Hydrologic model parameterization using dynamic Landsat-based vegetative estimates within a semiarid grassland

Authors: Kautz, Mark; Holifield Collins, Chandra; GUERTIN, D.P. - University Of Arizona; Goodrich, David - Dave; VAN LEEUWEN, W.J. - University Of Arizona; Williams, Christopher - Jason

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/13/2019
Publication Date: 5/15/2019
Citation: Kautz, M.A., Holifield Collins, C.D., Guertin, D., Goodrich, D.C., Van Leeuwen, W., Williams, C.J. 2019. Hydrologic model parameterization using dynamic Landsat-based vegetative estimates within a semiarid grassland. Journal of Hydrology. 575:1073-1086. https://doi.org/10.1016/j.jhydrol.2019.05.044.
DOI: https://doi.org/10.1016/j.jhydrol.2019.05.044

Interpretive Summary: Hydrologic models are an important tool for quantifying the health of rangelands through simulation of soil erosion and surface runoff. These models require inputs of ground and vegetative cover to produce accurate results. Obtaining these measurements across the large, heterogeneous landscapes typical in semiarid rangelands is difficult using traditional ground measurements. This research outlined a methodology for estimating these vegetative inputs using remotely sensed satellite imagery. Compared to traditional methods, the process outlined in this research improved model results for a series of rainfall-runoff events spanning 20 years. This procedure for model parameterization allows for changes in vegetation over time to be characterized, providing a more accurate representation of the hydrologic response to these alterations. These findings provide a significant improvement to representing time-varying vegetation within rangeland health decision support tools.

Technical Abstract: Changes in watershed vegetative cover from natural and anthropogenic causes including, climatic fluctuations, wildfires and land management practices, can result in increased surface water runoff and erosion. Hydrologic models play an important role in the decision support process for managing these landscape alterations. However, model parameterization requires quantified measures of watershed biophysical condition to generate accurate results. These inputs are often obtained from nationally available land cover data sets that are static in terms of vegetation condition and phenology. Obtaining vegetative data for model input of sufficient spatiotemporal resolution for long-term, watershed-scale change analysis has been a challenge. The purpose of this research was to assess the implications of parameterizing the event-based, Rangeland Hydrology and Erosion Model (RHEM) with dynamic, remotely sensed foliar cover data. The study was conducted on a small, instrumented, grassland watershed within the Walnut Gulch Experimental Watershed surrounding Tombstone, Arizona. A time series of foliar cover rasters was produced by calibrating Landsat-based Soil Adjusted Total Vegetation Index (SATVI) scenes with field measurements. Estimates of basal and litter cover were calculated using allometric relationships derived from ground-based transect data. The model was parameterized using these remotely sensed inputs for all recorded runoff events from 1996-2014. Model performance was improved using the remotely sensed foliar cover compared to using literature-based value associated with static national land cover classes. Significant (p < 0.05) correlation was shown for the linear relationships between foliar cover and SATVI, foliar cover and basal cover, and foliar cover and litter cover. The integration of Landsat-based vegetative data into RHEM shows potential for modelling on a broadened spatiotemporal scale, allowing for improved landscape characterization and the ability to track watershed response to long-term vegetation changes.