Science of the Total Environment 499 (2014) 481–496

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Comparative study of nitrate leaching models on a regional scale J. Roelsma ⁎, R.F.A. Hendriks Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands

H I G H L I G H T S • • • •

Nitrate concentrations were modelled on a regional scale for three study areas. The performance of four different nitrate leaching models were compared. The dynamic complex process oriented ANIMO model showed the best performance. If a comprehensive data set is not available the MM-WSV model is a good alternative.

a r t i c l e

i n f o

Article history: Received 15 March 2014 Received in revised form 8 July 2014 Accepted 8 July 2014 Available online 2 August 2014 Keywords: Groundwater Nitrate contamination Drinking water resource catchment Model evaluation ANIMO model NURP model

a b s t r a c t In Europe and North America the application of high levels of manure and fertilisers on agricultural land has led to high levels of nitrate concentrations in groundwater, in particular on sandy soils. For the evaluation of the development of the quality of groundwater a sound quantitative basis is needed. In this paper a comparison has been made between observations of nitrate concentrations in the upper groundwater and predictions of nitrate leaching models. Observations of nitrate concentrations in the upper groundwater at three different locations in regions with mainly sandy soils in the eastern and northern part of the Netherlands were used to test the performance of the simulation models to predict nitrate leaching to the upper groundwater. Four different types of simulation models of different levels of complexity and input data requirement were tested. These models are ANIMO (dynamic complex process oriented model), MM-WSV (meta-model), WOG (simple process oriented model) and NURP (semi-empiric model). The performance of the different simulation models was evaluated using statistical criteria. The dynamic complex process oriented ANIMO model showed the best model performance. The MM-WSV meta-model was the second best model, whilst the simple process oriented WOG model produced the worst model performance. The best model performance showed the dynamic complex process oriented ANIMO model in predicting the nitrate concentrations in the upper groundwater of the Klooster catchment. The good performance of the ANIMO model for this catchment can be explained by the additional information about the use of manure and fertilisers at farm level in this study area. The ANIMO model may be a good tool to predict nitrate concentrations in the upper groundwater on a regional scale. However, the use of a detailed process oriented simulation model requires a comprehensive set of input data. If such a comprehensive data-set is not available the MM-WSV model (meta-model) proves to be a good alternative. The WOG and NURP models are suitable for long term (N 8 years) predictions of average nitrate concentrations in the upper groundwater on a regional scale. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Contamination of groundwater with nitrate is a global problem (Spalding and Exner, 1993; McLay et al., 2001; Oenema et al., 2009) and threatens the quality of groundwater as a resource for drinking water in especially North America and the European Union (De Clerq et al., 2001; Shrestha et al., 2010; Chaudhuri et al., 2010). This contamination is commonly associated with diffuse sources such as intensive agriculture, high density of houses with unsewered sanitation, and ⁎ Corresponding author. Tel.: +31 317 486453; fax: +31 317 419000. E-mail address: [email protected] (J. Roelsma).

http://dx.doi.org/10.1016/j.scitotenv.2014.07.030 0048-9697/© 2014 Elsevier B.V. All rights reserved.

point sources such as irrigation using sewage effluent onto land (Keeney, 1986; Wakida and Lerner, 2005; Ledoux, et al., 2007; Almasri and Kaluarachchi, 2007). In the European Union, agriculture is the largest contributor of nitrogen pollution to groundwater since nitrogen fertilisers and manure are used in excessive amounts on agricultural land to increase yield and productivity (Oenema et al., 2007). In the European Union, mineral fertilisers account for almost 50% of nitrogen inputs into agricultural soils and manure for 40% (European Commission, 2002). The use of mineral fertilisers and animal manure increased until the late 1980s and then started to decline. But in the recent years it has increased in the European Union. The loss of nitrogen has led to increased nitrate concentrations in the groundwater

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of especially sandy soils (Scheidleder et al., 2000; Oenema et al., 1998; Fraters et al., 1998). In sandy regions in the Netherlands the 95% confidence interval estimate of the areal fraction where the EU standard was exceeded, was found to be 0.77–0.85 in the early 1990s (Boumans et al., 2008). Nowadays, the median nitrate concentrations in the phreatic and shallow groundwater of agricultural lands in sandy regions is 75 mg·l−1 NO3 in the Netherlands (De Goffau et al., 2012). In the Netherlands 65% of the drinking water is extracted from groundwater. A quarter of the groundwater wells, in particular those on the sandy soils, face rising levels of nitrate (Joosten et al., 1998; Visser, 2009). Since groundwater is a major source of drinking water, the health effects of ingesting nitrate in drinking water are a concern when nitrate levels are high (Cantor, 1997). Elevated nitrate concentrations in drinking water can cause methemoglobinemia in infants and stomach cancer in adults (Lee et al., 1991; Wolfe and Patz, 2002). For the quality of water intended for human consumption a guide level of 25 mg·l−1 NO3 is used, whilst the maximum admissible concentration according to European legislation is 50 mg·l−1 NO3 (EC, 1991). To predict changes the quality of groundwater a sound quantitative basis is needed. Different methods can be used to quantify nitrate leaching to the groundwater (Sonneveld et al., 2010). A monitoring network is a commonly known method of quantification for nitrate leaching. The main advantage of a monitoring network is that it measures the actual environmental quality. The main disadvantages are the spatial and temporal limitations of the monitoring results. Data derived from monitoring networks can neither be extrapolated into

the future nor be used to predict nitrate leaching for a different region or under other circumstances concerning e.g. nutrient management, land use or hydrology. The high economic costs of monitoring networks are another important disadvantage. Simulation models may contribute to overcome the main disadvantages of a monitoring network. Models allow investigation of the effects on nitrate leaching of parameters that are varying in space and time such as land use, use of fertilisation and climate. The use of simulation models has therefore become an important tool for decision makers (Fassio et al., 2005; Wolf et al., 2005a). Furthermore, the combination of nitrate monitoring networks with simulation models may explain measured values and thereby obtain a better understanding of the behaviour of nitrogen in soil and groundwater on a catchment scale. The objective of this paper is to compare the performance of four nitrate leaching models of different levels of complexity and input data requirement on a regional scale. The performance is assessed by comparing model results with observations of NO 3 concentrations in the upper groundwater at three different locations in the Netherlands. The measurements of nitrate concentrations in the upper groundwater were conducted in regions with mainly sandy soils in the eastern and northern part of the Netherlands. In these regions the probability of exceeding the EU limit value for nitrate in the upper groundwater is high (Boumans et al., 2008). Furthermore, in the regions where the measurements were conducted land use consists dominantly of dairy farming with grassland covering most of the area and maize cultivation as the second forage crop.

Fig. 1. Locations of the study area in the Netherlands; 1: the Drentsche Aa catchment, 2: Overijssel; 3: the Klooster catchment.

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2. Materials and methods

2.2. Assessment of data for model evaluation

2.1. Description of the study areas

The study areas Klooster catchment, Drentsche Aa catchment and the four different drinking water resource catchments in the Province of Overijssel were selected because of the availability of measurements of nitrate concentrations in the upper groundwater and the study areas are situated on sandy soils which are sensitive to leaching of nitrate to groundwater. For the Klooster catchment two data-sets of nitrate concentrations of the upper groundwater were collected. The first data-set contained observed nitrate concentrations of the upper groundwater from a survey conducted in October 2000 (Van Beek and Baggelaar, 2001). In this survey the agricultural land in the Klooster catchment was divided in 4 classes: grassland with a deep groundwater table in winter (N 180 cm below soil surface), grassland with a shallow groundwater table in winter (b 180 cm below soil surface), maize with a deep groundwater table in winter (N 180 cm below soil surface) and maize with a shallow groundwater table in winter (b 180 cm below soil surface). The numbers of fields selected in the catchment area were proportional to the area of each of the four groups of the combination of land use and groundwater table. A total of 48 fields on 15 dairy farms was selected. Within this set 18 fields with grassland and shallow groundwater table, 22 fields with grassland and deep groundwater table, 3 fields with maize and shallow groundwater table and 5 fields with maize and deep groundwater table were selected. The second data-set contained nitrate concentrations in the upper groundwater or soil moisture from a survey conducted in May 2001 (Roelsma et al., 2003; Smit et al., 2003). In this survey a stratified sampling was carried out (see Table 1). The strata were formed by overlaying maps of the catchment with the combinations of soil type, groundwater table depth class and land use. At each sampling point one groundwater sample was taken or when the groundwater level was deeper than 150 cm below soil surface a soil sample was taken in order to determine the nitrate content in the soil moisture that was extracted by centrifugation. In order to obtain a data-set comparable with the first data-set, only the data of the sampling points at grassland (29 samples) and maize land (12 samples) were used. In the Drentsche Aa catchment nitrate concentration data from the upper groundwater were obtained from the soil quality monitoring network of the Province of Drenthe (Seine, 1996; Roelsma and Knotters, 2009). The soil quality monitoring network consists of 67 monitoring sites of which 37 sites were located on agricultural land and 30 sites on nature areas or extensively managed grasslands. Of the 37 monitoring sites on agricultural land 30 sites were located on sandy soils (all Gleyic Podzols); the remaining 7 sites were located on peat soils. In order to obtain a data-set comparable with the data-set of the Klooster catchment only the measurements of the 30 monitoring sites on agricultural land with sandy soils were used. Of these sites, 15 were located on grassland and 15 on maize. For the period 2001–2012, annually samples were collected in the period from January till June on all monitoring sites in the Drentsche Aa catchment. For the four drinking water resource catchments in the Province of Overijssel, a study on the quality of the upper groundwater in terms of nitrate concentrations was conducted in 2011 and 2012 (Roelsma et al., 2013). The aim of the study was to estimate the average nitrate concentration in the upper groundwater in the study area. This area consisted of 13 participating dairy farms in the four resource catchments. The upper groundwater was sampled in autumn 2011 and in autumn 2012 at 170 sampling locations, selected by stratified simple random sampling. The strata were formed by overlaying maps of the 13 farms with the combinations of soil type, groundwater table depth class and land use. The number of sampling locations per stratum was chosen proportional to their area size, with a minimum of two. At each sampling location and time of sampling, the groundwater level was measured as well. The observations of nitrate concentrations in the upper groundwater are valid at the point scale, whilst the application scale of nitrate leaching models is larger (field scale or regional scale). To overcome

