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Flood type classification is an optimal tool to cluster floods with similar meteorological triggering conditions. Under climate change these flood types may change differently as well as new flood types develop. This paper presents a new methodology to classify flood types, particularly for use in climate change impact studies. A weather generator is coupled with a conceptual rainfall-runoff model to create long synthetic records of discharge to efficiently build an inventory with high number of flood events. Significant discharge days are classified into causal types using k-means clustering of temperature and precipitation indicators capturing differences in rainfall amount, antecedent rainfall and snow-cover and day of year. From climate projections of bias-corrected temperature and precipitation, future discharge and associated change in flood types are assessed. The approach is applied to two different Alpine catchments: the Ubaye region, a small catchment in France, dominated by rain-on-snow flood events during spring, and the larger Salzach catchment in Austria, affected more by rainfall summer/autumn flood events. The results show that the approach is able to reproduce the observed flood types in both catchments. Under future climate scenarios, the methodology identifies changes in the distribution of flood types and characteristics of the flood types in both study areas. The developed methodology has potential to be used flood impact assessment and disaster risk management as future changes in flood types will have implications for both the local social and ecological systems in the future.
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Author’s Accepted Manuscript
A new flood type classification method for use in
climate change impact studies
Thea Turkington, Korbinian Breinl, Janneke
Ettema, Dinand Alkema, Victor Jetten
PII: S2212-0947(15)30050-5
Reference: WACE125
To appear in: Weather and Climate Extremes
Received date: 18 November 2015
Revised date: 30 September 2016
Accepted date: 4 October 2016
Cite this article as: Thea Turkington, Korbinian Breinl, Janneke Ettema, Dinand
Alkema and Victor Jetten, A new flood type classification method for use in
climate change impact studies, Weather and Climate Extremes,
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A new flood type classification method for use in climate change impact studies
Thea Turkington1*, Korbinian Breinl2, Janneke Ettema1, Dinand Alkema1, and Victor Jetten1
1Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The
2Department of Geoinformatics, Paris-Lodron University of Salzburg, Salzburg, Austria
* Corresponding author: Thea Turkington email:
Flood type classification is an optimal tool to cluster floods with similar meteorological triggering
conditions. Under climate change these flood types may change differently as well as new flood types
develop. This paper presents a new methodology to classify flood types, particularly for use in climate
change impact studies. A weather generator is coupled with a conceptual rainfall-runoff model to create
long synthetic records of discharge to efficiently build an inventory with high number of flood events.
Significant discharge days are classified into causal types using k-means clustering of temperature and
precipitation indicators capturing differences in rainfall amount, antecedent rainfall and snow-cover and
day of year. From climate projections of bias-corrected temperature and precipitation, future discharge
and associated change in flood types are assessed. The approach is applied to two different Alpine
catchments: the Ubaye region, a small catchment in France, dominated by rain-on-snow flood events
during spring, and the larger Salzach catchment in Austria, affected more by rainfall summer/autumn
flood events. The results show that the approach is able to reproduce the observed flood types in both
catchments. Under future climate scenarios, the methodology identifies changes in the distribution of
flood types and characteristics of the flood types in both study areas. The developed methodology has
potential to be used flood impact assessment and disaster risk management as future changes in flood
types will have implications for both the local social and ecological systems in the future.
1. Introduction
Climate change will alter flooding around the globe, and therefore an increasing number of studies are
modelling the impact of climate change on floods, with the focus generally on changing magnitude and
frequency of the flood events (Booij, 2005; Gain et al., 2013; Raff et al., 2009). However, future
projections of the meteorological triggers, including heavy precipitation and snowmelt, may change
differently and alter the characteristics of the flood events (Hall et al., 2014). As a result, factors
associated with the causal type of flood such as seasonality and triggering conditions should be addressed
next to the change in frequency or magnitude of floods. Classifying flood events into different types can
place flooding into a wider climate context and help with exploring changes in future flood events.
Changes in flood types will have implications on both the local social and ecological systems and are
therefore important to consider when assessing future changes in flooding (Gain et al., 2013; Garner et al.,
Flood types can be distinguished based on the meteorological conditions of a flood event, such as amount
and distribution of precipitation, as well as antecedent conditions, such as snow depth and soil moisture.
Nied et al. (2014) identify three different approaches to describe flood events: (1) based on the flood
event description, (2) linking the flood with atmospheric circulation patterns, and (3) classification into
flood types. The first category describing the specific flood events covers studies with a detailed
examination of a particular event (e.g. the Danube flood in 2013 (Blöschl et al., 2013), the Mississippi
River flood in 1993 (Kunkel et al., 1994), and the Himalayan flood in 2013 (Dube et al., 2014)). The
second approach uses large scale atmospheric circulation patterns to identify similar atmospheric
triggering conditions that are linked with the probability of flood occurrence (e.g. Bárdossy and Filiz
(2005) Delgado et al. (2014); Pattison and Lane (2012), Prudhomme and Genevier (2011)). In the final
approach, individual flood events are clustered into different categories based on generating processes of
the events (e.g. Gaál et al. (2012); Merz and Blöschl (2008); Viglione et al. (2010)).
Of the three approaches for identifying flood types, the applicability of the method depends on the
purpose. The description of flood events allows for a singular flood event to be examined, without
necessarily a long record of events. However, the variables considered vary between case studies, in part
due to different data availability, making it difficult to compare between case studies (Nied et al., 2014).
Widely applied classification based on atmospheric conditions is hampered due to the small number of
actual flood events relative to the overall number of days (Nied et al., 2014; Prudhomme and Genevier,
2011), particularly on the local or regional scale where maybe only a handful of observed flood events
occurred over the past 100 years. Both the second and third categories have the potential to be used in
climate change impact studies, provided there are sufficient flood events, and that climate models are able
to reproduce the necessary atmospheric variables. Even with long complete records, a relationship
between flood events and large scale atmospheric circulations cannot be determined in many cases.
Therefore, the classification approach (approach three) will be applied here, as the characteristics of the
flood events are of concern in assessing the impact of climate change on flood types.
The variety of approaches to cluster flood events leads to different flood types. The approach to cluster
flood events depends on the region and triggering conditions as well as the data available. Merz and
Bloschl (2003) clustered flood types manually allowing a combination of different sources of information
to be used. They classified the flood events into five types: flash floods, short rain, long rain, rain-on-
snow, and snowmelt floods. Nied et al. (2014) used the previous classification of five flood types, and
then compared the soil moisture and atmospheric circulation patterns between flood types, highlighting
the importance of antecedent conditions for the different flood types. Alila and Mtiraoui (2002) clustered
flood events based on ENSO, storms (monsoonal storms, frontal storms and dissipating tropical
cyclones), with either two or three clusters for each classification for south-east and central Arizona in the
USA. Viglione et al. (2010) included catchment excess rainfall as part of the flood response for different
flood types in the Kamp catchment, Austria, while Gaál et al. (2012) clustered different Austrian
catchments including the month when it occurred. In each of these studies, to obtain sufficient number of
flood events, either less severe flood events were included or a large study area was defined,
incorporating discharge measurements from multiple locations a catchment or catchments. Therefore, we
introduce the use of a weather generator in combination with conceptual rainfall-runoff model to generate
long time series of discharge to classify flood types.
Little research has been done on how causal flood types explicitly will change in the future and recent
literature provides evidence that they will change along with potential indicators to use in classifications.
Arnell and Gosling (2014) found decreases in magnitude of spring floods for central Europe as a result of
smaller discharge peaks from rainfall than the previously snowmelt-generated ones. Possible future
changes in flood seasonality have also been identified in Switzerland due to changes in rainfall, snow
accumulation, and snow melt (Köplin et al., 2014). Current trends in rain-on-snow floods in the western
United States have a range of significant increasing and decreasing trends (McCabe et al., 2007). In the
future, parts of the same area are expected to shift from snow dominated winters to rain dominated
winters (Nolin and Daly, 2006). An increase of high temperature and heavy rainfall in Norway also
indicates an increase in winter/spring snowmelt floods (Benestad and Haugen, 2007; Vormoor et al.,
2015). While none of these studies considered changing flood types explicitly, they demonstrate that
changes in precipitation, both rainfall and snowfall and melt have the potential to alter flood types in a
This paper presents a methodology developed to classify flood types particularly for use in climate
change impact studies as it creates and analyzes long records (meteorological and flood events). To obtain
sufficiently long records of flood events for objective flood type classification, a multi-site weather
generator is coupled with the HBV rainfall-runoff model. The flood events are extracted from the
resulting 1200 years of simulated data, where a flood is defined as days with discharge that could
potentially lead to flood situations. In particular the discharge levels corresponded to the 2 (bank full
flow), 10, and 25 year return periods, with longer return periods were not considered in order to limit
spurious extrapolations. The flood events are separated into different flood types based on extreme
meteorological triggering conditions and flood timing (Section 2). To illustrate the developed
methodology, two European catchments are used as test sites with different sizes and dominant flood
types (Section 3 and 4). In this paper we apply the methodology to future climate projections and on the
past climate (Section 5), allowing new flood types to be identified that were absent in the past. Four
different climate projections are analyzed for each catchment for the period 2070 to 2099 to demonstrate
how changes in the future climate may alter flood types in the future. Sections 6 and 7 discuss and
conclude our findings.
2. Methods
In any classification method, a sufficient number of events is required to allow for clustering. In the case
of flood events within a single catchment there are often only a handful of events. While other studies
work around this through the using multiple catchments, this paper aims to classify the flood types within
a catchment by producing synthetic data based on observational records. Long time series of
meteorological and hydrological data is generated using a combination of a weather generator (Section
2.1) and model of discharge (Section 2.2). Once this synthetic time series is generated for past and future
climate, clustering of different flood types can be done (Section 2.3). A flow diagram of all steps of the
methodology is contained in the supplementary material.
