Hi, I'm an error. x

Chlorophyll Forecasting Bayesian Network Model

Authors: Carly Hansen
Owners: Carly Hansen
  • Contributions
    Joined

    Subject Areas:
    External Profiles:
    ResearcherID icon
Resource type: Composite Resource
Storage: The size of this resource is 6.2 KB
Created: Aug 17, 2018 at 7:14 a.m.
Last updated: Aug 17, 2018 at 7:26 a.m. by Carly Hansen
  • Contributions
    Joined

    Subject Areas:
    External Profiles:
    ResearcherID icon
Citation: See how to cite this resource
Sharing Status: Public
Views: 139
Downloads: 8
+1 Votes: Be the first one to  +1 this.  (You need to be logged in to rate this.)
Comments: No comments (yet)

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

Content

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/
CC-BY

Comments

There are currently no comments

New Comment

required