Peatlands represent globally significant soil carbon stores that have been accumulating for millennia under water-logged conditions. However, deepening water-table depths (WTD) from climate change or human-induced drainage could stimulate decomposition resulting in peatlands turning from carbon sinks to carbon sources. Contemporary WTD ranges of testate amoebae (TA) are commonly used to predict past WTD in peatlands using quantitative transfer function models.

This study is the first to compare TA-based WTD reconstructions to instrumentally-monitored WTD and hydrological model predictions using the MILLENNIA peatland model in order to examine past peatland responses to climate change and land management. Although there was very good agreement between monitored and modelled WTD, TA-reconstructed water table was consistently deeper. Predictions from a larger European TA transfer function data set were wetter, but the overall directional fit to observed WTD was better for a TA transfer function based on data from northern England. A regression-based offset correction was applied to the reconstructed WTD for the validation period (1931-2010). WTD was then predicted using available climate records as MILLENNIA model input and compared the offset-corrected TA reconstruction to MILLENNIA WTD predictions over an extended period (1750-1931) with available climate reconstructions. Although the comparison revealed striking similarities in predicted overall WTD patterns, particularly for a recent drier period (1965-1995), there were clear periods when TA-based WTD predictions underestimated (i.e. drier during 1830-1930) and overestimated (i.e. wetter during 1760-1830) past WTD compared to MILLENNIA model predictions. Importantly, simulated grouse moor management scenarios may explain the drier TA WTD predictions, resulting in considerable model predicted carbon losses and reduced methane emissions, mainly due to drainage.

This study demonstrates the value of a site-specific and combined data-model validation step towards using TA-derived moisture conditions to understand past climate-driven peatland development and carbon budgets alongside modelling likely management impacts.