2.1 Weather Generator
The semi-parametric daily-multisite weather generator from Breinl et al. (2014) was utilized to generate
long time series of daily 2m temperature and precipitation values to serve as input for the rainfall-runoff
model. The multi-site precipitation algorithm uses a univariate Markov process to represent sequences of
daily snapshot of precipitation amounts for multiple point locations within the catchment. The weather
generator was used in a so-called Reduced State Space setup (see Breinl (2015); Breinl et al. (2014)) to
reduce the duplication of observed precipitation sequences. Precipitation amounts were simulated by pure
resampling of observations ('bootstrap'), instead of using parametric distribution functions for
precipitation amounts as applied in Breinl et al. (2014). Parametric distributions were not applied to do
the complexity of altering compound distributions under future climate scenarios. For the temperature,
mean daily temperature was simulated with autoregressive-moving-average processes (ARMA). The
weather generator was set up monthly to account for seasonality of precipitation and temperature. In total
1200 years of daily temperature and precipitation were generated to drive the conceptual rainfall-runoff
model (see Section 2.2) for the observed period, as well as for each of the selected future climate
The multi-site weather generator has been successfully applied for the historical period in Alpine
catchments by Breinl (2015). It was found that the weather generator handles the spatial variability of
precipitation between rain gauges well, with a slight tendency to underestimate extreme dry spells, which
is a well-known issue of Markov based weather generation algorithms. The mean number of dry and wet
days was well simulated.
To generate future projections of precipitation, the time series of the resampled observational period
values were first generated by the weather generator and then the values replaced with the projected
precipitation amounts by reshuffling to maintain temporal and inter-site statistics. Future temperature
projections were generated by adding the projected monthly mean temperature shift to the observations, a
common technique in climate impact studies (e.g. Steinschneider and Brown (2013); Tao and Zhang
(2011)). These generated time series are fed to the HBV model to simulate future discharge.
2.2 HBV model
The conceptual HBV rainfall-runoff model was used to model historical and future discharge based on
observational records and future climate data (Bergström, 1976). The HBV model was selected as it
represents the main runoff generating processes, and due to the low computational costs, it can be used to
generate discharge time series longer than 1000 years. The model has also been used in numerous
previous studies (e.g. Booij (2005); Das et al. (2008); Gao et al. (2012); Steele-Dunne et al. (2008)). For
this study, the HBV-light model (version from Seibert and Vis (2012) was applied. The model
was used in a semi-distributed setup, with a single catchment sliced into ten elevation zones for
distributed snow modelling a well as three groundwater boxes. The HBV-light model uses time series of
daily precipitation, daily mean temperature and daily discharge data for calibration. For historical period,
multi-site precipitation from the weather generator in Section 2.1 was averaged through Thiessen
polygons, which turned out to be sufficient compared to other methods such as Kriging with external drift
(Breinl, 2015). The potential monthly evaporation was calculated after Thornthwaite (1948), as has been
used in previous studies in combination with the HBV model (e.g. Bergström et al. (2001), Akhtar et al.
(2008); Timalsina et al. (2015)).
The HBV model was calibrated and validated based on 20 years of observed temperature, precipitation,
and discharge as a 20 year period has been assessed to be sufficient length for use in climate change
impact studies (Vaze et al., 2010). For the calibration, the ranges of 15 model parameters were taken from
Seibert and Vis (2012), related to snow, soil moisture, response, and routing. In total 100 different
parameter sets were calibrated to account for equifinality (Beven, 1999) using a genetic algorithm
followed by Powell's quadratically convergent method for fine-tuning (Press et al., 2002). Further details
on the calibration and validation process as well as the model performance in both catchments can be
taken from Breinl (2015). After model calibration, synthetic discharge (1200 years) was generated by
feeding the temperature and precipitation time series from the weather generator in Section 2.1 into the
HBV model. This was done for both the past and future periods. As different magnitude flood events may
have differences in flood type characteristics, three discharge magnitudes were used. These were based on
the 2 (bank full), 10, and 25 year return period amounts (Q2, Q10, Q25) and were calculated empirically
based on the annual maximum daily discharge values.
2.3 Classification: Flood types
The two most important considerations in clustering flood types are the selection of meteorological
indices, and how to cluster the flood events based on these indicators. Flood types in mountainous
catchments include different combinations of intense-short-duration rainfall, high antecedent rainfall
decreasing catchment storage, and snow cover and melt (Merz and Blöschl, 2008). The clusters should
reflect these types, and therefore indicators should be able to capture differences.
The indicators representing four different components of flood generation were: 1) short (1-day) duration
precipitation, 2) antecedent precipitation over two or more days preceding the flood event, 3) daily and
antecedent 2m temperature, both absolute values and normalized temperature values based on time of
year, and 4) day of the year (DOY). The precise antecedent precipitation and temperature indicators were
selected based on their correlation with daily discharge. The period of antecedent precipitation that had
the highest correlation with discharge was selected, varying the period for two to 60 days before the
flood. Temperature in combination with precipitation may identify rainfall as opposed to snowfall, while
warm spring temperatures indicate snowmelt, and high temperatures in summer and autumn the
possibility of convective precipitation. DOY could indicate possible snow cover and snowmelt, or other
seasonally varying phenomena. For temperature, the period was allowed to vary from 1 to 60 days, using
both absolute and normalized values due to the strong seasonal signal. Temperature was normalized
() on a daily basis using:
 
where is the daily mean temperature,
is the average daily mean temperature for all values for the
same day of the year, and is the standard deviation for all values for the same day of the year.
To cluster the flood events into different types the indicators were analyzed by k-means clustering. K-
means clustering is an unsupervised clustering technique that separates events into different groups based
on one or more indicators. Previous uses include classification of groups of catchments with similar
precipitation and flood regimes (Parajka et al., 2010), as well as classifying atmospheric circulation
patterns (Huth et al., 2008). The iterative process groups each event into the cluster with the closest
centroid, after which the centroid is recalculated based on the mean values of all the events in the cluster.
When using multiple indicators for clustering, those with a larger variance will have a larger influence on
the center clusters, which can be mitigated by standardizing the indicators. The flood types were clustered
for each return period (Q2, Q10 and Q25) using two to all four indicators.
The silhouette index (SI, Rousseeuw (1987)) was used to evaluate the quality of the flood type clusters
and determine the final number of clusters. The SI for each cluster can be calculated using:
 (2)
where is the number of flood events in cluster, is the average Euclidean distance between an
observation and all observations in the next closest cluster, and is the average Euclidean distance
between and all other flood events in the same cluster. SI values vary between 1 and -1, with positive
values when they are likely to be correctly classified, negative when the likely belong in another cluster,
or near zero for no particular cluster.
The final flood type classification was selected based on the classification with the highest average SI
value from (2). It is possible that particular flood types are not observed at all return periods, or there may
not be a clear distinction between the more frequent flood types. Therefore, for each study area the final
flood type cluster indicators and number of clusters/types were allowed to vary between return periods.
Two different approaches were applied to assess how the flood types may change in the future. For both
approaches, 1200 years of future discharge were generated using weather generator enforced by the future
climate projections for temperature and precipitation and the HBV model. The first approach was to
detect changes in the distribution of historical flood types as a change in dominant flood type may have an
impact on the vulnerability or exposure of an area to flooding. To assess the change in distribution, the
flood events (Q2, Q10, Q25) were identified based on the historical discharge amounts, allowing the
relative change in number of flood frequency to be calculated. The future flood events were placed in the
closest historical cluster. A change in the distribution of flood events between the clusters indicates
possible changes in the dominant flood type or types in the future.
The second approach repeated the flood type classification using future discharge to allow for new flood
types to emerge. To maintain the same number of flood events for clustering, the discharge return periods
are re-calculated based on future discharge. The clustering is repeated, based on the four indicators and
average SI value. Both the number and the characteristics of the flood types can be compared to the
historical flood types to assess changes in future flood type characteristics. The projected temperature
values were normalized based on the historical time series, so as to be able to assess the difference in
temperature between the historical and new future flood types.
3. Study area
Two study areas were selected to demonstrate the applicability of the methodology under different
conditions: the Ubaye catchment (548 km2) in the southern French Alps and the Salzach catchment (4637
km2) in Austria (Figure 1). Both are located in the European Alps, a region that has warmed twice as fast
as the mean temperature for the Northern Hemisphere (Auer et al., 2007). The Alps have also experienced
a general retreat of glaciers, an poor snow conditions for winter tourism, with future changes in discharge
predicted to increase in winter and decrease in summer (Beniston et al., 2011). Currently, the Ubaye
catchment has a mountainous Mediterranean climate with snow on the upper reaches of the catchment for
approximately six months of the year (Remaître et al., 2011). It has an observed average annual
precipitation between 730mm and 985mm with the average annual daily maximum precipitation between
46mm and 53mm. As Salzach is located on the north side of the Alps, it has a predominately Alpine
climate experiencing annual maximum precipitation and flood maxima generally in summer (Parajka et
al., 2010). The Salzach catchment has an observed average annual precipitation varies between 1096mm
and 2035mm, while the average annual daily maximum precipitation is between 45mm and 84mm.
Figure 1. Map of the two study areas with the location of the rain and river gauges, including location where
temperature was also measured. The size and location of the EOBS grid cell is also shown for Ubaye.
Salzach and Ubaye catchments differ in size and average annual precipitation, they also differ in flood
seasonality as shown in previous flood hazard studies (Salzach: Stanzel et al. (2008), Ubaye: Ramesh
(2013)). The Ubaye River generally experiences spring flood events, where warm rain amplifies elevated
river levels due to snow melt (Ramesh, 2013). Summer flood events are more common for Salzach
catchment, which includes the August 2002 flood event where the discharge was the highest in the
previous 100 years (Ulbrich et al., 2003). More recently in June 2013, the Salzach catchment recorded
high discharge after four days of high precipitation with high antecedent soil moisture (Blöschl et al.,
4. Data selection
The Ubaye and Salzach catchments are covered by a hydrological network with more than 20 years of
measurements. The Ubaye catchment contains four rain gauges and measurements of mean daily
discharge covering the period 1971-2004. Observed gridded data from the ENSEMBLES project was
used for temperature (E-OBS -Haylock et al. (2008)), due to missing data and discontinuities in the
temperature record for the catchment. E_OBS data have been successfully used in previous flood related
studies (e.g. Freudiger et al. (2014), Ionita et al. (2014)). The Salzach catchment contains 18 rain gauges,
three temperature gauges, and measurements of mean daily discharge for the period 1987-2010. For input
into the HBV model the arithmetic mean of multiple temperature station was used for Salzach as it
resulted in higher model efficiency coefficients compared to using a single, centrally located, temperature
gauge. To calibrate the HBV model, a ten year period was selected (Salzach: 2001-2010, Ubaye: 1995-
2004). The validation period was for Salzach: 1988-1997, and for Ubaye: 1971-1980. Both calibration
periods contained significant flood events, 2002 in Salzach and 2003 in Ubaye.
