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The rising anthropogenic demand for water and increased variability of water levels is a challenge for lakes across North America. Many terminal lakes in the Great Basin struggle in the face of anthropogenic demand and climate change.

Mono Lake is a terminal lake in eastern California that supplies freshwater to a small fraction of Los Angeles residents. Chronic water level decline has been an issue at Mono Lake for the past century due to a combination of long-term diversions and drought. This has caused ecological and hydrologic damage in the basin, and despite previous conservation efforts and legislation, it still jeopardizes the future of Mono Lake. Mono Lake’s continuing struggle and the subsequent push for new water management legislation underscores the importance of hydrologic modeling of the past, present, and future to understand how water balance components interact and provide more information for policymakers in California.

In this research, we present a two-step statistical approach to tackle the modeling challenges implicit to Mono Lake. More specifically, we use a modeling framework (the Large Lake Statistical Water Balance Model (L2SWBM)) to close the historical water balance, quantify uncertainty, and provide a probabilistic basis for simulating water levels under different climate and water management conditions. Using results from the L2SWBM, we apply a regular vine copula to capture relationships between water balance components and allow for unlimited sampling and range.

The goal of this research is to create a probabilistic forecasting system of the Mono Lake water balance and equip decision makers/community members with an understanding of the impacts of both climate and human activities on Mono Lake. The forecasting system developed in the study area creates a tool to resimulate historical and future water levels under various diversion and climate scenarios. This allows for a deep analysis of the impacts of each climate and diversion scenario on Mono Lake water levels.