Modeling phosphorus in the environment - Chapter 17 (end) pptx

Section IV
Modeling in the Future
© 2007 by Taylor & Francis Group, LLC
405
17
Suggestions to Improve
Modeling of Phosphorus
David E. Radcliffe
University of Georgia, Athens, GA
Miguel L. Cabrera
University of Georgia, Athens, GA
CONTENTS
17.1 Introduction 405
17.2 Runoff 406
17.3 Soil Phosphorus Pools and Model Parameters 406
17.4 Leaching of Phosphorus 408
17.5 Best Management Practices 409
17.6 In-Stream Processes 410
17.7 Phosphorus Indices 411
17.8 Calibration and Uncertainty 411
17.9 Data to Support Phosphorus Modeling 412
17.10 Summary 413
References 413
17.1 INTRODUCTION
Much progress has been made in the science of modeling phosphorus (P) fate and
transport: from field-scale, event-based, empirical models to dynamic, watershed-
scale, process-based models. Nonetheless, some improvements are clearly needed
in today’s models. This chapter’s purpose is to identify the most necessary changes
to improve the modeling of P in the environment. Earlier chapters in this book have
been drawn on heavily in developing the ideas presented here. Suggestions are
grouped into eight general areas: (1) runoff; (2) soil P pools and model parameters;

(3) leaching; (4) best management practices (BMPs); (5) in-stream processes; (6)
P indices; (7) calibration and uncertainty; and (8) data for modeling.
© 2007 by Taylor & Francis Group, LLC
406 Modeling Phosphorus in the Environment
17.2 RUNOFF
Most of the current models use the Soil Conservation Service (SCS) curve number
approach (McCuen 1982) to estimate runoff. This approach is widely proven and
sensitive to land use and soils, but it limits models to a daily time step. Some models
use, or have the option of using, infiltration equations such as Green and Ampt
(1911). These equations can be used for shorter time steps, but they do not readily
reflect differences in land use. Furthermore, the infiltration equations are based on
a one-dimensional (vertical) approach to water flow and as a result estimate infil-
tration excess runoff in contrast to saturation excess runoff (Beven 2001). Research
in humid regions has shown that saturation excess runoff is generated during storms
in areas low in the landscape that vary in size (variable source area, or VSA)
depending on the antecedent soil water content because of lateral subsurface water
flow (Hewlett and Troendle 1975). It is essential not only that the correct amount
of runoff from a watershed be estimated but also that the area where runoff is
generated be identified. Combined with the areas that are sources of high concen-
trations of P in runoff, these are the critical source areas where management can be
focused to reduce P losses (Gburek and Sharpley 1998).
Models need to incorporate the VSA concept into their estimations of where
runoff occurs. This may move P simulation toward fully distributed watershed
models such as AnnAGNPS or ANSWERS-2000. With a fully distributed model,
digital elevation model (DEM) data can be used to generate a wetness index (Beven
2001) for each cell, which in turn can be used to identify areas that are likely to
receive lateral subsurface flow of water. Alternatively, it may be possible to incor-
porate the VSA concept into semidistributed models such as Soil Water Assessment
Tool (SWAT) and HSPF. According to Steenhuis et al. (1995), the SCS curve number
approach for estimating the amount of runoff is consistent with the VSA concept.

