June 2014

1.
Introduction
The Phase 5.3.2 (P532) HSPF watershed model uses the Agricultural Chemical Model (AGCHEM) and the Pervious Quality Model (PQUAL) modules for the simulation of nutrient export from land sources. AGCHEM simulates biological and chemical processes, while PQUAL simulates nutrient fluxes using a simpler coefficient-based method. Although AGCHEM is a robust module from academic and scientific standpoints, its high level of complexity represents an obstacle for comprehension within the management community and introduces variability in response to inputs that is not readily attributed to measureable watershed characteristics. Using only the
PQUAL module in phase 6 will be more straightforward for comprehension and decision implementation.
Unlike the AGCHEM module, the PQUAL module is not generally sensitive to inputs of nutrients such as atmospheric deposition, fertilizer, and manure. However, variations in these inputs are featured in management scenarios. The effect of changes in nutrient inputs on nutrient outflows from the land simulation must be estimated and then applied to the PQUAL simulation. These input-output relationships are referred to in this document as sensitivity.
This technique allows for the direct use of multiple models in the estimation of these sensitivities. The use of multiple models has been recommended to the CBP partnership by the
CBP Scientific and Technical Advisory Committee on several occasions (e.g., Friedrichs, et al,
2012).
Inputs, outputs, and sensitivity to inputs of existing watershed models used in the Chesapeake
Bay Watershed have been evaluated. The sensitivities obtained from the analysis of all models will be used to specify functional links between nutrient inputs and outputs. Sensitivities will be presented and then implemented within the PQUAL module in the phase 6 version of the
Chesapeake Bay Watershed Model.

2.
Objectives a) To compare watershed models inputs, outputs, and sensitivity to inputs. b) To build, present and implement PQUAL sensitivities in the next version of the Chesapeake
Bay Watershed model.
3.
Methods
Five different watershed models previously used in the Chesapeake Bay Watershed are included in this analysis: the Phase 5.3.2 (P532) HSPF watershed model, the Spatially
Referenced Regression on Watershed Attributes (SPARROW) model, the Agricultural Policy
Environmental Extender (APEX) model, Annual Phosphorus Loss Estimator (APLE) model, and the Functional Linear Concurrent M odel ( FLCM) .
The Phase 5.3.2 CBP Watershed Model
For the 2017 mid-point assessment, a shift is planned from a combined PQUAL and AGCHEM simulation, which was systematically used in the previous phases of the Chesapeake Bay watershed model, to a full version of PQUAL. Yet, the Chesapeake Bay Program must develop a new version of PQUAL as robust as the ACHEM prior to application. To this end, The
Chesapeake Bay Program Modeling Team has conducted a sensitivity analysis of the P532
AGCEHM simulation between all nutrient inputs and outputs in the Chesapeake Bay watershed.
This analysis incorporated input loadings and predicted yields information from fourteen different P532 scenarios, including simulations from 1985 to 2011. The scenario list includes runs such as the “No Action” scenario, in which all management practices are removed, and the
“E3” simulation, a theoretical scenario where maximum levels of pollution management control are applied to the fullest extent. All scenarios were simulated over a ten-year hydrologic period from 1991 to 2000, and average annual data of both inputs and outputs were used for the

analyses.
Multi-variate regressions, slopes between nutrient export and input loading, and ratios between nutrient export and input loading were calculated to identify P532 landuse sensitivities. These sensitivities are presented in Tables 1-4.
Table 1. Model sensitivity slopes between nutrient export and total loading on different land uses. TN: Total nitrogen, DIN; Dissolved inorganic nitrogen; ON: Organic nitrogen; TP: Total phosphorus; PO4: Phosphate; OP:
Organic phosphorus; W: with; w/o: without. Blank cells indicate not applicable.
Land use TN DIN ON TP PO4 OP
Forest (for)
High-tillage w manure (hwm)
0.05 0.04
0.21 0.15
0.003
0.07 0.12 0.11 0.015
High-tillage w/o manure (hom)
Low-tillage w manure (lwm)
Hay w nutrient management (hym)
0.48 0.46 0.02 0.1 0.1 0.002
0.19 0.14 0.06 0.1 0.09 0.01
Hay w/o nutrient management (hyo) 0.3 0.3 0.005
0.08 0.08 0
Alfalfa (alf)
Pasture (pas)
0.03 0.03 -0.002 0.1 0.1 0.001
0.06 0.05 0.013
Non-regulated pervious urban (npd) 0.12 0.1 0.012
Table 2. Model sensitivity slopes of multi-variate regression between total nitrogen (TN) export and different types of loading on different land uses. A: atmospheric deposition; M: Manure; F: Fertilizer; U: Uptake; L:

