Methodology and Interpretation

Total Phosphorus Concentration Predictions (mg/L)

Methodology

The 244 water-quality sampling locations were used to delineate 244 subwatersheds. The study area can be depicted as a grouping of the 244 subwatersheds, each of which contains a single hydrologic outlet (sometimes called a "pour point"). The 244 subwatersheds facilitate the mapping of landscape metrics in landscape reporting units. The 244 subwatersheds provide the basis for the statistical development of water-quality vulnerability indicators. It is important to understand that some of the 244 subwatersheds are nested completely within other larger subwatersheds, and thus the total area of the 244 subwatersheds exceeds the total study area. The value of using this unconventional view of the landscape is that the cumulative effects of landscape condition on water quality can be assessed, thereby increasing the predictive power of any determined relationships between land-cover and water-quality parameter. Thus, in the two- dimensional metric and indicator maps, solely a portion of some larger subwatersheds are shown, but in the digital browser there is an additional capability for viewing the nested (i.e., stacked) subwatersheds and viewing a synopsis of the landscape metrics and other pertinent information for all subwatersheds.

The Missouri land-cover data set and the Arkansas land-cover data set were imported and evaluated using Imagine image processing software (ERDAS v. 9.0). While evaluating the forest class in the Missouri land-cover data, it was discovered that the original classification did not fully capture the forest area. The forest areas that were lacking where filled in by classifying and merging a new forest layer from multiple Landsat Thematic Mapper (TM) imagery 'scenes'. The new Missouri land-cover was superimposed onto 2003 county Digital Ortho Quarter Quadrangles (DOQQ). The preliminary land-cover map was edited and updated to match the 2003 DOQQs, using aerial photographic interpretation techniques. Edits were made to the forest, urban, water, and agriculture classes. The Arkansas land-cover was then superimposed onto the DOQQs and updated. The updated state land-cover maps were exported as ArcInfo (ESRI ArcGIS v. 9.0) Grids. Each land-cover classification schema was aggregated to meet the project's classification scheme requirements. The land-cover grids for Missouri and Arkansas were merged to create a unified land-cover map for 2003, and used for statistical analyses of landscape metrics (i.e., Landscape Metric Maps - Analysis by Decade Status, and Landscape Metric Change Maps - Analysis by Decadal Interval).

For each of the selected sites, the watershed support area was delineated and a suite of landscape metrics was calculated. A total of 46 landscape metrics were tested among watersheds. Measured total phosphorous, total ammonia, and E. coli solely existed in 18, 6, and 15 sites, respectively. Landscape metrics were for year 2003 and surface water constituents were averaged over a period of 1997-2002. Each of the surface water constituents from the above sites was used in a Partial Least Squares analysis (PLS) to predict water-quality values for all of the 244 subwatersheds.

PLS is a multivariate analysis technique that permits analysis and prediction for data sets with missing values, with collinearity, and with a relatively small number of observations (for additional information about PLS see references, below). In the PLS analyses, both data sets (e.g. water and landscape variables) are first centered and scaled. A linear combination is composed on the independent variables (T = L_o W; T is the score and W is weight) forming a number of orthogonal latent variables [T] that are less in number (dimensions) than that of the original landscape variables. The linear combination in [T] is formed so that the covariance between [T] and the linear composition of the dependent variables are maximized (T& U; U = B_o V; U is the score and V is weight). Prediction of both water and landscape data will be via regression on the common latent variables (T). Modeling and prediction in PLS, therefore, is not solely based on the conditional distribution of the predictors (water variables) in the presence of independent variables (landscape variables), instead it accounts for both landscape and water together through [T].

