the Creative Commons Attribution 4.0 License.

the Creative Commons Attribution 4.0 License.

# Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models?

### Diego Avesani

### Patrick Zulian

### Aldo Fiori

### Alberto Bellin

Climate change impact studies on hydrological extremes often rely on hydrological models with parameters inferred through calibration procedures using observed meteorological data as input forcing. We show that this procedure can lead to a biased evaluation of the probability distribution of high streamflow extremes when climate models are used. As an alternative approach, we introduce a methodology, coined “Hydrological Calibration of eXtremes” (HyCoX), in which the calibration of the hydrological model, as driven by climate model output, is carried out by maximizing the probability that the modeled and observed high streamflow extremes belong to the same statistical population. The application to the Adige River catchment (southeastern Alps, Italy) by means of HYPERstreamHS, a distributed hydrological model, showed that this procedure preserves statistical coherence and produces reliable quantiles of the annual maximum streamflow to be used in assessment studies.

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The recognition that an altered climate may severely impact water availability and exacerbate floods and droughts has led to a flourish of climate change impact assessment studies over the past decades. Several studies have investigated the likely impact of climate change on hydrology using hydrological modeling performed with meteorological forcing obtained from an ensemble of projections from multiple climate models under different greenhouse gas emission scenarios (e.g., Kundzewicz et al., 2007; Todd et al., 2011; Wilby and Harris, 2006, for a comprehensive review). A wealth of studies have focused on long-term annual and/or seasonal changes in hydrological variables such as runoff, streamflow, snowmelt and soil moisture (e.g., Chiew et al., 2009; Majone et al., 2012; Buytaert and De Bièvre, 2012). Far fewer studies have addressed projected changes in hydrological extremes (i.e., floods and droughts), although they are expected to exert profound and dramatic impacts on agriculture, the economy, human health, energy and many other water-related sectors (e.g., Arnell 2011; Taye et al., 2011; Bouwer, 2013; Thornton et al., 2014).

The role of hydrological calibration and the way to perform it in climate change impact studies has been much debated in the hydrological community (e.g., Peel and Blöschl, 2011; Muñoz et al., 2013; Montanari et al., 2013; Thirel et al., 2014). According to the most commonly used approach, the hydrological model is first calibrated against the observed streamflow using observed meteorological data as input. The calibrated hydrological model is then run with climate models as input to assess the projected changes in selected indicators, including those related to extremes (e.g., flow quantiles; see Ngongondo et al., 2013; Aich et al., 2016; Pechlivanidis et al., 2017; Vetter et al., 2017; Hattermann et al., 2018). The drawbacks of such an approach are, however, twofold: (1) optimality in the reproduction of the time series of observed streamflow does not automatically imply optimality in the reproduction of extremes; and (2) due to epistemic uncertainty, a model calibrated with a given set of observations may respond differently when fed with projections obtained from climate change scenarios. Concerning this latter aspect, some studies have shown that the calibrated model parameters depend on the climatic characteristics of the input forcing used for the calibration of the hydrological model (e.g., Vaze et al., 2010; Laiti et al., 2018). Although recognized, this additional source of uncertainty is mostly ignored in climate change impact studies.

Several studies have suggested that observed streamflow extremes provide valuable information about the hydrological behavior of investigated catchments (Grubbs, 1969; Laio et al., 2010). Similarly, Perrin et al. (2007) and Seibert and Beven (2009) concluded that a limited number of streamflow extremes encapsulate a significant amount of information that may be useful for hydrological model calibration. Beven and Westerberg (2011) also suggested that, when dealing with extremes, including the entire time series might not be informative. This occurs, for instance, when streamflow extremes belong to a different population than ordinary flows (e.g., Calenda et al, 2009), such that the latter does not provide useful information for inferring the former. Hence, quantifying the influence of such extreme events on model calibration is still a challenge in hydrological studies (Brigode et al., 2015) as well as quantifying the uncertainty associated with these estimates (Honti et al., 2014).

To overcome the aforementioned limitations, we propose an innovative methodology in which the calibration of a hydrological model, as driven by climate models, is conducted by maximizing the probability that the modeled and observed streamflow extremes belong to the same population within the reference period. While the approach is exemplified in this work for high streamflows (given the broad interest in the topic), it can be applied to low flows as well (e.g., for drought assessment). The methodology, coined “Hydrological Calibration of eXtremes” (HyCoX), specifically targets climate change impact assessment studies and relies on the use of the two-sample Kolmogorov–Smirnov statistic (Smirnov, 1939) as an efficiency metric during the calibration procedure. We emphasize that the suggested approach is, by definition, a “goal-oriented” framework, as recently discussed in Fiori et al. (2016), Guthke (2017) and Laiti et al. (2018).

Studies adopting the two-sample Kolmogorov–Smirnov test to evaluate whether simulated hydrological variables are distributed according to a given probability distribution (e.g., Kleinen and Petschel-Held, 2007) are relatively common in the literature. This statistical test has also been used to detect changes in hydrological variables (e.g., Wang et al., 2008) as well as to verify if calibrated parameters of a hydrological model belong to a given probability distribution (e.g., Wu et al., 2017; Wang and Solomatine, 2019). This notwithstanding, we are not aware of existing studies adopting this statistical test in the context of hydrological model calibration oriented to the reproduction of extremes.

Therefore, the main objective of the present work is twofold. From one side, we introduce the HyCoX framework and assess its capability to reproduce observed high streamflow extremes using climate models, applied to the same time frame as that of the observational data, as input. On the other, the strength of the proposed methodology is tested against the commonly adopted procedure of calibrating the model using observational data.

The paper is organized as follows: Sect. 2 presents the hydrological modeling framework, the calibration metrics and the adopted statistical test; a description of the study area, the climate change projections, the observational hydrometeorological datasets and the setup of the simulations are summarized in Sect. 3; the main findings are presented and discussed in Sect. 4; and, finally, conclusions are drawn in Sect. 5.

