Hydrological pathways and nitrogen runoff in agricultural dominated catchments in Nordic and Baltic countries

Agriculture, Ecosystems and Environment 195 (2014) 211–219

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Hydrological pathways and nitrogen runoff in agricultural dominated catchments in Nordic and Baltic countries Johannes Deelstra a, *, Arvo Iital b , Arvydas Povilaitis c, Katarina Kyllmar d , Inga Greipsland a , Gitte Blicher-Mathiesen e , Viesturs Jansons f , Jari Koskiaho g, Ainis Lagzdins f a

Bioforsk - Norwegian Institute for Agricultural and Environmental Research, Frederik A. Dahls vei 20, N-1430 Ås, Norway Tallinn University of Technology, Ehitajate tee 5, EE-19086 Tallinn, Estonia Water Resources Engineering Institute, Aleksandras Stulginskis University, Universiteto 10, LT-53361 Kaunas, Lithuania d Swedish University of Agricultural Sciences, Department of Soil and Environment, Box 7014, SE-750 07 Uppsala, Sweden e Aarhus University, Institute for Bioscience, Vejlsøvej 25, DK-8600 Silkeborg, Denmark f Latvia University of Agriculture, Department of Environmental Engineering and Water Management, 19 Akademijas Street, LV-3001 Jelgava, Latvia g Finnish Environment Institute, Mechelininkatu 34a, FI-00251 Helsinki, Finland b c

A R T I C L E I N F O

A B S T R A C T

Article history: Received 1 February 2014 Received in revised form 28 May 2014 Accepted 3 June 2014 Available online xxx

Nitrogen (N) transport and retention in streams are largely determined by hydrological characteristics (e.g. water runoff, baseflow index (BFI) and flashiness index (FI)) in the catchment. It is important to know the impact of catchment characteristics such as land use, subsurface drainage intensity, elevation difference and catchment size on the hydrological properties and N loss. This paper presents a comparison of the magnitude and variation of the baseflow and flashiness in streams in relation to the selected geographical and drainage characteristics for thirty studied agriculture dominated catchments in the Nordic and Baltic countries and the effects it can have on N loss. The analysis included measured data from the total discharge and nitrogen loss at the catchment outlets for the period from the beginning of 1993 to 2011, although there is variation in the length of periods among catchments and countries. The study revealed that the rate of subsurface drainage systems and drainage intensity (given as lateral tile drainage spacing) were statistically significant explanatory variables in explaining differences in hydrological characteristics between catchments. There is a considerable increase in the FI, almost by a factor of three, when using hourly discharge values instead of average daily values, indicating that large diurnal variation in discharge can occur, especially at higher FI values. The analysis also showed that there is a negative relation between FI and the BFI, i.e. a high BFI corresponding to a low FI and vice versa. In general, there seems to be a positive relationship between long-term average runoff and N loss, with the highest runoff and N loss occurring in the Norwegian catchments. However, flow path can have a significant influence on the N loss. ã 2014 Published by Elsevier B.V.

Keywords: Flashiness index Baseflow index Runoff Subsurface drainage Catchment Nitrogen loss

1. Introduction Agriculture contributes nutrients to the environment, and is to a large degree responsible for the eutrophication of inland waters and coastal zones. In the Baltic Sea catchment area, the major anthropogenic source of waterborne nitrogen is diffuse inputs which constitute around 70% of the total load into surface waters within the catchment area. Agriculture alone contributes approximately 80% of the total reported diffuse load (Stålnacke, 1996;

* Corresponding author. Tel.: +47 92699501. E-mail address: [email protected] (J. Deelstra). http://dx.doi.org/10.1016/j.agee.2014.06.007 0167-8809/ ã 2014 Published by Elsevier B.V.

