ESTIMATION OF RIVERINE LOADS OF NITROGEN ANDPHOSPHORUS TO THE BALTIC SEA, 1970–1993

PER STÅLNACKE1∗, ANDERS GRIMVALL2, KARIN SUNDBLAD3 and ANDRZEJTONDERSKI4

1 Jordforsk- Centre for Soil and Environmental Research, N-1432 Ås, Norway2 Department ofMathematics, Linköping University, SE-581 83 Linköping, Sweden3 Department of Physics andMeasurement Technology, Linköping University, SE-581 83 Linköping, Sweden4 Department of

Water and Environmental Studies, Linköping University, SE-581 83 Linköping, Sweden(∗ author for correspondence, e-mail: per.stalnacke@jordforsk.nlh.no)

(Received 30 September 1997; accepted 18 May 1998)

Abstract. This article presents the results of the first critical examination of time series of riverinenutrient-load data for the entire Baltic Sea drainage area. Water quality data collected by or for thedifferent national environmental agencies were compiled and analysed statistically to identify andremove inconsistent or obviously incorrect observations. Moreover, sampling tours were undertakento acquire additional information about the present nutrient concentrations in the largest rivers in thestudy area. Gaps in the time series of approved data were then filled in by employing statistical inter-polation and extrapolation methods. Thereafter, the concentration and runoff data were combined toobtain estimates of monthly nutrient loads for the time period 1970–93. The results of the calculationsshowed that although there had been substantial changes in land use, atmospheric deposition andwastewater treatment in many parts of the study area, the total riverine loads of nitrogen (N) andphosphorus (P) to the Baltic Sea have been fairly constant since 1980, and most likely also since1970. Moreover, the interannual variation was clearly correlated to the runoff. The mean annualloads for the time period 1980–93 were found to be about 825 000 tonnes N and 41 000 tonnes P,respectively. This implies that (i) several other investigators have strongly underestimated the riverineloads of nutrients, especially the nitrogen, and that (ii) the riverine loads by far exceed the input to theBaltic Sea from other sources, i.e. atmospheric deposition, direct emissions from cities and industriesalong the Baltic Sea coast and nitrogen fixation by marine algae.

Keywords: Baltic Sea, nitrogen, phosphorous, riverine load, time series

1. Introduction

The Baltic Sea, together with the lakes and watercourses in its drainage basin,represents one of the most intensively monitored aquatic systems in the world, andeutrophication has been identified as a major threat to this system. The total area ofthe Baltic Sea drainage basin is 1 745 000 km2, and the long-term average inflowof freshwater with the rivers is 475 km3yr−1 or 15 130 m3s−1 (Bergström andCarlsson, 1994). Measurements in the marine environment have demonstrated thatthe concentrations of nitrogen (N) and phosphorus (P) have increased in most of thesubbasins of the Baltic Sea during the past few decades (Sandén and Rahm, 1993).

Environmental Monitoring and Assessment58: 173–200, 1999.© 1999Kluwer Academic Publishers. Printed in the Netherlands.

174 PER STÅLNACKE ET AL.

Furthermore, there are several observations of extensive algae blooms (Fleischeret al., 1987; Rosenberget al., 1990; FCC., 1991; Kahruet al., 1994) and oxygen-free bottoms (Bernes, 1988), particularly in the Baltic proper, the Western Balticand the Kattegat. Information about the input of nutrients to the Baltic Sea is alsoplentiful but more difficult to interpret. Although it is generally accepted that therehas been a dramatic increase in the input during the twentieth century (Larssonet al., 1985), studies of nutrient concentrations and loads in rivers have revealedonly a few indisputable examples of long-term upward trends (Hagebroet al.,1983; Kauppi, 1984; Grimvallet al., 1991; Tsirkunovet al., 1992; Stålnackeet al.,1997b). In addition, there is a remarkable lack of consensus regarding the presentinput of nutrients to the Baltic Sea.

Several investigators have tried to estimate the total riverine loads of nutrientsto the Baltic Sea, some of which are listed in the Appendix, and have arrived atvalues ranging from 267 000 to 979 000 tonnes yr−1 N and from 22 000 to 59 000tonnes yr−1 P (Pawlak, 1980; Maksimova, 1982; Larssonet al., 1985; Rosenberget al., 1990; Rybinski, 1990; SEPA, 1990a; MENR, 1990; Helcom, 1993; Enelland Fejes, 1995). The atmospheric deposition of nitrogen has also received con-siderable attention (Nehring and Wilde, 1979; Rodheet al., 1980; Helcom, 1989;Rosenberget al., 1990; MENR, 1990; Helcom, 1991; Areskoug, 1993; Heidam,1993; Lindforset al., 1993; Barrettet al., 1995) whereas information about thedeposition of phosphorus is more scarce (Nehring and Wilde, 1979; Rodheet al.,1980; Rosenberget al., 1990; SEPA, 1990a). The deposition estimates reported inthe cited studies range from 220 000 to 630 000 tonnes yr−1 N and from 1400 to9900 tonnes yr−1 P. Direct emissions from urban areas and industries along thecoast of the Baltic Sea play a surprisingly small role considering that this areahas 15-20 million inhabitants (Sweitzeret al., 1996). According to point sourceinventories compiled by Helcom (Helcom, 1993), such sources are responsible foronly 94 000 tonnes yr−1 N and 12 500 tonnes yr−1 P. Our own estimates that werebased on reports, studies by consultant agencies and assigned per capita emissionsof N and P, confirmed these figures. Nitrogen fixation in the Baltic Sea has been es-timated to account for an input of approximately 130 000 tonnes yr−1 N (Brattberg,1980).

In the present investigation, we focused our interest on riverine loads of nutri-ents measured at sampling sites close to the mouths of the studied rivers. In contrastto other investigators, who have estimated loads during short time periods or singleyears, we also tried to elucidate long-term trends and interannual and seasonalvariation in riverine loads. The main objectives of the study were as follows:

• to identify the major causes of the remarkable discrepancies between previ-ously published estimates of riverine loads of nutrients to the Baltic Sea;

• to produce new estimates of monthly and annual loads of nitrogen and phos-phorus to the main subbasins of the Baltic Sea for the time period 1970–1993;

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 175

• to demonstrate how various statistical techniques can be used to (i) reveal out-liers and abnormal interannual variation in reported data, (ii) to fill in gaps inreported time series of data, and (iii) to combine concentration and runoff datainto estimates of riverine loads.

