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Humans have increased the discharge of pollution, altered water flow regime and modified the morphology of rivers. All these actions have resulted in multiple pressures on freshwater ecosystems, undermining their biodiversity and ecological functioning. The European Union has adopted an ambitious water policy to reduce pressures and achieve a good ecological status for all water bodies. However, assessing multiple pressures on aquatic ecosystems and understanding their combined impact on the ecological status is challenging, especially at the large scale, though crucial to the planning of effective policies. Here, for the first time, we quantify multiple human pressures and their relationship with the ecological status for all European rivers. We considered ecological data collected across Europe and pressures assessed by pan-European models, including pollution, hydrological and hydromorphological alterations. We estimated that in one third of EU’s territory rivers are in good ecological status. We found that better ecological status is associated with the presence of natural areas in floodplains, while urbanisation and nutrient pollution are important predictors of ecological degradation. We explored scenarios of improvement of rivers ecological status for Europe. Our results strengthen the need to halt urban land take, curb nitrogen pollution and maintain and restore nature along rivers.
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Scientific RepoRts | 7: 205 | DOI:10.1038/s41598-017-00324-3
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Human pressures and ecological
status of European rivers
B. Grizzetti, A. Pistocchi, C. Liquete, A. Udias, F. Bouraoui & W. van de Bund
Humans have increased the discharge of pollution, altered water ow regime and modied the
morphology of rivers. All these actions have resulted in multiple pressures on freshwater ecosystems,
undermining their biodiversity and ecological functioning. The European Union has adopted an
ambitious water policy to reduce pressures and achieve a good ecological status for all water bodies.
However, assessing multiple pressures on aquatic ecosystems and understanding their combined
impact on the ecological status is challenging, especially at the large scale, though crucial to the
planning of eective policies. Here, for the rst time, we quantify multiple human pressures and their
relationship with the ecological status for all European rivers. We considered ecological data collected
across Europe and pressures assessed by pan-European models, including pollution, hydrological and
hydromorphological alterations. We estimated that in one third of EU’s territory rivers are in good
ecological status. We found that better ecological status is associated with the presence of natural
areas in oodplains, while urbanisation and nutrient pollution are important predictors of ecological
degradation. We explored scenarios of improvement of rivers ecological status for Europe. Our results
strengthen the need to halt urban land take, curb nitrogen pollution and maintain and restore nature
along rivers.
In the second half of the 20th century economic activities ourished in Europe while the status of rivers, lakes and
coastal waters chronically deteriorated1. Human activities have produced multiple pressures on waters, including
nutrient pollution2, 3, modications of river morphology4, 5, alterations of water ow regime6, 7 and the introduc-
tion of alien species8. Multiple pressures from land-based activities pose threats to human water security and
freshwater biodiversity9, and have produced cumulative eects in oceans and coastal waters10.
Natural spatio-temporal heterogeneity in rivers and oodplains is essential to support ecosystem biodiver-
sity11. However river regulation, such as ow alterations, channelization, dredging and river bank stabilization,
have reduced the connectivity in the riverine landscape and altered the uvial dynamics that support habitat
heterogeneity11. Similarly, the widespread construction of dams has diminished the natural disturbance patterns
in rivers, homogenizing ow regional dierences and creating cumulative transboundary eects12, 13. Freshwater
biodiversity is further threatened by water pollution related to human activities in the catchment, sh overex-
ploitation and the increase in the number of alien species14. All these actions have resulted in multiple pressures
on freshwater ecosystems that undermine their biodiversity and ecological functioning.
Disentangling and quantifying the cause and eect relationship between multiple pressures and ecological
functioning is challenging, especially when addressing large geographical areas like Europe. Firstly, the quanti-
cation of pressures on water systems is hampered by limited and spatially heterogeneous data. Secondly, multiple
pressures are acting concurrently on water bodies and their combined eect is poorly understood15. irdly, eco-
logical conditions are the result of impacts building up over time, local natural conditions and climatic variabil-
ity16, 17. Finally, ecological systems could change following non-linear patterns and regime shis, and restoration
measures do not necessarily return the ecological systems to their original state18. All these aspects contribute to a
great complexity in the link between multiple pressures and ecological status in water bodies. Yet understanding
this relationship is necessary to plan eective policies19, 20 and restoration measures21, as long-term availability of
water resources and many benets for people depend on healthy aquatic ecosystems22, 23.