PLS produces n-1 factors, with each factor containing a pair of scores (T_i, U_i). Linear combinations on each data set are called factors. The above was the extraction of the first factor. PLS extracts the second factor using the residuals from the first and finds the linear combinations of both data sets such that their covariance is maximized. This process is repeated by taking residuals from the previous factor, producing n-1 factors, where n is the number of observations. For example, if the number of sites (observations) is 89, then 88 factors will be produced. Not all of these factors are significant using the Cross Validation (CV) method; only the significant factors are used in the final model. When applying CV, data set is divided into groups (5 to 9 groups; see references in Nash et al., 2005). The fitted models are tested using the test data sets and the predicted values are compared with that of observed using PRESS (Predictive Residual Sum of Square) to assess the predictive ability of the model. SAS gives the root means PRESS and its significant level (the lower the value, the better the model is).

After defining the significant PLS factors; scores, weights and VIP (Variable Influence on Projection) are used to examine the strength of the relationship, irregularities and the contribution of the independent variable (landscape) in the model. If VIP for an independent variable is small in value, it implies that variable has a relatively small contribution to the model and may be deleted from the model. It was indicated VIP values of less than 0.8 are considered to be small. The quality of the model was determined by examining the residuals for both the response and the landscape variables. An examination of any possible outliers using residuals was carried out to finalize the fitted PLS model. SAS was used for statistical analyses.

Interpretation

The most significant factors in the total phosphorus PLS model are the percentage of barren land in the riparian zone (i.e., within 120 meters of streams), and the percentage of barren land and the density of stream networks within a subwatershed. The total phosphorus PLS model resulted in one significant factor explaining 91% of the variability in surface water total phosphorus concentration.

(The following excerpt is from Agricultural Phosphorus and Water-Quality - University of Missouri, Columbia Outreach and Extension - Report G9181 )

Nitrogen (N), phosphorus (P) and potassium (K), as essential macronutrients, are required for growth by all animals and plants. Lack of these nutrients can restrict growth. Farmers regularly apply fertilizers containing N, P and K to crops to increase yield.

Similarly, nutrient levels in surface water often restrict the growth of aquatic plant species. In freshwaters such as lakes and streams, phosphorus is typically the nutrient limiting growth, though occasionally nitrogen is the most limiting nutrient. Potassium is not a limiting element in water, so water-quality concerns focus on nitrogen and phosphorus.

Increasing the amount of nutrients entering a stream or lake will increase the growth of aquatic plants and other organisms. Although these nutrients are necessary, excessive levels overstimulate the lake or stream, reducing the quality of the water. The progressive deterioration of water-quality from overstimulation by nutrients is called eutrophication.

The following sequence characterizes changes in surface water-quality as a result of eutrophication:

• Increased algae growth
• Reduced water clarity
• Water treatment problems
  • Odor and bad taste
  • Increased filtration costs
  • Disinfectant byproducts with potential human health effects
• Reduced oxygen in the water
• Altered fisheries
• Fish kills
• Toxins from cyanobacteria (blue-green algae) affecting human and animal health

Once a stream or lake has excess phosphorus, it takes time to improve water-quality. Excess phosphorus cycles between the bottom sediments and the water long after the source of excess phosphorus has been eliminated.

The Ozarks are known for their clear (oligotrophic) streams and lakes. Other regions of Arkansas and Missouri have water that is less clear. These differences are generally understood to be a function of the geology and land use in a region. The Ozarks are dominated by forest and pasture on an old geologic landscape low in phosphorus. Much of the rest of Missouri has a greater percentage of agricultural land on a geological landscape that naturally supports higher phosphorus concentrations in water. However, all water resources can be impaired by excess nutrients from agricultural fields and changes in water-quality are more rapidly apparent in poorly nourished, oligotrophic water. A small increase in phosphorus concentration in these water bodies creates a dramatic decrease in water clarity.

Phosphorus is carried in runoff water from agricultural fields into streams, wetlands, and lakes. Phosphorus can travel attached to particles of soil or manure eroded by water into a stream. Phosphorous also dissolves into runoff water as it passes over the surface of the field.

There is little potential for phosphorus to leach through soil into groundwater. Soil particles have a large capacity to fix phosphorus in forms that are immobile in soil. Most soils filter out soluble phosphorus as water passes through the soil profile into groundwater. However, this filtration process can be overloaded or bypassed under certain conditions, allowing higher concentrations of phosphorus into groundwater. Cracking soils or areas with karst topography create channels in the soil that allow surface water to travel directly to groundwater. The capacity of soil to adsorb phosphorus can be overwhelmed on sandy soils or when the water table is close to the soil surface.