## 2.1 Hydrological modeling

Hydrological simulations were performed at a daily timescale with the HYPERstreamHS model (Avesani et al., 2021; Laiti et al., 2018; Larsen et al., 2021); HYPERstreamHS couples the HYPERstream routing scheme, recently proposed by Piccolroaz et al. (2016), with a continuous module for surface and subsurface flow generation. The HYPERstream routing scheme is specifically designed to facilitate coupling with climate models and, in general, with gridded climate datasets. HYPERstream can share the same computational grid as that of any overlaying product providing the meteorological forcing while still preserving the geomorphological dispersion of the river network (Rinaldo et al., 1991), irrespective of the grid resolution. This “perfect upscaling” (see Piccolroaz et al., 2016) is obtained via the application of suitable transfer functions derived from a high-resolution digital elevation model (DEM) of the study area. Separation between surface flow and infiltration was obtained using the continuous Soil Conservation Service Curve Number (SCS-CN) model (Michel et al., 2005); this model receives the total precipitation given by the sum of rainfall and snowmelt as input, with the latter being evaluated by the degree-day model coupled with mass balance, which includes snow accumulation (Rango and Martinec, 1995). The infiltrating water enters into a nonlinear bucket mimicking soil moisture dynamics (Majone et al., 2010), with evapotranspiration, which is computed by the Hargreaves and Samani (1982) model, and deep infiltration as output fluxes. Finally, deep infiltration enters a linear bucket used to represent return flow. The surface and subsurface flow generation module has already been successfully applied in previous studies conducted in Alpine catchments (Piccolroaz et al., 2015; Bellin et al., 2016; Galletti et al., 2021). The model requires a total of 12 parameters, which are assumed to be spatially uniform and to be determined through calibration. Spatial heterogeneity of evapotranspiration, infiltration and runoff generation was accounted for by computing all relevant properties for each macrocell (e.g., maximum infiltration capacity, average elevation, soil type and crop coefficient) based on available DEM and land-use/land-cover spatial maps. The list of the 12 parameters, including their units, a short description and the range of variation, is presented in Table 1. A detailed description of the hydrological model can be found in Laiti et al. (2018) and Avesani et al. (2021).

## 2.2 Hydrological model calibration

The HYPERstreamHS hydrological model was calibrated against streamflow observations using both the ADIGE observational dataset (see Sect. 3.2) and the output of three climate models under two respective emission scenarios as meteorological forcing. A short description of these datasets is provided in Sect. 3.3. Parameters were inferred by optimizing three efficiency metrics using the particle swarm optimization (PSO) algorithm (Kennedy and Eberhart, 1995). PSO is an iterative algorithm belonging to the swarm intelligence category, which is based on the exploration of the space of parameters by a set of particles, called “bees”. Particle's positions were first randomly initialized and then iteratively updated in the search for the optimal solution, with the location-updating procedure considering the memory of all locations visited by the whole collection (swarm) of particles.

The first metric is the classic Nash–Sutcliffe model efficiency (NSE; Nash and Sutcliffe, 1970), which is widely used in hydrological applications:

where *m* is the total number of daily time steps; *Q*_{s,i}(** θ**) and

*Q*

_{o,i}are the simulated (

*s*) and observed (

*o*) streamflow at time step

*i*, respectively; ${\overline{Q}}_{\mathrm{o}}$ is the mean of the observed values; and $\mathit{\theta}=({\mathit{\theta}}_{\mathrm{1}},\mathrm{\dots},{\mathit{\theta}}_{q})$ represents the

*q*=12 model parameters. As this metric considers the chronological time series of simulated and observed daily streamflow, it was applied only when the ADIGE observational dataset was used as meteorological input.

The second efficiency metric (*R*_{FDC}) is an adaptation of the objective
function proposed in Westerberg et al. (2011) to obtain a good match between
simulated, ${\widehat{Q}}_{\mathrm{s},\left(i\right)}(\mathit{\theta}$), and observed,
${\widehat{Q}}_{\mathrm{o},\left(i\right)}$, flow duration curves (FDCs; i.e., the ranked streamflow
values in descending order):

where ${\widehat{Q}}_{\mathrm{s},\left(i\right)}^{\mathrm{EP}}(\mathit{\theta}$) and ${\widehat{Q}}_{\mathrm{o},\left(i\right)}^{\mathrm{EP}}$
are the respective simulated and observed streamflow values at the *n*_{EP} evaluation points (EPs) in which the flow duration curves are partitioned,
and ${\overline{Q}}_{\mathrm{o}}$ is the mean of the observed time series. According to this metric,
*R*_{FDC}=1 when the two flow duration curves coincide (i.e., they are the
same at all of the EPs). Given that the flow duration curve is insensitive to
the chronologic sequence, *R*_{FDC} has been used as an objective function for
streamflow maxima obtained with both climate models and the ADIGE observational
dataset. Furthermore, following Westerberg et al. (2011), the
so-called volume method was employed in which EPs were identified as the
upper boundary of the elements with the same area $V/{n}_{\mathrm{EP}}$ below the FDC,
where *V* is the total streamflow volume (i.e., the total area below the FDC).
Given the same number of EPs, we remark that the procedure is performed
independently for observed and simulated FDCs, and it is indeed possible that
the total volume *V* under the curves and the water volume $V/{n}_{\mathrm{EP}}$ of the
*n*_{EP} intervals differ between observations and simulations. The water
volume pertaining to each interval and the total water volume of the
flow duration curve are computed using the right Riemann sum procedure
(Protter and Morrey, 1977). In the computations, we used *n*_{EP}=50, which has been shown to obtain the convergence of the numerical integration of Eq. (2)
irrespective of the algorithm adopted (Vogel and Fennessey,1994)

The third efficiency metric is the two-sample Kolmogorov–Smirnov (KS)
statistic (*D*_{n}):

where *F*_{s} and *F*_{o} are the empirical cumulative distribution
functions (ECDFs) of the simulated, ${Q}_{\mathrm{s},\left(i\right)}^{M}\left(\mathit{\theta}\right)$, and observed, ${Q}_{\mathrm{o},\left(i\right)}^{M}$,
samples of daily average annual streamflow maxima ranked in increasing
order, respectively, and *n* is the number of years considered in the
simulation (29 in the present work, 1 for each year of the investigated
period excluding the first 2; see Sect. 3.4). Before ranking the abovementioned streamflow maxima in increasing
order, annual streamflow maxima samples are extracted from the
chronological daily time series of observed and simulated streamflow,
respectively. Following this, ECDFs of the simulated and observed samples of
annual maxima are computed according to the classic Weibull formulation
(Weibull, 1939):

This metric, which is at the core of the proposed approach, aims to maximize
the probability that the modeled and observed samples of high streamflow
extremes belong to the same population. In other words, among all possible
sets of model parameters, we consider the one leading to the smallest
maximum absolute distance (*D*_{n}) between simulated and observed ECDFs of
daily annual streamflow maxima. As KS is not sensitive to the temporal
sequence of observed and simulated streamflows, similar to *R*_{FDC}, it has
been applied to climate projections in addition to the simulations with the
ADIGE observational dataset.

