reactions found in the UM-BBD database. This collection references those rules.

The “National Long-term Surveillance of Swiss Rivers” (NADUF) program was initiated in 1972 as a cooperative project between three institutes: Federal Office for the Environment (FOEN) Swiss Federal Institute of Aquatic Science and Technology (EAWAG) Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) (since 2003) The following institutes participated later: Amt für Umwelt und Energie des Kantons Basel-Stadt (AUE) Federal Institute of Metrology (METAS) With the NADUF program, the chemical-physical state of Swiss rivers as well as intermediate-term and long-term changes in concentration are observed. Furthermore, it provides data for scientific studies on biological, chemical and physical processes in river water. The NADUF network serves as a basic data and sampling facility to evaluate the effectiveness of water protection measures and for various scientific projects.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) contains information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. The goal of the UM-BBD is to provide information on microbial enzyme-catalyzed reactions that are important for biotechnology. This collection refers to enzyme information.

This is the collection associated with list S82 | EAWAGPMT | PMT Suspect List from Eawag on the NORMAN Suspect List Exchange.

https://www.norman-network.com/nds/SLE/

A PMT suspect screening list containing >1100 entries from Kiefer et al (2021), DOI: 10.1016/j.watres.2021.116994

This dataset is about: EAWAG Profiling Ion Analyser Data, Lake Rot, Switzerland (ROT20100914).

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) contains information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. The goal of the UM-BBD is to provide information on microbial enzyme-catalyzed reactions that are important for biotechnology. This collection refers to reaction information.

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

Interactive annotation tool that allows to perform labelling procedure of both time series and 2D data.

This MassBank record with Accession MSBNK-Eawag-EA009613 contains the MS2 mass spectrum of Prochloraz with the InChIkey TVLSRXXIMLFWEO-UHFFFAOYSA-N.

The dreissenid quagga Dreissena bugensis and zebra D. polymorpha mussels are invasive freshwater mussels in Europe and North America. These species strongly impact aquatic ecosystems, such as the food web through their high abundance, filtration rate. They spread quickly within and between waterbodies, and have the ability to colonize various substrates and depths. The zebra mussel invaded and established in Swiss lakes in the 1960s, whereas the quagga mussel was not detected until 2014. We collected all available data from cantonal as well as local authorities and other institutions to describe the colonization pattern of quagga mussels in Switzerland. We also collected data regarding the distribution of larval stages of the mussels, the so-called veliger larvae. We observed that in lakes colonized by the quagga mussel, veligers are present the whole year round, whereas they are absent in winter in lakes with only zebra mussels. Additionally, we present detailed information about the invasion and colonization pattern of quagga mussels in Lake Constance. Quagga mussels colonized the lakeshore within a few years (~ 2016-2018) and outcompeted zebra mussels, and have reached densities > 5000 ind. m-2 in the littoral zone, even at 80 m densities above 1000 ind. m-2 were found at some locations. At the end of the article, we discussed possibilities on how the spread of quagga mussels within and among northern perialpine lakes should be monitored and prevented in the future.

This MassBank record with Accession MSBNK-Eawag-EA273958 contains the MS2 mass spectrum of Climbazol with the InChIkey OWEGWHBOCFMBLP-UHFFFAOYSA-N.

This MassBank record with Accession MSBNK-Eawag-EA277003 contains the MS2 mass spectrum of Aspartame with the InChIkey IAOZJIPTCAWIRG-QWRGUYRKSA-N.

This MassBank record with Accession MSBNK-Eawag-EA275655 contains the MS2 mass spectrum of Acesulfame with the InChIkey YGCFIWIQZPHFLU-UHFFFAOYSA-N.

This MassBank record with Accession MSBNK-Eawag-EA257907 contains the MS2 mass spectrum of Cyclophosphamide with the InChIkey CMSMOCZEIVJLDB-UHFFFAOYSA-N.

This MassBank record with Accession MSBNK-Eawag-EQ01130707 contains the MS2 mass spectrum of Rotenone with the InChIkey JUVIOZPCNVVQFO-UHFFFAOYSA-N.

This MassBank record with Accession MSBNK-Eawag-EA271208 contains the MS2 mass spectrum of Pyrimethanil with the InChIkey ZLIBICFPKPWGIZ-UHFFFAOYSA-N.