In-Stream Nitrogen Attenuation: Model-Aggregation Effects and

Apr 5, 2005 - Department of Physical Geography and Quaternary Geology,. Stockholm University, SE-106 91, Stockholm, Sweden. Eutrophication problems ...
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Environ. Sci. Technol. 2005, 39, 3716-3722

In-Stream Nitrogen Attenuation: Model-Aggregation Effects and Implications for Coastal Nitrogen Impacts A M EÄ L I E D A R R A C Q * A N D GEORGIA DESTOUNI Department of Physical Geography and Quaternary Geology, Stockholm University, SE-106 91, Stockholm, Sweden

Eutrophication problems in coastal and marine waters worldwide emphasize the significance, for the scientific community as well as the whole society, of relevant quantification of catchment-scale nitrogen transport from land to coast. Different catchment-scale nitrogen budget models use, and base management recommendations on, quite different process representations of and spatial resolution approaches to in-stream nitrogen attenuation. We compare three different spatial resolution approaches to modeling nitrogen loss rates in streams of the same drainage basin. Results show that commonly used spatial model aggregation may lead to artificial decrease of calibrated nitrogen loss rates with increasing stream depth (or flow), in addition to any such dependences that may prevail in independently measurable reality. Coastal nitrogen impact predictions and practical management implications of large-scale model aggregation of nitrogen attenuation rates may further differ considerably from those based on rates from finer resolution modeling or independent measurements.

Introduction Catchment-scale nitrogen transport from land to coast participates in one of the greatest threats to coastal and marine environmental quality worldwide: the excess fertilization of ecosystems, creating eutrophication problems in the coastal zone. Nutrient load reductions are therefore required by national and international environmental regulation (such as the European Water Framework Directive) and political agreements (1), and must be based on relevant quantification of natural and anthropogenic nutrient loading to coastal and marine waters by relevant modeling of coupled physical and biogeochemical processes of nutrient transport and attenuation in catchments. Such modeling is therefore the subject of many recent scientific studies (2-13) and is crucial for efficient mitigation and management of nitrogen loads causing coastal and marine eutrophication (1, 14-17). Catchment-scale nutrient budget modeling, however, may currently be based on quite different model representations and spatial-temporal resolutions of various nitrogen transport-attenuation processes, as exemplified in Table 1 for the process of nitrogen attenuation in streams. Some model studies (2, 3, 9, 10) report then highly increasing nitrogen attenuation rates with decreasing stream depth or flow, * Corresponding author phone: +468164768; fax: +468164818; e-mail: [email protected]. 3716

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dendritic patterns of zones with high coastal nitrogen impacts that extend far upstream within a river basin, and nitrogen attenuation rates in lakes that are either similar to (2, 10) or higher than (9) those in streams. Other studies, based on other modeling and spatial-temporal resolution approaches (4, 5), report instead systematically high nitrogen attenuation in lakes and relatively small attenuation in streams, which decreases with stream volume, and resulting zones of high coastal nitrogen impact that are always located far downstream and close to the coast in a river basin. Catchment-scale nitrogen budget models may thus differ considerably in their representations and spatial resolutions of the in-stream nitrogen attenuation process (Table 1) and yield different results and practical implications for coastal nitrogen load management. Independent experimental measurements of nitrogen attenuation rates have been made in a range of different streamflows and depths (18), but have been interpreted with a model representation of the in-stream attenuation process other than the ones listed in Table 1 and have not been compared to the different calibrated rate results of the latter. Recent theoretical results, however, indicate that the spatial aggregation of calibrated in-stream nitrogen attenuation rates in catchment-scale nitrogen budget modeling may lead to significant model artifacts relative to independently measurable rates (19). In general, there have been few attempts to investigate and quantify the predictive and practical importance of such open nitrogen transport-attenuation issues and model differences. The main objective of the present paper is to make one such investigation of the possible effects and implications of different spatial model resolutions of the instream nitrogen transport-attenuation process. To address this objective, we use the Norrstro¨m drainage basin in Sweden as an example case study for realistic water flow and nitrogen transport-attenuation quantification.