Legume nitrogen fixation; w: with; w/o: without. Blank cells indicate not applicable.
Land use A M F U L
Forest (for)
High-tillage w manure (hwm)
0.05
0.35 0.19 0.29 -0.09 0.34
High-tillage w/o manure (hom)
Low-tillage w manure (lwm)
0.44 0.23 -0.004 0.43
0.33 0.16 0.31 -0.13 0.36
Hay w nutrient management (hym)
Hay w/o nutrient management (hyo) 0.27 0.02
Alfalfa (alf)
Pasture (pas)
0.22 0.08
0.15 0.06 0.1
Non-regulated pervious urban (npd) 0.15 0.07
Table 3. Model sensitivity slopes of multi-variate regression between total phosphorus (TP) export and different types of loading on different land uses. A: atmospheric deposition; M: Manure; F: Fertilizer; U: Uptake; L:
Legume nitrogen fixation; w: with; w/o: without. Blank cells indicate not applicable.
Land use A M F U L
Forest (for)
High-tillage w manure (hwm)
High-tillage w/o manure (hom)
Low-tillage w manure (lwm)
Hay w nutrient management (hym)
Hay w/o nutrient management (hyo)
Alfalfa (alf)
Pasture (pas)
Non-regulated pervious urban (npd)
0.13
0.11
0.09
0.11
0.1
0.09
0.1
0.15 0.09
Table 4. Ratio between nutrient exports and total loading on different land uses. TN: Total nitrogen, DIN;
Dissolved inorganic nitrogen; ON: Organic nitrogen; TP: Total phosphorus; PO4: Phosphate; OP: Organic

phosphorus; W: with; w/o: without. Blank cells indicate not applicable.
Landuse
Forest (for)
High-tillage w manure (hwm)
High-tillage w/o manure (hom)
Low-tillage w manure (lwm)
Hay w nutrient management
(hym)
Hay w/o nutrient management
(hyo)
Alfalfa (alf)
Pasture (pas)
Non-regulated pervious urban
(npd)
TN
0.29
0.28
0.23
0.26
0.37
0.73
0.07
0.22
DIN
0.13
0.17
0.2
0.16
0.3
0.62
0.05
0.07
ON
0.16
0.11
0.03
0.1
0.07
0.11
TP
0.02
0.15
0.1
0.1
0.09
0.08
0.09
PO4 OP
0.08 0.02
0.1 0.004
0.08 0.01
0.08
0.09
0.004
0.003
Spatially Referenced Regression on Watershed Attributes (SPARROW) model
SPARROW is a spatially explicit, mass-balance watershed model that uses nonlinear regressions to quantify the relationship between observed nutrient fluxes in nontidal streams and inputs and factors that affect their overland and in-stream fate and transport. The SPARROW model has been used to provide empirical estimates of the source, fate, and transport of nutrients across the United States including the Chesapeake Bay Watershed.
Ator et al. (2011) simulated the fate, transport, and flux of nitrogen and phosphorus in the
Chesapeake Bay Watershed using SPARROW, and the model was calibrated to mean annual conditions in 2002 in the Chesapeake Bay watershed. The NHDPlus segmentation and attributes developed by Wieczorek and LaMotte (2010a) were used in this study. The NHDPlus is a geospatial dataset that incorporates features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes stream networks based on the
1:100,000-scale NHD. Inputs and outputs from this study were compared to a P532 scenario run for a 10-year hydrology simulation period from 1991 to 2000 using a constant representational input data set for the year 2002.

The NHDplus atmospheric deposition attributes include inorganic nitrogen for the year 2002 compiled for every catchment of NHDPlus (Figure 1), and the atmosphere was assumed to be a negligible source of phosphorus. Both models have similar input rates and spatial distribution
(Figure 1). The wet atmospheric input load in 2002 by SPARROW and P532 were 195 and 183 millions of pounds, respectively. SPARROW only includes wet atmospheric deposition while
P532 includes both wet and dry inputs. The dry atmospheric deposition by P532 in the year
2002 was 276 millions of pounds.
Figure 1. P532 and SPARROW atmospheric deposition inputs for the year 2002.
Ruddy et al. (2006) created a data set that represents the estimated amount of nutrient inputs to the land surface, compiled for every catchment of NHDPlus for the conterminous United
States. Inorganic fertilizer estimates were calculated from the Association of American Plant
Food Control Officials fertilizer sales data, Census of Agriculture fertilizer expenditures, and U.S.
Census Bureau county population. P532, on the other hand, used crop uptake and Census of
Agriculture yields to determine the inorganic fertilizer application rates. Both models have similar nitrogen input rates; however, they did not show a similar spatial distribution (Figure
2).The nitrogen fertilizer input load in 2002 by SPARROW and P532 were 424 and 624 millions

