Assessing future extreme rainfall trends through multifractal scaling arguments: A CONUS-wide analysis based on NA-CORDEX model outputs
Authors: Emmanouil Stergios , Langousis Andreas , Nikolopoulos Efthymios I. , Anagnostou Emmanouil N. 
Published: March 2022

&lt;p&gt;The quantification of future flood risk, as well as the assessment of impacts attributed to the evolution of extreme rainfall events under rapidly changing climatic conditions, require multi-year information at adequately high spatiotemporal scales. The spatial and temporal evolution of regional extreme rainfall patterns, however, is quite challenging to describe due to natural climate variability and local topography. Hence, the use of conventional climate model outputs to evaluate the frequency of extreme events may not be conclusive due to significant epistemic uncertainties. &amp;#160;To date, there is limited knowledge on how extreme precipitation patterns will evolve under the influence of climate change, at spatiotemporal resolutions suitable for hydrological modeling, and considering the non-stationarity of rainfall as a process. In this study, we evaluate future trends related to extreme rainfall using hourly estimates acquired through the North American (NA) CORDEX Program (see Mearns et al., 2017), spanning from 1979 to 2100, over a 25-km CONUS-wide grid. In view of the practical importance of high spatial and temporal resolutions in hydrological modeling, we first simultaneously bias-correct and statistically downscale the NA-CORDEX model outputs, by using the two-component theoretical distribution framework described in Emmanouil et al. (2021), as well as the Stage IV weather radar-based gridded precipitation data (4-km spatial resolution) as a high-resolution reference. To investigate the validity of the yielded rainfall intensity quantiles, we use as benchmark the hourly rainfall measurements offered by NOAA&amp;#8217;s rain gauge network (National Centers for Environmental Information, 2017). Finally, to evaluate the effects of climate change on the spatial and temporal evolution of rare precipitation events while taking into consideration the nonstationary nature of rainfall, we apply a robust (Emmanouil et al., 2020) parametric approach founded on multifractal scaling arguments (Langousis et al., 2009) to sequential 10-year segments of the data, where conditions can be fairly assumed stationary. In view of revealing future infrastructure vulnerabilities over a wide range of characteristic temporal scales and exceedance probability levels, our analysis is founded on Intensity-Duration-Frequency (IDF) curves, which are derived using the previously acquired CORDEX-based, gridded (4-km), hourly precipitation estimates, and cover the entire CONUS for a period of 120 years.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Emmanouil, S., Langousis, A., Nikolopoulos, E. I., &amp; Anagnostou, E. N. (2020). Quantitative assessment of annual maxima, peaks-over-threshold and multifractal parametric approaches in estimating intensity-duration-frequency curves from short rainfall records. &lt;em&gt;Journal of Hydrology&lt;/em&gt;, &lt;em&gt;589&lt;/em&gt;, 125151. https://doi.org/10.1016/j.jhydrol.2020.125151&lt;/p&gt;&lt;p&gt;Emmanouil, S., Langousis, A., Nikolopoulos, E. I., &amp; Anagnostou, E. N. (2021). An ERA-5 Derived CONUS-Wide High-Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework. &lt;em&gt;Water Resources Research&lt;/em&gt;, &lt;em&gt;57&lt;/em&gt;(6), 1&amp;#8211;17. https://doi.org/10.1029/2020WR029548&lt;/p&gt;&lt;p&gt;Langousis, A., Veneziano, D., Furcolo, P., &amp; Lepore, C. (2009). Multifractal rainfall extremes: Theoretical analysis and practical estimation. &lt;em&gt;Chaos, Solitons and Fractals&lt;/em&gt;, &lt;em&gt;39&lt;/em&gt;(3), 1182&amp;#8211;1194. https://doi.org/10.1016/j.chaos.2007.06.004&lt;/p&gt;&lt;p&gt;Mearns, L. O., McGinnis, S., Korytina, D., Arritt, R., Biner, S., Bukovsky, M., et al. (2017). The NA-CORDEX dataset, version 1.0. &lt;em&gt;NCAR Climate Data Gateway. Boulder (CO): The North American CORDEX Program&lt;/em&gt;, &lt;em&gt;10&lt;/em&gt;.&lt;/p&gt;&lt;p&gt;National Centers for Environmental Information. (2017). Cooperative Observers Program Hourly Precipitation Dataset (C-HPD), Version 2.0 Beta. &lt;em&gt;NOAA National Centers for Environmental Information&lt;/em&gt;, [accessed July 17, 2020].&lt;/p&gt;