Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3246 3245 erent precipitation periods for peak discharge generation is ff The precipitation accumulating 0 to 3 days before an event is the most relevant for This discussion paper is/has beenSciences under (HESS). review Please for refer the to journal the Hydrology corresponding and final Earth paper System in HESS if available. Oeschger Centre for Climate Change Research and Institute of Geography, at the exit ofprobability large in lakes. catchments with As gentleage a topography, capacity high general infiltration rule, (karstic rates, wet cavities, and PRE-AP large deep stor- periods soils, enhance large the reservoirs). flood In contrast, floods were floods in Switzerland. PRE-AP precipitationence has on only flood a probability. weakperiods Floods and only region-specific were influ- in significantly the more Jura frequent Mountains, after in wet the PRE-AP western and eastern Swiss plateau, and Abstract Determining the role of di observed, (ii) the period 2uations to 3 generate days “significantly before flood higher days,period than when from normal” longer-lasting synoptic 4 precipitation days sit- amounts, tohave and one already (iii) month preconditioned the before thearation flood catchment. of days The antecedent when novelty precipitation previous ofbefore into wet this floods the episodes study or precursor may lies earlier, antecedent called inbefore precipitation PRE-AP) the floods, (4 and sep- days a the period shortsituation like range when e.g. precipitation a precipitation (0 stationary isber low-pressure to system). of often 3 Because days events driven we and consider because bythe a we one high “antecedent” work num- persistent and with daily “peak-triggering” weather precipitationduring precipitation. values, the The we flood whole do day precipitation not is separate recorded included in the short-range antecedent precipitation. crucial for both projecting future changesrange in flood flood probability and forecasting. for We short-ries and analyze prior medium- catchment-averaged to daily annual peakfloods precipitation discharge considered time events – (floods) se- more in than– Switzerland. 4000 allows The events to high from amount derive 101 of catchmentspeak significant discharge have information generation. been about Based analyzed on the thepose role analysis of a of antecedent precipitation new times precipitation separation series,before for of we flood pro- flood-related days, when precipitation the periods: maximum flood-triggering (i) precipitation the rates period are generally 0 to 1 day University of Bern, Bern, Switzerland Received: 12 February 2015 – Accepted: 25Correspondence February to: 2015 P. Froidevaux – ([email protected]) Published: 25 MarchPublished 2015 by Copernicus Publications on behalf of the European Geosciences Union. Flood triggering in Switzerland: thedaily role to of monthly preceding precipitation P. Froidevaux, J. Schwanbeck, R. Weingartner, C. Chevalier, and O. Martius Hydrol. Earth Syst. Sci. Discuss.,www.hydrol-earth-syst-sci-discuss.net/12/3245/2015/ 12, 3245–3288, 2015 doi:10.5194/hessd-12-3245-2015 © Author(s) 2015. CC Attribution 3.0 License. 5 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cient ffi ort is invested in ff cient, i.e. the fraction of the total ffi coe ff 3247 3248 , of 58 flash floods in Europe was statistically higher for ff occurs outside of river beds (Bezzola and, Hegg 2007). The devastating ff For several recent catastrophic flood events antecedent water storage was impor- However, damages in Switzerland often occur when small rivers overflow or when For the majority of catchments however, no significant correlation between precipi- 1993 was preceded byet a al., series2014). Antecedent of conditions high mightfloods: even precipitationMarchi be events et relevant for in al.(2010) the found the development of that watershed flash the (Barton runo tant. For example, Reagerstorage anomaly et for al.(2014) the 2011 pointrope Missouri to were floods. the preceded The floods importancethat by in influenced of above-average June 2013 precipitation a the inSchröter positive during flood Central et water discharge the Eu- al.(2015) by second furthercombination presaturating show half of that the of non this extraordinary soils May exceptionalthe precipitation (Grams flood with floods event et extremely of resulted high, al. from 2002tense initial2014). the also wetness. rainfall For episodes in in Centralcharges. the In Europe, southern firstUlbrich Switzerland, half et severe of flooding al.(2003) of August describe the that several Lago finally Maggiore in- in lead September to the extreme dis- surface runo designing continuous hydrological simulations whichtecedent allow to precipitation account time for year-long seriesand an- Buishand, when2007, assessing for discharge the Rhine extremes and (see Meuse basins). e.g. Wit rainfall that is routed intowetter runo antecedent precipitation. Theyare more however frequent also after found wetcur antecedent that, conditions following although in dry Central flash conditions Europe, in floods theythe primarily the antecedent oc- Mediterranean conditions region in andand the show streams, no Alpine-MediterraneanStucki dependence et region. on to al.(2012) For excessive underline large water the Swiss supply importance by lakes the of enhanced generation high melt of soil and historical precipitation saturation floods. over due several months for event of 1993 is a memorable(Hilker example et of how al., a2009). locallogical river Local processes can (depending generate floods high on in damages theshowers, Switzerland region, continuous floods result rainfall, may rain from be onBlöschl, a driven snow, by2003; large or short snowHelbling variety but and/or et of intense fluence glacier al., hydro- melt; of2006; see antecedentDiezigMerz precipitation and and for Weingartner , this2007). large Defining variety the in- of flood types is a complex conclude that the considerationto of represent a (understand, 3–4 days reconstruct, precipitation model, project) period Swiss should Alpine be floods. su 1 Introduction River flooding is oneland of (Hilker et the al., most2009 )the devastating and Alps and worldwide are (Munich costly often Re, Massacand natural caused2014). et hazards by Damaging al., high in flood1998; events precipitationdischarge Switzer- Hohenegger in during events et that floods al. , can last2008; alsoacteristics forStucki be of several et influenced the days al., by precipitation (e.g. 2012). bothcipitation event However, the and event, river spatial i.e. by and the the temporal antecedent statefactors char- is conditions. of the One the total of catchment water the beforeface storage water. most the in In important pre- the particular, antecedent form thebeen of importance snow, emphasized of soil (especially antecedent water, precipitation for ground for large water floods catchments). and has sur- For long example, e tation amounts and flood occurrencesare is omitted found in when the theextreme last floods precipitation three than amounts. days for annual before Moreover,matology floods floods the for with all PRE-AP a floods. high wasdischarge The frequency memory not and weak of higher was influence Prealpine, very for Alpine ofnevertheless close and poses PRE-AP to Southalpine the is cli- question Swiss a whether catchments.tion the Our clear impact for study indicator of floods long-term of in precursory a such precipita- short catchments is not overestimated in the general perception. We significantly less frequent afterreduced wet melt. PRE-AP periods in glacial catchments because of 5 5 10 25 15 20 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff coef- ff erent precipita- ff cient sample of events is ffi ect the resulting runo ff cient use of that information is pri- ffi erent precipitation accumulation peri- ff cient during floods is important only for ffi ect flash flood forecasting in the Southern coe ff 3250 3249 ff cients like in previous studies. Instead, we make use of two large networks of rain help to improve medium-rangethe amount flood of forecasting. antecedent Identifyinghelp precipitation to catchments is determine particularly where critical determinant regions for where floods an may e peaks? rence? frequent after wet conditions during that period? whether and how strongly that period influences flood probability? mordial for flood forecastingwater systems. storage For information example, fromstrate it satellite a is great data, now potential and for possible warningReager to systems et derive at weekly al.(2014) to demon- seasonal lead times. precipitation. Future changes(CH2011, in2011) precipitation but for generalsummer, Switzerland tendencies the are can most still be importantprecipitation derived season uncertain for from (due Alpine the to floods, projections.crease drier a In in clear soils) extreme decrease daily is precipitation in (dueThus, expected mean to depending to warmer on air, be whether see Rajczak short-termtant accompanied et or for al., by long-term floods,2013). flood a precipitation frequency is weak might more increase in- impor- or decrease in the future. moisture storage capacity, information regarding the current moisture state can ffi i. In the past 50 years, have floods in Switzerland been significantly more (or less) i. Because future flood frequency changes might depend on the role of antecedent v. How many days of antecedent precipitation are relevant for floods? ii. If they were more frequent, can we define catchment properties that determine ii. Due to the relatively long residence time of water in catchments with significant Apart from case studies and modeling studies of single catchments, the relationship A better understanding and quantification of the role played by antecedent precip- We aim to explicitly separate short-range and long-range antecedent precipitation iv. Which precipitation accumulation period is most closely related to flood occur- iii. Did extreme floods follow wetter antecedent conditions than smaller discharge gauges and river dischargeevents. stations The to underlying derive robust hypothesisences statistics is the from that amplitude a if large of aafter number peak period of wet discharges, of floods conditions antecedent should during precipitation be that influ- significantly period more provided frequent that a su and thus discuss theods. temporal The analysis separation comprises of thousands of di annual maximum discharge events in a large coe investigated. The following questions aretion addressed in periods particular before floods for di (e.g. 0–1, 3–14 days before floods): Here, we do not aim to quantify the role of antecedent precipitation by calculating runo between precipitation and floodingand systematic has manner never in been Switzerland. investigated in aitation comprehensive in thereasons: development of floods is crucial for flood hazard management for two task. A modeling study by a paramountPaschalis role et in) al.