modelling nitrogen dynamics for a mesoscale catchment using a minimum information requirement (mir)...
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Modelling nitrogen dynamics fora mesoscale catchment using aminimum information requirement(MIR) concept / Modélisation desdynamiques de l'azote pour unbassin versant de taille moyenne,utilisant le concept de besoinminimum d'informationMICHAEL EISELE a & CHRIS LEIBUNDGUT aa Institute of Hydrology, University of Freiburg ,Fahnenbergplatz, D-79098, Freiburg, Germany E-mail:Published online: 29 Dec 2009.
To cite this article: MICHAEL EISELE & CHRIS LEIBUNDGUT (2002) Modelling nitrogen dynamicsfor a mesoscale catchment using a minimum information requirement (MIR) concept /Modélisation des dynamiques de l'azote pour un bassin versant de taille moyenne, utilisant leconcept de besoin minimum d'information, Hydrological Sciences Journal, 47:5, 753-768, DOI:10.1080/02626660209492978
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HydrologicalSciences-Joumal-des Sciences Hydrologiques. 47(5) October 2002 753
Modelling nitrogen dynamics for a mesoscale catchment using a minimum information requirement (MIR) concept
MICHAEL EISELE & CHRIS LEIBUNDGUT Institute of Hydrology, University of Freiburg, Fahnenbergplatz, D-79098 Freiburg, Germany [email protected]; chris,[email protected]
Abstract Based on the water balance model LARSIM (Large Area Simulation Model), a model for the simulation of nitrogen transport was developed in a mesoscale catchment in southwest Germany. To meet the needs and constraints in river basin management, the nitrogen model was developed following the concept of minimum information requirement (MIR). The modelling concept uses only few calibration parameters and only easily accessible input data. Water balance, runoff generation and nitrogen transport were simulated on a 1-km" grid of sub-areas in which different land-use classes and soil characteristics were accounted. Temporal variability of the storage of mobile nitrogen were described using a monthly based mass balance. Nitrogen mobilization and transport was simulated using monthly values of different runoff components and data for soil properties, topography, hydrogeology and river network. The simulation was calibrated and validated using streamflow from two gauging stations and observed nitrogen concentrations at the catchment outlet, showing reasonable results for both streamflow and nitrogen dynamics. The results of the model application are discussed in the context of uncertainty problems and their implications for water management.
Key words river basin management; nitrogen dynamics; distributed catchment modelling; minimum information requirement; water balance model, LARSIM; nitrogen transport modelling; uncertainty; southwest Germany
Modélisation des dynamiques de l'azote pour un bassin versant de taille moyenne, utilisant le concept de besoin minimum d'information Résumé Sur la base du modèle de bilan hydrique LARSIM (Large Area Simulation Model—Modèle de simulation sur une grande zone), un modèle simulant le transport d'azote a été développé pour un bassin versant de taille moyenne du Sud-Ouest de l'Allemagne. Pour tenir compte des besoins et des contraintes de la gestion de bassin, le modèle de transport de l'azote a été construit selon le concept de besoin minimum d'information. Ce concept de modélisation n'utilise que peu de paramètres de calage et que des données d'entrée facilement disponibles. Le bilan hydrique, la production d'écoulement et le transport de l'azote ont été simulés sur des sous-régions, discrétisées selon un maillage de 1 km2, et en tenant compte des différentes classes d'occupation du sol et de pédologie. La variabilité temporelle du stockage d'azote mobile a été décrite grâce à un bilan massique mensuel. La mobilisation et le transport de l'azote ont été simulés à partir de valeurs mensuelles des différentes composantes de l'écoulement et de données sur les propriétés des sols, la topographie, l'hydrogéologie et le réseau hydrographique. La simulation a été calée et validée grâce aux valeurs de débit de deux stations de jaugeage et aux relevés de concentrations en azote à l'exutoire du bassin versant, qui montrent des résultats raisonnables pour les débits et les concentrations en azote. Les résultats de la mise en œuvre du modèle sont discutées dans le contexte des problèmes d'incertitude et de leurs implications dans la gestion de l'eau.
