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Human pressures and ecological status of European rivers

Scientific Reports

December 2017
7(1)

DOI:10.1038/s41598-017-00324-3

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CC BY 4.0

Authors:

Alberto Pistocchi

European Commission

Camino Liquete

European Commission

Angel Udias

European Commission

Show all 6 authorsHide

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.

Maps of pressures on European rivers. (a) Nitrogen concentration; (b) phosphorus concentration; (c) pollution from urban runoff; (d) water demand; (e) preservation of low flow at 25 th percentile; (f) preservation of low flow at 10 th percentile; (g) infrastructures in floodplains; (h) natural areas in floodplains; (i) urban areas in floodplains; (j) agricultural areas in floodplains; (k) artificial 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/software/arcgis).

…

Relationship between the indicators of pressures and the proxy of the ecological status. (a) Nitrogen concentration; (b) phosphorus concentration; (c) pollution from urban runoff; (d) water demand; (e) preservation of low flow (at 25 th percentile); (f) preservation of low flow (at 10 th percentile); (g) infrastructures in floodplains; (h) natural areas in floodplains; (i) urban areas in floodplains; (j) agricultural areas in floodplains; (k) artificial land cover in catchment area; (l) agricultural land cover in catchment area. The indicators of pressures are described in Table 1.

…

Model results. (a) Accuracy of classification using the regression tree (RT), logistic regression (LR) and random forest (RF) models. (b) Importance of the variables in the classification of the random forest method computed by the mean decrease Gini index 48, 49. The analysis refers to the period 2004-2009, for which data on the ecological status were reported and most of the pressures indicators were available.

…

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/software/arcgis).

…

Distribution of model accuracy and errors per country. The values within brackets indicate the number of catchments with available data. Results are based on the random forest method. The analysis refers to the period 2004-2009, for which data on the ecological status were reported and most of the pressures indicators were available.

…

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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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Scientific RepoRts | 7: 205 | DOI:10.1038/s41598-017-00324-3

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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Scientific RepoRts | 7: 205 | DOI:10.1038/s41598-017-00324-3

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 States42–44.

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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Scientific RepoRts | 7: 205 | DOI:10.1038/s41598-017-00324-3

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.

References

1. Meybec, M. Global analysis of river systems: from Earth system controls to Anthropocene syndromes. Philosophical Transactions

of the oyal Society B: Biological Sciences 358, 1935–1955 (2003).

2. Sutton, M. et al. e European Nitrogen Assessment. Cambridge University Press, Cambridge (2011).

3. Fowler, D. et al. e global nitrogen cycle in the Twenty-rst century. Philosophical Transactions of the oyal Society B: Biological

Sciences 368, 20130164 (2013).

4. Belletti, B., inaldi, M., Buijse, A. D., Gurnell, A. M. & Mosselman, E. A review of assessment methods for river hydromorphology.

Environmental Earth Sciences 73, 2079–2100 (2015).

5. Sweeney, B. W. et al. iparian deforestation, stream narrowing, and loss of stream ecosystem services. Proceedings of the National

Academy of Sciences of the United States of America 101, 14132–14137 (2004).

www.nature.com/scientificreports/

Scientific RepoRts | 7: 205 | DOI:10.1038/s41598-017-00324-3

6. Acreman, M. & Dunbar, M. J. Dening environmental river ow requirements - A review. Hydrology and Earth System Sciences 8,

861–876 (2004).

7. Po, N. L. & Zimmerman, J. . H. Ecological responses to altered ow regimes: A literature review to inform the science and

management of environmental ows. Freshwater Biology 55, 194–205 (2010).

8. Strayer, D. L. Alien species in fresh waters: Ecological eects, interactions with other stressors, and prospects for the future.

Freshwater Biology 55, 152–174 (2010).

9. Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).

10. Halpern, B. S. et al. Spat ial and temporal changes in cumulative human impacts on the world/‘s ocean. Nature Communicatio ns 6 (2015).

11. Ward, J. V., Tocner, . & Schiemer, F. Biodiversity of oodplain river ecosystems: Ecotones and connectivity. iver esearch and

Applications 15, 125–139 (1999).

