Journal of Hydrology 394 (2010) 334–346

Contents lists available at ScienceDirect

Journal of Hydrology

journal homepage: www.elsevier .com/locate / jhydrol

Has streamflow changed in the Nordic countries? – Recent trendsand comparisons to hydrological projections

Donna Wilson ⇑, Hege Hisdal, Deborah LawrenceNorwegian Water Resources and Energy Directorate (NVE), P.O. Box 5091, Majorstua, N-0301 Oslo, Norway

a r t i c l e i n f o s u m m a r y

Article history:Received 18 February 2010Received in revised form 6 September 2010Accepted 14 September 2010

This manuscript was handled byA. Bardossy, Editor in Chief, with theassistance of Haberlandt, Associate Editor

Keywords:Streamflow trendsNordic regionClimate changeFloodTemporal autocorrelationField significance

0022-1694/$ - see front matter � 2010 Elsevier B.V. Adoi:10.1016/j.jhydrol.2010.09.010

⇑ Corresponding author. Tel.: +47 22 95 92 03; fax:E-mail address: dowi@nve.no (D. Wilson).

A pan-Nordic dataset of 151 streamflow records was analysed to detect spatial and temporal changes instreamflow. Prior to undertaking analyses, all streamflow records with significant levels of autocorrela-tion were pre-whitened to remove the adverse effect of temporal autocorrelation on the test results.The Mann–Kendall trend test was applied to study changes in annual and seasonal streamflow as wellas floods and droughts for three periods: 1920–2005, 1941–2005 and 1961–2000. Field significancewas evaluated to determine the percentage of stations that are expected to show a trend due to the effectof cross-correlation. The period analysed and the selection of stations influenced the regional patternsfound, but the overall picture was that trends of increased streamflow dominate annual values and thewinter and spring seasons. Trends identified in summer flows differed between the three periods ana-lysed, whereas no trend was found for the autumn season. In all three periods, a signal towards earliersnowmelt floods was clear, as was the tendency towards more severe summer droughts in southernand eastern Norway. These trends in streamflow result from changes in both temperature and precipita-tion, but the temperature induced signal is stronger than precipitation influences. This is evident becausethe observed trends in winter and spring, where snowmelt is the dominant process, are greater than theannual trends. A qualitative comparison of the findings with available streamflow projections for theregion showed that the strongest trends found are generally consistent with future changes expectedin the projection periods, for example increased winter discharge and earlier snowmelt floods. However,there are predicted changes that are not reflected in past trends, such as the expected increase in autumndischarge in Norway. Hence, the changes expected because of increased temperatures are reflected in theobserved trends, whereas changes anticipated due to increases in precipitation are not.

� 2010 Elsevier B.V. All rights reserved.

1. Introduction

Climate change may cause changes in annual average stream-flow, in the seasonal distribution of flow or in the magnitude andfrequency of floods and droughts which could have extensive im-pacts on water management, agriculture and aquatic ecosystems.Countries such as Norway, Sweden, Iceland and Finland wheremuch of their electricity production system is based on hydro-power are especially sensitive to long-term variations in stream-flow. Hydrological changes may offer new opportunities forhydropower development, but could require new investment, de-sign, operation and management practices at new and existinghydroplants to take advantage of these opportunities. Conversely,increases in the magnitude of peak flows can have implicationsfor flood management and dam safety.

ll rights reserved.

+47 22 95 92 16.

This paper seeks to detect climate change signals in historicalstreamflow records and compares the results to published hydro-logical projections. Studies of climate change traditionally includeboth the development of possible future projections (as outlinedbelow) and the analysis of possible climate change signals in his-torical data. Studies based on historical data evaluate the presenceof trends or jumps over time relative to natural climate and hydro-logical variability. Changes might be detected in annual as well asseasonal or extreme values. In some cases, annual averages may re-main unchanged although a change in seasonal or extreme valuesmight be present. This paper focuses on detecting climate changesignals in historical streamflow records, addressing the questionof whether climate change is already impacting hydrology. TheMann–Kendall test, which is widely used in trend analyses (e.g.Mitosek, 1995; Hisdal et al., 2001) is used to detect trends in thestreamflow records, after accounting for the adverse effect of spa-tial and temporal autocorrelation within the datasets. To evaluateif observed trends in streamflow are consistent with scenario pro-jections, a qualitative comparison between the observed trendsand published hydrologic projections for streamflow is made.

D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346 335

Climate projections for Europe and the Nordic countries havebeen developed as part of large international research activities,e.g. the PRUDENCE (Prediction of Regional scenarios and Uncer-tainties for Defining European Climate change risks and Effects,http://prudence.dmi.dk/; Christensen et al., 2007a), ENSEMBLES(http://ensembles-eu.metoffice.com/) and national regional cli-mate modelling projects. PRUDENCE was a 3 years initiative(2001–2004) which produced a series of high-resolution climatechange scenarios for the period 2071–2100 for Europe, and charac-terised the variability and level of confidence in these scenarios.Following this, ENSEMBLES ran for 5 years (2004–2009) and fur-ther developed and assessed a range of future climate projectionsfor Europe to determine which are more probable than others(van der Linden, 2009). Parallel to these projects, regional climatemodelling has been undertaken in numerous coordinated nationalprojects (e.g. SWECLIM in Sweden, RegClim in Norway, FIGARE inFinland). The Regional Climate Modelling Programmes undertakenby individual countries developed regional climate change projec-tions for the Nordic region (e.g. Rummukainen et al., 2004a). Sev-eral studies, often undertaken as part of the regional climatemodelling projects, have simulated the potential impacts of cli-mate change on hydrological processes for a range of basins inthe Nordic region (e.g. Andréasson et al., 2004; Roald et al., 2006;Beldring et al., 2008). Reports of observed changes and the likelyfuture impacts of climate change for individual Nordic countriesare also available (e.g. Jørgensen et al., 2001; Rummukainenet al., 2004b; Tuomenvirta, 2004; Hanssen-Bauer et al., 2009).

Information about observed and anticipated changes in the cli-mate system are summarised in the Fourth Assessment Reports ofthe IPCC (Solomon et al., 2007; Parry et al., 2007). In Europe, tem-peratures increased throughout the 20th Century, with the greatestrates of change identified for the period 1979–2005 (Alcamo et al.,2007). Temperatures are also increasing at a faster rate in winterthan summer (Jones and Moberg, 2003) and snow cover has de-creased, particularly in the spring (Trenberth et al., 2007; Bateset al., 2008). Trends in precipitation across Europe are found tobe more spatially variable, but the greatest increases have been ob-served in Northern Europe (Klein Tank et al., 2002; Trenberth et al.,2007). Recent studies of the climate in Finland (Hyvärinen, 2003)Sweden (Lindström and Alexandersson, 2004) and Norway(Hanssen-Bauer et al., 2009) found that precipitation in thesecountries increased during the last 100 years, particularly sincethe end of the 1970s. In Trenberth et al. (2007) observed changesregarding hydrology are summarised, but it is concluded that sub-stantial uncertainty remains about trends in hydrological variablesbecause the detected changes vary considerably over space anddue to limitations with data quality and availability.

Individual national studies for the Nordic countries (for exam-ple Hyvärinen, 1998; Førland et al., 2000; Ovesen et al., 2000; Lind-ström and Bergström, 2004; Hanna et al., 2004; Jónsdóttir et al.,2008; Hisdal et al., 2010) also show that trends in climatic andhydrological variables, either natural or human induced, vary con-siderably between regions. An overview of studies of long time ser-ies of precipitation, temperature and streamflow in the Nordiccountries up to 2003 can be found in Hisdal et al. (2003). The na-tional studies vary both regarding the period and variables ana-lysed. To make a qualitative estimate of regional differences inthe changes found, it is possible to compare the national studies.However, the results would be uncertain due to the various timeperiods analysed and the different methods used. To study the re-gional distribution of changes, there is a need to study data fromseveral countries and to include a common time period and meth-odology. The latter is important because the trend or changesfound will be strongly influenced by the time period studied. A pre-vious Nordic study on regional differences in trends focused on an-nual and seasonal streamflow, and on the time period 1930–1980

(Hisdal et al., 1995). Over the last 15 years, streamflow recordshave been updated, and a similar study is possible for a longer per-iod. Hisdal et al. (2010) recently investigated changes in annual,seasonal, floods and droughts in the Nordic countries up to 2002.In this paper the analyses undertaken by Hisdal et al. (2010) arefurther refined by considering the effect of spatial and temporalautocorrelation, investigating trend magnitude instead of statisti-cal significance and updating the period of analysis to 2005. En-hanced climate change may result in more pronounced and morepersistent changes in streamflow, so that the likelihood of detect-ing changes is expected to increase over time (Kundzewicz, 2004).This is an argument for continuing to examine updated streamflowrecords, including at the pan-Nordic scale, to identify larger scaleregional differences in streamflow in terms of non-stationarityand climate variability. Furthermore, the majority of the studiesdetailed above exist only in national archives, which can be diffi-cult to obtain and use and is a motivation for making these resultsin this paper more widely available.

