Figure 2 . a – Spatial variation of the TT modes in the 166 catchments with an R² ≥ 0.6. b – Spatial variation of the overlapping retention in all of the analyzed catchments (n = 238). c – Histogram of the mode TTs. d – Histogram of the retention for the overlapping time (beige curve) and the convolved retention (grey curve) with their corresponding medians (dashed lines). e – Scatter plot of the overlapping retention versus the mode TTs, with the corresponding medians for both measures (dashed lines). Excluding one outlier with negative retention.
3.3. Controls of catchment’s response and retention
The PLSR for predicting the mode TTs in the selected catchments with a good fit (R2 ≥ 0.6) explained 49% of the total variance. Variables that are connected to the catchment’s hydroclimatological characteristics were found to be most important (Supporting Information Figure S4.1.). Potential evapotranspiration (PET) was analyzed as the most important variable (VIP 2.1) indicating longer mode TTs with higher PET. The seasonality index of precipitation (P_SI, see Supporting Information S1 for detailed description) was with an almost same VIP value (2.10 vs. 2.07) the second most influential predictor (VIP = 2.1). The higher the mean difference between monthly P averages and the annual average, the shorter the mode TT. The other three most important parameters indicate shorter TT related with 1) higher coefficients of variation of discharge (VIP = 1.9), 2) higher topographic wetness indices (TWI; VIP = 1.5) and 3) higher median winter discharges (VIP = 1.3).
The N retention across the catchments was well predicted by the PLSR (R2 = 0.72). Four of the five most important parameters (Supporting Information Figure S4.2.) referred to subsurface characteristics, while one predictor was a hydrological descriptor. High specific discharge was connected to low N retention and was the most important predictor (VIP = 2.3). Second most important factor for predicting retention was the depth to bedrock (VIP = 1.8). The positive coefficient indicated that a higher depth to bedrock is associated with a higher retention. Consolidated (VIP = 1.5) and porous aquifer materials (VIP = 1.4) were associated with low retention while vice versa unconsolidated aquifers (VIP = 1.4) favored higher retention.
4. Discussion
4.1. Nitrogen transport times and its controlling parameters
The high number of catchments showing a good fit between N input and riverine N export using a log-normal TTD indicate that the applied methodology is appropriate for the analyzed Western European catchments. This also shows that the temporal pattern of annual flow-weighted NO3-N concentrations observed in the streams is mainly controlled by the pattern of the diffuse N input.
The PLSR that explained 49% of the variability of mode TTs between the catchments, reveals the importance of hydroclimatic variables (via PET, precipitation and discharge variability, winter discharge) and morphology (via TWI), which is partly in line with previous knowledge that stated recharge rate (besides aquifer porosity and thickness) as a major control for mean groundwater travel times (Haitjema, 1995). We note the close connection between hydroclimatic descriptors (e.g. between long-term mean precipitation, PET, discharge; Supporting Information Figure S5.; as established through the Budyko (1974) framework), but only discuss here the ones ranked as most important for TTs according to the PLSR.
Especially regions with highest intra-annual precipitation seasonality (Figure 1c) like in the Armorican Massif and the Alpine foothills showed short TTs with modes shorter than 5 years. Precipitation seasonality, entailing changing wetness conditions, can cause changing aquifer connectivity (Blume & Van Meerveld, 2015; Roa-Garcia & Weiler, 2010), which is known as a major control of NO3 export from catchments (Molenat et al., 2008; Ocampo et al., 2006; Wriedt et al., 2007). In terms of hydrological connectivity, Birkel et al. (2015) and Yang et al. (2018) stated that the activation of shallow flow paths during runoff events favors young water ages. Hence, we hypothesize that these high-flow events efficiently export young NO3 from the shallow subsurface to the stream and thus lowers N TT scales. High median winter discharge as another VIP, common in the Alpine foothills favoring short TTs, is in line with our hypothesis and the previous findings by Wriedt et al. (2007). The correlation between high TWI values and short TTs for N may be also attributed to a prevalence of N exports by shallow subsurface flow paths: lowland catchments, characterized by higher TWI’s, show strong seasonal changes of discharging streams and the artificial drainage network (Van der Velde et al., 2009). As these drains favor rapid, shallow subsurface flows, their temporal connection during high-flow events favor short travel times (Van der Velde et al., 2009). Long N TTs were found in the western Massif Central and south of it where PET was highest among the study catchments and recharge likely low, corroborating Haitjema’s (1995) finding for groundwater travel times.