To protect and enhance water resources and aquatic ecosystems, since 2000 the European Union has adopted
an ambitious water policy, the Water Framework Directive (WFD)24, with the objective of reducing pressures and
achieving good ecological status for all European water bodies. With this aim, EU Member States had to assess
European Commission, Joint Research Centre (JRC), Directorate D—Sustainable Resources, via Enrico Fermi 2749,
21027, Ispra, Italy. Correspondence and requests for materials should be addressed to B.G. (email: bruna.grizzetti@
ec.europa.eu)
Received: 11 October 2016
Accepted: 21 February 2017
Published: xx xx xxxx
OPEN
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the ecological status of rivers, lakes and coastal waters in their territory, and established programmes of measures
to reduce signicant anthropogenic pressures aecting the status.
Here, for the rst time, we have characterised the main pressures acting on European rivers and explored their
relationship with the ecological status reported by EU Member States. Our analysis addressed three main ques-
tions: (1) How do multiple pressures aect the ecological status of European rivers? (2) To what extent has the EU
water policy target of good ecological status been achieved? and (3) How and where would measures to improve
the ecological status of rivers be eective?
Results
How do multiple pressures aect the ecological status of European rivers? To address this rst
question we quantied multiple pressures on European rivers and examined their relationship with reported data
on the ecological status.
According to a recent European Commission report25, the major pressures acting on European rivers are
related to pollution, hydrological changes and hydromorphological alterations. We considered 12 indicators that
could inform on these pressures (Table1): nitrogen and phosphorus concentration; pollution from urban runo;
water demand; alteration of natural low ow regimes (at 10th and 25th percentiles); density of infrastructure in
oodplains; natural areas in oodplains; articial and agricultural land cover in oodplains; and articial and
agricultural land cover in the drained area. We quantied these indicators at the spatial resolution of catchments
(180 km2 on average), using pan-European models and data sets (we used best available data for the period 2004–
2009, see ‘Methods’). e maps of pressures on European inland waters are shown in Fig.1.
In parallel, we computed a proxy indicator of the ecological status of rivers (at the same spatial resolution of
pressures), based on the data reported by EU Member States (Supplementary Information FigureS1). e eco-
logical status is an integrative evaluation of aquatic ecosystem health, designed to reect changes in community
structure and ecosystem functioning in response to anthropogenic pressures26. It is expressed in ve classes—
high, good, moderate, poor and bad—and its assessment is carried out by EU Member States (per single water
body), using biological assessment methods. e national classication scales are harmonised by intercalibration
to assure their consistency at the EU level. e target set by EU water policy is to reach a good ecological status
for all rivers (by 2015 or 2027). Our proxy indicator for the ecological status of European rivers covers 77% of the
EU’s surface. Out of this area, 38% is estimated to be in good or high ecological status, 42% in a moderate state
and the rest in poor or bad status.
When looking at the distribution of individual pressures per class of ecological status, we observe signicant
correlations and trends in the expected direction (Fig.2). For all indicators of pressures medians signicantly
dier per class of ecological status (Kruskall–Wallis test, p < 0.05). Nitrogen and phosphorus concentrations
increase towards poor and bad ecological classes, and the same happens for the indicators of hydromorphological
alterations in oodplains. Also, pressures related to urban and agricultural land in the drained area take higher
values in poor and bad classes, while greater maintenance of natural low ow and the presence of natural riparian
areas are related to good and high ecological status.
We explored the combined eects of multiple pressures on the achievement of good ecological status of rivers,
applying statistical classication methods (notably, regression tree (RT), logistic regression (LR) and random
forest (RF)). e accuracy of the models’ predictions was up to 0.74 (0.70 for RT, 0.72 for LR and 0.74 for RF
respectively, Fig.3a). e results of the models showed that the good ecological status of rivers is explained by
a combination of pressures, and the most important predictors are the presence of natural areas in oodplains,
nutrient concentration (especially nitrogen), infrastructures in oodplains and urbanisation and agriculture in
the drained catchment (Fig.3b).
