# Presentation: 861524

## SPARROW Modeling –

- With Special Emphasis on Modeling in the Mississippi River Basin and Upper Midwest

- By
-  Dale M. Robertson*, USGS, Wisconsin Water Science Center
- David A. Saad, Wisconsin WSC
-  Richard B. Alexander and Gregory E. Schwarz, USGS, National Center, Reston, VA

- Midwest Partnership Meeting
- Chicago, Ill.
- July 24, 2008

- dzrobert@usgs.gov
-    (608) 821-3867

## Hypoxia in the Gulf of Mexico

**Frequency of Occurrence 1985 - 1999**

**Rabalais, Turner, and Wiseman**

- Early results suggested this was driven by Nitrogen Loading from the watershed,
-  now maybe Nitrogen and Phosphorus

**Regional Water Quality Problems**

**Notes:**
A major water-quality problem that has developed in the Gulf of Mexico is Gulf hypoxia – anoxic bottom water

Horizontal extent
  - Largest zone of oxygen-depleted coastal waters in the U.S. and the entire western Atlantic Ocean.
  - It stretches from bird-foot delta to LA/TX border (equivalent to from Chicago to St. Louis.)

Causes
 N (NO3) and P loading from the Miss and Atch

Many State and Federal Agencies and Groups now want to take action to reduce the problem (loading from the watershed)

## Large-scale State Water Quality Problems

- SOLEC, 2008

**Large-scale State Water Quality Problems**

## Why do you want to know where the highest source are?

- Disproportionality of Loading (e.g., P runoff from Agricultural Areas or Urban Centers)

- Nowak et al. 2006

**Notes:**
Nowak, P., S. Bowen and P.E. Cabot. 2006. "Disproportionality as a Framework for Linking Social and Biophysical Systems." Society and Natural Resources 19:153-173 

Want to target top contributors to get the biggest bang for the efforts placed in the watershed.

## Wisconsin Buffer Initiative

- Potential = Estimated Load – Background Load

- Example of prioritizing efforts

## Impaired Waters Throughout the Mississippi/Atchafalaya River Basin

- Number of Locations Impaired By Nutrients in each HUC

- Target by: Number of Locations Identified as Impaired by nutrients

**NUMBER OF SITES IMPAIRED BY NUTRIENTS IN HUC8**

**Notes:**
Based on State 303(d) lists submitted and approved by the USEPA from 2002 to 2006 

## Nitrogen Source Distribution

**Nitrogen Source Distribution**

**Goolsby and others, 1999**

- Target by: Loading from Specific Locations

**Notes:**
A better approach would be to rely on real load estimates

Work of Don Goolsby and his team:
Goolsby, D. A., W. A. Battaglin, G. B. Lawrence, R. S. Artz, B. T. Aulenbach, R. P. Hooper, D. R. Keeney, and G. J. Stensland 1999. Flux and sources of nutrients in the Mississippi- Atchafalaya River Basin—topic 3 report for the integrated assessment on hypoxia in the Gulf of Mexico: Silver Spring, Md., NOAA Coastal Ocean Office, NOAA Coastal Ocean Program Decision Analysis Series No. 17, 130 p.

Used USGS stream gage network and calculated Nitrate flux at a particular station – divided by the area of it’s watershed.
  Computed Nitrate Yields to be able to compare among different size watersheds
  Highest Yields are from Illinois, Iowa, Indiana, southern Minnesota, and Ohio.

What’s going on in those watersheds?

This is the primary area of lands drained for agricultural use.  

## Use of Nitrogen yields from 42 basins to estimate Nitrogen yields throughout the Mississippi River Basin

**Use of Nitrogen yields from 42 basins to estimate Nitrogen yields throughout the Mississippi River Basin**

**modified from Goolsby and others, 1999**

**Total N yield = f (Land Use, Population, Atm. Dep.) **

- Measured Yields

- N Input

- Basin Attributes

- Target by: Refined Loading from Specific Locations

- Extrapolation with a regression model

**Notes:**
They then extended this work by looking at loading from 42 smaller basins and related these data to N inputs and GIS attributes of the basin

This would allow all of the basins to be ranked. However pretty crude (both the model and its predictions). All of the predictions are independent

Provided the ground work for SPARROW as you will see shortly.

