2017
DOI: 10.1111/1752-1688.12543
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Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages

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… Show more

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Cited by 18 publications
(21 citation statements)
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“…Unfortunately, the NorWeST model in its current form yields few insights about where thermal regimes are impaired by anthropogenic factors because the covariates we used from national data sets do not represent those factors. This limitation has been recognized previously (Moore et al, ; Wehrly et al, ) and once more detailed covariates are developed, they could be included in temperature model revisions to test for additional effects, identify effect locations through residual sensitivity analysis, and improve predictive accuracy (see Scown et al, for a relevant SSN example with stream nutrients). Useful covariates might include inventories of channel realignments, water diversions, or detailed measures of riparian canopy conditions.…”
Section: Discussionmentioning
confidence: 99%
“…Unfortunately, the NorWeST model in its current form yields few insights about where thermal regimes are impaired by anthropogenic factors because the covariates we used from national data sets do not represent those factors. This limitation has been recognized previously (Moore et al, ; Wehrly et al, ) and once more detailed covariates are developed, they could be included in temperature model revisions to test for additional effects, identify effect locations through residual sensitivity analysis, and improve predictive accuracy (see Scown et al, for a relevant SSN example with stream nutrients). Useful covariates might include inventories of channel realignments, water diversions, or detailed measures of riparian canopy conditions.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, spatial stream network modeling approaches (e.g., Peterson et al 2013;McGuire et al 2014;Ver Hoef et al 2014;McManus et al 2016;Scown et al 2017) are now available to further evaluate effects of headwater disturbance and cumulative downstream impacts. .…”
Section: Discussionmentioning
confidence: 99%
“…A similar approach of using assumed values of proposed mine impacts was used to develop a predictive model for downstream conductivity based on a weighted average approach at stream confluences ). In addition, spatial stream network modeling approaches (e.g., Peterson et al 2013;McGuire et al 2014;Ver Hoef et al 2014;McManus et al 2016;Scown et al 2017) are now available to further evaluate effects of headwater disturbance and cumulative downstream impacts. Such geospatial techniques are necessary to advance watershed management and next-generation watershed assessment.…”
Section: Discussionmentioning
confidence: 99%
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“…This is an issue for quantifying the environmental effects of different fertilizing systems, as manure and chemical fertilizers have different nitrous oxide emissions (a potent greenhouse gas) and nitrate leaching to groundwater (Basso & Ritchie, ; Charles et al, ; Tuomisto et al, ). Additionally, local data about septic systems and point sources have been shown to improve modeling when added to coarser national data (Scown et al, ). Approaches that use explicit sources, like NANI, generally estimate loads at large watershed or county scales and are used to predict riverine outputs (Goyette et al, ; Han & Allan, ; Hong et al, ), rather than informing specific changes in local management.…”
Section: Introductionmentioning
confidence: 99%