1. Introduction
There is great concern among hydrologists, watershed managers, and as policy makers about how different rainfall-runoff events influence hydrology as well as the loss of pollutants from tile-drained agricultural land [
1,
2]. Intensively managed tile-drained landscapes have been found to have a considerable potential for N and P losses, with regard to different soil conditions and cropping systems, especially during periods with elevated flows [
3,
4,
5,
6,
7,
8,
9]. Nutrient concentrations in drainage waters may change rapidly within a rainfall-runoff event due to variable pre-event and event soil/catchment moisture conditions, related soil biogeochemical processes [
10,
11,
12,
13], precipitation characteristics, and water flow paths into drainage and with different origin and residence times [
14,
15,
16,
17].
Tile-drained fields and catchments of various scales have been subject to many studies trying to quantify how different sampling strategies, monitoring schemes, and load calculation methods influence the assessment of runoff and nutrient fluxes from tile drains, from one season to longer periods. While there is in-depth research for individual fields or paired catchments [
8,
14,
18,
19,
20,
21], not many mid-term studies have been conducted to compare nutrient losses and load assessment methods for tile-drained fields or small catchments with different agricultural management and runoff characteristics, especially in central European conditions. In the Czech Republic, around 30% of the agricultural land is tile-drained, and in some regions land drainage exists on every field [
22,
23].
The basic classification of load estimation methods is usually in four categories: averaging methods, ratio estimators, period-weighted, and regression methods. For nitrate-N load estimation, two methods have been recommended by numerous authors: (1) linear interpolation, a period weighted method, and (2) flow-weighted mean concentration, an averaging method [
18,
24,
25]. Previous research has shown that the uncertainty due to infrequent sampling and nutrient load estimation approaches for drainage systems or small streams is often much greater than the uncertainty brought about by other steps in the sample collection process [
25,
26,
27]. In general, the uncertainties in annual or monthly nutrient load estimates are reported to be influenced both by the sampling interval and the load assessment methods and tend to increase with an increasing sampling interval for the majority of load estimation algorithms [
24,
25,
28]. Ratio methods are often reported as bei ng unsatisfactory compared with the two aforementioned simple methods. Regression based algorithms are also found to poorly characterize load variation (especially for nitrates) in drainage or small watercourses [
27,
29] despite the implementation of error correction techniques [
20,
30].
To our knowledge, only a few studies have been published to compare monitoring approaches and nutrient load assessment methods for tile-drained fields or small catchments [
6,
25,
27,
31]. These studies often conclude that the most dominant factors influencing nutrient fluxes are precipitation characteristics and catchment hydrological connectivity. This pertains, compared to nitrogen, to a greater extent to phosphorus, as high P concentrations in water from land drainage are associated predominantly with elevated flows, both from ploughland and grassland [
4,
5,
32,
33,
34]. Load assessment approaches without continuous discharge measurements and event sampling tend to underestimate P loads especially [
33,
35]. However, little is still known about how and to what extent various nutrient load assessment approaches, including those used in operational activities (e.g., of River Basin Authorities) may differ for small, tile-drained catchments, situated in sloping conditions. Further, there is an obvious research gap in the knowledge of the hydrological behaviour of small, tile-drained catchments with different soil and land-use characteristics, especially during various rainfall-runoff events captured by different monitoring strategies [
1,
36].
The necessity of the accurate assessment of nutrient loads from tile-drained fields or small catchments is invoked by watershed management and agricultural policy aims oriented towards diminishing nutrient and pesticides losses from drained land [
2,
36,
37]. Therefore, it is crucial to develop an approach to identify the processes responsible for the input of pollutants from land to water via tile drainage. To reveal the rapid discharge/concentration changes, describe the related processes, and estimate the true pollutant loads, a proper monitoring programme and appropriate methods for the evaluation of matter fluxes are necessary to support [
6,
18,
38] the design of field to catchment scale mitigation measures for tile drainage or on drained land, as well as when estimating their effects [
2,
9,
22,
38,
39,
40].
The aim of this study was to quantify the proportion of N and P losses in drainage waters from runoff events, assess the real N and P losses, and to compare six different methods for N and P load estimation at ten very small tile-drained catchments with different drainage and agricultural management characteristics in Czechia on a monthly and annual basis in three to five hydrological years between 2012 and 2016.