HELCOM, 2009). Several authors (e.g. Kauppi, 1979; Rekolainen,
kowski, 1999; De Wit, 2000; Mander et al., 1989; Zabłocki and Pien 2000; Vagstad et al., 2004; Iital et al., 2005) have described the relative importance of different factors, e.g. land use, fertilization rate, livestock density, topography and soil type, influencing the loss of nitrogen. Nutrient losses, especially nitrogen, are well correlated with variations in discharge (Stålnacke and Grimvall, 2000). However, when comparing the results of different water quality monitoring programmes in catchments with a relative high agricultural share, large differences in nutrient losses can be observed under otherwise almost similar climatological conditions and agricultural practices (Vagstad et al., 2004). Also, catchment scale can play a role in the nutrient loss processes. Deelstra et al.

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(2005) and Lagzdins et al. (2012) found a decrease in nitrogen concentration in Latvian catchments with an increase in catchment scale. In addition to a decrease in area-specific fertiliser application rates, it was concluded that flow processes also had an important impact on water chemistry. Similar findings were made by Tiemeyer et al. (2006) when studying nutrient losses in artificially drained catchments in north-east Germany. When comparing nutrient losses from small agricultural catchments in the Baltic and Nordic countries, Vagstad et al. (2004) found that high groundwater contributions, e.g. a higher share of the baseflow in the catchment discharge, might lead to lower nitrogen loss. Hydrological pathways are of great importance not only for the transport of nitrogen but also for nitrogen transformation processes in soils and the buffering capacities of the catchment

area. Thus, a good understanding of the hydrology is necessary to understand the processes leading to nitrogen loss and retention. Besides surface and groundwater flow, subsurface drainage systems are also an important pathway for both water and transport of nitrogen in agricultural dominated catchments (Deelstra 2013; Kværnø 2013). However, its magnitude is very much influenced by soil type and drainage systems, (for example Skaggs et al., 1994; Gilliam and Skaggs, 1986). Kladivko et al. (2004) and Nangia et al. (2009) showed the importance of drain spacing on the magnitude of these losses, indicating greater N loss with narrower drain spacing. A study carried out by Paasonen-Kivekàs et al. (1999) also showed the importance of subsurface drainage systems on transport of nitrogen in Finland, especially highlighting the effects of the macropore system on this transport. In the

Table 1 Catchment characteristics. Catchment

Area (km2)

Land use (%)

Precipitation (mm y
1)

Temperature ( C)

Soil texture

Height difference (m, min/max)

Draina spacing/depth (m)

Agriculture

Forest

Other land use

4.5 6.8 3.1 20.0 1.0 1.5 1.7 0.7

61 62 68 58 86 35 43 60

28 28 26 31 0 29 54 35

12 10 6 11 14 36 2 5

930 762 751 997 1278 1258 587 1429

6.3 5.3 4.4 6.1 8.5 5.2 2.9 8.2

Silty clay loam Silt, silty clay loam Loam Silty clay loam Loamy sand Loamy sand, peat Loamy sand Sand, loam

91/146 130/230 200/318 10/282 35/100 4/91 440/863 5/40

8–10/0.8–1.0 8–10/0.8–1.0 8–10/0.8–1.0 8–10/0.8–1.0 8–10/0.8–1.0 8–10/0.8–1.0 8–10/0.8–1.0 8–10/0.8–1.0

Sweden M36 N34 F26 O18 E21 I28 C6

7.9 13.9 1.8 7.7 16.3 4.8 33.1

86 85 71 92 89 78 59

4 5 10 2 4 11 32

10 10 19 7 6 11 9

719 886 1066 655 506 587 623

7.6 7.2 6.2 6.1 6 6.9 5.5

Clay, sandy loam Sandy loam, silt loam Sandy loam Clay Sandy loam Sandy loam Clay loam

