Effect of modelling scale on the assessment of climate change

impact on river runoff Effet de lrsquoeacutechelle de la modeacutelisation

sur lrsquoeacutevaluation de lrsquoimpact du changement climatique sur

lrsquoeacutecoulement fluvial

Mikołaj Piniewski1 Frank Voss2 Ilona Baumlrlund32 Tomasz Okruszko1 Zbigniew W

Kundzewicz45

1 Department of Hydraulic Engineering Warsaw University of Life Sciences Nowoursynowska Str

159 02-776 Warszawa Poland

mpiniewskilevissggwpl (Corresponding author) 2 Center for Environmental Systems Research University of Kassel Wilhelmshoumlher Allee 47 34117

Kassel Germany 3 Helmholtz Centre for Environmental Research-UFZ Bruumlckstrasse 3a 39114 Magdeburg Germany 4 Institute for Agricultural and Forest Environment Polish Academy of Sciences Bukowska Str 19 60-

809 Poznań Poland 5 Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam Germany

Received hellip accepted hellip open for discussion until hellip (dates are example text only)

Citation in reference format

Abstract

The effect of using two distributed hydrological models with different degrees of spatial

aggregation on the assessment of climate change impact on river runoff was investigated Analyses

were conducted in the Narew River basin situated in NE Poland using a global hydrological model

(WaterGAP) and a catchment-scale hydrological model (SWAT) Climate change was represented

in both models by projected changes in monthly temperature and precipitation between the period

2040-2069 and the baseline period resulting from two General Circulation Models IPSL-CM4 and

MIROC32 both coupled with the SRES A2 emission scenario The degree of consistency between

global and catchment model was very high for mean annual runoff and medium for indicators of

high and low runoff It was observed that SWAT generally suggests changes of larger magnitude

than WaterGAP for both climate models but the hydrological models were consistent in showing

the direction of change in monthly runoff Results indicate that a global model can be used in

Central and Eastern European lowlands to identify hot-spots where a catchment scale model should

be applied to evaluate eg the effectiveness of management options

Reacutesumeacute

Nous avons eacutetudieacute lrsquoeffet de lrsquousage de deux modegraveles hydrologiques distribueacutes avec diffeacuterents

degreacutes drsquoagreacutegation spatiale sur lrsquoeacutevaluation de lrsquoimpact du changement climatique sur

lrsquoeacutecoulement fluvial Les analyses ont eacuteteacute effectueacutees dans le bassin de la riviegravere Narew situeacutee dans

le nord-est de la Pologne agrave lrsquoaide drsquoun modegravele hydrologique global (WaterGAP) et drsquoun modegravele

hydrologique agrave lrsquoeacutechelle du bassin versant (SWAT) Le changement climatique a eacuteteacute repreacutesenteacute

dans les deux modegraveles par les changements des tempeacuteratures et des preacutecipitations mensuelles entre

la peacuteriode 2040-2069 et la peacuteriode de reacutefeacuterence projeteacutes par deux Modegraveles de Circulation

Geacuteneacuterale IPSL-CM4 et MIROC32 coupleacutes tous les deux avec le sceacutenario drsquoeacutemission SRES A2

Le degreacute de coheacuterence entre le modegravele global et le modegravele agrave lrsquoeacutechelle du bassin versant eacutetait tregraves

eacuteleveacute pour lrsquoeacutecoulement annuel moyen et moyen pour les indicateurs de haut et bas eacutecoulements

Nous avons observeacute que SWAT suggegravere geacuteneacuteralement des changements de plus grande ampleur

que WaterGAP pour les deux modegraveles climatiques mais les modegraveles hydrologiques eacutetaient

coheacuterents pour montrer la direction du changement de lrsquoeacutecoulement mensuel Les reacutesultats

indiquent que le modegravele global peut ecirctre utiliseacute sur les plaines en Europe centrale et orientale afin

drsquoidentifier les points chauds ougrave le modegravele agrave lrsquoeacutechelle du bassin versant devrait ecirctre appliqueacute pour

eacutevaluer par exemple lrsquoefficaciteacute des options de gestion

Key words global hydrological model catchment hydrological model WaterGAP SWAT climate

change Narew

Mots cleacutes modegravele hydrologique global modegravele agrave lrsquoeacutechelle du bassin versant WaterGAP SWAT

changement climatique Narew

1 INTRODUCTION

The most recent report of the Intergovernmental Panel on Climate Change (IPCC

2007) states that ldquowarming of the climate system is unequivocal as is now evident

from observations of increases in global air and ocean temperatures widespread

melting of snow and ice and rising global sea levelrdquo All available General

Circulation Models (GCMs) agree about the further increase of global mean

temperature in future projections Several local Polish studies also suggest that the

north-east of Poland has been subject to significant temperature increase Maksymiuk

et al (2008) detected an increasing statistically significant trend in winter air

temperature and a decreasing trend in snow cover thickness in the Biebrza River

basin Marszelewski and Skowron (2006) analysed various indicators related to lake

ice cover and winter air temperature and concluded that the recently observed

modifications of the ice cover regime on lakes reflect the increase in winter

temperature Trends and projections in precipitation another key driver of the

hydrological system are less consistent than for temperature Kundzewicz et al

(2008) argued that precipitation is not adequately simulated by the present climate

models Poland is one of the countries for which climate models largely disagree

about future precipitation projections Most of the models however predict an

increase in winter precipitation (Szwed et al 2010)

The common method for assessment of climate change impact on hydrology is

by using a pre-processed output from one or several General Circulation Models

(GCMs) as climatic input to hydrological models This step of pre-processing often

referred to as ldquodownscalingrdquo is essential due to the fact that the GCMs remain coarse

in spatial resolution and are unable to resolve several sub-grid scale features (Grotch

and MacCracken 1991) such as topography clouds and land use (Fowler et al 2007)

Anagnostopoulos et al (2010) showed that GCMs on their own cannot accurately

reconstruct the past even at sub-continental to continental scales and perform poorly

at regional scales This is one of the reasons why Kundzewicz and Stakhiv (2010)

concluded that climate models are not yet ldquoready for prime timerdquo in water resources

management applications On the contrary hydrological models widely used by the

water science community have undergone decades of peer review testing and

application in a wide range of conditions all over the globe They have been applied

not only in different geographical settings but also at various spatial scales from

hillslopes (Ambroise et al 1996) through small catchments (Zehe et al 2001) large

river basins (Barthel et al 2005) to the global scale (Hanasaki et al 2010 Haddeland

et al 2011) When we focus only on runoff prediction models another differentiating

feature is the discretisation strategy strongly related to computational requirements

from fully-distributed grid-element-based models such as Systegraveme Hydrologique

Europegraveen ndash SHE (Abbott et al 1986) and its successors such as SHETRAN (Bathurst

et al 1995) and MIKE SHE (Refsgaard and Storm 1995) to semi-distributed models

built on the concept of hydrological similarity such as TOPMODEL (Beven and

Kirkby 1979) SWAT (Arnold et al 1998 Neitsch et al 2005) or SWIM (Krysanova

et al 1998)

It is often assumed that spatially explicit and process-based models are best

suited to predict the effects of changing environmental conditions (Beven and Binley

1992) however problems with a priori estimation of model parameters make them

difficult to apply and instead semi-distributed models are often argued to be a more

practical alternative (Croke et al 2004) In particular Gassman et al (2007) in their

comprehensive review of SWAT model applications reported 22 peer-reviewed

papers devoted to climate change impact assessment using SWAT and this number

has been undoubtedly growing since then (eg Kingston and Taylor 2010 Parajuli

2010 Zhang et al 2011) including related models such as SWIM (Krysanova et al

1998) These assessments are typically done for small (496 km2 Zhang et al 2011)

medium (2098 km2 Kingston and Taylor 2010) and large catchments (13000 ndash

147423 km2 Huang et al 2010) For water resources management climate change

impact assessments made at the second and third of scales mentioned above are the

most desired These scales conform to the spatial units imposed by the Water

Framework Directive of the European Union (EC 2000) water districts and

catchments of water bodies (in Poland the latter are grouped into larger units such as

integratedconsolidated water bodies and water balance units cf Pusłowska-

Tyszewska et al (2006) Piniewski and Okruszko (2011)) When a broader

perspective is needed an alternative to using catchment models in climate change

impact studies is using large-scale (global or continental) models The examples

include MacPDM (Arnell 1999 Gosling and Arnell 2011) VIC (Nijssen et al 1997)

and WaterGAP (Alcamo et al 2003 Doumlll et al 2003) Haddeland et al (2011)

recently provided a comprehensive inter-comparison study in which six land surface

models and five global hydrological models participated

The future of Europersquos waters will be influenced by a combination of many

important environmental and socio-economic drivers In the project ldquoWater Scenarios

for Europe and Neighbouring Statesrdquo (SCENES Kaumlmaumlri et al 2008) a set of

qualitative and quantitative scenarios has been developed to describe freshwater

futures up to 2050 in the pan-European perspective covering the area from

Mediterranean rim countries and reaching from Caucasus to the White Sea in the East

The Narew River basin used in this study was selected as one of the SCENES Pilot

Areas

The objective of this study is to analyse the effect of modelling scale (using

semi-distributed hydrological models with different degrees of spatial aggregation) on

the assessment of climate change impact on river runoff Two models were selected

for the comparison the global hydrological model WaterGAP (Water A Global

Analysis and Prognosis) cf Alcamo et al (2003) Doumlll et al (2003) and a catchment-

scale hydrological model SWAT (Soil amp Water Assessment Tool) cf Arnold et al

(1998) Neitsch et al (2005) Consistent climate change signals derived from two

GCMs for the time period 2040 ndash 2069 drove the hydrological models and generic

hydrological indicators were evaluated such as mean annual runoff high and low

monthly runoff as well as indicators describing the seasonal cycle An implicit

assumption was that due to more spatially explicit catchment representation SWAT

can be used as a reference to evaluate WaterGAP as a tool to quantify hydrological

indicators related to climate change at a large catchment level In this respect it is

worth noting that the aim of this study was not to analyse which model performs

better in the Narew basin as such competition would be highly unfair for WaterGAP

because of different model input data different set-ups and different calibration

strategies In this study WaterGAP was not set up intentionally for the Narew basin

but was applied with its parameters set at the continental scale and calibrated using

river flow data from the Global Runoff Data Centre stations across Europe This

included a station on the Narew River Ostrołęka (GRDC ID 6458810) In contrast

SWAT set-up was tailored for the studied basin and its calibration involved numerous

gauging stations and discharge data with finer temporal resolution (Piniewski and

Okruszko 2011) Hence WaterGAP and SWAT were applied and evaluated in this

study as a global and a catchment model respectively consistently with their nature

Comparison studies of this kind (ie between global scale and catchment scale

models) are rare in literature For example the focus in Gosling et al (2011) was

mainly on comparing climate model uncertainty with hydrological model uncertainty

and due to the magnitude of their study (6 catchment models 6 study areas 7 GCMs)

rather little was reported on the explanation of different responses of global and

catchment models In this study the models were applied in a single study area the

Narew basin in the north-east of Poland which allowed a focus more on explaining

the discovered differences rather than comparing different types of uncertainty

2 MATERIALS AND METHODS

21 Study area

The River Narew situated in north-east of Poland (Fig1) is the right tributary of the

River Vistula and its total drainage area upstream from its mouth equals ca 75000

km2 However in this study the attention is focused only on the part of the basin

situated upstream of the Zambski Kościelne (hereafter referred to as ldquoZambskirdquo)

gauging station This part of the basin occupies ca 28000 km2 and is beyond the

reach of backwater effects from Lake Zegrzyńskie Approximately 5 of this area in

its upstream part lies in western Belarus ie outside of the territory of the Republic

of Poland

The Narew is a lowland river and its basin can be characterized by mean

altitude of 136 masl and flat topography This region is located in the temperate

climatic zone with moderately warm summers (mean July temperature equal to 17degC)

and cool winters (mean January temperature equal to -3degC) with annual mean

precipitation of ca 600 mm occurring mostly in summer months Snowmelt occurs

usually in early spring causing peak runoff in the rivers Soils are predominantly

loamy sands and sandy loams with significant contribution of organic soils in river

valleys Agriculture is the dominant land use in this area 46 of land is used as

arable land and 17 as meadows and pastures whereas 33 is occupied by forests

The remaining 4 of land is covered by wetlands lakes and urban areas

The Narew basin is a good study area for purely hydrological research since it

is not largely impacted by anthropogenic pressure The population density is

estimated as 59 people per km2 which is a low number compared to the average

density of 119 people per km2 for the whole of Poland Only 533 of population live

in cities and towns whereas the percentage of urban population in the whole of

Poland is considerably higher (613) There is only one city with population above

100000 inhabitants (the city of Białystok) whose surface and sub-surface water

abstractions as well as the treatment plant discharges cause the hydrograph alteration

of the Supraśl River however their impact on the Narew is negligible No heavy

industry is present in the study area whereas agriculture food and wood production

and tourism are the main sources of income for the inhabitants For further description

of the Narew basin see Okruszko and Giełczewski (2004)

