Application of a modified SEBAL evapotranspiration algorithm for improved understanding and prediction of hydrologic behavior in highly altered landscapes

Remote sensing data can provide critical information on spatial and temporal parameters that influence hydrologic behavior after acute or long-term disturbance, including urbanization, wildfire, and climate change. Numerous algorithms have been developed to estimate variables which otherwise are difficult to retrieve at appropriate scales, such as vegetation biomass, soil moisture, potential evapotranspiration (PET) and evapotranspiration (ET). The current presentation will focus on multi-platform algorithms that have been developed by the Hogue research group for estimating these key hydrologic variables. Specifically, we investigate the application of an ET product based on a modified Surface Energy Balance Algorithm (SEBAL) that integrates only Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) products to develop algorithm parameters. This presentation will focus on an overview of developed products and their application in disturbed watersheds – including highly urbanized landscapes of Los Angeles as well as regional watersheds affected by wildfire. The remotely-sensed ET estimates are also evaluated against a high resolution surface model, the Noah-SLUCA (single layer UCM) and residential water consumption data in Los Angeles. The integration of remote sensing data and products in changing environments will ultimately improve hydrologic predictions and help inform short and long-term water resources decisions.