Articles | Volume 29, issue 17
https://doi.org/10.5194/hess-29-4133-2025
© 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-29-4133-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: Image processing for continuous river turbidity monitoring – full-scale tests and potential applications
Domenico Miglino
CORRESPONDING AUTHOR
Dipartimento di Ingegneria Civile, Edile e Ambientale (DICEA), Università degli Studi di Napoli Federico II, Naples, 80125, Italy
Seifeddine Jomaa
Department of Aquatic Ecosystem Analysis and Management, Helmholtz Centre for Environmental Research – UFZ, Magdeburg, 39114, Germany
Michael Rode
Department of Aquatic Ecosystem Analysis and Management, Helmholtz Centre for Environmental Research – UFZ, Magdeburg, 39114, Germany
Institute of Environmental Science and Geography, University of Potsdam, Potsdam-Golm, 14476, Germany
Khim Cathleen Saddi
Dipartimento di Ingegneria Civile, Edile e Ambientale (DICEA), Università degli Studi di Napoli Federico II, Naples, 80125, Italy
SDC, Istituto Scuola Superiore Pavia (IUSS Pavia), Pavia, 27100, Italy
Department of Civil Engineering and Architecture, Ateneo de Naga University, Naga, 4400, Philippines
Francesco Isgrò
Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione (DIETI), Università degli Studi di Napoli Federico II, Naples, 80125, Italy
Salvatore Manfreda
Dipartimento di Ingegneria Civile, Edile e Ambientale (DICEA), Università degli Studi di Napoli Federico II, Naples, 80125, Italy
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Earth Syst. Sci. Data, 17, 3411–3430, https://doi.org/10.5194/essd-17-3411-2025, https://doi.org/10.5194/essd-17-3411-2025, 2025
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Many waterbodies undergo nutrient decline, called oligotrophication, globally, but a comprehensive dataset to understand ecosystem responses is lacking. The OLIGOTREND database comprises multi-decadal chlorophyll a and nutrient time series from rivers, lakes, and estuaries with 4.3 million observations from 1894 unique measurement locations. The database provides empirical evidence for oligotrophication responses with a spatial and temporal coverage that exceeds previous efforts.
Jingshui Huang, Dietrich Borchardt, and Michael Rode
EGUsphere, https://doi.org/10.5194/egusphere-2025-656, https://doi.org/10.5194/egusphere-2025-656, 2025
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Climate change is increasing low flows, yet how streams respond remains poorly understood. Using sensors in a German stream during the extreme 2018 drought, we found hotter water, more algae, and lower oxygen and nitrate levels. Daily oxygen swings intensified, and algae on the riverbed boosted gross primary productivity. Nitrate removal got more efficient. These changes highlight risks to water quality and ecosystems as droughts worsen, aiding efforts to protect rivers in a warming world.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
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The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
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We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
Matthias Koschorreck, Norbert Kamjunke, Uta Koedel, Michael Rode, Claudia Schuetze, and Ingeborg Bussmann
Biogeosciences, 21, 1613–1628, https://doi.org/10.5194/bg-21-1613-2024, https://doi.org/10.5194/bg-21-1613-2024, 2024
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We measured the emission of carbon dioxide (CO2) and methane (CH4) from different sites at the river Elbe in Germany over 3 days to find out what is more important for quantification: small-scale spatial variability or diurnal temporal variability. We found that CO2 emissions were very different between day and night, while CH4 emissions were more different between sites. Dried out river sediments contributed to CO2 emissions, while the side areas of the river were important CH4 sources.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
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Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Michael Rode, Jörg Tittel, Frido Reinstorf, Michael Schubert, Kay Knöller, Benjamin Gilfedder, Florian Merensky-Pöhlein, and Andreas Musolff
Hydrol. Earth Syst. Sci., 27, 1261–1277, https://doi.org/10.5194/hess-27-1261-2023, https://doi.org/10.5194/hess-27-1261-2023, 2023
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Agricultural catchments show elevated phosphorus (P) concentrations during summer low flow. In an agricultural stream, we found that phosphorus in groundwater was a major source of stream water phosphorus during low flow, and stream sediments derived from farmland are unlikely to have increased stream phosphorus concentrations during low water. We found no evidence that riparian wetlands contributed to soluble reactive (SR) P loads. Agricultural phosphorus was largely buffered in the soil zone.
Carolin Winter, Tam V. Nguyen, Andreas Musolff, Stefanie R. Lutz, Michael Rode, Rohini Kumar, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 27, 303–318, https://doi.org/10.5194/hess-27-303-2023, https://doi.org/10.5194/hess-27-303-2023, 2023
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The increasing frequency of severe and prolonged droughts threatens our freshwater resources. While we understand drought impacts on water quantity, its effects on water quality remain largely unknown. Here, we studied the impact of the unprecedented 2018–2019 drought in Central Europe on nitrate export in a heterogeneous mesoscale catchment in Germany. We show that severe drought can reduce a catchment's capacity to retain nitrogen, intensifying the internal pollution and export of nitrate.
Jingshui Huang, Dietrich Borchardt, and Michael Rode
Hydrol. Earth Syst. Sci., 26, 5817–5833, https://doi.org/10.5194/hess-26-5817-2022, https://doi.org/10.5194/hess-26-5817-2022, 2022
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In this study, we set up a water quality model using a 5-year paired high-frequency water quality dataset from a large agricultural stream. The simulations were compared with the 15 min interval measurements and showed very good fits. Based on these, we quantified the N uptake pathway rates and efficiencies at daily, seasonal, and yearly scales. This study offers an overarching understanding of N processing in large agricultural streams across different temporal scales.
Robert Ljubičić, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Peña-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, and Salvatore Manfreda
Hydrol. Earth Syst. Sci., 25, 5105–5132, https://doi.org/10.5194/hess-25-5105-2021, https://doi.org/10.5194/hess-25-5105-2021, 2021
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The rise of new technologies such as drones (unmanned aerial systems – UASs) has allowed widespread use of image velocimetry techniques in place of more traditional, usually slower, methods during hydrometric campaigns. In order to minimize the velocity estimation errors, one must stabilise the acquired videos. In this research, we compare the performance of different UAS video stabilisation tools and provide guidelines for their use in videos with different flight and ground conditions.
Salvatore Manfreda, Domenico Miglino, and Cinzia Albertini
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In this work, we introduce a new theoretically derived probability distribution of the outflows of in-line detention dams. The method may be used to evaluate the impact of detention dams on flood occurrences and attenuation of floods. This may help and support risk management planning and design.
Alonso Pizarro, Silvano F. Dal Sasso, Matthew T. Perks, and Salvatore Manfreda
Hydrol. Earth Syst. Sci., 24, 5173–5185, https://doi.org/10.5194/hess-24-5173-2020, https://doi.org/10.5194/hess-24-5173-2020, 2020
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An innovative approach is presented to optimise image-velocimetry performances for surface flow velocity estimates (and thus remotely sensed river discharges). Synthetic images were generated under different tracer characteristics using a numerical approach. Based on the results, the Seeding Distribution Index was introduced as a descriptor of the optimal portion of the video to analyse. A field case study was considered as a proof of concept of the proposed framework showing error reductions.
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Short summary
Turbidity is a key factor for water quality monitoring. Here, an image-based procedure is tested in a full-scale river monitoring experiment using digital cameras. This approach can enhance our understanding of the real-time status of waterbodies, overcoming the spatial and temporal resolution limitations of existing methods. It also facilitates early-warning systems, advances water research through increased data availability and reduces operating costs.
Turbidity is a key factor for water quality monitoring. Here, an image-based procedure is tested...