22 Hydrological models

The catchment-scale model used in this study was the SWAT model developed at the

Grassland Soil and Water Research Laboratory in Temple Texas USA It is a semi-

distributed catchment model developed mainly for meso- and large-scale applications

which can be applied to catchments of any size (from very small to large see eg

Gassman et al (2007)) provided that it is fed with necessary catchment-specific input

data In contrast the global-scale model used in this study was the WaterGAP model

developed at the Center for Environmental Systems Research University of Kassel

Germany It is a global hydrological model of water availability and water use that

comprises two main components Global Hydrology Model and Global Water Use

Model In this study the latter component was not applied since as mentioned

previously water use is not a significant issue in the Narew basin

Comparison of SWAT and WaterGAP in terms of their modelling approaches

and input data used for the Narew case study show both differences and similarities

between them (Table 1) The former model is a physically-based tool although it uses

many conceptual modelling approaches such as the US SCS curve number method

Instead of using grid cells the SWAT model subdivides a river basin into sub-

catchments connected by river network and further delineates hydrological response

units (HRUs) obtained through overlay of land use soil and slope maps in each sub-

catchment It is worth noting that the HRUs are lumped (ie non-spatially distributed)

units Current configuration of SWAT in the Narew basin uses 151 sub-catchments

and 1131 HRUs

Model issues compared in Table 1 are very general and do not cover many

substantial differences in parameterisations of hydrological processes First of all the

same processes can be modelled using different methods (eg potential

evapotranpiration ndash PET) and thus require different parameters secondly even if a

given process (eg snowmelt) is modelled using the same method the values of

associated parameters might be different

The current version of WaterGAP works with resolution of 5 arc minutes

which is one of the finest resolutions of state-of-the-art global models Mean HRU

area in SWAT of ca 24 km2 represents a finer resolution than that used in WaterGAP

(ca 51 km2) The relation between SWAT sub-basins and reaches and WaterGAP grid

mesh is illustrated in Fig 2

It is to be noted that SWAT as a catchment model was set up calibrated and

validated intentionally for the Narew basin whereas WaterGAP was used in its global

set-up In particular its parameters were not fine-tuned to better represent the study

area Four of the WaterGAP global calibration points were situated in the Vistula

basin Three of them were outside the Narew basin (the River San at Radomyśl the

River Vistula at Szczucin and Warszawa) and one was inside at the Lower Narew

(Ostrołęka cf Fig 1) Discharge values for calibration were obtained from the Global

Runoff Data Centre In this study we used the WaterGAP 30 model version which is

an upgrade from the version 21 as applied by Alcamo et al (2003) and Doumlll et al

(2003) One of its main improved features was the enhanced spatial resolution which

was adapted from 05deg to 5rsquo grid cell size For SWAT Piniewski and Okruszko

(2011) performed a spatially distributed calibration and validation in the Narew basin

for the time period 2001-2008 using SWAT2005 with the GIS interface ArcSWAT

23 which set the basis for future modelling activities using this tool In this study we

used the same version of the model and the model set-up which was recalibrated and

revalidated for the time period 1976-2000 As reported in Piniewski and Okruszko

(2011) eight SWAT parameters with the highest sensitivities were selected for auto-

calibration performed using ParaSol method (van Griensven and Meixner 2007)

Three most sensitive parameters were ESCO (soil evaporation compensation factor)

CN2 (curve number for moisture conditions II) and ALPHA_BF (baseflow alpha

factor) The main calibration criterion was Nash-Sutcliffe efficiency for daily flows

above 05 however other aspects such as maintaining the model bias below 25 and

visual inspection of low and high flow modelling were also taken into account The

calibration criteria were met in all 11 calibration gauges However spatial validation

performed at 12 additional upstream gauges demonstrated that the model performance

is significantly lower at smaller spatial scales

23 Climatic input data

The climate data used to drive the hydrological models can be divided into (1) the

observed data from the time period 1976-2000 representing the present-day climate

hereafter referred to as the baseline (2) the projected climate change data downscaled

from two General Circulation Models (GCMs) for the time period 2040-2069

representing the future climate hereafter referred to as the 2050s Both models

SWAT and WaterGAP used different data sources for the baseline period and

consistent climate change forcing for the 2050s

231 Baseline

In WaterGAP monthly values of the climate variables from the 10-min resolution

CRU TS 12 dataset (Mitchell el al 2004) were used The time series of the following

variables were used precipitation air temperature cloudiness and wet day frequency

Since WaterGAP simulates river discharges with a daily time step the climate input

data needed to be downscaled from monthly to daily values Downscaling procedures

are implicitly implemented in WaterGAP and were run during the simulations With

this temperature and cloudiness were downscaled with a cubic-spline-function

between the monthly averages which were assigned to the middle of each month

Precipitation was distributed equally over the number of wet days per month which

were distributed within the month using a two-state first-order Markov Chain

applying the parameterisation according to Geng et al (1986)

In contrast daily station data from the Polish Institute of Meteorology and

Water Management network were used as the climate input for precipitation and

temperature in SWAT Precipitation data came from 12 stations whereas temperature

data were taken from 7 stations Missing values were filled in either by manual

interpolation or with values taken from the public domain MARS-STAT database

(van der Goot and Orlandi 2003) This data source which provides daily time series

in 25 km grid for the whole of Europe was also used to provide daily data for further

climate variables required in SWAT wind speed relative humidity and solar

radiation Since SWAT does not perform any interpolation of climate data

precipitation and temperature were interpolated to the sub-basin level outside

ArcSWAT using the Thiessen polygon method

It is evident that the daily time scale of the climate data used in SWAT is more

adequate than the monthly time series of the original CRU dataset used in WaterGAP

which was internally downscaled to the daily time scale leading to a loss in daily

weather dynamics However it is difficult to say which of the models used the more

appropriate spatial resolution of climate data Even though 10-min resolution of the

CRU 12 dataset is theoretically much higher than resolution of the climate input used

in SWAT one has to bear in mind that CRU data are based on interpolation from

station data and hence the quality of SWAT climate input data should not be worse

than the quality of the CRU data set This assumption was verified by comparing

annual basin-averaged mean temperature and precipitation series (Fig 3) as well as

mean monthly values of temperature and precipitation (Fig 4) It is to be noted that

SWAT uses daily maximum and minimum temperature as the climatic input so in

order to enable direct comparison of this variable with that from WaterGAP we

estimated daily mean temperature as the arithmetic mean of daily maximum and

minimum temperature

Mean annual temperature time series used within SWAT and WaterGAP are

very well correlated with R2 equal to 094 (Fig 3(a)) Long-term mean temperature

used within WaterGAP is ca 03degC higher than that used within SWAT These higher

temperature values can be observed especially in spring and summer (Fig 4(a))

Nevertheless the differences between SWAT and WaterGAP temperature inputs are

rather small and they can be partly explained by the indirect method of comparison as

well as the different data sources

Annual precipitation series are also very well correlated (R2 equal to 082) and

there is hardly any long-term bias between the models (Fig 3(b)) The highest

difference between WaterGAP and SWAT (71 mm) was observed in 1995 The

monthly differences are also rather small (Fig 4(b)) which suggests that mean areal

precipitation derived from the CRU dataset is comparable to precipitation derived

from station data

232 Projections for 2050s

Consistent climate change signal of two types was applied to both hydrological

models The signal was derived from the output of two different GCMs IPSL-CM4

from the Institute Pierre Simon Laplace France (Marti et al 2006) and MIROC 32

from the Center for Climate System Research University of Tokyo Japan (Hasumi

and Emori 2004) both forced by the SRES-A2 emission scenario (IPCC 2007) The

development of socio-economic scenarios within the SCENES project was a

stakeholder driven process (Kok et al 2011) Climate scenario development was

however not part of the project and thus available GCM ndash emission scenario

combinations were selected Here the stakeholders played a key role in finally

concentrating on the IPCC SRES-A2 scenario emphasising the trigger role of climate

change in all SCENES storylines The analysis performed at pan-European scale in

the SCENES project revealed that across the range of GCMs driven by the A2

scenario climate projection by IPSL-CM4 is dry and by MIROC 32 is wet whereas

both project an increase in temperature

Monthly precipitation and temperature derived from GCMs needed to be

downscaled to a finer spatial resolution due to the fact that their original resolution

was too coarse compared to that of the catchment processes simulated by hydrological

models To this end first a simple bilinear interpolation approach was applied to

downscale GCM data to the resolution of WaterGAP grid cell

It is well known that present climate models contain considerable biases in

their climatology and do not fit gridded station data well (Kundzewicz and Stakhiv

2010) To reduce the GCM biases various bdquobias correctionrdquo methods were developed

In this study we applied the delta-change approach Based on the assumption that

GCMs more accurately simulate relative change than absolute values we assumed a

constant bias through time (Fowler et al 2007) In this method the delta change

factors (DCFs) are calculated at the monthly time scale using the future (here 2040-

2069) and present (1976-2000) GCM output For temperature (additive variable)

change factors are defined as arithmetic difference between the future and present

long-term means whereas for precipitation (multiplicative variable) as future to

present long-term mean ratios

Due to obvious differences between the hydrological models the final

versions of climate input representing 2050s (the middle decade from the climatic

standard normal 2040-2069) were derived in both models in a slightly different way

In WaterGAP gridded DCFs were first added to (in the case of temperature) or

multiplied by (in the case of precipitation) the monthly time series for respective grid

cells Next the number of wet days per month and the cloudiness were taken from the

baseline period in order to downscale monthly climate to daily climate as described

in the section above In SWAT there is an option of running climate change scenarios

by defining monthly change factors at sub-basin level (parameters RFINC and

TMPINC in sub files) and in such case the model automatically creates new daily

time series associated to scenarios by scaling the observed climate data for the

baseline In order to use this option the DCFs calculated beforehand at WaterGAP

grid scale were averaged over SWAT sub-catchments On average there were over 3

grid cells for a single sub-catchment (cf Fig 2 for the map of the modelling units)

Both climate models predict similar increase in mean annual temperature

however the seasonal variability of this increase is different (Fig 5(a)) For instance

in April and November the increase in temperature projected by IPSL-CM4 is over

1degC greater than the one projected by MIROC32 As regards precipitation there is

hardly any agreement between the two GCMs (Fig 5(b)) According to IPSL-CM4

relative changes in precipitation do not exceed +-25 for any month and mean

annual precipitation is almost the same as in the baseline According to MIROC32

there is an 11 increase in annual precipitation and quite a large variability of within-

year changes There is a largely different hence problematic behaviour of model

projections in two adjacent months July (15 decrease) and August (44 increase)

Two periods can be found where MIROC32 projects a substantial increase and IPSL-

CM4 a little change or even a decrease in precipitation (1) from March to April (2)

from August to October

24 Hydrological indicators

Standard goodness-of-fit measures were used to assess the model behaviour in the

baseline period The Nash-Sutcliffe efficiency (NSE) measures the relative magnitude

of the residual variance compared to the observed data variance (Nash and Sutcliffe

1970) whilst coefficient of determination (R2) describes the degree of co-linearity of

measured and modelled time series (Moriasi et al 2007) Percent bias is one of the

widely used error indices which measures the average tendency of the modelled data

to be larger or smaller than the observed data (Gupta et al 1999)

The response of hydrological models to the climate change forcing was

assessed by relating the modelled runoff from scenario simulations with the runoff

from the respective baseline simulations The impact assessment was done on three

levels

(1) Impact on the mean annual runoff Here one indicator was used the absolute

change in mean annual runoff relative to baseline

(2) Impact on the monthly extreme (highlow) runoff Here in the first step the

empirical flow duration curves (EFDCs) were used to make a visual inspection of

the extreme parts of the frequency distribution of monthly runoff (Smakhtin

2001) In the second step two particular indicators (single points from the

EFDCs) were reported the absolute changes in monthly Q10 and Q90 (defined as

the monthly runoff exceeded for 10 and 90 of the time respectively) relative

to the baseline period

(3) Impact on the seasonal cycle of runoff Here in the first step monthly runoff

hydrographs simulated by SWAT and WaterGAP for the baseline and under two

climate scenarios were analysed in order to interpret the main hydrograph

alterations In the second step the absolute changes in mean monthly runoff

relative to baseline were analysed in order to detect the seasonal pattern in the

differences between the future scenarios and baseline conditions and to measure

mean sensitivity of both models to the climate change signals

All above mentioned indicators (apart from the EFDC which was reported for

Zambski only) were evaluated at three sites within the catchment at the basin outlet

(Zambski) at the mouth of the Biebrza (Burzyn) and in the upper Narew at Suraż

(Fig 2)

3 RESULTS

Despite the fact that the main objective of our study is not to evaluate model

performance during the baseline period it is an essential step before analysing the

climate change impact on hydrological indicators The analysis of model behaviour in

the baseline period can bring an insight into the process of explaining differences

between the model behaviours in the future

31 Baseline

WaterGAP tends to underestimate mean monthly runoff in the baseline period at the

main catchment outlet (Zambski gauge) and two internal outlets (cf Fig 1) by 12 to

24 whilst SWAT does neither underestimate nor overestimate mean monthly runoff

by more than 8 (Table 2) As expected the SWAT-based estimates of Q10 and Q90

are closer to the measured ones than the WaterGAP-based estimates apart from Q90

at Burzyn Performance of SWAT at Zambski is apparently better than the

performance at Burzyn and Suraż which is very likely linked to the size of the

upstream catchment area (Piniewski and Okruszko 2011) In the case of WaterGAP

this spatial relationship does not exist the best performance is observed at Burzyn and

not in the main catchment outlet at Zambski

The SWAT model captures monthly variability better than the WaterGAP in

all three locations (Fig 6) Peak runoff in WaterGAP occurs as often in March as in

April whereas according to the measured data the peaks occur much more frequently

in April in the Narew basin Both models underestimate peak runoff (with one

exception of SWAT at Suraż) by 28-32 mm in the case of SWAT and 20-71 mm in

the case of WaterGAP As regards the low flow period in the Narew basin it lasts

from July to September In SWAT this period is shifted one month ahead whereas in

WaterGAP it lasts from September to February which is supposedly the largest

deficiency of the hydrograph simulation by WaterGAP The largest issue of the

SWAT-modelled hydrograph is in our opinion that the falling limb is decreasing too

gently It causes overestimation of runoff from May to July as most clearly seen at

