Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages
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....
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| Published in | Journal of the American Water Resources Association Vol. 53; no. 4; pp. 944 - 960 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Blackwell Publishing Ltd
01.08.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1093-474X 1752-1688 1752-1688 |
| DOI | 10.1111/1752-1688.12543 |
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| 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. |
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| AbstractList | 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. 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. 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 1290 km 2 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 that 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. 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 km 2 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. 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 1290 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 that 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.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 1290 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 that 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. 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 1290 km 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 that 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 W. Carson, John H. Nietch, Christopher T. McManus, Michael G. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30034212$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1002/9781118643525.ch6 10.1007/s00267-009-9330-8 10.1890/1051-0761(1998)008[0559:NPOSWW]2.0.CO;2 10.1007/s00267-009-9401-x 10.1007/s10661-005-9156-7 10.1080/00401706.1974.10489231 10.1890/08-1668.1 10.1371/journal.pone.0134757 10.1002/env.995 10.1021/es0716103 10.1061/(ASCE)EE.1943-7870.0000770 10.1007/s10651-006-0022-8 10.1111/j.1752-1688.1994.tb03315.x 10.1046/j.1365-2427.1997.d01-539.x 10.1016/j.envsoft.2014.06.019 10.1198/jasa.2009.ap08248 10.3133/fs20123020 10.1890/14-0935.1 10.1002/env.2284 10.1021/es2003167 10.1016/j.scitotenv.2007.03.036 10.1017/CBO9781139022422.010 10.1214/10-STS330 10.1002/rra.2978 10.1016/j.scitotenv.2008.08.002 10.18637/jss.v056.i02 10.1890/09-0822.1 10.1029/2005GB002496 10.2166/wst.2004.0150 10.1890/0012-9658(1999)080[2283:SHOSWN]2.0.CO;2 10.1007/PL00021506 10.1007/s10661-015-4504-8 10.1002/wat2.1023 10.1002/ecs2.1321 10.1007/s10661-013-3114-6 10.1073/pnas.1404820111 10.1007/s00267-008-9139-x 10.1039/C4EM00583J 10.1111/j.1752-1688.2011.00574.x 10.1111/j.1752-1688.2007.00045.x |
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| Copyright | 2017 American Water Resources Association. This article is a U.S. Government work and is in the public domain in the USA 2017 American Water Resources Association |
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| References | 1974; 16 2009; 44 2015; 17 2009; 43 2012 2016b 2007; 382 2011 2016a 2006; 13 2004; 49 2010; 105 2015; 52 2015; 32 2015; 187 2015; 10 2009 2014; 25 2006 2013; 185 2008; 400 2014; 111 1999; 80 2014; 60 1999 2010; 45 2014; 1 2010; 21 2016; 7 2010; 20 2015; 25 2005; 19 2010; 25 1997; 37 2003; 6 2013; 139 2006; 121 2016 2015 2011; 45 2014 2011; 47 2007; 42 2010; 91 2007; 43 2014; 56 1994; 30 1998; 8 Fox J. (e_1_2_8_12_1) 2011 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_26_1 Mercurio G. (e_1_2_8_29_1) 1999 e_1_2_8_3_1 e_1_2_8_7_1 e_1_2_8_9_1 USEPA (U.S. Environmental Protection Agency) (e_1_2_8_49_1) 2016 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_22_1 e_1_2_8_45_1 e_1_2_8_41_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_57_1 BOM (Bureau of Metereology) (e_1_2_8_5_1) 2015 e_1_2_8_55_1 Ohio EPA (Ohio Environmental Protection Agency) (e_1_2_8_32_1) 2009 USGS (U.S. Geological Survey) (e_1_2_8_51_1) 2015 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_30_1 McKay L. (e_1_2_8_28_1) 2012 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_48_1 e_1_2_8_2_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 Karcher S.C. (e_1_2_8_23_1) 2012 e_1_2_8_44_1 e_1_2_8_40_1 e_1_2_8_18_1 ESRI (Environmental Systems Research Institute) (e_1_2_8_10_1) 2014 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_58_1 Hill R.A. (e_1_2_8_17_1) 2015; 52 R Core Team (e_1_2_8_39_1) 2015 Ver Hoef J.M. (e_1_2_8_54_1) 2014; 56 e_1_2_8_31_1 e_1_2_8_56_1 e_1_2_8_33_1 e_1_2_8_52_1 e_1_2_8_50_1 |