2. Data Base

This study was based primarily on data collected for the national environmentalagencies of the Baltic Sea nations. Monthly runoff data for rivers in the Baltic Seadrainage area and daily runoff data for selected rivers in Sweden were obtainedfrom the Swedish Meteorological and Hydrological Institute (Bergström and Carls-son, 1994). Time series of nutrient concentrations at more than 110 sampling sites(see Table I) were obtained from the following agencies or institutions: the StateHydrological Institute in St. Petersburg (Russia); the Ministry of Environment inEstonia; the Latvian Hydrometeorological Agency in Riga (Latvia); the Environ-mental Research Centre in Vilnius (Lithuania); the Institute of Meteorology andWater Management in Gdansk and Wroclaw (Poland), the State Agency of WaterManagement and Coasts in Kiel and Institute of Freshwater Ecology and Fisheriesin Berlin (Germany); the National Environmental Research Institute in Silkeborg(Denmark); the Swedish University of Agricultural Sciences in Uppsala and Na-tional Board of Waters and the Environment in Helsinki (Finland). In most cases,data on the following parameters were available: ammonium nitrogen (NH4-N),nitrite and nitrate nitrogen (NO2-N and NO3-N), total nitrogen (total-N), phosphatephosphorus (PO4-P) and total phosphorus (total-P). However, total-N and total-P had not been measured in the standard monitoring programme of the formerSoviet Union. Most of the data series covered, at least, the period 1977–93, andthe sampling frequency ranged from twice a week in the Vistula River to aboutfour times a year in Estonian rivers prior to 1992. Detailed information about theduration of the sampling programmes and analysed chemical parameters is givenin Table I.

Most of the water quality data came from sites close to the mouths of therivers. However, a majority of the large cities in the study area, e.g. St. Peters-burg, Riga, Copenhagen and Stockholm, are located on or near the coast. Hence,values representing the amount of nutrients in the wastewater and stormwater fromthese cities were not included when calculating riverine loads. The largest inlandcities (>400 000 inhabitants) are Warsaw, Lodz and Krakow in the Vistula Basin,Wroclaw and Poznan in the Oder Basin, and Kaunas and Vilnius in the NemanBasin. For some of the rivers, data from adjacent sampling sites were pooled toproduce time series of data that were as complete as possible (see Table I).

To acquire further information about nutrient concentrations in the major riversin the study area, our research team undertook, respectively, 3, 7, 8 and 6 samplingtours to the Neva, Narva, Daugava and Neman Rivers (1992–1994) and organised

176 PER STÅLNACKE ET AL.

almost monthly sampling at several sites in the Vistula River (1991–1994). All ofthe samples we collected were brought frozen to Linköping University for analysisof nutrients. Information about the variation of nutrient concentrations within rivercross-sections was also obtained from our own sampling campaigns, in which upto nine water samples were taken in each of the investigated cross-sections.

3. Statistical methods

3.1. IDENTIFICATION OF SINGLE OUTLIERS IN THE TIME SERIES OF

CONCENTRATION DATA

A nonparametric method proposed by McLeodet al. (1983) was used to identifyoutliers in the collected time series of concentration data. This method comprisesthe following steps: (i) aggregation of observed values to a time series of monthlyvalues; (ii) decomposition of the time series of monthly values into three com-ponents representing trend, seasonal variation and random variation, respectively;(iii) identification of observations with unacceptably large random components andrecalculation of the components for trend and seasonal variation after removal ofsuch observations; (iv) recalculation of all random components and classificationof observations with unacceptably large random components as outliers; (v) re-placement of outliers with the sum of their trend and seasonal components and thelargest acceptable random component.

3.2. IDENTIFICATION OF SEQUENCES OF DATA OF QUESTIONABLE QUALITY

Identification of sequences of data of questionable quality was based primarily onexamination of the interannual variation in flow-weighted mean concentrations.All time series of such data were visually inspected to reveal remarkable shifts inobserved concentrations. In addition, coefficients of variation (CV) were calculatedas

s/c (1)

where

s2 = 1

N

N∑j=1

(cj − c

)2, c = 1

N

N∑j=1

cj ,

N denotes the number of years with a minimum of four observations andcjdenotes the flow-weighted mean concentration during thejth year. Data sets withremarkable concentration shifts or unusually large CVs were subjected to more de-tailed investigations involving the following: (i) calculation of CVs after removal oflinear trends; (ii) comparisons with concentrations observed in our own sampling

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 177

TABLE I

Total drainage basin areas, areas upstream of sampling sites for water quality and duration of sam-pling programmes. Unless otherwise stated, concentrations of NH4-N, NO2-N, NO3-N, total-N,PO4-P and total-P were measured

Country River Drainage basin Area upstream of Time period

area (km2) sampling site (km2) (yr)