Suraż (Fig 6(c))

Correlation of the annual time series of various water balance components

simulated by both models (only for runoff measured values could be included) is

illustrated in Fig 7 SWAT- and WaterGAP-based estimates of annual runoff are

correlated with measured ones with different strength (R2 is equal to 078 and 051

respectively) and the correlation between them is good (R2 is equal to 075) Other

water balance components are either moderately (PET1 R2 is equal to 052) or weakly

correlated (for actual evapotranspiration AET and soil water content R2 is equal to

022 and 037 respectively) It can be observed that there exists a bias in PET time

series especially in the first seven years of the simulation period when SWAT-based

PET estimates are ca 100 mm higher than WaterGAP-based estimates WaterGAP

simulates considerably higher AET than SWAT (with average difference being 44

mm) which partly explains its underestimation of runoff compared to SWAT by 22

mm in average Year-to-year soil water storage changes are presented in Fig 7(c)

instead of actual soil water content since the latter variable is difficult to compare

directly between the models The magnitude of soil water storage changes is

comparable between both models and does not exceed 20 mm in terms of the absolute

values

The analysis of the monthly dynamics of previously mentioned water balance

components can help explain the observed differences in runoff simulation (Fig 8)

Estimates of PET by WaterGAP are higher than by SWAT in the hottest months of

the year and lower during the rest of the year WaterGAP simulates significantly (51

mm) higher AET than SWAT in May and June which is reflected in the drop of soil

water content in these months by 72 mm in WaterGAP and only by 17 mm in SWAT

The decrease in soil saturation estimated by WaterGAP lasts until September which

is a potential reason for underestimation of runoff by WaterGAP that can be observed

in autumn and continues until February

32 Hydrological model responses to climate change forcing

321 Mean annual runoff

There is a large difference between the results driven by IPSL-CM4 and MIROC32

and a negligible difference between the results obtained for SWAT and WaterGAP

driven by the same climate model in all selected locations regarding the change in

mean annual runoff because of the GCMs when compared to the simulations in

baseline (Fig 9) The largest difference between SWAT- and WaterGAP-based

estimates of change in runoff is for IPSL-CM4 at Suraż where the runoff decrease

according to SWAT would be 412 mm and according to WaterGAP 278 mm

However the sign of projected change is the same in each case It is worthy of noting

that for all sites the differences between the results of a hydrological model driven

by two climate models are higher than the differences between the results of two

hydrological models driven by one climate model Hence the climate scenarios

largely contribute to the uncertainty of findings

322 High and low monthly runoff

The EFDC (Fig 10) indicates a decrease in both high and low runoff under IPSL-

CM4 for both SWAT and WaterGAP at any exceedance level The magnitude of this

decrease is variable however at the exceedance levels of 5-10 the consistency

between SWAT and WaterGAP is higher than at the exceedance levels below 5 (for

the low runoff part there is no clear relation in this regard) In the case of MIROC32

SWAT suggests an increase in high runoff at any exceedance level whereas

WaterGAP suggests a negligible change in runoff at the exceedance levels in the

1 As shown in Table 1 the models use different PET methods SWAT uses Penman-Monteith and

WaterGAP uses Priestley-Taylor

range 7-10 and a decrease below 7 Low runoff part of the EFDC shows that

under MIROC32 the WaterGAP model suggests an increase in runoff at any

exceedance level whereas SWAT suggests a small increase at the exceedance levels

between 90 and 91 and a negligible change above 91 Overall the analysis of the

EFDCs shows that the consistency between SWAT and WaterGAP is higher for

runoff corresponding to less extreme exceedance levels Hence hereafter we will

focus on Q10 as the high runoff indicator and Q90 as the low runoff indicator

The diversity in the change of Q10 and Q90 due to the selected GCMs with

regard to the baseline is larger than for the annual runoff (Fig 11 note that this figure

shows monthly and not annual runoff contrary to Fig 9) For Q10 at Zambski and

Burzyn IPSL-CM4 forcing causes higher decrease in the WaterGAP model than in

the SWAT model whilst at Suraż the decrease rate is higher in SWAT The

MIROC32 forcing causes an increase in SWAT and a negligible change in

WaterGAP In the case of Q90 for IPSL-CM4 forcing SWAT suggests a larger

decrease than WaterGAP whereas for MIROC32 the results are not spatially

consistent at Zambski both models suggest an increase in runoff whereas at Burzyn

and Suraż WaterGAP continues to show an increase whilst SWAT shows a decrease

It is worth noting that most of projected changes in runoff are considerable when

related to the measured Q90 (63 56 and 42 mm for Zambski Burzyn and Suraż

respectively)

The differences in low and high runoff are greater between climate scenarios

than between hydrological models (Figs 10 and 11) as in the mean annual runoff

case

323 The seasonal cycle

The projected seasonal cycle of runoff simulated by the hydrological models

illustrated in Fig 12 (baseline runoff is plotted for comparison) gives a general

impression about the hydrograph alteration caused by the climate change forcing

There is a consistency between the hydrological models under both climate scenarios

that peak monthly runoff will shift from April to March in all cases except for one ndash

SWAT-MIROC32-Burzyn combination In the latter case January is the month with

peak runoff however the difference between January and March is only 03 mm It is

equally worth noting that under IPSL-CM4 climate scenario not only shift in timing

can be observed but also a substantial decrease in peak runoff at all analysed sites and

for both models Under the MIROC32 climate scenario SWAT shows a moderate

decrease in peak runoff and WaterGAP shows a negligible change

The IPSL-CM4 climate model forcing is likely to significantly alter the

hydrographs in their low runoff part as well (Fig 12) Under this scenario according

to simulations with the help of SWAT model in the period between June and

November runoff will be lower than the minimum SWAT-modelled baseline monthly

runoff at all sites (at Suraż between July and November) According to simulations

with the help of WaterGAP runoff will be lower than the minimum WaterGAP-

modelled baseline monthly runoff for the period between August (or September in the

case of Suraż) and November It has to be remembered however that simulation of

the low runoff period in the baseline was less accurate in WaterGAP than in SWAT

(cf Fig 6)

Figure 13 gives a deeper insight into the seasonal aspects of runoff as it

presents the absolute deviations from baseline for each hydrological model each

climate model (GCM) and each site Two observations are noteworthy

(1) With a few exceptions the models are generally consistent in showing the

direction of change in mean monthly runoff Lack of consistency in the sign of

change occurred in only 4 out of 72 cases (neglecting very small changes up to

02 mm)

(2) The differences between changes simulated by SWAT and WaterGAP for a given

GCM are generally smaller than the differences between changes simulated by a

given model forced by IPSL-CM4 or MIROC32 The largest observed difference

between the departures from baseline simulated by SWAT and WaterGAP under a

given climate scenario equals 57 mm For the absolute changes in 4 out of 6

cases the largest differences occur in March

Analysis of the results from Fig 13 in relation to the climate forcing data

illustrated in Fig 5 results in the following points

(1) A uniform reaction of both models and both climate scenarios can be observed in

April at all sites This particular consistency between the models can be explained

by the fact that regardless different projections of precipitation change a high

temperature increase projected in winter by both models accelerates the

occurrence of peaks Hence in April which used to be the peak runoff month in

the baseline the hydrograph is already decreasing

(2) MIROC32 suggests an increase in temperature between May and June by 3-35

˚C and a relatively small change in precipitation This drives SWAT presumably

due to increased evapotranspiration to decrease the total runoff at Zambski in this

period by 57 mm compared to the baseline whilst the change in runoff in

WaterGAP is negligible Figure 8 suggests that this might be due to significant

overestimation of AET by WaterGAP in the baseline in May and June

(3) For the period from August to November a total increase in precipitation

according to MIROC32 is equal to 53 mm and increase in temperature stays in

the range 25-35 ˚C This drives SWAT to increase the total runoff in this period

by 84 mm compared to the baseline whilst the increase in WaterGAP equals 3

mm only

The above observations indicate that SWAT is more sensitive to various

seasonal climate change signals than WaterGAP Results reported in Table 3 confirm

this hypothesis It is interesting to note that (i) this measure of sensitivity is higher for

the MIROC32 model than for the IPSL-CM4 model and (ii) in the case of SWAT it

is much higher for the sub-catchments than for the whole basin while this is not the

case for WaterGAP This is the reason why the hydrological model inconsistency in

assessing the effect of climate change on monthly runoff is larger at Burzyn and Suraż

than at Zambski Indeed the number of months for which the differences between the

absolute changes simulated by SWAT and WaterGAP for any GCM do not exceed 1

mm (in terms of the absolute values) are equal to 9 2 and 3 for Zambski Burzyn and

Suraż respectively The number of months for which the same characteristics exceed

2 mm are equal to 5 15 and 11 respectively

4 DISCUSSION

The results of our analysis of the global and catchment-scale model responses to the

same climate change signal indicate that

(1) SWAT and WaterGAP were very consistent in showing the direction and

quantifying the magnitude of future change in mean annual runoff due to climate

change

(2) The consistency in identifying the high (Q10) and low (Q90) monthly runoff

change was not as good as for the mean annual runoff It was quite often observed

that when one model was showing a negligible change in these indicators the

other one was showing at least medium change As shown in Fig 10 for more

extreme indicators (eg Q5 and Q95) the difference between SWAT- and

WaterGAP-based estimates was even larger

(3) Some patterns of change in the seasonal cycle of runoff were comparable in both

models (eg earlier occurrence of peak runoff large decrease in April runoff)

while others were not (eg different responses to the August-November

precipitation increase from MIROC32) The magnitudes of projected seasonal

changes varied significantly the SWAT model showing overall more sensitivity

to climate change than the WaterGAP model

Our interpretation of these results is that the modelling scale does not have

much influence on the assessment of simple indicators and general descriptive

patterns whilst when it comes to more detailed indicators and in particular their

magnitudes the impact of the modelling scale is visible This partly corresponds to

the observation pointed out by several authors (Gosling et al 2011 Hughes et al

2011 Noacutebrega et al 2011) that the mean annual runoff can mask considerably greater

seasonal variations which are of high importance to water management

As regards the potential reasons for the differences between simulations by

SWAT and WaterGAP in climate change impact assessment it is not straightforward

to discriminate between the different model behaviour in the baseline and the different

model reaction to the climate change forcing Since the catchment-specific calibration

was not performed for the global model it was not surprising to observe generally

better behaviour of the catchment model in the baseline At present and very likely in

the near future the global models such as WaterGAP are not specifically calibrated

for catchments of the size of the Narew Hence an important question emerges which

process descriptions parameterisations in WaterGAP should be rethought in order to

reduce the uncertainty in climate change impact assessments The same question

should apply to SWAT however in this study we tacitly assume since SWAT

performed better in the baseline that its results are more reliable and can be used as

benchmark for WaterGAP

The comparison of the annual time series (Fig 7) and the seasonal dynamics

(Fig 8) of various water balance components revealed a large difference between

SWAT- and WaterGAP-based estimates of actual evapotranspiration (AET) and soil

water content We suppose that WaterGAP actually overestimates AET in May and

June This is consistent with a large decrease in soil water content in these months

compared to SWAT We expect that this results in too little soil moisture content in

summer months and in consequence as total runoff simulated in WaterGAP is a

nonlinear function of soil moisture (Bergstroumlm 1995 Doumlll 2003) in underestimation

of runoff starting from September and lasting until the soils are completely rewetted

(ie until February)

The above considerations suggest that either the main parameters controlling

vertical soil water balance in WaterGAP should be reconsidered or the process

description itself should be rethought Since the methods used for estimation of soil

water balance components in WaterGAP are well established and used in many other

models such as HBV (Bergstroumlm 1995) one should rather focus on the parameters In

particular three parameters may turn to be critical namely soil depth set to 1 m in

WaterGAP which may be too low total available water capacity within the effective

root zone (Ssmax) and runoff coefficient (γ) which is a WaterGAP calibration

parameter (Doumlll 2003) This statement is not restricted only to the Narew basin but

should apply also to other lowland river basins lying in the same climatic zone

range 7-10 and a decrease below 7 Low runoff part of the EFDC shows that

under MIROC32 the WaterGAP model suggests an increase in runoff at any

exceedance level whereas SWAT suggests a small increase at the exceedance levels

between 90 and 91 and a negligible change above 91 Overall the analysis of the

EFDCs shows that the consistency between SWAT and WaterGAP is higher for

runoff corresponding to less extreme exceedance levels Hence hereafter we will

focus on Q10 as the high runoff indicator and Q90 as the low runoff indicator

The diversity in the change of Q10 and Q90 due to the selected GCMs with

regard to the baseline is larger than for the annual runoff (Fig 11 note that this figure

shows monthly and not annual runoff contrary to Fig 9) For Q10 at Zambski and

Burzyn IPSL-CM4 forcing causes higher decrease in the WaterGAP model than in

the SWAT model whilst at Suraż the decrease rate is higher in SWAT The

MIROC32 forcing causes an increase in SWAT and a negligible change in

WaterGAP In the case of Q90 for IPSL-CM4 forcing SWAT suggests a larger

decrease than WaterGAP whereas for MIROC32 the results are not spatially

consistent at Zambski both models suggest an increase in runoff whereas at Burzyn

and Suraż WaterGAP continues to show an increase whilst SWAT shows a decrease

It is worth noting that most of projected changes in runoff are considerable when

related to the measured Q90 (63 56 and 42 mm for Zambski Burzyn and Suraż

respectively)