For future flood type analysis over the period 2070-2099, four future projections were selected from a set
of 15 future climate projections. Four projections were selected to analyze future flood types to maintain a
manageable number of future projections, as well as using many projections can tend to highlight the
central tendency, rather than extreme conditions (Raff et al., 2009). The full set of 15 originate from the
EURO-CORDEX dataset (Jacob et al., 2014). Model output from three RCMs (SMHI-RCA4,
DMI_HIRHAM5, KNMI-RACMO22E) driven by 4 different GCMs (ICHEC-EC_EARTH, MOHC-
HadGEM2_ES, IPSL-CM5a_MR, MPI-ESM_LR) and two representative concentration pathways,
RCP4.5 and RCP 8.5, were selected to cover a wide range of genealogy (Knutti et al., 2013). Details on
these 15 projections can be found in the supplementary material. From the set of 15, four projections
were selected using the method by Raff et al. (2009). This method is based on the mean temperature and
precipitation projected changes compared to the historical period, averaged over the catchment. Mean
changes in temperature and precipitation were used so that the results are not biased towards one
particular flood type, for example through selecting changes in extreme precipitation or spring
temperature. The selected projections represent combinations of warmer, milder, drier and wetter
conditions for time period 2070-2099.
A bias correction method was utilized for the four selected projections for each catchments, as model
biases may still remain in RCM data even though they reasonably reproduce meso-scale atmospheric
features (Frei et al., 2006). For this work, an empirical-quantile mapping technique (EQM) was chosen.
The bias correction methodology for precipitation was based on Themeßl et al. (2012), which has been
successfully applied in hydrological climate impact studies (e.g. Dobler et al. (2012); Finger et al.
(2012)). EQM transforms the empirical cumulative density distribution of the RCM data to match the
observed empirical distribution, requiring no assumption about underlying distributions. The bias-
correction method performed better than other methods in a range of mid-latitude climates, although may
be subject to over-tuning (Lafon et al., 2013).
From Themeßl et al. (2012), the corrected precipitation amounts (Xcorr) can be calculated using:
  (1)
- (2)
Where Xrawt,j is the precipitation amount on day t at point j, Xcorrt,i is the corrected RCM precipitation
amount on day t for gauge i, CFi is the correction factor at j with regards to i, and P is the probability of
Xraw based on the empirical cumulative distribution (ecdf) for daily precipitation values.
The correction factors were calculated monthly, as RCMs biases may differ between seasons (Frei et al.,
2006), as well as to align with the weather generator (Section 2.1). Time periods should be longer than 20
years, because for shorter periods results become sensitive to the precise time period chosen (Wood et al.,
2004), although results for longer time periods are increasingly likely to contain non-stationeries over the
period. The average correction factor for the five most extreme values was used for any unobserved
extreme precipitation value.
5. Flood typing
The generated time series from the combination weather generator and HBV model are described in
Section 5.1, including the results for the observational period as well as the future projections. These time
series form the base for the classification of flood types along with the indicators selected using the
historical data. As the catchments have different flood characteristics, the applicability of the flood type
classification is shown per catchment (Ubaye in Section 5.2 and Salzach in Section 5.3) for past and
future climates.
5.1 Data input for classification
5.1.1 Historical period and indicators
The generated discharge, precipitation and temperature for the historical period in both catchments are
characterized in Figure 2. The discharge time series was generated after calibrating the HBV model. The
average Nash-Sutcliffe efficiency (Nash and Sutcliffe, 1970) is computed as performance indicator for the
HBV model for was 0.87/0.82 for Salzach and 0.82/0.74 for Ubaye for the calibration/validation period.
As the HBV model has been tested for both catchments, details on comparison with observational records
can be found in Breinl (2015).
In the Ubaye catchment, the average daily precipitation was stable thought the year (1-2mm) with a small
peak in October/November (3mm). In the Salzach catchment there was a clear seasonal signal, with the
average daily precipitation lowest in December and January (2mm) and increasing to 6-7mm in July and
August. The temperature shows the same annual variation for the two regions, with a higher maximum
average temperature in the Salzach catchment. As a representation of extreme precipitation for the two
catchments, the average annual maximum daily precipitation from the weather generator was 45.2mm for
Ubaye and 42.3mm for Salzach.
Figure 2. Mean daily discharge (solid black) from the HBV model, with mean temperature (red dots) and
precipitation (blue dash) from the weather generator. Left: Ubaye for the period 1988-2010. Right: Salzach
for the period 1971-2004
The mean daily discharge for the Ubaye catchments peaks in the spring, with a second smaller peak in the
autumn. The second peak aligns with the peak precipitation period, while the first discharge peak may be
associated with snowmelt. For the Salzach catchment, the mean discharge and precipitation are highest
from late spring to early autumn. An increase in discharge around April/May, not matched in the mean
precipitation amounts, was likely caused in part by snowmelt. Higher discharge values in the Salzach
catchment could be explained by the difference is size compared to the Ubaye catchment.
The selection of exact indicators was based on the correlation with discharge using the generated time
series of precipitation, temperature, and discharge (Figure 2). Different antecedent periods were tested as
indicators in both catchments. The 15-day total precipitation and 5-day normalized temperature had the
highest correlation with discharge for the Salzach catchment (correlation coefficients of 0.61 and 0.60
respectively). For the Ubaye catchment, the antecedent period used for precipitation was 35 days and 4
days for normalized temperature (both with a correlation coefficient of 0.42). As the antecedent
normalized temperature had a higher correlation with discharge for both study areas, it was used as a
potential indicator instead of absolute temperature values. Table 1 lists the potential indicators for the
classification of flood types.
Table 1. Potential indicators for classification of flood types for the Ubaye and Salzach catchments
5.1.2 Future period
The mean changes in precipitation and temperature in each catchment for 15 different bias-corrected
climate projections are shown in Figure 3. All projections show an increase in the average annual
temperature for the future (2070-2099) compared to the historical period (Ubaye: 1971-2004, Salzach:
1987-2010). The largest increase in temperature for both study areas was more than 2.0% using the
MOHC-HadGEM2_ES- SMHI-RCA4 combination. For precipitation, the Ubaye catchment shows most
projections with drier conditions, while for Salzach most projections show the area becoming wetter. To
reduce the number of future projections, four were selected for each study area using the method
proposed by Raff et al. (2009). The SMHI-RCA4 runs with ICHEC-EC_EARTH as driving GCM were
selected for both catchment (red circle Figure 3), with the DMI_HIRHAM5 and ICHEC-EC_EARTH as
the other model combination for Ubaye, and SMHI-RCA4 and IPSL-EM5a_MR for Salzach.
Figure 3. Projection temperature and precipitation ratio comparing the period 2070-2099 with observational
period with Ubaye catchment (left) and the Salzach catchment (right). The selected projections circled in red
are for the following combinations: mild dry (Md), mild wet (Mw), warm dry (Wd) and warm wet (Ww). The
Short precipitation
1-day total (mm)
1-day total (mm)
Antecedent precipitation
35-day total (mm)
15 day total (mm)
4 day mean temperature
5 day mean temperature
Day of the year
Days from 31st Dec
Days from 31st Dec
colors represent the different driving GCMs: black ICHEC-EC_EARTH, blue MOHC-HadGEM2_ES, green
IPSL-EM5a_MR, orange MPI-ESM_LR, and the different RCMs are represented with different symbols:
circle SMHI-RCA4, star DMI_HIRHAM5, diamond KNMI-RACMO22E.
The eight projection circled in red were then fed to the weather generator and HBV model to produce four
times 1200 years of generated data for both the Ubaye and the Salzach catchments. Figure 4 shows the
mean daily precipitation, temperature and discharge per projection for the period 2070-2099. For Ubaye,
all projections had the highest mean precipitation amounts in September and October, extending into
August under the Wd projection. Furthermore, besides Wd, the other projections showed a clear seasonal
variation in precipitation with two peaks: one in September-October (3-6mm/day) and a second minor
peak in March May (2-3mm/day). The temperature had the same annual variation as in the historical
period, although warmer by 1-2°C, except for the winter temperatures for the two warmer projections
(Wd and Ww). For the Wd and Ww projections, the temperature was 5-7 degrees higher than in the
historical period elevating the mean temperature to above freezing. The changes in temperature and
precipitation led to a smaller spring discharge peak than observed in the historical period, particularly for
the Wd projection, and higher discharge amounts from October to November.
Figure 4. Mean daily discharge (solid black) from the HBV model, with mean temperature (red dots) and
precipitation (blue dash) from the weather generator. Left: Ubaye Right: Salzach. Both for the period 2070-
2099 and for each of the four projections Md, Mw, Wd, and Ww
For Salzach, the seasonal variation of mean precipitation varied between the four future projections in
Figure 4, with the Md projection being most similar to the historical period. The Mw and Ww projections
had an increase in the average daily precipitation of 7-8mm for July and August. For the Wd projection,
there were two precipitation peaks of 5-6mm, one in February to March and the other in June to
September. The temperature showed a similar distribution as the historical period with a 2-4°C increase
for the milder projections, Md and Mw, and a 4-6°C increase for the warmer projections, Wd and Ww.
For future discharge, the amount either stayed the same or increased for March to April, with lower
discharge between June and October. There was a second discharge peak in three of the projections
occurring in July to August for Mw and Ww projections and September to October for the Md projection.
5.2 Ubaye flood types
5.2.1 Historical period (1971-2004)
Per return period the dominant flood types in Ubaye catchment were determined for the historical period
1971-2004. The indicators that independently gave the highest correlation with discharge were: 1-day
precipitation (RR), antecedent 35-day precipitation (RRa), and antecedent 4-day normalized temperature
(Tna) in combination with the day-of-year (DOY). Only the temperature indicator was normalized, as it
was found that normalization of all the indicators resulted in poor separation of clusters (not shown).
Figure 5 shows the flood types where DOY versus precipitation is plotted (a) as well as the silhouette
value per event (b). For Q2, flood events were classified into two groups: a small cluster later in the year
with higher 1-day rainfall amounts (Type 1) and a second larger cluster earlier in the year (Type 2; Figure
5a). For the Q2 floods, the combination of 1-day precipitation (RR), temperature and day of the year gave
an average SI score of 0.94, indicating a near perfect separation between the two groups. Using the same
set of indicators and number of clusters, the SI values for Q10 and Q25 were 0.85 and 0.85, respectively.