Sweden Götaa 50 100 48 250 1970–1993

Ångermanälven 31 900 30 640 1970–1993

41 other riversb 318 400 314 360 1970–1993c

Finland Kemijoki 51 400 50 900 1970–1992

Kymijokia 37 200 36 500 1970–1992

30 other riversd 116 200 111 200 1970–1992c

Russia Nevaa 281 000 281 000 1977–1990

Lugaa 13 200 12 200 1973–1989e

Pregolia 15 500 13 600 1980–19905

Estonia/Russia Narvaa 56 200 56 100 1977–1993

Estonia Pärnu 6 910 5 200 1977–1993f

13 other riversg 9 200 8 300 1977–1993f

Latvia Daugavaa 87 900 84 100 1977–1993h

Lielupea 17 600 16 500 1977–1993h

6 other riversi 29 300 25 600 1977–1993h

Lithuania Nemana 98 200 94 500 1970–1993j

Poland Vistula 194 400 194 400 1980–1993k

8 other riversl 25 400 14 720 1990

Poland/Germany Odera 118 900 116 200 1980–1993m

Germany 5 riversn 23 800 Not available 1987–1988

Denmark 130 riverso 31 200 18 690 1989–1993

Total 1 613 910 1 532 960

a Combined data from two or more sampling sites.b Further information about rivers and samplingsites in Stålnackeet al., (1997b).c Time periods can vary among rivers.d Further informationabout rivers and sampling sites in Kauppi (1984).e Total-N data not available.f Total-N andtotal-P available only for 1992–1993.g Data from the Purtse, Kasari, Keila, Kunda, Valge, Jä-gala, Vihterpalu, Vääna, Püha, Vasalemma, Loobu, Selja and Pirita Rivers.h Total-N and total-Pavailable for 1990–1993 and 1985–1993, respectively.i Data from the Salaca, Gauja, Venta, Irbe,Saka and Barta Rivers.j PO4-P available only for 1974–1993, total-N and total-P available onlyfrom our own sampling tours 1992–1994.k Data from our own sampling campaign were used for1992–1993.l Data from the Rega, Parseta, Wieprza, Slupia, Lupawa, Leba, Reda and Ina Rivers.m Total-N and total-P available only for 1987–1993.n Data from the Koseler Au, Schwentine,Oldenburger Graben, Aalbeck and Wakenitz Rivers.o Data from the 130 rivers were compiledby the National Environmental Research Institute, 1989–1993. The total-N concentrations wereassumed to have increased by 3–4% annually during the period 1970–1978 and then remainedconstant (Kronvanget al., 1993). The total-P concentrations were assumed to have been constant1970–1988 and then decreased by 50–70% from 1989 to1993 (Grœsbøllet al., 1994).

178 PER STÅLNACKE ET AL.

campaigns; (iii) comparisons with concentrations observed in adjacent rivers ofsimilar character; (iv) examination of winter concentrations of nutrients in relationto land use and sewage emissions in the catchment.

3.3. TEMPORAL INTERPOLATION AND EXTRAPOLATION OF INCOMPLETE

TIME SERIES OF DATA

Short to moderately long gaps in time series of observed concentration data werefilled in by linear interpolation, i.e. plotted points representing observed valueswere connected with straight lines. In the present study, this method was used toproduce daily concentration values for all quarters with a minimum of one observedvalue, and such series of daily data were then aggregated to monthly arithmeticmeans. To fill in larger gaps, the previously cited method for detection of outliers ina series of monthly values (McLeodet al., 1983) was used to decompose the timeseries under consideration into trend, seasonal variation and irregular variation.Missing values were then replaced with the sum of the components for trend andseasonal variation. Gaps in time series of monthly runoff values were filled in byusing linear regression to establish relationships between the runoff in the riverunder consideration and the runoff in adjacent rivers for which more complete timeseries of runoff data were available.

3.4. EXTRAPOLATION OF CONCENTRATION AND RUNOFF DATA FROM

MONITORED TO NON-MONITORED CATCHMENTS

The drainage areas upstream of the sampling sites for water quality in the presentstudy covered 88% of the whole Baltic Sea drainage basin (see Table I). To estimatethe nutrient loads from areas downstream of these sampling sites and from thenormally small coastal segments between the monitored basins, we used observednutrient concentrations and area-normalised runoff values for adjacent basins ofsimilar character.

3.5. LOAD CALCULATIONS

By using the interpolation and extrapolation methods described above, completeseries of monthly runoff valuesq1, q2, . . ., qN and monthly mean concentrationsc1, c2, . . ., cN were produced. Riverine loads for individual years and for the entirestudy period were then calculated as

L1 =N∑j=1

qj cj (2)

whereN denotes the number of months in the selected study period.In a prestudy based on monthly concentration data and daily runoff data from

ten Swedish rivers, we also used two alternative methods to calculate riverine loads.

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 179

One of these methods was a direct analogue of formula (2) for daily data. By theuse of linear interpolation we produced a complete series of daily concentrationvalues. Riverine loads for arbitrary time periods were then estimated by summingup products of daily concentration and runoff data. The other method was basedon load estimates for the days when water samples were collected for analysisof nutrients. Such values were first plotted and connected with straight lines toproduce complete series of daily load data, and then aggregated to monthly datal1,l2, . . ., lN . Thereafter, loads for arbitrary time periods were calculated according to

L2 =N∑j=1

lj (3)

whereN denotes the number of months in the study period.

3.6. ESTIMATION OF TOTAL-N CONCENTRATIONS IN THE RIVERS OF THE

FORMER SOVIET UNION

Prior to 1990, measurements were rarely made to determine the total concentra-tions of chemically bound nitrogen (total-N) in the rivers of the Baltic States andRussia. However, we were able to obtain some total-N values from the 1980s forthe Neva and Narva Rivers in Russia and Estonia, although only NO2-N, NO3-Nand NH4-N values were available for other rivers in that part of the Baltic Seadrainage area (see Table I). To produce total-N values for all of the rivers for theentire study period, we took advantage of the fact that temporal trends in organicnitrogen, i.e. the difference between total-N and inorganic nitrogen, are normallyless pronounced than such trends in inorganic-N. To be more precise, we useddata from the past few years regarding total-N and inorganic nitrogen to estimatemonthly mean concentrations of organic-N, and these mean values were then addedto the time series of inorganic-N concentrations.

3.7. FLOW-NORMALISATION OF NUTRIENT LOADS TO THEBALTIC SEA

To facilitate interpretation of temporal changes in riverine loads of nutrients wecalculated flow-normalised loads to the Baltic Sea. First, the whole drainage basin(see Figure 1) was divided into five subareas consisting of the drainage basins ofthe Gulf of Bothnia, the Gulf of Finland, the Gulf of Riga, the Baltic Proper (theGulf of Riga, the Gulf of Finland and the Western Baltic excluded) and the WesternBaltic (Kattegat included). For each of these subbasins, we then calculated flow-weighted mean annual concentrationscij of nitrogen or phosphorus and annualrunoff valuesqij , wherei denotes the subbasin under consideration andj the yearof observation. Finally, flow-normalised loads were calculated as

c′.j =

∑i

qi.cij (4)

180 PER STÅLNACKE ET AL.

Figure 1.The Baltic Sea drainage basin and the main rivers referred to in the text.

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 181

whereqi. denotes the average runoff from theith subbasin.

