4.7 Article

Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework

期刊

REMOTE SENSING OF ENVIRONMENT
卷 246, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2020.111805

关键词

Wetlands; Ensemble Kalman filter; Land surface model; PEATCLSM; Radiative transfer modeling; Peatland hydrology; Groundwater table depth; Microtopography; Boreal zone; Microwave remote sensing; Soil moisture; Organic soil

资金

  1. Alexander von Humboldt Foundation
  2. Research Foundation Flanders [FWO1512817N]
  3. NASA Modeling, Analysis, and Prediction program
  4. NASA Department of Energy, Office of Science
  5. Ameriflux Network Management Project Support for UW ChEAS Cluster
  6. Global Institute for Water Security (University of Saskatchewan)
  7. BMUIKI project Restoring Peatlands and Applying Concepts for Sustainable Management in Belarus

向作者/读者索取更多资源

There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.

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