Schneiderman (Chapter 12, this volume) describes a new version of the Groundwater
Loading Function (GWLF) known as VSLF that uses the wetness index to distribute
runoff estimated by the SCS curve number method among hydrologic response units
(HRUs).
In the present authors’ opinion, models must consider both infiltration excess
and saturation excess processes in estimating where runoff will occur. Recent work
has shown that both soil properties and landscape topography are important in
predicting runoff (Soulsby et al. 2005; Tetzlaff et al. 2005).
17.3 SOIL PHOSPHORUS POOLS AND MODEL
PARAMETERS
Many of today’s watershed-scale models have evolved from field-scale models
developed in the 1980s. The soil P routines in these models are derived from concepts
current in the 1980s and implemented in the EPIC model (Williams 1995). These
routines need to be updated to reflect recent research. The most important improve-
ment will be to add a separate manure pool for P (Vadas et al. 2004). Current models
assume that P added in manure instantly becomes part of the soil P pools. However,
in grasslands where manure is not incorporated this is not the case, and models can
© 2007 by Taylor & Francis Group, LLC
Suggestions to Improve Modeling of Phosphorus 407
underestimate P losses in runoff from storms shortly after manure is applied (Pierson
et al. 2001). How these pools interact is an important question. DeLaune et al. (2004)
showed that the manure pool dominated soil test P as a source for up to 14 days
after application of broiler litter, but it is not known how much longer these pools
remain separate. Vadas et al. (2005a) developed equations for a separate manure
pool based on laboratory-derived relationships similar to what has been used earlier
for soil P pools (see Chapter 3). Vadas is also developing a full model of the manure
pool and its interaction with other pools (Vadas 2006). This model estimates dis-
solved organic P in runoff in addition to dissolved inorganic P. Dissolved organic P
in manure has been found to increase as the pH of the manure decreases after
application from its original pH of around 8 to a pH of 6, which is typical of

agricultural soils (Tasistro et al. 2006). It may also be important to model the
contribution to particulate P that comes from the manure pool as well as the P derived
from vegetation or residue pools (Sharpley and Smith 1992). Further research is
needed to determine how much P loss is due to losses from these pools.
Nelson and Parsons (Chapter 4, this volume) note that the EPIC approach leads
to an assumption of linear P sorption in soils, but sorption isotherms show that
sorption is highly nonlinear. Assuming linear sorption could lead to error in predict-
ing runoff and leaching losses, especially in soils with high concentrations of sorbed P.
Nonlinearity also arises in the data used to develop the laboratory-derived relation-
ships because of the effective depth of interaction and water-to-sediment ratio terms
(Chapter 3, this volume).
For many models, it is not clear what form of measurable soil P (e.g., total, soil
test, bioavailable, anion-exchangeable) corresponds to the different model soil P
pools (e.g., labile, active, residual). In the original papers by Jones et al. (1984) and
Sharpley et al. (1984) describing the EPIC approach, it was clear that the labile P
pool corresponded to anion-resin-exchangeable P (Sharpley 2000). Sharpley et al.
(1984) recognized that the most commonly measured forms of soil P were the
different soil test extractions (Bray-1, Mehlich-1, and Olsen P) and provided regres-
sion equations for converting soil test P concentrations to labile P concentrations.
This clarity has been lost in the documentation of some of the current models. In
the SWAT User’s Manual (Neitsch et al. 2002), a dissolved P pool is described, but
the equations are identical to those in the EPIC routines, so this is most likely labile
P. In HSPF, two soil P pools — dissolved and adsorbed — are simulated, but no
guidance is given on what forms of measurable P correspond to these pools. Models
have been found to be sensitive to the initial storages of soil P pools (Chapter 9,
this volume). Wherever possible, soil P pools in models should be related to mea-
surable forms of P in soils.
The rate of transfer between soil P pools in soil can be important. Vadas et al.
(2006) showed that SWAT does not include a delay that was part of the original EPIC
routine in transfer between the labile and active P pools when fertilizer is added. As