4. Results

4.1. IMPACT OF SINGLE OUTLIERS ON CALCULATED LOADS

Calculation of riverine loads with and without removal of outliers according to themethod of McLeodet al. (1983) showed that the presence of single outliers did notcause any serious problems in the load calculations. Although practically all timeseries of data contained observations that were classified as outliers, in most casessubstitution of such observations had a negligible impact on the average loads forthe entire study period and a small impact on the annual loads.

4.2. DATA QUALITY AS REFLECTED IN FLOW-WEIGHTED MEAN ANNUAL

CONCENTRATIONS

The time series plots in Figure 2 illustrate the temporal variation in the officiallyreported nitrate and phosphate concentrations for the largest rivers in the BalticSea drainage area. These plots show that some of the time series of flow-weightedmean annual concentrations had an interannual variation that called for further in-vestigations; this was particularly true for NO3-N in the Neman, Vistula and NarvaRivers and PO4-P in the Neva and Neman Rivers.

The coefficients of variation listed in Table II provide quantitative estimatesof the interannual variation in nutrient concentrations reported for various types ofrivers. The sampling sites on the Göta, Neva and Narva Rivers are all located down-stream of large lakes. The Kemijoki, Daugava, Neman, Vistula and Oder Rivers arelarge and only moderately regulated, whereas the Rönneå, Gudenå and OdenseåRivers are relatively small (drainage areas 952, 1289 and 303 km2, respectively)and characteristically have a flow that can vary rapidly with the weather conditions.Land is predominantely arable in the Neman, Vistula, Oder, Rönneå, Gudenå andOdenseå Basins. The largest point emissions of sewage are found in the Vistula,Oder and Neman Basins.

Inspection of the CVs for nitrate in Table II showed that the smallest value(0.12) was obtained for the flow-weighted mean annual concentrations at Troll-hättan on the Göta River in Sweden. This was expected because the sampling siteis located downstream of Lake Vänern, which has a hydraulic residence time ofalmost 10 yr. However, low CVs (0.14–0.21) were also obtained for relativelysmall streams in Denmark (the Gudenå and Odenseå Rivers) and Sweden (theRönneå River). In contrast, the Neman, Vistula and Narva Rivers had CVs rangingfrom 0.43 to 0.93, and there were marked shifts in observed concentrations in theanalysed time series of data. Closer examination of the nitrate concentrations re-ported for the Neman River showed that the winter values for the time periods1977–1980 and 1984–1993 were unrealistically low considering the following:

182 PER STÅLNACKE ET AL.

Figure 2.Flow-weighted mean annual concentrations of nitrate-N and phosphate-P in ten major riversin the Baltic Sea drainage basin. The values presented are based on concentration data reported bythe national environmental agencies.

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 183

(i) the high percentage of agricultural land (approximately 50%) in the drainagebasin; (ii) the substantial use of mineral nitrogen fertilisers in Lithuania duringthe main part of the study period (approximately 80 kg ha−1 arable land in the1980s); (iii) the fact that NO3-N concentrations of up to 4.0 mg L−1 were recordedin our own sampling campaigns in 1992–1994. Remarkable discrepancies betweennitrate concentrations reported for Lithuanian and Latvian sampling sites in thesame river (Lielupe) provided further indications of analytical or other problems.The large CV for nitrate at Kiezmark on the Vistula River was partly caused byinexplicably low NO3-N concentrations reported for the time period 1982–1983.The considerable interannual variation in the nitrate levels in the Narva River isremarkable inasmuch as the sampling site is located downstream of a large lake(Lake Peipus). Sudden drops in the NO3-N concentrations were also observed inother Estonian rivers in 1980–81 which further indicates temporary data qualityproblems.

Inspection of the CVs for PO4-P revealed that the largest interannual variationexisted in the data reported for the Neman River. Closer analysis of data showedthat the winter concentrations reported for the time periods 1974–1984 were un-realistically low in light of the sewage emissions from the cities of Kaunas andVilnius, whereas the concentrations reported thereafter were in good agreementwith the concentrations observed in our own sampling campaign. Relatively highCVs were also observed for the Göta, Neva and Narva Rivers, where the samplingsites were located downstream of large lakes. This was most probably due to thefact that the concentrations observed at these sites were rather close to the detectionlevel of the analytical procedures used.

4.3. DATA SETS REMOVED PRIOR TO THE LOAD CALCULATIONS

In general, the average levels and the temporal variation of reported nutrient con-centrations were credible. However, the study of coefficients of variation and otherattempts to assess the data quality showed that analytical or other problems haveinfluenced certain parts of reported time series of data. Therefore, we decided toremove some data sets prior to the calculations of riverine loads; the results givenin Table III show which of the data sets were removed and whether the removedvalues were exceptionally large or small. In addition, single outliers were replacedby less extreme values according to the procedure proposed by McLeodet al.(1983). However, as pointed out above, this had a marginal impact on calculatedloads.

4.4. COMPARISON OF LOAD ESTIMATORS

A special study of nitrogen and phosphorus loads in ten Swedish rivers showedthat the load estimators defined by formula (2) and (3) produced similar results. Inaccordance with empirical and theoretical calculations carried out by Rekolainenet al. (1991) and Reinelt and Grimvall (1992), the most pronounced differences

184 PER STÅLNACKE ET AL.

TABLE II

Estimated coefficients of variation (CVs) of flow-weighted mean annual con-centrations of NO3-N and PO4-P in various types of rivers. Unless otherwisestated, the CVs were calculated according to formula (1)

River Country CV of Length of CV of Length of

NO3-N study PO4-P study

period (yr) period (yr)

Göta Sweden 0.12 24 0.41 24

Rönneå Sweden 0.14 24 0.33 24

Kemi Finland 0.15 19 0.21 20

Neva Russia 0.15 11 0.47 11

Narva Russia/Estonia 0.43 19 0.45 21

Daugava Latvia 0.24 17 0.36 17

Neman Lithuania 0.93 21 0.97 18

Vistula Poland 0.48 12 0.33 12

Oder Poland 0.19 14 0.12 14

Gudenå Denmark 0.21 24 0.20a 23

Odenseå Denmark 0.21 24 0.25 22

a After removal of linear trend.

