Critical loads of acidity for surface waters

Henriksen, A.; Posch, Maximilian; Hultberg, Hans; Lien, L.

doi:10.1007/bf01186196

Cited by 101 publications

(122 citation statements)

References 6 publications

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“…The calculated critical loads are thus site specific (sensitive geological regions or not) and also depend on the local hydrology and precipitation. For full details see Henriksen (1988), Kämäri et al (1992) and Henriksen and Posch (2001). The critical loads for acidifying effects of nitrogen compounds, based upon nitrogen as the only cause of acidification, for the most sensitive lakes and streams are:…”

Section: Effects Of Nitrogen Deposition On Lakes and Streams (C1-partmentioning

confidence: 99%

“…This chapter summarises field and experimental evidence to establish critical loads for nitrogen deposition with respect to eutrophication or adverse ammonium effects. The acidifying effects of airborne nitrogen compounds to surface waters are only briefly summarized, as critical loads for acidity, including nitrogen, are well established for most waters (Henriksen, 1988;Kämäri et al, 1992;Henriksen and Posch, 2001). …”

Section: Introductionmentioning

confidence: 99%

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Potential Approaches to Developing Lichen-Based Critical Loads and Levels for Nitrogen, Sulfur and Metal-Containing Atmospheric Pollutants in North America

Glavich¹,

Geiser²

2008

The Bryologist

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Section: Effects Of Nitrogen Deposition On Lakes and Streams (C1-partmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Potential Approaches to Developing Lichen-Based Critical Loads and Levels for Nitrogen, Sulfur and Metal-Containing Atmospheric Pollutants in North America

Glavich¹,

Geiser²

2008

The Bryologist

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“…Brakke et al (1989) and later Henriksen and Posch (2001) have estimated background SO 4 concentrations in Norwegian lakes, based on linear regression of SO 4 * on BC * concentrations in lakes in parts of Norway receiving very low levels of acid deposition:…”

Section: Deposition Datamentioning

confidence: 99%

“…For Norwegian lakes the natural atmospheric background (intercept a) was estimated as 15 µeq l 1 by Brakke et al (1989) based on data from the mid1980s, but by 1995 had decreased to 8 µeq l -1 (Henriksen and Posch, 2001). This suggests strongly that areas of northwestern and northern Norway receive small but significant acid deposition, and that this deposition has decreased in parallel with decreases in southern Norway and elsewhere in Europe.…”

Section: Deposition Datamentioning

confidence: 99%

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Future recovery of acidified lakes in southern Norway predicted by the MAGIC model

Wright

Cosby

2003

Hydrol. Earth Syst. Sci.

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The acidification model MAGIC was used to predict recovery of small lakes in southernmost Norway to future reduction of acid deposition. A set of 60 small headwater lakes was sampled annually from either 1986 (35 lakes) or 1995 (25 lakes). Future acid deposition was assumed to follow implementation of current agreed legislation, including the Gothenburg protocol. Three scenarios of future N retention were used. Calibration of the sites to the observed time trends (19901999) as well as to one point in time considerably increased the robustness of the predictions. The modelled decline in SO 4 * concentrations in the lakes over the period 19862001 matched the observed decline closely. This strongly suggests that soil processes such as SO 4 adsorption/desorption and S reduction/oxidation do not delay the response of runoff by more than a few years. The slope of time trends in ANC over the period of observations was less steep than that observed, perhaps because the entire soil column does not interact actively with the soilwater that emerges as runoff. The lakes showed widely differing time trends in NO 3 concentrations over the period 19862000. The observed trends were not simulated by any of the three N scenarios. A model based on the C/ N ratio in soil was insufficient to account for N retention and leaching at these sites. The large differences in modelled NO 3 , however, produced only minor differences in ANC between the three scenarios. In the year 2050, the difference was only about 5 µeq l -1 . Future climate change entailing warming and increased precipitation could also increase NO 3 loss to surface waters. SO 4 * concentrations in the lakes were predicted to decrease in parallel with the future decreases in S deposition. Fully 80% of the expected decline to year 2025, however, had already occurred by the year 2000. Similarly, ANC concentrations were predicted to increase in the future, but again about 67% of the expected change has already occurred over the past 20 years. The recovery of ANC was predicted to be incomplete. Even after the CLE scenario (for future acid deposition) is implemented, the chemical conditions in about one-third of the lakes were predicted to be insufficient to support trout populations in the future. Thus, additional measures will be required if these lakes are to be restored.

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Machine learning and linear regression models to predict catchment‐level base cation weathering rates across the southern Appalachian Mountain region, USA

Povak

Hessburg

McDonnell

et al. 2014

Water Resources Research

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Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BC w ) as the response variable to identify key environmental correlates and predict a continuous map of BC w within the southern Appalachian Mountain region. More than 50 initial candidate predictor variables were submitted to a variety of conventional and machine learning regression models. Predictors included aspects of the underlying geology, soils, geomorphology, climate, topographic context, and acidic deposition rates. Low BC w rates were predicted in catchments with low precipitation, siliceous lithology, low soil clay, nitrogen and organic matter contents, and relatively high levels of canopy cover in mixed deciduous and coniferous forest types. Machine learning approaches, particularly random forest modeling, significantly improved model prediction of catchment-level BC w rates over traditional linear regression, with higher model accuracy and lower error rates. Our results confirmed findings from other studies, but also identified several influential climatic predictor variables, interactions, and nonlinearities among the predictors. Results reported here will be used to support regional sulfur critical loads modeling to identify areas impacted by industrially derived atmospheric S inputs. These methods are readily adapted to other regions where accurate CL estimates are required over broad spatial extents to inform policy and management decisions.

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Critical loads of acidity for surface waters

Cited by 101 publications

References 6 publications

Potential Approaches to Developing Lichen-Based Critical Loads and Levels for Nitrogen, Sulfur and Metal-Containing Atmospheric Pollutants in North America

Potential Approaches to Developing Lichen-Based Critical Loads and Levels for Nitrogen, Sulfur and Metal-Containing Atmospheric Pollutants in North America

Future recovery of acidified lakes in southern Norway predicted by the MAGIC model

Machine learning and linear regression models to predict catchment‐level base cation weathering rates across the southern Appalachian Mountain region, USA

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