Fleig, Anne K.

Frei, Christoph

Hisdal, Hege

Prudhomme, Christel

Schiemann, Reinhard

Tallasken, Lena M.

English

NERC Open Research Archive

Data0 60 120 180 240 300 36019601970198019902000daily droughts region GB1  1964 - 2001dayyear0 60 120 180 240 300 36019601970198019902000daily droughts region GB4  1964 - 2001dayyearReferences and Acknowledgements• However, there is a time delay (typically days-weeks) until observations from all stations are digitised and much less data are available for quasi real-time precipitation gridding.• How to approach this problem? Can weather-type information help in near-real time gridding of daily precipitation?Hydrological applications in COST 733Flood flow occurrence (CEH) Hydrological drought (UiO) Precipitation mapping (MeteoSwiss)Flood occurrence has been linked to weather types of the Grosswetterlagen classification (manual classification) in Germany [1]. This pan-European study evaluates (1) if such links exist anywhere in Western Europe, (2) if some weather types are associated to large-scale floods and (3) if results depend on the classification algorithm, using the objective classifications developed within COST733. The existence of significant relationships would show the hydrological relevance of the corresponding classification.  This could be exploited by using the same algorithms to derive Weather Types (WT) from GCM outputs and anticipate large-scale floods as a seasonal time frame or evaluate flood probability at a future multi-decadal time horizon.COST 733 mid-term Conference, 22-24 October 2008, Krakow, PolandMotivationAims and ObjectiveAims and ObjectivesGridded precipitation climatologies based on high-density station networks and spanning several decades are available in the Alpine region (e.g., Figure 1, [1]).Here,• We test a reduced-space optimal interpolation (RSOI) method for the construction of daily precipitation gridsfrom a sparse gauge network.• Stratify the method according to a weather types classification [2,3] and compare with results from the unstratified interpolation.Figure 1. Overview of the study area. (a) Gauges of the sparse network (51 stations; blue circles), the dense network (549 stations; red dots) and height of topography (in m; shading). (b,c) Long-term mean and standard deviation of daily precipitation (mm per day).Reduced-space optimal interpolation• is an interpolation method normally applied in climate reconstruction. Here, it is tested for near real-time gridding of dailly precipitation.• RSOI combines information from(i) high-quality precipitation grids based on the dense network (for calibration, not available in real-time)(ii) sparse gauge data available in quasi real-time• RSOI is based on principal component analysis of the calibration data, truncation of the data space, and the estimation of principal component scores from the sparse gauge data. See [4] for details.ExamplesFigure 2. Examples of gridded daily precipitation (mm/d). (left) Gridding from the sparse network in terms of a reference method[1], (centre) RSOI from the sparse network, (right) precipitation grid based on the dense network. The top two rows show cases with good interpolation results, the bottom row shows a case where gridding from the sparse network is very difficult. Numbers show the value of a mean-squared-error skill score used to evaluate the interpolations.Weather typesConclusion• Reduced-space optimal interpolation is suitable for gridding daily precipitation data.• For the setup tested here, RSOI clearly outperforms a reference method (based on climatological caling and the SYMAP algorithm, [1]).• Stratification with respect to weather types can improve the interpolations for some weather types.Figure 3. Boxplot statistics for the distributions of daily mean-squared-error skill scores for unstratified RSOI (black) and RSOI stratified with respect to the five PCACA[2,3] weather types(grey). For each weather type, the annotations are (i) the number of days in the calibration period, (ii) the number of days in the reconstruction period, (iii) the number of wet days (with domain-mean precip > 1 mm/d) considered in the evaluation, and (iv) the domain-mean precipitation for the weather type considered. Results are shown for different values of the reduced-space dimension L (a parameter of RSOI).References[1] Frei, C. and C. Schär, 1998: A precipitation climatology of the Alps from high-resolution rain-gauge observations. Int. J. Climatol., 18, 873-900.[2] PCACA weather types classification developed by Domingo Rasilla,Universidad de Cantabria, Departamento de Geografía, Spain.[3] COST Action 733: Harmonisation and Applications of Weather Type Classifications for European Regions, www.cost733.org.