## Total Nitrogen Load 

**Total Nitrogen Load**

- 1992 Nitrogen SPARROW Model Output     – Alexander and others, 2008

- Extrapolation with the SPARROW model

**Notes:**
See the accumulation as rivers get bigger

Alexander, R. B., R. A. Smith, G. E. Schwarz, E. W. Boyer, J. V. Nolan, and J. W. Brakebill, 2008. Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River Basin. Envir. Sci. and Tech. 42:822-830.

## SPARROW Water-Quality Model - DescriptionSPAtially Referenced Regression on Watershed Attributes http://water.usgs.gov/nawqa/sparrow; Smith et al. 1997

**Hybrid statistical and mechanistic process structure; mass-balance constraints; data-driven, nonlinear estimation of parameters**
**Separates land and in-stream processes**
**Once calibrated, the model has physically interpretable coefficients; model supports hypothesis testing and uncertainty estimation**
**Predictions of mean-annual flux reflect long-term, net effects of nutrient supply and loss processes in watersheds**

**Hybrid statistical and mechanistic process structure; mass-balance constraints; data-driven, nonlinear estimation of parameters**

**Notes:**
Here are the main characteristics of SPARROW – what makes it unique.

## SPARROW modeling approach:- Regress water-quality conditions (monitored load) on upstream sources and factors controlling transport- Incorporates in-stream decay of nutrients

- Sources

- Land-to-water
- transport

- SPARROW modeling approach:- Regress water-quality conditions (monitored load) on upstream sources and factors controlling transport- Incorporates in-stream decay of nutrients

- Monitored load

- Instream
- transport

**Notes:**
The SPARROW model uses a mass balance approach to predict annual nutrient loads in streams, based on nutrient sources or inputs and on landscape characteristics that influence the delivery of nutrients to the edge of the stream.

Calibration of SPARROW requires information about nutrient sources, Nutrient Sources:
• Point Sources
• Fertilizer
• Animal Agriculture
• Atmospheric Deposition
• Non-agricultural Runoff

Attributes that effect the transport of nutrients to the edge of the stream,
Process Variables
• Land to Water
– Temperature
– Soil Permeability
– Slope
– Stream Density
• In-Stream Decay
– Channel Size/Flow
– Velocity
– Reservoir Characteristics

 and information about the stream network itself, in order to track the movement of nutrients relative to nutrient loads that show up at monitoring stations.

## Nonlinear Regression Model

- SPARROWSPAtially Referenced Regressions On Watershed Attributes

**For each watershed**

- SPARROW Statistical Approach

**Notes:**
Here is the SPARROW model in equation form. Use nonlinear calibration techniques to find all of the coefficients in the model.

## 425 sites were used in the National 1992 SPARROW Models

**425 sites were used in the National 1992 SPARROW Models**

**Notes:**
Calibration based on sites from the USGS NASQAN program (most now discontinued)

## SPARROW’s Reach-Scale Mass BalanceReach network relates watershed datato monitored loads

**Notes:**
Theory behind SPARROW.

## SPARROW modeling approach:- Regress water-quality conditions (monitored load) on upstream sources and factors controlling transport- Incorporates in-stream decay of nutrients

- Sources

- Land-to-water
- transport

- SPARROW modeling approach:- Regress water-quality conditions (monitored load) on upstream sources and factors controlling transport- Incorporates in-stream decay of nutrients

- Monitored load

- Instream
- transport

**Notes:**
The SPARROW model uses a mass balance approach to predict annual nutrient loads in streams, based on nutrient sources or inputs and on landscape characteristics that influence the delivery of nutrients to the edge of the stream.

Calibration of SPARROW requires information about nutrient sources, Nutrient Sources:
• Point Sources
• Fertilizer
• Animal Agriculture
• Atmospheric Deposition
• Non-agricultural Runoff

Attributes that effect the transport of nutrients to the edge of the stream,
Process Variables
• Land to Water
– Temperature
– Soil Permeability
– Slope
– Stream Density
• In-Stream Decay
– Channel Size/Flow
– Velocity
– Reservoir Characteristics

 and information about the stream network itself, in order to track the movement of nutrients relative to nutrient loads that show up at monitoring stations.