Materials and Method Case Study Basin. The Norrstro¨m drainage basin in Sweden (Figure 1a) drains into the Baltic Sea, covers an area of 22 000 km2, which has a population of 1.7 million people, and includes most of the Lake Ma¨laren valley west of Stockholm. The water quality of Lake Ma¨laren is at stake for the regional supply of freshwater, and general water resource management in the Baltic Sea Region, with eutrophication being a significant problem in many inland and coastal Swedish waters. Parts of the basin are covered by heavily exploited agricultural areas, while urbanization and industrialization are additional sources of pressure around Stockholm and Lake Ma¨laren (6) (Figure 1b). The Norrstro¨m drainage basin further contains a number of water bodies, especially in the northwestern part of the basin, including also the third and fourth biggest lakes in Sweden: Lake Ma¨laren and Lake Hja¨lmaren. In addition, the basin area is characterized by a hilly and mostly forested northwestern part and a more flat and mostly agricultural eastern part. Basic Model Application. As a basis for water flow and nitrogen transport quantification in the Norrstro¨m basin, we use the POLFLOW model, which has also previously been applied to the Norrstro¨m (6) and other relatively large river basins and catchments, such as the Rhine and Elbe (7, 8, 11-13), for a considered five-year simulation period (19951999). The POLFLOW model is embedded in the raster-based Geographic Information System (GIS) modeling tool PCRaster, and allows for spatial process resolution at the finest scale of available site-specific GIS data, by directly integrating these 10.1021/es049740o CCC: $30.25

 2005 American Chemical Society Published on Web 04/05/2005

TABLE 1. Comparison between Different Catchment-Scale Nitrogen Transport and Attenuation Models, in Terms of Their Spatial and Temporal Resolution, and Representation of Nitrogen Attenuation in Streams and Lakes, as Quantified by the Dimensionless Attenuation Coefficient 1 - r ) (Min - Mout)/Min e 1, Relating Total Attenuated Nitrogen Mass (Min - Mout) to Total Nitrogen Mass Input (Min), with Mout Being Total Nitrogen Mass Output and r being the Complementary Nitrogen Delivery Coefficient, over the Model-Specific Spatial Grid and Temporal Resolution Scale model

spatial resolution

N attenuation coefficient, 1 - r, in streams

temporal resolution

POLFLOW (6-8, 11-13)

GIS pixel (1 km2 grid cell for this study)

steady-state (annual averages in this study), or dynamic (6, 8)

SPARROW first version (2, 9, 10)

stream reach

steady-state (annual averages)

1 a 1 + (rn1(1000slope + 1)(q + 1)m2 1 - exp(-kT)b

SPARROW second version (3) HBV-N (4, 5)

subcatchment

N attenuation coefficient, 1 - r, in lakes

dynamic (e.g., daily resolution)

1 - exp(-ksL)c

1 - exp(-kr/q)d

par(2)ciT10V e cinqin

par(3)ciT10A f cinqin

a rn1: calibrated nitrogen attenuation parameter in all pixels within stream network (s‚m-3).; rn2: calibrated coefficient for all pixels within stream network, determining the pixel-specific water flow (q) effect on nitrogen attenuation (-). q: uncalibrated average water flow for each pixel (m3‚s-1), resulting from the water flow module of the POLFLOW model. For pixels within lakes, q is average lake discharge normalized with lake surface area. slope: average topographic slope in each pixel. b k: calibrated first order in-stream /lake loss rate for each streamflow/lake class (day-1). T: selected characteristic travel time for each flow class in every stream reach/lake (days). c ks: calibrated first-order in-stream loss coefficient (km-1). L: length of stream channel for each flow class in every stream reach (m). d kr: calibrated loss rate for all lakes, called apparent settling velocity (m/yr). q: lake and reservoir discharge normalized with lake surface area (m/yr). e par(2): free calibration parameter (yr-1 Celsius-1). ci: stream concentration of inorg-N (kg/m3) during considered time period. T10: average of last 10 days temperature (°C). V: water volume in all streams of considered sub-catchment. cin: inflow concentration of inorg-N (kg‚m-3). qin: inflow (m3‚yr-1). f par(3): free calibration parameter (m‚yr-1°C-1). A: lake surface area within considered sub-catchment (m2).

