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Watershed Planning within a Quantitative Scenario Analysis Framework
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Toward snowpack runoff decision support.

Anne Heggli1, Benjamin Hatchett1, Andrew Schwartz2

  • 1Division of Atmospheric Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno 89512, NV, USA.

Iscience
|May 2, 2022
PubMed
Summary
This summary is machine-generated.

Rain-on-snow (ROS) events can cause major floods. This study develops a framework using hourly data to predict snowpack runoff, aiding water management decisions.

Keywords:
earth sciencesenvironmental eventenvironmental managementenvironmental monitoringwater resources engineering

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Area of Science:

  • Hydrology
  • Environmental Science
  • Climate Science

Background:

  • Rain-on-snow (ROS) events are a significant driver of major historical floods in the United States.
  • Increasing frequency and intensity of ROS events exacerbate existing uncertainties and risks in operational decision-making for water resource management.

Purpose of the Study:

  • To introduce a quality-controlled data framework for hourly snow water content, snow depth, precipitation, and temperature data.
  • To guide the development of an empirically based snowpack runoff decision support framework.
  • To aid in pattern recognition of weather and snowpack conditions conducive to snowpack runoff.

Main Methods:

  • Quality control of hourly meteorological and snowpack data (water years 2006-2019).
  • Development of a decision tree classification for rain-on-snow and warm day melt events.
  • Analysis of present weather and antecedent snowpack conditions.

Main Results:

  • Demonstrated a framework for learning present weather and antecedent snowpack risk from hourly data.
  • Identified key conditions associated with snowpack runoff events.
  • Established a foundation for developing basin-specific snowpack runoff decision support systems.

Conclusions:

  • The developed framework supports pattern recognition of conditions leading to snowpack runoff.
  • Hourly data analysis is crucial for understanding and predicting snowpack runoff events.
  • This research contributes to real-time guidance for water resource management, particularly concerning ROS events.