Most trend studies look at annual and seasonal values (e.g.Hyvärinen, 2003; Jónsdóttir et al., 2008). Less attention is givento extremes, especially droughts. Amongst the reasons are that ex-tremes are especially prone to man-made environmental changesand also more vulnerable to measurement errors (Boiten, 2000).Detected trends are therefore more difficult to relate to changesin climate.

In the latter half of the 20th century an increase in the fre-quency of heavy precipitation events has been observed in North-ern Europe (Trenberth et al., 2007). This increase has been greaterthan the increase in mean annual precipitation (Groisman et al.,2005). Grønås et al. (2005) observed this tendency in the westernpart of Norway and state that this agrees with expected changesfrom human induced climate change. Although, even if changesin annual streamflow usually follow changes in annual precipita-tion, the change in floods does not relate that well to the increasein heavy precipitation. Recent studies of trends in annual maxi-mum (Kundzewicz et al., 2005) and peak over threshold floods(Svensson et al., 2005) worldwide, and annual maximum floodsin Europe (Kundzewicz, 2005) conclude that although more in-tense precipitation has been documented, a coherent and generalincrease in high river flows could not be detected. In these previousstudies, streamflow records from Norway, Finland and Swedencovered different time periods, whereas, a common time periodis analysed in the present study to enable a spatial comparison oftrends. Annual maximum autumn and spring floods are also stud-ied separately to distinguish between rain and snowmelt floods.

In Hisdal et al. (2001) a pan–European study of trends in theseverity and frequency of summer drought was carried out. Forthe period 1962–1990, which included a relatively high densityof stations in Norway and Denmark, only one station within thesetwo countries had a significant trend. However, a tendencytowards more severe droughts was seen in southern and northernNorway and Denmark and less severe droughts in central Norway.The other Nordic countries had too few stations to draw any con-clusions. Trends in the low flow indices 7-day annual minimumand 30-day annual minimum based on a global dataset were stud-ied by Svensson et al. (2005). Only increases in low flows werefound in Europe, but this was interpreted as resulting from a largenumber of reservoirs becoming operational during the time peri-ods evaluated. In the present study, trends in droughts are onlyinvestigated for stations where reservoirs have insignificantinfluence.

The Nordic countries have a good spatial coverage of stream-flow records with a high level of quality control. Pristine basinswith a minimum of human interventions such as urbanization,deforestation and changes in storage capacity are available forstudies of extremes. Long-term data series enable documentation

336 D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346

of changes at inter-annual time scales and testing for trends incommon time intervals across the dataset. Such data provides aunique opportunity to investigate changes in streamflow in theNordic region.

This paper aims to address the question ‘has streamflow chan-ged in the Nordic countries?’ by means of an analysis of the tempo-ral and spatial variations in annual and seasonal streamflow, floodsand droughts. Results are compared to hydrological projections toinvestigate how observed trends relate to projected futurechanges. The next section describes data selection and the stream-flow variables analysed, followed by details of the methods ap-plied. Results are then presented and discussed with respect tothe time period and region analysed. The detected trends are thencompared to changes expected in the future, and final conclusionsare proposed.

2. Data

Daily data from 151 streamflow discharge gauging stations inDenmark, Finland, Iceland, Norway and Sweden were compiled.These data are stored in a common database, a Nordic version ofthe European Water Archive (EWA) of the Flow Regimes fromInternational Experimental and Network Data (FRIEND) Project(Roald et al., 1993; Rees and Demuth, 2000). For informationregarding data availability see the following link: http://ne-friend.bafg.de/servlet/is/7413/. Data were selected to cover the wholeNordic region with a common time period, from 1920 to 2005.The criteria for selecting series were that the records should be,as far as possible, unaffected by human induced changes in thebasin, and that the records should be continuous and as long aspossible. Even if most catchments in the Nordic region are onlysubject to minor land use changes and a minimum of waterabstractions for irrigation, industry and public water supply, longpristine series are hard to find. The longest series are often affectedby human activities in the basin, causing various forms of inhomo-geneity in the series. The series were therefore classified into threecategories:

(i) series suitable for the analysis of annual values;(ii) series suitable for (i) and the analysis of monthly values, i.e.

seasonal trends;(iii) series also suitable for (ii) and the analysis of daily values,

i.e. trends in extremes.

Only natural catchments were deemed suitable for the analysisof daily values. In addition, catchments were included for analysisof seasonal values if minor regulations in the catchment were notthought to influence mean seasonal flows or if flows have beennaturalised to account for such regulations. For the analysis of an-nual values, catchments were also included if they contained smalldams which were used only to store water within a single year.

To obtain a good spatial resolution a reasonable and commontime period should be selected. However, time series, as long aspossible, are important to study long-term variability. A best pos-sible Nordic coverage required a relatively short period to be se-lected (1961–2000). This period encompasses the total dataset(151 stations). Two additional sets of stations, 1941–2005(111 stations) and 1920–2005 (68 stations) were chosen to inves-tigate longer-term trends. The stations used for each of these threeperiods of analysis have data available for the full periods of anal-ysis, with no periods of missing data. However, analyses of trendsin seasonal streamflow and extremes further reduce the number ofstations due to the data quality classification detailed above. Aminimum number of stations, 42, were identified for analysis ofdata at a daily resolution for the period 1920–2005. The spatial

coverage of data is not uniform as a larger number of long recordsfrom pristine basins are available in Norway and Denmark com-pared to Sweden, Finland and Iceland. This is considered furtherin the methods and discussion sections. However, the dataset com-prises a high-quality, long-term set of homogeneous series of ade-quate spatial resolution with a minimum of human influence fordetecting trends caused by climate change, natural or humaninduced.

Trend studies were performed for the following 11 hydrologicalvariables:

� mean annual streamflow (calendar years);� mean seasonal values (winter: December–February; spring:

March–May; summer: June–August; autumn: September–November);� magnitude and timing (date) of the spring (1 March–15 July;

see below) flood peak;� magnitude and timing of the autumn (16 July–11 November;

see below) flood peak;� drought duration and deficit volume.

Nordic river flow regimes vary considerably. In Denmark, alongthe western coast of Norway and much of southern Sweden thereis no regular snow cover during the winter. In these areas floodevents most commonly occur in the autumn, while the lowestflows occur during the summer months caused by lack of precipi-tation and high evaporation losses. In the north, the east and themountainous regions, flood events tend to be induced by snowmeltand occur in the spring, while the lowest flows occur in winter dueto precipitation being stored as snow. There are also transition re-gions where the annual maximum flood and the lowest flows canbe found in any season.

In the Nordic countries the dominant flood mechanism differsbetween autumn and spring. Flood events tend to be thermally dri-ven (i.e. snowmelt) in the spring, whereas precipitation becomesthe dominant driver in the autumn. A rough differentiation of theflood generation mechanism was achieved by considering springand autumn floods separately. The spring flood period was definedto be 1 March–15 July, and the autumn flood period was 16 July–11November. Timing was defined as the date of the flood peak. InDenmark, parts of southern Sweden and south-western Icelandthe major floods usually occur during the winter season. For thisregion it could therefore be relevant to study floods for a seasonlasting from late autumn to early spring, but this study is limitedto the two periods defined above.

To perform a consistent analysis of droughts, it is necessary todistinguish between ‘summer droughts’ that are driven by a lackof precipitation and high evaporation and ‘winter droughts’ causedby precipitation being stored as snow. These two drought types areaffected in different ways by climate change. Here only ‘summerdroughts’ are considered. Most of the basins investigated in thisstudy have a regular spring flood, with the exception of Denmark,southern Sweden and the groundwater-fed rivers in south-westernIceland. Plots of maximum, median and minimum values ofstreamflow were used to identify the average timing of the springflood. The start of the summer season was defined to be at the endof the spring flood. The end of the summer season was defined tobe the date when the average temperature drops below 0 �C. Thismethod has previously been applied to define summer droughtseasons for many basins across Europe and is further discussedin Hisdal et al. (2001). Finally, three seasons were identified andthe stations were classified according to these. Season A (allyear/no season) includes Denmark, the four western-most basinsin Norway and the two south-western basins in Sweden. SeasonB (15 April–15 November) is applied to the coastal zone in Norway,two stations in southern Sweden and the three southern-most

D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346 337

stations in Finland. Season C (15 June–15 October) is used for thenorthern and inland catchments of Norway, Sweden and Finlandand the Icelandic stations.