of pounds, respectively.
Figure 2. P532 and SPARROW total nitrogen fertilizer inputs for the year 2002.
Both models have similar phosphorus input rates and similar spatial distribution (Figure 3). The phosphorus fertilizer input load in 2002 by SPARROW and P532 were 88 and 82 millions of pounds, respectively.

Figure 3. P532 and SPARROW total phosphorus fertilizer inputs for the year 2002.
The nitrogen and phosphorus content of livestock wastes was estimated using Census of
Agriculture. The procedures include: (1) estimating animal populations, (2) calculating nitrogen and phosphorus content of the animal manure, and (3) estimating the component of nitrogen and phosphorus from confined and unconfined livestock. SPARROW estimates of nitrogen input from manure do not account for loss through volatilization. These estimates represent the total nitrogen content in manure as excreted by each livestock group.
Both models have almost the same nitrogen and phosphorus input rates and spatial distribution
(Figure 4 and 5). The nitrogen manure input load in 2002 by SPARROW and P532 were 491 and
478 millions of pounds, respectively. The phosphorus manure input load in 2002 by SPARROW and P532 were 134.14 and 134.12 millions of pounds, respectively.

Figure 4. P532 and SPARROW total nitrogen manure inputs for the year 2002.
Figure 5. P532 and SPARROW total phosphorus manure inputs for the year 2002.
Despite differences in atmospheric and fertilizer input mass, spatial application rates and approach to assess water quality, the models showed almost the same output at 9 USGS stations (Figure 6 and 7).

Figure 6. P532 and SPARROW nitrogen outputs at 9 USGS stations.
Figure 7. P532 and SPARROW phosphorus outputs at 9 USGS stations.
SPARROW uses an estimated source-specific coefficient to interpret the proportion of the applied or deposited nutrient mass that is transported to the tributaries. For example, Ator et al. (2011) estimated that approximately 24% of the total nitrogen from fertilizer was

transported to the Chesapeake Bay tributaries in 2002 (a source-specific coefficient of 0.237).
Similarly, less than 6 % of total nitrogen from manure and total phosphorus from both fertilizer and manure reached watershed streams. Source-specific coefficients were 0.058, 0.038, and
0.025, respectively.
These rates can be interpreted as sensitivities to inputs. An additional pound of fertilizer input results in 0.237 lbs delivered to an NHD+ stream as an average for the entire Chesapeake basin.
The strength of SPARROW is that it is a regression approach that is based on observed data. A caveat is that these sensitivities are average sensitivities, not instantaneous sensitivities. The effect of adding a single pound of fertilizer to an unfertilized crop is likely different from the effect of adding an additional pound of fertilizer to an already overfertilized crop. An ideal sensitivity varies over the range of application or is derived from model behavior near typical application rates.
The Decision Support System (DSS) SPARROW is an online tool that allows mapping predictions of long-term average water quality conditions and changing nutrients source contributions. We conducted an exercise in the Susquehanna Basin to evaluate SPARROW sensitivity to inputs.
After adding 100% more fertilizer and manure, it was found that the DSS model is very sensitive to nitrogen fertilizer input but not very sensitive to nitrogen manure, or to phosphorus fertilizer or manure inputs. The mean and median sensitivity slopes (ratio between output and input) are in agreement with SPARROW’s manure and fertilizer source specific coefficients found previously in the literature (Figure 8 and 9).

Figure 8. DSS SPPAROW 2002 total nitrogen ratio between predicted yield and input load for the Susquehanna
Basin.

