Webb, Ryan W听1听;听Molotch, Noah听2听;听Fend, Michael听3
1听INSTAAR, 麻豆影院
2听Dept. of Geography, 麻豆影院
3听鲍狈础痴颁翱
Snow is an important component of the hydrologic cycle for numerous communities around the globe. An important consideration for water resource planning is snowmelt runoff timing. Runoff timing can be determined by the difficult to observe physical process of water movement through a seasonal snowpack. The aim of this study is to present a novel method that combines light detection and ranging (LiDAR) with ground penetrating radar (GPR) to nondestructively observe the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property (subdaily timescale). We applied these methods at three experimental plots across elevational gradients in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19% within the scale of meters. We also show the rapid changes in bulk liquid water content that occur over sub-daily time scales. Results of this study show the importance of the lateral flow of water in higher elevation snowpacks and how this process may change in a future climate. The presented methods have a reasonable amount of uncertainty in bulk liquid water content (maximum of 1.5%) making this an applicable method for future studies to observe the complex spatio-temporal dynamics of liquid water in snow.