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Assessment of Extreme Rainfall in the UK Marie Ekström 2003-04-02

Assessment of Extreme Rainfall in the UK

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Assessment of Extreme Rainfall in the UK. Marie Ekström. 2003-04-02. Sustainable Water: Uncertainty, Risk and Vulnerability in Europe ( SWURVE ). The project aims to study the impacts of climate variability and change on the sustainable use of water and its related activities in Europe. - PowerPoint PPT Presentation

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Page 1: Assessment of Extreme Rainfall in the UK

Assessment of Extreme Rainfall in the UK

Marie Ekström

2003-04-02

Page 2: Assessment of Extreme Rainfall in the UK

Sustainable Water: Uncertainty, Risk and Vulnerability in Europe

(SWURVE)The project aims to study the impacts of climate variability and change on the sustainable use of water and its related activities in Europe.

One objective for CRU is to look at changes in extreme rainfall over the UK, updating a study made by P. Jones and P. Ried (2001):

“Assessing future changes in extreme precipitation over Britain using regional climate model integrations. Int. J. Climatol. 21: 13371356.”

Page 3: Assessment of Extreme Rainfall in the UK

Return period magnitudes

• The return period can be seen as the average time between events of a given magnitude.

• The quantile of a return period (T) is an event magnitude so extreme that it has probability 1/T of being exceeded by any single event.

Example: the magnitude associated with a 10 and 50 year return period has a chance of 10 and 2 percent chance to occur within any one year.

Page 4: Assessment of Extreme Rainfall in the UK

Methodology

• Annual maxima were fitted to a extreme value type I distribution: Gumbel distribution.

• The Gumbel distribution parameters (location and scale) were estimated using probability weighted moments according to Palutikof et al. (1999):

“A review of methods to calculate extreme wind speeds. Met. Appl. 6: 119132.”

• Return period magnitudes (mm) were estimated for 5, 10, 20 and 50 year periods.

Page 5: Assessment of Extreme Rainfall in the UK

How good is the estimation?

Two tests were applied on all data sets:• Kolmogorov-Smirnov (matlab)• Anderson-Darling (assumed to be more sensitive to

deviations in the tail of the distribution than the older K-S test.)

Stephens M.A. 1977: Goodness of Fit for the Extreme Value Distribution. Biometrika, 64: 583-588.

Page 6: Assessment of Extreme Rainfall in the UK

HadRM3 grid used for the Gumbel analysis

• Control and Perturbed runs

• Scenario A2 (three ensamble members)

• 3* 31 years

Page 7: Assessment of Extreme Rainfall in the UK

Stations used for the Gumbel analysis

• 105 stations

• Time period 19601990

• For each station and year: less than 20 % chance that the annual maxima is missing

Page 8: Assessment of Extreme Rainfall in the UK

… and some more

UK Climate Impacts Programme (UKCIP): 5-day rainfall totals for a 5 by 5 km grid over the UK

Page 9: Assessment of Extreme Rainfall in the UK

Common grid

• 0.5º latitude by 0.5º longitude

• Regular grid (21,20).

Page 10: Assessment of Extreme Rainfall in the UK

Results

• Annual maxima: all return periods are displayed

• Seasonal maxima: only 10-year return period is shown

• Multiday maxima: only 10-year return period is shown.

Page 11: Assessment of Extreme Rainfall in the UK

Annual return period magnitudes (mm)

Page 12: Assessment of Extreme Rainfall in the UK

Relative difference (%) between obs. and HadRM3

Page 13: Assessment of Extreme Rainfall in the UK

Seasonal return period magnitudes HadRM3) (mm)

Page 14: Assessment of Extreme Rainfall in the UK

Relative difference (%) between HadRM3 and obs.

Page 15: Assessment of Extreme Rainfall in the UK

Return period magnitudes for multiday totals (mm)

Page 16: Assessment of Extreme Rainfall in the UK

Relative difference (%) between HadRM3 and obs.

Page 17: Assessment of Extreme Rainfall in the UK

Summary (10-year return period)

• On average the HadRM3 values are about +/- 20 % different to those of the observed values.

• Large overestimation is found for western Scotland.The main part of the overestimation is probably due to the sparseness of data in this region.

• The largest spatial difference is found during the summer season.