Acidification reversal in low mountain range streams of ... - Springer Link

Environ Monit Assess (2011) 174:65–89 DOI 10.1007/s10661-010-1758-z

Acidification reversal in low mountain range streams of Germany Carina Sucker · Klaus von Wilpert · Heike Puhlmann

Received: 15 March 2010 / Accepted: 29 September 2010 / Published online: 14 November 2010 © Springer Science+Business Media B.V. 2010

Abstract This study evaluates the acidification status and trends in streams of forested mountain ranges in Germany in consequence of reduced anthropogenic deposition since the mid 1980s. The analysis is based on water quality data for 86 longterm monitored streams in the Ore Mountains, the Bavarian Forest, the Fichtelgebirge, the Harz Mountains, the Spessart, the Black Forest, the Thuringian Forest, and the Rheinisches Schiefergebirge of Germany and the Vosges of France. Within the observation period, which starts for the individual streams between 1980 and 2001 and ends between 1990 and 2009, trends in chemical water quality were calculated with the Seasonal Mann Kendall Test. About 87% of the streams show significant (p < 0.05) negative trends in sulfate. The general reduction in acid deposition resulted in increased pH values (significant for 66% of the streams) and subsequently decreased base cation concentrations in the stream water (for calcium significant in 58% and mag-

C. Sucker (B) · K. von Wilpert · H. Puhlmann Forest Research Institute of Baden-Württemberg, Wonnhaldestr. 4, 791 00 Freiburg, Germany e-mail: [email protected] K. von Wilpert e-mail: [email protected] H. Puhlmann e-mail: [email protected]

nesium 49% of the streams). Reaction products of acidification such as aluminum (significant for 50%) or manganese (significant for 69%) also decreased. Nitrate (52% with significant decrease) and chloride (38% with significant increase) have less pronounced trends and more variable spatial patterns. For the quotient of acidification, which is the ratio of the sum of base cations and the sum of acid anions, no clear trend is observed: in 44% of the monitored streams values significantly decreased and in 23% values significantly increased. A notable observation is the increasing DOC concentration, which is significant for 55% of the observed streams. Keywords Water quality · Acidification · Forested catchments · Deposition · Germany

Introduction Forested catchments are considered to guarantee a high quality of surface and drinking water, because disturbance of element budgets through management impacts is usually small there; hence, the contamination with nitrate and pesticides is comparatively low (Gäth and Frede 1991). However, forests are not per se guarantors for good water quality. Stream acidification is a prevalent problem, particularly in catchments with bedrocks

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poor in base content (Rhode et al. 1995). Adequate forestry can help to counteract effects of pollutant deposition such as soil acidification and nitrogen saturation. Intensive forestry, as it is practiced in some of our catchments, brings about problems for the drinking water treatment such as increased water turbidity, occurrence of herbicides and pesticides, or pulses of dissolved organic carbon in the raw water (Sudbrack, personal communication). Ensuring a high drinking water quality by adequate forest management is, therefore, of public and political concern. Deposition history and present development At the beginning of the twentieth century, acid deposition was predominantly restricted to the close vicinity of industrial and urban emission sources. With the overall trend of taller chimneys in the 1950s, air pollutants were transported to higher atmospheric layers, and a mainly regional phenomenon became a large-scale global problem. Anthropogenic sulfur dioxide and nitrogen oxides in the atmosphere acted as strong acids increasing the natural acidity of rainwater. Since the early 1980s, the “acid rain” phenomenon was associated with the observed extensive “forest dieback.” In the last two decades, the emission of acidifying pollutants was reduced significantly as a result of stringent air purification policy at national and European level. Between 1990 and 2008, the emissions of oxidized sulfur compounds decreased by 91%, of oxidized nitrogen by 52%, and of ammonia by 13% (UBA 2009). Regardless of this observed reduction, the nitrogen input, originating in equal arts from fuel combustion (individual traffic and power plants) and stock breeding in agriculture (von Wilpert 2007), presently remains at a high level (Gauger et al. 2008; Meesenburg et al. 2009; von Wilpert 2007). The total acid input therefore still exceeds the receptiveness and the buffer capacity of many forest systems (UBA 2009; UNECE 2009; von Wilpert 2007).

Environ Monit Assess (2011) 174:65–89

ongoing acidification of the soils. von Wilpert (2007) gives a concise overview on the causes and processes of soil acidification. Under natural conditions, soil acidification is mainly caused by organic acids (podsolisation) and is normally restricted to the upper 30 to 50 cm. Under the influence of stronger acids like sulfate or nitrate, acidification intensities can reach pH values substantially below 5 and furthermore, can propagate into deeper soil horizons. As a consequence, cation acids like iron, manganese, aluminum, or heavy metal cations are mobilized and leached from the soil zone (Bihl 2004). Wolff and Riek (1998) observed that, although acid deposition was already decreasing by this time, the water quality, with respect to pH, nitrogen, sulfate, and aluminum, in the German low mountain ranges generally did not show a positive development. The ongoing acidification leads to a leakage of alkaline cations (sodium, magnesium, potassium), which affects root growth and activity and eventually derogates the filter functions of the soil and the plants (Jordi 2005). Nitrogen saturation Anthropogenic nitrogen input leads to a substantial increase of nitrogen availability. This causes not only an increase in growth of almost all plant species but also a tendency towards nutrient imbalances. NO− 3 can leach from the rooting zone if nitrogen saturation surpasses the uptake capacity of forest ecosystems (critical loads). The deposition threshold above which NO− 3 leakage occurs is not very sharp and largely depends on the individual ecosystem characteristics. In most European case studies, NO− 3 output occurs above a total nitrogen input of between 10 and 25 kg ha−1 a−1 (Aber et al. 1989; Dise et al. 1998). In order to protect the groundwater and drinking water quality in the long run, the stabilization of the forest soil systems is one of the most urgent needs (Bihl 2004).

Soil acidification

Adequate forestry

Frequently, the seepage water from forest ecosystems is subject to indirect strain caused by the

The endangered soil functions can be supported by adapted forest management practices, e.g., tree


species selection, type of thinning, or type of forest regeneration. As an example, the amount of the deposited substances depends on the direct input through precipitation and on the ability of the trees to comb the air with their needles and leaves. Numerous studies, e.g. Körner (1996), Hegg et al. (2004), Zirlewagen and von Wilpert (2002), have shown that element concentrations below spruce are much higher than below beech stands. Low harvesting intensity and careful selection of tree species can lead to tighter element cycles, thus decreasing acidification and leaching of base cations (Kreutzer 1994). Lenz et al. (1994) pointed out that element removal with harvested wood biomass in the Fichtelgebirge Mountains led to an acid load within the soil comparable to the anthropogenic acid deposition. Additionally, liming or amelioration of soils can improve base saturation. However, German legislation obliges forestry to preservation of soil and site quality through adequate forest management only in rather fuzzy terms, such as the general rule that forest must be replanted after harvest. The federal forest laws contain no specified technical directives on how to optimize the water preservation function of forests through specific management features. Furthermore, while the principal mechanisms of silvicultural options for stabilizing forest ecosystems and improving drinking water quality are understood, a quantitative evaluation of the effectiveness of the various possible management practices is rather difficult due to the complex interactions between deposition, site/forest properties and water/element fluxes.

Recent studies on trends in stream water quality For several decades, surface water acidification has been recognized as a major environmental problem in many parts of Europe and North America. Recent studies frequently provided evidence for (at least partial) recovery from acidification in response to decreasing emissions of acidifying pollutants (e.g., Davies et al. 2005; Evans et al. 2001a; Harriman et al. 2003; Kopácek et al. 1998, 2002; Majer et al. 2003; Skjelkvale et al. 2005, 2007; Stoddard et al. 1999; Veselý et al.

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2002). Evans et al. (2001a) found that in Europe, recovery of surface waters is most pronounced in the Czech Republic and Slovakia, moderate in Scandinavia and the UK, and comparatively weak in Germany. They observed wide-spread decreases in sulfate, base cations (particularly calcium), and aluminum and increases in acid neutralizing capacity and pH. Nitrate had a much weaker and more varied pattern with no significant trend at 62% of the sites, decreases at some sites in Scandinavia in the Czech Republic, Slovakia and Germany, and increases at some sites in Italy and the UK. Skjelkvale et al. (2005) also stated a largely varying extent of recovery from acidification in Europe and North America, depending on a range of factors including the magnitude of deposition changes and catchment characteristics. Most regions showed decreasing sulfate concentrations and improvement in at least one indicator of chemical recovery (alkalinity, acid neutralizing capacity, or pH). Nitrate remained largely unchanged and dissolved organic carbon increased significantly in half of the regions. Numerous studies focus on the regional development of surface waters in Germany. A comprehensive hydrochemical monitoring program for acidified surface waters is coordinated by the Federal Environmental Agency (UBA). This monitoring program includes 24 streams and nine lakes throughout Germany and, for some of them, more than 40 years of continuous measurements are available (Schaumburg et al. 2008, 2010). Objectives of this monitoring program are (1) recording the degree of acidification and the geographical distribution of acidified streams within Germany, (2) documentation of changes in the chemical and biological status, (3) examination of the effectiveness of actions taken for reducing the sulfur and nitrogen emissions, and (4) specifying the demand and possible actions for further enhancement of chemical and biological water quality. Regions with formerly very large sulfur and nitrogen deposits (e.g., Ore Mountains, Harz Mountains) responded to the drastic deposition reductions with a distinct improvement of their acidification status. Other regions which were less affected by pollutant deposition (e.g.,

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Black Forest, Bavarian Forest) may, in the long run, even attain a near-natural status unless other incidents (e.g., forest die-back due to bark beetles or wind throw) do not countervail this positive development (Schaumburg et al. 2010). Alewell et al. (2001) investigated nine streams in the Harz Mountains, the Fichtelgebirge, the Bavarian Forest, the Spessart, and the Black Forest for the EU project “Recover:2010” and considered observations mainly from 1987/1988 to end of 1999s. They estimated element fluxes and budgets and assessed the biological recovery (microinvertebrates and diatom communities) in the investigated streams as response to two decades of decreasing acid deposition. Although Alewell et al. (2001) did not observe a major acidification reversal, they found signs of improvement such as a decrease in the level and frequency of extreme values of pH, acid neutralizing capacity, and aluminum concentrations and generally (weakly) decreasing sulfate concentrations. Nitrogen compounds in precipitation or stream water remained largely unchanged. Increasing base cation fluxes were interpreted as an indicator for an ongoing soil acidification. Ulrich et al. (2006) analyzed measurements (1993 to 1999) from seven reservoirs and 22 tributaries in the Ore Mountains in eastern Germany. They assessed the response of the chemical water quality to decreased acidic atmospheric deposition and tried to quantify the influence of forest soil liming on hydrochemical recovery. About 85% of the waters showed a rapid reversal from acidification, manifested by declining concentrations of protons, nitrate, sulfate, and reactive aluminum. Lorz et al. (2003) investigated another stream in a limed forest catchment of the Ore Mountains and satisfactorily reproduced the main trends (observation period 1993 to 1999) in pH, nitrate, sulfate, and aluminum concentrations using a lumped-parameter model for groundwater acidification (MAGIC). Despite the regular ameliorative liming of the catchment, calcium concentrations slightly declined. Lorz et al. (2003) concluded that in general, the short-term effects of ameliorative liming are low and recovery from acidification can only take place over long-term periods. Westermann (2000) analyzed 10 streams in Rhineland-Palatinate for the years 1983 to 1999


and found slowly weakening acidification conditions (large sulfate decrease, slightly increasing pH, reduction of aluminum peaks) whereas nitrate largely remained on a constant level. Increasing calcium and magnesium concentrations in the 1990s but possibly also the increasing concentrations of dissolved organic carbon, were interpreted as liming effects. Significant changes in sulfate, nitrate, calcium and aluminum were observed by Feger et al. (1995) in 85 streams in the Black Forest between two repetitive inventories in 1984 and 1994. Besides deposition patterns, the pedological–geological conditions as well as the vegetation mainly governed the stream water chemistry. The present study comprises the measurements of the above-mentioned studies and expands the database by including more recent data as well as data from 34 (= 40% of the presented dataset) other streams. This comprehensive dataset gives a broad overview of the development of water quality in streams of mountainous regions in Germany. The selected variables illustrate the main stream water responses to changes in acid deposition, with sulfate, nitrate and chloride representing the major acidifying anions, and pH value, aluminum, and manganese providing measures of stream water acidity and toxicity. In addition, concentrations of the base cations calcium and magnesium were considered as indicators of the depletion of the soils’ buffering capacity and/or increased base cation leaching from soils through high fluxes 2− − of mobile anions (NO− 3 , SO4 , Cl ). The hydrochemical dataset is complemented by a broad set of data on soil characteristics, geology, deposition, forest stand characteristics, forest management, and soil protective liming, which can be used to assess the effects of forest management options for preservation of water quality under the given environmental conditions.

