Uncertainty of modelled flow regime for flow-ecological assessment in Mediterranean environments

Supplementary Information

1. Overview of hydrological model processes and input data

Table SI1.1. Overview of hydrological model processes

Hydrological process methods

LISFLOOD mHM ADSWAT EUSWAT EVSWAT HYPERstream

Snow & snowmelt

Degree-day Degree-day

Interception Storage based equation using daily LAI (Merriam; Aston; Von Hoyningen-Huene)

Maximum interception using mean monthly leaf area index (LAI)

Curve Number CN method

Potential evapotranspiration (PET)

Penman-Monteith

EartH2Observe project, 2017)

Hargreaves-Samani

Penman-Monteith Penman-Monteith

Hargreaves-Samani

Actual evapotranspiration

Derived from PET using plant canopy, limited by soil moisture

Derived from PET; differentiation between canopy, soil, water bodies, and sealed areas

Derived from PET as a function of plant canopy, soil sublimation and evaporation

Derived from PET , linearly limited by soil moisture

Below roots drainage

Percolation to baseflow compartment

Surface runoff Surplus rainfall and snowmelt

Surface runoff from impervious areas

Curve Number CN method; rational method for peak runoff

compared to infiltration rate

(linear reservoir exceedance)

Lateral flow At 5km scale, each cell is supposed to contain river, and no subsurface lateral flow between pixels; surface flow using kinematic wave method

Fast and slow interflow (nonlinear reservoirs with saturation excess)

Kinematic storage model Non-linear bucket model

Baseflow Groundwater linear reservoir with quick and slow component

Groundwater linear reservoir

nonlinear reservoir approach Baseflow recession constant

Linear reservoir approach

Streamflow routing

Double Kinematic wave routing

Muskingum river routing method

variable storage coefficient method WFIUH routing

Abstractions Abstractions per sector given as user input; irrigation water demand calculated using evapotranspiration deficit in irrigated areas, multiplied by efficiency, conveyance loss and safety coefficients

NA NA Vandecasteele et al. (2014)

Abstractions applied in response to soil water deficit

NA

Reservoirs Several hundreds of reservoirs included, with

NA NA NA NA

estimated outflow behaviour

Others Lakes included, with estimated outflow behaviour; environmental/minimum flow requirement included

Tile drainage: Hooghoudt-Kirkham-Drainmod;irrigation as auto-irrigation

References Van der Knijff et al., 2008; De Roo et al, 2000.

Samaniego et al. (2010); Kumar et al. (2013); Rakovec et al. (2016)

Neitsch et al. 2011Tuo et al., 2016.

Malagó et al., 2015; 2017

Gamvroudis (2016); Gamvroudis et al., (2015;2017)

Piccolroaz et al., 2016; Bellin et al., 2016; Majone et al., 2010

Table SI1.2. Overview of hydrological model input data

Hydrological process methods

LISFLOOD mHM ADSWAT EUSWAT EVSWAT HYPERstream

Climate P: JRC EFAS gridded precipitation product 1990-2014 at 5km resolutionT: JRC-EFAS gridded product

P and T: E-OBS: Haylock et al., 2008

Autonomous Province of Trento (http://www.meteotrentino.it) and Autonomous Province of Bolzano(http://www.provincia.bz.it/meteo/home.as)

EFAS METEO (Ntegeva et al., 2013)

Hellenic Ministry of Rural Development and Food; Hellenic Ministry for the Environment, Physical Planning and Public Works; daily precipitation and air temperature

Autonomous Province of Trento (http://www.meteotrentino.it) and Autonomous Province of Bolzano(http://www.provincia.bz.it/meteo/home.as)

Land use CORINE Land Cover (European Environment Agency, 2013a) at 5x5 km resolution but with 100m sub-grid classes