12. Po, N. L., Olden, J. D., Merritt, D. M. & Pepin, D. M. Homogenization of regional river dynamics by dams and global biodiversity

implications. Proceedings of the National Academy of Sciences of the United States of America 104, 5732–5737 (2007).

13. Ziv, G., Baran, E., Nam, S., odriguez-Iturbe, I. & Levin, S. A. Trading-o sh biodiversity, food security, and hydropower in the

Meong iver Basin. Proceedings of the National Academy of Sciences of the United States of America 109, 5609–5614 (2012).

14. Butchart, S. H. M. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168 (2010).

15. Nõges, P. et al. Quantied biotic and abiotic responses to mu ltiple stress in freshwater, marine and ground waters. Science of the Total

Environment 540, 43–52 (2016).

16. Brucet, S. et al. Fish diversity in European laes: Geographical factors dominate over anthropogenic pressures. Freshwater Biology

58, 1779–1793 (2013).

17. Nõges, P., Van De Bund, W., Cardoso, A. C. & Heisanen, A. S. Impact of climatic variability on parameters used in typology and

ecological quality assessment of surface waters - Implications on the Water Framewor Directive. Hydrobiologia 584, 373–379 (2007).

18. Scheer, M., Carpenter, S., Foley, J. A., Fole, C. & Waler, B. Catastrophic shis in ecosystems. Nature 413, 591–596 (2001).

19. Hering, D. et al. Managing aquatic ecosystems and water resources under multiple stress–An introduction to the MAS project.

Science of the Total Environment 503–504, 10–21 (2015).

20. Navarro-Ortega, A. et al. Managing the eects of multiple stressors on aquatic ecosystems under water scarcity. e GLOBAQUA

project. Science of the Total Environment 503–504, 3–9 (2015).

21. Teichert, N., Borja, A., Chust, G., Uriarte, A. & Lepage, M. estoring sh ecological quality in estuaries: Implication of interactive

and cumulative eects among anthropogenic stressors. Science of the Total Environment 542, Part A, 383-393 (2016).

22. MEA. Millennium Ecosystem Assessment. (Ecosystems and human well-being: Wetlands and water. Synthesis. World esources

Institute: Washington, DC, 2005).

23. Guerry, A. D. et al. Natural capital and ecosystem services informing decisions: From promise to practice. Proceedings of the National

Academy of Sciences 112, 7348–7355 (2015).

24. European Parliament and Council (2000), Directive 2000/60/EC establishing a framewor for communit y action in the eld of water

policy, Ocial Journal of the European Union L 327, 22.12.2000.

25. European Commission. e water framewor directive and the oods directive: actions towards the ‘good status’ of EU water and to

reduce ood riss, COM(2015) 120 (2015).

26. Heisanen, A. S., van de Bund, W., Cardoso, A. C. & Nõges, P. Water Science and Technology 49, 169–177 (2004).

27. Homann, C. C. & Baattrup-Pedersen, A. e-establishing freshwater wetlands in Denmar. Ecological Engin eering 30, 157–166 (2007).

28. enöfält, B. M., Jansson, . & Nilsson, C. Eects of hydropower generation and opportunities for environmental ow management

in Swedish riverine ecosystems. Freshwater Biology 55, 49–67 (2010).

29. European C ommission. Ecological ows in the implementation of the Water Framewor Directive. Guidance Document No. 31.

Technical eport-2015-086 (2015).

30. Allan, J. D. et al. Joint analysis of stressors and ecosystem services to enhance restoration eectiveness. Proceedings of the National

Academy of Sciences 110, 372–377 (2013).

31. Grizzetti, B., Lanzanova, D., Liquete, C., eynaud, A. & Cardoso, A. C. Assessing water ecosystem services for water resource

management. Env ironmental Science and Policy 61, 194–203 (2016).

32. Pistocchi, A. Hydrological impacts of soil sealing and urban land tae. In: Urban Expansion, Land Cover and Soil Ecosystem Services,

edited by C. Gardi, outledge, in press, ISBN 978-1-138-88509-7 (2017).

