Environmental Modeling & Assessment

, Volume 15, Issue 2, pp 93–109 | Cite as

How Do River Nitrate Concentrations Respond to Changes in Land-use? A Modelling Case Study of Headwaters in the River Derwent Catchment, North Yorkshire, UK

  • Michael G. Hutchins
  • Amelie Deflandre-Vlandas
  • Paulette E. Posen
  • Helen N. Davies
  • Colin Neal
Article

Abstract

A combined semi-distributed hydrological model (CASCADE/QUESTOR) is used to evaluate the steady-state that may be achieved after changes in land-use or management and to explore what additional factors need to be considered in representing catchment processes. Two rural headwater catchments of the River Derwent (North Yorkshire, UK) were studied where significant change in land-use occurred in the 1990s and the early 2000s. Much larger increases in mean nitrate concentration (55%) were observed in the catchment with significant groundwater influence (Pickering Beck) compared with the surface water-dominated catchment (13% increase). The increases in Pickering Beck were considerably greater than could be explained by the model in terms of land-use change. Consequently, the study serves to focus attention on the long-term increases in nitrate concentration reported in major UK aquifers and the ongoing and chronic impact this trend is likely to be having on surface water concentrations. For river environments, where groundwater is a source, such trends will mask the impact of measures proposed to reduce the risk of nitrate leaching from agricultural land. Model estimates of within-channel losses account for 15–40% of nitrate entering rivers.

Keywords

Nitrate Water quality Model Catchment Land management Agriculture 

1 Introduction

Nitrate is a key nutrient within riverine environments and is of environmental concern in relation to issues of eutrophication and potable water supplies [36] and is profoundly important across Europe with regards to basin-scale management under the EU Water Framework Directive [24]. There is considerable evidence of strong links between nutrient concentrations and loads in rivers and the proportion of agricultural land in the basins which they drain [37, 43]. Evidence is apparent in many basins [59], including the Humber, UK [19], a catchment of which, the Derwent [60], is the focus of a case study reported here. A wealth of experimental monitoring has revealed that the rate of applied fertiliser is likely to have a significant impact on nutrient losses in terms of exports from agricultural plots [29]. Changes in agricultural practices have been linked to increased river nitrate concentrations in some Scottish catchments [25]. The attribution of wider impacts in waterbodies, however, has often been reliant on modelling studies [50, 62] largely due to the paucity of clear evidence of uni-directional change in nutrient loadings.

A key question arises: in terms of nitrate concentrations, how quickly would effects of altered manure and fertiliser regimes or broader land-use modifications be seen in waterbodies? The answer is unclear and subject to many uncertainties such as prevailing weather conditions, crop yields, soil processes and hydrological pathways (reflected in catchment residence times and associated retention, retardation and release of nutrients). In particular, storage in groundwater is difficult to quantify. Whilst on a regional scale, it does not appear to exceed 3% of the rate of anthropogenically derived inputs, it may be more important in localised areas [39]. In this respect, in-river processes are also important, determined by bioavailability, nutrient limitation and flow conditions and other physical factors. Even at the plot-scale, despite the direct evidence potentially available through the deciphering of data from controlled manipulation experiments, identification of long-term steady-state conditions following a change may not be possible. This is due to experimental plots undergoing continual management change in order to test a wide variety of new and ongoing scientific hypotheses.

Conceptual understanding of catchment-scale nitrogen dynamics is embodied in models with a detailed physical basis such as those described by Young et al. [85], Arnold et al. [4] and Wade et al. [78]. In contrast, many models developed to address the consequences of strategic, policy-driven changes in land management to reduce nutrient loss are structurally simpler and largely based on empiricism. In this regard, the use of simple models is pragmatic and appropriate [35] given the difficulties (and introduction of associated uncertainties) in estimating a wide range of environmental parameters across geographically extensive areas. Such models typically work on the assumption that land-use-specific quantities of nutrient will be available for leaching and that this quantity is dependent on an assumed level of fertiliser and manure input (e.g. [3, 22, 44]). Despite having the important capability to provide insights of eventual steady-state conditions, estimating the dynamics of response is unlikely to be possible. Hence, when using such models of this type, it is important to make additional and explicit consideration of the likely timescales of change.

Here, we present a modelling study of two sub-catchments of the River Derwent (North Yorkshire, UK): the River Dove and Pickering Beck. It provides an example of the use of a relatively simple model (of the second type discussed above) to explore the likely consequences of land-use and land management change on nitrate-N (NO3-N) concentrations in waterbodies at steady-state. These are set in the context of the inherent inertia of catchment systems. Availability of national land-cover and agricultural census datasets allow derivation of two land-use datasets representative of the periods 1990–1993 and 2000–2003. Data from these sub-catchments indicate substantial change between these periods with a shift towards types of agricultural land-use which have an inherently higher risk of NO3-N leaching. In this respect, and given the absence of known point source inputs, the sub-catchments form an excellent case study to explore impacts of land management-induced changes in N loading on NO3-N concentrations. Modelling is used:
  1. 1.

    To quantify the likely change in NO3-N concentrations if the system were to have reached steady-state. Here, land-use changes are treated in isolation, the climatic effects being removed by using the same 1990–1993 meteorological datasets for both periods.

     
  2. 2.

    To indicate additional impacts of short-term climate on NO3-N concentrations, which has been shown to be important in a number of catchments in the north of UK using other models (e.g. [21]).

     

In the light of evidence of drivers and dynamics of long-term change summarised in Section 2, the predictions are evaluated and an explanation of changes in NO3-N concentration in the long-term offered. The research provides an example of the management issues for the Humber, a major basin within the UK and a key area for integrated environmental research and management (ChREAM project, [5]).

