Hydrologic Model Parameterization Using Dynamic Landsat-Based Foliar Cover Estimates for Runoff Simulation on a Semiarid Grassland Watershed

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 an a priori value based on 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.