CORINE Land Cover (European Environment Agency, 2013a) at 0.5x0.5 km² resolution

Corine Land Cover 2006 (CLC2006) 100 m × 100 m

CAPRI,SAGE,HYDE 3,GLC2000

Corine Land Cover CLC2000 (EEA, 2007); scale 1:100,000

Corine Land Cover 2006 (CLC2006) 100 m × 100 m

DEM EU-DEM (European Environment Agency, 2013b) at 5x5 km resolution

EU-DEM (European Environment Agency, 2013b) at 0.5x0.5 km² resolution

90m NASA SRTM CCM2 DEM (Vogt et al., 2007)

90m NASA SRTM 30m EU-DEM (http://www.eea.europa.eu/data-and-maps/data/eu-dem

Soil ISRIC 1km SoilGrids data (Hengl et al, 2017)

Harmonized World Soil Database v 1.2 (http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-

Food and Agriculture Organization (FAO, 1995; 2008): 1:1,500,000

Adige River basin Authority (www.bacino-adige.it)

soil-database-v12/en/) at 0.5x0.5 km² resolution

River Discharge

The Global Runoff Data Centre, GRDC, http://www.bafg.de/GRDC/EN/Home/homepage_node.html

Various national providers through JRC EFAS system.

Ebro: Confederación Hidrográfica Ebro (CEH; http://ceh-flumen64.cedex.es/general/default.htm); the Global Runoff Data Centre, GRDC, http://www.bafg.de/GRDC/EN/Home/homepage_node.html;

Sava: Environmental 333333Agency of the Republic of Slovenia, ARSO, www.arso.gov.si; and national water agency of Croatia, Hrvatske vode, www.voda.hr); the Serbian Environmental Protection Agency (www. sepa.gov.rs)

Autonomous Province of Trento and Bolzano

Autonomous Provinces of Trento and Bolzano

Others Geology: International Hydrogeological Map of Europe for groundwater aquifers

River Network:

Geology: International Hydrogeological Map of Europe 1:1,500,000 (IHME1500; http://www.bgr.bund.de/EN/Themen/W

River network:EU-DEM product http://www.eea.europa.eu/data-and-maps/data/eu-dem

River network: CCM2 (Vogt et al. 2007)Global Drainage Map (www.uni-

JRC, 2000. asser/Projekte/laufend/Beratung/Ihme1500/ihme1500_projektbeschr_en.html) at 0.5x0.5 km² resolution;Leaf Area Index: MODIS (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php) at 0.5x0.5 km² resolution;

frankfurt.de)

References section SI.1

EartH2Observe project; 2017. https://wci.earth2observe.eu/thredds/catalog/deltares/PET/wrr2/0.083degree/penmanmonteith/catalog.html [ accessed Apr 2017]

European Environment Agency (EEA), 2007. Corine Land Cover Changes 1990–2000 by Country.

European Environment Agency (EEA), 2013a. Corine Land Cover 2006 seamless vector data (Version 17). http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-3.

European Environment Agency (EEA), Digital Elevation Model over Europe (EU-DEM), 2013b, http://www.eea.europa.eu/data-and-maps/data/eu-dem.

FAO, 1995. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization of the United Nations, Rome

Haylock, M. R.; Hofstra, N.; Klein Tank, A. M. G.; Klok, E. J.; Jones, P. D.; New, M., A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres 2008, 113, (D20), doi:10.1029/2008JD010201.

Lehner, R., Döll, P., 2004. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296 (1–4), 1–22.

Ntegeka, V., Salamon, P., Gomes, G., Sint, H., Lorini, V. and & Thielen, J. 2013. EFAS-Meteo: A European daily high-resolution gridded meteorological data set for 1990 – 2011.

Vogt, J., Soille, P., de Jager, A., Rimaviciute, E., Mehl, W., Foisneau, S., Bodis, K., Dusart, J. Paracchini, M.L., Haastrup, P., Bamps, C. 2007. A pan-European River and catchment Database. JRC Reference Reports, EUR 229220 EN.