To what extent has the EU water policy target of good ecological status been achieved? To
examine this second question, we estimated the level of achievement of the EU water policy objective, using the
relationship established by modelling (RF). We estimated the probability of meeting the policy target of good eco-
logical status for all EU rivers in catchments with complete data on pressures (89% of the EU’s surface), therefore,
also in areas where direct measurements of ecological status were not available. According to our estimations, the
proportion of the EU surface where rivers meet the water policy target, with a probability of at least 70%, is 32%
(Fig.4).
e distribution of the model’s accuracy and error type per country can provide more insights (Fig.5). False
negatives (9%, the country reports meeting the target while the model predicts not meeting the target) could indi-
cate where pressures are overestimated by the European assessment or local measures are not taken into account.
For example, this could be the case of Denmark, where substantial investments have been made in the restoration
of wetlands27. On the other hand, false positives (17%, the country reports not meeting the target while the model
predicts meeting the target) could suggest where pressures are underestimates or not captured by the current
indicators. is could be the case of Sweden, where local water ow modications could be the reason for not
achieving the good ecological status28. Among errors, dominance of false positives could characterise countries
that adopt stricter rules or more conservative reference status in the implementation of the WFD, compared to
the average of EU countries. Contrarily, dominance of false negatives might occur for countries that have slightly
lower standards or consider a partially impacted ecological status as reference conditions for the water bodies.
Besides misrepresentation of pressures and local measures, or difference in reference status among the
national assessments, another reason that could explain the model errors is a dierent interaction of multiple
pressures according to river typology or ecological regions. However, overall, discrepancies between model pre-
dictions and the ecological status reported by the countries are spread homogeneously across the study area,
indicating no particular bias in the assessments by Member States. is is an encouraging signal considering
the large eort spent on the national assessments and on the intercalibration of methods among Member States.
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How and where would measures to improve the ecological status of rivers be eective? To
shed light on this third question, we examined the eects of measures to improve the ecological status of rivers
through scenario analysis. We tested the scenario of concurrently reducing nitrogen pollution and increasing nat-
ural areas in oodplains (using RT, LR and RF models, Fig.6), as these pressures were among the most signicant
variables explaining the good ecological status (according to the results of the RF, Fig.3). e analysis showed that
4% of EU catchments with degraded rivers would achieve a good ecological status by reducing nitrogen pollution
and increasing natural areas in oodplains by 10%, and up to 8% of catchments could meet the policy target if
Pressure Indicator
(acronym)How the indicator is estimated (reference
year and available spatial coverage*)
Pollution
Nitrogen
concentrations in
rivers (Nconc)
Estimated nitrogen concentration in rivers
(mgN/l), based on the model GREEN35. (2005;
EU-28+)
Phosphorus
concentrations in
rivers (Pconc)
Estimated phosphorus concentration in rivers
(mgP/l), based on the model GREEN35. (2005;
EU-28+)
Diuse pollution
from urban runo
(Heaney)
Relative intensity of the potential pollution
load from urban runo (dimensionless),
estimated by the Heaney model34, 36. e
indicator is designed to reproduce potential
pollution and not specic contaminants, based
on urban land cover (CLC 2006), annual
precipitation and population. (2006; EU-28,
without GR and CY)
Hydrological alterations
Total water demand
(WatDemand)
Total water demand in the catchment upstream
(mm/day) (ref. 34 based on ref. 37). (2006;
EU-28, without CY)
Low ow alteration
at 25%-ile (Q25)
Ratio between the number of days the water
ow is below the 25%-ile with and without
water abstractions (fraction)34. e ow
duration curve without abstractions is used
to dene the ow threshold of Q25%-ile. e
indicator is computed using the estimations
of the hydrological model LISFLOOD37,
considering baseline conditions including
water abstractions and an ideal undisturbed
case with no abstractions. (2006; EU-28,
without CY)
Low ow alteration
at 10%-ile (Q10)
Ratio between the number of days the water
ow is below the 10%-ile with and without
water abstractions (fraction)34. e ow
duration curve without abstractions is used
to dene the ow threshold of Q10%-ile. e
indicator is computed using the estimations
of the hydrological model LISFLOOD37,
considering baseline conditions including
water abstractions and an ideal undisturbed