2 Review of Evidence of Long-Term Changes in Nitrate-N Leaching

Until the 1980s and the UK implementation of the EC Drinking Water Directive, there were many cases identified of long-term increases in river nitrate concentrations (nitrate as nitrogen: NO3-N) [36]. These are summarised by Jose [45] and Johnes and Burt [43], largely indicating change in southern and eastern UK. The adverse river impacts were broadly coincident with increased loadings from point sources along with an intensification of agricultural activity, which is reflected and summarised by Sylvester-Bradley [74] and Davies and Sylvester-Bradley [20] who discuss trends in crop-specific N application and N surplus in the context of varietal genetic developments. A levelling off of leachable NO3-N since the mid-1980s is apparent. It should be noted that DON can also be important, representing around 20% of total nitrogen leached in some agricultural systems where rainfall input is high [76], and the fraction appears to become increasingly labile as systems become more saturated with nitrogen under increased loading [55].

However, it would be naive to assume that relationships between NO3-N leached and changes either in land-use (such as ploughing up of grassland) or fertiliser N applications are simple or manifested rapidly [12]. A great wealth of data arising from plot experiments is of potential worth in this regard. However, these experiments had originally all been designed for a myriad of different purposes (e.g. identifying impacts of crop rotation and residual matter on N mineralisation and leaching) and were often ultimately focused on economic optimisation of fertiliser recommendation with knowledge of field history. In this way, the design of experiments to meet specific research objectives have often, for the purpose of identifying long-term change in leachable NO3-N, resulted in the introduction of confounding factors. Nevertheless, there is considerable evidence of complex responses at the field scale. For example, under cereals, tracer studies using 15N indicate the efficiency of crop utilisation, showing that less than 2% of applied inorganic nitrogen was present in soil in inorganic forms at harvest [67]. Potato crops may be less efficient, yet Davies and Sylvester-Bradley [20] observed only 8% remaining at harvest. Even in long-term plots where fertilisation rates have been very high (e.g. 144 kg ha−1 per year) the contribution of N originally added as fertiliser (at any time not just in the given year) to the pool of leachable NO3-N is likely only to be around 50% [28]. Davies [18] asserts that “the effect on NO3-N leaching of even large reductions in fertiliser N input would initially be comparatively small, only achieving substantial reductions after many years as organic N reserves and soil fertility decline”. As yields and crop residues have increased in tandem with development of new crop varieties in the past 100 years, there has been an attendant increase in soil organic matter accumulation which provides enhanced sources for slow and persistent generation (through mineralisation) of leachable NO3-N.

Some quantification of change in land-use from pasture to arable cropping and its effects has been made in small catchments; Worrall and Burt [83] pinpointed the importance of the unlocking of reserves of organic N present in grassland soils in regulating NO3-N concentrations. The link between renewed availability of organic N and an increased capacity for soil denitrification in response to increased supply was identified. Sequestration of N, apparently progressing at an exponentially decreasing rate, occurs when land reverts to pasture [84]. Addiscott and Mirza [1] assert that the impact of reserves of organic material acting as sources and sinks of leachable NO3-N should be given due consideration in assessing and modelling catchment-scale river NO3-N responses.

At field scale alone, it may take up to 30 years to reach a steady-state in respect of leached NO3-N following a management change [66]. Stabilisation changes in soil organic N may take much longer still. Notwithstanding the delaying and moderating effects of catchment-scale processes, including the impacts of groundwater residence time (which according to isotope studies of Bohlke and Denver [7] usually exceeds 20 years), these observations at field and small catchment-scale themselves have important implications for the feasibility of implementing current legislation drawn up to improve the chemical and ecological quality of waterbodies (the EU WFD: [24]).

Of relevance to the scale of large catchments, East European states have experienced 50–90% decrease in fertiliser use in recent years [72], with an increase in grassland at the expense of arable land. The changes have occurred since the end of the Communist regimes and provide a valuable case study of river nutrient impacts of broad-scale management shifts. From the limited data available, any evidence of steady-state having been reached is hard to find. In rivers draining to the Baltic, spatial surveying showed that largest NO3-N losses were actually observed in sites with largest N deficits. Mirroring many conclusions from field scale work, Vagstad et al. [75] identified that soil mineralisation rather than fertilisers are the main source of leached NO3-N. The delay in response to changing fertiliser regime is exacerbated by the dampening effect of groundwater retention allowing for deceleration of response (e.g. inefficient field drainage) and for denitrification to take place. Evidence from Latvia reported by Stalnacke et al. [71] suggests that stream denitrification is more efficient in the smaller streams and drainage ditches than in large channels. With a notable exception being the 157,000-km2 Tisza River in Hungary [72], riverine NO3-N trends in response to land-use change are more noticeable in smaller East European basins (< 1,000 km2). Examples include Slovakian studies [63] and the Estonian sub-catchments of Lake Peipsi [53]. An overall lack of response in East European rivers to land-use change in the late 1980s suggests that reductions in NO3-N export from catchments are likely to be harder and slower to achieve than responses to increases in N loading. According to Grimvall et al. [32], this is due largely to sustained N release from humus layers and groundwater aquifers. Even the rapidity of increases is not always apparent, especially in large catchments, for example, as shown by Stow et al. [73] in North Carolina. Export coefficient modelling over long-term can illustrate the expected change in nutrient leaching and comparison with observed concentration trends. There is an example of such a study from the River Kennet, UK [81] where, arguably, river data show a 20-year lag in the marked increase attributed to land practice changes and population expansion in the 1950s and 1960s and that this has yet to reach a steady-state.

To summarise, the review reveals that change in NO3-N concentration will be slow and possibly incomplete, i.e. of lesser magnitude than the driving changes in N inputs. Delay and diminution of change is likely to be increasingly apparent on moving up from plot to catchment-scale due to the dampening effects of many catchment hydrological and biogeochemical processes. The model chosen for the Derwent study does not represent the dynamics of how such changes in input are manifested in terms of river NO3-N concentration. It can, however, be used to quantify an expected eventual steady-state condition.

