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The main objective of this work was to develop regional settings of the NANI budgeting tool that will address the significant variation in agricultural practices and resulting nutrient accountings among European countries. NANI (Net Anthropogenic Nitrogen Inputs), first introduced by Howarth et al. (1996), estimate the human‐induced nitrogen inputs to a watershed and have been shown to be a good predictor of riverine nitrogen export at a large scale, multi‐year average basis. NANI have been calculated as the sum of four major components: atmospheric N deposition, fertilizer N application, agricultural N fixation, and net food and feed imports, which in turn are composed of crop and animal N production (negative fluxes removing N from watersheds) and animal and human N consumption (positive fluxes adding N to watersheds). Assuming approximate steady-state behavior, riverine N export is a fixed proportion of net nitrogen inputs. Similar calculations can be made for phosphorus (P) inputs, though because atmospheric deposition of P is usually considered negligible and there is no analog in P for atmospheric fixation, the calculation of Net Anthropogenic Phosphorus Inputs (NAPI) reduces to accounting for P fertilizer and P in net food/feed terms. While this document is primarily concerned with calculating NANI, we also describe the data sources and assumptions used to make the parallel calculations of NAPI. Version 2.0 of the Toolbox described in this document is an improvement of version 1.0 developed for US watersheds (http://www.eeb.cornell.edu/biogeo/nanc/nani/nani.htm; Hong et al. 2011). Version 1.0 allows the user to calculate NANI in any area within the contiguous United States (e.g., watershed, county, etc.) from nationally available databases downloadable from the Internet. The toolbox consists of a set of tools that: (1) calculate the proportions of various regions (political or gridded) in which data are collected that fall into areas of interest such as watersheds (“NANI‐GIS tools”), (2) extract and organize relevant data downloaded from web‐based datasets to be used by the accounting tools (“NANI‐extraction tools”), and (3) calculate NANI, their components, and other relevant items such as animal excretion (“NANI-.‐accounting tools”). While attempting to apply version 1.0 of the toolbox to Baltic Sea catchments, we found that the calculation of NANI in Baltic Sea catchments is more challenging than in US watersheds, mainly for two reasons: • Watersheds span international boundaries. Significant variation in agricultural practices and resulting nutrient accountings among European countries exist. For example, a substantial gradient in agricultural practices is expected among the former EU countries, new EU member states with transitional economies, and Belarus and Russia. • Gaps and uncertainties in the available data are much greater than those in the US. In general, the problem of missing information is more severe for the transitional countries, Belarus, and Russia, requiring numerous assumptions and guesswork to be made to deal with the insufficient data issue. Version 2.0 of the Toolbox describe in this document has several modules and improvements added to version 1.0 (which assumes spatially uniform agricultural practices, i.e., fixed values for all the NANI parameters, supported by the availability of well‐established and standardized datasets) to address the above difficulties. These improvements include: • Allowing spatial variation of NANI parameters (in this example, country‐specific NANI parameters) (Sections 4, 5.1, and 5.2) • Distribution of regional data (e.g., country-level crop production) into smaller spatial units (e.g., grid cells containing crop area information) (Section 5.3) • Making post‐calculation adjustments and refinements by accepting auxiliary datasets and manual calculations from the user (Section 3) In the following sections we describe the calculation of NANI and their components in the Baltic Sea catchments, with details of data availability, input preparation, and step-by‐step procedure of the use of various tools, and provide some preliminary results. In addition, Appendix 1 described parameter values used to create NAPI estimates following an accounting methodology in parallel to that for NANI.

In this thesis empirical relationships and modeling tools are used to describe the relationship between human activities and meso- and large scale riverine N fluxes from land to sea. On a large scale Paper I showed that by only having knowledge about human population size and runoff one can estimate the riverine export of DIN (r2= 0.76). In Paper II we included two other important anthropogenic N inputs, i.e. atmospheric deposition and primary emission (PE) from animals. In most of the catchments the PE from animals were larger than the PE from humans. Hence, development of livestock is important and increased animal protein consumption by humans might increase the riverine N export. Scenario analysis (Paper II) show that climate change is expected to both decrease and increase the riverine N export depending on which part of the catchment is modeled. In the southern and eastern parts of the Baltic Sea catchment there is large potential for N reductions from point sources (Papers III & V). The diffuse sources are more difficult to decrease and a reduction of mineral fertilizer does not always lead to reduced N loadings because the agricultural systems can buffer even a slight surplus (Paper III). There is inertia in the catchments which can be seen in for example in the northern part of the catchment. Here atmospheric N deposition is almost as high as in the southern part but the nitrogen flux from these rivers is not elevated. These northern river catchments have N exports of the same magnitude as the natural background (Paper IV), indicating that the atmospheric N deposition is retained in the system and probably taken up by N limited boreal forests. However, important reductions can be achieved in the agricultural sector by detailed management of the planted land and animal manure. The highest sensitivity is in catchments with high animal density and high specific discharge, primarily draining to Kattegat and Danish Straits (Paper II & IV). At the time of doctoral dissertation the following publications were unpublished and had a status as follows: Paper 2: Manuscript. Paper 4: Manuscript