Mots clefs gestion des bassins versants; dynamique de l'azote; modélisation distribuée des bassins versants; besoin minimum d'information; modèle de bilan hydrique, LARSIM; modélisation du transport d'azote; incertitude; Sud-Ouest de l'Allemagne
Open for discussion until I April 2003
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INTRODUCTION
The new European Water Framework Directive obliges water authorities to develop river basin management plans. A central focus within this new instrument lies in the documentation of management measures by which a good status of surface water and groundwater quality can be achieved. The use of distributed catchment models has become a common strategy to predict the effects of measures on solute transport and water quality within river basins (Johnes & Heathwaite, 1997; Thorsen et al, 1996; Donigian et al, 1995). In the field of water quality modelling, many models are available and described in the literature (e.g. Arnold et al, 1993; Thorsen et al, 1996; Johanson et al, 1996). All of these models contain detailed deterministic descriptions of the water and nutrient cycles, which require intensive data collection as well as the estimation and calibration of many model parameters. Although most modelling studies have shown acceptable results in test catchments with good data availability (e.g. Eisele et al, 2001; Zehe et al. 2001), model applications in mesoscale nonexperimental catchments are limited by lack of available data. In the context of river basin management, models of low information requirement are needed, which can be applied using commonly available data sets. The concept of minimum information requirement (MIR) is characterized by a simple model configuration, which seeks to mimic the output of physically-based models using simple process descriptions and few input parameters (Anthony et al, 1996; Quinn et al, 1999). Still, models designed for water management purposes should preserve a level of complexity and variability in space and time which makes the simulation results sensitive to key environmental parameters (Van Herpe et al, 1999). An investigation of the impact of land-use change or a change of fertilizer applications on nitrogen concentrations in surface water and groundwater in a catchment requires a realistic description of the nitrogen cycle in the soil as well as the nitrogen transport through the hydrological system. Predictions of solute transport can be made only on the basis of a realistic description of the hydrological components (Donigian et al, 1995).
In recent literature concerning river basin modelling, problems of input data requirements and the uncertainty of parameters and model structures are widely discussed (Uhlenbrook et al, 1999; Beven, 2001). Simple model concepts using a small number of calibration parameters are less susceptible to the problem of parameter uncertainty. Nevertheless, a simple model concept implies uncertainties about the realistic process description. The uncertainty concerning the model structure can be reduced by the inclusion of different independent data sets in the validation procedure (Uhlenbrook & Leibundgut, 2002).
The objective of this study is the implementation of a distributed catchment model as a tool for prediction of nitrogen dynamics under the constraint of generally accessible data sets. The approach to achieve this goal was the development of a MIR model for the simulation of nitrogen budgets and transport on the basis of an existing conceptual water balance model. The uncertainties of the simulation results are assessed using sensitivity analysis and checks of internal model variables against additional data.
STUDY AREA
The study was carried out in the catchment of the Seefelder Aach (272 km") located in the southwest of Germany. The river is a tributary to Lake Constance, its catchment is
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Modelling nitrogen dynamics for a mesoscale catchment 755
embedded in a hilly landscape formed by the last glaciation. The geology is formed by glacial sediments and tertiary molasses on the hillslopes. Alluvial sediments, glacio-fluvial sediments and some moorland areas are located in the valley bottoms. In the lower part of the catchment, these sediments form considerable alluvial aquifers. Due to the glacial morphology, the topography is very inhomogeneous, with small hills and valleys, canyons and substantial alluvial plains. The altitude reaches from 399 to 840 m a.s.l. The soils are mostly classified as ortic luvisols, with some gleysols in the valley floors and moor areas. Agriculture is the dominant form of land use in the catchment. Arable land (wheat, rye and corn) and grassland are found in the higher regions of the northern part, whereas the steeper slopes and valley bottoms are mostly covered with forest and some grassland. In the southern part, the valleys are dominated by intensive agriculture (wheat, rye, barley, corn, hop, apple plantations) with some forest and grassland on the hills. Settlements are mainly located in the alluvial valley bottoms. Due to intensive land use in parts of the catchment, the water quality standards for surface waters, concerning nitrogen and phosphorus, are not met.
The catchment shows a pluvial runoff regime with 1045 mm of mean annual precipitation and a mean annual streamflow of 365 mm. Actual évapotranspiration values calculated from the residue in the water balance equation are too high considering the climate in the region; the calculated actual évapotranspiration (Penman-Monteith method) was 620 mm. A possible explanation is a lateral groundwater bypass in the alluvial aquifer at the catchment outlet.
METHODS
Water balance model
In a first step, the conceptual distributed water balance model LARSIM (Large Area Simulation Model, Bremicker, 2000) was applied to the catchment described above. It was chosen because it can be applied using easily accessible input data and requires calibration for only a few model parameters. A detailed description of the model concept is given by Bremicker (2000).