Figure 9. DSS SPPAROW 2002 total phosphorus ratio between predicted yield and input load for the
Susquehanna Basin.
The Agricultural Policy / Environmental eXtender (APEX) model
USGS-NRCS (2013) evaluated the impacts of conservation adoption on cropland in the
Chesapeake Bay watershed. The APEX model was used to estimate field-level effects of the crop management and conservation practices. Concurrently, the SWAT model (Soil and Water
Assessment Tool) was used to simulate non-point source loadings from land uses other than cropland and to route instream loads from one watershed to another.
The APEX model was developed to extend the Environmental Policy Integrated Climate (EPIC) model capabilities of simulating land management impacts for small-medium watersheds and

heterogeneous farms. APEX simulates day-to-day farming activities, wind and water erosion, loss or gain of soil organic carbon, and edge-of-field losses of water, soil, and nutrients.
HSPF and SWAT usually report a good performance when simulating streamflow and nutrients.
Im et al. (2003) found that HSPF streamflow and nutrient simulations more closely approximate observed values than those predicted by SWAT in an urbanizing watershed in northeastern
Virginia. Moriasi et al. (2007) has reviewed ranges of daily and monthly model calibration statistics for surface runoff, streamflow, sediment, and nutrients from several publications, and both HSPF and SWAT reported satisfactory calibration statistics. Based on HSPF and SWAT capabilities, we could anticipate that the models would produce similar results in the
Chesapeake Bay.
Very little information has been found to evaluate APEX and SWAT in the Chesapeake Bay region, and no spatial information or tabular detailed information has been publicly available for review. However, information from a study published by USGS-NRCS (2013), which included total inputs and outputs for four subregions of the Chesapeake Bay watershed, was used to calculate the ratio between predicted yields and input loads.
Functional Linear Concurrent M odel ( FLCM)
FLCM employs a functional linear concurrent approach to predict loads in forested regions by using combinations of satellite-derived data such as vegetation and disturbance indexes (Figure
10), nitrogen atmospheric deposition, precipitation and physiographic and land cover parameters.

Figure 10. FLCM monthly Normalized Difference Vegetation Index (NDVI) satellite-derived data for eight major basins of the Chesapeake Bay watershed.
The model was calibrated for the Chesapeake Bay region and the simulation period was from
2001 to 2009. Parameters and inputs vary by month, enabling predictions to be made on a monthly basis. Inputs and outputs are available for every major basin of the Chesapeake Bay watershed (Figure 11).

Figure 11. FLCM monthly predicted nitrate in forest regions for eight major basins of the Chesapeake Bay watershed.
Functional links between nutrient inputs and outputs were also calculated using this model. The ratio between monthly yield and monthly input load was calculated for every major basin of the
Chesapeake Bay watershed from 2001 to 2009 (Figure12). The median ratio was 0.26 and the average was 0.33.

Figure 12. FLCM monthly total nitrogen ratio between monthly yield and input load for eight major basins of the
Chesapeake Bay watershed.
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
0 0,2 0,4 0,6 0,8 1 1,2 1,4
RATIO
Figure 13. Cumulative distribution function of total nitrogen ratio between monthly yield and input load.

Annual Phosphorus Loss Estimator (APLE) Model
The APLE model is a Microsoft Excel spreadsheet model that runs on an annual time step. The model simulates sediment bound and dissolved P loss in surface runoff. It does not consider subsurface loss of P through leaching to groundwater or artificial drainage networks. It is intended to simulate edge-of-field P loss for uniform fields of several hectares in size, or smaller
(Vadas, 2014).
APLE was used to simulate phosphorus loss in every county of the Chesapeake Bay watershed and in five types of agricultural landuses from 1992 to 2005 (Mulkey, in prep). The landuses simulated were high-till with manure (hwm), high-till with manure nutrient management (nhi), low-till with manure (lwm), low-till with manure nutrient management (nlo), and pasture (pas).
APLE model’s phosphorus sensitivity to inputs was assessed by calculating the ratio between annual predicted yield and input loads for every county of the Chesapeake Bay watershed.
Figures 14 - 18 show APLE’s sensitivity distribution for the entire simulation period.
The implementation of the APLE model will likely take one of two forms following further study.
APLE may be used directly to estimate phosphorus losses from cropland as part of each scenario run. Though this model closely matches recent research, its implementation would preclude the use of multiple models for estimating phosphorus. Alternatively, multiple runs of
APLE could be used to establish relationships between inputs and yield specific to each region’s characteristics. This prediction could then be used with predictions from other models in an ensemble framework.
Approximately 90% of hwm and nhi landuse ratios and 98% of lwm, nlo, and pas landuse ratios were lower than one. Hwm and nhi landuse showed higher median and average ratios than the rest of the landuses. The median and the average ratios are listed in Table 5.

