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Chlorophyll Forecasting Bayesian Network Model

Authors: Carly Hansen
Owners: Carly Hansen
Resource type:Composite Resource
Created:Aug 17, 2018 at 7:14 a.m.
Last updated: Aug 17, 2018 at 7:26 a.m. by Carly Hansen

Abstract

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.

Subject Keywords

Utah Lake,Harmful Algal Bloom,Forecasting model,Chlorophyll

How to cite

Hansen, C. (2018). Chlorophyll Forecasting Bayesian Network Model, HydroShare, http://www.hydroshare.org/resource/27f81cb47f814e32adc48f5fb9d02fa5

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

  http://creativecommons.org/licenses/by/4.0/
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    Authors

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    Name Organization Address Phone Author Identifiers
    Carly Hansen University of Utah 9209159353

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