Examining the Robustness of a Spatial Bootstrap Regional Approach for Radar-Based Hourly Precipitation Frequency Analysis

Hisham Eldardiry,Emad Habib

doi:10.3390/rs12223767

Abstract

Radar-based Quantitative Precipitation Estimates (QPE) provide rainfall products with high temporal and spatial resolutions as opposed to sparse observations from rain gauges. Radar-based QPE’s have been widely used in many hydrological and meteorological applications; however, using these high-resolution products in the development of Precipitation Frequency Estimates (PFE) is impeded by their typically short-record availability. The current study evaluates the robustness of a spatial bootstrap regional approach, in comparison to a pixel-based (i.e., at site) approach, to derive PFEs using hourly radar-based multi-sensor precipitation estimation (MPE) product over the state of Louisiana in the US. The spatial bootstrap sampling technique augments the local pixel sample by incorporating rainfall data from surrounding pixels with decreasing importance when distance increases. We modeled extreme hourly rainfall data based on annual maximum series (AMS) using the generalized extreme value statistical distribution. The results showed a reduction in the uncertainty bounds of the PFEs when using the regional spatial bootstrap approach compared to the pixel-based estimation, with an average reduction of 10% and 2% in the 2- and 5-year return periods, respectively. Using gauge-based PFE’s as a reference, the spatial bootstrap regional approach outperforms the pixel-based approach in terms of robustness to outliers identified in the radar-based AMS of some pixels. However, the systematic bias inherent to radar-based QPE especially for extreme rainfall cases, appear to cause considerable underestimation in PFEs in both the pixel-based and the regional approaches.

Highlights

Rainfall plays a critical role in the earth’s water and energy cycle over a wide range of spatiotemporal scales
Radar precipitation estimates provide new possibilities to investigate the climatology of extreme rainfall at high spatial resolutions and over large areas [7]
Our results indicated that the spatial bootstrap technique can provide spatially smoother distribution parameters and associated quantiles compared to the pixel-based approach, which reduces the unrealistically high variations between neighboring pixels over the fine-resolution radar grid (4-km × 4-km in the case of Stage IV)

Summary

Introduction

Rainfall plays a critical role in the earth’s water and energy cycle over a wide range of spatiotemporal scales. Accurate quantitative estimation of rainfall is an important input for engineering design applications where Precipitation Frequency Estimates (PFE) are highly sought [1]. Probabilistic modeling and statistical analysis techniques of extreme rainfall are used to provide PFE information and characterize the relationships between three important precipitation variables: intensity (or depth), duration, and frequency [2]. Such relationships are usually referred to as Intensity-Duration-Frequency (IDF) or Depth-Duration-Frequency (DDF) curves. Statistics derived from IDF or DDF curves are typically used to develop design storms, which are used as an input for a variety of engineering applications such as design of dams, levees, reservoirs, and urban sewer systems [3]

Methods

Results

Discussion

Conclusion