18/87 4/72 146/173 64/86 102/130 32/43 20/60

15/1.0 20/1.0 20/0.9 10–12/1.0 20/1.0 20/1.0 15/1.0

Finland Savijoki Haapajyrä Löytäneenoja

15.4 6.1 5.6

39 58 77

57 26 20

4 16 3

644 545 604

5.8 4.5 5.1

Clay and moraine Clay and peat Clay and sand

50/75 24/45 35/55

20/1.0 20/1.0 20/1.0

Estonia Räpu Rägina

24.9 21.1

61 53

29 47

10 0

716 656

6 6.3

Sandy clay loam Sandy clay loam

59/73 18/35

18–22/0.9 18–22/0.9

Latvia Berze Mellupite

3.7 9.6

98 69

1 27

2 4

589 666

7.5 6.4

Silty clay loam Loam

17/23 74/88

18–22/1.1 15–25/1.2

Lithuania Graisupis Vardas Lyžena

14.2 7.5 1.7

69 73 97

29 25 2

2 2 1

716 561 661

5.7 7.3 7.2

Loam Loamy sand Sandy loam

60/70 130/180 114/172

16–20/0.9(78) 16–24/1.0(73) 18–26/1.0

Denmark Højvads Rende Odderbæk Horndrup bæk Lillebæk Bolbro bæk

9.9 11.4 5.5 4.7 8.2

65 98 82 89 99

27 2 18 2 1

9 0 0 9 0

706 732 949 921 834

6.5 9.4 8.4 8.5 9.3

Loamy sand Sand Loamy sand Loamy sand Sand

2/24 11/58 41/171 5/40 25/39

12/1.0(72) 12/1.0(10) –/– 8/1.0(8) –/–

Norway Skuterud Mørdre Kolstad Hotran Time Naurstad Volbu Vasshaglona

a

In case <80% of agriculture area is artificial drained, information provided.

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analysis of runoff at the catchment outlets, no differentiation can be made between the different flow paths contributing to the total runoff. However, to quantify the contribution from groundwater in the total runoff, techniques can be used to differentiate between fast and slow flow processes, the slow flow representing the groundwater contribution or baseflow. One methodology is the determination of the base flow index (BFI), i.e. the contribution of the slow flow or groundwater flow in the total runoff measured at the catchment outlet. An overview of different methods to differentiate between fast and slow flow processes in the catchment is given by Brodie and Hostetler (2005). Baker et al. (2004) developed a flashiness index (FI), reflecting the frequency and rapidity of short term changes in daily runoff values, representing the fast flow. The objective of this study has been to find a relation between catchment characteristics and the hydrological characteristics of BFI and FI. The catchment characteristics considered were subsurface drainage density, catchment size, land use and elevation difference. The hydrological characteristics have furthermore been used to assess the differences in nitrogen loss between catchments. 2. Materials and methods 2.1. Description of catchments Long-term monitoring data on discharge and N loss from thirty agricultural dominated catchments in the Nordic-Baltic region, covering the period 1992–2011, have been used in this analysis. The main characteristics of the studied agricultural catchments are presented in Table 1. For an overview of the location of the catchments, the reader is referred to Stålnacke et al. (2014). All the catchments in the seven countries are a part of environmental monitoring programmes, providing information about the nutrient concentrations and losses to inland surface waters. There is a large variation in catchment size, the smallest one being The time catchment in Norway (1.0 km2), the largest one being C6 in Sweden (33.1 km2). The proportion of agricultural land varies from 35% in the Naurstad catchment, Norway to 99% in Bolbro bæk, Denmark. Only three out of thirty catchments have a proportion of agricultural land of less than 50% while in twenty-three catchments, agriculture represents more than 60% of the total area. In all but one catchment forest is present; in six catchments this represents more than 30% of the total land area. A third land use type was identified, encompassing the other land use forms represented by urban areas and scattered dwellings, and in some cases peat land. In all catchments different soil types are present, varying from sand to clay soils. The soil types representing the main share of agricultural land are indicated in Table 1. Soil types play an important role in determining subsurface drainage design and in some cases are not in need of artificial drainage, exemplified by the Horndrup and Bolbro bæk catchments in Denmark. However in many cases artificial drainage is needed to drain excess water during the autumn and spring period, facilitating tillage operation and early land preparation. In the studied catchments, drain spacings vary from 8 to 26 m with depths varying 0.8–1.2 m below soil surface (Table 1). The topography of the catchments varies from relatively flat to hilly. The catchments in Norway have the largest range in elevation difference, varying from 65 to 423 m. The catchment with the smallest elevation difference is the Berze catchment, located in Latvia. 2.2. Discharge measurement, water sampling and calculation of nitrogen loss In all catchments the discharge is measured continuously using either a mechanical recorder or data logger in combination with a