33. Bouraoui, F., Grizzetti, B. and Aloe, A. Long term nutrient loads entering European seas, JC scientic and technical reports, EU

24726 EN, Publications Oce of the European Union, Luxembourg (2011).

34. Pistocchi, A. et al. Assessment of the eectiveness of reported water framewor directive programmes of measures. —Part I—Pan-

European scale screening of the pressures addressed by Member States, JC Technical eports, EU 27465 EN, Publications Oce

of the European Union, Luxembourg (2015).

35. Grizzetti, B., Bouraoui, F. & Aloe, A. Changes of nitrogen and phosphorus loads to European seas. Global Change Biology 18,

769–782 (2012).

36. Heaney, J., Huber, W. and Nix, S. J. Storm water management model—Level I—Preliminar y screening procedures, EPA-600/2-76-275,

Cincinnati (1976).

37. De oo, A. et al. A multi-criteria optimisation of scenarios for the protection of water resources in Europe, JC scientic and policy

reports, JC75919, Publications Oce of the European Union, Luxembourg (2012).

38. Clerici, N. et al. Pan-European distribution modelling of stream riparian zones based on multi-source Earth Observation data.

Ecological Indicators 24, 211–223 (2013).

39. European Environment Agency. aster data on land cover for the CLC2006 inventory—Version 17 (12/2013). Processed by the

European Topic Centre on Spatial Information and Analysis (http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-

2006-raster-3) (last modied 10 December 2015) (2014).

40. OpenStreetMap https://www.openstreetmap.org/ (accessed in March 2014) (2014).

41. Weissteiner, C. J., Bouraoui, F. & Aloe, A. eduction of nitrogen and phosphorus loads to European rivers by riparian buer zones.

nowledge and Management of Aquatic Ecosystem 08 (2013).

42. Bir, S. et al. Three hundred ways to assess Europe’s surface waters: An almost complete overview of biological methods to

implement the Water Framewor Directive. Ecological Indicators 18, 31–41 (2012).

43. Poiane, S. et al. A hitchhier’s guide to European lae ecological assessment and intercalibration. Ecological Indicators 52, 533–544 (2015).

44. Poiane, S. et al. Benthic macroinvertebrates in lae ecological assessment: A review of methods, intercalibration and practical

recommendations. Science of the Total Environment 543, 123–134 (2016).

45. European Environment Agency, WISE WFD database, http://www.eea.europa.eu/data-and-maps/data/wise_wfd (last modied 6

May 2015) (2012).

46. Breiman, L., Friedman, J., Stone, C. J. & Olshen, . A. Classication and regression trees. CC press (1984).

47. McCullagh, P. & Nelder, J. A. Generalized linear models. (Chapman and Hall: London, U, 1983).

48. Breiman, L. andom forests. Machine learning 45, 5–32 (2001).

49. Liaw, A. and Wiener, M. The randomForest manual. r-project.org (https://cran.r-project.org/web/pacages/randomForest/

randomForest.pdf) (2015).

www.nature.com/scientificreports/

Scientific RepoRts | 7: 205 | DOI:10.1038/s41598-017-00324-3

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.

is work is licensed under a Creative Commons Attribution 4.0 International License. e images

or other third party material in this article are included in the article’s Creative Commons license,