case with no abstractions. (2006; EU-28,
without CY)
Hydro-morphological alterations
Density of
infrastructures
in oodplains
(INFRoodp)
Density of infrastructure (roads and railways)
in the oodplains (km/km2)34, 40. (dates not
available, data extracted in 2014; EU-28,
without HR)
Natural areas
in oodplains
(NAToodp)
Fraction of the oodplain occupied by natural
elements30, 38. (2000; EU-28, without HR)
Articial land
cover in oodplains
(URBoodp)
Fraction of urban land use (CLC 2006 class:
articial areas) in the oodplains34. (2006; EU-
28, without GR and HR)
Agricultural land
cover in oodplains
(AGRoodp)
Fraction of agricultural land use (CLC 2006
class: arable land and permanent crops) in
the oodplains34. (2006; EU-28, without GR
and HR)
Integrated
Articial land
cover in catchment
area (catchURB)
Fraction of catchment area which is urban
(CLC 2006 class: articial areas)34. (2006; EU-
28, without GR and HR)
Agricultural land
cover in catchment
area (catchAGRI)
Fraction of catchment area which is
agricultural (CLC 2006 class: arable land and
permanent crops)34. (2006; EU-28, without
GR and HR)
Table 1. Pressures considered in the study and the respective indicators. (*) As at January 2017 the European
Union (EU) is composed of 28 Member States (MS): Belgium (BE), Bulgaria (BG), Czech Republic (CZ),
Denmark (DK), Germany (DE), Estonia (EE), Ireland (IE), Greece (GR), Spain (ES), France (FR), Croatia
(HR), Italy (IT), Cyprus (CY), Latvia (LV), Lithuania (LT), Luxembourg (LU), Hungary (HU), Malta (MT),
Netherlands (NL), Austria (AU), Poland (PO), Portugal (PT), Romania (RO), Slovenia (SI), Slovakia (SK),
Finland (FI), Sweden (SE) and United Kingdom (GB).
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the same measures were raised to 20%. However, this is a conservative estimation, as the methods adopted do not
include the eect of improving the ecological quality in one catchment on the downstream area.
Yet the scenario analysis helps us understand how addressing a combination of pressures can aect the eco-
logical status compared to changes in single pressures (which are presented in Supplementary Information
FiguresS2, S3 and S4) and where measures are likely to yield good ecological status. According to our results,
the predicted increase in good ecological status by simultaneously reducing nitrogen concentration in rivers and
enhancing natural areas in oodplains is slightly higher than the sum of the predicted increase by changing the
two pressures independently, showing a synergistic eect.
Discussion
Statistical classication models, as adopted here, cannot bring strong evidence of a causal relationship between
the pressures and the ecological status, but they can unveil patterns. Our results show that the ecological status
of European rivers can be explained by multiple pressures, and in particular by a combination of local pressures
(i.e. hydromorphological alterations) and catchment pressures (i.e. nutrient pollution and land use). Measures
to improve the ecological quality of rivers should consider these two dimensions, as well as synergistic eects of
acting simultaneously on more pressures.
In our analysis, ow regime alteration and water abstractions appeared less signicant. ey were probably
not completely represented by selected indicators or spatial information. At the same time, it is currently under
debate whether the present assessment of the ecological status suciently accounts for hydrological alterations of
river ecosystems29. Other pressures not included in this study might also be relevant to explaining the ecological
status, such as the disruption of upstream-downstream connectivity, historical impacts having legacy eects and
the introduction of invasive species. In addition, the river typology could explain the dierent impact of similar
pressures combination.
e joint eort of EU Member States in monitoring the ecological status remains crucial to ensuring that
eective measures for protecting and restoring aquatic ecosystems are deployed, considering the panoply of vital
ecosystem services they provide30, 31. Similarly, models and remote sensing data represent useful tools to assess
multiple pressures across Europe, especially in less data intensive areas.
Our results indicate that maintaining natural oodplains and limiting nitrogen pollution can be key meas-
ures to improve the ecological status of rivers and achieve water policy goals, producing synergetic eects. ey
also suggest that preserving natural land cover as opposed to urban sprawling, which erodes the capacity of the
Figure 1. Maps of pressures on European rivers. (a) Nitrogen concentration; (b) phosphorus concentration; (c)
pollution from urban runo; (d) water demand; (e) preservation of low ow at 25th percentile; (f) preservation
of low ow at 10th percentile; (g) infrastructures in oodplains; (h) natural areas in oodplains; (i) urban areas
in oodplains; (j) agricultural areas in oodplains; (k) articial land cover in catchment area; (l) agricultural
land cover in catchment area. Details of the pressures indicators are in Table1. Maps generated with ArcGIS
10.1 for desktop (http://www.esri.com/soware/arcgis).