Materials and methods For the trend analyses, data were compiled for 86 streams from various mountain ranges in Germany (Fig. 1). The streams cover a wide


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Fig. 1 Left investigated streams and delineation of regions 1 to 5; stream numbers correspond to those in Appendix, numbers in grey boxes mark regions. Right main mountains ranges covered by regions 1 to 5

Region 1 Ore Mountains Fichtelgebirge Bavarian Forest Region 2 Harz Mountains Region 3 Spessart Black Forest Vosges 3233 28 31 30 29

6970 67 68

Rheinisches Schiefergebirge 5965 4 52 64 62 63 5354

66

5

50

46 45 3738 35 36 42 4447 41 43 40

Thuringian Forest Region 5

2

82

78 72 8586 76 83 84 717581

Region 4

8 23 35 15 9 4 14 2 22 7 12 6 1 11 17 24

34

1

48 49

2627 25

3

range of landscapes in Saxony, Saxony-Anhalt, Thuringia, Lower Saxony, North-Rhine-Westphalia, Rhineland-Palatinate, Baden-Württemberg, and Bavaria, and range from high-altitude catchments in the Black Forest and the Bavarian Forest to lower mountain ranges in the Rheinische Schiefergebirge (Table 2). Appendix contains a detailed list of all investigated streams and their catchment characteristics. Most data of deposition, hydrochemistry, and stream flow as well as comprehensive information on geology, soils and vegetation were obtained through personal communication. Where available, data were also derived from previous publications: Ditsche-Kuru (2003) (streams 1–2, 10, 12, 15, 20, 22 28, 35–37, 51, 53, 66, 82–86); Ulrich et al. (2006) (streams 1–6, 10, 12, 14– 22); Armbruster et al. (2003) (streams 23 and 40); Diefenbach-Fries and Beudert (2007) (stream 27); Moritz and Bittersohl (2000) (stream 25); Beudert and Klöcking (2007) (streams 25–27); Alewell et al. (2001) (streams 24–25, 31–34, 40, 42); Meesenburg et al. (2001) (streams 31–33); Sucker et al. (2009) (streams 35, 37, 40–41); Armbruster (1998) (streams 40–42); von Wilpert

and Puhlmann (2007) (stream 39); OHGE (2010) (stream 50); Westermann (2000) (streams 73–81). Meteorological data for all catchments were obtained from the German Weather Service. The geology in the regions (Appendix) comprises different varieties of bedrocks with an extremely poor base content (quarzite, granite, rhyolite, and phyllite), mainly found in regions 1, 4, and 5, and bedrocks with a slightly higher base content (gneiss, mica schist, argillite, sandstones, claystones, and greywacke), which were found mostly in regions 2 and 3. Thus, the latter have a higher potential to buffer acidic deposition. In most catchments, the underlying bedrock has a low water permeability and fens developed in depressions and flat plateaus. The soils (base-poor cambiols, haplic or cambic pozols, podzolic cambisols, peaty gleys, stagnogley, and gley) are acid (pH(CaCl2) < 4) and have a low base saturation (< 20%) (Alewell et al. 2001; Armbruster 1998; Lorz et al. 2003; Meesenburg et al. 2001; Moritz and Bittersohl 2000; Raben et al. 2000; Schaumburg et al. 2010). Consequently, streams are poorly buffered. This study

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considers exclusively catchments with a forest percentage of at least 60% (Appendix). Norway spruce (Picea abies) is the dominant forest tree, whilst beech (Fagus sylvatica) and other tree species are of minor relevance. Landuse was derived from CORINE landcover data (status mapped for the year 2000). This was the only landuse information available for all catchments, although for some catchments, we had more detailed information on landuse and its temporal changes. However, CORINE data also contains a landuse class “transitional woodland shrub”, which gives a hint as to structural changes in the forest cover, e.g. as a result of wind throw or bark beetle infestation. The streams were grouped into regions mainly according to their geographic location (Fig. 1). In some cases, distant streams were grouped together because of similar geological conditions (e.g. streams 25 to 27 grouped in region 1) or similar deposition patterns (stream 34 grouped in region 3). The typical deposition regimes in these regions were classified into deposition types according to Wellbrock et al. (2005), which is based on the source and the total amount of atmospheric emissions from 1989 (Table 1). Wellbrock’s classification constitutes a comprehensive assessment of deposition patterns in Germany for a time when deposition was at large still at a high level. Deposition type 1 is characterized by medium values for atmospheric deposition. In deposition type 2, atmospheric input of potential acid and sulfur was twice as high as in type 1. Nitrogen input was slightly above the German average but relatively low compared to sulfur deposition. Deposition type 3 is characterized by low deposition of sulfur, potential acid, and sodium compared with

Table 1 Deposition types according to Wellbrock et al. (2005)

the German average. Typical is a high percentage of reduced nitrogen of the total N input from agricultural nitrogen emissions together with low calcium and magnesium deposition. Deposition type 4 showed high sea-borne sodium and magnesium deposition with a high percentage of ammonia nitrogen of the total N deposition from agricultural emissions. Deposition type 5 consists of sites with extremely high depositions of sulfur substances, nitrogen, and potential acid resulting from large nearby sources of emission (energy production, lignite combustion, livestock farming, and fertilization). Deposition type 6 is characterized by low atmospheric deposition. Compared to type 3, this type contains a greater amount of oxidized nitrogen as well as a higher deposition of calcium, magnesium, and potassium. Region 1 is located in southeast Germany and surrounds the western Bohemian Basin. It includes catchments in the Ore Mountains (streams 1 to 23), in the Fichtelgebirge (stream 24) and in the Bavarian Forest (streams 25 to 27). Most catchments in the Ore Mountains belong to deposition type 5 and received extremely high depositions of acidifying pollutants. Since the mid 1980s, emissions of total inorganic sulfur were largely reduced in these catchments. Contrarily, nitrogen compounds decreased only slightly, and they presently dominate the atmospheric input of acidity. Until ∼1995, alkaline emission components declined more than the acidic ones due to insufficient flue gas desulfurization.

Deposition type

Ntotal [kmol ha−1 yr−1 ]

Potential acid [kmol ha−1 yr−1 ]

SOx –S [kmol ha−1 yr−1 ]

1 2 3 4 5 6

2.63 2.41 2.92 2.92 4.02 2.07

5.50 9.57 5.32 5.32 19.75 4.56

2.96 7.16 2.53 2.53 15.82 2.55


This caused precipitation acidity to rise five times above its level in West Germany (Ulrich et al. 2006). The Fichtelgebirge is classified as Wellbrock’s deposition type 2 with deposition amounting to only half of that in deposition type 1. The Bavarian Forest is strongly influenced by agricultural nitrogen emissions and characterized by deposition type 3. Accordingly, the deposit of total inorganic S and N compounds has declined only moderately since the 1990s. Region 2 is located in Central Germany, with catchments in the southeast (streams 28 to 30) and northwest (streams 31 to 33) of the Harz Mountains. Atmospheric deposition in the catchments northwest of the Harz Mountains had been influenced by both long-range transport and by local sources due to the mining industry. The latter resulted in increased deposition rates during the last millennium. The smelting of sulfur containing ores emitted large amounts of sulfur, which were, in parts, deposited in the region. Unlike Wellbrock et al. (2005) who classified these catchments into deposition type 6 (low atmospheric input), our data (Alewell et al. 2001; Schaumburg et al. 2010) suggest a classification into type 2 (high atmospheric input). Since the mid 1980s, sulfate deposition has decreased drastically. The formerly high nitrogen deposition (with a high percentage of ammonia nitrogen) decreased only slightly. The catchments in the southeast of the Harz Mountains experienced low atmospheric input in the past and accordingly, are classified as deposition type 6. Region 3 is located in southwest Germany, in the Spessart (stream 34), in the Black Forest (streams 35 to 49) and includes

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one stream (no. 50) in the Vosges/ France. Most of the catchments in the Black Forest belong to deposition type 1 (medium atmospheric input). Acid deposition decreased substantially whereas nitrate deposition remained at a high level (von Wilpert et al. 2010). Some catchments in the Black Forest as well as the Spessart catchment belong to deposition type 6 (low atmospheric input). Region 4 groups the streams in the Thuringian Forest (no. 51 to 65). This region belongs to deposition type 2, with higher sulfur and potential acid deposition than German average. Until 1990, most elements were deposited in the form of fly ashes. Since then, the emissions of total inorganic S and N compounds have been reduced dramatically. Region 5 is located in West Germany and subsumes various catchments in the Rheinische Schiefergebirge (streams 66 to 86). This region is characterized by deposition type 1. Sulfur deposition in this region originated mainly from fossil fuels combustion in the heavily industrialized Ruhr Area at the northern edge of this region. N deposition is still comparatively high due to extensive agriculture in the neighboring Benelux states. For each catchment of the analyzed streams, the long-term buffering capacity in the soil was assessed by the critical load values for acids published by UBA (2000). The concept of critical loads combines soil chemical characteristics like base saturation with an assessment of the longterm mobilization of base cations through weathering of primary minerals and leaching of base cations with seepage water. The maps of UBA (2000) give only rough estimates of the spatial patterns of critical loads, and their value for local interpretations is rather limited. However, region-specific information on soil acidification

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Table 2 Characteristics of the monitored streams and their catchments in each region Region

Total number of streams

Catchment area [ha]

Altitude range [m a.s.l].

