Articles | Volume 22, issue 10
Hydrol. Earth Syst. Sci., 22, 5259–5280, 2018
https://doi.org/10.5194/hess-22-5259-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 22, 5259–5280, 2018
https://doi.org/10.5194/hess-22-5259-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article 15 Oct 2018
Research article | 15 Oct 2018
Rainfall disaggregation for hydrological modeling: is there a need for spatial consistence?
Hannes Müller-Thomy et al.
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Cited
9 citations as recorded by crossref.
- Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach B. Winter et al. 10.5194/nhess-20-1689-2020
- Integrating an hourly weather generator with an hourly rainfall SWAT model for climate change impact assessment in the Ru River Basin, China X. Yang et al. 10.1016/j.atmosres.2020.105062
- Propagation of uncertainties in interpolated rainfields to runoff errors Y. Gyasi-Agyei 10.1080/02626667.2019.1593989
- Daily vs. hourly simulation for estimating future flood peaks in mesoscale catchments M. Beylich et al. 10.2166/nh.2021.152
- Does the complexity in temporal precipitation disaggregation matter for a lumped hydrological model? H. Müller-Thomy & A. Sikorska-Senoner 10.1080/02626667.2019.1638926
- Modelling the interplay of future changes and wastewater management measures on the microbiological river water quality considering safe drinking water production K. Demeter et al. 10.1016/j.scitotenv.2020.144278
- Can we estimate flood frequency with point-process spatial-temporal rainfall models? Y. Chen et al. 10.1016/j.jhydrol.2021.126667
- Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation H. Müller-Thomy 10.5194/hess-24-169-2020
- Modelling forest snow processes with a new version of WaSiM K. Förster et al. 10.1080/02626667.2018.1518626
8 citations as recorded by crossref.
- Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach B. Winter et al. 10.5194/nhess-20-1689-2020
- Integrating an hourly weather generator with an hourly rainfall SWAT model for climate change impact assessment in the Ru River Basin, China X. Yang et al. 10.1016/j.atmosres.2020.105062
- Propagation of uncertainties in interpolated rainfields to runoff errors Y. Gyasi-Agyei 10.1080/02626667.2019.1593989
- Daily vs. hourly simulation for estimating future flood peaks in mesoscale catchments M. Beylich et al. 10.2166/nh.2021.152
- Does the complexity in temporal precipitation disaggregation matter for a lumped hydrological model? H. Müller-Thomy & A. Sikorska-Senoner 10.1080/02626667.2019.1638926
- Modelling the interplay of future changes and wastewater management measures on the microbiological river water quality considering safe drinking water production K. Demeter et al. 10.1016/j.scitotenv.2020.144278
- Can we estimate flood frequency with point-process spatial-temporal rainfall models? Y. Chen et al. 10.1016/j.jhydrol.2021.126667
- Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation H. Müller-Thomy 10.5194/hess-24-169-2020
1 citations as recorded by crossref.
Latest update: 21 Sep 2021
Short summary
Rainfall time series are disaggregated from daily to hourly values to be used for rainfall–runoff modeling of mesoscale catchments. Spatial rainfall consistency is implemented afterwards using simulated annealing. With the calibration process applied, observed runoff statistics (e.g., summer and winter peak flows) are represented well. However, rainfall datasets with under- or over-estimation of spatial consistency lead to similar results, so the need for a good representation can be questioned.
Rainfall time series are disaggregated from daily to hourly values to be used for...