noted by Chaubey et al. (Chapter 7, this volume), several improvements to the P
routines in SWAT are under consideration, including slowing the transfer of added
(fertilizer) P from labile into active pools and adding a separate manure pool.
Most models estimate the dissolved inorganic P concentration in runoff by mul-
tiplying the concentration of dissolved or labile P in the topsoil by an extraction
© 2007 by Taylor & Francis Group, LLC
408 Modeling Phosphorus in the Environment
coefficient. The National P Project (Vadas et al. 2005b) resulted in a very large number
of rainfall simulation studies performed across the U.S. where dissolved inorganic P
concentration in runoff was regressed on different forms of P in soil. This information
on extraction coefficients needs to be incorporated into model databases.
HSPF uses a number of soil parameters in the soil P routines — and in the
hydrology routines — that are unique to the model and not commonly measured in
soils. A database of parameter values used in models by experienced users is provided
in HSPFParm (Donigian et al. 1999), which is very useful. A technical note for
selecting values for hydrology parameters is available (EPA 2004), as well as a new
technical note for sediment parameters (EPA 2006). A similar technical note for
selecting P parameters is needed.
17.4 LEACHING OF PHOSPHORUS
Concern over leaching of P is relatively new, as recent research has shown it is an
important pathway for subsurface losses to nearby streams, especially in deep sands
and in systems with tile drains (Nelson et al. 2005; Sims et al. 1998). As noted by
Nelson and Parsons (Chapter 4, this volume), leaching also affects the estimation of
runoff in that it removes P from the topsoil that would otherwise be available for runoff
loss. They point out that in soils with low P sorption capacities, leaching can potentially
remove more P from the root zone than crop uptake. Leaching is also important in long-
term modeling studies where it becomes a significant component of P mass balances.
However, current models either ignore P leaching or treat it in a simplistic
manner. AnnAGNPS, ANSWERS-2000, and WEND-P do not model leaching of P
or losses by tile drains. SWAT uses a field-capacity water balance (FCWB) approach

to estimate vertical flux of water within the soil profile (up to 10 layers) and includes
preferential crack flow. The concentration of P in leaching water is determined by
the labile P concentration in the top soil layer. HSPF uses an empirical equation to
determine percolation of water, and user-specified reduction factors are used to
determine how much P is transported with the percolating water. GWLF does not
model leaching explicitly, but the concentration of P in groundwater, which affects
stream base-flow P concentration, is a user-specified input or a function of the percent
of agricultural land use in the basin.
A better description of the P leaching process needs to be included in models.
Modeling may benefit from some of the same improvements mentioned for improv-
ing the modeling of runoff — a separate manure pool because manure P, especially
when surface applied, will not be immediately available for leaching and because
adsorption is likely to be nonlinear. Models need to be able to estimate tile drain
flow and the concentration of P in tile drains. It may be possible to estimate the P
concentration in leachate by using a relationship with soil test P in the topsoil.
Nathan and Parsons (Chapter 4, this volume) refer to these as quantity–intensity
relationships. Preferential flow should also be incorporated into future models if it
is not already present. Research has shown that it is especially important in fields
with tile drains (Kladivko et al. 1991). Particulate P as well as dissolved P may
move via preferential flow (Chapter 4, this volume). Model validation should include
comparisons of model estimations with P distributions in the soil profile.
© 2007 by Taylor & Francis Group, LLC
Suggestions to Improve Modeling of Phosphorus 409
Model developers seem aware of the need for improving the simulation of P
leaching. SWAT2003 has improved tile drain simulation, and further testing of the
preferential flow model in SWAT is stated goal (Chapter 7, this volume). ANSWERS-
2000 is undergoing major revision to link the model’s subsurface flow components
with the groundwater model MODFLOW (Chapter 10, this volume).
17.5 BEST MANAGEMENT PRACTICES
Today, watershed-scale models are being used to test scenarios for reducing P loads

