Improving Predictive Models of In-Stream Phosphorus Concentration Based on Nationally-Available Spatial Data Coverages
(2017) In Journal of the American Water Resources Association 53(4). p.944-960- Abstract
- Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km2 watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and... (More)
- Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km2 watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a36c8af0-416c-48ae-98c5-a08c814e335c
- author
- Scown, Murray LU ; McManus, Michael ; Carson, John and Nietch, Christopher
- organization
- publishing date
- 2017-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- water quality, spatial data, autocorrelation, statistical modelling, phosphorus (P), river
- in
- Journal of the American Water Resources Association
- volume
- 53
- issue
- 4
- pages
- 944 - 960
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85021780053
- wos:000407043700014
- pmid:30034212
- ISSN
- 1752-1688
- DOI
- 10.1111/1752-1688.12543
- language
- English
- LU publication?
- yes
- id
- a36c8af0-416c-48ae-98c5-a08c814e335c
- date added to LUP
- 2017-07-11 12:17:09
- date last changed
- 2025-04-04 14:27:31
@article{a36c8af0-416c-48ae-98c5-a08c814e335c,
abstract = {{Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km2 watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.}},
author = {{Scown, Murray and McManus, Michael and Carson, John and Nietch, Christopher}},
issn = {{1752-1688}},
keywords = {{water quality; spatial data; autocorrelation; statistical modelling; phosphorus (P); river}},
language = {{eng}},
number = {{4}},
pages = {{944--960}},
publisher = {{Wiley-Blackwell}},
series = {{Journal of the American Water Resources Association}},
title = {{Improving Predictive Models of In-Stream Phosphorus Concentration Based on Nationally-Available Spatial Data Coverages}},
url = {{http://dx.doi.org/10.1111/1752-1688.12543}},
doi = {{10.1111/1752-1688.12543}},
volume = {{53}},
year = {{2017}},
}