EFAS upgrade for the extended model domain : technical documentation.
Date: 2019-03-11
Institutions: Joint Research Centre
Subjects: documentation, environmental risk prevention, flood, hydrology, information system, meteorology, natural disaster, research report

EFAS upgrade for the extended 
model domain 

Technical documentation 

2019

EUR 29323 EN

 
 
 
 
 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science 
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the European Commission nor any person acting on behalf of the Commission is responsible for the use that 
might be made of this publication. 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC111610  

PDF   ISBN 978-92-79-92881-9    ISSN  1831-9424      doi:10.2760/806324 

Ispra: European Commission, 2019 

© European Union, 2019 

Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents 
is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). 

For any use or reproduction of photos or other material that is not under the EU copyright, permission must be 
sought directly from the copyright holders. 

How to cite this report: L. Arnal, S.-S. Asp, C. Baugh, A. de Roo, J. Disperati, F. Dottori, R. Garcia, M. Garcia-
Padilla, E. Gelati, G. Gomes, M. Kalas, B. Krzeminski, M. Latini, V. Lorini, C. Mazzetti, M. Mikulickova, D. 
Muraro, C. Prudhomme, A. Rauthe-Schöch, K. Rehfeldt, P. Salamon, C. Schweim, J.O. Skoien, P. Smith, E. 
Sprokkereef, V. Thiemig, F. Wetterhall, M. Ziese, EFAS upgrade for the extended model domain – technical 
documentation, EUR 29323 EN, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-
79-92881-9, doi:10.2760/806324, JRC111610. 

All images © European Union 2019 

 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Acknowledgements ................................................................................................ 2 

1  Introduction ...................................................................................................... 3 

1.1  Geographical projection ................................................................................ 3 

1.2  Geographical domain extension ..................................................................... 3 

2  Upgrade in the meteorological input ..................................................................... 5 

2.1  New interpolation scheme ............................................................................. 5 

2.2  Historic meteorological input ......................................................................... 5 

2.3  Real-time meteorological input ...................................................................... 6 

3  Upgrade in LISFLOOD static input maps ............................................................... 8 

3.1  Land cover .................................................................................................. 8 

3.2  Digital elevation model, channel network and related parameters ...................... 8 

3.3  Soil and related parameters .......................................................................... 9 

3.4  Leaf area index ........................................................................................... 9 

3.5  Water demand, abstraction and consumption maps ......................................... 9 

3.6  Reservoirs ................................................................................................ 10 

4  Upgrade in the hydrological model LISFLOOD ...................................................... 11 

5  Upgrade in the model calibration ....................................................................... 12 

5.1  Calibration algorithm .................................................................................. 12 

5.2  Calibration parameters ............................................................................... 12 

5.3  Hydrological observations and calibration / validation periods .......................... 13 

5.4  Calibration results ...................................................................................... 14 

6  Upgrade in other related EFAS products ............................................................. 17 

6.1  Updated simulated return periods ................................................................ 17 

6.2  Updated post-processing hydrographs .......................................................... 18 

6.3  Updated flash flood products ....................................................................... 18 

6.4  Updated seasonal forecasts ......................................................................... 18 

6.5  Updated flood hazard and impact assessment ............................................... 19 

7  Forecast skill score evaluation ........................................................................... 21 

References ......................................................................................................... 22 

Annexes ............................................................................................................. 24 

Annex 1. List of stations used in the calibration and calibration results ................... 24 

i 

Acknowledgements 

We  would  like  to  acknowledge  the  contributions  from  the  Copernicus  early  warning 
hydrological  and  meteorological  data  collection  center  as  well  as  the  contributions  from 
the  Copernicus  flood  early  warning  computational  and  dissemination  centers.  The 
European  Flood  Awareness  System  is  financed  as  part  of  the  Copernicus  Emergency 
Management Service. 

Authors 

L.  Arnal,  S.-S.  Asp,  C.  Baugh,  J.  Disperati,  A.  de  Roo,  F.  Dottori,  R.  Garcia,  M.  Garcia-
Padilla, E. Gelati, G. Gomes, M. Kalas, B. Krzeminski, M. Latini, V. Lorini, C. Mazzetti, M. 
Mikulickova,  D.  Muraro,  C.  Prudhomme,  A.  Rauthe-Schöch,  K.  Rehfeldt,  P.  Salamon,  C. 
Schweim, J.O. Skoien, P. Smith, E. Sprokkereef, V. Thiemig, F. Wetterhall, M. Ziese 

2 

1  Introduction 

This  document  briefly  describes  all  the  relevant  changes  performed  for  the  latest  major 
upgrade  of  the  operational  European  Flood  Awareness  System  (EFAS);  the  operational 
implementation of the upgrade is planned for 5 April 2018.  

Two main changes were conducted: 

1)  Change  in  the  geographical  projection  used  in  EFAS  to  an  INSPIRE-compliant 
projection; 

2) Change in the geographical limits of the service, to include more river basins located 
in the eastern and southern neighbourhood countries of the EU. 

As  part  of  the  upgrade,  meteorological  and  static  input,  the  hydrological  model,  the 
calibration  and  other  EFAS  products  depending  on  a  model  climatology  were  also 
changed. 

1.1  Geographical projection 

The  geographical  projection  was  upgraded  to  the  INSPIRE  compliant  ETRS89  Lambert 
Azimuthal Equal Area Coordinate Reference System (ETRS-LAEA). ETRS-LAEA is a single 
projected  coordinate  reference  system  for  the  pan-European  area.  It  is  based  on  the 
ETRS89 geodetic datum and the GRS80 ellipsoid, and is the recommended projection for 
pan-European  statistical  mapping  at  all  scales  or  for  other  purposes  where  true  area 
representation is required, 

Its defining parameters can be found here: http://spatialreference.org/ref/epsg/3035/  

Proj4 format: 

Proj4js.defs["EPSG:3035"]  =  "  +proj=laea  +lat_0=52  +lon_0=10  +x_0=4321000 

+y_0=3210000 +ellps=GRS80 +units=m +no_defs " 

1.2  Geographical domain extension 

The spatial coverage of the operational European Flood Awareness System was enlarged 
towards  the  east  and  the  south  (see  green  boundary  box  in  Error!  Reference  source 
not found.), to include more river basins located near the eastern and southern borders 
of the EU.  

Figure 1 shows the current and new limits of the EFAS domain, defined as:  

•  Top: 5500000 (including Scandinavia) 

•  Left: 2500000 (including the Iberian Peninsula) 

•  Right: 7500000 (including Turkey) 

•  Bottom:  750000  (including  coastal  catchments  of  the  southern  shore  of  the 

Mediterranean Sea) 

The grey areas of Figure 1 show the hydrological modelling extent, and includes the most 
of the rivers within the new domain limits. Some river basins in the north-eastern corner 
and  southern  part  of  the  model  domain  were  excluded  either  due  to  a  lack  of  data,  or 
because  part  of  their  catchment  area  was  outside  the  new  domain  limits  (e.g.  the  Nile 
river).   

3 

Figure 1: Green (new) and red (current) domain limits for EFAS, with in grey the limits of the 
hydrological modelling. 

4 

 
 
 
2  Upgrade in the meteorological input 

Meteorological  data  are  needed  to  drive  the  hydrological  model  LISFLOOD  during 
calibration, the long-term run and to calculate the initial conditions for the forecasts. The 
following meteorological inputs with a daily time step were generated: 

Parameter 

Abbreviation of grid  Unit 

Precipitation 

Min temperature 

Max temperature 

Wind speed 

Solar radiation 

Vapor pressure 

PR 

TN 

TX 

WS 

RG 

PD 

mm 

°C 

°C 

m/s 
J / (m2 d) 
hPa 

The  changes  introduced  in  the  meteorological  input  for  the  update  to  the  extended 
domain are described in the sections below.  

2.1  New interpolation scheme 

The  interpolation  scheme  SPHEREMAP  is  now  used  instead  of  the  inverse  distance 
weighting  (IDW)  in  the  extended  domain.  SPHEREMAP  is  the  adaptation  to  spherical 
coordinates  of  Shepard's  inverse  distance  weighting  (Shepard,  1968),  and  an  extension 
of the currently applied IDW. The interpolation weight equation depends on the distance 
and  number  of  available  input  stations.  In  particular,  the  interpolation  procedure  takes 
into account the clustering of stations, so that weights of clustered stations were lower so 
that these data do not bias in the final product. 

Although  IDW  requires  more  reduced  computational  effort,  SPHEREMAP  generates  grids 
more  reliably  and  with  lower  overall  uncertainty,  in  particular  compared  with 
interpolation  schemes  such  as  ordinary  kriging.  In  addition,  SPHEREMAP  procedure  is 
robust to locally higher density of input data.  

The uncertainty of gridded fields for all variables will be produced using an adaptation of 
Yamamoto's procedure (Yamamoto, 2000).  

2.2  Historic meteorological input 

The  following  gridded  precipitation  datasets  have  been  included  for  the  historic 
meteorological input, in addition to the station data: 

•  EURO4M-APGD: Pan-Alpine precipitation grid dataset with a spacing of 5 km and 
a  daily  time  resolution  from  01.01.1971  –  31.12.2008.  For  further  details  see 
Isotta et al. (2014). 

•  INCA-Analysis Austria: Precipitation grid with a spacing of 1 km and a 3 hourly 
further  details  see 

from  01.01.2003 

to  present.  For 

time 
resolution 
http://www.inca-ce.eu/  

•  ERA-Interim  GPCP  corrected:  Precipitation  grid  with  a  spacing  of  0.75  degree 
and a 6 hourly time resolution from 01.01.1979 – 31.10.2016. For further details 
https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-
see 
interim-land  

•  Carpat-Clim: Precipitation grid with a spatial resolution of 0.1° x 0.1° and a daily 
time  resolution  from  01.01.1970  –  31.12.2010.  For  more  information  visit 
http://www.carpatclim-eu.org/pages/home/.  

Where  necessary,  the  gridded  datasets  were  aggregated  to  a  daily  time  resolution.  A 
subset  of  grid  points  were  treated  as  “virtual”  stations  in  the  final  interpolation 
procedure.  ERA-Interim  GPCP  corrected  was  only  used  in  the  southern,  the  eastern 

5 

border of the model domain and in Iceland due to a lack of meteorological observations 
(see Figure 2).   

Figure 2: Only regions highlighted in blue used ERA-Interim GPCP-corrected in the interpolation of 
historic meteorological grids 

In  addition  to  these  changes,  all  data  providers  have  been  checked  for  new  /updated 
historic data, including Quality Assurance introduced (e.g. minimum temperature).  

2.3  Real-time meteorological input 

The  overall  number  of  stations  for  interpolation  and  gridding  used  for  the  real-time 
meteorological input was increased with: 

•  more data providers 

• 

inclusion of the INCA Analysis Austria gridded dataset.  

Figure 3 illustrates the precipitation observations used to produce the daily precipitation 
map  for  10  February  2018.  Table  2  shows  the  average  number  of  observations  per 
meteorological  variables  as  currently  available  for  the  real-time  meteorological  grids. 
Note  that  the  number  of  available  meteorological  stations  can  vary  strongly  especially 
for  dates  further  in  the  past  and  that  more  data  providers  and  stations  are  added 
continuously.  

6 

 
 
Figure 3: Overview of meteorological observations included for the daily precipitation grid from 10 
February 2018.   

Table  1:  Approximate  number  of  observations  available  for  the  production  of  the  near  real-time 
meteorological grids (example: 10 February 2018).  

Parameter 

Precipitation 

Min temperature 

Max temperature 

Wind speed 

Solar radiation 

Vapor pressure 

Approximate number of observations 
for near real-time meteo grids  

~ 5.600 

~ 8.900 

~ 8.900 

~ 6.600 

~ 4.000 

~ 3.700 

7 

 
 
3  Upgrade in LISFLOOD static input maps 

Due  to  the  change  in  projection  and  the  extension  of  the  model  domain  all  static  input 
maps required by LISFLOOD were upgraded or newly calculated as described below. 

3.1  Land cover 

To  derive  the  necessary  land  cover  information  for  the  extended  model  domain  the 
following datasets have been used: 

•  Corine  land  cover  data  set  2006  refined  by  Batista  et  al.  2012  based  on  Corine 

CLC 2006, version 13. 

•  Corine land cover 2000 - Version 16 (04/2012) - Raster data on land cover for the 

CLC2000, 100m resolution. 

•  GlobCover2009 (GC2009) database (Arino 2010) (to extend the missing areas  of 

the European land cover database) 

•  Pan-European Forest/Non-Forest Map 2006 (Kempeneers et al. 2011) 

•  EEA Soil Sealing (or imperviousness) (EEA 2013, Kopecky and Kahabka 2009). 

All  three  land  cover  datasets  were  merged  into  one  to  create  a  complete  land  cover 
dataset  for  the  extended  model  domain.  The  spatially  dominant  value  was  used  to 
upscale  the  information  to  a  5km  grid.  The  merged  land  cover  dataset,  the  forest/non-
forest  map  and  soil  sealing  maps  were  used  to  generate  some  of  the  necessary  input 
maps for LISFLOOD: 

•  Percentage of forest coverage 

•  Percentage of impermeable surface 

•  Percentage of water coverage 

•  Crop coefficient 

•  Crop group number 

•  Manning’s roughness 

3.2  Digital  elevation  model,  channel  network  and  related 

parameters 

The  following  datasets  were  used  as  a  digital  elevation  model  and  used  as  base 
information  to  derive  parameters  required  for  the  calculation  of  the  surface  runoff  in 
LISFLOOD  such  as  slope,  river  network,  channel  length/gradient/bottom/Mannings’ 
roughness/bankful depth: 

•  Shuttle Radar Topography Mission ( SRTM) version 4.1 at 90m resolution  

•  EU-DEM (Copernicus Land Monitoring Service) 

•  HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives 

at multiple Scales) (Lehner et al., 2008) 

•  Pan-European  River  and  Catchment  Database  was  developed  by  the  Catchment 
Characterisation  and  Modelling  (CCM)  activity of  the  Joint  Research  Centre  (JRC) 
(Vogt et al. 2007a) 

8 

 
 
 
   
The  digital  elevation  model  was  projected  to  LAEA  ETRS89  with  the  bilinear  resampling 
technique.  

The  local  drain  direction  (LDD)  is  the  essential  component  to  connect  the  grid  cells, 
express  the  flow  direction  from  one  cell  to  another,  and  form  a  river  network  from  the 
river spring to their mouth. The LDD was derived from the digital elevation model using 
void filling and hydrological conditioning methods as outlined by Lehner et al. 2008, and 
the stream burning methods used by Hiederer and de Roo, 2003 and Vogt et al. 2007. 

The river network was upscaled to a courser resolution (5km for LISFLOOD; and 1 km for 
the  Flash  Flood  indicator  ERIC)  based  on  the  tracing  method  ‘Flexible  Location  of 
Waterways’ (FLOW) from Yamazaki et al., 2009. 

Channel  geometry  maps  such  as  length,  slope,  width  and  depth  of  the  main  channel 
inside  a  grid  cell  were  computed  by  combining  the  LDD  map  with  the  digital  elevation 
maps using empirical relationships.  

3.3  Soil and related parameters 

Soil related parameters were derived by combining the following datasets: 

•  European Soil Database (ESDB) of the European Soil Bureau 

•  Harmonized World Soil Database 1.2 (HWSD) - Version 1.2 7 March, 2012 

•  HYPRES database (Wösten et al. 1999) 

This includes maps for soil texture, bulk density, organic matter and soil depth. 

3.4  Leaf area index 

The  Leaf  Area  Index  (LAI)  reflects  the  foliage  density  per  ground  surface,  and  plays  an 
important  role  in  the  interception  of  water  and  evapotranspiration.    Leaf  area  is  not  a 
static  map  but  evolves  during  the  year.  The  development  of  vegetation  over  time  is 
accounted for in LISFLOOD by a stack of 36 LAI maps (each representing a time span of 
~10 days).  

Here  we  used  reprocessed  LAI  products  derived  from  SPOT-VGT  data  (CYCLOPES  and 
geoland2) available at the World Data Center for Remote Sensing of the Atmosphere. 

3.5  Water demand, abstraction and consumption maps 

Irrigated  areas  were  derived  from  the  pan-European  irrigation  map  of  Wriedt  et  al 
(2009),  based  on  regional  European  statistics,  European  land  use  and  global  irrigation 
maps. The map provides spatial information on the distribution of irrigated areas per crop 
type, allowing to determine irrigated areas at the level of spatial modelling units. 

LISFLOOD  differentiates  the  source  of  the  abstracted  water  used  for  irrigation  as  a 
percentage from: 

• 

surface water 

•  groundwater 

•  non-conventional sources (e.g. desalination plants) 

These 
(http://www.fao.org/nr/water/aquastat/irrigationmap/index10.stm). 

percentages 

derived 

were 

from 

the 

FAO/Aquastat 

website 

9 

 
 
 
 
Water  demands  for  the  livestock  sector,  energy  production  and  cooling,  and  the 
manufacturing industry are derived from downscaled Eurostat data mainly, as described 
by Vandecasteele et al (2013). Disaggregation was done using 100m land use data. 

3.6  Reservoirs 

The location of reservoirs was derived from the following sources: 

•  GRanD database (Global Reservoir and Dam database) (2011); 

•  Global Lakes and Wetlands Database (GLWD, Lehner & Doell, 2004) 

A  total  of  1454  reservoirs  were  included  (see  Figure  4).  For  almost  all  reservoirs,  only 
basic parameters as location and total volume of the reservoir are available. Many of the 
reservoir  steering  parameters  were  estimated,  with  some  parameters  included  in  the 
calibration procedure. 

Figure 4: Reservoirs included in the hydrological modelling of the new extended domain 

10 

 
 
4  Upgrade in the hydrological model LISFLOOD 

In  comparison  to  the  currently  running  LISFLOOD  version  in  EFAS  the  following  major 
changes have been introduced: 

•  Water infiltration into the soil is now parameterized using three soil layers instead 

of two  

•  Open water evaporation was added 

•  Water  abstraction  from  livestock,  agriculture,  manufacturing  industry,  energy 
production  (cooling  water  needs),  and  public  sector  water  use.  Actual  water 
abstraction  is  calculated  while  checking  if  the  demand  can  actually  be  met. 
Specifically,  the  model  takes  into  account  if  the  water  is  abstracted  from 
groundwater, lakes or reservoirs. The remaining water is abstracted – if available 
– from the river surface water. 

•  Environmental  flow  option.  Currently  the  10%  percentile  of  discharge  is  taken, 
calculated over the entire year. Abstraction can be limited if e-flow is to be respected. 

• 

Inputs  and  outputs  are  now  in  NetCDF  format,  PCRaster  maps  are  still  possible, 
but will be phased out 

A number of bug-fixes and minor modifications were introduced to the existing LISFLOOD 
modules. 

11 

 
 
5  Upgrade in the model calibration 

5.1  Calibration algorithm 

The calibration of the LISFLOOD model parameters was modified to use an Evolutionary 
Algorithm (EA). EA is a population-based optimization algorithm in which each individual 
(e.g. a vector of model parameters) in a large population represents a candidate solution 
for  the  optimization  problem.  EAs  modify  and  improve  the  population  through  evolution 
over a range of generations, and ultimately identify the best performing individual. 