The differences in low and high runoff are greater between climate scenarios

than between hydrological models (Figs 10 and 11) as in the mean annual runoff

case

323 The seasonal cycle

The projected seasonal cycle of runoff simulated by the hydrological models

illustrated in Fig 12 (baseline runoff is plotted for comparison) gives a general

impression about the hydrograph alteration caused by the climate change forcing

There is a consistency between the hydrological models under both climate scenarios

that peak monthly runoff will shift from April to March in all cases except for one ndash

SWAT-MIROC32-Burzyn combination In the latter case January is the month with

peak runoff however the difference between January and March is only 03 mm It is

equally worth noting that under IPSL-CM4 climate scenario not only shift in timing

can be observed but also a substantial decrease in peak runoff at all analysed sites and

for both models Under the MIROC32 climate scenario SWAT shows a moderate

decrease in peak runoff and WaterGAP shows a negligible change

The IPSL-CM4 climate model forcing is likely to significantly alter the

hydrographs in their low runoff part as well (Fig 12) Under this scenario according

to simulations with the help of SWAT model in the period between June and

November runoff will be lower than the minimum SWAT-modelled baseline monthly

runoff at all sites (at Suraż between July and November) According to simulations

with the help of WaterGAP runoff will be lower than the minimum WaterGAP-

modelled baseline monthly runoff for the period between August (or September in the

case of Suraż) and November It has to be remembered however that simulation of

the low runoff period in the baseline was less accurate in WaterGAP than in SWAT

(cf Fig 6)

Figure 13 gives a deeper insight into the seasonal aspects of runoff as it

presents the absolute deviations from baseline for each hydrological model each

climate model (GCM) and each site Two observations are noteworthy

(1) With a few exceptions the models are generally consistent in showing the

direction of change in mean monthly runoff Lack of consistency in the sign of

change occurred in only 4 out of 72 cases (neglecting very small changes up to

02 mm)

(2) The differences between changes simulated by SWAT and WaterGAP for a given

GCM are generally smaller than the differences between changes simulated by a

given model forced by IPSL-CM4 or MIROC32 The largest observed difference

between the departures from baseline simulated by SWAT and WaterGAP under a

given climate scenario equals 57 mm For the absolute changes in 4 out of 6

cases the largest differences occur in March

Analysis of the results from Fig 13 in relation to the climate forcing data

illustrated in Fig 5 results in the following points

(1) A uniform reaction of both models and both climate scenarios can be observed in

April at all sites This particular consistency between the models can be explained

by the fact that regardless different projections of precipitation change a high

temperature increase projected in winter by both models accelerates the

occurrence of peaks Hence in April which used to be the peak runoff month in

the baseline the hydrograph is already decreasing

(2) MIROC32 suggests an increase in temperature between May and June by 3-35

˚C and a relatively small change in precipitation This drives SWAT presumably

due to increased evapotranspiration to decrease the total runoff at Zambski in this

period by 57 mm compared to the baseline whilst the change in runoff in

WaterGAP is negligible Figure 8 suggests that this might be due to significant

overestimation of AET by WaterGAP in the baseline in May and June

(3) For the period from August to November a total increase in precipitation

according to MIROC32 is equal to 53 mm and increase in temperature stays in

the range 25-35 ˚C This drives SWAT to increase the total runoff in this period

by 84 mm compared to the baseline whilst the increase in WaterGAP equals 3

mm only

The above observations indicate that SWAT is more sensitive to various

seasonal climate change signals than WaterGAP Results reported in Table 3 confirm

this hypothesis It is interesting to note that (i) this measure of sensitivity is higher for

the MIROC32 model than for the IPSL-CM4 model and (ii) in the case of SWAT it

is much higher for the sub-catchments than for the whole basin while this is not the

case for WaterGAP This is the reason why the hydrological model inconsistency in

assessing the effect of climate change on monthly runoff is larger at Burzyn and Suraż

than at Zambski Indeed the number of months for which the differences between the

absolute changes simulated by SWAT and WaterGAP for any GCM do not exceed 1

mm (in terms of the absolute values) are equal to 9 2 and 3 for Zambski Burzyn and

Suraż respectively The number of months for which the same characteristics exceed

2 mm are equal to 5 15 and 11 respectively

4 DISCUSSION

The results of our analysis of the global and catchment-scale model responses to the

same climate change signal indicate that

(1) SWAT and WaterGAP were very consistent in showing the direction and

quantifying the magnitude of future change in mean annual runoff due to climate

change

(2) The consistency in identifying the high (Q10) and low (Q90) monthly runoff

change was not as good as for the mean annual runoff It was quite often observed

that when one model was showing a negligible change in these indicators the

other one was showing at least medium change As shown in Fig 10 for more

extreme indicators (eg Q5 and Q95) the difference between SWAT- and

WaterGAP-based estimates was even larger

(3) Some patterns of change in the seasonal cycle of runoff were comparable in both

models (eg earlier occurrence of peak runoff large decrease in April runoff)

while others were not (eg different responses to the August-November

precipitation increase from MIROC32) The magnitudes of projected seasonal

changes varied significantly the SWAT model showing overall more sensitivity

to climate change than the WaterGAP model

Our interpretation of these results is that the modelling scale does not have

much influence on the assessment of simple indicators and general descriptive

patterns whilst when it comes to more detailed indicators and in particular their

magnitudes the impact of the modelling scale is visible This partly corresponds to

the observation pointed out by several authors (Gosling et al 2011 Hughes et al

2011 Noacutebrega et al 2011) that the mean annual runoff can mask considerably greater

seasonal variations which are of high importance to water management

As regards the potential reasons for the differences between simulations by

SWAT and WaterGAP in climate change impact assessment it is not straightforward

to discriminate between the different model behaviour in the baseline and the different

model reaction to the climate change forcing Since the catchment-specific calibration

was not performed for the global model it was not surprising to observe generally

better behaviour of the catchment model in the baseline At present and very likely in

the near future the global models such as WaterGAP are not specifically calibrated

for catchments of the size of the Narew Hence an important question emerges which

process descriptions parameterisations in WaterGAP should be rethought in order to

reduce the uncertainty in climate change impact assessments The same question

should apply to SWAT however in this study we tacitly assume since SWAT

performed better in the baseline that its results are more reliable and can be used as

benchmark for WaterGAP

The comparison of the annual time series (Fig 7) and the seasonal dynamics

(Fig 8) of various water balance components revealed a large difference between

SWAT- and WaterGAP-based estimates of actual evapotranspiration (AET) and soil

water content We suppose that WaterGAP actually overestimates AET in May and

June This is consistent with a large decrease in soil water content in these months

compared to SWAT We expect that this results in too little soil moisture content in

summer months and in consequence as total runoff simulated in WaterGAP is a

nonlinear function of soil moisture (Bergstroumlm 1995 Doumlll 2003) in underestimation

of runoff starting from September and lasting until the soils are completely rewetted

(ie until February)

The above considerations suggest that either the main parameters controlling

vertical soil water balance in WaterGAP should be reconsidered or the process

description itself should be rethought Since the methods used for estimation of soil

water balance components in WaterGAP are well established and used in many other

models such as HBV (Bergstroumlm 1995) one should rather focus on the parameters In

particular three parameters may turn to be critical namely soil depth set to 1 m in

WaterGAP which may be too low total available water capacity within the effective

root zone (Ssmax) and runoff coefficient (γ) which is a WaterGAP calibration

parameter (Doumlll 2003) This statement is not restricted only to the Narew basin but

should apply also to other lowland river basins lying in the same climatic zone

change occurred in only 4 out of 72 cases (neglecting very small changes up to

02 mm)

(2) The differences between changes simulated by SWAT and WaterGAP for a given

GCM are generally smaller than the differences between changes simulated by a

given model forced by IPSL-CM4 or MIROC32 The largest observed difference

between the departures from baseline simulated by SWAT and WaterGAP under a

given climate scenario equals 57 mm For the absolute changes in 4 out of 6

cases the largest differences occur in March

Analysis of the results from Fig 13 in relation to the climate forcing data

illustrated in Fig 5 results in the following points

(1) A uniform reaction of both models and both climate scenarios can be observed in

April at all sites This particular consistency between the models can be explained

by the fact that regardless different projections of precipitation change a high

temperature increase projected in winter by both models accelerates the

occurrence of peaks Hence in April which used to be the peak runoff month in

the baseline the hydrograph is already decreasing

(2) MIROC32 suggests an increase in temperature between May and June by 3-35

˚C and a relatively small change in precipitation This drives SWAT presumably

due to increased evapotranspiration to decrease the total runoff at Zambski in this

period by 57 mm compared to the baseline whilst the change in runoff in

WaterGAP is negligible Figure 8 suggests that this might be due to significant

overestimation of AET by WaterGAP in the baseline in May and June

(3) For the period from August to November a total increase in precipitation

according to MIROC32 is equal to 53 mm and increase in temperature stays in

the range 25-35 ˚C This drives SWAT to increase the total runoff in this period

by 84 mm compared to the baseline whilst the increase in WaterGAP equals 3

mm only

The above observations indicate that SWAT is more sensitive to various

seasonal climate change signals than WaterGAP Results reported in Table 3 confirm

this hypothesis It is interesting to note that (i) this measure of sensitivity is higher for

the MIROC32 model than for the IPSL-CM4 model and (ii) in the case of SWAT it

is much higher for the sub-catchments than for the whole basin while this is not the

case for WaterGAP This is the reason why the hydrological model inconsistency in

assessing the effect of climate change on monthly runoff is larger at Burzyn and Suraż

than at Zambski Indeed the number of months for which the differences between the

absolute changes simulated by SWAT and WaterGAP for any GCM do not exceed 1

mm (in terms of the absolute values) are equal to 9 2 and 3 for Zambski Burzyn and

Suraż respectively The number of months for which the same characteristics exceed

2 mm are equal to 5 15 and 11 respectively

4 DISCUSSION

The results of our analysis of the global and catchment-scale model responses to the

same climate change signal indicate that

(1) SWAT and WaterGAP were very consistent in showing the direction and

quantifying the magnitude of future change in mean annual runoff due to climate

change

(2) The consistency in identifying the high (Q10) and low (Q90) monthly runoff

change was not as good as for the mean annual runoff It was quite often observed

that when one model was showing a negligible change in these indicators the

other one was showing at least medium change As shown in Fig 10 for more

extreme indicators (eg Q5 and Q95) the difference between SWAT- and

WaterGAP-based estimates was even larger

(3) Some patterns of change in the seasonal cycle of runoff were comparable in both

models (eg earlier occurrence of peak runoff large decrease in April runoff)

while others were not (eg different responses to the August-November

precipitation increase from MIROC32) The magnitudes of projected seasonal

changes varied significantly the SWAT model showing overall more sensitivity

to climate change than the WaterGAP model

Our interpretation of these results is that the modelling scale does not have

much influence on the assessment of simple indicators and general descriptive

patterns whilst when it comes to more detailed indicators and in particular their

magnitudes the impact of the modelling scale is visible This partly corresponds to

the observation pointed out by several authors (Gosling et al 2011 Hughes et al

2011 Noacutebrega et al 2011) that the mean annual runoff can mask considerably greater

seasonal variations which are of high importance to water management

As regards the potential reasons for the differences between simulations by

SWAT and WaterGAP in climate change impact assessment it is not straightforward

to discriminate between the different model behaviour in the baseline and the different

model reaction to the climate change forcing Since the catchment-specific calibration

was not performed for the global model it was not surprising to observe generally

better behaviour of the catchment model in the baseline At present and very likely in

the near future the global models such as WaterGAP are not specifically calibrated

for catchments of the size of the Narew Hence an important question emerges which

process descriptions parameterisations in WaterGAP should be rethought in order to

reduce the uncertainty in climate change impact assessments The same question

should apply to SWAT however in this study we tacitly assume since SWAT

performed better in the baseline that its results are more reliable and can be used as

benchmark for WaterGAP

The comparison of the annual time series (Fig 7) and the seasonal dynamics

(Fig 8) of various water balance components revealed a large difference between

SWAT- and WaterGAP-based estimates of actual evapotranspiration (AET) and soil

water content We suppose that WaterGAP actually overestimates AET in May and

June This is consistent with a large decrease in soil water content in these months

compared to SWAT We expect that this results in too little soil moisture content in

summer months and in consequence as total runoff simulated in WaterGAP is a

nonlinear function of soil moisture (Bergstroumlm 1995 Doumlll 2003) in underestimation

of runoff starting from September and lasting until the soils are completely rewetted

(ie until February)

The above considerations suggest that either the main parameters controlling

vertical soil water balance in WaterGAP should be reconsidered or the process

description itself should be rethought Since the methods used for estimation of soil

water balance components in WaterGAP are well established and used in many other

models such as HBV (Bergstroumlm 1995) one should rather focus on the parameters In

particular three parameters may turn to be critical namely soil depth set to 1 m in

