H017-02
Leveraging soil moisture assimilation to improve land surface seasonal dynamics in permafrost regions

Monday, 7 December 2020: 10:34
Virtual
Akhilesh Sivaraman Nair1, J. Indu1,2, Olga Makarieva3 and Nataliia Nesterova4, (1)Indian Institute of Technology Bombay, Department of Civil Engineering, Mumbai, India, (2)Indian Institute of Technology Bombay, Interdisciplinary Programme in Climate Studies, Mumbai, India, (3)St. Petersburg State University, Melnikov Permafrost Institute, Yakutsk, Russia, (4)St. Petersburg State University, Hydrograph Model Research Group, Yakutsk, Russia
Recorded Presentation
Abstract:
Soil moisture (SM) is a crucial component of the land surface model (LSM), having strong influence on the exchange of water and energy fluxes between land and atmosphere. SM simulation over permafrost region remains challenging owing to its high heterogeneity. Even though satellite observations provide reliable SM estimates, its resulting improvement on LSM simulations in permafrost region is unknown. This work explores the ability of satellite SM assimilation to improve LSM seasonal dynamics using blended SM from the European Space Agency’s Climate Change Initiative (ESA CCI). Present study uses Noah LSM model owing to its ability to reflect the impact of soil moisture on the soil thermodynamics and surface energy balance [Nair and Indu, 2016; 2019]. The SM observation is assimilated using the Ensemble Kalman Filter (EnKF) approach over the Iya River basin (in South-Eastern Siberia, Russia), which has a catchment area of 14500 sq. m. As the region is affected by permafrost, assimilation is carried out only during the summer season (June to August). Results from our study indicate the potential of assimilation in improving the soil temperature estimates during summer and autumn (September to October). Assimilation reduces the dry bias in Noah LSM which is particularly evident in the north region of the Iya basin. The results further indicate strong coupling between SM and surface energy balance that lets assimilation reduce the sensible heat flux over the northern region of the river basin.

References:

Nair, A. S., and Indu, J., 2019. "Improvement of land surface model simulations over India via data assimilation of satellite-based soil moisture products", Journal of Hydrology, 573, 406-421

Nair, A.S., Indu, J., 2016. Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture. Remote Sensing. doi:10.3390/rs8120976

Acknowledgement: To conduct this work, authors acknowledge the support from the BRICS project to “Detect and predict the impacts of climate change on the flow regimes of rivers originated from plateaus in Asia” .

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