3.1. Phosphorus Concentrations
summarizes the measured discharge rates, as well as the measured TP-P concentrations during the fixed-schedule sampling for the study period. The highest maximum and mean discharge values were measured in winter and spring following the seasonal periods with wet soils, low transpiration, and snow melt. The TP-P concentrations ranged from 2.0 to 65.7 μg·L−1
across all seasons, with the highest maximum and mean values observed in the winter. The mean concentrations during winter, summer, and fall are within the same ranges (10.6, 7.8, and 9.3 μg·L−1
), while the mean concentration during spring is significantly lower (4.6 μg·L−1
). From Table 2
it also can be seen that the maximum sampled discharge is smaller than the maximum total discharge throughout the season. This indicates that the fixed-schedule sampling scheme did not capture many of the high-flow conditions.
summarizes the measured discharge values and TP-P-concentrations during the storm event-based monitoring. Event 1 (September 2010) had the highest discharge (31.6 L·s−1
), with TP-P concentrations ranging from 2.7 to 60.9 μg·L−1
during the event. The highest concentrations during a storm event was found during Event 2, with TP-P concentration ranging from 21 to 202 μg·L−1
and with discharge as high as 8.5 L·s−1
Event 3 had the lowest measured discharge of the sampled storm events, with TP-P concentrations ranging from values below the detection limit up to 78 μg·L−1, with a mean concentration of 11.4 μg·L−1 for the whole event.
The results show that the dynamics of TP-P concentrations are highly variable, both over the course of seasons, during low flow periods, and during storm events (i.e., high-flow periods). As shown in Figure 2
, the TP-P concentrations varied over several orders of magnitude, ranging from values below the detection limit to very high concentrations after heavy rainfall events. The highest concentrations were measured during events in summer and fall after long dry periods with little or no precipitation.
The increased concentration of TP-P during the summer and fall seasons indicates that during dry periods P accumulates in the soil, which then is flushed by precipitation water during rain events [6
]. Recent studies have demonstrated that dry and rewetting cycles, which occur more often during summer and fall seasons, increase the export of P from the organic layers and upper mineral horizons [26
]. In forest soils, mobilized P is typically transported via preferential flow pathways, such as root channels, stone surfaces, and at the interface between organic and mineral horizons laterally down slope to streams [8
]. Lateral subsurface flow is frequently the dominant discharge generation process in small forested mountain catchments [28
]. In the Eastern Ore Mountains, lateral subsurface flow is driven by the characteristics of periglacial cover beds over bedrock, which are prevalent in this area [29
]. During dry periods with low soil moisture, the formation of near surface lateral flow is more frequent [29
], which explains the rapid catchment response to storm events and the higher TP-P concentrations.
As can be seen from the results of the different sampling schemes (e.g., fixed-schedule and event-based, compared mean and max concentrations in Table 2
and Table 3
), the highly dynamic changes in P concentrations were not fully captured by the fixed-schedule sampling at either the weekly or bi-weekly time scale. During the dry summer and fall seasons, where P exports from soils are driven primarily by the change of accumulation and flushing (as discussed above), P concentrations in streams in forested catchments can change drastically within a day, or even at sub-daily time scales. A weekly or bi-weekly sampling scheme is therefore not able to entirely capture those changes, and does not provide an adequate enough representation of P dynamics necessary for process understanding [13
]. This information gap will therefore increase process uncertainty [11
], which will propagate into the estimation of P export fluxes [10
3.2. Phosphorus Fluxes
provides a summary of the evaluation criteria for each of the applied loadflex models. All criteria were calculated during the independent validation time period (see Figure 1
). A wide range of values for the coefficient of determination (R²) were observed from both the fixed-schedule and extended sample data sets. Values for the prediction models ranged from 0.41 to 0.67 for the fixed-schedule sampling data set, and from 0.37 to 0.97 for the extended sampling data set. It is clear that for both data sets, only a limited number of models established a good relationship between the predicted and the observed TP-P loads. The MAE measures the difference between predicted and observed TP-P loads. MAE values close to zero indicate a good agreement between the prediction and observations. All prediction models based on the two data sets had small values for MAE, with values ranging from 0.05 to 0.27 g·ha−1
over the 2-year validation period. The inclusion of data from the event sampling (extended data set) led to an improvement in the relationship between observed and predicted TP-P loads, and only to small increases in the MAE. Therefore, it can be concluded that the prediction quality was increased with the inclusion of the event-based data. For analysis of the TP-P flux export rates, the models Trian. I., Reg. Mod. 4, and Reg. Mod. 7 will be referred to as LF-R (based on the fixed-schedule sampling data set) and the models Lin. I., Trian. I., and Dist. Weigh. I. will be referred to as LF-E (based on the extended data set). These models were chosen because they showed the best fit to the independent validation data set. The other models were not considered in the analysis of the export fluxes.
