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This file describes the contents of 9 files and folders loaded to the Dryad data depository by Lise Montefiore.
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1. vulnerability.csv
2. vulnerability.zip
3. nutrients_yields.csv
4. TN_yields_uncertainties.zip
5. TP_yields_uncertainties.zip
6. nutrient_yields_variability.csv
7. exposure_variability.csv
8. average_event_precipitation.csv
9. figures_reproduction.R

These files contain the data, analysis, and results associated with the paper "Vulnerability of Estuarine Systems in the Contiguous United States to Water Quality Change Under Future Climate and Land-Use” published at Earth’ s Future. 

Contact Lise Montefiore (lisemonte@gmail.com) or Natalie Nelson (nnelson4@ncsu.edu) with questions about these datasets.s. 

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Metadata & details about each of the 6 files and 3 folders. 
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1. vulnerability.csv 

Data from the vulnerability’s component scores. A score of 0 represents a low exposure, sensitivity score, or adaptive capacity score, while a score of 1 represents a high exposure, sensitivity, or high adaptive capacity score. 

Column headings and descriptions:

“name”: watershed’s name included in the analysis
“state”: state’s abbreviation name in which the watershed was located. A watershed can be shared with multiple states
“region”: region where the watershed is located
“A1B_45_TN”: average change score for TN under RCP 4.5 and A1B, rescaled from 0 to 1 (exposure component)
“A2_45_TN”: average change score for TN under RCP 4.5 and A2, rescaled from 0 to 1  (exposure component)
“B1_45_TN”: average change score for TN under RCP 4.5 and B1, rescaled from 0 to 1 (exposure component)
“B2_45_TN”: average change score for TN under RCP 4.5 and B2, rescaled from 0 to 1 (exposure component)
“A1B_85_TN”: average change score for TN under RCP 8.5 and A1B, rescaled from 0 to 1 (exposure component)
“A2_85_TN”: average change score for TN under RCP 8.5 and A2, rescaled from 0 to 1 (exposure component)
“B1_85_TN”: average change score for TN under RCP 8.5 and B1, rescaled from 0 to 1 (exposure component)
“B2_85_TN”: average change score for TN under RCP 8.5 and B2, rescaled from 0 to 1 (exposure component)
“A1B_45_TP”: average change score for TP under RCP 4.5 and A1B, rescaled from 0 to 1 (exposure component)
“A2_45_TP”: average change score for TP under RCP 4.5 and A2, rescaled from 0 to 1 (exposure component)
“B1_45_TP”: average change score for TP under RCP 4.5 and B1, rescaled from 0 to 1 (exposure component)
“B2_45_TP”: average change score for TP under RCP 4.5 and B2, rescaled from 0 to 1 (exposure component)
“A1B_85_TP”: average change score for TP under RCP 8.5 and A1B, rescaled from 0 to 1 (exposure component)
“A2_85_TP”: average change score for TP under RCP 8.5 and A2, rescaled from 0 to 1 (exposure component)
“B1_85_TP”: average change score for TP under RCP 8.5 and B1, rescaled from 0 to 1 (exposure component)
“B2_85_TP”: average change score for TP under RCP 8.5 and B2, rescaled from 0 to 1 (exposure component)
“eutrophication”: eutrophication scores (sensitivity component)
“susceptibility_TN”: physical susceptibility to increased TN loading, rescaled from 0 to 1 (sensitivity component)
“susceptibility_TP”: physical susceptibility to increased TP loading, rescaled from 0 to 1 (sensitivity component)
“budget”: amount of the annual state budget dedicated to environmental and natural resources departments normalized by the population of the respective state, rescaled from 0 to 1 (human  adaptive capacity component)
“climate_plan”: the existence of a state climate adaptation plan, rescaled from 0 to 1 (human adaptive capacity component)
“wq_criteria” : the state adoption of numeric water quality parameter criteria rescaled from 0 to 1 (human adaptive capacity component)
“human_ac_legislative”: average scores of the “budget”, “climate_plan”, and “wq_criteria” columns (human adaptive capacity component)   
“review”: the number of published peer-reviewed research articles related to climate change, nutrients, and eutrophication of the estuary, rescaled from 0 to 1 (human adaptive capacity component)
“monitoring”: the number of published peer-reviewed research articles related to climate change, nutrients, and eutrophication of the estuary, rescaled from 0 to 1 (human adaptive capacity component)
“faculty_number”: the number of academic staff in R1 and R2 universities in the states where the watershed was located, normalized by the state population, rescaled from 0 to 1
“human_ac_knowledge”: average scores of the “review”, “monitoring”, and “faculty_number” columns (human adaptive capacity component)
“overall_human_ac”: average scores of the ““human_ac_legislative” and “human_ac_knowledge” (human adaptive capacity component)
 “wetlands”: wetlands density per watershed score, rescaled from 0 to 1 (natural adaptive capacity component)