213

discharge measurement structure. The discharge measurement structures used vary among the catchments and V-notches, broadcrested weirs and crump weirs are used. In all the cases, the discharge is calculated based on the recorded water level and a known head to discharge relation for the measuring structure. Composite water quality samples are collected for the majority of catchments on a volume proportional basis and in cases where no data logger is available, on a time proportional basis. The nitrogen loss for a sampling period is calculated on the basis of the measured discharge and concentrations in composite samples. 2.3. Flashiness index and baseflow index The studied hydrological characteristics involved both the flashiness index and the baseflow index. Flashiness, or rate of change, refers to how quickly flow changes from one condition to another and has been widely used to describe urban hydrology and the effects of urban development on stream hydrology (Schoonover et al., 2006). In this case, the coefficient of variation (CV) is an indicator of the flashiness. Schoonover et al. (2006) also showed that the watersheds with a high CV had a corresponding low BFI. Similar findings were made by Deelstra et al. (2008) in a comparative study on hydrology in small basins. Jordan et al. (2004) when investigating the patterns of phosphorus (P) transfer from fertilised soils to streams and processes responsible for these losses used flashiness in explaining phosphorus transport processes. In their study the flashiness was represented by the Q5:Q95 ratio, being the 5 and 95 percentile from the flow duration curve and where a high Q5 discharges or low Q95 discharge (or a combination of both) will yield a high ratio. Baker et al. (2004) developed a flashiness index (FI, Eq. (1)) which among others was used to detect changes in the hydrological behaviour due to changes in the landscape. In analysing the hydrology from catchments, varying in size from 10 to more than 104 km2, they showed that the FI decreased with an increase in the catchment scale. An advantage of the index is that it is independent of the annual discharge in a catchment; the FI combines characteristics such as CV and the Q5:Q95 ratio. The flashiness index (FI) is obtained by calculating the total path length of flow, divided by the sum of the average daily discharges (Eq. (1)). n X jqi
qi
1 j

FIday ¼

i¼1

n X qi

(1)

i¼1

The total path length is equal to the sum, usually over one year, of the absolute values of the day to day changes in the average daily discharge values represented by qi and qi
1 on day (i) and day (i
1), respectively. The index is dimensionless, meaning that similar results are obtained when replacing the discharge (m3 s
1) by the runoff per unit area (mm) or total daily discharge volumes (m3). When the flashiness index is based on average daily discharge values (FIday), it does not take into account the diurnal variation in discharge, which under specific conditions can vary considerably. Therefore the flashiness index for all studied catchments also has been calculated based on hourly discharge values (FIh). In this case the total path length is equal to the sum of the differences between the hourly discharge values. Baker et al. (2004) tested the effect of using hourly instead of daily average discharge values and found for smaller catchments a considerable increase in the FI due to an increase in the total path length by a factor of 3. A multiple regression analysis was carried out to study the effect of different catchment attributes on the flashiness. Four different attributes were identified, as input to the analysis

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Fig. 1. Relationship between the baseflow (BFI) and flashiness index (FIday) in thirty studied catchments.

