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

2020 ◽  
Vol 12 (20) ◽  
pp. 3405 ◽  
Author(s):  
Manoj K. Nambiar ◽  
Jaison Thomas Ambadan ◽  
Tracy Rowlandson ◽  
Paul Bartlett ◽  
Erica Tetlock ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Hydrological Simulation ◽  
Near Surface ◽  
Land Surface Scheme ◽  
Brightness Temperatures ◽  
Canadian Land Surface Scheme ◽  
Ocean Salinity ◽  
Surface Scheme

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.

scholarly journals The Effect of Soil and Vegetation Parameters in the ECMWF Land Surface Scheme

2004 ◽  
Vol 5 (6) ◽  
pp. 1131-1146 ◽  
Author(s):  
H. Richter ◽  
A. W. Western ◽  
F. H. S. Chiew
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Soil Parameters ◽  
Model Parameters ◽  
Lower Boundary Condition ◽  
Land Surface Scheme ◽  
Vegetation Parameters ◽  
Surface Scheme

Abstract Numerical Weather Prediction (NWP) and climate models are sensitive to evapotranspiration at the land surface. This sensitivity requires the prediction of realistic surface moisture and heat fluxes by land surface models that provide the lower boundary condition for the atmospheric models. This paper compares simulations of a stand-alone version of the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface scheme, or the Viterbo and Beljaars scheme (VB95), with various soil and vegetation parameter sets against soil moisture observations across the Murrumbidgee River catchment in southeast Australia. The study is, in part, motivated by the adoption of VB95 as the operational land surface scheme by the Australian Bureau of Meteorology in 1999. VB95 can model the temporal fluctuations in soil moisture, and therefore the moisture fluxes, fairly realistically. The monthly model latent heat flux is also fairly insensitive to soil or vegetation parameters. The VB95 soil moisture is sensitive to the soil and, to a lesser degree, the vegetation parameters. The model exhibits a significant (generally wet) bias in the absolute soil moisture that varies spatially. The use of the best Australia-wide available soils and vegetation information did not improve VB95 simulations consistently, compared with the original model parameters. Comparisons of model and observed soil moistures revealed that more realistic soil parameters are needed to reduce the model soil moisture bias. Given currently available continent-wide soils parameters, any initialization of soil moisture with observed values would likely result in significant flux errors. The soil moisture bias could be largely eliminated by using soil parameters that were derived directly from the actual soil moisture observations. Such parameters, however, are only available at very few point locations.

scholarly journals Sensitivity tests of the Canadian Land Surface Scheme (CLASS) for Arctic tundra

1997 ◽  
Vol 25 ◽  
pp. 46-50 ◽  
Author(s):  
Jeffrey S. Tilley ◽  
William L. Chapman ◽  
Wanli Wu
Keyword(s):  
Land Surface ◽  
Initial Conditions ◽  
Soil Depth ◽  
Arctic Tundra ◽  
Systems Science ◽  
Initial Water ◽  
Land Surface Scheme ◽  
Internal Parameters ◽  
Canadian Land Surface Scheme ◽  
Surface Scheme

We have conducted tests of the Canadian Land Surface Scheme (CLASS V2.5) for Arctic tundra applications. Our tests emphasize sensitivities to initial conditions, external forcings and internal parameters, and focus on the Alaskan North Slope during the summer of 1992. Observational data from the National Science foundation (NSF), Arctic Systems Science (ARCSS), Land/Atmosphere/Ice Interactions (LAII) Flux Study is available to serve as forcing and validation for our simulations.Comparisons of the runs show strong sensitivities to the composition and depth of the soil layers, and we find that a minimum total soil depth of 5.0 m is needed to maintain permafrost. The response of the soil to diurnal variations in forcing is strong, while sensitivities to other internal parameters, as well as to precipitation, were relatively small. Some sensitivity to air temperatures and radiative fluxes, particularly the incoming shortwave flux, was also present. Significant sensitivity to the specification of the initial water and ice contents of the soil was found, while the sensitivity to initial soil temperature was somewhat less.

scholarly journals Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

Parametrization of Peatland Hydraulic Properties for the Canadian Land Surface Scheme

2019 ◽  
pp. 93-105
Author(s):  
Matthew G. Letts ◽  
Nigel T. Roulet ◽  
Neil T. Comer ◽  
Michael R. Skarupa ◽  
Diana L. Verseghy
Keyword(s):  
Land Surface ◽  
Hydraulic Properties ◽  
Land Surface Scheme ◽  
Canadian Land Surface Scheme ◽  
Surface Scheme

scholarly journals Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 542 ◽  
Author(s):  
Mohammed Dabboor ◽  
Leqiang Sun ◽  
Marco Carrera ◽  
Matthew Friesen ◽  
Amine Merzouki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Soil Texture ◽  
Land Surface ◽  
Goodness Of Fit ◽  
Retrieval Algorithm ◽  
Surface Model ◽  
Land Surface Scheme ◽  
Sar Imagery ◽  
Surface Scheme

Soil moisture is a key variable in Earth systems, controlling the exchange of water andenergy between land and atmosphere. Thus, understanding its spatiotemporal distribution andvariability is important. Environment and Climate Change Canada (ECCC) has developed a newland surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS landsurface scheme features sophisticated parameterizations of hydrological processes, including watertransport through the soil. It has been shown to provide more accurate simulations of the temporaland spatial distribution of soil moisture compared to the current operational land surface scheme.Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, wesimulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme overan experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps wereproduced between May and November 2015. Simulated soil moisture values were compared withestimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture andAgri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic ApertureRadar (SAR) imagery. Statistical analysis of the results showed an overall promising performanceof the SVS land surface scheme in simulating soil moisture values at high resolution scale.Investigation of the SVS output was conducted both independently of the soil texture, and as afunction of the soil texture. The SVS model tends to perform slightly better over coarser texturedsoils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulatedSVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. TheRoot Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. Theunbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore,results show an Index of Agreement (IA) between the simulated and the derived soil moisturealways higher than 0.90.

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