The study was conducted in three different areas in the eastern and northern part of the Netherlands (Fig. 1). At two of the three locations groundwater is used for the abstraction of drinking water. At the third location (the Drentsche Aa) drinking water is extracted from the surface water. Because of the abstraction of groundwater or surface water for the purpose of drinking water use, the nitrate concentrations in the upper groundwater at these locations have been monitored. The first study area, the Klooster catchment, is a drinking water resource catchment area of 25 km2 and is located in the eastern part of the Netherland. Land use in the Klooster catchment mainly consists of agriculture (70%) and forest (25%). The remaining 5% of the area in the catchment is urban area. Grassland is the dominant land use (48%), followed by maize cultivation (16%). Only a small part of the area is used for arable crops (6%). The soils in the Klooster catchment are characterised as non-calcareous sandy soils of which approximately 80% are classified as Gleyic Podzols (FAO, 2002). The remaining soils are Umbric Gleysols, Haplic Arenosols and Fimic Anthrosols. On the Dutch Soil Map (Stiboka, 1981) groundwater dynamics are characterised by groundwater table classes (GTC). Each GTC classifies a combination of a mean highest groundwater table (MHW) and a mean lowest groundwater table (MLW) (Finke, 2000). The GTC ranges from GTC I (very wet) till GTC VIII (very dry). According to the Soil Map approximately 10 km2 of the Klooster catchment has a GTC VI, 8 km2 has a GTC VII and 6 km2 has a GTCV. The second study area, the Drentsche Aa catchment, is situated in the northern part of the Netherlands (Fig. 1). The drainage area of the catchment is about 300 km 2 . The Drentsche Aa is a small stream system of which a large part is still in the original meandering state. Land use in the Drentsche Aa catchment mainly consists of agriculture (52%) and nature (32%). The remaining 16% of the area in the catchment is built-up area and surface waters. Grassland is the dominant land use (27%), followed by arable crops (20%). Only a small part of the area is used for maize cultivation (5%). Sandy soils are the dominant soil types in the Drentsche Aa catchment (N 90%). Close to the stream peat soils can be found. The sandy soils in the Drentsche Aa catchment are characterised as noncalcareous sandy soils of which approximately 80% are classified as Gleyic Podzols. The remaining soils are Umbric Gleysols and Carbic Podzols. According to the Dutch Soil Map (Stiboka, 1981) approximately 15% of the area of the Drentsche Aa catchment has a MLW b 120 cm below soil surface (GTC II, III and IV). 25% of the Drentsche Aa catchment has a GTCV, 35% has a GTC VI and 25% of the area of the catchment has a MHW N 80 cm below soil surface (GTC VII and VIII). The third study area is situated in the eastern part of the Netherlands and consists of four different drinking water resource catchments in the Province of Overijssel (Fig. 1). The total area of the four resource catchments is about 70 km2 . Land use consists mainly of agriculture (61%) and nature (25%). The remaining 14% of the area is urban area. In the four resource catchments in Overijssel grassland is the dominant land use (41%), followed by maize cultivation (14%). Only a small part of the area is used for arable crops (6%). In the four drinking water resource catchments sandy soils are the dominant soil types. The sandy soils are characterised as non-calcareous sandy soils of which approximately 40% are classified as Gleyic Podzols. The remaining soils are Fimic Anthrosols (33%), Umbric Gleysols (15%), Histic Gleysols (10%) and Carbic Podzols (2%). According to the Dutch Soil Map (Stiboka, 1981) approximately 52% of the area of the four drinking water resource catchments has a GTC VII (dry), 33% has a GTC VI and 15% has a GTC V (intermediate wet).

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Table 1 Temporal and spatial averaged observed nitrate concentrations in the upper groundwater and characteristics of the different strata of the study area. Data-set

't Klooster 't Klooster 't Klooster 't Klooster 't Klooster 't Klooster 't Klooster 't Klooster 't Klooster 't Klooster Drentsche Aa Drentsche Aa Overijssel Overijssel Overijssel Overijssel Overijssel Overijssel Overijssel Overijssel Overijssel Overijssel Overijssel Overijssel a b

Period

2000/2001 2000/2001 2000/2001 2000/2001 2000/2001 2000/2001 2000/2001 2000/2001 2000/2001 2000/2001 2001–2012 2001–2012 2011–2012 2011–2012 2011–2012 2011–2012 2011–2012 2011–2012 2011–2012 2011–2012 2011–2012 2011–2012 2011–2012 2011–2012

Stratum Number

Land use

Soil type (Soil Map Code)a

Groundwater table classb

Area (ha)

n

NO3 (mg·l−1)

1 2 3 4 5 6 7 8 9 10 1 2 1 2 3 4 5 6 7 8 9 10 11 12

Grassland Grassland Grassland Grassland Grassland Grassland Maize Maize Maize Maize Grassland Maize Grassland Grassland Grassland Grassland Grassland Grassland Grassland Grassland Grassland Maize Maize Maize

Gleyic Podzols (Hn21) Gleyic Podzols (Hn21) Gleyic Podzols (Hn21) Gleyic Podzols (Hn23) Gleyic Podzols (Hn23) Fimic Anthrosols (zEZ21) Gleyic Podzols (Hn21) Gleyic Podzols (Hn21) Gleyic Podzols (Hn23) Fimic Anthrosols (zEZ21) Gleyic Podzols (Hn23) Gleyic Podzols (Hn21) Umbric Gleysols (pZg23) Fimic Anthrosols (zEZ21) Fimic Anthrosols (zEZ21) Carbic Podzols (Hd21) Histic Gleysols (vWz) Histic Gleysols (vWz) Gleyic Podzols (Hn21) Gleyic Podzols (Hn21) Gleyic Podzols (Hn21) Umbric Gleysols (pZg23) Fimic Anthrosols (zEZ21) Gleyic Podzols (Hn21)

V VI VII V VI VII V VI VI VII VI VII VI VI VII VII V VI V VI VII VI VII VII

191 203 121 64 119 83 48 46 43 24 5932 3389 22 16 27 5 15 5 15 17 36 8 24 13

114 59 61 44 37 32 26 28 9 8 146 150 36 20 42 10 16 14 24 14 40 20 49 20

92 111 110 72 76 100 107 159 83 93 78 102 66 120 79 128 51 28 111 124 71 66 132 161

Soils with code 23 contain more loam. The groundwater table classes ranges from V (intermediate wet) to VII (dry).

this discrepancy, the observation data of the different data-sets were stratified on basis of soil type, groundwater table class and land use. For the Klooster catchment this resulted in 10 different strata consisting of areas with sizes ranging from 24 ha till 203 ha (Table 1). For the Drentsche Aa catchment the stratification resulted in two strata (one grassland and one maize). For the four drinking water resource catchments in the Province of Overijssel a stratification was already conducted. 2.3. Simulation models In order to predict the leaching of nitrate to groundwater different types of models can be used. In this study the performances of four different types of nitrate leaching simulation models were compared: 1) Dynamic detailed process oriented model 2) Meta-model 3) Simple process oriented model 4) Empiric model A dynamic detailed process oriented model describes the main processes of the nutrient cycle on a detailed temporal and spatial scale. The strength of these types of simulation models is that effects of different strategies of nutrient management, water management and land use changes can be evaluated. A disadvantage is that they normally require large amounts of input data at a detailed temporal and spatial scale. In many cases, such detailed data may not be available, at least not at the larger scale. To overcome the need for extensive data collection for dynamic detailed process oriented models, a meta-model may be used. A meta-model is a simple approximation of the input/output behaviour of a detailed dynamic model. For this purpose, the output of the detailed dynamic model is related to its most relevant input data by means of fitting of a regression model. The obtained regression model is called ‘meta-model’ (Kleijnen and van Groenendaal, 1992; Forsman, 2002; Kleijnen, 2006). The application of meta-models is restricted to a fixed time period and therefore they do not provide dynamic simulations. Another restriction is that a meta-model can