3 Case Study Background: Catchment and Model Description

A hypothesis to guide the modelling case study is proposed: “over a 14 year period, changes in river NO3-N concentration can be satisfactorily explained by climate variability and changes in land-use within that time”.

3.1 Study Catchment

Modelling was undertaken for the 1,586 km2 Derwent catchment draining to Buttercrambe (NGR SE 731587) in North Yorkshire, UK (Fig. 1). Annual average rainfall is 779 mm, although it exceeds 1,000 mm in the North York Moors at the northern edge of the catchment. Of this rainfall, approximately 59% is accounted for as evapotranspiration [14]. In 2000, land-cover proportions were 42%, 27%, 15%, and 13% for arable, grass, woodland and upland cover, respectively [27]. The remainder was urban and suburban. Soil permeability varies greatly across the catchment, with consequent spatial differences in stream baseflow index (BFI) [42] at gauged sites (Table 1).
Fig. 1

Location map of River Derwent and its tributaries, North Yorkshire, UK

Table 1

Summary details of rivers, catchments and gauging stations mentioned in the text and in Fig. 1 [14]

NGR

River

Site

Catchment area (km2)

Base flow index

Rainfall (mm yr−1)

SE 731587

Derwent

Buttercrambe

1,586.0

0.68

779

SE 990853

Derwent

West Ayton

127.0

0.71

870

SE 833774

Derwent

Low Marishes

457.5

0.80

735

SE 652902

Hodge Beck

Cherry Farm

37.1

0.54

962

SE 705855

Dove

Kirkby Mills

59.2

0.58

926

SE 737821

Seven

Normanby

121.6

0.36

910

SE 791819

Pickering Beck

Ings Bridge

68.6

0.65

860

N.B. NGR SE 000000 is equivalent to NGR 40004000

The River Dove and Pickering Beck drain rural catchments with moorland headwaters. There are no significant known point sources of N in either catchment. The catchments are of similar size but differ in that the Pickering Beck is more extensively underlain by the major aquifer of the Corallian group (primarily calcareous sandstone). A summary of the land-use percentage classifications in the sub-catchments and an indication of change since the 1990s are given in Table 2. The 1990–1993 land-use data sets are a combination of the 1991 land-cover map of Great Britain [26] with 1992 cropping and livestock data from Defra small area statistics. The land-use data sets for 2000–2003 are derived from a similar combination of LCM2000 [27] and EDINA agcensus 2-km grid data aggregated from the Defra 2004 Agricultural Census cropping and livestock statistics. In the northern part of the Derwent catchment (encompassing Pickering Beck and River Dove), average annual rainfall was approximately 20% higher in 2000–2003 relative to 1990–1993.
Table 2

Land-use percentages in the Pickering Beck and River Dove

 

Temporary grass

Permanent grass

Cereals

Other crops

Set aside

Non-agricultural land

Pickering Beck 1990–1993

7.1

22.1

16.5

2.7

0.0

51.4

Pickering Beck 2000–2003

4.3

18.3

27.0

1.2

2.9

46.4

River Dove 1990–1993

5.5

26.3

9.4

1.0

0.0

57.7

River Dove 2000–2003

2.9

31.6

8.3

1.8

1.6

53.8

The urban and suburban contribution to the non-agricultural category is negligible in all data sets. The “Other crops” category comprises crops with high N residues (including potatoes, oilseed rape and sugar beet)

3.2 CASCADE/QUESTOR Application

The CASCADE/QUESTOR model operates on a daily time-step, representing dynamics of diffuse pollution (CASCADE; [17]), point source pollution and in-river processes (QUESTOR; [8]). A semi-distributed spatial representation is used, a catchment being split up into hydrologically isolated hydrological response units (HRUs) of approximate size of 5 km2. Given the level of spatial detail provided by EDINA (agcensus data), modelling at a finer spatial resolution is not justifiable. Kirkby Mills and Ings Bridge, on the River Dove and Pickering Beck respectively, lie at the start of the QUESTOR network and flow and NO3-N concentrations at these locations are modelled using the diffuse pollution model (CASCADE) alone. Model setup was largely as described by Hutchins et al. [40] with a different procedure being used to define N inputs, which are calculated on a monthly basis and are specific to a range of land-uses with a number of different sub-categories of arable land being considered. Fertiliser use and soil type are accounted for as they have a bearing on rates of organic matter mineralisation [2]. Model inputs have been refined relative to the lumped approach used by Hutchins et al. [40] so as to include sensitivity to these variables. For arable land-uses, the N inputs are now considered to be from three sources: (1) soil mineralisation, (2) managed additions, and (3) atmospheric deposition. These are described in the first of three sub-sections below. Denitrification of mineralised crop residue N in arable soils was not included. A second sub-section covers grassland N inputs. Non-agricultural N inputs remain as described by Hutchins et al. [40]. The third sub-section describes the procedure for calibrating catchment hydrology. NO3-N concentrations are only calibrated in QUESTOR (e.g. using denitrification rates) not in CASCADE. A simplified diagrammatic representation of how the component parts of the model fit together (Fig. 2) illustrates the linkages between model codes and input data sources.
Fig. 2

Diagram illustrating the CASCADE/QUESTOR N model. Input data are indicated in bold italic type. Discrete model codes are represented by shaded boxes and their use described in Section 3.2.1 (MANNER) and 3.2.2 (N-CYCLE). Rates of mineralisation and plant uptake/fixation are quantified during the process of defining leachable N in arable soils (see Section 3.2.1; arable N inputs). Methods of modelling soil hydrology and in-river processes are described by Hutchins et al. [40]

3.2.1 Arable Land N Inputs

The first component to the N input (soil mineralisation) was calculated based on the concept that NO3-N available for leaching is supplied through the mineralisation of topsoil organic matter. Two pools of mineralisable N were considered: (1) readily decomposable material derived from plant residues (Ndpm) and (2) more recalcitrant (largely humic) material (Nrpm). Quantities were determined on a daily basis by first-order kinetics with different decay coefficients [48]. The processes are dependent on soil temperature, and values appropriate to the Derwent were taken from Green and Harding [31].

