Articles | Volume 19, issue 1
https://doi.org/10.5194/hess-19-485-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-19-485-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Jan 2015
Research article |
| 26 Jan 2015
Precipitation variability within an urban monitoring network via microcanonical cascade generators
P. Licznar
Faculty of Environmental Engineering, Wroclaw University of Technology, Wrocław, Poland
C. De Michele
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
W. Adamowski
Institute of Environmental Engineering, John Paul II Catholic University of Lublin, Stalowa Wola, Poland
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20 citations as recorded by crossref.
- A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields M. Schleiss 10.5194/hess-24-3699-2020
- Multifractal Properties of Temporal Rainfall Series in Peninsular Malaysia S. Mariam Saad & N. Ismail 10.1088/1755-1315/616/1/012014
- Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany B. Shehu et al. 10.5194/hess-27-1109-2023
- 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
- The spatiotemporal variations of global rainfall erosivity and erosive rainfall event based on half-hourly satellite rainfall data Q. Yang et al. 10.1016/j.catena.2025.108831
- Precipitation Type Specific Radar Reflectivity-rain Rate Relationships for Warsaw, Poland P. Licznar & W. Krajewski 10.1515/acgeo-2016-0071
- Future Projection of Extreme Rainfall for Flood Management due to Climate Change in an Urban Area S. Halder & U. Saha 10.1061/JSWBAY.0000954
- Incorporating parameter dependencies into temporal downscaling of extreme rainfall using a random cascade approach N. McIntyre et al. 10.1016/j.jhydrol.2016.09.057
- 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
- Evaluating the reliability of stormwater treatment systems under various future climate conditions K. Zhang et al. 10.1016/j.jhydrol.2018.10.056
- Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation H. Müller-Thomy 10.5194/hess-24-169-2020
- Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach B. Szeląg et al. 10.5194/hess-24-595-2020
- 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
- Using Probable Maximum Precipitation to Bound the Disaggregation of Rainfall N. McIntyre & A. Bárdossy 10.3390/w9070496
- Development of cluster analysis methodology for identification of model rainfall hyetographs and its application at an urban precipitation field scale K. Mikołajewski et al. 10.1016/j.scitotenv.2022.154588
- Disaggregation of rainfall from daily to 1-hour scale through integrated MMRC-copula modelling P. Biswas & U. Saha 10.1016/j.jhydrol.2024.132338
- Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology H. Müller & U. Haberlandt 10.1016/j.jhydrol.2016.01.031
- Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries I. Pohle et al. 10.1016/j.jhydrol.2018.04.063
- Prioritizing neighborhoods for intervention to mitigate urban small disasters triggered by rainfall C. Canon-Barriga et al. 10.1080/1573062X.2022.2026981
- Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling S. Papalexiou 10.1029/2021WR031641
1 citations as recorded by crossref.
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