Information Theory of Multiscale Simulations and Data Assimilation
Abstract
Mountain environments are highly heterogeneous. Rugged terrain drives spatial variation in soils, climate, and hydrology, which influences vegetation distribution. Wind, in conjunction with topography and vegetation in turn control the deposition and redistribution of snow. The development of snow banks and scour zones produces local variability in water supply and growing season length. These interrelated factors contribute to high levels of spatial and temporal variability in alpine ecosystem productivity. Leading to the development of 'hot spots' and 'hot moments', where certain areas and times contribute disproportionately more to regional observations. However, many of these processes operate at sub 10m to sub 1m spatial resolutions at daily to weekly time scales. Consequently, these processes are not resolved with widely used moderate resolution satellite remote sensing, and, are often poorly represented by point measurements. Unmanned aerial systems (UAS) or drones, provide a way to bridge this spatiotemporal gap in our understanding of alpine ecohydrology by enabling the collection of centimeter resolution multispectral imagery on demand at low cost.
From June-August 2017 we deployed a custom built multispectral (visible, near infrared, thermal infrared) UAS at weekly interval over the Niwot Ridge Long Term Ecological Research sites saddle catchment; which crosses the tree line transition zone in the Colorado Rockies. We used digital elevation models to estimate snow depth, and quantify topographic variables, and thermal imagery to map surface/subsurface hydrologic flowpaths. Using weekly NDVI as a proxy for vegetation productivity we explore spatiotemporal patterns in ecosystem response across a 40ha study site at 25cm resolution. The impact of various ecohydrologic forcings (e.g. topography, energy availability, soil moisture, snow depth, growing season length) on vegetation productivity were examined. Aspect, snowmelt supply and growing season length are identified as primary controls on vegetation productivity. This presentation aims to provide more than just pretty pictures, by leveraging the high spatial and temporal resolution of UAS imagery to develop quantitative datasets suitable for investigating dynamic ecohydrologic processes in complex mountain environments.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.H13B..08B
- Keywords:
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- 0439 Ecosystems;
- structure and dynamics;
- BIOGEOSCIENCESDE: 1807 Climate impacts;
- HYDROLOGYDE: 1813 Eco-hydrology;
- HYDROLOGYDE: 1899 General or miscellaneous;
- HYDROLOGY