The water balance model was applied for the period January 1987-December 1996 on a daily time step. A spatial distribution was achieved by dividing the catchment into a grid network of sub-areas with a size of 1 km2. The topology of the sub-areas and the representation of the river network were derived from a digital elevation model (50 m x 50 m). Within the sub-areas, different soil characteristics and land-use classes were accounted by their relative proportions. Soil properties (field capacity and plant-available field capacity) were needed for the soil module of LARSIM as well as for the nitrogen transport simulation. They were derived from a digital soil map with a scale of 1:200 000. The land-use classification was taken from a Landsat TM satellite image with a spatial resolution of 30 m x 30 m. The hydrometeorological input was calculated from 20 raingauges and seven meteorological stations, which were available in the region around the catchment. For calibration and validation of the water balance simulation, time series of streamflow from two gauging stations were available. The calibration was carried out separately for the gauging stations of the main river and the tributary in the period from January 1987 to December 1991. Model performance was evaluated by calculating the model efficiency (i?c«) and using its logarithmic values
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(logoff) (Nash & Sutcliffe, 1970). As a tool for automatic calibration was not implemented, the calibration was carried out with a half-automatic calibration procedure. First of all, the model was calibrated manually for each parameter. In the next step, a sensitivity analysis was carried out. The optimum parameter set was extracted by an analysis of the model efficiencies, which were computed for combinations of parameter values. After calibration and validation, the water balance model was used to compute runoff components for each sub-area. The time series produced were aggregated to monthly values and used as input data for the nitrogen transport simulation. In the modelling concept of LARSIM, lateral groundwater fluxes between the sub-areas are not implemented. To account for the lateral transport, the routing of the groundwater components was altered from the original model outputs in parts of the catchment according to the geological properties.
Nitrogen transport model
The nitrogen simulation was developed on the basis of spatially-distributed nutrient balances for all permeable land surfaces using the calculation methods described in Bach et al. (1998). Statistical data (mean annual values for the local community areas) for cultivated crops, crop yields and number of cattle, as well as regional statistics for atmospheric inputs, were used as input data to calculate the annual nitrogen input, plant uptake and surplus for the different land-use types taken from the Landsat TM image. On the basis of this annual nutrient balance and the water balance simulation, a monthly nutrient balance and transport simulation was developed. Figure 1 shows a visualization of the modelling concept, which is described below. The temporal
nitrogen mobilization
N-Balance field capacity
water balance model
simulation of runoff
N storage in Soil Leaching Flushing
runoff corn Don ents
denitrificati on delay
nitrogen transport | d | f t j s e
input ^ ^
into rïvsr
results
concentrations and loads along riusr network
^:-v,;.;;!
spatial resolution (1 krr' end)
input from point sources, transport, retention in nusr system
Fig. 1 Modelling concept of the nitrogen transport simulation.
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Modelling nitrogen dynamics for a mesoscale catchment 757
variability of mobile nitrogen in the soil was calculated from the nitrogen input and a monthly balance (accounting plant uptake, mineralization, denitrifi cation, immobilization and loss from water-related transport). Monthly values for mobile nitrogen were calculated as follows:
Nstor(tï) = Nstor(ti - 1 ) + Nin{ti) - Nnpu(ti) - Nd„(ti) ~ N0llt{ti - 1 ) ( 1 )
where ti is the time step; Nstor is the soil storage of mobile nitrogen; Nin is nitrogen input (organic and inorganic fertilizer, atmospheric deposition); N„pu is the net plant uptake of nitrogen; Nd„ is nitrogen transformed by denitrification and released into the atmosphere; and Nout is nitrogen loss from runoff (see also equation (6)); all in kg ha"1.
The initial value for Nstor was set at 30% of the mean annual nitrogen surplus in the specific land-use class and sub-area. This corresponds to mean measured values of mineral nitrogen in the region taken from the agricultural survey. Net mineralization or immobilization of nitrogen were expected to be negligible. Data for denitrification in soil are rarely given in the literature. For agricultural land use and the soil type ortic luvisol, which is the dominating soil type in the catchment, Wendland (1992) assumes an annual denitrification of c. 30 kg ha"1. To account for seasonal variations, Nd„ was set to a mean value of 0.7% of the monthly value of Nslor and was shifted with a temperature-dependent potential function. This approach resulted in annual flux of 10-35 kg ha"1 of denitrified N, dependent on the nitrogen surplus in the land-use class and sub-area. Values for fertilizer applications, atmospheric deposition and net plant uptake were derived from the annual nutrient balance and the seasonality of agricultural practices described in the literature (Prahsuhn et al, 1996; Frede & Dabbert, 1998).