WaterGAP which may be too low total available water capacity within the effective

root zone (Ssmax) and runoff coefficient (γ) which is a WaterGAP calibration

parameter (Doumlll 2003) This statement is not restricted only to the Narew basin but

should apply also to other lowland river basins lying in the same climatic zone

other one was showing at least medium change As shown in Fig 10 for more

extreme indicators (eg Q5 and Q95) the difference between SWAT- and

WaterGAP-based estimates was even larger

(3) Some patterns of change in the seasonal cycle of runoff were comparable in both

models (eg earlier occurrence of peak runoff large decrease in April runoff)

while others were not (eg different responses to the August-November

precipitation increase from MIROC32) The magnitudes of projected seasonal

changes varied significantly the SWAT model showing overall more sensitivity

to climate change than the WaterGAP model

Our interpretation of these results is that the modelling scale does not have

much influence on the assessment of simple indicators and general descriptive

patterns whilst when it comes to more detailed indicators and in particular their

magnitudes the impact of the modelling scale is visible This partly corresponds to

the observation pointed out by several authors (Gosling et al 2011 Hughes et al

2011 Noacutebrega et al 2011) that the mean annual runoff can mask considerably greater

seasonal variations which are of high importance to water management

As regards the potential reasons for the differences between simulations by

SWAT and WaterGAP in climate change impact assessment it is not straightforward

to discriminate between the different model behaviour in the baseline and the different

model reaction to the climate change forcing Since the catchment-specific calibration

was not performed for the global model it was not surprising to observe generally

better behaviour of the catchment model in the baseline At present and very likely in

the near future the global models such as WaterGAP are not specifically calibrated

for catchments of the size of the Narew Hence an important question emerges which

process descriptions parameterisations in WaterGAP should be rethought in order to

reduce the uncertainty in climate change impact assessments The same question

should apply to SWAT however in this study we tacitly assume since SWAT

performed better in the baseline that its results are more reliable and can be used as

benchmark for WaterGAP

The comparison of the annual time series (Fig 7) and the seasonal dynamics

(Fig 8) of various water balance components revealed a large difference between

SWAT- and WaterGAP-based estimates of actual evapotranspiration (AET) and soil

water content We suppose that WaterGAP actually overestimates AET in May and

June This is consistent with a large decrease in soil water content in these months

compared to SWAT We expect that this results in too little soil moisture content in

summer months and in consequence as total runoff simulated in WaterGAP is a

nonlinear function of soil moisture (Bergstroumlm 1995 Doumlll 2003) in underestimation

of runoff starting from September and lasting until the soils are completely rewetted

(ie until February)

The above considerations suggest that either the main parameters controlling

vertical soil water balance in WaterGAP should be reconsidered or the process

description itself should be rethought Since the methods used for estimation of soil

water balance components in WaterGAP are well established and used in many other

models such as HBV (Bergstroumlm 1995) one should rather focus on the parameters In

particular three parameters may turn to be critical namely soil depth set to 1 m in

WaterGAP which may be too low total available water capacity within the effective

root zone (Ssmax) and runoff coefficient (γ) which is a WaterGAP calibration

parameter (Doumlll 2003) This statement is not restricted only to the Narew basin but

should apply also to other lowland river basins lying in the same climatic zone

Differences in snowmelt estimation might be another reason for differences

between SWAT- and WaterGAP-based estimates especially those related to winter

and spring runoff generation It was observed that peak runoff in the baseline period

occurred quicker in WaterGAP than in SWAT and in the observation records (Fig 6)

which was likely caused by the fact that snow cover was thawing quicker in

WaterGAP Both models are using degree-day approach to estimate snowmelt

However although snowmelt base temperature was set to 0degC in both models two

other important parameters controlling snowmelt were set to different values Firstly

snowfall temperature was set to 1degC in SWAT and 0degC in WaterGAP Secondly

degree-day factor (DDF) in WaterGAP was set to values ranging from 15 to 7 mm d-1

degC ndash1 depending on the land cover type whereas in SWAT this parameter ranged

between 05 (21 Dec) and 15 (21 Jun) as a unique value for the whole basin like all

snow-related parameters in SWAT Higher DDFs in WaterGAP induced quicker

snowmelt and since there was less snow accumulated (due to lower snowfall

temperature) peak runoff occurred up to 1 month in advance Verzano and Menzel

(2009) compared hydrographs modelled in WaterGAP with measured ones in two

large basins situated in the Alps and the Scandinavian Mountains and also found out

that WaterGAP underestimated winter runoff but the magnitude of this

underestimation was smaller It requires further studies to examine if improvement of

estimation of peak runoff occurrence in WaterGAP could be reached by manipulating

snow-related parameters Another possible reason for too rapid snowmelt in

WaterGAP could be that the global hydrological model internally generates daily

climate input time series out of the monthly CRU dataset which in the case of

temperature and especially temperatures around snowmelt events may affect

simulated runoff stronger than in any other season of the year

Although differences between SWAT- and WaterGAP-based estimates in

assessing the effect of climate change on runoff are undeniable it is worth noting that

the inter-GCM differences are even larger and this is where the uncertainty is

dominating In particular the largest difference between estimates of the mean annual

runoff using IPSL-CM4 and MIROC32 is equal to 56 mm whereas differences

between SWAT- and WaterGAP-based estimates do not exceed 13 mm (Fig 9) It is

also interesting to note that regardless whether it was a decrease or an increase in the

monthly runoff due to the climate change forcing the reaction of SWAT was in 63

out of 72 cases (2 models 3 sites 12 months) more pronounced than in WaterGAP

(Fig 13 and Table 2) The SWAT model is equally sensitive to climate change

forcing from IPSL-CM4 and MIROC32 whereas the WaterGAP model shows

significantly lower sensitivity to the latter model Since the difference between the

climate models is mainly in future precipitation changes we suppose that there exists

a mechanism in WaterGAP which triggers a more pronounced reaction to a climate

model with a large temperature increase and a little change in precipitation than to a

model with similar temperature increase and a considerable increase in precipitation

It was noted that the differences between SWAT and WaterGAP are smaller

for the whole catchment (Zambski) than for its two sub-catchments (Burzyn and

Suraż occupying 24 and 12 of the whole catchment area respectively) This can be

explained by the fact that various model inputs have higher uncertainty for smaller

areas whilst for larger areas the differences are likely to cancel out (Qi and Grunwald

2005) Piniewski and Okruszko (2011) who performed spatial calibration and

validation of SWAT in the Narew basin noted also that the goodness-of-fit measures

were connected to the catchment area ie the smaller the catchment the lower NSE

value

5 CONCLUSIONS AND OUTLOOK

The results of our study show that the global model is able to capture some of the

major responses to the climate change forcing Given the fact that the setup

calibration and validation of a SWAT-type catchment model requires a lot of time

human and financial resources whilst the results of the global model are available at

hand2 we can recommend using the latter for climate change impact assessments on

general level for instance for indicators such as mean annual runoff direction of

change in monthly runoff or shift in timing of peak runoff We are not in position to

extend this recommendation for the pan-European scale but we believe that for the

river basins situated in the same climatic zone (such as the Central and Eastern

European lowlands) this statement should hold true However for more sophisticated

assessments taking into account eg the magnitudes of changes in mean and extreme

monthly runoff the local model has advantages over the global one In practice for

instance in the Polish case WaterGAP could be used for the country-wide general

assessment and SWAT-type model could be applied in selected hot spots of special

interest to water managers or decision-makers

As regards the reasons for the identified inconsistencies in the model results

we have found some evidence that if there is any part of WaterGAP that could be

improved in the future it is the modelling of vertical soil water balance and in

particular soil parameterisation We found out that soil over-drying in summer and

autumn is a likely reason for the underestimation of runoff in autumn and winter

In order to gain more insight into the cross-scale issues related to climate

change impact assessments it would be beneficial to use the approach undertaken in

this paper for several more case study river basins situated in different parts of the

European continent This should be straightforward provided that the local models

(not necessarily SWAT) are already setup and calibrated for the baseline period

similar to the one used in WaterGAP Given that there is a considerable uncertainty

across different global models in hydrological projections (Haddeland et al 2011)

such a study could also be a valuable complement to the study of Gosling et al (2011)

who found out that it is equally feasible to apply the global hydrological model Mac-

PDM09 (Gosling and Arnell 2011) as it is to apply a catchment model to explore

catchment-scale changes in runoff due to global warming from an ensemble of

GCMs

Further impacts of our findings on water management in the Narew basin

should be analysed in the aspects of water use (domestic industrial and agricultural)

and environmental flows In the first case there is no evidence that relative changes

even in the low flow period may alter the water use possibility assuming the current

use level as well as projected future water use (Giełczewski et al 2011) in this region

with low population density In contrast environmental flows should be a concern of

the nature conservation authorities High ecological values of riparian wetlands

located in the basins of the rivers Biebrza and Narew are strongly depending on the

availability of a flood pulse in spring (Okruszko et al 2005) Shifting of the

inundation period may significantly change the habitat condition for both spawning of

phytophilous fish species such as pike and wels catfish (Piniewski et al 2011) as well

2 The SCENES WebService (httpwwwcesrdeSCENES_WebService) [last accessed 11042012]

as for the waterfowl bird community The buffering capacity of particular ecosystems

andor adaptation strategies should be considered in the further study

Acknowledgements The authors gratefully acknowledge financial support for the

project Water Scenarios for Europe and Neighbouring States (SCENES) from the

European Commission (FP6 contract 036822) The authors appreciate constructive

comments made by two anonymous referees that helped us clarify our presentation

and generally improve the paper

REFERENCES Alcamo J Doumlll P Henrichs T Kaspar F Lehner B Roumlsch T and Siebert S 2003

Development and testing of the WaterGAP 2 global model of water use and availability

Hydrological Sciences Journal 48(3) 317ndash337

Ambroise B Beven K and Freer J 1996 Toward a generalization of the TOPMODEL concepts

Topographic indices of hydrological similarity Water Resouces Research 32(7) 2135-2145

Anagnostopoulos G G Koutsoyiannis D Christofides A Efstratiadis A and Mamassis N 2010

A comparison of local and aggregated climate model outputs with observed data

Hydrological Sciences Journal 55(7) 1094ndash1110

Arnell N W 1999 A simple water balance model for the simulation of streamflow over a large

geographic domain Journal of Hydrology 217 314ndash335

Arnold J G Srinavasan R Muttiah R S and Williams J R 1998 Large area hydrologic modelling

and assessment Part 1 Model development Journal of American Water Resources

Association 34 73-89

Barthel R Rojanschi V Wolf J and Braun J 2005 Large-scale water resources management

within the framework of GLOWA-Danube Part A The groundwater model Physics and

Chemistry of the Earth 30(6-7) 372-382

Bergstroumlm S 1995 The HBV model In Computer Models of Watershed Hydrology (ed by V P

Singh) Water Resources Publications 443ndash476

Beven K J and Binley A 1992 The future of distributed models model calibration and uncertainty

prediction Hydrological Processes 6 279ndash298

Beven KJ and Kirkby MJ 1979 A physically based variable contributing area model of basin

hydrology Hydrological Sciences Bulletin 24(1) 43-69

Croke B F W Merritt W S and Jakeman A J 2004 A dynamic model for predicting hydrologic

response to land cover changes in gauged and ungauged catchments Journal of Hydrology

291 115-131

Doumlll P Kaspar F and Lehner B 2003 A global hydrological model for deriving water availability

indicators model tuning and validation Journal of Hydrology 270 105-134

EC (European Communities) 2000 Establishing a framework for community action in the field of

water policy Directive 200060EC of the European Parliament and of the Council of 23

October 2000 Official Journal of the European Communities Brussels Belgium cf

httpeur-lexeuropaeuLexUriServLexUriServdouri=CELEX32000L0060ENHTML

[last accessed 11042011]

Fowler H J Blenkinsop S and Tebaldi C 2007 Linking climate change modelling to impacts

studies recent advances in downscaling techniques for hydrological modelling International

Journal of Climatology 27 1547-1578

Gassman PW Reyes MR Green CH and Arnold JG 2007 The Soil and Water Assessment

Tool Historical development applications and future research directions Transactions of the

ASABE 50 1211-1250

Geng S Penning F W T and Supit I 1986 A simple method for generating daily rainfall data

Agricultural and Forest Meteorology 36 363ndash376

Giełczewski M Stelmaszczyk M Piniewski M and Okruszko T 2011 How can we involve

stakeholders in the development of water scenarios Narew River Basin case study Journal of

Water and Climate Change 2(2-3) 166-179

Gosling S N and Arnell N W 2011 Simulating current global river runoff with a global

hydrological model model revisions validation and sensitivity analysis Hydrological

Processes 25(7) 1129-1145

Gosling S N Taylor R G Arnell N W and Todd M C 2011 A comparative analysis of

projected impacts of climate change on river runoff from global and catchment-scale

hydrological models Hydrology and Earth System Sciences 15 279-294

Grotch S L and MacCracken M C 1991 The use of general circulation models to predict regional

climatic change Journal of Climate 4 286ndash303

Gupta H V Sorooshian S and Yapo P O 1999 Status of automatic calibration for hydrologic

models Comparison with multilevel expert calibration Journal of Hydrologic Engineering

4(2) 135-143

Haddeland I Clark D B Franssen W Ludwig F Voszlig F Arnell N W Bertrand N Best M

Folwell S Gerten D Gomes S Gosling S N Hagemann S Hanasaki N Harding R

Heinke J Kabat P Koirala S Oki T Polcher J Stacke T Viterbo P Weedon G P

and Yeh P 2011 Multi-model estimate of the global terrestrial water balance setup and first

results Journal of Hydrometeorology (doi 1011752011JHM13241)

Hanasaki N Inuzuka T Kanae S and Oki T 2010 An estimation of global virtual water flow and

sources of water withdrawal for major crops and livestock products using a global

hydrological model Journal of Hydrology 384(3-4) 232-244

Hasumi H and Emori S (eds) 2004 K-1 coupled model (MIROC) description K-1 Technical Report

