Nitrogen source apportionment—a comparison between a dynamic and a statistical model

Lidén, Rikard; Vasilyev, A.; Stålnacke, Per; Loigu, Enn; Wittgren, Hans Bertil

doi:10.1016/s0304-3800(98)00146-x

Cited by 20 publications

(17 citation statements)

References 16 publications

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“…They found that a majority of these catchments had a unit-area loss of between 600 and 2500 kg km −2 . In addition, our results showed that the nitrogen losses from agricultural land were almost four times higher than the corresponding losses from forested land (Table 1), which is found to be realistic and in line with other results (Lidèn et al, 1999;Vassiljev and Stålnacke, 2005;Vassiljev et al, 2008) …”

Section: Parameterisation Resultssupporting

confidence: 92%

“…It should be noted that the model inputs are areas of the land cover and not the percentages which will decrease the risk of multicollinearity. Experiences with the MESAW models, as also given in the previously quoted papers, in different geographical areas (Lidèn et al, 1999;Vassiljev and Stålnacke, 2005;Vassiljev et al, 2008;Povilaitis et al, 2012) have not indicated any problem with possible interrelated explanatory variables. In addition, the parameter estimates showed reasonable stability; little change occurred in the values of the most statistically significant model coefficients when additional variables were added in exploratory regressions (Table 1).…”

Section: Uncertainty Aspects and Outlookmentioning

confidence: 73%

“…This model approach uses non-linear regression for simultaneous estimation of export coefficients to surface waters for the different specified land cover or soil categories and retention coefficients for pollutants in river basins. Examples of application of the MESAW model are given in Lidèn et al (1999), Vassiljev and Stålnacke (2005), Vassiljev et al (2008) and Povilaitis et al (2012). MESAW has many features in common with the more well-known SPARROW model developed in the USA (Smith et al, 1997;Alexander et al, 2000).…”

Section: The Mesaw Model and Model Parameterisationmentioning

confidence: 99%

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Nitrogen surface water retention in the Baltic Sea drainage basin

Stålnacke

Pengerud

Vassiljev

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. In this paper, we estimate the surface water retention of nitrogen (N) in all the 117 drainage basins to the Baltic Sea with the use of a statistical model (MESAW) for source apportionment of riverine loads of pollutants. Our results show that the MESAW model was able to estimate the N load at the river mouth of 88 Baltic Sea rivers, for which we had observed data, with a sufficient degree of precision and accuracy. The estimated retention parameters were also statistically significant. Our results show that around 380 000 t of N are annually retained in surface waters draining to the Baltic Sea. The total annual riverine load from the 117 basins to the Baltic Sea was estimated at 570 000 t of N, giving a total surface water N retention of around 40 %. In terms of absolute retention values, three major river basins account for 50 % of the total retention in the 117 basins; i.e. around 104 000 t of N are retained in Neva, 55 000 t in Vistula and 32 000 t in Oder. The largest retention was found in river basins with a high percentage of lakes as indicated by a strong relationship between N retention (%) and share of lake area in the river drainage areas. For example in Göta älv, we estimated a total N retention of 72 %, whereof 67 % of the retention occurred in the lakes of that drainage area (Lake Vänern primarily). The obtained results will hopefully enable the Helsinki Commission (HELCOM) to refine the nutrient load targets in the Baltic Sea Action Plan (BSAP), as well as to better identify cost-efficient measures to reduce nutrient loadings to the Baltic Sea.

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Section: Parameterisation Resultssupporting

confidence: 92%

Section: Uncertainty Aspects and Outlookmentioning

confidence: 73%

Section: The Mesaw Model and Model Parameterisationmentioning

confidence: 99%

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Nitrogen surface water retention in the Baltic Sea drainage basin

Stålnacke

Pengerud

Vassiljev

et al. 2015

Hydrol. Earth Syst. Sci.