2. Station monthly modelled and observed streamflow (mm/month) in 1990-2015.

Color legend: Observed discharge = black, ADSWAT = grey: UNITN = Brown; EVSWAT = Green; mHM = orange; EUSWAT = pink, Lisflood = blue; UniTNat = cyan; LisfQNat = dark blue. Horizontal dashed lines indicate ERFA upper and lower thresholds (5th and 95th percentile exceedance flow estimated for one decade according to observed discharge (black) or LisfQnat (grey).

2.1 Adige

1990 1995 2000 2005 2010 2015

02

46

810

Index

Stre

amflo

w (m

m/m

onth

)

Caminata

1990 1995 2000 2005 2010 2015

02

46

8

Index

Stre

amflo

w (m

m/m

onth

)

Male

1990 1995 2000 2005 2010 2015

02

46

8

Index

Stre

amflo

w (m

m/m

onth

)

Mezzolombardo

1990 1995 2000 2005 2010 2015

01

23

45

6

Index

Stre

amflo

w (m

m/m

onth

)

Bronzolo

1990 1995 2000 2005 2010 2015

01

23

45

6

Index

Stre

amflo

w (m

m/m

onth

)

Trento ponte S. Lorenzo

2.2 Ebro

1990 1995 2000 2005 2010 2015

02

46

810

12

Index

Stre

amflo

w (m

m/m

onth

)

Seo de Urgel

1990 1995 2000 2005 2010 2015

02

46

Index

Stre

amflo

w (m

m/m

onth

)

Miranda

1990 1995 2000 2005 2010 2015

01

23

45

Index

Stre

amflo

w (m

m/m

onth

)

Yesa

1990 1995 2000 2005 2010 2015

0.0

0.1

0.2

0.3

0.4

0.5

Index

Stre

amflo

w (m

m/m

onth

)

Grisen

1990 1995 2000 2005 2010 2015

0.0

0.5

1.0

1.5

Index

Stre

amflo

w (m

m/m

onth

)

Asco Coca

2.3. Evrotas

1990 1995 2000 2005 2010 2015

02

46

8

Index

Stre

amflo

w (m

m/m

onth

)

Vivari Sellasia

1990 1995 2000 2005 2010 2015

02

46

8

Index

Stre

amflo

w (m

m/m

onth

)

Kelefina Kladas

1990 1995 2000 2005 2010 2015

02

46

8

Index

Stre

amflo

w (m

m/m

onth

)

Sparti Bridge

1990 1995 2000 2005 2010 2015

01

23

45

6

Index

Stre

amflo

w (m

m/m

onth

)

Vrontamas Bridge

2.4 Sava

1990 1995 2000 2005 2010 2015

02

46

Index

Stre

amflo

w (m

m/m

onth

)

Prijepolje

1990 1995 2000 2005 2010 2015

02

46

810

12

Index

Stre

amflo

w (m

m/m

onth

)

Litija

1990 1995 2000 2005 2010 2015

01

23

45

Index

Stre

amflo

w (m

m/m

onth

)

Slavonski Brod

1990 1995 2000 2005 2010 2015

01

23

4

Index

Stre

amflo

w (m

m/m

onth

)

Sremska Mitrovica

0 2 4 6

02

46

Simulated daily flow (mean per month; mm/d)

Observed daily flow (mean per month; mm/d)

Pre

dict

ed e

rror (

mm

/d)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

-3-2

-10

12

Prediction error by month (all models)

Month

Pre

dict

ed e

rror (

mm

/d)

S2.19. Above: modelled vs observed mean daily flow/month . below: boxplot of monthly prediction errors in data ensemble (all staiton-model combinations)

3. Station flow regime indicators estimated from observed flow or with models over the reference decade 2000-2009.

0

200

400

600

800

1000

Annu

al fl

ow

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

80

100

120

140

160

180

200

Day

50

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

-1

0

1

2

3

win

ter

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

-1

0

1

2

3

4

sprin

g

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0

2

4

6

sum

mer

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

-1

0

1

2

3

4

autu

mn

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

SI3.1. Water Resource Indicators (WRIs, Shresta et al., 2014) in the reference decade calculated with hydrological models and from observed flow per station. Model legend: Observed flow Q = empty black circle; ADSWAT = grey; Hyper = brown; mHM = orange, EVSWAT = green, Lisflood = empty blue circle.

