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Chlorophyll Forecasting Bayesian Network Model

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Created: Aug 17, 2018 at 7:14 a.m.
Last updated: Aug 17, 2018 at 7:26 a.m.
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Forecasting conditions that are indicative of algal blooms can help provide an early warning for monitoring and water management agencies. This script creates a seasonal (monthly) forecasting model which uses hydrologic and climate data from earlier in the season to predict chlorophyll concentrations throughout the late summer months. The accompanying data includes time series of monthly average extreme chlorophyll values, average streamflows, snow water equivalent, temperatures, and precipitation totals in or near Utah Lake.

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How to Cite

Hansen, C. (2018). Chlorophyll Forecasting Bayesian Network Model, HydroShare,

This resource is shared under the Creative Commons Attribution CC BY.


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