## SPARROW Sources and Transport Attributes for Latest NATIONAL Model

_**NUTRIENT SOURCES (1992)**_
- Urban and population sources
- Atmospheric N deposition
- Farm fertilizer use allocated to major crops:
- County fertilizer sales and expenditures; crop acreage
- NLCD agricultural land use
- State application rates (corn, soybeans, cotton, wheat, other crops)
- Corn/soybean rotations
- N2 fixation – cultivated lands
- Animal manure:
- Non-recoverable on pasture/rangelands
- Recoverable on crops
- Natural and residual sources (lands in forest, barren, shrub)

**Notes:**
Many of these GIS layers were the best available about 2 years ago.

## SPARROW Delivery of Agricultural Nutrients to Streams

**Notes:**
This is how nutrients from Agricultural areas are handled. 

Coefficients are needed to describe each pathway.

The %’s reflect what is the breakdown is in Wisconsin for Manure.

## SPARROW Sources andTransport Attributes

_**NUTRIENT SOURCES (1992)**_
- Urban and population sources
- Atmospheric N deposition
- Farm fertilizer use allocated to major crops:
- County fertilizer sales and expenditures; crop acreage
- NLCD agricultural land use
- State application rates (corn, soybeans, cotton, wheat, other crops)
- Corn/soybean rotations
- N2 fixation – cultivated lands
- Animal manure:
- Non-recoverable on pasture/rangelands
- Recoverable on crops
- Natural and residual sources (lands in forest, barren, shrub)

_**AQUATIC ATTENUATION**_
- Streams
- First-order decay ~ f(water travel time, flow and depth)
- Reservoirs
- First-order decay ~ f(areal hydraulic load—ratio of outflow to surface area)

_**LAND-TO-WATER DELIVERY**_
- Climate (precipitation, temperature)
- Soils (permeability)
- Topography/subsurface (slope, specific catchment area)
- Artificial drainage (tiles, ditches)

## SPARROW Rates of Aquatic Nutrient Loss

**SPARROW Rates of Aquatic Nutrient Loss**
- Nutrient removal rate declines in larger rivers
- and more rapidly flushed reservoirs

- Nitrogen literature rates from Howarth et al. 1996; Seitzinger et al. 2002;
- 	Bohlke et al. 2004; Mulholland et al. 2004)

**STREAMS**

**RESERVOIRS**

**Notes:**
The theory behind instream and in-reservoir losses. 

## Nonlinear Regression Model

- SPARROWSPAtially Referenced Regressions On Watershed Attributes

**For each watershed**

- With all of the data > now use a nonlinear regression to calibrate the model

**Notes:**
Here is the SPARROW model in equation form. 

With all of the information computed for each site, nonlinear regression is used to compute the (7-15) coefficients for the model. Use only parameters that are statistically significant.

## SPARROW Predictions of Nitrogen Flux

**SPARROW Predictions of Nitrogen Flux**
**USEPA RF1 - 62,000 reaches nationally (~3,200 Upper Miss.) ~ HUC12**

- SPARROWSPAtially Referenced Regressions On Watershed Attributes

**Notes:**
From all of this we can estimate the loads and yields from all of the streams in the area of interest.

This can allow us to rank the basins, get an idea of what are the major sources of the loads.

## Definitions:

**Notes:**
Different products from SPARROW.

## Total Nitrogen Load 

**Total Nitrogen Load**

- 1992 Nitrogen SPARROW Model Output     – Alexander and others, 2008

- Types of SPARROW Output

**Notes:**
You can see:
Most of the loads originate near the streams

See the accumulation as rivers get bigger

## Total Nitrogen – Incremental Yield

**Total Nitrogen – Incremental Yield**

- 1992 Nitrogen SPARROW Model Output

- Nitrogen Yield (kg/km2)

- Types of SPARROW Output

**Notes:**
Load added between line segments

Standardized by drainage area

Good for taking action – at any spatial scale

However much of this load does not make it to the ultimate receiving area

## Percentage of Stream Nutrients Delivered to the Gulf of Mexico

**Total Nitrogen**

**Total Phosphorus**

**Notes:**
Demonstration of how instream and reservoir losses affects what makes it to the Gulf.

## Total Nitrogen – Delivered Incremental Yield to the Gulf

**Total Nitrogen – Delivered Incremental Yield to the Gulf**

- 1992 Nitrogen SPARROW Model Output

- Nitrogen Delivered Yield (kg/km2)

- Types of SPARROW Output

**Notes:**
Therefore for the Gulf what we would really like to know is how much of the load actually originates from individual areas (in other words, we want to compensate for the instream losses (N2 formation – P deposition)

Could rank on this scale
BUT – this would mean we are predicting to basins much smaller than the data used to calibrate the model. So we really want to smooth things. You will see later this is why we look at things at the HUC8 scale right now. Future models will be capable of predicting things at a finer scale.