(2014 mediating showedThe the that discharge initial soil response saturation conditions of can a also small play Prealpine significantly catchment. a catchments with intermediate subsurfacemoisture water conditions storage is capacity; negligible for i.e. catchments theage with either role capacity. very Also, of large reports initial or very from Alpine small catchmentsRanzi stor- with et relatively al.(2007) shallowvalues on and of observed permeable antecedent floods soil precipitation in layersficient, do mesoscale conclude at not that “. dramatically least . a . duringmoisture conditions major than floods. generally assumed This . . indicates . ”. a smaller sensitivity to initial soil Swiss Alps (Liechti et al., of2013). initial However, moistureNorbiato conditions et on al.(2009) the found runo that the impact 5 5 10 20 25 15 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) and ective 2 ff ce of the En- ffi 90 % by the precipitation > grid covering the Swiss territory ◦ ). Catchments within the same size cat- 2 en and be covered = 2 3252 3251 1000 km > ), mesoscale catchments (Meso, 100–1000 km 2 . The stations measure water level from which a discharge value 1 counting of basins, i.e.mized. small catchments located in larger catchments, is mini- dataset. 2011. http://www.bafu.admin.ch/index.html?lang i. The discharge time series must cover at least 20 years during the period 1961– 1 ii. The area must be larger than 10 km Assessment of human influence on peak discharges (e.g. hydropower dams and/or The selected catchments were subdivided according to their size into microscale We use gridded daily precipitation accumulations constructed from interpolation of iv. A homogeneous representation of the Swiss territory is ensured and multiple iii. The possible human influence on the HQs must be minimal. discharge regulation) requires detailed knowledge about water management in each egory never overlap spatially,Macro catchments but and Micro Meso catchments catchments in can Macro catchments. be contained in Meso and macroscale catchments (Macro, catchments (Micro, 10–100 km rain gauge but theadequately results captured from in each Sect. catchment. 4.4 show that the flood-relevant precipitation3 is Methods 3.1 Selection and classification of catchments We selected 101 catchments based on the following criteria: sample of catchments representative of theThis various hydrological analysis regions of is Switzerland. uniqueand, for to our Switzerland knowledge, in also regard unprecedented worldwide. to the amount of floods2 considered Data The events analyzed in thismeasurements study are (called 4257 floods annual maximum hereafter).the instantaneous period They discharge 1961 were to recorded 2011. at The 101 data is stations provided during by the Swiss Federal O vironment (FOEN) for the period 1961–2011 (hereaftergauges varies RhiresD, from see approximately, MeteoSwiss 4002011). toresolution 500 The of throughout number this the of time dataset, period.15–20 km. The given e Some by the of typical the inter-station smallest distance, catchments is investigated approximately here may not contain any a dense network06:00 of to rain 06:00 UTC) gauges are (see available onFrei a and 0.02 Schär, by1998). 0.02 The daily sums (from if required, have beennual corrected maximum discharge by events hydraulic arethe modeling denoted 20 and year HQ floods hereafter. expert will HQs judgment.mated be exceeding return All denoted the periods HQ5 5 an- and of and HQ20, moreHere respectively. than those Note 100 floods that years HQs are have20 of been simply years). esti- recorded included The in in distinction the of the lastmaintain higher HQ20 decades. a return sample large sample periods (return size. than period 20 larger years than is avoided in order to is obtained through aIn rating the curve case that of is extreme based floods, the on discharge regular values discharge have measurements. been manually checked and, 5 5 20 15 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 %), Nival (mean altitude > 1900 m and glacial coverage > ective drainage areas of the stations ff 3254 3253 2300 m and glacial coverage > erent catchment subsamples are: Micro (52 catchments), Meso ff erent because of the complex underground flow (see e.g. Malard and Jeannin, ff In summary, the di Area-averaged precipitation time series were obtained by combining the gridded pre- The Swiss landscape contains distinct geographical and hydrological regions: the From Glacial to Nival to Pluvial, the flood seasonality decreases but a maximum 6 % or mean altitude 1200 m) and Pluvial regimes. Then, all catchments from the southern side of the cipitation data with the topographical catchment areas. 3.3 Definition of precipitation periods A set of1). precipitationble Most accumulation PAPs periods represent (PAPs)flood a was day precipitation defined and sum (summarized twoindices over in more (API). a A PAPs Ta- detailed are particular descriptionis based period of given on the before in PAPs the and the Sect. concept thewithin4.1. motivation the of for For period antecedent choosing example, from them precipitation 14 PAPday D4-14 to of 4 is the days catchment-averaged the prior precipitationprecipitation to precipitation time the sums sum flood series corresponding that day. (not PAPs to occurred areical only flood calculated for distribution days for flood of each are days). all then The precipitation compared sums. to the The climatolog- climatological distribution is defined as (35 catchments), Macroments), (8 Pluvial catchments), (31 Glacialments.) and catchments), (19 Lakes catchments), Meridional Exitscatchment. Nival (8 (7 (17 catchments). catchments), See catch- 3 Jurassien Table for (12 a3.2 catch- brief description of Derivation of each precipitation time seriesWe for each identified catchment catchmenta purely area topography-based boundaries approachresolution. to for For a most each of digital the elevation discharge Swiss model territory, station (DEM) the with by e a applying 10 m can be expectedCritical to regions be are reasonably thethe close highly Prealps, karstic to where areas the the incantly catchments the hydrological di derived Jura and Mountains topographical from and catchments2013). the some The tend DEM. areas most to critical of be catchments signifi- were not considered for the analysis. this diversity, a typical hydrological regimecatchment has (see been Fig.1). attributed This to classification eachand of Micro hydrological Weingartner(1985); and regimes Meso see follows also Aschwanden separationWeingartner and criteria(1992). Aschwanden is A theallow first mean us set to elevation of distinguish and> between the Glacial glacier (mean coverage. altitude These properties flood frequency in summer isa maintained. maximum Meridional flood catchments are frequencyfloods characterized in with by fall rain and onKöplin snow summer et as and al., a2014). Jurassien major catchments flood by process winter (see e.g. Piock-Ellena et, al. 2000; Alps (Prealps, High Alps,Each Southern Alps), region the shows Swiss specific Plateau hydro-meteorological and properties. the Jura In Mountains. order to account for catchment. Some ofSwitzerland (see this table of information plateand 5.6 is from MesoAschwanden catchments and available Spreafico , withinfluence within).1995 no Only was Micro the or tolerated low for Hydrologicalthe human Macro majority Atlas influence catchments. of were Discharge of large(hereafter selected. is “Lakes Swiss Some Exits”). regulated Karstic lakes human at catchments and with theremoved very based the exits complex on underground lake of expert flow exit knowledge. were stations are analyzed separately > Alps were joined inand the, Frei Meridional2005) group. andthe The flood specific specific geology precipitation seasonality regime (crystalline, ( Köplinmotivated this (Schmidli poor et choice. infiltration Similarly,, al. the rates, catchments2014) inJurassien steep the regime of Jura slopes, Mountains type this were and because joined group, weak of inplateaus, the their soils) as gentle shared well specific slopes, as morphology highstreams and infiltration due geology to rates (high the and calcareous important and karstic network bedrock). of underground 5 5 20 25 15 10 10 15 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | t p is (1) ≈ K )) is ) 1 p β (1 . A signifi- exp( T p/ ), of a flood ) is propor- p = p (1 O p/ 0.8, is chosen and = ) are calculated. The t ( K T value of the most significant . For days when floods were 0 for all other days. The model t p is by definition small (we look at = ) p . The odds ratio ( 45 days is an optimal compromise t 1 ( ± erent PAPs for peak discharge gen- T β ff )). t ( . Here, T is below 1) of the odds, T O PAP | 3256 3255 is the day for which API is calculated and 1 and PAP n 1 ) and precipitation PAP ) is the probability of observing a flood at day i − t t i = ( ( = value of 0.8 is used for all catchments. The decay P ) y p ) n t t ( between 0.7 and 0.9. We use the indices API2 and K ( K T y ( K + P 0 for all other days. For days when the PAP exceeded PAP ... )), and = T = x ects of precipitation seasonality and maximizing the clima- + ) (2) ): ) ff t t 2 − t ( ( ( − T i p y (1 P 2 x/ PAP K 1 + β log( 1 and 1 + − ers the advantage of avoiding the assumption that logit( = i = 0 ff ) ) β t x KP ( = y + is the daily precipitation sum, is a proxy for diverse water fluxes that lead to a reduction of the water stored i )) . Statistical testing can assess the significance of the predictor PAP t P P value implies that “the exceedance of a given precipitation threshold significantly p ( K p = p i 45 days range for each day of the calendar year. For example, let us assume that erent thresholds (P50, P75, P90, P95, P99) and the Binary daily time series of floods In Sect. 4.4 we assess the importance of the di Beside the simple precipitation sums, more complex indices for antecedent precipita- We are particularly interested in the value of Note that working with binary predictors is not mandatory in logistic regression. Here ± ff tion, i.e. APIs are used. e.g. APIsKohler have been and commonly Linsley used, Ray et in1951; al.