only be used within the range of input data for which it is derived from the dynamic detailed process oriented model. An alternative for a less complex model is a simple process oriented model. Such a model is based on a restricted number of processes. The strength of these types of models is their transparency and the small amount of input data needed. Their weakness is the limitation to a restricted number of strategies of nutrient management or water management which can be evaluated. In contrast to the other types of models an empiric model is not process oriented but is based on direct cause–effect relations that are obtained from observations in the field or in laboratory experiments. The strength of an empiric model is the validity for the ‘real life’ situation. Its weakness is that it is only valid for the considered experimental situation and that it does not provide an explanation of the results based on processes. For this study four simulation models which are used in the Netherlands were selected for the comparison of their performance simulating nitrate concentrations in the upper groundwater on a regional scale. The ANIMO model was selected as a dynamic, complex process oriented model. The ANIMO model is a one-dimensional model for simulating the carbon, nitrogen and phosphorus cycle in a wide variety of soils and the nutrient emissions to groundwater and surface waters (Groenendijk and Kroes, 1999; Kroes and Roelsma, 1998; Groenendijk et al., 2005). The model is commonly used to quantify the impact of nutrient management strategies on leaching of nitrogen (and phosphorus) from agricultural land to groundwater and surface waters in the Netherlands (Boers et al., 1997; Wolf et al., 2003, 2005a, b; RIVM, 2004). The ANIMO model has been used in a couple of European Union projects for a detailed intercomparison of simulation models for quantification of nitrogen losses to groundwater and surface water (Vereecken et al., 1991; Schoumans and Silgram, 2003). Also outside the Netherlands the ANIMO model has been used to calculate the leaching of nitrogen (and phosphorus) to groundwater and surface waters (Marinov et al., 2005; Kroes and Roelsma, 2007; McGechan and Hooda, 2010). The ANIMO model incorporates addition of organic materials and nutrients to the soil system by fertilisation, root residues and harvest losses and the redistribution of these materials by tillage as well as accumulation and decomposition of soil organic matter in

J. Roelsma, R.F.A. Hendriks / Science of the Total Environment 499 (2014) 481–496

harvest losses N-plant shoots dying roots

fertilizer

materials

N-org. parts

485

deposition dry + wet

manure

NH3

N-mineral

N2 N2 O

N-plant roots NO 3 - N

N-fresh org. matter

N-exudates

N-dissolved org. matter NH 4 - N

N-humus

adsorbed NH 4 - N leaching

Fig. 2. Relational diagram of the N cycle in the ANIMO model. Source: Kroes and Roelsma, 1998

relation to quality and composition of different organic materials (Fig. 2). Crop uptake of nitrogen (and phosphorus) in relation to the nutrient status of arable crops and grassland is also considered by the ANIMO model. In addition, the ANIMO model offers the option to use the nutrient uptake and amount of crop residues calculated by an external crop simulation model. In this study the model output of an external crop module as part of the integrated modelling system STONE was used (see Section 2.4). The ANIMO model involves nitrification and denitrification as a function of the oxygen demand of transformation processes and the diffusive properties of the soil. The influences of environmental factors such as pH, temperature, aeration and drought condition on the transformation rates are included in the model. Hydrological data, such as water fluxes and moisture contents of the distinct soil compartments, are supplied by an external model that describes water flow in the unsaturated/saturated zone and discharge to the surface waters on a field plot or regional scale. In this study the model SWAP (Van Dam et al., 1997) was used for simulating unsaturated water flow. For the meta-model the MM-WSV model was selected. Mol-Dijkstra et al. (1999) have derived a set of regression equations for nitrogen and phosphorus leaching to groundwater and surface waters by statistical analysis of the input and output of the more complex process oriented models used in the Aquatic Outlook project (Boers et al., 1997). This project consists of a modelling framework of the following four components: 1) a regional groundwater (DEMGEN-model; Delft Hydraulics, 1986) linked with a model for simulating unsaturated water flow (SWAP model; Van Dam et al., 1997); 2) a model for the

distribution of manure and fertiliser applications based on the calculations of the Workgroup Uniform Calculation of Manure and Minerals (WUM; CBS, 2010); 3) the ANIMO-model for calculations of the nitrogen and phosphorus emissions to groundwater and surface waters on a national scale and 4) a spatial discretisation of 3634 unique combinations of land use, soil types, groundwater tables and fertilisation levels. The output of the model runs of the 3634 unique combinations was related to the most relevant input data of the modelling framework. A set of regression equations was fitted to find the best-fitting relation between the model input and output. This set of regression equations is called the MM-WSV meta-model. The MM-WSV model calculates a year average nitrate concentration at a predefined depth (e.g. upper part of the groundwater; Table 2). The most important input characteristics describing nitrate leaching are soil type, land use, mean highest groundwater table, upward seepage quantity and quality, and net nitrogen surplus of the soil. The MM-WSV model reads as follows: NO3 ¼ c‐Soil þ c‐Land use þ c‐N‐Seepage  N‐Seepage þ c‐Seepage  Seepage þ c‐MHW  MHW þ c‐N‐surplus  N‐surplus

ð1Þ

where NO3 = predicted nitrate concentration in the upper groundwater [mg·l− 1]; c-Soil, c-Land use, c-N-Seepage, c-Seepage, c-MHW and c-N-surplus = different constants depending on soil type, land use, seepage, mean highest groundwater table and nitrogen surplus; N-Seepage = amount of nitrogen in seepage [kg·ha−1·a−1]; Seepage =

Table 2 Selected nitrate leaching models. Type of model

Name of the model

Number of parameters

Temporal resolution of output

Spatial (vertical) resolution

Detailed process oriented model Meta-model Simple process oriented model Empiric model

ANIMO MM-WSV WOG NURP

160 7 5 17

Day Year Year Year

Soil compartment Upper metre groundwater Upper metre groundwater Upper metre groundwater

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amount of seepage [mm·a−1]; MHW = mean highest groundwater table [m]; N-surplus = nitrogen surplus [kg·ha−1·a−1]. In a study of Schröder et al. (2007) an estimation has been made of the amount of nitrogen surplus which can be permitted on sandy soils without exceeding the target of 50 mg·l nitrate in groundwater. In this study, a model was built to explore at which rate combinations of nitrogen fertiliser and cattle manure applied to grassland and maize achieve this nitrate target. This model is called the WOG model. The WOG model is a simple process oriented model which relates (allowable) inputs to (required) water quality (Schröder et al., 2009). The main input of the WOG model is the nitrogen surplus. The nitrogen surplus is defined as the total input of nitrogen (i.e. sum of manure-N, mineral fertiliser N, soil mineral N at the onset of the growing season, deposition of atmospheric N, biologically fixed N and N mineralized from soil organic matter) minus the total output of nitrogen (i.e. the sum of crop N which is removed by either grazing or harvests, N investments in (new) crop residues and N stored in the organic N fraction of manure if it is not mineralized in the first 12 months after application). In the WOG model for each combination of land use (grassland or arable land), soil type (sandy soils, clay soils or peat soils) and groundwater table class leaching fractions have been determined using the monitoring data from the Mineral Policy Monitoring Programme (LMM) (Fraters et al., 2012). For different combinations of land use and groundwater table class leaching fractions are given in Table 3. The nitrate concentration predicted by the WOG model is a function of nitrate surplus, leaching fraction and precipitation surplus: NO3 ¼ N‐surplus  c‐lf=precipitation‐surplus  62=14  100

ð2Þ

where NO3 = predicted nitrate concentration in the upper groundwater [mg·l−1]; N-surplus = nitrogen surplus [kg·ha−1·a−1]; c-lf = constant for leaching fraction depending on soil type, land use, and groundwater table class (−); precipitation-surplus is amount of precipitation minus quantity of evapotranspiration [mm·a−1]. The NURP model was selected as an empiric model. The NURP model is based on empirical data of nitrogen input and soil mineral nitrogen conducted from dairy farms (Vellinga et al., 1997). The nitrate concentration in the upper groundwater in the NURP model is calculated as a function of soil mineral nitrogen and groundwater table for both grassland and maize cultivation. Therefore, the NURP model can be characterised as a semi-empiric model. The soil mineral nitrogen in the NURP model is simulated as an empiric function of fertilisation, grazing system, supplemental feeding and milk production per cow. The NURP model was developed for making guidelines on utilisation of fertilisers and manure and grassland management to reduce nitrate leaching from dairy farms on sandy soils. It was applied for the justification of the request of the Dutch government to the European Commission for a derogation from the manure standard of 170 kg·ha− 1·a−1 nitrogen (in 2003) according to the EC Nitrates

Table 3 Leaching fractions for different combinations of land use and groundwater table class for sandy soils (Fraters et al., 2012). Ground water table class