## Regional Contributions to the Stream Nutrient Flux to the Gulf of Mexico - 1992

**Regional Watersheds**

- Conclusions from the Natonal SPARROW model applied to the Miss. River Basin
- Ohio River Basin in the largest contributor to the Gulf of Mexico

## Nutrient Source Contributionsto Stream Flux:

## Sources Contributions to Stream Nutrient Flux - 1992 Mississippi River at St. Francisville, LA

- Conclusions from the National SPARROW model applied to the Miss. River Basin

**Notes:**
Note the differences in source terms for N and P.

Uncaptured manure is the real source for TP.

## Total Nitrogen – Delivered Incremental Yield to the Gulf

**Total Nitrogen – Delivered Incremental Yield to the Gulf**
**Can be used to rank basins**

- 1992 Nitrogen SPARROW Model Output

- Nitrogen Delivered Yield (kg/km2)

**Notes:**
Therefore for the Gulf what we would really like to know is how much of the load actually originates from individual areas (in other words
Compensate for the instream losses (N2 formation – P deposition)

Could rank on this scale
BUT – this would mean we are predicting to basins much smaller than the data used to calibrate the model.

## U.S. EPA’s Plan to Reduce Gulf Hypoxia

- Identify and Implement Action in the Highest Opportunity Watersheds in the Mississippi/Atchafalaya River Basin

**Notes:**
EPA is now taking action to reduce the Loading to the Gulf

Here is their plan...
This presentation shows a way that USGS SPARROW Model can help identify watersheds contributing the greatest amounts of nutrients to the northern Gulf of Mexico.

## MARB Watershed RankingsNutrient Delivery to the Gulf of Mexico for 2002

- Where are the major sources of nutrients?
-  - the highest delivered incremental yields.

- First step, apply the 1992 SPARROW models to conditions in 2002

## Total Nitrogen – 

**Total Nitrogen – **
**Delivered Incremental Yield**
- HUC 8 Scale (818 basins)

- 1992 Nitrogen SPARROW Model Output for 2002

- Delivered TN Incremental Yields (kg/km2) to the Gulf

**Notes:**
So we decided to summarize the data at a HUC 8 scale – fairly close to the scale used to calibrate the model 

Maybe a little too crude but we decided we would start there

## Possible reductions in Load based on Highest Contributors

- Accumulated Nitrogen Loading

- SPARROW Model Output for 2002

**Notes:**
Simply ranked the loads.
This graph shows a possible 50% reduction in load based on the highest contributing watersheds.

The 8-digit HUC watersheds are located along the x-axis, the total nitrogen loadings are on the y-axis, and the percent reductions are on the z-axis. If you trace the horizontal red line from the y-axis to the z-axis it shows that a total nitrogen load of about 700,000 Kg equates to a 50% total reduction in load from the MARB. 

The blue line indicates the cumulative incremental load of the ranked watersheds from highest loads to lowest loads (left to right). 

The intersection of the three lines (black, red, and blue) suggests that to achieve a 50% reduction in total nitrogen loads requires reducing the entire load of nitrogen from the top 150 ranked 8-digit HUCS.

## But is a total reduction in loads from HUCs possible?

**Notes:**
This graph shows four watershed reduction scenarios; from 25 to 100 percent removal of nitrogen from the 8-digit HUC watersheds. Depending on the scenario, it would take 150 watersheds at 100% removal, about 225 watersheds at 75% removal, more than 500 watersheds at only 50% removal, and that at only 25% removal the goal of 50% reduction in total loadings of nitrogen could not be achieved.