0.00.51.01.52.02.53.0

MD

F

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0.3

0.4

0.5

0.6

0.7

CV

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0.20.3

0.40.50.60.7

Skew

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0.0

0.1

0.2

0.3

0.4

0.5

Kurt

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0.0

0.10.2

0.30.4

0.50.6

AR1

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0.2

0.4

0.6

0.8

1.0

1.2

Ampl

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

-1.5

-1.0

-0.5

0.0

0.5

1.0

Phas

e

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

SI3.2. Mag7 IHAs (Archfield et al., 2012) in the reference decade calculated with hydrological models and from observed flow per station. Model legend: Observed flow Q = empty black circle; ADSWAT = grey; Hyper = brown; mHM = orange, EVSWAT = green, Lisflood = empty blue circle.

0

1

2

3

4

5

E85

Cam

#6.

04M

al #

2.68

Mez

#3.

7Br

o #2

.9Tr

e #2

.47

Pri #

3.46

Lit #

4.14

Sla

#2.6

3Sr

e #2

.43

Seo

#2.1

5Ye

s #0

.94

Mir

#0.8

4G

ri #0

.05

Asc

#0.5

4Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

20

40

60

80

100

MA2

6

Cam

#18

Mal

#23

Mez

#41

Bro

#20

Tre

#23

Pri #

40Li

t #46

Sla

#37

Sre

#29

Seo

#52

Yes

#44

Mir

#75

Gri

#83

Asc

#44

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

0

2

4

6

8

MH

10

Cam

#5.

33M

al #

3.58

Mez

#3.

77Br

o #3

.63

Tre

#3.3

3Pr

i #3.

54Li

t #7.

53Sl

a #1

.9Sr

e #1

.74

Seo

#1.5

6Ye

s #0

.29

Mir

#0.8

8G

ri #0

.03

Asc

#0.4

2Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

20

40

60

80

100

120

140

ML1

8

Cam

#6.

53M

al #

22.6

4M

ez #

26.1

5Br

o #1

1.68

Tre

#14.

97Pr

i #31

.96

Lit #

23.6

3Sl

a #2

5.61

Sre

#24.

64Se

o #7

8Ye

s #7

7.94

Mir

#32.

91G

ri #5

5.33

Asc

#33.

23Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

0.4

0.6

0.8

1.0

ML2

0

Cam

#0.

77M

al #

0.72

Mez

#0.

5Br

o #0

.77

Tre

#0.7

1Pr

i #0.

67Li

t #0.

65Sl

a #0

.73

Sre

#0.7

8Se

o #0

.63

Yes

#0.4

7M

ir #0

.56

Gri

#0.5

8As

c #0

.69

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

0

1

2

3

4

SEP_

mea

n

Cam

#3.

13M

al #

1.23

Mez

#1.

97Br

o #1

.67

Tre

#1.3

6Pr

i #0.

57Li

t #2.

1Sl

a #0

.77

Sre

#0.7

2Se

o #0

.32

Yes

#0.2

1M

ir #0

.26

Gri

#0.0

1As

c #0

.2Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

SI3.3. Magnitude IHAs (selected from Murphy et al., 2013) in the reference decade calculated with hydrological models and from observed flow per station. Model legend: Observed flow Q = empty black circle; ADSWAT = grey; Hyper = brown; mHM = orange, EVSWAT = green, Lisflood = empty blue circle.

0.0

0.2

0.4

0.6

0.8

TA1

Cam

#0.