Following harvest, an initial value (1st Sept) of Ndpm was taken as the N remaining from crop residues, i.e. the difference between figures of N offtake and N uptake specified for a range of crops by Sylvester-Bradley [74]. These values (given in Table 3 for crop classifications used by CASCADE) assume N applications are at an economic optimum. If applications stray from this optimum, the values of Lord [52] may be used to define the likely change in crop residue resultant from a unit change in added N. These gradients, which differ above and below the optimum level, are summarised elsewhere [20].
Table 3

N potentially available (kg ha−1) from crop residues (Ndpm) from main crop types found in Derwent catchment

Crop type

Residual N, kg ha−1

Crop type

Residual N, kg ha−1

Winter wheat

38

Potatoes

93

Winter barley

33

Sugar beet

130

Spring barley

15

Peas and beans

70

Other cereals

36

Oilseed rape

130

Linseed

20

  
The value of the Nrpm fraction is dependent on soil type and uses data readily available from national datasets [33] on organic matter content of soils under different land-uses. Soil organic carbon is then estimated from organic matter values [38]. Estimates of amounts of available N for crop uptake, as a contribution mineralised specifically from this organic matter, are given by MAFF [56]. Using the first-order kinetics described above, these can then be related to an initial (1st Sept) pool of Nrpm (analogous to the initial pool of Ndpm from crop residues). Initial values of Nrpm (given in Table 4) were determined based on Hydrology of Soil Types (HOST) class [9].
Table 4

Values by soil HOST class of Nrpm (kg ha−1) specified on 1st September each year

HOST

arable Nrpm

HOST

arable Nrpm

HOST

arable Nrpm

1

117

11

N/A

21

116

2

119

12

N/A

22

273

3

19

13

54

23

132

4

117

14

64

24

116

5

33

15

N/A

25

220

6

62

16

23

26

679

7

26

17

167

27

N/A

8

90

18

23

28

N/A

9

397

19

85

29

N/A

10

397

20

127

  

(N/A = insufficient data available to calculate values)

In the period between crop harvest and rapid spring crop growth, the mineralised pools of NO3-N are available for leaching. However, to discriminate between spring and winter-sown crops, a temperature-dependent plant uptake factor was included [82]. For winter-sown crops, this factor was applied to the combined pools of Nrpm and Ndpm in October and November of each year and serves to reduce the amount available for leaching in those months. When verified against data reported for various plot experiments [2], total net mineralisation rates (Nrpm + Ndpm) simulated by the model appear satisfactory.

In terms of managed additions to arable land (the second N input component), impacts of adding fertiliser are assumed to be manifested in the quantity of N in crop residue (as described above). If additions are made above the economic optimum, these quantities will start to increase significantly [52]. National surveys of fertiliser use report annually on use of organic manures [10], revealing that cattle farmyard manure (FYM) is applied to 59% of fields and cattle slurry to 28% of fields. Data suggest that, though high in N, other manure types (e.g. pig and poultry manures and slurries) are only applied to a very small percentage of fields. For poultry manure, there is also high geographical uncertainty, in terms of where they are actually applied, as BSFP [10] data reveal that there is a significant level of importing of such material. For these reasons, organic manure types other than cattle are excluded from the calculation of N inputs. Under mean regional climate, the MANNER model [15] was used to determine the amount of NO3-N likely to be leached from a range of representative application quantities, timings and incorporation routines for both cattle FYM and cattle slurry. For each of the crop types considered, MANNER indicated that an additional 2 kg ha−1 per month of NO3-N available for leaching should be included for the months of November, December and January.

For the Derwent, atmospheric inputs (the third N input component) were assumed spatially invariant for the purposes of modelling. A value of 20 kg ha−1 per year is quoted by Smith et al. [70]. For case study purposes the value was deemed unchanging on an annual time-step and was partitioned on a monthly basis using information on seasonal variability of atmospheric inputs [30]. It was assumed that for nitrogen-fixing crops (peas and beans), farmers allow for the nutritional value of deposited N in their fertiliser calculations and monthly inputs from atmospheric N are not assumed to contribute to the NO3-N available for leaching between April and August inclusive.

3.2.2 Grassland N Inputs

For grasslands, HRU-resolution agricultural census data were used in conjunction with typical stocking density rates [61] to determine the proportions of grassland (temporary and permanent) used for beef, dairy and cutting systems, respectively. BSFP [10] data indicate that typical fertiliser application rates for grasslands are 155 kg N ha−1 and 85 kg N ha−1 for temporary (less than 5 years since last seeding/re-seeding age) and permanent (age greater than 5 years) pasture, respectively. These figures were used as input to the N-CYCLE model [64] which, for regional mean climate and estimated atmospheric N input, was run many times using a range of grassland ages and soil texture classes. In the absence of readily accessible historic records, census data were used to indicate the likelihood that land was previously arable rather than pasture prior to seeding. From the N-CYCLE applications, mean values of leachable NO3-N per annum for temporary and permanent grassland were determined for each of the three grassland systems. These annual values were uniformly distributed across the months at which soils are typically at hydrological field capacity (September–March inclusive). For the model applications described here, based on assessment of the spatial variability in abundance of the different grassland systems, uniform catchment-wide annual values of 21 and 10.5 kg ha−1 NO3-N available for leaching were deemed sufficient and used for temporary and permanent grassland, respectively.

3.2.3 Calibration of Hydrology

Whilst there were suitable data from the Dove and Pickering Beck in the two periods of interest, intermittent problems with flow data quality at one of the sites are apparent at other times. Therefore, for consistency, and to illustrate the level of robustness of a model-based method for identifying relations between land-use change and water quality, calibration was undertaken in two other catchments chosen to have similar characteristics to the Dove and Pickering Beck. This approach to calibration is in keeping with the primary purpose of the method as an exploratory tool in appraisal and management of large heterogeneous basins/regions.