## 2.3 Evaluation of statistical coherence

After calibration, the statistical coherence between the observed and simulated
samples of high streamflow extremes was evaluated by employing the two-sample
Kolmogorov–Smirnov test (Smirnov, 1939), applied under the null hypothesis
that the two samples are drawn from the same underlying distribution. In the
two-tail application of interest here, the test statistic *D*_{n} is given
by Eq. (3). The closer that *D*_{n} is to zero, the more likely it is that the two
samples are drawn from the same population. In addition, the two-sample
Kolmogorov–Smirnov test returns a *p* value corresponding to the computed
*D*_{n} statistic (Conover, 1999). The larger the *p* value, the stronger the
evidence in favor of the null hypothesis (i.e., that the samples are drawn
from the same distribution).

In this study, the *p* value has been used as a measure of the statistical
coherence between samples of simulated and observed high streamflow
extremes. Furthermore, this evaluation step has been performed a posteriori
for each simulation experiment described in Sect. 3.4.

## 2.4 Probability distribution computation and confidence intervals

The theoretical probability distributions of simulated and observed annual
streamflow maxima were obtained by fitting an extreme value type I Gumbel distribution (Gumbel, 1941), $P\left(Q\le q\right)=\mathrm{exp}\left[-\mathrm{exp}\left[-\mathit{\beta}\left(q-u\right)\right]\right]$, to the respective samples using the maximum likelihood estimation (MLE)
method (Hosking, 1985). The Pearson
chi-square test (Pearson, 1990) with a confidence level of *α*_{s}=0.05 was then applied to validate the parameters *β* and *u* provided by
the MLE. Extrapolation of high quantiles (i.e., estimation of quantiles for
a return period larger than the available number of observation and
simulation years) of observed and simulated annual streamflow maxima was
then performed for all of the simulation experiments described in Sect. 3.4.

Confidence intervals of observed streamflow ECDFs were computed using parametric bootstrapping (Efron, 1982) under the assumption that the quantity of interest was distributed according to the abovementioned parametric Gumbel probability distribution. In particular, a 90 % confidence band was estimated using 10 000 uniform random samples from the underlying inferred distribution.

## 3.1 Study area

To exemplify the application of the methodology, the upper part of the Adige
River basin (Italy), located in the southeastern Alpine region (see Fig. 1), at the Trento gauging station (46^{∘}04^{′}13^{′′} N, 11^{∘}06^{′}54.8^{′′} E; drainage area of about 9850 km^{2}) was selected
as a case study. The Adige River originates at the Reschen Pass (close to the
Alpine divide) and ends its course after 410 km in the northern Adriatic
Sea. It is a typical Alpine river basin, with terrain elevations ranging
from 185 at Trento to 3500 m a.s.l. at the Italian–Austrian border.
The region's morphology is characterized by deep valleys and high mountain crests.

The climate of the river basin is characterized by relatively dry and cold winters followed by humid summers and autumns. Streamflow is minimum in winter, when precipitation falls as snow over most of the river basin, and shows two maxima: one occurring early in summer, due to snow melting, and the other in autumn, triggered by intense cyclonic storms. The average annual precipitation ranges from 500 mm in the northwest of the region to 1600 mm in the southern part of the basin (Lutz et al., 2016; Diamantini et al., 2018; Laiti et al., 2018). A projected decrease in snowfall in winter and anticipated earlier snowmelt, essentially due to rising temperatures associated with global warming (Gobiet et al., 2014; Gampe et al., 2016), will likely affect the Adige streamflow regime by the second half of the 21st century (Bard et al., 2015; Majone et al., 2016). This may have relevant consequences on water resources and hydropower production, which is particularly relevant in this region of the Alps (Zolezzi et al., 2009; Bellin et al., 2016; Majone et al., 2016; Avesani et al., 2022); the reader is referred to Chiogna et al. (2016) for a comprehensive review of the hydrological stressors acting in the Adige Basin as well as the area's ecological status.

## 3.2 Observational datasets

The ADIGE regional dataset, developed by Mallucci et al. (2019) using the
meteorological stations within the catchment and in the nearby Austrian
territory bounding the catchment from the north, was used as observational
precipitation and temperature dataset within the 1950–2010 time window.
ADIGE was selected because it is the most accurate gridded meteorological
dataset of the investigated river basin (as shown in the recent paper by
Laiti et al., 2018). Meteorological data at the selected stations were
provided by the Austrian Zentralanstalt für Meteorologie und Geodynamik
(https://www.zamg.ac.at/cms/de/aktuell, last access: 15 July 2022) and the meteorological offices of the Autonomous Provinces
of Trento (https://www.meteotrentino.it/#!/home, last access: 15 July 2022) and Bolzano (https://weather.provinz.bz.it/Default.asp, last access: 15 July 2022).
The time series were interpolated over a 1 km grid at a daily time step
using the kriging with external drift algorithm (Goovaerts, 1997; Journel
and Rossi, 1989), with an exponential semivariogram using the 16
closest neighboring stations in the linear combination providing the
estimate. The optimal spatial distribution model was selected by Mallucci et al. (2019) according to the leave-one-out cross-validation procedure and was
applied to both the ordinary kriging and kriging with external drift algorithms.
Several semivariogram models (i.e., Gaussian, spherical and exponential
models) and different numbers of neighboring stations (namely 8, 16 and 32
stations) were tested, and the model that provided the minimum average absolute
error of daily estimates was identified. As described in Mallucci et al. (2019), the optimal semivariogram model was the exponential one, which provided an
average absolute error of the daily estimates of about 1.32 mm for
precipitation and 0.02 ^{∘}C for temperature – both comparable with
the error estimates provided by widely used datasets available for the
Alpine region, such as the Alpine precipitation grid dataset (APGD; Isotta et al., 2014). Daily streamflow at the
Ponte San Lorenzo gauging station in Trento and the Bronzolo gauging station (see Fig. 1)
were provided by the Hydrological Offices of the Autonomous Province of
Trento (https://www.floods.it/public/index.php, last access: 15 July 2022) and Bolzano (https://meteo.provincia.bz.it/default.asp, last access: 15 July 2022), respectively.

