MILLENNIA model:

Peatlands in the UK have mostly been 'growing' for the last 6-8k years, since after the ice-age. Basically, plants take up carbon from the atmosphere through photosynthesis and put this carbon into plant biomass (leaves and roots). However, plants or parts of their biomass die annually, decomposing according to their litter quality and the environmental conditions (temperature and moisture). Therefore annual litter cohorts are laid down, mostly within the layer above the mean water table (acrotelm), and as plant matter decays only very slowly under wet and cold conditions in the uplands, the undecomposed organic matter is 'locked away' over time as the carbon enters the saturated zone (catotelm) - as long as conditions do not change (i.e. high water tables and low temperatures). Consequently, over millennia organic matter accumulates, leading to peat formation. The MILLENNIA model (Heinemeyer et al., 2010) captures this long-term peat accumulation in relation to variable (i.e. Holocene) climate data, estimates of mean annual temperature (MAT) and mean annual precipitation (MAP), and resulting changes water table and corresponding vegetation dynamics over time. The model can switch from long-term build up during the Holocene to recent predictions using available climate data from actual locations. The model has been validated and successfully compared to other UK peatland models in Clark et al. (2010) who pointed out several advantages of this model over others (mainly the dynamic (and total) peat depth, dynamic water table and vegetation feedbacks). We have since further developed the monthly model in collaboration with M. Carroll on his UKPopNet cranefly work (i.e. Carroll et al., 2015). This model performs well in comparison to site hydrological data (see previous figure), a prerequisite for modelling accurate carbon dynamics. However, the winter months still show some discrepancies to site data as there is no modelled snow representation. However, annual mean temperatures also seem crucial for key decomposers in peatlands, enchytraeids (see Briones et al., 2007), which will be subjected to climate change alongside other biota crucial for bird populations, such as crane flies (Carroll et al., 2011). We will therefore run the MILLENNIA model with climate change scenarios to determine likely impacts on such key species.

Importantly, the field flux measurements and the empirical models of carbon uptake (photosynthesis) and release (respiration) in connection with root exclusion will allow comparing field based estimates of net primary productivity (NPP) to predictions by the MILLENNIA model based on the real site climate data. Currently, the MILLENNIA model derives net carbon inputs (NPP) based on a simple empirical relationship to actual evapotranspiration (AET). This will offer an important validation for the MILLENNIA model and enable comparison of the simple versus a more process level driven calculation of NPP (considering light conditions and vegetation specific NPP estimates). A further opportunity will be the model inclusion of available vegetation and treatment specific water flux data. This will enable an estimation of the treatment impact on the water balance and discharge (flow patterns) in connection with runoff relationships to water table dynamics and vegetation. We will, over time, attempt to achieve this goal in a combination of GIS and the MILLENNIA model, also as part of the up-scaling attempts to larger regions (making use of the variance estimates from the ‘between site’ comparison to provide an uncertainty measure on model input parameters and processes).

Above: the MILLENNIA model, considering past climate (during the Holocene - in the UK in general about 6,000-8,000 years before present), dynamic vegetation (litter quality) and water table as affected by climate and rooting depth (evapotranspiration), topography and drainage .

Below: preliminary modeled water table depth (WTD) data compared to site measurements from the three sites (note the good overall agreement, but lower WTD at the Nidderdale and Whitendale sites - likely due to site specific drainage - STDEV ~4 cm) v.1 (left) unadjusted model runs; v.2 (right) adjusted model runs, increasing bedrock drainage (modeled via hydraulic conductivity and specific yield in the bedrock) at Nidderdale and Whitendale.

Below Figures: We did a comparison of modeled versus paleao-ecological water table depth (WTD) reconstructions (Heinemeyer & Swindles, 2018) for Moor House (NNR), a well-studied upland blanket bog site in the UK, which revealed a very good agreement between the two methods (Figure 4 & 5); not only did the WTD predictions agree over an about 100 year time period, they also agreed with the available actual water table measurements over the past ~20 years (ECN monitoring; data citation code: ‘ECN:AH2/12 & ECN: AH2/14’), and the model allowed comparing water table depth for an unmanaged (wet) and a grouse moor managed scenario (dry) including maintenance of drainage ditches and burn rotations (Figure 6). Most interesting was the period between 1840s and 1940s, an unexpectedly drier period based on TA predicted hydrological conditions compared to the wetter conditions predicted by the MILLENNIA model based on climate only (Figure 6). Moor House was a formal shooting estate throughout exactly this period (i.e. 1842-1951), based on grouse bags and predator control information (ECN data provided by Rob Rose from the Centre for Ecology and Hydrology (CEH) Lancaster, personal communication). Therefore, burning in connection with drainage seems to be the most likely factor explaining the water-table lowering:

Most importantly, the model allowed us to explore the grouse moor management impacts of drainage and burning (at Moor House since about the 1850s). We included drainage impacts with a cycle of natural infilling and maintenance and burning reducing net primary productivity (NPP) as part of a burn management cycle (i.e. natural regrowth over time). Please see the actual paper for more information (Heinemeyer & Swindles, 2018). Importantly, the model enables making predictions of changes in carbon accumulation, C fluxes and GHG emissions (CO₂ & CH₄ emissions) for both scenarios (i.e. unmanaged 'natural' vs. managed 'grouse moor'):