to sensitive water bodies because of limits imposed by Total Maximum Daily Loads
(TMDLs) (Reckhow et al. 2001), source water assessment (EPA 1997), or lawsuits
(Blackstock 2003). This is usually done in part by reducing loads from nonpoint
sources through the use of best management practices (BMPs). Despite the wide-
spread use of models for this purpose, BMPs are usually modeled in a simplistic
manner (Chapter 15, this volume).
All the models in this volume allow for simulation of BMPs to some degree.
The most extensive list of BMPs occurs in SWAT and AnnAGNPS. Common BMPs
simulated are crop rotations, tillage, and nutrient management (i.e., timing, type,
and rate). However, in most cases a simple reduction factor is used to simulate the
effect of BMPs. Often the reduction factor for a given BMP is the same for different
pollutants. For example, in SWAT the reduction factor for filter trips is specified by
the user, but it is the same for sediment and dissolved P. Research has shown that
filter strips are not as effective in removing dissolved P as they are in removing
sediment (Chapter 15, this volume).
One of the most important BMPs that needs improvement in modeling is the
use of riparian buffers, since these are commonly used in implementing TMDLs.
Semidistributed models such as SWAT and HSPF do not model overland flow or
subsurface flow from one land use to another, so it is difficult to model the effect
of buffers beyond using a simple reduction factor. Buffers can be more easily
modeled in fully distributed models such as AnnAGNPS or ANSWERS-2000. This,
along with identification of variable source areas of runoff, may be another reason
for moving toward fully distributed models. Including buffers as separate land uses
may require improvements in the leaching and lateral flow predictions as well
(Chapter 4, this volume). The SWAT model developers are considering the addition
of vegetated filter strips and riparian zones, but it will require reconfiguration of
HRUs to allow more detailed variation in topography and management practices
(Chapter 7, this volume). The advantages of this approach are being weighed against
the fact that increased complexity will make it less user friendly. Lowrance et al.
(2000) developed the Riparian Ecosystem Management Model (REMM), which

simulates all of the important P and hydrologic processes thought to occur in riparian
buffers, given the runoff and subsurface lateral flow inputs from adjoining fields. It
may be possible to use REMM to parameterize HRUs that consist of a field with a
riparian buffer and thereby avoid developing a fully distributed model.
Gitau and Vieth (Chapter 15, this volume) recommend that the cost of BMPs
be included in models. They also suggest that the interaction between BMPs needs
to be simulated since management scenarios will consider more than one type of BMP.
© 2007 by Taylor & Francis Group, LLC
410 Modeling Phosphorus in the Environment
Interesting work is being done on using optimization methods to determine the most
effective suite of BMPs to achieve a given reduction (Chapter 15, this volume).
Multi-objective functions can be used that include cost and the effect on other
pollutants such as nitrogen and sediment. This type of capability is also needed in
calibration and uncertainty analysis (see Chapter 6).
The long-term effectiveness of BMPs must also be considered. WEND-P, which
uses a long-term mass balance approach at an annual time step, reminds readers that
to be effective in the long term BMPs must affect P storage in the watershed.
17.6 IN-STREAM PROCESSES
A clear trend in the modeling of P has emerged to scale up. Models have moved
from field scale models such as EPIC and GLEAMS to watershed scale models such
as AnnAGNPS, ANSWERS-2000, GWLF, HSPF, SWAT, and WEND-P. At first the
watershed models were used for single events on ephemeral or small, first-order
streams, but now they are being used for basins that are thousands of square kilo-
meters in area. In modeling P, the objective is often to predict the load to a sensitive
lake, reservoir, or estuary. Modeling of P processes in large lakes, reservoirs, and
estuaries is usually done with separate, specialized models, and for the near future
it does not seem likely that this will change.
To estimate accurate loads at this scale, it is essential that in-stream processes
affecting the transport of P and sediment be included in models (see Chapters 1 and
water body and the bioavailability of this P. Processes change with flow (i.e., storm

