Catchment Modelling Strategies for Faecal
Indicator Organisms:
Options Review and Recommendations
Project Code
WW0220
Workpakage 1; Milestone 2
Interim Report on Literature Review Listing
Sources Accessed
January 2011
Project Team
David Oliver* (Principal Author of this Report),
John Crowther**, Phil Haygarth***, Louise Heathwaite***,
David Kay**, Trevor Page***
* Stirling University; ** Aberystwyth University; *** Lancaster University
Correspondence to:David Kay
[email protected]
01570 423565 (Tel and Fax)
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GLOSSARY
Black-box model: A model which does not explicitly represent the actual processes in
converting the model input into a model output.
Calibration: The fitting of model predictions with measured data through the changing of
model input parameters relating to some accepted criteria.
Continuous model: The application of a model to continuous data (e.g. long term) as
opposed to discrete data (e.g. storm event)
Deterministic: A deterministic model is one of knowable outcome: having an outcome that
can be predicted because all of its causes are either known or the same as those of a
previous event.
Empirical model: A model developed on empirical observations of the system under study
Export coefficient model: A ‘black-box’ modelling approach whereby, for a given climatic
regime, a particular land use class is determined to export characteristic quantities of a
contaminant over a defined time period.
Fully-distributed: The attributes of the catchment being modelled are distributed throughout
the landscape (e.g. via a grid).
Fuzzy model: Deals with information that is approximate rather than accurate
Grey-box model: Provides some physical process-representation but some of the
processes are approximated
Lumped model: Simplification of a distributed physical system whereby processes are
grouped into spatial units of similar functioning such as ‘hydrological response units’
Model evaluation: Assessment of the model with respect to its intended objectives and may
include some reporting on model structural and parameter uncertainties, and parameter
sensitivity. Model evaluation is often undertaken as part of validating whether the model is ‘fit
for purpose’.
Model structure: The conceptualisation of the system under study into a model
representation and numerical design.
Monte-carlo simulation: The use of repeated random sampling from apriori specified
parameter distributions to generate results.
Parameterisation: The process of assigning values to parameters that represent particular
processes or functions within a model structure. This can be undertaken using expert
opinion, literature searches, via new experimentation and field studies or via calibration (see
above). A lack of spatial or temporal data can inhibit parameterisation.
Physically-based: A model whereby the structure attempts to represent processes, such as
those governing contaminant inputs, mobilisation and delivery, in a physically-meaningful
and spatially distributed manner. The extent of process representation is dictated in part by
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underlying hydrological model and process equations. Physically-based models can be
deterministic or stochastic.
Probabilistic model: A statistical approach to estimate the probability of a given event
based on historical data.
Process based: see physically-based
Semi-distributed: In contrast to a fully distributed model different land use classes within a
catchment, or sub catchment boundaries, are modelled simultaneously rather than as
explicitly individually defined units typical of a distributed model. This likens the approach to
a lumped model in some ways but similar land units may not be contiguous
Stochastic: Non-deterministic behaviour involving a random element. Stochastic models
aim to represent the likelihood of different outcomes given similar inputs
Uncertainty analysis: Model uncertainty can relate to parameter uncertainty and model
structural uncertainty as well as the uncertainty associated with uncertain inputs and
evaluation data. Ultimately, a reduction and characterisation of uncertainty in model
predictions should form part of the modelling process which may help in the reduction of
uncertainty.
Validation: For the purposes of this review this term is used to mean that a model is tested
as being fit for purpose rather than as being truly valid.
White-box model: A model representation of a system where all necessary information is
known and available
INTRODUCTION
DEFRA have utilised export coefficient models to characterise faecal indicator organism
(FIO) flux at the catchment scale and determine FIO source apportionment through the
FIOSA project (Kay, Anthony et al. 2010). FIOSA comprises an empirically-based, but
‘black-box’, modelling approach. While useful, export coefficient approaches are only able to
provide a limited disaggregation of process understanding at the catchment scale. The
growing requirement for the design of ‘programmes of measures’ by Article 11 of the Water
Framework Directive (WFD), to prevent impairment of Annex IV ‘protected areas’ (i.e.
including bathing and shellfish harvesting waters), is generating an imperative for the
development of more ‘white-box’, or process-based, modelling capacity. This is needed in
order to differentiate specific (spatial) effects of land management practices when combined
with catchment responses to hydrological drivers at relevant timescales. In turn this will
underpin requirement and design of remediation strategies (particularly in livestock farming
areas) to facilitate integrated management of diffuse and point-source FIO fluxes.