| References_xml | – year: 2011 – volume: 19 start-page: GB4S07 year: 2005 article-title: Modeling Nutrient (N, P, Si) Budget in the Seine Watershed: Application of the Riverstrahler Model Using Data from Local to Global Scale Resolution publication-title: Global Biogeochemical Cycles – year: 2009 – year: 2016b – volume: 60 start-page: 320 year: 2014 end-page: 330 article-title: Improving the Predictive Power of Spatial Statistical Models of Stream Macroinvertebrates Using Weighted Autocovariance Functions publication-title: Environmental Modelling & Software – volume: 16 start-page: 499 year: 1974 end-page: 511 article-title: Regressions by Leaps and Bounds publication-title: Technometrics – volume: 52 start-page: 1 year: 2015 end-page: 9 article-title: The Stream‐Catchment (Streamcat) Dataset: A Database of Watershed Metrics for the Conterminous United States publication-title: Journal of the American Water Resources Association – volume: 6 start-page: 407 year: 2003 end-page: 423 article-title: Effects of Land Cover on Stream Ecosystems: Roles of Empirical Models and Scaling Issues publication-title: Ecosystems – volume: 111 start-page: 7030 year: 2014 end-page: 7035 article-title: Network Analysis Reveals Multiscale Controls on Streamwater Chemistry publication-title: Proceedings of the National Academy of Sciences – volume: 25 start-page: 289 year: 2010 end-page: 310 article-title: To Explain or to Predict? publication-title: Statistical Science – volume: 30 start-page: 605 year: 1994 end-page: 611 article-title: Characterization of Surface‐Water Quality along a Watershed Disturbance Gradient publication-title: Water Resources Bulletin – volume: 121 start-page: 571 year: 2006 end-page: 596 article-title: Patterns of Spatial Autocorrelation in Stream Water Chemistry publication-title: Environmental Monitoring and Assessment – volume: 21 start-page: 439 year: 2010 end-page: 456 article-title: Spatial Modelling and Prediction on River Networks: Up Model, Down Model or Hybrid? publication-title: Environmetrics – year: 2014 – year: 2016a – volume: 25 start-page: 306 year: 2014 end-page: 323 article-title: Spatial Sampling on Streams: Principles for Inference on Aquatic Networks publication-title: Environmetrics – volume: 7 start-page: e01321 year: 2016 article-title: Modeling Lake Trophic State: A Random Forest Approach publication-title: Ecosphere – volume: 382 start-page: 1 year: 2007 end-page: 13 article-title: Defining the Sources of Low‐Flow Phosphorus Transfers in Complex Catchments publication-title: Science of the Total Environment – volume: 80 start-page: 2283 year: 1999 end-page: 2298 article-title: Spatial Heterogeneity of Stream Water Nutrient Concentrations over Successional Time publication-title: Ecology – volume: 10 start-page: e0134757 year: 2015 article-title: Downstream Warming and Headwater Acidity May Diminish Coldwater Habitat in Southern Appalachian Mountain Streams publication-title: PLoS ONE – volume: 49 start-page: 1 year: 2004 end-page: 10 article-title: Estimates of Diffuse Phosphorus Sources in Surface Waters of the United States Using a Spatially Referenced Watershed Model publication-title: Water Science and Technology – volume: 400 start-page: 379 year: 2008 end-page: 395 article-title: Delivery and Cycling of Phosphorus in Rivers: A Review publication-title: Science of the Total Environment – start-page: 126 year: 2012 end-page: 150 – volume: 45 start-page: 336 year: 2010 end-page: 350 article-title: Influences of Spatial Scale and Soil Permeability on Relationships between Land Cover and Baseflow Stream Nutrient Concentrations publication-title: Environmental Management – volume: 32 start-page: 1654 year: 2015 end-page: 1671 article-title: A Watershed Integrity Definition and Assessment Approach