11M

al #

0.24

Mez

#0.

1Br

o #0

.09

Tre

#0.1

5Pr

i #0.

17Li

t #0.

07Sl

a #0

.32

Sre

#0.2

1Se

o #0

.63

Yes

#0.8

1M

ir #0

.72

Gri

#0.7

1As

c #0

.71

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

50

100

150

200

250

300

350

TH1

Cam

#18

5.67

Mal

#19

3.5

Mez

#74

.75

Bro

#202

.23

Tre

#254

.59

Pri #

359.

21Li

t #31

9.36

Sla

#42.

54Sr

e #9

2.85

Seo

#129

.86

Yes

#64.

13M

ir #4

0.71

Gri

#113

.74

Asc

#84.

68Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

50

100

150

200

250

300

350

TL1

Cam

#49

.29

Mal

#42

.75

Mez

#24

2.52

Bro

#59.

32Tr

e #4

4.96

Pri #

266.

53Li

t #25

5.34

Sla

#246

.01

Sre

#240

.51

Seo

#277

.12

Yes

#262

.61

Mir

#298

.12

Gri

#178

.64

Asc

#303

.39

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

2

4

6

8

10

12

FH6

Cam

#8

Mal

#5.

33M

ez #

0.88

Bro

#4Tr

e #3

.22

Pri #

5.33

Lit #

6.56

Sla

#3.1

1Sr

e #1

.11

Seo

#6.6

7Ye

s #4

.38

Mir

#5.8

9G

ri #5

.43

Asc

#5.1

2Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

0

1

2

3

4

5

FH7

Cam

#0.

56M

al #

0.89

Mez

#0.

12Br

o #0

.22

Tre

#0.5

6Pr

i #1.

33Li

t #1

Sla

#0Sr

e #0

Seo

#2.3

3Ye

s #5

.12

Mir

#4.1

1G

ri #2

.71

Asc

#1.7

5Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

30

40

50

60

70

80

FL2

Cam

#55

.8M

al #

41.9

2M

ez #

40.2

8Br

o #4

0.58

Tre

#49.

54Pr

i #47

.24

Lit #

47.3

5Sl

a #3

9.46

Sre

#37.

18Se

o #7

1.56

Yes

#81.

82M

ir #5

6.38

Gri

#60.

38As

c #8

8.35

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

SI3.4. Timing and frequency IHAs (selected from Murphy et al., 2013) in the reference decade calculated with hydrological models and from observed flow per station. Model legend: Observed flow Q = empty black circle; ADSWAT = grey; Hyper = brown; mHM = orange, EVSWAT = green, Lisflood = empty blue circle.

2

4

6

8

10

12

14

DH

13

Cam

#4.

03M

al #

3.27

Mez

#1.

82Br

o #2

.61

Tre

#2.6

4Pr

i #3.

37Li

t #2.

91Sl

a #2

.98

Sre

#2.5

2Se

o #7

.51

Yes

#9.7

6M

ir #4

.95

Gri

#10.

8As

c #4

.31

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

40

60

80

100

DH

16

Cam

#50

.17

Mal

#55

.68

Mez

#90

.3Br

o #8

1.83

Tre

#68.

41Pr

i #76

.66

Lit #

54.8

9Sl

a #4

9.6

Sre

#43.

4Se

o #3

5.18

Yes

#83.

32M

ir #5

3.76

Gri

#60.

31As

c #6

1.1

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

50

100

150

DL6

Cam

#15

.19

Mal

#33

.44

Mez

#50

.57

Bro

#18.

57Tr

e #4

0.9

Pri #

18.4

9Li

t #13

.59

Sla

#19.

31Sr

e #2

6.52

Seo

#98.

39Ye

s #1

9.52

Mir

#29.

42G

ri #N

AAs

c #1

6.21

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

0.1

0.2

0.3

0.4

RA5

Cam

#0.