River flow is considered to indicate drought conditions whenthe flow is below a specific threshold, Qthres. This method proposedby Yevjevich (1967) was originally based on the statistical theoryof runs for analysing sequential time series with a time resolutionof 1 month or longer. The approach has previously been used to se-lect droughts from a daily hydrograph for trend studies (Hisdalet al., 2001). The method allows streamflow droughts to be charac-terised in terms of their duration, di, deficit volume (severity), vi,minimum flow, Qmin and time of occurrence (Fig. 1). During a pro-longed dry period it is often observed that flow exceeds the thresh-old level for a short period of time, thereby dividing a large droughtinto a number of minor droughts that are mutually dependent. To,as far as possible, pool dependent droughts and remove minordroughts, an 11-day moving average procedure was adopted, asrecommended by Tallaksen et al. (1997). The threshold level, Qthres,was determined for each station by using the 30th percentile (Q70),from the flow duration curve (i.e. the flow which is exceeded 70%of the time in the given period and season). A suitable droughtthreshold may be chosen in a number of ways. The choice isamongst others a function of the hydrological regime under studyand the type of analysis to be carried out. Hisdal et al. (2002, 2004)discuss this and conclude that the thresholds in the range betweenthe 10th and 30th percentile are reasonable choices for perennialrivers. When undertaking trend analysis it is an advantage to haveone drought every year. Choosing the 30th percentile ensures this.Finally, the drought events were extracted for the three time peri-ods for the available stations. Two drought characteristics wereanalysed:

� annual maximum drought duration (days);� annual maximum deficit volume (m3).

We chose not to analyse low flow data (i.e. annual daily mini-mum flows) because they are especially prone to measurement er-rors caused by factors such as altered cross-sections, weed growthand backwater effects. This might influence a trend study of dailyminimum series, but drought as defined here, is more robust in thisrespect.

3. Methods

Temporal autocorrelation within a time series and cross-correlation between sites in a dataset can both affect the abilityof a trend test to assess trend significance (Burn and Hag Elnur,

Time (

Q (m

3 /s)

Q0

Fig. 1. Definition of drought characteristics. (This article was published in DevelopmenCopyright Elsevier, 2004.) The value of Q0 is set to Q70 in the present study.

2002; Yue and Wang, 2002). High autocorrelation can also affectthe estimate of trend magnitude (Zhang and Zwiers, 2004). Yueet al. (2003) state that positive serial correlation increases the pos-sibility that the Mann–Kendall test rejects a hypothesis of no trend,when there is actually no trend. A basic assumption of the Mann–Kendall test is, therefore, that the data being analysed are a collec-tion of random independent variables. Time series of hydrologicalvariables may exhibit serial correlation, because of storage proper-ties in the basin and frequently require pre-whitening to removeautocorrelation prior to trend analysis.

The consistency of trends detected across a region is often stud-ied by plotting results of at-site tests on a map. However, if data arespatially correlated and a trend is found at one site, it is likely thattrends will also be found at other nearby sites. As the spatial cov-erage of stations is seldom uniform, a trend in a region with a rel-atively high density of stations will often result in a larger totalpercentage of trends being identified, compared to a trend beingidentified in a region with fewer stations. This non-uniformitycan bias an assessment of regional trends. As for temporal autocor-relation, ignoring spatial correlation in a dataset can also result in anull hypothesis of no trend being rejected more frequently than itshould be when considering overall trends across a region (Douglaset al., 2000; Yue et al., 2003; Burn and Hag Elnur, 2002; Renardet al., 2008). Pre-whitening, trend test and field significance proce-dures were applied in this work and are detailed in the followingsub-sections.

3.1. Pre-whitening

Autocorrelation coefficients were calculated for each of the 11hydrological variables, at each station, for each of the three timeperiods. The hydrological variables at many stations have signifi-cant partial correlations (at the 5% level) at a time lag of 1 year,with data for some stations also possessing significant partialcorrelations over longer time lags. In particular, three Icelandic(Dynjandi- Brúará, Kljáfoss and Skálabrekka) and one Danish sta-tion (Lindenborg Bro) have high autocorrelation of annual values,possibly due to the buffering effect of groundwater aquifers. OneSwedish catchment (Kallio) characterised by large lakes showshigh autocorrelation of winter flows. Two Danish stations (Linden-borg Bro and Lindebjerg) have high autocorrelation of droughtduration and drought volume likely to be due to the regulating ef-fect of groundwater aquifers. The autocorrelations amounted to asmuch as 0.76, which suggests that they may not only influence sig-nificance levels, but also the estimate of trend magnitude (seeFig. 2 of Zhang and Zwiers (2004)). Pre-whitening of all 11 hydro-logical variables was undertaken to remove short-term persistence

days)

ts in Water Science, 48, Hisdal et al., Hydrological drought characteristics, Fig. 5.7.

338 D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346

following the iterative procedure described in Appendix A of Wangand Swail (2001). Pre-whitening can remove a proportion of thetrend from a dataset, but this iterative procedure minimises thisproblem so that the pre-whitened time series possesses the sametrend as the original series (Wang and Swail, 2001; Zhang andZwiers, 2004; Liu et al., 2009). Following the pre-whitening proce-dure, partial correlations were recalculated (for lag = 1, 2, . . . , 20).This showed a notable reduction in the autocorrelation of eachtime series, with few partial correlations remaining significant atthe 5% level. No further attempt was made to remove the remain-ing low levels of autocorrelation in the datasets. Birsan et al. (2005)state that where autocorrelation coefficients are low, thedifferences between the results of a trend test prior to and afterpre-whitening are small. The Mann–Kendall test (Section 3.3)was performed on the pre-whitened time series.

3.2. Trend test

According to Yue and Pilon (2004), even if the underlyingdistribution is known to be approximately normal, the power ofa parametric test is only slightly higher than the power of a non-parametric alternative. For non-normal series, rank-based tests,including the Mann–Kendall test, are found to have an increasedability to detect trends compared to slope-based tests. The distri-bution of the streamflow variables being analysed will differ acrossspace and can be highly skewed. This is particularly the case withfloods and droughts which are typically characterised by manyminor and few major events. Hence, a non-parametric test isfavourable.

In Kundzewicz and Robson (2004) resampling methods arepointed out as particularly suitable for hydrological data. Hisdalet al. (2001) applied two tests, a resampling test and the widelyused non-parametric Mann–Kendall test, to investigate trends instreamflow droughts in Europe. A strong agreement between thetwo tests was found.

The Mann–Kendall test searches for a trend in a time serieswithout specifying whether the trend is linear or nonlinear, butdoes assume that the trend is monotonic. An example of an appli-cation of the test can be found in Mitosek (1995), who compareddifferent trend tests to detect signals of climate variability andchange in monthly and annual discharges of 176 series fromall over the world. Further references of application to hydro-climatological time series can be found in Yue and Pilon (2004).A description of the test can be found in Salas (1993). The Mann–Kendall S statistic is given by (Burn and Hag Elnur, 2002):

S ¼Xn�1

i¼1

Xn

j¼i�1

SgnðXj � XiÞ ð1Þ

where Xi and Xj are the sequential data values, n is the dataset re-cord length, and

Sgn ðhÞ ¼þ1 h > 00 if h ¼ 0�1 h < 0

8><>:

Table 1Critical values of the Mann–Kendall statistic for the identification of strong

Period ofanalysis

Number of annualdata values ineach station time seriesa

Range of

1920–2005 85 �3750 to1941–2005 64 �2016 to1961–2000 39 �741 to

a The number of annual data values in each series is equal to the numb

3.3. Significance testing

In the present study, trends are discussed in terms of their trendmagnitude and direction. Critical values of the Mann–Kendall Sstatistic (Smax; Table 1) are used to identify strong and weak trends.The Smax values are defined using p-values (two-sided) of 0.05 and0.3, which indicate the magnitude of the trend is likely to be in theupper or lower 2.5% and 15%, respectively, of the statistical distri-bution. Because the data series within each study period are all ofthe same length, and pre-whitening has been undertaken to pre-serve the original trend magnitude, analysing trends in terms ofeither magnitude or significance levels should nominally beroughly equivalent. However, this paper focuses on trend magni-tude because long-term persistence (as opposed to short-term per-sistence removed by pre-whitening) of many hydroclimatic timeseries can cause trend test significance to be highly sensitive tothe trend test used (Cohn and Lins, 2005). In particular, Cohn andLins found that statistical significance is difficult to assess becauseit depends on assumptions being made about the underlying sto-chastic process. They illustrate using the Northern Hemispheretemperature record that trend significance can vary by up to 25orders of magnitude depending on the trend test used. Statisticalsignificance is often cited to bolster scientific argument, but asCohn and Lins, and more recently Clarke (2010) acknowledge, theconcept of statistical significance is meaningless when discussingpoorly understood systems and simply reporting the identificationof significant trends does little to advance hydrologicalunderstanding.

