Articles | Volume 27, issue 6
https://doi.org/10.5194/hess-27-1325-2023
© Author(s) 2023. 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-27-1325-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Mar 2023
Research article |
| 28 Mar 2023
| 28 Mar 2023
Incorporating experimentally derived streamflow contributions into model parameterization to improve discharge prediction
Andreas Hartmann
CORRESPONDING AUTHOR
Chair of Hydrological Modeling and Water Resources, University of Freiburg, Freiburg, Germany
Institute of Groundwater Management, Technical University of Dresden, 01069 Dresden, Germany
Department of Civil Engineering, University of Bristol, Bristol, United Kingdom
Jean-Lionel Payeur-Poirier
Department of Hydrology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany
Luisa Hopp
Department of Hydrology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany
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The study examines how spatial soil moisture variability influences SVAT model calibration and the estimation of groundwater recharge in forest ecosystems. We show that model-inherent uncertainties affect predictions more strongly than soil moisture variability itself. Our results demonstrate that reliable groundwater recharge can be achieved using data from just six to seven profiles, providing practical guidance for more efficient field monitoring and model calibration.
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Subsurface stormflow (SSF) is one of the least studied and therefore least understood runoff generation processes because detecting and quantifying SSF is extremely challenging. We present an ongoing concerted experimental effort to systematically investigate SSF across four catchments using a variety of methods covering different spatial scales. Centerpiece of this effort is the construction of 12 large trenches to capture and monitor SSF.
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To understand the impact of external factors on groundwater level modelling using a 1-D convolutional neural network (CNN) model, we train, validate, and tune individual CNN models for 505 wells distributed across Lower Saxony, Germany. We then evaluate the performance of these models against available geospatial and time series features. This study provides new insights into the relationship between these factors and the accuracy of groundwater modelling.
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Karstic recharge processes have mainly been explored using discharge analysis despite the high influence of the heterogeneous surface on hydrological processes. In this paper, we introduce an event-based method which allows for recharge estimation from soil moisture measurements. The method was tested at a karst catchment in Germany but can be applied to other karst areas with precipitation and soil moisture data available. It will allow for a better characterization of karst recharge processes.
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Analysis of karst spring recession is essential for management of groundwater. In karst, recession is dominated by slow and fast components; separating these components is by manual and subjective approaches. In our study, we tested the applicability of automated streamflow recession extraction procedures for a karst spring. Results showed that, by simple modification, streamflow extraction methods can identify slow and fast components: derived recession parameters are within reasonable ranges.
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We adapt the informal Kling–Gupta efficiency (KGE) with a gamma distribution to apply it as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS) method. Our adapted approach performs as well as the formal likelihood function for exploring posterior distributions of model parameters. The adapted KGE is superior to the formal likelihood function for calibrations combining multiple observations with different lengths, frequencies and units.
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This study presents a work to investigate the feasibility of using EC to predict the discharge in a typical karst catchment. We found that the spring discharge can be well predicted by EC in storms using LSTM (Long Short Term Memory) model, while the prediction has relatively large uncertainties in small recharge events. To establish a roust LSTM model for long-term discharge prediction from EC in ungauged catchments, the random or fixed-interval discharge monitoring strategy is recommended.
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Short summary
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Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP (The Inter-Sectoral Impact Model Intercomparison Project) groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-812, https://doi.org/10.5194/essd-2025-812, 2026
Preprint under review for ESSD
Short summary
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We developed the first global database of water from karst springs and cave drips that records different forms of oxygen and hydrogen, which naturally trace how rainwater moves through rocks. By gathering and checking thousands of measurements from around the globe and linking them with flow and rainfall data, the database provides a comprehensive view of water movement, allows scientists to compare regions, understand groundwater processes, and support sustainable water management worldwide.
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Subsurface stormflow (SSF) is a key runoff mechanism in upland environments. The analysis of SSF at two trenched hillslopes showed that SSF volume was controlled by total rainfall and initial wetness, with threshold behaviour observed at one site. Peakflow depended on rainfall amount, with initial wetness and rainfall intensity being important for small and large events, respectively. The rate at which SSF increased was linked to rainfall intensity and amount.
