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
European Drought CentreAnne K. Fleig
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
Hydrological drought: “Homogenous” regions
Great Britain
Denmark
European Drought CentreAnne K. Fleig
University of Oslo,Norway
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 )
European Drought CentreAnne K. Fleig
University of Oslo,Norway
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 )
European Drought CentreAnne K. Fleig
University of Oslo,Norway
Hydrological droughts: Regional series
RDI > 0
RDI > 0.4
RDI > 0.7
European Drought CentreAnne K. Fleig
University of Oslo,Norway
Selection of Weather Type Classifications
European Drought CentreAnne K. Fleig
University of Oslo,Norway
Differences in:
• methods: PCA, cluster analysis, etc;
• input data: MSLP, Z500, etc;
• number of types;
• similarity index.
Selection of Weather Type Classifications
European Drought CentreAnne K. Fleig
University of Oslo,Norway
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
Selection of Weather Type Classifications
European Drought CentreAnne K. Fleig
University of Oslo,Norway
Selection of Weather Type Classifications
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
Selection of Weather Type Classifications
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
Selection of Weather Type Classifications
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
• 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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
European Drought CentreAnne K. Fleig
University of Oslo,Norway
European Drought CentreAnne K. Fleig
University of Oslo,Norway
• 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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
• mean FA is positive or negative for summer / winter / event / summer and event
Comparison of identified WTs with composites
European Drought CentreAnne K. Fleig
University of Oslo,Norway
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
• 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
European Drought CentreAnne K. Fleig
University of Oslo,Norway
• 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