Assessment of the PMPs and Design Storms Estimated from the IMERG Satellite Precipitation Data

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., HUC-08).Chapter 4 covers the modeling of design storms using IMERG data. PMP depths first need to be transformed into Hyetographs, serving as crucial inputs for modeling probable maximum floods (PMF). The modeled IMERG design storms (transformed to percentage precipitation) were juxtaposed with those from the NOAA-Atlas-14 at nine-percentile distributions (10% to 90%) for twenty-eight regions and seven zones. Across the regions and zones, the results indicate that NOAA-Atlas-14 is more frontloaded IMERG. The study found IMERG and NOAA-Atlas-14 to agree most at the 40th to 60th percentile.In conclusion, this research provides an alternative means of estimating PMPs in both gaged and ungaged locations and modeling design storms from them. The evaluation results suggest IMERG estimated PMPs had good agreement with NOAA observed data for 2360 NOAA stations around Conus, thereby begging for further evaluation in regions beyond Conus.

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2024

Author dc:creator

• Ekpetere, Kenneth Okechukwu

Advisor dc:contributor.advisor

• Li, Xingong

• Geography
• Hydrologic sciences
• Atmospheric sciences
• Area Interpolation
• Area Reduction Factors
• Design Storm
• IMERG
• Probable Maximum Precipitation
• Watershed PMP

Statement dc:rights

• Copyright held by the author.

Language dc:language.iso

en

Dc Identifier Other

http://dissertations.umi.com/ku:19532

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/38522