Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions - Champagne, Catherine; White, Jenelle; Berg, Aaron A.; Bélair, Stéphane; Carrera, Marco L. (Remote Sensing, 2019)
Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X2 = 89 on Day of Year 129) and then the RDPS (peak X2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.

Warm Season Evaluation of Soil Moisture Prediction in the Soil, Vegetation, and Snow (SVS) Scheme - Alavi, Nasim; Bélair, Stéphane; Fortin, Vincent; Zhang, Shunli; Husain, Syed Zahid; Carrera, Marco L.; Abrahamowicz, Maria (Journal of Hydrometeorology, 2016)
AbstractA new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in highe...

Assimilation of SMAP brightness temperatures in environment and climate change Canada's new land surface parameterization scheme - Carrera, Marco L.; Belair, Stephane; Bilodeau, Bernard; Abrahamowicz, Maria; Alavi, Nasim; Russell, Albert; Wang, Xihong (IGARSS 2017 - 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017)

The Multibudget Soil, Vegetation, and Snow (SVS) Scheme for Land Surface Parameterization: Offline Warm Season Evaluation - Husain, Syed Zahid; Alavi, Nasim; Bélair, Stéphane; Carrera, Marco L.; Zhang, Shunli; Fortin, Vincent; Abrahamowicz, Maria; Gauthier, Nathalie (Journal of Hydrometeorology, 2016)
AbstractA new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conduc...

Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil - Dabboor, Mohammed; Sun, Lili; Sun, Leqiang; Carrera, Marco L.; Friesen, Matthew; Merzouki, Amine; McNairn, Heather; Powers, Jarrett; Bélair, Stéphane (Water, 2019)
Soil moisture is a key variable in Earth systems, controlling the exchange of water and energy between land and atmosphere. Thus, understanding its spatiotemporal distribution and variability is important. Environment and Climate Change Canada (ECCC) has developed a new land surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS land surface scheme features sophisticated parameterizations of hydrological processes, including water transport through the soil. It has been shown to provide more accurate simulations of the temporal and 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, we simulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme over an experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps were produced between May and November 2015. Simulated soil moisture values were compared with estimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture and Agri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic Aperture Radar (SAR) imagery. Statistical analysis of the results showed an overall promising performance of 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 a function of the soil texture. The SVS model tends to perform slightly better over coarser textured soils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulated SVS 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. The Root Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113 over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. The unbiased 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 moisture always higher than 0.90.

Spaceborne L-Band Radiometry in Environment and Climate Change Canada (ECCC)’S Numerical Analysis and Prediction Systems - Belair, Stephane; Carrera, Marco L.; Abrahamowicz, Maria; Alavi, Nasim; Badawy, Bakr; Shahabadi, Maziar Bani; Bilodeau, Bernard; Charpentier, Dorothee; Deacu, Daniel; Durnford, Dorothy; Gaborit, Etienne; Gasset, Nicolas (IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019)