The nitrogen transport with surface runoff (Nsr) and the leaching or flushing of nitrogen (/%) were simulated on the basis of surface runoff, subsurface runoff (subsurface direct flow, interflow and baseflow) and field capacity. Monthly values of mobilized nitrogen were calculated according to the following equations, using two parameters describing the mobility of nitrogen with regard to the different flow paths:
Ns,(ti) = Nxlor(ti}Fracsr(ti) (2)
FracUti) = (Q,M) IFQ-MCsr (3)
Nif[ti) = Nstor(ti) Fracyiti) (4)
Fracijitï) = (Qss{ti)IFC)-MCif (5)
NUti) = Nsr(ti) + N,j(ti) (6)
where Nsr is nitrogen in surface runoff (kg ha" ); Fracsr is the fraction of nitrogen storage mobilized from surface runoff; Qsr is surface runoff (mm); FC is the field capacity (mm); MCsr is a mobility coefficient for nitrogen in surface runoff (calibration); Ny'is leached or flushed nitrogen in the subsurface components (kg ha"1); Frac if is the fraction of nitrogen storage mobilized with the subsurface components; Qss is subsurface runoff (mm); and MCy is a mobility coefficient for nitrogen in subsurface runoff (calibration).
Equations (3) and (5) describe the process of nitrogen mobilization according to the hydrological situation and the drainage efficiency. The terms (Qsl(ti)IFC) and (Qss(ti)/FC) can be interpreted as expressions of the soil moisture content. This concept is similar to that of the N-flushing hypothesis, in which the flushing or leaching of
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nitrogen is estimated based on the soil saturation deficit (Creed et ai, 1996; Hornberger et al., 1994).
Leached or flushed nitrogen loads are exposed to different transformation processes depending on flow path and transport time (e.g. Behrendt et al, 1999). Nitrogen mobilized with the subsurface components (Ny) was therefore divided into a flushed fraction, mobilized with the interflow component (Nf), and a leached fraction, mobilized with the groundwater component (Ni). This division was carried out for each time step according to the partition of the runoff components. Using the calculated leached nitrogen loads and the monthly values of groundwater flow, the nitrogen concentration in groundwater recharge was calculated.
The denitrification loss in the interflow component was calculated using a function based on the topographical index (Beven & Kirkby, 1979), temperature, runoff regime and a calibration parameter. The simple form of the topographical index was used as an indicator for the presence of saturated areas in which denitrification rates are higher than in hillslope or hilltop areas. The topographical index, I,opo, was first calculated from the digital elevation model for each 50 m x 50 m grid cell as follows:
Ilopo = ln(a/tans/p) (7)
where a is the source contributing area (nr, the maximum value was set to the cell size of the model sub areas (1 000 000 nr)); and sip is the slope (°).
The mean value of I,opo in each sub-area was calculated and normalized with the mean value for the whole catchment. For each sub-area, monthly values for the fraction of denitrification loss, DLf, were calculated as follows:
DLj(ti) = 1 - IMmm(Denit- TF(ti)-RF(ti)) DL0) > 0 (8)
where /Mlorm is the mean value of Ilopo in the sub-area normalized by the mean value of Ii„p„ for the whole catchment; Denit is a denitrification factor (calibration); TF is a monthly temperature factor (calculated from the temperature regime in the catchment); and RF is a monthly runoff factor (calculated from the runoff regime in the catchment).
The load (emission) of flushed nitrogen that reaches the river system, Nfem
(kg ha~'), was then calculated as follows:
Nfeill(ti) = Nj(ti)il-DLj(ti)) (9)
where Nf -is the nitrogen flushed out with the interflow (kg ha"1). The denitrification rate of the leached nitrogen, which is transported via the
groundwater flow path, is a function of transport time through the unsaturated and saturated zones into the river system. Due to a lack of data regarding depth of groundwater table and hydrogeological properties in most areas of the catchment, this process had to be described in a lumped way using a denitrification parameter (DLg). This parameter was estimated using the ratio of mean nitrogen concentrations in groundwater recharge and concentrations in streamflow during baseflow conditions.