VIII VII VI V* V IV III* III II* II I

Leaching fraction (–) Grassland

Arable land

0.44 0.37 0.29 0.21 0.22 0.19 0.14 0.04 0.02 0.02 0.02

0.90 0.75 0.59 0.43 0.45 0.39 0.28 0.07 0.05 0.05 0.05

Directive (Willems et al., 2000). The mathematical formulation of the NURP model has not been published in the theoretical description of the model (Vellinga et al., 1997). The four selected models differ in degree of complexity and number of input parameters, and also in temporal and spatial resolution of the output data (Table 2). Only the dynamic complex process oriented ANIMO model can supply output data on a daily basis. Furthermore, the output data of the MM-WSV model, the simple process oriented WOG model and the empiric NURP model are fixed at a predefined depth, whilst the user of the dynamic complex process oriented ANIMO model has a certain degree of freedom for choosing the vertical resolution of the output data. The four models also differ in the nature of the mathematical description of their internal processes and/or relationships. ANIMO is a process oriented model, the other three models are derived from statistical analyses of cause–effect-relationships on the basis of empirical data (measured or generated by models). ANIMO basically applies analytical descriptions with continuous variables, whilst the other three models use descriptions with a mix of continuous aggregated and discrete variables (classes). In this sense ‘continuous aggregated’ applies to continuous variables that are temporally and/or spatially aggregated and/or are composed of other variables (e.g. N-surplus = N-input minus N-uptake). On the basis of this, we characterise the four models as shown in Table 4. It shows that MM-WSV is closer to ANIMO than WOG and NURP, because of a continuous description of the hydrology. This is surely of significance, as hydrological processes are of great importance to the nitrogen and nitrate processes in soil (e.g. effects of soil moisture on N-mineralisation, nitrification and denitrification). 2.4. Model inputs For each of the three catchments of the study area a stratification was made on basis of land use, soil type and groundwater table class. Model simulations were performed solely for the strata for which validation data were available (see Section 2.2). For each strata model inputs were obtained from the integrated modelling system STONE (Wolf et al., 2003). The STONE model was designed for evaluation at the national scale of the effects of changes in the agricultural sector (e.g. changes in fertiliser recommendations and cropping patterns) and in policy measures (e.g. EU nitrate directive for groundwater) for the leaching of nitrogen and phosphorus from agricultural land areas to groundwater and surface waters. The STONE model consists of several modules of which a module for calculation of supply of manure of nitrogen and phosphorus in both manure and mineral fertilisers to the soil, a module for the calculation of atmospheric nitrogen deposition, a module for soil hydrological simulations (SWAP model) and a module for nutrient cycling in soils and nutrient fluxes to groundwater and surface waters (ANIMO model). The soil processes of the integrated modelling system STONE are calculated with the ANIMO model system for 6405 unique spatial units (plots) with respect to land use, soil type, hydrology, fertilisation levels, meteorological districts, etc. For each strata of the three catchments a STONE plot was selected which had the most similarity with the characteristics of the strata (i.e. land use, soil type, groundwater table class). The STONE data base contains input data for each STONE plot concerning fertilisation, crop uptake,

Table 4 Characterisation of the four models according to the nature of their descriptions and variables of processes and relationships of the main domains soil, land use, hydrology and N input. Soil & land use Hydrology N input Continuous Continuous aggregated

Continuous

Discrete classes

Continuous

Continuous aggregated

Discrete classes

ANIMO –

– MM–WSV

– WOG & NURP

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487

Table 5 Description of the main input parameters of the four models according to their type of variable in Table 4.

Input

ANIMO

domain

Continuous

MM–WSV Continuous

WOG

Discrete classes

aggregated Soil

Physical, (bio)chemical &

Land use



21 types; works via: c–Soil, c–Land use, c–

initial N concentrations

MHW, c–N–surplus1

physical & chemical crop



4 crop types;

properties, N uptake crop

Discrete

Continuous

aggregated

classes

aggregated



hydraulic properties,

NURP

Continuous

3 types;

Discrete classes



3 types



2 types

works via: c–lf2 –

2 crop types; works via: c–lf2

works via: c–Soil, c–Land use, c– MHW, c–N–surplus1

Meteorology

Precipitation and



Annual actual

Hydrology

evapotranspiration on

Annual

daily basis

MHW3, 5



Parameters for





11 GTC6;

Additions (daily basis)

Annual N–

and properties of organic

surplus4

Number of livestock

10 GTC6



works via: c–lf2 –

Annual N–



Annual

surplus4



effective N

and mineral N–fertilizers Management



surplus3

downward seepage Fertilization



precipitation

potential

fertilization –







Annual

grazing



number of livestock, milk production

1

Eq. (1); 2Eq. (2); 3derived from SWAP output; 4derived from ANIMO output; 5mean highest ground water table; 6groundwater table class.

soil characteristics, soil hydrological data and meteorological data. Most input data were derived from the data of the STONE plots. However some data were obtained from regional sources: for the application of manure and mineral fertiliser regional data were used for the Klooster catchment, meteorological data were retrieved from local KNMI precipitation stations in the catchments and observations of groundwater levels in the catchments were used to calibrate the hydrological SWAP model. In Table 5 the different types of model inputs of the ANIMO model, the MM-WSV model, the WOG model and the NURP model are summarised according to the characterisation of the models in Table 4. For the main input domains, the model inputs are distinguished into continuous, continuous aggregated and discrete variables. The process oriented ANIMO model requires mainly continuous variables. The other three, less complex models use both continuous aggregated and discrete variables. The continuous aggregated variables of MM-WSV and WOG are all derived by aggregation of SWAP and ANIMO output. MM-WSV is the only less complex model that uses continuous aggregated hydrological input. Both other less complex models use discrete groundwater table classes to describe hydrological influence on nitrate concentrations in soil. This implies absence of temporal variation of hydrological impact in the simulations.

2.4.1. Regional input data on manure and mineral fertilisers Fertilisation of soils is one of the main driving factors for nitrate leaching to groundwater. For (regional) application of nitrate leaching models information about the use of fertilisers and manure is essential. One method to obtain this information is to use farm statistics and characteristics to calculate the manure production and the application of fertilisers. For the Klooster catchment a classification of farm types was used to calculate the applied amount of fertilisers and manure of dairy farms (Rougoor and Jansen, 2001). This classification of farm types is suitable for dairy farms and dairy farms in combination with pig holding. For other types of farms it is not suitable, because there is no manure production (arable farming) or the area of land for applying the produced manure is limited (pig holding, poultry holding). Each dairy farm in the Klooster catchment has been assigned to a farm type based on farm statistics and characteristics. A farm type is characterised by area of grassland and maize, number of animals, composition of animals and milk production. In the Klooster catchment six different farm types were distinguished (Table 6), classified into two sets: specialised dairy farms (Mv5, Mv1 and Mv7) and dairy farms in combination with pig holding (Mv55, Mv11 and Mv77). Within both sets of farm types a classification was made based on the amount of milk production and hence the amount of manure production and fertilisation

Table 6 Characteristics of dairy farm types in the Klooster catchment for the period 2000–2001. Code

Farm type

Number of farms

Average farm size (ha)

Milk production (kg·ha−1·a−1)

Mv5 Mv1 Mv7 Mv55 Mv11 Mv77

Specialised dairy farms Specialised dairy farms Specialised dairy farms Dairy farm in combination with pig holding Dairy farm in combination with pig holding Dairy farm in combination with pig holding

7 11 9 10 22 19

23 33 29 24 32 22

b11,250 11,250–13,750 N13,750 b11,250 11,250–13,750 N13,750

Applied amount of manure (m3·ha−1)

Applied amount of fertilisers (kg·ha−1 N)

Grassland

Maize

Grassland

Maize

50 53 73 53 61 65

43 50 39 45 35 45

250 250 190 215 189 189

30 30 30 30 30 30

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Table 7 Model inputs of the four selected nitrate leaching models and three study areas. Input parameter

Dimension

Description

Input value

Input range

Drentsche Aa ANIMO θsat Ksat Norg

m3.m−3 cm.d−1 kg·ha−1 N

Vol. moisture content saturation Saturated hydraulic conductivity Initial organic N in top soilb

Nmin

kg·ha−1 N

Initial mineral N in top soilb

Precipitationc Atm. deposition Additiona

mm·a−1 kg·ha−1·a−1 N kg·ha−1 ·a−1N

Total yearly precipitation Total yearly atmospheric deposition Total yearly addition of nitrogen (fertilisers and manure)

Crop residues

kg·ha−1 ·a−1N

Total yearly crop residues

Crop uptake

kg·ha−1·a−1N

Total yearly crop uptake

– – 12443 (grass) 11279 (maize) 30 (grass) 19 (maize) 906 22.4 387 (grass) 214 (maize) 259 (grass) 24 (maize) 543 (grass) 185 (maize)

0.38–0.43 9.7–15.6 – – – – 706–1121 20.5–24.0 326–464 182–285 233–304 22–28 487–635 171–212

– – cm-soil surface

Soil type Land use: grassland and maize Mean highest groundwater table

kg·ha−1 ·a−1N

Nitrogen surplus

– – 90 (grass) 158 (maize) 104 (grass) 75 (maize)

– – 36–136 76–236 71–143 49–124

– – mm·a−1

Soil type Land use: grassland and maize Precipitation surplus

N-surplus

kg·ha−1 ·a−1 N

Nitrogen surplus

Leaching fraction



Leaching fraction of the N-surplus

– – 346 (grass) 415 (maize) 104 (grass) 75 (maize) 0.37 (grass) 0.90 (maize)