However further analysis on Q10 and Q25 identified a third group that split the Type 1 floods into two
smaller clusters. The two clusters also add the antecedent 35-day precipitation as an indicator and
provided a more compact range of conditions under which the flood events occurred. The average SI
score changed to 0.68 for the Q10 floods and 0.73 for the Q25 floods. The majority of individual SI
values were above 0.5 in Figure 5b, indicating that these flood events were most similar to other flood
events in their cluster. However, when introducing three flood types some SI values dropped to near zero,
particularly for Q10 floods, indicating that there is no preferred cluster for these flood events. The
average SI value and cluster center values per indicator are listed in Table 2 per return period and flood
For all return periods Type 2 floods occurred between September and December with higher than normal
temperatures. The warmer temperatures indicated that the rainfall may come from warmer convective
events. The associated mean 1-day precipitation amounts are 44.5 mm, 53.2 and 45.6 mm for Q2, Q10
and Q25, respectively (Table 2); values close to the observed annual daily maximum precipitation. The
related antecedent precipitation mean values increased with increasing return period indicating higher soil
moisture that may lead to more runoff during the short rain events.
Table 2. Cluster center values for the different flood types for Ubaye under the historical climate. RR is the 1-
day precipitation amount, RRa is the 35-day antecedent precipitation and Tna is the normalized 4-day
antecedent temperature. The values in bold are used for the cluster centers.
Ubaye flood types
Type 1
Type 2
Figure 5. Clustering of flood types for the Ubaye catchment (A) with the individual silhouette values (B) for
the historical period. Green stars indicate Type 2, blue circles Type 1/1a, and light blue diamonds for Type 1b
flood events. In each instance, only the antecedent and DOY indicators are shown.
Type 1 floods occurred between March and July. Compared to Type 2 floods, Type 1 floods had a lower
Tna, but still generally higher than normal (Table 2). The warmer temperatures in spring may have been
associated with increased snowmelt, rain on snow, or more rainfall rather than snowfall. The 1-day
precipitation amounts for this flood type were lower than for Type 2 floods (Figure 5a and Table 2), but
higher than the mean values in Figure 2. Therefore it is unlikely that there were snowmelt floods in the
Ubaye catchment, a type outlined by Merz and Blöschl (2008), rather, two groups of Rain-Snow floods
(here labelled Type 1a and Type 1b) separated by antecedent precipitation amounts. Type 1b floods had
higher antecedent precipitation with lower 1-day precipitation compared to Type 1a, as can be seen in
Figure 5 and Table 2. As the temperature indicator covered a shorter time period than the antecedent
precipitation, it is not possible to assess whether all the precipitation is snow or rain using the indicators
alone. Further investigation of the HBV output data of a select number of the Q25 Type 1b floods showed
lower temperatures the preceding weeks, only warming to above normal temperatures in the days before
the flood event. In these instances, increased precipitation likely built up the snowpack, especially at
higher elevations, which eventually melted and increased the discharge levels. Type 1 floods accounted
for more than 90% of the flood events in the generated time series, with an equal split between Type 1a
and Type 1b for Q10 and Q25.
As a performance check, the characteristics of the above generated flood types were compared with real
floods documented in the catchment. The highest measured discharge amount between 1970 and 2010
was in May 2008, and had similar values for the indicators as the flood Type 1a for Q25. The recorded 1-
day precipitation was more than 40mm at the rain gauges in Figure 1, with above normal temperature.
Considering high observed discharge events, most Q2 events occurred during the March to July period,
with only three events that could be classed as Type 2 events. Based on the observed times series, it was
not possible to discern Type 1a and Type 1b floods, as there were only four measured Q10 floods and one
Q25; too few to cluster. The comparison shows that types of floods captured by the flood classification
method appear to be similar to those observed in the Ubaye catchment.
5.2.2 Future flood types (2070-2099)
The future flood types were first analyzed for changes in the flood type frequency compared to the
historical period (approach 1). Figure 6 shows the relative change in number of flood events for each
flood type and return period with the historical period (H) as reference (the grey band indicating the 99%
random sampling range of historical time series). For Ubaye, all four projections for Q2, Q10, and Q25
events had in increase in overall flood frequency in 2070-2099, as the total length of each bar is greater
than the grey horizontal band in Figure 6. The overall increase was due to a strong increase in the number
of Type 2 floods (green) for all projections: a flood type that accounted for less than 10% of the events in
the historical period. The increase in these events primarily came from an increase in the 1-day
precipitation during autumn (see Figure 4). There was no consistent change projected in Type 1 for Q2
and Types 1a and 1b for Q10 and Q25. Overall, there was a potential shift in flood types from Type 1 to
Type 2 floods.
Figure 6. Approach 1: Number of high discharge events relative to the historical period, split into flood type
(blue = Type 1/1a, light blue = Type 1b, green = Type 2, H = for the historical period). The horizontal grey
box indicates the 99% random sampling range from the historical period. Amounts above 1 indicate an
increase in overall flood frequency and below 1 represents a decrease. The Q2, Q10, Q25 refer to the
discharge amount in the historical period. Md, Mw, Wd, and Ww correspond to the projections selected in
Figure 3.
In the second approach, future flood type clusters were re-classified to account for potential changes in
the climate of the catchment that alter the flood types themselves. The center values of the clusters are in
Table 3 for each projection (Wd, Ww, Md and Mw). Figure 7 shows the clustering of flood types based
on the indicators DOY and 1-day rainfall for each return period and climate scenario. The individual SI
values for Ubaye are in the supplementary material.
The future flood types in Ubaye were similar to the historical period, except for projection Wd (Figure 7).
Under Wd, Q2 events occurred throughout the year, as opposed to the defined spring and autumn periods
observed historically. The three projections Mw, Ww, and Md showed two distinct periods of the year
with flood events, as seen with the separation in the DOY between the Type 2 and Type 1 floods in
Figure 7. Under the Md projection, Type 2 could be split for Q10 and Q25 floods, where Type 2a
experienced higher 1-day precipitation amounts and lower antecedent precipitation than Type 2b flood
events (Figure 7a and Table 3). All four future projections resulted in fewer Type 1 floods and a
separation could no longer be made between Type 1a and 1b floods as in the historical period. For the Q2
events, the average SI value was similar to the historical period, while Q10 and Q25 had higher average
SI values than in the historical period, indicative of a clearer separation between the future flood types.
Figure 7. Clustering of future flood types for the Ubaye catchment for the period 2070-2099 per selected
projection. Green stars indicate Type 2 floods, blue circles Type 1 floods, red diamonds for Type 3 floods with
indicator 1-day rainfall on the x-axis and indicator DOY on the y-axis two. The average SI value is shown the
in top right corner.
Table 3. New cluster centers for future flood types in the Ubaye catchment (2070-2099) for the four future
projections. RR is the 1-day precipitation amount, RRa is the 35-day antecedent precipitation and Tna is the
normalized 4-day antecedent temperature. The values in bold are used for the cluster centers.
Although similar clusters were detected in the future for the Ubaye catchment, shifts in timing and cluster
center values for indicators were projected. The two warmer projections (Wd and Ww) had the Type 1
flood types occurring earlier in the year than historically (on average in March, as opposed to May from
the simulated flood events or the May 2008 flood event). Type 2 floods occurred on average at the same
time of the year as found in the historical data, although some of the Q2 floods occur in December in all
projections (Figure 7), which was not seen in the historical period (Figure 5a). For all projections, the
cluster center values for 1-day precipitation were higher (Table 3) than the historical values (Table 2).
The antecedent precipitation values were lower. All temperature values were on average much warmer
than in the historical period, consistent with a warming climate.
5.3 Salzach flood types
5.3.1 Historical period (1987-2010)
For each return period the dominant flood types in the Salzach catchment were determined for the
historical period 1987-2010. The indicators that gave the highest correlation with discharge were 1-day
precipitation (RR), antecedent 15-day precipitation (RRa), and antecedent 5-day temperature (Tna).
Mild, dry
Mild, wet
Warm, dry
Warm, wet
Figure 8 shows the DOY and precipitation per flood event (a) as well as the silhouette value for each
event (b). For the Q2 floods using all four indicators, there were two flood types from the classification.
The first type were flood events earlier in the year with warmer than normal temperatures and moderate
1-day precipitation (Type 1). A second type occurred later in the year with higher 1-day precipitation and
normal or colder than normal temperatures (Type 2; Figure 8a). The average SI value for this
classification was 0.68. The separation between clusters became more distinct for the Q10 and Q25 flood
events, with average SI values of 0.84 and 0.74 respectively. Antecedent precipitation was also not used
for the Q10 and Q25 events to classify the clusters, due to lower SI values (0.79 and 0.44 respectively if
included). For the Q25 flood events, the Type 2 events could be split into two clusters, ones with lower 1-
day precipitation amounts and cooler temperatures that occurred earlier in the year (Type 2a) and flood
events with higher 1-day precipitation amounts and temperatures near normal (Type 2b). Most of the
silhouette values imply a good fit with values above 0.5 in Figure 8b, however, especially for Q2 events,
there are near zero values, demonstrating that some flood events did not clearly fit in a particular flood
type. The average SI value and cluster center values for each of the indicators are listed in Table 4.
Table 4. Cluster center values for the different discharge magnitudes for Salzach under the historical climate.
RR is the 1-day precipitation amount, RRa is the 15-day antecedent precipitation and Tna is the normalized
5-day antecedent temperature. The values in bold are used for the cluster centers.
Salzach flood types
Q2, SI = 0.68
Q10, SI = 0.84
Q25, SI = 0.74
For all return periods, the Type 2 flood events occurred between July and October, with the Type 2a
events occurring between July and August and the Type 2b between August and October (Q25 only). All
Type 2 floods had on average 1-day precipitation amounts higher than the average annual daily
maximum, except for the Type 2a events that were slightly lower (Table 4). The temperature was
generally cooler than normal for these events, indicating that the rainfall may have originated from low
pressure systems, rather than local convection. However, for the Type 2b events, the temperatures were
on average near normal, possibly due a more balanced mixture of synoptically driven rainfall triggered
flood events and local convective rainfall triggered flood events. Overall the Type 2 flood events were the
dominant flood type in the simulated time series for Salzach, accounting for more than 65% of the flood
Figure 8. Clustering of flood types for the Salzach catchment (A), with the individual silhouette values (B).
Green stars indicate Type 2/2a, blue circles Type 1, with red diamonds for Type 2b flood events. Only the RR
and DOY indicators are shown.