1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
0,0001 0,001 0,01 0,1 1 10
RATIO
Figure 14. Cumulative distribution function of total phosphorus ratio between yield and input load for high-till with manure (hwm) landuse.
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
0,0001 0,001 0,01 0,1 1 10 100
RATIO
Figure 15. Cumulative distribution function of total phosphorus ratio between yield and input load for high-till with manure nutrient management (nhi) landuse.

1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
0,00001 0,0001 0,001 0,01
RATIO
0,1 1 10
Figure 16. Cumulative distribution function of total phosphorus ratio between yield and input load for low-till with manure (lwm) landuse.
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
0,00001 0,0001 0,001 0,01
RATIO
0,1 1 10
Figure 17. Cumulative distribution function of total phosphorus ratio between yield and input load for low-till with manure nutrient management (nlo) landuse.

0,7
0,6
0,5
0,4
0,3
1
0,9
0,8
0,2
0,1
0
0,00001 0,0001 0,001 0,01 0,1 1 10
RATIO
Figure 18. Cumulative distribution function of total phosphorus ratio between yield and input load for pasture
(pas) landuse.
Table 5. APLE median and average ratio between predicted yield and input loads.
Landuse Median Ratio Average Ratio high-till with manure (hwm) 0.247 high-till with manure nutrient management (nhi) 0.234
0.448
0.466 low-till with manure (lwm) 0.103 low-till with manure nutrient management (nlo) 0.097 pasture (pas) 0.081
0.186
0.191
0.155
4.
Results and Recommendations
The main similarities between P532 and SPARROW in their simulations of 2002 conditions were that both models: 1) used similar methods to calculate manure inputs, 2) showed similar manure and phosphorus fertilizer input rates and spatial distribution, 3) had almost the same total manure input, and 4) showed almost the same output at 9 USGS stations and from the entire Chesapeake Bay watershed.

The main differences found between SPARROW and P532 consisted of the following: 1)
SPARROW uses only wet atmospheric deposition while P532 includes both wet and dry, 2) the models used different methods to calculate fertilizer inputs, 3) SPARROW total nitrogen fertilizer input was significantly lower than the P532, and 4) the models did not show similar nitrogen fertilizer input rates or spatial distribution.
Preston and Brakebill (1999), Ator et al. (2011), Moore et al. (2011), and Preston et al. (2011) have published studies in the Chesapeake Bay Watershed region using the SPARROW model. All
SPARROW source-specific coefficients found in the literature were compiled and used in the sensitivity to input comparison.
The sensitivities obtained from APEX, SPARROW, APLE, and P532 models that represent the relationship between nutrient predicted yields and input loads for cropland are shown in
Figures 19, 20, 21, and 22. We recommend using the median of the distribution of all model coefficients to build the sensitivity to inputs in PQUAL agricultural landuses (Figures 19, 20, 21, and 22).
We recommend using sensitivity values of 0.255 and 0.225 to calculate fertilizer and manure nitrogen losses, respectively. We can interpret it as for every pound of nitrogen fertilizer that is entered into PQUAL, 0.255 pounds would make it to the tributaries.

Figure 19. APEX, P532, and SPARROW models sensitivity to fertilizer nitrogen inputs.
Figure 20. APEX, P532, and SPARROW models sensitivity to manure nitrogen inputs.
We recommend using a sensitivity value of 0.1 to calculate both fertilizer and manure phosphorus losses. We can interpret it as for every pound of phosphorus fertilizer that is put entered into PQUAL, 0.1 pounds would reach the tributaries. Future versions of this document will highlight a dependence on storage rather than inputs.

Figure 21. APEX, APLE, and P532, and SPARROW models sensitivity to fertilizer phosphorus inputs.
Figure 22. APEX, APLE, P532, and SPARROW models sensitivity to manure phosphorus inputs.
In forested regions, the P532 ratio between yield and input load was 0.29, whereas the FLCM median ratio was 0.26 and the average was 0.33. We recommend using a sensitivity value of
0.29 to calculate total nitrogen losses in forested regions. It may be interpreted as for every pound of atmospheric deposition that is entered into PQUAL, 0.29 pounds would make it to the tributaries.

In grasslands, the P532 ratio between yield and input load was 0.07, whereas the APLE median ratio was 0.081 and the average was 0.155. We recommend using a sensitivity value of 0.081 to calculate total nitrogen losses in grasslands. We can interpret it as for every pound of atmospheric deposition that is entered into PQUAL, 0.08 pounds would reach the tributaries.

