Articles | Volume 28, issue 19
https://doi.org/10.5194/hess-28-4427-2024
© Author(s) 2024. 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-28-4427-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
08 Oct 2024
Research article | Highlight paper |
| 08 Oct 2024
| 08 Oct 2024
Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept
Dept. of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland
Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Dept. of Earth and Planetary Science, University of California, Berkeley, CA 94720, USA
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Cited
22 citations as recorded by crossref.
- Hydrologic connectivity amplifies riverine N 2 O emission hot spots and hot moments across the contiguous United States M. Hu et al. https://doi.org/10.1073/pnas.2524113123
- AMAformer: Dynamic and accurate forecasting of water quality in aquaculture with adaptive multi-kernel decomposition and attention Y. Li et al. https://doi.org/10.1016/j.envsoft.2026.107023
- Enhanced identification of watershed hydrological extremes using an improved drought index incorporating hydrological non-stationarity X. Pang et al. https://doi.org/10.1016/j.jhydrol.2026.135910
- Flash flood forecasting in North East England through weak label-guided mixture of experts with multi-scale explainability J. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103402
- Impact of climate change on hydrological processes in data-scarce regions: A case study in Tianshan Mountain, Central Asia H. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103553
- Multi-scale coupled water–system dynamics under power-law heterogeneity S. Rao https://doi.org/10.1016/j.advwatres.2026.105353
- Data-driven estimation of the hydrologic response using generalized additive models Q. Duchemin et al. https://doi.org/10.5194/gmd-18-8663-2025
- Catchment hydrological response and transport are affected differently by precipitation intensity and antecedent wetness J. Knapp et al. https://doi.org/10.5194/hess-29-3673-2025
- Assessing Nonstationary Hydroclimatic Impacts on Streamflow in the Soan River Basin, Pakistan, Using Mann–Kendall Test and Artificial Neural Network Technique R. Din et al. https://doi.org/10.3390/hydrology13040106
- A runoff prediction method for arid regions integrating physics-guided signal extraction and temporally adaptive feature selection Z. Li et al. https://doi.org/10.1016/j.ejrh.2025.103034
- Rain-snow-ice dynamics substantially changed the hydrological regime in arid inland river basins S. Han et al. https://doi.org/10.1016/j.ejrh.2026.103615
- Tree planting and soil conservation measures have strongly attenuated storm runoff responses on the Chinese Loess Plateau S. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.134039
- Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
- Annual memory in the terrestrial water cycle W. Berghuijs et al. https://doi.org/10.5194/hess-29-1319-2025
- Climatic, topographic, and groundwater controls on runoff response to precipitation: evidence from a large-sample data set Z. Eslami et al. https://doi.org/10.5194/hess-29-5121-2025
- The Elusive Runoff Generation: Understanding Thresholds and Pathways in a Dry Sedimentary Plain M. Niborski et al. https://doi.org/10.1002/hyp.70399
- Controls on magnitude and timing of peak runoff response to rainfall across the continental US M. Li et al. https://doi.org/10.1088/1748-9326/ae49a2
- Enhanced Spatiotemporal Relationship-Guided Deep Learning for Water Quality Prediction R. Chen et al. https://doi.org/10.3390/w18020185
- Investigating the Dry–Wet Differentiation of the Yellow River Basin Driven by Climate Change and Anthropogenic Activities Q. Yu et al. https://doi.org/10.3390/rs18070974
- Quantifying controls on rapid and delayed runoff response in double-peak hydrographs using ensemble rainfall-runoff analysis (ERRA) H. Gao et al. https://doi.org/10.5194/hess-29-6529-2025
- Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al. https://doi.org/10.5194/hess-29-4761-2025
- From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025
22 citations as recorded by crossref.
