Use of Basin Internal Observations for Uncertainty Quantification and Propagation in a Distributed Hydrologic Model

Uncertainty characterization of hydrologic models becomes vital, particularly in the context of extreme events (i.e. floods).  These extreme events are rare, which means the models constructed based on observed data will have very few samples of such events. This has consequences on model development as well as parameterization.  In response to this challenge, this study aims to leverage basin internal observations of streamflow and soil moisture in an effort to capture areas of model weakness for the Russian River Basin in Northern California in the context of a distributed hydrologic model designed for operational use. At the center of this study is the National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) and its conceptually-based Sacramento Heat Transfer with enhanced EvapoTranspiration (SAC-HTET) submodel for rainfall-runoff generation. The Russian River Basin is instrumented with 11 streamflow gauges from the USGS and 7 multi-depth NOAA Physical Sciences Division soil moisture observation stations with most records dating back to 2010.  It is the multitude of streamflow gauges that makes this basin of particular interest to track the propagation and accumulation of uncertainty through the channel routing of the model, and the soil moisture observations allow for a check on model state behavior during sub-basin processes. Utilizing an ensemble-based approach and prescribed precipitation forcing uncertainty, the distribution of uncertainty (in the form of streamflow and soil moisture) is evaluated at various interior basin locations over the course of wet season events of this “flashy” basin. As is often the case, flows in the Russian River Basin are modified by manmade structures such as dams, whose operators’ choices of reservoir release are not readily captured by the hydrologic model.  Therefore, a special effort in this work is to focus on uncertainty behavior through these structures, and to reduce uncertainty using the aforementioned observations in regions unaffected by these structures.