A comparison of remote sensing approaches to previous hydrologic model results to determine the potential to estimate changes in water yield from forest restoration and thinning projects.

Forest restoration, or reducing the vegetation density of overgrown forestland, is effective at reducing high-severity wildfire risk across the western US. Restoration can also increase the water yield from forests in many different geographic regions, but the effects are highly variable depending on the individual watershed location, vegetation, and climate characteristics.Accurate, affordable, and scalable measurement of water yield enhancement following ecologically-based forest restoration would enable a full accounting of the water volume benefits from specific forest restoration projects. This approach could allow the costof restoration to be shared among multiple downstream beneficiaries, including hydroelectric power and water utilities.Several methods are commonly used to measure changes in water yield following forest restoration. The traditional paired-watershed approach is a robust and proven method, but expensive and time-consuming, resulting in a poor fit for the requirement of a quick and mobile application to assess the impacts of scalable forest restoration. Using sophisticated hydrologic modeling is more suitable to a low-cost scalable approach, but the accuracy of models can vary widely depending on model selection and calibration data availability. Model simulations can be calibrated exclusively using remote-sensing and other existing data (e.g. temperature records and streamflow on major rivers) however additional ground-based data can dramatically improve model accuracy. Installing and maintaining ground-based monitoring equipment, however, is also expensive and time-consuming. For scalable application across large restoration watersheds, understanding the relative modeling accuracy value of each measurement component would allow prioritization of limited measurement resources.As of yet, no comprehensive analysis hasbeen performed in the California Sierras to evaluate the opportunity to use remote sensing data on quarterly and annual time steps to measure changes in water yield due to vegetation change. While sophisticated hydrologic modeling has been completed showing significant changes in water yield, we propose evaluating how well remote sensing approaches would have correlated with these past modeling results.We propose to develop a remote-sensing-based watershed scale tool for determining water yield changes following forest restoration in the California Sierra Nevada.To create this framework, we will evaluate the accuracy of existing physically-based hydrologic models compared to results we will predict using different remote sensing data for assessing water yield changes following forest restoration.