Thomas Fichtner, Yuly Juliana Aguilar Avila, Andreas Hartmann, Stefan Seeger, Martin Maier, and Stephan Raspe
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The study examines how spatial soil moisture variability influences SVAT model calibration and the estimation of groundwater recharge in forest ecosystems. We show that model-inherent uncertainties affect predictions more strongly than soil moisture variability itself. Our results demonstrate that reliable groundwater recharge can be achieved using data from just six to seven profiles, providing practical guidance for more efficient field monitoring and model calibration.
Theresa Blume, Peter Chifflard, Stefan Achleitner, Andreas Hartmann, Stefan Hergarten, Luisa Hopp, Bernhard Kohl, Florian Leese, Ilja van Meerveld, Christian Reinhardt-Imjela, and Markus Weiler
EGUsphere, https://doi.org/10.5194/egusphere-2025-4424, https://doi.org/10.5194/egusphere-2025-4424, 2025
Short summary
Short summary
Subsurface stormflow (SSF) is one of the least studied and therefore least understood runoff generation processes because detecting and quantifying SSF is extremely challenging. We present an ongoing concerted experimental effort to systematically investigate SSF across four catchments using a variety of methods covering different spatial scales. Centerpiece of this effort is the construction of 12 large trenches to capture and monitor SSF.
Markus Giese, Yvan Caballero, Andreas Hartmann, and Jean-Baptiste Charlier
Hydrol. Earth Syst. Sci., 29, 3037–3054, https://doi.org/10.5194/hess-29-3037-2025, https://doi.org/10.5194/hess-29-3037-2025, 2025
Short summary
Short summary
Karst springs respond quickly to environmental changes, making them crucial to understanding climate impacts on groundwater. This study analyses long-term trends in precipitation, temperature, and discharge from more than 50 springs across Europe. Results show that while historical discharge trends align with those of rivers, recent changes are driven by rising temperatures rather than precipitation. These findings highlight climate-driven shifts in groundwater recharge and storage processes.
Pia Ebeling, Andreas Musolff, Rohini Kumar, Andreas Hartmann, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 29, 2925–2950, https://doi.org/10.5194/hess-29-2925-2025, https://doi.org/10.5194/hess-29-2925-2025, 2025
Short summary
Short summary
Groundwater is a crucial resource at risk due to droughts. To understand drought effects on groundwater levels in Germany, we grouped 6626 wells into six regional and two national patterns. Weather explained half of the level variations with varied response times. Shallow groundwater responds fast and is more vulnerable to short droughts (a few months). Dampened deep heads buffer short droughts but suffer from long droughts and recoveries. Two nationwide trend patterns were linked to human water use.
Katharina Blaurock, Burkhard Beudert, and Luisa Hopp
Hydrol. Earth Syst. Sci., 29, 2377–2391, https://doi.org/10.5194/hess-29-2377-2025, https://doi.org/10.5194/hess-29-2377-2025, 2025
Short summary
Short summary
The release of carbon from landscapes into streams is one important component within the global carbon cycle. We measured the concentrations of dissolved organic carbon (DOC), one of the forms in which carbon can be present, in the streams of three nested forested subcatchments over 12 months. The export of DOC is closely linked to water flow processes within the subcatchments, but the interplay of soils, vegetation, topography, and microclimate results in distinct seasonal DOC release patterns.
Mariana Gomez, Maximilian Nölscher, Andreas Hartmann, and Stefan Broda
Hydrol. Earth Syst. Sci., 28, 4407–4425, https://doi.org/10.5194/hess-28-4407-2024, https://doi.org/10.5194/hess-28-4407-2024, 2024
Short summary
Short summary
To understand the impact of external factors on groundwater level modelling using a 1-D convolutional neural network (CNN) model, we train, validate, and tune individual CNN models for 505 wells distributed across Lower Saxony, Germany. We then evaluate the performance of these models against available geospatial and time series features. This study provides new insights into the relationship between these factors and the accuracy of groundwater modelling.