(2003): hydrologyPui for et decades al., (see 2011). We follow theAPI method of Baillifard tological sample size (91compared days to per 1820–4550 year other values). times 20–50 years means that each value is where the decay factor. Here, a constant factor in a catchment. In this study, a decay rate of 20 % per day, i.e. reflects roughly typical conditionsinsensitive to in a Switzerland tested (Baillifard range et of al., ).2003 Results are API4 that include all(hereafter days also of called PAPs). the time series up3.4 to 2 and Logistic 4 regression days before theThe flood underlying hypothesis day of this studya is that, significant if signal a can PAP is be importanton detected for the using flood generation, other the logistic hand, regression. impliesthat A this either lack influence that of is the significance too PAP weak has to no be influence significant on during flood the probability investigated or period. where logit( and the odds ratio canability thus be understood as a multiplicative factor for the flood prob- is then fitted as follows: logit( a measure for the increaseoccurring (or when decrease the if PAP exceeds percentile cant changes the flood probability”. this choice o a given percentile threshold given the predictor, i.e. test is selected (the corresponding thresholds and odd ratios are not discussed). time series contain approximatelyrecorded 7000 to 18 000 days yearly discharge maxima and even rarer events) and we can therefore set tional to the percentile oflar the argument precipitation could period; be anperformed found. assumption with A for predefined drawback which is no thresholds.di however particu- Here, that the the logistic regression regressions can are only tested be for 5 eration at each catchment. Aarately test using is a performed logistic for regression each model. catchment and each PAP sep- a a flood occurred on theprecipitation 1 accumulations June between 2000. 17 Therespective April D4-14 percentile and of of 16 that D4-14 July daymine is from is the calculated. compared 1979 percentile For value to to for each all 2011 each flood 11 A and PAP. day event range the we of can thus deter- between minimizing the e 5 5 15 20 10 25 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 back- − at the gauge, ff 10 represents the distribution of pre- − = x 3257 3258 3 also show high precipitation sums with medians − 2 and − 2 days around high precipitation events like it is enhanced around ± erent precipitation periods preceding Swiss floods ff Daily precipitation sums correspond to the 06:00 to 06:00 UTC accumulations and In order to determine the optimal separation of precipitation periods for the sample Precipitation 2 to 3 days before floods is also greater than climatology in all catch- amounting to climatological percentiles 75 and 60, respectively. From day rence, especially for the floods ini.e. Micro the catchments. time The response between time precipitation of and catchments, registration of the related runo are therefore shifted by 5This h partly compared explains to the discharge one-day peaks shift recorded on between calendar maximum days. precipitation and HQ occur- wards, the precipitation distributionbe is slightly very enhanced up closewhen subsamples to to of 10 climatology, catchments are to although analyzedprecipitation 15 (Fig.2b–d). it days is The tends before maximum recorded median floods. to 0–1 daily daysbefore Similar before HQ results days HQ at are days Lakes at observed Exits.before A Micro HQ clearly days catchments enhanced is and median only 1–2 precipitation found days prior at to Lakes 4 Exits. days plays a role as well. We(hereafter therefore the group the PAP called flood days D0-1; andprecipitation see the rates also preceding1). Table are days This together most isfor likely. the Micro As time and shown range Macro in when catchments whereas intense Fig.2boccurs for and 2 Lakes days Exits c, before the this floods mostIntense assumption (because intense precipitation precipitation is of events valid longer responsible response forminutes times flood in due peaks to the might lake case bebetween retention). of very precipitation flash short and floods) (hours floodswindows. or but does the not daily allow resolution for of a the further dataments and separation and, interestingly, the of precipitation shift remains theter also time greater floods than in climatologywhich 2 Fig.2a. days shows af- An the results explanation ofmum an for precipitation analysis this days similar instead to phenomenon of thesimilarly flood can one enhanced days. of be In Fig.2a Fig.2e, but found the applied in precipitation to distribution maxi- Fig.2e, is flood events. The typical time scaleto of some precipitating persistence weather of systems the daily over weather Europe situations leads so that daily precipitation time series after all floods. For example,cipitation the sums boxplot recorded at 10recorded days before 10 days all prior floodsmost to (4257 often the values recorded one 4257 ofexceeded day daily flood before in precipitation days). floods 75 % when Moderate of the98th to climatological 80th the percentile. intense local cases During seasonal flood precipitation and days, percentileto the is the median is percentile median precipitation 93. only precipitation The amounts sum days corresponds to the of events considered, the precipitationure2a distribution shows is the first distributions investigated day of by daily day. Fig- precipitation sums for every day prior to and 4 Results Hereafter, we will uselanguage, percentiles we to define a describe set precipitation of intensities. expressions (see To2). simplify Table 4.1 the Defining di The first challenge istriggering to precipitation distinguish can between be eventlasting in and between the a pre-event form few precipitation. hours ofconducive Flood to synoptically to several driven repeated days precipitation when precipitationcipitation (periods the events) events synoptic and/or (typically situation localized convective). is and Ideally, particularly alogical short flood-by-flood model lived analysis should high using be a pre- intense performed hydro- precipitation to rate determine and the theevents exact discharge that time peak, would lag as be between well attributedof the as to a most to a cyclone. merge particular However, a all synopticbecause precipitation case-by-case situation, the analysis daily such resolution is as of beyond the therates the passage data on scope does sub of not daily allow this timescales forconsidered. study an and Instead, first evaluation second we of because search precipitation for ofrepresent simple the the indices, very precipitation i.e. large associated PAPs number that with will of all (on events floods average) in best Swiss rivers. 5 5 10 15 20 25 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 − 3 and − erent PAPs are ff 10 days corresponds to < 2 when precipitation is clearly higher than + 3260 3259 3 to day − 14 (PRE-AP). The large majority of floods are associated − erent events. Extreme or very intense precipitation (return period ff 4 and day P90 and thus to less than 20–30 mm in 2 days (see Fig.3a and d). There − < The logarithmic scale of return periods in4 Figs. 5 and underlines the fact that return In hydrology, “antecedent precipitation” typically implies all the precipitation preced- 100 days) is frequently associated with floods, but a majority of catchments also ex- 4) is only based on averaged statistics and although flood-triggering events can be are more floods without intensePluvial D0-1 regime. in The Nival D0-1 and invial Glacial Jurassien group. and regimes D0-1 Meridional as is groups slightly compared lower is toExits. in comparable HQ5 the Macro to s catchments the and and Plu- HQ20 clearly sHQs, the tend weakest although to for they be Lakes can associated withof also longer less be return than triggered periods 10 by of days),ments. non-intense D0-1 especially Interestingly, than precipitation extreme at D0-1 (return s Lakes periods often Exits,indicating occur as widespread simultaneously well in events. as several Most catchments, inin of Glacial 1978, them 1987, and correspond 1990, Nival 1999, to catch- 2002, extraordinary 2005, flood and events 20074.2.2 and involve several HQ20 Precursor s. antecedent precipitation Figure5 is similar to Fig.4 butbetween shows day the PAP D4-14, i.e. the accumulated precipitation a percentile with return periods ofHQ5 s PRE-AP and lower than HQ20 scases 10 days, are of i.e. unusually not not wet(like associated unusually in PRE-AP with wet. 1972, typically 1993, In higher 1999 occur general PRE-AP and simultaneously 2006). than at HQs manyperiods and catchments of the D4-14 are rare one several orders cannot of expect magnitude D4-14 lower to than be those systematically of extreme D0-1. as However, this 11 day period often ex- 4.2.1 The 2 days precipitation Figure4 shows the 2 day PAP (D0-1)(HQ) associated of with each each annual catchment. maximumnitude discharge The between return di periods of D0-1 varyperience by HQs several during orders low of or mag- moderate D0-1. A return period > 4.2 Overview of the precipitation associatedWe with start Swiss the floods analysis withwith an Swiss floods overview (event of and the pre-event variability precipitation). of the precipitation associated are autocorrelated.2a Figure thus highlights a time window centered between day − defined over a wide rangeguish explicitly of long time range scales; antecedent precipitation weitation from choose is a obvious this period in simple when rainfall formulation unusualexcludes time precip- the to series. last distin- We 3 strongly days before emphasize floods. that hereafter PRE-AP ing the very last flood-triggeringinto event. the Here short-range we antecedent separate precipitation flood-precedingtecedent precipitation and precipitation what PRE-AP. Although we this define sharp as separation the (between precursor days an- and day 0 and ranging from day shown in Fig.3. For example, thecatchments P99.9 by of D0-1 the in rightmost summer orange50 is % boxplot summarized of for in all the Fig.3a. Macro Macro TheD0-1 P99.9 catchments at exceeds and Macro 94 reaches catchments mmorange 156 is for mm and in at the general blue one lower boxplot). catchment. in The winter P99.9 than of in summer (compare the usual. We identify it as theerate time range high when precipitation. the flood-producing Two weather morebefore situations PAPs gen- floods are in thus order definedevents which to (periods D0-3 range capture and back precipitation D2-3). tosubsequently associated The 3 defined “precursor days with antecedent as longer-lasting precipitation” the weather (PRE-AP)PRE-AP period is are finishing D4-6, D4-14 4 days andis D4-30. before To floods. also complete the PAPs applied representing set to ofspectively). PAPs, APIs a Hereafter, similar the (see separation analysis API2 iscomparison, and precipitation based API4, sums on [mm] stopped corresponding seasonal to 2 percentiles percentiles and of of di 4 the days PAPs. before For floods, re- 5 5 10 15 20 25 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent flood ff erent periods ff 50. < erent flood samples (deviations from ff P90) for 90 % of the events (6 % P90–95 > 3262 3261 P90). Only 10 % of the floods were preceded by > P90). For D2-3, intense and very intense precipitation 90 % < = P99 P50). On the other hand, no significant departure from climatol- > < 61 % + 99 of D4-14 is as frequently observed as P > preceding Swiss floods P99) for 61 % of the floods and intense ( Although no significant signal is found, PRE-AP was nevertheless slightly wetter In summary, the flood events considered in this study, with the exception of Lakes Figure6 shows the distribution of PAPs for di For HQ5 s in Micro catchments (Fig.6a), precipitation during D0-1 was very intense Figure6e and f shows the results for HQs and HQ20 s in all catchments. During D0- The statistics of Meso and