to Support Strategic Management of Watersheds publication-title: River Research and Applications – year: 2015 – start-page: 103 year: 2016 end-page: 131 – volume: 56 start-page: 1 year: 2014 end-page: 43 article-title: SSN: An R Package for Spatial Statistical Modeling on Stream Networks publication-title: Journal of Statistical Software – volume: 17 start-page: 956 year: 2015 end-page: 964 article-title: Correlation of Trace Contaminants to Wastewater Management Practices in Small Watersheds publication-title: Environmental Science: Processes & Impacts – volume: 56 start-page: 1 year: 2014 end-page: 17 article-title: Stars: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data publication-title: Journal of Statistical Software – volume: 1 start-page: 277 year: 2014 end-page: 294 article-title: Applications of Spatial Statistical Network Models to Stream Data publication-title: Wiley Interdisciplinary Reviews: Water – volume: 44 start-page: 505 year: 2009 end-page: 523 article-title: Long‐Term Effects of Changing Land Use Practices on Surface Water Quality in a Coastal River and Lagoonal Estuary publication-title: Environmental Management – volume: 8 start-page: 559 year: 1998 end-page: 568 article-title: Nonpoint Pollution of Surface Waters with Phosphorus and Nitrogen publication-title: Ecological Applications – volume: 43 start-page: 594 year: 2007 end-page: 604 article-title: Relationship of Land‐Use/Land‐Cover Patterns and Surface‐Water Quality in the Mullica River Basin publication-title: Journal of the American Water Resources Association – volume: 45 start-page: 5652 year: 2011 end-page: 5659 article-title: Combining Land Use Information and Small Stream Sampling with PCR‐Based Methods for Better Characterization of Diffuse Sources of Human Fecal Pollution publication-title: Environmental Science & Technology – volume: 185 start-page: 7485 year: 2013 end-page: 7499 article-title: Automated Riverine Landscape Characterization: GIS‐Based Tools for Watershed‐Scale Research, Assessment, and Management publication-title: Environmental Monitoring and Assessment – volume: 13 start-page: 449 year: 2006 end-page: 464 article-title: Spatial Statistical Models That Use Flow and Stream Distance publication-title: Environmental and Ecological Statistics – volume: 187 start-page: 1 year: 2015 end-page: 16 article-title: Combining and Aggregating Environmental Data for Status and Trend Assessments: Challenges and Approaches publication-title: Environmental Monitoring and Assessment – year: 2012 – volume: 139 start-page: 1413 year: 2013 end-page: 1423 article-title: Alternative Land‐Use Method for Spatially Informed Watershed Management Decision Making Using SWAT publication-title: Journal of Environmental Engineering – volume: 25 start-page: 943 year: 2015 end-page: 955 article-title: The Importance of Lake‐Specific Characteristics for Water Quality across the Continental United States publication-title: Ecological Applications – volume: 47 start-page: 1011 year: 2011 end-page: 1033 article-title: Nutrient Inputs to the Laurentian Great Lakes by Source and Watershed Estimated Using Sparrow Watershed Models publication-title: Journal of the American Water Resources Association – volume: 91 start-page: 644 year: 2010 end-page: 651 article-title: A Mixed‐Model Moving‐Average Approach to Geostatistical Modeling in Stream Networks publication-title: Ecology – year: 2006 – volume: 42 start-page: 822 year: 2007 end-page: 830 article-title: Differences in Phosphorus and Nitrogen Delivery to the Gulf of Mexico from the Mississippi River Basin publication-title: Environmental Science & Technology – volume: 43 start-page: 69 year: 2009 end-page: 83 article-title: Landscape Planning for Agricultural Nonpoint Source Pollution Reduction III: Assessing Phosphorus and Sediment Reduction Potential publication-title: Environmental Management – volume: 105 start-page: 6 year: 2010 end-page: 18 article-title: A Moving Average Approach for Spatial Statistical Models of Stream Networks publication-title: Journal of the American Statistical Association – volume: 37 start-page: 193 year: 1997 end-page: 208 article-title: Landscape Influences