3.4. Field significance testing

Field significance testing determines the percentage of stationsexpected to show a trend, for critical values of the Mann–Kendall Sstatistic (see Section 3.3), purely by chance, due to the effect ofcross-correlation. Renard et al. (2008) compared several methodsfor assessing field significance using synthetic datasets and founda bootstrap method to be an adequate and robust tool. A bootstrapprocedure described by Burn and Hag Elnur (2002) was used todetermine the percentage of stations expected to show a trenddue to the effect of cross-correlation. Where the number of ob-served trends is greater than the number of trends expected, theresults can be considered field significant. In this paper, 2000 boot-strap samples were selected to establish the percentage of stationsexpected to show a trend, which is the upper limit of the numberrecommended by Davison and Hinkley (1997).

4. Results and discussion

The results are assessed in two different ways: summary plotsof the stations exhibiting positive and negative trends, and mapsof the spatial variability of the trends (all periods). The resultsare discussed in terms of strong and weak trends as defined inSection 3.3. Because of the quality classification, the results areas far as possible not influenced by human interference in the

and weak trends.

S Smax

(strongtrends)

Smax

(weaktrends)

3750 6�518 or P518 6�274 or P2742016 6�339 or P339 6�180 or P180

741 6�163 or P163 6�87 or P87

er of years minus one, as each time series has been pre-whitened.

1920-2005 1941-2005 1961-2000

0

5

10

15

20

25

30

annu

al

win

ter

sprin

g

sum

mer

autu

mn

annu

al

win

ter

sprin

g

sum

mer

autu

mn

annu

al

win

ter

sprin

g

sum

mer

autu

mn

Num

ber o

f sta

tions

(%)

Strong positive trend Strong negative trend

Fig. 2. Summary of strong trends for annual and seasonal flow in all time intervals. Positive trends indicate a tendency towards increased flows, whereas negative trendsindicate a tendency towards reduced flows.

D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346 339

catchment. Hence, the detected changes are caused either by nat-ural climate variability or by climate change.

4.1. Summary statistics

The percentages of stations with strong positive and negativetrends are summarised in Fig. 2 (annual and seasonal flow) andFig. 3 (extremes). All results are field significant, i.e. for both thestrong and weak trends the percentage of stations showing a trendis greater than that expected by chance, with the followingexceptions:

� Autumn flows (1941–2005 strong and weak trends, 1961–2000weak trends).� Timing of the autumn flood (1961–2000 weak trends).� Drought duration (1961–2000 weak trends).� Drought volume (1961–2000 weak trends).

Three issues should be kept in mind when discussing theresults; (i) only few records are available for the period1920–2005, (ii) in the trend analysis observations in the first andlast few years are more important than the period of analysis(Hisdal et al., 2001), and (iii) although field significance has beencalculated, this procedure only identifies whether results are fieldsignificant at the Nordic scale and does not identify which stationsare spatially correlated or remove spatial correlation from thedatasets. A further study would be required to conclude whetherstations at the regional scale are correlated, but this is beyondthe scope of this paper.

More than 6% of stations show strong trends in all annual andseasonal variables for all periods, except for autumn flow (Fig. 2).For the extremes, fewer strong trends are evident, particularly dur-ing the 1961–2000 period, but the trend in the magnitude of thespring and winter peaks is clearest with more than 7% of stationsshowing a trend in all three periods (Fig. 3). In general a larger pro-portion of trends are found in annual and seasonal values than theextremes. Overall positive trends, towards increasing streamflow,dominate annual and seasonal flows. Regarding annual discharge,a positive trend is found for about 17% of the stations in the period1961–2000. For the two longer periods, 1920–2005 and 1941–2005, this number is reduced to between 7% and 8%. The winterand spring seasons are both dominated by positive trends in all

periods. The clearest trends are identified for winter flows, withmore than 12% of stations showing a strong trend in each period.For the period 1920–2005, over 26% of stations show strong trendsin spring flows, but for the two shorter periods fewer strong trendsare identified (7–9%). For the summer season strong trends areidentified at between 6% and 14% of stations, but the positive dom-inance in the period 1961–2000 is replaced by a negative domi-nance in the two longer periods, 1920–2005 and 1941–2005.Less than 4% of the stations show a strong trend for the autumnseason.

Concerning floods, the picture is less clear (Fig. 3). For floodpeaks (spring and autumn), roughly equal numbers of positiveand negative trends are identified for the two shorter periods,1941–2005 and 1961–2000. For the 1920–2005 period, a trend to-wards decreasing streamflow dominates. For the longer two peri-ods, 1920–2005 and 1941–2005, there is a clear dominance ofnegative trends for the timing of the spring flood, indicating a trendtowards earlier spring floods. For the shorter 1961–2000 periodonly 2% of stations show a strong trend in the timing of the springflood. However, regardless of the period analysed, there is a cleardominance of positive trends for the timing of the autumn flood,meaning a trend towards later autumn floods. For droughts, trendstowards increasing drought durations and volumes dominate allperiods, indicating a tendency towards more severe droughts,although only between 3% and 7% of stations show a strong trendin any of the periods.

4.2. Trends in annual discharge

Trends in annual streamflow are presented in Fig. 4. Strongtrends are indicated with large blue circles (strong positive trend)and large red circles (strong negative trend). Weak trends are indi-cated with smaller circles, light blue if positive, orange if negative.Green circles indicate no trend was found.

Positive trends are identified for stations in south-westernNorway in all three periods. A positive trend is identified for afew stations in northern Sweden in the period 1921–2005, but agreater number of positive trends are identified for this region in1941–2005 and 1961–2005, with strong trends for a large numberof stations in the later period. Positive trends are also identified forsouthern Sweden for the period 1961–2000, and western Finland

1920-2005 1941-2005 1961-2000

0

5

10

15

20

25

autu

mn

peak

autu

mn

timin

g

sprin

g pe

ak

sprin

g tim

ing

drou

ght d

urat

ion

drou

ght v

olum

e

autu

mn

peak

autu

mn

timin

g

sprin

g pe

ak

sprin

g tim

ing

drou

ght d

urat

ion

drou

ght v

olum

e

autu

mn

peak

autu

mn

timin

g

sprin

g pe

ak

sprin

g tim

ing

drou

ght d

urat

ion

drou

ght v

olum

e

Num

ber o

f sta

tions

(%)

Strong positive trend Strong negative trend

Fig. 3. Summary of strong trends for flood and drought in all time intervals. Positive trends indicate a tendency towards more severe floods and droughts, whereas negativetrends indicate a tendency towards less severe floods and droughts.

Fig. 4. Trends in annual streamflow for the periods 1920–2005 (left), 1941–2005 (middle) and 1961–2000 (right).

340 D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346

in the periods 1941–2005 and 1961–2000. In Denmark, positivetrends are identified at a limited number of stations in all periods,but most stations show no trend in the periods 1941–2005 and1961–2000.

Generally, in a zone from central Norway through eastern Nor-way and into central Sweden there are no trends. A limited numberof stations, especially in southern Norway and southern Swedenhave a negative trend in the period 1920–2005.

Almost all stations in Iceland show no trend in streamflow dur-ing the period 1961–2000. These results are in agreement with thefindings of Jónsdóttir et al. (2005, 2008). Weak trends in Icelandduring the period 1941–2005 are apparent but only two stationsare of that length. A station to the north (Reykjafoss) has a weakpositive trend, while a station to the south-west (Ellidaarstøø)has a weak negative trend.

By examining individual series (non-pre-whitened), temporalcharacteristics of annual streamflow can be identified. There wereseveral relatively dry years from the late 1960s to around 1980 at

many stations (e.g. Lindström and Bergström, 2004; Hanssen-Bauer et al., 2009). The early 1940s were also dry across much ofthe Nordic region, but with some wet years in central Norway(Hanssen-Bauer et al., 1995). This explains why central Norwayis without trends, and why many series have strong positive trendsin the two shorter periods. The 1920s were cool, but with abundantprecipitation in many regions (e.g. Lindström and Bergström, 2004;Førland et al., 2000), which helps explain why most series arewithout any trend in the 1920–2005 period. Precipitation in wes-tern Norway has increased by 20% over the last 100 years, withthe increase being most marked since the end of the 1970s(Hanssen-Bauer et al., 2009). Glacier streams in western Norwaypeaked in the warm 1930s and have had increasing discharge sincethe mid 1960s because of rising rainfall and temperatures, the laterof which has increased melt-induced streamflow (Førland et al.,2000; Stranden and Skaugen, 2009). Precipitation in northernSweden has also increased, particularly from the late 1940s(Lindström and Alexandersson, 2004), and explains why a positive

D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346 341

trend is apparent at so many Swedish stations in the two shorterperiods. In Denmark, the positive trends identified at a limitednumber of stations correspond to a general increase in precipita-tion throughout the study period (Ovesen et al., 2000). In Finland,a strong positive trend is found at only one station (Niskakosi) forthe period 1941–2005. This is partially caused by the occurrence ofa sequence of two very dry years in the early 1940s. At this station,flows in 1941 and 1942 were 26% and 52%, respectively, of the1941–2005 long-term mean.