For the River Dove, calibrated parameters were derived using 1987–1988 data from Hodge Beck, an adjacent catchment of similar rainfall and BFI (Table 1). In terms of hydrogeology, Pickering Beck is a challenge for rainfall-runoff modelling in that a limestone aquifer and impermeable grits both occur extensively. Calibrated parameters for Pickering Beck were derived in the drier catchment of the Derwent above Low Marishes (1991–1992 data), which has a somewhat higher BFI value (Table 1). In making this choice, a higher priority was assigned to the accurate simulation of low flows in Pickering Beck as opposed to storm flows. The dominance of sub-surface flow pathways is greatest at low flows and hence, total river nitrate loads are primarily determined by leaching from the soil matrix at such times. Nash–Sutcliffe model efficiency statistics however are more sensitive to errors at high flows.

A fuller account of the calibration process and the application of calibrated parameters across the entire Derwent catchment are given in Appendix 1. Model start dates were 1st Jan 1989 and 1st Jan 1999 for the 1990–1993 and 2000–2003 applications, respectively. This was to allow for equilibration of water and N in the soil stores, which started empty of water content and at roughly 50% of the annual total soil accumulated N (distributed 2:1 between the upper and lower layers). Warm up periods of 1 year were also used prior to the calibration exercises described above.

4 Results

4.1 Summary Description

Land-use statistics integrated over all 282 HRUs contributing to the Derwent at Buttercrambe reveal that the difference in estimates of mean NO3-N available for leaching on an annual basis is less than 3%. Therefore, detailed results are presented here solely for the River Dove and Pickering Beck sub-catchments. The various NO3-N model applications undertaken are listed in Table 5. The Model 2 runs were conducted to indicate the level of change expected to occur at eventual steady-state solely in response to the land-use change in the absence of any climate-induced hydrological effects.
Table 5

Model applications

 

Land-use inputs

1990–1993

2000–2003

Climate inputs (rainfall, PET)

1990–1993

Pickering Model 1

Pickering Model 2

Dove Model 1

Dove Model 2

2000–2003

Not tested

Pickering Model 3

Dove Model 3

For the River Dove and Pickering Beck, the main features of the results are:
  • As a basis for modelling NO3-N, flow simulation is acceptable (in terms of flow duration curves: Fig. 3), although mean flows are underestimated.
    Fig. 3

    Flow duration curves for a River Dove and b Pickering Beck. Mean absolute percentage error statistics: River Dove, 6.8 (1990–1993) and 18.4 (2000–2003) Pickering Beck, 13.2 (1990–1993) and 12.5 (2000–2003)

  • Overestimation of NO3-N concentration in all model applications; see Figs. 4 (Pickering Beck) and 5 (River Dove). This is in part due to the underestimation of flow together with other factors, notably denitrification which is not included in headwater channel simulation.
    Fig. 4

    Observed and CASCADE modelled NO3-N in Pickering Beck. Model applications are defined in Table 5

    Fig. 5

    Observed and CASCADE modelled NO3-N in River Dove. Model applications are defined in Table 5

  • Land-use changes indicate an increase in NO3-N leaching. However, this is moderated by the diluting effect of wetter conditions in 2000–2003 (Table 6).
    Table 6

    Summary statistics of modelled flow, NO3-N concentration and NO3-N load

     

    Mean flow (m3 s−1)

    Mean mg N L−1

    kg N ha−1 yr−1

    Pickering Model 1

    0.62 (0.82)

    5.90 (obs = 2.78)

    17.1 (obs = 9.5)

    Pickering Model 2

     

    7.64

    22.0

    Pickering Model 3

    0.86 (0.99)

    6.25 (obs = 4.30)

    22.5 (obs = 18.4)

    Dove Model 1

    0.82 (0.94)

    3.72 (obs = 1.18)

    14.7 (obs = 7.7)

    Dove Model 2

     

    4.48

    17.9

    Dove Model 3

    1.01 (1.27)

    3.63 (obs = 1.33)

    16.8 (obs = 10.4)

    For comparison, observed data (EA flows and Harmonised Monitoring Scheme water quality) are quoted in brackets. Loads estimated from observed data were calculated using Method 5 of Littlewood et al. [51]

  • Observed data show that mean values and the positive upward trend in NO3-N concentrations are much greater in Pickering Beck than in River Dove (Table 6).

Although simulation of flow percentile values is largely adequate, there is a universal tendency for the model to underestimate variability at the extremes, particularly in Pickering Beck. During 2000–2003, moderate flows were underestimated (more notably in the River Dove). The curves reflect substantially higher rainfall in the early 2000s compared with the early 1990s. A very low value for 1st percentile observed flow at Pickering Beck in 2000–2003 was due to temporary gauging problems giving spuriously low flow values which bias the statistics. The value is anomalous and should be disregarded. The failure to represent extreme conditions results in underestimation of high flows, and for both periods, mean flow is underestimated in both catchments (Table 6). Underestimation of high flows is generally more substantial in Pickering Beck, being an unsurprising consequence of the calibration procedure employed (outlined in Section 3.2.3). This is reflected in model efficiency statistics. Values of 0.15 and 0.30 for 1990–1993 and 2000–2003, respectively, are attained for Pickering Beck whereas higher values are attained for River Dove (0.53 in 1990–1993 and 0.63 in 2000–2003).

The N observations in the two rivers almost exclusively represent baseflow conditions (only six of 184 observations occurred at flows exceeding Q95). Both sub-catchments have in excess of 40% agricultural land and although Pickering Beck has a considerably higher proportion of tilled land (with higher levels of N in crop residues than other land-cover types) the differences in mean observed NO3-N concentrations are large. The observed increases in NO3-N concentration between 1990–1993 and 2000–2003 differ substantially in magnitude between the two catchments: in the Pickering Beck, an increase of 55% was seen whereas, in contrast, the River Dove showed only a 13% increase. In the Dove, it appears there were some higher observed concentrations in the intervening period (late 1990s) lasting only transiently, yet, at around the same time a step-change may have occurred in the Pickering Beck. The temporal increase in NO3-N load and concentration in Pickering Beck is captured by the model but not to the extent indicated by the observed data. Simulations indicate little change in the River Dove.