## 3.3 Climate change projections

Climate projections used in the present work were derived from the combination of general circulation models (GCMs) and regional climate models (RCMs) available from the EURO-CORDEX initiative under Representative Concentration Pathway (RCP) 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively) at a spatial resolution of about 12 km (EUR-11, https://www.euro-cordex.net/, last access: 15 July 2022, Jacob et al., 2014). To reduce the computational burden of the hydrological modeling experiments, we adopted the model sub-selection proposed by Vrzel et al. (2019), who applied a hierarchical clustering approach (Wilcke and Bärring, 2016) in selected European river basins (including the Adige) to reduce the number of available climate model (CM) simulations (i.e., GCM–RCM combinations) while preserving the variability of the ensemble of climate change signals. In particular, model reduction involved five steps: (1) identification of the meteorological variables; (2) transformation of variables into orthogonal and therewith uncorrelated variables using singular vector decomposition; (3) identification of the optimum number of clusters; (4) hierarchical clustering to group the simulations; and, finally, (5) selection of the simulations closest to the group's mean as representative. This procedure led to the selection of the three GCM–RCM combinations (out of the 12 available), here referred to as CLMcom, KNMI and SMHI (see Table 2).

These three GCM–RCM combinations provide projections of likely future
climate changes for the mid-term horizon (2040–2070), with the
1980–2010 time window selected as the period of reference. The projected climate change
meteorological signals in the Adige Basin are discussed in Gampe et al. (2016).
Both the RCP4.5 and RCP8.5 emission scenarios are available for all of the
combinations, thereby leading to a total of six CMs being investigated
in the present study (see Table 2). As GCM–RCM combinations are prone
to model biases, especially in complex terrain (Kotlarski et al., 2014),
bias correction is needed to accurately reproduce historical meteorological
forcing during the reference period. In the present work, we rely on
products retrieved from EURO-CORDEX; these products are available as bias-corrected values and were corrected with
the distribution-based scaling approach (DBS; Yang et al., 2010) using the MESoscale ANalysis system (MESAN) gridded reanalysis datasets of daily precipitation
and temperature as
observations (Landelius et al., 2016). Basin-averaged monthly mean
precipitation and temperature of the six CMs are presented in Fig. 2, with
reference to the 1980–2010 period, along with those of the ADIGE dataset.
Notice that the CMs differ slightly between the two RCPs as a consequence of (i)
the bias correction method adopted, which matches observed and simulated
frequency distributions rather than the observations, and (ii) the fact that the correction
performed with reference to the 1989–2010 period is extended to the previous
9 years to obtain bias-corrected scenarios for the entire reference period
(1980–2010). This is needed because MESAN data are only available for the
former period. Figure 2a and b show that basin-averaged monthly
mean time series of the six CMs for both variables are in close agreement with ADIGE, with
the largest deviations observed in May for precipitation (differences in the
range of 15–21 mm) and in December for temperature (differences in the
range of 1.3–1.9 ^{∘}C), respectively. Accordingly, differences at
the annual scale are rather small, as highlighted in the insets of Fig. 2a
and b. ECDFs of the basin-averaged daily precipitation (Fig. 2c) and temperature (Fig. 2d) for both
ADIGE and the six CMs are presented in Fig. 2. For precipitation,
no appreciable differences are observed between the CMs and ADIGE throughout the
entire range of variability. For the temperature (Fig. 2d), small
differences are observed which decrease progressively as temperature increases
and become undetectable at high temperatures. Overall, these results
indicate that the output of the CMs is in good agreement with the observations
during the reference period, a statement which is also valid for the
extremes of precipitation and temperature, which are indeed at the base of
our approach.

## 3.4 The setup of simulations

All of the simulations were performed with the HYPERstreamHS hydrological model using a daily time step and the 5 km computational grid depicted in Fig. 1. Accordingly, precipitation and temperature provided by the ADIGE dataset and by the six CM simulations presented in Sect. 3.3 were projected onto this grid using the nearest-neighbor method. Notice that the contributing area of the macrocells at the border of the domain was reduced by the amount belonging to the neighboring basin, so as to preserve the overall contributing area of the investigated case study.

In a first set of simulations presented in Sect. 4.1, the HYPERstreamHS
model was calibrated at the Trento gauging station using the NSE,
KS and *R*_{FDC} metrics as objective functions and the 1980–2010 period as a
reference. In order to ease the presentation of results, these three
parameterizations are hereafter called NSE-ADIGE, KS-ADIGE and
*R*_{FDC}-ADIGE, respectively. Validation of the modeling framework was
then performed for these three parameterizations by computing the
efficiency metrics at the Bronzolo gauging station (drainage area of about
6000 km^{2}; see Fig. 1) within the same time window as well as at the Trento
gauging station in the 1950–1980 period (which was not used for calibration).

In a second set of simulations (presented in Sect. 4.2), we assessed whether
the model calibrated with observational data and fed with precipitation and
temperature obtained from climate models produced samples of annual
streamflow maxima that were statistically coherent with the observations. Here, we
considered simulations performed in the 1980–2010 period using
precipitation and temperature from the three GCM–RCM combinations, selected
as described in Sect. 3.3, for both respective RCP4.5 and RCP8.5 emission
scenarios and for a total of six CM combinations (see Table 2). The parameters
of the hydrological model were those referring to the NSE-ADIGE, KS-ADIGE and
*R*_{FDC}-ADIGE parameterizations.

In Sect. 4.3, we present the results of the calibration experiments
performed using the precipitation and temperature
distributions provided by the six CMs for the 1980–2010 period in HYPERstreamHS with KS and
*R*_{FDC} as objective functions. Following the procedure described in Sect. 2.4, extrapolations were then performed under the assumption that simulated
and observed ECDFs were distributed according to the parametric Gumbel
probability distribution. The Pearson chi-square test was then applied to
verify the inferred model.