The adoption or development of a modelling approach for diffuse pollution should always
consider a number of critical factors. Most notably these should include: a clear statement of
data needs (both for process representation (and constraint) but also for model evaluation
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purposes, an awareness of the importance of spatial and temporal scales for model
predictions, and finally an evaluation of the uncertainty in model predictive capability. The
other major consideration should be the type of modelling approach to use (Oliver,
Heathwaite et al. 2009). By that we mean where on the scale (ranging from simplistic, black
box modelling through to highly complex physically based modelling) the chosen approach
will need to function.
The prediction of catchment contributions to watercourse pollution has seen a number of
process-based modelling platforms developed over the last few decades, particularly for
prediction of nitrate and phosphate loading of surface waters (see review by Merritt for
assessment of modelling platforms for sediment and sediment-associated nutrients). In
contrast, model development for FIOs is less mature – a direct consequence of the limited
extent of the scientific evidence base for, and ‘historical’ interest in, microbial contaminants
relative to phosphorus (P) and nitrogen (N). That said, the need has never been greater to
consider existing modelling platforms (developed for other agricultural pollutants) in order to
evaluate their suitability for accommodating a microbial sub-routine given the looming
requirements of the WFD. Indeed, excellent progress has already been made in terms of
initial developments of such microbial submodels and some prior reporting of comparative
model evaluations already exists in the literature (e.g. (Borah and Bera 2003; Borah and
Bera 2004; Coffey, Cummins et al. 2007). Here, we build on this existing review material but
crucially we also extend the evaluation of modelling platforms to a key cluster of tools
developed specifically for UK application to agricultural pollution prediction. This does not
imply a preference for UK specific models but simply recognises: (i) a lack of their
consideration in published literature appraising microbiological modelling capability
previously, and brings this up-to-date; and (ii) that there may be more efficient uptake by UK
regulators through bolting on of different modelling components to existing UK models.
Importantly, the research team fully appreciate that we should not limit ourselves to the fact
that model structures are right for UK needs with regard to prediction of microbial
watercourse pollution; they may not be, and if not it raises difficult issues surrounding the
ease at which existing model structures (and associated code) can be adapted. In evaluating
existing model frameworks it will be paramount to keep in mind the purpose of this review.
The aim is to consider the range of modelling frameworks currently available and to propose,
after a thorough balanced interrogation, a selection of the most suitable contenders for
potential development in order to accommodate a microbial submodel. The ultimate
selection must be undertaken in conjunction with the Project Advisory Group.
THE SUITABILITY OF KEY INDICATORS FOR A MODEL PLATFORM
Given that the focus of this review is to consider the extension of modelling platforms to
accommodate a microbial submodel it is important to highlight the key requirements of
catchment scale process-based models of agricultural pollution for FIO suitability. Each of
the models evaluated in this document will be compared against the following defined ‘model
needs’ to provide a yardstick for model comparison:
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-
Hydrological representation: the driving routine that underpins the majority of
models of diffuse agricultural pollution is one grounded in hydrology. Thus, a
hydrological model accommodates a suite of flow-governing equations to deliver on
distinct stages of a model simulation. For example, one of the most frequently used
modules is a subroutine for calculating surface runoff. This needs to account for
variation in land use type, topography, soil type, vegetation cover, precipitation and
land management practice (e.g. manure applications, livestock grazing etc). Processbased models attempt to represent some of the (albeit in a simplified way) physical
rules and processes observed under real-world scenarios including surface runoff,
subsurface flow, and channel flow via these submodel components. Flow routing is
undisputedly a critical element in models designed to predict diffuse pollution impacts
on receiving waters. One of the first models claimed to have successfully integrated
all submodules necessary for catchment chemical hydrology was the Stanford
Watershed Model (SWM). A derivative of SWM is the Hydrological Simulation
program – FORTRAN (HSPF). European equivalents of a comprehensive catchment
model include the Systeme Hydrologique Europeen (SHE), which has been
succeeded by MIKE SHE (a catchment-scale, physically based, spatially distributed
model for water flow and sediment transport). In some cases, many more processes
are represented and this in turn can lead to the creation of incredibly complex model
structures that have no quantitative equivalent using current field measurements.