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“…Mathematical models can help us to estimate loads from different nutrient sources and their temporal variations. Even though these models have been developed for prediction purposes and to provide improved process understanding, results can be used to estimate source apportionments (Liden et al 1999). Nutrient load simulations and source apportionment can be performed by statistical models (Grizzetti et al 2005), export coefficient models (ECMs) , and mechanistic models (Liden et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Variations in source apportionments of nutrient load among seasons and hydrological years in a semi-arid watershed: GWLF model results

Zhang

et al. 2014

Environ Sci Pollut Res

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Quantifying source apportionments of nutrient load and their variations among seasons and hydrological years can provide useful information for watershed nutrient load reduction programs. There are large seasonal and inter-annual variations in nutrient loads and their sources in semi-arid watersheds that have a monsoon climate. The Generalized Watershed Loading Function model was used to simulate monthly nutrient loads from 2004 to 2011 in the Liu River watershed, Northern China. Model results were used to investigate nutrient load contributions from different sources, temporal variations of source apportionments and the differences in the behavior of total nitrogen (TN) and total phosphorus (TP). Examination of source apportionments for different seasons showed that point sources were the main source of TN and TP in the non-flood season, whereas contributions from diffuse sources, such as rural runoff, soil erosion, and urban areas, were much higher in the flood season. Furthermore, results for three typical hydrological years showed that the contribution ratios of nutrient loads from point sources increased as streamflow decreased, while contribution ratios from rural runoff and urban area increased as streamflow increased. Further, there were significant differences between TN and TP sources on different time scales. Our findings suggest that priority actions and management measures should be changed for different time periods and hydrological conditions, and that different strategies should be used to reduce loads of nitrogen and phosphorus effectively.

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“…Therefore, a large number of water bodies without water quality monitoring excludes the possibility of using sophisticated models. For decision-makers and scientists faced with a specific water management problem, it is therefore essential to choose a method which meets the often limited available input data, gives the wanted results and is economically feasible (Lidén et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment and Retention of Nutrients and Organic Matter in the Merkys River Basin in Southern Lithuania/Skirtingų Šaltinių Poveikis Biogeninių Ir Organinių Medžiagų Pernašai Ir Sulaikymui Merkio Upės Baseine Pietų Lietuvoje/ Распределение И Задержание Биогенных И Органических Веществ В Бассейне Реки Meркис В Южной Части Литвы

Povilaitis¹

2008

Journal of Environmental Engineering and Landscape Management

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Journal of Environmental Engineering and Landscape ManagementISSNSource apportionment and retention of nutrients and organic matter in the merkys river basin in southern Lithuania Arvydas PovilaitisTo cite this article: Arvydas Povilaitis (2008) Source apportionment and retention of nutrients and organic matter in the merkys river basin in southern Lithuania, Journal of Environmental Engineering and Landscape Management, 16:4, 195-204, DOI: 10.3846/1648-6897.2008 To link to this article: https://doi.org/10. 3846/1648-6897.2008.16.195-204 Abstract. The assessment of the type of human activity in a basin area that may cause an impact on the status of a water body is needed for successful implementation of the EU Water Framework Directive. Lack of necessary information often makes it difficult to perform the task. Therefore, the statistical MESAW model based on export coefficients approach has been used in this study for evaluation of the impact of different sources of nutrients and organic matter on the water quality in the Merkys River in southern Lithuania. The model was tested on the basis of data from 5 water quality monitoring sites with corresponding subbasin data on land use, point sources and atmospheric deposition. Nonlinear regression was used for simultaneous estimation of the export coefficients and retention. The results revealed that the impact of anthropogenic sources accounted for 73% of COD, 56% of BOD, 90% of N tot and 78% of P tot loads measured in the Merkys River. Forest and wetlands contribute from 9.5 to 44% to the corresponding load. The retention in the Merkys River, Basin was found to be high for nitrogen and phosphorus and low for organic matter.

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Nitrogen source apportionment—a comparison between a dynamic and a statistical model

Cited by 20 publications

References 16 publications

Nitrogen surface water retention in the Baltic Sea drainage basin

Nitrogen surface water retention in the Baltic Sea drainage basin

Variations in source apportionments of nutrient load among seasons and hydrological years in a semi-arid watershed: GWLF model results

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