This paper describes a versatile stochastic daily weather generator (WeaGETS) for producing daily precipitation, maximum and minimum temperatures (Tmax and Tmin). The performance of WeaGETS is demonstrated with respect to the generation of precipitation, Tmax and Tmin for two Canadian meteorological stations. The results show that the widely used first-order Markov model is adequate for producing precipitation occurrence, but it underestimates the longest dry spell for dry station. The higher-order models have positive effects. The gamma distribution is consistently better than the exponential distribution at generating precipitation quantity. The conditional scheme is good at simulating Tmax and Tmin. The spectral correction approach built in WeaGETS successfully preserves the observed low-frequency variability and autocorrelation functions of precipitation and temperatures.
Knowledge of the moisture content of soil is valuable for hydrology and climate studies, as well as for yield prediction or agricultural planning. As part of the long-term research plan at the Canada Centre for Remote Sensing (CCRS) to establish a relationship between radar backscatter and the spatial and temporal variations of soil moisture, a series of experiments were conducted in the Canadian prairies in 1988.This paper examines radar backscatter as a function of soil moisture, plant type, and phenological development. Airborne data were acquired by the CCRS C-band SAR of a test site near Outlook, Saskatchewan, in June and August 1988. The digitally recorded and processed imagery were externally calibrated via point targets of known radar cross-section. Soil dielectric measurements were collected using a portable dielectric probe in fields with similar surface roughness characteristics. These measurements were used as input to a model developed by CCRS for estimating soil volumetric water content.This paper describes the development of relationships between soil moisture under wheat and canola canopies and radar backscatter. The relationships were developed using the relatively calibrated SAR, the estimate of soil volumetric water content derived from soil dielectric measurements, plant type, and phenological development. The analysis indicated a strong correlation between radar backscatter and volumetric soil moisture under both wheat and canola canopies and that the relationship is dependent on crop type and phenological development.
Abstract. Lakes are important sources of freshwater and provide essential ecosystem services. Monitoring their spatial and temporal variability, and their functions, is an important task within the development of sustainable water management strategies. The Surface Water and Ocean Topography (SWOT) mission will provide continuous information on the dynamics of continental (rivers, lakes, wetlands and reservoirs) and ocean water bodies. This work aims to contribute to the international effort evaluating the SWOT satellite (2022 launch) performance for water balance assessment over large lakes (e.g., >100 km2). For this purpose, a hydrodynamic model was set up over Mamawi Lake, Canada, and different wind scenarios on lake hydrodynamics were simulated. The derived water surface elevations (WSEs) were compared to synthetic elevations produced by the Jet Propulsion Laboratory (JPL) SWOT high resolution (SWOT-HR) simulator. Moreover, water storages and net flows were retrieved from different possible SWOT orbital configurations and synthetic gauge measurements. In general, a good agreement was found between the WSE simulated from the model and those mimicked by the SWOT-HR simulator. Depending on the wind scenario, errors ranged between approximately −2 and 5 cm for mean error and from 30 to 70 cm root mean square error. Low spatial coverage of the lake was found to generate important biases in the retrievals of water volume or net flow between two satellite passes in the presence of local heterogeneities in WSE. However, the precision of retrievals was found to increase as spatial coverage increases, becoming more reliable than the retrievals from three synthetic gauges when spatial coverage approaches 100 %, demonstrating the capabilities of the future SWOT mission in monitoring dynamic WSE for large lakes across Canada.
The future Surface Water and Ocean Topography (SWOT) satellite mission will provide images of surface water topography for inland water bodies and oceans. Over land, water surface elevation will be retrieved at 10 cm accuracy for water bodies with areas > 250 m x 250 m and rivers with widths > 100 m, when averaging over 1 km2. Studies have shown that the Ka-band used by SWOTs main payload can be affected by aquatic and emergent riparian vegetation, which in turn could influence SWOT capacity to correctly observe water extent. The current study investigates effects of aquatic and emergent riparian vegetation on SWOT water extent and water surface elevation (WSE) detection capabilities through the use of NASA/JPLs SWOT simulator (HR). Data from the AirSWOT airborne campaign over Mamawi Lake (163 km2) in the Peace-Athabasca Delta (PAD; Alberta, Canada), are used to establish a land cover classification and backscattering values for simulation inputs. Simulation results have shown that aquatic vegetation has a negligible effect on the SWOT signal. Yet, simulations showed that water extent misclassification can occur for water with emergent riparian vegetation in the specific case of wetlands surrounding lakes (i.e., small differences in backscattering values between surrounding land and water with emergent riparian vegetation). Simulations featuring the smallest difference between emergent riparian vegetation and land (1.3 dB) showed a 32% to 35% lake extent reduction from true extent. As expected, this study reveals that estimating water extent from SWOT in very wet environments with emergent vegetation can be challenging.
Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi-site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1-12. DOI: 10.1111/j.1752-1688.2010.00514.x Abstract: A multi-site weather generator has been developed using the concept of spatial autocorrelation. The multi-site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi-site weather generator compared to a uni-site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in-depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi-site and uni-site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi-site weather generator significantly outperformed that using the uni-site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons.