In POLFLOW (11-13), resulting runoff, groundwater recharge indices, and groundwater residence times from its water flow module are used as input to its nitrogen transport module. The flow module yields annual average (over 10 years) runoff as the difference between annual average precipitation and actual evapotranspiration, where the latter is estimated as a function of precipitation and temperature. Groundwater recharge indices are related to topographic slope, soil type, texture, aquifer type, groundwater level, land cover, and January temperature, and groundwater residence times are related to aquifer conductivity, porosity, thickness, local slope, and groundwater recharge. Nitrogen transport pathways can be vertical as well as horizontal, over and/or through the soil surface into underlying groundwater before reaching the streams. Input data for nitrogen emissions are taken from the Swedish national database of nutrient sources at the drainage basin scale (20), with six different types of nitrogen sources given for our specific case study: point sources (industries and wastewater treatment plants), atmospherically deposited nitrogen on lakes, runoff from urban areas, runoff from agricultural land, and diffuse emissions from private no-sewage systems and clear-cuts. Modeled annual average water discharges resulted from the uncalibrated water flow module (see Table SI-1 in Supporting Information for independent data sources), whereas modeled discharges of total nitrogen (TN) resulted from 5-parameter calibration of the nitrogen transport module (see Table SI-2 in Supporting Information for calibration parameter description). FIGURE 1. (a) Location map of the Norrstro1 m drainage basin among all Baltic Sea drainage basins; (b) land cover map of the Norrstro1 m basin, and locations of nitrogen emission point sources (industries and wastewater treatment plant (WWTP)). data with mathematical process functions, in a single, readily available, and easy-to-use programming language. A 1 × 1 km2 GIS pixel was the spatial resolution scale of available GIS data in this study.

Figure 2b shows the resulting agreement between modeled and measured annual averages (based on monthly data) of water discharge at 25 observation stations in streams of the Norrstro¨m basin (see locations in Figure 2a). The value of R2 for the water discharge model was 0.992, with a prediction bias, in terms of median prediction error, of -17.4%, and a prediction error variability (interquartile range) of 33.3%. Figure 2c shows the resulting goodness-of-fit between calibrated (5-parameter calibration; Table SI-2) VOL. 39, NO. 10, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. (a) Locations of measurement stations for streamwater flow and nitrogen concentration in water; and validation of water and total nitrogen (TN) discharges from the POLFLOW model of the Norrstro1 m basin (6) in terms of: (b) comparison between uncalibrated modeled and measured (see source in Table SI-1) annual average water discharges in different monitoring stations for streamwater flow (Figure 2a); and (c) comparison between model calibrated and observed annual average TN discharges in different monitoring stations for nitrogen concentration (Figure 2a). The modeled and observation-based total annual average TN load at the outlet of the Norrstro1 m basin is 3185 T/year. model results and observation-based (from monthly concentration measurements and annual average water discharge) annual average values of TN discharge at 62 measurement stations (see locations in Figure 2a). The value of R2 for the TN discharge model was 0.980, with a prediction bias, in terms of median prediction error, of -4.35%, and a prediction error variability (interquartile range) of 51%. In general, we expect the site-specific goodness-of-fit of the POLFLOW model results to be similar to what other models in Table 1 may be capable of, since all these models are already proven to be sufficiently flexible for successful calibration to site data in different catchments around the world (see references in Table 1). Comparative Nitrogen Loss Rate and Coastal Impact Calculations. We aim here at directly comparing the relatively fine-resolution POLFLOW grid-cell results on in-stream nitrogen attenuation rates with corresponding spatially aggregated rates and their respective coastal impact predictions to quantify potential effects of spatial model aggregation and parameter averaging. On the basis of the available POLFLOW water flow and nitrogen transport results for the Norrstro¨m basin, we therefore derive first-order nitrogen loss rates and coastal impacts in three different ways, corresponding to three different modeling and spatial averaging approaches from the ones listed in Table 1 (see also Table SI-3 for notation summary and explanation). First, we calculate for every grid cell the resulting cellspecific dimensionless nitrogen delivery coefficients for total nitrogen, Rcell ) (Mout/Min)cell e 1, with Min and Mout being total nitrogen mass input and output for each cell, respectively. Local first-order nitrogen loss rates can then be further calculated for each cell as kcell ) -ln(Rcell)/Tcell, with Tcell being nitrogen travel time through the cell, obtained as Tcell )Lcell 3718