39M

al #

0.46

Mez

#0.

48Br

o #0

.45

Tre

#0.4

6Pr

i #0.

24Li

t #0.

29Sl

a #0

.34

Sre

#0.3

8Se

o #0

.4Ye

s #0

.13

Mir

#0.3

4G

ri #0

.31

Asc

#0.4

2Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

RA7

Cam

#0.

05M

al #

0.08

Mez

#0.

13Br

o #0

.07

Tre

#0.1

Pri #

0.05

Lit #

0.08

Sla

#0.0

5Sr

e #0

.04

Seo

#0.2

2Ye

s #0

.14

Mir

#0.0

9G

ri #0

.16

Asc

#0.0

5Vi

v #N

AKe

l #N

ASp

a #N

AVr

o #N

A

50

100

150

RA8

Cam

#12

8.89

Mal

#16

5.78

Mez

#19

7.38

Bro

#160

.44

Tre

#152

.78

Pri #

68.3

3Li

t #11

0Sl

a #6

5.33

Sre

#76.

44Se

o #1

60.8

9Ye

s #5

2.38

Mir

#116

.89

Gri

#110

.43

Asc

#161

.12

Viv

#NA

Kel #

NA

Spa

#NA

Vro

#NA

SI3.5. Duration and rate of change IHAs (selected from Murphy et al., 2013) in the reference decade calculated with hydrological models and from observed flow per station. Model legend: Observed flow Q = empty black circle; ADSWAT = grey; Hyper = brown; mHM = orange, EVSWAT = green, Lisflood = empty blue circle.

0

20

40

60

80

med

_Jan

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0

20

40

60

80

IQR

_Jan

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

020406080

100120

med

_Apr

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0

20

40

60

80

IQR

_Apr

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0

50

100

150

200

med

_Jul

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0102030405060

IQR

_Jul

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

020406080

100120

med

_Oct

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0

20

40

60

IQR

_Oct

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

SI3.6. Timing MFRIs (Laize’ et al., 2014) in the reference decade calculated with hydrological models and from observed flow per station. Model legend: Observed flow Q = empty black circle; ADSWAT = grey; Hyper = brown; mHM = orange, EVSWAT = green, Lisflood = empty blue circle.

0.00.51.01.52.02.53.0

med

_h

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0.00.51.01.52.02.53.0

IQR

_h

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0

2

4

6

med

_low

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0

1

2

3

4

IQR

_low

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

2

4

6

8

10

12

Mon

_h

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

2

4

6

8

10

Mon

_low

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0.00.51.01.52.02.53.0

med

_seq

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

0.0

0.5

1.0

1.5

2.0

IQR

_seq

Cam M

alM

ez Bro

Tre

Pri

Lit

Sla

Sre

Seo

Yes

Mir

Gri

Asc

Viv

Kel

Spa

Vro

SI3.7. Magnitude, frequency and duration MFRIs (Laize’ et al., 2014) in the reference decade calculated with hydrological models and from observed flow per station. Model legend: Observed flow Q = empty black circle; ADSWAT = grey; Hyper = brown; mHM = orange, EVSWAT = green, Lisflood = empty blue circle.

4. Enlarged scatter plot of modelled vs observed indicators for the reference decade 2000-2009

0 200 400 600 800 1000

020

040

060

080

010

00

Annual flow (mm/y)

Observed indicator

Mod

elle

d in

dica

tor

80 100 120 140 160 180 200

8010

012

014

016

018

020

0Day 50 (Julian day)

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

median winter flow (mm/d)

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

median spring flow (mm/d)

Observed indicator

Mod

elle

d in

dica

tor

0 1 2 3 4 5

01

23

45

median summer flow (mm/d)

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

median autumn flow (mm/d)

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Mean Daily Flow (mm/d)

