Stochastic Disaggregation of Daily Rainfall Using Barlett Lewis Rectangular Pulse Model (BLRPM): A Case Study of Middle Gujarat

Bhavin Ram *

Anand Agricultural University, Anand, Gujarat, India.

Murari Lal Gaur

Anand Agricultural University, Anand, Gujarat, India.

G. R. Patel

Anand Agricultural University, Anand, Gujarat, India.

A. N. Kunapara

Anand Agricultural University, Anand, Gujarat, India.

P. A. Damor

Junagadh Agricultural University, Gujarat, India.

*Author to whom correspondence should be addressed.


Having accurate and ample data on rains is the sole golden input for deciding ultimate success of any progressive efforts towards natural resource management. Ultimate conquest of any pertinent schemes on developing and managing watersheds, canals, commands, irrigation net-works, soil-erosion, soil-conservation, drylands, forests, pastures, livestock, land use changes and many ecology-based errands; is entirely governs by the precision, relevancy and quality of rainfall data. Even the ending success of present days smart hydrologic models, modelling entirely remains regulated by the precision & relevance of rainfall data used therein. Most commonly available rain data happens to be daily rain values. However, for precise planning at microscale, we need to have its finer sub-daily temporal distribution. Rainfall disaggregation is a newly emerging applied option where utilities of advanced stochastic architecture is utilized across the globe to offer desired location specific and even rainy day specific best possible temporal disaggregated outcomes. Present paper offers some of the crisped outcomes from a detailed study performed in Gujarat. The predictive ability of one of the most popular BLRP model in this regard is shared by incorporating its basic architecture followed by its predictive performances on randomised sample rainy days covering 6 explicit locations in middle Gujarat region of western India. Preliminary findings reported herein will serve as a food for thought for smarter ways of managing water, land, watersheds and ecology. The BLRP model for rainfall disaggregation has the potential to improve the accuracy of rainfall estimates, facilitate efficient water management, improve hydrological modeling, facilitate climate change analysis, and be cost-effective.

Keywords: Rainfall disaggregation, BLRMP, stochastic disaggregation, hydro-meteorological

How to Cite

Ram , B., Gaur , M. L., Patel , G. R., Kunapara , A. N., & Damor , P. A. (2023). Stochastic Disaggregation of Daily Rainfall Using Barlett Lewis Rectangular Pulse Model (BLRPM): A Case Study of Middle Gujarat. International Journal of Environment and Climate Change, 13(4), 37–47.


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Buytaert W, Célleri R, De Bièvre B, Cisneros F, Wyseure G, Deckers J, Hofstede R. Human impact on the hydrology of the andean páramos. Earth-Science Reviews, 2006;79(1–2):53–72. Available:

Basist A, Bell GD, Meentemeyer V. Statistical relationships between topography and precipitation patterns. Journal of Climate. 1994;7(9):1305–1315.

Allamano P, Claps P, Laio F. Global warming increases flood risk azmierczak in mountainous areas. Geophysical Research Letters. 2009;36(24). Available:

Gaur Murari Lal. "Catchment scale forest-water interfaces for pollution management." Water Quality, Assessment and Management in India. Cham: Springer International Publishing. 2022:71-111.

Parmar Urvashiben, Gaur Murari Lal.. Judging spatio-temporal variability of key river water quality vis-à-vis land use changes in Mahi Basin. Journal of Soil and Water Conservation. 2022;21(4):385-393. ISSN: 022-457X DOI: 10.5958/2455-7145.2022.00049.2

Huff FA. Time distribution of rainfall in heavy storms. Water Resources Research. 1967;3(4):1007–1019. Available:

El- Sayed EAH. Development of synthetic rainfall distribution curves for Sinai area. Ain Shams Engineering Journal. 2018;9(4): 1949–1957. Available:

Koutsoyiannis D. A nonlinear disaggrega-tion method with a reduced parameter set for simulation of hydrologic series. Water Resources Research. 1992;28(12):3175–3191. Available:

Guntner A, Olsson J, Calver A, Gannon B. Cascade-based disaggregation of continuous rainfall time series: the influence of climate. Hydrology and Earth System Sciences. 2001;5(2):145–164. Available:

Muller TH, Wallner M, Förster K. Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence? Hydrology and Earth System Sciences. 2018;22(10):5259–5280. Available:

Muller TH, Sikorska AE. Does the complexity in temporal precipitation disaggregation matter for a lumped hydrological model? Hydrological Sciences Journal. 2019;64(12):1453–1471. Available:

Miao Q, Yang D, Yang H, Li Z. Establishing a rainfall threshold for flash flood warnings in China’s mountainous areas based on a distributed hydrological model. Journal of Hydrology. 2016;541: 371–386. Available:

Rodriguez-Iturbe I, de Power BF, Valdes JB. Rectangular pulses point process models for rainfall: Analysis of empirical data. Journal of Geophysical Research. 1987;92(D8):9645. Available:

Ritschel C. BLRPM: Stochastic rainfall generator Bartlett-Lewis rectangular pulse model. R Package, Version 1.0; 2017. Available:https://CRAN.R

Cheng CT, et al. Disaggregation of daily rainfall into hourly values using the Bartlett-Lewis rectangular pulse model in Taiwan. Hydrology and Earth System Sciences. 2015;19(6):2803-2814. DOI: 10.5194/hess-19-2803-2015

Seo JH, Kim TW. Development of a high-resolution rainfall dataset using the Bartlett-Lewis rectangular pulse model. Journal of Hydrology. 2016;540:775-785. DOI: 10.1016/j.jhydrol.2016.07.041

Khatami S, Coulibaly P. Disaggregation of daily precipitation data into hourly values using the Bartlett-Lewis rectangular pulse model. Journal of Hydrology. 2017;545: 257-269. DOI: 10.1016/j.jhydrol.2016.12.027

R Studio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA; 2020. Available: