Abstract
Precipitation extremes and associated hydrological hazards pose a significant global risk to society and economy. To be effective, mitigation strategies require the best possible estimation of the intensity and frequency of precipitation extremes. Traditional approaches to precipitation frequency analysis rely on long-term records from in-situ observations, which are limited in terms of global coverage. Satellite-based precipitation products provide global coverage, but errors in these estimates may lead to large biases in the quantification of extremes. Previous studies have demonstrated the ability of the novel Metastatistical Extreme Value Distribution (MEVD) framework to provide robust estimates of high quantiles in the presence of short-term data records and the uncertainties typical of remote sensing precipitation products. Here, we evaluate MEVD-based precipitation frequency analyses for four widely used quasi-global precipitation products (IMERG-v6, GSMaP-v6, CMORPH-v1.0, and MSWEP-v2) over high-density gauge networks in five hydroclimatic regions (Austria, Italy, Florida, Texas, and Arizona). We show dependence of MEVD-based estimation error on the characteristics of each dataset and the hydroclimatic region. Additionally, we evaluate the sub-grid variability of extreme precipitation and demonstrate the impact of spatial scale mismatch (that is, single in-situ gauge versus satellite pixel) on the frequency analysis of extremes. This work provides an assessment of the use of MEVD for estimating precipitation extremes from globally available datasets and an understanding of the variability of sub-daily precipitation extremes in different hydroclimatic regions of the world.
| Original language | American English |
|---|---|
| Article number | 125564 |
| Journal | Journal of Hydrology |
| Volume | 590 |
| DOIs | |
| State | Published - Nov 2020 |
Bibliographical note
Funding Information:This study was supported by Eversource Energy. Francesco Marra was funded by the Israeli Science Foundation [grant n. 1069/18]. Data for the Austrian case study were downloaded from the WegenerNet portal (https://wegenernet.org/portal/, accessed 2019/01). Data for the north Italy case study are from the Alto Adige network and were provided by Prof. Marco Borga, University of Padova. Data for the Florida and Texas case studies were downloaded from the NASA Goddard Space Flight Center's GPM ground validation data archive (https://gpm-gv.gsfc.nasa.gov/Gauge/index.html, accessed on 2019/01). Data from the Arizona case study were downloaded from Walnut Gulch Experimental Watershed network in Southwest Watershed Research Center (SWRC) (https://www.tucson.ars.ag.gov/dap/, accessed on 2019/02). The authors would like to thank Hylke Beck for sharing the MSWEP data.
Funding Information:
This study was supported by Eversource Energy. Francesco Marra was funded by the Israeli Science Foundation [grant n. 1069/18]. Data for the Austrian case study were downloaded from the WegenerNet portal (https://wegenernet.org/portal/, accessed 2019/01). Data for the north Italy case study are from the Alto Adige network and were provided by Prof. Marco Borga, University of Padova. Data for the Florida and Texas case studies were downloaded from the NASA Goddard Space Flight Center’s GPM ground validation data archive (https://gpm-gv.gsfc.nasa.gov/Gauge/index.html, accessed on 2019/01). Data from the Arizona case study were downloaded from Walnut Gulch Experimental Watershed network in Southwest Watershed Research Center (SWRC) (https://www.tucson.ars.ag.gov/dap/, accessed on 2019/02). The authors would like to thank Hylke Beck for sharing the MSWEP data.
Publisher Copyright:
© 2020
Keywords
- High-density rain gauges
- Precipitation extremes
- Satellite-based precipitation products
- Uncertainty