Romane Berthelin, Tunde Olarinoye, Michael Rinderer, Matías Mudarra, Dominic Demand, Mirjam Scheller, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 27, 385–400, https://doi.org/10.5194/hess-27-385-2023, https://doi.org/10.5194/hess-27-385-2023, 2023
Short summary
Short summary
Karstic recharge processes have mainly been explored using discharge analysis despite the high influence of the heterogeneous surface on hydrological processes. In this paper, we introduce an event-based method which allows for recharge estimation from soil moisture measurements. The method was tested at a karst catchment in Germany but can be applied to other karst areas with precipitation and soil moisture data available. It will allow for a better characterization of karst recharge processes.
Tunde Olarinoye, Tom Gleeson, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5431–5447, https://doi.org/10.5194/hess-26-5431-2022, https://doi.org/10.5194/hess-26-5431-2022, 2022
Short summary
Short summary
Analysis of karst spring recession is essential for management of groundwater. In karst, recession is dominated by slow and fast components; separating these components is by manual and subjective approaches. In our study, we tested the applicability of automated streamflow recession extraction procedures for a karst spring. Results showed that, by simple modification, streamflow extraction methods can identify slow and fast components: derived recession parameters are within reasonable ranges.
Yan Liu, Jaime Fernández-Ortega, Matías Mudarra, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5341–5355, https://doi.org/10.5194/hess-26-5341-2022, https://doi.org/10.5194/hess-26-5341-2022, 2022
Short summary
Short summary
We adapt the informal Kling–Gupta efficiency (KGE) with a gamma distribution to apply it as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS) method. Our adapted approach performs as well as the formal likelihood function for exploring posterior distributions of model parameters. The adapted KGE is superior to the formal likelihood function for calibrations combining multiple observations with different lengths, frequencies and units.
Yong Chang, Benjamin Mewes, and Andreas Hartmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-77, https://doi.org/10.5194/hess-2022-77, 2022
Preprint withdrawn
Short summary
Short summary
This study presents a work to investigate the feasibility of using EC to predict the discharge in a typical karst catchment. We found that the spring discharge can be well predicted by EC in storms using LSTM (Long Short Term Memory) model, while the prediction has relatively large uncertainties in small recharge events. To establish a roust LSTM model for long-term discharge prediction from EC in ungauged catchments, the random or fixed-interval discharge monitoring strategy is recommended.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Katharina Blaurock, Burkhard Beudert, Benjamin S. Gilfedder, Jan H. Fleckenstein, Stefan Peiffer, and Luisa Hopp
Hydrol. Earth Syst. Sci., 25, 5133–5151, https://doi.org/10.5194/hess-25-5133-2021, https://doi.org/10.5194/hess-25-5133-2021, 2021
Short summary
Short summary
Dissolved organic carbon (DOC) is an important part of the global carbon cycle with regards to carbon storage, greenhouse gas emissions and drinking water treatment. In this study, we compared DOC export of a small, forested catchment during precipitation events after dry and wet preconditions. We found that the DOC export from areas that are usually important for DOC export was inhibited after long drought periods.
Tesfalem Abraham, Yan Liu, Sirak Tekleab, and Andreas Hartmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-271, https://doi.org/10.5194/hess-2021-271, 2021
Preprint withdrawn
Short summary
Short summary
In this study we demonstrate the use of global data products for the regionalization of model parameters. We combine three steps of uncertainty quantification from the parameter sampling, best parameter sets identification, and spatial cross-validation. Our results show the best validation parameters provide the most robust regionalization results, and the uncertainties from the regionalization in the ungauged catchments are higher than those obtained from simulations in the gauged catchments.
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Short summary
We advance our understanding of including information derived from environmental tracers into hydrological modeling. We present a simple approach that integrates streamflow observations and tracer-derived streamflow contributions for model parameter estimation. We consider multiple observed streamflow components and their variation over time to quantify the impact of their inclusion for streamflow prediction at the catchment scale.
We advance our understanding of including information derived from environmental tracers into...