(Table 1). The first attribute (attr1) represents the share of agricultural land in the catchment having a subsurface drainage system. If agricultural land represents 60% of the total catchment area and 50% of the agricultural land is drained, the value for attr1 is 0.5  0.6 = 0.3. Attribute 2 (attr2) represents the drainage density index varying from 1 to 5. In this case, the drainage density index 1 represents the case of no subsurface drainage, index 2 represents a drain spacing >20 m, index 3 represents a drain spacing of 16–20 m, index 4 represents a drain spacing of 11–15 m while index 5 represents a drain spacing <10 m. Attribute 3 (attr3) represents the

combined share of forest and other land use in the catchment. Attribute 4 (attr4) represents a proxy for average slope in the catchment, calculated as the height difference (m) divided by the catchment area (km2), the height difference being the difference between the maximum and minimum elevation in the catchment. For the studied catchments the baseflow index (BFI) has also been calculated. The BFI is a measure of the proportion of groundwater flow in the total runoff measured at the catchment outlet. In our study we used the method developed by Gustard et al. (1992); which is based on a smoothed minima technique,

Table 2 Average base flow index (BFI), flashiness index (FI), yearly runoff and nitrogen loss (N-loss (kg ha
1)). Catchment

Data period

BFI (%)

FIdaya

FIha

Runoff (mm)

N-loss (kg ha
1)b

Skuterud Mørdre Kolstad Hotran Time Naurstad Volbu Vasshaglona M36 N34 F26 O18 E21 I28 C6 Savijoki Haapajyrä Löytäneenoja Räpu Rägina Berze Mellupite Graisupis Lyzena Vardas Højvads Rende Odderbæk Horndrup bæk Lillebæk Bolbro bæk

94–11 93–11 92–11 93–07 96–98; 95–11 94–11 99–11 93–09 05–09 06–09 97–09 97–09 96–09 96–09 99–08 99–08 99–08 99–05; 00–02; 95–04 98–05 97–10 97–10 97–10 00–05; 91–92; 91–92; 91–10 07–11

21 14 38 21 32 20 51 59 18 52 35 23 35 29 28 30 37 29 55 37 26 27 28 37 40 60 76 63 54 87

0.58 0.56 0.29 0.63 0.49 0.58 0.19 0.29 0.65 0.32 0.41 0.54 0.28 0.37 0.41 0.40 0.25 0.38 0.18 0.18 0.35 0.34 0.29 0.25 0.27 0.14 0.13 0.24 0.24 0.05

1.97 1.64 0.96 1.88 1.63 1.62 0.70 1.49 1.32 0.71 0.95 1.27 0.39 0.65 0.75

545 312 346 730 803 1128 286 1246 264 407 539 377 186 157 226 334 233 263 289 245 175 222 172 227 237 130 223 251 237 514

47.2 23.1 40.0 55.4 54.5 28.7 21.0 102.8 22.3 20.2 21.5 21.5 19.3 17.4 10.0 19.7 29.4 18.6 13.8 1.7 15.3 9.0 15.7 7.0 11.3 15.4 14.1 21.0 25.5 5.7

a b

05–11

07–08 04–10

07–08; 10 95 – 10 94–04; 10

FIday and FIh calculated on average daily and hourly discharge values. Nitrogen loss from agricultural area.

0.29 0.32 0.62 0.69

0.17 0.28 0.63 0.68 0.09

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utilising the minima of 5-day non-overlapping periods derived from the hydrograph. The baseflow hydrograph is generated by connecting a subset of points selected from this minima series. The BFI was calculated on the basis of average daily discharge values for a period of one year. The share of slow flow contribution in the total yearly runoff, i.e. baseflow contribution (BFI) is calculated as BFIð%Þ ¼

Q sf  100 Qt

(2)

where Qsf and Qt are the slow flow contribution and total runoff respectively. In our study the calculations of the BFI have been carried out using the average daily discharge values. 3. Results and discussion 3.1. Baseflow index There is a large variation in the BFI values among the catchments, 14 and 87% in Skuterud and Bolbro bæk respectively (Fig. 1, Table 2). The high BFI for the Bolbro bæk catchment might be explained by the fact that the dominating soil type is sand and no subsurface drainage system is installed. Excess precipitation in this case recharges the groundwater which in turn is being drained to the Bolbro bæk. Among the ten catchments with the highest BFI, five are Danish catchments with BFI varying 54–87%. For the eight Norwegian catchments with a drain spacing, L = 8–10 m, the BFI was 14 and 59% for Skuterud and Vasshaglona respectively. With narrow drain spacings one would not expect a high BFI value. The high BFI in this case might be an indication that excess water is bypassing the subsurface drainage systems as groundwater flow leaves the catchment. The catchment is characterised by a very high mean annual precipitation (1429 mm). This probably contributes to the elevated baseflow in addition to the dominating sand and loam soils with reasonably good infiltration capacity. Among the Swedish catchments, the lowest BFI values (18–28%) were obtained for the catchments with clay soils (M36, O18 and C6). These catchments also had the smallest distance in drain spacing (10–15 m). The largest BFI (52%) was at N34 with sandy loam and silt loam soils. Permeable soils in combination with outflow of