2. vulnerability.zip 

This folder contains the shapefile vulnerability.shp and its associated files. The shapefile has the same columns as file 1 and contains the geospatial information of the 112 estuarine watersheds. 
This file was used to produce Figure 6 in the main manuscript and Figures S8-9 and S14 in the supporting information. 

3. nutrients_yields.csv

Annual and seasonal TN and TP yields computed for 15 combinations of historical and projected land-use (i.e. A1B, A2, B1, B2),  and climate (I.e., RCPs 4.5 and 8.5) scenarios.
This file was used to produce Figure 3 in the main manuscript and Figure S7 in the supporting information. 

Column headings and descriptions:

“name”: watershed’s name included in the analysis
“state”: state’s abbreviation name in which the watershed was located. A watershed can be shared with multiple states
“region”: regions where the watersheds are located
state”: states in which the watersheds are located
“area”: area of the watersheds in km2
“Yield”: TN or TP yields in kg/km2
“land_use”: land-use model and scenario. “NLCD 2016”, and “A1B”, “A2”,  “B1”,  “B2” represent historical, and projected land-use scenarios, respectively.
“Period”: annual or seasonal 
“climate”: historical or projected climate (I.e., RCPs 4.5 and 8.5)
“nutrient”: TN or TP

4. TN_yields_uncertainties.zip

This folder contains the shapefile TN_yields_uncertainties.shp and its associated files. This file was used to produce Figure 4 in the manuscript. 

Column headings and descriptions:

“name”: watershed’s name included in the analysis
“region”: regions where the watersheds are located
“p90_rcp45”: 90th percentile of ∆TN,A  measured from the 20 climate models and 4 LULC scenarios (A1B, A2, B1, B2) under RCP 4.5 
“p90_rcp85” :  90thof ∆TN,A  measured from the 20 climate models and 4 LULC (A1B, A2, B1, B2) scenarios under RCP 8.5 
“p90_rcp45_85”:  90th of ∆TN,A  measured from the 20 climate models and 4 LULC (A1B, A2, B1, B2) and 2 climate scenarios (RCPs 4.5 and 8.5)  
“sd_rcp45”: standard deviation of ∆TN,A  measured from the 20 climate models and 4 LULC scenarios (A1B, A2, B1, B2) under RCP 4.5 
“sd_rcp85”: standard deviation of ∆TN,A  measured from the 20 climate models and 4 LULC (A1B, A2, B1, B2) scenarios under RCP 8.5 
“sd_rcp45_85”: standard deviation of ∆TN,A  measured from the 20 climate models and 4 LULC (A1B, A2, B1, B2) and 2 climate scenarios (RCPs 4.5 and 8.5)

5. TP_yields_uncertainties.zip

This folder contains the shapefile TP_yields_uncertainties.shp and its associated files. This file was used to produce Figure 4 in the manuscript. 

Column headings and descriptions:

“name”: watershed’s name included in the analysis
“region”: regions where the watersheds are located
“p90_rcp45”: 90th percentile of ∆TP,A  measured from the 20 climate models and 4 LULC scenarios (A1B, A2, B1, B2) under RCP 4.5 
“p90_rcp85” :  90th of ∆TP,A  measured from the 20 climate models and 4 LULC (A1B, A2, B1, B2) scenarios under RCP 8.5 
“p90_rcp45_85”:  90th of ∆TP,A  measured from the 20 climate models and 4 LULC (A1B, A2, B1, B2) and 2 climate scenarios (RCPs 4.5 and 8.5)  
“sd_rcp45”: standard deviation of ∆TP,A  measured from the 20 climate models and 4 LULC scenarios (A1B, A2, B1, B2) under RCP 4.5 
“sd_rcp85”: standard deviation of ∆TP,A  measured from the 20 climate models and 4 LULC (A1B, A2, B1, B2) scenarios under RCP 8.5 
“sd_rcp45_85”: standard deviation of ∆TP,A  measured from the 20 climate models and 4 LULC (A1B, A2, B1, B2) and 2 climate scenarios (RCPs 4.5 and 8.5)

6. nutrient_yields_variability.csv

This file was used to create Figure 5 in the main manuscript. Data contains projected annual nutrient yields simulated with STEPLgrid for different climate models under the four LULC scenarios and two climate scenarios. The 10th and 90th percentiles of the nutrient yields were computed using the 20 climate models outputs and were compared to the outputs corresponding to the multi-model ensemble mean. 

