Hyperresolution Land Surface Modeling in the Context of SMAP Cal–Val

Abstract

This study explores the performance of Environment Canada’s Surface Prediction System (SPS) in comparison to in situ observations from the Brightwater Creek soil moisture observation network with respect to soil moisture and soil temperature. To do so, SPS is run at hyperresolution (100 m) over a small domain in southern Saskatchewan (Canada) during the summer of 2014. It is shown that with initial conditions and surface condition forcings based on observations, SPS can simulate soil moisture and soil temperature evolution over time with high accuracy (mean bias of 0.01 m3 m−3 and −0.52°C, respectively). However, the modeled spatial variability is generally much weaker than observed. This is likely related to the model’s use of uniform soil texture, the lack of small-scale orography, as well as a predefined crop growth cycle in SPS. Nonetheless, the spatial averages of simulated soil conditions over the domain are very similar to those observed, suggesting that both are representative of large-scale conditions. Thus, in the context of the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) project, this study shows that both simulated and in situ observations can be upscaled to allow future comparison with upcoming satellite data.

Regional scale spatial and temporal variability of soil moisture in a prairie region

“…For example, Garnaud et al . () attempted to model the observed spatial variably in soil moisture over this domain by using a land surface scheme run at ultra‐fine resolution (100 m).…”

“…It is anticipated that these results can establish accuracy metrics for land surface modelling or can be applied in soil moisture upscaling or downscaling applications (Crow et al, 2012). For example, Garnaud et al (2016) attempted to model the observed spatial variably in soil moisture over this domain by using a land surface scheme run at ultrafine resolution (100 m).…”

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, 11, x 3 of 23 comparative analysis, we investigate the level of agreement between the simulated and estimated soil moisture with respect to the evolution and the variability in time and space of soil moisture. Thus, the present study is complementary to and expands upon the study by Garnaud et al [25], though it focuses on the capability of the SVS model to capture the spatial variability of the soil moisture, in contrary to the study by Garnaud et al [25] which mainly focused on the feasibility of the SVS model to capture the soil moisture evolution over time. An experimental site located southwest of Winnipeg, Manitoba, Canada, was selected for the purpose of our study.…”

“…A good agreement between the simulated and observed soil moisture was reported. However, a visual interpretation of the results showed that the modeled soil moisture spatial variability was much weaker than the observed [25]. In another study [13], the performance of an updated version of the SVS model using in situ soil moisture observations was evaluated in a study area which was located within our current experimental site.…”

“…A first evaluation of the performance of the SVS model in simulating soil moisture at high resolution was presented in [25]. In that study, soil moisture was simulated over an experimental site in Manitoba, Canada, using the SVS model for the entire summer of 2014 and the simulation results were compared with in situ soil moisture observations.…”