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.