where QH was estimated as PE multiplied by the mean water usage per
capita in Germany (126L/day) (42). The sum of QSW and QSIW were obtained
from the German statistical yearbook (42) and proportional to PE
distributed to the WWTP. The QR was obtained by the mesoscale
Hydrological Model (mHM) developed by (43). The Q10 low flow was used
for UDF calculation (10% of the time the measured discharge is lower
than Q10), because this discharge is an established design criterion for
the treatment requirements of wastewater discharges in Germany (44).
This criterion was also recently used for US wide estimates on effects
of water pollution by micropollutants (45). The stream order ω for each
WWTP effluent location was derived from the EU Hydro river network (46).
Finally, WWTPs were assigned to the RWBs of the EU-WFD (47)).
Agricultural land use fraction (ALF) as a proxy for non-point sources
ALF is defined as percentage of agricultural land use of an area that
drains to a river segment. It can be interpreted as proxy of an
integrated impact to a certain river segment originating from diffuse
sources.
The specific area that drains to a stream segment is available within
the EU Hydro data set (46, 48). Details about deriving the drainage
areas per segment are given in (48) and SI. The land use within the
drainage area of a segment was obtained from the CORINE Land Cover data
set (CLC) (49), which distinguishes 5 classes of land use on the highest
level: 1) artificial surfaces, 2) agricultural surfaces, 3) forests and
semi-natural areas, 4) wetlands and 5) water bodies. For more details on
CLC, see SI and (49). A description of the algorithm used to derive ALF
is given in SI.
Assessment of ecological health of rivers
Ecological status is used within the WFD as a measure of the ecological
health of rivers (13) (12). Ecological status is an assessment of the
quality of the structure and functioning of surface water ecosystems and
is available for almost all SWBs in Europe (47). Ecological status is
categorical with possible values high , good ,moderate , poor , and bad .
Ecoregions in Germany
The three ecoregions were selected following the German LAWA (German
states water association) organization: a) Alps and Alpine foothills,
altitude > 800 m, b) Central highlands, altitude ca. 200 -
800 m and higher, c) Central plains, altitude < 200 m (50,
51). The ecoregions are as mandatory information included in the WFD
data set (12).
Data sets used in the study
An overview of all used data and the list of sources is given in SI
Table 2. All used data are available on public websites except the WWTP
data for PE < 2000 (referred to as small WWTP) and mHM
discharge data. These small WWTP data underlie some confidentiality
constraints from the local authorities, therefore these data cannot be
made publicly available, but the authors are willing to work with those
needing access to these data. The mHM result data can be requested from
the authors.
Statistical methods used
Data were separated in two subsets based on smaller streams with ω ≤ 3
and larger streams with ω > 3. The plots and statistics are
related to these groups. Boxplots were used for characterization of
distribution of ecological status in relation to UDF and ALF for both
subsets (Fig. 2). We used the non-parametric Kruskal-Wallis test to
assess if there are statistically significant differences between groups
according to their ecological status (high , good ,moderate , poor , bad ). The test compares the medians
of the five groups within both subsets to determine if the samples come
from the same population. A multiple comparison procedure was used in a
way that all pairs of groups are tested against each other with the
Bonferroni method (52). The medians of the five groups within each
subset were tested for a positive trend using the nonparametric
Mann–Kendall and Sen’s methods (53). The significance level alpha was
set to 0.05 for all statistical tests.
Acknowledgments
We thank the authorities of 13 federal states in Germany for providing
us data for WWTPs with PE < 2000 to research projects with UFZ
under a confidentiality agreement from the German states’ authorities.
Due to confidentiality constraints, these data cannot be made publicly
available, but the authors are willing to work with those needing access
to these data for scientific purposes.
This research was a part of the series of International Summer Workshops
on “Complex Networks: Structure and Functions,” held during 2015–2018
in Seoul, South Korea; Dresden, Germany; West Lafayette, IN;
Gainesville, FL; and Ft. Collins, CO. The authors extend their
appreciation to all the colleagues who participated in this
interdisciplinary, collaborative research effort. We are grateful to the
organizations and logistical support by the institutions that hosted
these Summer Workshops.
Competing interests: The authors declare that they have no
competing interests.