1 Center for Climate System Research University of Tokyo Japan

Huang S Krysanova V Osterle H and Hattermann FF 2010 Simulation of spatiotemporal

dynamics of water fluxes in Germany under climate change Hydrological Processes 24(23)

3289-3306

Hughes D A Kingston D G and Todd M C 2011 Uncertainty in water resources availability in

the Okavango River Basin as a result of climate change Hydrology and Earth System

Sciences 15 931-941

IPCC (Intergovernmental Panel on Climate Change) 2007 Summary for Policymakers In Climate

Change 2007 The Physical Science Basis (ed by S Solomon D Qin M Manning Z Chen

M Marquis K B Averyt M Tignor and H L Miller) Contribution of Working Group I to

the Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge

University Press Cambridge UK and New York USA

Kaumlmaumlri J Alcamo J Baumlrlund I Duel H Farquharson F Floumlrke M Fry M Houghton-Carr H

Kabat P Kaljonen M Kok K Meijer K S Rekolainen S Sendzimir J Varjopuro R

and Villars N 2008 Envisioning the future of water in Europe ndash the SCENES project E-

WAter Official Publication of the European Water Association

httpwwwewaonlinedeportaleewaewansfhomereadformampobjectid=19D821CE3A88D7

E4C12574FF0043F31E [last accessed 11042011] Kingston D G and Taylor R G 2010 Sources of uncertainty in climate change impacts on river

discharge and groundwater in a headwater catchment of the Upper Nile Basin Uganda

Hydrology and Earth Sysem Sciences 23(6) 1297-1308 Kok K Van Vliet M Dubel A Sendzimir J and Baumlrlund I 2011 Combining participative

backcasting and exploratory scenario development Experiences from the SCENES project

Technological Forecasting and Social Change doi101016jtechfore201101004 [in press] Krysanova V Muumlller-Wohlfeil D I and Becker A 1998 Development and test of a spatially

distributed hydrological water quality model for mesoscale watersheds Ecological

Modelling 106 261-289

Kundzewicz Z W and Stakhiv E Z 2010 Are climate models ldquoready for prime timerdquo in water

resources management applications or is more research needed Hydrological Sciences

Journal 55(7) 1085-1089

Kundzewicz Z W Mata L J Arnell N W Doumlll P Jimenez B Miller K Oki T Şen Z and

Shiklomanov I 2008 The implications of projected climate change for freshwater resources

and their management Hydrological Sciences Journal 53(1) 3ndash10

Maksymiuk A Furmańczyk K Ignar S Krupa J and Okruszko T 2008 Analysis of climatic and

hydrologic parameters variability in the Biebrza River basin Scientific Review Engineering

and Environmental Sciences 41(7) 59-68 [In Polish]

Marszelewski W and Skowron R 2006 Ice cover as an indicator of winter air temperature changes

case study of the Polish Lowland lakes Hydrological Sciences Journal 51(2) 336-349

Marti O Braconnot P Bellier J Benshila R Bony S Brockmann P Cadule P Caubel A

Denvil S Dufresne J-L Fairhead L Filiberti M-A Foujols M-A T Fichefet T

Friedlingstein P Gosse H Grandpeix J-Y Hourdin F Krinner G Leacutevy C Madec G

Musat I de Noblet N Polcher J and Talandier C 2006 The new IPSL climate system

model IPSL-CM4 Note du Pocircle de Modeacutelisation 26 ISSN 1288-1619

Mitchell T D Carter T Hulme M New M and Jones P 2004 A comprehensive set of climate

scenarios for Europe and the globe Tyndall Working Paper 55

Moriasi D N Arnold J G van Liew M W Bingner R L Harmel R D and Veith T L 2007

Model evaluation guidelines for systematic quantification of accuracy in watershed

simulations Transactions of the ASABE 50(3) 885-900

Nash JE and Sutcliffe JV 1970 River flow forecasting through conceptual models part I mdash A

discussion of principles Journal of Hydrology 10(3) 282ndash290

Neitsch S L Arnold J G Kiniry J R and Williams J R 2005 Soil and Water Assessment Tool

Theoretical Documentation Version 2005 GSWRL-BRC Temple

Nijssen B Lettenmaier D P Liang X Wetzel S W and Wood E F 1997 Streamflow

simulation for continental-scale river basins Water Resources Research 33(4) 711-724

Noacutebrega M T Collischonn W Tucci C E M and Paz A R 2011 Uncertainty in climate change

impacts on water resources in the Rio Grande Basin Brazil Hydrology and Earth System

Sciences 15 585-595

Okruszko T Dembek W and Wasilewicz M 2005 Plant communities response to floodwater

conditions in Ławki Marsh in the River Biebrza Lower Basin Poland Ecohydrology amp

Hydrobiology 5(1) 15-21

Okruszko T and Giełczewski M 2004 Integrated River Basin Management ndash The Narew River Case

Study Kasseler Wasserbau-Mitteilungen Universitaumlt Kassel 14 59-68

Parajuli P B 2010 Assessing sensitivity of hydrologic responses to climate change from forested

watershed in Mississippi Hydrological Processes 24(26) 3785-3797

Piniewski M and Okruszko T 2011 Multi-site calibration and validation of the hydrological

component of SWAT in a large lowland catchment In Modelling of Hydrological Processes

in the Narew Catchment (ed by D Świątek and T Okruszko) Geoplanet Earth and Planetary

Sciences Springer-Verlag Berlin Heidelberg 15-41

Piniewski M Acreman M C Stratford C S Okruszko T Giełczewski M Teodorowicz M

Rycharski M and Oświecimska-Piasko Z 2011 Estimation of environmental flows in semi-

natural lowland rivers - the Narew basin case study Polish Journal of Environmental Studies

20(5) 1281-1293

Pusłowska-Tyszewska D Kindler J and Tyszewski S 2006 Elements of water management

planning according to EU Water Framework Directive in the catchment of Upper Narew

Journal of Water and Land Development 10 15-38

Qi C and Grunwald S 2005 GIS-based hydrologic modeling in the Sandusky watershed using

SWAT Transactions of the ASABE 48(1) 169-180

Smakhtin V U 2001 Low flow hydrology a review Journal of Hydrology 240 147ndash186

Szwed M Karg G Pińskwar I Radziejewski M Graczyk D Kędziora A Kundzewicz Z W

2010 Climate change and its effect on agriculture water resources and human health sectors

in Poland Natural Hazards and Earth System Sciences 10 1725-1737

van der Goot E and Orlandi S 2003 Technical description of interpolation and processing of

meteorological data in CGMS Institute for Environment and Sustainability Ispra

httpmarsjrcitmarsAbout-usAGRI4CASTData-distributionData-Distribution-Grid-

Weather-Doc [last accessed 11042011]

van Griensven A and Meixner T 2007 A global and efficient multi-objective auto-calibration and

uncertainty estimation method for water quality catchment models Journal of

Hydroinformatics 094 277-291

Verzano K and Menzel L 2009 Snow conditions in mountains and climate change ndash a global view

In Hydrology in Mountain Regions Observations Processes and Dynamics (Proceedings of

Symposium HS1003 at 147 IUGG2007 Perugia July 2007) (ed by D Marks R Hock M

Lehning M Hayashi and R Gurney) 147-154 Wallingford IAHS Press IAHS Publ 326

Zehe E Maurer T Ihringer J and Plate E 2001 Modeling water flow and mass transport in a loess

catchment Physics and Chemistry of the Earth 26(7-8) 487-507

Zhang H Huang G H Wang D and Zhang X 2011 Uncertainty assessment of climate change

impacts on the hydrology of small prairie wetlands Journal of Hydrology 396(1-2) 94-103

Table 1 Comparison of SWAT and WaterGAP modelling conceptsapproaches and input data used

Aspect SWAT WG

Modelling

approach

Basic unit Hydrologic Response Unit 5 by 5 grid cell

Potential

evapotranspiration

(PET)

Penman-Monteith method Priestley-Taylor method

Actual

evapotranspiration

(AET)

Evaporation from canopy +

sublimation + plant water uptake +

soil evaporation

Evaporation from canopy +

sublimation +

evapotranspiration from

vegetated soil

Snowmelt Degree-day method

Surface runoff Modified SCS curve number

method HBV method

Redistribution in

soil

Storage routing method between up

to 10 soil layers

No redistribution one soil

layer

Soil water content Allowed range of variation from the

absolute zero to saturation

Allowed range of variation

from the wilting point to the

field capacity

Groundwater

storage

Two groundwater storages (shallow

unconfined and deep confined) One groundwater storage

Baseflow Recession constant method Linear storage equation

Flood routing Variable storage coefficient method Linear storage equation

Input data

Drainage topology Based on 30m resolution DEM and

stream network map

Based on the global drainage

direction map DDM5

Land use map Corine Land Cover 2000

Soil map Based on ca 3400 benchmark soil

profiles in the Narew basin FAO

Climate

Daily data from 12 precipitation

stations and 7 climate stations

(temperature) + daily data from

MARS-STAT database for other

variables

Monthly data from the CRU

10 resolution global dataset

Table 2 SWAT and WaterGAP monthly runoff simulation statistics and goodness-of-fit measures in

the baseline

Gauge Area [km2] Category Qmean Q10 Q90 NSE R2 Bias []

Zambski 27500

measured 134 226 63

SWAT 136 235 56 072 073 -2

WaterGAP 117 208 49 035 050 12

Burzyn 6800

measured 146 249 56

SWAT 144 276 38 059 061 1

WaterGAP 111 206 51 047 058 24

Suraż 3280

measured 126 259 42

SWAT 136 306 21 061 071 -8

WaterGAP 101 211 20 030 045 20

Table 3 The averages of the absolute changes in monthly runoff [mm] for all combinations of GCMs

hydrological models and sites

Location IPSL-CM4 MIROC32

SWAT WaterGAP SWAT WaterGAP

Zambski 33 29 33 21

Burzyn 47 28 45 20

Suraż 49 33 46 22

Fig 1 Map of the study area

Fig 2 Spatial discretisation of the Narew basin in SWAT and WaterGAP

50

55

60

65

70

75

80

85

90

1975 1980 1985 1990 1995 2000

Tem

pera

ture

[deg

C]

400

450

500

550

600

650

700

750

1975 1980 1985 1990 1995 2000

Pre

cip

itation [

mm

]

WaterGAP

SWAT

(a) (b)

Fig 3 Annual basin-averaged mean temperature (a) and precipitation (b) in the baseline period

-5

0

5

10

15

20

J F M A M J J A S O N D

Tem

pera

ture

[deg

C]

0

20

40

60

80

J F M A M J J A S O N DP

recip

itation [

mm

] WaterGAP

SWAT

(a) (b)

Fig 4 Mean monthly basin-averaged temperature (a) and precipitation (b) in the baseline period

-30

-10

10

30

50

J F M A M J J A S O N D

Re

lative

ch

an

ge

[

] IPSL-CM4

MIROC32

0

1

2

3

4

5

J F M A M J J A S O N D

Ab

so

lute

ch

an

ge

[d

eg

C

]

(a)

(b)

Fig 5 Basin-averaged changes in temperature (a) and precipitation (b) from IPSL-CM4 and

MIROC32

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

measuredSWATWaterGAP

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

0

5

10

15

20

25

30

J F M A M J J A S O N D

Ru

no

ff [m

m]

(a) Narew at Zambski

(b) Biebrza at Burzyn

(c) Narew at Suraż

Fig 6 Mean measured and simulated monthly runoff in the baseline at three analysed locations

450

500

550

600

650

700

1975 1980 1985 1990 1995 2000

PE

T [

mm

]

(a)

350

375

400

425

450

475

500

1975 1980 1985 1990 1995 2000

AE

T [

mm

]

(b)

80

100

120

140

160

180

200

220

1975 1980 1985 1990 1995 2000

Runoff

[m

m]

WaterGAP

SWAT

measured

(d)

-20

-15

-10

-5

0

5

10

15

20

1975 1980 1985 1990 1995 2000

Sto

rage c

hange in S

W [

mm

]

(c)

Fig 7 Annual time series of the basin-averaged water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (year-to-year) (d) Runoff

0

5

10

15

20

25

J F M A M J J A S O N DR

unoff

[m

m]

WaterGAP

SWAT

(d)

0

15

30

45

60

75

90

J F M A M J J A S O N D

AE

T [

mm

]

(b)

0

20

40

60

80

100

120

J F M A M J J A S O N D

PE

T [

mm

]

(a)

-40

-30

-20

-10

0

10

20

30

J F M A M J J A S O N DSto

rage c

hange in S

W

[mm

] (c)

Fig 8 Basin-averaged monthly dynamics of the water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (month-to-month) (d) Runoff

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

SWAT

WaterGAP-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

Rru

no

ff c

han

ge [

mm

] (a) Narew at Zambski (b) Biebrza at Burzyn (c) Narew at Suraż

Fig 9 Absolute changes in mean annual runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

10

20

30

40

50

60

70

0 2 4 6 8 10

Equalled or Exceeded

Ru

no

ff [

mm

]

Baseline_SWAT

IPSL-CM4_SWAT

MIROC32_SWAT

Baseline_WaterGAP

IPSL-CM4_WG

MIROC32_WG

Q5

Q10

(a)

0

2

4

6

8

90 92 94 96 98 100

Equalled or Exceeded

Q95

Q90

(b)

Fig 10 Empirical flow duration curves (EFDCs) of the monthly runoff simulated by SWAT and

WaterGAP for the baseline and two climate scenarios at Zambski (a) high runoff (b) low runoff

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

Ch

an

ge in

Q10 [

mm

]