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/vcell from the known cell length, Lcell, along the mean flow direction and the water velocity expression used in previous applications (3): vcell ) 0.36 (qcell)0.241, with qcell being the cellspecific streamwater flow. This local first-order nitrogen loss rate kcell should correspond in physical meaning, even though not necessarily in spatial aggregation scale, to the streamreach-aggregated loss rate k of expression (2) in Table 1 (2, 9, 10). Second, we calculate, from the known underlying POLFLOW grid-cell results also subcatchment-specific dimensionless delivery coefficients Rsc ) (Mout/Min)sc e 1, with Min and Mout here being average annual total nitrogen mass input and output for each subcatchment, respectively. Corresponding subcatchment attenuation coefficients 1 - Rsc e 1 should then in both physical meaning and spatial aggregation scale be consistent with attenuation coefficient commonly reported in applications of the HBV-N model (4, 5; see also Table 1). Third, we calculate subcatchment-specific nitrogen loss rates for each subcatchment as ksc ) -ln(Rsc)/Tsc, with Tsc being nitrogen travel time through the subcatchment, as obtained from the expression used in application of the first version of the SPARROW model (2): Tsc) 0.5 (-0.0065 + 0.2642Asc0.3), with Asc being total subcatchment drainage area. These subcatchment-specific nitrogen loss rates ksc should in both physical meaning and spatial aggregation scale be consistent with stream-reach aggregated loss rates commonly reported in several applications of the first version of the SPARROW model (2, 9, 10; see also Table 1). The above three different quantifications of nitrogen loss rates and delivery coefficients can then be further used to calculate associated coastal nitrogen impacts, in ways consistent with different spatial aggregation scales and

FIGURE 3. In-stream nitrogen loss rate at grid-cell-scale, kcell (day-1), in relation to topographic slope (-) and water flow qcell (m3/s) as obtained directly from the POLFLOW model at the grid cell scale for (a) low topographic slope (0.025). modeling approaches among the ones listed in Table 1 (see Table SI-3 for notation summary and overview). First, coastal nitrogen impacts are calculated directly from the known gridcell nitrogen loss rates produced by the POLFLOW model as a Nimpact ) ∏Mcell exp(-kcellTcell), with Mcell being the total number of downstream cells, along the main stream pathway to the coast, from each considered grid-cell location within the drainage basin. Second, coastal nitrogen impacts are calculated from the subcatchment-aggregated loss-rates as b N ˜ impact ) ∏Mcell exp(-kav sc Tcell), following the spatial aggregation and impact-modeling approach of previous SPARROW applications (2, 3, 9). Third, coastal nitrogen impacts are calculated from the subcatchment-aggregated deliveryc coefficients as N ˜ impact ) ∏Msc Rsc, with Msc being the total number of downstream subcatchments, along the main stream pathway to the coast, following the spatial aggregation and impact-modeling approach of previous HBV-N applications (4, 5).

Results and Discussion Spatial-Aggregation Effects on Modeled In-Stream Nitrogen Loss Rates. Resulting grid-cell nitrogen loss rates, kcell, as produced directly from the POLFLOW model results are shown in Figure 3 to be highly variable, and more dependent on local topographic slope than on local streamwater flow. av Resulting average values of grid-cell loss rates kcell (kcell ) for different stream-depth classes are illustrated in Figure 4 (thick solid line) in direct comparison with previously reported results of average stream-reach-aggregated loss rates (2, 3, 9, 10; thin lines). In contrast to the latter, the average nitrogen av loss rates at grid-cell scale, kcell , do not exhibit any significant decrease with increasing stream depth, even though there is considerable variability around these fairly constant average values depending on local stream conditions (Figure 3), as has also been observed by direct local nitrogen uptake measurements (18). Resulting average values of subcatchment-aggregated loss rates ksc (kav sc ) for different stream-depth classes are also illustrated in Figure 4 (thick dotted line) and do exhibit a decreasing trend with increasing stream depth, in contrast av to the corresponding average grid-cell values kcell , but consistent with previous model results of average streamreach-aggregated rates (2, 3, 9, 10). From our certain knowledge of underlying modeled rate results on the finer grid-cell scale of the present case study, we then know with certainty that the decreasing trend with increasing stream depth that is exhibited by the average subcatchmentaggregated rates, kav sc , does not correctly represent the average magnitude and depth-dependence behavior of av underlying grid-cell rates (kcell , thick solid line in Figure 4), but is a pure spatial aggregation artifact. The exhibited spatial aggregation artifact of kav sc supports the previous theoretical indication (19) that such artifacts