- Hydrologic connectivity amplifies riverine N 2 O emission hot spots and hot moments across the contiguous United States M. Hu et al. https://doi.org/10.1073/pnas.2524113123
- AMAformer: Dynamic and accurate forecasting of water quality in aquaculture with adaptive multi-kernel decomposition and attention Y. Li et al. https://doi.org/10.1016/j.envsoft.2026.107023
- Enhanced identification of watershed hydrological extremes using an improved drought index incorporating hydrological non-stationarity X. Pang et al. https://doi.org/10.1016/j.jhydrol.2026.135910
- Flash flood forecasting in North East England through weak label-guided mixture of experts with multi-scale explainability J. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103402
- Impact of climate change on hydrological processes in data-scarce regions: A case study in Tianshan Mountain, Central Asia H. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103553
- Multi-scale coupled water–system dynamics under power-law heterogeneity S. Rao https://doi.org/10.1016/j.advwatres.2026.105353
- Data-driven estimation of the hydrologic response using generalized additive models Q. Duchemin et al. https://doi.org/10.5194/gmd-18-8663-2025
- Catchment hydrological response and transport are affected differently by precipitation intensity and antecedent wetness J. Knapp et al. https://doi.org/10.5194/hess-29-3673-2025
- Assessing Nonstationary Hydroclimatic Impacts on Streamflow in the Soan River Basin, Pakistan, Using Mann–Kendall Test and Artificial Neural Network Technique R. Din et al. https://doi.org/10.3390/hydrology13040106
- A runoff prediction method for arid regions integrating physics-guided signal extraction and temporally adaptive feature selection Z. Li et al. https://doi.org/10.1016/j.ejrh.2025.103034
- Rain-snow-ice dynamics substantially changed the hydrological regime in arid inland river basins S. Han et al. https://doi.org/10.1016/j.ejrh.2026.103615
- Tree planting and soil conservation measures have strongly attenuated storm runoff responses on the Chinese Loess Plateau S. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.134039
- Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
- Annual memory in the terrestrial water cycle W. Berghuijs et al. https://doi.org/10.5194/hess-29-1319-2025
- Climatic, topographic, and groundwater controls on runoff response to precipitation: evidence from a large-sample data set Z. Eslami et al. https://doi.org/10.5194/hess-29-5121-2025
- The Elusive Runoff Generation: Understanding Thresholds and Pathways in a Dry Sedimentary Plain M. Niborski et al. https://doi.org/10.1002/hyp.70399
- Controls on magnitude and timing of peak runoff response to rainfall across the continental US M. Li et al. https://doi.org/10.1088/1748-9326/ae49a2
- Enhanced Spatiotemporal Relationship-Guided Deep Learning for Water Quality Prediction R. Chen et al. https://doi.org/10.3390/w18020185
- Investigating the Dry–Wet Differentiation of the Yellow River Basin Driven by Climate Change and Anthropogenic Activities Q. Yu et al. https://doi.org/10.3390/rs18070974
- Quantifying controls on rapid and delayed runoff response in double-peak hydrographs using ensemble rainfall-runoff analysis (ERRA) H. Gao et al. https://doi.org/10.5194/hess-29-6529-2025
- Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al. https://doi.org/10.5194/hess-29-4761-2025
- From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025
Saved (final revised paper)
Latest update: 08 Jul 2026
Editorial statement
A rigorous analytical hydrological model for Ensemble Rainfall-Runoff Analysis (ERRA) of catchments. The paper gives the concepts, mathematical details and several proof-of-concepts. ERRA was designed as a tool for iterative and exploration of hydrological data, through trial and error with analyses of varying degrees of complexity. The broad geosciences community could benefit from this statistical approach. The discussion cites some good examples of applications possible in hydrology, but potential also in other related fields as well.
A rigorous analytical hydrological model for Ensemble Rainfall-Runoff Analysis (ERRA) of...
Short summary
Here, I present a new way to quantify how streamflow responds to rainfall across a range of timescales. This approach can estimate how different rainfall intensities affect streamflow. It can also quantify how runoff response to rainfall varies, depending on how wet the landscape already is before the rain falls. This may help us to understand processes and landscape properties that regulate streamflow and to assess the susceptibility of different landscapes to flooding.
Here, I present a new way to quantify how streamflow responds to rainfall across a range of...