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

(b) Biebrza at Burzyn(a) Narew at Zambski (c) Narew at Suraż

-2

-1

0

1

2

IPSL-CM4 MIROC32

Ch

an

ge in

Q90 [

mm

]

-2

-1

0

1

2

IPSL-CM4 MIROC32

-2

-1

0

1

2

IPSL-CM4 MIROC32

SWAT

WaterGAP

(d) Narew at Zambski (e) Biebrza at Burzyn (f) Narew at Suraż

Fig 11 Absolute changes in monthly Q10 (a-c) and Q90 (d-f) relative to baseline under two GCMs as

simulated by SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(b) Biebrza at Burzyn

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(c) Narew at Suraż

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

Table 2 SWAT and WaterGAP monthly runoff simulation statistics and goodness-of-fit measures in

the baseline

Gauge Area [km2] Category Qmean Q10 Q90 NSE R2 Bias []

Zambski 27500

measured 134 226 63

SWAT 136 235 56 072 073 -2

WaterGAP 117 208 49 035 050 12

Burzyn 6800

measured 146 249 56

SWAT 144 276 38 059 061 1

WaterGAP 111 206 51 047 058 24

Suraż 3280

measured 126 259 42

SWAT 136 306 21 061 071 -8

WaterGAP 101 211 20 030 045 20

Table 3 The averages of the absolute changes in monthly runoff [mm] for all combinations of GCMs

hydrological models and sites

Location IPSL-CM4 MIROC32

SWAT WaterGAP SWAT WaterGAP

Zambski 33 29 33 21

Burzyn 47 28 45 20

Suraż 49 33 46 22

Fig 1 Map of the study area

Fig 2 Spatial discretisation of the Narew basin in SWAT and WaterGAP

50

55

60

65

70

75

80

85

90

1975 1980 1985 1990 1995 2000

Tem

pera

ture

[deg

C]

400

450

500

550

600

650

700

750

1975 1980 1985 1990 1995 2000

Pre

cip

itation [

mm

]

WaterGAP

SWAT

(a) (b)

Fig 3 Annual basin-averaged mean temperature (a) and precipitation (b) in the baseline period

-5

0

5

10

15

20

J F M A M J J A S O N D

Tem

pera

ture

[deg

C]

0

20

40

60

80

J F M A M J J A S O N DP

recip

itation [

mm

] WaterGAP

SWAT

(a) (b)

Fig 4 Mean monthly basin-averaged temperature (a) and precipitation (b) in the baseline period

-30

-10

10

30

50

J F M A M J J A S O N D

Re

lative

ch

an

ge

[

] IPSL-CM4

MIROC32

0

1

2

3

4

5

J F M A M J J A S O N D

Ab

so

lute

ch

an

ge

[d

eg

C

]

(a)

(b)

Fig 5 Basin-averaged changes in temperature (a) and precipitation (b) from IPSL-CM4 and

MIROC32

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

measuredSWATWaterGAP

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

0

5

10

15

20

25

30

J F M A M J J A S O N D

Ru

no

ff [m

m]

(a) Narew at Zambski

(b) Biebrza at Burzyn

(c) Narew at Suraż

Fig 6 Mean measured and simulated monthly runoff in the baseline at three analysed locations

450

500

550

600

650

700

1975 1980 1985 1990 1995 2000

PE

T [

mm

]

(a)

350

375

400

425

450

475

500

1975 1980 1985 1990 1995 2000

AE

T [

mm

]

(b)

80

100

120

140

160

180

200

220

1975 1980 1985 1990 1995 2000

Runoff

[m

m]

WaterGAP

SWAT

measured

(d)

-20

-15

-10

-5

0

5

10

15

20

1975 1980 1985 1990 1995 2000

Sto

rage c

hange in S

W [

mm

]

(c)

Fig 7 Annual time series of the basin-averaged water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (year-to-year) (d) Runoff

0

5

10

15

20

25

J F M A M J J A S O N DR

unoff

[m

m]

WaterGAP

SWAT

(d)

0

15

30

45

60

75

90

J F M A M J J A S O N D

AE

T [

mm

]

(b)

0

20

40

60

80

100

120

J F M A M J J A S O N D

PE

T [

mm

]

(a)

-40

-30

-20

-10

0

10

20

30

J F M A M J J A S O N DSto

rage c

hange in S

W

[mm

] (c)

Fig 8 Basin-averaged monthly dynamics of the water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (month-to-month) (d) Runoff

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

SWAT

WaterGAP-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

Rru

no

ff c

han

ge [

mm

] (a) Narew at Zambski (b) Biebrza at Burzyn (c) Narew at Suraż

Fig 9 Absolute changes in mean annual runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

10

20

30

40

50

60

70

0 2 4 6 8 10

Equalled or Exceeded

Ru

no

ff [

mm

]

Baseline_SWAT

IPSL-CM4_SWAT

MIROC32_SWAT

Baseline_WaterGAP

IPSL-CM4_WG

MIROC32_WG

Q5

Q10

(a)

0

2

4

6

8

90 92 94 96 98 100

Equalled or Exceeded

Q95

Q90

(b)

Fig 10 Empirical flow duration curves (EFDCs) of the monthly runoff simulated by SWAT and

WaterGAP for the baseline and two climate scenarios at Zambski (a) high runoff (b) low runoff

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

Ch

an

ge in

Q10 [

mm

]

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

(b) Biebrza at Burzyn(a) Narew at Zambski (c) Narew at Suraż

-2

-1

0

1

2

IPSL-CM4 MIROC32

Ch

an

ge in

Q90 [

mm

]

-2

-1

0

1

2

IPSL-CM4 MIROC32

-2

-1

0

1

2

IPSL-CM4 MIROC32

SWAT

WaterGAP

(d) Narew at Zambski (e) Biebrza at Burzyn (f) Narew at Suraż

Fig 11 Absolute changes in monthly Q10 (a-c) and Q90 (d-f) relative to baseline under two GCMs as

simulated by SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(b) Biebrza at Burzyn

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(c) Narew at Suraż

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

Table 3 The averages of the absolute changes in monthly runoff [mm] for all combinations of GCMs

hydrological models and sites

Location IPSL-CM4 MIROC32

SWAT WaterGAP SWAT WaterGAP

Zambski 33 29 33 21

Burzyn 47 28 45 20

Suraż 49 33 46 22

Fig 1 Map of the study area

Fig 2 Spatial discretisation of the Narew basin in SWAT and WaterGAP

50

55

60

65

70

75

80

85

90

1975 1980 1985 1990 1995 2000

Tem

pera

ture

[deg

C]

400

450

500

550

600

650

700

750

1975 1980 1985 1990 1995 2000

Pre

cip

itation [

mm

]

WaterGAP

SWAT

(a) (b)

Fig 3 Annual basin-averaged mean temperature (a) and precipitation (b) in the baseline period

-5

0

5

10

15

20

J F M A M J J A S O N D

Tem

pera

ture

[deg

C]

0

20

40

60

80

J F M A M J J A S O N DP

recip

itation [

mm

] WaterGAP

SWAT

(a) (b)

Fig 4 Mean monthly basin-averaged temperature (a) and precipitation (b) in the baseline period

-30

-10

10

30

50

J F M A M J J A S O N D

Re

lative

ch

an

ge

[

] IPSL-CM4

MIROC32

0

1

2

3

4

5

J F M A M J J A S O N D

Ab

so

lute

ch

an

ge

[d

eg

C

]

(a)

(b)

Fig 5 Basin-averaged changes in temperature (a) and precipitation (b) from IPSL-CM4 and

MIROC32

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

measuredSWATWaterGAP

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

0

5

10

15

20

25

30

J F M A M J J A S O N D

Ru

no

ff [m

m]

(a) Narew at Zambski

(b) Biebrza at Burzyn

(c) Narew at Suraż

Fig 6 Mean measured and simulated monthly runoff in the baseline at three analysed locations

450

500

550

600

650

700

1975 1980 1985 1990 1995 2000

PE

T [

mm

]

(a)

350

375

400

425

450

475

500

1975 1980 1985 1990 1995 2000

AE

T [

mm

]

(b)

80

100

120

140

160

180

200

220

1975 1980 1985 1990 1995 2000

Runoff

[m

m]

WaterGAP

SWAT

measured

(d)

-20

-15

-10

-5

0

5

10

15

20

1975 1980 1985 1990 1995 2000

Sto

rage c

hange in S

W [

mm

]

(c)

Fig 7 Annual time series of the basin-averaged water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (year-to-year) (d) Runoff

0

5

10

15

20

25

J F M A M J J A S O N DR

unoff

[m

m]

WaterGAP

SWAT

(d)

0

15

30

45

60

75

90

J F M A M J J A S O N D

AE

T [

mm

]

(b)

0

20

40

60

80

100

120

J F M A M J J A S O N D

PE

T [

mm

]

(a)

-40

-30

-20

-10

0

10

20

30

J F M A M J J A S O N DSto

rage c

hange in S

W

[mm

] (c)

Fig 8 Basin-averaged monthly dynamics of the water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (month-to-month) (d) Runoff

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

SWAT

WaterGAP-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

Rru

no

ff c

han

ge [

mm

] (a) Narew at Zambski (b) Biebrza at Burzyn (c) Narew at Suraż

Fig 9 Absolute changes in mean annual runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

10

20

30

40

50

60

70

0 2 4 6 8 10

Equalled or Exceeded

Ru

no

ff [

mm

]

Baseline_SWAT

IPSL-CM4_SWAT

MIROC32_SWAT

Baseline_WaterGAP

IPSL-CM4_WG

MIROC32_WG

Q5

Q10

(a)

0

2

4

6

8

90 92 94 96 98 100

Equalled or Exceeded

Q95

Q90

(b)

Fig 10 Empirical flow duration curves (EFDCs) of the monthly runoff simulated by SWAT and

WaterGAP for the baseline and two climate scenarios at Zambski (a) high runoff (b) low runoff

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

Ch

an

ge in

Q10 [

mm

]

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

(b) Biebrza at Burzyn(a) Narew at Zambski (c) Narew at Suraż

-2

-1

0

1

2

IPSL-CM4 MIROC32

Ch

an

ge in

Q90 [

mm

]

-2

-1

0

1

2

IPSL-CM4 MIROC32

-2

-1

0

1

2

IPSL-CM4 MIROC32

SWAT

WaterGAP

(d) Narew at Zambski (e) Biebrza at Burzyn (f) Narew at Suraż

Fig 11 Absolute changes in monthly Q10 (a-c) and Q90 (d-f) relative to baseline under two GCMs as

simulated by SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(b) Biebrza at Burzyn

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(c) Narew at Suraż

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

Fig 1 Map of the study area

Fig 2 Spatial discretisation of the Narew basin in SWAT and WaterGAP

50

55

60

65

70

75

80

85

90

1975 1980 1985 1990 1995 2000

Tem

pera

ture

[deg

C]

400

450

500

550

600

650

700

750

1975 1980 1985 1990 1995 2000

Pre

cip

itation [

mm

]

WaterGAP

SWAT

(a) (b)

Fig 3 Annual basin-averaged mean temperature (a) and precipitation (b) in the baseline period

-5

0

5

10

15

20

J F M A M J J A S O N D

Tem

pera

ture

[deg

C]

0

20

40

60

80

J F M A M J J A S O N DP

recip

itation [

mm

] WaterGAP

SWAT

(a) (b)

Fig 4 Mean monthly basin-averaged temperature (a) and precipitation (b) in the baseline period

-30

-10

10

30

50

J F M A M J J A S O N D

Re

lative

ch

an

ge

[

] IPSL-CM4

MIROC32

0

1

2

3

4

5

J F M A M J J A S O N D

Ab

so

lute

ch

an

ge

[d

eg

C

]

(a)

(b)

Fig 5 Basin-averaged changes in temperature (a) and precipitation (b) from IPSL-CM4 and

MIROC32

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

measuredSWATWaterGAP

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

0

5

10

15

20

25

30

J F M A M J J A S O N D

Ru

no

ff [m

m]

(a) Narew at Zambski

(b) Biebrza at Burzyn

(c) Narew at Suraż

Fig 6 Mean measured and simulated monthly runoff in the baseline at three analysed locations

450

500

550

600

650

700

1975 1980 1985 1990 1995 2000

PE

T [

mm

]

(a)

350

375

400

425

450

475

500

1975 1980 1985 1990 1995 2000

AE

T [

mm

]

(b)

80

100

120

140

160

180

200

220

1975 1980 1985 1990 1995 2000

Runoff

[m

m]

WaterGAP

SWAT

measured

(d)

-20

-15

-10

-5

0

5

10

15

20

1975 1980 1985 1990 1995 2000

Sto

rage c

hange in S

W [

mm

]

(c)

Fig 7 Annual time series of the basin-averaged water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (year-to-year) (d) Runoff

0

5

10

15

20

25

J F M A M J J A S O N DR

unoff

[m

m]

WaterGAP

SWAT

(d)

0

15

30

45

60

75

90

J F M A M J J A S O N D

AE

T [

mm

]

(b)

0

20

40

60

80

100

120

J F M A M J J A S O N D

PE

T [

mm

]

(a)

-40

-30

-20

-10

0

10

20

30

J F M A M J J A S O N DSto

rage c

hange in S

W

[mm

] (c)

Fig 8 Basin-averaged monthly dynamics of the water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (month-to-month) (d) Runoff

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

SWAT

WaterGAP-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

Rru

no

ff c

han

ge [

mm

] (a) Narew at Zambski (b) Biebrza at Burzyn (c) Narew at Suraż

Fig 9 Absolute changes in mean annual runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