## Identification of the Top Contributors

- Nitrogen SPARROW Model Output for 2002

- Need to identify where these 150 HUCs are

**Notes:**
So we can find the TOP 150 – which represents about 750,000,000 kgs or about 50 % of the load

## Total Nitrogen – 

**Total Nitrogen – **
**Delivered Incremental Yield**
- Top 150 HUCs

- 1992 Nitrogen SPARROW Model Output

- Percentiles of Top 150

- Top 150 represent ~50% of the Total Load

- Ranking the HUCs – 2002 Conditions

- But... How certain are we of these Rankings??

**Notes:**
Assuming that 50% reduction is desired as a goal, this graph shows the top 150 watersheds that would need to reduce their loadings by 100%. This would mean that action would primarily be taken in these 150 areas.

This has led to lots of discussion. Many of the areas on the map don’t want to be included and many areas not identified on the map want to be included. Therefore we want to see how certain we are about the ranking of watersheds from high to low loadings.

(Some of the reasons for these disparities in perception of being in or out will be discussed later in this presentation when we compare delivered loads to the Gulf of Mexico versus delivered loads to local receiving waters and the differences between the two.)

Therefore we want to see how certain we are of this ranking.

## Ranked Incremental Nitrogen Yields From the HUCS, with 90 % CI’s

- Output from SPARROW with 90% Confidence Limits

- 0

- 1,000

- 2,000

- 3,000

- 4,000

- 5,000

- 6,000

- 7,000

- 8,000

- 9,000

- 0

- 25

- 50

- 75

- 100

- 125

- 150

- 175

- 200

**Original Rank**

**Incremental N Yield x10****6**** kg/km2**

**Notes:**
To do this first we wanted to see how confident we are in our estimates of the delivered incremental yields.

Here are the 90 % confidence intervals (CI) for total nitrogen load (y-axis) of the top 200 watersheds (x-axis) shown for each watershed by the vertical bars. The 90% CI for the 15th highest ranked watershed is indicated by the pink curve showing that it’s rank also has some variability (error). So there are actually two sources of error associated with each of the 8-digit watershed; the error associated with the load estimate and the error associated with the rank.

These CI are pretty wide, but this incorporates all of the errors in load computations and errors in the predictions with the SPARROW model.

So how certain can we be of which watersheds are in the TOP 150?

To further examine this we wanted to look at this rank by incorporating this uncertainty.

## Bootstrapping Procedure for Selecting Specific Values 

- Bootstrapping Procedure for Selecting Specific Values

-  Distribution of the Ranks > Confidence Limits on the Ranks
- How often each sites ranks in the Top X > Confidence in being in the Top X Basins

- 50
- 60
- 70
- 80
- 90
- 100

- 50
- 60
- 70
- 80
- 90
- 100

- 50
- 60
- 70
- 80
- 90
- 100

- Original Distribution in SPARROW PREDICTIONS

**Notes:**
To do this we used a bootstrapping resampling procedure to choose specific values for each of the HUCs – we did this 200 times.

Here is what I mean by that
Here is the distribution of a few of the sites.
In iteration 1 we chose one value from each of the distributions and then ranked all of these values. In iteration 2, we did the same thing and then reranked them. We did this 200 times.

From this we can put CIs on our ranking and determine how often each of the sites occurs in the TOP X.

## 90% Confidence Intervals for Yields and Ranks

- Horizontal Bars demonstrate the 90% confidence limits on the individual ranks

**Notes:**
Vertical lines demonstrate 90% CI on Predictions
Horizontal lines demonstrate 90% CI on Ranking

This slide shows that for example, a watershed with an original rank of 40 could possibly rank anywhere from 8 to 227.

## Confidence of HUCS Being In or Not In the Top 150 HUCs

- Different colors demonstrate how confident we are that a specific HUC is in or not in the top 150 contributors to the Gulf

**Notes:**
This graph shows the 75%, 90%, and 95% CI that specific HUC8s are in the top 150 sites (by color). 

So with 90 % confidence we can say the 11 sites are in the TOP 100 (diamond symbols near y-axis) and 513 sites are NOT in the TOP 150 (shown in light gray area of chart.) (90% CI means there is 1 chance in 10 that the watershed ranks outside its predicted range) 

The pink areas of the chart are areas where the rank is uncertain of either being in or out of the top 150 watersheds at CI less than 75%.

So given the original uncertainty we can not say many of the HUCs were really that different.