TABLE III

Removed data sets in time series of observed water-quality dataa

River Parameter Time period Average level

of removed data

All Swedish rivers NO3-N 1970 Low

Total-P 1970 High

Neva PO4-P 1986 High

Narva NH4-N 1970–1978 High

NO3-N 1979 High

All Estonian rivers NO3-N 1980–1981 Low

Neman PO4-P 1974–1987 Low

NO3-N 1977–1980 Low

NH4-N 1983–1991 Low

Pregolia NO3-N 1984–1987 Low

NH4-N 1986 High

PO4-P 1980–1985 Low

Vistula NO3-N 1980–1983 Low

Total-P 1989 Low

a In addition, single outliers were removed in a majority of the studiedtime series of data.

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 185

Figure 3.Estimated mean annual riverine loads of total-N in ten Swedish rivers, 1970–1990. Esti-mation was done by linear interpolation of observed concentrations (L1d and L1m) and momentaryloads (L2). L1d denotes the load estimator in formula 2 applied to daily discharge data and L1m thesame load estimator applied to monthly discharge data. L2 denotes the load estimator defined byformula 3.

were observed for rivers with simultaneous flow and concentration peaks. This canbe exemplified by the results obtained for the unregulated Torne River in northernSweden (see Figure 3). In general, however, the differences between the two typesof load estimates were small. Furthermore, formula (2) produced almost identi-cal results when concentration measurements from monthly water sampling werecombined, with respectively, daily and mean monthly runoff data (see Figure 3).Therefore, we decided to use the same load estimator for all rivers, and all load cal-culations presented below were based on formula (2) with runoff and concentrationdata aggregated to months.

4.5. TIME-AVERAGED RIVERINE LOADS TO THEBALTIC SEA AND ITS MAJOR

SUBBASINS

By using the data base and the statistical procedures described in previous sections,we estimated average annual riverine loads of nutrients discharged to the differentsubbasins of the Baltic Sea (Table IV). The most striking result is the value repre-senting the total riverine load to the entire Baltic Sea. Our estimates of the averageannual riverine loads for the time period 1980–93, i.e. 825 000 tonnes yr−1 N and41 000 tonnes yr−1 P, are substantially higher than the loads reported by Helcom(Helcom, 1993) and several other investigators (see Appendix). Further comments

186 PER STÅLNACKE ET AL.

regarding the discrepancies between different load estimates are presented in thediscussion.

The results itemized in Table IV also show the dominating role of the six largestrivers in the study area, i.e. the Neva, Narva, Daugava, Neman, Vistula and Oder.Together, these rivers were responsible for almost half of the riverine load of totalnitrogen and more than half of the riverine load of total phosphorus discharged tothe Baltic Sea. The nutrient loads observed in the Vistula, Oder and the NemanRivers were particularly large: the Vistula alone accounted for more than 14% ofthe total nitrogen load and the Oder for 16% of the total phosphorus load. Thedrainage area upstream of St. Petersburg in the basin of the Neva River accountedfor 16% of the total runoff to the Baltic Sea, but only 7 and 8% of the nitrogen andphosphorus load, respectively. Inorganic-N, mainly nitrate, played a dominatingrole in the western and southern part of the drainage area, whereas organic nitrogendominated the riverine export to the Gulf of Bothnia.

When the riverine loads of nutrients were normalised with respect to drainageareas, a notable spatial variation became apparent. First and foremost, there was asubstantial difference in area-specific nutrient loads between the sparsely populatednorthern parts of the Baltic Sea drainage basin and the densly populated southernparts (Table V). In northern Sweden, for example, the area-specific riverine loadof total-N was about 1.2 kg yr−1 ha−1, whereas the corresponding load in theOder River was about 6.0 kg yr−1 ha−1. However, there were also other notabledifferences. The highest area-specific nitrogen loads were observed in Denmark,where they reached an average level of 22.8 kg yr−1 ha−1 (see e.g. Grœsbøllet al.,1994). The phosphorus loads were markedly higher in the Neman and Oder Basins(0.55 and 0.56 kg yr−1 ha−1, respectively) than in the Daugava and Vistula Basins(0.15 and 0.28 kg yr−1 ha−1, respectively).

4.6. TEMPORAL VARIATION IN RIVERINE LOADS

The total riverine loads of nitrogen and phosphorus were slightly higher in the1980s than in the 1970s (Figure 4). However, the interannual variation in nutrientloads was clearly correlated to the interannual variation in runoff. Accordingly, asubstantial part of the interannual variation was removed by normalising the esti-mated annual nutrient loads with respect to the total runoff and the distribution ofthe runoff among the major subbasins of the Baltic Sea. In particular, normalisationremoved practically all signs of long-term upward or downward trends in nitrogenduring the study period (Figure 5). Considering the flow-normalised phosphorusloads, a slight upward tendency can be seen in the 1970s and a slight downwardtendency during the last few years of the study period.

Examination of long-term trends in nutrient loads in individual rivers confirmedthat, at most of the investigated sites, natural variation in runoff was the main causeof interannual variation in riverine loads of nutrients. However, in some rivers,factors other than runoff obviously played a significant role. The Daugava River in

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 187

TABLE IV

Time-averaged runoff and riverine loads of NO3-N, total-N, PO4-P and total-P to each ofthe major subbasins of the Baltic Sea. Study period: 1980–1993

Baltic Sea Runoff NO3-N Total-N PO4-P Total-P

subbasin/river (109 m3 yr−1) (t yr−1) (t yr−1) (t yr−1) (t yr−1)