Phosphorus losses from agricultural fields can be divided into three categories:

• Flash losses of soluble phosphorus soon after application of manure or fertilizer
• Slow leak losses of soluble phosphorus
• Erosion events.

Flash losses of soluble phosphorus
Manure and fertilizer have vastly higher concentrations of soluble phosphorus than soil. If a rainfall event causing runoff occurs soon after a surface application, the concentration of soluble phosphorus in the runoff can be more than 100 times higher than normal.

Over time, highly soluble manure and fertilizer phosphorus on the soil surface will react with the soil reducing soluble phosphorus in runoff back to initial levels. Normal levels return over the course of a month in warm soils, but this process takes longer in cold soils. Manure and fertilizer application is not recommended on frozen or snow-covered soils because phosphorus never has a chance to react with the soil before runoff occurs.

Research from Arkansas on poultry litter and swine manure applied to pastures shows that soluble phosphorus concentrations increase in direct proportion to increasing application rate in these flash phosphorus loss events.

Flash soluble phosphorus losses have high concentrations of phosphorus in a form that is readily available to aquatic organisms. These events occur with runoff soon after a surface application of phosphorus or when phosphorus is surface applied to frozen or snow-covered fields. However, one ill-timed application can contribute more phosphorus to surface water than is lost by all other processes over the course of a year or more.

To minimize flash soluble phosphorus losses:

• Apply phosphorus sources below the surface.
• Surface-apply phosphorus sources during periods of the year when runoff is unlikely.
• Surface-apply phosphorus sources only on fields with a low potential for runoff.
• Do not surface-apply phosphorus sources to frozen or snow-covered soils.
• Maintain buffer strips around water resources where no phosphorus is applied.
• Add alum or a similar treatment to manure to reduce the availability of phosphorus.

Slow leak losses of soluble phosphorus
All soils naturally release some soluble phosphorus into surface runoff. The concentration of soluble phosphorus in runoff is affected by the soil test phosphorus level of the soil.

Soil tests for phosphorus were developed to help estimate phosphorus fertilizer requirements for crops. Research on soils from other states indicate that soils near optimum soil test levels for growing crops typically supports soluble phosphorus concentrations of 0.5 ppm or less.

Considerable evidence suggests that soluble phosphorus concentration in runoff increases in direct proportion to increasing soil test phosphorus levels. This linear relationship changes from soil to soil.

To minimize slow leak soluble phosphorus loss:

• Apply phosphorus only to fields that have an agronomic need for phosphorus.
• Reduce the amount of annual runoff from agricultural fields through crop selection and soil conservation practices.
• Maintain buffer strips around water resources where no phosphorus is applied.

Erosion losses
When runoff water gains sufficient energy to cause soil erosion, the amount of phosphorus lost from the field increases dramatically. Reducing erosion losses through reduced or no-till on corn or wheat can reduce total phosphorus losses by 50 percent or more.

In soil, total phosphorus is much higher than the soluble phosphorus content. Soil particles have a tremendous capacity to fix soluble phosphorus allowing only a small proportion of the total and plant-available phosphorus to exist in the soluble form.

The natural sorting of soil particles during erosion causes those with the highest phosphorus concentration to be carried with runoff. Soils with higher soil test phosphorus levels will have higher phosphorus content in eroded particles.

To minimize erosion losses of phosphorus:

• Adopt soil conservation practices to minimize soil erosion.
• Maintain buffer strips around water resources where no phosphorus is applied.

Reducing the quantity of phosphorus reaching streams, wetlands and lakes will lead to long-term improvements in water-quality of impaired lakes and streams.