Mean annual precipitation (1980–2008) [mm]

Mean annual temperature (1980–2008) [◦ C]

Critical load [eq ha−1 yr−1 ]

1 2 3 4 5

27 6 17 15 21

15 to 3,080 15 to 594 9 to 643 37 to 2,211 25 to 1,624

430 to 1,440 430 to 900 190 to 1,490 480 to 900 330 to 820

900 to 1,530 920 to 1,360 1,060 to 2,000 1,150 to 1,330 860 to 1,380

5.3 to 8.5 7.2 to 7.8 5.1 to 10.2 6.6 to 7.4 7.4 to 9.3

500 to 2,500 (mean 1,722) 3,450 to 3,550 (mean 3,500) 1,500 to 3,500 (mean 2,313) 1,500 to 2,500 (mean 1,833) 1,500 to 3,500 (mean 2,548)

and buffering capacities were not available for the entire set of the investigated catchments. Minimum, maximum and mean values of the critical loads for each region, as derived from the maps of UBA (2000), are presented in Table 2. The data used in this study were collected from a large number of different ecosystem studies and monitoring sites and include measurements from various hydrochemical laboratories with varying analysis methods. Thus, heterogeneity in dataset characteristics is inevitable, due to e.g. varying reporting levels and sampling frequencies (from weekly to monthly). A considerable problem is caused by the length of the observation periods which vary largely between and within the regions. For 29% of the investigated streams, observation periods are longer than 20 years (mainly streams in region 2 and 5), 54% of the streams have continuous data for 11 to 20 years (mainly streams in regions 1 and 3) and further 17% have data for 8 to 10 years (mainly from region 4). The shortest observation period (8 years) is available for stream no. 7 in region 1, the longest ones (29 years) for streams no. 26 and 31 in region 1 and 2. In the trend analysis, a set of ten key variables − was considered: sulfate (SO2− 4 ), nitrate (NO3 ), − 2+ chloride (Cl ), calcium (Ca ), magnesium (Mg2+ ), acid strength (pH), aluminum (Al3+ ), manganese (Mn2+ ), dissolved organic carbon (DOC), and quotient of acidification (QA). QA is defined as the quotient of the sum of base cations (Ca2+ , Mg2+ ) and the sum of acid anions (NO− 3, − , and Cl ): SO2− 4 QA =

Ca2+ + Mg2+ 2− − NO− 3 + SO4 + Cl

(1)

where all concentrations are given in eq l−1 . From Eq. 1 follows that QA < 1 indicates active dissociated strong mineral acids in the water. QA = 1 indicates that all basic cations are accompanied by anions of strong acids and, therefore, provide no buffer capacity because they are dissolved neutral salts. For QA > 1, the water has buffer capacity because the portion of basic cations which exceeds the sum of strong mineral acid anions is accompanied by anions of hydrogen carbonate or of weak organic acids. We preferred calculation of QA over acid neutralizing capacity (ANC), derived from the charge balance of base cations and strong acid anions, because of sparsely available data on sodium and potassium concentrations and due to analytical uncertainties as described by Evans et al. (2001b). The selected hydrochemical parameters were analyzed in almost all investigated streams. For all chemical parameters, long-term trends were analyzed using the complete observation period for each individual stream and chemical parameter. For a homogenization of the dataset, all measurements were aggregated to monthly mean values. Values less than the reporting level were set at half of the reporting level. Values which were greater or less than mean ± 3 standard deviations were considered to be outliers and removed from the data set. For testing the hypothesis of significant positive or negative linear trends in the monthly averaged measurements, the non-parametric Seasonal Mann–Kendall test (Gilbert 1987; Hirsch and Slack 1984) was used with the software package MATLAB (Software: Mann–Kendall Tau-b with Sen’s Method (enhanced) by Burkey (2009)). This test is robust with regard to non-normality


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as well as to missing or “less-than” values in the datasets. This test groups data into monthly blocks to identify persistent long-term trends. The Z test was used for accepting or rejecting the hypothesis of significant positive or negative trends. The level of significance was set at p = 0.05. Trend slopes were estimated according to the method of Sen (1968), as individual slope ci for data pair {xij, xik } and the month i for i = 1..12

xij − xik ci = ( j − k)

(2)

where, xij is the value for the month i of the year j, and xik is the value for the month i of the year k, where j > k. From the set of individual slopes, c, the median seasonal trend slope C was derived. Fig. 2 Ranked C for all analyzed streams; black bars mark significant positive or negative trends ( p < 0.05), white bars mark nonsignificant trends; QA quotient of acidification

Results and discussion Observed trends For the analyses of the medium to long-term trends of the individual streams, the entire individual observation periods were used because we need observation periods as long as possible for a well-defined trend recognition. In Fig. 2, the estimated median trend slopes, C, for each chemical parameter are ranked over all investigated streams, from streams with low C values to streams with high C values. Black bars mark streams with significant trend according to the Z statistics; white bars mark streams without significant trend. This figure allows identifying general patterns of concentration changes across all investigated streams regardless of their belonging to a specific region.

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The estimated trends in our dataset confirm a wide-spread recovery from acidification in response to decreasing emissions of acidifying pollutants as reported in many recent studies. Significant decreases in stream water SO2− 4 concentrations were observed in 87% of the streams. This complies with observations of Schaumburg et al. (2008), who found significant negative SO2− 4 trends for 86% of 28 investigated streams and lakes in Germany. The observation reflects the steep reduction of sulfur deposition, although release of sulfur stored in soils may have damped this response in some regions (Alewell et al. 2001; Prechtel et al. 2001). In comparison to SO2− 4 , our data on NO− show only weak and more variable 3 trends with significant negative trends in 52% and significant positive trends in 25% of the monitored streams. Again, this complies with the results of previous studies (e.g. Alewell et al. 2000, 2001; Schaumburg et al. 2008; Westermann 2000). No clear trend could be found for chloride (Fig. 2), and streams had both significant positive (38% of the streams) and significant negative trends (29%). Chloride deposition originates from natural (sea-spray in oceanic areas) and industrial sources (combustion of PVC, lignite power plants). Due to the two different principle sources of Cl− depositions, Cl− concentrations vary with distance to the sea but also to industrial regions and, therefore, have no clearly interpretable spatial pattern. The most important base cations in aquatic systems, calcium and magnesium, are mobilized by weathering and cation exchange and respond indirectly to the decreases in SO2− 4 and NO− deposition. The reduced acid input led to 3 a reduction of neutralizing processes in the soil and thereby reduced the release of base cations to the stream water. Significant negative trends were observed in 58% of the streams for Ca2+ and 49% for Mg2+ , whereas significant positive trends were found in only 18% of the streams for Ca2+ and 17% for Mg2+ . An integral measure of stream acidification is the QA; it reflects the buffering capacity of the stream water. No clear trend can currently be observed for QA, with 44% of all streams showing significant negative trends and 23% showing significant positive


trends (Fig. 2). Other studies (Alewell et al. 2001; Evans et al. 2001a) used the acid neutralizing capacity ANC for describing the status of stream acidification and found similar trends to those in our data. However, some studies (Schaumburg et al. 2008; Ulrich et al. 2006; Westermann 2000) report slightly significant positive trends for ANC or alkalinity. The pH value characterizes the actual acid strength in the water and is biologically most relevant because aquatic biota have very explicitly defined pH ranges favorable for their lives. In contrast to the QA, the pH value shows a clear trend towards increasing values in almost all streams (Fig. 2). Sixty-six percent of the streams exhibit significant positive trends whilst only 7% show significant negative trends. Positive pH trends were also reported in Schaumburg et al. (2008), where 52% of the streams and lakes showed significant positive trends. Aluminum and manganese leach from the soil zone as a result of deep soil acidification. Aluminum poses a severe thread for aquatic organisms due to its highly toxic effect (Baker and Schofield 1982; Driscoll et al. 1980). High manganese concentration in the raw water makes drinking water treatment cumbersome and costly, since the raw water needs to be filtered and supplied with bases up to pH 9 (Gray 2008). In consequence of widespread positive trends in precipitation and stream water pH, significant negative trends in Al3+ have been observed in 50%, for Mn2+ in 69% of the investigated streams. However, some of the streams showed increasing trends (18% for Al3+ , 3% for Mn2+ ). Decreasing Al3+ concentrations in surface waters of Germany were also reported by Evans et al. (2001a), Ulrich et al. (2006), and Westermann (2000), whereas Alewell et al. (2001) found no general negative trend. An indicator of organic acidity is DOC. It is a broad classification for organic molecules of varied origin and composition in aquatic systems. The main source of DOC is leaching of decomposed organic matter from soils into stream water. DOC is an important source of carbon and energy for microorganisms and thus plays an important role in many chemical and photochemical reactions and transformations. Increases in stream water DOC


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concentrations occurred in most of the analyzed streams (Fig. 2, 55% significant). Only 9% of the streams showed significant negative trends. Among all investigated streams, streams no. 3 to 5 show the highest median changes in SO2− 4 , 2+ 2+ 3+ 2+ NO− , Ca , Mg , Al , and Mn . These streams 3 belong to region 1, which received extremely high deposition in the past, and furthermore, are influenced by abandoned interconnected drinking water wells and the influence of near-surface groundwater. Figure 2 also shows that the ob2+ served trends are highest for SO2− 4 , lower for Ca , − 2+ − 3+ Mg , Cl , Al ,and NO3 , and are small for Mn2+ .

median absolute values for each parameter of all individual streams are summarized per region as median, minimum and maximum values in Table 3. Thus, the mean regional characteristic can be explained through predictors which are active in distinct regions, while the variation around the median values provides an impression about the potential which could be controlled, at least partially, by forest management. In order to allow for an unbiased comparison between regions, the length of time series considered was uniformly limited to the period between 1999 and 2008, for which continuous measurements in almost all streams were available. In addition, Table 3 summarizes in a second block (below the absolute concentrations) the identified trends for each chemical parameter and each region. The median trend of a given chemical parameter for a specific region, C*, is given together with the percentage of streams with significant positive or

Variation within and between regions In order to provide information about the absolute level of the hydrochemical parameters and their variation within and between regions, the Table 3 Upper part: absolute concentrations (median, minimum, maximum), light/dark grey shading = lowest/ highest values; lower part: median trends C* within each region (light/dark grey shading = highest/lowest Absolute conc. Parameter/Region

n 1 2 3 4

SO42- [µmolc l-1]

27 5 16 15 17 430.79 497.38

88.07 312.32 229.03

NO3- [µmolc l-1]

27 5 16 15 21

70.96

55.00

27.82

Cl- [µmolc l-1]

27 5 16 15 8 121.30

87.45

Ca2+ [µmolc l-1]

27 5 16 15 17 264.47 384.93 154.69 359.28 164.67

Mg2+ [µmolc l-1] QA [-] pH [-] Al3+ [µmolc l-1]

27 27 26 26

Mn2+ [µmolc l-1]

27 3 15 8 12

5.90

0.18

0.22

0.50

2.71

0.15

0.11

0.09

0.04

0.25

45.14

0.36

2.44

2.57

6.13

DOC [mg l-1]

27 5 16 15 14

3.60

1.50

1.40

1.00

3.05

1.60

0.92

0.50

0.50

0.90

9.38

1.75

4.60

3.40

7.30

SO42- [µmolc l-1yr-1] 27 6 17 15 17 -10.64

-6.17

Δ C* -1.04

-7.81

-5.21

0

0

0

0

0

100

67

88

53

-2.15

0.24

0

0.04

0.03

11

17

35

21

38

89

33

47

21

33

0.21

-2.52

0.12

2.12

-0.45

44

17

47

21

38

30

83

24

7

38 41

Trends

5 6 5 5

16 8 16 15

15 13 15 15

5

1

2

17 246.81 361.99 8 0.75 0.96 21 5.36 6.80 17 44.67 2.11

n

NO3- [µmolc l-1yr-1] 27 6 17 14 21

median 3

values), and percentage of streams with significant positive/negative trends (light/dark grey shading = < 25%/ > 75%)

4

5

1

2

minimum 3 4

5

1

2

maximum 3 4

5

54.14 221.20 49.14 174.90

80.99 1378.36 832.85 458.07 397.68 410.18

57.12

17.74

48.63

31.45

43.72 111.42 136.81

16.93

80.73 19.18

81.69

63.27 106.95 172.77 1.08 0.80 0.79 6.54 6.90 6.77 5.56 5.23 28.35

4.76

41.05

94.81 166.34 39.92 164.67 41.14 144.06 10.61 0.41 0.89 0.50 4.29 6.35 5.00 3.22 2.06 1.67

391.91 121.01

84.83 1047.90 603.79 474.05 499.00 414.17

65.82 115.18 0.65 0.43 6.00 4.75 1.78 4.67

740.44 389.76 246.81 255.04 436.03 1.54 1.38 2.89 1.83 1.15 7.50 7.26 7.40 7.40 7.71 308.23 12.23 31.25 35.58 85.45

Significant positive

Cl- [µmolc l-1yr-1]

27 6 17 14 8

Ca2+ [µmolc l-1yr-1]

27 4 17 15 17

-8.83

-5.51

0 -16.22

Mg2+ [µmolc l-1yr-1] 27 4 17 13 17

-2.29

-3.04

0

-1.37

91.69 119.35 180.63

73.34 105.78 1173.48 131.17 121.30 234.13 629.06

Significant negative 100

0

15

0

18

0

41

85

75

41

40

0.24

11

0

12

0

47

74

75

29

31

35

59

0

29

33

57

QA [yr-1]