Gulf of Bothnia

Kemijoki 18.9 1 100 6 900 140 420

Lule R. 16.7 560 3 300 40 180

Ångermanälven R. 17.1 890 4 650 35 200

Indalsälven R. 15.3 1 390 4 540 25 140

Other monitored rivers 111.5 15 980 60 250 1 020 3 250

Non-monitored rivers 24.3 7 960 20 750 610 1 220

Subtotal 203.8 27 880 100 390 1 870 5 400

Gulf of Finland

Neva R. 81.7 21 260 55 590 1 200 3 210

Narva R. 14.3 4 010 26 400 290 750

Other monitored rivers 18.5 11 830 17 110 550 660

Non-monitored rivers 7.7 8 620 26 870 560 1 510

Subtotal 122.2 45 720 125 970 2 600 6 130

Gulf of Riga

Daugava R. 23.3 28 680 70 130 970 1 330

Other monitored rivers 9.7 21 480 35 940 450 670

Non-monitored rivers 3.3 4 120 7 160 90 240

Subtotal 36.3 54 280 113 230 1 510 2 240

Baltic propera

Neman R. 20.5 31 650 58 340a 4 140 5 410

Vistula R. 32.4 59 280 119 080 3 570 5 510

Oder R. 16.3 41 900 71 430 2 510 6 630

Other monitored rivers 19.9 18 760 36 430 900 1 340

Non-monitored rivers 23.9 41 410 77 740 2 100 4 300

Subtotal 113.0 193 000 363 020 13 220 23 190

Western Balticb

Göta R. 18.0 10 080 16 250 60 300

Other monitored rivers 11.4 47 620 57 210 880 1 860

Non-monitored rivers 8.0 38 980 49 830 1 110 2 040

Subtotal 37.4 96 680 123 290 2 050 4 200

Total 512.7 417 560 825 900 21 250 41 160

a Excluding the G. of Bothnia, the G. of Finland and the G. of Riga.b Including the Kattegat.

188 PER STÅLNACKE ET AL.

TABLE V

Time-averaged, area-specific riverine load of nutrients (kg ha−1 yr−1) to differentsubbasins of the Baltic Sea. Study period: 1980–1993

Baltic Sea subbasin Drainage basin NO3-N Total-N PO4-P Total-P

areaa

(km2) (kg ha−1 yr−1)

Gulf of Bothnia 491 000 0.57 2.04 0.038 0.110

Gulf of Finland 421 000 1.09 2.99 0.062 0.146

Gulf of Riga 132 000 4.11 8.58 0.114 0.170

Baltic proper 584 000 3.30 6.22 0.226 0.397

Western Balticb 101 000 9.57 12.21 0.203 0.416

Total 1 729 000 2.42 4.78 0.123 0.238

a Areas according to Bergström and Carlsson (1994).b Including the Kattegat.

Latvia and the Gudenå River in Denmark are two examples of rivers in which therewas a clear increase in the concentration of NO3-N from the 1970s to the 1980s.A special study of nitrogen trends in Swedish rivers, which has been described ina separate article (Stålnackeet al., 1997b), revealed several cases of weak upwardtrends but only few downward trends.

The most striking phosphorus trends represent changes that occurred during thelast five years of the study period which most likely can be attributed to reducedpoint source emissions. Kronvang and co-workers (1993; 1996) have demonstratedthat, in Denmark, phosphorus loads decreased due to reduced point emissions, andthe data collected for the present study indicated a decrease also in the Oder River.

The seasonal distribution of the riverine loads of nutrients (calculated for thetime period 1980–1990) was characterised by a pronounced peak during the springflow and comparatively low loads in summer (Figure 6). This was particularly truefor the rivers flowing into the Gulfs of Bothnia, Riga and Finland. Regarding thetotal riverine loads of nutrients to the Baltic Sea, April and May accounted forabout 27% of the nitrogen and 31% of the phosphorus.

5. Discussion

The following issues will be discussed:

• sources of uncertainty in estimated loads;• discrepancies between the load estimates in the present study and in previously

published studies;

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 189

Figure 4a.Annual riverine load of nitrogen (bars) and annual river water discharge (line) to differentparts of the Baltic Sea, 1970–1993.

190 PER STÅLNACKE ET AL.

Figure 4b. Annual riverine load of phosphorus (bars) and annual river water discharge (line) todifferent parts of the Baltic Sea, 1970–1993.

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 191

Figure 5. Annual riverine loads of nitrogen and phosphorus to the Baltic Sea, 1970–1993. Thetwo curves represents loads before (dashed thin line) and after (solid or dashed thick line)flow-normalisation.

• causes of spatial and temporal variation in estimated loads.

5.1. SOURCES OF UNCERTAINTY INESTIMATED LOADS

The uncertainty of calculated load estimates is a function of (i) the accuracy of col-lected raw data, (ii) the completeness of the monitoring programmes, and (iii) thestatistical methods used to combine observed nutrient concentrations and runoffdata into load estimates. To ensure the best possible data quality, our study wasbased almost exclusively on data from national monitoring programmes. This im-plies that relatively stable organisations have been responsible for the quality assur-ance of collected data and that the analytical procedures used to determine nutrientconcentrations have been thoroughly documented and evaluated. Nevertheless, thetime series plots in Figure 2 and the discussion regarding that figure revealed thatsome of the data regarding nutrient concentrations were obviously incorrect. Wetook the standpoint that the inconsistencies in data were due to temporary problemsin the quality assurance of reported data and that statistical procedures could beused to identify subsets of data of questionable quality. Furthermore, we assumed

192 PER STÅLNACKE ET AL.

Figure 6.Monthly means of riverine loads of total-N and total-P to different parts of the Baltic Sea.

that the true interannual variation in flow-weighted nutrient concentrations is small,unless hydrological conditions are very extreme. The relatively low CVs observedfor several of the rivers listed in Table II support this assumption. Studies of otherEuropean rivers, such as the Rhine and the Meuse, have provided further evidencethat time series of annual flow-weighted nutrient concentrations normally formrelatively smooth curves (van Dijket al., 1996). This justified the use of coefficientsof variation as indicators of problematic time series of data.

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 193

It is difficult distinguishing between accurate and less accurate observationswithin a given time series of data. Our method can be characterised as the makingof an expert judgement based on the following: (i) the presence of inexplicableshifts in reported nutrient concentrations, (ii) the results of our own sampling cam-paigns, and (iii) literature data regarding nutrient concentrations in different typesof river basins. We can hope that observations influenced by analytical errors orimproper sample handling were efficiently removed. However, we can not objec-tively determine the success of this operation, and data quality issues are thereforecrucial for the reliability of calculated riverine loads. The most striking example ofthis is the large uncertainty regarding nutrient concentrations in the Neman River.