Observed indicator

Mod

elle

d in

dica

tor

0.3 0.4 0.5 0.6 0.7

0.3

0.4

0.5

0.6

0.7

Coeff of Variation

Observed indicator

Mod

elle

d in

dica

tor

0.2 0.3 0.4 0.5 0.6 0.7

0.2

0.3

0.4

0.5

0.6

0.7

Skeweness

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.1 0.2 0.3 0.4 0.5

0.0

0.1

0.2

0.3

0.4

0.5

Kurtosis

Observed indicator

Mod

elle

d in

dica

tor

0.2 0.4 0.6 0.8 1.0 1.2

0.2

0.4

0.6

0.8

1.0

1.2

Amplitude

Observed indicator

Mod

elle

d in

dica

tor

-1.5 -1.0 -0.5 0.0 0.5

-1.5

-1.0

-0.5

0.0

0.5

Phase

Observed indicator

Mod

elle

d in

dica

tor

0.30 0.35 0.40 0.45 0.50 0.55

0.30

0.35

0.40

0.45

0.50

0.55

AR1

Observed indicator

Mod

elle

d in

dica

tor

0 1 2 3 4 5 6

01

23

45

6E85

Observed indicator

Mod

elle

d in

dica

tor

20 40 60 80 100

2040

6080

100

MA26

Observed indicator

Mod

elle

d in

dica

tor

0 2 4 6 8

02

46

8MH10

Observed indicator

Mod

elle

d in

dica

tor

20 40 60 80 100

2040

6080

100

ML18

Observed indicator

Mod

elle

d in

dica

tor

0.4 0.6 0.8 1.0

0.4

0.6

0.8

1.0

ML20

Observed indicator

Mod

elle

d in

dica

tor

0 1 2 3 4

01

23

4SEP_mean

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

0.8

TA1

Observed indicator

Mod

elle

d in

dica

tor

50 100 150 200 250 300 350

5010

015

020

025

030

035

0TH1

Observed indicator

Mod

elle

d in

dica

tor

2 4 6 8 10 12

24

68

1012

FH6

Observed indicator

Mod

elle

d in

dica

tor

50 100 150 200 250 300 350

5010

015

020

025

030

035

0TL1

Observed indicator

Mod

elle

d in

dica

tor

0 1 2 3 4 5

01

23

45

FH7

Observed indicator

Mod

elle

d in

dica

tor

30 40 50 60 70 80 90

3040

5060

7080

90FL2

Observed indicator

Mod

elle

d in

dica

tor

2 4 6 8 10

24

68

10DH13

Observed indicator

Mod

elle

d in

dica

tor

40 60 80 100

4060

8010

0DH16

Observed indicator

Mod

elle

d in

dica

tor

0 20 40 60 80 100

020

4060

8010

0DL6

Observed indicator

Mod

elle

d in

dica

tor

0.1 0.2 0.3 0.4 0.5 0.6

0.1

0.2

0.3

0.4

0.5

0.6

RA5

Observed indicator

Mod

elle

d in

dica

tor

-0.1 0.0 0.1 0.2 0.3

-0.1

0.0

0.1

0.2

0.3

RA7

Observed indicator

Mod

elle

d in

dica

tor

50 100 150 200

5010

015

020

0RA8

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.5

1.0

1.5

2.0

2.5

med_h

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

IQR_h

Observed indicator

Mod

elle

d in

dica

tor

0 2 4 6

02

46

med_low

Observed indicator

Mod

elle

d in

dica

tor

0 1 2 3 4 5

01

23

45

IQR_low

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

med_Jan

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

IQR_Jan

Observed indicator

Mod

elle

d in

dica

tor

0 1 2 3 4

01

23

4med_Apr

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.5

1.0

1.5

2.0

2.5

IQR_Apr

Observed indicator

Mod

elle

d in

dica

tor

0 1 2 3 4 5 6

01

23

45

6med_Jul

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