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

license, visit http://creativecommons.org/licenses/by/4.0/

Supplementary Material

Data

March 2017

B. Grizzetti · Alberto Pistocchi · Camino Liquete · Angel Udias · Wouter van de Bund

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The plausible impacts of climate change threat water resources sustainability in Mediterranean regions, where they are already under pressure. Hydrological modelling might aid assessing this threat, but realistic models are needed to guarantee accurate simulations. This work presents a hydrological model for the Guajaraz River basin (central Spain) using SWAT+. This basin contains the homonym reservoir and supplies the historic city of Toledo. An innovative calibration procedure, “inverse calibration”, was designed towards achieving a model that simulates the hydrological processes realistically rather than just providing satisfactory metrics: hard calibration results were filtered by two soft indices, the runoff coefficient and the groundwater contribution, to constrain parameter values. The methodology was successful, solved model structural problems (i.e. equifinality) and yielded a realistic model to simulate climate change. Then, five out of sixteen climate models were selected after calculating a suitability index, and they were simulated under two emissions scenarios (mid and high) and two time horizons (mid and long term). As expected, results foresee more severe hydrological impacts for the high emissions scenario and towards the end of the 21st century, with an average decrease in reservoir inflow up to 28% (or even up to 35% when analysing median values). Results also reveal that quickflow will be more affected than baseflow. These insights might aid on decision-making towards sustainable water management in a region where studies of this kind are scarce.

Water Quality Prediction using Random Forest Algorithm and Optimization

Article

Sep 2024

Deshinta Arrova Dewi

In the field of environmental conservation, the integration of Artificial Intelligence (AI) into pollution control strategies offers a transformative approach with significant potential. This paper presents a study on the application of AI techniques, specifically Random Forest algorithms, to predict and manage water quality in river systems. The objective of this research was to evaluate the performance of Random Forest models in comparison to Artificial Neural Networks (ANNs) for predicting the Water Quality Index (WQI). The study's findings revealed that the Random Forest model achieved a Mean Absolute Error (MAE) of 7.87 and a Root Mean Squared Error (RMSE) of 27.99, significantly outperforming the ANN model, which had a MAE of 121.40 and an RMSE of 215.04. These results demonstrate the superior accuracy and reliability of the Random Forest algorithm in capturing complex environmental data patterns. The novelty of this research lies in its comprehensive comparison of AI models for environmental monitoring, providing a data-driven approach to improving water quality management. This contribution is particularly relevant in the context of achieving Sustainable Development Goal (SDG) 6, which focuses on ensuring clean water and sanitation. By advancing traditional environmental planning methods with AI, this study highlights the potential of these technologies to make a substantial impact on environmental protection efforts.

Linking functional habitat and fish population dynamics modeling to improve river rehabilitation planning and assessment

Article

Full-text available

Sep 2024
J ENVIRON MANAGE

In-stream habitat enhancement is widely used to improve ecological conditions in rivers, often prioritizing key fish life stages such as spawning and juvenile development. However, no standard approaches exist to predict their effects on fish recruitment and populations. Here, we use a spatially-explicit population dynamics model that integrates functional habitat dynamics to assess the impact of two rehabilitation measures in a hydropower-impacted section of the Inn River (SE Germany) on the recruitment potential of four rheophilic and lithophilic fish species — grayling, nase, barbel, and chub. Rehabilitation measures implemented included the construction of a bypass channel and an island side-channel system to improve both longitudinal connectivity and habitat conditions. In addition, we analyzed two alternatives, which would enhance functional availability of nursery habitats from actual 33.2% to 66.8% and 95.3%, respectively. The results suggest that the improved habitat conditions will yield on average additional 14.9 individuals/ha (5.6 kg/ha) of the target species. However, the limited accessibility of usable nursery habitat constitutes a significant recruitment bottleneck for all species. In the alternative scenarios, the increase of functional connectivity will result in average densities of 17.9 and 25.8 individuals/ha, respectively. However, potential further improvements are species-specific, because of distinct population responses to spawning-to-nursery habitat ratios, with density changes varying between -11.7% for grayling and +172.6% for chub. This study not only demonstrates the applicability of the modeling approach for assessing and planning rehabilitation measures but also emphasizes the importance of considering habitat ratios and their functional connectivity to optimize recruitment potential.