Within this review we are particularly interested in assessing the potential of models
to simulate the capture of faecally-derived microbial pollutants via hydrological
processes and their subsequent routing through the catchment drainage network.
Thus, where evident we will make clear the potential for hydrological submodels to
entrain sporadic livestock excretions (sources of FIOs) and likewise their feasibility
for representing in-stream processing of FIOs.
-
Time-step: The temporal resolution of the model routines are extremely important if
we need to think about capturing the dynamic response of event-driven pollutants
such as FIOs. Logically, monthly time-steps would appear to be inappropriate for
accurate capture of any water quality response to short term rainfall events, and
instead a daily if not sub daily routine would appear to be needed. The timestep for
FIOs is governed by the likely exceedence periods and in our view this can be very
short for bathing waters and shellfish harvesting waters, thus hourly resolution would
be the ideal. This will be explored within the body of this review
-
Spatial-scale: The over-arching remit of evaluating modelling capability to underpin
prediction and design of remediation strategies (particularly in livestock farming
areas) to facilitate integrated management of diffuse and point-source FIO fluxes
dictates a catchment-scale approach. However, it will be important to consider the
importance of arbitrary 1km2 gridded distributed models versus models that delineate
hydrological response units (HRU’s) or the equivalent based on common landscape
functionality. How important is such delineation? Such issues are discussed by Lane
et al. (2009) and incorporated in the body of this review.
-
Diffuse and point source contributions: In addition to diffuse source FIO inputs to
stream loadings there will need to be some consideration of how point source FIO
inputs are accounted for within the catchment context. Point sources will be
numerous both in terms of quantity but also type (e.g. wastewater treatment works,
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farmyards, leaking septic tanks). Of particular interest will be the scope for modelling
platforms to account for farmyard FIO contributions yet at the same time we need to
be critical in assessing what data are actually available for farmyard contributions to
constrain the model. It will be important to understand how variable such data may
be. It is likely that such point sources will prove complex to accommodate within any
model structure because of the variability of farmyards associated with different
agricultural enterprises.
-
Ability to represent lifecycle processes (such as regrowth and die-off) within
model parameterisation: Many diffuse pollutants are of a non-conservative nature
because of uptake by plants or their degradation potential. For FIOs the model will
need to be able to account for cell die-off and regrowth potential within different
catchment matrices. Likewise storage and release within the catchment though this is
probably much more important for P than for FIOs.
-
Recognition of in-stream processes: Given that the overall aim of the review is to
evaluate the predictive capability of modelling platforms for FIO it is important to
account for all of the key sources and sinks of microbial pollutants, one of which is
stream-bed sediment. The ability of including in-stream processing within modelling
routines will be considered. There is evidence available in the literature to highlight
that streambed storage is important (Muirhead, Davies-Colley et al. 2004; Cho,
Pachepsky et al. 2010).
-
Ability to account for mitigation impacts: Most models should be able to account
for changes in the catchment that relate to management interventions through the
alteration of parameter values. Any reference to trialled modelling of mitigation
measures within existing modelling platforms will be highlighted within this review.
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Licensing (cost): The likelihood of licensing requirements presenting a hindrance to
model development will be considered. Collaborating with those who hold the license
is clearly one option to circumvent those problems but this does not pave the way
forward for an open-access web-based platform that is favoured by Defra. An opensource web-based approach would encourage the development of any modelling
platform and allow a more rapid evolution of the model by the research community.