vs. base flow) and stream order (Vanotte et al. 1980). Stream water column P con-
centrations may be heavily buffered by elevated bed sediment (benthic) P concentra-
tions in watersheds with a history of large P losses. This may delay the reduction in
P loading to lakes or reservoirs in response to the implementation of BMPs.
Most current models ignore or poorly model in-stream processes. Only three of
the models discussed in this volume include in-stream processes: HSPF, SWAT, and
GWLF. HSPF provides the most comprehensive modeling of in-stream processes,
taking into consideration benthic release of dissolved P, sorption and desorption of
P to suspended sediment in the water column (which varies for different sediment
size fractions), settling of suspended sediment, scouring of bed and bank sediment,
uptake by phytoplankton or benthic algae and respiration and inorganic excretion
by zooplankton. Atmospheric deposition is also considered. SWAT includes settling
of organic P, benthic release of P, and uptake by algae but does not simulate
interaction between dissolved P and suspended sediment. GWLF models in-stream
processes as a single, lumped stream segment. It includes channel erosion and
transformation between dissolved and particulate P under base-flow conditions.
It is necessary to know the initial stores of benthic P for models, and studies
that have measured equilibrium P concentrations (EPC
0
) of bed sediments may be
useful in this regard (Sharpley et al. 2002). They may also provide an integrated
measure of historical local inputs (Page et al. 2005).
It may be possible to use measurements of P uptake lengths in streams to
determine if models are correctly estimating losses of P in streams. As described by
© 2007 by Taylor & Francis Group, LLC
5, this volume). Stream processes also affect the form of P that reaches a sensitive
Suggestions to Improve Modeling of Phosphorus 411
Haggard and Sharpley (Chapter 5, this volume), uptake length is measured by
applying a constant source of dissolved P to a stream and measuring concentrations
of dissolved P in the stream at various distances below the input point. Concentra-

tions typically decay exponentially with distance, and the inverse of the exponential
decay coefficient is the uptake length (Newbold et al. 1981, 1983). It should be
possible to do a similar experiment with a watershed model by including a point
source and examining the concentrations of P in downstream reaches. Concentrations
estimated by the model should have a decay coefficient that is on the order of that
measured in streams of the region.
17.7 PHOSPHORUS INDICES
In the present authors’ opinion, P indices (PIs) can be considered a type of model
representing one end of the spectrum in that they are usually used at the field scale
and estimate long-term averages (one to five years). In most cases, they estimate
a risk index of P loss, not an actual loss, but there are three states where the PI
in fact estimates P loss (Chapter 13, this volume). PIs are probably weakest in
their treatment of transport, and in this area they need improvements in some of
the same areas as the more complex, dynamic, watershed-scale models. These
include identification of critical source areas, better treatment of P leaching, and
better quantification of the effects of BMPs. Better methods for rapidly calculating
erosion under different scenarios are needed, perhaps by linking PI software to
Revised Universal Soil Loss Equation, version 2 (RUSLE2) programs. PIs also
need to be linked to databases of soils, hydrography, and land use.
Other needed improvements include the development of a preliminary risk
assessment that only considers a field’s inherent transport risk factors such as soil
type, slope, and distance from water body (Chapter 13, this volume). PIs need to be
integrated into comprehensive manure management software such as the Manure
Management Planner (Chapter 14, this volume).
17.8 CALIBRATION AND UNCERTAINTY
Dynamic watershed-scale P models are necessarily calibrated because of the large
number of parameters, many of which operate at a scale beyond which measurements
can be made — called noncommensurability by Beven et al. (Chapter 6, this volume).
Sensitivity analysis can be used to reduce the number of calibrated parameters to only
those that have a large effect on estimations. Because watershed models are frequently