The goodness-of-fit of each individual is evaluated based on selected objective functions. 
For the LISFLOOD calibration we used the modified Kling-Gupta efficiency criteria (KGE; 
Gupta et al., 2009) as objective function.  

The  KGE’  is  a  performance  indicator  based  on  the  equal  weighting  of  three  sub-
components:  linear  correlation  (r)  [-],  bias  (β)  [-]  and  variability  (γ)  [-],  between 
simulated (s) and observed (o) discharge. It is defined as follows: 
)
1

KGE

-=
1'

)2
1

)
1

(
r

(
g

+

+

(

b

2

2

b =

m
m

s

o

g

=

CV
s
CV
o

=

s m
s
s m
o

s

o

where  r  is  the  Pearson  product-moment  correlation  coefficient,  µ  is  the  mean  discharge 
[m³/s],  CV  is  the  coefficient  of  variation  [-]  and  σ  is  the  standard  deviation  of  the 
discharge [m³/s]. The optimum of KGE’, r, β and γ is at unity. The actual value of KGE’ 
gives the lower limit of any of the three sub-components. 

The reason for choosing KGE’ over the often used Nash-Sutcliffe Efficiency (NSE) is that 
KGE’ has been demonstrated to improve the bias and variability ratio considerably during 
calibration,  while  the  correlation  is  only  slightly  decreased.  In  contrast,  NSE  has  shown 
the  tendency  to  underestimate  the  variability  of  flows  and  exhibit  less  efficiency  in 
constraining the bias. For a full discussion of the advantages of using KGE over NSE see 
(Gupta et al., 2009). 

5.2  Calibration parameters 

LISFLOOD  calibration  parameters  were  selected  based  on  prior  expert  knowledge.  The 
selected  parameters  relate  to  groundwater  processes,  channel  routing,  snow  melt  and 
reservoir/lake simulation and are listed in Table 2. 

12 

-
-
-
 
 
 
 
 
Table  2:  Calibration  parameters  of  LISFLOOD.  Parameters  with  *  are  only  calibrated  if  a  lake  or 
reservoir is in the relevant sub-catchment area. 

Parameter Name 

Units 

ID 

Range 

Min 

Max 

Default 
Value 

Upper Zone Time Constant 

Lower Zone Time Constant 

[d] 

[d] 

UZTC 

LZTC 

3 

40 

40 

10 

500 

100 

Groundwater Percolation Value 

[mm/d] 

GwPV 

0.01 

2 

Groundwater Loss 

Power in the infiltration’s equation 

Power in the preferential flow equation 

Multiplier  applied  to  the  Manning’s 
roughness maps of the channel system 

Multiplier  applied  to  the  Manning’s 
roughness  maps  for  the  second  line  of 
routing 

[-] 

[-] 

[-] 

[-] 

[-] 

GwLoss 

0.0 

0.5 

b_Xinan 

0.01 

PPrefFlow 

0.5 

1 

8 

CCM 

0.1 

15 

CCM2 

0.1 

15 

Snow Melt Coefficient 

[mm /˚C d] 

SMC 

Lake Multiplier* 

Adjust Normal Flood* 

Fraction  of 
outflow* 

the 

reservoir  normal 

[-] 

[-] 

[-] 

2.5 

0.5 

6.5 

2 

0.01 

0.99 

0.8 

LM 

ANF 

RNM 

0.25 

5 

1 

0.8 

0.0 

0.5 

4 

3 

3 

4 

1 

5.3  Hydrological observations and calibration / validation periods 

Reliable discharge data is necessary in order to calibrate the model and to evaluation its 
skill. The following criteria was used to select calibration stations: 

•  Drainage area larger than >500 km2. 

•  A minimum of 5 years with daily discharge data within the period Jan-1990 up to 
Dec-2014. If the record length is greater than 10 years calibration and validation 
periods  are  split  into  equally  long  parts.  If  shorter  than  10  years,  5  years  were 
reserved for the calibration and the remaining used as for the validation.  

•  Station locations that would improve spatial coverage of the calibrated stations in 
Europe;  exclusion  of  stations  that  are  redundant  for  calibration  purposes  (i.e. 
located very close to another station on the same river).  

However, in some exceptional cases, stations that did not fulfil all of the previous criteria 
were selected for calibration when no better alternative was available in the nearby area 
or  based  on  specific  request  from  the  EFAS  partner.  This  resulted  in  a  total  of  717 
calibration stations presented in Figure 6. 

Note that in regions where calibration station density is very low or no calibration station 
exists  at  all  this  was  due  to  either  insufficient  historic  data  or  no  data  available  at  all. 
Furthermore, data provided later than May 2016 was not included in this calibration. It is 
foreseen  to  increase  the  number  of  calibration  points  constantly  in  the  forthcoming 
calibrations with new available data from the EFAS partners. 

13 

 
 
 
5.4  Calibration results 

The  hydrological  skill  of  LISFLOOD  is  expressed  by  the  Kling-Gupta  Efficiency.  Table  3 
provides a brief summary on the KGE achieved during calibration and validation. 75 % of 
all stations score a KGE higher than 0.5 during calibration, and 57 % during validation.  

Table 3: Brief summary on skill scores of calibration / validation 

calibration 

validation 

KGE 

> 0.75 

no. of 
stations 
303 

> 0.5 – 0.75 

240 

> 0.2 – 0.5 

> 0 – 0.2 

≤0 

91 

36 

47 

[%] 

42 

33 

13 

5 

7 

no. of 
stations 
174 

235 

172 

44 

73 

[%] 

24 

33 

24 

6 

10 

∑ 717 

∑ 698 

Figure 6 shows the spatial distribution of the hydrological skill across the EFAS domain. It 
is  clearly  noticeable  that  the  skill  is  not  homogeneously  distributed  across  Europe,  with 
higher  skills  in  large  parts  of  Central  Europe,  and  lower  skill  mostly  in  Spain  caused  by 
the  strong  influence  of  reservoirs.  Users  should  remember  that  a  low  score  during 
calibration/validation  is  not  necessarily  an  indicator  for  decreased  forecast 
performance as EFAS forecasts  are compared to model derived thresholds (see 
Section  6.1)  which  eliminates  systematic  bias  that  could  lead  to  an  overall 
lower score of the calibration/validation! The updated EFAS forecast skill scores will 
be provided with the release of the new EFAS web interface.  

Note  that  the  hydrological  skill  will  be  available  as  separate  layer  with  the  EFAS  web 
interface  after  the  release  of  the  extended  domain.  An  example  of  the  detailed 
information  on  calibration  and  validation  is  shown  in  Error!  Reference  source  not 
found.. A detailed list of stations used for calibration and validation as well as associated 
results can furthermore be found in Annex 1. 

14 

 
 
 
 
 
 
Figure 5: Example of the available calibration and validation summary information available in the 
“Hydrological Skill” layer in the EFAS interface. 

15 

 
Figure 6: Hydrological skill of EFAS at the calibration locations. Colour coding denotes the quality of 
the  KGE  during  calibration  (left  half  of  square)  and  validation  (right  half  of  the  square).  Dark 
green: KGE > 0.75; Green: KGE 0.5 – 0.75, Light green: KGE 0.2 – 0.5; Orange: 0 – 0.2; Red: < 
0. Note: the hydrological skill of the calibration will be available as separate layer. 

16 

 
 
 
6  Upgrade in other related EFAS products 

6.1  Updated simulated return periods 

EFAS  warnings  are  issued  following  a  comparison  of  the  forecast  discharge  with  model 
discharge  associated  with  different  return  levels.  The  return  level  discharge  thresholds 
are calculated from a long-term discharge climatology generated from running LISFLOOD 
with forcing from 1-Jan-1990 until 31-Dec-2016, excluding simulations from 1990 (model 
warm  up  phase).  The  Gumbel  distribution  using  L-Moments  was  used  to  calculate  the 
relevant  discharge  return  periods.  Figure  7  shows  the  simulated  discharge  associated 
with the 100 year return period.  

Figure  7:  100-year  return  period  discharges  [m3/s]  as  derived  from  the  long  term  run  of  the 
calibrated LISFLOOD model set up.  

17 

 
 
6.2  Updated post-processing hydrographs 

The  post-processing  hydrographs  were  updated  to  use  climatological  information  from 
the newly calibrated LISFLOOD long term simulations. Furthermore, the number of post-
processing hydrographs has been extended significantly to include more locations where 
real-time and sufficient historic river discharge information is available. 

6.3  Updated flash flood products 

Flash  flood  products  computed  from  the  ERIC  [Raynaud  et  al.,  2015;  European  Runoff 
Index  based  on  Climatology]  methodology,  have  been  updated  to  use  climatological 
information  from  the  newly  calibrated  LISFLOOD  long  term  simulations.  Soil  moisture is 
one  of  the  required  simulated  variables,  previously  this  was  a  single  output  from 
LISFLOOD but now the upper soil layer has been divided into two top layers and separate 
calculations  are  performed  for  the  forested  and  non-forested  fractions  in  each  of  these. 
Therefore  it  is  necessary  to  combine  these  soil  moisture  data  into  a  single  value  of  the 
upper level soil moisture Sm, using the following equation: 

(cid:1)(cid:2) =

(cid:4)(cid:5)(cid:6) ∗ (cid:8)(cid:9)(cid:6) ∗ (cid:10)1 − (cid:13)(cid:13)(cid:14)(cid:15)(cid:16)(cid:17)(cid:18) + (cid:4)(cid:5)(cid:20) ∗ (cid:8)(cid:9)(cid:20) ∗ (cid:10)1 − (cid:13)(cid:13)(cid:14)(cid:15)(cid:16)(cid:17)(cid:18) + (cid:10)(cid:5)(cid:6)(cid:21) ∗ (cid:8)(cid:9)(cid:6)(cid:21) ∗ (cid:13)(cid:13)(cid:14)(cid:15)(cid:16)(cid:17) + (cid:10)(cid:5)(cid:20)(cid:21) ∗ (cid:8)(cid:9)(cid:20)(cid:21) ∗ (cid:13)(cid:13)(cid:14)(cid:15)(cid:16)(cid:17)
(cid:4)(cid:10)(cid:8)(cid:9)(cid:6) + (cid:8)(cid:9)(cid:20)(cid:17) ∗ (cid:10)1 − (cid:13)(cid:13)(cid:14)(cid:15)(cid:16)(cid:17)(cid:18) + (cid:4)(cid:10)(cid:8)(cid:9)(cid:6)(cid:21) + (cid:8)(cid:9)(cid:20)(cid:21)(cid:17) ∗ (cid:13)(cid:13)(cid:14)(cid:15)(cid:16)(cid:18)

where  θ1  =  soil  moisture  in  layer  1  non-forested  fraction  [m/m],  θ2  =  soil  moisture  in 
layer  2  non-forested  fraction  [m/m],  θ1F  =  soil  moisture  in  layer  1  forested  fraction 
[m/m],  θ2F  =  soil  moisture  in  layer  2  forested  fraction  [m/m],  ffrac  =  forested  fraction, 
sd1 =  soil depth of layer 1 non-forested fraction [mm], sd2 = soil depth of layer 2 non-
forested  fraction  [mm],  sd1F  =  soil  depth  of  layer  1  forested  fraction  [mm],  and  sd2F  = 
soil depth of layer 2 forested fraction [mm]. 

Apart  from  the  above,  the  calculation  methodology  for  the  ERIC  climatological  and 
forecast  products  has  remained  unchanged.  Also  unchanged  is  the  spatial  extent  of  the 
products,  as  this is  limited  by  the  coverage  of  the  COSMO-LEPS  forecasts  meaning  that 
ERIC products cannot be extended eastwards. Work is ongoing to investigate filling these 
missing areas using forcings from the ECMWF global ensemble NWP.  

6.4  Updated seasonal forecasts 

The seasonal forecasts were updated by both extending to the new domain and switching 
to  the  new  System  5  seasonal  forecasts  (SEAS5)  from  ECMWF.  The  main  change  with 
SEAS5  in  comparison  to  the  old  model  cycle  is  a  much  higher  spatial  resolution,  36  km 
compared with 80 km, a better ocean model and a 25-member hindcast period compared 
to only 15 members in the old version. The release date is set to the 5th of each month, 
so the dissemination will also be earlier. 

The sub-basins for the eastwards expansion have been recalculated for the new domain 
(Figure  8).  The  seasonal  forecasts  will  be  presented  as  before  with  probabilities  of 
exceeding  high  and  low  percentiles  calculated  from  the  water  balance.  However,  we 
foresee  that  new  products  will  be  developed  for  the  seasonal  and  sub-seasonal  scale  in 
the near future. 

18 

 
 
 
 
Figure 8: Water regions used for the seasonal outlook 

6.5  Updated flood hazard and impact assessment 

The rapid risk assessment procedure is used to estimate the flood extent as well as the 
potential flood impacts based on EFAS forecasts.  Every time a flood event is forecasted 
in EFAS, the procedure identifies those river sections where the magnitude of forecasted 
peak  discharge  is  expected  to  exceed  the  local  flood  protection  level.  For  these  river 
sections,  the  procedure  delineates  flood  prone  areas  using  a  catalogue  of  flood  hazard 
maps covering all the EFAS river network. These event-based hazard maps are combined 
with  exposure  information  to  assess  several  categories  of  impacts,  such  as  affected 
population, roads and cities, the total extent of urban and agricultural areas affected and 
direct  economic  losses.  A  full  description  of  the  procedure  is  reported  in  Dottori  et  al. 
(2017).  

In  order  to  apply  the  procedure  to  the  extended  domain,  additional  flood  simulations 
have been carried out to produce flood hazard maps for the new areas covered in EFAS. 
The  simulations  were  run  using  the  latest  LISFLOOD  climatology  and  the  geographical 
layers described in Sections 3.1 and 3.2. As example is reported in Figure 9. 

19 

 
 
Figure 9: flood extent of the 1-in 100 year flood map for the Dniepr river basin (marked by purple 
line).  

In addition, the existing database of flood protection levels has been integrated using the 
FLOPROS  global  dataset  (Scussolini  et  al.,  2016)  and  information  on  design    protection 
levels, where available.  

The land cover data are derived based on the procedure described in the Section 3.1. 
The population potentially affected is calculated using the 250m resolution GHSL 
POPULATION GRID, resampled to 100m resolution grid GHSL (2016). The potential 
affected cities are derived d from the map of World Cities developed by ESRI (2017). The 
affected roads will be based on the data set of Herrera (2015), merged with global data 
set from SEDAC (Global Roads Open Access Data Set (gROADS), v1 (1980 – 2010). 

Finally, vulnerability functions have been integrated using the global set of flood damage 
functions produced by Huizinga et al. (2017). 

20 

 
 
 
 
 
7  Forecast skill score evaluation 

The  skill  scores  will  be  expanded  with  new  scores  and  be  presented  through  the  new 
EFAS interface  on a more interactive and dynamic way. This will allow the user to have 
more  detailed  information  on  the  forecast  performance  for  certain  locations,  seasons, 
flow  regime  et  cetera.  However,  this  development  will  be  released  with  the  new  EFAS 
web interface and not in the release of the extended domain. 

21 

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23 

 
Annexes 

Annex 1. List of stations used in the calibration and calibration results 

ID 

Country 

Basin 

River 

Station 

C008  RO 
C012  AT 
C015 
SK 
C016  RO 
C017  BG 
C018  RO 
C019  RO 
C020  MD 
C021  RO 
C023  HR 
C024  HR 
C025  HR 
C026  HR 
C028  RO 
C029  RO 
C030  RO 
C031  HU 
C032  HU 
C034 
SK 

C037  RO 
C039  RO 
C041  RO 

Arges 
Drava 
Hron 
Ialomita 
Iskur 
Jiu 
Olt 
Prut 
Prut 
Sava 
Sava 
Sava 
Sava 
Siret 
Siret 
Siret 
Tisza 
Tisza 
Tisza 

Tisza 
Tisza 
Tisza 

Arges 
Mur 
Hron 
Bratul Borcea 
Iskur 
Jiu 
Olt 
Prut 
Prut 
Sava 
Sava 
Crnac 
Sava 
Siret 
Siret 
Siret 
Tisza 
Sajó 
Bodrog 

Crisul Alb 
Mures 
Mures 

Budesti 
Bruck 
Brehy 
Slobozia 
Kunino 
Podari 
Cornet 
Oancea 
Radauti 
Zagreb 
Jasenovac 
Davor 
Zupanja 
Dragesti 
Lungoci 
Siret 
Jasztelek 
Felsozsolca 
Streda nad 
Bodrogom 
Gyula 
Ocna 
Arad 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

0.71 
0.89 
0.71 
0.22 
0.5 
0.71 
0.74 
0.64 
0.69 
0.86 
0.86 
0.64 
0.74 
0.72 
0.73 
0.54 
0.76 
0.47 
0.68 

0.54 
0.64 
0.81 

0.45 
0.78 
0.43 
-0.48 
0.06 
0.51 
0.56 
0.39 
0.52 
0.75 
0.74 
0.56 
0.63 
0.59 
0.52 
0.3 
0.54 
0.02 
0.42 

0.41 
0.52 
0.65 

0.72 
0.89 
0.71 
0.71 
0.5 
0.74 
0.77 
0.67 
0.74 
0.87 
0.87 
0.9 
0.87 
0.78 
0.75 
0.59 
0.79 
0.49 
0.7 

0.66 
0.74 
0.82 

5.9 
2 
-2.6 
28.2 
-3.8 
-9.4 
-9.1 
-5.5 
-10.8 
0.1 
0.4 
23.2 
13.9 
-11.1 
-7.4 
-8.3 
10.5 
-12.2 
-8.7 

-21.4 
-16.5 
-4.8 

-0.06 
0.82 
0.32 
0 
0.18 
0.34 
0.49 
0.02 
0.45 
0.8 
0.83 
0.42 
0.58 
0.32 
0.17 
0.25 
-8.74 
- 
0.41 

-0.22 
-0.05 
0.48 

-0.23 
0.75 
-0.36 
-0.53 
-0.08 
-0.12 
0.19 
-0.19 
0.25 
0.72 
0.68 
0.21 
0.35 
0.21 
0.02 
0.05 
-111.32 
- 
0.36 

0.24 
0.88 
0.41 
0.16 
0.39 
0.46 
0.61 
0.21 
0.59 
0.89 
0.85 
0.86 
0.82 
0.55 
0.44 
0.37 
-0.08 
- 
0.74 

-0.51 
-0.48 
0.09 

0.15 
0.26 
0.67 

-59.8 
-8.7 
-33.1 
-50.4 
-41.9 
-36.5 
-30.4 
-28.1 
-31.5 
9.8 
6.4 
25.1 
15 
-39.6 
-43.9 
-22.1 
306.5 
- 
-39.2 

-71 
-61.6 
-39.9 

24 

 
ID 

Country 

Basin 

River 

Station 

C045  HU 
C046 
SK 

C047  DE 

C048  DE 

C050  AT 

C051  CZ 

C052  AT 

C053  DE 

C054  AT 

C055  CZ 

C056  DE 

C057  AT 

C058  DE 

C059  DE 

C060 
C061 

SK 
YU 

Tisza 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Upper 
Danube 
Vah 
Velika 