Clearly this may restrict the applicability of some of the existing models.
LITERATURE SEARCH STRATEGY
There are a considerable number of model platforms from around the world and the
following text provides a brief summary of those identified as having greatest potential for
further development with regard to microbial prediction. Web of Knowledge was the principal
engine for the literature search using combinations of key words as shown in Table 1. This
text summary has been condensed into two accompanying Excel summary tables for each
model and an initial reference bank has been created within Endnote for further exploitation
(containing 230 FIO [or diffuse pollution related] catchment-scale modelling references). This
document therefore serves as a precursor to a more detailed evaluation of the required input
parameters necessary for their functioning (see linked excel spread-sheet).
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Table 1: Search criteria used in combination within Web of Knowledge
SUMMARY OF MODEL PLATFORMS FOR CONSIDERATION
PSYCHIC: PSYCHIC is a process based catchment scale model of P and sediment
transfers developed in the UK. Specifically, it models P and suspended sediment
mobilisation in land runoff and their subsequent delivery to watercourses (Davison, Withers
et al. 2008). It is packaged as a decision support system and operates through the coupling
of hydrological and land management information. The PSYCHIC platform offers end-users
a dual scale application allowing for catchment scale prediction using nationally available
datasets, but also harnessing more detailed user-supplied information for field-scale utility. A
variety of transfer pathways are accounted for and include: release of desorbable soil P,
detachment of suspended sediment (SS) and associated particulate P, incidental P losses
from manure and fertiliser applications, losses from hard standings, artificial drainage
routings, point sources and surface runoff.
A number of caveats are apparent when PSYCHIC is operated at the catchment scale. For
example, the model is not programmed to account for bank erosion as a contributor of P
loading (nor in-stream processing of P for that matter). PSYCHIC uses a monthly time-step
and the spatial scale of operation allows for the accumulation of 1km2 (or smaller where
possible) spatial data that the model combines with management information derived from
Ag Census returns and relevant survey responses.
As with any process-based model PSYCHIC has a number of data requirements in order to
function. These are outlined briefly in the following list (though a considerable number are
considered practical constraints on uptake of PSYCHIC as a modelling platform of choice,
largely because of licensing issues):
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1. Manure Management Database (held by ADAS), combining agricultural census data
with the survey of fertiliser practice;
2. MAGPIE (held by ADAS): used in PSYCHIC as a database of land use type, the
number of livestock per ha of managed grass and the proportion of each crop grown
per ha;
3. NATMAP (held by NSRI): series based, 1km2 spatial data set of % coverage of each
soil series;
4. National Soil Inventory (held by NSRI): physical properties associated with each of
the soil series under different land use conditions;
5. HOST (Held by CEH/NSRI): Classification of the soils of the UK;
6. DEM;
7. Census data (no of people per km2);
8. Climate Surface (held by Climate Research Unit, UEA) – climate attributes including
rainfall, rain days, wind speed, sun hours, maximum temperature, minimum
temperature;
9. Drainage density (river network; CEH);
10. Index of proximity to surface water (connectivity).
Clearly, a number of datasets are the property of research institutions and or consultancies
and the model code itself is held by ADAS and is not open access. The EA do have a
PSYCHIC 1 version (Neil Preedy is the contact at the EA who would know such details)1.