Depending on the hydrogeological conditions, solutes in the groundwater are delayed before they reach the river system. The output signal of the single linear storage describing the groundwater storage of the water balance model represents only the mobile part of the water stored in the groundwater aquifer (Mehlhom, 1999; Behrendt et al, 1999). This mobile fraction reacts on the input signal after a short time (for example, the groundwater outflow increases a few days after a rain event). Depending on the fraction of immobile water in the system, the residence time of the
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groundwater can be much longer. The delay of a hydrochemical compound in hydro-logical system can be described by a dispersion-translation function (Mehlhorn, 1999). This function was parameterized using a mean residence time and a dispersion parameter, which were corrected according to the length of the lateral flow path. The mean residence times in the catchment were estimated based on investigations in groundwater and springs in the years 1991 and 1992. From these measurements, a mean residence time of nine years for a flow distance of 2000 m and a dispersion coefficient of 0.07 was estimated. Concentrations for the pre-modelling period were derived by extrapolating the concentrations in groundwater recharge from the modelling period (reflecting trend and the seasonal behaviour). The trend was estimated using data for general temporal development of nitrogen balances (Prahsuhn et al, 1996; Behrendt et ai, 1999) showing a slight decrease in the nitrogen surplus in the pre-modelling decade. Based on the calculated delay, the groundwater-related nitrogen emission into the river system, Ngem (kg ha"1), was calculated as follows:
Ngem{ti) = (1 -DLgyW2Qh(ti)^TCNlf(DTM) (10)
where DLg is the fraction of denitrification loss in the groundwater (calibration); Qi, is the baseflow = groundwater outflow (mm); T is the turnover time (months); CM is the concentration of nitrogen in the groundwater recharge (mg F1) and j\DTM) is a function of the dispersion-translation model.
In each sub-area, nitrogen concentrations were calculated for all runoff components and the different loads entering the river system were aggregated. For each river segment, the input loads from diffuse sources were calculated from the sum of all upstream areas. At different points along the river network, input loads from point sources (e.g. sewage plants) were added. For the retention of nitrogen in the river system, an empirical function based on the hydraulic impact (annual volume of stream-flow divided by the surface area of the water bodies) was used (equations (11) and (12); Behrendt et al., 1999). Finally, the monthly nitrogen concentration in each river segment was calculated (equation (13)):
HL(a) = Q(a)l{L-W) (11)
R,,(ti) = (33-HL{a;0MyRF(ti) (12)
CN(t() = midti) + Ep ,v(?/))(l + (1/(1 + RM) x 100]/g(?/) (13)
where HL{a) is the annual hydraulic impact (m); Q{a) is the annual streamflow (m3); L is the length of the upstream river network (m); W is the mean channel width (m); RL is the nitrogen retention in the river system (load weighted); RF is a runoff factor calculated from the annual runoff regime; EllN is the nitrogen input from diffuse sources; Ep,\ is the nitrogen input from point sources (kg ha~'); C.v is the nitrogen concentration in the river segment (mg F1); and Q is the mean monthly streamflow in the river segment (mm).
For the calibration of the nitrogen transport, simulation measurements of nitrogen concentrations at the catchment outlet for the period 1987-1991 were used. After an initial manual calibration, a sensitivity analysis was carried out to identify the optimal parameter set.
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RESULTS AND DISCUSSION
Water balance model
In the calibration procedure for the water balance model, about 250 model runs were carried out. Two parameters describing the behaviour of the soil storage and runoff generation processes and the storage coefficients for surface runoff and interflow were found to be highly sensitive (Table 1). The storage coefficient for the groundwater showed only a very small sensitivity over a wide range of values and was finally quantified as the mean of the insensitive range. A first estimate of the optimum parameter values was quantified by printing the model efficiency in a two-dimensional parameter space for the sensitive parameters Dmm and P (Fig. 2(a)). Then the results of Reff for the parameter space for the storage coefficients EQD and EQI were analysed (Fig. 2(b)). In both plots, the efficiency maximum was identified by checking the log/îefr values (not shown) in addition. By combining the best fits of both parameter
Table 1 Calibrated parameter values of the Seefelder Aach water balance model .
Parameter Function Unit Value Sensitivity £>min Lower limit for interflow generat ion m m day'1 13 High Anax Upper limit for interflow generat ion m m day"1 15 M e d i u m B(bsf) Parameter describing the form of soil moisture day"1 0.2 High
saturation function (Parti t ion: direct runoff/soil storage)
(J Drainage index for lower soil storage 1 day"1 0.006 High EQD Storage coefficient for direct runoff - 523 High EQI Storage coefficient for interflow - 5550 High EQB Storage coefficient for groundwater - 130000 Low
11 14 16 1? 19 20 22 3600 4800 5400 5700 6300 6600 7200 8400
Dmin (mm/d) EQI
Fig. 2 Interpolated lines of the objective function, Rct(, in the parameter space: (a) Dmir vs P and (b) EDI vs EQD.