Column headings and descriptions:

“name”: watershed’s name included in the analysis
“region”: regions where the watersheds are located
“state”: state’s abbreviation name in which the watershed was located. A watershed can be shared with multiple states
“area”: area of the watersheds in km2
“land_use”: land-use scenarios  (A1B, A2,  B1,  B2)
“climate”: climate scenarios (RCP45, RCP85)
“nutrient”: TN or TP
“variable”:  10th and percentiles of nutrient yields using using the 20 MACA climate models outputs. MM corresponds to  nutrient yields computed with the multi-model ensemble mean 
“value”: nutrient yields in kg/km2

7. exposure_variability.csv

This file was used to create Figure 7 in the main manuscript and Figures S10-13 in the supporting information. 

Column headings and descriptions:

“name”: watershed’s name included in the analysis
“MM”: exposure score computed the multi-model ensemble mean
“variable”: range of exposure score values
“value”: number of times the exposure scores fell within the given ranges when calculated for each climate model output 
“MM-label”: the symbol “+” means that the exposure score was computed with the multi-model ensemble mean, NA otherwise
“nutrient”: TP or TN
“rcp”: climate scenarios (rcp45 or rcp85)
“lulc”: land-use scenarios (A1B, A2,  B1,  B2)

8. average_event_precipitation.csv

This file was used to create Figure S6 in the supporting information. 

Column headings and descriptions:

“region”: regions where the watersheds are located
“year”: year for which event mean precipitation was calculated
“climate”: historical or projected (rcp45 or rcp85)
“Mean”: average annual average event precipitation (mm) the averaging climate ensemble
“Max”: maximum annual average event precipitation (mm) from the climate model ensemble 
“Min”: minimum annual average event precipitation (mm) from the climate model ensemble 

9. figures_reproduction.Rmd

RMarkdown document that displays the code used to reproduce Figures 3, 5, and 7 in the main manuscript. The files need to be loaded in your directory, which need to be added in the Rscript. 

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#   Existing data used 
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The water quality model STEPLgrid was previously tested by Montefiore and Nelson (2022) for the study area. Hydrological soil groups were retrieved from gSSURGO (Soil Survey Staff, n.d.) and HYSOGs250m dataset (Ross et al., 2018). Historical precipitation for the period from 1990 to 2020 was obtained from the gridMET dataset (Abatzoglou, 2013). Projected precipitation data for the period from 2035 to 2065 were obtained from the outputs of twenty climate models from the Coupled Model Intercomparison Project 5 (CMIP5) ensemble downscaled with Multivariate Adaptive Constructed Analogs (MACA) (Abatzoglou and Brown, 2012). The 2016 National Land Cover Database (NLCD) was used to represent current land-use and land-cover (LULC) (Homer et al., 2012; Jin et al., 2019). LULC projections for the year 2050 were used from the USGS FOREcasting SCEnarios (FORE-SCE) model (Sohl et al., 2007). The spatial importance of large-scale continuous and predictable/seasonal upwelling along the CONUS coasts was retrieved from Hoekstra et al. (2010). The degree of existing eutrophication for each study estuary was obtained from the 2008 National Estuarine Eutrophication Assessment (Bricker et al., 2008). The Dissolved Concentration Potential (DCP) and Particle Retention Efficiency (PRE) were developed by NOAA (1989) and their values were calculated by Engle et al. (2007) for the estuaries studied. Research-intensive universities were identified through the Carnegie database (About Carnegie Classification, n.d). Long-term water quality information were retrieved from Oelsner et al. (2017) and Oelsner and Stets (2019). The annual state budgets dedicated to environmental and natural resources departments from 2011 and 2015 were retrieved from Ballotpedia (n.d.). The status of climate adaptation plans per state was retrieved from the Georgetown Climate Center (n.d.). The development status of EPA-approved nitrogen, phosphorus, and Chl-a were retrieved from the EPA (U.S. EPA, 2016). Wetland areas were retrieved from the National Wetlands Inventory (NWI) (U.S. Fish & Wildlife Service, 2018). gSSURGO data (Soil Survey Staff, n.d.) were accessed through ArcGIS. The maps and plots were created with QGIS (QGIS.org, 2018) and R (R Core Team, 2020). 