on Water Chemistry in Mid‐Western Stream Ecosystems publication-title: Freshwater Biology – volume: 20 start-page: 1350 year: 2010 end-page: 1371 article-title: Effects of Climate Change and Wildfire on Stream Temperatures and Salmonid Thermal Habitat in a Mountain River Network publication-title: Ecological Applications – year: 1999 – ident: e_1_2_8_44_1 doi: 10.1002/9781118643525.ch6 – volume-title: Australian Hydrological Geospatial Fabric Version 2 year: 2015 ident: e_1_2_8_5_1 – ident: e_1_2_8_31_1 – ident: e_1_2_8_42_1 doi: 10.1007/s00267-009-9330-8 – ident: e_1_2_8_6_1 doi: 10.1890/1051-0761(1998)008[0559:NPOSWW]2.0.CO;2 – ident: e_1_2_8_7_1 doi: 10.1007/s00267-009-9401-x – ident: e_1_2_8_36_1 doi: 10.1007/s10661-005-9156-7 – volume-title: Sparrow Decision Support System—Upper Midwest Total Phosphorus in Water Model year: 2015 ident: e_1_2_8_51_1 – ident: e_1_2_8_14_1 doi: 10.1080/00401706.1974.10489231 – ident: e_1_2_8_37_1 doi: 10.1890/08-1668.1 – volume-title: Discharge Monitoring Report Pollutant Loading Tool year: 2016 ident: e_1_2_8_49_1 – ident: e_1_2_8_26_1 doi: 10.1371/journal.pone.0134757 – ident: e_1_2_8_15_1 doi: 10.1002/env.995 – volume-title: Examining the Feasibility of Water Quality Trading in the East Fork Watershed Case Study year: 2012 ident: e_1_2_8_23_1 – ident: e_1_2_8_3_1 doi: 10.1021/es0716103 – volume-title: R: A Language and Environment for Statistical Computing year: 2015 ident: e_1_2_8_39_1 – ident: e_1_2_8_24_1 doi: 10.1061/(ASCE)EE.1943-7870.0000770 – ident: e_1_2_8_52_1 doi: 10.1007/s10651-006-0022-8 – ident: e_1_2_8_57_1 doi: 10.1111/j.1752-1688.1994.tb03315.x – ident: e_1_2_8_22_1 doi: 10.1046/j.1365-2427.1997.d01-539.x – ident: e_1_2_8_13_1 doi: 10.1016/j.envsoft.2014.06.019 – ident: e_1_2_8_53_1 doi: 10.1198/jasa.2009.ap08248 – ident: e_1_2_8_19_1 doi: 10.3133/fs20123020 – ident: e_1_2_8_40_1 doi: 10.1890/14-0935.1 – ident: e_1_2_8_47_1 doi: 10.1002/env.2284 – ident: e_1_2_8_34_1 doi: 10.1021/es2003167 – ident: e_1_2_8_4_1 doi: 10.1016/j.scitotenv.2007.03.036 – ident: e_1_2_8_33_1 doi: 10.1017/CBO9781139022422.010 – ident: e_1_2_8_46_1 doi: 10.1214/10-STS330 – volume: 52 start-page: 1 year: 2015 ident: e_1_2_8_17_1 article-title: The Stream‐Catchment (Streamcat) Dataset: A Database of Watershed Metrics for the Conterminous United States publication-title: Journal of the American Water Resources Association – ident: e_1_2_8_50_1 – volume-title: Guide to Using 1995–1997 Maryland Biological Stream Survey Data year: 1999 ident: e_1_2_8_29_1 – volume-title: An R Companion to Applied Regression year: 2011 ident: e_1_2_8_12_1 – volume: 56 start-page: 1 year: 2014 ident: e_1_2_8_54_1 article-title: SSN: An R Package for Spatial Statistical Modeling on Stream Networks publication-title: Journal of Statistical Software – ident: e_1_2_8_11_1 doi: 10.1002/rra.2978 – ident: e_1_2_8_56_1 doi: 10.1016/j.scitotenv.2008.08.002 – ident: e_1_2_8_38_1 doi: 10.18637/jss.v056.i02 – ident: e_1_2_8_20_1 doi: 10.1890/09-0822.1 – ident: e_1_2_8_45_1 doi: 10.1029/2005GB002496 – ident: e_1_2_8_2_1 doi: 10.2166/wst.2004.0150 – ident: e_1_2_8_8_1 doi: 10.1890/0012-9658(1999)080[2283:SHOSWN]2.0.CO;2 – ident: e_1_2_8_48_1 doi: 10.1007/PL00021506 – ident: e_1_2_8_16_1 – ident: e_1_2_8_25_1 doi: 10.1007/s10661-015-4504-8 – ident: e_1_2_8_21_1 doi: 10.1002/wat2.1023 – ident: e_1_2_8_35_1 – ident: e_1_2_8_18_1 doi: 10.1002/ecs2.1321 – ident: e_1_2_8_30_1 – ident: e_1_2_8_55_1 doi: 10.1007/s10661-013-3114-6 – volume-title: Ohio EPA Manual of Surveillance Methods and Quality Assurance Practices year: 2009 ident: e_1_2_8_32_1 – ident: e_1_2_8_27_1 doi: 10.1073/pnas.1404820111 – volume-title: NHDPlus Version 2: User Guide year: 2012 ident: e_1_2_8_28_1 – ident: e_1_2_8_9_1 doi: 10.1007/s00267-008-9139-x – ident: e_1_2_8_43_1 doi: 10.1039/C4EM00583J – volume-title: ArcGIS Desktop: Release 10.2.2 year: 2014 ident: e_1_2_8_10_1 – ident: e_1_2_8_41_1 doi: 10.1111/j.1752-1688.2011.00574.x – ident: e_1_2_8_58_1 doi: 10.1111/j.1752-1688.2007.00045.x |
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| Title | Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages |
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