Date of Award

8-1-2019

Degree Name

Master of Science

Department

Civil Engineering

First Advisor

Bravo, Rolando

Abstract

It is evident that reliable hydrologic prediction and water resource management are still a challenge for water resource engineers and planners, due to the unavailability of good network of hydro-meteorological stations. Especially, the situation of observation stations is not improving in developing countries. Ubiquity of satellite-based precipitation products has availed hydrologic studies in ungaged regions; however, assessment of satellite estimates is consequential before using it for hydrological utilities. Moreover, assessment of satellite precipitation products is important for the betterment of quality of precipitation estimates. The assessment requires comparison of gridded satellite precipitation products with point gage observations. Thus, it is also necessary to identify better methods of comparison among the point-based gage data and gridded satellite products. In this study, rainfall estimates from Integrated Multi-satellite Retrievals for Global Precipitation Measurement mission (IMERG) at monthly temporal scale were evaluated over Sugar Creek Basin from May 2014 to April 2018. Initially, monthly IMERG at was evaluated using three different methods of comparision: a) Ordinary Kriging (OK) b) Inverse Distance Weighting (IDW) c) Point-to-Pixel. Secondly, IMERG data were downscaled to finer resolution (1km) using the Random Forest (RF) algorithm and evaluated against gage observation using point-to-pixel method. Results showed, IMERG at monthly temporal resolution showed better results when evaluated using OK and IDW, compared to point-to-pixel method. Monthly IMERG performed well in capturing temporal variability of rainfall and estimated the quantity of rainfall efficiently, however, it slightly overestimated the precipitation. Besides, RF algorithm showed promising performance in spatial downscaling of IMERG at monthly scale. Downscaled results at finer resolution successfully captured the spatial distribution and temporal variability of rainfall with acceptable accuracy.

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