Assessing water ecosystem services for water resource management

Article

Full-text available

Jul 2016
ENVIRON SCI POLICY

Ana Cristina Cardoso

Ecosystem service concepts can offer a valuable approach for linking human and nature, and arguments for the conservation and restoration of natural ecosystems. Despite an increasing interest in the topic, the application of these concepts for water resource management has been hampered by the lack of practical definitions and methodologies. In this study we review and analyse the current literature and propose an approach for assessing and valuing ecosystem services in the context of water management. In particular, to study the link between multiple pressures, ecological status and delivery of ecosystem services in aquatic ecosystems under different scenarios of measures or future changes. This is of interest for the development of River Basin Management Plans under the EU Water Framework Directive. We provide a list of proxies/indicators of natural capacity, actual flow and social benefit for the biophysical assessment of the ecosystem services. We advocate the use of indicators of sustainability, combining information on capacity and flow of services. We also suggest methods for economic valuation of aquatic ecosystem for each service and spatial scale of application. We argue that biophysical assessment and economic valuation should be conducted jointly to account for the different values of ecosystem services (ecologic, social and economic) and to strengthen the recognition of human dependency on nature. The proposed approach can be used for assessing the benefits of conservation and restoration of aquatic ecosystems in the implementation of the EU water policy.

Benthic macroinvertebrates in lake ecological assessment: A review of methods, intercalibration and practical recommendations

Article

Full-text available

Feb 2016
SCI TOTAL ENVIRON

Legislation in Europe has been adopted to determine and improve the ecological integrity of inland and coastal waters. Assessment is based on four biotic groups, including benthic macroinvertebrate communities. For lakes, benthic invertebrates have been recognized as one of themost difficult organismgroups to use in ecological assessment, and hitherto their use in ecological assessment has been limited. In this study, we review and intercalibrate 13 benthic invertebrate-based tools across Europe. These assessment tools address different human impacts: acidification (3 methods), eutrophication (3 methods), morphological alterations (2 methods), and a combination of the last two (5 methods). For intercalibration, the methods were grouped into fourintercalibration groups, according to the habitat sampled and putative pressure. Boundaries of the ‘good ecological status’ were compared and harmonized using direct or indirect comparison approaches. To enable indirect comparison of the methods, three common pressure indices and two common biological multimetric indices were developed for larger geographical areas. Additionally, we identified the best-performing methods based on their responsiveness to different human impacts. Based on these experiences, we provide practical recommendations for the development and harmonization of benthic invertebrate assessment methods in lakes and similar habitats.

Hydrological impact of soil sealing and urban land take

Chapter

Apr 2017

Alberto Pistocchi

Generalized Linear Models

Book

Jan 1983

Classification And Regression Trees

Book

Oct 2017

The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

Directive 2000/60/EC—establishing a framework for community action in the field of water policy

Article

Jan 2000

European Commission

Managing aquatic ecosystems and water resources under multiple stress - An introduction to the MARS project

Article

Jan 2014
SCI TOTAL ENVIRON

Water resources globally are affected by a complex mixture of stressors resulting from a range of drivers, including urban and agricultural land use, hydropower generation and climate change. Understanding how stressors interfere and impact upon ecological status and ecosystem services is essential for developing effective River Basin Management Plans and shaping future environmental policy. This paper details the nature of these problems for Europe's water resources and the need to find solutions at a range of spatial scales. In terms of the latter, we describe the aims and approaches of the EU-funded project MARS (Managing Aquatic ecosystems and water Resources under multiple Stress) and the conceptual and analytical framework that it is adopting to provide this knowledge, understanding and tools needed to address multiple stressors. MARS is operating at three scales: At the water body scale, the mechanistic understanding of stressor interactions and their impact upon water resources, ecological status and ecosystem services will be examined through multi-factorial experiments and the analysis of long time-series. At the river basin scale, modelling and empirical approaches will be adopted to characterise relationships between multiple stressors and ecological responses, functions, services and water resources. The effects of future land use and mitigation scenarios in 16 European river basins will be assessed. At the European scale, large-scale spatial analysis will be carried out to identify the relationships amongst stress intensity, ecological status and service provision, with a special focus on large transboundary rivers, lakes and fish. The project will support managers and policy makers in the practical implementation of the Water Framework Directive (WFD), of related legislation and of the Blueprint to Safeguard Europe's Water Resources by advising the 3rd River Basin Management Planning cycle, the revision of the WFD and by developing new tools for diagnosing and predicting multiple stressors.

Assessing water ecosystem services for water resource management

Article