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Modelling nitrogen dynamics for a mesoscale catchment 761
Qobs
Q sim
(b)
o 01.87 07.87 01.88 07.88 01.89 07.89 01.90 07.90 01.91 07.91
Qobs
Q sim
20
0
01.92 07.92 01.93 07.93 01.94 07.94 01.95 07.95 01.96 07.96
Fig. 3 Simulated and observed mean daily streamflow for the Seefelder Aach gauging station: (a) for the calibration period; and (b) for the validation period.
combinations, the optimum parameter set was identified. The calibrated parameter values are given in Table 1. For the calibration period, a model efficiency of j?enr= 0.84 and logoff = 0.85 was reached. Then the model was validated for the period January 1992-December 1996. In this period, the model efficiency decreased to values of Reft = 0.62 and logoff = 0.72.
The poorer efficiency in the validation period results from an overestimation of streamflow and from errors in the simulation of peak flows (Fig. 3). One reason for these simulation errors was the inadequate precipitation data set with only two precipitation collectors located inside the catchment. The regionalization with the inverse distance method was not able to represent the heterogeneity of precipitation in the catchment. In particular, the summer events, which occurred more often in the validation period, were not captured well. An improvement of the precipitation input should be achieved by using radar data for rainfall intensity. Another reason for simulation errors might be a possible groundwater bypass, which is not accounted for in the water balance simulation.
The sensitivity analysis revealed that the optimum parameter set for the water balance model was hard to find using manual or half-automatic calibration techniques. Similarly high efficiencies can be achieved with different parameter sets. This problem of equally good parameter sets has been observed in other modelling studies (e.g. Uhlenbrook et al,, 1999; Seven, 2001). An improvement of the calibration, or at least a quantification of the uncertainty, should be achieved with an automatic calibration procedure. The observed parameter uncertainty can lead to errors in the nitrogen
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transport simulation, because the partition of the different runoff components depends on the combination of parameters. Therefore, further investigations of runoff components in the catchment, using natural and artificial tracers, are needed to reduce model and parameter uncertainty.
Nitrogen transport model
The simulation was carried out based on the water balance simulation described above, using the parameter set given in Table 1. After a first sensitivity analysis of the nitrogen transport model, three parameters were found to be sensitive to the objective functions: i?err, logoff and r~ (calibration parameters are listed in Table 2). The parameter DLg was estimated using the ratio of mean concentration in groundwater recharge and baseflow concentrations. For the denitrification factor, Denit, an upper limit was identified (for Denit > 1.2, the denitrification rates in all sub-areas reached zero). For the possible combinations of MCif and Denit, the values of i?eff and logoff were determined and plotted (Fig. 4). A single global optimum could be identified from the combination of both plots. With this combination, a model performance of Reir = 0.45, logoff = 0.48 and r of 0.5 was reached. The nitrogen simulation was validated for the period 1992-1996, achieving a model performance of Relj = 0.28, log/? e fr=0.33androf0.5.
Table 2 Calibrated parameter values of the nitrogen transport model.
Parameter
MC,r
MClf
Denit DLg
Function Mobility coefficient for surface runoff Mobility coefficient for subsurface runoff (leaching and flushing of nitrogen) Denitrification parameter for flushed nitrogen Fraction of denitrification loss in groundwater component
Unit --
--
Value 0.001 0.026
1.1 0.70
Sensitivity low high
high medium
The simulation of monthly nitrogen concentrations at the catchment outlet (Fig. 5) showed acceptable results for the calibration and validation periods. Although the observed concentrations were not fitted exactly, the general seasonal behaviour was modelled well, showing high concentrations in winter and spring and a decrease during the low-flow period in summer and autumn. In the calibration procedure, an optimum parameter set was identified. In modelling studies using more sophisticated water quality models, problems of parameter identification have occurred (Eisele et ai, 2001; Zehe et al, 2001). The use of a simple model concept has clear advantages with regard to the problem of parameter uncertainty. Nevertheless, the optimized parameter set is only valid for the time series of observed concentrations at the catchment outlet. A calibration using other data, such as groundwater concentrations or nitrogen loads, might lead to a different parameter set. The use of time series of nitrogen loads calculated from observed concentrations and measured streamflow for the calibration procedure was problematic because the calculation of the loads implies uncertainties in itself. Furthermore, the overestimation of discharge produced by the water balance model is very likely to produce an overestimation of the simulated nitrogen loads.
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0.024 0.027 0.028 0.031 0.033 0.036 0.024 0.027 0.028 0.031 0.033 0.036
(a) MC„ (b) MC„
Fig. 4 Interpolated lines of (a) AC|T and (b) log/?en in the parameter space MCy vs Denit.