Type 1 flood events occurred between March and July, with most of the Q10 and Q25 floods occurring
between March and May. The average 1-day rainfall and antecedent precipitation were the same between
the three return periods for this type, with the 1-day rainfall between 30-36mm higher than normal for this
time of year, but lower than the average annual daily maximum. The 15-day antecedent precipitation was
on average 135-155mm, double the average amount. The temperature was warmer than normal for all
Type 1 events (Table 4), indicating that warmer temperatures in spring may be associated with increased
snowmelt, or more rainfall rather than snowfall, as in Ubaye. The cluster center values for the temperature
indicator also increased with increasing return period (Table 4), indicating either more snowmelt, or more
rapid snowmelt. Overall the Type 1 flood events accounted for 10-35% of the total Q2, Q10, and Q25
floods in the Salzach catchment.
The characteristics of the generated flood types was compared with real flood events document in the
Salzach catchment. For the August 2002 flood event, the 1-day rainfall amounts in some places exceeded
the 100-year return level period, with heavy precipitation also recorded in the weeks before the event
(Ulbrich et al., 2003). These are characteristic of the Type 2 flood events described in Table 4 and Figure
8a, although slightly earlier in the year than average for the simulated data. More recently the early June
2013 flood occurred after three days of heavy precipitation combined with high antecedent moisture
conditions in part due to snow melt (Blöschl et al., 2013). This flood bares resemblance to the Type 1
flood events, where snowmelt appears to play a role, alongside heavy precipitation and higher than
normal antecedent precipitation. Overall, the flood types captured through the classification of generated
data appear to be similar to the observed flood types.
5.3.2 Future flood types (2070-2099)
The change in frequency of each of the flood types was analyzed first (approach 1). Figure 9 shows the
relative change in number of flood event for each flood types and return period compared to the historical
period (H). For each return period, three projections of flood events show an increase in overall
frequency, with only the Md total bar length below the grey horizontal band in Figure 9. The Md
projection was also unique between projections for the individual flood types, where the milder, drier
projection had a decrease in Type 2 flood events and no change in the Type 1 flood events. For the other
three projections, Mw, Wd, Ww, each flood type had an increase in frequency, although the increase was
small for Type 2a events for the Q25 Mw projection. For all return periods, the Type 1 flood events had
the greatest increase in frequency, becoming the dominant flood type. For the two warmer projections,
Wd and Ww, there were still more Type 2 flood events than Type 1. Overall the results for the Salzach
catchment show that the distribution of flood types may shift to more events earlier in the year, although
Type 2 flood types remained the dominant type, except in the Mw projection.
Figure 9 Approach 1: Number of high discharge events relative to the historical period, split into flood type
(blue = Type 1, green = Type 2/2a, red = Type 2b, H = for the historical period). The horizontal grey box
indicates the 99% random sampling range from the historical period. Amounts above 1 indicate an increase
in overall flood frequency and below 1 represents a decrease. The Q2, Q10, Q25 refer to the discharge
amount in the historical period. Md, Mw, Wd, and Ww correspond to the projections selected in Figure 3.
In approach 2, the future flood types were re-classified to account for possible changes in flood type
characteristics by 2070-2099. The center values for the indicators for each projection (Md, Mw, Wd, Ww)
and return period are in Table 5. Figure 10 shows the clustering of flood types based on the indicators
DOY and 1-day rainfall, with the individual SI values in the supplementary material.
Table 5. Cluster center values for the different discharge magnitudes for Salzach under the historical climate.
RR is the 1-day precipitation amount, RRa is the 15-day antecedent precipitation and Tna is the normalized
5-day antecedent temperature. The values in bold are used for the cluster centers.
Salzach flood types
(A)Mild, dry
(B) Mild, wet
(C) Warm, dry
(D) Warm, wet
Figure 10. Clustering of future flood types for the Salzach catchment for the period 2070-2099 per selected
projection. In the case of two flood types, green stars indicate Type 2 floods, blue circles Type 1 floods. In
other cases, red diamond and orange squares indicate Type 3 floods, and purple stars indicate a subset of
Type 2 floods. In each plot shows the indicator 1-day rainfall on the x-axis and indicator DOY on the y-axis
two. The average SI value is shown the in top right corner.
For Salzach there was a larger difference between the historical and future flood types compared with
Ubaye. The most similar flood types from the Md projection retained the Type 1 and Type 2 events,
although they occurred over a larger portion of the year (Figure 10a). The Mw projection future flood
type characteristics had the largest contrast from the historical period (Figure 10b). This was the only
projection that did not use DOY in all of the flood type classifications (Table 5). Four flood types were
identified for Q2 events based on only temperature and 1-day precipitation, with the flood events that had
the highest 1-day precipitation and coldest normalized temperature occurring in spring. Only two flood
types were defined for the Q10 events, one group only occurring in spring, with higher 1-day precipitation
values, and a second group that occurred throughout the year with higher antecedent precipitation. For the
Q25 flood events, three types were identified with the inclusion of the DOY indicator. For the two
warmer projections, Wd and Ww, between two and four clusters were found based on 1-day precipitation,
temperature, and DOY with higher 1-day totals (Figure 10c, d). Flood types 2a and 1 were similar to the
flood types 2 and 1 from the historical period. However, in both cases a third type, Type 2b, was also
observed, occurring in November and December with abnormally high temperatures, much later in the
year than observed in the historical period. For the Wd projection, a fourth type, Type 2c, was also
observed and occurred in June. As with Ubaye, all temperatures were higher than normal, as would be
expected in a warmer climate.
5. Discussion
The developed flood type classification methodology was able to define the main historical flood types
for both tested catchments as result of temporal data expansion by using weather generator combined with
the HBV rainfall-runoff model. Separation between flood types based on the SI value depended on both
the catchment characteristics as well as the number of flood events in the cluster. The separation was less
clear for lower return period floods (Q2) in the Salzach catchment than Ubaye, which could be linked to
the two distinct peaks in the precipitation distribution in Ubaye that were absent in Salzach (Figure 2).
Generally, there was an increase in SI value between the flood types with higher return period, for both
catchments and as well as for historical as future periods. A reason could be that more frequent discharge
events can occur in a wider range of conditions, while the extreme flood event conditions only occur
under specific combinations of indicator values, possibly linked to certain atmospheric situations such as
atmospheric blocking leading to persistent rain over the catchment.
The developed methodology employs four types of indicators using only time series of temperature,
precipitation data from the weather generator and the DOY. The selected indicators have strongest
correlation with generated discharge, but could limit the number of flood types. Other flood types, such as
snowmelt, may be difficult to capture with only temperature and precipitation indicators (Gelfan, 2010). It
is possible that new indicators should be used for clustering future flood events or other catchments.
Using other indicators as well as antecedent periods for the temperature, precipitation, and DOY may
alter the mean indicator values per flood type, and possibly the flood types themselves. Furthermore, the
decision not to standardize all the indicators would have affected the cluster centers as those with smaller
variance had a smaller influence on the cluster centers, particularly in this case temperature. During
preliminary analysis, standardizing the indicators decreased the performance of the clustering and
therefore was not included. For other regions or indicators, however, weighting and standardization of
variables may be a viable option where the separation between clusters is less clear. Overall, for both test
catchments most of the silhouette values were greater than 0.5, indicating that these two, frequently
measured meteorological variables, temperature and precipitation, along with day of the year can be used
to distinguish two or three clearly different flood types.
Previous work shows that hydrologic future projections are potentially sensitive to the GCM, RCM,
rainfall-runoff model and downscaling method used (e.g. Dobler et al. (2012); Wood et al. (2004)). Here
climate model projections were selected based on mean changes in temperature and precipitation (Section
4), although Figures 6 and 9 do not show consistent changes in flood types between the selections beyond
the milder, drier projections showing the least number of flood events. These differences suggest that
selecting projections based on mean changes in temperature and precipitation may not directly relate to
the changes in flood types, although selecting the mean values reduces the assumptions on the governing
factors for flood events. The selected indicators assume that the GCMs and RCMs were able to project
future changes in precipitation, while GCMs are known to have limited skill in capturing factors driving
regional precipitation, which would affect future projections of precipitation, and therefore flood types in
this study (Asadieh and Krakauer, 2015; Merz et al., 2014). Furthermore, the weather generator assumed
no change in autocorrelation, or inter-site correlation, rather focusing on changes in precipitation amounts
as well as temperature. Spatial changes in precipitation can in some instances cause greater changes in
discharge amounts than temporal changes (Perdigão and Blöschl, 2014). Not changing the autocorrelation
might partly explain why the Type 2b floods saw a larger increase in frequency compared to those with
larger antecedent precipitation (Type 2a). However, future projections in temperature and precipitation
amounts still led to changes in the dominant flood types in a catchment as well as the flood frequency,
although the range of future flood frequencies and flood types for the study areas may actually be greater
than presented here. A more detailed study in changing flood types for a particular area should possibly
consider more projections, as well as changes in land use and other catchment characteristics, as this may
also influence future flooding.
Two approaches were provided to assess changes in the flood types under four future climate scenarios.
These approaches were complementary to each other as one estimates changes in frequency of the
historical flood types, where the second assesses whether future precipitation and temperature would lead
to (dis)similar flood types compared to the historical period. Changes in the dominant flood type can have
implications for local land use practices. For example, in the Ubaye catchment during summer the flood
plains are used for farming and camping, as the historical flood events have occurred during spring.
However, if summer and autumn floods become the dominant flood type, as projected in Section 5.2.2,
this will have implications for exposure in the area. Changes in the characteristics, as in approach two, are
also important, such as the decrease in the temperature indicator for the Type 1 floods in the Mw
projection, even under a warmer climate.
Flood types for the historical period may be inherent to the combination of weather generator and specific
rainfall-runoff model, enforced by historical observational records of precipitation and temperature. Two
flood types were found, Type 1 and 2, which are similar to Rain-Snow and Short Rain floods respectively
as classified in Merz and Blöschl (2008). Other flood types listed in the previous work, Snowmelt and
Long Rain, were not distinguished through the flood type classification. Instead, in cases where there
were three or more flood types, the types generally split one of the main clusters, based on which was the
dominant flood type in the catchment. Even when considering the new flood types for 2070-2099 the
Rain-Snow and Short Rain floods remained the two clear flood types from Merz and Blöschl (2008), even
if the characteristics of the flood type were different. It is possible that a flood type, such as Snowmelt,
could trigger only discharge with shorter return periods in the catchments, and not generate high
discharge levels. The ability of the method to capture snowmelt floods could be confirmed through future
work in a catchment where these flood types occurred.
