Toward snowpack runoff decision support

• Published in: iScience 2022-05, Vol.25 (5), p.104240-104240, Article 104240
• Main Authors: Heggli, Anne, Hatchett, Benjamin, Schwartz, Andrew, Bardsley, Tim, Hand, Emily
• Format: Article
• Language: English
• Subjects: earth sciences; environmental event; environmental management; environmental monitoring; water resources engineering
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2022.104240

Rain-on-snow (ROS) events are commonly linked to large historic floods in the United States. Projected increases in the frequency and magnitude of ROS multiply existing uncertainties and risks in operational decision making. Here, we introduce a framework for quality-controlling hourly snow water content, snow depth, precipitation, and temperature data to guide the development of an empirically based snowpack runoff decision support framework at the Central Sierra Snow Laboratory for water years 2006–2019. This framework considers the potential for terrestrial water input from the snowpack through decision tree classification of rain-on-snow and warm day melt events to aid in pattern recognition of prominent weather and antecedent snowpack conditions capable of producing snowpack runoff. Our work demonstrates how (1) present weather and (2) antecedent snowpack risk can be “learned” from hourly data to support eventual development of basin-specific snowpack runoff decision support systems aimed at providing real-time guidance for water resource management. [Display omitted] •A framework for a snowpack runoff decision support is introduced using station data•Soil moisture data are used to identify periods of terrestrial water input•Semi-automated quality control procedures improved model accuracy by up to 25.7%•Snowmelt is not a primary source of runoff during rain-on-snow events Earth sciences; Environmental event; Environmental management; Environmental monitoring; Water resources engineering