Supplementary 1: Selected COUP Parameters and their ranges used in the Monte Carlo runs used for calibrating upland (U transect). This was adapted to riparian soil (R transect) to evaluate if similar parameter ranges could depict soil temperature processes between the two profiles. The parameters denoted as stars represent COUP parameters whose values fell outside the Monte Carlo ranges to effectively calibrate R transect. For example, *1 denotes 0.1m while *2 denotes 0.5 oC-1. Model validity was tested with available data in 2007 in R transect.Module / Parameter / Definition / Process / Range / Units
pack / CritDepthSnowCover*1 / Thickness of snow height / Lower boundary / 0.3678 – 0.6145 / m
DensityCoefMass / Mass coefficient of snow density / Shrinking / 0.5753 – 0.9589 / 1/m
DensityOfNewSnow / Density of new snow / Shrinking / 90 - 150 / Kg/m3
MeltCoefAirTemp / Snow melt air temperature function / Snow melting / 1.5 – 2.5 / Kg/oC m2day
MeltCoefGlobRad / Snow melt global radiation function / Snow melting / 2.45e-08 - 4.09e-08 / Kg/J
SThermalCondCoef / Thermal conductivity of snow / Heat convection / 1.88e-06 – 3.12e-06 / Wm5/oCkg2
Soil Thermal / Organic C1 / Organic soil type / Heat conduction / 1.14e-2 – 1.90e-2 / Nil
Organic C2 / Organic soil type / Heat conduction / 7.50e-04 – 1.20e-3 / Nil
HeatProdCoef_B*2 / Heat production coefficient / Heat source / 0.0723 – 0.1205 / 1/oC
Supplementary 2: Bias correction of regional climate model
Data used for climate modelling was based on local meteorological data from Svartberget and ENSEMBLES data archive (Vander Linden and Mitchell, 2009). The control run was defined as temperature and precipitation series from 1981-2010 and future runs as 2061-2090. In this study, we utilized 15 regional climate models (RCMs) that were driven by different global climate models (GCMs). Each RCM was based on Special Reports on Emission scenario A1B and has a resolution of 25 km2, an area exceeding the size of our study catchment. This led to disagreement between the local observations and RCM outputs (also referred to as biases) and as a result, RCM values are not used directly as model inputs for climate impact assessment unless subjected to post processing (bias correction). It is worthwhile to note that the greenhouse gas scenario (based on Special Report on Emission Scenarios, SRES) presented here is old and has been replaced with Representative Concentration Pathway (RCP) based scenarios. However, we are using the former in this study as a follow up to compare the result with our earlier study that utilized this SRES scenario data (Jungqvist et al. 2014). More detailed information about the bias correction method described here can be found in our earlier studies (Oni et al. 2014, 2015, 2016; Teutschbein et al. 2015).
Underlying RCMs for the bias corrected site-specific climate scenarios, their notation, driving GCMs, emission scenarios and performing institutes.No. / Notation / Institute / RCM / Resolution / Driving GCM / Emission scenario
1 / C41_HAD / C4I / RCA3 / 25 km / HadCM3Q16 / A1B
2 / DMI_ARP / DMI / HIRHAM5 / 25 km / ARPEGE / A1B
3 / DMI_BXM / DMI / HIRHAM5 / 25 km / BCM / A1B
4 / DMI_ECH / DMI / HIRHAM5 / 25 km / ECHAM5 / A1B
5 / ETHZ / ETHZ / CLM / 25 km / HadCM3Q0 / A1B
6 / HC_HAD0 / HC / HadRM3Q0 / 25 km / HadCM3Q0 / A1B
7 / HC_HAD3 / HC / HadRM3Q16 / 25 km / HadCM3Q16 / A1B
8 / HC_HAD16 / HC / HadRM3Q3 / 25 km / HadCM3Q3 / A1B
9 / KNMI / KNMI / RACMO / 25 km / ECHAM5 / A1B
10 / MPI / MPI / REMO / 25 km / ECHAM5 / A1B
11 / SMHI_BCM / SMHI / RCA / 25 km / BCM / A1B
12 / SMHI_ECH / SMHI / RCA / 25 km / ECHAM5 / A1B
13 / SMHI_HAD / SMHI / RCA / 25 km / HadCM3Q3 / A1B
14 / CNRM / CNRM / Aladin / 25 km / ARPEGE / A1B
15 / ICTP / ICTP / RegCM / 25 km / ECHAM5 / A1B
Supplementary 3: Monthly mean precipitation (a) and temperature (b) for the control period 1981–2010 as simulated by 15 individual Regional Climate Models (RCMs) (blue/red thin curves). Observations (black circles) and the RCM-ensemble means (blue/red thick curve) are also displayed. Seasonal changes in precipitation (c) and temperature (d) as projected by the ensemble median of 15 RCMs from the control run 1981-2010 (light blue/red) to the future scenario 2061-2090 (dark blue/red). Observations (1981-2010) are shown as black circles.
Supplementary 4: COUP model validation to independent dataset in 2007 in Riparian transect, showing credibility of the model in simulating soil temperature under present day condition. No data was available during this period in Upland soil transect.
Supplementary 5: Seasonal assessments of COUP model calibration to upper, middle and lower soil temperature series across a gradient of riparian (R; column 1) and upland (U; column 2) soils. In R transect; upper, middle and bottom layer represent 10cm, 20cm and 60cm respectively while these represent 12cm, 20cm and 50cm respectively in U transect.
Supplementary 6: Comparison of simple soil temperature model of Jungkvist et al. (2014) applied to middle layer of upland soil in nearby Gammtratten catchment with middle soil layer based on complex soil temperature model presented in this study. Both models were based on the same ensemble of 15 RCM runs. The whiskers show the annual standard deviation.
Supplementary 7: COUP model calibration to the uppermost 6cm in the upland (U) and 5cm in the riparian soil (R) layers. This shows the uncertainty in simulating surface soil temperature due to stronger influence of air temperature.
Jungqvist, G., Oni, S. K., Teutschbein, C., & Futter, M. N. (2014). Effect of climate change on soil temperature in Swedish boreal forests. PloS one, 9(4), e93957.
Oni, S., Futter, M., Ledesma, J., Teutschbein, C., Buttle, J., & Laudon, H. (2016). Using dry and wet year hydroclimatic extremes to guide future hydrologic projections. Hydrology and Earth System Sciences, 20(7), 2811-2825.
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Oni, S. K., Tiwari, T., Ledesma, J. L., Ågren, A. M., Teutschbein, C., Schelker, J., . . . Futter, M. N. (2015). Local‐and landscape‐scale impacts of clear‐cuts and climate change on surface water dissolved organic carbon in boreal forests. Journal of Geophysical Research: Biogeosciences, 120(11), 2402-2426
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