10

20

30

40

50

60

70

0 2 4 6 8 10

Equalled or Exceeded

Ru

no

ff [

mm

]

Baseline_SWAT

IPSL-CM4_SWAT

MIROC32_SWAT

Baseline_WaterGAP

IPSL-CM4_WG

MIROC32_WG

Q5

Q10

(a)

0

2

4

6

8

90 92 94 96 98 100

Equalled or Exceeded

Q95

Q90

(b)

Fig 10 Empirical flow duration curves (EFDCs) of the monthly runoff simulated by SWAT and

WaterGAP for the baseline and two climate scenarios at Zambski (a) high runoff (b) low runoff

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

Ch

an

ge in

Q10 [

mm

]

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

(b) Biebrza at Burzyn(a) Narew at Zambski (c) Narew at Suraż

-2

-1

0

1

2

IPSL-CM4 MIROC32

Ch

an

ge in

Q90 [

mm

]

-2

-1

0

1

2

IPSL-CM4 MIROC32

-2

-1

0

1

2

IPSL-CM4 MIROC32

SWAT

WaterGAP

(d) Narew at Zambski (e) Biebrza at Burzyn (f) Narew at Suraż

Fig 11 Absolute changes in monthly Q10 (a-c) and Q90 (d-f) relative to baseline under two GCMs as

simulated by SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(b) Biebrza at Burzyn

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(c) Narew at Suraż

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

50

55

60

65

70

75

80

85

90

1975 1980 1985 1990 1995 2000

Tem

pera

ture

[deg

C]

400

450

500

550

600

650

700

750

1975 1980 1985 1990 1995 2000

Pre

cip

itation [

mm

]

WaterGAP

SWAT

(a) (b)

Fig 3 Annual basin-averaged mean temperature (a) and precipitation (b) in the baseline period

-5

0

5

10

15

20

J F M A M J J A S O N D

Tem

pera

ture

[deg

C]

0

20

40

60

80

J F M A M J J A S O N DP

recip

itation [

mm

] WaterGAP

SWAT

(a) (b)

Fig 4 Mean monthly basin-averaged temperature (a) and precipitation (b) in the baseline period

-30

-10

10

30

50

J F M A M J J A S O N D

Re

lative

ch

an

ge

[

] IPSL-CM4

MIROC32

0

1

2

3

4

5

J F M A M J J A S O N D

Ab

so

lute

ch

an

ge

[d

eg

C

]

(a)

(b)

Fig 5 Basin-averaged changes in temperature (a) and precipitation (b) from IPSL-CM4 and

MIROC32

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

measuredSWATWaterGAP

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

0

5

10

15

20

25

30

J F M A M J J A S O N D

Ru

no

ff [m

m]

(a) Narew at Zambski

(b) Biebrza at Burzyn

(c) Narew at Suraż

Fig 6 Mean measured and simulated monthly runoff in the baseline at three analysed locations

450

500

550

600

650

700

1975 1980 1985 1990 1995 2000

PE

T [

mm

]

(a)

350

375

400

425

450

475

500

1975 1980 1985 1990 1995 2000

AE

T [

mm

]

(b)

80

100

120

140

160

180

200

220

1975 1980 1985 1990 1995 2000

Runoff

[m

m]

WaterGAP

SWAT

measured

(d)

-20

-15

-10

-5

0

5

10

15

20

1975 1980 1985 1990 1995 2000

Sto

rage c

hange in S

W [

mm

]

(c)

Fig 7 Annual time series of the basin-averaged water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (year-to-year) (d) Runoff

0

5

10

15

20

25

J F M A M J J A S O N DR

unoff

[m

m]

WaterGAP

SWAT

(d)

0

15

30

45

60

75

90

J F M A M J J A S O N D

AE

T [

mm

]

(b)

0

20

40

60

80

100

120

J F M A M J J A S O N D

PE

T [

mm

]

(a)

-40

-30

-20

-10

0

10

20

30

J F M A M J J A S O N DSto

rage c

hange in S

W

[mm

] (c)

Fig 8 Basin-averaged monthly dynamics of the water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (month-to-month) (d) Runoff

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

SWAT

WaterGAP-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

Rru

no

ff c

han

ge [

mm

] (a) Narew at Zambski (b) Biebrza at Burzyn (c) Narew at Suraż

Fig 9 Absolute changes in mean annual runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

10

20

30

40

50

60

70

0 2 4 6 8 10

Equalled or Exceeded

Ru

no

ff [

mm

]

Baseline_SWAT

IPSL-CM4_SWAT

MIROC32_SWAT

Baseline_WaterGAP

IPSL-CM4_WG

MIROC32_WG

Q5

Q10

(a)

0

2

4

6

8

90 92 94 96 98 100

Equalled or Exceeded

Q95

Q90

(b)

Fig 10 Empirical flow duration curves (EFDCs) of the monthly runoff simulated by SWAT and

WaterGAP for the baseline and two climate scenarios at Zambski (a) high runoff (b) low runoff

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

Ch

an

ge in

Q10 [

mm

]

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

(b) Biebrza at Burzyn(a) Narew at Zambski (c) Narew at Suraż

-2

-1

0

1

2

IPSL-CM4 MIROC32

Ch

an

ge in

Q90 [

mm

]

-2

-1

0

1

2

IPSL-CM4 MIROC32

-2

-1

0

1

2

IPSL-CM4 MIROC32

SWAT

WaterGAP

(d) Narew at Zambski (e) Biebrza at Burzyn (f) Narew at Suraż

Fig 11 Absolute changes in monthly Q10 (a-c) and Q90 (d-f) relative to baseline under two GCMs as

simulated by SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(b) Biebrza at Burzyn

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(c) Narew at Suraż

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

measuredSWATWaterGAP

0

5

10

1520

25

30

35

J F M A M J J A S O N D

Ru

no

ff [m

m]

0

5

10

15

20

25

30

J F M A M J J A S O N D

Ru

no

ff [m

m]

(a) Narew at Zambski

(b) Biebrza at Burzyn

(c) Narew at Suraż

Fig 6 Mean measured and simulated monthly runoff in the baseline at three analysed locations

450

500

550

600

650

700

1975 1980 1985 1990 1995 2000

PE

T [

mm

]

(a)

350

375

400

425

450

475

500

1975 1980 1985 1990 1995 2000

AE

T [

mm

]

(b)

80

100

120

140

160

180

200

220

1975 1980 1985 1990 1995 2000

Runoff

[m

m]

WaterGAP

SWAT

measured

(d)

-20

-15

-10

-5

0

5

10

15

20

1975 1980 1985 1990 1995 2000

Sto

rage c

hange in S

W [

mm

]

(c)

Fig 7 Annual time series of the basin-averaged water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (year-to-year) (d) Runoff

0

5

10

15

20

25

J F M A M J J A S O N DR

unoff

[m

m]

WaterGAP

SWAT

(d)

0

15

30

45

60

75

90

J F M A M J J A S O N D

AE

T [

mm

]

(b)

0

20

40

60

80

100

120

J F M A M J J A S O N D

PE

T [

mm

]

(a)

-40

-30

-20

-10

0

10

20

30

J F M A M J J A S O N DSto

rage c

hange in S

W

[mm

] (c)

Fig 8 Basin-averaged monthly dynamics of the water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (month-to-month) (d) Runoff

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

SWAT

WaterGAP-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

Rru

no

ff c

han

ge [

mm

] (a) Narew at Zambski (b) Biebrza at Burzyn (c) Narew at Suraż

Fig 9 Absolute changes in mean annual runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

10

20

30

40

50

60

70

0 2 4 6 8 10

Equalled or Exceeded

Ru

no

ff [

mm

]

Baseline_SWAT

IPSL-CM4_SWAT

MIROC32_SWAT

Baseline_WaterGAP

IPSL-CM4_WG

MIROC32_WG

Q5

Q10

(a)

0

2

4

6

8

90 92 94 96 98 100

Equalled or Exceeded

Q95

Q90

(b)

Fig 10 Empirical flow duration curves (EFDCs) of the monthly runoff simulated by SWAT and

WaterGAP for the baseline and two climate scenarios at Zambski (a) high runoff (b) low runoff

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

Ch

an

ge in

Q10 [

mm

]

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

(b) Biebrza at Burzyn(a) Narew at Zambski (c) Narew at Suraż

-2

-1

0

1

2

IPSL-CM4 MIROC32

Ch

an

ge in

Q90 [

mm

]

-2

-1

0

1

2

IPSL-CM4 MIROC32

-2

-1

0

1

2

IPSL-CM4 MIROC32

SWAT

WaterGAP

(d) Narew at Zambski (e) Biebrza at Burzyn (f) Narew at Suraż

Fig 11 Absolute changes in monthly Q10 (a-c) and Q90 (d-f) relative to baseline under two GCMs as

simulated by SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(b) Biebrza at Burzyn

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(c) Narew at Suraż

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

5

10

15

20

25

J F M A M J J A S O N DR

unoff

[m

m]

WaterGAP

SWAT

(d)

0

15

30

45

60

75

90

J F M A M J J A S O N D

AE

T [

mm

]

(b)

0

20

40

60

80

100

120

J F M A M J J A S O N D

PE

T [

mm

]

(a)

-40

-30

-20

-10

0

10

20

30

J F M A M J J A S O N DSto

rage c

hange in S

W

[mm

] (c)

Fig 8 Basin-averaged monthly dynamics of the water balance components in the baseline period as

simulated by WaterGAP and SWAT (a) Potential Evapotranspiration (b) Actual Evapotranspiration

(c) Storage change in soil water (month-to-month) (d) Runoff

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

SWAT

WaterGAP-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

-50

-40

-30

-20

-10

0

10

20

IPSL-CM4 MIROC32

Rru

no

ff c

han

ge [

mm

] (a) Narew at Zambski (b) Biebrza at Burzyn (c) Narew at Suraż

Fig 9 Absolute changes in mean annual runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

0

10

20

30

40

50

60

70

0 2 4 6 8 10

Equalled or Exceeded

Ru

no

ff [

mm

]

Baseline_SWAT

IPSL-CM4_SWAT

MIROC32_SWAT

Baseline_WaterGAP

IPSL-CM4_WG

MIROC32_WG

Q5

Q10

(a)

0

2

4

6

8

90 92 94 96 98 100

Equalled or Exceeded

Q95

Q90

(b)

Fig 10 Empirical flow duration curves (EFDCs) of the monthly runoff simulated by SWAT and

WaterGAP for the baseline and two climate scenarios at Zambski (a) high runoff (b) low runoff

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

Ch

an

ge in

Q10 [

mm

]

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

(b) Biebrza at Burzyn(a) Narew at Zambski (c) Narew at Suraż

-2

-1

0

1

2

IPSL-CM4 MIROC32

Ch

an

ge in

Q90 [

mm

]

-2

-1

0

1

2

IPSL-CM4 MIROC32

-2

-1

0

1

2

IPSL-CM4 MIROC32

SWAT

WaterGAP

(d) Narew at Zambski (e) Biebrza at Burzyn (f) Narew at Suraż

Fig 11 Absolute changes in monthly Q10 (a-c) and Q90 (d-f) relative to baseline under two GCMs as

simulated by SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(b) Biebrza at Burzyn

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(c) Narew at Suraż

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

Ch

an

ge in

Q10 [

mm

]

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

-8

-6

-4

-2

0

2

4

IPSL-CM4 MIROC32

(b) Biebrza at Burzyn(a) Narew at Zambski (c) Narew at Suraż

-2

-1

0

1

2

IPSL-CM4 MIROC32

Ch

an

ge in

Q90 [

mm

]

-2

-1

0

1

2

IPSL-CM4 MIROC32

-2

-1

0

1

2

IPSL-CM4 MIROC32

SWAT

WaterGAP

(d) Narew at Zambski (e) Biebrza at Burzyn (f) Narew at Suraż

Fig 11 Absolute changes in monthly Q10 (a-c) and Q90 (d-f) relative to baseline under two GCMs as

simulated by SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(b) Biebrza at Burzyn

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

(c) Narew at Suraż

-14

-10

-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

0

5

10

15

20

25

J F M A M J J A S O N D

Runoff [m

m]

(b) Biebrza at Burzyn

0

5

10

15

20

25

30

J F M A M J J A S O N D

Runoff [m

m]

(c) Narew at Suraż

0

5

10

15

20

25

30

35

J F M A M J J A S O N D

Runoff [m

m]

IP S L -C M 4_S W A T

IP S L -C M 4_W aterG A P

M IR O C 32_S W A T

M IR O C 32_W aterG A P

B as el in e_S W A T

B as el in e_W aterG A P

Fig 12 Monthly runoff hydrographs simulated by SWAT and WaterGAP for the baseline and under

two climate scenarios

(a) Narew at Zambski

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-10

-6

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J F M A M J J A S O N D

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[m

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(b) Biebrza at Burzyn

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J F M A M J J A S O N D

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[m

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(c) Narew at Suraż

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J F M A M J J A S O N D

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[m

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IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż

(a) Narew at Zambski

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-10

-6

-2

2

6

10

J F M A M J J A S O N D

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no

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an

ge

[m

m]

(b) Biebrza at Burzyn

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-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

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ge

[m

m]

(c) Narew at Suraż

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-6

-2

2

6

10

J F M A M J J A S O N D

Ru

no

ff ch

an

ge

[m

m]

IPSL-CM 4_SWAT

IPSL-CM 4_WaterGAP

M IROC32_SWAT

M IROC32_WaterGAP

Fig 13 Absolute changes in mean monthly runoff relative to baseline under two GCMs as simulated by

SWAT and WaterGAP at Zambski Burzyn and Suraż