While to the authors knowledge there has been little coverage of changes in future flood types for Alpine
catchments, the results found here are similar to other studies for the two catchments. Hall et al. (2014)
concluded that an increase in future extreme precipitation events with mean precipitation increases over
northern Europe and decrease in southern areas will results in different changes in flood frequency
between catchments in the future. For the Ubaye catchment, Saez et al. (2013) hypothesize that future
warming could enhance snowmelt during the spring, although from the results in Section 5.2 this appears
to be offset by the decrease in antecedent precipitation. The increase in temperature in both Figure 4 and
Table 3 is consistent with future warming in the area (Malet et al., 2007; Rousselot et al., 2012). For the
Salzach, previous work found no clear trend in flood frequency (Dobler et al., 2011), although the authors
commented that higher spring temperatures could lead to more frequency flooding events in this season.
The similarities between this work and previous studies implies that the even with the limitations of
method outlined above the method produces reasonable results using relatively straightforward method.
6. Conclusion
This paper demonstrated a methodology developed for detecting present and future flood types. Long
time series of discharge were generated using a weather generator coupled with a rainfall-runoff model to
provide sufficient flood events for classification into different causal types. The types were determined to
be sufficiently different based on the silhouette index. Future climate scenarios were assessed using bias
correction of different RCM climate projections and to train the weather generator. The methodology was
applied in two European Alpine catchments, Ubaye and Salzach, for both the historical period, and the
future period (2070-2099).
The flood type classification was based on a set of temperature and precipitation indicators as well as day
of the year. In this work, the selection of indicators was based on correlation with historical discharge.
Our findings showed that the methodology was able to reliably reproduce the observed flood types for the
two catchments. Care is needed in the selection of the indicator values however, as the variables used
will affect the final flood types.
When looking at the future projections, both study areas showed potential changes in the distribution of
flood types, as well as the types themselves. For the Ubaye catchment, flood events may shift from Rain-
Snow (Type 1) dominated floods to Short Rain (Type 2), a type that currently accounts for less than 10%
of flood events. Re-clustering of flood types shows changes in the characteristics of the flood events, with
higher average daily precipitation values and flood events both later and earlier in the year in the future.
For the Salzach catchment, Short Rain (Type 2) floods may remain the dominant flood type, although it is
possible there is an increase in Rain-Snow floods (Type 1), and overall flood frequency. Re-classifying of
the future flood events for this catchment also found changes in the flood type characteristics with events
occurring throughout the year, and in some instances particularly higher daily precipitation in spring.
Although only a limited number of climate projections were considered, the results showed the potential
of the methodology developed to assess the full range of possible future changes in flood types for the
Therefore, this methodology identifies realistic flood types, and can be used to assess future changes in
flood types. The methodology has potential to be applied to higher return periods and other catchments as
long as the observational records of precipitation, temperature and flood events are of good quality and
length. Changes in flood types are an important consideration for future research as the changes will have
an impact on the local social and ecological systems and have implications for future flood management.
 under Grant
Agreement No. 263953. Data were provided by the Central Institution for Meteorology and Geodynamics
(ZAMG), the Federal Ministry of Agriculture, Forestry, Environment and Water Management, and the
German Meteorological Service (DWD) and Météo France. We acknowledge the World Climate
Research Programme's Working Group on Regional Climate, and the Working Group on Coupled
Modelling. We also thank the climate modelling groups (supplementary material) for producing and
making available their model output.
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... Thus, it is of great significance to improve the physical justification of TCMD by classifying flood types beforehand [3,7,28]. Classification of different FGMs is not only beneficial for the fulfilment of IID assumption in flood frequency analysis, but can help put flood events into a wider climate situation and analyze the future evolution of flood events [34][35][36]. ...
... FGMs can be classified into different types using either the meteorological triggers (e.g., different precipitation types and contribution of precipitation associated with a flood event, compared with snowmelt) or the basin characteristics (e.g., land-cover types, snow cover and soil moisture), since the interaction between meteorological factors and underlying surface dominates the formation of a flood event [7,34,[37][38][39][40][41][42]. Turkington et al. [34] highlighted the significance and applicability of classifying FGMs into different categories, and they generated a long sequence of hydrological data by combining the weather generator and a conceptual (Hydrologiska Byråns Vattenbalansavdelning) HBV model, and dividing flood types according to four meteorological indicators using the k-means clustering method. ...
... FGMs can be classified into different types using either the meteorological triggers (e.g., different precipitation types and contribution of precipitation associated with a flood event, compared with snowmelt) or the basin characteristics (e.g., land-cover types, snow cover and soil moisture), since the interaction between meteorological factors and underlying surface dominates the formation of a flood event [7,34,[37][38][39][40][41][42]. Turkington et al. [34] highlighted the significance and applicability of classifying FGMs into different categories, and they generated a long sequence of hydrological data by combining the weather generator and a conceptual (Hydrologiska Byråns Vattenbalansavdelning) HBV model, and dividing flood types according to four meteorological indicators using the k-means clustering method. However, as discussed by Turkington et al. [34], the selected indicators are likely to fail to capture FGMs such as snowmelt-induced flood. ...
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The fundamental assumption of flood frequency analysis is that flood samples are generated by the same flood generation mechanism (FGM). However, flood events are usually triggered by the interaction of meteorological factors and watershed properties, which results in different FMGs. To solve this problem, researchers have put forward traditional two-component mixture distributions (TCMD-T) without clearly linking each component distribution to an explicit FGM. In order to improve the physical meaning of mixture distributions in seasonal snow-covered areas, the ratio of rainfall to flood volume (referred to as rainfall–flood ratio, RF) method was used to classify distinct FGMs. Thus, the weighting coefficient of each component distribution was determined in advance in the rainfall–flood ratio based TCMD (TCMD-RF). TCMD-RF model was applied to 34 basins in Norway. The results showed that flood types can be clearly divided into rain-on-snow-induced flood, snowmelt-induced flood and rainfall-induced flood. Moreover, the design flood and associated uncertainties were also estimated. It is found that TCMD-RF model can reduce the uncertainties of design flood by 20% compared with TCMD-T. The superiority of TCMD-RF is attributed to its clear classification of FGMs, thus determining the weighting coefficients without optimization and simplifying the parameter estimation procedure of mixture distributions.
... Flooding origins depend on the water source and on the reasons and processes causing the water level to rise including spatial patterns and characteristics of flood seasonality (warm or cold periods etc.) [22,[34][35][36]. In particular, in terms of origin and drivers, riverine floods can be triggered and developed by hydrometeorological conditions through precipitation, temperature, evaporation, snow accumulating and melting processes, and high soil moisture [11,16,21,22,35,36]; coastal floodsby high tides, combined with low atmospheric pressures and strong winds inducing a storm surging [16][17][18][19]37]. ...
... Flooding origins depend on the water source and on the reasons and processes causing the water level to rise including spatial patterns and characteristics of flood seasonality (warm or cold periods etc.) [22,[34][35][36]. In particular, in terms of origin and drivers, riverine floods can be triggered and developed by hydrometeorological conditions through precipitation, temperature, evaporation, snow accumulating and melting processes, and high soil moisture [11,16,21,22,35,36]; coastal floodsby high tides, combined with low atmospheric pressures and strong winds inducing a storm surging [16][17][18][19]37]. There are also many unusual flood cases [2], including groundwater flooding caused by high seepage through permeable, river-connected alluvial aquifers [38][39][40], tsunamis flooding [41], floods because of dam disasters [42][43][44], dike and levee breaches [45,46], floods caused by landslide dam collapses [47] and glacial lake outburst floods [48], backwater floods [49,50], debris flows and mudflows floods [51,52], etc. ...
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This paper explores some aspects relating to retrospective predicting the confirmed monetary losses caused by the disastrous floods of 1980, 1986, and 1998 in the Tisza River basin within the Transcarpathian region of Ukraine. The research was based on two time series – the losses because of past floods and the maxima water discharges gauged at the hydrological station near the village of Vylok, Vynohradiv district. The main aim of the research was to make out whether it had been the possibility to predict the losses due to those floods in advance.In solving the task, there was revealed and modelled the dependence of the risk of losses due to the floods in Transcarpathia on the maximum water discharges of the Tisza River gauged at the “Vylok” hydrological station. Predicting was based on the hypothesis of the stationary random process for maximum water discharges, which allowed using an empirical distribution function of a random variable regarding flood water discharges assessing the risk of flood losses.Retrospective predicting of the losses caused by the floods of 1980, 1986, and 1998 was carried out by means of a combined situational-inductive predictive modelling method (CSIPMM), being an original author’s development. The method relates to predicting the behaviour of complex dynamic systems based on monitoring findings presented as time series data reflecting evolutions of a resulting (dependent) variable and an explaining (independent) variable (predictor). The method uses extrapolation-regression type models. According to this method, the prediction task is performed in two stages. The first stage realises the retrospective situational modelling task aiming to obtain a set of simple regressions (situational models) built on data of sample time series. The situational models are accepted to be adequate or relevant ones only within certain periods of time determined as situations. In the second stage, based on the generalization (on an ensemble) of the obtained retrospective situational models, inductive “levels” models are built, which reflect the behaviour of a controlled parameter of the system or process (a resulting variable) at several fixed values of a predictor in time. The inductive models are used in extrapolative predicting situational models belonging to future periods (situations).In total, three predictions were made: (1) taking into account the annual maximum flood discharges from 1954 to 1979 (before the flood of 1980); (2) the same from 1954 to 1985 (before the flood of 1986); (3) the same from 1954 to 1997 (before the flood of 1998). The study found that there had been a possibility to predict the confirmed monetary losses inflicted by the flood of 1986 and 1998 (relative predicting errors of 7.2-8.7% and 6.0-12.8% depending on the prediction options).
... Xu and Peng (2015) took the precipitation distribution, time variance of precipitation intensity and some hydrology characteristics as the classification indicators to cluster floods, and used a rough set to identify rules for flood forecast. Turkington et al. (2016) first generated by a weather generator and a conceptual rainfall-runoff model, and then used temperature and precipitation indicators to achieve flood classification in two basin of Austria. The methods above are all designed by enough information for extracting indicators. ...
... Different from previous models using indicators extracted from geography, hydrology, meteorology, and human activities (Ren et al. 2010;Turkington et al. 2016;Xu and Peng 2015), we propose a new simple model to cluster historic floods by the occurrence and development of floods. The similarity of the occurrence and development of each flood has been described as the similarity of the simulated model for each flood in the study. ...
