Remote sensing of snow and its application to hydrometeorological studies in western Canada

Snow plays a vital role in the energy and water budgets of drainage basins of western Canada. Various remote sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer (AMSR-E) and Special Sensor Microwave/Imager (SSM/I) have been launched to map the snow cover extent (SCE), snow cover fraction (SCF), and snow water equivalent (SWE) across the globe. However, the distribution and variability of snow inferred from remote sensing products have not been comprehensively investigated in western Canada owing to its complex topography and harsh environment.;In this thesis, a new approach referred to as the spatial filter (SF) method is developed to decrease the cloud coverage in the MODIS snow products. At the same time, the new snow products are evaluated based on in-situ observations of snow depth in the QRB. Then the relationships between SCF from MODIS, topography, and hydrometeorology of the QRB are explored. In addition, various retrieval algorithms of SWE from microwave remote sensing are tested in the QRB. At last, the Environment Canada algorithms of SWE from SSM/I are adopted to produce new SCF products evaluated with the MODIS snow products. The relationships between SWE and SCF from SSM/I and hydrometeorology are also investigated in the MRB. The main findings of this thesis are as follows. Firstly, the SF method reduces the average cloud coverage in the QRB from 15% for MODIS 8-day snow products to 9%. The overall accuracy (OA) of MODIS snow products achieves ≈ 90% accuracy in the QRB. Secondly, the SCF and snow cover duration (SCD) are largely controlled by the topography of this alpine watershed. For example, the gradient of SCF with elevation (d(SCF)/dz) during the snowmelt season is 8% (100 m)-1 in the QRB. Mean gradients of SCD with elevation are 3.8, 4.3, and 11.6 days (100 m)-1 for the snow onset season, snowmelt season, and entire year, respectively in the QRB. Thirdly, for SWE retrieval algorithms from microwave remote sensing, significant relationships between brightness temperatures (TB) difference and in-situ SWE exist only when the snow accumulation is less than a threshold of 250 mm or 400 mm that varies at the different in-situ stations in the QRB. Overall, AMSR-E provides better estimates of retrieved SWE than SSM/I. Compared to the algorithms based on TB difference, the artificial neural network (ANN) for SSM/I and AMSR-E performs much better in the QRB. At last, significant relationships exist between the snow distribution and hydrometeorology of western Canada watersheds. For example, an aggregated 1°C rise in average air temperature during spring leads to a 10-day advance in reaching 50% SCF (SCF50%) in the QRB. The correlation coefficient between normalized SCE of the SF and normalized streamflow is -0.84 (p<0.001) for snow ablation seasons in the QRB. The correlation coefficients between SCF and discharge and between SWE and discharge are 0.87 (p<0.001) and 0.84 (p<0.001) in the MRB, respectively. The studies in this thesis will contribute to the monitoring of snow in remote northern areas where observation stations are sparse and the analyses of the relationships between the snow and hydrometeorology in western Canada are not well understood.;So far, little research has been conducted on SCE-streamflow and SCE-SWE-runoff models focusing on Canadian watersheds where snow cover is very important for human well being. Although microwave remote sensing of snow is widely developed and applied in Canada, the retrieval of SWE in western Canada is not as well established owing to the complex topography in this area. Therefore, the Quesnel River Basin (QRB) of British Columbia is selected as a primary test site to develop and test SCE-streamflow and SCE-SWE-runoff models. Then the Mackenzie River Basin (MRB) is chosen as a secondary test site to apply the Environment Canada (EC) SWE retrieval algorithms to upscale the hydrometeorological research.