5.
References a) Ator, S., J. Brakebill, and J. Blomquist. 2011. Sources, Fate, and Transport of Nitrogen and
Phosphorus in the Chesapeake Bay Watershed: An Empirical Model. USGS. Available online at
http://pubs.usgs.gov/sir/2011/5167/ b) Friedrichs, M., K.G. Sellner, and M.A. Johnston. 2012. USING MULTIPLE MODELS FOR
MANAGEMENT IN THE CHESAPEAKE BAY: A SHALLOW WATER PILOT PROJECT. Chesapeake Bay
Program Scientific and Technical Advisory Committee Report. No. 12-003, Edgewater, MD. 2012
09. c) Im, S., K. Brannan, S. Mostaghimi, and J. Cho. 2003. A Comparison of SWAT and HSPF
Models for Simulating Hydrologic and Water Quality Responses from an Urbanizing Watershed.
ASAE. Paper Number: 032175. d) Moore, R., C. Johnston, R. Smith, and B. Milstead. 2011. Source and Delivery of Nutrients to
Receiving Waters in the Northeastern and Mid-Atlantic Regions of the United States. JAWRA.
47(5):965-990. e) Moriasi, D., J. Arnold, M. Van Liew, R. Bingner, R. Harmel, and T. Veith. 2007. Model
Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations.
ASABE. Vol. 50(3): 885−900. f) Preston, S., R. Alexander, G. Schwarz, and C. Crawford. 2011. Factors Affecting Stream
Nutrient Loads: A Synthesis Of Regional Sparrow Model Results For The Continental United
States. JAWRA. 47(5):891-915. g) Preston, S. and J. Brakebill. 1999. Application of Spatially Referenced Regression Modeling for the Evaluation of Total Nitrogen Loading in the Chesapeake Bay Watershed. U.S. Geological

Survey. Available online at http://md.water.usgs.gov/publications/wrir-99-
4054/pdf/SPARROW.pdf h) Ruddy, B., D. Lorenz, and D. Mueller. 2006. County-Level Estimates of Nutrient Inputs to the
Land Surface of the Conterminous United States, 1982–2001. USGS. Available online at http://pubs.usgs.gov/sir/2006/5012/ i) Smith, R., G. Schwarz, and R. Alexander, 1997, Regional interpretation of water-quality monitoring data, Water Resources Research, v. 33, no. 12, pp. 2781-2798. Available online at http://water.usgs.gov/nawqa/sparrow/intro/intro.html j) Schwarz, G., A. Hoos, R. Alexander, and R. Smith. 2006. The SPARROW Surface Water-
Quality Model—Theory, Applications and User Documentation: U.S. Geological Survey,
Techniques and Methods. Available online at http://water.usgs.gov/nawqa/sparrow/sparrowmod.html k) USDA-NRCS. 2013. Impacts of Conservation Adoption on Cultivated Acres of Cropland in the
Chesapeake Bay Region, 2003-06 to 2011. 113p. Available online at http://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/technical/nra/ceap/?cid=stelprdb1
240074 l) Vadas, P. 2014. Annual Phosphorus Loss Estimator - Theoretical Documentation (Version
2.4). Available online at http://ars.usda.gov/Services/docs.htm?docid=21763 m) Wieczorek, M., and A. LaMotte. 2010a. Attributes for NHDPlus Catchments (version 1.1) for the Conterminous United States: Average Atmospheric (Wet) Deposition of Inorganic Nitrogen,
2002. USGS. Available online at
http://water.usgs.gov/GIS/metadata/usgswrd/XML/nhd_atdep.xml

n) Wieczorek, M., and A. LaMotte. 2010b. Attributes for NHDPlus Catchments (version 1.1) for the Conterminous Unites States: Nutrient Inputs from Fertilizer and Manure, Nitrogen and
Phosphorus (N&P), 2002. USGS. Available online at
http://water.usgs.gov/GIS/metadata/usgswrd/XML/nhd_nutrients.xml o) Wieczorek, M., and A. LaMotte. 2010c. Attributes for NHDPlus Catchments (version 1.1) for the Conterminous United States: Nutrient Application (phosphorus and nitrogen) for Fertilizer and Manure Applied to Crops, 2002. USGS. Available online at
http://water.usgs.gov/GIS/metadata/usgswrd/XML/nhd_cropsplit02.xml