The clear link between TTs for N and hydroclimatic settings make catchment N transport vulnerable to the changing future climate. Based on past observations since the 1960s, the intensity of extreme weather has been predicted to increase in most parts of Europe (EC, 2009). Hydroclimatic projection studies in general suggest drier conditions in Atlantic climatic zones in Europe in terms of longer drought durations and lower low flows under warming climates (Marx et al., 2018; Samaniego et al., 2018). Both extremes, heavy precipitation events and longer droughts, are more likely. According to the discussed influence of precipitation and discharge variability on N dynamics, TTs are supposed to decrease in the future. The stronger ET with increasing temperature (Donnelly et al., 2017) is counteracting this trend by favoring longer TTs. Since the climate is expected to manifest differently within Europe, reliable predictions on future N TTs on regional scales will need further research.
Despite a high number of catchments with a good fit using our TT estimations, we acknowledge the inherent uncertainties and limitations of the database as well as of the method itself. With better knowledge on the temporal evolution of waste water inputs and anthropogenic modifications in the catchment hydrology, like damming, more reliable TT estimations and a potentially better explainability among the catchments may have been possible. Furthermore the method, assuming a constant log-normal TTD, is only supposed to mirror the dominant long-term TT behavior, disregarding known temporal variability of water travel times in catchments (Benettin et al., 2013; Botter et al., 2011; Harman, 2015; Van der Velde et al., 2010). Moreover, we estimated TTs from the small fraction of total N inputs that left the catchment as NO3-N (median 28%). Long-term tracer studies using labeled 15N compounds (e.g. Sebilo et al., 2013) hold promising avenues for a more detailed and hedged evaluation of the fate of N.
4.2. Nitrogen retention and controlling parameters
According to the PLSR, the variability in retention among the catchments was mainly explained by subsurface properties that can be connected to biogeochemical conditions and the specific discharge. This finding was in line with Merz et al. (2009) and Nolan et al. (2002), who stated that spatial differences in NO3 retention or contamination, respectively, result from a combination of the geochemical environment and the hydraulic conditions. We argue that the highly-ranked subsurface predictors describe favorable biogeochemical conditions for either permanent removal by denitrification or storage in the soils as biogeochemical legacy.
Areas with a high depth to bedrock and an unconsolidated aquifer (Figure 1d), which showed retention above 75%, were particularly common in the Northern German Lowlands and in the Alpine foothills. This is in line with Ebeling et al. (2020), who attributed areas with large depth to bedrock and unconsolidated (sedimentary) aquifers to natural attenuation or retention processes based on riverine NO3-N concentration-discharge relationships. Unconsolidated deposits in the terrestrial subsurface, like in the Northern German Lowlands, are often associated with iron sulphide minerals (pyrite; Bouwman et al., 2013). The pyrite oxidation acts as electron donor for denitrification under anaerobic conditions (Zhang et al., 2009). For the unconsolidated aquifers in northern Germany, a recent study (Knoll et al., 2020) connected the high denitrification potential to strongly anaerobic redox conditions in the groundwater. Although denitrification permanently removes N from the catchment, it can be a source for N2O, an important greenhouse gas, being 300-fold more effective in trapping heat than carbon dioxide (Griffis et al., 2017). Lastly, long-term consumption of reactants via denitrification can alter the reduction capacity of the aquifer (Merz et al., 2009), decreasing the catchment’s N retention over time.
In contrast to northern Germany, for the unconsolidated sediments in the Alpine foothills different studies (BMU, 2003; Knoll et al., 2020) proposed aerobic subsurface conditions, hindering denitrification. Also Ebeling et al. (2020) found in this area evidence for a lack of denitrification. Excluding denitrification and long TTs (see Section 4.1.), we hypothesize biogeochemical legacy as a likely process of the high retention in the Alpine foothills. In comparison to northern Germany, soils here contain higher degrees of silt and clay. These grain sizes are prone to microaggregate formation and anion sorption, both sequestering organic N in the mineral subsoil for long periods of time (Bingham & Cotrufo, 2016; Von Lützow et al., 2006). Also mineral N fixed on clays can make a significant contribution to the soil N stock (Allred et al., 2007; Stevenson, 1986).
In contrast, areas with a high share of consolidated subsurface materials and a small depth to bedrock, like the Armorican Massif, parts of the Massif Central or the Harz Mountains showed N retention below 75%. In general, denitrification and biogeochemical legacies can only evolve if favorable biogeochemical conditions in soils and groundwater are abundant in the catchment. An important part for denitrification is the contact area and contact time with organic-rich soils (Bouwman et al., 2013). Due to abundant crystalline rocks, water moves along fissures in the weathered zone (Wyns et al., 2004), while it is dependent on joints and fractures in deeper depth (Wendland et al., 2007). Hence, there is only a limited reactive surface for NO3 within the areas dominated by consolidated materials (Wendland et al., 2007). Furthermore, Knoll et al. (2020) showed oxic conditions in consolidated units for Germany that do not allow for denitrification in groundwater.