27 4 17 9

7

-0.006

0.001

0.004 -0.011 -0.003

19

50

35

11

14

pH [yr-1]

26 6 17 15 21

0.021

0.024

0.005

0.000

0.030

85

83

47

33

76

0

0

18

13

5

Al3+ [µmolc l-1yr-1]

26 3 16 10 17

-2.51

-0.04

0.05

-0.03

-0.88

8

33

44

10

12

69

67

19

10

71

Mn2+ [µmolc l-1yr-1] 27 4 16 2 12

-0.22

-0.04

-0.02

-0.15

-0.03

7

0

0

0

0

78

100

63

50

50

0.06

-0.01

0.01

0.03

0.08

59

0

24

71

86

4

40

18

0

7

DOC [mg l-1yr-1]

27 5 17 14 14

Shadings for QA (quotient of acidification) and pH adverse. Absolute concentrations based on data from 1999 to 2008, trends based on data from entire individual measuring period for each stream

76

significant negative trends. In addition, the distribution of the C values of the individual streams in each region is presented in Fig. 3. In the following, the estimated trends are discussed for each individual chemical parameter, and possible explanations are given for the observed variation within and between regions. Sulfate SO2− is the dominant anion in many soil solu4 tions and waters. The consequences of SO2− 4 release into the waters are ongoing cation leaching (Reuss and Johnson 1986) from deeply weathered soils—with a high soil SO2− 4 and proton storage

Fig. 3 Range of C observed within each region. Boxes with notches represent range of C (25th to 75th percentiles, with red line at median); QA quotient of acidification; confidence intervals indicate 10th and 90th percentiles; plus signs indicate outliers). Note that all C are included in this figure, regardless of significance


capacity—and thus a delay of acidification reversal of soils and waters (Alewell et al. 2000; Veselý et al. 1998; Kopácek et al. 2002; Prechtel et al. 2001). SO2− concentrations are lowest in region 3 4 and highest in regions 1 and 2 (Table 3), following roughly the German-wide deposition patterns as described by Wellbrock et al. (2005). Generally, the streams with the highest deposition also had the largest observed decrease in stream water SO2− 4 . The largest trends were estimated for the streams in the Ore Mountains of region 1 (−10.6 μmolc l−1 year−1 ). Regions 2 and 4 also showed a distinct SO2− 4 reduction in stream water (−6.17 and −7.81 μmolc l−1 year−1 ). With the


exception of regions 3 and 5—where the deposition level in the past was generally lower—in all regions observed SO2− 4 reductions in the stream water amount to more than −6 μmolc l−1 year−1 (Table 3). By far the largest variation among the individual streams can be observed in region 1 (Fig. 3). Region 3, with the lowest deposition in the past, exhibits both, small concentration trends as well as a small variation between the various streams. Nitrate − As for SO2− 4 , NO3 concentrations are lowest in region 3, whereas regions 4 and 1 show comparatively high concentrations (Table 3). Trends are highly variable within the same region (Fig. 3). In region 1, NO− 3 decrease was largest in catchments with comparatively large proportions of natural grassland and agricultural land (streams no. 3, 5, 8, 9, 12, 14). In the 1970s, parts of these catchments were intensively used by agriculture connected with extensive use of organic and inorganic fertilizers as well as stock breeding. Consequently, NO− 3 concentration in surface water increased dramatically until around 1979/1980 and remained at a high level until 1992/1993 (Pütz et al. 2002). Since then, fertilizer use declined drastically, initiated by federal regulations of Saxony which came into effect in 1994. Another possible explanation for decreasing trends in the Ore Mountains is a higher N uptake as a result of extensive reforestation and the continuous recovery and increasing vitality of damaged forests (Ulrich et al. 2006), which, according to Armbruster et al. (2003), could also explain the large NO− 3 decrease of stream 23 (−8.87 μmolc l−1 year−1 ). Regardless of the landuse impact, the majority of streams in the Ore Mountains and the Fichtelgebirge (region 1) show significant negative trends (Fig. 3). This can be explained by the economic crash in this region which followed the political changes in 1989, and which led to drastically reduced nitrogen deposition. Contrarily to most other streams of region 1, the streams in the Bavarian Forest (nos. 25 to 27) exhibit large increases in NO− 3 concentrations. Since 1993, these catchments have been

77

affected by extensive forest damage due to a severe bark beetle infestation (Alewell et al. 2001). Also, snow break can affect the NO− 3 trends. This could be observed in stream no. 41, which shows among all streams of region 3, the largest NO− 3 increase (+0.84μmolc l−1 year−1 , Appendix), resulting from wide-spread tree damage during the winter season of 1996/1997 (Fink et al. 1999). In region 2, the streams 31–33 in the northwest of the Harz Mountains show decreases, in contrast to the southeast streams. Wright et al. (2001) mentioned for these streams that the decreases are not explained by changes in N deposition, climate or management practices. Chloride By far the lowest concentrations of Cl− were found in region 3. The highest concentrations were observed in region 5, which can be explained by the comparatively low distance to the North Sea and, hence, larger sea-salt input. In regions 1 and 4, increased concentration can neither be explained by the vicinity to the sea nor by primary Cl− content of the bedrock. There, the main factor is Cl− deposition from lignite combustion deposited with fly ashes (Kaufmann and Nussbaumer 1999). Within these regions, chloride trends showed a high degree of heterogeneity, with exception of region 3 (Fig. 3). De-icing salts applied to roads in winter can influence the concentration additionally, as was reported for example for stream no. 23 (Armbruster et al. 2003). Calcium and magnesium Along with the reduction in particulate emissions, base cation deposition has been reduced in many forest ecosystems (Driscoll et al. 1989; Meesenburg et al. 1995). Temporal changes in 2− the leaching of the anions NO− 3 and SO4 have strong effects on cation losses. As a consequence of large reductions in SO2− 4 , concentrations of Ca2+ and Mg2+ in stream water generally decreased in our study. The spatial patterns of Ca2+ and Mg2+ concentrations resemble the deposition patterns, but they are modified by the natural

78

base content of the soil and bedrock substrates. In region 2, the basic cation concentrations are over-proportionally high, because of the slightly more carbonate-rich bedrocks, like greywacke and sandstones. This finding is supported by the critical load values (Table 2), which are about 27 to 51% higher in region 2 than in the other regions. The comparatively high level of basic cations in regions 1 and 4 possibly originates from fly ash resulting from lignite combustion. Large decreases of base cation concentrations are observed in regions 1, 2, and 4 (Fig. 3, Table 3), which experienced a high deposition input in the past. The largest variation among the individual streams can be observed for Ca2+ and Mg2+ in region 1. As for NO− 3 , the bark beetle-affected streams in the Bavarian Forest (streams 25 and 27 in region 1) showed positive trends, contrary to most other streams of this region. Another example for positive Ca2+ and Mg2+ trends can be observed in streams 73 to 81 in region 5, which Westermann (2000) argues to be effected by forest liming with dolomite rich in Mg2+ . Alewell et al. (2001) investigated the base cation flux for six streams in Germany and found increasing net loss of base cations from all ecosystems, which the authors interpreted as an effect of increased soil acidification. QA QA reflects the buffering capacity of the stream water. Values of QA slightly lower than 1 (0.75– 0.96) were observed in all regions expect for region 3, where median QA was 1.08. This indicates that strong mineral acid anions dominate the stream water chemistry. Large maxima of QA values (1.2–2.9) point out that throughout the regions, some streams are well buffered, possibly an buffering effect of deep aquifers. Examination of individual regions reveals a high degree of heterogeneity (Fig. 3). Region 4 shows the largest variation of change in QA from the largest significant negative trend for stream 57 (−0.049 year−1 ) to the largest positive trend for stream 54 (+0.055 year−1 ). Only 44% of the streams show significant trends. Region 1 displays also a high variation of QA trends with dominat-


ing negative trends, all other regions display low variety of QA trends around zero. pH Region 1 comprises the most acidified streams with median pH values of slightly above 5. All other regions have median pH values above 6.5 (Table 3). Since the pH value partly depends on the activity of Al3+ , it follows only roughly the spatial pattern of strong acid anion concentrations (Table 3). The wide-spread pH increase can be explained by reduced concentrations of strong mineral acids (particularly, sulfuric acid) and by reduced concentrations of “cation acids” (Al3+ , Mn2+ ). Without exception, all streams in regions 1 and 2 show increasing trends (Fig. 3), whereas in the other regions negative trends could be observed. The largest increase in stream water pH occurred in region 5 (+0.03 year−1 ). The largest variation among the individual streams can be observed in region 5. Aluminum and manganese Since the leaching of anions is always connected to cation leaching, the ongoing release of SO2− 4 delays a reversal of stream water acidification due to leaching of Al3+ and Mn2+ (Alewell et al. 2001). Al3+ and Mn2+ concentrations are lowest in region 2, and highest in region 1. The spatial patterns of Al3+ and Mn2+ trends resemble the deposition patterns. The decrease of Al3+ and Mn2+ is following from reduced acidity which attenuates dissolution of bedrock minerals, favors precipitation of secondary mineral phases, and alters sorption exchange processes on organic and mineral surfaces (Ulrich et al. 2006). In comparison to Al3+ trends, Mn2+ trends are less pronounced. The largest reductions were observed in region 1 (−2.51 μmolc l−1 year−1 for Al3+ , −0.22 μmolc l−1 year−1 for Mn2+ ; Fig. 3), which received extremely high deposition input in the past. All other regions experienced much less pronounced decreases. A considerable number of streams even show significant positive trends in Al3+ . However, the magnitude of this increase is small compared with the decrease observed


in most other streams. The estimated increase is mainly caused by acid peaks which result from intense rain fall or snow melt and subsequent fast surface runoff. Dissolved organic carbon (DOC) The lowest DOC concentrations are observed in region 4, the highest in region 1. Significant increases in stream water DOC concentrations occurred in most of the analyzed streams in regions 1, 4, and 5 (Table 3, Fig. 3). The variation among the individual streams is largest in regions 1 and 5. Drains of moorland (data not presented) could explain the higher increasing DOC concentrations in region 1 (Ulrich et al. 2006). Westermann (2000) assumed that the microbial activity in most forest soils of the catchments in region 5 has increased during the 1990s due to continuously improving living conditions since the 1980s (decreasing acid deposition, extensive forest liming). Accordingly, the low DOC values at the beginning of the observations could be interpreted as indicator for strongly acidified soil conditions which favored the accumulation of thick raw humus layers on the forest floor, and which in the 1990s slowly started regenerating. Ecological impact of acidification Based on the range and temporal dynamics of pH observations, Braukmann (2001) classified streams into four acidity types from “never acidic” (class I), over “episodically weakly acidic, pre-

Table 4 Percentage of streams per region falling into one of the acidification classes I to IV according to Braukmann (2001) (data from 1999 to 2008)

Region 1 Region 2 Region 3 Region 4 Region 5 Average over all regions %

79

dominantly neutral” (class II), and “periodically critically acidic” (class III) to “permanently acidic to very acidic” (class IV). Table 4 applies this classification to the investigated streams in each region: 31% of all the streams belong to class I, 27% to class II, 11% to class III, and 31% to class IV. In Fig. 4, the pH values are ranked for the individual streams according to the observed median value (observation period 1999 to 2008) and grouped into the four acidity types. Among all investigated streams, median pH values range from 4.3 to 7.7. The boxes in Fig. 4 show the variation in the individual streams within the observation period. Variation in time is minimal in class I and increases to the more acid sensitive streams in classes II, III, and IV. In class IV, the variation in time is again minimal below median pH < 5. The highest pH levels were measured in region 4 (Table 3); consequently, almost all streams (93%) in that region belong to acidification classes I or II (Table 4). Lowest pH levels were observed in region 1; therefore, a high proportion of streams belong to acidification class IV. Braukmann’s classification is a useful indicator for eco-toxidity since the relation between pH and Al3+ concentration is strongly inversely proportional (Fig. 4). For the acidity type IV (pH usually below 5.5), observed Al3+ concentrations are frequently above the drinking water threshold of 0.2 mg l−1 . Al3+ concentrations are usually below this threshold for streams of acidity type III (pH normally below 6.5 and above 5.0). Streams of acidity type I (pH usually well above 6.5) and II (pH usually well above 6.5 with infrequent pH