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Figure 2. Relationship between the indicators of pressures and the proxy of the ecological status. (a) Nitrogen
concentration; (b) phosphorus concentration; (c) pollution from urban runo; (d) water demand; (e)
preservation of low ow (at 25th percentile); (f) preservation of low ow (at 10th percentile); (g) infrastructures
in oodplains; (h) natural areas in oodplains; (i) urban areas in oodplains; (j) agricultural areas in
oodplains; (k) articial land cover in catchment area; (l) agricultural land cover in catchment area. e
indicators of pressures are described in Table1.
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Figure 3. Model results. (a) Accuracy of classication using the regression tree (RT), logistic regression (LR)
and random forest (RF) models. (b) Importance of the variables in the classication of the random forest
method computed by the mean decrease Gini index48, 49. e analysis refers to the period 2004–2009, for which
data on the ecological status were reported and most of the pressures indicators were available.
Figure 4. Probability of good ecological status of rivers. Values estimated by the random forest method applied
to all catchments with complete data on pressures (89% of EU). Map generated with ArcGIS 10.1 for desktop
(http://www.esri.com/soware/arcgis).
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Figure 5. Distribution of model accuracy and errors per country. e values within brackets indicate the
number of catchments with available data. Results are based on the random forest method. e analysis refers
to the period 2004–2009, for which data on the ecological status were reported and most of the pressures
indicators were available.
Figure 6. Scenarios of measures for improvement of river ecological status. e scenarios are simulated by
the three classication methods: regression tree (RT), logistic regression (LR) and random forest (RF). e
scenarios ‘measures for improvement’ estimate the eects of contemporary reduction of nitrogen concentration
in rivers and the increase of natural areas in oodplains, considering improvement rates of 10% and 20%.
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ecosystem to buer pressures32, should be seen an investment in ecosystem resilience. Overall, our results con-
rm evidence of the need to halt urban land take, curb nutrient pollution and preserve natural areas along water
courses, in order to protect the ecological quality of rivers and ensure future benets for humans.
Methods
Spatial extent and resolution. e area covered by the study is the European Union (EU). As at January
2017 the EU is composed of 28 Member States (notably Belgium, Bulgaria, Czech Republic, Denmark, Germany,
Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta,
Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom). We
based the spatial analysis on a consistent hydrological geodatabase covering Europe, with elementary catchments
of 180 km2 on average, called the HydroEurope database33. For inland waters the EU is divided into 23 187 catch-
ments, corresponding to 4 098 757 km2. In the study, we referred to this area as reference for the EU, although it is
slightly less (7%) than the EU surface, as small coastal catchments are not included in the database.
Multiple pressures. The anthropogenic pressures on aquatic ecosystems were identified based on the
Common Implementation Strategy (CIS) and the rst River Basin Management Plans (RBMPs) submitted by the
EU Member States25, 34. e main types of pressures reported for river ecosystems were nutrient and chemical pol-
lution, hydrological alterations and morphological modications. We proposed a set of 12 indicators that could
inform on the quantitative presence of these pressures and could be computed consistently across Europe, using
already established models or available spatial data, considering the best available data for the period 2004–2009.
e indicators of pressures proposed in this study are summarised in Table1, including the available reference
year and the spatial coverage. For pollution, nitrogen and phosphorus concentration in surface waters were con-
sidered, based on the nutrient loads estimated by the GREEN model combined with water ow estimated by a
simple hydrological model based on a Budyko framework35. In addition, load from urban runo was estimated by
an indicator accounting for urban population and rainfall, derived from the loading function proposed by Heaney
et al.36, as described in Pistocchi et al.34. For hydrological alteration, the total water demand was derived from the
European maps at 5 km resolution used as input by the LISFLOOD hydrological model37. ese include water
demand for irrigation, public supply, industry (including energy production) and livestock. e indicators of ow
regime alteration were computed as the number of days in which the actual stream ow is below the 10th and 25th
natural ow percentile, normalised by the corresponding natural duration (i.e. 36.5 and 91.25 days respectively).