4.3. Trends in seasonal discharge

For analyses of seasonal trends, 128 stations are available forthe period 1961–2000, 94 for 1941–2005 and 56 for the longestperiod 1920–2005. For Finland and Sweden the number of stationsis almost doubled when using data for the period 1941–2005, com-pared to that available for the period 1920–2005.

For some stations, winter values may be less accurate due totypically very small discharge values. Small deviations from the

Fig. 5. Trends in seasonal streamflow for the periods 1920–2

‘true’ value might lead to large percentage deviations. Also, icejamming and ice cover might affect the stage–discharge relation-ship. Attention must therefore be given when interpreting resultsfor the winter season. Fig. 5a shows a clear positive trend for thewinter season in many regions and for all periods. Exceptions arethe west coast of central and northern regions of Norway, the east-ern part of Finland, northern and eastern Denmark and Icelandwhere there are no strong positive trends in any of the periods.The trend toward increased winter flow diminishes for the north-ern parts of Norway, Sweden and Finland and for Denmark forthe period 1961–2000. Positive trends in winter runoff might becaused by a general increase in temperature during recent decadesin combination with increased winter precipitation in many re-gions (Førland et al., 2000; Moberg et al., 2006). The main patterncan also be observed for the period 1920–2005 which starts withseveral cold and wet years in the 1920s.

A marked increase in streamflow has also taken place in theNordic countries during spring (Fig. 5b), likely caused by an in-crease in both precipitation and temperature. This corresponds

005 (left), 1941–2005 (middle) and 1961–2000 (right).

Fig. 5 (continued)

342 D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346

with an earlier snowmelt and spring flood for many catchments inNorway, Sweden and Finland, and Jutland in Denmark (Fig. 6).There are only a few stations with positive trends which aresituated in the coastal region of south-west Norway, south-eastSweden and southern Finland.

Different seasons often have opposite trends (Hisdal et al.,1995). This is apparent for the periods 1920–2005 and 1941–2005, when comparing the summer season to winter and spring.During summer (Fig. 5c) there is a tendency for reduced stream-flow in the central, southern western and eastern regions of Nor-way in the periods 1920–2005 and 1941–2005. A change in thetiming of the spring flood could be the main reason. In Denmark,central Sweden and Finland there are also a limited number of sta-tions with negative trends during at least one of the three periodsbeing analysed. In contrast, an increase in summer streamflow isidentified for central, western and northern Norway, northernSweden, southern Finland and parts of western Iceland for the per-iod 1961–2000. In Iceland, a negative trend in spring temperature(Jónsdóttir et al., 2005) has delayed the snowmelt in some basins

from the spring to the summer period, generating an increase insummer discharge.

For the autumn season there are few strong trends, and moststations show no trend at all (Fig. 5d). In the period 1920–2005,some stations along the Norwegian coast show a weak positivetrend, while in the period 1961–2000 stations in the same regionshow a weak negative trend.

4.4. Trends in floods

Spatio-temporal patterns in the magnitude and timing of au-tumn floods are less clear than those for mean seasonal and annualvalues. For the longest period, 1920–2005, most stations (57%) incentral, southern, eastern and western regions of Norway have atendency towards reduced autumn flood magnitudes, with 19%of these stations showing a strong trend. For the periods 1941–2005 and 1961–2000 many of the negative trends disappear, buta tendency towards decreased autumn floods is found along thewest coast of Norway. Positive trends are found at a limited

Fig. 6. Trends in the timing of the spring flood peak for the periods 1920–2005 (left), 1941–2005 (middle) and 1961–2000 (right).

D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346 343

number of stations in Northern Sweden during the period 1941–2005, but in the 1961–2000 period positive trends dominate.

At inland stations in Norway and western Sweden there is a ten-dency for autumn floods to occur later during the periods 1920–2005 and 1941–2005. In the shorter period, 1961–2000, this trendalmost disappears except in southern Norway. In Denmark, wes-tern Norway and eastern Sweden there are a limited number ofstations with weak negative trends, but the stations with trendsdiffer between time periods.

The magnitude of the spring flood shows no systematic trendseither in time or in space. However, there is a clear tendency to-wards an earlier spring flood in each of the Nordic countries, ex-cept for Iceland (Fig. 6). This tendency is found both in regionshaving a snowmelt flood (inland of Norway, Sweden and Finland)and in regions dominated by rain floods (Denmark and south-western Sweden). This picture is repeated for all three periods,but the proportion of strong trends identified in Denmark, south-ern Norway and southern Sweden for the period 1961–2000 isnotably higher than during the other periods and is difficult to ex-plain. Ovesen et al. (2000) found that precipitation has increased in

Fig. 7. Trends in drought deficit volume for the periods 1920–

Denmark throughout the period 1920–2000, but seasonal changesin the timing of precipitation were not investigated.

The change in timing in inland catchments is likely to be causedby increased temperatures which increase melt-induced stream-flow. In Finland, the spring temperature has increased in recentdecades as shown by Tuomenvirta (2004) who found that themean temperature in 1963–2002 was 1.8 �C higher than in1847–1876. The Icelandic stations are the exception to this ten-dency. In Iceland, most stations show no trend in the timing ofthe spring flood, but some stations in the period 1961–2000 showa weak trend towards larger and later floods. This is caused by anegative trend in spring temperature. During a cold spring thespring flood is delayed, more snow accumulates and the probabil-ity of a sudden warming followed by a large spring flood becomeshigher.

4.5. Trends in drought

The spatial trend patterns in drought duration and deficit vol-ume are consistent, although the number of strong trends is

2005 (left), 1941–2005 (middle) and 1961–2000 (right).

344 D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346

slightly greater for drought deficit volume. Therefore, only droughtdeficit volume trends are shown (Fig. 7). In contrast to the othervariables, strong positive trends are indicated in red indicating atrend towards more severe droughts and strong negative trendsare indicated in dark blue, which indicates a tendency towards lesssevere droughts. For all time periods there is a tendency towardsmore severe summer droughts in southern and eastern Norway.Although, the percentage of stations exhibiting trends for thesedrought variables is not as large as for summer season flows. Forthe periods 1941–2005 and 1961–2005, western Norway hastrends indicating less severe summer droughts, but this patternis less evident for the period 1920–2005. For the period 1961–2000 western Iceland and southern Finland have more negativethan positive trends, which indicates that droughts have becomeless severe in these regions.

5. Comparison with available projections

To assess the effects of future climate changes on basin hydrol-ogy, detailed climate information in both time and space areneeded. Hanssen-Bauer et al. (2005) provide a review of climateprojections for Scandinavia (Denmark, Norway and Sweden) basedon statistically downscaled climate scenarios. Although the resultsvary, there are several features in common. Temperature projec-tions for the 21st century predict that the greatest increases intemperature will occur with increasing distance from the coast,at higher latitudes and in the winter season. Precipitation projec-tions are less consistent, as the projected changes are linked to pro-jected changes in the atmospheric circulation, which differsconsiderably between climate models. However, annual precipita-tion is expected to increase, with the increase most pronounced forthe winter season. For the summer period, the projections show anincrease in northern Scandinavia, with several projections indicat-ing a reduction in parts of southern Scandinavia. These findings areconsistent with the recent IPCC Fourth Assessment report (Chris-tensen et al., 2007b), the PRUDENCE (http://prudence.dmi.dk/)and ENSEMBLES (van der Linden and Mitchell, 2009) projects. Sce-narios also predict more days with intense precipitation and fewerdays with light precipitation although regional differences are evi-dent (Skaugen et al., 2003; Boberg et al., 2007; Caroletti and Bars-tad, 2009).