Macro catchments (Fig.6bIn and contrast, c) HQ5 s resemble at the Lakes ones Exits of (Fig.6d) were triggered by significantly higher than 23 % P95–99 > responses to PAPs. Previous studiesdepends showed not that only typical on flood-triggering catchmentvarious precipitation size catchment (investigated properties in the (e.g. Helbling previousMerz section), et and but al., Blöschl, also2006; 2003 ; on includeDiezigWeingartner mean and et Weingartner , al., elevation,2007).2003; slope,underground Potentially cavities). land important The cover, properties soil hydrologicaland serve regimes type, as encompass geology a framework some and for of interpreting reservoirs the this (lakes, following variability analysis. floods for particular catchments, particular flood types, and4.4 particular flood seasons. Catchment by catchment analysis Here, we use logisticeach regression catchment: to is address the theOr occurrence in following of other question HQs words: are for influenced floodsto each by more investigate whether the (or PAP the less) and amount large frequent of variety after of precipitation? wet Swiss periods? basins We is thereby associated aim with di than climatology before floodsare in presented Switzerland. in Consequently, more the detailed next analyses sections to explore the correlation between PRE-AP and enhanced frequency of wet weeksfloods is did only not found happen for after Lakes significantly Exits. wetter For other nor catchments, drier PRE-AP in general. Exit floods, frequently co-occurthe with day intense before precipitation (D0-1).tion during Longer-lasting during the D2-3. multi-day flood The events daycompared slightly also and/or to larger Micro generate departure catchments high from indicatesHelbling climatology precipita- a et higher during importance D2-3 al.(2006) of atlonger-lasting already longer-lasting Marco precipitation events. showed at the that sub-dailymulti-day larger scale; events. Regarding here catchments precipitation we 4 can are or extend more more those days before findings sensitive HQ to days, to a significantly and HQ20 s is surprisinglyfor similar HQ20 during s the than other for HQs periods and (D2-3 PRE-AP is is only basically slightly the higher same). cludes the heavy precipitation (whichon happens just to before further the quantify flood). these We qualitative will observations. now move 4.3 Quantification of the precipitation intensity during di ( The overview of flood-precipitation in thePAP last D0-1 50 was years intense revealed that or precipitation extreme14) during for was a not. majority of Thisfloods, floods raises still but the PRE-AP tends question (during to of PAP be D4- wetter whether than D4-14, climatology. although notclimatology extreme significant at before the 99 % level are outside of the gray zones). 1, very intense precipitation isit twice is as prior frequent to prior all to annual HQ20 HQs s (45 (80 % % of of all all floods). floods) However, the as precipitation prior to HQs + no or moderate precipitation ( was also significantly more frequent thanis usual very although weak the compared deviation from tothan climatology usual D0-1. (only Drier 35 % percentiles were also significantly less frequent ogy is found for the PRE-APrule, PAPs (D4-6, the D4-14, conditions D4-30). This were meansfloods not that, in significantly as Micro a wetter catchments. general than usual earlier than 3Micro days catchments. before usual precipitation during all PAPsthe (and P not only during D0-1 and D2-3). For example, 5 5 25 20 15 10 10 25 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4; i.e. / erent PAPs (see de- ff contribution from melt during ff 0.001) in Fig.7a indicate that, in < value P in the main streams is recorded. Soils in the 3264 3263 ff ect the flood probability. By comparing the results of D0-30 ff value with an odd ratio smaller than 1 for D4-14. These are the exact 6 catchments In summary, the role of long-term antecedent precipitation for flood generation de- Enhanced flood frequency after wet periods is less surprising. The Swiss Plateau, 7 Figure shows the results of the logistic regression for the di A reduced flood frequency following wet periods (like found for the glacial catch- tributor to the floods discharges. 4.4.1 Antecedent precipitation indices (APIs) We also tested the power ofpared APIs (see to1) Table for simple statistically precipitation predictingday floods sums. and as API2, the com- day like preceding D2-3, theinstead flood omits but of information accounts for about for only the the whole 2D2-3 days. flood antecedent precipitation The is results a for better botha periods (more weaker are predictor significant) similar for flood in 11 most predictor catchments. catchments. than API2 API2 allows for us 12 to catchments, distinguish and the relevance of pends strongly on the regionAP and/or periods on enhance the hydrological HQportant regime probability processes considered. where and Wet soil decrease PRE- HQ saturation probability and where reservoir melt filling water is are an im- important con- especially the western part,need to is be a saturated relativelyJura before are flat large typically runo area thinner characterizedderground but karstic by very cavities. deep A permeable karstic soils and undergroundvoirs, that this the network water can region level contain is of important which wellBall reser- influences and known the Martin, flow for response2012). its in un- surface streams (see e.g. the flood seasonnegative (summer, correlation see is e.g. probably due(lowerVerbunt to temperature, et the reduced al., fact sunshinebaseflow that2003; and prolonged inKöplin hence periods those et reduced of catchmentstion wet melt) al., so events weather would can2014). that be lead contributions less The likely totime from to series a short generate of annual lower and discharge glacial intense peaks.cycle catchments Indeed, precipita- in discharge are summer, typically revealing characterized theand by importance discharge a generation. of pronounced The high baseflow diurnal extended temperature continuously periods and rises of sunshine nice from for weather day melt which to are day therefore in particularly case conducive of to floods. tails in Sect. 3.4). For example, triangles ( every catchment investigated, floods werelar significantly threshold more of frequent D0-1 whenfalls a was during particu- exceeded. D0-1 In hastation other a that words, significant falls the impact during amountmost on D2-3 of catchments, flood (Fig.7b) precipitation with frequency. also that the The significantlyments. exception amount impacts of With of the most regard precipi- flood Glacial topatterns frequency and PRE-AP can few in in be Nival D4-6, distinguished. and Wetfrequency D4-14 Pluvial antecedent mainly catch- and in periods the D4-30 significantly northwest (Fig.7c–e), enhance andlakes the northeast clear except Switzerland, Lake flood regional Thun as well (nb. 111). aster In at wet the contrast, floods exit periods of were in all significantly some lessP Glacial frequent catchments. af- Indeed, six catchmentswith show more a than significant 25 %AP glacial does coverage. For not the significantly rest a of Switzerland, the amount of PRE- approximately 12–13 HQs. Itan is explanation almost must impossible thereforelocated to at be get high found. elevations, 0 Glacial exhibitterized HQs steep catchments by just slopes very are and by short typically lack chance response deep small and times soils. and and They a are large charac- runo ments) seems counterintuitive. Thethe most most significant glaciated negative catchmentsignificance correlation (the is is Aletsch obtained found glacier inHQs for this catchment, recorded correspond case nb. to with 865). the the The 25 % threshold highest wettest P75 D4-14. The because expected none value is of 51 the 51 with D4-30, it emerges30) that in a floods large are majorityfloods of significantly catchments lead associated only to because with high heavyduring wet precipitation monthly the 3–4 months accumulations. days rest before (D0- Indeed, of D4-30 thecatchments. indicates month has that no precipitation significant impact on the flood probability for most 5 5 25 20 15 10 10 25 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff erent flood seasons. ff erent catchment sam- ff erent flood seasons). Assuming that ff 3265 3266 erent hydrological regimes and di ff 1–3 years while HQ1 s correspond to a median D0-1 of = erent catchment groups, di ff cient is enhanced by wetter PRE-AP (and thus more saturated soils), floods ffi coe ff Figure9 shows D0-3 and D4-14 of all flood events for di For the Micro, Meso and Macro catchments in Fig.8a, larger floods correspond to Figure8a can be directly compared to Fig.8b. For Micro, Meso and Macro catch- Figure8c–f investigates di In8, Fig. the flood-precipitation is simply summarized by the median return period of For no regime and noamplitude. season Even is at the amount Jurassien of catchments,more PRE-AP where frequent precipitation we linked found after to that wet theHQ1 floods flood s. periods, are HQ20 significantly s are not associated with4.6 wetter periods than Can weaker precipitation trigger floodsIn if the PRE-AP is previous higher? sections,combinations of the PRE-AP PAPs and wereruno short-range investigated precipitation separately. events Here formight we single happen show floods. in the association If with the weaker triggering events. ples. As already inferredremarkably from between single Fig.4, events precipitation andprecipitation accumulations is the highest portion before in of floods Glacialthe floods and vary linear lacking Nival regression catchments. intense between The triggering D0-3 greenfor and lines clarity). D4-14 in Fig.9 for The HQ5show PRE-AP regression events allow (only lines weaker HQ5 weather s address events are toJurassien, the shown Meridional generate and HQ5s? following Lakes Indeed, Exit question: it catchments,weather seems did HQ5 events that s tend that for wet to the were periods be triggered associated by of weaker with higher values of PRE-AP. This is in contrast of D0-1 of 400–1000only days 60 days). Incatchments. contrast, At HQ20 s those catchments, are as related much to precipitation clearly falls higher 2 D2-3 to only 3 days at before Macro the higher D0-1 than smaller floods (HQ20 s are associated with a median return period ments, the return periodsother of hand, D0-3 the in mediansize Fig.8b (close PRE-AP to are the is similar climatological remarkablyHQ20 to median). s Moreover, close the than the before PRE-AP ones to HQ1 was of s. normalLakes not A Exits. higher D0-1. for change before On in each the PRE-AP catchment with flood magnitude is only found at flood magnitudes, di the precipitation distribution isperiod should equal be to equal climatology to 2 before days (delimited floods, by the solid median lines in return the graphs). HQ20 s as falls 0D0-1 to because 1 of days the before long allSect. time HQs.4.1). delay At between Lakes precipitation Exits, and D2-3 gauged discharge is (see more