e actual and natural ow duration curves were estimated using the LISFLOOD model under the 2006 baseline
conditions, in presence and in absence of water abstractions respectively34, 37. A series of (proxy) indicators of
hydromorphological pressures were considered to reect the conditions of oodplains, including the share of
the oodplain occupied by agricultural land, by articial areas and by natural areas (riparian functional areas),
and the density of infrastructures (roads and railways) in the oodplain. Floodplains were identied through the
data set described by Clerici et al.38. Agricultural and articial land cover shares were estimated on the basis of
the CORINE Land Cover 2006 map39. Infrastructures were extracted from the freely accessible OpenStreetMap
data set40. e presence of riparian functional areas was calculated as the average riparian vegetation buer width
divided by the oodplain width, where the average riparian vegetation buer width was derived by aggregation
of the vegetation maps developed by Weissteiner et al.41. All variables relating to oodplains were aggregated at
1 km resolution across the stream network. Finally, the fraction of the drained catchment occupied by urban areas
and by agricultural land were considered as two additional integrated indicators of pressures on rivers related to
the land use in the catchment. All pressures indicators were computed or aggregated at the spatial resolution of
catchments of the HydroEurope database33 (Fig.1).
Ecological status. e ecological status is a synthetic judgement that represents the condition of water bod-
ies as high, good, moderate, poor or bad, based on assessment methods for biological quality elements (BQEs,
that are phytoplankton, ora, invertebrate fauna and sh fauna), combined with information on physico-chemical
and hydromorphological conditions. e ecological status is dened in general terms by the WFD, which is the
EU water law; then each individual Member State develops national assessment methods. Depending on the
Member State, the assessment of the ecological status was based on full BQEs, pressure assessments, expert judge-
ment or combinations of the above. is variability in approaches limits the methodological consistency across
the EU. However, classication scales for the biological classication methods have been intercalibrated across
EU Member States4244.
For this study, we used ecological status data from River Basin Management Plans reported according to
Article 13 of the WFD, extracted from the WISE2 database, compiled by the European Environment Agency45,
including data from 2004 to 2009. For each monitored river stretch the data set reports the class of ecological
status or potential and the length of the stretch. A river stretch is dened as a water body in the WFD. Only the
coordinates of the centroid of each water body were available for this study, while the geographic delineation of
the stretch was not available at the European scale. To overcome this lack of information and the dierent spatial
density of monitoring across the EU, we developed a proxy indicator of the ecological status of rivers that could
be representative at the scale of HydroEurope catchments, the same spatial unit at which pressure indicators were
aggregated. For each catchment, we considered the ecological status assigned to all centroids of water bodies fall-
ing in that catchment, yielding valid and usable data for 79 630 water bodies across the EU. en, for each catch-
ment, we computed the percentage of monitored river length under each class of ecological status (with 1 = High,
2 = G ood, 3 = Moderate, 4 = Poor, 5 = Bad) and the dominant class CMODE (corresponding to the mode), i.e.
the class that appears most oen in the total monitored length of the observations. CMODE takes values between
1 and 5, corresponding to the ve classes of ecological status (Supplementary Information FigureS1). We also
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considered a simple Boolean variable called TARGET to indicate if the good ecological status is met or not.
TARGET takes value 0 when the sum of percentages of monitored river length in high and good ecological status
is higher than the sum of percentages in moderate, poor and bad status, and takes value 1 otherwise. erefore,
TARGET is a proxy indicator of meeting the WFD target of good ecological status.
Data sample. e spatial extent covered by the 12 pressures indicators varies depending on the input data
used in each pressure assessment (see the specic extent covered in Table1). We did not have complete informa-
tion on pressures for four countries—Greece, Croatia, Cyprus and Malta—whose surface represents about 5% of
the EU. We could develop a completed data set of pressures for 89% of the EU’s surface (85% of catchments). Data
on rivers’ ecological status were available for 15 052 catchments of HydroEurope (65% of EU catchments, 77% of
the EU’s surface). In total, there were 13 651 catchments with complete indicators of pressures and complete data
on ecological status that we used for the models’ calibration. is represents 59% of the catchments and 71% of
the EU’s surface. e temporal extent of the analysis refers to the period 2004–2009, for which data on the ecolog-
ical status were reported and most of the pressures indicators were available.
Analysis. We explored the data distribution and correlation, and we performed a factor analysis. We analysed
the distribution of values of each indicator of pressures per class of ecological status, using the most frequent
status class reported per catchment CMODE as proxy for the ecological status. We assessed for all indicators of
pressures that the medians per class of ecological status were signicantly dierent (p < 0.05) by a Kruskall–Wallis
test (Fig.2).