For all time windows and all simulations, the first 2 years were used as
spin-up and were, therefore, excluded from the computation of model performance.
Furthermore, statistical coherence between simulated and observed samples of
annual streamflow maxima was evaluated a posteriori using the *p* values
associated with the Kolmogorov–Smirnov two-sample test described in Sect. 2.3.

The effects of calibrations conducted using different meteorological forcing (observational data and CMs simulations) on model parameters are investigated in Sect. 4.4 with reference to the KS metric. For each calibration experiment performed with the PSO algorithm, we considered 100 particles that, with a maximum number of 400 iterations, lead to a maximum of 40 000 hydrological simulations for each external forcing. The parameter ranges considered during the search for the optimal solution are presented in Table 1, and they have been set by means of preliminary simulations so as to minimize the probability of excluding combinations of parameters which lead to behavioral solutions (Beven and Binley, 1992). In addition, as a metric of uncertainty for the calibrated parameter, we considered the range, $\overline{d}$, between the maximum and minimum value of each parameter in the 200 simulations presenting the highest efficiency metric (see Piccolroaz et al., 2015). We remark that the procedure adopted here aims to only to quantify the differences in the range of calibrated parameters and not to perform a full uncertainty analysis of predictions.

Finally, in Sect. 4.5 the projected changes in high flow extremes in the
future period from 2040 to 2070 are evaluated. For each CM, we considered the
following parameterizations obtained during calibration in the reference
period: (1) calibrations with KS and *R*_{FDC} as objective functions and (2)
NSE-ADIGE as representative of a standard calibration procedure using the
observational dataset ADIGE as input forcing.

## 4.1 Simulations using the ADIGE observational dataset

Figure 3a shows the simulated ECDFs obtained using the three metrics, NSE,
KS and *R*_{FDC}, as objective functions and the ADIGE observational dataset
as input forcing. Table 3 shows the associated *p* values of the
Kolmogorov–Smirnov test. From a statistical viewpoint, all three metrics
provide simulated samples of annual streamflow maxima belonging to the same
population as the observed ones, given that *p**>*0.05 in all cases, with a maximum for KS (*p*=1.000) and a minimum for *R*_{FDC} (*p*=0.372).
However, calibration conducted using KS as an objective function leads to
NSE and *R*_{FDC} values (0.4 and 0.564, respectively; see Table 3) that
are lower than those obtained when calibration is performed by (separately) optimizing these two metrics (NSE=0.822 and *R*_{FDC}=0.975,
respectively; see Table 3). This is in accordance with several studies
showing that the adoption of a given metric in calibration may lead to
suboptimal results for other metrics because each of them is sensitive to
specific aspects of the time series with its limitations and trade-offs (see,
e.g., Schaefli and Gupta, 2007; Gupta et al., 2009; Mcmillan et al., 2017;
Fenicia et al., 2018). This latter limitation is, in our opinion, outweighed
by the improvements in representing the ECDFs of observed high flow extremes
when the model is calibrated explicitly considering such information, i.e.,
by minimizing the KS metric. Accordingly, in our analyses, the use of
different efficiency metrics leads to different simulated ECDFs and, hence, to
different *p* values in the application of the statistical coherence test (see Table 3).

Validation of the hydrological modeling framework was performed by
evaluating the model performance in the time frame from 1952 to 1980, which was not used for
calibration, at the Ponte San Lorenzo gauging station in Trento. The
validation was done using the ADIGE dataset as input and the
parameterizations obtained by calibrating the model in the 1982–2010 time frame (i.e., NSE-ADIGE, *R*_{FDC}-ADIGE and KS-ADIGE, as described
above). The NSE-ADIGE and *R*_{FDC}-ADIGE parameterizations led to NSE and
*R*_{FDC} values that
were only slightly lower than those obtained in calibration (NSE=0.803 and *R*_{FDC}=0.804; see Table 3). The KS-ADIGE
parameterization led to an increase in the KS from 0.067 during calibration to
0.233 during validation, which was still rather small. The limited modification of the
efficiency metrics during validation is an encouraging result which shows that
the HYPERstreamHS model provides a good representation of the hydrological
system independent of the metric adopted during calibration. The simulated and
observed ECDFs of annual streamflow maxima and the associated *p* value of the
Kolmogorov–Smirnov test are presented in Fig. 3b. Reproduction of the observed
ECDF is satisfactorily for all three parameterizations, particularly for
high flow quantiles, with *p* values in the range between 0.222 and 0.372 (see
also Table 3). The three parameterizations provide simulated samples of
annual streamflow maxima belonging to the same population of observations
(also in the time window from 1952 to 1980); the reduction in the *p* value from
calibration to validation is significant but rather common in hydrological
models.

Spatial validation of the modeling framework was also performed by
simulating streamflow at the Bronzolo gauging station (see Fig. 1) during the
same time window as the calibration conducted at the Trento gauging station
(1982–2010). Similarly to the previous case, efficiency metrics during
validation evidence a small reduction in performance with respect to those
obtained during calibration (see Table 3). On the other hand, the results
presented in Fig. 3c highlight an excellent reproduction of the observed
ECDF of annual streamflow maxima for all three parameterizations, with the
associated *p* values in the range between 0.791 (NSE-ADIGE) and 0.951
(*R*_{FDC}-ADIGE and KS-ADIGE). The latter is a noteworthy result which
indicates that the parameterization obtained using KS as an objective
function is reliable, although relying on a limited number of observations,
and does not introduce distortion in the spatial representation of the
hydrological processes, particularly those controlling high streamflow
events (i.e., runoff generation and streamflow concentration processes). This
latter aspect will be further investigated in Sect. 4.4.

