Bark beetle impacts on remotely sensed evapotranspiration in the Colorado Rocky Mountains
Date
2019-01
Authors
Knowles, John F., author
Molotoch, Noah P., author
Journal Title
Journal ISSN
Volume Title
Abstract
Bark beetles represent a major ongoing forest disturbance throughout the southern Rocky Mountains with unknown implications for hydrological partitioning between the abiotic (evaporation) and biotic (transpiration) components of the total evapotranspiration (ET) flux. Since changes in ET are linked to both groundwater and surface water recharge processes, this scenario has the potential to affect water delivery to agricultural, industrial, and residential consumers downstream. Accordingly, this research used satellite remote sensing, eddy covariance, and hydrological modeling approaches to independently quantify the impact of bark beetles on growing season ET, the transpiration fraction of ET (T/ET), and streamflow across a range of spatial scales throughout the 144,462 km2 EPA Level III Southern Rocky Mountain ecoregion. The results of this work demonstrate statistically significant post-disturbance ET reductions between 9% (remote sensing) and 28% (eddy covariance) relative to pre-disturbance conditions. Further, commensurate decreases in transpiration and T/ET from disturbed areas suggest that the total ET flux was primarily sensitive to changes in transpiration. In the context of the water balance, the Variable Infiltration Capacity (VIC) hydrological model simulated decreased canopy interception and increased soil moisture as a result of beetle disturbance, which increased streamflow by 9%. Factoring in the number of grid cells that were disturbed, bark beetles decreased ET by 62,000 acre-feet and increased streamflow by 54,000 acre-feet between 2000 and 2013. These results will benefit water managers tasked with forecasting water resources from disturbed areas both now and in the future.
Description
January 2019.
Rights Access
Subject
evapotranspiration
Rocky Mountains
partitioning
streamflow
bark beetles
remote sensing
eddy covariance
hydrological model