We applied statistical methods to investigate how multiple pressures can explain the ecological status in riv-
ers, using the variable TARGET as indicator of meeting the policy objective in each catchment. Specically, we
considered three types of classication techniques: regression trees46 (RT), logistic regression47 (LR) and random
forest48 (RF). ese methods establish a classication of catchments using the information embedded in the data.
We applied the three methods using the complete data set on pressures and ecological status. is means that the
temporal extent of the analysis does not refer to a specic year but is centred on the period 2004–2009.
For the analysis the three classication methods (RT, LR and RF) were applied 200 times using random sam-
ples (without replacement) extracted from the data set. Each iteration included three steps: 1. randomly balance
the data set (as the number of catchments with TARGET = 1 exceeded those with TARGET = 0); 2. randomly
select, out of the balanced data set, a training sample (80% of data) and a testing sample (the 20% remaining); and
3. run the three models (RT, LR and RF) using the training sample (model calibration). en the accuracy of the
models was measured using the testing sample (model verication), as the ratio of samples (catchments) whose
value (TARGET) is correctly predicted over the total number of samples (Fig.3). e overall accuracy of each
method was reported as the median of the 200 model runs.
e RT and RF models were set including all 12 pressure indicators as explanatory variables. e LR model
was run rstly with 12 pressures and then including only the signicant variables (p < 0.1 two-sided) and with
sign coherent with the expected physical eect on ecological status. e importance of the variables in the classi-
cation of the random forest method was computed by the mean decrease Gini index48, 49.
We used the RF method (and the variable TARGET) to predict the probability of meeting the policy target of
good ecological status in all EU catchments for which we had complete pressures indicators (89% of the EU’s sur-
face) (Fig.4). For reporting the EU’s area meeting the policy target we considered a probability threshold of 0.7.
Similarly, we based the analysis of predictions’ accuracy and errors per EU country on the RF method (Fig.5),
showing where modelled and reported ecological status were in agreement on meeting (T0) or non-meeting (T1)
the policy target of good ecological status, and the frequency of false positive (F0, the model predicts meeting
the target while the reported data indicate lower ecological status) and false negative (F1, the model predicts not
meeting the target while the reported data indicate at least good ecological status).
Finally, we simulated two types of scenarios: the eect of measures for improvement of the ecological status
(Fig.6) and the eect of further degradation (Supplementary Information FigureS5), using the three methods,
RT, LR and RF (and the variable TARGET). In the scenario ‘measures for improvement’ we tested the concurrent
reduction of nitrogen concentration in rivers (10% and 20%) and increase of natural areas in oodplains
(+10% and +20%), while in the scenario ‘further degradation’ we simulated the simultaneous increase of nitro-
gen concentration in rivers (+10% and +20%) and reduction of natural areas in oodplains (10% and 20%).
e eects of the changes were quantied as the increase rate of catchments predicted in good ecological status
(meeting the target of the water policy) compared to the baseline. For the scenarios, the models were run accord-
ing to the three-step iteration presented above, and the eects tested on the catchments correctly classied by the
models. We reported the overall expected eect of the scenarios as the average of the medians of the three models’
predictions. We also simulated a variation of ±10% and ±20% of one pressure at a time, using the three methods
(RT, LR and RF). e results are shown in the Supplementary Information FiguresS2, S3 and S4.
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Acknowledgements
is research has been supported by the institutional programme of the Joint Research Centre of the European
Commission and the EU-funded seventh framework programme for research and technological development
projects: MARS (grant agreement no. 603378) and GLOBAQUA (grant agreement no. 603629). e authors
would like to acknowledge the work of the river basin authorities, institutions and researchers in EU Member
States for contributing to the assessments of ecological status and the European Environment Agency for
providing the data.
Author Contributions
B.G. and A.P. conceived the study. All authors contributed with ideas to the analysis. B.G., A.P., C.L., F.B, W.v.B.
assessed the indicators of pressures and the proxy of ecological status. A.U. developed the statistical modelling and
R code. B.G., A.P., C.L., A.U., F.B. performed statistical and spatial analysis. B.G., A.P., C.L. wrote the manuscript.
All authors discussed the results and commented on the manuscript.
Additional Information
Supplementary information accompanies this paper at doi:10.1038/s41598-017-00324-3
Competing Interests: e authors declare that they have no competing interests.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
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unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material. To view a copy of this
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© e Author(s) 2017

Supplementary resource (1)

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