Number of Acidification class analyzed streams I [%] II [%]

III [%]

IV [%]

26 5 16 15 21 83

4 20 25 7 10 11

62

23 40 25 53 29 31

12 40 25 40 33 27

25 29 31

80


Fig. 4 Distribution of pH, QA (quotient of acidification) and Al3+ concentrations, ranked by mean pH value (data from 1999 to 2008). Each box with notches shows the range (25th to 75th percentiles, with the median) of concen-

trations; dashed lines indicate 10th and 90th percentiles; plus signs indicate outliers. Numbers above uppermost plot and below lowest plot are stream numbers (Appendix and Fig. 1)

declines below 6.0) show low Al3+ concentrations with only occasional exceedance of the drinking water threshold. Unlike Al3+ , QA is only roughly linked to pH since the primary base pool is an independent cause of variation. Therefore, QA is highly variable within and between the different acidity classes (Fig. 4).

the magnitude of deposition change and catchment characteristics. The variation of the individual parameters is in contrast to the other regions lowest in region 3 (Spessart, Black Forest, and Vosges). This region, with the lowest deposition in the past, exhibits both, small concentration trends as well as a small variation between the various streams. Generally, the streams with the highest deposition also had the largest observed changes in trends in stream water. Influencing factors were beneath deposition of sulfur and nitrogen compounds also the specific soils and vegetation, which respond to their reductions. Among them, sulfate desorption, organic S cycling in soils, and factors affecting N cycling in catchments are important. The results indicate widespread recovery from acidification, but more sites show increasing pH

Conclusions The investigated streams in Germany responded in an unexpected rapid way to the reduced anthropogenic deposition since the mid 1980s. The extent of recovery from acidification varies over time, between regions and between sites within regions, depending on a range of factors including


than increasing QA. Indicators of the recovery of stream water acidification were in detail: • • • • •

SO2− 4 concentrations in stream water are decreasing steady in all regions Trends in NO− 3 are highly variable, but slightly decreasing QA and Cl− showed no clear trend Steep increases were observed in pH values Reduction of eco-toxical acidification products Al3+ and Mn2+

The processes responsible for the increased DOC concentrations are complex and not entirely understood (Porcal et al. 2009; Sucker and Krause 2010). Freeman et al. (2001) and Harriman et al. (2003) emphasized that enhanced mineralization through climate change and increasing temperature would result in increased DOC release from forest soils, especially in fens and bogs. Subsoil acidification seems to play a major role since the most positive DOC trends were found in regions 1 (Fig. 3), which are characterized by low critical load values (Table 2) and correspondingly, low pH values. Also, von Wilpert and Zirlewagen (2007) found in heavily acidified areas with podzol soil types that increasing DOC activity in seepage water was caused by the dissolution of organic matter from Bhs horizons through high proton activity in the soil solution. An increase of DOC release from soils caused by soil acidification was also reported by Zech et al. (1994) for the Bavarian Forest. Lorz and Schneider (2003) and Grunewald et al. (2004) hypothesized that soil protective liming would result in DOC mobilization from soils. In the guidelines of liming procedure were organic wet sites explicitly excluded. Hildebrand (1991) and Hildebrand and Schack-Kirchner (2000) identified the process of DOC mobilization in acid forest soils after forest liming to be a “reaction of neutralization where the stronger Fulvo acids remove the weaker carbonic acid from its salt, the carbonate.” However, this reaction is limited to the uppermost soil layers (Hildebrand and Schack-Kirchner 2000) due to the tendency of organic acids for complexation, triggered though multi-valent cations like Ca2+ and Al3+ , which causes polymerization of organic acids in the subsoil and eliminates them from the soil solution. Furthermore, the pH rises along the flow path

81

in the soil to values where fulvo acids are no more dissociated. But the main objection against the hypothesis that soil protective liming would increase DOC release from forest soils on the long run is the fact that this process is limited in time and must finish when the amount of carbonate distributed with liming is exhausted by this neutralization reaction. Another argument against the hypothesis is the fact that DOC is rising in the whole northern hemisphere since the 1990s. Thus, this process is very unlikely to play a key role in the observed long-term increase of DOC release. As a first step of this evaluation, in this paper trend analyses in the water quality of individual streams are presented in a descriptive way, but an attempt is made to identify general patterns of chemical change at a region-wide scale. For explaining the observed trends in water quality, deposition, geology, and streamflow data as well as information on forest management practices (liming) and natural disturbances (storms, bark beetle infestation) are considered in more or less exemplarily terms.

Acknowledgements We would like to thank all persons who provided data to this study, namely from institutes in Bavaria: Büro für Angewandte Hydrologie München, Nationalparkverwaltung Bayerischer Wald, LfU (Bayrisches Landesamt für Umwelt); in Saxony: Landestalsperrenverwaltung des Freistaates Sachsen, Staatsbetrieb Sachsenforst, TU Dresden (Institut für Bodenkunde und Standortslehre); in Saxony-Anhalt: Talsperrenbetrieb Sachsen-Anhalt (AöR); in Thuringia: Thüringer Fernwasserversorgung; in Lower Saxony: Northwest German Forest Research Station; in NorthRhine-Westphalia: WSW Energie & Wasser AG, WAG Nordeifel mbH, Wasserverband Aabachtalsperre; in Rhineland-Palatinate: Stadtwerke Idar-Oberstein, SWT Stadtwerke Trier Versorgungs-GmbH LUWG, Landesamt für Umwelt, Wasserwirtschaft und Gewerbeaufsicht Rheinland-Pfalz; in Baden-Württemberg: Zweckverband Wasserversorgung Kleine Kinzig, LfU (Landesanstalt f. Umweltschutz Baden-Württemberg, Karlsruhe Abt 4 Wasser), LUBW Landesanstalt für Umwelt, Messungen und Naturschutz Baden-Württemberg, Forstliche Versuchs- und Forschungsanstalt BW. We kindly acknowledge also the help of H. Meesenburg (Department of Environmental Control, Forest Research Institute of Lower Saxony), R. Sudbrack (Referat Wassergütebewirtschaftung, Landestalsperrenverwaltung des Freistaates Sachsen), K.-H. Feger (Institute of Soil Science and Site Ecology, Dresden University of Technology) and two anonymous reviewers for substantially improving the manuscript.

82


Appendix Table 5 Streams investigated in trend analysis Stream number

Ore Mountains 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Fichtelgebirge 24 Bavarian Forest 25 26 27 Harz Mountains 28 29 30 31 32 33 Spessart 34 Black Forest 35 36 37 38 39

Stream name/ Measuring point [water reservoir]

Latitude

Longitude

Data from–to

Geology

CORINE 2000 [%] bf/cf/tws/ag/ua

Neudecker Bach [Sosa] Kleine Bockau/Messwehr [Sosa] Querenbach [Stollberg] Seitental 1 [Stollberg] Seitental 2 [Stollberg] Geigenbach/Werda [Werda] Geigenbach/Siehdichfür [Werda] Gimmlitz/Kalkwerk [Lichtenberg] Rauschenbach//Flöha [Rauschenbach] Lampertsbach [Cranzahl] Lampertsbach/Hanggraben Fallwehr [Cranzahl] Moritzbach [Cranzahl] Lautenbach/Klatschmühle [Neunzehnhain 1] Lautenbach/Lautenbach [Neunzehnhain 1I] Gänsebach [Neunzehnhain 1I] Wilzsch [Carlsfeld] Bach Südost [Carlsfeld] Bach Ost [Carlsfeld] Saubach [Muldenberg] Weiße Mulde [Muldenberg] Bach Südost [Muldenberg] Rote Mulde [Muldenberg] Rotherdbach

50◦ 28 50◦ 28 50◦ 42 50◦ 43 50◦ 42 50◦ 27 50◦ 26 50◦ 48 50◦ 42

12◦ 39 12◦ 39 12◦ 48 12◦ 49 12◦ 48 12◦ 20 12◦ 20 13◦ 29 13◦ 32

E E E E E E E E E

1993–2008 1993–2008 1993–2008 1993–2008 1993–2008 1993–2008 2001–2008 1993–2008 1993–2008

Granite Granite Phyllite Phyllite Phyllite Phyllite Phyllite Gneiss Gneiss

0/100/0/0/0 0/100/0/0/0 33/33/0/34/0 0/100/0/0/0 0/89/0/11/0 0/82/0/13/5 0/100/0/0/0 0/56/0/43/1 0/69/15/16/0

50◦ 29 N 50◦ 28 N

13◦ 0 E 12◦ 59 E

1993–2008 1994–2008

Gneiss Gneiss

0/90/8/0/2 0/88/10/0/2

50◦ 29 N 50◦ 44 N

13◦ 0 E 13◦ 9 E

1993–2008 1993–2008

Gneiss Mica schist

0/88/0/10/2 0/80/0/19/0

50◦ 42 N

13◦ 9 E

1993–2008

Mica schist

0/72/1/27/0

50◦ 42 50◦ 25 50◦ 24 50◦ 25 50◦ 24 50◦ 24 50◦ 24 50◦ 24 50◦ 47

13◦ 8 E 12◦ 35 E 12◦ 36 E 12◦ 36 E 12◦ 25 E 12◦ 24 E 12◦ 24 E 12◦ 22 E 13◦ 44 E

1993–2008 1993–2008 1993–2008 1993–2008 1993–2008 1993–2008 1994–2008 1993–2008 1994–2006

Mica schist Granite Granite Granite Phyllite Phyllite Phyllite Phyllite Rhyolite

0/100/0/0/0 0/81/19/0/0 0/87/13/0/0 0/88/12/0/0 0/100/0/0/0 0/97/0/3/0 0/100/0/0/0 0/99/0/0/1 0/100/0/0/0

N N N N N N N N N

N N N N N N N N N

Lehstenbach

50◦ 8 N

11◦ 53 E

1987–2008

Granite

0/100/0/0/0

Markungsgraben Große Ohe/Taferlruck Forellenbach

48◦ 57 N 48◦ 56 N 48◦ 56 N

13◦ 25 E 13◦ 25 E 13◦ 25 E

1987–2007 1980–2008 1990–2007

Granite Granite Granite

24/10/66/0/0 26/46/28/0/0 22/50/28/0/0

Zillierbach Brook/Borntal [Neustadt] Krebsbach [Neustadt] Lange Bramke Dicke Bramke Steile Bramke

51◦ 47 51◦ 34 51◦ 35 51◦ 51 51◦ 51 51◦ 51

10◦ 46 10◦ 52 10◦ 52 10◦ 25 10◦ 26 10◦ 26

1980–1990 1999–2009 1999–2009 1980–2008 1980–2008 1987–2008

Greywacke Greywacke Greywacke Sandstone Sandstone Sandstone

0/99/1/0/0 87/0/0/13/0 62/38/0/0/0 0/100/0/0/0 0/100/0/0/0 0/100/0/0/0

Metzenbach

49◦ 54 N

9◦ 27 E

1987–2008

Sandstone

76/23/0/0/1

Huttenbächle [Kleine Kinzig] Kleine Kinzig [Kleine Kinzig] Teufelsbächle [Kleine Kinzig] Weiherbergbach [Kleine Kinzig] Brook/Conventwald

