Investigating the Annual Water Balance of a High-Altitude Watershed Using Near-Real Time Lidar Data Integration into a Physically Based Snowmelt Model

Knowledge of the amount of water stored in the mountain snowpack is crucial for flood prevention, drought mitigation, and energy production in the Western United States. In modeling terms, the most important component of the hydrologic water balance is the precipitation input to the system. Determining where and how much precipitation falls in mountain catchments, however, is the most difficult problem with regards to closing the water balance. The work presented in this dissertation details the modeling portion of the NASA Airborne Snow Observatory (ASO) using the iSnobal physically based snow model. This combination of remote sensing and modeling at 50 m resolution provides the most accurate quasi-operational estimates of snow distribution ever produced over a mountain basin. The first chapter supplies the background and motivation for undertaking this dissertation and presents a brief introduction to the following chapters. Chapter 2 describes the methods used for periodically inserting the ASO-derived snow depths into iSnobal over a consecutive four-year period (2013–2016) in the Tuolumne River Basin in California’s Sierra Nevada. Chapter 3 provides a background for how the forcing data for our modeling approach was derived in near-real time and addresses the problem of reproducibility in the hydrologic sciences. Chapter 4 examines the water balance over the Tuolumne Basin using ASO-derived snow depth updates to iSnobal in three very dissimilar water years (2015–2017). For validation of the modeled evapotranspiration using the water balance approach, we use an independent satellite-derived estimate of annual evapotranspiration and show that the basin runoff efficiency is related to total precipitation input for each year. Finally, Chapter 5 presents a summary of the previous chapters and provides a direction for moving the research detailed in this dissertation forward. The combined results of these studies will help usher in a shift toward more wide-spread use of physics-based models for operational predictions of water storage and runoff