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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29 citations as recorded by crossref.
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- A Cox Process with State-Dependent Exponential Pulses to Model Rainfall N. Ramesh et al. https://doi.org/10.1007/s11269-021-03028-6
- Deriving rainfall IDF curves using modified Bartlett-Lewis rectangular pulses (BLRP) model for Babylon City, Iraq S. Al-Jalili et al. https://doi.org/10.1016/j.rineng.2024.103028
- A long short-term memory model for sub-hourly temporal disaggregation of precipitation H. Oates et al. https://doi.org/10.1007/s00477-025-02996-0
- Future multivariate weather generation by combining Bartlett-Lewis and vine copula models J. Van de Velde et al. https://doi.org/10.1080/02626667.2022.2144322
- Investigation of rainfall disaggregation with flexible timescales based on point process models X. Qin & C. Dai https://doi.org/10.1016/j.jhydrol.2024.131101
- Nomogram-Based Rainwater Harvesting Design for a Sustainable Residential Water Supply R. Magini et al. https://doi.org/10.3390/su17135801
- Intra-Storm Pattern Recognition through Fuzzy Clustering K. Vantas & E. Sidiropoulos https://doi.org/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 https://doi.org/10.3390/w14132123
29 citations as recorded by crossref.
- Context-Specific Selection of Poisson cluster rainfall models for accurate urban hydrological simulations T. Lian et al. https://doi.org/10.1016/j.jhydrol.2024.132637
- Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data N. Ebers et al. https://doi.org/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. https://doi.org/10.1016/j.jhydrol.2022.127743
- Incorporation of weather parameters in MMRC-K model for rainfall disaggregation D. Chowdhury et al. https://doi.org/10.1007/s00477-024-02863-4
- Can we estimate flood frequency with point-process spatial-temporal rainfall models? Y. Chen et al. https://doi.org/10.1016/j.jhydrol.2021.126667
- A semi-parametric hourly space–time weather generator R. Pidoto & U. Haberlandt https://doi.org/10.5194/hess-27-3957-2023
- Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
- Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling S. Papalexiou https://doi.org/10.1029/2021WR031641
- Modelling rainfall with a Bartlett–Lewis process: pyBL (v1.0.0), a Python software package and an application with short records C. Wei et al. https://doi.org/10.5194/gmd-18-1357-2025
- 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 https://doi.org/10.1016/j.jhydrol.2023.129478
- Modelling convective cell life cycles with a copula-based approach C. Tseng et al. https://doi.org/10.5194/hess-29-1-2025
- Improvement of extreme rainfall characteristics for disaggregation of rainfall using MMRC with machine learning based DBSCAN clustering algorithm D. Chowdhury & U. Saha https://doi.org/10.1007/s12145-024-01309-3
- NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process J. Diez-Sierra et al. https://doi.org/10.5194/gmd-16-5035-2023
- Accurate reproduction of sub-hourly rainfall extremes in Poisson cluster rainfall models with a variable sinusoidal pulse C. Tai et al. https://doi.org/10.1016/j.jhydrol.2025.134821
- A simple scheme to adjust Poisson cluster rectangular pulse rainfall models for improved performance at sub-hourly timescales J. Park et al. https://doi.org/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 https://doi.org/10.3390/hydrology8020076
- A stochastic approach to simulate realistic continuous snow depth time series J. Park & D. Kim https://doi.org/10.1016/j.jhydrol.2022.128980
- Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites R. Pidoto et al. https://doi.org/10.5194/esurf-10-851-2022
- Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation D. Dao et al. https://doi.org/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 https://doi.org/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. https://doi.org/10.3390/atmos14060985
- A Cox Process with State-Dependent Exponential Pulses to Model Rainfall N. Ramesh et al. https://doi.org/10.1007/s11269-021-03028-6
- Deriving rainfall IDF curves using modified Bartlett-Lewis rectangular pulses (BLRP) model for Babylon City, Iraq S. Al-Jalili et al. https://doi.org/10.1016/j.rineng.2024.103028
- A long short-term memory model for sub-hourly temporal disaggregation of precipitation H. Oates et al. https://doi.org/10.1007/s00477-025-02996-0
- Future multivariate weather generation by combining Bartlett-Lewis and vine copula models J. Van de Velde et al. https://doi.org/10.1080/02626667.2022.2144322
- Investigation of rainfall disaggregation with flexible timescales based on point process models X. Qin & C. Dai https://doi.org/10.1016/j.jhydrol.2024.131101
- Nomogram-Based Rainwater Harvesting Design for a Sustainable Residential Water Supply R. Magini et al. https://doi.org/10.3390/su17135801
- Intra-Storm Pattern Recognition through Fuzzy Clustering K. Vantas & E. Sidiropoulos https://doi.org/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 https://doi.org/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...
