Delivering of Water to the Critical Zone and Knowledge to Society: Synthesizing High Spatiotemporal Resolution Model and Remote Sensing Data in Snow-Dominated, Mountain Watersheds

Abstract:

The critical zone is the reactive skin of the terrestrial Earth and plays a key role in sustaining ecosystem services that billions of people depend on. The patterns and rates of water delivery to the critical zone exhibit variability across an enormous range of spatiotemporal scales in seasonally snow-dominated, mountain watersheds. Not coincidentally, they are also among the most sparsely instrumented. Climate change is not only shifting patterns of precipitation, snow accumulation, and snow melt, it is also changing ecohydrologic cycles in mountain landscapes and the reliability of what instrumental records exist there. Data from models and remote sensing platforms, therefore, fill key knowledge, information, and data gaps in characterizing patterns and changes in mountain snowpacks. These data can simultaneously support applications like streamflow forecasting and ecological modeling. We report on an ongoing effort to combine regional climate modeling at convection-permitting scales and remote sensing data fusion approaches to synthesize historical records that can be used to advance our understanding of snowpacks and ecohydrologic processes in mountain landscapes. Specifically, in the East River watershed in central Colorado, we are using the Weather Research and Forecasting (WRF) model to create a 30 year long hydrometeorologic dataset at 1 km spatial and 1 hour temporal resolution. Simultaneously, we are using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to create a 20 year long snow cover map at 30 m spatial and daily temporal resolution. Together, these datasets are used to test hypotheses about geomorphic controls on the accumulation, retention, and ablation of snow in the watershed, ecological associations between vegetation and snow cover dynamics, and support subsequent modeling efforts. In addition to advancing scientific understanding, these data increasingly support resource management applications as managers cope with data and information gaps in an era of rapid change. As such, these efforts point the way to a model of research that simultaneously advances fundamental process understanding, but also the ability to meet the needs of stakeholders.