## 4.2 Simulations using parameterizations derived from calibrations with observed ground data

Here, we analyze the case in which HYPERstreamHS was run in the time frame
from 1982 to 2010 using the meteorological variables produced by the
climate models and the three parameterizations, NSE-ADIGE, *R*_{FDC}-ADIGE,
KS-ADIGE (described in Sect. 3.4), as input. Visual inspection of Fig. 4a, b and c
shows that, for high quantiles, the simulated ECDFs are often outside the
90 % confidence interval of the Gumbel distribution fitted to observations
for all of the considered combinations of CMs and parameterizations. The
*p* values of these validation runs are shown in the last three columns of
Table 4. In particular, these three parameterizations lead to *p* values
that are always lower than *p*=0.372 for all of the considered CMs and emission
scenarios (see Table 4). NSE-ADIGE and *R*_{FDC}-ADIGE show the
lowest *p* values on average, with KS-ADIGE performing slightly better: *p*=0.372 for
KNMI and SMHI under the RCP8.5 scenario (see Fig. 4b and c and Table 4).
Inspection of Table 4 also reveals that values of *p*<0.05, and thus
simulated ECDFs not belonging to the same population as the measured one,
are obtained with the CLMcom model for both the NSE-ADIGE and KS-ADIGE
parameterizations under both emission scenarios as well as with the KNMI model for the
NSE-ADIGE and *R*_{FDC}-ADIGE parameterizations under RCP4.5.

The above results highlight how classic approaches based on feeding
hydrological models, calibrated using observed meteorological data and
employing customary efficiency metrics (i.e., NSE and *R*_{FDC}), with
meteorological forcing provided by climate models produce results
characterized by low statistical coherence with the observational data.
Furthermore, our results indicate that the same drawback arises when
employing parameterizations obtained with a calibration approach optimizing
the desired statistic of extremes but still using observational data as
input (i.e., KS-ADIGE in Fig. 4a, b and c). These results are in
agreement with previous studies evidencing that the hydrological models,
calibrated against observed data, that perform well within a baseline period
may not be accurate nor consistent for simulating streamflow under future
climate conditions (Brigode et al., 2013; Lespinas et al., 2014). Indeed, it
is recognized that the use of different datasets can lead to different
optimized parameters that will partially account for their specific climate
characteristics (Yapo et al. 1996; Vaze et al., 2010; Laiti et al., 2018).
Furthermore, it is acknowledged that climate change impact simulations are
affected by uncertainty in climate modeling, but the calibration
strategy adopted during the reference period also plays a role (Lespinas et al.,
2014; Mizukami et al., 2019). In this respect, we showed that the
statistical coherence between climate scenarios and observations (i.e., high
streamflow extremes in our case) should be preserved during hydrological
calibration, at least in the reference period. This latter aspect will be
further discussed in the ensuing Sect. 4.3.

## 4.3 Performance of the hydrological model calibrated using climate model outputs as input

Table 4 summarizes the efficiency metrics and the *p* values of the
calibration experiments performed using the
precipitation and temperature distributions provided by the six selected
CMs with KS and *R*_{FDC} as the objective functions in HYPERstreamHS. Simulations refer to the 1982–2010 period. When KS was used in calibration, all six simulations
provided samples of annual streamflow maxima that had a high probability
(i.e., *p*=1.000, column 8 of Table 4) of belonging to the same population of
observed values. A similar conclusion was reached for the objective function
*R*_{FDC} but with lower *p* values (column 7 of Table 4), although these values were
larger than *p*=0.05 – the level of significance customarily adopted in the
statistical literature to reject the null hypothesis. The lowest *p* value was
obtained with the CLMcom climate model under the RCP4.5 emission scenario
with *R*_{FDC} as the objective function (*p*=0.222; see column 7 of Table 4).
Consistently, the absolute maximum distances between the ECDFs of observed
and simulated samples obtained using *R*_{FDC} as the calibration metric are
always larger than those obtained using KS (see the third and fifth columns
in Table 4). When calibration is performed with KS, the results are
satisfactorily with respect to the *R*_{FDC} metric, which is in the
range between 0.449 and 0.804 for all of the CMs (see the fourth column in
Table 4). As *R*_{FDC} employs the entire time series of observational
data, this result evidences the fact that using the KS metric in calibration does not
introduce model overparameterization, despite the reduced number of
observational data used (i.e., 29 values of observed daily annual streamflow
maxima).

The appreciable difference between the observed and simulated ECDFs obtained in
the calibration experiments conducted using the KS and *R*_{FDC} metrics is
highlighted in Fig. 5. Figure 5 shows that the ECDFs obtained by
extracting the annual maxima from the simulations calibrated with KS as the
objective function are in a better agreement with the observed ECDFs than
those obtained by calibrating with *R*_{FDC}. This comparison highlights
that the KS metric is preferable to *R*_{FDC} when dealing with high flow
extremes, thereby strengthening the approach envisaged here of
directly addressing the desired statistics of extremes in calibration instead of
calibrating the hydrological model on the entire streamflow record.

The literature reports a few examples of hydrological models calibrated using tailored information instead of the entire observed streamflow time series (e.g., Montanari and Toth, 2007; Blazkova and Beven, 2009; Westerberg et al., 2011; Lindenschmidt, 2017). However, these approaches are typically adopted for reproducing the basin response to observed meteorological forcing and have not been applied (to our best knowledge) in combination with GCM–RCM simulations in climate change impact studies. The only example somewhat similar to our approach that we found in the literature is that of Honti et al. (2014), who used a stochastic weather generator trained by observed weather time series coupled with observed discharge data to sample the posterior distribution of model parameters. The adoption of a time-independent calibration, for which time shift does not influence the objective function, has the intrinsic advantage of allowing the use of GCM–RCM runs conducted without the assimilation of observational data, as in our case. In fact, these runs provide time-slice experiments representing a stationary climate for both reference and future periods (see, e.g., Majone et al., 2012) and, by definition, cannot be used in the context of a classic day-by-day hydrological comparison experiment with observed historical data (see, e.g., Eden et al., 2014).

