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 cost of 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 stream flow 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 has been 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. OBJECTIVES: Forest restoration, or reducing the vegetation density of overgrown forestland, can be effective at reducing high-severity wildfire risk across the western US (Agee and Skinner, 2005; Finney et al. 2008). Restoration may also increase the water yield from forests in some cases in many different geographic regions (Stednick, 1996; Brown et al., 2005), 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. More sophisticated measurement and tools could allow the costs of 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 stream flow 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. To our knowledge, no comprehensive analysis has yet been 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 using the RHESSys model 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. Our overall R&D goal is to demonstrate the feasibility of a customizable and scalable landscape scale water quantity evaluation framework that can be broadly applied to quantify changes in watershed yield following forest restoration across the western US. At the core of this framework will be a rigorous decision-making matrix based on technical, logistical, and environmental feasibility, along with cost and water yield prediction accuracy associated with different hydrologic measurement scenarios. We will initially assess the feasibility of this framework using data from an existing research project in the northern Sierra at the Last Chance study site (Figure 2, pg 10). Last Chance site has been intensively instrumented, studied, and modeled by Dr. Roger Bales and Dr. Martha Conklin at the Sierra Nevada Research Institute (SNRI - researchers affiliated with SNRI are a subaward recipient). This completed analysis using past complex hydrologic models will be used as a "best-case" hydrologic measurement scenario. Our feasibility assessment will focus on comparing the water yield accuracy, cost, and feasibility of broadly applicable and low-cost hydrologic quantification scenarios based on remote sensing data. This will be done by acquiring the MODIS satellite NDVI data for the Last Chance site that overlaps the period of measurements and treatments. We will investigate the data requirements, accuracy, and application of the remote sensing approach for comparison to existing hydrologic model outputs for this watershed. The measurement scenarios will consist of: Scenario 1: Quantification of water yield response to forest treatments comparing past RHESSys model outputs to remote sensing-based evapotranspiration estimates (both MODIS and NDVI-ET relationship) using a water balance over varying time steps. Scenario 2: Quantification of water yield response to forest treatments using the MODIS evapotranspiration estimates in both treated and adjacent untreated watersheds. Specific technical questions that will be addressed during this feasibility phase to quantify changes in water yield will allow us to determine the relative accuracy of each of the scenarios: Is there a statistically significant difference in quantifying the water balance using the remote sensing approach compared to the physical modeling at the 95% confidence level? Are the remote sensing approaches significantly correlated with the direction and magnitude of water yield changes in response to forest restoration? What is the estimated cost for predicting changes in streamflow following forest restoration using data-intensive modeling versus remote sensing? What is the feasibility to scale-up a remote sensing water quantity framework across the American River watershed and other Sierra Nevada catchments?