4.2 Impact of Propagated Errors on Model Output

When considering the Nash–Sutcliffe efficiency statistic, hydrological model performance for the two sub-catchments compares acceptably to other similar recent studies especially given that application was at sites different to where calibration was undertaken and furthermore, for different time periods. Other studies, comparable both in terms of the complexity of model structure and parameterisation and in terms of input data needs, cite performance under calibration or validation for the location at which calibration was carried out. For example, Bell et al. [6] achieved an efficiency value for the Derwent at Buttercrambe of 0.20 (compared with a value of 0.26 in our studies). Silgram et al. [69] report efficiency values of between 0.40 and 0.85 for diffuse pollution modelling applications in the nearby River Ouse catchment. More generally, for hydrological modelling in the UK using the water quality models NIRAMS, SWAT and INCA (by [21, 41, 77] respectively), a substantial number of efficiency values below 0.5 have been reported amongst the more prevalent higher values. Returning to this study, the CASCADE calibrations for Hodge Beck and Low Marishes (Derwent) yielded efficiency values of 0.66 and 0.77, respectively.

Errors due to the hydrological component of the model do introduce some bias to the estimates of nitrate concentration. Estimates using the method of Anthony et al. [3] suggest that during the wetter 2000–2003 period all nitrate available for leaching would have been leached to the rivers whereas during 1990–1993, small proportions (9.7% and 10.5% in Pickering Beck and Dove, respectively) would have been redirected to other parts of the soil nitrogen cycle due to lower rainfall. Comparison of the modelled loads for Pickering Beck and Dove Models 2 and 3 (Table 6) suggest that CASCADE was simulating the leaching of virtually all available soil nitrate in the earlier period. Knowing this discrepancy and the consistent underestimation of flow by CASCADE, it is possible to apply adjustment factors (Table 7) to the modelled nitrate concentrations and loads presented in Table 6. In all four cases where observations are quoted, the outcome of this exercise is lower simulated concentrations, though these are still overestimates. Likewise, loads are still overestimated. Following these adjustments what remains most apparent is the inability of the model to explain the increase in time of nitrate concentrations and loads in Pickering Beck.
Table 7

Modified simulated NO3-N concentrations and loads allowing for bias in flow modelling and in estimates of the fraction of soil nitrate leached (represented by the first and second adjustment multipliers respectively)

 

Original simulation (mg N L−1)

Original simulation (kg N ha−1)

First adjustment multiplier

Second adjustment multiplier

Modified simulation (mg N L−1)

Modified simulation (kg N ha−1)

Pickering Model 1

5.90

17.1

0.76

0.90

4.01

15.4

Pickering Model 3

6.25

22.5

0.87

1.00

5.43

22.5

Dove Model 1

3.72

14.7

0.87

0.90

2.92

13.2

Dove Model 3

3.63

16.8

0.80

1.00

2.89

16.8

There are other sources of uncertainty, notably those associated with in-river processes and land-use mapping which are discussed below. Nitrogen sinks via stream channel denitrification were not represented in the upper reaches of Pickering Beck and the River Dove because the QUESTOR network is a truncated representation of reality. The form of the CASCADE/QUESTOR model linkage did not include explicit representation of high-velocity headwater stream channel environments. Doubtless, there is considerable scope for NO3-N retention to take place where headwaters are slower-flowing and in ditches draining land with high NO3-N loads, as stated by Stalnacke et al. [71]. Heathwaite [34] gives a detailed overview of in-stream N transformation processes. Identification of such processes and their representation in models is an important ongoing research need. Inclusion of a more extensive representation of the river network is outside of the scope of this paper. Model performance for 1990–1993 (Fig. 6) at a location roughly 4 km downstream of the Pickering Beck monitoring site indicates that, with inclusion of denitrification at rates similar to those reported in previous modelling studies (e.g. 0.5–1.5 t N day−1: as quoted by Eatherall et al. [23] for the nearby River Wharfe), mean NO3-N concentrations are not being overestimated. Typically, in Pickering Beck and River Dove, denitrification rates of between 0.25 (15–20% of N inputs) and 0.7 t N day−1 (35–40% of inputs) were estimated by the model for the 1990–1993 and 2000–2003 periods, respectively. However, time-series responses in Pickering Beck are not well represented.
Fig. 6

Observed and CASCADE/QUESTOR modelled NO3-N in Pickering Beck downstream of trout farm

Modelling the link between land-use and river N concentrations in small sub-catchments encompasses some uncertainties in catchment N residence time and also land-use mapping. Historic, as well as contemporary, land-use may influence river N content in the two periods of interest. For Pickering Beck the model includes two HRUs at the downstream end that only partially lie within the catchment delineated by the N sampling site. Splitting up HRUs is not justified (Section 3.2). These two HRUs, although representing less than 15% of the total modelled catchment area, are almost exclusively tilled land with much higher mean N inputs than the remainder of the catchment.

5 Discussion

Achieving a close goodness-of-fit to observations is not the primary objective of this modelling study. Here, in line with Wade et al. [79], the main role of the model is to focus on improving understanding of the less well-defined components of the catchment system. More specifically, it is used to understand the role that actual land-use change has on impacts in terms of river NO3-N concentrations and how these impacts may differ between catchments. The model applications, as they stand, isolate impacts of land-use change and avoid confounding factors of in-river processes. Results permitted further isolation of confounding factors due to climate (e.g. diluting and flushing effects) to be made.

5.1 Differences Between Catchments

There are two key points in this respect:
  • The differences in NO3-N concentration observed between the two catchments are larger than can be predicted by land-use factors alone.

