Modeling of Nitrate Loads and Concentrations in the Raccoon River

Date: 
Feb 2014

Issue

Stream water quality is strongly linked to land use, hydrology and precipitation. Land use in the Raccoon River Watershed is overwhelmingly committed to corn and soybean production. Understanding how climate and weather link to production practices, and how this affects environmental outcomes, is crucial to quantify water quality improvements. Better assessments are needed so policymakers can optimize investment of limited public resources toward improving water quality.

Objective

This project will develop statistical models to describe changes in seasonal concentrations and loads of nitrate in the Raccoon River at Van Meter from 1974 to the present, and how these relate to agricultural production practices.

Approach

Predictors used in the statistical models will be related to climate, agriculture and the economy. For climate, basin-wide seasonally averaged rainfall and basin-wide rainfall for the month prior to the season to model will be used. For agriculture, predictors such as rate, form and timing of nitrogen applications, manure use, tillage practices and corn and soybean acres planted and harvested will be considered. For the economy, seasonal pricing of nitrogen forms and average annual price of corn and soybean for the year prior to the one to model will be used. The development of these models will allow the investigation of the sensitivity of nitrate loads and concentrations to different combinations of predictors.

Project Updates

December 2015

FINAL REPORT

The main objective of this project was to develop statistical models to describe temporal changes in nitrate concentrations in the Raccoon River at Van Meter that relate the response variable (monthly nitrate concentrations) to predictors that are potentially useful in describing its variability. The predictors that were considered were related to climate and agriculture. The modeling results show it is possible to successfully describe monthly flow-weighted average concentrations for the Raccoon River over the 1974-2013 period. Plus, researchers found that baseflow and planted soybean acreage are the two predictors most often identified as important.

September 2015

This project was designed to see if it is possible to develop statistical models to describe changes in nitrate concentrations in the Raccoon River at Van Meter for the period from 1974 to 2013. The predictors considered were related to climate and agriculture. The modeling results show it is possible to successfully describe monthly flow-weighted average concentrations for the Raccoon River. The researchers found baseflow and the previous year’s planted soybean acreage were the two predictors most often identified as important.

June 2015

Statistical models have been developed to describe the monthly flow-weighted concentrations of nitrates in the Raccoon River, from March to August, for the period 1974 to 2013. The modeling results show it is possible to successfully describe monthly flow-weighted average concentrations for the Raccoon River. The researchers found baseflow and planted soybean acreage are the two predictors most often identified as important. 

March 2015

This project involves the development of statistical models able to describe the monthly flow-weighted concentrations of nitrates in the Raccoon River. Data are available from 1974 to 2014. The work has focused on the March-August time period, which contributes 60-90% of the annual nitrate concentrations. The goal of the project is to model the year-to-year variations in nitrate concentrations in terms of climate and agriculture-related predictors. Initial modeling efforts have provided promising results.

December 2014

Understanding how climate and weather link to production practices, and how this affects environmental outcomes, is crucial to quantify water quality improvements. The project will develop statistical models to describe changes in seasonal concentrations and loads of nitrate in the Raccoon River at Van Meter from 1974 to the present, and how these relate to agricultural production practices. Predictors used in the statistical models will be related to climate, agriculture and the economy. Data collection and organization as well as some preliminary modeling efforts are underway.

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