– – 131–584 197–648 71–143 49–124 0.37–0.37 0.90–0.90

NURP Soil type Land use GTC N-input-year N-input-fertilisers N-input-org.man Number live stocks Milk production

– – – kg·ha−1 ·a−1 N kg·ha−1 ·a−1N kg·ha−1 ·a−1 N lsu·ha−1·a−1 kg·cow−1·a−1

Soil type Land use: grassland and maize Groundwater table class Total yearly nitrogen input (grassland) Total yearly input fertilisers (maize) Total yearly input organic part manure (maize) Yearly number of live stocks Annual milk production

– – – 311 27 128 2.11 8000

– – VI–VII 264–390 1–81 121–166 2.11–2.11 8000–8000

Overijssel ANIMO θsat Ksat Norg

m3.m−3 cm·d−1 kg·ha−1 N

Vol. moisture content saturation Saturated hydraulic conductivity Initial organic N in top soilb

Nmin

kg·ha−1 N

Initial mineral N in top soilb

Precipitationc Atm. deposition Additiona

mm·a−1 kg·ha−1 ·a−1N kg·ha−1 ·a−1 N

Total yearly precipitation Total yearly atmospheric deposition Total yearly addition of nitrogen (fertilisers and manure)

Crop residues

kg·ha−1 ·a−1N

Total yearly crop residues

Crop uptake

kg·ha−1 ·a−1 N

Total yearly crop uptake

– – (Grass) (Maize) (Grass) (Maize) 806 28.7 366 (grass) 186 (maize) 277 (grass) 23 (maize) 572 (grass) 179 (maize)

0.36–0.45 5.5–17.8 11279–15044 10552–14220 6–45 14–45 793–819 21.5–39.7 331–497 185–186 224–356 23–24 467–692 178–181

– – cm-soil surface

Soil type Land use: grassland and maize Mean highest groundwater table

kg·ha−1 ·a−1N

Nitrogen surplus

– – 78 (grass) 105 (maize) 92 (grass) 58 (maize)

– – 42–110 64–141 42–138 57–59

– – mm·a−1

Soil type Land use: grassland and maize Precipitation surplus

N-surplus

kg·ha−1 ·a−1N

Nitrogen surplus

Leaching fraction



Leaching fraction of the N-surplus

– – 292 (grass) 317 (maize) 92 (grass) 58 (maize) 0.33 (grass) 0.75 (maize)

– – 287–301 316–317 42–138 57–59 0.29–0.37 0.59–0.90

MM-WSV Soil type Land use MHW N-surplus WOG Soil type Land use Precipitation surplus

MM-WSV Soil type Land use MHW N-surplus WOG Soil type Land use Precipitation surplus

J. Roelsma, R.F.A. Hendriks / Science of the Total Environment 499 (2014) 481–496

489

Table 7 (continued) Input parameter

Dimension

Description

Input value

Input range

Overijssel NURP Soil type Land use GTC N-input-year N-input-fertilisers N-input-org.man Number live stocks Milk production

– – – kg·ha−1 ·a−1N kg·ha−1 ·a−1N kg·ha−1 ·a−1N lsu·ha−1·a−1 kg·cow−1·a−1

Soil type Land use: grassland and maize Groundwater table class Total yearly nitrogen input (grassland) Total yearly input fertilisers (maize) Total yearly input organic part manure (maize) Yearly number of live stocks Annual milk production

– – – 285 10 125 2.11 8000

– – V–VII* 243–297 10–11 124–125 2.11–2.11 8000–8000

Klooster ANIMO θsat Ksat Norg

m3.m−3 cm·d−1 kg·ha−1 N

Vol. moisture content saturation Saturated hydraulic conductivity Initial organic N in top soilb

Nmin

kg·ha−1 N

Initial mineral N in top soilb

Precipitationc Atm. deposition Additiona

mm·a−1 kg·ha−1 ·a−1N kg·ha−1 ·a−1 N

Total yearly precipitation Total yearly atmospheric deposition Total yearly addition of nitrogen (fertilisers and manure)

Crop residues

kg·ha−1 ·a−1 N

Total yearly crop residues

– – (Grass) (Maize) (Grass) (Maize) 975 37.2 471 (grass) 307 (maize) 317 (grass) 29 (maize) 665 (grass) 274 (maize)

0.38–0.43 9.7–15.6 12159–12649 11062–11080 59–102 73–75 967–984 36.0–38.3 300–561 300–320 317–317 28–29 497–748 215–304

– – 59 (grass) 70 (maize) 161 (grass) 100 (maize)

– – 45–77 53–99 138–195 65–158

– – 285 (grass) 343 (maize) 161 (grass) 100 (maize) 0.29 (grass) 0.60 (maize)

– – 277–298 332–374 138–195 65–158 0.22–0.37 0.45–0.75

Crop uptake MM-WSV Soil type Land use MHW

kg·ha

−1

−1

·a

N

Total yearly crop uptake

– – cm-soil surface

Soil type Land use: grassland and maize Mean highest groundwater table

kg·ha−1 ·a−1N

Nitrogen surplus

– – mm·a−1

Soil type Land use: grassland and maize Precipitation surplus

N-surplus

kg·ha−1 ·a−1 N

Nitrogen surplus

Leaching fraction



Leaching fraction of the N-surplus

– – – kg·ha−1 ·a−1 N kg·ha−1 ·a−1N kg·ha−1 ·a− 1N lsu·ha−1·a−1 kg·cow−1·a−1

Soil type Land use: grassland and maize Groundwater table class Total yearly nitrogen input (grassland) Total yearly input fertilisers (maize) Total yearly input organic part manure (maize) Yearly number of live stocks Annual milk production

– – – 354 60 171 1.79 7599

– – VI–VII* 225–421 60–60 165–180 1.30–2.20 7437–7692

a−1 a−1 a−1

N mineralisation first order rate constant Nitrification first order rate constant Denitrification first order rate constant

0.0075 365 22

– – –

N-surplus WOG Soil type Land use Precipitation surplus

NURP Soil type Land use GTC N-input-year N-input-fertilisers N-input-org.man Number live stocks Milk production For all study areas ANIMO Kmin Knit Kden

a The addition in the ANIMO is divided into addition from fertilisers and manure and also divided into a mineral part of the addition and an organic part of the addition. The ANIMO model requires the number and dates of additions. b Top 35 cm for grass, top 50 cm for maize c The SWAP model requires daily input of precipitation

level. Farm types Mv5 and Mv55 were classified as farm types with less than 11,250 kg·ha− 1 ·a − 1 milk production. Farm types Mv1 and Mv11 were classified as farm types with a milk production between 11,250 and 13,750 kg·ha− 1·a− 1. Farm types Mv7 and Mv77 were classified as farm types with more than 13,750 kg·ha− 1·a− 1 milk production. The applied amount of manure and fertilisers of the farm types has been calibrated with the amount of fertilisation level of a number of farms in the Klooster catchment which were collected by a survey of participants of dairy farmers in the project “Nitrate reduction in the Klooster catchment” (Nieuwenhuis, 2003). In this survey also data about the agricultural management of the different farms were

collected, such as dates and number of application of manure and fertilisers, grazing period and grazing number and number of cuts of grassland. For the Drentsche Aa catchment and the four drinking water resource catchments in the Province of Overijssel no additional information about farm statistics and characteristics were available. Instead of using farm statistics and characteristics for these two study areas information about the use of fertilisation was derived from the study in the context of the National Manure Legislation using the calculation models MAMBO and STONE (Groenendijk et al., 2012). Most input data of the dynamic complex process oriented ANIMO model were derived from the STONE data base. The STONE data base

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contains input data for each STONE plot concerning fertilisation, crop uptake, soil characteristics, soil hydrological data and meteorological data. Data concerning agricultural management such as number and dates of fertiliser and manure addition, time and amount of grazing, time of sowing and harvesting of crops, and ploughing. were also derived from the STONE data base. The significant input data of the ANIMO model is given in Table 7. The input data of MM-WSV meta-model and the WOG model are partly derived from the STONE data base. Another part of the input data of these two models originates from the output data of the SWAP and ANIMO model. This relationship between output data of the ANIMO model and input data of the MM-WSV model and the WOG model is indicated in Table 5. This concerns the mean highest groundwater table (MHW) and the precipitation surplus (both SWAP), and the nitrogen surplus (ANIMO). The latter is calculated as the sum of atmospheric deposition, addition of fertilisers and manure, and crop residues (all input data of ANIMO) minus gross crop uptake (output of ANIMO). Table 7 offers the input data of the combinations of the four models and the three study areas. 2.4.2. Meteorological input data From each catchment the measured precipitation of a local KNMI precipitation station and the evaporation data of a main meteorological station were used as model input for the hydrological SWAP model. The SWAP model is able to calculate the actual annual precipitation surplus (annual precipitation minus actual annual soil evaporation and plant evaporation and interception) which is input data of the WOG model (Table 5).