The main model components include modules that account for: water balance and
hydrological pathways (using the mean climate drainage model [MCDM]); sediment loss
(using the modified Morgan-Morgan-Finney Model); incidental losses (determined by rainfall
intensity and cumulative rainfall since application); solubilisation of P in soil (a function of soil
Olsen P); Particulate P loss with eroded sediment (including an account of particle size
distribution effects); and delivery as determined by hydrological connectivity (which strictly
speaking is transfer potential rather than deliver (Beven et al. 2005). The PSYCHIC model
can therefore be divided into a combination of processes governing water balance and those
governing mobilisation and delivery. Values for surface and subsurface flow volumes
associated with each of the land use classes are derived using the MCDM. An area
weighted approach is then adopted to obtain the overall proportion of different flow pathways
in each grid cell, and the relative importance elucidated via reference to soil properties. In
the end the assigning of the importance of those pathways will depend on what processes
you want to represent and what information will be available to constrain/evaluate those
processes. The final model can then be run including uncertainty bounds. PSYCHIC makes
inroads in attempting to account for hard standings as key contributors to SS and P inputs to
streams and uses hydrologically effective rainfall (HER) as a factor in determining their
importance. The model architects note that more work is needed to develop this element
(Davison, Withers et al. 2008). Similarly, in attempting to include septic tank inputs within the
PSYCHIC framework the ‘educated guess’ of particular proportions of the population being
connected to mains sewers is again recognised by the model architects as a weakness in
1
The project team needs to identify whether the EA have the code or just an executable
version (probably the latter) – we would need to consult with Neil to determine what exactly
we could vary. If we don’t have the code we cannot add an FIO component without going
back to ADAS – speak with Neil. [Louise: We will not have access to the code].
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design. As a final point, the application of the prototype PSYCHIC model has been linked to
feedback from catchment dwellers and stakeholders. Such local knowledge ensured that
predictions provided by PSYCHIC could be fine-tuned and reality-checked (Collins,
Stromqvist et al. 2007) though this can be difficult to obtain when models are transferred to
other catchments unless community engagement is already well established.
Potential to consider as microbial modelling platform?: WEAK, largely because of
monthly time-step approach and complex licensing issues and access to original
code – though discussion with EA is perhaps needed on this. The monthly time-step
must be considered as too low a resolution for prediction of contaminants that are so
heavily dominated by incidental surface-driven losses. The lack of accounting for
bank erosion is not a major limitation for FIOs though the omission of in-stream
processing capability could be problematic given the likely range of differential die-off
and resuspension rates of FIOs and particle-bound FIOs within the water column and
stream bed sediment. An additional bolt-on model could be feasible. The attempt to
include hard-standing contributions of diffuse pollutants is of high interest in terms of
transferability to FIO modelling though the robustness of the hard standing
component is questionable. This is because HER is coupled with a number of
additional coefficients that are essentially preliminary estimates. Also, it is perhaps
more favourable to discount models that operate using 1km2 grids. Instead, model
platforms that use HRUs could designate catchment Critical Source Areas (CSAs) as
the most important HRU. 1 km grids are often used for other reasons - commonly
because data is often available in GIS grid-based format and therefore convenient but
the best model is not necessarily grid-based. More realistic and logical, but slightly
harder to deal with, is the delineation of a catchment into CSA and non-CSA areas
(e.g. PEDAL2) or HRUs or Functional Units (FUs) (e.g. SWAT, eWATER). Beyond this
issue we also need to query the appropriate spatial scale for some of the transfer
pathways and sources e.g. hardstandings, where a very detailed scale me be crucial
for these known hot-spots.
An interesting comment on PSYCHIC (with generic applicability to other models) can
be drawn from information published in the final report to Defra (2005) whereby a
number of questions were raised in terms of the usefulness of the UK Environment
Agency (EA) data archive. Load estimates based on EA data were actually shown to
be unreliable with significant bias and imprecision and to circumvent this weakness,
it was apparent that testing of model outputs should be undertaken with targeted and
detailed monitoring of flow, SS and P (Defra, 2005). While the likelihood of PSYCHIC
being recommended as a modelling platform suitable for FIOs is rather low given a
number of limiting factors already identified, this point does raise a generic point of
utmost importance that will be a general concern for any modelling platform. For
FIOs, in-stream empirical fate and transport data is extremely limited but would be
needed to parameterise process-based FIO models at the catchment scale. This
brings issues of specific model evaluation requirements to the fore. Clearly, a means
of testing and evaluating modelling capability within reliable data constraints is vital
to ensure any degree of confidence in model outputs. Interestingly, the final report to
Defra recognised that the ‘integration of current understanding of sediment and P
loss risk into reliable quantitative predictive models is less well advanced than for
nitrate’. This is an important statement that is magnified when applied to the existing
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FIO evidence base. This reinforces the policy need for enhanced water quality
monitoring programmes (potentially via the DTC projects) to provide measured data
with which to evaluate FIO load estimates.