Quantiles of daily annual streamflow maxima as a function of the return
period at the Trento gauging station are shown in Fig. 6, where the results
obtained by calibrating the hydrological model with the meteorological input
provided by the climate models (with the KS and *R*_{FDC} metrics as
objective functions) are compared with those obtained using the same
meteorological input but employing the NSE-ADIGE, *R*_{FDC}-ADIGE and KS-ADIGE
parameterizations. Visual inspection of Fig. 6 reveals that, for all return
periods, parameterizations obtained by calibrating with the observed
precipitation and temperature data, as provided by the ADIGE dataset,
significantly underestimate the quantiles of the observations and fall
outside the confidence interval of the fitted Gumbel distribution (i.e.,
outside the gray area). The only exceptions are the quantiles derived from
simulations conducted with the KNMI (KS-ADIGE; dotted line in Fig. 6c) and
CLMcom (all three metrics; Fig. 6a) climate models under the RCP4.5 emission scenario. We note,
however, that these curves are obtained with forward simulations, providing
low KS *p* values with respect to the other cases
(always lower than *p*=0.222). Instead, quantiles obtained from simulations
optimized directly using climate models and with KS as the metric are in a very
good agreement with the experimental data, whereas those obtained using
*R*_{FDC} are outside or at the lower bound of the interval of confidence,
although they are generally in a better agreement with the quantiles of the
experimental data than those obtained with the aforementioned NSE-ADIGE,
*R*_{FDC}-ADIGE and KS-ADIGE parametrizations. Exceptions to this are the quantiles
obtained with CLMcom and KNMI under the RCP4.5 emission scenario with *R*_{FDC}
as the metric, which are characterized by the largest deviations from
observations (see Fig. 6a and c, respectively). We attribute this
occurrence to the additional source of uncertainty arising from the
extrapolation procedure (i.e., the selection of the probability distribution
and of the statistical inference method for the parameters, MLE in our
case). The confidence interval of the fitted Gumbel distribution to the
observational data (gray area) widens as the return period increases;
this is in line with the recent findings of Meresa and Romanowicz (2017),
who showed that errors in fitting theoretical distribution models to
annual maxima streamflow series might contribute significantly to the
overall uncertainty associated with projections of future hydrological
extremes.

## 4.4 Model parameters

The results presented in the previous sections highlight that better
statistical coherence between observations and simulations (performed with
CM simulations as input) was achieved by optimizing the desired statistics
of extremes, in our case KS (see the curves labeled KS in Figs. 5 and 6),
in the calibration of the hydrological model. Starting from this evidence,
we investigated the effect (on the model parameters) of performing the calibration
using either observed data or meteorological data derived from CMs with KS as the
objective function. Figure 7 shows the range ($\overline{d}$) between the maximum
and minimum values, which is represented here by the length of the vertical bar of
each parameter among the 200 accepted values corresponding to the
behavioral models (see Sect. 3.4), and the corresponding optimal
parameter set, which is represented by a horizontal segment. The values of
the parameters are normalized with respect to their range (see Table 1) such
that they are directly comparable. In all simulations the normalized
parameter range $\overline{d}$ is well distributed between zero and one, indicating a
proper choice of the parameter range in the PSO algorithm, although the optimal value was located close to the boundary of the
search domain for a
few parameters. As shown in Fig. 7 the majority of the parameters
obtained using the proposed approach span a range ($\overline{d}$) that is
similar to (or slightly larger than), in terms of amplitude, that obtained for
KS-ADIGE, thereby supporting the conclusion that calibration using CM
simulations does not lead (for either RCP) to bias parameterizations. Figure 7 also shows that, for most of the parameters, simulations performed with CMs
lead to generally overlapping ranges for $\overline{d}$ with respect to the case
in which the ADIGE observational dataset was used. The largest deviations in
terms of $\overline{d}$ are observed for KS-KNMI, particularly under the RCP8.5
emission scenario. Notably, the parameters shaping the continuous
soil moisture accounting module result in values of the optimum which are
very similar for all cases (see *q*_{ref}, *μ* and *c*_{fc} in Fig. 7a and b). Visual inspection of Fig. 7 also highlights that the parameters
controlling runoff generation and streamflow concentration (in particular,
*v*, *c*_{s}, *q*_{ref} and *c*_{fc}) present very good identifiability (i.e.,
a small range, $\overline{d}$). This is not the case for parameters controlling
snow melting and groundwater contribution, with the latter being relevant only for
low flow conditions (see *k* in Fig. 7a and b). These results, as well as
the good performance obtained in the validation runs presented in
Sect. 4.1, suggest that, although the model is calibrated considering a
limited number of observations, the maxima are
well reproduced in the continuous simulations, but this is achieved only if the interaction between the
precipitation and streamflow relevant during high flow extremes is correctly
reproduced. We cannot exclude that additional analyses could be envisioned
for improving the identifiability of some parameters (e.g., by reducing the
number of model parameters or introducing constraints in the parameter's range) in applications dealing with different hydrological models and
different data availabilities (e.g., lower number of streamflow extremes).
However, the analysis presented here provides clear evidence that the
parameterizations derived from the use of the KS metric are reliable.

The differences observed in the optimal values of model parameters are due to the use of meteorological forcing datasets with different capabilities to reproduce the present climate. Along with the concepts brought forward here, this source of uncertainty can be addressed effectively via calibration of the hydrological model to the quantities of interest (i.e., the observed streamflow statistics of extremes) using the forcing provided by a specific CM as input. This approach can be seen as a “hydrologically based bias correction” and is rooted in the adoption of a goal-oriented calibration framework (see, e.g., Laiti et al., 2018), as stated in the Sect. 1.

## 4.5 Projected changes in streamflow quantiles

Figure 8 shows the annual maximum streamflow at the Trento gauging station
as a function of the return period in the future time window (2040–2070) and
for the six selected CMs. Visual inspection of Fig. 8 confirms that, in all
cases, using the standard calibration (i.e., NSE-ADIGE) of the hydrological
model leads to a significant underestimation of all quantiles with respect
to using KS and *R*_{FDC}. This is in agreement with the results obtained
for the reference period (see Fig. 6), where simulations using the NSE-ADIGE
parameterization provided streamflow quantiles systematically lower than
those obtained with the CMs. In addition, KS-based calibrations always
provide larger quantiles with respect to the cases in which the *R*_{FDC}
metric is adopted (considering the same RCP emission scenario). We note that the adoption of the KS metric is preferable, as it provided an almost
perfect match with observed streamflow quantiles in the calibration period
(see Fig. 6).