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The classification and identification can increase the prediction accuracy effectively due to the complexity and regularity of flood formation. However, it is difficult to extract the influence indicators, especially in data-sparse basins. This research proposes a framework for flood classification and dynamic flood forecast identification in data-sparse basins. The framework starts from a new perspective for flood classification and introduces the concept of forgetting mechanism for flood identification. In the framework, the Data-Based Mechanistic (DBM) forecasting model, a data-driven model with a physically mechanistic interpretation, has been selected as the basic simulated model; then a flood classification model based on DBM and the process of flood occurrence and development has been built to classify floods and generate the corresponding sub-cluster models, and the similarity of the process of flood occurrence and development for each flood is described as the similarity of the simulated model trained for each flood; the forgetting mechanism, which can eliminate the out-of-date data gradually to reduce the influence of the misleading information, is coupled with the deterministic coefficient to identify one of the sub-models for the dynamic flood forecast. The framework has been tested in Shihuiyao Basin, Northeastern China. Results show that the average deterministic coefficients of the proposed framework are 0.87 and 0.86, which are 0.05 and 0.16 higher than those without classification and identification (0.82 and 0.70). The established framework provides a new idea for flood classification and identification, which has the advantages of ease of use, good generality, and low data requirements.
... Such a system will subsequently aid policy and decisionmakers in developing resilience-guided risk management strategies, accounting for the four attributes of resilience. Classification and data driven models require a sufficient number of observations in a dataset to allow for meaningful classification and clustering [23]. While this necessitates the accessibility to a large volume of high quality data, there are also alternative ways to account for missing data within an employable dataset. ...
... There have been numerous ANN techniques developed to date, each of which may befit a specific application (e.g., self-organizing maps, recurrent neural networks, and feed-forward back-propagation neural networks). However, ANN is more commonly employed in predictive algorithms [54,56,57] and pattern recognition applications [23,36,55,58]. For the study presented herein, SOM was utilized using the Deep Learning Toolbox in MATLAB, where the Kohonen rule was adopted [55,59]. ...
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Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies.
... Hydrological classification of flood events based on hydrometeorological forcing within catchments (for example, rainfall and snowmelt) and catchment states (for example, soil moisture) is one of the most commonly used methods for flood typology 54 . It can be further categorized into two approaches: the decision tree 21,22,24,25 and the statistical clustering algorithm 55,56 . In this study, we combined these two approaches to classify flood types by first constructing a decision tree to define the criteria for each flood type, this tree consisting of decision attributes and the attribute thresholds ( Supplementary Fig. 4 and Table 1). ...
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An intensified hydrological cycle with global warming is expected to increase the intensity and frequency of extreme precipitation events. However, whether and to what extent the enhanced extreme precipitation translates into changes in river floods remains controversial. Here we demonstrate that previously reported unapparent or even negative responses of river flood discharge (defined as annual maximum discharge) to extreme precipitation increases are largely caused by mixing the signals of floods with different generating mechanisms. Stratifying by flood type, we show a positive response of rainstorm-induced floods to extreme precipitation increases. However, this response is almost entirely offset by concurrent decreases in snow-related floods, leading to an overall unapparent change in total global floods in both historical observations and future climate projections. Our findings highlight an increasing rainstorm-induced flood risk under warming and the importance of distinguishing flood-generating mechanisms in assessing flood changes and associated social-economic and environmental risks.
... However, future projections of the meteorological tr iggers, including heavy precipitation and snowmelt, may change differently and alter the characteristics of the flood events (Hall et al., 2014). As a result, factors related to the causal sort of flood-like seasonality and triggering conditions should be addressed next to the change in frequency or magnitude of floods (Turkington et al., 2016). ...
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Flooding is usually thought of as a result of heavy rainfall, snowmelt, land subsidence, rising of groundwater, dam failures. Coastal flooding, river flooding, flash flooding, urban flooding , and snowmelt flooding are kinds of flooding. Among them, flash flooding and river flooding occurs often, and negatively impacts human lives and economic asset in Ethiopia. As flood risk and its impacts are increasing from time to time in Ethiopia, the main objective of this paper is to review flooding and it`s coping mechanisms in Ethiopia. The factors affecting flooding are the size of the catchments, the intensity of rainfall and amount of precipitation that fall under the watershed and tributaries, topography, the presence and absence of vegetation, anthropogenic activity within the catchment areas and catchment area are discussed within this paper. And also, the mechanism of reducing flood impacts like flood alert and metrological forecasting agency established but the cop ing and mitigation strategies are very low in Ethiopia. So that, forward suggestions are these strategies need to supplement one another, their coordination must include "multi-actor, multi-level, and multi-sector involvement and is realized for instance, by collaborative leadership. Stakeholder and community involvement and a standard knowledge basis also are fundamental. Check dams, terracing, bunds, percolation tanks, and storage tanks were proposed for various locations across the watershed as effective landscape-based flood risk mitigation strategies. Moreover, the coping strategies are very needed like insurance, perennial crops farming along with flood-prone areas and within the river's bank, and the waterway management is more needed to reducing the river flood inundate. This review recommends new policy approaches that will increase the effectiveness of the present flood coping strategies to sustainably address the impact of flooding on human health.
... An earlier definition [16]) extends this temporal limit to 12 h. It is worth mentioning that similar precipitation patterns, but different hydrological antecedent conditions, may influence flood occurrence and severity, as well as catchment response during weather events that appear similar [17] (iii) early warning capacity. Classification criteria based on early warning capacity deviate from the scientific definition, which is linked to the dynamics of the phenomenon. ...
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Central Italy is characterized by complex orography. The territorial response to heavy precipitation may activate different processes in terms of hydrogeological hazards. Floods, flash floods, and wet mass movements are the main ground effects triggered by heavy or persistent rainfall. The main aim of this work is to present a unique tool that is based on a distributed hydrological model, able to predict different rainfall-induced phenomena, and essential for the civil protection early warning activity. The Cetemps Hydrological Model is applied to the detection of hydrologically stressed areas over a spatial domain covering the central part of Italy during a weather event that occurred in 2014. The validation of three hydrological stress indices is proposed over a geographical area of approximately 64,500 km2 that includes catchments of varying size and physiography. The indices were used to identify areas subject to floods, flash floods, or landslides. Main results showed very high accuracies (~90%) for all proposed indices, with flood false alarms growing downstream to larger basins, but very close to zero in most cases. The three indices can give complementary information about the predominant phenomenon and are able to distinguish fluvial floods from pluvial floods. Nevertheless, the results were influenced by the presence of artificial reservoirs that regulated flood wave propagation, therefore, indices timing slightly worsen downstream in larger basins.
This study aims to develop risk analysis methodologies on floods, analyze floods produced on the Suhu River in Pechea village (Galați county), and factors that favor flooding. Flood analysis represents one of the main concerns of researchers in hydrology in the context of climate change. It is increasingly leaving its mark on the frequency of precipitation and, implicitly, on the production of floods. We presented the definitions of floods, and we presented the study area in the first part of the article. The monthly and seasonal frequency of floods were analyzed, and there were calculated specific parameters of a flood produced in the study area. Then, the factors that favor the occurrence of floods were analyzed. The results obtained will contribute to the complete information on floods in small basins in the plain area on the Romanian territory.
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Climate change may systematically impact hydrometeorological processes and their interactions, resulting in changes in flooding mechanisms. Identifying such changes is important for flood forecasting and projection. Currently, there is a lack of observational evidence regarding trends in flooding mechanisms in Europe, which requires reliable methods to disentangle emerging patterns from the complex interactions between flood drivers. Recently, numerous studies have demonstrated the skill of machine learning (ML) for predictions in hydrology, e.g., for predicting river discharge based on its relationship with meteorological drivers. The relationship, if explained properly, may provide us with new insights into hydrological processes. Here, by using a novel explainable ML framework, combined with cluster analysis, we identify three primary patterns that drive 53 968 annual maximum discharge events in around a thousand European catchments. The patterns can be associated with three catchment-wide river flooding mechanisms: recent precipitation, antecedent precipitation (i.e., excessive soil moisture), and snowmelt. The results indicate that over half of the studied catchments are controlled by a combination of the above mechanisms, especially recent precipitation in combination with excessive soil moisture, which is the dominant mechanism in one-third of the catchments. Over the past 70 years, significant changes in the dominant flooding mechanisms have been detected within a number of European catchments. Generally, the number of snowmelt-induced floods has decreased significantly, whereas floods driven by recent precipitation have increased. The detected changes in flooding mechanisms are consistent with the expected climate change responses, and we highlight the risks associated with the resulting impact on flooding seasonality and magnitude. Overall, the study offers a new perspective on understanding changes in weather and climate extreme events by using explainable ML and demonstrates the prospect of future scientific discoveries supported by artificial intelligence.
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Dünya genelinde kentleşme artmaktadır ve buna bağlı olarak kentsel nüfus da artmaktadır. Hızlı kentsel nüfus artışının sonucu olarak yetersiz drenaj sistemleri nedeniyle taşkın yağışı durumunda, binaların bodrum katlarını su basması, ulaşım yollarının kapanması gibi can ve mal kaybına neden olabilecek hasarlar meydana gelmektedir. Bu çalışmada, Malatya ili için arazi kullanım türü ve sızma durumlarına bağlı üç farklı senaryo kapsamında taşkın yayılım haritaları oluşturulmuştur. Bu senaryolar, modelde; arazi kullanım türünün olmadığı, arazi kullanım türünün olduğu ve arazi kullanım türü ile yüzeysel akış değerlerinin birlikte olduğu durumuna dayanmaktadır. Çalışmanın, hidrolojik veya hidrodinamik modellerde arazi kullanımı ve yüzeysel akış verilerinin kullanımı hakkında detaylar sunması amaçlanmıştır. Bu kapsamda, InfoWorks ICM yazılımı kullanılarak oluşturulan modeller, model doğruluğunu arttırması için sayısal yükseklik modeli, bina konum verileri, arazi kullanım türü ve gelecekte meydana gelebilecek yağış yükseklikleri verileriyle desteklenmiştir. Sonuçlar, arazi kullanım türü ile yüzeysel akış değerlerinin birlikte kullanıldığı modele dayalı senaryo da diğer iki senaryoya göre havza genelinde daha az akış kollarının oluştuğunu göstermiştir.