## HUCS In or Potentially In The Top 150 For TN

**TOTAL NITROGEN,**
**DELIVERED INCREMENTAL YIELDS, CATEGORY**

**Notes:**
Here are the categories from the previous slide presented in a geographical manner for all 818 HUCs. The pink watersheds are uncertain (more than 1 chance in 4 of being in or out of the top 150).

## Comparison of Original Top 150 HUCs with Potential Top 150ish HUCs

- Original Top 150

- Potential Top 150

- Many areas in Iowa, Kentucky, and Tennessee are hard to say with confidence are in or out of the top 150

**TOTAL NITROGEN,**
**DELIVERED INCREMENTAL YIELDS, KG/KM2**

**TOTAL NITROGEN,**
**DELIVERED INCREMENTAL YIELDS, RANK**

**Notes:**
How does this compare with our original TOP 150?

The big differences is that we really can’t say there is a shift EASTward.

Basins in IOWA and northern Missouri are now possibly back in the top 150. 

## Total Phosphorus

- Comparison of Original Top 150 HUCs with Potential Top 150ish HUCs
- Larger area possibly in the top 150 for TP because of less precise SPARROW model and smaller range in predicted yields

**TOTAL PHOSPHORUS,**
**DELIVERED INCREMENTAL YIELDS, CATEGORY**

**TOTAL PHOSPHORUS,**
**DELIVERED INCREMENTAL YIELDS, RANK**

**Notes:**
This slide is similar to the previous slide for total nitrogen but for total phosphorus

## Original Top 100 – Total N

- Original Top 100 – Total P

- Total Nitrogen

- Total Phosphorus

- Comparison of areas of dominant TN and TP contributors

**TOTAL PHOSPHORUS,**
**DELIVERED INCREMENTAL YIELDS, CATEGORY**

**TOTAL PHOSPHORUS,**
**DELIVERED INCREMENTAL YIELDS, RANK**

**TOTAL NITROGEN,**
**DELIVERED INCREMENTAL YIELDS, CATEGORY**

**TOTAL NITROGEN,**
**DELIVERED INCREMENTAL YIELDS, RANK**

**Notes:**
Here are all four maps side by side.

## What reductions can be obtained by keying on a specific number of HUCs?

- What reductions can be obtained by keying on a specific number of HUCs?

- ~150 HUC basins needed to get 50% reduction with 100% removal

- ~220 HUC basins needed to get 50% reduction with 75% removal

**Notes:**
Repeating... It shows various reduction scenarios and how the reduction level selected results in including varying numbers of watersheds to meet the desired percent reduction in load to the GOM.

## Comparison of Ranked Basins with Impaired Waters Throughout the Basin

- Ranked Yields

- Impaired Waters

- 2002

- Very different ranking from these two approaches

**TOTAL NITROGEN,**
**DELIVERED INCREMENTAL YIELDS, KG/KM2**

**NUMBER OF SITES IMPAIRED BY NUTRIENTS IN HUC8**

**Notes:**
Impaired waters are based on State 303(d) lists submitted and approved by the USEPA from 2002 to 2006 
How does our SPARROW model of delivered incremental loads to the GOM compare with where the States see the problems within their borders? Their view may be MUCH different compared to the view of reducing loads to the GOM. The top graph shows the map indicating the top 150 watersheds contributing the most nutrients to the GOM. The bottom map shows the state watersheds impaired by nutrients. 

The maps are different, most notably the bottom map shows highlighted areas of local watershed concerns in states such as Wisconsin, the Dakotas and Montana, and in Kansas, Oklahoma, Tennessee, Kentucky, and western Pennsylvania.

## Uses of Ranking Procedures for Individual States Nutrient Yields for 2002 

- Where are the major losses of nutrients in the State?
- - most detremental effects in local streams
-  the highest incremental yields.

**Notes:**
So let’s look at the problem from a “State” perspective for awhile. 

## Ranking Based on

- Delivered Incremental Yields – Downstream Effects			      What is reaching the Gulf

- Delivered TP Incremental Yields (kg/km2) to the Gulf

**Notes:**
This map focuses on the State of Tennessee and shows the HUC watersheds of highest rank for loadings of total phosphorus to the GOM. Most highlighted watersheds are in the western part of the state and downstream from reservoirs.

