European Drought Centre

European Drought CentreAnne K. Fleig

University of Oslo,Norway European Drought Centre

Test-application of COST-733

WTCs: Hydrological drought in

North-Western Europe

Objective

Identification of weather types associated with the development of severe hydrological drought

- in Denmark and UK

- Regional hydrological drought defined as deficit in streamflow


University of Oslo,Norway

Data:• 38 streamflow stations from Great Britain• 23 streamflow station in Denmark• daily data• natural or naturalized• at least 30 years of data• common standard period: 1964 – 2003

Hydrological drought

Drought definition• For each station: threshold level method (Qtr=90-

percentile)• Identification of regions with “homogeneous”

drought occurrence Regional drought series



Hydrological drought: “Homogenous” regions

Great Britain

Denmark



Index definition

• Daily series

• Percentage of stations experiencing drought weighted by catchment area

“drought affected proportion of area within one region”

RDI: 0 - 1

Hydrological drought: Regional drought index

Regional drought definition• RDI > 0.7Seasonal series: number of drought days per summer

(summer season: 16 Apr – 15 Oct )



Hydrological droughts: Regional series

RDI > 0

RDI > 0.4

RDI > 0.7



Selection of Weather Type Classifications



Differences in:

• methods: PCA, cluster analysis, etc;

• input data: MSLP, Z500, etc;

• number of types;

• similarity index.




Differences in:

• methods: PCA, cluster analysis, etc;

• input data: MSLP, Z500, etc;

• number of types;

• similarity index.

First choices for domain 00:

OGWL, SANDRA, TPCAV, TPCA07, PCACA, EZ500C30, EZ500C10





WTC Input data

Method Types Comments

OGWL SLP& Z500

Pattern recognition 29 - WTs 2 and 1 most frequent during summer but do not dominate completely

SANDRA SLP Cluster analysis 18 - few types dominate during summer: 1-4 during high summer and also WT5 during early summer

PCACA SLP S-mode PCA + clustering

11 - few types dominate during summer: WTs 1, 7, 10 and spring: WTs 11, 4 and other

TPCA07 SLP T-mode PCA 7 - WT1 most frequent Jun-Sep; May-Jun also WTs 7 and 4; WTs 2 and 3 during autumn, winter, spring

TPCAV SLP T-mode PCA 12 - WTs 1 and 8 most frequent during summer; WT2 aut, winter, spring and WT3 during winter but all not dominate

EZ500C30 Z500 Geometr. 3D direction of the vertical vector + cosine similarity index

30 - WT1 dominates throughout the year with more than 48 %, 77% in Aug and 73 % in Sep; WT2 around 10 % all year; WT3 ≥ 6% and peaks Jun-Jul ≥ 12%

EZ500C10 Z500 “ 10 - WT1 dominates throughout the year ≥ 70 %, 94% in Aug and 91 % in Sep; WT2 around 7 % all year and peaks in Jul with 10 %; WT3 around 7 % Dec-May




T P C AV, domain 00

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

J an F eb Mar Apr May J un J ul Aug S ep Oct Nov Dec

1

2

3

4

5

6

7

8

9

10

11

12

P C AC A, domain 00

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

J an F eb Mar Apr May J un J ul Aug S ep Oct Nov Dec

1

2

3

4

5

6

7

8

9

10

11




WTC Input data

Method Types Comments

OGWL SLP& Z500

Pattern recognition 29 - WTs 2 and 1 most frequent during summer but do not dominate completely

SANDRA SLP Cluster analysis 18 - few types dominate during summer: 1-4 during high summer and also WT5 during early summer

PCACA SLP S-mode PCA + clustering

11 - few types dominate during summer: WTs 1, 7, 10 and spring: WTs 11, 4 and other

TPCA07 SLP T-mode PCA 7 - WT1 most frequent Jun-Sep; May-Jun also WTs 7 and 4; WTs 2 and 3 during autumn, winter, spring

TPCAV SLP T-mode PCA 12 - WTs 1 and 8 most frequent during summer; WT2 aut, winter, spring and WT3 during winter but all not dominate

EZ500C30 Z500 Geometr. 3D direction of the vertical vector + cosine similarity index

30 - WT1 dominates throughout the year with more than 48 %, 77% in Aug and 73 % in Sep; WT2 around 10 % all year; WT3 ≥ 6% and peaks Jun-Jul ≥ 12%

EZ500C10 Z500 “ 10 - WT1 dominates throughout the year ≥ 70 %, 94% in Aug and 91 % in Sep; WT2 around 7 % all year and peaks in Jul with 10 %; WT3 around 7 % Dec-May



• Identify WTs which are more frequent during droughts and periods leading up to severe drought events

Daily series

- first 20 days of the 8 most severe events (= longest droughts)

Seasonal series

- 3 summers with most drought days - preceding winters

WTs associated with hydrological drought



Frequency anomaly (FA)

• Fds = Frequency during drought period (1 year)

• Fn = Frequency during that period of the year (1964 – 2001)

FA = (Fds - μFn) / σFn

WTs associated with hydrological drought



• different WTs seem to be related with drought

• WTs can have a high positive FA for one event and negative FA for other events

• also for WTCs with only few different types

WTs related to severe hydrological droughts



• mean FA is positive or negative for summer / winter / event / summer and event

Comparison of identified WTs with composites



Do WTs with high precipitation have positive mean FA?

- TPCA07: no.- TPCAV: a few times for the summer series.- SANDRA: yes, but only once among the first five

WTs;more often for summer series.

- OGWL: yes, sometimes; more often for summer series.

Comparison of identified WTs with composites



Groups of WTs:

• positive mean FA over all summers

• positive mean FA over all events

• positive mean FA over all summers with maximum

• positive mean FA over all events with maximum

• negative mean FA over all summers

• negative mean FA over all events

• subjective selection based on composites: positive

• subjective selection based on composites: negative

Correlation analysis for seasonal series



Results:

• highest correlation with group: positive mean FA over all summers

• WTCs with highest correlation:OGWL and SANDRA

• general low R2

Correlation analysis for seasonal series



Further work

• correlation analysis between daily RDI-series and WT-frequencies during the 30 / 60 / 90 and 180 last days

• chose WTC for domain 04 and drought from Great Britain



• selection method works better for TPCAV and TPCA07

• problematic when few types dominate too much (SANDRA)

• but highest correlation with OGWL and SANDRA

• disadvantage of TPCAV and TPCA07:same WT has contrasting effects during summer and winter

Conclusions



• selection method works better for TPCAV and TPCA07

• problematic when few types dominate too much (SANDRA)

• but highest correlation with OGWL and SANDRA

• disadvantage of TPCAV and TPCA07:same WT has contrasting effects during summer and winter

WTC based on other input data (total water column) ?

Conclusions

European Drought Centre

Documents

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