used in the regulatory arena for TMDLs (Reckhow et al. 2001), source water assessment
(EPA 1997), and lawsuits (Blackstock 2003), some measure of the uncertainty of
estimations must be included.
Methods and software have been developed for performing sensitivity analysis,
for automating calibration, and for quantifying uncertainty, but these tools are usually
not integral parts of today’s models. In fact, some model users oppose the use of
autocalibration tools because they feel that model users should be limited to expert
hydrologists (Gupta et al. 2003). The present authors disagree and recommend that
calibration and uncertainty tools be integrated into modern models.
© 2007 by Taylor & Francis Group, LLC
412 Modeling Phosphorus in the Environment
A good example of this type of approach is contained in the latest version of
SWAT, which incorporates an autocalibration capability described by van Griensven
(2002). It includes sensitivity analysis using the one-factor-at-a-time approach. Once
the significant parameters are identified, the Shuffled Complex Evolution (SCE)
algorithm is used for autocalibration (Chapter 7, this volume). Another example is
a new version of GWLF that has a built-in calibration tool (Chapter 12, this volume).
Automated calibration tools may be easier to use with stream-flow data (where
daily or more frequent measurements are frequently available) than with P data,
which may be collected at monthly intervals in many TMDL data sets. For the near
future it may be necessary to accept the idea of equifinality (Chapter 6, this volume);
no unique set of parameter values probably provides the most accurate model
estimation. But it may be possible to separate parameter values into those that are
plausible (behavior) and those that are not (nonbehavior). All of this points to the
importance of monitoring data.
17.9 DATA TO SUPPORT PHOSPHORUS MODELING
Edge-of-field and stream-monitoring data are essential for calibrating and evaluating
watershed-scale models (Chapter 16, this volume). One of the most difficult variables
to measure accurately is flow, but it is essential for calculating loads and for cali-
brating and evaluating models. Advantage should be taken wherever possible of U.S.

Geological Survey (USGS) sites that measure daily discharge on large streams.
Usually, these sites do not have automated storm samplers installed, so they are an
ideal location for partnering among universities, the U.S. Department of Agriculture
(USDA), and USGS.
The development of automated storm samplers represents a huge advance in the
ability to collect stream-water-quality data. Frequently when models are calibrated
for the estimation of sediment and P concentrations in streams, there are only one
or two samples during a storm. In that situation, the model should estimate concen-
trations that are at least as large as those measured. Only with a well-defined
chemograph can a comparison be made to the model estimations and can there be
some assurance that the model processes are correct. Optimum sampling schemes
for automated samplers need to be further developed and tested (Chapter 16, this
volume). The type of sampling will likely be different depending on whether the
aim is to calculate an annual load or to calibrate a dynamic model.
Even when monitoring data are not used for calibrating dynamic models,
annual loads calculated from monitoring data from small streams with a predom-
inant land use are very useful for comparison with unit area loads calculated from
dynamic models as well as export-coefficient loading models. Edge-of-field data
should also be used to assess the accuracy of and to improve PIs. Ideally, studies
should employ a nested design where edge-of-field and low-order stream-moni-
toring sites are located within the watershed where the high-order stream is
monitored. Last, better quantification of the uncertainty (error) in monitoring data
is needed.
© 2007 by Taylor & Francis Group, LLC
Suggestions to Improve Modeling of Phosphorus 413
17.10 SUMMARY
Much progress has been made in the modeling of P in the environment over the past
30 years. To continue this progress, the following improvements to modeling P are
suggested.
• The estimation of the areas where runoff occurs must be improved through

the incorporation of saturation excess as well as infiltration excess pro-
cesses. Better information on where these areas occur will allow identi-
fication of the critical source areas where runoff and P sources coincide
and where management needs to be focused.
• Modeling of soil P pools needs to be improved by adding a separate
manure pool, relating the forms of P in model pools with forms of P
measured in soils, and refining the rates of movement between P pools.
• Modeling of leaching of P needs to be improved by using a more process-
based approach to estimating the flux of percolating water and the con-
centrations of P in leachate. This is especially important in soils with ditch
or tile drainage.
•A more process-based approach is needed in modeling the effects of BMPs
and their interactions. Riparian buffers are especially important in this
regard.
• Simulation of in-stream processes must be improved in most of the current
models if they are to be used to predict P loading to sensitive lakes,
reservoirs, and estuaries in large watersheds. Stream measurements of
equilibrium P concentrations and P uptake lengths offer opportunities to
assess model in-stream estimations.
• Calibration and uncertainty tools need to be an integral part of future
models. Uncertainty in model estimations must be quantified as models
are increasingly used for regulatory purposes such as TMDL development.
• Monitoring data are critical for modeling. Long-term studies with a nested
design of automated samplers are needed in partnership with the USGS
stream-monitoring network.
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