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How do you predict streamflow in the absence of field data?

This challenge is particularly vexing in snowy mountain regions. Critical data are often absent and prediction efforts are hampered by lack of timely snow data. Predicting low flows is particularly important where water resources are limited.

Remote sensing, cloud computing, and interactive web-based mapping tools offer a new paradigm for delivering key streamflow data to water resource managers.

This prototype project develops cloud-based computing tools to predict streamflow in watersheds where snowmelt is a major contributor. The project builds on an existing Snow Cover Forecast Model (SCFM) that predicts mean monthly streamflow with a one month lead time (Sproles et al. 2016). The model and its calculations do not require programming skills or proprietary software.

The model and calculations are described in three sections. We recommend that you go through them in order.

The first stop – A basic description of the model, how it works, and the data requirements.
Next – A video tutorial of the cloud computing framework, how calculations of Snow Cover Frequency (SCF) are made, and how to export the cloud-based data.
And finally – A hands-on tutorial of the model with examples from three different watersheds.

Funding for this project is provided by the Federation of Earth Science Information Partners (ESIP).

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