Abstract:
Vast areas of the boreal zone are covered by peatlands, i.e., wetlands with highly-organic surface soil layers. There is an urgent need to include peatland-specific processes and relevant remote sensing data in Earth system models to better understand the changing land-atmosphere interactions over Northern peatlands and the impact of the changing climate on the hydrology and peat carbon stock. In this study, we assimilate L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) into the Catchment land surface model (CLSM) to improve the simulation of Northern peatland hydrology from 2010 through 2018. We compared four simulation experiments: two open loop and two data assimilation simulations, either using the default CLSM or a recently-developed peatland-specific adaptation of it (PEAT-CLSM). The assimilation system uses a spatially distributed ensemble Kalman filter to update soil moisture and groundwater table depth. The simulation experiments were evaluated against an in-situ dataset of groundwater table depth in about 20 natural and semi-natural peatlands that were large enough to be representative for the corresponding 81-km2 model grid cells. We further compared the filter diagnostics of the two different assimilation runs. For PEAT-CLSM, Tb data assimilation increased the temporal Pearson correlation (R) and anomaly correlation (aR) between simulated and measured groundwater table from 0.53 and 0.38 (open-loop) to 0.58 and 0.45 (analysis), respectively. The generally lower coefficients of 0.30 (R) and 0.09 (aR) for the default CLSM also improved after Tb data assimilation to values of 0.39 (R) and 0.28 (aR). However, even with Tb data assimilation, the skill of CLSM remained inferior to that of PEAT-CLSM. The superiority of the PEAT-CLSM was also supported by a reduction of the temporal variance of the innovations (observed Tb – forecasted Tb) over 94 % of the Northern peatland area. The variance reduction was 20 % on average and largest over the large peatland areas of the Western Siberian (25 %) and Hudson Bay Lowlands (40 %). This study demonstrates, for the first time, an improved description of the peatland hydrological dynamics by the assimilation of SMOS L-band brightness data into a global land surface model.