•"TIN sim
TIN obs
W'vA
01.87 01.88 01.89 01.90 01.91 01.92 01.93 01.94 01.95 01.96
Fig. 5 Measured and simulated mean monthly concentrations of total inorganic nitrogen (TIN) for the Seefelder Aach gauging station.
To gain an insight into the internal model behaviour, different variables produced by the subroutines were analysed. The simulated values of mineral nitrogen in the soil storage lie in the same range as regional mean values for March given by the agricultural survey (20-80 kg ha"1). The rates of mobilized nitrogen (40-70% of the annual nitrogen surplus) are comparable to values given in the literature (e.g. Wendland et al, 1993).
As a test for the nitrogen transport simulation on the sub-catchment scale, the spatial variability of simulated loads and concentrations along the river network was analysed. Measurements along the river network were only available for the years 1998 and 1999 (with a sampling interval of two weeks), but not for the modelling
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764 Michael Eisele & Chris Leibundgut
Main r iver (Seefelder Aach)
800
Tr ibutary: (Deggenhauser Aach)
_ 800
oi 600 "*>,,..., pi ~^~"̂ ra ~ "
j= O o 400 0 - ~ -.
"O I o n O û a ^ ^
= 2 0 0 o •* < <u " O i l I I I I
I I I 0 D9 D10D11
Mean annua! specific discharge (simulated) ™»®Mean TIN-Concentration (simulated)
o Mean TIN-lnput-Load (simulated)
• Mean TIN-Concentration (observed 1998-1999) A Mean TIN-Load (simulated)
Fig. 6 Mean values of simulated and observed concentrations of total inorganic nitrogen (TIN), simulated TIN-input loads, TIN-loads and specific discharge along the river network.
period. From the longer time series of measured concentrations at the catchment outlet (1988-1999), it could be proved that there was no significant trend in concentrations. For this reason, the mean concentrations from the modelling period and from the period 1998-1999 are considered to be comparable. Comparison of the means of the measured concentrations at sampling points along the river network for 1998-1999 and the mean simulated concentrations for the modelling period showed a good agreement, with the exception of one sampling point (Fig. 6). This indicates that the spatial variability of the nitrogen input into the river system is reproduced well by the model. The high concentration levels in simulated as well as measured concentrations in the headwater area of the tributary were traced back to high manure applications from cattle farming in this area. Towards the catchment outlet, the concentration level decreases. The model results (Fig. 6) indicate that, following the main stream downwards, a slightly increasing specific discharge is causing a dilution effect, whereas, in the tributary, the concentration decrease is a consequence of the lower nitrogen input loads in its downstream part. The relationship between the area-weighted nitrogen inputs and the area-weighted nitrogen loads shows that the retention of nitrogen in the river plays an important role, with an increase in the absolute values of retained nitrogen towards the catchment outlet. Retention of nutrients in the river system mainly occurs in the upper layers of the river sediments (hyporheic interstitial) and is, therefore, a function of the hydro-morphological properties of the river channel (Brunke & Gonser, 1997). Although the model description for this process is very coarse, using only streamflow and channel width as predictors, the importance of
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Modelling nitrogen dynamics for a mesoscale catchment 765
nitrogen retention and denitrification is evident. The observed concentration decrease shows that not only the reduction of emissions but also the nutrient retention in the river system provides a high potential for the improvement of water quality. In the context of river basin management, this demonstrates the importance of the maintenance and restoration of the hydro-ecological function of the river channels.
To test the simulation of the groundwater-related nitrogen transport, the simulated time series of groundwater concentrations were analysed. Data of nitrogen concentrations were available for the hydrological year 1991 for a spring located at the geological borderline between the glacial sediments and the tertiary. The comparison of the measured concentrations and the simulated concentrations for this sub-area showed a good agreement with only small deviations (Fig. 7(a)). Therefore it seems likely that the temporal variability of the groundwater concentrations is reproduced well by the dispersion-translation function. For the whole modelling period, the concentrations show a small downward trend, which is mainly the result of the trend in concentrations of the pre-modelling period (Fig. 7(b)). As there were no significant peaks in the nitrogen input and only small changes in the concentration during low flow periods (Fig. 5), the uncertainty about the residence time distribution of the groundwater remains. Although this does not affect the simulation results at the catchment outlet, predictions of concentration levels in the groundwater of sub-areas might be produced with errors.