The incompleteness of the collected time series of data had several aspects:(i) some of the collected time series of data only went back to 1977 or even 1980(see Table I); (ii) measurements of total-N and total-P were normally not includedin the monitoring programmes in the Soviet Union; (iii) nutrient concentrationswere not available for the catchments between the monitored river basins; (iv) re-ported nutrient concentrations represented grab samples. To handle the first typeof gap, we assumed that both the annual flow-weighted nutrient concentrationsand their seasonal distributions had been constant from 1970 to the start of thetime series under consideration. This very crude assumption can be justified by thefollowing facts:

• obvious water quality trends were only present in a small minority of the morethan 100 rivers for which fairly complete data were available• two of the largest rivers for which data were missing prior to 1977 (the Neva

and Narva Rivers) do still have so low nutrient concentrations that there is littleroom for significant historical increases• the other major rivers for which data from the 1970s were missing (the Dau-

gava, Oder and Vistula Rivers) have all been shown to respond slowly, or not atall, to recent changes in land use (Stålnackeet al., 1997a; Lazniket al., 1998;Löfgrenet al., 1998; Grimvallet al., 1998).

The reconstruction of missing total-P and total-N data for the former Soviet Unionwas achieved by assuming that the temporal trends in these parameters were iden-tical to the trends noted for PO4-P and for the sum of NO3-N and NH4-N, respec-tively. This assumption was justified by the absence of clear trends in collectedtime series of organic nitrogen and in the difference between total-P and PO4-P.The lack of data regarding the coastal segments between the major river basinswas mainly a problem in the Swedish and Finnish parts of the Baltic Sea drainagebasin (see Table IV). In the entire Baltic Sea drainage basin, the monitored areawas 88% of the total area (see Table I). Compared to the sources of uncertaintyalready discussed, sampling frequency was found to be a minor problem. Lowsampling frequency can introduce serious errors in load estimates for small rivers(Rekolainenet al., 1991; Reinelt and Grimvall, 1992; Kronvanget al., 1996). How-

194 PER STÅLNACKE ET AL.

ever, this problem is less pronounced for the large rivers that dominate the input ofnutrients to the Baltic Sea. This has been demonstrated by Tonderskiet al. (1995),who studied large rivers in Eastern and Western Europe, and by Grimvall (1996)who performed a similar study of data on Swedish rivers. In addition, the findingspresented by Tonderskiet al. (1995) and the results obtained in our own samplingin the Neva, Narva, Daugava and Neman Rivers showed that monitoring strategiesbased on one sample per cross-section normally do not introduce any significantbias in the load estimates.

The choice of load estimator has been addressed by several scientists (Verhoffet al., 1980; Dolanet al., 1981; Richards and Holloway, 1987; Prestonet al., 1989).If the flow peaks are very pronounced and tend to coincide in time with concen-tration peaks, estimators based on the assumption that concentrations vary linearlybetween sampling occasions can be biased (Rekolainenet al., 1991; Reinelt andGrimvall, 1992; Kronvanget al., 1996). However, the results obtained for selectedSwedish rivers in the present study and the findings presented by Tonderski andco-workers (1995) in their study of large rivers strongly indicated that the choiceof load estimator is of minor importance in a Baltic Sea perspective.

In summary, large gaps in collected data and questionable quality of some nu-trient concentration data were identified as the main sources of uncertainty in theestimated nutrient loads. The design of the sampling programmes and the choiceof load estimator were of minor importance.

5.2. DISCREPANCIES BETWEEN LOADESTIMATES IN DIFFERENT STUDIES

Our estimates of the total riverine loads of nutrients to the Baltic Sea were generallyhigher than the estimates published by several other scientists and organisations(Pawlak, 1980; Maksimova, 1982; Larssonet al., 1985; Rybinski, 1989; Rosenberget al., 1990; Helcom, 1993). To some extent, this incongruity can be explained bydifferences in the study areas or study periods. Pawlak (1980), for example, did notinclude riverine loads from the Soviet Union to the Baltic proper and Gulf of Riga,and neither Pawlak (1980) nor Larssonet al. (1985) included loads to the Kattegatin their estimates. However, closer examination of the data presented in Figure 4,Table IV and the Appendix shows that there are considerable discrepancies thatmust be explained by factors other than study areas or study periods. Inasmuch asrelatively relible information about runoff has been generally available for quitesome time (Mikulski, 1970; Mikulski, 1982), we can conclude that different inves-tigators have made remarkably different estimates of the nutrient concentrations inat least some of the major rivers and subbasins in the Baltic Sea drainage basin.

The discrepancies between the results of our study and the results of the secondpollution load compilation undertaken by Helcom (1993) can be explained by thefollowing characteristics of the load estimates published by Helcom: (i) concentra-tions of inorganic nitrogen and phosphorus were used instead of concentrations oftotal-N and total-P, when data regarding organic forms were not available; (ii) the

ESTIMATION OF RIVERINE LOADS OF NITROGEN AND PHOSPHORUS 195

riverine loads of nutrients for some areas, e.g. Lithuania, were unrealistically low;(iii) contributions from small rivers (Q<5 m3 s−1) and from coastal areas betweenthe monitored rivers were not estimated; and (iv) the load estimates referred to oneparticular year (1990) in which the runoff was below the average runoff for ourstudy period.

The Swedish Ministry of Environment and Natural Resources (MENR, 1990)and a follow-up article by Enell and Fejes (1995) have presented the highest loadestimates. In contrast to all other investigators, who used measured concentrationsand runoff in monitored rivers, they assigned emission coefficients to differenttypes of point and nonpoint sources, and combined such coefficients with assignedretention coefficients (LRF, 1988). Furthermore, point source emissions from in-dustries and cities along the coast were combined with riverine loads into a totalinput from land in the just cited studies. If the latter is is taken into account, theirestimate of the total nitrogen load to the Baltic Sea is only about 5% higher thanour estimate.

5.3. CAUSES OF SPATIAL AND TEMPORAL VARIATION IN ESTIMATED LOADS

The results of the present study indicated that the riverine loads of nutrients tothe Baltic Sea have been remarkably stable since 1970 and that natural variationin runoff is the main cause of interannual variation in the presented load esti-mates. Concerning the estimates for the 1970s, the methods used to fill in gapsin observed time series of data may have removed some interannual variation andsuppressed long-term trends. However, the load estimates for 1980 to 1993 werealmost exclusively based on actually observed concentrations.