FIGURE 4. Resulting average in-stream nitrogen loss rates (day-1) av av kcell for local grid-cell-scale and ksc for subcatchment-aggregated, for the Norrstro1 m basin, in comparison with previous SPARROW results for other watershed studies, related to mean stream depth (m). For comparison, the grid cell-averaged loss rate in lakes was of the same order (0.220185 day-1) as the grid-cell average inav stream loss rate kcell . The mean stream depth D is obtained from the expression used in SPARROW-model application (2): D ) 0.2612q0.3966, with q being the uncalibrated cell-specific streamwater flow, qcell, as obtained by the water flow module of the POLFLOW model for computations at the grid cell scale (1 km2 GIS pixel), and q being the subcatchment-averaged streamwater flow for computations at the subcatchment scale. may occur from use of a single characteristic solute travel time Tsc for representing the physical transport effects of a whole range of different travel times from various distributed stream inputs of nitrogen to a given subcatchment/streamreach outlet. This travel time representation problem arises also in the present numerical study, because there exists also here a whole range of different nitrogen travel times from the inputs to each grid-cell of a stream network to the associated subcatchment outlet. It is then clear that a single travel time value Tsc cannot possibly represent such a whole range of travel times that may prevail in a subcatchment, and may or may not even adequately represent the mean travel time value of this whole range, depending on how Tsc is quantified. It is further also clear that use of misleading Tsc values for calculating associated subcatchment-aggregated attenuation rates as ksc ) -ln(Rsc)/Tsc will yield a misleading VOL. 39, NO. 10, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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rate estimate relative to underlying grid-cell rates kcell ) -ln(Rcell)/Tcell. What cannot be clear from such reasoning alone is how large and significant the resulting magnitude and possible depth-dependence differences may be between the spatially aggregated subcatchment rates and the known underlying grid-scale rates. The result summary in Figure 4 provides a quantitative answer to precisely this question for the conditions of the present example case study. An important general implication of this example quantification is that one cannot a priori neglect the possibility of significant differences occurring also under other spatial model aggregation and case study conditions, but has to be able to prove that they are insignificant before neglecting them. The indications of previous analytical studies (19) are that such misleading rate differences will increase with increasing Tsc value and increasing sub-aggregation variability of solute travel times. Other studies (21-23) have shown that solute travel time variability in streams may indeed be very large and greatly complicate also the quantification of a relevant mean travel time measure. In general, use of a simple, single characteristic solute travel time value Tsc may thus be far from representative of either a realistic mean value or the variability range of prevailing solute travel time distribution in streams over large model aggregation scales. In the following, we continue to use the Norrstro¨m case study for example quantification of potential predictive and practical implications of this travel time representation problem. Spatial Aggregation Effects on Predicted Coastal Nitrogen Impacts. Three different coastal nitrogen impact maps (Figure 5a-c) result from the three different quantifications of loss rates/delivery factors along the stream pathways to a the coast: Nimpact (Figure 5a), based on the local grid-cell loss b rates kcell (Figure 3); N ˜ impact (Figure 5b), based on average subcatchment-aggregated loss rates kav sc (thick dotted line in c Figure 4); and N ˜ impact (Figure 5c), based on the subcatchment-aggregated delivery factors Rsc. The impact map b N ˜ impact in Figure 5b exhibits the dendritic pattern of zones with relatively high coastal nitrogen impacts extending far upstream within the drainage basin, which is consistent with and characteristic of previous studies using stream-reachaggregated loss rates for such impact calculations (2, 3, 9). c Furthermore, the impact map N ˜ impact in Figure 5c is consistent with previously reported inverse results on downc stream nitrogen attenuation (1 - N ˜ impact ) in the Norrstro¨m drainage basin (see specific Figures 4B in (4) and 5 in (5)). a The impact map Nimpact (Figure 5a), obtained directly from local grid-cell loss rates, shows a much higher impact from the eastern than from the central-western part of the b Norrstro¨m drainage basin, whereas the impact maps N ˜ impact c (Figure 5b) and N ˜ impact (Figure 5c), obtained from subcatchment aggregated loss rates and delivery factors, respectively, considerably underestimate the impact differences between the eastern and the central-western parts of the basin. Figure 6 further illustrates the predicted resulting efficiency of diverse nitrogen reduction strategies based on the three different underlying coastal nitrogen impact maps (Figure 5a-c). Efficiency of a nitrogen reduction strategy is here quantified as the resulting change in coastal nitrogen load at the outlet of the Norrstro¨m basin in response to a unit mass change of nitrogen inputs to streams in: either (strategy A) only the eastern part of basin; or (strategy B) only the central-western part of the basin; or (strategy C) uniformly over the entire basin. Results shown in Figure 6 imply that, according to the a Nimpact impact calculations (Figure 5a), one can, for instance, achieve a 50% reduction of nitrogen discharge to the Baltic Sea (required by international agreement (1); corresponding in absolute terms to a coastal load reduction at the outlet of the Norrstro¨m basin of 1600 T/year) by reducing nitrogen 3720