Tisza 
Mosoni-Duna 

Lech 

Donau 

Enns 

Szeged 
Bratislava 

Augsburg 

Berg 

Steyer 

Morava 

Straznice 

Traun 

Donau 

March 

Wels 

Wiblingen 

Záhorská Ves 

Inn 

Passau-Ingling 

Salzach 

Burghausen 

Oberaudorf 

Inn 

Donau 

Vah 
Morava 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

0.69 
0.92 

0.8 

0.91 

0.81 

0.85 

0.54 
0.85 

0.77 
0.93 

-15.6 
0.7 

0.29 
0.9 

-0.35 
0.81 

0.42 
0.91 

0.6 

0.81 

-0.5 

0.79 

0.63 

0.8 

0.83 

0.92 

1.2 

0.88 

0.76 

0.89 

-38.8 
0.2 

-1.1 

1.7 

0.65 

0.82 

-4.3 

0.78 

0.69 

0.84 

-10.6 

0.71 

0.85 

-2.3 

0.49 

0.04 

0.68 

-28.1 

0.8 

0.61 

0.8 

-0.9 

0.7 

0.69 

0.84 

-10.3 

0.62 

0.76 

0.89 

0.77 

0.82 

0.45 

0.82 

25.3 

0.68 

0.42 

0.77 

17.7 

0.54 

0.8 

6.3 

4.3 

0.51 

0.35 

0.67 

0.48 

0.2 

0.6 

0.84 

0.92 

-3.4 

0.88 

0.85 

0.93 

-27 

-31 

-6 

0.66 

0.84 

-11.8 

0.76 

0.66 

0.84 

-13.8 

0.72 

0.87 

-9.1 

0.8 

0.78 

0.9 

-11.1 

Hofkirchen 

0.9 

0.82 

0.91 

2.2 

0.89 

0.79 

0.91 

4.2 

Sala 
Lubicevsky Most 

0.69 
0.9 

0.41 
0.81 

0.7 
0.9 

-1.8 
-1.3 

0.38 
0.4 

-0.43 
0.27 

0.52 
0.75 

-27.4 
-46.4 

25 

Stary Rybnik 

Židlochovice 

0.8 

0.59 

0.8 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C063 
FR 
C064 
FR 
C065 
FR 
C066 
FR 
C067 
FR 
C068 
FR 
C069 
FR 
C070 
FR 
C071 
FR 
C072 
FR 
C073 
FR 
C074 
FR 
C075 
FR 
C076 
FR 
C077 
FR 
C078 
FR 
C079 
FR 
C080 
FR 
C081 
FR 
C082 
FR 
C083 
FR 
C084 
FR 
C085  BE 
C086 
FR 
C091 
FR 

Morava 
Adour 
Adour 
Adour 
Adour 
Garonne 
Garonne 
Garonne 
Garonne 
Garonne 
Garonne 
Garonne 
Loire 
Loire 
Loire 
Loire 
Loire 
Loire 
Loire 
Loire 
Loire 
Loire 
Loire 
Maas 
Maas 
Rhone 

Adour 
Adour 
Gave de Pau 
Gave 
Garonne 
Tarn 
Garonne 
Lot 
Vezere 
Isle 
Dordogne 
Loire 
Allier 
Vienne 
Creuse 
Creuse 
Vienne 
Loire 
Loire 
Loire 
Loire 
Loire 
Meuse 
Meuse 
Saone 

0.86 
0.8 
0.39 
0.33 
0.63 
0.71 
0.82 
0.61 
0.71 
0.87 
0.46 
0.78 
0.72 
0.88 
0.81 
0.87 
0.9 
0.87 
0.84 
0.83 
0.93 
0.95 
0.87 
0.83 
0.84 

Audon 
Cahuzac 
Gave de Pau 
Escos 
Verdun 
Villemur 
Tonneins 
Faycelles 
Campagne 
Abzac 
Cénac-et-Saint-Julien 
Villerest 
Moulins 
Lussac les Chateaux 
Ciron 
Leugny 
Nouatre 
Durtal 
Nevers 
Blois 
Saumur 
Nantes 
Borgharen 
Chooz 
Ray 

26 

0.73 
0.62 
0.3 
0.24 
0.5 
0.55 
0.73 
0.53 
0.69 
0.74 
0.46 
0.55 
0.57 
0.76 
0.62 
0.74 
0.81 
0.76 
0.69 
0.71 
0.86 
0.89 
0.83 
0.77 
0.69 

0.86 
0.82 
0.76 
0.6 
0.73 
0.75 
0.86 
0.75 
0.85 
0.87 
0.82 
0.78 
0.77 
0.88 
0.82 
0.87 
0.91 
0.88 
0.84 
0.85 
0.93 
0.95 
0.92 
0.88 
0.84 

2.1 
7.5 
-39.2 
-37.7 
-14.1 
-8.9 
-4.7 
-19.8 
-15.6 
3.3 
-36.6 
-0.2 
-10.3 
0.4 
3.1 
1.8 
2.6 
5.2 
-0.9 
-5.1 
1.1 
0.1 
9.9 
-6.6 
-0.2 

0.77 
0.61 
0.45 
0.28 
0.76 
0.62 
0.83 
0.72 
0.88 
0.53 
0.48 
0.63 
0.78 
0.85 
0.84 
0.86 
0.81 
0.65 
0.77 
0.78 
0.82 
0.9 
0.84 
0.89 
0.8 

0.6 
0.51 
0.38 
0.24 
0.56 
0.62 
0.74 
0.45 
0.76 
0.54 
0.52 
0.28 
0.6 
0.72 
0.7 
0.75 
0.76 
0.59 
0.56 
0.63 
0.77 
0.86 
0.83 
0.81 
0.63 

0.82 
0.85 
0.74 
0.61 
0.77 
0.8 
0.86 
0.72 
0.88 
0.88 
0.81 
0.72 
0.79 
0.86 
0.84 
0.88 
0.9 
0.86 
0.8 
0.82 
0.9 
0.93 
0.93 
0.9 
0.83 

11.3 
28.9 
-31.1 
-40.3 
-2.4 
-19.2 
3.2 
-2.4 
-0.5 
37.8 
-31.4 
15.4 
-2 
6.6 
-1.6 
6.7 
14.4 
25.1 
8.7 
11 
14.9 
7.2 
13 
-2.4 
10.3 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C092 
FR 
C094  CH 
C095 
FR 
C097 
FR 
C098 
FR 
C099 
FR 
C100 
FR 

C101 
C102 
C103 
C104 
C105 
C106 
C107 
C108 
C109 
C110 
C111 
C112 
C113 
C114 
C115 
C116 
C117 
C118 

FR 
FR 
FR 
FR 
FR 
FR 
FR 
FR 
FR 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 

Rhone 
Rhone 
Rhone 
Rhone 
Rhone 
Rhone 
Rhone 

Seine 
Seine 
Seine 
Seine 
Seine 
Seine 
Seine 
Seine 
Seine 
Duero 
Duero 
Duero 
Duero 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 

Saone 
Rhone 
Rhone 
Rhone 
Rhone 
Isere 
Durance 

Yonne 
Armancon 
Yonne 
Loing 
Marne 
Marne 
Aisne 
Oise 
Seine 
Duero 
Duero 
Duero 
Duero 
Ebro 
Ebro 
Gallego 
Segre 
Ebro 

0.91 
0.65 
0.85 
0.91 
0.91 
0.83 
0.72 

0.88 
0.89 
0.92 
0.88 
0.81 
0.8 
0.84 
0.9 
0.94 
0.78 
0.81 
0.93 
0.84 
0.85 
0.82 
-0.32 
0.62 
0.22 

Pagny-la-Ville 
Pougny 
Lyon 
Viviers 
Beaucaire 
Beaumont-Monteux 
Saint-Paul-lès-
Durance 
Gurgy 
Brienon 
Courlon 
Episy 
La Ferté-sous-Jouarre 
Gournay 
Givry 
Sempigny 
Poissy 

HerreraDeDuero 
Villamarciel 
Zamora 
Castejon 
Zaragoza 
Zaragoza 
Seros 
Tortosa 

27 

0.82 
0.33 
0.7 
0.83 
0.82 
0.66 
0.47 

0.78 
0.78 
0.84 
0.79 
0.7 
0.65 
0.76 
0.85 
0.89 
0.55 
0.64 
0.87 
0.81 
0.77 
0.73 
-0.09 
0.29 
0.17 

0.91 
0.65 
0.85 
0.91 
0.91 
0.84 
0.72 

0.89 
0.89 
0.92 
0.9 
0.86 
0.82 
0.87 
0.93 
0.94 
0.78 
0.81 
0.93 
0.92 
0.88 
0.86 
0.49 
0.65 
0.51 

0.4 
-3.5 
0.5 
0.3 
1.9 
-4 
-2.1 

-2.8 
2.7 
1 
6.2 
-11.2 
-2.8 
-5.3 
5.9 
1 
-1.4 
2.3 
-0.9 
6.7 
-3.6 
-3.8 
117.8 
11.9 
-28.9 

0.87 
0.71 
0.85 
0.91 
0.89 
0.72 
0.6 

0.78 
0.8 
0.79 
0.82 
0.88 
0.86 
0.88 
0.8 
0.83 
0.6 
0.01 
0.41 
0.66 
0.8 
0.74 
-0.68 
0.37 
0.23 

0.76 
0.44 
0.7 
0.83 
0.82 
0.45 
0.28 

0.67 
0.77 
0.74 
0.82 
0.75 
0.72 
0.77 
0.82 
0.84 
0.42 
-0.63 
0.34 
0.71 
0.65 
0.57 
-0.36 
0.12 
0.14 

0.88 
0.72 
0.85 
0.91 
0.91 
0.72 
0.61 

0.86 
0.89 
0.9 
0.92 
0.88 
0.86 
0.89 
0.93 
0.94 
0.83 
0.8 
0.88 
0.93 
0.83 
0.82 
0.45 
0.46 
0.42 

4.2 
-6.8 
1.4 
-2 
3 
-4.8 
4.3 

14.7 
16.7 
13.2 
16 
1.1 
-3 
3.5 
15.5 
12.8 
-16.9 
38.5 
29.3 
16.6 
10.5 
15.4 
158.1 
14.8 
-16 

 
ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C119 
C120 

C121 
C122 

C125 
C126 
C127 
C130 
C131 
C133 
C136 
C138 
C139 
C140 
C142 
C144 
C145 
C146 
C147 
C148 
C149 
C152 
C153 
C154 

ES 
ES 

ES 
ES 

ES 
ES 
ES 
IT 
IT 
IT 
IT 
IT 
IT 
IT 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 

Guadiana 
Guadiana 

Guadiana 
Guadiana 

Guadiana 
Guadiana 

Guadiana 
Zujar 

Jucar 
Jucar 
Mijares 
Po 
Po 
Po 
Po 
Po 
Po 
Po 
Segura 
Segura 
Segura 
Tejo 
Tejo 
Tejo 
Tejo 
Turia 
Turia 
Turia 

Jucar 
Jucar 
Mijares 
Po 
Sesia 
Lago di Mergozzo 
Po 
Po 
Po 
Po 
Segura 
Segura 
Segura 
Tajo 
Tajo 
Jarama 
Henares 
Turia o Guadalviar 
Turia o Guadalviar 
Turia o Guadalviar 

0.17 
-4.98 

0.44 
-8.35 

-0.23 
-0.21 
0.47 
0.81 
0.72 
0.78 
0.76 
0.92 
0.92 
0.89 
-0.1 
-6.79 
-8.4 
0.57 
0.47 
0.66 
0.7 
0.27 
0.5 
0.16 

Luciana 
Villanueva de la 
Serena 
Azud de Badajoz 
Villanueva de la 
Serena 
Picazo 
Alcalá del Júcar 
Villarreal 
Carignano 
Palestro 
Candoglia 
Spessa 
Cremona 
Borgoforte 
Lagoscuro 
Almadenes 
Contraparada 
Beniel 
Trillo 
La Portusa 
Mejorada del Campo 
Espinillos 
Teruel 
Zagra 
La Presa 

28 

-0.39 
-0.69 

0.51 
0.38 

0.53 
-74.73 

0.78 
0.42 

-0.53 
-1.44 
0.08 
0.63 
0.53 
0.64 
0.79 
0.84 
0.85 
0.83 
-0.88 
-59.66 
-79.47 
0.28 
0.21 
0.58 
0.41 
-0.64 
-0.13 
-1.13 

-0.12 
-0.21 
0.5 
0.82 
0.74 
0.81 
0.91 
0.92 
0.93 
0.91 
-0.02 
-0.02 
0.28 
0.6 
0.84 
0.8 
0.71 
0.41 
0.55 
0.46 

61.1 
594.6 

-11.2 
646.6 

-25.4 
1.9 
13.7 
-7.6 
-0.1 
-7 
-17.1 
-0.9 
-2.4 
-5.4 
-31.3 
685 
836.2 
-6.5 
27.3 
26.7 
5.9 
34.3 
10.3 
40.5 

0.06 
0.26 

0.62 
-1.58 

-0.24 
-0.19 
0.33 
0.69 
0.63 
0.75 
0.66 
0.85 
0.87 
0.85 
0 
- 
-3.62 
0.63 
0.8 
0.75 
0.72 
-0.16 
0.62 
0.43 

0.06 
0.36 

0.49 
0.62 

0.64 
-1.77 

0.81 
0.3 

-0.4 
-1.61 
0.09 
0.56 
0.56 
0.69 
0.62 
0.74 
0.82 
0.78 
-0.5 
- 
-25.05 
0.36 
0.73 
0.69 
0.57 
-0.49 
0.35 
0.28 

-0.06 
-0.11 
0.46 
0.75 
0.75 
0.83 
0.86 
0.89 
0.92 
0.91 
0.12 
- 
0.14 
0.65 
0.9 
0.83 
0.78 
0.67 
0.73 
0.55 

78 
37.4 

-8.1 
245.1 

-39.4 
-42 
-26.4 
-1.7 
-8.9 
0.1 
-26.5 
-0.6 
-9.8 
-11.2 
-30.2 
- 
385.1 
-3.6 
5.7 
9.8 
14.6 
103.5 
25.9 
-7.7 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C155  DE 
C156  CZ 
C157  CZ 
C158  CZ 
C159  CZ 
C160  CZ 
C161  CZ 
C162  CZ 
C163  DE 
C164  DE 
C165  DE 
C166  DE 
C167  DE 
C169  DE 
C170  DE 
C171  DE 
C172  DE 
C175 
LT 
C177 
LT 
C178  DE 
C179  PL 
C180  PL 
C181  PL 
C182  PL 
C183  PL 
C184  CZ 

Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 
Nemnus 
Nemnus 
Oder 
Oder 
Oder 
Oder 
Oder 
Oder 
Oder 

Elbe 
Labe 
Labe 
Sazava 
Luznice 
Vltava 
Berounka 
Ohre 
Elbe 
Elbe 
Mulde 
Saale 
Unstrut 
Saale 
Elbe 
Elbe 
Elbe 
Nemunas 
Neris 
Oder 
Odra 
Odra 
Odra 
Odra 
Odra 
Olse 

0.91 
0.88 
0.87 
0.7 
0.89 
0.85 
0.77 
0.8 
0.92 
0.91 
0.5 
0.86 
0.87 
0.89 
0.89 
0.89 
0.9 
0.87 
0.84 
0.77 
0.77 
0.78 
0.65 
0.75 
0.58 
0.82 

0.83 
0.79 
0.74 
0.46 
0.77 
0.71 
0.54 
0.6 
0.85 
0.82 
-0.09 
0.71 
0.75 
0.78 
0.79 
0.79 
0.79 
0.74 
0.7 
0.52 
0.52 
0.57 
0.39 
0.62 
0.39 
0.64 

0.92 
0.9 
0.88 
0.71 
0.89 
0.86 
0.77 
0.82 
0.92 
0.91 
0.53 
0.86 
0.88 
0.89 
0.9 
0.89 
0.9 
0.87 
0.86 
0.78 
0.78 
0.81 
0.67 
0.86 
0.65 
0.82 

2.9 
3.9 
1.3 
1 
-0.8 
5.2 
4.2 
5.4 
1.5 
1 
-10.9 
1.8 
2.6 
2 
2.1 
-0.2 
1.8 
1.3 
5.7 
5.7 
0.4 
6 
-2.9 
10.4 
-11.3 
1.4 

0.75 
0.78 
0.48 
0.27 
0.71 
0.67 
0.69 
0.48 
0.77 
0.81 
0.82 
0.72 
0.81 
0.83 
0.85 
0.87 
0.89 
0.4 
0.41 
0.74 
0.68 
0.67 
0.53 
0.2 
0.63 
0.66 

0.53 
0.56 
0.38 
-0.87 
0.41 
0.33 
0.44 
0.15 
0.59 
0.61 
0.66 
0.65 
0.71 
0.71 
0.71 
0.74 
0.78 
-0.08 
-0.3 
0.48 
0.31 
0.35 
-0.03 
-0.77 
0.27 
0.47 

0.79 
0.8 
0.73 
0.39 
0.75 
0.73 
0.78 
0.77 
0.8 
0.82 
0.84 
0.84 
0.86 
0.88 
0.85 
0.87 
0.89 
0.78 
0.65 
0.78 
0.69 
0.68 
0.64 
0.62 
0.74 
0.77 

-13.4 
-7.2 
-31.1 
-8.1 
3.2 
7.1 
-10.1 
36.3 
-11.8 
-2.6 
8.3 
-21 
-11.7 
-10.4 
-4.2 
-3.5 
-1.3 
24.6 
28.4 
-1.2 
-0.9 
6.1 
8.1 
22.3 
7.7 
-22.9 

Dresden 
Usti 
Brandys 
SazavaPorice 
LuzniceBechyne 
PrahaChuchle 
Beroun 
OhreLouny 
Torgau 
LuthWittenberg 
Golzern 
Camburg 
Laucha 
Calbe 
Tangermuende 
Wittenberge 
NeuDarchau 
Smalininkai 
Jonava 
Hohensaaten 
Slubice 
Polecko 
Scinawa 
Olawa 
Miedonia 
Bohumin 

29 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C185  CZ 
C186  CZ 
C189  PL 
C190  PL 
C191  PL 
C193  PL 
C194  PL 
C196  PL 
C197  PL 
C199  PL 
C200  DE 
C201  CH 
C202  DE 
C203  DE 
C204  DE 
C207  DE 
C208  DE 
C209  DE 
C211  DE 
C212  CH 
C213  CH 
C214  DE 
C217  PL 
C218  PL 
C220  PL 
C222  PL 

Oder 
Oder 
Oder 
Oder 
Oder 
Oder 
Oder 
Oder 
Oder 
Oder 
Rhine 
Rhine 
Rhine 
Rhine 
Rhine 
Rhine 
Rhine 
Rhine 
Rhine 
Rhine 
Rhine 
Rhine 
Vistula 
Vistula 
Vistula 
Vistula 