48◦ 24 N 48◦ 25 N 48◦ 24 N 48◦ 24 N 48◦ 1 N

8◦ 22 8◦ 21 8◦ 21 8◦ 21 7◦ 58

1986–2008 1989–2003 1989–2008 1989–2003 1991–2009

Sandstone Sandstone Sandstone Sandstone Gneiss

0/100/0/0/0 0/100/0/0/0 0/100/0/0/0 0/100/0/0/0 50/50/0/0/0

N N N N N N

E E E E E E

E E E E E


83

Table 5 (continued) Stream number

40 41 42


Latitude

Longitude

Data from–to

Geology


ARINUS Brook S1 [Schluchsee] ARINUS Brook S4 [Schluchsee] ARINUS Brook V1 (Villingen) St.Wilhelmer Talbach Zastlerbach Kaltenbach Dürreychbach Goldersbach Steinbach Itterhof

47◦ 49 N 47◦ 49 N 48◦ 3 N

8◦ 6 E 8◦ 6 E 8◦ 21 E

1987–2007 1989–2007 1988–1997

Granite Granite Sandstone

0/100/0/0/0 0/100/0/0/0 0/100/0/0/0

47◦ 53 47◦ 54 48◦ 37 48◦ 45 47◦ 52 49◦ 26 49◦ 29

N N N N N N N

7◦ 59 E 8◦ 1 E 8◦ 24 E 8◦ 27 E 8◦ 3 E 8◦ 44 E 9◦ 0 E

1987–2008 1993–2008 1986–2008 1987–2008 1986–2008 1988–2008 1989–2005

Gneiss Gneiss Sandstone Sandstone Granite Sandstone Sandstone

14/66/0/19/0 12/83/0/5/0 0/100/0/0/0 0/67/33/0/0 0/99/0/1/0 18/82/0/0/0 56/44/0/0/0

48◦ 12 N

7◦ 11 E

1985–2005

Granite

11/86/0/3/0

50◦ 46 N

10◦ 37 E

1995–2009

Molasse

0/100/0/0/0

50◦ 46 N

10◦ 37 E

1999–2009

Granite

0/94/6/0/0

50◦ 29 N

11◦ 2 E

1995–2009

Argillite

0/82/0/15/3

50◦ 29 N

11◦ 5 E

1999–2009

Sandstone

0/100/0/0/0

50◦ 35 50◦ 45 50◦ 45 50◦ 45 50◦ 45

N N N N N

10◦ 45 10◦ 39 10◦ 39 10◦ 39 10◦ 39

E E E E E

1999–2009 1999–2009 1999–2009 1999–2009 1999–2009

Granite Granite Granite Granite Granite

19/81/0/0/0 0/89/4/7/0 0/91/9/0/0 0/100/0/0/0 0/90/5/5/0

50◦ 34 50◦ 34 50◦ 33 50◦ 34 50◦ 44 50◦ 45

N N N N N N

10◦ 53 10◦ 52 10◦ 54 10◦ 52 10◦ 43 10◦ 41

E E E E E E

1999–2009 1999–2009 1999–2009 1999–2009 1998–2009 1998–2009

Argillite Granite Granite Granite Granite Granite

17/83/0/0/0 13/77/0/6/4 17/59/0/21/3 7/76/0/7/9 0/97/0/0/3 0/100/0/0/0

43 44 45 46 47 48 49 Vosges (France) 50 Strengbach Thuringian Forest 51 Apfelstädt [Tambach-Dietharz] 52 Mittelwassergrund [Tambach-Dietharz] 53 Schwarza/Mündung [Scheibe-Alsbach] 54 Brook/Lager [Scheibe-Alsbach] 55 Finstere Erle [Erletor] 56 Haselbach [Schmalwasser] 57 Schmalwasser [Schmalwasser] 58 Walsbach [Schmalwasser] 59 Schmalwasser/Mündung [Schmalwasser] 60 Gabelbach [Schönbrunn] 61 Schleuse [Schönbrunn] 62 Tannenbach [Schönbrunn] 63 Trenkbach [Schönbrunn] 64 Silbergraben [Ohra] 65 Kernwasser [Ohra] Rheinisches Schiefergebirge 66 Saarscherbach/Stauwurzel [Kall] 67 Murmecke [Aabach] 68 Großer Aabach [Aabach] 69 Karpkebach [Aabach] 70 Kleiner Aabach [Aabach] 71 Riverisbach [Riveris] 72 Thielenbach [Riveris] 73 Ahringsbach 74 Bleidenbach/Mündung 75 Bleidenbach/Oberlauf 76 Ellerbach 77 Fischbach 78 Gräfenbach 79 Idarbach 80 Traunbach/Oberlauf

50◦ 38 N

6◦ 18 E

1998–2009

Phyllite

11/88/0/1/0

51◦ 29 51◦ 29 51◦ 30 51◦ 29 49◦ 42 49◦ 42 49◦ 55 49◦ 39 49◦ 39 49◦ 53 49◦ 48 49◦ 55 49◦ 44 49◦ 43

8◦ 44 E 8◦ 44 E 8◦ 46 E 8◦ 45 E 6◦ 46 E 6◦ 47 E 7◦ 13 E 7◦ 5 E 7◦ 4 E 7◦ 37 E 7◦ 13 E 7◦ 37 E 7◦ 7 E 7◦ 7 E

1986–2008 1986–2008 1986–2008 1986–2008 1992–2009 1992–2009 1983–2009 1984–2009 1982–2009 1982–2009 1982–2009 1982–2009 1982–2009 1982–2009

Greywacke Greywacke Greywacke Greywacke Greywacke Greywacke Argillite Argillite Quarzite Quarzite Quarzite Quarzite Quarzite Quarzite

31/56/0/11/2 21/52/0/24/3 74/26/0/1/0 65/35/0/0/0 53/42/0/2/0 24/51/0/28/1 27/73/0/0/0 55/46/0/0/0 40/60/0/0/0 26/27/51/0/0 11/89/0/0/0 26/67/7/0/0 14/82/4/0/0 30/57/12/0/0

N N N N N N N N N N N N N N

84



81 82 83 84 85 86


Latitude

Longitude

Data from–to

Geology


Traunbach/Börfink Markbach [Kerspe] Waldbach/Denkmal [Steinbach] Waldbach/oberhalb Denkmal [Steinbach] Bach ohne Namen [Steinbach] Steinbach [Steinbach]

49◦ 42 N 51◦ 9 N 49◦ 47 N

7◦ 5 E 7◦ 33 E 7◦ 11 E

1984–2009 1994–2008 1989–2009

Quarzite Clay- and siltstone Quarzite

47/44/5/3/0 8/92/0/0/0 26/64/10/1/0

49◦ 47 N

7◦ 10 E

1991–2009

Quarzite

39/61/0/0/0

49◦ 47 N

7◦ 11 E

1985–2009

Quarzite

20/80/0/0/0

49◦ 47 N

7◦ 11 E

1989–2009

Quarzite

27/73/0/0/0

bf broad-leaved forest, cf coniferous forest, tws transitional woodland shrub; ag agricultural areas and natural grassland, ua urban areas

Table 6 C for all analyzed streams Stream number

Al3+ [μeq l−1 year−1 ]

Ca2+ [μeq l−1 year−1 ]

Cl− [μeq l−1 year−1 ]

DOC [mg l−1 year−1 ]

Mg2+ [μeq l−1 year−1 ]

Mn2+ [μeq l−1 year−1 ]

NO− 3 [μeq l−1 year−1 ]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

−4.65* −7.39* −0.667* −27.6* −18.2* −4.59* −2.25* 0.111* 0.278* −0.048 −0.222 −1.49* 0.043 −0.074 −0.188* −3.50* −4.38* −2.78* −3.34* −4.08* −9.29* −3.15* −20.5* −1.22* 0.185

−12.8* −6.24* −25.0* −25.0* −22.9* −22.0* −10.9* −17.5* −16.6* 3.33* 13.3* −7.07* −4.51* −3.60* −2.83* −9.98* −7.98* −6.65* −18.3* −9.98* −12.8* −8.83* −13.1* −1.75* 1.25* −0.341* 1.29* −4.99

0.000 −0.282* 13.8* −3.02* −3.22* 7.44* 26.3* 0.705 −3.29* −0.403* 0.209 −0.513 3.17* 14.4* −0.769* 0.000 0.353* 0.000 17.3* 6.04* 0.282* 6.85* 15.8* 4.80* −0.353* −0.651* −0.049 2.68* −11.3* −6.35* −0.355* −3.36*

0.122* 0.005 −0.018* −0.012 −0.007 0.150* 0.100 0.030* 0.013 0.005 −0.021 0.150* 0.006 0.003 0.034* 0.274* 0.056* 0.230* 0.135* 0.134* 0.133* 0.178* 0.131* 0.170* 0.034* 0.011 0.067*

−6.17* −3.09* −20.1* −24.7* −22.3* −6.58* 0.000 −7.47* −9.40* 1.03 2.74* −2.19* −2.74* −5.48* −1.37* −3.53* −1.54* −1.37* 0.000 −4.11* −1.03* −2.29* −5.87* −0.686* 0.343* 0.000 0.754* 0.000

−0.567* −0.437* −0.733* −1.97* −1.33* −0.629* −0.582* −0.076* −0.046* 0.055* −0.020 −0.050* −0.036* 0.014 −0.073* −0.312* −0.330* −0.218* 0.319* −0.728* −0.546* −0.659* −0.364* −0.061* 0.000 −0.010* 0.001 −0.455*

−0.592* −7.12*

−0.011* −0.064*

−2.15* −3.00* −14.2* −6.79* −8.44* −1.23* −1.34* −9.14* −9.68* −6.33* −5.36* −7.56* −4.37* −5.03* −3.23* −1.97* −1.61* −1.45* −1.88* −0.538* −1.78* −0.513* −8.87* −1.05* 2.42* 0.878* 5.92* 0.591 8.33 3.23* −0.107 −6.89*

0.049

−0.051* −0.043*

−1.06* −7.30*

−0.125* 0.017 −0.010 0.005

pH [year−1 ]

SO2− 4 [μeq l−1 year−1 ]

QA [year−1 ]

0.053* 0.060* 0.028* 0.018* 0.010* 0.057* 0.020 0.014* 0.021* 0.020* 0.035* 0.061* 0.012* 0.014* 0.018* 0.030* 0.023* 0.028* 0.025* 0.059* 0.021* 0.070* 0.010 0.001 0.000

−22.1* −9.58* −40.2* −59.7* −40.4* −16.0* −13.0* −25.0* −17.4* −2.43* −5.52* −8.07* −13.3* −13.4* −6.02* −9.37* −6.77* −5.44* −15.4* −9.89* −12.9* −10.6* −38.7* −5.21* −1.67* −1.93* −1.37* 0.000 −58.3* −3.82 −1.22* −8.53*

−0.012* −0.005 −0.006* −0.001 −0.005* −0.023* −0.017* 0.006* 0.000 0.027* 0.052* 0.001 0.002 −0.008* −0.006* −0.021* −0.015* −0.013* −0.027* −0.030* −0.011* −0.016* −0.001 −0.003* 0.006* 0.007* −0.024* −0.004

0.012* 0.025* 0.025* 0.075* 0.002 0.022*

−0.001 0.003*


85









pH [year−1 ]


QA [year−1 ]

33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82

0.016* 0.051 0.061* 0.097* 0.088* 0.022 0.000 −0.564* −0.499* −3.55* 0.171* 0.232* 0.056 −0.109 0.375* 0.156* 0.028

−1.67* −0.940* 0.027 2.28* −0.384* 0.157* −1.29* 0.118* 0.009 −0.150 0.000 1.62* 1.54* 1.41* 0.201* 0.376* 0.651 −0.185* 22.0* 8.31 −0.564 2.82 −2.82* −19.0 13.4*