3. Results
3.1. Dynamics of N and P Concentrations
The concentration values of N-NO3, P-PO4, and Ptot in drainage waters differed profoundly across the sites, between hydrological years, and within regular sampling (RS) and event sampling (ES) schemes. In RSs, N-NO3 concentrations were 1.0 to 91.5 mg/L (median 19.4 mg/L), P-PO4 ranged between 0.001 and 0.424 mg/L (median 0.020 mg/L), and concentrations of Ptot were 0.001 to 0.747 mg/L (median 0.046 mg/L). In the ES, N-NO3 concentrations were 0.452 to 144.9 mg/L (median 14.9 mg/L), P-PO4 ranged between 0.001 to 2.174 mg/L (median 0.043 mg/L), and Ptot was between 0.001 to 3.231 mg/L (median 0.120 mg/L).
The concentrations of nutrients adjusted to hydrology (
Cfw) were usually lower for N-NO
3 in REs compared to RSs, whereas for both P-PO
4 and P
tot, the opposite was true. This means that during an RE, a dilution of N-NO
3 in drainage waters usually prevailed, whilst for both P-PO
4 and P
tot, the concentrations usually rose with elevated discharge (
Figure 4a–c). The only exceptions were sites PD1 and PD2, with
Cfw N-NO
3 being higher in REs than in RSs. No statistically significant correlation between drainage discharge and any of the substances was discovered during baseflow conditions. During an RE, positive correlations between discharge and both P forms were found, although with a broad range of slopes and shapes of the regression curves (data not shown).
3.2. Runoff Events and Their Proportion in the Total Runoff, N and P Loads
The average number of REs per hydrological year, across all the sites during the whole monitored period, was 13 (2 to 25), and these lasted on average for 30 (5.3 to 81.3) days within a hydrological year; see
Table 3. The portion of REs sampled in one season varied from 30% to 45% of the total number of REs (
Table 3). The within-period differences among the number and magnitudes of REs were induced most probably by different precipitation amounts and their variable time-distribution across the evaluated period (hydrological years). The lowest number of REs was monitored in the very dry year 2015 (an RE lasted on average 14 days, with an average 12% share on the total runoff), whereas in the wet year 2013, an RE lasted on average 48.5 days, with the mean share on the total runoff being 38.5%. The proportion of REs in the annual total runoff and in the N-NO
3, P-PO
4, and P
tot loads across all the monitored sites and periods was on average 24.5% (1.8–91.1%), 23.8% (1.7–85.5%), 41.6% (2.1–98.1%), and 40.3% (1.4–96.5%), respectively; see
Table 3 and
Figure 5 for details.
3.3. Comparison of Load Assessment Methods Annual Load Schemes
The annual true loads (M6) for N-NO3, P-PO4, and Ptot across all the monitored sites and periods were on average 19.01 kg·ha−1 y−1 (0.39–60.50), 32.09 g·ha−1·y−1 (0.64–131.85), and 99.28 g·ha−1·y−1 (3.83–337.65), respectively. The lowest N-NO3 loads were measured at the grassland sites (KL, V1) and at the ploughed P53 site, which exhibits an obvious episodic runoff pattern. The N-NO3 annual load from ploughed sites was on average around 30 kg·ha−1·y−1. The P-PO4 and Ptot loads were comparable at the ploughed and grazed catchment (V1). The lowest P-PO4 and Ptot loads were measured at sites KL and P53, the catchments with the smallest total runoff.
The nutrient load assessment algorithms differed according to the employed method of discharge monitoring and, to a lesser extent, the method of nutrient concentration monitoring (calculation), with the exception of monitoring during the REs (M6). Nutrient loads, calculated by M1 to M5, in relation to M6, along with the loads in REs, are depicted on
Figure 6a,b, and the basic statistics on M1 to M5 accuracy related to the true load (M6) on an annual basis for all the substances, sites, and monitored periods are shown in
Table 4. For annual N-NO
3 loads, M1 was the least accurate method, when accounting for the average 3.2% underestimation (−27.5 to 64.5%) of the M6 load. The M1 algorithm was also the least accurate for annual phosphorus loads; for P-PO
4, it underestimated M6 loads by 27.9% on average (−93.3% to 47.5%), and, for P
tot, it underestimated M6 loads by 28.2% on average (−78.2% to 4.2%). None of the variables (nutrient loads according to M1 to M6 across all the sites and monitored periods) was affected significantly by the methods (ANOVA;
p > 0.05).