Odra 
Odra 
Jezioro Turawskie 
Nysa Klodzka 
Bystrzyca 
Nysa Luzycka 
Warta 
Warta 
Warta 
Notec 
Rhein 
Rhein 
Rhein 
Rhein 
Rhein 
Mosel 
Mosel 
Neckar 
Main 
Aare 
Aare 
Saar 
Wisla 
Orzyc 
Bug 
Wieprz 

Svinov 
Dehylov 
Turawa 
Skorogoszcz 
Jarnoltow 
Gubin 
Gorzow 
Poznan 
Slawsk 
Drezdenko 
Rekingen 
Diepoldsau 
Kaub 
Duesseldorf 
Worms 
Perl 
Cochem 
Lauffen 
Frankfurt 
Untersiggenthal 
Murgenthal 
Fremersdorf 
Sandomierz 
Zambski Ko?cielne 
Frankopol 
Kosmin 

0.72 
0.76 
0.61 
0.76 
0.67 
0.86 
0.71 
0.81 
0.79 
-0.62 
0.7 
0.76 
0.93 
0.95 
0.94 
0.84 
0.9 
0.87 
0.93 
0.93 
0.92 
0.89 
0.9 
0.61 
0.76 
0.7 

30 

0.56 
0.54 
0.32 
0.6 
0.36 
0.73 
0.41 
0.62 
0.58 
-4.99 
0.55 
0.75 
0.9 
0.9 
0.88 
0.78 
0.84 
0.74 
0.85 
0.89 
0.85 
0.79 
0.79 
0.37 
0.54 
0.39 

0.76 
0.76 
0.63 
0.78 
0.67 
0.86 
0.75 
0.81 
0.81 
0.54 
0.93 
0.89 
0.95 
0.95 
0.94 
0.89 
0.92 
0.87 
0.93 
0.95 
0.93 
0.9 
0.9 
0.66 
0.78 
0.71 

-4 
0.9 
6.5 
-2.4 
2.9 
1.2 
9.9 
1.2 
-2.1 
54.9 
-21.5 
-14.8 
-1.4 
0.3 
-0.5 
-7.2 
-2.6 
2.2 
1 
-2.9 
-0.7 
2.2 
0.9 
-9.6 
7.3 
6.2 

0.67 
0.58 
0.31 
0.56 
0.44 
0.72 
0.63 
0.57 
0.73 
-0.47 
0.68 
0.75 
0.94 
0.92 
0.93 
0.89 
0.92 
0.69 
0.87 
0.92 
0.87 
0.85 
0.83 
0.5 
0.65 
0.4 

0.48 
0.1 
-0.24 
0.39 
0.4 
0.6 
0.19 
0.1 
0.44 
-4.42 
0.53 
0.71 
0.89 
0.88 
0.86 
0.78 
0.85 
0.45 
0.8 
0.88 
0.83 
0.76 
0.66 
0.44 
0.38 
0.22 

0.75 
0.62 
0.33 
0.64 
0.64 
0.81 
0.71 
0.7 
0.76 
0.5 
0.91 
0.88 
0.94 
0.94 
0.93 
0.89 
0.93 
0.8 
0.91 
0.94 
0.91 
0.89 
0.84 
0.78 
0.74 
0.6 

-20.9 
-8.1 
15.7 
-5.2 
17.9 
-16.7 
9.8 
9.8 
2.5 
34.9 
-20.1 
-15.9 
1.7 
0.7 
1.3 
1.3 
-1.7 
11.3 
4.5 
-1.6 
-2.7 
-4.4 
4.2 
-21 
22.6 
-20.4 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C223  PL 
C224  PL 
C225  PL 
C227  DE 
C228  DE 
C229  DE 
C230 
SE 

C231 
SE 
C232 
SE 
C234  NO 
C235  NO 
C236 
SE 

C237 
C238 
C239 
C241 
C242 
C243 
C244 
C245 
C246 
C247 
C248 
C249 

FI 
SE 
SE 
FI 
FI 
FI 
FI 
FI 
SE 
SE 
SE 
SE 

Vistula 
Vistula 
Vistula 
Weser 
Weser 
Weser 
Angermanelv
en 
Dalaelven 
Eman 
Glomma 
Losna 
Gota 

Iijoki 
Indalselven 
Kalixaelven 
Kemijoki 
Kemijoki 
Kemijoki 
Kymijoki 
Kymijoki 
Lagan 
Ljungan 
Ljusnan 
Luleaelven 

San 
Wisloka 
Dunajec 
Weser 
Weser 
Weser 
Sollefteae Krv 

Nisko 
Mielec 
Zabno 
Intschede 
Hann. Münden 
Porta 
Angermanaelven 

Dalaelven 
Eman 
Glomma 
Losna 
Gota 

Aelvkarleby 
Emsfors Bruk 
Elverum 
Losna 
Vaenersborg 
Vargoens 
Raasakka 
Iijoki 
Bergeforsens Krv 
Indalselven 
Raektfors 
Kalixaelven 
Isohaara 
Kemijoki 
Marraskoski Iisinki 
Ounasjoki 
Kemijoki 
Seitakorva 
Leppavesi-Paijanne  Vaajakoski 
Kymijoki 
Lagan 
Ljungan 
Ljusnan 
Luleaelven 

Anjala 
Angabaecks 
Skallboele 
Ljusne Stroemmar 
Bodens 

0.83 
0.69 
0.8 
0.94 
0.93 
0.92 
0.45 

0.63 
0.82 
0.56 
0.36 
0.73 

0.7 
0.51 
0.48 
0.85 
0.89 
0.57 
0.74 
0.63 
0.86 
0.66 
0.72 
0.49 

0.67 
0.57 
0.6 
0.91 
0.87 
0.84 
-0.02 

0.49 
0.64 
0.59 
0.43 
0.55 

0.72 
0.04 
0.63 
0.75 
0.82 
0.05 
0.46 
0.31 
0.75 
0.45 
0.59 
-0.12 

0.84 
0.76 
0.8 
0.95 
0.93 
0.92 
0.5 

0.73 
0.82 
0.89 
0.89 
0.77 

0.87 
0.57 
0.92 
0.89 
0.91 
0.64 
0.74 
0.63 
0.89 
0.7 
0.78 
0.55 

1 
-12.9 
2.9 
-1.1 
-0.6 
1.7 
-17.5 

-10.4 
0.9 
-30.1 
-43.6 
-8.6 

-17.8 
-17.4 
-36.3 
0.5 
-4.2 
-3.3 
2 
0.2 
6.3 
-9.5 
-8.9 
-20 

0.53 
0.64 
0.7 
0.87 
0.78 
0.83 
0.48 

0.62 
0.55 
0.54 
0.38 
0.13 

0.42 
0.43 
0.52 
0.68 
0.67 
0.62 
0.3 
0.3 
0.58 
0.61 
0.72 
0.21 

0.4 
0.53 
0.44 
0.91 
0.84 
0.84 
0.1 

0.31 
0.4 
0.58 
0.48 
-0.53 

0.43 
0 
0.65 
0.68 
0.8 
0.36 
-1.25 
-1.28 
0.38 
0.38 
0.45 
-0.96 

0.72 
0.73 
0.77 
0.96 
0.93 
0.93 
0.65 

0.67 
0.78 
0.88 
0.88 
0.29 

0.79 
0.61 
0.89 
0.85 
0.92 
0.7 
0.41 
0.37 
0.84 
0.74 
0.75 
0.26 

-29.5 
-9.4 
11.7 
-10 
-14.5 
-11.2 
-28.6 

-15.6 
-34.2 
-32.6 
-42.2 
-35.2 

-40.7 
-26.9 
-34.7 
-19.5 
-26.7 
-20.3 
-31.4 
-28.9 
-36.7 
-23.3 
-11.7 
-26 

31 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C250 
C251 
C252 
C253 

SE 
FI 
SE 
SE 

C254  NO 
C255 
SE 
C258 
FI 
C259 
IE 
C260 
IE 
C261 
IE 
C264 
IE 
C268 
IE 
C272 
IE 
C273 
IE 
C276 
IE 
C277 
IE 
C278 
IE 
C279 
IE 
C281 
IE 
C282 
IE 
C283 
ES 
C284 
ES 
C285 
ES 
C286 
ES 
C287 
ES 

Maelaren 
Oulujoki 
Piteaelven 
Skellefteaelve
n 
Tanano 
Umeaelven 
Vuoksi 
Boyne 
Boyne 
Boyne 
Barrow 
Suir 
Shannon 
Shannon 
Shannon 
Shannon 
Clare 
Clare 
Erne 
Erne 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 

Maelaren 
Oulujoki 
Piteaelven 
Skellefteaelven 

Tanano 
Umeaelven 
Vuoksi 
Boyne 
Blackwater Kells 
Boyne 
Barrow 
Suir 
Maigue 
Mulkear 
Suck 
Inny 
Clare 
Cong 
Annalee 
Erne 
Ega 
Arga 
Aragon 
Jalon 
Gállego 

Oevre Stockholm 
Meriskoski 
Sikfors 
Kvistforsens 

Polmak Nye 
Umeae 
Tainionkoski 
Navan Weir 
Liscartan 
Slane Castle 
Levitstown 
Caher Park 
Castleroberts 
Annacotty 
Bellagill 
Ballymahon 
Corrofin 
Cong Weir 
Butlers Bridge 
Belturbet 
Andosilla 
Funes 
Caparroso 
Cetina 
Ardisa 

0.6 
0.06 
0.74 
0.48 

0.51 
0.77 
0.63 
0.86 
0.76 
0.06 
0.89 
0.86 
0.81 
0.68 
0.92 
0.91 
0.73 
0.66 
0.54 
0.58 
0.78 
0.75 
0.29 
-0.13 
-2.95 

0.69 
-0.92 
0.82 
-0.05 

0.71 
0.62 
0.34 
0.74 
0.74 
0.05 
0.78 
0.84 
0.65 
0.64 
0.86 
0.84 
0.65 
0.72 
0.57 
0.62 
0.64 
0.7 
0.4 
-0.68 
-1.6 

0.87 
0.06 
0.94 
0.61 

0.92 
0.83 
0.66 
0.87 
0.87 
0.85 
0.89 
0.92 
0.82 
0.81 
0.93 
0.92 
0.81 
0.92 
0.91 
0.92 
0.82 
0.84 
0.67 
0.42 
0.5 

-7 
-10.9 
-18.2 
-25.6 

-34.1 
-14.9 
-5.6 
3 
-10.3 
-64.2 
2.1 
-7.2 
5.3 
-16.7 
4.1 
1.2 
-10 
-21.5 
33.9 
-35.2 
11.8 
-12.4 
55.7 
95 
391.8 

0.36 
-0.09 
0.65 
0.38 

0.5 
0.59 
0.34 
0.87 
0.56 
-0.02 
0.82 
0.65 
0.84 
0.57 
0.9 
0.77 
0.78 
0.42 
0.75 
0.28 
0.75 
0.59 
0.44 
-0.04 
-5.18 

0.34 
-1.8 
0.77 
-0.16 

0.74 
0.44 
-1.36 
0.76 
0.58 
-0.12 
0.74 
0.73 
0.74 
0.62 
0.89 
0.73 
0.67 
0.27 
0.63 
0.18 
0.55 
0.53 
0.46 
-0.24 
-1.46 

0.69 
0 
0.93 
0.57 

0.93 
0.79 
0.39 
0.88 
0.82 
0.82 
0.86 
0.89 
0.87 
0.83 
0.95 
0.87 
0.82 
0.85 
0.82 
0.81 
0.76 
0.75 
0.7 
0.35 
0.26 

-35.2 
-43.2 
-21.5 
-30.2 

-36.8 
-25.6 
-23.5 
-3.1 
-28.8 
-70.5 
-6.1 
-18.1 
9 
-25.2 
-6 
-14.6 
-7.6 
-39.5 
-15.5 
-55.9 
-1.5 
-25.9 
-16.1 
79.9 
613.4 

32 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C288 
C289 
C290 
C291 
C292 
C293 
C294 
C295 
C296 
C297 
C298 
C299 
C300 
C301 
C302 
C303 
C304 
C306 
C307 
C308 

C309 
C310 
C311 
C312 
C313 

ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 

ES 
ES 
ES 
ES 
ES 

Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 

Ebro 
Ebro 
Ebro 
Ebro 
Ebro 

Esera 
Martín 
Cinca 
Bergantes 
Najerilla 
Pancrudo 
Jiloca 
Isabena 
Jiloca 
Esca 
Irati 
Ega 
Ayuda 
Jalon 
Alcanadre 
Nela 
Oca 
Guadalope 
Gallego 
Noguera 
Ribagorzana 
Bayas 
Aragon 
Algas 
Manubles 
Alcanadre 

0.71 
-0.84 
0.61 
0.2 
0.58 
-2.46 
0.23 
0.75 
0.55 
0.77 
0.52 
0.79 
0.57 
0.5 
0.66 
0.51 
0.74 
-3.55 
0.62 
0.35 

0.44 
0.73 
0.4 
-0.23 
0.65 

Graus 
Hijar 
Fraga 
Zorita 
Torremontalbo 
Navarrete del Río 
Calamocha 
Capella 
Morata de Jiloca 
Sigues 
Liedena 
Estella 
Berantevilla 
Grisen 
Lascellas 
Trespaderne 
Ona 
Caspe 
Anzanigo 
El Pont de Suert 

Miranda de Ebro 
Yesa 
Batea 
Ateca 
Ballobar 

33 

0.57 
-0.54 
0.39 
-0.18 
0.22 
-0.3 
-0.44 
0.57 
0.23 
0.58 
0.43 
0.63 
0.3 
0.37 
0.48 
0.32 
0.6 
-0.74 
0.48 
0.2 

0.51 
0.55 
0.37 
0.16 
0.34 

0.76 
0.25 
0.68 
0.54 
0.59 
0.48 
0.47 
0.78 
0.7 
0.78 
0.66 
0.8 
0.61 
0.72 
0.78 
0.59 
0.81 
0.32 
0.73 
0.71 

0.74 
0.76 
0.61 
0.65 
0.65 

-6.6 
168.4 
16 
61.4 
-0.5 
342 
55.9 
12.9 
30.8 
5.5 
-10.1 
0.2 
12.3 
41.4 
23.6 
-7.6 
17 
450.4 
-15.3 
-38.4 

-27.8 
-6.1 
30.7 
117.9 
3.6 

0.51 
-6.11 
0.53 
-0.25 
0.43 
-4.09 
-0.65 
0.43 
-0.22 
0.64 
0.43 
0.69 
0.11 
-0.52 
0.66 
0.51 
0.03 
-3.47 
0.59 
0.44 

0.32 
0.59 
-0.21 
-0.79 
0.47 

0.35 
-39.48 
0.36 
-3.06 
-0.16 
-0.85 
-4.41 
0.3 
-1.41 
0.39 
0.43 
0.45 
-0.09 
-0.42 
0.35 
0.23 
-1.01 
-2.47 
0.38 
0.05 

0.33 
0.34 
-1.58 
0.18 
0.41 

0.61 
0.19 
0.64 
0.13 
0.56 
0.18 
0.42 
0.56 
0.49 
0.66 
0.68 
0.7 
0.64 
0.74 
0.67 
0.64 
0.57 
0.15 
0.64 
0.53 

0.59 
0.62 
0.24 
0.59 
0.65 

-8.2 
460.9 
-16.1 
18.6 
30 
502.1 
122 
3.4 
97.7 
0.6 
-17.1 
-0.8 
78.3 
142.3 
7 
32.5 
60.6 
434.4 
-2.7 
-25.7 

-33 
-3.4 
79.1 
173.5 
-13.9 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C314 
ES 
C315  CZ 
C316  CZ 
C317  CZ 
C318  CZ 
C319  CZ 
C320 
SK 
C321  CZ 
C322 
SE 

C323 

SE 

C324 
SE 
C325  AT 
C326  AT 
C327  AT 
C328  AT 
C329  AT 
C330  AT 
C332  DE 
C333  DE 
C334  DE 
C335  DE 
C338  DE 
C340  DE 
C341  DE 

Ebro 
Elbe 
Elbe 
Elbe 
Morava 
Morava 
Morava 
Morava 
LakeVatternC
atchment 
Ulme 

Torne 
Danube 
Danube 
Inn 
Inn 
Mur 
Mur 
Rhine 
Rhine 
Rhine 
Main 
Rhine 
Main 
Rhine 

Tiron 
Otava 
Elbe 
Jizera 
Morava 
Thaya 
Morava 
Becva 
Motala Stroem 

Vindelaelven 
(Umeaelven) 
Torneaelven 
Danube 
Danube 
Inn 
Inn 
Mur 
Mur 
Nahe 
Rhine 
Rhine 
Main 
Lahn 
Main 
Neckar 

0.76 
0.11 
0.88 
0.6 
0.72 
0.72 
0.73 
0.74 
0.55 

0.83 

0.93 
0.89 
0.91 
0.8 
0.91 
0.9 
0.91 
0.85 
0.94 
0.93 
0.88 
0.87 
0.9 
0.87 

Haro 
Pisek 
Decin 
Zelezny Brod 
Moravicany 
Breclav Ladna 
Moravsky Jan 
Teplice 
Glan 

Sorsele 2 

Kukkolankoski Oevre 
Kienstock 
Korneuburg 
Innsbruck 
Schaerding 
Spielfeld 
Friesach 
Grolsheim 
Mainz 
Speyer 
Kleinheubach 
Leun (Neu) 
Wuerzburg 
Rockenau Ska 

34 

0.56 
-0.46 
0.8 
0.49 
0.62 
0.45 
0.47 
0.59 
0.37 

0.79 
0.7 
0.91 
0.72 
0.8 
0.73 
0.77 
0.78 
0.7 

10.5 
64.7 
3.9 
-17.5 
-13.6 
9 
7.5 
-7.8 
-19.4 

-12.37 
0.07 
0.7 
0.23 
0.58 
0.63 
0.2 
0.47 
0.27 

-105 
-0.85 
0.51 
0.07 
0.38 
0.52 
0.05 
0.39 
-0.09 

0.31 
0.71 
0.78 
0.53 
0.75 
0.72 
0.46 
0.69 
0.71 

1121.8 
39.7 
-18.4 
-47.9 
-32.4 
-0.6 
-33.6 
-42.4 
-48.8 

0.81 

0.9 

-8.9 

0.76 

0.78 

0.89 

-12 

0.9 
0.79 
0.83 
0.75 
0.82 
0.82 
0.82 
0.71 
0.88 
0.87 
0.75 
0.75 
0.81 
0.73 

0.95 
0.9 
0.91 
0.88 
0.91 
0.91 
0.91 
0.85 
0.94 
0.94 
0.88 
0.87 
0.9 
0.87 

-3.2 
1 
-0.2 
-11.1 
-1.9 
-1.5 
2 
0.2 
0.3 
-1.4 
2.5 
1.2 
-1.5 
1.4 

0.86 
0.92 
0.9 
0.75 
0.9 
0.84 
0.87 
0.85 
0.92 
0.91 
0.73 
0.88 
0.87 
0.63 

0.88 
0.84 
0.83 
0.78 
0.86 
0.75 
0.79 
0.75 
0.87 
0.85 
0.59 
0.83 
0.79 
0.4 

