Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2791-2020
© 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-24-2791-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 May 2020
Research article |
| 29 May 2020
| 29 May 2020
Modelling rainfall with a Bartlett–Lewis process: new developments
Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
Li-Pen Wang
Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
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Cited
21 citations as recorded by crossref.
- A stochastic approach to simulate realistic continuous snow depth time series J. Park & D. Kim 10.1016/j.jhydrol.2022.128980
- Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data N. Ebers et al. 10.5194/nhess-24-2025-2024
- Coupling of satellite-derived precipitation products with Bartlett-Lewis model to estimate intensity-frequency-duration curves for remote areas M. Islam et al. 10.1016/j.jhydrol.2022.127743
- 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
- Can we estimate flood frequency with point-process spatial-temporal rainfall models? Y. Chen et al. 10.1016/j.jhydrol.2021.126667
- A semi-parametric hourly space–time weather generator R. Pidoto & U. Haberlandt 10.5194/hess-27-3957-2023
- Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling S. Papalexiou 10.1029/2021WR031641
- Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation D. Dao et al. 10.1007/s00477-021-02049-2
- Stochastic streamflow and dissolved silica dynamics with application to the worst-case long-run evaluation of water environment H. Yoshioka & Y. Yoshioka 10.1007/s11081-022-09743-2
- Bartlett–Lewis Model Calibrated with Satellite-Derived Precipitation Data to Estimate Daily Peak 15 Min Rainfall Intensity M. Islam et al. 10.3390/atmos14060985
- A Cox Process with State-Dependent Exponential Pulses to Model Rainfall N. Ramesh et al. 10.1007/s11269-021-03028-6
- 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
- Deriving rainfall IDF curves using modified Bartlett-Lewis rectangular pulses (BLRP) model for Babylon City, Iraq S. Al-Jalili et al. 10.1016/j.rineng.2024.103028
- 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
- Future multivariate weather generation by combining Bartlett-Lewis and vine copula models J. Van de Velde et al. 10.1080/02626667.2022.2144322
- NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process J. Diez-Sierra et al. 10.5194/gmd-16-5035-2023
- A simple scheme to adjust Poisson cluster rectangular pulse rainfall models for improved performance at sub-hourly timescales J. Park et al. 10.1016/j.jhydrol.2021.126296
- STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series D. De Luca & A. Petroselli 10.3390/hydrology8020076
- Investigation of rainfall disaggregation with flexible timescales based on point process models X. Qin & C. Dai 10.1016/j.jhydrol.2024.131101
- Intra-Storm Pattern Recognition through Fuzzy Clustering K. Vantas & E. Sidiropoulos 10.3390/hydrology8020057
- Generating Continuous Rainfall Time Series with High Temporal Resolution by Using a Stochastic Rainfall Generator with a Copula and Modified Huff Rainfall Curves D. Nguyen & S. Chen 10.3390/w14132123
21 citations as recorded by crossref.
- A stochastic approach to simulate realistic continuous snow depth time series J. Park & D. Kim 10.1016/j.jhydrol.2022.128980
- Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data N. Ebers et al. 10.5194/nhess-24-2025-2024
- Coupling of satellite-derived precipitation products with Bartlett-Lewis model to estimate intensity-frequency-duration curves for remote areas M. Islam et al. 10.1016/j.jhydrol.2022.127743
- 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
- Can we estimate flood frequency with point-process spatial-temporal rainfall models? Y. Chen et al. 10.1016/j.jhydrol.2021.126667
- A semi-parametric hourly space–time weather generator R. Pidoto & U. Haberlandt 10.5194/hess-27-3957-2023
- Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling S. Papalexiou 10.1029/2021WR031641
- Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation D. Dao et al. 10.1007/s00477-021-02049-2
- Stochastic streamflow and dissolved silica dynamics with application to the worst-case long-run evaluation of water environment H. Yoshioka & Y. Yoshioka 10.1007/s11081-022-09743-2
- Bartlett–Lewis Model Calibrated with Satellite-Derived Precipitation Data to Estimate Daily Peak 15 Min Rainfall Intensity M. Islam et al. 10.3390/atmos14060985
- A Cox Process with State-Dependent Exponential Pulses to Model Rainfall N. Ramesh et al. 10.1007/s11269-021-03028-6
- 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
- Deriving rainfall IDF curves using modified Bartlett-Lewis rectangular pulses (BLRP) model for Babylon City, Iraq S. Al-Jalili et al. 10.1016/j.rineng.2024.103028
- 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
- Future multivariate weather generation by combining Bartlett-Lewis and vine copula models J. Van de Velde et al. 10.1080/02626667.2022.2144322
- NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process J. Diez-Sierra et al. 10.5194/gmd-16-5035-2023
- A simple scheme to adjust Poisson cluster rectangular pulse rainfall models for improved performance at sub-hourly timescales J. Park et al. 10.1016/j.jhydrol.2021.126296
- STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series D. De Luca & A. Petroselli 10.3390/hydrology8020076
- Investigation of rainfall disaggregation with flexible timescales based on point process models X. Qin & C. Dai 10.1016/j.jhydrol.2024.131101
- Intra-Storm Pattern Recognition through Fuzzy Clustering K. Vantas & E. Sidiropoulos 10.3390/hydrology8020057
- Generating Continuous Rainfall Time Series with High Temporal Resolution by Using a Stochastic Rainfall Generator with a Copula and Modified Huff Rainfall Curves D. Nguyen & S. Chen 10.3390/w14132123
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Short summary
The randomised Bartlett–Lewis (RBL) model is widely used to synthesise rainfall time series with realistic statistical features. However, it tended to underestimate rainfall extremes at sub-hourly and hourly timescales. In this paper, we revisit the derivation of equations that represent rainfall properties and compare statistical estimation methods that impact model calibration. These changes effectively improved the RBL model's capacity to reproduce sub-hourly and hourly rainfall extremes.
The randomised Bartlett–Lewis (RBL) model is widely used to synthesise rainfall time series with...
