Collaborative Research: Unraveling Orographic Precipitation Patterns by Combined Hydrologic and Atmospheric Analysis

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Focusing on the Southern Sierra Nevada, California, we will use distributed snow and streamflow measurements (which date back to the 1920s) to assess what precipitation must have been for each basin on annual and storm-by-storm time-scales. Using iterative hydrologic simulations across a range of model structures in a Bayesian framework, we will determine which precipitation amounts and gradients (and with what uncertainty) best fit available measurements of streamflow and snow accumulation. This methodology will provide a ground-based estimate of high-elevation precipitation for times and locations where no high-altitude rain gauges are available and will allow us to assess long-term change. We will then use these new hydrologic-based precipitation datasets as a benchmark for atmospheric model performance, and will compare these with an ensemble of 14-year regional atmospheric model (WRF) simulations generated using different boundary conditions and microphysics schemes. This will allow us to assess sources of model uncertainty in annual patterns of orographic precipitation. Accurate precipitation inputs, both total amounts and changes with elevation, are critical to model streamflow in mountainous regions. However, in general these regions are grossly under-sampled in terms of precipitation measurements, and those gauges that do exist are notoriously unreliable. Atmospheric models can predict precipitation rates and distributions. However, development and improvement of these atmospheric models has been hindered by the lack of direct precipitation measurements that caused hydrologists problems in the first place.Our most reliable measurements from these high-altitude areas (distributed streamflow and snow water equivalent) are useful clues for estimating historic spatial and temporal distributions of precipitation. Our research approach integrates both meteorology and hydrology, using hydrology as a tool to better understand meteorology, and providing a long-term benchmark that can be used to improve our forecasts and advance science in both fields. This work will improve understanding of mountain precipitation for both individual storms and how those storms aggregate over an entire season. This knowledge is crucial for forecasting short-term floods, seasonal water resources, and long-term climate sensitivity. Efforts will focus on the Southern Sierra Nevada, California, where short-term and long-term forecasts have historically had lower accuracy than other regions, but where those same forecasts are critical for the San Joaquin Valley agriculture industry, valued at over $30 billion. The research results will also contribute to atmospheric model development, such that these models can be used more successfully to predict mountain precipitation worldwide.