In a next step, concentration levels for 1995 in different wells in the catchment were compared to the 1995 means of the simulated concentrations (Fig. 7(c)). The map of deviations (Fig. 7(c)) reveals that, in the alluvial aquifer in the confluence area of the Seefelder Aach and its main tributary, Deggenhausener Aach, the concentration levels were underestimated by the model. On the one hand, this underestimation might
a: Nitrogen concentration in groundwater (simulated) and c: Deviation of simulated TIN concentration s in
spring water (observed) groundwater from observations in wells
• TIN observed »- " ,— TIN simulated
• • • ' • " * * * * " • " • • • • • • *^ « + +
10.90 11.90 01.91 02.91 04.91 06.91 07.91 09.91
b: Simulated ground water outflo wand nitrogen concentrations
A&A&^k&Â&&&&&&&fti&£fi&.
Spring
• Dev (mg/l) Mean TIN-concentration in groundwater outflow 0.5 m Small dev
Simulated base flow at the catchmrtet outlet
Jan. 87 Jan. 88 Jan. 89 Jan. 90 Jan. 91 Jan. 92 Jan. 93 Jan.94 Jan. 95 Jan. 96
Fig. 7 Results of the nitrogen transport simulation for groundwater.
negative dev. strong negative dev.
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be a consequence of an inadequate description of the nitrogen input, as the agricultural practices are quantified for the spatial resolution of the communal land areas. To get a better representation of the spatial variability of the nitrogen input in this area, data about the characteristics of individual farms and a better representation of the soil properties are needed. On the other hand, errors might be produced due to an inadequate reproduction of the lateral groundwater flow paths and the corresponding residence times. As lateral fluxes of groundwater between the sub-areas of the model had to be estimated based on geological maps, the mixing of waters from different areas in the aquifer might be reproduced falsely. This problem of uncertainty regarding the groundwater flow paths and transit times has been revealed in other studies (e.g. Behrendt et al, 1999). A better representation of the groundwater flow paths may be achieved using data of groundwater tables and dynamics or simulation results of three-dimensional groundwater models (Groenendijk & Boers, 1999).
CONCLUSIONS
To simulate the nitrogen dynamics and nitrogen budget of a mesoscale catchment using commonly available data sets, a MIR model for nitrogen transport was developed on the basis of a water balance simulation. The efficiency of the modelling approach was proved by the reasonable results for the simulation of streamflow and nitrogen concentrations at the catchment outlet. The described concept requires calibration for only a few parameters. In comparison with other models, it can be seen as a hybrid between empirical predictions of water quality used by nutrient balance models (e.g. MONERIS, Behrendt et al., 1999), or simple chemical mixing approaches (e.g. Jain, 2000), and deterministic conceptual catchment water quality models with a more physical basis (e.g. SWAT, Arnold et al, 1993; HSPF, Johanson et al, 1996). Due to the spatially-distributed and temporally-variable approach, it is possible to identify areas and periods with high mobilization potential. The described nitrogen simulation can be applied in any catchment, where a grid-based water balance model has been implemented. As LARSIM is currently applied in many mesoscale catchments, the simulation of nitrogen transport within the European Water Framework Directive procedure appears to be possible.
As the nitrogen model uses the results of a water balance model, the water balance simulation concept and efficiency has a large impact on the nitrogen simulation. Although a good parameter set was found, the reliability of the runoff generation routine and the partitioning of the runoff components is uncertain. The modelling errors in the spatial variability of groundwater concentrations showed that the use of additional data sets in the validation procedure is crucial to prove the validity of conceptual models and to identify uncertainties of the simulations.
A better representation of the hydrological processes in the modelling system can only be achieved by the use of additional data concerning runoff generation processes, residence times and flow paths of the groundwater component (Uhlenbrook & Leibundgut, 2002). Following a MIR concept which should be applicable with readily available data, such a data collection procedure cannot be included in the modelling work. Consequently, uncertainty about the modelling concept and realistic process description will remain when using models of this kind. However, in the context of river basin management, a compromise between applicability of models and process
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representation has to be discussed. Nevertheless, efforts of uncertainty identification and prediction are needed to ensure the validity of model results which may form the basis of management decisions.
Acknowledgements The research reported in this contribution was supported by the BW-PLUS programme. The local authorities and regional water authorities, the Environmental Agency and the Agricultural Agency of Baden-Wurttemberg, Germany (Landesanstalt fur Umweltschutz, Landesanstalt fur Pflanzenbau) are acknowledged for the provision of data and literature. The Consulting Bureau Dr K. Ludwig is acknowledged for the provision of the model LARSIM and pre-processing of input data. Thanks are due to S. Uhlenbrook, M. Hauns and H. Gôtz for critical reviews.
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Received 3 December 2001; accepted 17 May 2002
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