Materials and methods
Site description
Langtjern is a forested, boreal lake catchment in southeast Norway
(Figure 1) (4.8 km2; 510–750 m.a.s.l; 60.371 N, 9.727
E) where monitoring of water chemistry started in 1972 under the
national monitoring programs for air pollution effects on surface waters
(Lund et al. 2018; Vogt & Skancke 2022). The catchment includes a lake
(0.23 km2) and its land cover is dominated by forest
(75% forested with low productivity Scots pine forest and 5% of
productive Norway spruce forest), located on shallow mineral soils, and
peatland (20%). Since the 1950s, when some small-scale forest harvest
was conducted, the catchment has been free from direct human
disturbances. The geology consists of till generated from felsic
gneisses and granites (de Wit et al. 2018). Mean annual temperature,
precipitation, and discharge (1974-2022) are 2.6°C, 834 mm, and 634 mm,
respectively.
Data sources
Streamwater discharge and chemistry
Annual values for discharge and flow-weighted streamwater chemistry
(1974–2022) were derived from the routine monitoring conducted by NIVA
as part of the Norwegian national environmental monitoring programmes
(Vogt & Skancke 2022). Water level is monitored continuously by a weir
at the outlet and converted to discharge using standardized
stage-discharge relationships. Streamwater samples are collected weekly
at lake outlet and analysed for major chemical components (pH, labile
Al, alkalinity), major anions (SO4, Cl,
NO3), major cations (Ca, Mg, Na, K) and TOC at NIVA.
Analytical methods have evolved since monitoring began in 1973 and
currently entail automated ion-chromatography. In 1986, monitoring of
TOC started. Acid Neutralizing Capacity (ANC) is calculated as the sum
of base cations minus the sum of strong acidic anions. ANC corrected for
organic acidity, ANCoaa (Lydersen et al. 2004), is calculated as ANC +
1/3*site density of TOC, where site density is -10.2 µeq/mg C. The
charge balance of the streamwater chemistry was estimated as the
difference between the equivalent sum of all major cations and major
anions, and the anion deficit was attributed to organic acidity (OA).
Charge density (CD) of TOC was calculated as the anion deficit divided
by TOC. For NH4+ and
F-, no full time series were available, but earlier
data show that their concentrations are low and that they largely
outweigh each other in terms of equivalent concentrations. Labile Al was
assumed to have charge density of 2+ while the
contribution of bicarbonate was set at zero, an acceptable assumption at
pH below 5.5.
Element fluxes at the catchment outlet were calculated for each day
using measured water discharges at the sampling point multiplied by the
solute concentration interpolated from the weekly samples. The daily
calculated data were then aggregated to annual fluxes of each element.
Soil element pools
Soils were sampled with the purpose to estimate catchment soil pools of
exchangeable cations, and following the same sampling design, in 1983
(Reuss 1990), 1991 (Stuanes et al. 1995) and 2001 (SFT 2001) and
reported in Larssen (2005). Standard soil depth for Langtjern was
estimated to be 40 cm, including the O horizon. Catchment exchangeable
base cation soil pools were calculated by multiplying bulk density with
layer depth and soil base cation concentration, where the B horizon was
assumed to extend to 40 cm soil depth. Only mineral forest soils were
sampled, and the soil base cation stores reported in in Larssen (2005)
only represent forest soils.
Atmospheric deposition
Total annual deposition inputs (the sum of wet and dry deposition of
SO4, NH4, NO3, Cl and
major cations) to MAGIC for the period 1850-2100 are derived from air
quality monitoring at the station Brekkebygda (390 masl.,
60.29o N, 9.76o E, 7 km south of
Langtjern) for 1974-2022, a hindcast for 1860-1973 and projections for
2023-2100. The air quality and deposition monitoring for 1974-2022,
resulting in annual wet deposition, follows analytical methods, quality
control and flux calculations are described in Aas et al. (2022). The
mass balance approach incorporated in the MAGIC model requires
adjustment of the wet deposition data, since the deposition station data
are not entirely representative for the catchment: dry deposition is not
measured, and precipitation amounts may differ from the catchment given
the lower elevation of the deposition station. The adjustment is done in
two steps. First, dry deposition is estimated using Cl as a conservative
tracer, assuming that all Cl originates from sea salt aerosols. For the
1974–2022, the ratio of inputs of Cl (wet deposition) to outputs
(streamwater flux of Cl) was 0.898, where the difference
(1-0.102 ) is assumed to be from dry deposition. The dry
deposition of Cl is assumed to be associated with dry deposition of
cations and marine sulphate (mSO4) following their
relative proportions in seawater (as given by the following molar
elemental ratios: Ca/Cl : 0.037, Mg/Cl : 0.196, Na/Cl : 0.856, K/Cl :
0.018, SO4/Cl: 0.103). Second, we assume that long term
catchment outputs of S (in streamwater) equal anthropogenic
(SO4*) and marine inputs (mSO4) to the
catchment, resulting in the scaling factor 0.98 for anthropogenic
SO4 deposition. Historical (1860-1973) and future
(2023-2100) deposition of sea salts and base cations was assumed to
equal averaged total deposition over 1974-2022. For
SO4*, NH4 and NO3,
historical deposition was estimated from the modelled deposition for the
Langtjern grid square by EMEP based on historical emissions of air
pollutants in Europe (Schopp et al. 2003), and scaled to Langtjern using
the ratio of the long term averaged EMEP deposition of the grid square
and Langtjern deposition, estimated as described above. Future
deposition of SO4*, NH4 and
NO3 was assumed to follow the European emissions of the
CLRTAP current legislation scenario (CLE) for the Langtjern grid square
forward to the year 2050 and then held constant to the year 2100.The scenarios were supplied by the Coordination Centre for Effects of
the CLRTAP. Future deposition was scaled to Langtjern deposition
1974-2022 data to follow the same relative trends as in the CLE grid
square.