extreme than the PAPs for a flood sample. This allows us to compare various flood samples (di dry periods for floodingthe in flood Glacial to capture catchmentsconditions this but followed signal. D2-3 by However, wet is combining conditions D2-3 too arein and Gsteig important short (nb. D4-6 for 387), and flood indicates for formation too that example.nal in Both close dry is the periods to Lütschine found. cancel out Searching inPRE-AP. for Each API2 the of and the best no 4 significant period periods sig- predictor also (D4-6, at appears D4-14, D4-30, several to API4) catchments. be isprecipitation D4-30 complex the sum is with most over significant regard rarely a flood to the monthlyAPI4 period best is is predictor, slightly not indicating more a that often powerfulsystematic. a measure the APIs better for are flood measure widely probability. than usedFedora D4-6 in and and hydrology D4-14, (see Beschta, although e.g. 1989; thisKohlerstudyHeggen, is and cannot not Linsley2001; confirm, Ray thatTramblay1951; et theysums. al. , explain flood2012) frequency but better our than integrative simple precipitation 4.5 Impacts of short range precipitationIn and the PRE-AP on previous flood sections,whether magnitude the floods impact are ofmagnitude more is PAPs frequent investigated on after as HQ wellsmaller wet (i.e. floods). probability periods). whether was larger Here, floods discussed the follow (i.e. wetter impact periods on than the flood 5 5 10 15 20 25 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cient changes ffi coe ff erently related to PRE-AP ff cient is independent on the ffi coe ff 3268 3267 formation is well established. Indeed, models showed ff erences in discharge (see e.g. Berthet et, al. 2009; Pathiraja et al., ff ect flood probability in Australia (see e.g. Pui et al., 2011). ff erent regions. (i) Annual floods are significantly more frequent after wet PRE-AP ff The lack of correlation between precipitation during PRE-AP and the occurrence of Our findings are not necessarily in contradiction with these studies. We find that the Moreover, several limitations inherent to the statistical experiment must be consid- We call weekly to monthly precipitation periods preceding floods by more than 3 days The statistical results do not mean that the runo annual floods at the majorityence of of catchments soil may saturation appear on surprising runo given that the influ- peaks. Our results thus highlightfluence that floods long precipitation (occurrence periods, and if amplitude)role at in should all, the not only Swiss be weakly Alps. in- overestimatedrelevant. We and emphasize that that a their 4 day period must be considered as most that for the samelead triggering to precipitation strong event, variations di in antecedent moisture can strongly with the amountland of (e.g. antecedentSpreafico et precipitation al., forhave).2003 various been Moreover, soil weekly described to types as monthlyfloods in important precipitation (see Switzer- anomalies e.g. factorsUlbrich for etfound the al., to2003; development a Grams of et extreme al., 2014; European Schröter et al., 2015) and was ered in order to correctly appreciate the results: amount of PRE-AP. Ourgenerate analysis a simply significant shows signalnevertheless that when expect this to 20–50 dependence be floods on iscant per the influence too catchment safe on side the weak are flood when to investigated. occurrence5 stating at We tests that a for a particular each catchment PRE-AP catchment. and Indeed, hasP90, each we no P95 PAP performed (we signifi- tested or if P99 the exceedance of of the the P50, PAP P75, significantly changes the flood probability). Significance 2012). Also, artificial rainfall experiments showed that the runo role of PRE-AP isthat the very absence dependent of link onPrealpine, between Alpine the PRE-AP (except and hydrological glaciers) flood regime occurrence and is southern of specific Alpine the to catchments. the catchments Swiss so in di periods in most Jurassien catchments, innortheastern some Pluvial Switzerland, catchments and of at northwestern lakes and quent exits. (ii) after Annual wet floods are PRE-AP significantlynot periods less significantly in fre- related glacial to catchments.Swiss the (iii) occurrence catchments. The of Also, amount annual PRE-AP ofat floods is PRE-AP lakes in absolutely is exits the (for not rest all relatedThe catchment (the to sizes, precipitation majority) all flood outside of hydrological of magnitude regimes a except and 4 during day all period seasons). is not related to the amplitude of discharge “PRE-AP” (PREcursor Antecedent Precipitation)cal periods. analysis The shows that comprehensive the statisti- occurrence of annual floods is di to Glacial catchments where weaker events triggerflood HQ5 forecasting, s after it drier would periods. be Regarding of interesting event to precipitation define isscatter a required in minimum to observations threshold: trigger shows what that aland amount defining HQ5 because such given floods a that can thresholdflood PRE-AP occur is sample impossible for is in which for association known? such Switzer- The a withExits. threshold all There, would a types be realistic HQ20 of isceptionally occurred precipitation. the wet without set The period of precipitation only of HQ20 in s PRE-AP.wet In at the Lakes contrast, periods last all of 3 HQ20 days s PRE-APmight but occurring be required after after a at an minimum not threshold ex- least unusually as of a D0-3 well for but D0-3 HQ20 it s of does in return not Macro seem and period to Meridional of catchments depend on 100 days. PRE-AP. There 5 Discussion Our results are based onmon a synoptic signature and of statistical antecedent approach precipitationland. that to In emphasizes a this the large section, com- sample wedata comment of on and flood the put events specific in our limitations Switzer- results of in this the approach context and of of previous our work. 5 5 25 10 15 20 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent Swiss hydrological ff erent precipitation periods ff erent precipitation periods for the generation er new opportunities with this regard. Also, ff ff 3270 3269 cients of some catchments (e.g. Paschalis et al., 2014). Pre- ffi ects flood frequency everywhere except in the high Alps. It is more- coe ff ff value of 5 %. The 2 day sum (0–1 days before floods) is clearly the best correlated with both the At the short range, we do not separate antecedent precipitation from the precipitation Our results could be refined by including information about the precipitation phase. In summary, the small scale distribution of precipitation, the precipitation phase and Antecedent precipitation is not antecedent moisture. Extending the results to the The small-scale time and space distribution of precipitation is an important determi- P lier periods however, we findquency that for PRE-AP the has majority hadnitude of no of Swiss significant floods catchments impact was intypes on also the flood except independent last fre- at on 50 years. lakes thesuggest Moreover, exits. magnitude the that The of researchers mag- focus PRE-AP influence oning in of 2 antecedent all PRE-AP to precipitation catchment is 4 of days pastrisk thus precipitation Alpine from periods overall floods climate when weak. or reconstruct- projections. We when Long thus inferring range future Alpine antecedent flood precipitation periods preceding flood frequency and thealso flood significantly a magnitude. Theover precipitation related 2 to flood to magnitude 3 days at lakes before exits floods and in large catchments. Regarding ear- amount of precipitation related to flood frequency and flood magnitude? event directly triggering the dischargeseveral peak. days Instead, and we adress consider the accumulations following over question: over which preceeding period is the of thousands of annual floods inantecedent Switzerland. precipitation In as contrast to all previousprecipitation studies the event, that we water define explicitly that separateand antecedent fell long-range precipitation before into antecedent the the precipitation short-range verytation based time last on series the flood-triggering and autocorrelation reflecting thethe of synoptic daily 0–3 time days precipi- scale. period The short-range beforeAP). encompasses This floods novel and distinction the allowsprecipitation to long periods specifically range for address the flood the generation. earlier role of period several (called antecedent PRE- for the occurrence and amplitude ofregions. peak discharges in the di 6 Conclusions We quantify statistically the influence of di peak discharges which are not addressedthe in role this of study. Our antecedent results precipitationsented are amounts here strictly on cannot limited a to be supra-daily directlygive scale. related The general to specific statistics and hydrological pre- robust processes. indications Instead, they on the relevance of di We chose not to distinguishing between from snowfall the and strong rainfall variations becauseinvolving of of hourly the uncertainty precipitation snowline aris- data on the may sub-daily o time scale. Future work the land surface state (soil moisture, snow cover) are other contributors to the final cipitation events can be veryand/or local localized and precipitation imply events can rapidlyand thus varying point be measurement-based rainfall precipitation smoothed rates. dataset Some outregard used short or here. very missed The in coarse PAPs are theprevents representations with daily- us this of from describing real theit precipitation sub-daily is events. flood-triggering unlikely to While precipitation impact characteristics, this the main limitation findings of our study; namely the role of PRE-AP. the presence of abut a snow high-resolution cover snow cover stronglySnow dataset was influences is therefore not the not available for considered. flow the response period of to investigation. precipitation role of antecedent moisture wouldthe require scope to use of land thisemphasize surface models study that which our given is results the beyond and are large limited that number to the of thegenerate events role moisture discharge of considered. state peaks, antecedent We especially may precipitation at thus better amounts the must time represent scale the covered bynant disposition PRE-AP. of of the a runo catchment to was established even if only onea of these 5 tests lead to a flood probability change with 5 5 25 20 15 10 25 15 10 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ects of an- ff ce of the En- ffi cient data is available. ffi simulation using an antecedent precipitation in- ff 3272 3271 de (last access: 29 January 2015), 2007. 