Remote sensing of snow using active and passive microwave systems

点击翻译按钮获取中文摘要

Snow and glacier melt provide water for more than one sixth of the world's population yet observed changes and model predictions of snowmelt supply suggest that by 2050 future water demands in several parts of Canada and Eurasia are at risk of not being met by snow melt runoff (Mankin et al., 2015). Snow is also important for business activities. For example, the British Columbia ski industry revenue for 2012-13 was $1.3 billion which represents 9% of all annual provincial tourism activity. Yet snow also attracts significant economic costs. Hazards created by flooding from rapid spring snowmelt can also be devastating. For example, the recent 2013 flood in the South Saskatchewan and Elk river basins have been projected to cost in excess of $6 billion (Pomeroy et al. 2015), making it one of the costliest natural disasters in recent Canadian history. While snow accumulation is an important resource, its management is becoming increasingly challenging as the winter snow season regime changes with global warming. For example, the snow season is shortening with the average snow melt date shifting earlier in the season (Derksen and Brown, 2012). Changes impact the ski industry, food production and the hydroelectric power generation in regions that rely on snowmelt runoff.******Snow water equivalent (SWE) accumulation is of keen importance for water resource managers because it reveals how much water is stored in snow. The goal of this proposal is to increase our understanding and use of active and passive microwave remote sensing systems improve our estimation of SWE, both for climatology and water resources applications. Three research objectives are proposed. First, to exploit the use of radar measurements to estimate SWE in snow covered terrain at L, C, X and Ku frequencies. Knowledge about the radar response from underlying organic soils, buried vegetation within snow and from snow covered forest landscapes is incomplete yet important to improve estimates of SWE in the presence of vegetation (low or tall stand canopies). In addition, interferometric imaging radar observations of snow have demonstrated strong sensitivity to SWE (Leinss et al. 2015) and this proposal will confirm these findings over a wider range of snow environments. Second, the proposal will focus on combining different satellite multi-sensor snow products. This will provide insights into agreements and disagreements between existing data and will inform how the uncertainties in the SWE estimates might be reduced to create a more robust time series of SWE. Third, the research will use techniques and methods deployed in the first two objectives to develop and explore new science applications. For example, my Snowtweets research project, which seeks to connect with school programmes to collect snow depth data, will map multi-sensor SWE data in combination with crowdsourced snow depth data to provide a powerful multi-source approach to SWE mapping.