Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4425-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-22-4425-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
22 Aug 2018
Research article |
| 22 Aug 2018
How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps
Swiss Federal Research Institute WSL, Birmensdorf, 8903,
Switzerland
Department of Geography, University of Zurich, Zurich, 8057,
Switzerland
Massimiliano Zappa
Swiss Federal Research Institute WSL, Birmensdorf, 8903,
Switzerland
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- Deep learning models to predict flood events in fast-flowing watersheds M. Luppichini et al. 10.1016/j.scitotenv.2021.151885
- A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations R. Schneider et al. 10.1016/j.jhydrol.2022.128339
- Developing a comprehensive methodology for evaluating economic impacts of floods in Canada, Mexico and the United States Z. Adeel et al. 10.1016/j.ijdrr.2020.101861
- Upscaling of Denitrification Rates from Point to Catchment Scales for Modeling of Nitrate Transport and Retention H. Kim et al. 10.1021/acs.est.1c04593
- Parameter allocation approach for runoff simulation in an arid catchment using the KINEROS2 hydrological model D. Ghonchepour et al. 10.1111/nrm.12364
- CleverRiver: an open source and free Google Colab toolkit for deep-learning river-flow models M. Luppichini et al. 10.1007/s12145-022-00903-7
- Techniques to evaluate the modifier process of National Weather Service flood forecasts Z. Zhu et al. 10.1016/j.hydroa.2020.100073
- Ensemble flood forecasting considering dominant runoff processes – Part 1: Set-up and application to nested basins (Emme, Switzerland) M. Antonetti et al. 10.5194/nhess-19-19-2019
- Modeling streamflow variability at the regional scale: (2) Development of a bespoke distributed conceptual model F. Fenicia et al. 10.1016/j.jhydrol.2021.127286
- Enhancing Water Temperature Prediction in Stratified Reservoirs: A Process-Guided Deep Learning Approach S. Kim & S. Chung 10.3390/w15173096
- OpenForecast v2: Development and Benchmarking of the First National-Scale Operational Runoff Forecasting System in Russia G. Ayzel 10.3390/hydrology8010003
- IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling B. Mohammadi et al. 10.1038/s41598-022-16215-1
- Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. 10.5194/hess-24-1319-2020
- Finding behavioral parameterization for a 1-D water balance model by multi-criteria evaluation M. Casper et al. 10.2478/johh-2019-0005
- Mapping Small-Scale Irrigation Areas Using Expert Decision Rules and the Random Forest Classifier in Northern Ethiopia A. Mohammedshum et al. 10.3390/rs15245647
- A Hybrid Model for Fast and Probabilistic Urban Pluvial Flood Prediction X. Li & P. Willems 10.1029/2019WR025128
- A parameter allocation approach for flow simulation using the WetSpa‐Python model A. Bahremand et al. 10.1002/hyp.13992
- Machine learning models to complete rainfall time series databases affected by missing or anomalous data A. Lupi et al. 10.1007/s12145-023-01122-4
- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
- A Regularization Approach to Improve the Sequential Calibration of a Semidistributed Hydrological Model A. de Lavenne et al. 10.1029/2018WR024266
20 citations as recorded by crossref.
- Deep learning models to predict flood events in fast-flowing watersheds M. Luppichini et al. 10.1016/j.scitotenv.2021.151885
- A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations R. Schneider et al. 10.1016/j.jhydrol.2022.128339
- Developing a comprehensive methodology for evaluating economic impacts of floods in Canada, Mexico and the United States Z. Adeel et al. 10.1016/j.ijdrr.2020.101861
- Upscaling of Denitrification Rates from Point to Catchment Scales for Modeling of Nitrate Transport and Retention H. Kim et al. 10.1021/acs.est.1c04593
- Parameter allocation approach for runoff simulation in an arid catchment using the KINEROS2 hydrological model D. Ghonchepour et al. 10.1111/nrm.12364
- CleverRiver: an open source and free Google Colab toolkit for deep-learning river-flow models M. Luppichini et al. 10.1007/s12145-022-00903-7
- Techniques to evaluate the modifier process of National Weather Service flood forecasts Z. Zhu et al. 10.1016/j.hydroa.2020.100073
- Ensemble flood forecasting considering dominant runoff processes – Part 1: Set-up and application to nested basins (Emme, Switzerland) M. Antonetti et al. 10.5194/nhess-19-19-2019
- Modeling streamflow variability at the regional scale: (2) Development of a bespoke distributed conceptual model F. Fenicia et al. 10.1016/j.jhydrol.2021.127286
- Enhancing Water Temperature Prediction in Stratified Reservoirs: A Process-Guided Deep Learning Approach S. Kim & S. Chung 10.3390/w15173096
- OpenForecast v2: Development and Benchmarking of the First National-Scale Operational Runoff Forecasting System in Russia G. Ayzel 10.3390/hydrology8010003
- IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling B. Mohammadi et al. 10.1038/s41598-022-16215-1
- Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. 10.5194/hess-24-1319-2020
- Finding behavioral parameterization for a 1-D water balance model by multi-criteria evaluation M. Casper et al. 10.2478/johh-2019-0005
- Mapping Small-Scale Irrigation Areas Using Expert Decision Rules and the Random Forest Classifier in Northern Ethiopia A. Mohammedshum et al. 10.3390/rs15245647
- A Hybrid Model for Fast and Probabilistic Urban Pluvial Flood Prediction X. Li & P. Willems 10.1029/2019WR025128
- A parameter allocation approach for flow simulation using the WetSpa‐Python model A. Bahremand et al. 10.1002/hyp.13992
- Machine learning models to complete rainfall time series databases affected by missing or anomalous data A. Lupi et al. 10.1007/s12145-023-01122-4
- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
- A Regularization Approach to Improve the Sequential Calibration of a Semidistributed Hydrological Model A. de Lavenne et al. 10.1029/2018WR024266
Latest update: 20 Nov 2024
Short summary
We developed 60 modelling chain combinations based on either experimentalists' (bottom-up) or modellers' (top-down) thinking and forced them with data of increasing accuracy. Results showed that the differences in performance arising from the forcing data were due to compensation effects. We also found that modellers' and experimentalists' concept of
model realismdiffers, and the level of detail a model should have to reproduce the processes expected must be agreed in advance.
We developed 60 modelling chain combinations based on either experimentalists' (bottom-up) or...
