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Passive Imaging
Published in Iain H. Woodhouse, Introduction to Microwave Remote Sensing, 2006
Snow is also a key factor in the hydrological cycle, the hydrological properties being characterised by a “snow water equivalent” (SWE). This avoids trying to represent the complex structural differences in the snow by simply quantifying how much water the snow contains. The value of SWE is most apparent when considering the potential run-off from a snowpack after it has melted — forecasting snowmelt runoff within a catchment is a fundamental input to flood prediction models.
Changes to rainfall, snowfall, and runoff events during the autumn–winter transition in the Rocky Mountains of North America
Published in Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2020
Paul H. Whitfield, Kevin R. Shook
The accumulation of snow water equivalent (SWE) is affected by variability in the cool-season climate. The variability of SWE is affected by both temperature and precipitation, with strong spatial discontinuities; temperature is more important in New Mexico and precipitation more important in Idaho and Colorado (Cayan 1996). Snow is the largest component of surface water storage in most basins in western North America, and the number of melt events during winter has increased (Mote et al. 2005); the largest increases have occurred in the warmest mountain areas. The spatial patterns and elevational dependence of trends are climatic, as trends in the north follow precipitation trends whereas trends in the south are more complicated (Mote et al. 2005). In the Columbia Basin, Hamlet and Lettenmaier (1999) showed earlier spring peak flows and smaller flows in the April–September period, with increased winter flows. High-elevation regions in the Rocky Mountains are relatively insensitive to temperature trends, and trends in snowpack are connected to trends in precipitation (Hamlet et al. 2005).
Using artificial neural networks to estimate snow water equivalent from snow depth
Published in Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2020
J. Odry, M. A. Boucher, P. Cantet, S. Lachance-Cloutier, R. Turcotte, P. Y. St-Louis
Snow is known to exert a dominant control on runoff-generating processes in high latitude and mountainous areas (Doesken and Judson 1997; Barnett, Adam, and Lettenmaier 2005; Hock et al. 2006). Therefore, estimating the state of the snowpack in near-real time is necessary for proper water resource management. Measurement of the different characteristics of the snowpack can be performed in multiple ways, from remote sensing to ground measurements (Kinar and Pomeroy 2015), depending on the variable of interest and the required degree of accuracy. Most of the time, snow water equivalent (SWE) is considered as the most important variable, as it represents the amount of water held within the snowpack expressed per unit of area (Seibert et al. 2015). Mathematically, SWE is the product of the local snow depth by the vertically averaged snow density. Ground measurements of SWE are more expensive than measurements of snow depth; Sturm et al. (2010) estimates SWE measurements to be 20× more time-consuming than those for snow depth. Their study also suggests that historical databases of snow measurements contain much more snow depth data than SWE data. These statements emphasize the need for tools to estimate snow density accurately to derive SWE from snow depth measurements. One could argue that the existence of automatic SWE sensors, such as snow pillows, radioisotopes, or radar devices (Kinar and Pomeroy 2015), as well as the development of remote sensing-based snowpack estimates, would make snow density estimating tools irrelevant. Nevertheless, the costs associated with direct SWE estimation with those techniques remain prohibitive, consequently they are rarely employed operationally. It is also worthwhile to be able to estimate SWE from historical time series of snow depth.