provides the predicted annual TP-P fluxes for the different groups of load estimation techniques (LF-R, LF-E, Webb-R, and Webb-E). All methods show a similar pattern, with higher TP-P exports in 2010 and lower in 2011 and 2012. Three single storm events were monitored in 2010 and 2011, during which Webb-E estimated the highest TP-P export, followed by LF-E, Webb-R, and LF-R. No storm events were recorded in 2012, during which LF-E predicted slightly higher export rates than the other three groups. The amount of predicted TP-P exports followed the observed annual rainfall, with 2010 having the highest rainfall and 2011 the lowest.
The TP-P fluxes follow the variation of discharge and concentration over the course of the seasons, as can be seen from Figure 3
(flux rates), Figure 2
(concentration and discharge), and Table 2
. In winter 2009/10, higher concentrations of TP-P were measured, but lower discharge was recorded (see Table 2
), which results in fluxes between 7 to 10 g·ha−1
across all methods. In winter 2010/11, higher discharge values and lower TP-P concentrations resulted in slightly higher export rates when compared to the preceding winter season, with values ranging between 8 and 15 g·ha−1
. The highest TP-P concentrations were measured in the winter of 2011/12, with average discharge amounts ranging from 14 to 25 g·ha−1
. For spring 2010 and 2012, flux rates ranged from 9 to 13 g·ha−1
across all methods. Both seasons were comparable in terms of discharge rates, with maximum flow rates of 39.1 and 32.2 L·s−1
, and average discharge of 5.6 and 5.7 L·s−1
for spring 2010 and spring 2012, respectively. The only significant difference was the higher rainfall of 284 mm in spring 2010, compared to 181 in spring 2012. It can be assumed that much of the discharge in spring 2012 was induced by snowmelt, since the preceding winter seasons had average discharges with high precipitation of 531 mm, of which a significant part was in the form of snow. The TP-P fluxes in spring 2011 were almost 50% lower compared to the other spring season, with rates ranging from 4.5 to 7.5 g·ha−1
. The maximum and average discharge was also lower in spring 2011 compared to the other spring seasons, with values of 17.2 and 3.5 L·s−1
. For summer 2010, all methods predicted TP-P fluxes around 10 g·ha−1
, with relatively high mean and maximum TP-P concentrations, and medium average discharge and higher maximum discharge. For the summer 2011, the estimated TP-P flux rates across the applied methods differed significantly, with loads ranging from 4 to 40 g·ha−1
. For this season, a single storm event-based sample was available (see Table 3
, Event 2), where high TP-P concentrations were observed.