INCA: The INCA model is a process-based, dynamic, semi-distributed and integrated
catchment model. It is highly parameterised and therefore requires a large number of input
variables some of which are poorly understood and others which are deemed to be
impossible to measure (Dean, Freer et al. 2009). Essentially, INCA is a dynamic massbalance model designed to provide a process based representation of plant/soil dynamics,
instream biogeochemistry, hydrology and the fate and distribution of chemicals in aquatic
and terrestrial environments. The platform simulates water flow and associated water quality
and attempts to track hydrological flow pathways operating in both the land and riverine
phase. Its dynamic nature means that day-to-day variations (i.e. changes in daily mean
values) in flow and water quality are included for both the land and stream phase
components. The nature of the model is such that changes in land use or climate on water
flow and associated quality can be simulated. The approach is one whereby a simple mixing
model is applied to water and contaminants of interest linked to different land uses (up to 6
user defined classes) within each reach and the resultant is then routed along the main
stream. It is important to note the semi-distributed nature of INCA and to not confuse it with a
fully-distributed model. The purpose of the INCA platform is not to model catchment land
surface in explicit detail. Instead different land use classes and sub catchment boundaries
are modelled simultaneously with output from these different spatial components providing
subsequent input data for a multi-reach river model. The hydrological land-use model
component of INCA is driven by a daily mass balance and facilitates the calculation of daily
water flows for soil, groundwater and leaching to the river system for up to 6 land use
classes using a daily time step.
The river model component uses conventional non-linear reservoir dynamics to simulate the
routing of water down the river network. Therefore, the river model operates in a similar
manner to that associated with ANSWERS, ANSWERS-continuous and HSPF (see later
descriptions) to route water down the river system. In addition to solving flow equations,
there is a requirement to solve NO3-N and NH4-N mass balance equations in soils and
groundwater zones.
The INCA platform is complex. It has to be because it attempts to describe a large number of
factors and processes in the catchment environment. Such modelling philosophies are
limited by the availability of sufficient and reliable data even for small catchment areas
(Dean, Freer et al. 2009). The model platform was originally developed to predict daily time
series of soil-water NO3 and NH4 concentrations and fluxes (including temporal variations in
flow paths and N transformations) (Wade, Whitehead et al. 2002). A full model description is
presented in Whitehead et al. (1998).
Briefly, the hydrological model is constructed from three component parts, namely: (i)
MORECS – the Met Office rainfall and evapotranspiration calculation system (converting
rainfall into HER to drive water transfers); (ii) a system allowing simulation of the effects of
the land surface on flow (using a two-box approach whereby principal reservoirs of water are
deemed to be the reactive soil zone and groundwater); and (iii) river flow model (based on
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mass balance of flow as discussed above) (Whitehead, Wilson et al. 1998). N inputs to each
subcatchment are derived from land use and DTM data. Land use is also used as an
approximate surrogate for soil type for a number of characteristics that can influence N
transformations.
In 2002 Wade et al. published an updated version of INCA. The general structure remained
largely unchanged and so the equations are still founded on the 4th order Runge-Kutta
technique enabling simultaneous solution of model equations. This in turn ensures that no
process representation within the model is able to take precedence over another. What did
constitute a major amendment was the inclusion of retention volumes. Thus, the simulation
of long term changes in water and N stored in soil could be simulated and this removed the
previous oversimplification whereby water storage volume within the catchment depended
only on the rainfall input. The corresponding soil drainage volume stored in the soil could
now respond rapidly to water inflow – and could be conceptualised as macropore, drain or
piston flow and impact on the rising limbs of hydrographs. Conversely, the soil retention
volume represented the proportion of water in the soil that responds much more slowly and
is comparable with the field capacity concept (and water associated with soil micropores).
