Articles | Volume 24, issue 1
https://doi.org/10.5194/hess-24-169-2020
© Author(s) 2020. 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-24-169-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2020
Research article |
| 14 Jan 2020
| 14 Jan 2020
Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation
Hannes Müller-Thomy
CORRESPONDING AUTHOR
Institute of Hydraulic Engineering and Water Resources Management,
Vienna University of Technology, Karlsplatz 13/222, 1040 Vienna, Austria
previously published under the name Hannes Müller
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18 citations as recorded by crossref.
- A probabilistic-deterministic approach for assessing climate change effects on infection risks downstream of sewage emissions from CSOs J. Derx et al. 10.1016/j.watres.2023.120746
- Applicability of statistical and deep-learning models for rainfall disaggregation at metropolitan stations in India D. Bhattacharyya et al. 10.1016/j.ejrh.2023.101616
- Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data N. Ebers et al. 10.5194/nhess-24-2025-2024
- Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites R. Pidoto et al. 10.5194/esurf-10-851-2022
- Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models: case studies from Germany and South Korea I. Vorobevskii et al. 10.5194/hess-28-391-2024
- Improvement of extreme rainfall characteristics for disaggregation of rainfall using MMRC with machine learning based DBSCAN clustering algorithm D. Chowdhury & U. Saha 10.1007/s12145-024-01309-3
- Introduction of k-means clustering into random cascade model for disaggregation of rainfall from daily to 1-hour resolution with improved preservation of extreme rainfall P. Deka & U. Saha 10.1016/j.jhydrol.2023.129478
- Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling S. Papalexiou 10.1029/2021WR031641
- Estimation of future extreme rainfall in Barcelona (Spain) under monofractal hypothesis R. Monjo et al. 10.1002/joc.8072
- Estimation of the G2P Design Storm from a Rainfall Convectivity Index R. Balbastre-Soldevila et al. 10.3390/w13141943
- SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models R. Hales et al. 10.3390/hydrology9070113
- Ordinal network-based affine invariant Riemannian measure and its expansion: powerful similarity measure tools for complex systems Z. Wang et al. 10.1007/s11071-022-07991-6
- Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia M. Alexopoulos et al. 10.5194/hess-27-2559-2023
- Multifractal Properties of Temporal Rainfall Series in Peninsular Malaysia S. Mariam Saad & N. Ismail 10.1088/1755-1315/616/1/012014
- Assessing the impact of climate change on Combined Sewer Overflows based on small time step future rainfall timeseries and long-term continuous sewer network modelling F. Gogien et al. 10.1016/j.watres.2022.119504
- Forecasting green roof detention performance by temporal downscaling of precipitation time-series projections V. Pons et al. 10.5194/hess-26-2855-2022
- Sub‐daily rainfall intermittency: Is it really stochastic? A test at two Australian locations D. Dunkerley 10.1002/joc.8344
- Review: Fractal Geometry in Precipitation R. Monjo & O. Meseguer-Ruiz 10.3390/atmos15010135
Latest update: 20 Nov 2024
Short summary
Simulation of highly dynamic floods requires high-resolution rainfall time series. Observed time series of that kind are often too short; rainfall generation is the only solution. The applied rainfall generator tends to underestimate the process memory of the rainfall. By modifications of the rainfall generator and a subsequent optimisation method the process memory is improved significantly. Flood simulations are expected to be more trustable using the rainfall time series generated like this.
Simulation of highly dynamic floods requires high-resolution rainfall time series. Observed time...
