Understanding the relationship between extreme rainfall scaling and dew point temperature

Master Thesis (2020)
Author(s)

D. Teruel Cano (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

Marie-Claire Ten Veldhuis – Mentor (TU Delft - Water Resources)

Marc Schleiss – Graduation committee member (TU Delft - Atmospheric Remote Sensing)

R. J. van der Ent – Coach (TU Delft - Water Resources)

Ruben Dahm – Coach (Deltares)

Frederiek Sperna Weiland – Coach (Deltares)

Faculty
Civil Engineering & Geosciences
Copyright
© 2020 David Teruel Cano
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 David Teruel Cano
Graduation Date
31-07-2020
Awarding Institution
Delft University of Technology
Programme
Water Management
Faculty
Civil Engineering & Geosciences
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Abstract

Due to global warming, temperatures are expected to rise and, with it, the increase in moisture holding capacity of the atmosphere. Changes in precipitation extremes with temperature are governed by the Clausius-Clapeyron relationship (CC), which states that precipitation increases 7% per degree of warming. While global models and observations are consistent with CC, research at hourly observations have showed deviations in the CC relationship, showing steeper increase up to 14% per degree (super-CC) and <7% per degree for larger temperatures (sub-CC). Studies have shown that scaling rates can be influenced by several factors, such as the length of observations, rainfall percentiles or the timescale of rainfall, among others. Apart from methodological limitations, research on scaling rates is not readily available in all parts of the world, with scarce studies carried out in the African continent. In this context, there is a need for better understanding the relationship between precipitation and temperature as well as the implications for future extremes. To address these challenges, this research investigated 3 main topics. Firstly, existing methodologies were compared and analysed for 353 rain gauges in Germany. Using as extreme rainfall indicators wet percentiles (80th-99th quantile), 3 methods were tested and statistically compared to estimate scaling rates with dew point temperature (DPT): (i) binning methods, (ii) quantile regression and (iii) change-point models at an hourly timescale. Secondly, the presence of super-CC was evaluated for storm events instead of fixed hourly intervals. For that, data from Germany at 10-min resolution was used. The last step involved the analysis of scaling rates for a dense network of newly available rain stations in 4 countries in sub-Saharan Africa: Ghana,Kenya, Tanzania and Uganda. The results from measuring the scaling of extreme precipitation with DPT shows that two regimes arise for Germany: for DPT below 13ºC- 15ºC the scaling rates are close to the CC rate across all quantiles. For warmer DPT of 15ºC or more, scaling rates above the CC are observed, with increasing slope for the highest quantiles. The study of storm events shows that, for mean intensity and peak rainfall intensity, there is a strong sensitivity to DPT, with higher DPT associated with higher rainfall. The analysis of storm duration indicates that shorter events are more frequent for larger temperatures than for shorter ones. The study in sub-Saharan countries shows consistent negative scaling rates with dew point temperature. In addition, for large surface air temperatures (SAT), there is a decrease in relative humidity, suggesting that there is a general lack of moisture availability at the higher SAT ranges. To better understand the physical processes that influence these results, the use of alternative predictors that describe the atmospheric moisture are proposed. In a future warming world, current literature indicates that the greatest rainfall increases will be more visible for short-duration storms rather than increases in annual mean precipitation. As such, within this project, a novel analysis on scaling rates for storm properties shows the potential of these approach to obtain detailed information on the storm properties and their relationship with temperature, which could be used in storm simulations and projects characterising current or future climate.

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