0.94 
0.92 
0.92 
0.92 
0.93 
0.88 
0.9 
0.88 
0.94 
0.92 
0.85 
0.92 
0.9 
0.81 

-10.9 
-1.9 
2.7 
-17.1 
-3.5 
-8.7 
-7.8 
-8.7 
3.7 
-2.6 
10.6 
-7.6 
8.9 
12.8 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C342  DE 
C343  DE 
C344  DE 
C346  DE 
C347  DE 
C348  DE 
C349  DE 
C350  DE 
C351  DE 

C353  DE 
C354  DE 

C355  DE 
C356  DE 
C357  DE 
C358  DE 
C360  DE 
C361  DE 
C362  DE 
C363  DE 
C364  DE 
C365  DE 
C367  NL 
C368  NL 
C370 
SI 

Rhine 
Mosel 
Weser 
Weser 
Ems 
Elbe 
Elbe 
Saale 
Havel 

Mulde 
Havel 

Elbe 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Inn 
Oder 
Maas 
Rhine 
Sava 

Neckar 
Moselle 
Aller 
Werra 
Ems 
Elbe 
Elbe 
Saale 
Havel 

Vereinigte Mulde 
Spree 

Schwarze Elster 
Iller 
Danube 
Altmuehl 
Danube 
Danube 
Danube 
Isar 
Inn 
Oder 
Meuse 
Rhine 
Sava 

Plochingen 
Trier Up 
Rethem 
Letzter Heller 
Versen 
Barby 
Aken 
Halle Trotha 
Rathenow 
Hauptschleuse Up 
Bad Dueben 
Berlin 
Muehlendamm Op 
Bad Liebenwerda 
Kempten 
Ingolstadt 
Eichstaett 
Achleiten 
Oberndorf 
Pfelling 
Landau 
Wasserburg 
Eisenhuettenstadt 
Megen Dorp 
Lobith 
Hrastnik 

0.83 
0.92 
0.92 
0.92 
0.91 
0.89 
0.88 
0.88 
0.75 

0.69 
0.76 

0.75 
0.78 
0.9 
0.88 
0.93 
0.91 
0.91 
0.86 
0.88 
0.76 
0.9 
0.94 
0.86 

35 

0.66 
0.84 
0.84 
0.85 
0.82 
0.8 
0.77 
0.75 
0.55 

0.83 
0.92 
0.92 
0.92 
0.91 
0.91 
0.89 
0.88 
0.76 

0.37 
0.51 

0.74 
0.77 

0.51 
0.58 
0.8 
0.77 
0.87 
0.82 
0.83 
0.72 
0.82 
0.53 
0.81 
0.89 
0.71 

0.76 
0.78 
0.9 
0.89 
0.94 
0.91 
0.92 
0.87 
0.91 
0.79 
0.91 
0.95 
0.86 

1.8 
0.5 
-0.4 
1.4 
2.2 
4.7 
3.8 
1.3 
-1.8 

7.9 
1.5 

8 
-0.7 
0 
5 
0.4 
0.9 
1.5 
2.8 
-6.3 
3.6 
0 
1.2 
2.1 

0.82 
0.93 
0.88 
0.79 
0.86 
0.85 
0.83 
0.8 
0.75 

0.78 
0.67 

0.68 
0.76 
0.89 
0.83 
0.92 
0.89 
0.9 
0.84 
0.85 
0.69 
0.74 
0.91 
0.87 

0.72 
0.86 
0.83 
0.83 
0.81 
0.73 
0.66 
0.7 
0.49 

0.88 
0.93 
0.93 
0.93 
0.92 
0.86 
0.84 
0.87 
0.75 

0.66 
0.37 

0.83 
0.74 

0.4 
0.54 
0.78 
0.73 
0.85 
0.79 
0.82 
0.69 
0.84 
0.36 
0.71 
0.88 
0.79 

0.78 
0.77 
0.89 
0.87 
0.93 
0.9 
0.91 
0.86 
0.93 
0.71 
0.91 
0.95 
0.91 

9.8 
-0.3 
-8 
-12.5 
10.6 
-4.5 
-0.9 
-14.7 
-4.3 

12.9 
-18.7 

-6.8 
-4.6 
2.2 
11.7 
-2 
1.4 
3.1 
2.3 
-7.7 
-10.6 
16 
3.9 
6.2 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C371 
SI 
C372 
SI 
C373 
SI 
C374 
SI 
C377 
SI 
C380  UK 
C383  UK 

C386  UK 
C390  NO 
C391  NO 
C392  NO 
C393  NO 
C394  NO 
C395  NO 
C396  NO 
C397  NO 
C398  NO 
C399  NO 
C400  NO 
C401  NO 
C402  NO 
C403  RO 
C404  RO 
C405  RO 
C406  RO 

Sava 
Sava 
Sava 
Sava 
Isonzo 
R. Annan 
Orrin / Black 
Water 
Lune 
Vosso 
Otra 
Begna 
Glomma 
Glomma 
Glomma 
Gauldalen 
Namsen 
Goto 
Vefsna 
Barduelva 
Altaelva 
Altaelva 
Danube 
Olt 
Danube 
Danube 

Sava 
Krka 
Savinja 
Ljubljanica 
So?a / Isonzo 
Annan 
Conon 

Lune 
Vosso 
Otra 
Dramselv 
Glama 
Glama 
Otta 
Gaula 
Namsen 
Klara 
Vefsna 
Maalselv 
Kautokeinoelva 
Altaelva 
Danube 
Olt 
Danube 
Danube 

Radovljica 
Podbocje 
Veliko Sirje 
Moste 
Solkan 
Brydekirk 
Moy Bridge 

Caton 
Bulken 
Heisel 
Dovikfoss 
Langnes 
Skarnes 
Lalm 
Gaulfoss 
Bertnem 
Nybergsund 
Lakfors 
Malangsfoss 
Masi 
Kista 
Bazias 
Hoghiz 
Zimnicea 
Ceatal Izmail 

0.68 
0.81 
0.7 
0.89 
0.78 
0.84 
0.56 

0.39 
0.58 
0.62 
0.73 
0.61 
0.65 
0.28 
0.65 
0.43 
0.72 
0.68 
0.28 
0.44 
0.5 
0.87 
0.66 
0.89 
0.89 

0.48 
0.67 
0.38 
0.79 
0.57 
0.68 
0.55 

0.44 
0.58 
0.37 
0.53 
0.59 
0.62 
0.41 
0.74 
0.34 
0.78 
0.7 
0.37 
0.61 
0.65 
0.77 
0.52 
0.79 
0.78 

0.71 
0.83 
0.71 
0.9 
0.78 
0.84 
0.85 

0.73 
0.81 
0.71 
0.8 
0.91 
0.84 
0.89 
0.89 
0.71 
0.92 
0.86 
0.85 
0.86 
0.88 
0.91 
0.74 
0.91 
0.89 

-7.8 
-5.2 
4.7 
3.3 
0.7 
0.6 
-29.7 

-38.3 
-26.7 
-17.7 
-14.7 
-23.3 
-19.1 
-50.6 
-22.4 
-34 
-17.7 
-20.1 
-48.3 
-36.5 
-32.5 
7.9 
-14.1 
5.7 
1.7 

0.75 
0.84 
0.75 
0.89 
0.79 
0.79 
0.06 

0.1 
0.53 
0.55 
0.67 
0.59 
0.59 
0.21 
0.56 
0.37 
0.68 
0.61 
0.21 
0.55 
0.53 
0.8 
0.46 
0.8 
0.75 

0.57 
0.7 
0.56 
0.82 
0.6 
0.57 
-0.26 

0.19 
0.57 
0.23 
0.43 
0.6 
0.73 
0.26 
0.72 
0.29 
0.75 
0.64 
0.33 
0.71 
0.71 
0.63 
0.39 
0.6 
0.47 

0.77 
0.85 
0.76 
0.91 
0.8 
0.79 
0.7 

0.61 
0.82 
0.68 
0.8 
0.9 
0.91 
0.78 
0.9 
0.74 
0.89 
0.84 
0.87 
0.89 
0.91 
0.84 
0.64 
0.82 
0.77 

-6.7 
-5.7 
0.1 
-5.3 
-3.2 
-1.3 
-62.4 

-56.2 
-28.4 
-23.2 
-22.5 
-24.5 
-7.6 
-51.6 
-28 
-41.2 
-11 
-26.6 
-55.6 
-27.6 
-32.9 
0.4 
-14.1 
-4.8 
-8.3 

36 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C407  RO 
C408  RO 
C409  RO 
C410  RO 
C411 
FI 
C412 
FI 
C413 
FI 

C414 
FI 
C415 
FI 
C416  CH 
C417  CH 
C418  CH 

C419  CH 
C421  CH 
C422  CH 
C423  CH 
C425  CH 
C426  CH 
C427  CH 
C436 
LV 
C437 
LV 
C438 
LV 
C439 
LV 
C440 
LT 

Danube 
Mures 
Timis 
Tisza 
Pasvikely 
Tana 
Kokemaenjok
i 
Oulujoki 
Oulujoki 
Aare 
Rhine 
Rhine 

Rhine 
Rhine 
Rhine 
Rhone 
Inn 
Po 
Po 
Venta 
Gauja 
Zap Dvina 
Lielupe 
Vilnya / Neris 

Ialomita 
Maros 
Timis 
Szamos 
Paatsjoki 
Tana 
Kokemaenjoki 

Oulujoki 
Oulujoki 
Aare 
Rhine 
Rhine 

Rhine 
Reuss 
Thur 
Rhone 
Inn 
Ticino 
Maggia 
Venta 
Gauja 
Daugava 
Lielupe 
Sventoji 

0.63 
0.76 
0.8 
0.68 
0.19 
0.48 
0.66 

0.7 
-0.11 
0.89 
0.88 
0.91 

0.75 
0.8 
0.71 
0.79 
0.81 
0.75 
0.12 
0.78 
0.83 
0.91 
0.81 
0.84 

Cosereni 
Alba Iulia 
Sag 
Satu Mare 
Lake Inari Outlet 
Onnelansuvanto 
Harjavalta 

Lake Lentua Outlet 
Jylhämä 
Bern Schoenau 
Basel Rheinhalle 
Neuhausen 
Flurlingerbruecke 
Domat/Ems 
Mellingen 
Andelfingen 
Porte Du Scex 
Martinsbruck 
Bellinzona 
Locarno Solduno 
Kuldiga 
Valmiera 
Daugavpils 
Mezotne 
Ukmerge 

37 

0.31 
0.51 
0.63 
0.53 
-0.76 
0.69 
0.42 

0.55 
-1.02 
0.84 
0.87 
0.91 

0.69 
0.8 
0.47 
0.6 
0.71 
0.59 
-0.08 
0.62 
0.67 
0.84 
0.62 
0.67 

0.64 
0.76 
0.81 
0.75 
0.24 
0.92 
0.68 

0.77 
-0.1 
0.92 
0.95 
0.97 

0.86 
0.92 
0.72 
0.83 
0.85 
0.78 
0.59 
0.8 
0.84 
0.92 
0.81 
0.84 

0.5 
2 
-2 
-14.8 
-26 
-36 
-5.6 

-11.4 
-5.5 
-5.2 
-6.8 
-8 

-14.5 
-12.1 
-4.1 
-11.8 
-6.8 
-8.8 
75.8 
-3.8 
5.4 
1.8 
2.5 
2.6 

0.44 
0.57 
0.38 
0.37 
0.43 
0.4 
0.39 

0.49 
-0.24 
0.88 
0.87 
0.86 

0.62 
0.8 
0.66 
0.79 
0.63 
0.73 
0.2 
- 
- 
0.82 
- 
0.7 

-0.27 
0.32 
-0.21 
0.1 
-0.22 
0.65 
-0.14 

0.18 
-1.58 
0.82 
0.84 
0.86 

0.61 
0.73 
0.36 
0.63 
0.67 
0.56 
-0.05 
- 
- 
0.64 
- 
0.46 

0.57 
0.71 
0.45 
0.63 
0.56 
0.9 
0.56 

0.67 
-0.17 
0.92 
0.94 
0.97 

0.83 
0.89 
0.67 
0.86 
0.85 
0.76 
0.61 
- 
- 
0.83 
- 
0.77 

-10.5 
-31.8 
-27.3 
-47.5 
-31 
-43.9 
-35.5 

-32.7 
-39.3 
-6.1 
-7.8 
-9.7 

-18.1 
-12.1 
-5.6 
-9.6 
-12.7 
-8.6 
66.4 
- 
- 
4 
- 
18.4 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C442  DE 
C443  DE 
C444  CZ 
C445  DE 
C446 
SL 
C447 
SL 
C448 
SL 
C449 
SL 
C450  HR 
C453  HR 
C454  HR 
C455  HR 
C459  HR 
C463  HR 
C464 
IT 
C465 
IT 
C466 
IT 
C468 
IT 
C471 
IT 
C472 
IT 
C473 
IT 
C475 
IT 
C476 
IT 
C481 
IT 
C482 
IT 
C483 
IT 

Morava 
Danube 
Danube 
Morava 
Danube 
Vah 
Hron 
Danube 
Sava 
Sava 
Sava 
Sava 
Sava 
Sava 
Po 
Reno 
Po 
Po 
Po 
Po 
Po 
Po 
Po 
Po 
Po 
Po 

Wornitz 
Naab 
Jihlava 
Regen 
Vah 
Vah 
Hron 
Hron 
Kupa 
Kupa 
Kupa 
Una 
Sava 
Sava 
Po 
Reno 
Tanaro 
Po 
Tanaro 
Stura Demonte 
Bormida 
Bormida 
Po 
Po 
Baltea 
Cervo 

0.83 
0.92 
0.66 
0.75 
0.77 
0.76 
0.68 
0.72 
0.3 
0.16 
0.33 
0.68 
0.85 
0.73 
0.88 
0.71 
0.5 
0.81 
0.84 
0.86 
0.67 
0.72 
0.79 
0.49 
0.51 
0.49 

Harburg 
Haitzenhofen 
Ivancice 
Kienhof 
Liptovskymikulas 
Nitra 
Banskabystrica 
Hron 
Kupari  
Jamnicka Kiselica 
Farkasic 
Donja Suvaja 
Rugvica 
Slavonski Brod 
Boretto 
Casalecchio Chiusa 
Farigliano 
Piacenza 
Asti 
Fossano 
Alessandria 
Cassine 
Isola S.Antonio 
Castiglione Torinese 
Hône - Ponte Dora 
Quinto Vercellese 

38 

0.67 
0.84 
0.38 
0.5 
0.54 
0.52 
0.33 
0.44 
-0.14 
-0.06 
0.23 
0.64 
0.74 
0.62 
0.85 
0.46 
0.5 
0.83 
0.72 
0.73 
0.46 
0.48 
0.74 
0.65 
0.45 
0.43 

0.84 
0.92 
0.67 
0.76 
0.8 
0.78 
0.7 
0.76 
0.72 
0.86 
0.88 
0.8 
0.87 
0.88 
0.92 
0.74 
0.77 
0.92 
0.85 
0.86 
0.7 
0.72 
0.87 
0.88 
0.81 
0.69 

4.2 
1.7 
3 
2 
8.5 
7.1 
6.7 
9.9 
46 
60.7 
48.1 
-14.5 
2.5 
15.8 
-4.1 
13.2 
-30.8 
-12.3 
-5.8 
-0.4 
8.1 
-1.9 
-12.4 
48 
41.4 
-26.5 

0.73 
0.81 
0.5 
0.73 
0.36 
- 
0.28 
0.21 
0.29 
0.07 
0.27 
0.65 
0.75 
0.45 
0.87 
0.63 
- 
0.82 
- 
0.58 
0.57 
0.74 
0.83 
- 
0.46 
- 

0.55 
0.72 
0.49 
0.47 
-0.33 
- 
-0.51 
-0.49 
-0.09 
-0.24 
0.15 
0.64 
0.74 
0.28 
0.82 
0.36 
- 
0.8 
- 
0.2 
0.51 
0.56 
0.77 
- 
-0.07 
- 

0.76 
0.86 
0.72 
0.73 
0.5 
- 
0.36 
0.34 
0.75 
0.87 
0.88 
0.81 
0.91 
0.87 
0.92 
0.65 
- 
0.91 
- 
0.66 
0.72 
0.77 
0.88 
- 
0.69 
- 

9 
-10.8 
-17.8 
-1.5 
-38.4 
- 
-34 
-43.2 
47.2 
65.6 
54.2 
-14.3 
13.7 
22.7 
-9.7 
-7.4 
- 
-12.4 
- 
20.3 
24 
7.9 
-11.1 
- 
14.9 
- 

ID 

Country 

Basin 

River 

Station 

C487 
C488 

FR 
FR 

C490 
C491 
C493 
C494 
C495 
C496 
C497 

C498 
C499 
C500 
C501 
C502 
C503 
C504 

SP 
SP 
SP 
SP 
SP 
SP 
SP 

SP 
SP 
SP 
SP 
SP 
SP 
SP 

L'Erdre 
Loire-
Atlantique 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 

Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 

C505 

SP 

Guadalquivir 

C506 

SP 

Guadalquivir 

C507 
C508 
C509 
C510 

SP 
SP 
SP 
SP 

Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 

L'Erdre 
La Sevre Nantaise 

Nantes 
Nantes 

Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadalquivir 
Guadiana Menor o 
Baza 
Guadalen 
Guadalmena 
Guadalimar 
Grande 
Jandula o Fresneda 
Yeguas o Pradillo 
Cuzna o 
Guadalmellato 
Cuzna o 
Guadalmellato 
Guadajoz o 
Almedinilla 
Guadiato 
Bembezar 
Genil 
Huesna 

Guadalb Men 
Guadiel Men 
Genil Loja 
Genil Ecija 
Corbones Ca 
Guadiamar 
Negratin 

Guadalen 
Guadalmena 
Giribaile 
Rumblar 
Jandula 
Yeguas 
Guadalmellato 

San Rafael De 
Navallana 
Vadomojon 

Puente Nuevo 
Bembezar 
Iznajar 
Huesna 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