The calculation groups based on the fixed-schedule sampling (LF-R and Webb-R) gave equal flux rates of around 4 g·ha−1
, whereas the methods based on the extended data differed significantly, with a value of 24 g·ha−1
for LF-E and 40 g·ha−1
for Webb-E. For summer 2012, all methods predicted low flux rates of 2 to 3 g·ha−1
, due to low discharge and low TP-P concentrations during a long dry period. As in summer 2012, for fall 2010, information of a single storm event was available, which resulted in a wide range of estimated TP-P fluxes across the applied methods (ranging from 12 to 53 g·ha−1
). For this season as well, the flux calculation based on the fixed-schedule sampling provided comparable rates, with 12 and 14 g·ha−1
for LF-R and Webb-R, respectively. The calculations made using the storm event data resulted in much higher values, with 31 g·ha−1
for LF-E and 53 g·ha−1
for Webb-E. Despite the inclusion of a single storm event for fall 2011, the calculated exports across all applied methods were relatively low, with flux rates ranging from 1.7 to 4 g·ha−1
. For this season, the flux estimation for LF-R and Webb-R were in the same range (approx. 1.7 g·ha−1
) and the estimates for LF-E and Webb-E were slightly higher (3.3 to 4.1 g·ha−1
). The monitored event was relatively short, with low discharge and medium TP-P concentrations (Table 2
). For fall 2012, all methods estimated low export rates of around 1.3 g·ha−1
, due to a dry period with low discharge and medium TP-P concentrations.
The three monitored single storm events differed in magnitude of discharge and measured TP-P concentrations, as well as in duration of the storm and preceding conditions (e.g., length of dry periods; see also Table 3
and Figure 2
). The different conditions resulted in different export rates of P during those events (Figure 4
). The calculated export rates vary also across the applied method and the underlying data. For Event 1 in fall 2010, export rates varied from 2.1 to 12.3 g·ha−1
between the different methods. The lowest flux estimation was given by LF-R, whereas LF-E and Webb-E predicted much higher loads (12.3 and 9.8 g·ha−1
, respectively). According to the LF-E method, this single event produced 40% of the total TP-P exports in fall 2010. For LF-R and Webb-E, the shares were 17% and 18%, respectively. For the total flux in year 2010, the portions were 19%, 5%, and 12% for LF-E, LF-R, and Webb-E. During the second monitored event in summer 2011, 0.2 to 2.4 g·ha−1
TP-P were exported. As for Event 1, the LF-R method predicted much lower flux rates than LF-E and Webb-E (2.4 and 2.0 g·ha−1
, respectively). This event was characterized by high P concentrations but low discharge and a short duration. This event occurred after a long dry period with low precipitation and discharge in the preceding weeks, so P could have accumulated in the organic and mineral layer of the soil, which then flushed out during the event. The share for the total TP-P flux in summer 2011 was 17%, 4%, and 5% for LF-E, LF-R, and Webb-E. The event contributed to 7% of the total TP-P flux in year 2011 for LF-E, 1% for LF-R, and 4% for Webb-E. The third monitored event occurred in fall 2011 after a long dry period with low-flow conditions. For this event, LF-E calculated export rates of 0.8 g·ha−1
, whereas LF-R and Webb-E calculated 0.1 and 0.2 g·ha−1
. This event contributed to 24% of the annual total TP-P flux for LF-E, 6% for LF-R, and 5% for Webb-E. The shares for the total flux in year 2011 were 2%, 0.4%, and 0.4% for LF-E, LF-R, and Webb-E, respectively.
The predicted annual export fluxes of TP-P ranged from 18.5 to 83.2 g·ha−1
depending on the year and selected load calculation method. These values are in the same range as previous studies for watersheds in other regions of Europe [4
], but lower than findings for catchments dominated by monsoonal influence [10
]. Further, the seasonality of the export is driven by high discharge due either to snow melt in the winter/spring or due to rainfall events following a dry period where P could accumulate in the soil. It was observed that the event-based export of P can contribute up to 40% to the seasonal and 19% to the annual export fluxes, which highlights the importance of monitoring stormflow events for flux estimation and process understanding. These results support the findings of numerous studies [9
] which have found that time discrete sampling schemes at the monthly or bi-weekly time scale are unable to capture short-term changes in solute concentrations, and therefore can lead to a bias in load calculations. Ide et al. [18
] suggested that a certain number of monitored stormflow events are necessary to quantify export fluxes with low bias. The results of our study also suggest that the differences in the flux estimates between the selected methods are smaller than the differences due to the sampling scheme (i.e., see the error bars in Figure 3
and Figure 4
). Finally, we can assume that for the quantification of P export fluxes from forests, an adequate monitoring scheme of stream exports is necessary to be able to capture short-term changes in P concentrations.