Stefan Kunz

Bundesweite Entwicklung der Grundwasserstände seit 1991: Langzeittrends, Variabilität und Auswirkungen der jüngsten Trockenphase

Maria Wetzel, Stefan Broda, Mariana Gomez, Markus Zaepke et autres

Bundesweite Entwicklung der Grundwasserstände seit 1991: Langzeittrends, Variabilität und Auswirkungen der jüngsten Trockenphase

Maria Wetzel, Stefan Broda, Mariana Gomez, Markus Zaepke et autres

Zusammenfassung Die Studie untersucht die Entwicklung der Grundwasserstände in Deutschland anhand von 5844 Messstellen sowie deren Reaktionsdynamik gegenüber klimatologischen Antrieben im Referenzzeitraum 1991–2020. Zusätzlich wird die Entwicklung seit 2018 im Kontext der außergewöhnlichen Dürreperiode betrachtet, um die Reaktionsfähigkeit und Vulnerabilität der Grundwassersysteme …

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27 Years of Freshwater Fish and Invertebrate Assemblage Data in the United States

Michael B. Mahon, Devin K. Jones, Ryan A. Hill, Terry N. Brown et autres

ABSTRACT Motivation United States federal biomonitoring programs assess the status of freshwater ecosystems using standardised sampling protocols. While most data from these efforts are publicly available, new users face barriers in applying these data appropriately. Here, we introduce unified assemblage databases for …

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Consistency of stream insect trait responses to instream pesticide exposure across five U.S. regions

Stefan Kunz, Ian R. Waite, Patrick W. Moran, Lisa H. Nowell et autres

Trait-environment relationships are a promising tool to understand how environmental factors shape freshwater communities, particularly on large scales. However, inconsistent responses of the community trait composition to environmental gradients have been reported for freshwater invertebrates. One reason for these discrepancies may be …

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Validation strategies for deep learning-based groundwater level time series prediction using exogenous meteorological input features

Fabienne Doll, Tanja Liesch, Maria Wetzel, Stefan Kunz et autres

Abstract. Due to the growing reliance on machine learning (ML) approaches for predicting groundwater levels (GWL), it is important to examine the methods used for performance estimation. A suitable performance estimation method provides the most accurate estimate of the accuracy the model …

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Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany

Stefan Kunz, Alexander Schulz, Maria Wetzel, Maximilian Nölscher et autres

Reliable predictions of groundwater levels are crucial for sustainable groundwater resource management, which needs to balance diverse water needs and to address potential ecological consequences of groundwater depletion. Machine learning (ML) approaches for time series forecasting have shown promising accuracy for groundwater …

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Multitemporal and Multiscale Feature Attribution Methods to Understand the Impact of Climatic and Land Use Features on the Prediction of Groundwater Levels

Michael Engel, Stefan Kunz, Maria Wetzel, Marco Körner

Groundwater is a critical resource for drinking water supply, agriculture, and ecosystems in general. In regions facing water scarcity, such as Brandenburg (Germany), effective groundwater management is essential. This requires accurate assessments of groundwater dynamics, which data-driven models can deliver through efficient …

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Deep Learning Models for Seasonal Groundwater Level Prediction

Stefan Kunz, Maria Wetzel, Michael Engel, Stefan Broda

The development of purely data-driven approaches for groundwater level prediction is crucial for sustainable groundwater management, offering the ability to predict groundwater levels across numerous monitoring wells and large geographical regions. Especially in arid regions, groundwater resources are under pressure, as seen …

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Predicting Decadal Groundwater Levels in Brandenburg: Deep Learning Approaches for Sustainable Management

Stefan Broda, Stefan Kunz, Maria Wetzel, Lena Katharina Schmidt et autres

The federal state of Brandenburg is characterized by over 3,000 lakes and hundreds of kilometres of rivers and thus is one of Germany's most water-rich regions, but also ranks among the country's driest states in terms of precipitation. Climate change exacerbates this …

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Reply on RC1

Stefan Kunz

Abstract. Reliable predictions of groundwater levels are crucial for a sustainable groundwater resource management, which needs to balance diverse water needs and to address potential ecological consequences of groundwater depletion. Machine Learning (ML) approaches for time series prediction, in particular, have shown …

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Reply on RC2

Stefan Kunz

Abstract. Reliable predictions of groundwater levels are crucial for a sustainable groundwater resource management, which needs to balance diverse water needs and to address potential ecological consequences of groundwater depletion. Machine Learning (ML) approaches for time series prediction, in particular, have shown …

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Reply on RC3

Stefan Kunz

Abstract. Reliable predictions of groundwater levels are crucial for a sustainable groundwater resource management, which needs to balance diverse water needs and to address potential ecological consequences of groundwater depletion. Machine Learning (ML) approaches for time series prediction, in particular, have shown …

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