0.78 
0.86 

0.56 
-1.53 
0.8 
0.07 
-1.84 
0.7 
0.09 

0.69 
0.5 
0.67 
0.42 
0.48 
-9.7 
0.18 

0.64 
0.73 

0.82 
0.86 

0.3 
-1.43 
0.66 
-1.32 
-2.86 
0.42 
-1.1 

0.42 
0.43 
0.34 
0.42 
0.41 
-57.86 
0.2 

0.64 
0.76 
0.82 
0.22 
0.35 
0.71 
0.14 

0.7 
0.65 
0.67 
0.66 
0.64 
0.64 
0.45 

12.9 
-0.6 

26.3 
226.3 
6.1 
37.5 
260.1 
8.9 
24 

9.7 
-1.2 
1.9 
-14 
-15.2 
844.5 
-16.3 

0.8 
0.78 

0.51 
0.22 
0.68 
0.59 
-0.39 
0.57 
-0.03 

0.52 
-0.01 
0.06 
0.47 
0.54 
-6.16 
0.09 

0.66 
0.72 

0.82 
0.85 

0.35 
0.1 
0.48 
0.27 
-0.64 
0.49 
-0.73 

0.17 
-0.1 
0.02 
0.42 
0.09 
-18.63 
0.18 

0.61 
0.48 
0.71 
0.6 
0.66 
0.84 
-0.01 

0.6 
0.14 
0.24 
0.65 
0.58 
0.66 
0.44 

0.4 

0.21 

0.5 

-12.3 

0.42 

0.26 

0.53 

0.77 

0.43 
0.51 
0.27 
0.41 

0.57 

0.78 

7.3 

0.43 

0.28 

0.55 

0.24 
0.38 
0.1 
0.31 

0.52 
0.62 
0.39 
0.56 

-9.5 
-8.5 
-11.5 
-5.3 

0.3 
0.52 
0.29 
0.23 

0.33 
0.41 
0.25 
0.44 

0.58 
0.64 
0.52 
0.77 

6.1 
-4.7 

-16.1 
52.9 
5.5 
4.3 
117.8 
26.2 
11.8 

25.5 
-13.6 
2.9 
-5.4 
17.6 
621.1 
-22.5 

-0.6 

-8.6 

-19.3 
-4.8 
-21.7 
-41.3 

39 

ID 

Country 

Basin 

River 

Station 

C511 
C512 
C513 
C514 
C515 
C516 
C517 
C518 

C519 
C520 
C521 
C522 
C523 
C524 
C525 
C526 
C527 

SP 
SP 
SP 
SP 
SP 
SP 
SP 
SP 

SP 
SP 
SP 
SP 
SP 
SP 
SP 
SP 
SP 

C528 

SP 

C529 
C530 
C531 
C532 

SP 
SP 
SP 
SP 

Guadalquivir 
Guadalquivir 
Guadalquivir 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 

Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 
Ebro 

Ebro 

Viar 
Guadalquivir 
Huelva 
Ebro 
Ebro 
Aragán 
Cina 
Noguera 
Ribagorzana 
Segre 
Piedra 
Guadalope 
Segre 
Segre 
Irati 
Ebro 
Soton 
Noguera 
Ribagorzana 
Esera 

Guadiana 
Guadiana 
Ebro 
Guadiana 

Zujar 
Guadiana 
Ebro 
Ciguela 

C533 

SP 

Guadiana 

Zancara 

El Pintado 
Alcala Del Rio 
Zufre 
Mequinenza 
Ebro 
Yesa 
Grado I 
Santa Ana 

Oliana 
La Tranquera 
Santolea 
San Lorenzo Mongay 
Rialb 
Itoiz  
Ribarroja 
La Sotonera 
Escales 

Barasona-Joaquin 
Costa 
Zujar 
Orellana 
Miranda de Ebro 
Villafranca de los 
Caballeros 
Alcazar 

40 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

0.42 
0.67 
0.33 
-0.22 
-0.25 
-0.16 
0.3 
0.06 

0.55 
0.08 
0.35 
0.13 
0.34 
0.12 
0.03 
-0.32 
0.29 

0.29 
0.69 
0.19 
-0.31 
-0.03 
-0.28 
0.18 
-0.03 

0.42 
-0.08 
-0.39 
0.17 
0.1 
-0.24 
0.04 
-0.45 
-0.05 

0.55 
0.84 
0.46 
0.38 
0.19 
0.58 
0.56 
0.32 

0.67 
0.22 
0.4 
0.42 
0.45 
0.23 
0.38 
0 
0.44 

-9.8 
15.2 
-10.1 
-71.4 
-31.1 
-69.4 
-31.6 
-25.4 

-17.2 
-22.3 
19.9 
-16.8 
-20.4 
-29.2 
-36.6 
-55 
-28.9 

0.24 
0.68 
0.05 
-0.25 
-0.39 
-0.26 
0.23 
-0.09 

0.39 
-0.07 
-0.33 
0.07 
0.35 
0.21 
0.13 
-0.36 
0.05 

0.36 
0.77 
0.27 
-0.35 
-0.06 
-0.42 
0.05 
-0.03 

0.01 
-0.26 
-3.39 
-0.67 
-0.27 
0.22 
-0.11 
-0.43 
-0.24 

0.63 
0.9 
0.66 
0.29 
-0.09 
0.3 
0.42 
0.13 

0.42 
0.04 
0.19 
0.33 
0.38 
0.48 
0.29 
0 
0.16 

-36.5 
3.2 
-53 
-69.1 
14.3 
-67.1 
-24.5 
-0.8 

8.6 
-10.6 
20.7 
64.2 
-18.9 
-14.9 
-25.1 
-56 
-19.3 

0.57 

0.33 

0.62 

-8.3 

0.23 

0.04 

0.35 

-14.9 

-0.04 
0.04 
0.54 
0.4 

-1.12 
0.14 
0.23 
0.15 

-0.04 
0.42 
0.56 
0.75 

4.7 
-47.3 
-6.5 
41.4 

0.27 
-0.17 
0.16 
0.04 

-0.41 
0.03 
-0.75 
-1.52 

0.31 
0.27 
0.57 
0.38 

21.8 
-55.2 
48.5 
33.1 

-10.65 

-16.65 

0.79 

1104.8 

-1796.29 

-14041.2 

0.11  179384.9 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C536  PL 
C537  DE 

C538  PL 
C539  PL 
C540  PL 
C541  PL 
C542  PL 
C543  PL 
C544  PL 
C545  PL 
C546  PL 
C547  PL 
C548  PL 
C549  PL 
C550  PL 
C551  PL 
C554  PL 
C555  PL 
C556  PL 
C557  PL 
C558  DE 
C559  CZ 
C560  CZ 
C561  CZ 
C562  CZ 

Pregolya 
Elbe 

Vistula 
Vistula 
Oder 
Oder 
Oder 
Vistula 
Vistula 
Vistula 
Vistula 
Oder 
Vistula 
Vistula 
Vistula 
Vistula 
Pregolya 
Vistula 
Vistula 
Oder 
Elbe 
Elbe 
Elbe 
Elbe 
Elbe 

Lyna tributary 
Havel 

Skawa 
Vistula tributary 
Warta 
Warta 
Warta 
Narew 
Vistula 
Narew tributary 
Wkra 
Oder tributary 
San 
Dunajec 
Narew tributary 
Zakhidnyy Buh 
Lyna 
Vistula tibutary 
Narew 
Gwda 
Tepla Vltava 
Elbe 
Tepla Vltava 
Uhlava 
Uhlava 

Prosna 
Sachsenhausen 
Sohlabsturz 
Zator 
Proszowki 
Dzialoszyn 
Podgorze 
Dabie 
Strekowa Gora 
Smolice 
Bialobrzeg Blizszy 
Borkowo 
Osetno 
Gusin 
Stary Sacz 
Burzyn 
Kepa Polska 
Sepopol 
Lochow 
Dobrylas 
Pila 
Malliss Op 
Nemcice 
Ceske Budejovice 
Hracholusky Mze 
Lhota Radbuza 

0.84 
0.77 

0.69 
0.61 
0.81 
0.74 
0.67 
0.63 
0.84 
0.39 
0.8 
0.59 
0.88 
-0.18 
0.61 
0.8 
0.81 
0.65 
0.62 
0.01 
0.83 
0.85 
0.62 
0.75 
0.41 

0.72 
0.59 

0.85 
0.8 

0.57 
0.38 
0.63 
0.51 
0.35 
0.22 
0.69 
-0.33 
0.66 
0.37 
0.77 
-0.02 
0.37 
0.63 
0.65 
0.43 
0.14 
-0.57 
0.68 
0.71 
0.25 
0.58 
0.08 

0.76 
0.65 
0.83 
0.74 
0.67 
0.64 
0.85 
0.41 
0.82 
0.63 
0.89 
0.08 
0.65 
0.81 
0.82 
0.68 
0.65 
0.03 
0.85 
0.85 
0.69 
0.81 
0.59 

2.2 
10.2 

-7.9 
-6.9 
6.4 
0.3 
1.6 
5.5 
-1.6 
7.3 
-6.4 
-0.8 
1.4 
-8.9 
-8.5 
-2.5 
0 
8.6 
9 
-8.5 
8.6 
-4.1 
14.6 
14.9 
41.8 

0.72 
0.75 

0.59 
0.58 
0.57 
0.72 
0.64 
0.64 
0.68 
0.39 
0.74 
0.59 
0.78 
0.28 
0.75 
0.73 
0.75 
0.28 
0.69 
-4.22 
0.76 
0.39 
0.52 
0.49 
0.43 

0.49 
0.59 

0.73 
0.8 

0.26 
0.45 
0.06 
0.47 
0.31 
0.51 
0.52 
-0.18 
0.56 
0.46 
0.58 
-0.46 
0.58 
0.54 
0.51 
0.05 
0.39 
-33.84 
0.71 
0.15 
0.11 
0.38 
-0.06 

0.6 
0.68 
0.65 
0.73 
0.65 
0.74 
0.73 
0.42 
0.77 
0.68 
0.8 
0.67 
0.79 
0.76 
0.76 
0.37 
0.69 
0.37 
0.9 
0.6 
0.74 
0.67 
0.73 

7 
13.6 

-4.3 
-7.5 
9.7 
8.4 
-6.5 
14.4 
-5.5 
-18.1 
-7.7 
0.7 
-8.8 
18.6 
-12.5 
-7.3 
-5.9 
10.6 
3.2 
-14.5 
19.8 
-35.6 
24 
-26.3 
1.7 

41 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C563  PL 
C564  PL 
C565  PL 
C566 
FI 
C567 
FI 
C568 
FI 
C569 
FI 
C570 
FI 

C571 
C572 
C573 
C574 

FI 
FI 
FI 
FI 

C575 

FI 

C576 
C577 

C578 
C579 

C580 
C581 
C582 
C583 
C584 

FI 
FI 

FI 
FI 

FI 
FI 
FI 
FI 
FI 

Vistula 
Vistula 
Vistula 
NA 
Lapuanjoki 
NA 
Pyhtjoki 
Kuivajoki 

Simojoki 
Kalajoki 
Kyronjoki 
NA 

NA 

NA 
NA 

Neva 

Neva 
Kokem 
Kokem 
Kokem 
Kokem 
Kokem 

San 
Nida 
Narew tributary 
Eurajoki 
Lapuan 
Lestijoki 
Tolpankoski 
Kuiva 

Simo 
Kala 
NA 
Vantaanjoki 

Rzeszow 
Pinczow 
Makow Mazowiecki 
Pappilankoski 
Keppo 
Saarenpaa 
Pyhajoki 
Luujoki Haaran Ala 
Puolelle 
Simo 
Nikakoski 
Skatila Lansorsund 
Oulunkyla 
NearTheMouth 

Pielisjoki 
Kallavesi 

Huopanankoski 
Porvoonjoki 

Leppavesi-Paijanne  Vaajakoski / 
Haapakoski 
Kivijarvi 
Vakkola / Near The 
Mouth 
Kaltimo 
Kallavesi-Konnus-
Karvio 
Lempäälä 
Vilppulankoski 
Muroleenkoski 
Hartolankoski 
Maurialankoski 

Kokemaen 
Kokemaen 
Loimijoki 

Kuokkalankoski 
NA 

0.8 
0.78 
-0.25 
0.77 
0.83 
0.74 
0.78 
0.74 

0.67 
0.71 
0.66 
0.84 

0.74 

0.68 
0.83 

0.8 
0.69 

0.7 
0.42 
0.46 
0.53 
0.83 

0.61 
0.56 
-0.04 
0.56 
0.7 
0.62 
0.62 
0.65 

0.67 
0.47 
0.55 
0.69 

0.8 
0.79 
0.03 
0.77 
0.84 
0.79 
0.8 
0.81 

0.84 
0.72 
0.75 
0.85 

0.47 

0.74 

0.39 
0.68 

0.68 
0.84 

0.6 
0.4 

0.8 
0.69 

0.43 
-0.38 
-0.27 
0.25 
0.67 

0.71 
0.57 
0.58 
0.57 
0.83 

-1.9 
6.3 
-6.6 
4.9 
-1 
-2.6 
-1.8 
-9.4 

-18.5 
0.7 
-16.9 
5.2 

0.8 

0.7 
5.5 

-4.8 
-1.4 

-0.1 
23.2 
11.2 
-4.4 
0.7 

0.6 
0.48 
-1.05 
0.34 
0.42 
0.51 
0.44 
0.54 

0.49 
0.53 
0.35 
0.75 

0.46 
-0.13 
-6.46 
0.22 
0.06 
0.36 
0.33 
0.66 

0.63 
0.22 
0.3 
0.5 

0.68 
0.64 
0.39 
0.65 
0.59 
0.66 
0.67 
0.85 

0.85 
0.61 
0.66 
0.75 

-7 
8.4 
-3.5 
-42.8 
-40.7 
-30 
-41.3 
-27 

-32.4 
-24.5 
-47.3 
2.8 

0.3 

-1.28 

0.41 

-32.3 

0.35 
0.63 

0.66 
0.41 

0.55 
0.25 
0.22 
0.31 
0.58 

-0.47 
0.59 

0.48 
0.79 

0.12 
-0.43 

0.71 
0.51 

-0.04 
-0.98 
-1.18 
-0.32 
0.42 

0.59 
0.41 
0.35 
0.44 
0.68 

-37.7 
-21.9 

-16.8 
-33.1 

-12.5 
-14.1 
-38.7 
-33.3 
-19.4 

42 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C585 
C586 
C587 
C588 

C589 
C590 
C591 
C592 
C593 
C594 
C595 

C596 
C597 

FI 
FI 
FI 
FI 

FI 
FI 
FI 
FI 
FI 
FI 
FI 

FI 
FI 

C598 

FI 

C599 
C600 
C601 
C602 
C603 
C604 
C605 
C606 
C607 

FI 
FI 
FI 
FI 
FI 
FI 
FI 
FI 
FI 

NA 
NA 
NA 

Kyronjoki 
Kyronjoki 
NA 
NA 

Siikajoki 
Neva 
Neva 

Neva 
Neva 
NA 

NA 

NA 
NA 
NA 
NA 

Iijoki 
Iijoki 
Iijoki 
NA 
NA 

Karvian 
NA 
Narpion 
Kyronjoki 

Kyronjoki 

Ahtavan 
Perhon 
Siika 
Vuoksi 
NA 
NA 

NA 
NA 

Kymijoki 

NA 
NA 
Kymijoki 
NA 
NA 
Iijoki 
NA 
Livojoki 
Kitinen 

0.39 
0.6 
0.65 
0.38 

-0.06 
0.7 
0.75 
0.73 
0.8 
0.74 
0.76 

-0.67 
0.62 

0.71 

0.77 
0.02 
0.58 
0.3 
0.75 
0.5 
0.86 
0.83 
0.24 

Lankoski 
Perus 
Allmanningsforsen 
Kauhajoen 
Saannpato 
Jalasjoen Saannpato 
Herrfors 
Kaitfors 
Lankela 
Lieksankoski 
Koitajoki_Lylykoski 
Onkivesi_Viannonkos
ki 
Kuolimo-Luusua 
Karnajarvi_Kellankos
ki 
Kiimasjarvi_Hietama
nkoski 
Hankavesi-Luusua 
Jaasjarvi-Luusua 
Mankala 
Verla 
Haukipudas 
Vaatajansuvanto 
Jaurakkajarvi-Luusua 
Hanhikoski 
Kokkosniva 

43 

-0.33 
0.22 
0.36 
-0.18 

-0.02 
0.42 
0.56 
0.5 
0.63 
0.49 
0.59 

0.56 
0.6 
0.66 
0.63 

0.7 
0.7 
0.76 
0.74 
0.82 
0.74 
0.78 

-5.76 
0.16 

0.2 
0.65 

27.5 
2.2 
-2.5 
32.5 

95.7 
2.2 
0.1 
0.7 
-6.1 
-2.9 
-3.6 

59 
9 

0.15 
0.41 
0.59 
0.57 

0.71 
0.31 
0.49 
0.37 
0.74 
0.68 
0.47 

-0.4 
0.42 

-0.01 
0.33 
0.38 
0.34 

0.45 
-0.86 
0.1 
0.17 
0.48 
0.53 
0.4 

0.56 
0.65 
0.7 
0.74 

0.72 
0.45 
0.6 
0.58 
0.78 
0.76 
0.7 

-3.94 
-0.19 

0.16 
0.53 

-60.1 
-35.4 
-27.2 
28.9 

-4 
-33.9 
-32.1 
-42 
-13.1 
-14.8 
-35.9 

31.4 
-32.5 

0.47 

0.72 

-1.1 

0.44 

-0.11 

0.52 

-28.9 

0.57 
-2.14 
0.26 
-0.83 
0.55 
-0.14 
0.75 
0.69 
-0.11 

0.78 
0.46 
0.59 
0.51 
0.79 
0.6 
0.87 
0.84 
0.61 

-3.2 
37.4 
-1.3 
17.7 
12 
-4.3 
0.4 
0.9 
-45.8 

0.33 
0.09 
0.15 
0.31 
0.71 
0.49 
0.51 
0.61 
0.18 

-1.23 
-1.55 
-1.67 
-0.76 
0.62 
-0.15 
0.52 
0.57 
-0.01 

0.44 
0.34 
0.21 
0.4 
0.81 
0.53 
0.8 
0.78 
0.65 

-30.3 
5.7 
-31.2 
15.5 
-20.2 
-20.8 
-40.2 
-25.6 
-53.5 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C608 
FI 
C609 
FI 
C610 
FI 
C611 
FI 
C612 
FI 
C613 
FI 
C614 
FI 
C615 
FI 
C616 
FI 
C617 
SE 
C618 
SE 
C619 
SE 
C620 
SE 
C621 
SE 
C622  UK 
C623  NO 
C624  UK 
C625 
FI 
C626 
C627 
C628 
C629 
C630 
C631 
C632 
C633 

SE 
SE 
SE 
SE 
SE 
SE 
SE 
SE 

NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
Helge 
NA 
NA 
NA 
NA 

Bann 
Moel 
Severn 
NA 
NA 
NA 
Daläven 
Daläven 
NA 
NA 
Indals 
Umeliven 

Kemijoki 
NA 
Ounasjoki 
Inarijoki 
NA 
Ivalojoki 
Patsoyoki 
NA 
Kitkajoki 
Helge 
NA 
Viskan 
Moaelven 
Oereaelven 
Lower Bann 
Neiden 
Severn 
Munio 

Pite 
Munio 
Dal 
Vasterdal 
NA 
NA 
Indals 
Umea 

0.86 
0.87 
0.73 
0.6 
0.7 
0.62 
0.2 
0.87 
0.7 
0.84 
0.85 
0.8 
0.74 
0.83 
0.79 
0.2 
0.89 
0.65 
0.62 
0.74 
0.31 
0.86 
0.83 
0.7 
0.01 
0.25 

Kemihaara 
Vikajärvi-Luusua 
Köngäs 
Karigasniemi 
Juutuanjoki 
Pajakoski 
Kaitakoski 
Oulankajoki 
Kayla 
Torsebro Krv 
Morrum 
Asbro 3 
Vaestersel 
Torrboele 
Movanagher 
Neset 
Bewdley 
Muonio 
Gransel 
Kallio 2 
Grada Krv 
Mockfjard Krv 
Saffle_Damm 
Edsvalla_Krv 
Hissmofors_Krv 
Harrsele_Krv 