the observations. The optimal value for EF is 1. If EF b 0, the simulated values are worse than simply using the observed mean. 2 2 X  Xn  n Oi−O − i¼1 Pi−Oi i¼1 EF ¼ 2 Xn  Oi−O i¼1

The Coefficient of Variance (CV) is a measure of the proportion of the total variance of observed data explained by the predicted data. Therefore, CV is a measure of the sensitivity of the model for the input parameters. The optimal value is 1. If CV N 1 the variance in the predicted values is smaller than the variance of the observed data, which indicates that the model is not sensitive to variation in model input. n  X

CV ¼

2 Oi −O

i¼1

n  2 X P i −P

2.5. Model evaluation Evaluation and comparison of the performance of simulation models can be based upon visual or graphical comparison of the simulated values produced by the model with actual measured values. One problem associated with such methods, however, is that the difference between measured and simulated values cannot easily be quantified, and consequently it is not easy to compare the performance of different models in a quantitative way. The use of statistical methods allows us to quantify the comparison of the performance of different models (Jégo et al., 2008; Beaudoin et al., 2008). In this study five different statistical evaluation procedures (statistical criteria) were used for characterising model performance according to Willmott et al. (1985), Loague and Green (1991) and Smith et al. (1996). The Root Mean Square Error (RMSE) used by Loague and Green (1991) provides a percentage term for the total difference between the predicted and the observed values, proportioned against the mean observed value. The optimal value is 0. sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xn ðPi−OiÞ2 RMSE ¼ i¼1 n

RMSE% ¼

RMSE O

 100:

ð3Þ

Model Efficiency (EF) provides a standard method for assessing the accuracy of simulations by comparing the variance of predicted from observed values to the variance of observed values from the mean of

ð5Þ

i¼1

The Coefficient of Residual Mass (CRM) gives an indication of the consistent errors in the distribution of all simulated values across all measurements with no consideration of the order of the measurements. The optimal value is 0. A negative value of CRM indicates that the majority of predicted values are greater than the measured values (overestimation of the observations); a positive value indicates that the majority of predicted values are less than the measured values (underestimation of the observations). n X

2.4.3. Hydrological data The hydrological SWAP model was calibrated against the observed groundwater levels at the monitoring sites. The parameter of the model that was calibrated was the average value of the bottom flux. The calibrated SWAP model was used to determine the groundwater table class and mean highest groundwater table (MHW) of the different strata of the catchments as input data for the MM-WSV model (Table 5).

ð4Þ

CRM ¼

i¼1

n X Oi − Pi n X

i¼1

ð6Þ

Oi

i¼1

The Sample Correlation Coefficient (r) provides an estimate of the correlation coefficient for the whole population (Draper and Smith, 1966). The optimal value is 1. If r = −1, there is a negative correlation. If r = 0 there is no correlation between simulations and measurements and the values are not linearly related.   Xn  Oi−O Pi−P i¼1 ð7Þ r¼  2 ½ X  2 ½ Xn  n Pi−P Oi−O i¼1 i¼1 where n is the number of observations, Oi is the observed value, O is the mean of the observed values, Pi is the value predicted by the model and P is the mean of the predicted values. In this study the results of the statistical evaluation were used in a comparative way. There is no objective method to evaluate the interrelation of the different outcomes of the different statistical criteria. To evaluate the overall performances of the different models graphical displays were used. The combined assessment approach can be useful for making comparative evaluations of model performance between alternative or competing models (Loague and Green, 1991). 3. Results 3.1. Observed nitrate concentrations The average measured nitrate concentrations in the upper groundwater for the different strata of study area are shown in Table 1. For the ten strata of the Klooster catchment the average observed values of the nitrate concentrations range from 72 to 159 mg·l−1 NO3 with a mean nitrate concentration of mg·l−1 NO3 for the study area. The average observed nitrate concentration in the upper groundwater in grassland locations was 96 mg·l−1 NO3, whilst at the locations on maize an average observed nitrate concentration of 113 mg·l−1 NO3 was found.

J. Roelsma, R.F.A. Hendriks / Science of the Total Environment 499 (2014) 481–496

200

200

Nitrate concentrations (mg.l-1 NO3)

180

Observed Simulated Stdev observations

160 140

180

Observed Simulated Stdev observations

160

120

99

100

96

100

82 80

71

60

40

40

20

20

0

ANIMO MMWSV

WOG

87

88

91 77

80

60

200

Nitrate concentrations (mg.l-1 NO3)

140

120 100

491

NURP

0

ANIMO MMWSV

WOG

78

NURP

Nitrate concentrations (mg.l-1 NO3)

180

Observed Simulated Stdev observations

160 140 120 100

94

92

89 80

80 60

51

40 20 0

ANIMO MMWSV

WOG

NURP

Fig. 3. Comparison of observed and simulated mean nitrate concentrations in the upper groundwater as predicted by the four nitrate leaching models for the Klooster catchment (top left), the Drentsche Aa catchment (top right) and the four resource catchments in Overijssel (bottom left).

For the stratum grassland in the study area of the Drentsche Aa catchment an average observed value of 78 mg·l−1 NO3 over the period 2001–2012 was found, whilst for the same period for stratum maize an average observed values of 102 mg·l−1 NO3 was obtained. For the total study area in the Drentsche Aa catchment a mean nitrate concentration of 87 mg·l−1 NO3 over the period 2001–2012 was found. The large variation in soil types and groundwater tables in the four different drinking water resource catchments in the Province of Overijssel is well expressed by the larger spread (28–161 mg·l−1) in observed nitrate concentrations compared to the other two study areas (Table 1). In strata with soil type Histic Gleysols and a groundwater table of V or VI (intermediate wet) the lowest nitrate concentrations were found (28 and 51 mg·l − 1 NO3). On the other hand, in strata with soil type Gleyic Podzols or Fimic Anthrosols with a groundwater table of VII (dry) and land use maize the highest nitrate concentrations were observed (132 and 161 mg·l− 1 NO3). For the whole study area in Overijssel a mean nitrate concentration of 94 mg·l− 1 NO3 was obtained. The average observed nitrate concentration in the upper groundwater in grassland locations in the four drinking water resource catchments was 85 mg·l− 1 NO3, whilst at the locations in maize an average observed nitrate concentrations of 128 mg·l− 1 NO3 was found. 3.2. Model evaluation Fig. 3 depicts the observed and calculated mean nitrate concentrations of the different models. This figure shows a good prediction

of the mean nitrate concentrations by the ANIMO model and the MM-WSV model. The WOG model and the NURP model both underestimated the observed mean nitrate concentration. Especially, the WOG model showed a poor performance with respect to the simulated mean nitrate concentration. Fig. 4 shows the observed and simulated nitrate concentrations of the 24 different strata of the three study areas for respectively the ANIMO model, the MM-WSV model, the WOG model and the NURP model. Comparing the simulated nitrate concentrations in the upper groundwater, large differences are found between the simulation models in the variance of the model predictions. For the 24 strata the dynamic, complex process oriented model ANIMO calculated a minimum and maximum value of 31 and 189 mg·l− 1 NO3, respectively, the meta-model MM-WSV calculated a minimum and maximum value of 26 and 134 mg·l− 1 NO3 , respectively, the less complex model WOG calculated minimum and maximum values of 16 and 192 mg·l − 1 NO 3 , respectively, and the semi-empiric simulation model NURP calculated minimum and maximum values of 47 and 122 mg·l− 1 NO3 respectively (Fig. 4). From the graphs it is clear that the models ANIMO, MM-WSV and WOG showed good performances with respect to variance of simulated nitrate concentration in comparison with observed nitrate concentrations. The NURP model on the other hand showed little variance in the predicted nitrate concentrations: 47–122 mg·l−1 NO3. The four models perform quite differently for the three catchments, but also within the catchments large differences can be found. Both the WOG model and the NURP model underestimated the observations

492

J. Roelsma, R.F.A. Hendriks / Science of the Total Environment 499 (2014) 481–496

200

200

Simulated NO3 concentrations (mg.l-1)

Simulated NO3 concentrations (mg.l-1)

ANIMO

MM-WSV

150

150

100

100

50

50

Observed NO3 concentrations (mg.l-1)

0 0 200

50

100

Observed NO3 concentrations (mg.l-1)

0 200

150

0 200

Simulated NO3 concentrations (mg.l-1)

150

200

NURP

150

150

100

100

50

50

Observed NO3 concentrations (mg.l-1)

0

100

Simulated NO3 concentrations (mg.l-1)

WOG

0

50

50

100

150

Observed NO3 concentrations (mg.l-1)

0 200

0

50

100

150

200

Fig. 4. Scatterplot of simulated versus observed nitrate concentrations in the upper groundwater for the simulations of the ANIMO model (top left), the meta-model MM-WSV (top right), the WOG model (bottom left) and the NURP model (bottom right). : the Klooster catchment; : the Drentsche Aa catchment; : the four resource catchments in Overijssel.



of most of the strata of the four different drinking water resource catchments in the Province of Overijssel (Fig. 4). Especially the WOG model underestimated the observations of the different strata within the four catchments of Overijssel. For the different strata of the Drentsche Aa catchment on the other hand a number of strata were overestimated by the WOG model, but the average simulated nitrate concentration was underestimated by the WOG model for this

Cumulative frequency distribution 100% 90% 80% Observed ANIMO MM-WSV WOG NURP median

70% 60%

Table 8 Model performance statistics for the simulation models for the three study areas.

