Assessment of the PMPs and Design Storms Estimated from the IMERG Satellite Precipitation Data
type: CreativeWork
description: The goal of this research is to leverage the Integrated Multi-satellite Retrievals for Global (IMERG) precipitation dataset to estimate probable maximum precipitation (PMP) at pixel and watershed levels, and to model design storms. The IMERG data provides an opportunity to extend PMPs and design storms in data-scarce locations. This dissertation consists of five chapters. Chapter 1 provides a summary introduction of the dissertation, chapter 2 through chapter 4 presents the core research, and chapter 5 is the conclusion.Chapter 2 derived PMP at nine different durations (30-min up to 72-hr) from IMERG precipitation estimates from 2001-2022 and assessed them using corresponding information from 2360 National Oceanic and Atmospheric Administration (NOAA) gauge stations in the conterminous U.S. The World Meteorological Organization (WMO) PMP estimation method was found to improve the Hershfield PMP statistical approach. For each study duration, the mean statistics from the IMERG annual maximum value series (a key input for the WMO method) were compared to corresponding statistics from NOAA-Atlas-14 across the stations, exhibiting high correlation with low relative bias as duration increased.Chapter 3 extends the PMP estimation to the watershed level because the flood modeling unit is the watershed. Chapter 3 implements two approaches for estimating watershed PMP (WPMP) using IMERG data: the Area Interpolation Method (AIM), derived from precipitation time-series data, and the NOAA-Alas-14 Area Reduction Factor Method (NAM), which transforms gridded IMERG PMP to watershed PMP using Area Reduction Factors (ARFs). The analysis was conducted on 3,671 watersheds, categorized as HUC-08 (90), HUC-10 (501), and HUC-12 (3080), across nine different durations (0.5-hr, 1-hr, 2-hr, 3-hr, 6-hr, 12-hr, 24-hr, 48-hr, and 72-hr) in Kansas, USA. The assessment showed a greater agreement between the AIM and NAM approach in small watersheds (i.e., HUC-12) than in larger watersheds (e.g., H…
abstract: The goal of this research is to leverage the Integrated Multi-satellite Retrievals for Global (IMERG) precipitation dataset to estimate probable maximum precipitation (PMP) at pixel and watershed levels, and to model design storms. The IMERG data provides an opportunity to extend PMPs and design storms in data-scarce locations. This dissertation consists of five chapters. Chapter 1 provides a summary introduction of the dissertation, chapter 2 through chapter 4 presents the core research, and chapter 5 is the conclusion.Chapter 2 derived PMP at nine different durations (30-min up to 72-hr) from IMERG precipitation estimates from 2001-2022 and assessed them using corresponding information from 2360 National Oceanic and Atmospheric Administration (NOAA) gauge stations in the conterminous U.S. The World Meteorological Organization (WMO) PMP estimation method was found to improve the Hershfield PMP statistical approach. For each study duration, the mean statistics from the IMERG annual maximum value series (a key input for the WMO method) were compared to corresponding statistics from NOAA-Atlas-14 across the stations, exhibiting high correlation with low relative bias as duration increased.Chapter 3 extends the PMP estimation to the watershed level because the flood modeling unit is the watershed. Chapter 3 implements two approaches for estimating watershed PMP (WPMP) using IMERG data: the Area Interpolation Method (AIM), derived from precipitation time-series data, and the NOAA-Alas-14 Area Reduction Factor Method (NAM), which transforms gridded IMERG PMP to watershed PMP using Area Reduction Factors (ARFs). The analysis was conducted on 3,671 watersheds, categorized as HUC-08 (90), HUC-10 (501), and HUC-12 (3080), across nine different durations (0.5-hr, 1-hr, 2-hr, 3-hr, 6-hr, 12-hr, 24-hr, 48-hr, and 72-hr) in Kansas, USA. The assessment showed a greater agreement between the AIM and NAM approach in small watersheds (i.e., HUC-12) than in larger watersheds (e.g., H…
author: Ekpetere, Kenneth Okechukwu
language: en
keywords: Geography, Hydrologic sciences, Atmospheric sciences, Area Interpolation, Area Reduction Factors, Design Storm, IMERG, Probable Maximum Precipitation, Watershed PMP
date published: 2024-05-31
date modified: 2026-07-24T02:45:19Z
provider: University of Kansas
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Kansas

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1808/38522