Spatial downscaling and analysis of satellite and ground-based rainfall for hydrological modelling

Abstract

Precipitation is a vital element of the global hydrological and energy cycles, which is intermittent and highly variable in both space and time. Accurate measurements of precipitation at various space and time scales are important for hydrologists, agriculturalists, weather forecasters, and climate scientists. There are three approaches for measuring rainfall namely, (i) in-situ measurements; (ii) radars and (iii) satellite. The records of precipitation from in-situ measurements and radars are limited mostly to areas where they can be deployed, and measurements from those instruments are sparse. Due to the high variability of rainfall, a large rain gauge network becomes essential to capture the gradient, which is often not available. On the other hand, satellite rainfall estimates which provide high-resolution spatial data, with continuous temporal coverage can be used in place of gauge observations or to supplement gauge observations. Given that satellite rainfall estimates are contaminated by sources such as temporal sampling, instrument and algorithm error it is important to rigorously evaluate, validate and correct them to have a high level of confidence in using these satellite-based precipitation products for hydrological and meteorological applications