44 

0.75 
0.76 
0.75 
0.7 
0.73 
0.68 
-0.76 
0.75 
0.33 
0.73 
0.71 
0.79 
0.7 
0.81 
0.71 
-0.36 
0.79 
0.79 
0.66 
0.8 
0.03 
0.72 
0.67 
0.61 
-0.67 
-0.22 

0.87 
0.88 
0.87 
0.87 
0.88 
0.84 
0.25 
0.87 
0.75 
0.86 
0.85 
0.9 
0.85 
0.9 
0.84 
0.56 
0.9 
0.92 
0.88 
0.91 
0.43 
0.86 
0.83 
0.79 
0.04 
0.35 

-2.8 
-1.4 
-13.2 
-25.5 
-18.4 
-22.7 
-25 
0.3 
8.3 
-4.5 
0.4 
-10.4 
-14 
-6.4 
-4.5 
61.3 
0.8 
-24.4 
-25.9 
-17.1 
-17.6 
0.7 
-1.2 
-7.3 
-11.9 
-21.5 

0.63 
0.83 
0.53 
0.48 
0.65 
0.44 
0.44 
0.73 
0.55 
0.52 
0.85 
0.52 
0.44 
0.45 
0.63 
0.23 
0.39 
0.59 
0.67 
0.7 
0.2 
0.83 
0.57 
0.54 
0.12 
0.35 

0.68 
0.71 
0.67 
0.65 
0.74 
0.64 
-0.2 
0.67 
0.06 
0.57 
0.82 
0.56 
0.5 
0.41 
0.68 
-0.24 
0.46 
0.76 
0.76 
0.87 
-0.32 
0.73 
0.22 
0.28 
-0.54 
-0.18 

0.86 
0.87 
0.86 
0.86 
0.89 
0.87 
0.56 
0.83 
0.56 
0.89 
0.92 
0.86 
0.77 
0.65 
0.87 
0.69 
0.82 
0.92 
0.92 
0.96 
0.24 
0.86 
0.71 
0.62 
0.17 
0.49 

-26.3 
8.3 
-30.9 
-32.9 
-26 
-38.4 
-30.8 
-17.7 
-5.3 
-38.6 
-11.1 
-36.6 
-32.2 
-14.6 
-23.4 
56.3 
-48.9 
-32.2 
-23.9 
-22.8 
-12.2 
-7.3 
-31.9 
-19.5 
-20 
-27.9 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C634 
SE 
C635 
SE 
C636 
SE 
C637  GB 
C638 
SE 
C639 
SE 
C640 
SE 
C641 
SE 
C642  NO 
C643  NO 
C644  NO 
C645  NO 
C646  NO 
C647  NO 
C649  NO 
C650  NO 
C651  NO 
C652  NO 
C653  GB 

C654  GB 
C655  GB 
C656  GB 
C657  DE 
C658  DE 
C659 

IE 

NA 
Umeliven 
NA 
Tay 
NA 
NA 
NA 
NA 
Glomma 
NA 
NA 
NA 
NA 
NA 
NA 
Stjordalselva 
NA 
NA 
Thames 

NA 

Tweed 
Wye 
Weser 
Weser 
NA 

NA 

Vindel 
Gide 
Tay 
Ljungan 
NA 

Ljusnan 
Ljusnan 
Glomma 
Hallingdalselva 
Dokka 
Begna 
Fyreselv 
Numedalslågen 

Mandalselva 
Stjordalselva 
Rauma 
Orkla 
Thames 

Trent 
Tweed 
Wye 
Leine 
NA 
Slane 

Yngeredsforsen 
Granaker 
Bjorna_Krv 
Ballathie 
Ostavallselet 
Nybro 
Donje_Krv 
Svegs_Krv 
Barkaldfoss 
Skalfoss 
Kistefoss 
Stromstoa 
Eikhomkilen 
Holmfoss 
Kjolemo 
Hegra Bru 
Rauma v/Venge 
Syrstad 
Kingston upon 
Thames 
Colwick 
Norham 
Redbrook 
Herrenhausen 
Uttershausen 
Scarrawalsh 

0.9 
0.8 
0.72 
0.39 
0.45 
0.65 
0.73 
0.56 
0.88 
0.59 
0.53 
0.68 
0.31 
0.77 
0.86 
0.39 
0.41 
0.61 
0.92 

0.82 
0.79 
0.83 
0.9 
0.81 
0.73 

0.81 
0.83 
0.56 
0.4 
0.1 
0.52 
0.55 
0.2 
0.77 
0.39 
0.2 
0.62 
0 
0.63 
0.75 
0.31 
0.54 
0.52 
0.84 

0.69 
0.59 
0.72 
0.82 
0.63 
0.68 

0.91 
0.92 
0.76 
0.86 
0.52 
0.74 
0.77 
0.59 
0.88 
0.77 
0.55 
0.82 
0.49 
0.8 
0.87 
0.73 
0.88 
0.77 
0.92 

0.84 
0.79 
0.85 
0.91 
0.81 
0.83 

3.3 
-12.9 
-5.8 
-42.5 
-16.8 
-9.1 
-9.5 
-13 
0.7 
-26.7 
-5.1 
-16.2 
-26.7 
-7.1 
-3.8 
-38.9 
-39.8 
-21.9 
-0.1 

-4.8 
-1.2 
-4 
-0.6 
1.9 
-12.2 

0.75 
0.79 
0.64 
0.14 
0.49 
0.55 
0.73 
0.6 
0.63 
0.53 
0.46 
0.65 
0.21 
0.68 
0.76 
0.3 
0.4 
0.55 
0.82 

0.81 
0.77 
0.48 
0.85 
0.75 
0.52 

0.77 
0.82 
0.54 
0 
0.11 
0.47 
0.49 
0.21 
0.74 
0.16 
0.1 
0.56 
-0.31 
0.5 
0.63 
0.31 
0.53 
0.54 
0.8 

0.72 
0.55 
0.55 
0.86 
0.75 
0.54 

0.91 
0.91 
0.74 
0.76 
0.55 
0.74 
0.75 
0.61 
0.88 
0.73 
0.52 
0.81 
0.33 
0.76 
0.81 
0.76 
0.88 
0.79 
0.9 

0.86 
0.8 
0.8 
0.93 
0.88 
0.82 

-15.9 
-12.9 
-4.4 
-58.2 
-19.2 
-22.1 
-11 
-8.5 
-22 
-33.7 
-16.2 
-19.8 
-27 
-15.8 
-10.6 
-41.1 
-42.3 
-23.4 
-7.6 

-12.1 
7.6 
-34.7 
-8.9 
-17.3 
-32.4 

45 

 
ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

IE 

C660 
C661 

IE 
C662 
IE 
C663 
IE 
C664 
IE 
C665 
IE 
C666 
IE 
C667 
IE 
C670 
IE 
C671  GB 
C672  GB 
C673  GB 
C674  GB 
C675  GB 
C676  GB 
C677  GB 
C678  GB 
C679 

GB 

C680 

GB 
C681  GB 
C682  GB 
C683  GB 
C685 
IT 
C686 
IT 

Barrow 

Barrow 
Barrow 
Barrow 
Barrow 
NA 
NA 

Shannon 
Moy 
Foyle 
NA 

Spey 
Dee 
Tyne 
Eden 
NA 
NA 
Severn 

Barrow 
Barrow 

Nore 
Nore 
Suir 
Blackwater 
Blackwater 
Brosna 
Moy 
Mourne 
Clyde 
Spey 
Dee 
Tyne 
Eden 
Ouse 
Tywi 
Avon 

Royal Oak 
Graiguenamanagh U-
S 
Johns Bridge 
Brownsbarn 
Clonmel 
Ballyduff 
Killavullen 
Moystown 
Rahans 
Drumnabuoy House 
Blairstone 
Boat O Brig 
Park 
Bywell 
Sheepmount 
Skelton 
Nantgaredig 
Evesham 

Mersey 

Mersey 

Adelphi Weir 

Lower Tyne 
Exe 
R. Teifi 

Po 
Arno 

NA 

Exe 
Teifi 
Pellice 
Sieve 

Leven Bridge 
Thorverton 
Glan Teifi 
Villafranca 
Rosano 

0.91 
0.92 

0.76 
0.86 
0.75 
0.8 
0.7 
0.85 
0.81 
0.67 
0.69 
0.72 
0.39 
0.64 
0.81 
0.63 
0.65 
0.81 

0.65 

-4.31 
0.69 
0.56 
0.83 
0.58 

0.83 
0.86 

0.92 
0.94 

0.75 
0.79 
0.77 
0.7 
0.63 
0.78 
0.79 
0.65 
0.6 
0.55 
0.35 
0.55 
0.62 
0.57 
0.73 
0.61 

0.88 
0.89 
0.9 
0.85 
0.81 
0.88 
0.9 
0.82 
0.79 
0.76 
0.7 
0.75 
0.81 
0.76 
0.89 
0.81 

1.6 
2 

-13.1 
-6.4 
-16.8 
-7.9 
-16.8 
-2.4 
-9.1 
-16.4 
-15.9 
-8.5 
-37 
-13.1 
-0.2 
-12.5 
-22.5 
0.5 

0.7 
0.91 

0.63 
0.57 
0.52 
0.78 
0.75 
0.82 
0.75 
0.49 
0.77 
0.62 
0.38 
0.55 
0.62 
0.41 
0.33 
0.79 

0.74 
0.84 

0.89 
0.92 

0.7 
0.65 
0.62 
0.72 
0.67 
0.76 
0.76 
0.59 
0.66 
0.53 
0.36 
0.5 
0.55 
0.33 
0.4 
0.62 

0.87 
0.86 
0.89 
0.86 
0.82 
0.88 
0.9 
0.83 
0.82 
0.76 
0.7 
0.72 
0.76 
0.61 
0.78 
0.84 

-19.7 
-3.3 

-21.2 
-26.4 
-28.4 
-7.9 
-8.1 
-11.2 
-17.5 
-30.8 
-7.2 
-19.3 
-37 
-15.4 
-18.5 
-23.4 
-48.4 
5.7 

0.32 

0.66 

8.3 

0.56 

0.29 

0.6 

-11.5 

-6.74 
0.65 
0.58 
0.7 
0.33 

0.49 
0.88 
0.8 
0.85 
0.74 

525.1 
21.5 
-25 
7.5 
27.2 

-5.01 
0.79 
0.44 
0.84 
0.75 

-6.57 
0.68 
0.4 
0.69 
0.49 

0.45 
0.85 
0.7 
0.85 
0.76 

596.5 
-14.7 
-33.6 
5 
-0.5 

46 

 
 
ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

IT 
IT 
IT 

FR 
FR 
FR 

FR 
FR 
FR 
FR 
FR 
FR 
FR 
FR 

C687 
C688 
C689 
C690 

C691 
C692 
C693 

C694 
C695 
C696 
C697 
C698 
C699 
C700 
C701 

FR 
C702 
FR 
C703 
FR 
C704 
FR 
C705 
FR 
C706  DE 
C707  DE 
C708  DE 
C709  DE 
C710 
FR 

Arno 
Adige 
Po 
NA 

Seine 
Seine 

Seine 
Rhine 
Seine 
NA 

Vilaine 
Loire 
Loire 
Loire 

Lay 
Garonne 
Garonne 
Hrrault 
Aude 
Rhine 
Rhine 
Rhine 
Rhine 
Loire 

Canale Imperiale 
Adige 
Panaro 
Vire 

Therain 
Eure 
Essonne 

Meurthe 
Seine 
Blavet 
Vilaine 
Sarthe 
Loir 
Clain 
Lay 

Dropt 
Gers 
Herault 
Aude 
Regnitz 
Main 
Aisch 
Frankische Saale 
Sarthe 

0.59 
0.78 
0.66 
0.8 

0.83 
0.86 
0.34 

0.54 
0.83 
0.77 
0.85 
0.86 
0.71 
0.86 
0.8 

0.67 
0.75 
0.71 
0.61 
0.79 
0.89 
0.81 
0.86 
0.92 

S Giovanni Alla Vena 
Boara Pisan 
Bomporto 
Montmartin-en-
Graignes 
Maysel 
Louviers 
Ballancourt-Sur-
Essonne 
Malzeville 
Mery-Sur-Seine 
Languidic 
Rieux 
Neuville-Sur-Sarthe 
Villavard 
Dissay 
Mareuil-Sur-Lay-
Dissais 
Loubens 
Layrac 
Agde 
Moussan 
Huettendorf 
Kemmern 
Laufermuehle 
Wolfsmuenster 
Saint-Denis-d'Anjou 

47 

0.4 
0.57 
0.42 
0.74 

0.77 
0.76 
-0.84 

0.62 
0.67 
0.77 
0.79 
0.76 
0.49 
0.74 
0.7 

0.53 
0.56 
0.47 
0.36 
0.58 
0.8 
0.66 
0.73 
0.85 

0.78 
0.79 
0.68 
0.86 

0.91 
0.9 
0.83 

0.85 
0.83 
0.88 
0.89 
0.88 
0.8 
0.87 
0.87 

0.78 
0.79 
0.72 
0.64 
0.8 
0.9 
0.84 
0.86 
0.92 

29.6 
3.9 
-6.3 
-6.7 

10 
6.5 
34.9 

-30.4 
0.4 
-10 
-4.7 
-2.2 
13.3 
3.8 
11.7 

24.9 
12.4 
-1.6 
-9.4 
2.1 
0.5 
8.6 
3 
0.5 

0.49 
0.76 
0.57 
0.55 

0.64 
0.75 
-0.07 

0.56 
0.77 
0.68 
0.63 
0.87 
0.52 
0.75 
0.84 

0.73 
0.29 
0.43 
0.6 
0.74 
0.78 
0.62 
0.85 
0.91 

0.5 
0.53 
0.19 
0.69 

0.47 
0.53 
-3.91 

0.62 
0.67 
0.67 
0.55 
0.78 
0.42 
0.52 
0.7 

0.59 
0.07 
0.48 
0.61 
0.48 
0.75 
0.43 
0.75 
0.82 

0.88 
0.83 
0.58 
0.89 

0.87 
0.81 
0.5 

0.83 
0.87 
0.82 
0.86 
0.89 
0.8 
0.77 
0.84 

0.8 
0.74 
0.72 
0.8 
0.78 
0.88 
0.79 
0.87 
0.91 

37.1 
11.4 
9.3 
-26.2 

20 
2.3 
37.5 

-26.4 
16.8 
-4.7 
19.7 
-0.2 
38.8 
-10.3 
-0.2 

18 
54.9 
-37.2 
-6.2 
10.2 
-17.9 
24.7 
-6.8 
-0.2 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C711 
C712 
C713 
C714 

C715 
C716 
C717 
C718 
C719 
C720 
C721 
C722 

C723 
C724 
C725 
C726 
C727 
C728 

C729 
C730 
C731 
C732 
C733 

FR 
FR 
FR 

FR 
FR 
FR 
FR 
FR 
FR 
FR 
FR 

FR 
FR 
FR 
FR 
FR 
FR 

FR 
FR 
FR 
FR 
FR 

FR 

Rhine 
Seine 
Rhine 

Garonne 
Garonne 
Garonne 
Garonne 
Rhine 
Adour 
Loire 
Loire 

Ill 
Marne 
Seille 
Salat 

Ariege 
Save 
Tarn 
Orne 
Midouze 
Loire 
Allier 
Dore 

Ain 
Seille 
Loue 

Loire 
Rhone 
Rhone 
Rhone 
Meuse / Maas  Semoy 
Meuse / Maas  Chiers 
Moder 

Rhine 
Meuse / Maas  Meuse 
Loire 
Rhone 
Rhone 

Mayenne 
Tille 
Ognon 
Sioule 

Loire 

0.52 
0.7 
0.85 
0.46 

0.44 
0.64 
0.72 
0.82 
0.81 
0.66 
0.69 
0.49 

0.8 
0.85 
-1.22 
0.69 
0.86 
0.66 

0.83 
0.83 
0.85 
0.91 
0.39 

Strasbourg 
Frignicourt 
Metz 
Roquefort-Sur-
Garonne 
Auterive 
Larra 
Marsal 
Rosselange 
Campagne 
Bas-En-Basset 
Vic-Le-Comte 
Saint-Gervais-Sous-
Meymont 
Chazey-Sur-Ain 
Saint-Usuge 
Parcey 
Haulme 
Carignan 
Schweighouse-Sur-
Moder 
Vaucouleurs 
Chambellay 
Arceau 
Pesmes 
Saint-Priest-Des-
Champs 

48 

-0.11 
0.59 
0.73 
0.39 

0.25 
0.42 
0.51 
0.68 
0.76 
0.35 
0.56 
0.04 

0.6 
0.73 
-3.93 
0.67 
0.73 
0.45 

0.66 
0.74 
0.83 
0.83 
0.31 

0.56 
0.78 
0.86 
0.68 

0.6 
0.68 
0.74 
0.83 
0.89 
0.66 
0.77 
0.49 

0.8 
0.87 
0.88 
0.83 
0.87 
0.79 

0.83 
0.86 
0.92 
0.92 
0.62 

2.6 
-9.3 
7.1 
-27.3 

-26 
8.2 
-9 
0.9 
14.9 
-3.3 
-14.2 
-2.9 

0.1 
5.5 
160.7 
-16.7 
4.2 
24.7 

0.2 
-5.5 
12 
2.6 
-31 

0.71 
0.74 
0.72 
0.53 

0.69 
0.35 
0.7 
0.66 
0.77 
0.33 
0.8 
0.38 

0.74 
0.86 
-1.04 
0.67 
0.47 
0.32 

0.78 
0.83 
0.85 
0.89 
0.56 

0.38 
0.56 
0.67 
0.41 

0.43 
0.2 
0.6 
0.54 
0.61 
-0.58 
0.64 
-0.29 

0.5 
0.71 
-3.24 
0.75 
0.41 
-0.27 

0.57 
0.75 
0.81 
0.79 
0.42 

0.73 
0.77 
0.88 
0.69 

0.72 
0.78 
0.79 
0.8 
0.82 
0.43 
0.81 
0.39 

0.75 
0.86 
0.91 
0.88 
0.89 
0.72 

0.79 
0.87 
0.91 
0.9 
0.67 

3.8 
6.6 
19.5 
-24.8 

-13.3 
50.9 
-14.8 
26.2 
13 
-22.2 
3.8 
8.8 

0.6 
1.8 
142.4 
-21.4 
18.5 
32.5 

7.2 
-3.2 
11.5 
-1.9 
-18.1 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