## Ranking Based on Incremental Yields – Local Effects

- 		    What is reaching streams and nearby reservoirs

- TP Incremental Yields (kg/km2)

**Notes:**
This map focuses on the State of Tennessee and shows the HUC watersheds of highest rank for loadings of total phosphorus making it to the end of each HUC in Tennessee. Most highlighted watersheds are in the central part of the state and shows that many of the watersheds with the highest total phosphorus loads drain into reservoirs. Reservoirs act as a trap for sediment and phosphorus.

## Major Reservoirs remove much of the TP in the streams prior to reaching the Gulf

## Downstream (Gulf) Effects

- Delivered Incremental Yield

- Incremental Yield

- Ranking by Delivered Incremental Yield

- Ranking by Incremental Yield

- Local (stream/reservoir) Effects

- Possible Ranking Approaches by Individual States may alter areas States want to focus on

- Delivered TP Incremental Yields (kg/km2) to the Gulf

- TP Incremental Yields (kg/km2)

- Delivered TP Incremental Yields to the Gulf (rank)

- TP Incremental Yields rank)

**Notes:**
These set of 4 graphs show the same series but as on the previous maps but explore the use of contrasted versus shaded color schemes. 

## Ongoing Development of  SPARROW models

**Notes:**
All of the ranking I have talked about are based on the NATIONAL SPARROW model developed by Alexander and others, 2008

Because of these large Confidence Limits there are now several SPARROW models being developed.

Steven Preston
USGS, 
302-734-2506, ext 230
spreston@usgs.gov

## Take Advantage of Updated GIS Data and additional water-quality data 

- Take Advantage of Updated GIS Data and additional water-quality data
- from Other USGS and Other Agency Programs

- Sites used in National Models

- Sites being used in Regional Models

**Notes:**
Increased amount of data because data have now been obtained from the major sampling agency(s) in each state.

## Mississippi River SPARROW Model

**Notes:**
Main contact for the Mississippi River (MARB) SPARROW model is:
Dale Robertson
USGS Wisconsin Water Science Center
Middleton WI 53562
608-821-3867
dzrobert@usgs.gov

## Improvements with Regional SPARROW Models

**1. ****Better spatial resolution** – More sites and especially more smaller sites, should lead to more accurate predictions at smaller scales.
**2. ****Reductions in biases - ****more data applicable to the specific region should reduce biases****.**
**3. Better definition of source terms** – better point-source data, more sites in unique areas, possible better local GIS inputs.

## Improvements with Regional SPARROW Models – Continued

**4. Better description of ****source terms****. Addressing the issue of Pasture/Grassland as a major source of P (in 1992 and 2002 models).**
**5. Better description of ****stream delivery**** using sites with smaller drainages.**
**6. Better description of ****2002 conditions****, using all 2002 data.**
**7. Better able to address more regional and local questions (such as ****effects of ethanol**** capacity).**

## Better able to address more regional and local questions

## Future Models will Provide Better Definition of Ranking to Identify Major Source Areas

**TOTAL NITROGEN,**
**DELIVERED INCREMENTAL YIELDS, CATEGORY**

## SPARROW Decision Support Tools

- Being Developed by
- Nate Booth and Eric Everman
- U.S. Geological Survey, Wisconsin WSC
- 2008

**Notes:**
Decision support tools #1 on National Academy review.

Underdevelopment right now.

## What is it?

- GoogleMaps for SPARROW
- Run management scenarios
- Rank basins
- Understand flux by source
- View maps of the results
- Caveat scenarios with error estimates
- Deliver model and model predictions
- Also... a service for other models to grab model estimates (feed into the models discussed in the Midwest Partnership)

## Example of Possible Analysis

- What is the spatial pattern of Total Nitrogen Load in the Michigan area?
- What is the Nitrogen Load entering Lake Michigan?
- What HUC8s are the largest contributors?
- What percentage of Total Nitrogen entering in a specific district makes it into Lake Michigan?
- What percentage of the Total Nitrogen entering Lake Michigan comes from my jurisdiction?

## UI - Similar to Google Maps

**National Nitrogen Model**

## Easy Scenario Testing

## SPARROW Modeling

-           Questions????

- Dale Robertson
- USGS, Wisconsin Water Science Center
- _[dzrobert@usgs.gov](mailto:*dzrobert@usgs.gov)_
-  (608) 821-3867