Transferring phosphorus loss from one field in the watershed to another may not reduce the amount of phosphorus reaching a stream or lake. This is of particular importance to farmers applying manure. Reducing the rate of manure by half but covering twice the area within a watershed may not reduce the amount of phosphorus reaching the stream. If runoff is more likely on the additional land receiving manure, losses could be greater than the full rate on a field with lower runoff potential. The quantity of phosphorus in surface runoff can be lowered either by reducing runoff quantity or by reducing the phosphorus concentration of runoff. Farmers who apply phosphorus should adopt practices that limit runoff from their fields soon after application. High-testing soils should be cropped and tilled in a manner to minimize runoff and erosion. Buffers between agricultural fields and water resources are a key component of lowering phosphorus concentrations.

Good management requires balancing conflicting objectives. For example, incorporating phosphorus into the soil eliminates flash phosphorus losses and may reduce soil test phosphorus levels on the surface, reducing slow leak losses. Tillage associated with incorporation may promote erosion, which increases the potential for phosphorus loss. Banding the phosphorus source while following the contour of the land will incorporate the phosphorus source with little increase in erosion potential.

Extensive tillage should never be used to lower surface soil test phosphorus, particularly on highly erodable land. Erosion losses from tillage will be much more damaging to the water resource than a high-testing soil surface with little erosion.

Phosphorus is primarily a surface water-quality issue. The ability of soil particles to adsorb soluble phosphorus limits the movement of phosphorus through soil. Soil particles strip soluble phosphorus compounds from the water as it leaches through the soil profile. Phosphorus levels in soil leachate can be 10 percent of surface runoff concentrations. Most Missouri soils have a tremendous capacity to adsorb phosphorus, particularly the highly weathered soils in the Ozark region.

There are many sources of phosphorus in the landscape. Impaired water can be affected by point sources of phosphorus such as industrial effluent and wastewater treatment plants and by nonpoint sources such as agricultural fields, urban runoff, and septic systems.

In any watershed the contribution of agriculture versus other sources of phosphorus will depend on the relative mix of sources and activities in the watershed. For example, in the Table Rock Lake watershed, the wastewater treatment plant for the city of Springfield, the third largest city in the state, empties into the James River, which flows into the lake. A boom in construction along the shore has likely increased erosion losses and contributions from septic systems. The poultry industry also has dramatically expanded in recent years. Conversion from timber to other agricultural uses may also be a factor. Apportioning the relative contribution of these various nonpoint phosphorus sources requires detailed field-based study, a time-consuming and expensive process. Landscape analyses in this browser are intended as an initial approach to solving this complex challenge.

References

Helland, I. S., 1988. On the structure of partial least square regression. Commun. Statist. Simula. 17(2), 581-607.

Lindberg, W., Persson, J-A, and Wold, S., 1983. Partial Least-Square method for spectrofluorimetric analysis of mixture of humic acid and lignisulfonate. Anal. Chem. 55, 643-648.

Nash, M.S., Chaloud, D., and Lopez, R.D., 2005. Multivariate Analyses (Canonical Correlation Analysis and Partial Least Square, PLS) to Model and Assess the Association of Landscape Metrics to Surface Water Chemical and Biological Properties using Savannah River Basin Data. Untied States Environmental Protection Agency. EPA/600/X-05/004. 82pp.

SAS (SAS Institute), 1998. Version 9 User's Guide. SAS Institute. Inc., Cary, NC.

Wold, S., 1995. PLS for multivariate Linear Modeling. In: H. van de Waterbeemd (Editor), Chemometric methods in molecular design methods and principles in medicinal chemistry. Verlag-Chemie, Weinheim, Germany, p.195-218.

Quantile: Each class contains an approximately equal number (count) of features. A quantile classification is well-suited to linearly distributed data. Because features are grouped by the number within each class, the resulting map can be misleading, in that similar features can be separated into adjacent classes, or features with widely different values can be lumped into the same class. This distortion can be minimized by increasing the number of classes. For continuity of the browser content, and consistency among maps, legend gradients are from higher values (red) to lower values (green).

Metric input GIS data:

• 244 Subwatersheds - Metadata

• 2003 Land-Cover - Metadata

• Stream Network - Metadata

• Water-quality sampling locations - Metadata