Climate
Weather data, including daily average temperature (tm, ̵ͦC), daily
precipitation (rr, mm) and evaporation (gwb_eva, mm), was downloaded
using NVE’s Grid Time Series (GTS) API. The catchment area of Lake
Langtjern was first delineated based on a 10×10 m digital elevation
model for Norway (https://www.geonorge.no/, DTM 10 Terrengmodell
(UTM33)) as described by de Wit et al. (2023). Weather data were then
downloaded for each 1x1km grid (partially or entirely) overlapping with
the catchment area and then area-weighted averaged to one value for the
catchment. Projected 90% confidence interval of changes in temperature,
precipitation, and evaporation for scenario RCP8.5 for 2071-2100
relative to reference period 1971-2000 were taken from Hanssen-Bauer et
al. (2015) to generate climate data over 2023-2100. Temperature (delta
T), precipitation (delta P) and discharge (delta Q) increases from
1971-2000 to 2071-2100 were +3.0°C to +5.6°C (median: +4.2°C), +8% to
+29% (median: +15%) and -2% to +16% (median: +8%), respectively. A
locally adapted scenario RCP8.5* was derived by adding delta T, P and Q
to 2023 based on the difference between averaged T, P and Q at Langtjern
for 2000-2022 and the reference period 1971-2000.
Model description and calibration
MAGIC (Model of Acidification of Groundwater In Catchments) is a
process-oriented semi-distributed mass balance model for biochemical
processes involving ions and nutrients at catchment scale (Cosby et al.
2001; Cosby et al. 1985), originally used to predict air pollution
effects on surface waters and further developed to also include effects
of land use and climate change. This setup of MAGIC includes two
connected compartments, e.g., soil and surface water (stream or lake),
where the soil runoff is sent to the surface water compartment. The
solutes (SO4, NO3, NH4,
Cl, Ca, Mg, Na, and K) concentrations are computed as the mass balance
between atmospheric deposition, bedrock weathering, retention (for N
species) and export through runoff. A new version of MAGIC, MAGIC-Forest
(Norling et al. this issue) with modules for hydrology, forest growth,
soil carbon accumulation and SO4-dependent organic
matter solubility, has been implemented in the open-source Mobius
modelling framework (Norling et al. 2021). The Mobius framework has a
modern graphical user interface and scripting for interaction with
models, allowing for user-friendly advanced auto-calibration and
sensitivity analysis.
In the current MAGIC application, we start from the calibration
conducted by Larssen (2005) which described streamwater chemistry
responses to reductions in S deposition during the period 1974–2003. We
included monitoring data for the subsequent period 2004–2022. New
features in MAGIC used in the current application are i) the
SO4-solubility control of TOC
(\(c_{\text{OA}}=c_{OA,0}-c_{\text{SO}_{4}}f_{\text{SO}_{4}})\)background concentrations of organic acids reduced with factor
fSO4 multiplied with SO4) (Norling et
al. this issue) and ii) a factor to change weathering rates (only used
in a sensitivity analysis;\(w_{y}=w_{0}(1+max(0,\left(y-y_{0}\right){)\alpha}_{w})\), i.e.
the rate stays constant at \(w_{0}\) until year \(y_{0}\), and increases
by a linearly with slope \(\alpha_{w}\) after that). Table SI 1
describes which parameters are fixed and which are calibrated. Soil
characteristics are based on soil element pools as described earlier.
The model was calibrated manually, and optimization of the model
parameters (Table SI 1) was done by comparing observed and modelled
annual FWM concentrations of ANC, H+, labile Al,
SO4, NO3, Cl, Ca and Mg and organic
acids. During calibration, we also ensured that other modelled variables
were showing an acceptable fit to observations such as total and
cation-specific soil base saturation and FWM concentrations of
Aln+, Na and K. The best set of parameters was
selected using expert judgement anchored in an evaluation of combined
performance metrics: the Nash-Sutcliffe Efficiency (NSE), the root-mean
square error (RMSE), the coefficient of determination
(r2) and the bias (summation of difference between
model and empirical estimate, divided by nr of estimates), in addition
to comparison with soil base saturation. The automated optimization
implemented in Mobius resulted in similar model performance as the
manual calibration for base cation weathering rates, but manual
optimization was chosen over automated since the automated optimization
did not result in converging parameter values.
Scenarios
Scenarios for future climate and deposition always used CLE for future
deposition. The scenarios are referenced as ConstantClimate (CLE
deposition and no climate change), RCP8.5 (CLE scenario and RCP8.5) and
RCP8.5* (CLE scenario and RCP8.5*).