3248 = ce of Meteorology (MeteoSwiss). , M., and Sartori, M.: Rockfall hazard mapping along a mountainous ffi ff The authors gratefully acknowledge the Swiss Federal O erences in return periods of precipitation prior to floods of a similar mag- ff er new potential with this regard although the use of radar data to achieve this ff Our findings are derived from extensive observationsAlthough and our results can are be specific expected to to Swiss be catchments,The large the di method presented here tecedent hydrogeologic conditions onin flood North-Central magnitude Florida, and recharge J.UF00091523/00614 Undergraduate to(last Res., the access: 13, Floridan 29 10 aquifer January pp., 2015), available 2012. at: 3264 http://ufdc.ufl.edu/ of extreme precipitation events in switzerland, Mon. 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Res., Grid-Data 39, Products, Daily 1340, Precipitation (final anal- Munich Re: NatCatSERVICE Database, Münchener Rückversicherungs-Gesellschaft, Munich, 5 5 10 25 30 15 20 15 30 10 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3276 3275 erent precipitation accumulation periods (PAPs) used in this study. ff The di D0-1D2-3 climatological percentile of theD0-3D4-6 2 days precipitation sum (from 0 to 1 days before ” the flood day) ” ” 2 days 4 days 3 days ” ” ” 2 to 3 days 0 to 3 days 4 to 6 days ” ” ” API2API4 ” ” API API (2 days 4 days before the flood day) ” D4-14D4-30D0-30 ” ” ” 11 days 27 days 31 days ” ” ” 4 to 14 days 4 to 30 days 0 to 30 days ” ” ” Weather patterns and hydro-climatological precursors1868, of extreme Meteorol. floods Z., in 21, Switzerland 531–550, since ,10.1127/0941-2948/2012/368 doi: 2012. , 3247 3248 Estimation of antecedent wetness conditions forEarth flood Syst. modelling in Sci., northern 16, Morocco, 4375–4386, Hydrol. , doi:10.5194/hess-16-4375-2012 2012. 3265 pean floods of Augustclimatic 2002: change, Weather, Part 58, 2 434–442,, doi:10.1256/wea.61.03B – 2003. Synoptic3248, causes3268 role and of considerations snow and with glaciers in respect36–55, alpine, doi:10.1016/S0022-1694(03)00251-8 to river 2003. basins and3264 their distributed modeling, J. Hydrol., 282, Flows, available at: (last access:http://hades.unibe.ch/en/products/druckausgabe/gewaesser/tafel5_02 29 January 2015), 1992. 3253 on examples from Switzerland,X, J. 2003. Hydrol.,3262 282, 10–24, doi:10.1016/S0022-1694(03)00249- the Rhine and Meuse Basins,3247 Rijkswaterstaat RIZA/KNMI, Lelystad, the Netherlands, 2007. Table 1. Stucki, P., Rickli, R., Brönnimann, S., Martius, O., Wanner, H., Grebner, D., and Luterbacher, J.: Tramblay, Y., Bouaicha, R., Brocca, L., Dorigo, W., Bouvier, C.,Ulbrich, Camici, U., S., Brücher, and T., Fink, Servat, A., E.: Leckebusch, G., Krüger, A., and Pinto,Verbunt, J.: M., The Gurtz, central J., Euro- Jasper, K., Lang, H., Warmerdam, P., and Zappa, M.:Weingartner, R. The and hydrological Aschwanden, H.: Discharge regime the Basis for the Estimation of Average Weingartner, R., Barben, M., and Spreafico, M.: Floods in mountain areas anWit, overview M. based J. M. and Buishand, T. A.: Generator of Rainfall and Discharge Extremes (GRADE) for 5 10 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1000 days 2 days 2 days 10 days < > > > ] Station Height [m] Avg. Height [m] Glacier coverage [%] Hydro. Regime 2 3278 3277 P50 P50 P99.9 P90 Area [km < > > > Y erent intensities of precipitation. ff coord X drier wetter expressionextreme percentilevery intense returnintense period P99.9-P99moderate 100–1000 days unusually wet P99-P90 P90-P75 10–100 days 4–10 days Summary of catchment properties for the selected stations. Catchments are sorted Expressions used to define di 882831 Steinenbach – Kaltbrunn. Steinenbrugg Steinach932 – Steinach 721 215 Sionge833 – 229 Vuippens. 745 Château Aach789 – Salmsach. Hungerbühl 19.1 Bibere – Kerzers863 572 420 Langeten 750 – 760 167 Huttwil. 451 540 744 Häberenbad 410 262 610 268 400 45.3 24.2 48.5 629 560 1112 219 135 581 681 280 59.9 406 201 850 406 50.1 0 597 862 710 480 443 Pluvial 766 0 0 0 540 0 Pluvial Pluvial Pluvial 0 Pluvial Pluvial 844821 Ferrerabach –945 Trun Alpbach751 – Erstfeld. Bodenberg Rein838 da Sumvitg – Sumvitg. Gornernbach Encardens803 – Kiental Ova da735 Cluozza – 718 Zernez 810 Witenwasserenreuss792 – Realp 167 Simme 690 – Oberried/Lenk 688 560 Rhone753 (Rotten) – Gletsch 185 120848 21.8 Kander740 – 717 Gasterntal. 795 Staldi Dischmabach 20.6778 – Davos. 179 Kriegsmatte 550 680 950 Hinterrhein922 – 624 450 Hinterrhein 804 930 160 Rosegbach 130793 – 155 Pontresina 130 174 1490 12.5 830 Chamuerabach782 – La 786 Punt-Chamues-ch 220 602 630 Lonza – 30.7 670 Blatten 810 1022 25.6 183 370 141 Berninabach 26.9 660865 – 791 430 Pontresina 157 200 621 080387 160 600 144 Massa 43.3 260 – 35.7 1220 Blatten bei890 38.9 Naters Lütschine 2450 – Gsteig 735 480765 1575 73.3 1280 40.7 788 Poschiavino 810 1509 – 154948 2200 680 La Rösa Krummbach 151750 – 690 Klusmatten Chli 1668799 Schliere 1096 – 53.7 789 440 Alpnach. 1761 Chilch Allenbach Erli826 2461 – 66.5 Adelboden 151 320 1720 Grosstalbach822 – 1470 Isenthal 643 2427 700 6.7 Ova 2270 dal916 629 2368 130 Fuorn – 137 290 Zernez. Minster 107 Punt862 – 27.7 la 663 Euthal. 140 800 910 Drossa Rüti Taschinasbach 1584 –852 Grüsch. 2372 2370 Wasserf.Lietha 199 570 1766 Saltina 644 810 195 500 560 2719 633720 – 802 130 120 Brig 767 930 77.8 Thur 2549 17.3 119 170 – 420 790 168 Stein. 142 200 010 2600 Iltishag 206 420 21.8 Grande Eau – 12.7 Glacial Aigle 1804 17.3 608 710284 19.8 55.3 2.2 379 14.1 Glacial 685 500 148637 2360 300 63 1446 Muota 196 050 – 2716 1520 Ingenbohl 34.6 2.1 Simme 52.2 – Oberwil 28.8 Glacial 453 704 425 1.5 43.9 43.5 Glacial 1795 215 2617 1707 310 1860 Glacial 585 Glacial 666 2945 736 17.2 020 59.2 2630 1297 Glacial 30.1 Glacial 228 250 563 642 Glacial 975 220 767 1370 129 129 825 630 Glacial Glacial 2276 2331 2283 18.7 84 2050 688 230 1768 894 77.7 132 Glacial 206 140 65.9 600 1856 060 36.5 Glacial 167 090 1820 316 0 850 0.02 Glacial 0.35 3 344 677 414 17.4 1351 0.04 Glacial Glacial 0 438 9.3 1448 777 Nival Nival 2050 Nival 1560 Glacial Nival Nival 0 1360 Nival Nival 1640 0 5.1 1.8 Nival 0.08 3.7 Nival Nival Nival Nival Nival 1252 Sellenbodenbach – Neuenkirch1240 Biber –1251 Biberbrugg Alp 658 530 –1022 Einsiedeln 218 290 Goldach –1118 Goldach1128 10.5 Rot – Roggwil Gürbe –1231 Burgistein. Pfandersmatt1151 Worble – Ittigen 697 240 Veveyse – 515 Vevey. Copet 223 280 605 890 698 181 640 880 31.9 753 190 223 020 261 590 53.7 615 46.4 49.8 630 260 554 675 825 231 650 146 565 569 603 005 840 53.6 202 455 62.2 399 0 60.5 1009 1044 436 399 1155 833 522 Pluvial 0 586 1108 0 0 679 0 Pluvial Pluvial 0 0 Pluvial Pluvial 0 Pluvial Pluvial Pluvial 1250 Goneri – Oberwald1064 Poschiavino – Le Prese 670 520 153 830 40 803 490 130 530 1385 16911431017 Engelberger Aa – Buochs. Flugplatz Plessur 2377 – Chur 96711171127 673 555 Kander – Hondrich 202 Landquart 870 – Felsenbach 14.2 227 2170 757 443 975 Glacial 765 365 191 925 617 6.5 790 204 910 168 400 263 616 496 1620 Glacial 573 571 650 4.3 1850 1800 1900 Nival 0 1.4 7.9 Nival Nival Nival Number Name coord based on hydrological regimeSwiss and coordinates increasing (CH1903). size from top to bottom. Locations are given in Table 3. Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ] Station Height [m] Avg. Height [m] Glacier coverage [%] Hydro. Regime 2 3280 3279 Area [km Y coord X Swiss river discharge stations selected for this study. Colors refer to the hydrological Continued. 67 Ticino – Bellinzona5132 Reuss – Mellingen47 Rhône – Porte du Scex Aare – Brugg 721 245 117 025 1515 662 830 557 660 252 580 133 280 3382 220 5244 657 000 259 360 11 345 726 377 1680 332 1240 2130 0.7 1010 2.8 14.3 Macro 2 Macro Macro Macro 959 Aubonne-Allaman. Le972 Coulet829 Seyon –946 Valangin Suze – Sonceboz Dünnern960 – Olten. Hammermühle 520 720915 Venoge-Ecublens. 147 Les 410 Bois Ergolz380 – Liestal 634 330 91.4 244 Birs 480 – Münchenstein. Hofmatt 559 370 532 196 040871 579 810 206 810 154 160843 390 227 350 Breggia – 613 570 Chiasso. Ponte di Cassarate 112 Polenta769 – 231 Pregassona 263 080 150 400 722 315 Calancasca – 622 270 Buseno 911 78 320 259 750 890 630 383 47.4 261 642 718 010 750 97 268 380 729 440 73.9 255 127 970 180 305 700 0527 1050656 120 Lorze377 – Frauenthal 740 Tresa – 0917 Ponte Tresa. Rocchetta Linth 291111 – Weesen. Biäsche Reuss – 927 Luzern. 590 Jurassien Geissmattbrücke Aare – 0 Thun 0 746 0 709 580 665 330 92 145 Jurassien 0 211 800 990 674 725 715 160 615 2251 229 221 845 380 0 Jurassien 0 Jurassien 1950 Jurassien 1061 259 Jurassien 268 432 0 613 230 Meridional Jurassien 179 280 419 1.1 390 2466 1500 800 Meridional Meridional 1580 690 548 4.2 0 2.5 1760 0 Lake Exit Lake Exit Lake Exit 9.5 Lake Exit Lake Exit 854 Bied du Locle – La Rançonnière 545 025 211 575 38879975 Riale di Calneggia – 819 Cavergno. Magliasina Pontit – Magliaso. Ponte 684 970 135 960 711 620 24 NA 93 290785136 Inn764 – 34.3 Tarasp Thur – Andelfingen 890 Limmat942 – Baden. Limmatpromenade NA Rhein – 665 640 Bad Ragaz. ARA 295 258 690 1996 2396 Jurassien 693 510 757 272 090 500 920 816 800 209 600 185 910 351 1696 0 4455 1584 356 0 1130 491 1183 Meridional Meridional 770 2390 1.1 1930 Macro 0 5.1 1.9 Macro Macro Macro 834528 Urnäsch – Hundwil. Äschentobel Murg911 – Wängi Necker –898 Mogelsberg. Aachsäge 740 170888 244 800 Mentue650 – Yvonand. La Mauguettaz Langeten977 – Lotzwil 727 110 Gürbe 64.5549 – Belp. Mülimatt 247 290 545 Murg 440978 – Frauenfeld Töss – 180962 875 Neftenbach Sense 88.2883 – Thörishaus.Sensematt Wigger938 – 714 105 Zofingen 105 747 Broye – Payerne. 261 Caserne 720 d’aviation Glatt944 – Rheinsfelden 593 350 604825 810 561 660 626 840 78.9 606 Kleine 193 020 Emme 192 – 680 187 Littau. 320 226 535 Reussbühl Thur – Jonschwil. 449 709 Mühlau 540 1085 352 691 460 117 269 392 660 115 664 263 220 820 466 637 213 580 200 212 959 237 678 080 040 342 269 679 477 720 723 553 675 522 441 500 368 252 720 0 416 390 650 493 389 431 0 1068 426 837 710 713 0 336 Pluvial 534 580 650 0 1050 Pluvial 660 0 Pluvial 498 0 0 0 1030 0 Pluvial 0 0 Pluvial 0 Pluvial Pluvial Pluvial 0 0 Pluvial Pluvial Pluvial Pluvial Pluvial Pluvial 1173 Promenthouse – Gland. Route Suisse 510 0801150 140 080 Allaine – Boncourt. Frontière1139 100 Areuse – Boudry 567 830 261 394 200 2151287 Vedeggio – Agno1241 1037 554 350 Verzasca – Lavertezzo. Campi 199 940 366 377 708 420 122 920 0 559 714 110 186 444 95 680 105 Jurassien 4901253 0 10601170 Rhône – Genève. Halle de 281 Aare l’Ile – Brügg. Ägerten 1672 Jurassien 499 890 0 117 850 898 7987 588 220 219 020 0 Jurassien 8293 369 0 Meridional 428 1670 Meridional 1150 9.4 2.9 Lake Exit Lake Exit 1254 Scheulte – Vicques 599 485 244 150 72.81255 Riale di Pincascia – Lavertezzo 463 708 060 123 950 44.4 785 536 0 1708 Jurassien 0 Meridional 1066 Lorze –1140 Baar Lorze – Zug. Letzi1100 683 300 680 600 228 Emme 070 – 226 070 Emmenmatt 84.7 101 455 417 623 610 200 420 443 866 825 638 0 0 1070 Pluvial Pluvial 0 Pluvial Number Name coord Figure 1. regimes in the legend. Stationsthropogenic at influence on lakes the exits discharge are (lakesrefer shown exits to by are3 Table thus triangleswhich analyzed to provide separately). highlight The brief numbers the descriptions strong of an- the catchments. Table 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . (a, d) ● ● ● P99.9 P99.9 ● ● P99 P99 P95 P95 . APIs are shown in P90 P90 (b, e) erent PAPs. Statistics from P75 P75 ff 30 30 30 30 30 P50 P50