−0.015* 0.017 −0.007* 0.000 0.000 0.010 −0.049* 0.012 0.043* 0.045 0.000 0.010* 0.031* 0.017 0.050* −0.007 −0.038* 0.010 0.020* 0.100* 0.033* −0.020 0.033* 0.067* 0.050* 0.000

−5.49* −0.914* −0.008 0.064* −0.916* 0.000 −1.54* −0.148* 0.486* −0.055 0.000 0.000 0.000 0.000 0.000 0.000 0.000 −0.813* −1.37* −5.48* 0.000 4.11 0.000

−0.010* −0.026* −0.004* −0.003* −0.029* 0.007 −0.024* −0.017* −0.018* −0.082* 0.000 0.000 −0.015 −0.073* 0.000 −0.040* 0.006

−2.37* −0.941* −0.582* −1.45* −2.42* −3.00* −3.49* 0.393* 0.835* 0.029* 0.000 0.000 −0.090* −0.105* 0.000 0.251* 0.450* 0.759* −2.17* 1.61* −0.806 −0.282 3.14* 0.000 0.081 −2.22

−9.11* −4.35* −0.390* −0.416* −1.67* −0.833* −0.780* −1.37* −2.26* −1.04* −0.416* 0.000 −0.892* −0.390* −1.62* −2.08* −1.04 −4.90* −12.2* −7.81 −10.4* −4.16 −8.33* −4.34 −11.2* 0.347 −7.81 −6.25 −9.37* −7.81* −16.0* −8.33* −6.02 −8.33*

−0.436* −0.334* 0.000 −0.908* −2.50* −3.11* −0.882* −6.87* −2.83* −4.14* −1.10* −0.284*

−1.66* −2.66* 4.26 0.554* 0.499* 3.08* 0.000 −3.84* 1.56* 0.499* −0.263 −2.20*

0.017* 0.007* −0.012* 0.011* 0.005 0.029* −0.039* 0.005 0.010* −0.013 −0.014* 0.000 0.000 0.050* 0.023* 0.033* 0.000 0.012* −0.021* −0.011 0.040* 0.050* 0.038* 0.000 0.000 0.000 −0.013 0.067* 0.002 0.000 0.006 0.012* −0.018* 0.045* −0.006* 0.001 0.003 0.003 0.030* 0.006 0.030* 0.057* 0.044* 0.079* 0.033* 0.028* 0.008* 0.044* 0.027* 0.028*

0.003* 0.006* −0.008* −0.003 0.004* 0.023* 0.006* −0.005* 0.005* 0.016 0.013 −0.028* −0.005* 0.004 0.007 0.005* 0.000 −0.001* −0.011* −0.042* 0.018* 0.055

−0.890 −0.334 0.056 0.111 0.371 −0.741 −0.278

−6.03* −1.66* −1.58* 0.000 −2.38* 0.000 −2.84* −0.664* −0.767* −0.389 1.75* 0.624 0.000 1.43* 0.000 1.25* 0.000 −2.88* −4.16* −16.6* −1.25 0.000 −8.32 −16.6* −20.0* −17.5* −18.3 −16.2* −11.6 −25.0 −21.6 −6.24 −4.99 0.000

−3.01* −2.08* −4.82* −1.67* −3.55* −11.7* −3.47* −13.5* −4.53* −8.12* −3.23* −5.21*

−0.005* −0.003*

0.723* −3.34* 0.482 −0.111

−5.08 1.41 2.82 −11.3 8.46 4.70* 0.000 5.64*

0.013 0.033* 0.067* 0.000 0.033* 0.033* 0.117*

0.564* −2.01* 0.040* 0.050* 0.057*

−2.82*

0.063* 0.083* 0.014* 0.097* 0.067* −0.026*

0.000 −0.306*

−5.48* −2.29 0.000 0.000 −2.74 −2.06 −0.514 −8.23* −4.70*

0.000 −1.37* 4.11* 2.06* 2.15* 4.11* 0.686* 0.000 1.94* 1.79* 1.03* 0.235

−0.050* −0.021 0.002 −0.033 0.024 −0.073* −0.059*

−0.023*

1.27 1.61 2.44* 0.403 −1.13* −1.61* 0.346 −4.62* −4.84* −1.32* −1.40* 0.868* 2.82* −0.755* −0.038 0.293* −0.202 0.032 −0.799* 0.403* −0.217* −0.015 13.4*

−0.049*

−0.004 −0.008 −0.030 −0.015 −0.010*

86










pH [year−1 ]


QA [year−1 ]

83 84 85 86

0.209* −1.44* 0.056 0.549*

−0.021* −2.18* −1.64* 1.20*

−1.41 0.504 −7.47* 11.7*

0.230* 0.135 0.308* 0.248*

−2.19* −4.00* −3.81* −0.806*

−0.018* −0.205* −0.028 −0.017

1.01* 2.02* 0.110 0.856*

0.031* 0.125* 0.033* 0.026*

−5.78* −19.1* −15.5* −7.06*

0.001 0.020* −0.007 −0.002*

References Aber, J. D., Nadelhoffer, K. J., Steuder, P., & Melillo, J. M. (1989). Nitrogen saturation in northern forest ecosystems. BioScience, 39, 378–386. Alewell, C., Armbruster, M., Bittersohl, J., Evans, C. D., Meesenburg, H., Moritz, K., et al. (2001). Are there signs of aquatic recovery after two decades of reduced acid deposition in the low mountain ranges of Germany? Hydrology and Earth System Science, 5, 367–378. Alewell, C., Manderscheid, B., Bittersohl, J., & Meesenburg, H. (2000). Is acidification still an ecological threat? Nature, 407, 856–857. Armbruster, M. (1998). Zeitliche Dynamik der Wasserund Elementflüsse in Waldökosystemen. Freiburger Bodenkundliche Abhandlungen, 38, 1–301. Armbruster, M., Abiy, M., & Feger, K. H. (2003). The biogeochemistry of two forested catchments in the Black Forest and the eastern Ore Mountains (Germany) Effects of changing atmospheric inputs on soil solution and streamwater chemistry. Biogeochemistry, 65, 341– 368. Baker, J. P., & Schofield, C. L. (1982). Aluminum toxicity to fish in acidic waters. Water Air Soil Pollution, 18, 289–309. Beudert, B., & Klöcking, B. (2007). Große Ohe: Impact of bark beetle infestation on the water and matter budget of a forested catchment. In: H. Puhlmann, & R. Schwarze (Eds.), Forest hydrology – Results of research in Germany and Russia (pp. 41–63). Koblenz: Part I. IHP-HWRP-Berichte, H. 6. Bihl, C. (2004). Erschließung und Einsatz mineralischer Sekundärrohstof fe im Bodenschutz im Wald. Deutsche Nationalbiliothek. Braukmann, U. (2001). Stream acidification in South Germany – Chemical and biological assessment methods and trends. Aquatic Ecology, 35, 207–232. Burkey, J. (2009). Mann-Kendall Tau-b with Sen’s Method (enhanced). Available online at http://www. mathworks.in/matlabcentral/fileexchange/11190. Accessed on 19 July 2010. Davies, J. J. L., Jenkins, A., Monteith, D. T., Evans, C. D., & Cooper, D. M. (2005). Trends in surface water chemistry of acidified UK Freshwaters, 1988–2002. Environmental Pollution, 137, 27–39.

Diefenbach-Fries, H., & Beudert, B. (2007). Report on national ICP IM activities in Germany. Fifteen years of monitoring in the Forellenbach area—Using mass balances, bioindication, and modelling approaches to detect air pollution effects in a rapidly changing ecosystem: Main results. In: S. Kleemola, & M. Forsius (Eds.), 16th Annual Report 2007 (pp. 63– 81). UNECE ICP Integrated Monitoring. The Finnish Environment 26/2007, Finnish Environment Institute, Helsinki. Dise, N., Matzner, E., & Gundersen, P. (1998). Nitrogen status of European forest ecosystems. Water Air Soil Pollution, 105(1/2), 143–154. Ditsche-Kuru, P. (2003). Wald in Wasserschutzgebieten von Trinkwassertalsperren - Zusammenfassung und Auswertung der ATT-Untersuchungsprogramme (p. 110) (unpublished report). Driscoll, C. T., Bisogni, J. J., & Schofield, C. L. (1980). Effect of aluminum speciation on fish in dilute acidified waters. Nature, 248, 161–164. Driscoll, C. T., Likens, G. E., Hedin, L. O., Eaton, J. S., & Borman, F. H. (1989). Changes in the chemistry of surface waters. Environmental Science & Technology, 23, 137–143. Evans, C. D., Cullen, J. M., Alewell, C., Marchetto, A., Moldan, F., Kopácek, J., et al. (2001a). Recovery from acidification in European surface waters. Hydrology and Earth System Science, 5, 283–297. Evans, C. D., Harriman, R., Monteith, D. T., & Jenkins, A. (2001b). Assessing the suitability of acid neutralising capacity as a measure of long-term trends in acidic waters based on two parallel datasets. Water Air Soil Pollution, 130, 1541–1546. Feger, K. H., Martin, D., & Zöttl, H. W. (1995). Entwicklung der Gewässerazidität im Schwarzwald - sind depositionsbedingte Veränderungen erkennbar? Die Naturwissenschaften, 82, 420–423. Fink, S., Feger, K. H., Gülpen, M., Armbruster, M., & Lorenz, K. (1999). Magnesium-Mangelvergilbung an Fichte – Einfluss von frühsommerlicher Trockenheit und. Dolomit-Kalkung. FZKA-BWPLUSBerichtsreihe 25. Available online at http://bwplus.fzk. de/berichte/SBer/PEF197001SBer.pdf. Accessed on 26 July 2010. Freeman, C., Evans, C. D., Monteith, D. T., Reynolds, B., & Fenner, N. (2001). Export of organic carbon from peat soils. Nature, 412, 785.

Environ Monit Assess (2011) 174:65–89 Gäth, S., & Frede, H. G. (1991). Einfluss der Landnutzungsform auf die Nitratbelastung des Grundwassers im Osthessischen Bergland. Mitteilungen Deutsche Bodenkundliche Gesellschaft, 66, 943–946. Gauger, T., Haenel, H.-D., Rösemann, C., Dämmgen, U., Bleeker, A., Erisman, J. W., et al. (2008). National Implementation of the UNECE Convention on Longrange Transboundary Air Pollution (Effects) / Nationale Umsetzung UNECE-Luftreinhaltekonvention (Wirkungen): Part 1: Deposition Loads: Methods, modelling and mapping results, trends. BMU/UBA 204 63 252. UBA-Texte 38/08 (1). ISSN 1862-4804. Gilbert, R. O. (1987). Statistical methods for environmental pollution monitoring. New York: Van Nostrand Reinhold Inc. Gray, N. F. (2008). Drinking water quality: Problems and solutions (2nd ed.). Cambridge University Press. Grunewald, K., Scheithauer, J., Böhm, A., & Pavlik, D. (2004). Einzugsgebietsbewirtschaftung von Trinkwassertalsperren im Erzgebirge unter dem Aspekt veränderter Huminstoffeinträge. In: Bronstert, et al. (Ed.), Forum für Hydrologie und Wasserbewirtschaftung, Heft 05, Band 1, München. (pp. 265–272). Harriman, R., Watt, A. W., Christie, A. E. G., Moore, D. W., McCartney, A. G., & Taylor, E. M. (2003). Quantifying the effects of forestry practices on the recovery of upland streams and lochs from acidification. The Science of the Total Environment, 310, 101–111. Hegg, C., Jeisy, M., & Waldner, P. (2004). Wald und Trinkwasser, Eine Literaturstudie. Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, WSL, Birmensdorf. Hildebrand, E. E. (1991). The influence of forest site fertilisation on soil solution chemistry. Berichtsband des Seminars des ECE/FAO/ILOGemeinschaftsausschusses vom 26.-30.6.1990 in München, über Schonung und Verbesserung des Bodens als Grundlage nachhaltiger Forstwirtschaft (pp. 193–204). Bonn MELF. Hildebrand, E. E., & Schack-Kirchner, H. (2000). Initial effects of lime and rock powder application on soil solution chemistry in a dystric cambisol - Results of model experiments. Nutrient Cycling in Agroecosystems, 56, 69–78. Hirsch, R. M., & Slack, J. R. (1984). A nonparametric trend test for seasonal data with serial dependence. Water Resouces Research, 20(6), 727–732. Jordi, B. (2005). Der Waldboden – ein optimaler Filter. WSL – Umwelt. Kaufmann, H., & Nussbaumer, T. (1999). Bildung und Eigenschaften von Chlorverbindungen bei Verbrennung biogener Brennstoffe. Gefahrstof fe – Reinhaltung der Luft, 59(7/8), 267–272. Kopácek, J., Hejzlar, J., Stuchlík, E., Fott, J., & Vesely’, J. (1998). Reversibility of acidification of mountain lakes after reduction in nitrogen and sulphur emissions in Central Europe. Limnology Oceanography, 43, 357– 361. Kopácek, J., Stuchlík, E., Veselý, J., Schaumburg, J., Anderson, I., Fott, J., et al. (2002). Hysteresis in re-