For Norway, these changes are reflected in streamflow projec-tions based on the HadAM3H (Emission scenario A2 and B2) andthe ECHAM4/OPYC3 (Emission scenario B2) Global ClimateModels. Possible future streamflow projections for 2071–2100(Engen-Skaugen et al., 2005; Roald et al., 2004, 2006; Beldringet al., 2008; Hanssen-Bauer et al., 2009) are simulated by a Grid-ded Water Balance Model (Beldring et al., 2003). The projectionsvary, but there are some common features indicating increasedannual discharge in most of Norway, but a slight decrease forsome basins in southern, south-eastern and parts of northernNorway. This differs from our findings of observed trends, as in-creased flows were mainly found in south-western Norway.However, the increase in winter and spring discharge found inthe projections agrees with the detected trends. The projectionsshow a reduction in summer flow in most of the country,whereas a decreasing summer discharge trend is only foundfor the central, southern, western and eastern regions of Norway,and not northern Norway. The earlier timing of the spring flood,as found in the observed streamflow, is also indicated by theprojections.

The major differences between the trends and the projectionsare found for the autumn season. The projections indicate an in-crease in flow for the whole country whereas few trends in thisdirection were found in each of the periods analysed. The predicted

reduction in spring flood magnitude in regimes with a dominantsnowmelt flood and increased autumn floods close to the coast,which corresponds to the expected increase in intense precipita-tion events, is not reflected in this study of past trends. However,projections of future flood magnitudes are very uncertain due largeuncertainties in how rare climate events which generate floods arelikely to change in the future. Drought projections as defined inthis study are not available.

The most systematic change in observed streamflow in Swedenis an increase in winter and spring streamflow, and a trend towardsan earlier spring flood. This is a consequence of unusually hightemperatures and precipitation in recent years (Lindström andAlexandersson, 2004). The increased temperatures have led to ashorter period for snow accumulation and to earlier snowmelt.These observations agree with streamflow projections (e.g. Bergs-tröm et al., 2001; Andréasson et al., 2004). Andréasson et al.(2004) presented hydrological projections based on the A2 andB2 scenarios for 2071–2100, compared to a control climate of1961–1990. The projections generally suggest that in the futurewe will experience decreasing streamflow during summer insouth-eastern Sweden, decreasing spring flood peaks and increas-ing autumn flood peaks. These patterns are not, however, evidentin observed trends.

For Finland, Denmark and Iceland there are no publications onstreamflow projections. The discussion for these countries is there-fore based on precipitation and temperature projections. Climateprojections for Finland point to an increase in precipitation, partic-ularly in the winter and in the spring (http://prudence.dmi.dk/;van der Linden and Mitchell, 2009). Consequently, mean annualflow is expected to increase, but due to the predicted milder win-ters, spring floods may decrease in central and southern parts ofthe country. Summer and autumn floods are expected to becomemore severe; on the other hand, summer droughts might becomemore frequent and intense. The analysis of the Finnish time seriesincluded in this study only reveals some indication of thesechanges in southern Finland.

For Denmark, temperature is expected to increase in all seasons(http://prudence.dmi.dk/; Kjellström, 2004; Räisänen et al., 2004;van der Linden and Mitchell, 2009). Precipitation increases are pre-dicted for annual, winter and to a less extent spring and autumnvalues (van der Linden and Mitchell, 2009). Summer precipitationprojections indicate that rainfall will decrease in this season. As aresult, increased winter and decreased summer flow, in particular,are expected. The trends show an increase in winter flow for theperiods 1920–2005 and 1961–2000, but not for 1941–2005. Noclear trends are identified for the summer period.

The trends observed in the Icelandic 1961–2000 streamflowand precipitation series show some similarities, but also some dif-ferences with the trends predicted for the future by the multi-model ensemble mean of RCM simulations (A1B scenario) in theENSEMBLES project (van der Linden and Mitchell, 2009). The pro-jections predict only small annual changes in precipitation in Ice-land up to 2050, which is consistent with the absence of trendsin observed streamflow over the periods analysed. However, theclimate projections indicate that annual precipitation may increasesubstantially in north-eastern Iceland from 1961–1990 to 2071–2100 (van der Linden and Mitchell, 2009). This would continuethe trend observed for historical precipitation (Jónsdóttir et al.,2005, 2008), but not streamflow.

6. Conclusions

An analysis of trends in Nordic streamflow is described basedon annual as well as seasonal and extreme values. In some catch-ments, there are no changes, but distinct regional patterns are

D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346 345

found for the periods observed. Therefore, it can be said thatstreamflow in the Nordic countries has changed, even though someof the trends found depend on the period analysed.

There are large areas with increased annual discharge for theperiods 1941–2005 and 1961–2000. This increase is mostly absentin 1920–2005 due to some wet, but cold years in the beginning ofthis period. It should be noted, however, that few stations show astrong trend towards decreased annual flows. This is partially a re-sult of all periods ending in 2000/2005, where the last 10/15 yearsincludes many mild and wet years. Studying other periods couldhave changed this picture as the trends found are a result of theperiod analysed.

During the winter and spring seasons there has been a nota-ble increase in streamflow in large parts of the Nordic region.This is also a result of the warm and wet period after about1990. However, the positive trend is also present for the longestperiod which includes the cool and wet 1920s. This can be ex-plained by the low temperatures leading to precipitation beingstored as snow in these seasons in the 1920s; hence even ifthe annual discharge was high, the winter and spring dischargewas not. The summer discharge in the 1920s was high, eitherdue to snowmelt, abundant precipitation or a combination ofboth, leading to a distinct negative trend in summer streamflowfor the period 1920–2005. A decrease in summer flow was alsoidentified for the period 1941–2005, whereas there was an in-crease in central, western and northern Norway and northernSweden for the period 1961–2000. For the autumn period veryfew trends are identified.

Recent flood and drought events are often thought of as a con-sequence of climate change. Our findings do not support a theoryof increasing rain floods and decreasing snowmelt floods as, forexample, found in the projections for Norway (Roald et al., 2006),as no clear patterns are found related to the magnitude of the floodpeaks. The fact that the 1920s were cool is also reflected in the ten-dency for spring flood peaks to occur earlier over the period 1920–2005, since snowmelt is likely to occur earlier in warmer years.

These trends in annual and seasonal streamflow, floods anddroughts result from changes in both temperature and precipita-tion, but we can conclude that the temperature induced signal ismore clearly reflected in streamflow than precipitation influences.For instance, the relative increase in streamflow in the winter andspring, is larger than the increase in annual flows. This is a conse-quence of temperature affecting the timing of snowmelt and, thus,the seasonal distribution of flows rather than annual totals. Simi-larly, a change in the timing of the spring flood (except for Iceland)has been observed due to changes in the timing of snowmelt. Inaddition, a tendency towards more severe summer droughts insouthern and western Norway was found, which can be explainedby the gradually increasing summer temperatures (Hanssen-Baueret al., 2009).

Acknowledgements

The research described in this paper was supported by the Cli-mate and Energy and Climate and Energy Systems projects fundedby Nordic Energy Research, the Nordic energy sector and the na-tional hydrometeorological institutions. The organisations thatcontributed data to the database; the National Environmental Re-search Institute – Denmark, the Norwegian Water Resources andEnergy Directorate, the Finnish Environment Institute, the SwedishMeteorological and Hydrological Institute and the National EnergyAuthority in Iceland, are gratefully acknowledged. We thank T.Fjeldstad and E. Klausen for loading the data into the database,and are grateful to an earlier reviewer who acknowledged the issueof significance against long-term persistence.

References

Alcamo, J., Moreno, J.M., Nováky, B., Bindi, M., Corobov, R., Devoy, R.J.N.,Giannakopoulos, C., Martin, E., Olesen, J.E., Shvidenko, A., 2007. Europe. In:Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E.(Eds.), Climate Change 2007: Impacts, Adaptation and Vulnerability.Contribution of Working Group II to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge University Press,Cambridge, pp. 541–580.

Andréasson, J., Bergström, S., Carlsson, B., Graham, L.P., Lindström, G., 2004.Hydrological change – climate change impact simulations for Sweden. AmbioXXXIII (4–5), 228–234.

Bates, B.C., Kundzewicz, Z.W., Wu, S., Palutikof, J.P., 2008. Climate Change andWater. Technical Paper of the Intergovernmental Panel on Climate Change, IPCCSecretariat, Geneva.

Beldring, S., Engeland, K., Roald, L.A., Sælthun, N.R., Voksø, A., 2003. Estimation ofparameters in a distributed precipitation-runoff model for Norway. HESS 7,304–316.

Beldring, S., Engen-Skaugen, T., Førland, E.J., Roald, L.A., 2008. Climate changeimpacts on hydrological processes in Norway based on two methods fortransferring regional climate model results to meteorological station sites.Tellus 60A, 439–450.

Bergström, S., Carlsson, B., Gardelin, M., Lindström, G., Pettersson, A.,Rummukainen, M., 2001. Climate change impacts on runoff in Sweden –assessments by global climate models, dynamical downscaling andhydrological modelling. Clim. Res. 16 (2), 101–112.