[4] Kaplan, A., Y. Kushnir, M.A. Cane, and M.B. Blumenthal, 1997: Reduced space optimal analysis for historical datasets: 136 of Atlantic sea surface temperatures. J. Geophys. Res., 102, 27835-27860.Data and methodsDaily flow series from over 400 catchments where obtained from the EWA, BRDC, NRFA & HYDRO archives.Flood events were identified following the Peak-over-Threshold method [2] (average of 3 peaks per year). Two hypothesis were tested and their significance level evaluated:   • PI1: Is a weather type occurring more frequently during a flood event than usual?)1//(*1001,−=seasoniseasonseasoniseasonseasonWT daynWTwithdaynFlooddaynWTwithFlooddaynPIi• PI2: Is the persistence of a weather type followed by a flood event?*)0,()(2 NkdayskforiWTpriPI ≤≤≥==PI2 is compared with the binomial probability of at least k days out of N* of WTi using historical frequencies of occurrence.  Here N* = 10 days .  For each station, a catalogue of flood event was constructed, the weather type associated with each event and up to N* preceding days  identified and PI1 and PI2 calculated.ResultsFigure 1 illustrates PI1 for winter for two WTs of the objective Hess Brezowksiclassification (OGWL) derived from ERA-40 re-analyses developed within the COST733 [3]. For the great majority of stations analysed, WT2 occurs in winter more often 3 days before a flood event than on any other days (a).  At the opposite, there is no significant association between WT9 and winter flood occurrence (b)[1] Duckstein L, Bardossy A, Bogardi I (1993) Linkage between the occurrence of daily atmospheric circulation patterns and floods: an Arizona case study. Journal of Hydrology 143:413-428[2] Bayliss AC, Jones RC (1993) Peaks-over-threshold flood database: summary statistics and seasonality, Vol 121. Institute of Hydrology, Wallingford[3] James PM (2007) An objective classification method for Hess and BrezowskyGrosswetterlagen over Europe. Theoretical and Applied Climatology 88:17-42 Global Runoff Data Centre (http://grdc.bafg.de/servlet/is/Entry.987.Display/)European Water Archive (http://ewa.bafg.de/)UK National River Flow Archive (http://www.ceh.ac.uk/data/nrfa/index.html)Banque Hydro, France (http://www.hydro.eaufrance.fr/). ReferencesFigure 1. PI1 for WT2 (a) and WT9 (b) of the OGWL occurring with a lag 3 before a winter flood event.  Black dots show significant results, grey dots show stations were the frequency anomaly is not significant.(a)(b)Figure 2 shows that all over Europe (a) WT2 occurred at least once in a window of 7 days before a winter flood event and significantly more often than would be expected by chance (PI2 with N* = 7 days, k=1). Over 60% of the tested sites have a conditional probability PI2 greater than 30 : WT2 occurred more than 30% of the time before a flood event (b), more often in this season than would be expected by chance. Figure 2. Map of PI2 assessing the occurrence of WT2 (OGWL) at least once during the 7 days before a winter flood event (a), and  percentage of catchments with PI2 greater than 30 (b).  Same key as Figure 1.  (b)(a)For each indicator and time lag considered, results are displayed on maps showing, for a given WT, the associated indicator value (size of dots) and level of significance (colour).  The Pan-European relevance of a weather type is assessed by histograms of the percentage of stations falling into a certain bracket value. The results presented were obtained with OGWL and investigated possible association of some Weather Types with the occurrence of large floods in Europe.  Evidence was found that, in winter, WT2 occurs more often before a flood than the rest of the time and when a flood occurred, more than 30% of the time WT2 occurred at least once in the 7 days preceding the flood, more often than expected from chance alone. The increase of WT2 occurrence in the future might thus be associated with higher flood risk in Europe.  Further research is needed to test other classifications, and to evaluate if at the opposite, some WT never precede a flood (e.g. WT9), which could suggest lower flooding risk if their occurrence increases. ConclusionIn addition to flood, drought is the other hydrological extreme that can cause severe problems. Droughts are slowly developing and severity increases with increasing duration and extend. By identifying weather types (WT) which contribute to the development of droughts, hydroclimatological processes leading to severe hydrological droughts can be studied. The objective of this study is to compare objective weather type classifications (WTCs), with respect to analyzing links between weather types and hydrological drought in north-western Europe. The inter-comparison considers (1) classification algorithm, (2) input variables and (3) number of defined WTs.Regional hydrological drought: Daily Regional Drought Area Index series (RDAI; 1964–2001) for four regions in Great Britain and two in Denmark [1].RDAI: - based on at-site droughtseries derived from streamflow records [2];- represents the proportion of the total drought-affected catchment area within a region. Drought: - defined as RDAI > 0.7. Regional hydrological drought characteristics: vary between the regions, e.g. - short but frequent droughts (Scotland, GB1), - few but long droughts (southern and central England,  GB4; Figure 1).Figure 1 Regional drought development in regions GB1 and GB4; Red: 0.7<RDAI≤1; Orange: 0.5<RDAI≤0.7; Yellow: 0<RDAI≤0.5.ResultsFigure 2 Number of regions for which a WTC (1) obtained a correlation coefficient r> 0.4 and 0.5 for the daily (green bars) and seasonal (blue bars) analysis, respectively, (2) is among the five WTCs with highest r-values (crosses).Conclusions[1] Fleig AK, Tallaksen L M, Hisdal H, Hannah DM & Stahl K: Regional hydrological droughts and objective Grosswetterlagen in north-western Europe (in prep.). [2] Fleig AK, Tallaksen LM, Hisdal H. & Demuth, S. (2006) A global evaluation of discharge drought characteristics. Hydrol. Earth Syst. Sci. 10, 535–552.[3] COST733-Harmonisation and Applications of Weather Type Classifications of European Regions. http://cost733.org/.[4] Gerstengarbe FW, Werner PC & Rüge U (1999) Katalog der Großwetterlagen Europas nach Paul Hess und Helmuth Brezowsky 1881-1992. 5th ed. http://www.pik-potsdam.de/~uwerner/gwl/[5] James PM (2007) An objective classification method for Hess and Brezowsky Not all WTCs are suitable to identify WTsassociated with regional hydrological drought; depends on classification algorithm; influences of input variables and number of WTs vary between algorithms. Best results with LWT [5]; above average results with OGWL [6]; both are objective WTCs based on subjective WTCs (Lamb Weather Types [4] and HBGWL); HBGWL results are much below average. Objective WTCs considering the expert knowledge of a subjective WTC may be preferable over both purely mathematically defined WTCs and subjective WTC catalogues.Weather Type Classifications: Daily catalogues of the 71 automatic WTCs from COST733 [3] and the subjective Hess-Brezowsky Grosswetterlagen (HBGWL [4]) are used: - 24 different classification algorithms;- one or two sets of input variables (SLP as common one);- different numbers of WTs (approx. 9, 18 and 27).Grosswetterlagen over Europe. Theor. Appl. Climatol. 88, 17–42[6] James PM: Second-Generation Lamb Weather Types - A new generic classification method with evenly-tempered type frequencies. 6th Annual Meeting of the EMS / 6th ECAC, 4-8 September 2006.The authors greatly acknowledge:- Cedric Laize and the UK National River Flow Archive, CEH (http://www.ceh.ac.uk/data/nrfa/index.html, for the British discharge data; - the National Environmental Research Institute in Denmark for  the Danish discharge data for the Nordic Energy Christel Prudhomme1, Anne K. Fleig2, Reinhard Schiemann3, Christoph Frei3, Lena M. Tallaksen2 & Hege Hisdal21 Centre for Ecology and Hydrology, Wallingford, OX108BB, UK, chrp@ceh.ac.uk; 2 Department of Geosciences, UiO, P.O.Box 1047, Blindern, 0316 Oslo, Norway, a.k.fleig@geo.uio.no3 Federal Office of Meteorology and Climatology MeteoSwiss, Krähbühlstrasse 58, 8044 Zurich, Switzerland, reinhard.schiemann@meteoswiss.ch0123456Number of regionsCKMEANSC09CKMEANSC18CKMEANSC27EZ850C10EZ850C20EZ850C30ESLPC09ESLPC18ESLPC27GWTGWTC10GWTC18GWTC26KHC09KHC18KHC27LITADVELITC18LITTCLUNDLUNDC09LUNDC18LUNDC27LWT2C10LWT2C18LWT2NNWNNWC09NNWC18NNWC27P27P27C08P27C16P27C27PCACAPCACAC09PCACAC18PCACAC27PCAXTRPCAXTRC09PCAXTRC18PCAXTRKMPCAXTRKMC09PCAXTRKMC18PETISCOPETISCOC09PETISCOC18PETISCOC27SANDRASANDRAC09SANDRAC18SANDRAC27SANDRASSANDRASC09SANDRASC18SANDRASC27TPCAVTPCA07TPCAC09TPCAC18TPCAC27WLKC733WLKC09WLKC18WLKC28HBGWLHBGWTOGWLOGWLSLPPECZELYPERRETSCHUEEPPZAMGdaily r>0.4 seasonal r>0.5 best 5 daily best 5 seasonalIdentification of WTs, which may be associated with the development of hydrological drought (for each region): I: Frequencies of WTs prior to and during the five most severe droughts events (Fe,WT) are compared to the normal frequencies of the weather type during the same period of the year for 1961–2001 (FWT), - WTs with Method 0  >   FFF51  = FA51e WTe,WTe,WTe,WT ∑=−σµµ- Duration of the considered period depends on the regional drought characteristics, (dregion = 30 to 180 days).II: Correlation analyses for the summer period only (16  April – 15 October) between WTpos-frequencies (total of all WTs in WTpos) and drought to compare WTCs:- daily: moving dregion–day sums of WTpos-frequencies; and daily RDAI;- seasonal: total summer WTpos-frequencies; and the number of drought days during the summer (for GB4 WTpos-frequencies during the summer + the previous winter are used).are selected as group WTpos.

Hydrological applications in COST 733

http://nora.nerc.ac.uk/8621/1/COST733_MidTermConfWG4Hydrology_Final.pdf

Hydrological applications in COST 733

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