IQR_Jul

Observed indicator

Mod

elle

d in

dica

tor

0 1 2 3

01

23

med_Oct

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.5

1.0

1.5

2.0

2.5

IQR_Oct

Observed indicator

Mod

elle

d in

dica

tor

2 4 6 8 10 12

24

68

1012

Mon_h

Observed indicator

Mod

elle

d in

dica

tor

2 4 6 8 10

24

68

10Mon_low

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

med_seq

Observed indicator

Mod

elle

d in

dica

tor

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

IQR_seq

Observed indicator

Mod

elle

d in

dica

tor

5. Impact of period length (ten, 15, or 20 years)

10 yrs 15 yrs 20 yrs

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Mean

10 yrs 15 yrs 20 yrs0.

00.

51.

01.

52.

02.

5

StDev

10 yrs 15 yrs 20 yrs

1.0

1.5

2.0

2.5

3.0

Skew

10 yrs 15 yrs 20 yrs

05

1015

Kurt

SI5.1. Changes in mean, standard deviation, skweness and kurtosis of observed daily flow when enlarging the period of analysis.

10 yrs 15 yrs 20 yrs

0.0

0.5

1.0

1.5

RM

SE

10 yrs 15 yrs 20 yrs

0.4

0.6

0.8

1.0

1.2

1.4

RS

R

10 yrs 15 yrs 20 yrs

-50

050

100

PB

IAS

10 yrs 15 yrs 20 yrs

-1.0

-0.5

0.0

0.5

NS

E

SI5.2. Boxplot of monthly model performances (RMSE in mm/month, RSR, PIAS and NSE) when enlarging the period of analysis for the data ensembe (all station-model combinations).

0.0

0.2

0.4

0.6

0.8

1.0

Cam

Exceedance probability

Q m

3/s

0.1

1

10

100

0.0

0.2

0.4

0.6

0.8

1.0

Mez

Exceedance probability

Q m

3/s

1

10

100

0.0

0.2

0.4

0.6

0.8

1.0

Mal

Exceedance probability

Q m

3/s

1

10

1000.

0

0.2

0.4

0.6

0.8

1.0

Bro

Exceedance probability

Q m

3/s

10

100

1000

0.0

0.2

0.4

0.6

0.8

1.0

Tre

Exceedance probability

Q m

3/s

10

100

1000

SI5.3. Adige stations flow duration curves for ten (continuous lines) or 20 years (dashed lines). Color legend: Observed flow Q = black; HYPERstream = brown; mHM = orange; Lisflood = blue.

0.0

0.2

0.4

0.6

0.8

1.0

Seo

Exceedance probability

Q m

3/s

0.1

1

10

100

0.0

0.2

0.4

0.6

0.8

1.0

Yes

Exceedance probabilityQ

m3/

s

1

10

100

1000

0.0

0.2

0.4

0.6

0.8

1.0

Gri

Exceedance probability

Q m

3/s

0.01

0.1

1

10

100

0.0

0.2

0.4

0.6

0.8

1.0

Asc

Exceedance probability

Q m

3/s

100

1000

SI5.4. Ebro stations flow duration curves for ten (continuous lines) or 20 years (dashed lines). Color legend: Observed flow Q = black; HYPERstream = brown; mHM = orange; Lisflood = blue.

0.0

0.2

0.4

0.6

0.8

1.0

Pri

Exceedance probability

Q m

3/s

0.1

1

10

100

1000

0.0

0.2

0.4

0.6

0.8

1.0

Lit

Exceedance probabilityQ

m3/

s

10

100

1000

0.0

0.2

0.4

0.6

0.8

1.0

Sla

Exceedance probability

Q m

3/s

100

1000

0.0

0.2

0.4

0.6

0.8

1.0

Sre

Exceedance probability

Q m

3/s

100

1000

SI5.5. Sava stations flow duration curves for ten (continuous lines) or 20 years (dashed lines). Color legend: Observed flow Q = black; HYPERstream = brown; mHM = orange; Lisflood = blue.