Birsan, M.-V., Molnar, P., Burlando, P., Pfaundler, M., 2005. Streamflow trends inSwitzerland. J. Hydrol. 314, 312–329.

Boberg, F., Berg, P., Thejll, P., Christensen, J.H., 2007. Analysis of Temporal Changesin Precipitation Frequencies using PRUDENCE Data. Danish Climate CentreReport 07-03. Danish Meteorological Institute. <http://www.dmi.dk/dmi/dkc07-03.pdf>.

Boiten, W., 2000. Hydrometry. Balkema, Rotterdam.Burn, D.H., Hag Elnur, M.A., 2002. Detection of trends and hydrologic variability. J.

Hydrol. 255, 107–122.Caroletti, G.N., Barstad, I., 2009. Future extreme precipitation assessment in

Western Norway – using a linear model approach. Hydrol. Earth Syst. Sci.Discuss. 6, 7539–7579.

Christensen, J.H., Carter, T.R., Rummukainen, M., Amanatidis, G., 2007a. Evaluatingthe performance and utility of regional climate models: the PRUDENCE project.Clim. Change 81 (Suppl. 1), 1–6. doi:10.1007/s10584-006-9211-6.

Christensen, J.H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli,R.K., Kwon, W.-T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C.G.,Räisänen, J., Rinke, A., Sarr, A., Whetton, P., 2007b. Regional climate projections.In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor,M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge University Press,Cambridge and New York, pp. 849–940.

Clarke, R., 2010. On the (mis) use of statistical methods in hydro-climatologicalresearch. Hydrol. Sci. J. 55, 139–144.

Cohn, T.A., Lins, H.F., 2005. Nature’s style: naturally trendy. Geophys. Res. Lett. 32(23). doi:10.1029/2005GL024476. Art. No. L23402.

Davison, A.C., Hinkley, D.V., 1997. Bootstrap Methods and their Application.Cambridge University Press, Cambridge.

Douglas, E.M., Vogel, R.M., Kroll, C.N., 2000. Trends in floods and low flows in theUnited States: impact of spatial correlation. J. Hydrol. 240, 90–105.

Engen-Skaugen, T., Roald, L.A., Beldring, S., Førland, E.J., Tveito, O.E., Engeland, K.,Benestad, R., 2005. Climate Change Impacts on Water Balance in Norway. Met.Report No. 1/2005, Climate, Oslo.

Førland, E., Roald, L.A., Tveito, O.E., Hanssen-Bauer, I., 2000. Past and FutureVariations in Climate and Runoff in Norway. DNMI-Report 19/2000, Climate,Oslo.

Groisman, P.Y., Knight, R.W., Easterling, D.R., Karl, T.R., Hegerl, G.C., Razuvaev, V.N.,2005. Trends in intense precipitation in the climate record. J. Clim. 18, 1326–1350.

Grønås, S., Kvamme, D., Teigen, R., 2005. Og regnet strømmet ned på jorden (And itPoured over the Earth, in Norwegian), Cicerone, vol. 14, issue 1. Centre forInternational Climate and Environmental Research, Oslo, pp. 24–26.

Hanna, E., Jónsson, T., Box, J.E., 2004. An analysis of Icelandic climate since thenineteenth century. Int. J. Climatol. 24, 1193–1210.

Hanssen-Bauer, I., Førland, E.J., Tveito, O.E., 1995. Trends and Variability in AnnualPrecipitation in Norway, Klima-Report 27/95. Norwegian MeteorologicalInstitute.

Hanssen-Bauer, I., Achberger, C., Benestad, R.E., Chen, D., Førland, E.J., 2005. Review:statistical downscaling of climate scenarios over Scandinavia. Clim. Res. 29,255–268.

Hanssen-Bauer, I., Drange, H., Førland, E.J., Roald, L.A., Børsheim, K.Y., Hisdal, H.,Lawrence, D., Nesje, A., Sandven, S., Sorteberg, A., Sundby, S., Vasskog, K.,Ådlandsvik, B., 2009. Klima i Norge 2100 Bakgrunnsmateriale til NOUKlimatilpassing (Climate in Norway 2100 Background Material to the NOUClimate Adaption). Norsk klimasenter, Oslo.

Hisdal, H., Erup, J., Gudmundsson, K., Hiltunen, T., Jutman, T., Ovesen, N.B., Roald,L.A., 1995. Historical Runoff Variations in the Nordic Countries. NHP-Report No.37. Norwegian Hydrological Council, Oslo.

346 D. Wilson et al. / Journal of Hydrology 394 (2010) 334–346

Hisdal, H., Stahl, K., Tallaksen, L.M., Demuth, S., 2001. Have streamflow droughts inEurope become more severe or frequent? Int. J. Climatol. 21, 317–333.

Hisdal, H., Tallaksen, L.M., Frigessi, A., 2002. Handling non-extreme events inextreme value modelling of streamflow droughts. In: van Lanen, H.A.J., Demuth,S. (Eds.), FRIEND 2002 – Regional Hydrology: Bridging the Gap betweenResearch and Practice. IAHS Publ. No. 274, pp. 281–288.

Hisdal, H., Holmqvist, E., Hyvärinen, V., Jónsson, P., Kuusisto, E., Larsen, S.E.,Lindström, G., Ovesen, N.B., Roald, L.A., 2003. Long Time Series – a Review ofNordic Studies, Climate, Water and Energy Project. Report No. 2, Reykjavik.

Hisdal, H., Tallaksen, L.M., Clausen, B., Peters, E., Gustard, A., 2004. Hydrologicaldrought characteristics. In: Tallaksen, L.M., van Lanen, H.A.J. (Eds.), HydrologicalDrought – Processes and Estimation Methods for Streamflow and Groundwater,Developments in Water Science, vol. 48. Elsevier Science B.V, Amsterdam, pp.139–198.

Hisdal, H., Holmqvist, E., Jónsdóttir, J.F., Jónsson, P., Kuusisto, E., Lindström, G.,Roald, L.A., 2010. Has Streamflow Changed in the Nordic Countries? Report 1-2010. Norwegian Water Resources and Energy Directorate, Oslo.

Hyvärinen, V., 1998. Observed trends and fluctuations in hydrological time series inFinland – a review. In: Proceedings of the Second International Conference onClimate and Water, Espoo, Finland, pp. 1064–1070.

Hyvärinen, V., 2003. Trends and characteristics of hydrological time series inFinland. Nordic Hydrol. 34 (1/2), 71–90.

Jones, P.D., Moberg, A., 2003. Hemispheric and large scale surface air temperaturevariations: an extensive revision and an update to 2001. J. Clim. 16, 206–223.

Jónsdóttir, J.F., Jónsson, P., Uvo, C.B., 2005. Trend analysis of Icelandic discharge,precipitation and temperature series. Nordic Hydrol. 37 (4–5), 365–376.

Jónsdóttir, J.F., Uvo, C.B., Clarke, R.T., 2008. Trend analysis in Icelandic discharge,temperature and precipitation series by parametric methods. Hydrol. Res. 39,425–436.

Jørgensen, A.M.K., Fenger, J., Halsnæs, K., 2001. Climate Change Research – DanishContributions. Gads Forlag, Copenhagen.

Kjellström, E., 2004. Recent and future signatures of climate change in Europe.Ambio 33 (4–5), 193–198.

Klein Tank, A.M.G., Wijngarrd, J.B., Konnen, G.P., Bohm, R., Demaree, A., Gocheve, M.,Mileta, S., Pashiardis, L., Hejkrlik, C., Kern-Hansen, R., Heino, P., Bessemoulin, G.,Muller-Westermeier, G., Tzanakou, M., Szalai, S., Palsdottir, D., Fitzgerald, D.,Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., vanEngelen, A.F.V., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V.,Nieplova, E., Cegnar, T., López, J.A., Dahlstrom, B., Moberg, A., Kirchhofer, W.,Ceylan, A., Pachaliuk, O., Alexander, L.V., Petrovic, P., 2002. Daily dataset of20th-century surface air temperature and precipitation series for the EuropeanClimate Assessment. Int. J. Climatol. 22, 1441–1453.

Kundzewicz, Z.W., 2004. Searching for change in hydrological data – editorial.Hydrol. Sci. J. 49 (1), 3–6.

Kundzewicz, Z.W., 2005. Change detection in high river flows in Europe. In: Franks,S., Wagener, T., Bøgh, B., Gupta, H.V., Bastidas, L., Nobre, C., de Oliveira Gãlvao, C.(Eds.), Regional Hydrological Impacts of Climate Change – HydroclimaticVariability. IAHS Publ. 296, pp. 71–80.