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groundwater that originates from outside the catchment (Kyllmar et al., 2005) explains this relatively high value. Drainage systems are installed to facilitate agriculture as the natural drainage capacity of the dominating soil types was unable to provide good crop growth conditions and aid farming operations during both the autumn and spring. Although the natural drainage condition of soils can be poor, some recharge to the groundwater might still have existed. It is anticipated that this recharge, however small in magnitude, would have been further reduced through the installation of the artificial drainage system. This in turn would have resulted in a reduced slow flow contribution in stream runoff generation. On the other hand, research in the US showed that artificial drainage systems led to an increase in baseflow contribution in major rivers in Iowa (Schilling and Helmers, 2008; Schilling et al., 2012). Kumar et al. (2009) and Ahiablama et al. (2013) also report that the increased stream flow trends in Indiana were related to subsurface drainage systems among other factors. In our study, no long-time series on water runoff in agricultural catchments are available to be able to investigate baseflow contribution in the total runoff before and after the installation of tile drainage systems. 3.2. Catchment characteristics and flashiness index The flashiness index was calculated using both daily and hourly discharge values, and the results are presented in Table 2. The FIday varies from 0.06 to 0.65 between the Bolbro bæk and M36 catchments respectively. Among the ten catchments with the highest FIday, five are located in Norway, four in Sweden and one in Finland. The five lowest FIday values were obtained in three Danish and the Estonian catchments. There is a negative relation between the BFI and FI, which is not unexpected as they represent the types of opposing flow processes, i.e. a slow and a fast flow process (Fig. 1). Similar results were obtained by Schoonover et al. (2006). Spatial scale, e.g. the size of the watershed can have an effect on the FIday. Baker et al. (2004) reported a decrease in FI with an increase in scale from less than 10 to more than 10,000 km2. In our study the effect of the total catchment area on the FI is not present (Fig. 2), the reason being the relative small variation in size of 0.1–33 km2.

Fig. 2. The relationship between catchment area and FIday in thirty studied catchments.

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Fig. 3. The average yearly calculated and estimated FIday in the thirty studied catchments.

Another catchment characteristic which might have an influence on the FI is the subsurface drainage system. Deelstra et al., 2008; Deelstra, 2013 showed that the FI measured on subsurface runoff at field scale catchments nested within larger catchments were in the same order of magnitude as the FI at catchment scale. In addition to agricultural land, the proportion of forest, other types of land use and topography might affect the flashiness. The multiple regression analysis gave the following relation between the flashiness index and catchments attributes as: FIday ¼ 0:239  attr1 þ 0:059  attr2 þ 0:215  attr3
0:001  attr4
0:033

(3)

A good level of agreement was obtained between the estimated and measured FIday, with a coefficient of determination R2 = 0.49. The analysis gives a very high influence on the FIday from the agricultural area in the catchment provided with artificial drainage (attr1), indicated by the regression coefficient (b1 = 0.239, with corresponding p-value <0.009). Although less strong, there is also a positive relation between FIday and the drainage intensity index (attr2) with a regression coefficient of (b2 = 0.059, with corresponding p-value <0.004). The very good dependency between FIday and attr1 and attr2 respectively is a confirmation of earlier findings by Deelstra et al., 2008; Deelstra, 2013 that subsurface drainage significantly can affect runoff behaviour at the catchment outlet. Also a positive relation between FIday and attr3 was obtained, with a regression

Fig. 4. Relationship between FIday and FIh in thirty studied catchments.

