Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-5131-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-5131-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Neural networks in catchment hydrology: a comparative study of different algorithms in an ensemble of ungauged basins in Germany
Max Weißenborn
CORRESPONDING AUTHOR
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
Lutz Breuer
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Senckenbergstraße 3, 35392 Giessen, Germany
Tobias Houska
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
Institute of Soil Science and Site Ecology, TU Dresden, Dresden, Germany
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Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 5005–5029, https://doi.org/10.5194/hess-29-5005-2025, https://doi.org/10.5194/hess-29-5005-2025, 2025
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Three process-based and four data-driven hydrological models are compared using different training data. We found that process-based models perform better with small datasets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Elizabeth Gachibu Wangari, Ricky Mwangada Mwanake, Tobias Houska, David Kraus, Gretchen Maria Gettel, Ralf Kiese, Lutz Breuer, and Klaus Butterbach-Bahl
Biogeosciences, 20, 5029–5067, https://doi.org/10.5194/bg-20-5029-2023, https://doi.org/10.5194/bg-20-5029-2023, 2023
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Agricultural landscapes act as sinks or sources of the greenhouse gases (GHGs) CO2, CH4, or N2O. Various physicochemical and biological processes control the fluxes of these GHGs between ecosystems and the atmosphere. Therefore, fluxes depend on environmental conditions such as soil moisture, soil temperature, or soil parameters, which result in large spatial and temporal variations of GHG fluxes. Here, we describe an example of how this variation may be studied and analyzed.
Ricky Mwangada Mwanake, Gretchen Maria Gettel, Elizabeth Gachibu Wangari, Clarissa Glaser, Tobias Houska, Lutz Breuer, Klaus Butterbach-Bahl, and Ralf Kiese
Biogeosciences, 20, 3395–3422, https://doi.org/10.5194/bg-20-3395-2023, https://doi.org/10.5194/bg-20-3395-2023, 2023
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Despite occupying <1 %; of the globe, streams are significant sources of greenhouse gas (GHG) emissions. In this study, we determined anthropogenic effects on GHG emissions from streams. We found that anthropogenic-influenced streams had up to 20 times more annual GHG emissions than natural ones and were also responsible for seasonal peaks. Anthropogenic influences also altered declining GHG flux trends with stream size, with potential impacts on stream-size-based spatial upscaling techniques.
Jaqueline Stenfert Kroese, John N. Quinton, Suzanne R. Jacobs, Lutz Breuer, and Mariana C. Rufino
SOIL, 7, 53–70, https://doi.org/10.5194/soil-7-53-2021, https://doi.org/10.5194/soil-7-53-2021, 2021
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Particulate macronutrient concentrations were up to 3-fold higher in a natural forest catchment compared to fertilized agricultural catchments. Although the particulate macronutrient concentrations were lower in the smallholder agriculture catchment, because of higher sediment loads from that catchment, the total particulate macronutrient loads were higher. Land management practices should be focused on agricultural land to reduce the loss of soil carbon and nutrients to the stream.
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Short summary
Our study compares neural network models for predicting discharge in ungauged basins. We evaluated convolutional neural networks (CNNs), long short-term memory (LSTM) and gated recurrent units (GRUs) using 28 years of weather data. CNNs showed the best accuracy, while GRUs were faster and nearly as accurate. Adding static features improved all models. The research enhances flood forecasting and water management in regions lacking direct measurements, offering efficient and accurate predictive tools.
Our study compares neural network models for predicting discharge in ungauged basins. We...