10 yrs of Improved Groundwater Table Estimates in Northern Peatlands Through Assimilation of Passive Microwave Observations into PEATCLSM

Abstract

<p>Groundwater table depth and peat moisture, exert a first order control on a range of biogeochemical and -physical peatland processes, and the susceptibility to peat fires. Therefore, one of the first critical measures to identify “peatlands under pressure” is the change of hydrological conditions, e.g. due to changing climatic conditions or direct “hydraulic” human influence. In this presentation, we introduce a new opportunity for the global-scale monitoring of moisture conditions in peatlands. 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 2019. We compare 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 (PEATCLSM, Bechtold et al. 2019). The assimilation system uses a spatially distributed ensemble Kalman filter to update soil moisture and groundwater table depth. The simulation experiments are evaluated against an in-situ dataset of groundwater table depth in about 20 natural and semi-natural peatlands that are large enough to be dominant in the corresponding 81-km<sup>2</sup> model grid cells. For PEATCLSM, Tb data assimilation increases 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. Time series comparison at monitoring sites demonstrates how the assimilation effectively corrects for remaining deficiencies in model physics and/or errors of the global meteorological data forcing the model. The generally lower coefficients of 0.30 (R) and 0.09 (aR) for the default CLSM also improve after Tb data assimilation to values of 0.39 (R) and 0.28 (aR). However, even with Tb data assimilation, the skill of CLSM remains inferior to that of PEATCLSM. The more realistic model physics of PEATCLSM are also supported by a reduction of the Tb misfits (observed Tb – forecasted Tb) over 94 % of the Northern peatland area. The temporal variance of Tb misfits is reduced by 20 % on average and is 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 estimation of the peatland hydrological dynamics by the assimilation of SMOS L-band brightness data into a global land surface model and suggests a new route of research focusing on the incorporation of additional satellite observations into peatland-specific modeling schemes.</p><p>Bechtold, M., De Lannoy, G.J M., Koster, R.D., Reichle, R.H., et al. (2019). PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 11 (7), 2130-2162. doi: 10.1029/2018MS001574.</p>

Drought and Waterlogging Stress Regimes in Northern Peatlands Detected Through Satellite Retrieved Solar‐Induced Chlorophyll Fluorescence

“…TROPOMI SIF retrieval products are available for download from Koehler and Frankenberg (2020), CERES PAR data from NASA/LARC/SD/ASDC (2017), and modeled peatland WTD data from Bechtold et al (2023). In situ GPP data is available at databases indicated in Table S1 in Supporting Information S1.…”