3.4. Monthly Load Schemes
The monthly nutrient true loads (M6) for N-NO
3, P-PO
4, and P
tot across all the monitored sites and periods were on average 1.52 kg·ha
−1·month
−1 (0.01–16.41), 2.81 g·ha
−1·month
−1 (0.003–48.85), and 6.87 g·ha
−1·month
−1 (0.004–139.32), respectively. The accuracy and the
RMSE of M3 to M5 (M1 and M2 only on annual basis) as related to the true loads (
M6) are given in
Table 5 and
Table 6, and the accuracy for all the sites over the monitored period is displayed in
Figure 7a,b. In the monthly load schemes, the results showed that the algorithm that did not use a continuous record of discharge (M3) under- or overestimated nutrient loads the most. For N-NO
3, the average underestimation by M3 on a monthly basis was 10.5%, whereas for P-PO
4 and P
tot it was 176% and 59%, respectively. The extensive underestimation of monthly P-PO
4 loads was due to the fact that, at some sites (P53, V1, VP1), the P-PO
4 loss occurred solely during REs. As seen in
Table 5 and
Table 6, M4 and M5 gave comparable results; i.e., for N-NO
3 loads, the underestimation was on average only around 3%, for P-PO
4 loads it was on average around 100% and for P
tot it was 35% to 40%.
5. Conclusions
This study compared six methods for nitrogen and phosphorus load calculation in ten small tile-drained sloped catchments in both annual and monthly time steps based on data from three to five hydrological years. Further, for the monitored sites and periods, the share of runoff events in the total runoff of N-NO3, P-PO4, and Ptot was quantified. A novel semi-automated algorithm for the identification and selection of runoff events from continuous flow using drainage water temperature was proposed and applied for load calculation in the unsampled events. The share of runoff events in N loads was on average 5% to 30% of the total annual load, whereas for P (dissolved and total), the share of runoff events was on average 10% to 80% of the total annual load. The most precise methods for nutrient load estimation were those with the use of continuous measurement of the discharge (M4, M5). However, without sampling the runoff events, these methods showed considerable uncertainties, especially for phosphorus load estimation. The methods based on point monitoring discharge and water quality (M1 to M3), commonly employed in practice when balancing non-point pollution sources in small tile-drained agricultural catchments, underestimated the annual loads of N by 10% to 20% and of P by 30% to 80%. For the monthly loads, this study demonstrated that methods without continuous flow measurements and with infrequent sampling tended to underestimate N and especially P loads by percentage values in the tens to the hundreds. The variability in loads between the years and sites was caused most probably by the dissimilar runoff patterns between particular sites and between particular years, which is not captured by grab sampling and regular monitoring at fixed intervals.
Based on the acquired results, we recommend continuous flow and water temperature monitoring, as well as event-based sampling programmes, to be employed on tile drainage outlets, drainage mahnoles, or ditches. Although such very small catchments as observed in the present study could hardly be the subject of routine (nation-wide or large catchment) monitoring programmes, we emphasize the need to pay increased attention to the hydrochemical role of tile drainage since these systems strongly impact water quality and quantity in agriculturally exploited headwater catchments. This work also provided regionally representative N and P loads, which we consider to be of great value when balancing the non-point pollution sources of small water courses and designing appropriate mitigation measures for tile-drained land. Future work could focus on the linkages between catchment characteristics, nutrient load dynamics, and load calculation uncertainties provided by different methods in different seasons. Further, a more detailed hydrological description of the sites such as runoff flashiness, baseflow index, antecedent moisture conditions, or water catchment residence time will be employed in a follow-up study in order to better explain the magnitude of uncertainty and the dynamics of various algorithms across different catchments [
27,
34,
46,
47].