Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme

Manoj K Nambiar,Aaron A Berg,Erica Tetlock,Jaison Thomas Ambadan,Tracy Rowlandson,Paul Bartlett

doi:10.3390/rs12203405

Manoj K Nambiar, Aaron A Berg + Show 4 more

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https://doi.org/10.3390/rs12203405

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Abstract

Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land surface component of the Canadian Earth System Model (CESM), and the Canadian Seasonal and Interannual Prediction System (CanSIPS). Our results indicated that assimilating the SMOS products improved the soil moisture simulation skill of the CLASS. Near surface soil moisture assimilation also resulted in improved forecasts of root zone soil moisture (RZSM) values. Although both techniques resulted in improved forecasts of RZSM, assimilation of TB resulted in the superior estimates.

Highlights

Soil moisture (SM) is one of the most important variables affecting the land surface water and energy budgets
In both the assimilation approaches, we can see that the skill reduces in the beginning and shows improvement through time; that is because the system takes time to reach a steady state
Numerical weather prediction and seasonal to interannual hydrometeorological forecasts rely on the accuracy of the land surface initialization, in particular soil moisture and snow

Summary

Introduction

Soil moisture (SM) is one of the most important variables affecting the land surface water and energy budgets. It is one of the major components that impacts the terrestrial water, energy and biogeochemical cycles by constraining the evapotranspiration from land [1,2,3] It is involved in a series of feedback processes at different scales from local to global and is one of the key initial condition variables for climate and weather prediction models [4]. It impacts the ground water recharge and river outflow by controlling the division of rainfall into infiltration, percolation and runoff in a basin [5,6]. Accurate spatial and temporal estimation of SM is essential in many fields such as agriculture, ecology, water resource management, flood and drought forecasting as well as predicting the impacts of future climate change [11,12]

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