Abstract:

The Western United States has recently endured a decades long dry period marked by several unprecedented droughts. As the climate warms, these droughts are expected to get worse as increased evaporation and decreased snowpack drive aridification across the region. This has prompted concern among water resource decision makers and the communities they serve, highlighting the need for urgent adaptive efforts. While there is a general scientific consensus around the well constrained anthropogenic temperature increases across the WUS and their associated drying, our understanding of how climate change will impact hydrologic conditions at regional scales is still quite poor. This gap poses a substantial barrier to water resource decision makers as they begin to adapt our systems and infrastructure to changing future conditions.

The work presented here aims to advance our understanding of WUS water resources under climate change through the application of various regional modeling techniques and to explore ways of contextualizing scientific knowledge for use in decision making. We first use dynamical downscaling and pseudo global warming techniques to carry out an attribution of the impact of climate change on the 2020-2021 WUS drought, focusing on important water resource metrics. Next, we use machine learning techniques to produce the first large ensemble dataset of future Sierra Nevada streamflow. We use this dataset to evaluate the changing frequency and intensity of hydrologic extremes and whiplash events through a water budget lens. Lastly, we investigate how changing Sierra Nevada conditions will impact Mono Lake using an ensemble of downscaled GCMs to predict future Mono Lake levels. We then carry out an exploration of potential management solutions for Mono Lake through the application of Robust Decision Making techniques, which allow for the consideration of multiple and conflicting objectives.