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

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.