c) API at Macro cat. f) API at Micro cat.

400 300 200 100 0 400 300 200 100 0 20 20 20 20 20

● ●

mm mm a) Floods at All cat. a) Floods at All e) Max. prec. at All cat. P99.9 P99.9 b) Floods at Micro cat. d) Floods at Lake Exits d) Floods at Lake c) Floods at Macro cat. ● ● 10 10 10 10 10 P99 P99 P95 P95 ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●●●●●●●●●●●●●●●● ●●●●●●●● ●● ●●●●●●●●●● ●● ●●●● ●● ●●●●●●● ●●●●● ● ●●●●●● ●●●●●●●●●● ●●●●●● ● ●●●●●●●● ●●● ●●●●●●●●●●●● ●●●●●●●● ●●●●●●● ● ●●●●● ● ●●●●●● ●●●●●●●●● ●●●● ●●●● ●●● ●●●●●●●● ● ●●● ● ●● ● ●●●●● ●● ● ●●●●● ●●●●● ● ●●●●● ● ● ● ●●●● ● ● ●● ●●● ●● ● ●● ● ●●●●● ● ● ● ● ● ●●●●●● ●● ● ● ● ● ●●● ●● ●● ●● ● ● ● ● ● ● 0 0 0 0 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● P90 P90 3282 3281 Days after annual floods after annual Days floods after annual Days floods after annual Days floods after annual Days P75 P75 −10 −10 −10 −10 −10 Days after annual maximum daily precip. maximum after annual Days P50 P50 10 represents the distribution of daily precipitation per- b) D4−14 at Macro cat. e) D4−14 at Micro cat.

−

400 300 200 100 400 300 200 100 0 , but applied to annual maximum precipitation days instead 0

−20 −20 −20 −20 −20

● ● =

mm mm (a) x P99.9 P99.9 ● ● ● catchments are shown on the top and bottom row, respectively. P99 P99 −30 −30 −30 −30 −30 0 0 0 0 0

75 50 25 75 50 25 75 50 25 75 50 25 75 50 25

100 100 100 100 100 Percentiles of climatology of Percentiles climatology of Percentiles climatology of Percentiles climatology of Percentiles climatology of Percentiles but for floods in Micro catchments, Macro catchments and lakes ex- P95 P95 (d–f) (a) P90 P90 P75 P75 winter summer P50 P50

d) D0−1 at Micro cat. a) D0−1 at Macro cat.

Same as and Micro

200 100 0 400 300 200 100 0 400 300

The distribution of daily precipitation before and after all flood events is shown in

Absolute values of the climatological percentiles for the di

mm mm (a–c) (b–d) The same procedure as in . (e) . For example, the boxplot at Accumulations over 11 days corresponding to D4-14 are shown in (c, f) Accumulations over 2 days which correspond to the PAPs D0-1 or D2-3 are shown in Macro Figure 3. centiles 10 days prior tocatchments). the The middle 4257 line annual ofpercentile flood the events boxplots range, shows analyzed and the in the median,range. this the whiskers study boxes comprise end (all the at HQs 25–75 from a all deviation from the mean of 1.5 the interquartile Figure 2. (a) its. of annual flood days. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | axis, see y Meridional, ma- = 1000 100 10 1 RP [days] 1000 100 10 Jurassien, red RP [days] = 2010 2010 2000 2000 1990 1990 Years 3284 3283 Pluvial, orange Years = 1980 1980 1970 1970 1960 67 51 32 47

862 852 720 284 637 882 831 932 833 789 863 834 528 911 898 888 650 977 549 978 962 883 938 944 825 854 959 972 829 946 960 915 380 879 975 871 843 769 785 136 764 942 527 656 377 917 111 844 821 945 751 838 803 735 792 753 848 740 778 922 793 782 865 387 890 765 948 750 799 826 822 916