J. Deelstra et al. / Agriculture, Ecosystems and Environment 195 (2014) 211–219

217

Fig. 5. Relation between runoff and nitrogen loss for Denmark, Sweden and Norway.

coefficient of (b3 = 0.215, with corresponding p-value <0.131). This is an indication that more detailed information is needed concerning the effects of forest and other land use on runoff generation in agricultural dominated catchments. A negative (though statistically non-significant) relation was found between FIday and the average slope in the catchment (attr4), having a regression coefficient of b4 = –0.001, with corresponding p-value <0.066. Also a more detailed analysis on the effect of topography on flashiness is needed as a positive relation was expected between topography and the flashiness index. McGuire et al. (2005) showed a strong correlation between landscape characteristics such as topography on the residence time. The original and estimated values for FIday are presented in Fig. 3. FIday values can hide the real true hydrological variations. Baker et al. (2004) reported an increase in the FI by a factor of 1.14–3.44 when comparing with hourly based values for the FI. In our case when calculating the FI using hourly discharge values, a considerable increase in the FI was obtained by a factor of almost 3 on average for all the catchments, with minimum and maximum of 1.2 and 4.8, respectively (Fig. 4, Table 2). Important in this respect is that, especially for a higher FI value, such an increase has to be taken into consideration for example when designing water sampling systems or in the design of hydro-technical implementations in agricultural dominated catchments. Deelstra and Iital (2008) observed that the magnitude of the diurnal variation in discharge is also important when considering analyses of the nutrient loss processes, in this case especially those related to total phosphorus. 3.3. Nitrogen loss at catchment scale There is a large variation in nitrogen loss between the studied catchments; 1.7 and 102.8 kg ha
1 for Rägina and Vasshaglona, respectively (Table 2). In general there seems to be a positive relation between runoff and nitrogen loss, with the highest runoff and nitrogen loss occurring in the Norwegian catchments. A similar finding was made by Vagstad et al. (2004) in an earlier study on nitrogen loss in the Nordic-Baltic countries. However, when comparing runoff and nitrogen loss for Norway, Sweden and Denmark separately, different relations exist (Fig. 5). While there is a positive relation between runoff and nitrogen loss for the

Norwegian and Swedish catchments, this is negative for the Danish catchments. One possible reason for this difference might be attributed to the dominating flow processes generating the nitrogen loss. Our statistical analysis showed that the subsurface drainage has a significant influence on the flashiness index and thereby also on the runoff generation. The analysis showed at the same time a negative relation between FI and BFI, with an increase in BFI leading to a decrease in FI. Several studies have been carried out concerning the effects of subsurface drainage systems on nitrogen transport. A comparative analysis of nutrient loss through surface and subsurface runoff in three field scale catchments in Norway (Kværnø, 2013) showed that the proportion of nitrogen loss through the artificial drainage system, compared to surface runoff, was more than 80%. Bechmann (2014) showed that the nitrogen concentrations in outflow from drains in nested catchments were higher than the concentrations at the catchment outlets. The main reason for this was runoff contribution from non-agricultural areas but also in-stream nitrogen processes such as denitrification and uptake of nitrogen by in-stream vegetation. Similar findings were made by Tiemeyer et al. (2006); and Iital and Loigu (2001). Also, drainage design can influence the nitrogen transport. Both Kladivko et al. (2004) and Nangia et al. (2009) showed that nitrogen loss increased with a reduction in drain spacing. In addition, Bjorneberg et al. (1996) observed that up to 85% of the annual nitrate runoff through subsurface drains occurred during the off season. Also, Rossi et al. (1991) observed that nitrogen runoff through subsurface drainage systems was highest during the off season, corresponding to the increased drainage runoff during the same period. Similar results were obtained by Deelstra et al. (2011) when analysing the runoff and nitrogen loss at the catchment scale for some selected catchments in Norway. Bechmann et al. (2014) showed that the nitrogen balance, being the difference between the amounts of nitrogen applied to the field through fertiliser, animal manure and atmospheric deposition and removed through the harvested product for the Danish and Norwegian catchments were in the same order of magnitude. At the same time the Danish catchments have a lower N loss compared to all but two of the Norwegian catchments (Table 2). A possible reason for this difference can be due to flow processes.