50%

Data-set

Model

RMSE (mg·l−1) NO3

RMSE %

EF (−)

CV (−)

CRM (−)

r

't Klooster

ANIMO MM-WSV WOG NURP ANIMO MM-WSV WOG NURP ANIMO MM-WSV WOG NURP

13.3 35.4 41.7 26.5 20.4 27.6 58.9 29.4 26.5 32.9 52.9 36.6 0

13.3 35.3 41.6 26.4 23.4 31.7 67.7 33.8 28.0 34.8 55.9 38.6 0

0.68 −1.29 −2.17 −0.28 0.53 0.13 −2.95 0.01 0.52 0.26 −0.92 0.08 1

0.52 1.25 1.07 2.20 1.25 2.17 0.55 17.48 1.71 1.38 4.23 5.38 1

−0.05 0.06 0.26 0.11 −0.01 −0.04 0.12 0.10 0.07 0.12 0.48 0.18 0

0.96 0.23 0.00 0.29 0.74 0.45 −0.37 0.34 0.74 0.63 0.76 0.55 1

40% 30% 20% Drentsche Aa

10%

NO3 concentrations (mg.l-1)

0% 0

25

50

75

100

125

150

175

200

Fig. 5. Cumulative frequency distributions of the observed and simulated nitrate concentrations in the upper groundwater. The dotted line depicts the median value of the distribution curves.

Overijssel

Optimum

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catchment with 11%. In comparison to the other catchments this is the lowest underestimation (Klooster catchment 29% underestimation; four catchments of Overijssel 46% underestimation). Also the NURP model shows the lowest underestimation for the Drentsche Aa catchment: 10% (Klooster catchment 17% underestimation; four catchments of Overijssel 15% underestimation). The cumulative frequency distributions of the observed and simulated nitrate concentrations are shown in Fig. 5. The median values of observed and simulated concentrations are very close for the ANIMO model and the MM-WSV model. Also the lower 50% of the distribution curves of the simulations of these two models are very close to the curve of the observations. Yet, both models tended to underpredict the upper 25% of the observations. This especially counts for the MM-WSV model. The NURP model underestimated and the WOG model strongly underestimated the median of the observations. The cumulative frequency distribution confirms the low variance in the predicted nitrate concentrations of the NURP model: the slope of the distribution curve is extremely steep. Consequently, the lower 20% of the concentrations was strongly overpredicted whilst the upper 60% was strongly underpredicted by the model. The WOG model exhibited a general tendency to strongly underpredict the observations. Another important model output result is the predicted relative area with NO3 concentrations below the target level of 50 mg·l−1 NO3 in the upper groundwater (Fig. 5). With respect to this relative area, both the ANIMO model and the MM-WSV model showed a good result. They both predicted a value of 11% for this relative area, which is very close to the value of 13% that follows from the observed concentrations. The WOG model overestimated this relative area by almost a factor 3 (35%), whilst the NURP model underestimated it by more than a factor 3 (4%). Table 8 summarises the results of the five model performance statistics for the 24 different strata. Studying of these statistical results leads to the following observations: (1) RMSE; for all three study areas the RMSE values are unambiguous: the ANIMO model has the lowest (closest to the optimum value of zero) and the WOG model the highest RMSE, indicating that of the four selected models ANIMO has the highest accuracy and WOG the lowest. The MM-WSV model is mostly (67%) second in accuracy and the NURP model mostly third. Another observation is important: per model the ratio between the RMSE values of the three study areas is almost always such that the lowest RMSEs are found for the Klooster catchment. This especially counts for ANIMO, for which the RMSE-ratio between the Klooster catchment and the other two study areas is around two, indicating a strong deviation between the ANIMO performance for the Klooster catchment and for both other catchments. The exception is the MM-WSV model whose RMSE values differ little between the three catchments. (2) EF; the results of the Model Efficiency are straightforward and lead to dividing the models in two groups: the ANIMO model that for all catchments has an EF greater than 0.5, (half of the optimum value) and the other models with EFs that strongly differ between catchments and that are mostly negative indicating a performance of the models that is worse than taking the observed means. Nevertheless, the MM-WSV model is the best performing model within the last group with two EF scores greater than nil. Also the EF points out that the combination of the ANIMO model and the Klooster catchment provides the best modelling results. (3) CV; the values of this measure reflect the variance in predicted data of the models. The result of the Coefficient of Variance for all three study areas is less conclusive. Both the ANIMO model and the WOG and MM-WSV model have a CV closest to the optimum value of 1 for only one study area, respectively the Drentsche Aa catchment, the Klooster catchment and the four different drinking

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water resource catchments in the Province of Overijssel. Most values of CV are greater than 1, indicating less variance in predicted data compared to the observed values. Especially the NURP model has CVs greater than 1 (between 2.2 and 17.5), which indicates that the NURP model is less sensitive to variance in the input parameters. Only the ANIMO model and the WOG model have a CV less than 1 (both 0.5) for one study area (respectively the Klooster catchment and the Drentsche Aa catchment), indicating more variance in predicted data for these two models compared to the observed values. The CV points out that the combination of the WOG model and the Klooster catchment provides the best modelling results. (4) CRM; the tendency for underestimation or overestimation of simulation models is reflected by the values for CRM. Both the CRM values obtained by the ANIMO model and the MM-WSV model are closest to the optimum value of 0. For all three study areas the ANIMO model has the lowest CRM, indicating a slight underestimation (positive values) or a slight overestimation (negative values). The WOG model on the other hand has the highest CRM, which indicates that the WOG model strongly underestimated the median of the observations. The MM-WSV model has also for all three study areas low values of CRM, both positive (underestimations) and negative (overestimations) values. For the catchments in the Province of Overijssel the CRM value is the highest, indicating that the majority of predicted values of the MM-WSV model were less than the measured values. (5) r; the Sample Correlation Coefficient shows one outstanding high value of 0.96, that is very close to the optimum of one, for the ANIMO model in case of the Klooster catchment. The results for the ANIMO model and the other two catchments are second best with values of 0.74. These scores are strongly in line with the results of all other statistical measures for the ANIMOcatchment combinations. The MM-WSV model and the NURP model show consistent results for r as well, but with substantial lower scores than the ANIMO model. The WOG model displays an inconsistent behaviour in terms of r between the catchments, indicating that the correlation between simulations and observations for this model strongly depends on the catchment. 4. Discussion Different types of models can be used to quantify nitrate leaching to groundwater on a regional scale (Britz and Leip, 2009; Vereecken et al., 1991; Schmidt et al., 2008; Lord and Anthony, 2000). However, the evaluation of the model performances using statistical criteria in order to compare the outcome of the different types of models is not that straightforward, because each statistical criterion has a specific focus on the model performance. It depends on the aim of the study which statistical criteria are the most adequate. The objective of this study was to compare the performance of four nitrate leaching models of different levels of complexity and input data requirement for predicting nitrate concentrations in the upper groundwater on a regional scale. Regarding this objective, it is important that the predicted nitrate concentrations are in agreement with the observed nitrate concentrations on a regional scale. Therefore, the statistical criteria Root Mean Square Error (RMSE) and Model Efficiency (EF) are sound methods to evaluate the accuracy of the predicted nitrate concentration (Kersebaum et al., 2007). For all three study areas the ANIMO model showed the best performance for the statistical criterion RMSE, indicating that of the four selected models ANIMO has the highest accuracy. Also, the ANIMO model showed for all three study areas the best predictions of the mean nitrate concentration in the upper groundwater. This indicates that the ANIMO model is suitable for nitrate concentration prediction on the level of the 24 strata (local scale) as well as on a regional scale.