87 versal of Central European mountain lakes from atmospheric acidification. Water Air Soil Pollution, 2, 91–114, Focus. Körner, J. (1996). Abflussbildung, Interflow und Stoffbilanz im Schönbuch Waldgebiet (206 pp.). Institut und Museum für Geologie und Paläontologie der Universität Tübingen. Kreutzer, K. (1994). Folgerungen aus der Höglwaldforschung. AFZ-Der Wald, 14, 769–774. Lenz, R. J. M., Müller, A., & Erhard, M. (1994). Veränderungen der Säureneutralisationskapazität nordostbayerischer Wälder. Forstarchiv, 65, 172–182. Lorz, C., Hruška, J., & Krám, P. (2003). Modeling and monitoring of long-term acidification in an upland catchment of the Western Ore Mountains, SE Germany, SE-Germany. The Science of the Total Environment, 310, 153–161. Lorz, C., & Schneider, B. (2003). Regenerierung eines versauerten Fließgewässers im Oberen Westerzgebirge. In: Freiburger Forstliche Forschung, Heft 49, 137–151. Majer, V., Cosby, B. J., Kopacek, J., & Veselý, J. (2003). Modelling reversibility of Central European mountain lakes from acidification: Part I – The Bohemian forest. Hydrology and Earth System Science, 7, 494– 509. Meesenburg, H., Eichhorn, J., & Meiwes, K. J. (2009). Atmospheric deposition and canopy interaction. In: R. Brumme, & P. K. Khanna (Eds.), Functioning and management of European beech ecosystems. Ecological Studies (pp. 265–302). Berlin: Springer. Meesenburg, H., Meiwes, K. J., & Rademacher, P. (1995). Long term trends in atmospheric deposition and seepage output in northwest German forest ecosystems. Water Air Soil Pollution, 85, 611–616. Meesenburg, H., Meiwes, K. J., Wagner, M., & Prenzel, J. (2001). Ecosystem effects after ameliorative liming of a catchment at the Harz mountains, Germany. In: W. J. Horst, et al. (Eds.), Plant nutrition - Food security and sustainability of agro-ecosystem (pp. 914–915). Netherlands: Kluver Academic Publishers. Moritz, K., & Bittersohl, J. (2000). Turnover of nitrogen and acidfication in the small headwater catchment Markungsgraben. Silva Gabreta, 4, 63–70. OHGE (2010). Observatoire Hydro-Géochimique de l’Environnement. Available online at http://ohge. u-strasbg.fr/index.html. Accessed on 18 July 2010. Porcal, P., Koprivnjak, J. F., Molot, L. A., & Dillon, P. J. (2009). Humic substances-part 7: The biogeochemistry of dissolved organic carbon and its interactions with climate change. Environmental Science and Pollution Research, 16, 714–726. Prechtel, A., Alewell, C., Armbruster, M., Bittersohl, J., Cullen, J. M., Evans, C. D., et al. (2001). Response of sulphur dynamics in European catchments to decreasing sulphate deposition. Hydrology and Earth System Science, 5(3), 311–325. Pütz, K., Reichelt, P., Sudbrack, R., & Friemel, M. (2002). Nitratbericht Sächsischer Trinkwassertalsperren – Bericht der Landestalsperrenverwaltung des Freistaates Sachsen zur Belastung der sächsischen

88 Talsperren mit Nitrat bis zum Jahre 2002. Pirna (pp. 55). Available online at http://www. smul.sachsen.de/de/wu/organisation/staatsbetriebe/ltv/ downloads/nitratbericht2002_b.pdf. Accessed on 27 July 2010. Raben, G., Andreae, H., & Meyer-Heisig, M. (2000). Longterm acid load and its consequences in forest ecosystems of Saxony (Germany). Water Air Soil Pollution, 122, 93–103. Reuss, J. O., & Johnson, D. W. (1986). Acid deposition and the acidification of soils and waters. Ecological Studies, 59, 199. New York: Springer. Rhode, H., Grenfelt, P., Wisniewski, J., Ågren, C., Bengtsson, G., Hultberg, H., et al. (1995). Acid Reign’ 95? In Conference summary statement from the 5th international conference on acidic deposition. Science and policy (pp. 1–15). Göteborg: Kluwer Academic Publishers. Schaumburg, J., Maetze, A., Lehmann, R., & Coring, E. (2010). Monitoringprogramm für versauerte Gewässer durch Luftschadstoffe in der Bundesrepublik Deutschland im Rahmen der ECE - Bericht der Jahre 2007–2008; Bayerisches Landesamt für Umwelt im Auftrag des Umweltbundesamtes, München (134 p.). Schaumburg, J., Kifinger, B., Lehmann, R., Maetze, A., Coring, E., Baltzer, S., et al. (2008). Monitoringprogramm für versauerte Gewässer durch Luftschadstoffe in der Bundesrepublik Deutschland im Rahmen der ECE. Bericht der Jahre 2005–2006, Bayerisches Landesamt für Umwelt im Auftrag des Umweltbundesamtes, München (235 p). Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 63, 1379–1389. Skjelkvale, B. L., Borg, H., Hindar, A., & Wilander, A. (2007). Large scale patterns of chemical recovery in lakes in Norway and Sweden: Importance of seasalt episodes and changes in dissolved organic carbon. Applied Geochemistry, 22(6), 1174–1180. Skjelkvale, B. L., Stoddard, J. L., Jeffers, J. N. R., Tørseth, K., Høgasen, T., Bowman, J., et al. (2005). Regional scale evidence for improvements in surface water chemistry 1990–2001. Environmental Pollution, 137, 165–176. Stoddard, J. L., Jeffries, D. S., Lükewille, A., Clair, T. A., Dillon, P. J., Driscoll, C. T., et al. (1999). Regional trends in aquatic recovery from acidification in North America and Europe. Nature, 401, 575–578. Sucker, C., & Krause, K. (2010). Increasing dissolved organic carbon concentrations in freshwaters: What is the actual driver? iForest 3: 106–108. Available online at http://www.sisef.it/iforest/show.php?id=546. Accessed on 25 July 2010. Sucker, C., Puhlmann, H., Zirlewagen, D., Wilpert, K. V., & Feger, K. H. (2009). Bodenschutzkalkungen in Wäldern zur Verbesserung der Wasserqualität – Vergleichende Untersuchungen auf Einzugsgebietsebene. Hydrologie und Wasserbewirtschaftung, 4, 250–262. UBA (= Umweltbundesamt) (2000). Bestimmung und Kartierung der Critical Loads &

Environ Monit Assess (2011) 174:65–89 Levels für Deutschland. Available online at http://www.umweltbundesamt.de/umweltbeobachtung/ uid/mapping/karte_clmax_s.htm. Accessed on 25 January 2010. UBA (= Umweltbundesamt) (Ed.) (2009). National trend tables for the German atmospheric emission reporting 1990–2008. Available online at http:// www.umweltbundesamt.de/emissionen/archiv/EM_ Entwicklung_in_D_Trendtabelle_LUFT_v2.1.2_EUSubmission_2010.xls.zip. Accessed on 6 July 2010. Ulrich, K. U., Paul, L., & Meybohm, A. (2006). Response of drinking-water reservoir ecosystems to decreased acidic atmospheric deposition in SE Germany: Trends of chemical reversal. Environmental Pollution, 141, 42–53. UNECE (2009). The condition of forests in Europe, 2009 executive report, ICP forests and European commission, Hamburg and Brussels. Available online at http://www.icp-forests.org/pdf/ER2005.pdf. Accessed on 6 July 2010. Veselý, J., Hruška, J., Norton, S. A., & Johnson, C. E. (1998). Trends in the chemistry of acidified Bohemian lakes from 1984 to 1995: I. Major solutes. Water Air Soil Pollution, 108, 107–127. Veselý, J., Majer, V., & Norton, S. A. (2002). Heterogeneous response of central European streams to decreased acidic atmospheric deposition. Environmental Pollution, 120, 275–281. von Wilpert, K. (2007). Chemical deposition and seepage water quality in forests. In: H. Puhlmann, & R. Schwarze (Eds.), Forest hydrology – Results of research in Germany and Russia (pp. 23–36). Koblenz: Part I. IHP-HWRP-Berichte, H. 6. von Wilpert, K., & Puhlmann, H. (2007). Conventwald: Silvicultural management of seepage water quality. In: H. Puhlmann, & R. Schwarze (Eds.), Forest hydrology – results of research in Germany and Russia (pp. 63–90). Koblenz: Part I. IHP-HWRP-Berichte, H. 6. von Wilpert, K., Schäffer, J., Holzmann, S., Meining, S., Zirlewagen, D., & Augustin, N. (2010). Was Waldzustandserfassung und Forstliche Umweltüberwachung bewirkt haben – Ableitung eines langfristigen Kalkungsprogramms. AFZDerWald, 3, 20–25. von Wilpert, K., & Zirlewagen, D. (2007). Forestry Management options to maintain sustainability – Element budgets at Level II sites in South – West Germany. In: J. Eichhorn (Ed.), Forests in a changing environment – Results of 20 years ICP forests monitoring (Vol. 142, pp. 170–179). Schriften aus der Forstlichen Fakultät Universität Göttingen. Wellbrock, N., Riek, W., & Wolff, B. (2005). Characterisation of and changes in the atmospheric deposition situation in German forest ecosystems using multivariate statistics. European Journal Forest Research, 124, 261– 271. Westermann, F. (2000). Versauerung von Fließgewässern in Rheinland-Pfalz. Untersuchungen von Bachoberläufen im Hunsrück 1983–1999 - Entwicklungen und Trends (113 pp.). Landesamt für Wasserwirtschaft Bericht 206/00. Mainz.

Environ Monit Assess (2011) 174:65–89 Wolff, B., & Riek, W. (1998). Chemischer Waldbodenzustand in Deutschland, Ergebnisse der Bodenanalysen im Rahmen der BZE. Allgemeine Forst-Zeitschrift, Der Wald, 53(10), 503–506. Wright, R. F., Alewell, C., Cullen, J., Evans, C. D., Marchetto, A., Moldan, F., et al. (2001). Trends in nitrogen deposition and leaching in acid-sensitive streams in Europe. Hydrology and Earth System Science, 5, 299–310.

89 Zech, W., Guggenberger, G., & Schulten, H. R. (1994). Budgets and chemistry of dissolved organic carbon in forest soils: Effects of anthropogenic soil acidification. The Science of the Total Environment, 152, 49– 62. Zirlewagen, D., & von Wilpert, K. (2002). Was hat Waldbau mit Trinkwasservorsorge zu tun? Schriftenreihe Freiburger Forstliche Forschung, 18, 309– 319.

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