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Four out of five Danish catchments have no or very few artificial drainage systems installed, resulting in a very high BFI compared to the Norwegian catchments (Table 2). This is an indication of a much larger share of groundwater contribution in the total runoff measured at the catchment outlet. Postma et al. (1991), in a study carried out in Denmark, showed that the major component responsible for the removal of nitrate from recharge to the groundwater was pyrite, serving as an electron donor in the oxidation of nitrate. This process is mainly responsible for the low N loss in the Bolbro catchments. Molenat et al. (2002) also concluded that pyrite was responsible for the reduction of nitrate transfer from agriculture to open streams in a small agricultural catchment in France. However, contrary to the Danish catchments, the largest N loss in the Swedish catchments (38 kg ha
1) was observed in catchment N34 which also had the highest BFI. In this case denitrification processes as caused by pyrite, do not occur due to different origins of bedrock and sedimentary deposits (Kyllmar, 2005). These contradicting results indicate that additional research should be carried out on processes related to N loss and retention. 4. Conclusions A negative relation exists between FIday and BFI, i.e. a low BFI corresponding to a high FIday and vice versa. A considerable increase in the FI, by a factor of almost 3, was obtained when calculating the FI using hourly discharge values. A multiple regression analysis was carried out between FIday and catchment attributes, showing that a significantly positive relation between FIday and the subsurface drainage system exists. This is an indication of the importance of subsurface drainage systems in runoff and nitrogen loss generation. More information is needed concerning the effects of forest, other land use and topography on runoff generation. In general a positive relation between runoff and nitrogen loss exists, with the highest runoff and nitrogen loss occurring in the Norwegian catchments. However, conditions such as the presence of groundwater contribution in the total runoff can affect N loss. Acknowledgements The authors would like to thank the referees for their valuable comments and suggestions. Also, special thanks go to the organisers of this special issue. The study was for the Norwegian authors performed under the research grant ‘AGRI-LOSS’ of the Norwegian Research Council. References Ahiablama, L., Chaubeya, I., Engel, B., Cherkauer, K., Merwadec, V., 2013. Estimation of annual baseflow at ungauged sites in Indiana USA. J. Hydrol. 476, 13–27. Baker, D.B., Richards, R.P., Timothy, T., Loftus, T.T., Kramer, J.W., 2004. A new flashiness index: characteristics and applications to midwestern rivers and streams. J. Am. Water Resour. Assoc. (JAWRA) 40, 503–522. Bechmann, M., Andersen, H.E., Blicher-Mathiesen, G., Kyllmar, K., Iital, A., Jansons, V., 2014. Strategies for nitrogen application and the consequences for nitrogen surplus and water quality in Nordic-Baltic countries. Agric. Ecosyst. Environ. (in this issue). Bechmann, M., 2014. Long term monitoring of nitrogen in surface and subsurface runoff from small agricultural dominated catchments in Norway. Agric. Ecosyst. Environ. (in this issue). Bjorneberg, D.L., Kanwar, R.S., Melvin, S.W., 1996. Seasonal changes in flow and nitrate-N loss from subsurface drains. Trans. ASAE 39 (3), 961–976. Brodie, R.S., Hostetler, S., 2005. A review of techniques for analysing baseflow from stream hydrographs. Proceedings of the NZHS-IAH-NZSSS 2005 Conference, Auckland, New Zealand. 28 November–2 December http://data.daff.gov.au/brs/ brsShop/data/iah05_baseflow_final.pdf (Verified 27.5.14). De Wit, M., 2000. Modelling nutrient fluxes from source to river load: a macroscopic analysis applied to the Rhine and Elbe basins. Hydrobiologia 410, 123–130.

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