Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...

Chlorophyll Forecasting Bayesian Network Model


Authors:
Owners: This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource.
Type: 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.
Citation: See how to cite this resource
Sharing Status: Public
Views: 1668
Downloads: 48
+1 Votes: Be the first one to 
 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