1240 1251 1022 1128 1118 1231 1151 1066 1140 1100 1254 1173 1150 1139 1255 1287 1241 1170 1250 1064 1143 1017 1117 1127 1252 Stations Nival, green 1960 =

32 47 67 51

825 854 959 972 829 946 960 915 380 879 975 871 843 769 785 136 764 942 527 656 377 917 111 844 821 945 751 838 803 735 792 753 848 740 778 922 793 782 865 387 890 765 948 750 799 826 822 916 862 852 720 284 637 882 831 932 833 789 863 834 528 911 898 888 650 977 549 978 962 883 938 944 1066 1140 1100 1254 1173 1150 1139 1255 1287 1241 1170 1250 1064 1143 1017 1117 1127 1252 1240 1251 1022 1128 1118 1231 1151 Stations Lakes Exits. = Glacial, cyan = Overview of all flood events. All river discharge stations (numbers on the Same as Fig.4 but for PRE-AP (D4-14). Macro, brown = Figure 5. Figure 4. 3)Table cover at leastthe 20 years return in period the ofHQ20 s 1961–2011 the are two-days period. marked precipitation Forregime with sum type each squares and (D0-1) annual by and is dischargecolors: increasing triangles, indicated peak, blue size respectively. by from The colors. topgenta catchments to HQ5 s bottom. are and Hydrological sorted regimes by are indicated by Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (c) D0- (f) D4-30, (e) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● HQ20 s in all catch- P99−100 P99−100 P99−100 Meso catchments, (f) ● ● ● ● ● ●● ●● ● ● ●● ● ● (b) D4-14, P95−99 P95−99 P95−99 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● (d) h) API4 b) D2−3 P90−95 P90−95 P90−95 f) D0−30 ● d) D4−14 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● P75−90 P75−90 P75−90 Percentiles bins Percentiles bins Percentiles bins Percentiles ● ● ● ● ● ● ● ● ● D4-6, ● ● ● ● ● All HQs and ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● (c) ● ● ● P50−75 P50−75 P50−75 ● ● ● ● ● ● ● (e) ● ● ● ● ● ● ● ● b) Meso cat., 305 HQ5 events b) Meso cat., Exits cat., 75 HQ5 events d) Lakes f) All cat., 159 HQ20 events ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●

P0−50 P0−50 P0−50

0.6 0.4 0.2 0.0 0.6 0.4 0.2 0.0 0.6 0.4 0.2 0.0 1.0 1.0 0.8 1.0 0.8 0.8 Micro catchments,

D2-3,

Relative frequency Relative frequency Relative frequency Relative (a) ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● (b) ● ● 3286 3285 P99−100 P99−100 P99−100 ● ● ● value is displayed symbolically (squares, dots and g) API2 c) D4−6 a) D0−1 ● e) D4−30 ● ● ● P−val. > 0.05 > 0.05 P−val. P−val. >0.001 >0.001 0.05> P−val. 0.05> P−val. <0.001 <0.001 P−val. P−val. ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● P ●● ● ● ● ● ● ● D0−1 D2−3 D4−6 D4−14 D4−30 ● P95−99 P95−99 P95−99 ● ● ● ● ● ● ● D0-1, ● ● ● ● ● ● ● − − − − − ● ● ● HQ5 s in ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● P90−95 P90−95 P90−95 ● ● ● (a) ● ● ● ● ● ● ● ● ● ● erent precipitation periods for the occurrence of annual floods ● ff ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● (a–d) ● ● ● P75−90 P75−90 P75−90 Percentiles bins Percentiles bins Percentiles bins Percentiles ● Lakes Exits catchments. ● ●● ● ● ● ● ● ● ● ● ● ● ● ● GlacialGlacial Nival Nival PluvialPluvial Jurassien Jurassien MeridionalMeridional MarcoscaleMarcoscale exits exits Lake Lake ●● ●● ●● ●● ●● ●● P50−75 P50−75 P50−75 (d) a) Micro cat., 387 HQ5 events a) Micro cat., c) Macro cat., 76 HQ5 events e) All cat., 4308 HQ1 events ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● API4. For each precipitation period and each catchment, the following

P0−50 P0−50 P0−50

1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0

(h) Relative frequency Relative frequency Relative frequency Relative Relative frequency of precipitation percentiles for several PAPs before floods. Each The relevance of the di API2, and (g) 30, triangles indicate a non-, weakly-, andthe strongly-significant symbols influence, refer respectively). to The the colors hydrologicalnificant regimes of of correlation, the i.e. catchments. Circles the denoteflood exceedance a probability. of negatively Negative sig- a correlations given are precipitation almost threshold exclusively found significantly in reduces Glacial catchments. question is addressed usingthreshold logistic significantly regression: change does the theP90, flood exceedance P95, of probability? P99) a Several and thresholds given the precipitation are most tested significant (P50, P75, ments. Gray shadings representcentile the bin. 99 % level of significance of the frequency of each per- Figure 7. is tested using logistic regression Figure 6. colored line represents a PAP. Macro catchments and Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff Nival, (b) and di 20 20 20 (c, d) 20 20 500 500 500 Glacial, 20 20 20 20 5 5 5 5 (a) 5 erent flood samples. and the right column 20 5 5 20 20 ff 5 5 100 100 100 y 20 5 5 b) D0−3 vs. D4−14, diff. sizes diff. D4−14, b) D0−3 vs. 1 1 1 1 50 50 50 f) D0−3 vs. D4−14, diff. seasons D4−14, diff. f) D0−3 vs. 1 d) D0−3 vs. D4−14, diff. regimes D4−14, diff. d) D0−3 vs. 5 1 1 5 1 1 1 1 1 1 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● 5000 5000 5000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● 10 10 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● b) Nival cat. b) Nival ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● f) Macro cat. ● ● ● ● ● ● ● ● ● median return period [days] of D0−3 median return period of D0−3 [days] median return period [days] of D0−3 Lakes Exits. For each discharge ● ● ● ● ● ● ● ● ● ● 5 5 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 500 500 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● d) Jurassien cat. d) Jurassien ● ● ● ● ● ● ● Glacial Nival Pluvial Jurassien Meridional Winter Spring Summer Autumn Micro Meso Macro Lakes ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● and D2-3 in ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 2 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● (g) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● x ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 50 ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 1 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Return period of D3−0 [days] Return period of D3−0 [days] Return period of D3−0 [days] ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ●

● ● ● ● ● ● ● ● ● 10 10 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 10 5 2 1 50 10 5 2 1 50 10 5 2 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 5 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 500 erent hydrological regimes ● ● ● ● ●

● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● median return period of D4−14 [days] D4−14 of period return median [days] D4−14 of period return median median return period of D4−14 [days] D4−14 of period return median ● ● g) Lakes Exits cat. g) Lakes ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ff ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 1 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

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●

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● ● ● 5000 500 50 10 5 1 5000 500 50 10 5 1 5000 500 50 10 5 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● 50

● ●

● erent catchment samples: ● ● ● ● , di ● ● Return period of D4−14 [days] D4−14 of period Return [days] D4−14 of period Return Return period of D4−14 [days] D4−14 of period Return ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ff ● ● ● ● ● ● Macro and ● 5000 5000 5000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Return period of D3−0 [days] ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3287 3288 ● ● ● ● ● ● ● 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● . Annual floods are shown by gray dots (shadings ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● c) Pluvial cat. ● ● ● (f) a) Glacial cat. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 500 500 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● y ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● (a, b) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● e) Meridional cat. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 500 50 10 5 1 ● 5000 ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● 50 ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● [days] D4−14 of period Return ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 500 500 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Return period of D3−0 [days] Return period of D3−0 [days] Return period of D3−0 [days] ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 10 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 5 5 ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● 5 1 1 1 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 100 100 100 Meridional,

1 a) D0−1 vs. D2−3, diff. sizes diff. D2−3, a) D0−1 vs.

1

5000 500 50 10 5 1 5000 500 50 10 5 1 5000 500 50 10 5 1 1 1 1 c) D0−1 vs. D2−3, diff. regimes D2−3, diff. c) D0−1 vs. 1 1

e) D0−1 vs. D2−3, diff. seasons D2−3, diff. e) D0−1 vs.

50 50 50 1 Return period of D4−14 [days] D4−14 of period Return Return period of D4−14 [days] D4−14 of period Return Return period of D4−14 [days] D4−14 of period Return 1 and D4-14 in (e) 1 . The numbers 1, 5 and 20 indicate median return periods associated x 5 . The left column shows D0-1 in y 20 10 10 10 median return period of D0−1 [days] median return period [days] of D0−1 median return period [days] of D0−1 5 5 5 1 Winter Spring Summer Autumn Glacial Nival Pluvial Jurassien Meridional Micro Meso Macro Lakes erent catchment sizes (e, f) ● ● ● ● ● ● ● ● ● ● ● ff 2 2 2 Jurassien,

1 1 1

10 5 2 1 50 10 5 2 1 10 5 2 1 50 50 (d)

Median return periods of flood-associated precipitation for di

Flood-associated precipitation for di and D4-14 in

median return period of D2−3 [days] D2−3 of period return median median return period of D2−3 [days] D2−3 of period return median median return period of D2−3 [days] D2−3 of period return median x Pluvial, with all HQs, with all HQ5 s, and with all HQ20 s, respectively. They are joined together by a line. Figure 9. peak, D0-3 is shownindicate in the density ofshow the dots), linear HQ5 regression s of by the green HQ5 s. dots and HQ20 s by red triangles. Green lines ent flood seasons D0-3 in (c) The rows show di Figure 8.