DOI

10.17077/etd.0b4ewvlj

Document Type

Thesis

Date of Degree

Spring 2017

Degree Name

MS (Master of Science)

Degree In

Civil and Environmental Engineering

First Advisor

Krajewski, Witold F.

First Committee Member

Krajewski, Witold F.

Second Committee Member

Bradley, Austin A.

Third Committee Member

Weber, Larry J.

Abstract

In the agricultural state of Iowa, water quality research is of great importance for monitoring and managing the health of aquatic systems. Among many water quality parameters, water temperature is a critical variable that governs the rates of chemical and biological processes which affect river health. The main objective of this thesis is to develop a real-time high resolution predictive stream temperature model for the entire state of Iowa. A statistical model based solely on the water-air temperature relationship was developed using logistic regression approach. With hourly High Resolution Rapid Refresh (HRRR) air temperature estimations, the implemented stream temperature model produces current state-wide estimations. The results are updated hourly in real-time and presented on a web-based visualization platform: the Iowa Water Quality Information System, Beta version (IWQIS Beta). Streams of 4th order and up are color-coded according to the estimated temperatures. Hourly forecasts for lead time of up to 18 hours are also available.

A model was developed separately for spring (March to May), summer (June to August), and autumn (September to November) seasons. 2016 model estimation results generate less than 3 °C average RMSE for the three seasons, with a summer season RMSE of below 2 °C. The model is transferrable to basins of different catchment sizes within the state of Iowa and requires hourly air temperature as the only input variable. The product will assist Iowa water quality research and provide information to support public management decisions.

Public Abstract

In the agricultural state of Iowa, water quality research is of great importance for monitoring and managing aquatic systems health. Among many water quality parameters, water temperature is a critical variable that governs the rates of chemical and biological processes which affect river health. A statistical model was developed to produce real- time high resolution predictive stream temperature for the entire state of Iowa. The implemented model generates current-hour stream temperature estimations state-wide. Forecasts for 18 hours in advance are also available. The hourly estimation results are updated in real-time and presented on a web-based visualization platform: the Iowa Water Quality Information System, Beta version (IWQIS Beta). This product will assist Iowa water quality research and provide information to support public management decisions.

Keywords

logistic regression model, prediction, real-time, Water temperature

Pages

x, 91 pages

Bibliography

Includes bibliographical references (pages 89-91).

Copyright

Copyright © 2017 Yibing Su

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