  • Seasonality in observed NO3-N concentration (1999–2004) differs markedly between the two catchments (Fig. 7a) and is only reproduced by the model in the River Dove, not Pickering Beck.
    Fig. 7

    a Mean NO3-N concentration by month (1999–2004); b NO3-N concentration plotted against flow (1999–2004)

As annual rainfall patterns were similar in the catchments due to their close proximity and comparable relief, land-use factors are effectively summarised by the modelled annual mean loads. For example, if the loads simulated by Pickering Model 1 and Dove Model 1 are compared (Table 6), it is apparent that a difference of less than 20% might be expected. However, observed concentration data suggest that Pickering Beck has over twice the NO3-N load of River Dove. Clearly, there are additional sources of N contributing in the Pickering Beck but not in the River Dove.

Plots of mean NO3-N concentration for each month of the year (Fig. 7a) indicate summer maxima in the Pickering Beck but winter maxima in River Dove. The model structure, even when adapted to represent groundwater hydrological response, can only partially capture the seasonal NO3-N trends seen in Pickering Beck. Winter minima are simulated in periods when there is a preponderance of large winter dilution events in very high flow (as in 2000–2003). Otherwise, winter maxima are simulated (e.g. Pickering Model 1: Fig. 6). Summer maxima could only be fully reproduced in model output if an additional deep sub-surface source of N were to be included which conceptually would represent relics of historic N inputs percolating through the unsaturated zone. Winter maxima are typically seen in agricultural catchments [65, 80] with flow dominated by shallow sub-surface components which become enriched in NO3-N during the autumn and winter months. In this respect, seasonality is represented adequately by Dove Models 1 and 3, although the observed data indicate that winter peak N concentrations occur later than simulation suggests. This indicates a within-catchment retardation of N not captured by the model. A similar effect is seen in Pickering Beck (Fig. 6). Scatter-plots of flow at time of sampling against NO3-N concentration (Fig. 7b) clearly show an inverse relationship for the Pickering Beck and a positive relationship for the River Dove.

The large differences between the two catchments in terms of seasonal NO3-N dynamics reflect and support findings from other researchers (e.g. [43, 54]) suggesting the importance of groundwater sources in catchments which, like Pickering Beck, do not exhibit winter river NO3-N maxima. In Pickering Beck, the flow and seasonality-related relationships were much less strong pre-1998, which may reflect an increase in recent years in the groundwater NO3-N concentration (see Section 5.2).

5.2 Temporal Changes in Nitrate-N Concentration

Between the two periods (1990–1993 and 2000–2003), changes in land-use:
  • are sufficient to adequately explain the small increase in NO3-N concentration in River Dove and

  • can only explain a minor part of the increase in NO3-N concentration in Pickering Beck.

If land-use change were to have been the predominant driver of River Dove NO3-N concentration change, as seems likely, the rapidity of change is noteworthy as there is no evidence from the modelling to suggest that a steady-state had not already been reached by the 2000–2003 period. Highest concentrations were observed in the mid- to late-1990s, a period for which time-series model results are not shown, due to absence of a suitable combination of land-cover and agricultural census information to generate a land-use data set. However, continuation of Dove Model 1 application through to 1997 reveals higher simulated NO3-N concentrations in 1997 than in other years (Fig. 8), broadly coinciding with highest observed values. The reason for the modelled temporal pattern is that due to wetter conditions in the late-1990s relative to the late-1980s, the vast majority of the leachable NO3-N was transferred to the river rather than being retained as a residual in the soil. In the early-2000s, when conditions were wetter still, lower concentrations were simulated due to the effects of greater dilution.
Fig. 8

Mean simulated NO3-N concentration by year. For each sub-catchment Model 1 was used up to 31 Dec 1997 and Model 3 used thereafter

The substantially wetter nature of 2000–2003 relative to 1990–1993 is also important in interpretation of trends in Pickering Beck. If hydrological conditions had remained unaltered, the model simulates an increase of approximately 30% in mean NO3-N concentration (difference between Pickering Model 1 and 2). Even this figure represents a much less substantial change than the observed increase (55%), yet the effect of hydrology lowers this modelled increase to just 6% (comparing Pickering Models 1 and 3). Four hypotheses are put forward to explain the full magnitude of change, and these are discussed below.
  1. 1.

    Higher amounts of NO3-N available for leaching due to consideration of minor (high N use) crops and possible impacts of change in fertiliser usage on cereals and grasslands could account for an increase in NO3-N loads. To ease computational requirements, the CASCADE model does not consider all the possible combinations of soil type and land-use in a catchment. However, over 98% of land-use in the Pickering Beck sub-catchment was accounted for in the model and, in any case, potatoes and oilseed rape were both explicitly included. Currently available evidence on fertiliser use [10] does not suggest an increase in application rate for the main agricultural land-uses in the catchment.

     
  2. 2.

    An increase in numbers of livestock types not considered in the model application. The only appreciable change in the sub-catchment is in the lower reaches where an order of magnitude increase in poultry numbers has taken place. In conjunction with the impacts of land-use change between 1990–1993 and 2000–2003 (captured by the model application), such an increase could fully account for the observed increase in concentration in Pickering Beck. Estimates using the MANNER model [15] suggest poultry in the Pickering Beck catchment could contribute an approximate annual load to the river in excess of 3 kg ha−1 in the 2000–2003 period. Ordnance Survey maps at 1:25,000 scale show a large poultry farm situated very close to the river in the lower reaches of the sub-catchment. However, at the monitoring site, the median soluble reactive phosphorus concentration (that would in high concentration indicate the importance of such a point source input) is very low (40 μg L−1), and there is no indication of any temporal trend. The lack of P input to the river suggest that the poultry manure (typical N/P mass ratio of 2:1) has either not reached the channel in the time period under consideration or has been exported out of the catchment. The second explanation is most likely.

     
  3. 3.