Moreover, Fig. 8 shows that projected changes in high flows extremes
depend on the selected CM and emission scenario. Projected streamflow
quantiles under RCP8.5 are larger than those under RCP4.5 for all of the CMs.
In general, the projected streamflow quantiles do not exceed those obtained
by fitting the Gumbel distribution to the observational data of the
1982–2010 period (continuous black lines in Figs. 6 and 8), with the exceptions
of the CLMcom and SMHI models under the RCP8.5 emission scenario and SMHI under the RCP4.5 emission scenario when the KS metric
is adopted. These results are in line with other recent contributions which
concluded that the sign and magnitude of projected changes in high flow
extremes will vary significantly with the location of the investigated river
basin, the climate models used, the emission scenario and the
selection of the time window (Ngongondo et al., 2013; Aich et al., 2016;
Pechlivanidis et al., 2017; Vetter at al., 2017). Our results are in line
with the analysis of Brunner et al. (2019), who implemented a stochastic
framework to simulate future streamflow time series in 19 regions of
Switzerland and concluded that future maximum streamflow will increase and
decrease in rainfall-dominated and snowmelt-dominated regions, respectively.
Similarly, Di Sante et al. (2019) showed that a moderate increase in high
flow magnitude (return time of 100 years) is projected for large river
basins (drained area >10 000 km^{2}) in the central Europe
region under the RCP8.5 scenario and considering a mid-century time slice.

In this work, we proposed the HyCoX methodological framework in which the calibration of the hydrological model is carried out by maximizing the probability that the modeled and observed high streamflow extremes belong to the same statistical population. The proposed framework is goal-oriented and aims at improving the estimation of streamflow extremes by directly calibrating the selected hydrological model to the quantities of interest (i.e., flow statistics instead of time series) using the meteorological data provided by climate models as input. In particular, the framework relies on the use of the two-sample Kolmogorov–Smirnov (KS) statistic as the objective function during the calibration procedure. This approach ensures statistical coherence between scenarios and observations in the reference period, and it likely preserves statistical coherence in the future climate change scenario runs performed to project changes in streamflow extremes. The goal-oriented approach envisaged in this work can be applied to a variety of hydrological scenarios and modeling approaches. Furthermore, we remark that the HyCoX methodology is not a metric-dependent approach, and any type of metric assessing the statistical coherence between observed and simulated streamflow extremes can be employed without any loss of generality.

The proposed procedure is exemplified via the application of six climate
models and observational data to the analysis of the annual maximum
streamflow of the Adige River basin (Italy) using the HYPERstreamHS distributed
hydrological model. While the approach is exemplified here for
high flows, it can be applied to low flows as well (e.g., for drought
assessment). The results highlight that adopting the KS is preferable to other
popular metrics (e.g., the NSE or fit to flow duration curve, *R*_{FDC}) when
dealing with high streamflow extremes. This validates our hypothesis that directly addressing the statistics of the extremes under consideration during
the calibration exercise leads to coherent and reliable hydrological models
for assessing the impact of climate change. We warn that such an approach
may lead to suboptimal performance if the target is different from the one
employed in this study, which is in line with the goal-oriented framework pursued here. Alternatively, a multi-objective approach could be envisioned to
investigate the trade-off in model performance emerging from the use of
multiple metrics, including the one proposed here. This latter aspect is
indeed beyond the objective of the present contribution, although it is worthy
of further analysis. Furthermore, the investigation of optimal values
highlighted that direct calibration using CM outputs and KS as the objective
function leads to unbiased identification of model parameters.

Overall, we showed that the way in which the hydrological model is calibrated against observations assumes paramount importance in climate change impact assessments on streamflow extremes. In particular, we highlighted how the classic approach of calibrating using daily streamflow observations with observed meteorological data can lead to a biased probability distribution of streamflow extremes when climate models are used as input forcing during the reference period, with high streamflow quantiles being dramatically underestimated with respect to the fitted distribution of the observed extremes. Extrapolations performed using the proposed calibration procedure, with input provided by CMs, are instead more reliable, and they provide a good match with observed quantiles.

The model code used is available upon request from the corresponding author.

The EURO-CORDEX datasets are available at https://www.euro-cordex.net/060378/index.php.en (Earth System Grid Federation, 2022). The ADIGE dataset is available upon request from the corresponding author. Streamflow data are available upon request from the Hydrological Offices of the Autonomous Province of Trento (https://www.floods.it/public/index.php, last access: 15 July 2022) and Bolzano (https://meteo.provincia.bz.it/default.asp, last access: 15 July 2022).

BM was responsible for conceiving the study; developing the methodology; acquiring funding; carrying out the investigation; developing the software; preparing, reviewing and editing the manuscript; and supervising the study. DA contributed to developing the software, carrying out the investigation, creating the figures, curating the data, and reviewing and editing the manuscript. PZ was responsible for software development and data curation. AF conceived the study, developed the methodology, reviewed and edited the manuscript, and supervised the study. AB conceived the study, developed the methodology, acquired funding, reviewed and edited the manuscript, and supervised the study.

The contact author has declared that none of the authors has any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research received financial support from the Italian Ministry of Education, University and Research (MIUR) Department of Excellence (grant no. L.232/2016) and from the Energy-oriented Centre of Excellence (EoCoE-II; grant no. 824158) within the Horizon2020 framework of the European Union. Bruno Majone acknowledges support from the “Seasonal Hydrological-Econometric forecasting for hydropower optimization” (SHE) project, funded within the framework of the call for projects “Research Südtirol/Alto Adige 2019” of the Autonomous Province of Bozen/Bolzano – South Tyrol. Diego Avesani acknowledges support from the European Union – FSE-REACT-EU, PON Research and Innovation 2014–2020 DM1062/2021. The authors are grateful to the climate modeling groups listed in Table 2 of this paper for producing and making available their model output within the EURO-CORDEX initiative (https://www.euro-cordex.net/index.php.en, last access: 15 July 2022). Streamflow data were kindly provided by the Service for Hydraulic Works of the Autonomous Province of Trento (https://www.floods.it/public/index.php, last access: 15 July 2022) and the Hydrological Office of the Autonomous Province of Bolzano (https://meteo.provincia.bz.it/default.asp, last access: 15 July 2022). We also wish to thank the two anonymous referees whose comments and suggestions helped improve and clarify this paper.

This research has been supported by the Ministero dell'Istruzione, dell'Università e della Ricerca, Department of Excellence (grant no. L.232/2016); the European Commission, Horizon 2020 framework program EoCoE-II (grant no. 824158); and the Provincia autonoma di Bolzano – Alto Adige (project SHE). Diego Avesani also acknowledges support from the European Union – FSE-REACT-EU, PON Research and Innovation 2014–2020 DM1062/2021.

This paper was edited by Yi He and reviewed by two anonymous referees.

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