FR 
FR 
FR 
FR 
FR 
FR 
FR 
FR 
FR 

C734 
C735 
C736 
C737 
C738 
C739 
C740 
C741 
C742 
C743 

FR 
C744 
FR 
C745 
FR 
C746  DE 
C747 

DE 
C748  DE 
C749 

DE 
C750  DE 
C751  DE 
C752  DE 
C753  NA 
C754 
SK 
C755 
SK 
C756  HU 
C757  HU 

NA 
Garonne 
Garonne 
Seine 
Rhine 
Loire 
Rhone 
Loire 
Loire 

Eyre 
Cele 
Lot 
Epte 
Moselle 
Sauldre 
Doubs 
Gartempe 
Arroux 
Truyere 

Garonne 
Garonne 
Garonne 
Meuse / Maas  Niers 

Bais 
Garonne 

Schussen 

Rhine 
Rhine 

Rhine 
Weser 
Weser 
Weser 
NA 

Danube 
Danube 
Danube 
Danube 

NA 
Kinzig 

Aller 
Eder 
Fulda 
Kysuca 
Sajo 
Hornad 
Zala 
Danube 

0.88 
0.7 
0.73 
0.83 
0.56 
0.84 
0.9 
0.85 
0.76 
0.71 

0.74 
0.45 
0.7 
0.78 

0.77 
0.89 

0.88 
0.7 
0.87 
0.73 
0.65 
0.81 
0.64 
0.82 

Salles 
Orniac 
Lassouts 
Fourges 
Tonnoy 
Selles-Sur-Cher 
Rochefort-Sur-Nenon 
Montmorillon 
Rigny-Sur-Arroux 
Sainte-Genevieve-
Sur-Argence 
Nerac 
A_Saint-Gaudens 
Goch 
Gerbertshaus 

Giessen 
Hanau 

Celle 
Affoldern 
Grebenau 
Kysucké Nové Mesto 
Lenartovce 
Zdana 
Zalaapati 
Mohacs 

49 

0.86 
0.55 
0.46 
0.82 
0.6 
0.73 
0.8 
0.71 
0.61 
0.44 

0.51 
0.36 
0.46 
0.58 

0.94 
0.75 
0.74 
0.94 
0.81 
0.86 
0.9 
0.85 
0.79 
0.71 

0.78 
0.74 
0.85 
0.8 

0.54 
0.79 

0.77 
0.9 

0.77 
0.39 
0.75 
0.49 
0.27 
0.61 
0.25 
0.72 

0.89 
0.7 
0.88 
0.77 
0.67 
0.82 
0.64 
0.9 

9.6 
-8.8 
5.3 
10 
-26.2 
-0.1 
2.5 
0 
-7.1 
-2 

11.5 
-33.3 
19.9 
6.8 

3.1 
4.5 

3 
3 
2.5 
10.5 
6.2 
2.4 
4.4 
7.1 

0.61 
0.74 
0.56 
0.76 
0.83 
0.83 
0.86 
0.86 
0.77 
0.69 

0.29 
0.64 
0.47 
0.72 

0.75 
0.82 

0.77 
0.53 
0.85 
0.22 
-0.24 
0 
- 
0.71 

0.6 
0.51 
0.26 
0.66 
0.65 
0.76 
0.84 
0.72 
0.57 
0.36 

0.13 
0.5 
0.38 
0.45 

0.9 
0.75 
0.76 
0.89 
0.83 
0.9 
0.92 
0.87 
0.78 
0.69 

0.75 
0.75 
0.91 
0.74 

0.55 
0.75 

0.77 
0.89 

0.64 
0.48 
0.8 
0.29 
-0.3 
-0.31 
- 
0.46 

0.86 
0.73 
0.9 
0.66 
0.29 
0.49 
- 
0.8 

29.4 
-4.9 
21.5 
17.9 
-0.3 
8.7 
-4.7 
1.6 
1.9 
-0.6 

60.4 
-17.6 
27.5 
10.3 

6.5 
12.3 

-1.9 
-25 
-8.8 
-49.9 
-80.9 
-69.7 
- 
3.1 

 
 
ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

C758  RO 
C759  RO 
C760  RO 
C761  RO 
C762  UA 
C765  AT 
C770 
SK 
C772  BG 
C773  BG 
C774  BG 
C775  CZ 
C776  RO 
C777  RO 
C780  AT 
C781  RS 
C782  RS 
C783  BG 
C785  BG 
C786  RO 
C790  BA-RS 
C792  BA-RS 
C793  BA-RS 
C794  HR 
C797  HR 
C798  HR 
C799 
SI 

Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Strimonas 
Danube 
Danube 
Danube 
Danube 
Danube 

Danube 
Danube 
Danube 
Danube 

Crasna 
Crisul Negru 
Viseul 
Danube 
Siret 
Lech 

Latorytsya 
Ogosta 
Osum 
Vit 
Dyje 
Târnava Mare 
Strei 

Enns 
Donau 
Lim 
Strimonas 
NA 
NA 
Sana 
Bosna 
Vrbanja 
Sava 
Kupa 
Orljava 
Sava 

0.2 
0.44 
0.37 
0.81 
0.48 
0.79 
0.7 
0.48 
0.65 
0.61 
0.71 
0.68 
0.73 
0.87 
0.82 
0.62 
0.76 
0.53 
0.57 
0.56 
0.75 
0.3 
0.82 
-0.26 
0.66 
0.82 

-0.55 
0.25 
0.26 
0.64 
0.08 
0.58 
0.36 
0.06 
0.28 
0.27 
0.46 
0.41 
0.45 
0.74 
0.62 
0.55 
0.55 
0.22 
0.19 
0.48 
0.5 
0.26 
0.64 
-1.22 
0.4 
0.65 

0.2 
0.54 
0.65 
0.84 
0.51 
0.8 
0.71 
0.49 
0.66 
0.62 
0.75 
0.7 
0.73 
0.87 
0.85 
0.79 
0.76 
0.56 
0.57 
0.71 
0.75 
0.54 
0.82 
0.79 
0.73 
0.82 

-1.1 
-13.6 
-39 
2.1 
13.8 
2 
0 
-6.1 
6 
-2.6 
12.8 
-9.4 
3.4 
-0.7 
8.9 
-24.7 
-0.4 
-10.5 
-1.8 
-18.7 
-2.3 
-32.4 
0.7 
85.7 
19.5 
-1.1 

- 
0.49 
-0.09 
0.78 
- 
0.81 
0.38 
- 
0.04 
0.01 
0.66 
-0.16 
0.02 
0.88 
0.8 
0.21 
-0.2 
-0.11 
0.24 
0.7 
0.62 
0.46 
0.88 
-0.33 
- 
0.86 

- 
0.27 
-0.38 
0.58 
- 
0.74 
0.2 
- 
-0.04 
-0.16 
0.64 
-2.02 
-0.14 
0.77 
0.63 
0.13 
-0.14 
-0.02 
-0.07 
0.49 
0.49 
0.29 
0.8 
-1.08 
- 
0.76 

- 
0.58 
0.4 
0.79 
- 
0.87 
0.65 
- 
0.33 
0.2 
0.8 
0.03 
0.29 
0.88 
0.81 
0.78 
0.44 
0.29 
0.35 
0.72 
0.76 
0.58 
0.9 
0.85 
- 
0.89 

- 
-20 
-70.2 
-4.3 
- 
-10.6 
-48.8 
- 
-53.3 
-44.3 
14.7 
-51.2 
-40.7 
-3.7 
-5 
-62.2 
-66.1 
-60.6 
-28.1 
-4.4 
-27.2 
-22.3 
6.8 
99.4 
- 
6.5 

Agerdomajor 
Zerind 
Bistra 
Lom 
Storozhynets 
Lechaschau 
Ve?ké Kapušany 
Miziya 
Izgrev 
Tarnene 
Tr Vn Dvur Dyje 
Blaj 
Petreni 
Admont 
Smederevo 
Prijepolje 
Kresna 
Karantzi 
Oradea 
Prijedor 
Doboj 
Vrbanja 
Podsused 
Brodarci 
Pleternica 
Litija I 

50 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

C800 
SI 
C801 
SI 
C802 
SI 
C803  HR 
C804  BA-Fed 
C805  BA-Fed 
C806  BA-Fed 
C807  BA-Fed 
C808  BA-Fed 
C810  RS 
C812  RS 
C813  RS 
C814  RS 
C815  RS 
C816  RS 
C817  RS 
C818 
C819 
C820 
C821 
C822 
C823 
C824 
C825 
C826 
C827 

ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 

Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Miio 
Miio 
Miio 
Miio 
Miio 
Miio 
Guadalquivir 
Segura 
Segura 
Segura 

Sora 
Sotla 
Savinja 
Sava 
Sana 
Vrbas 
Vrbas 
Lašva 
Krivaja 
Sava 
Vapa 
Jadar 
Kolubara 
Kolubara 
Tamnava 
Tisza 
Mino 
Mino 
Mino 
Mino 
Mino 
Mino 
Guadalquivir 
Segura 
Segura 
Segura 

0.69 
0.7 
0.61 
0.54 
0.5 
0.65 
0.09 
0.71 
0.71 
0.89 
0.61 
0.76 
0.59 
0.68 
0.66 
0.63 
-0.39 
0.81 
0.77 
0.55 
0.22 
0.08 
-15.16 
0.59 
0.14 
-0.78 

0.36 
0.44 
0.19 
0.36 
0.4 
0.26 
-0.16 
0.4 
0.43 
0.79 
0.45 
0.53 
0.41 
0.48 
0.53 
0.39 
-0.48 
0.72 
0.74 
0.68 
0.21 
-0.01 
-209.84 
0.36 
-0.03 
-1.74 

Suha 
Rakovec 
Nazarje 
Jasenovac 
Sanski Most 
Daljan 
Kozluk Jajce 
Merdani 
Olovo 
S.Mitrovica 
Cedovo 
Lesnica 
Valjevo 
Slovac 
Cemanov Most 
Senta / Szeged 
Sil Las Portas 
Sil Belesar 
Sil Los Peares 
Sil San Esteban 
Sil Barcena 
Sil Bao 
La Brenaii 
Fuensanta 
Cenajo 
La Pedrera 

51 

validation 
KGE 

BIAS 
[%] 

NSE 

r 

BIAS [%] 

7.7 
12.1 
0 
33.7 
-28.6 
7.7 
-53.4 
-2.7 
7.8 
2.2 
28.3 
7 
0.4 
-1 
19.3 
25.7 
-74.3 
-1.9 
-3.9 
-25.9 
-39.9 
-54.8 
1057.5 
-3.6 
-7.3 
-95.6 

0.78 
0.71 
0.72 
0.11 
- 
- 
- 
- 
- 
0.79 
0.52 
0.35 
0.38 
0.84 
0.37 
0.67 
-0.42 
0.72 
0.73 
0.68 
0.13 
0.14 
-6.34 
0.09 
-0.21 
-0.84 

0.57 
0.62 
0.51 
-0.31 
- 
- 
- 
- 
- 
0.6 
0.57 
0.3 
0.33 
0.68 
0.31 
0.42 
-0.28 
0.63 
0.63 
0.64 
0.05 
-0.01 
-13.05 
0.06 
-0.08 
-1.5 

0.8 
0.79 
0.77 
0.9 
- 
- 
- 
- 
- 
0.82 
0.8 
0.61 
0.57 
0.84 
0.59 
0.73 
-0.03 
0.8 
0.8 
0.83 
0.51 
0.5 
0.52 
0.37 
0.17 
-0.28 

5.3 
10.9 
16.1 
72.7 
- 
- 
- 
- 
- 
-7 
40.7 
-42.3 
6.5 
2.2 
40 
-18.3 
-64.1 
-8.2 
-3.2 
-22.1 
-39.7 
-46.8 
678.6 
-30.5 
-48 
-93.3 

r 

0.7 
0.73 
0.62 
0.86 
0.69 
0.69 
0.47 
0.71 
0.73 
0.9 
0.81 
0.77 
0.66 
0.71 
0.75 
0.81 
0.11 
0.85 
0.86 
0.87 
0.67 
0.6 
0 
0.63 
0.26 
-0.18 

ID 

Country 

Basin 

River 

Station 

calibration 
KGE 

NSE 

C828 
C829 
C830 
C831 
C832 
C833 
C834 
C835 
C836 
C837 
C838 
C839 
C840 
C841 
C842 
C843 
C844 
C846 
C848 
C849 
C850 
C851 
C852 
C853 
C854 

ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 

ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 
ES 

ES 

Guadalquivir 
Guadalquivir 
Ebro 
Ebro 
Ebro 
Tajo 
Tajo 
Tajo 
Tajo 
Tajo 
Tajo 
Tajo 
Tajo 
NA 

Jrcar 
Jrcar 
Jrcar 
Guadiana 
Guadiana 
Guadiana 
Guadiana 
Duero 
Duero 
Duero 

Duero 

Guadalquivir 
Guadalquivir 
Ebro 
Ebro 
Ebro 
Tajo 
Tajo 
Tajo 
Tajo 
Tajo 
Tajo 
Tajo 
Tajo 
Júcar  
Júcar  
Júcar  
Júcar  
Guadiana 
Guadiana 
Guadiana 
Guadiana 
Duero 
Duero 
Duero 
Luna 

-0.12 
-0.12 
0.33 
0.29 
-0.03 
0.19 
0.43 
0.49 
-0.05 
0.1 
0.38 
-0.19 
0.25 
0.03 
0.18 
0.21 
-0.21 
0.15 
-0.49 
0.41 
0.1 
-0.07 
0.15 
-0.06 
-0.12 

Tranco De Beas 
La Fernandina 
Talarn O Tremp 
Mediano 
Canelles 
Burguillo 
Cedillo 
Valdecanas 
Gabriel Y Galan 
Entrepenas 
Atazar 
Buendia 
Alcantara 
Benageber 
Tous 
Contreras 
Alarcon 
Garcia De Sola 
Alange 
Cijara 
Serena 
Cuerda Del Pozo 
Cernadilla 
Aguilar De Campoo 
Barrios De Luna 

52 

r 

-0.06 
0.25 
0.69 
0.6 
0.26 
0.49 
0.81 
0.59 
0.31 
0.19 
0.39 
-0.03 
0.68 
0.12 
0.32 
0.43 
-0.16 
0.53 
-0.02 
0.55 
0.21 
0.16 
0.68 
0.37 
0.26 

-0.57 
0.06 
0.19 
0.21 
0 
0.18 
0.55 
0.34 
0.01 
-0.13 
-0.17 
-0.25 
0.35 
-0.29 
-0.04 
0.16 
-0.67 
0.23 
-3.43 
0.3 
-0.09 
-0.12 
0.28 
-0.06 
-0.05 

validation 
KGE 

BIAS 
[%] 

NSE 

r 

BIAS [%] 

-19.8 
5.2 
-40.3 
-33.2 
-20.8 
-29.5 
-25.9 
-3.9 
-42.8 
-1 
-6 
-17.8 
-35.4 
14.6 
-19.4 
18.4 
-13.7 
-42.4 
72.5 
-12.1 
4.8 
-35.5 
-53.5 
-46.9 
-37.6 

-0.48 
-0.12 
0.41 
0.21 
-0.14 
0.17 
0.37 
0.66 
-0.01 
0.01 
0.32 
-0.37 
0.26 
0.21 
0.05 
0.1 
-0.51 
-0.03 
-0.13 
0.5 
0.26 
-0.5 
0.06 
-0.13 
-0.29 

-0.06 
0.05 
-0.1 
0.08 
-0.04 
0.21 
0.54 
0.57 
0.04 
-0.14 
0.15 
-0.24 
0.4 
-0.26 
-0.79 
0.09 
-0.62 
0.01 
-2.03 
0.48 
-0.48 
-0.33 
0.05 
-0.15 
-0.15 

-0.04 
0.22 
0.44 
0.48 
0.14 
0.51 
0.82 
0.76 
0.33 
0.13 
0.49 
0.07 
0.73 
0.25 
0.25 
0.32 
-0.08 
0.25 
0.14 
0.7 
0.26 
-0.02 
0.4 
0.06 
0.12 

-41.5 
10.9 
-17.4 
-30.9 
-18.5 
-31.9 
-36.2 
-12.6 
-41.5 
-2.9 
-29.6 
-56.3 
-46.8 
11.6 
-51 
-12.1 
-71.7 
-39.3 
46.1 
-13.3 
3.3 
-61.6 
-49.4 
-16.2 
-45.9 

 
ID 

Country 

Basin 

River 

Station 

C855 
ES 
C856 
ES 
C857 
ES 
C858 
ES 
C859 
ES 
C860  RS 
C862  RS 
C863  RS 
C864  RS 
C865  RS 
C867  RS 
C868  RS 
C870  RS 
C871  RS 
C872  RS 
C873  RS 
C875  RS 
C876  RS 

Duero 
Duero 
Duero 
Duero 
Duero 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 
Danube 

Duero 

Tormes 
Tormes 
Esla 
Esla 
Dunav 
Ibar 
Južna Orava 
Južna Orava 
Nišava 
Zapadna Orava 
Dunav 
Drina 
Drina 
Lim 
Lim 
Mileševka 
Jadar 

Porma 
Santa Teresa 
Almendra 
Riano 
Ricobayo 
Na 
Na 
Na 
Na 
Na 
Na 
Na 
Na 
Na 
Na 
Na 
Na 
Na 

calibration 
KGE 

NSE 

r 

BIAS 
[%] 

validation 
KGE 

NSE 

r 

BIAS [%] 

-0.16 
0.04 
0.14 
-0.15 
0.5 
0.69 
0.82 
0.84 
0.67 
0.77 
0.87 
0.87 
0.73 
0.74 
0.78 
0.74 
0.36 
0.49 

-0.27 
0.06 
-0.26 
-0.3 
0.54 
0.43 
0.63 
0.69 
0.43 
0.55 
0.73 
0.76 
0.52 
0.57 
0.59 
0.64 
0.19 
0.3 

0.38 
0.41 
0.22 
0.51 
0.76 
0.85 
0.82 
0.85 
0.69 
0.77 
0.87 
0.89 
0.75 
0.79 
0.79 
0.81 
0.65 
0.57 

-55.8 
-45 
-23.9 
-62.7 
-21 
14.3 
3.4 
-2.9 
-5.2 
-2.3 
0.5 
6.4 
-8 
-13.8 
-4.1 
-8.5 
53.9 
-5.2 

-0.22 
0.03 
-0.06 
-0.19 
0.72 
0.8 
0.32 
0.36 
0.38 
0.45 
0.27 
0.8 
0.23 
0.2 
0.31 
0.33 
0.43 
0.36 

-0.36 
0.07 
-0.68 
-0.28 
0.46 
0.61 
0.03 
0.27 
0.17 
0.15 
0.07 
0.6 
-0.24 
-0.12 
0.23 
0.24 
0.28 
0.12 

0.23 
0.41 
-0.03 
0.22 
0.72 
0.87 
0.7 
0.72 
0.59 
0.63 
0.67 
0.81 
0.62 
0.63 
0.77 
0.74 
0.57 
0.47 

-49.5 
-46.1 
-10.9 
-44.9 
-5 
12.4 
-55.5 
-48.4 
-41.4 
-38.5 
-54.9 
-2.1 
-59.4 
-57.2 
-57.4 
-51 
-23.1 
-26.9 

53 

54 

 
 
 
GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the 
address of the centre nearest you at: http://europea.eu/contact 

On the phone or by email 

Europe Direct is a service that answers your questions about the European Union. You can contact this 
service: 

- by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

- at the following standard number: +32 22999696, or 

- by electronic mail via: http://europa.eu/contact 

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa 
website at: http://europa.eu 

EU publications 
You can download or order free and priced EU publications from EU Bookshop at: 
http://bookshop.europa.eu. Multiple copies of free publications may be obtained by contacting Europe 
Direct or your local information centre (see http://europa.eu/contact). 

 
 
 
 
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doi:10.2760/806324 

ISBN 978-92-79-92881-9