References:

Abatzoglou, J. T. (2013).Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. https://doi.org/10.1002/joc.3413
Abatzoglou, J.T., & Brown, T.J. (2012). A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology, 32(5), 772–780. https://doi.org/10.1002/joc.2312
About Carnegie Classification (n.d). Carnegie Classif. Inst. High. Educ. URL http://carnegieclassifications.iu.edu/2010/
Ballotpedia (n.d.).  Environmental spending in the 50 states. Ballotpedia. URL https://ballotpedia.org/Environmental_spending_in_the_50_states (accessed 11.3.21).
Bricker, S.B., Longstaff, B., Dennison, W., Jones, A., Boicourt, K., Wicks, C., & Woerner, J. (2008). Effects of nutrient enrichment in the nation’s estuaries: a decade of change. Harmful Algae, 8(1), 21-32.
Engle, V. D., Kurtz, J. C., Smith, L. M., Chancy, C., & Bourgeois, P. (2007). A classification of US estuaries based on physical and hydrologic attributes. Environmental Monitoring and Assessment, 129(1), 397-412. https://doi.org/10.1007/s10661-006-9372-9
Georgetown Climate Center, n.d. State Adaptation Progress Tracker. georgetownclimatecenter.org. URL https://www.georgetownclimate.org/adaptation/plans.html.
Hoekstra, J. M., Molnar, J. L., Jennings, M., Revenga, C., Spalding, M. D., Boucher, et al. (2010). The atlas of global conservation: changes, challenges, and opportunities to make a difference (pp. 1-272). Berkeley: University of California Press.
Homer, C.G., Fry, J.A., & Barnes, C.A., (2012). The National Land Cover Database (Report No. 2012–3020), Fact Sheet. Reston, VA. https://doi.org/10.3133/fs20123020
Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., et al. (2019). Overall methodology design for the United States national land cover database 2016 products. Remote Sensing, 11(24), 2971. https:/doi.org/10.3390/rs11242971
Montefiore, L. R., & Nelson, N. G. (2022). Can a simple water quality model effectively estimate runoff-driven nutrient loads to estuarine systems? A national-scale comparison of STEPLgrid and SPARROW. Environmental Modelling & Software, 150, 105344. https://doi.org/10.1016/j.envsoft.2022.105344
NOAA (1989). Susceptibility and status of Gulf of Mexico estuaries to nutrient discharges. Strategic assessment of near-coastal waters. Summary report (No. PB-93-112357/XAB). Delaware Univ., Newark, DE (United States). Coll. of Marine Studies.
Oelsner, G.P., Sprague, L.A., Murphy, J.C., Zuellig, R.E., Johnson, H.M., Ryberg, K.R., et al. (2017). Water-quality trends in the Nation’s rivers and streams, 1972–2012—Data preparation, statistical methods, and trend results (Scientific Investigations Report No. Report 2017–5006), Scientific Investigations Report. Geological Survey.
Oelsner, G. P., & Stets, E. G. (2019). Recent trends in nutrient and sediment loading to coastal areas of the conterminous US: Insights and global context. Science of the Total Environment, 654, 1225-1240. https://doi.org/10.1016/j.scitotenv.2018.10.437
Ross, C.W., L. Prihodko, J.Y. Anchang, S.S. Kumar, W. Ji, and N.P. Hanan. (2018). Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1566
Sohl, T. L., Sayler, K. L., Drummond, M. A., & Loveland, T. R. (2007). The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling. Journal of Land Use Science, 2(2), 103-126. https://doi.org/10.1080/17474230701218202
Soil Survey Staff (n.d.). Gridded Soil Survey Geographic (gSSURGO) Database for the Conterminous United States. United States Department of Agriculture, Natural Resources Conservation Service.
U.S. EPA (2016). State Progress Toward Developing Numeric Nutrient Water Quality Criteria for Nitrogen and Phosphorus. URL https://www.epa.gov/nutrient-policy-data/state-progress-toward-developing-numeric-nutrient-water-quality-criteria
U.S. Fish & Wildlife Service (2018). National Wetlands Inventory. URL https://data.nal.usda.gov/dataset/national-wetlands-inventory