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The MM-WSV model is second in accuracy (RMSE values: 32%, 35% and 35%), closely followed by the NURP model (RMSE values: 26%, 34% and 39%). Both the MM-WSV model and NURP model showed a negative value for the Model Efficiency in one study area (the Klooster catchment), indicating a performance of MM-WSV and NURP that is worse than taking the observed means. For the two remaining study areas positive values for EF were obtained for both the models MM-WSV and NURP. For all three study areas the MM-WSV model showed the second best predictions of the mean nitrate concentration. This shows that both the MM-WSV model and the NURP model are second best suitable for nitrate predictions on a local scale, but that the MM-WSV model is better suitable for nitrate predictions on a regional scale. The WOG model showed poor results on both local and regional scales. In general the best performance of the dynamic complex process oriented ANIMO model for all combinations of study areas and statistical criteria can be attributed to the extent of detail in the temporal and spatial discretisation, process descriptions and (continuous) input variables of the model. Within this study, the ANIMO model can therefore be adopted as a (relative) benchmark to judge the performances of the other models and as a tool for analysing their results. In addition, the MM-WSV model can be considered as a statistical analysis of the ANIMO model in the sense of input–output (nitrate concentration) relationships. In general, the statistic values of the MM-WSV model are better than those of the WOG and NURP models, indicating that the MM-WSV model can be used as well for analysing the performance of the two other simple models. From the statistical analysis of ANIMO from which MM-WSV is derived it follows that the mean highest ground water table (MHW), as a continuous (aggregated) input variable, is an important explanatory variable of the MM-WSV regression model (Eq. (1), Tables 4 and 5). In this aspect, the model differs from the WOG and NURP models that use the discrete variable Groundwater Table Class (GTC) to account for the influence of hydrology. This implies that these two models are expected to show less dynamic behaviour than the ANIMO and MM-WSV models. This is indeed reflected in their high values of CV (N 1, variance of predictions is less than variance of observations) and in Fig. 4: model predictions tend to group around a median value of 60 and 80 mg·l−1 NO3, respectively. The WOG model especially underestimated the median of the observations for all three study areas. The two driving factors of the WOG model are the input parameters nitrogen surplus and leaching fraction which determine the part of the nitrogen surplus leaching to the groundwater (Fraters et al., 2012 and Table 3). In particular for the four resource catchments in Overijssel the simulated nitrate concentrations were underestimated. For the two years (2011 and 2012) of nitrate measurements in these catchments, leaching fractions were derived from the nitrogen balances calculated by the ANIMO model as the ratio between the nitrogen load leaching to the groundwater and the nitrogen surplus. For these two years the ANIMO model calculated an average leaching fraction for the 12 strata of the catchments of 0.86 and 0.74, respectively, with an average for both years of 0.80. The average leaching fraction of the WOG model for the four catchments of Overijssel amounted to 0.43, about half of the value derived by the ANIMO model. Moreover, for the three strata of arable crops (B2A, E3A and P3A) the ANIMO model calculated a leaching fraction greater than one, indicating an additional source of nitrogen leaching to the groundwater besides the nitrogen surplus. This additional source is most likely the mineral nitrogen and the mineralisation of organic matter stored in the top of the soil profile. The mineral nitrogen storage amounts to about 30 kg·ha− 1 N whilst the potential N-mineralisation is estimated at 0.0075 (Kmin) × 12400 (average Norg) = 93 kg·ha−1·a−1 N (Table 7). Thus, mineralisation, followed by fast (Knit is about 50000 × Kmin; Table 7) nitrification seems to be the largest additional nitrate source for leaching. The nitrogen surplus equals 58 kg·ha− 1·a− 1 (Table 7), two third of the estimated mineralisation.

However, whether the potential mineralisation will be fully met, depends strongly on soil temperature and oxygen supply. The latter is correlated with moisture content and (thus) with groundwater table elevation. Especially the high groundwater tables are relevant as these hamper oxygen supply into the soil which inhibits mineralisation and nitrification and promotes denitrification. Models that lack a description or statistical term steered by a continuous (aggregated) input variable to account for these phenomena, will most likely not be able to catch the dynamics of nitrate sources (mineralisation and nitrification) and sinks (denitrification) in the soil. This counts for the WOG model and the NURP model that do lack a continuous input variable to describe groundwater table dynamics. On average for the 12 strata of Overijssel catchments the difference between the two leaching fractions of the WOG model and the ANIMO model which can be addressed to nitrogen release from mineral and organic nitrogen storage in the topsoil is 0.80–0.43 = 0.37. When this fraction is added to the average nitrate concentration in the upper groundwater calculated by the WOG model (50.8 mg·l−1 NO3) the calculated average nitrate concentration will be (0.43 + 0.37) / 0.43 × 50.8 = 94.4 mg·l−1 NO3. This value equals the observed average nitrate concentration of the four catchments of Overijssel. Thus, the absence of taking into account of the nitrogen release from the mineral and organic nitrogen storage in the top soil that can leach to the groundwater seems to be a good explanation of the underestimation by the WOG model. Long-term nitrate monitoring surveys have shown that in some years the observed nitrate concentrations in the upper groundwater are higher than can be explained by only nitrogen inputs to the soil (e.g. nitrogen surplus) and in some years lower (Boumans and Fraters, 2011). But if the data-set of the nitrate measurements is long enough (N8 years) the effect of higher observed nitrate concentrations due to nitrogen release from mineralisation and lower observed nitrate concentrations due to nitrogen accumulation or denitrification in the soil averages out. The best results of the WOG model in terms of the lowest underestimation of the average nitrate concentration for the Drentsche Aa catchment can be explained by this phenomenon, because this catchment has the longest data-set in time: 2001 till 2012. A long term (N8 years) average can be better predicted by models that use a constant discrete variable to describe effect of groundwater table than short term averages. The GTC that the WOG model and the NURP model apply is defined for a period of at least 8 years. The explanation of the underestimation of the WOG model seems also to be the reason for the underestimation by the NURP model. In the NURP model the calculated nitrate concentration in the upper groundwater is a function of nitrogen input by manure and fertilisers and groundwater table. Analogous to the WOG model the groundwater table variable (GTC) in the NURP model can be seen as a variable that accounts for hydrological influences that affect the nitrate processes in soil mineralisation, nitrification and denitrification. A GTC factor of 1.0 applies to groundwater table VII* and a factor between 0.6 and 0.9 to the groundwater tables V to VII. Similar to the WOG model the performance of the NURP model in terms of the smallest underestimation of the observed nitrate concentrations showed the best result for the Drentsche Aa catchment. This is also reflected by the lowest value of CRM for this catchment. Examination of the performance of the four nitrate leaching models for the different strata of the three study areas does not result in a consistent picture. When the RMSE for each strata and model is plotted against the characterisation of the strata, the strata with the highest RMSE are in most cases a combination of the WOG model and land use maize. Taking into account the nitrate which is released from the soil by mineralisation and nitrification, especially for maize cultivation on sandy soils, and by adding this part onto the existing leaching fractions of the WOG model would possibly improve the performance of this model.

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The best model performance showed the dynamic complex process oriented ANIMO model in predicting the nitrate concentrations in the upper groundwater of the Klooster catchment. The good performance of the ANIMO model for this catchment relative to the performance of the model for the two other catchments can be explained by the additional information about the use of manure and fertilisers at farm level in this study area. In this study the dynamic complex process oriented ANIMO model showed the best overall model performance. However, the actual performance of a model also depends on the amount of input data needed and the availability and quality of these data. Taking into consideration the large amount of input data needed to run the ANIMO model (Table 2), the MM-WSV model is a good second option in cases that not all input data is available and only a general overview of the nitrate concentrations in the upper groundwater in a region is needed. 5. Conclusions Based on the evaluation of the results of five different statistical criteria applied in three study areas the dynamic complex process oriented ANIMO model showed for 12 of 15 combinations of statistical criteria and study area the best performance in predicting nitrate concentrations in the upper groundwater. The ANIMO model showed good results for both the statistical criteria Root Mean Square Error (RMSE) and Model Efficiency (EF), indicating a good accuracy of the predicted nitrate concentrations. Therefore, the dynamic complex process oriented ANIMO model is a good quantification tool to predict the nitrate concentration to groundwater on both local and regional scales. However, the use of a detailed process oriented simulation model like the ANIMO model requires a comprehensive set of input data. If such a comprehensive data-set is not available the MM-WSV model (meta-model) proves to be a good alternative to quantify the nitrate concentrations in the upper groundwater on a regional scale. The WOG and NURP models both lack a continuous (aggregated) input variable to describe the dynamic influence of groundwater table elevation on the nitrate related soil processes mineralisation, nitrification and denitrification. These models are therefore more suitable for long term (N8 years) predictions of average nitrate concentrations in the upper groundwater on a regional scale. Conflict of interest There is no conflict of interest. Acknowledgements The authors thankfully acknowledge Stef Hoogveld of the Province of Gelderland, Anton Dries of the Province of Drenthe and Sander van Lienden of the Province of Overijssel for providing the data on observed nitrate concentrations in groundwater. We also want to thank Piet Groenendijk, Oscar Schoumans and Gert-Jan Noij (Alterra, Wageningen University and Research Centre) for their comments to an earlier version of this paper. References Almasri MN, Kaluarachchi JJ. Modeling nitrate contamination of groundwater in agricultural watersheds. J Hydrol 2007;343:211–29. Beaudoin N, Launay M, Sauboua E, Ponsardin G, Mary B. Evaluation of the soil crop model STICS over 8 years against the “on farm” database of Bruyères catchment. Eur J Agron 2008;29:46–57. Actual and future nitrogen and phosphate loads from agriculture to surface waters (in Dutch). In: Boers PCM, Boogaard HL, Hoogeveen J, Kroes JG, Noij IGAM, Roest CWJ, et al, editors. Report 532. Wageningen, The Netherlands: The Winand Staring Centre; 1997. [217 pp.]. Boumans LJM, Fraters B. Nitraatconcentraties in het bovenste grondwater van de zandregio en de invloed van het mestbeleid. Visualisatie afname in de periode 1992

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Comparative study of nitrate leaching models on a regional scale.

In Europe and North America the application of high levels of manure and fertilisers on agricultural land has led to high levels of nitrate concentrat...
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