Kundzewicz, Z.W., Robson, A.J., 2004. Change detection in hydrological data – areview of the methodology. Hydrol. Sci. J. 49 (1), 7–19.

Kundzewicz, Z.W., Graczyk, D., Maurer, T., Pinskwar, I., Radziejewski, M., Svensson,C., Szwed, M., 2005. Trend detection in river flow series: 1. Annual maximumflow. Hydrol. Sci. J. 50 (5), 797–810.

Lindström, G., Alexandersson, H., 2004. Recent mild and wet years in relation tolong observation records and future climate change in Sweden. Ambio XXXIII(4–5), 183–186.

Lindström, G., Bergström, S., 2004. Runoff trends in Sweden 1807–2002. Hydrol. Sci.J. 49 (1), 69–83.

Liu, S., Mo, X., Zhao, W., Naeimi, V., Dai, D., Shu, C., Mao, L., 2009. Temporalvariations of soil moisture over the Wuding River basin assessed with an eco-hydrological model, in-situ observations and remote sensing. Hydrol. EarthSyst. Sci. 13, 1375–1398.

Mitosek, H.T., 1995. Climate variability and change within the discharge time series:a statistical approach. Clim. Change 29, 101–116.

Moberg, A., Jones, P.D., Lister, D., Walther, A., Brunet, M., Jacobeit, J., Alexander, L.V.,Della-Marta, P.M., Luterbacher, J., Yiou, P., Chen, D., Klein Tank, A.M.G., Saladié,O., Sigró, J., Aguilar, E., Alexandersson, H., Almarza, C., Auer, I., Barriendos, M.,Begert, M., Bergström, H., Böhm, R., Butler, C.J., Caesar, J., Drebs, A., Founda, D.,Gerstengarbe, F.-W., Micela, G., Maugeri, M., Österle, H., Pandzic, K., Petrakis, M.,Srnec, L., Tolasz, R., Tuomenvirta, H., Werner, P.C., Linderholm, H., Philipp, A.,Wanner, H., Xoplaki, E., 2006. Indices for daily temperature and precipitationextremes in Europe analysed for the period 1901–2000. J. Geophys. Res. 111.doi:10.1029/2006JD007103. Art No. D22106.

Ovesen, N.B., Iversen, H.L., Larsen, S.E., Müller-Wohlfeil, D-I., Svendsen, L.M., Blicher,A.S., Jensen, P.M., 2000. Afstrømning i danske vandløb. (Runoff in DanishWatercourses, in Danish). Faglig rapport fra DMU, Nr. 340. DanmarksMiljøundersøgelser, Silkeborg, Denmark.

Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E. (Eds.),. Climate Change 2007: Impacts, Adaption and Vulnerability. Contribution of

Working Group II to the Fourth Assessment Report of the IntergovernmentalPanel on Climate Change. Cambridge University Press, Cambridge.

Räisänen, J., Hansson, U., Ullerstig, A., Döscher, R., Graham, L.P., Jones, C., Meier,H.E.M., Samuelsson, P., Willén, U., 2004. European climate in the late twenty-first century: regional simulations with two driving global models and twoforcing scenarios. Clim. Dynam. 22, 13–31.

Rees, G., Demuth, S., 2000. The application of modern information systemtechnology in the European FRIEND project. Moderne hydrologischeinformationssysteme. Wasser Boden 52 (13), 9–13.

Renard, B., Lang, M., Pois, P., Dupeyrat, A., Mestre, O., Niel, H., Sauquet, E.,Prudhomme, C., Parey, S., Paquet, E., Neppel, L., Gailhard, J., 2008. Regionalmethods for trend detection: assessing field significance and regionalconsistency. Water Resour. Res. 44. doi:10.1029/2007WR006268. Art. No.W08419 2008.

Roald, L.A., Wesselink, A.J., Arnell, N.W., Dixon, J.M., Rees, H.G., Andrews, A.J., 1993.European water archive. In: Gustard, A. (Ed.), Flow Regimes from InternationalExperimental and Network Data (FRIEND). Institute of Hydrology, Wallingford,pp. 7–20.

Roald, L.A., Beldring, S., Skaugen, T., 2004. Scenarios of annual and seasonal runoff inNorway. In: Järvet, A. (Ed.), XXIII Nordic Hydrological Conference – SelectedArticles NHP Report 48, vol. I, pp. 71–76.

Roald, L.A., Beldring, S., Engen-Skaugen, T., Førland, E.J., Benestad, R., 2006. ClimateChange Impacts on Streamflow in Norway. Norwegian Water Resources andEnergy Directorate, Report A1-2006.

Rummukainen, M., Bergström, S., Persson, G., Rodhe, J., Tjernström, M., 2004a. TheSwedish regional climate modelling programme, SWECLIM: a review. AmbioXXXIII (4–5), 176–182.

Rummukainen, M., Bergström, S., Persson, G., Rodhe, J., Tjernström, M., (Eds.),2004b. Ambio Special Issue: Swedish Regional Climate Modelling Programme.Ambio XXXIII (4–5), 175–274.

Salas, J.D., 1993. Analysis and modelling of hydrologic time series. In: Maidment,D.R. (Ed.), Handbook of Hydrology. McGraw-Hill, New York, pp. 19.1–19.72.

Skaugen, T., Astrup, M., Roald, L.A., Førland, E.J., 2003. Scenarios of extreme dailyprecipitation for Norway under climate change. Nordic Hydrol. 15 (1), 1–13.

Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M.,Miller, H.L. (Eds.), 2007. Climate Change 2007: The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge University Press,Cambridge and New York.

Stranden, H.B., Skaugen, T., 2009. Trends in Annual Maximum Snow WaterEquivalent in Norway (1914–2008). Norwegian Water Resources and EnergyDirectorate, Report 3-2009, Oslo.

Svensson, C., Kundzewicz, Z.W., Maurer, T., 2005. Trend detection in river flowseries: 2. Flood and low-flow index series. Hydrol. Sci. J. 50 (5), 811–824.

Tallaksen, L.M., Madsen, H., Clausen, B., 1997. On the definition and modelling ofstreamflow drought duration and deficit volume. Hydrol. Sci. J. 42 (1), 15–33.

Trenberth, K.E., Jones, P.D., Ambenje, P., Bojariu, R., Easterling, D., Klein Tank, A.,Parker, D., Rahimzadeh, F., Renwick, J.A., Rusticucci, M., Soden, B., Zhai, P., 2007.Observations: surface and atmospheric climate change. In: Solomon, S., Qin, D.,Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.),Climate Change 2007: The Physical Science Basis. Contribution of WorkingGroup I to the Fourth Assessment Report of the Intergovernmental Panel onClimate Change. Cambridge University Press, Cambridge and New York, pp.237–336.

Tuomenvirta, H., 2004. Reliable Estimation of Climatic Variations in Finland. FinnishMeteorological Institute Contributions no. 43. Finnish Meteorological Institute,Helsinki.

van der Linden, P., 2009. Introduction to the ENSEMBLES Project. ENSEMBLES:Climate Change and its Impacts: Summary of Research and Results from theENSEMBLES Project. Met Office Hadley Centre, Exeter, UK. pp. 3–6.

van der Linden, P., Mitchell, J.F.B. (Eds.), 2009. ENSEMBLES: Climate Change and itsImpacts: Summary of Research and Results from the ENSEMBLES Project. MetOffice Hadley Centre, Exeter, UK.

Wang, X.L.L., Swail, V.R., 2001. Changes of extreme wave heights in NorthernHemisphere oceans and related atmospheric circulation regimes. J. Clim. 14(10), 2204–2221.

Yevjevich, V., 1967. An Objective Approach to Definition and Investigations ofContinental Hydrologic Droughts. Hydrology Papers 23. Colorado StateUniversity, Fort Collins.

Yue, S., Pilon, P., 2004. A comparison of the power of the t test, Mann–Kendall andbootstrap tests for trend detection. Hydrol. Sci. J. 49 (1), 21–37.

Yue, S., Wang, C.Y., 2002. Regional streamflow trend detection with consideration ofboth temporal and spatial correlation. Int. J. Climatol. 22, 933–946.

Yue, S., Pilon, P., Phinney, B., 2003. Canadian streamflow trend detection: impact ofserial and cross-correlation. Hydrol. Sci. J. 48 (1), 51–63.

Zhang, X., Zwiers, F.W., 2004. Comment on ‘‘Applicability of prewhitening toeliminate the influence of serial correlation on the Mann–Kendall test” bySheng Yue and Chun Yuan Wang. Water Resour. Res. 40 (3). doi:10.1029/2003WR002073. Art. No. W03805.