The Application of a Snowpack Runoff Decision Support System for Rain‐on‐Snow Events

Abstract Accurately forecasting hydrologic outcomes of rain‐on‐snow (ROS) events is critical for water management and flood mitigation, not only in the western U.S., but globally. This study further refines a Snowpack Runoff Decision Support System (SR‐DSS) using hourly Natural Resource Conservation Service SNOw TELemetry (SNOTEL) data to identify thresholds for present weather and antecedent snowpack conditions that favor liquid water input into soils. This study applies the revised SR‐DSS to the unimpaired Upper Carson River watershed in the eastern Sierra Nevada, USA. We compare the SR‐DSS detection of ROS liquid water input potential to observed soil moisture, streamflow, and an existing operational snowpack‐runoff model framework (SNOW‐17 and SAC‐SMA). Information provided by the SR‐DSS enables forecasters to make real‐time adjustments to SNOW‐17 parameters, including rain‐snow temperature thresholds or melt factors, as conditions evolve in ways that the model alone might not capture. We provide the predictive probability (0.43–0.83) for the SR‐DSS thresholds that identify the potential of ROS liquid water input from 2006 to 2022. Multiple case studies demonstrate that the SR‐DSS alone captures the onset of liquid water input and provides evidence of how it can help performance assessment of operational models (SNOW‐17 and SAC‐SMA). Our results demonstrate that SR‐DSS can enhance situational awareness by providing snowpack and weather conditions in a time‐relevant manner for forecasting and decision‐making. Increasing the technical readiness and capability for integration into operations of the SR‐DSS through further development and evaluation will enhance the resiliency and flexibility of water management systems.