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Knowles et al., 2018


On the use of a snow aridity index to predict remotely sensed forest productivity in the presence of bark beetle disturbance.

Knowles, JK, L. Lestak, N.P. Molotch (2018)
Water Resources Research, 53: 4891-4906  


We used multiple sources of remotely sensed and ground based information to evaluate thespatiotemporal variability of snowpack accumulation, potential evapotranspiration (PET), and NormalizedDifference Vegetation Index (NDVI) throughout the Southern Rocky Mountain ecoregion, USA. Relationshipsbetween these variables were used to establish baseline values of expected forest productivity given waterand energy inputs. Although both the snow water equivalent (SWE) and a snow aridity index (SAI), whichused SWE to normalize PET, were significant predictors of the long-term (1989–2012) NDVI, SAI explained11% more NDVI variability than SWE. Deviations from these relationships were subsequently explored in thecontext of widespread forest mortality due to bark beetles. Over the entire study area, NDVI was lower perunit SAI in beetle-disturbed compared to undisturbed areas during snow-related drought; however, bothSAI and NDVI were spatially heterogeneous within this domain. As a result, we selected three focus areasinside the larger study area within which to isolate the relative impacts of SAI and disturbance on NDVIusing multivariate linear regression. These models explained 66%–85% of the NDVI and further suggestedthat both SAI and disturbance effects were significant, although the disturbance effect was generallygreater. These results establish the utility of SAI as a measure of moisture limitation in snow-dominated sys-tems and demonstrate a reduction in forest productivity due to bark beetle disturbance that is particularlyevident during drought conditions resultant from low snow accumulation during the winter.


Knowles, JK, L. Lestak, N.P. Molotch (2018): On the use of a snow aridity index to predict remotely sensed forest productivity in the presence of bark beetle disturbance . Water Resources Research, 53: 4891-4906. DOI: 10.1002/2016WR019887

This Paper/Book acknowledges NSF CZO grant support.