The only hydrological predictor for N retention was the specific discharge. High specific discharges were found in the Armorican Massif, the western part of the Massif Central, in the Harz Mountains and the southern Alpine foothills, were often spatially connected to areas with consolidated subsurface materials and had N retention below 75%. High discharge areas connect to short residence times in the catchment compartments like root zone, aquifer or riparian zone and therefore decreases denitrification efficiency through a reduced contact time (e.g. Howarth et al., 2006; Kunkel & Wendland, 2006; Wendland et al., 2007). This assumption is in line with a recent study by Dupas et al. (2020), arguing that higher runoff lowers denitrification. Tesoriero et al. (2017) and Knoll et al. (2020) stated high recharge rates as important predictors for aerobic conditions. Furthermore, high discharge may be driven by a high degree of shallow flow paths (Birkel et al., 2015; Yang et al., 2018), favoring a fast wash-out of N or an export before immobilization, thus decreasing retention as well.
With regard to climate change, the increase in European rainfall erosivity is estimated in the range from 10 to 15% until 2050 (Panagos et al., 2015). Especially in southern France and Germany, this may cause soil loss in arable lands up to 10 t ha-1yr-1 (Panagos et al., 2015). We argue that such mobilization of soils with high biogeochemical legacy (e.g. Alpine foothills) can contribute to further deterioration of downstream river water quality.
4.3. Joint analysis of nitrogen transport times and retention
The joint analyses of N TT estimations and N retention (Figure 2e) revealed a discrepancy between the two in the studied catchments. The rather observed short TTs indicate that the largest part (75th-percentile) of N input should have been exported after at least 20 years. In contrast, the observed retention indicates that 72% of total N input was not exported. The retention was similarly high (70%) when convolving N input taking into consideration estimated TTs. The missing relation between TTs and retention as well as the different predictors for both through the PLSR, indicate that hydrologic legacies of N alone could not explain the failure of measures to improve water quality in Western European catchments (e.g. Bouraoui & Grizzetti, 2011), despite decreasing N-inputs. We rather assume a dominance of non-hydrologic retention, namely biogeochemical legacy and denitrification.
After the implementation of regulations such as the EU Nitrate Directive (CEC, 1991), the diffuse N input decreased between the 1980s and 2010s by more than 20 kg N ha-1 yr-1(36%) in the studied Western European catchments. The responses of riverine N loads to this decrease in input was limited (< 1.5 kg N ha-1 yr-1). Hence, the retention decreased but catchments still received (in the 2010s) excess N of almost 30 kg N ha-1 every year, which is two-thirds of the diffuse input.
Besides failure to implement good agricultural practices, these results imply either a hindered substantial exploitation of the (already massive) biogeochemical legacy by mineralization and/or an ongoing exhaustion of the catchment’s denitrification potential.
According to the discussed subsurface and hydrological catchment characteristics favoring biogeochemical legacy, and due to the specific conditions required for effective denitrification that are only fulfilled in a few areas, we argue that biogeochemical legacy is the dominant retention process in most of the study catchments. We explain the missing catchment response for decreasing N inputs with the buffer effect stemming from the accumulated biogeochemical legacy acting as a secondary source and constituting a system inert to decreasing N inputs. A biogeochemical dominance was also found in a recent study for catchments in northwestern France (Dupas et al., 2020). They concluded two-third of the retention being stored in the subsoil with the potential to recycle this N in the agroecosystem. Also Ascott et al. (2017) concluded that the vadose zone is globally a significant NO3 store. If not being recycled and in light of limited denitrification potential, the stored N would further leach to the deeper subsurface (or groundwater), when being mineralized again (Van Meter & Basu, 2015). The missing export of three-quarters of the past N inputs in the study catchments therefore constitutes a huge challenge for efforts to reach effective water quality improvements now and in the future.
5. Conclusions and implications
In this study we used long-term time series of N input and riverine NO3-N output from 238 Western European catchments to estimate the N TTs, retention amount as well as the controlling catchment characteristics for both.
The analysis of catchment responses revealed peak TTs around 5 years with 70% of the catchments showing a peak export within the first 10 years after N enters the system. Hence, when assessing the effectiveness of measures, catchment managers have to be aware of the hydrological transport dependent decrease in N concentrations after around 5 years that should not falsely be attributed to successfully taken measures. Conversely, assessing the effect of regulations on the N input before the arrival of needed peak TTs, is not recommended.
Data
Please note that the used data base adheres to Enabling FAIR Data Project requirements and is referenced in the manuscript linking to the data bases and repositories.
Water quality data for France is publicly available athttp://naiades.eaufrance.fr/ . Water quantity data for France are available at http://hydro.eaufrance.fr/ . Diffuse N input data for France were derived from Poisvert et al. (2017).
Water quality and quantity data for Germany are available athttps://www.hydroshare.org/resource/a42addcbd59a466a9aa56472dfef8721/(Musolff, 2020).
Catchment characteristics for Germany and France are available at https://www.hydroshare.org/resource/c7d4df3ba74647f0aa83ae92be2e294b/ (Ebeling & Dupas, 2020).
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