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Flood estimation and flood management have traditionally been the domain of hydrologists, water resources engineers and statisticians, and disciplinary approaches abound. Dominant views have been shaped; one example is the catchment perspective: floods are formed and influenced by the interaction of local, catchment-specific characteristics, such as meteorology, topography and geology. These traditional views have been beneficial, but they have a narrow framing. In this paper we contrast traditional views with broader perspectives that are emerging from an improved understanding of the climatic context of floods. We come to the following conclusions: (1) extending the traditional system boundaries (local catchment, recent decades, hydrological/hydraulic processes) opens up exciting possibilities for better understanding and improved tools for flood risk assessment and management. (2) Statistical approaches in flood estimation need to be complemented by the search for the causal mechanisms and dominant processes in the atmosphere, catchment and river system that leave their fingerprints on flood characteristics. (3) Natural climate variability leads to time-varying flood characteristics, and this variation may be partially quantifiable and predictable, with the perspective of dynamic, climate-informed flood risk management. (4) Efforts are needed to fully account for factors that contribute to changes in all three risk components (hazard, exposure, vulnerability) and to better understand the interactions between society and floods. (5) Given the global scale and societal importance, we call for the organization of an international multidisciplinary collaboration and data-sharing initiative to further understand the links between climate and flooding and to advance flood research.
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Flood hazard projections under climate change are typically derived by applying model chains consisting of the following elements: "emission scenario – global climate model – downscaling, possibly including bias correction – hydrological model – flood frequency analysis". To date, this approach yields very uncertain results, due to the difficulties of global and regional climate models to represent precipitation. The implementation of such model chains requires major efforts, and their complexity is high. We propose for the Mekong River an alternative approach which is based on a shortened model chain: "emission scenario – global climate model – non-stationary flood frequency model". The underlying idea is to use a link between the Western Pacific monsoon and local flood characteristics: the variance of the monsoon drives a non-stationary flood frequency model, yielding a direct estimate of flood probabilities. This approach bypasses the uncertain precipitation, since the monsoon variance is derived from large-scale wind fields which are better represented by climate models. The simplicity of the monsoon–flood link allows deriving large ensembles of flood projections under climate change. We conclude that this is a worthwhile, complementary approach to the typical model chains in catchments where a substantial link between climate and floods is found.
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Floods and droughts are recurrent events with characteristics of frequency, magnitude, duration and timing occupying the opposing extremes of natural river flow regimes. This hydrological variability, driven by climate and meteorology and modified by river basin processes, is a key determinant of physicochemical river habitat influencing the structure and function of freshwater communities. A changing (warming) climate is projected to alter water and heat inputs to river systems that drive river flow, and thus, hydrological processes may be subject to unprecedented future change, resulting in potentially unprecedented river flow extremes. We review the hydroclimatology of extreme river flows in changing climates and draw case studies from the European temperate regions. Specifically, we adopt a ‘catchment perspective’, in which an understanding of meteorological and hydrological processes is used to (i) conceptually define extreme river flows, (ii) explain the (natural) climatic and catchment processes that drive extreme river flows, (iii) discuss future potential changes driven by an anthropogenically modified climate and (iv) identify uncertainties associated with projections of future climate‐driven hydrological shifts.
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Climate change is likely to impact the seasonality and generation processes of floods in the Nordic countries, which has direct implications for flood risk assessment, design flood estimation, and hydropower production management. Using a multi-model/multi-parameter approach to simulate daily discharge for a reference (1961–1990) and a future (2071–2099) period, we analysed the projected changes in flood seasonality and generation processes in six catchments with mixed snowmelt/rainfall regimes under the current climate in Norway. The multi-model/multi-parameter ensemble consists of (i) eight combinations of global and regional climate models, (ii) two methods for adjusting the climate model output to the catchment scale, and (iii) one conceptual hydrological model with 25 calibrated parameter sets. Results indicate that autumn/winter events become more frequent in all catchments considered, which leads to an intensification of the current autumn/winter flood regime for the coastal catchments, a reduction of the dominance of spring/summer flood regimes in a high-mountain catchment, and a possible systematic shift in the current flood regimes from spring/summer to autumn/winter in the two catchments located in northern and south-eastern Norway. The changes in flood regimes result from increasing event magnitudes or frequencies, or a combination of both during autumn and winter. Changes towards more dominant autumn/winter events correspond to an increasing relevance of rainfall as a flood generating process (FGP) which is most pronounced in those catchments with the largest shifts in flood seasonality. Here, rainfall replaces snowmelt as the dominant FGP primarily due to increasing temperature. We further analysed the ensemble components in contributing to overall uncertainty in the projected changes and found that the climate projections and the methods for downscaling or bias correction tend to be the largest contributors. The relative role of hydrological parameter uncertainty, however, is highest for those catchments showing the largest changes in flood seasonality, which confirms the lack of robustness in hydrological model parameterization for simulations under transient hydrometeorological conditions.
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This paper deals with the question whether a lumped hydrological model driven with lumped daily precipitation time series from a univariate single-site weather generator can produce equally good results, compared to using a multivariate multi-site weather generator, where synthetic precipitation is first generated at multiple sites and subsequently lumped. Three different weather generators were tested, which were a univariate “Richardson type” model, an adapted univariate Richardson type model with an improved reproduction of the autocorrelation of precipitation amounts, and a semi-parametric multi-site weather generator. The three modelling systems were evaluated in two Alpine study areas by comparing the hydrological output in regard to monthly and daily statistics as well as extreme design flows. The application of a univariate Richardson type weather generator to lumped precipitation time series requires additional attention. Established parametric distribution functions for single site precipitation turned out to be unsuitable for lumped precipitation time series and lead to a large bias in the hydrological simulations. Combining a multi-site weather generator with a hydrological model produced the least bias.
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In January 2011 a rain-on-snow (RoS) event caused floods in the major river basins in central Europe, i.e. the Rhine, Danube, Weser, Elbe, Oder, and Ems. This event prompted the questions of how to define a RoS event and whether those events have become more frequent. Based on the flood of January 2011 and on other known events of the past, threshold values for potentially flood-generating RoS events were determined. Consequently events with rainfall of at least 3 mm on a snowpack of at least 10 mm snow water equivalent (SWE) and for which the sum of rainfall and snowmelt contains a minimum of 20% snowmelt were analysed. RoS events were estimated for the time period 1950–2011 and for the entire study area based on a temperature index snow model driven with a European-scale gridded data set of daily climate (E-OBS data). Frequencies and magnitudes of the modelled events differ depending on the elevation range. When distinguishing alpine, upland, and lowland basins, we found that upland basins are most influenced by RoS events. Overall, the frequency of rainfall increased during winter, while the frequency of snowfall decreased during spring. A decrease in the frequency of RoS events from April to May has been observed in all upland basins since 1990. In contrast, the results suggest an increasing trend in the magnitude and frequency of RoS days in January and February for most of the lowland and upland basins. These results suggest that the flood hazard from RoS events in the early winter season has increased in the medium-elevation mountain ranges of central Europe, especially in the Rhine, Weser, and Elbe river basins.
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Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical (1901–2010) annual-maximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to sample them spatially and temporally in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901, with the largest changes in the deep tropics. Annual-maximum daily precipitation (Rx1day) has increased faster in the observations than in most of the CMIP5 models. On a global scale, the observational annual-maximum daily precipitation has increased by an average of 5.73 mm over the last 110 years, or 8.5% in relative terms. This corresponds to an increase of 10% K−1 in global warming since 1901, which is larger than the average of climate models, with 8.3% K−1. The average rate of increase in extreme precipitation per K of warming in both models and observations is higher than the rate of increase in atmospheric water vapor content per K of warming expected from the Clausius–Clapeyron equation. We expect our findings to help inform assessments of precipitation-related hazards such as flooding, droughts and storms.
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Over the past decades Europe has experienced heavy floods with major consequences for thousands of people and billions of Euros worth of damage. In particular, the summer 2013 flood in Central Europe showed how vulnerable modern society is to hydrological extremes and emphasizes once more the need for improved forecast methods of such extreme climatic events. Based on a multiple linear regression model, it is shown here that 55% of the June 2013 Elbe River extreme discharge could have been predicted using May precipitation, soil moisture and sea level pressure. Moreover, our model was able to predict more than 75% of the total Elbe River discharge for June 2013 (in terms of magnitude) by incorporating also the amount of precipitation recorded during the days prior the flood, but the predicted discharge for the June 2013 event was still underestimated by 25%. Given that all predictors used in the model are available at the end of each month, the forecast scheme can be used to predict extreme events and to provide early warnings for upcoming floods. The forecast methodology could be efficient for other rivers also, depending on their location and their climatic background.
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Flood generation is triggered by the interaction of the hydrological pre-conditions and the meteorological conditions at different space-time scales. This interaction results in floods of diverse characteristics, e.g. spatial flood extent and temporal flood progression. While previous studies have either linked flood occurrence to weather patterns neglecting the hydrological pre-conditions or categorised floods according to their generating mechanisms into flood types, this study combines both approaches. Exemplary for the Elbe River basin, the influence of pre-event soil moisture as an indicator of hydrological pre-conditions, on the link between weather patterns and flood occurrence is investigated. Flood favouring soil moisture and weather patterns as well as their combined influence on flood occurrence are examined. Flood types are identified and linked to soil moisture and weather patterns. The results show that the flood favouring hydro-meteorological patterns vary between seasons and can be linked to flood types. The highest flood potential for long-rain floods is associated with a weather pattern that is often identified in the presence of so called 'Vb' cyclones. Rain-on-snow and snowmelt floods are associated with westerly and north-westerly wind directions. In the analysis period, 18% of weather patterns only caused flooding in case of preceding soil saturation. The presented concept is part of a paradigm shift from pure flood frequency analysis to a frequency analysis that bases itself on process understanding by describing flood occurrence and characteristics in dependence of hydro-meteorological patterns. (C) 2014 The Authors. Published by Elsevier B.V.
The ice conditions in a regulated river will depend on the climatic changes as well as the changes to the hydropower operation strategies in the future. The existing literature shows that very few studies have been carried out to investigate the impact of climate change on the river ice regime, which is important for operation of hydropower in cold climates. In this study, a series of modelling tools have been used to transform the climate change signal in terms of precipitation and air temperature into cross-section based river ice assessment in a basin with a complicated hydropower system. The study is based on the EURO-CORDEX climate change data extracted from a regional climate model driven by a suite of five general circulation models with three representative concentration pathways. Hydrological model simulation results show that the winter and spring flow will be increased, which will have an impact on the river ice conditions towards the middle and end of this century. Reservoir hydro power model simulation shows that the production flows in the winter will be increased in the future. River ice model simulation shows the number of days with freezing water temperature are reduced in the future climate, and correspondingly days with frazil ice are reduced at most of the locations in the study area. The future period with ice cover will also be shortened. The paper also demonstrates a general methodology and procedure to simulate future ice conditions in a regulated river combining multiple models and data sets. © 2015, National Research Council of Canada. All rights Reserved.