Detailed studies of the spatial variation in nutrient loads and the causes of suchvariation were outside the scope of the present study. However, it is interesting tonote that the area-specific riverine loads of nitrogen for the Vistula and Oder Basinsare substantially lower than the loads reported for the Rhine Basin (Behrendt, 1996;Tonderskiet al., 1994). Furthermore, there is strong evidence that hydrogeologicalfactors, in particular the specific runoff, has a very strong impact on the export ofnutrients from a river basin (Behrendt, 1996).

6. Concluding Remarks

• The results of this study show that, during the period 1980–1993, the averageriverine loads of nutrients discharged to the Baltic Sea amounted to approx-imately 825 000 tonnes yr−1 N and 41 000 tonnes yr−1 P. This implies thatseveral other investigators have strongly underestimated the riverine loads andthat the input via the rivers by far exceeded the input of nutrients to the BalticSea along other routes.• The interannual variation in riverine loads of nitrogen and phosphorus was

largely due to natural variation in runoff. After flow-normalisation, there were

196 PER STÅLNACKE ET AL.

only weak signs of upward and downward trends in the total riverine loads ofnutrients to the Baltic Sea during the period 1970 to 1993.• The six largest rivers in the eastern and southern parts of the Baltic Sea drainage

basin were shown to be responsible for about half the total riverine export ofnitrogen and phosphorus to the sea.• Calculation of area-specific riverine loads demonstrated remarkable differences

between the sparsely populated almost pristine areas in the north and the denselypopulated and intensively cultivated areas in the south. However, it was alsoobvious that the low specific runoff in the southeastern parts of the Baltic Seadrainage basin reduced the impact of the large population and intensive landuse.• Incomplete data, in particular regarding total-N and total-P, and the presence

of inconsistent or obviously incorrect concentration data for some of the majorrivers were identified as the main sources of uncertainty in estimated riverineloads.

Acknowledgements

The study was carried out within the research project ‘Large Scale Environmen-tal Effects and Ecological Processes in the Baltic Sea’ (SEPA, 1990b) and wasfinanced by the Swedish Environmental Protection Agency (SEPA). The authorsare grateful to all agencies and institutions that have supplied data, and provideduseful information about sources of uncertainty in reported data. In particular wewould like to thank Dr. Boris Skakalsky (State Hydrological Institute, St. Peters-burg), Dr. Enn Loigu (Tallinn Technical University), Dr. Andrus Meiner (EstonianEnvironment Information Centre), Dr. Mendel Laznik (Latvian Hydrometeorolog-ical Agency, Riga), Dr. Elzbieta Niemirycz (Institute of Meteorology and WaterManagement, Gdansk), Ms. Rafalina Korol (Institute of Meteorology and WaterManagement, Wroclaw), Dr. Horst Behrendt (Institute of Freshwater Ecology andFisheries, Berlin), Mr. Bert Karlsson (Swedish University of Agricultural Sciences)and Mr. Bengt Carlsson (Swedish Meteorological and Hydrological Institute). Ms.Susanne Karlsson performed the analysis of nutrients in the water samples broughtto Linköping University. Mrs. Patrica Ödman revised the English.

The views and results expressed in this paper are the author’s and do not neces-sarily reflect the views of the different monitoring agencies and data holders.

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Appendix

Previously published estimates of riverine loads of nitrogen and phosphorus (expressed in tonnes yr−1)

Author(s) Pawlak Larssonet al. Rybinski Rosenberget al.d MENRd Helcom

Publication year 1980 1985 1990 1990 1990 1993

Study period 1972–1977 1977–1983 1985–1989 1982–1987 1985–1990 1990

Substance N P N P N P N P N P N P

Basin Country

Gulf of Bothnia Sweden 45 500 2 370 37 000 2 170 n.e. n.e. 54 500 2 600 54 000 2 600 40 729 1 957Finland 37 000 3 000 33 400 2 200 n.e. n.e. 54 100 3 660 39 000 3 660 45 160 2 692

Baltic proper, Sweden 32 600 870 21 000 750 n.e. n.e. 44 300e 1 780e 35 750 1 090 24 618 683Gulf of Riga Finland 7 600 420 7 900 400 n.e. n.e. 16 300 860 30 200 860 17 260 604and Gulf of Russia and the 35 600a 3 650a 130 000 5 600 n.e. n.e. 130 300 5 880 396 000 19 120 212 696 10 058Finland Baltic states

Poland 91 400 5 700 350 000 34 300 n.e. n.e. 109 900 19 100 272 500 22 000 112 034 12 774Germany 3 255 1 325 3 300 1 300 n.e. n.e. 3 600 380 21 700 1 460 3 094 323Denmark c c 1 100 20 n.e. n.e. 51 000e 7 860e 680 70 1 930 63

Kattegat and Sweden 2 480b 127b 7 200b 210b n.e. n.e. 37 000f 900f 44 000 1 600 35 286 1 267the Western Baltic Denmark 9 790b 3 150b 29 300b 620b n.e. n.e. 18 000f 1 900f 46 000 3 600 69 511 2 685

Germany n.e. 1 500 20 300 2 600 n.e. n.e. 16 400 2 370 39 000 2 800 5 421 254

Total All countries 265 225 22 112 640 500 50 170 692 894 38 119 535 400 47 290 978 830 58 860 567 739 33 360

Sweden 80 580b 3 367b 65 200b 3 130b 129 316 4 140 135 800 5 280 133 750 5 290 100 633 3 907Finland 44 600 3 420 41 300 2 600 72 906 3 759 70 400 4 520 69 200 4 520 62 420 3 296Russia and the 35 600a 3 650a 130 000 5 600 124 474 7 510 130 300 5 880 396 000 19 120 212 696 10 058Baltic statesPoland 91 400 5 700 350 000 34 300 246 039 17 022 109 900 19 100 272 500 22 000 112 034 12 774Germany 3 255g 2 825 23 600 3 900 54 459 3 315 20 000 2 750 60 700 4 260 8 515 577Denmark 9 790b 3 150b 30 400b 640b 65 700 2 373 69 000 9 760 46 680 3 670 71 441 2 748

n.e. Not estimated.a Loads to the Gulf of Riga and the Baltic propernot included.b Loads to the Kattegatnot included.c Included in the figures for theWestern Baltic.d Loads from coastal point sources included.e Loads to Western Baltic included.f Loads to Western Balticnot included.g Loads from formerGFRnot included.

198 PER STÅLNACKE ET AL.

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