This study aims at bridging the gap between freshwater and marine eutrophication studies by presenting (1) a cross-system analysis of the relationship between chlorophyll and the total nitrogen (TN) to total phosphorus (TP) ratio (2) a general model to predict concentrations of cyanobacteria from data on TP, the TN/TP-ratio, salinity and temperature, and (3) a general trophic level classification for aquatic systems based on chlorophyll classes (for oligo-, meso-, eu-and hypertrophic systems). The data compiled in this study concerns more than 500 lakes and coastal areas covering a very wide domain in terms of nutrient concentrations and salinity. There was no simple relationship between the TN/TP-ratio and empirical chlorophyll concentrations or concentrations of cyanobacteria. Variations in TP rather than TN generally seem to be more important to predict variations among systems in chlorophyll-a and cyanobacteria. Different "bioavailable" forms of the nutrients (DIN, DIP, phosphate, nitrate, etc.) have been shown to have very high coefficients of variation (CV), which means that many samples are needed to obtain reliable empirical data which are necessary in models aiming for high predictive power and practical usefulness.

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Linear trend analysis of time series is standard procedure in many scientific disciplines. If the number of data is large, a trend may be statistically significant even if data are scattered far from the trend line. This study introduces and tests a quality criterion for time trends referred to as statistical meaningfulness, which is a stricter quality criterion for trends than high statistical significance. The time series is divided into intervals and interval mean values are calculated. Thereafter, r2 and p values are calculated from regressions concerning time and interval mean values. If r2≥0.65 at p≤0.05 in any of these regressions, then the trend is regarded as statistically meaningful. Out of ten investigated time series from different scientific disciplines, five displayed statistically meaningful trends. A Microsoft Excel application (add-in) was developed which can perform statistical meaningfulness tests and which may increase the operationality of the test. The presented method for distinguishing statistically meaningful trends should be reasonably uncomplicated for researchers with basic statistics skills and may thus be useful for determining which trends are worth analysing further, for instance with respect to causal factors. The method can also be used for determining which segments of a time trend may be particularly worthwhile to focus on.

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This paper presents an improved version of a general, process-based mass-balance model (LakeMab/ LEEDS) for phosphorus in entire lakes (the ecosystem scale). The focus in this work is set on the boundary conditions, i.e., the domain of the model, and critical tests to reveal those boundary conditions using data from a wide limnological range. The basic structure of the model, and many key equations have been presented and motivated before, but this work presents several new developments. The LakeMab-model is based on ordinary differential equations regulating inflow, outflow and internal fluxes and the temporal resolution is one month to reflect seasonal variations. The model consists of four compartments: surface water, deep water, sediment on accumulation areas and sediment on areas of erosion and transportation. The separation between the surface-water layer and the deep-water layer is not done from water temperature data, but from sedimentological criteria (from the theoretical wave base, which regulates where wind/ wave-induced resuspension of fine sediments occurs). There are algorithms for processes regulating internal fluxes and internal loading, e.g., sedimentation, resuspension, diffusion, mixing and burial. Critical model tests were made using data from 41 lakes of very different character and the results show that the model could predict mean monthly TP-concentrations in water very well (generally within the uncertainty bands given by the empirical data). The model is even easier to apply than the well-known OECD and Vollenweider models due to more easily accessed driving variables.Keywords Eutrophication . Mass-balance model . Phosphorus . Lakes . Processes . Fluxes . Sedimentation . Resuspension . Mixing . Suspended particulate matter Background and AimPhosphorus abatement has substantially improved the water quality in many anthropogenically eutrophicated lakes (Sas 1989;Jeppesen et al. 2005). Models for predicting lake response from phosphorus reductions have thus far been rather imprecise and results from abatement programs have sometimes been disappointingly modest (Sas 1989). Phosphorus is since long recognised as a crucial limiting nutrient for lake primary production (Schindler 1977(Schindler , 1978Bierman 1980;Chapra 1980;Boynton et al. 1982;Wetzel 1983;Persson and Jansson 1988;Boers et al. 1993). The literature on phosphorus in lakes is extensive. The famous Vollenweider model (Vollenweider 1968(Vollenweider , 1976 and later versions, e.g., OECD 1982), and the analysis behind this modelling, constitutes a fundamental base for water management (Wetzel 2001; Håkanson and Water Air Soil Pollut (2008) Boulion 2002). Lake modelling has gone through great changes recently with respect to predictive power.As a consequence of the Chernobyl nuclear accident, the pulse of radionuclides that subsequently passed along European ecosystem pathways has revealed, and made it possible to quantify, important transport routes (Håkanson 2000). Many algorithms that quantify these fluxes are vali...

Eutrophication of the Baltic Sea is considered a major threat to its ecological status. We present and discuss Polish riverine flow normalized loads of total nitrogen (TN) and total phosphorus (TP) discharged into the Baltic Sea in (i)1988–2014, (ii) periods of maximum TN (1992–1994), TP (1988–1991) emission, (iii) the reference period (1997–2003) established by the Helsinki Commission (HELCOM), (iv) 2012–2014, last years of our study. Despite considerable nutrient load reductions prior to the HELCOM reference period, Poland is expected to reduce TN and TP loads by 30% and 66%, respectively. In the light of our historical and up-to-date findings defining ecological status of the Baltic Sea, we suggest that the proposed TP load reduction is overestimated and its realization may lead to (i) undesirable consequences for the Baltic ecosystem, (ii) would require a decline in TP concentrations to 0.067 mg P dm-3 (the Vistula River) and 0.083 mg P dm-3 (the Oder River), values reported for pre-industrial times. The current nutrient concentrations in the Vistula and Oder safely comply with the requirements of the Water Framework Directive. We also comment on the top-down and bottom-up effect resulting in quantitative and qualitative reorganization of the Baltic ecosystem, a phenomenon already observed in the Baltic Sea.

Lake models that predict phosphorus (P) concentrations from P-loading have provided important knowledge enabling successful restoration of many eutrophic lakes during the last decades. However, the first-generation (static) models were rather imprecise and some nutrient abatement programs have therefore produced disappointingly modest results. This study compares 12 first-generation models with three newer ones. These newer models are dynamic (time-dependent), and general in the sense that they work without any further calibration for lakes from a wide limnological domain. However, static models are more accessible to nonspecialists. Predictions of P concentrations were compared with empirical long-term data from a multi-lake survey, as well as to data from transient conditions in six lakes. Dynamic models were found to predict P concentrations with much higher certainty than static models. One general dynamic model, LakeMab, works for both deep and shallow lakes and can, in contrast to static models, predict P fluxes and particulate and dissolved P, both in surface waters and deep waters. PCLake, another general dynamic model, has advantages that resemble those of LakeMab, except that it needs three or four more input variables and is only valid for shallow lakes.