    An increase in rates of nitrification and mineralisation processes in soils as a result of prolonged dry conditions experienced across England between 1995 and 1997. Evidence of elevated river NO3-N concentrations in the Windrush in 1998 has been attributed to this mechanism (particularly in woodland soils) by Moorcroft et al. [57]. Extensive studies in the US [46] have also shown that nitrification is enhanced on drying and re-wetting of soils leading to marked year-on-year variability in N export and a clear decrease in N retention in wetter years. Despite this evidence, it would seem unlikely that such climate-related factors would result in a sustained longer-term trend.

     
  4. 4.

    Impact of higher groundwater NO3-N concentrations. There is much evidence to suggest that concentrations in major aquifers (of which for the UK the Jurassic Corallian group is included) have gradually been increasing in recent decades [11, 13]. Typically, present-day concentrations are up to 30% higher than in 1990 and are likely to violate EC Nitrates Directive levels. In many cases, sampling boreholes are impacted upon by water percolating from agricultural land through the unsaturated zone. High levels of NO3-N resulting from peaks in agricultural N surpluses in the 1970s are likely to eventually reach the water table and thenceforth the river network. The National Rivers Authority [58] reported an increase in groundwater NO3-N levels in samples from the Corallian limestone, indicating their motivation to prevent further degradation of the quality of the water resource. In the region, groundwater quality problems are of local concern to water companies [49]. It seems highly probable that changes in groundwater NO3-N concentration are impacting upon current and recent NO3-N concentrations in Pickering Beck.

     

The fourth hypothesis, in conjunction with impacts of the change in land-use since the early 1990s, clearly provides the most plausible explanation for the large increase in NO3-N concentration in recent years in Pickering Beck. The lack of evidence of change in the River Dove, which is not impacted by Corallian groundwaters to any great extent, gives indirect support to the chosen hypothesis.

6 Conclusions

Application of the diffuse pollution model (CASCADE) to flow and NO3-N dynamics in two rural sub-catchments of the River Derwent has provided insights into the significance of current land-use on NO3-N impacts in rivers. This has been achieved by considering two land-use datasets approximately a decade apart. In both sub-catchments, the more recent land-use data should have resulted in higher potential leaching of NO3-N to waterbodies. Observed data reflect these differences, indicating evidence of increasing NO3-N concentrations. Use of the model allows the changes to be set in the context of current climate and hydrological controls and in-river processes. For example, in Pickering Beck, an increase of 29% (for both load and concentration) driven solely by land-use change is estimated by the model. When including climatic effects the load increase is higher (31%), yet, the concentration increase is just 6% due to the wetter conditions of more recent years. However, additional inclusion of in-stream processes (as represented in QUESTOR and illustrated for a location a short distance downstream), reduces the NO3-N load by approximately 30%. It seems likely that headwater streams and ditches in agricultural areas of the sub-catchments act as significant sinks for NO3-N. The process of denitrification can be exploited in the management of rivers of high NO3-N concentration. Stream restoration measures to increase flow path retention times [68], for example by allowing reconnection of channels and associated floodplains [47], lead to increased denitrification rates.

Returning to our case study, differing responses are seen in Pickering Beck and River Dove and, although in these cases the NO3-N concentrations themselves are not at levels to cause legislative concern, explaining the contrast has important implications for general understanding of long-term nutrient dynamics in catchments. Large increases in NO3-N concentration have occurred in Pickering Beck since the early 1990s. Modelling shows that these cannot be explained by changes in land-use observed in the intervening period. Furthermore, it would not have been expected that land-use change could explain the rapidity of increases in NO3-N concentration. This is because research evidence, summarised in Section 2, suggests sizeable moderating and delaying factors which are manifested as the integration of (1) soil profile biochemical N cycling processes and (2) catchment-scale transit times typical of groundwater-influenced systems. Effectively, a steady-state condition resulting specifically from the land-use change is unlikely to have been reached by 2004. The changes in the Pickering Beck are explicable, however, in the light of long-term increases in groundwater NO3-N concentrations. There is much concern in the UK over recent and ongoing increases in NO3-N in major aquifers, and it seems this is having a discernible effect on recent trends in the water quality of the Pickering Beck. Case study data and modelling indicate that there is not a significant groundwater component to flow in the River Dove. Nitrate-N concentrations show evidence of change, but to a lesser extent than that seen in Pickering Beck.

In terms of the hypothesis proposed at the start of Section 3 (“over a 14-year period, changes in river NO3-N concentration can be satisfactorily explained by climate variability and changes in land-use within that time”) we draw the following conclusions. For Pickering Beck, there is clear evidence of other factors and the hypothesis is refuted. The impact of groundwater flow contribution suggests that NO3-N leaching due to historic (pre-1990) land-use is still strongly influencing river concentrations. For the River Dove, despite the general literature evidence to the contrary the hypothesis cannot be refuted, although in reality there are probably additional drivers. However, there is a clear need for additional incorporation of within-channel sources and sinks of N, raising issues of model structure and equifinality of parameterisation.

Notes

Acknowledgements

The authors thank Andrew Johnson (CEH) for comments on the manuscript. EDINA at Edinburgh University Data Library and Defra are acknowledged as the sources of the Agricultural Census data. The work was underpinned in part by funds from CEH, NERC and the Environment Agency. The analysis undertaken in this paper is stimulated, in particular, by a new initiative, the Catchment Hydrology, Resources, Economics and Management (ChREAM) project, funded under the joint ESRC, BBSRC and NERC Rural Economy and Land Use (RELU) programme (award number RES-227-25-0024).

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Michael G. Hutchins
    • 1
  • Amelie Deflandre-Vlandas
    • 1
  • Paulette E. Posen
    • 2
  • Helen N. Davies
    • 1
  • Colin Neal
    • 1
  1. 1.Centre for Ecology and Hydrology WallingfordWallingfordUK
  2. 2.School of Environmental SciencesUniversity of East AngliaNorwichUK

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