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FIGURE 5. Maps of the resulting coastal nitrogen impact along the stream pathway to the coast, for different zones within the Norrstro1 m drainage basin as obtained (a) from underlying grid-cell loss rates av kcell, (b) from average subcatchment-aggregated loss rates ksc for different stream depths, consistent with previous catchment-scale studies (2, 9, 10), and (c) from subcatchment-aggregated nitrogen delivery factors rsc, consistent with previous catchment-scale studies (4, 5).

inputs to the streams of only the eastern part of the basin by 2300 T/year, following strategy A. Achievement of the same (1600 T/year) coastal load reduction by use of strategy B or C, however, would require much higher reduction of nitrogen stream inputs, specifically a reduction of 5400 T/year to streams in the central-western part of the basin (strategy B), or a reduction of 4000 T/year uniformly to streams all over the basin (strategy C). The possible significant environmental and/or economic gain by management of nitrogen stream inputs in primarily the eastern part of the basin (strategy A), rather than in the central-western part (strategy B), or uniformly over the whole basin (strategy C) is not predictable by the alternative impact maps in Figure 5b and c. The latter instead predict that a 50% coastal nitrogendischarge reduction requires a reduction of nitrogen inputs

FIGURE 6. Resulting coastal load reduction efficiency of different strategies (A-C) for spatially allocating nitrogen reduction measures within the Norrstro1 m basin, quantified as the achieved coastal load reduction in response to a unit mass reduction of nitrogen inputs to streams in: either (strategy A) only the eastern part of the basin; or (strategy B) only the central-western part of the basin; or (strategy a b C) uniformly over the whole basin; based on the different coastal nitrogen impact calculations, Nimpact (Figure 5a), N ˜ impact (Figure 5b), c and N ˜ impact (Figure 5c). to streams of 2600-4500 T/year, more or less regardless of where in the basin this stream-input reduction takes place. In other words, the fine-resolution impact calculation a Nimpact (Figure 5a) suggests that introducing measures for reduction of nitrogen inputs to streams of only in the eastern part of the basin (strategy A) is 2.4 times more efficient than introducing the same measures only in the central-western part of the basin (strategy B), and 1.7 times more efficient than introducing the same measures evenly over the whole basin (strategy C). The subcatchment-aggregated impact b c (Figure 5b) and N ˜ impact (Figure 5c) calculations N ˜ impact instead suggest that introducing such measures only in the eastern part of the basin (strategy A) is only 1.2-1.6 times more efficient than introducing them only in the centralwestern part of the basin (strategy B), or uniformly over the whole basin (strategy C). Nitrogen transport-attenuation modeling that misses or misallocates the really high and/or low impact zones in a drainage basin may thus imply that one misses the opportunity to adopt an optimal nonhomogeneous allocation of nitrogen reduction measures within the basin and thereby achieve either: (a) a considerably higher reduction of coastal nitrogen loading for the same spent resources (same costs for higher resulting net benefits); or (b) the same coastal nitrogen loading reduction with considerably less spent resources (less cost for the same resulting net benefits) (15-17). In general, we have in this paper exemplified and compared quantitatively the possible effects and implications of differing spatial process and parameter aggregation procedures in catchment-scale nitrogen budget models. This comparison was enabled by representing in-stream nitrogen attenuation results for different model aggregation scales in terms of: (i) the same attenuation rate parameter (Figure 4); (ii) the same predictive quantity of coastal nitrogen impact (Figure 5); and (iii) the same predictive quantity of achieved coastal load reduction by different spatial abatement strategies (Figure 6). Through the comparison, we show that calibrated in-stream nitrogen loss rates on scales of whole subcatchments may exhibit an artificial decrease with increasing stream-depth, in addition to any such depthdependence that may occur in independently observable reality. A main implication of this result is that calibrated large-scale parameters should not be expected to have the same magnitude, physical meaning, and cause-effect responses as corresponding, independently measurable or calibrated parameters on smaller scales. In addition, we show that differing spatial aggregation of processes and parameters in different catchment-scale nitrogen budget models may

lead to considerable and practically important differences in predictions of coastal nitrogen impacts (Figure 5) and associated management implications (Figure 6).

Acknowledgments We thank the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS) for providing financial support for this work.

Supporting Information Available Independent data sources for the POLFLOW water flow module (Table SI-1), calibration parameter description for the POLFLOW nitrogen transport module (Table SI-2), and notation summary and explanation of calculations of nitrogen loss rates and coastal impacts (Table SI-3) (pdf). This material is available free of charge via the Internet at http:// pubs.acs.org.

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Received for review February 18, 2004. Revised manuscript received February 18, 2005. Accepted March 3, 2005. ES049740O