The other notable changes were the conversion of process equations to be represented in
terms of loads rather than concentrations and that a fertilizer submodel was removed and
replaced with a daily-timeseries of mass inputs from an additional read-in file (allowing for
multiple fertiliser applications within a single year). Example catchment applications of the N
version INCA are available (e.g.(Jarvie, Wade et al. 2002; Rankinen, Lepisto et al. 2002).
A number of versions (in various degrees of development) of INCA are discussed in the
literature including INCA-N (flow, nitrate and ammonia), INCA-P (flow, TP,DP, PP,
macrophytes), INCA-SED (flow, sediment including size fractions, stream power, shear
velocities), INCA-C (DOC, DIC, Particulate C), INCA-metals (flow and metals) and INCA-Trit
(for radioactive pollution events). There is no FIO version of the INCA platform. INCA-P is
considered here as an example of extension of the INCA modelling philosophy. INCA-P
evolved as a platform to model the transport and retention of P in terrestrial and aquatic
environments, and to simulate bed sediment resuspension and suspended sediment
deposition. There are three key components of INCA-P (according to (Dean, Freer et al.
2009)): (i) land phase hydrological model; (ii) land phase P model; and (iii) in-stream P
model (an in-stream processing component would be useful for FIO modelling). These three
components calculate discharge through different pathways, P stores and transformations in
soil and groundwater and P processes operating within the stream, respectively. The landuse hydrological model provides a daily mass balance of water flows at a daily time step.
Data describing typical land management practices (grazing season duration, application
timings and quantities etc) are required. The key assumptions made are that fertiliser,
wastewater and manure inputs are equal across a particular land use class, irrespective of
geographical positioning within a catchment. Similarly, the P process rates are the same
irrespective of catchment location, though can still vary according to spatial variations in soil
moisture and temperature and finally initial stores of P and water associated with each land
use class are the same irrespective of location within the catchment (Wade, Durand et al.
2002). These assumptions are basically included as an attempt to simplify the model
complexity (though given the structure and parameter requirements this does not mean that
the model itself is particularly simple). In fact, INCA-P is more complex than INCA because
the controls on catchment scale P transport require considerations of P adsorption on
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sediments and subsequent sediment associated transfer within stream systems (Neal,
Whitehead et al. 2002). To demonstrate the complexity of the model structure there are 93
parameters to populate if the system is treated homogeneously. The application of the model
to the River Lugg catchment, as an example, required 901 parameters to cover 22 stream
reaches and 6 land use types.
Potential to consider as microbial modelling platform?: WEAK, largely because it is
so complex, but the in-stream component could be of interest. However, this would
depend on where the similarities lie for in-stream processing on chemical versus
microbiological pollutants because it may be that FIOs require a completely different
approach. For INCA-P it is important to note that recommendations following
applications to different catchments have suggested a need to obtain detailed point
source data and a more general assessment of relative contributions of point and
diffuse sources (Dean, Freer et al. 2009). Critically, the current monitoring networks of
the EA are not yet sufficient to meet this need.
SCIMAP: First and foremost it is important to stress that Scimap is not considered a water
quality model (c.f. Simcat). However, nor is it a conventional rainfall-runoff model. Instead
Scimap operates in order to inform model-users where in a particular catchment you have
tributaries, fields or parts of fields that are producing risk. Essentially, it is a tool to prioritise
where to take action in a catchment, and is termed a risk-based’ model, so is not a processbased platform. Scimap uses a high resolution DEM and landcover map to profile where the
risks are most likely to be and therefore only uses readily available data. A strong advantage
is that it is open (controlled) access, meaning it is open access without charge but the output
could be used incorrectly so the model architects keep abreast of who is using it and for
what purpose. Scimap has been developed to predict fine sediment risk, P risk and is
starting to develop N risk (to an extent). While Scimap only looks at relative risk within a
catchment rather than absolute risk between different catchments there are modifications for
Scimap P and N to provide absolute results and any relative model could be calibrated to
give some absolute results.
The underlying hydrological concepts of SCIMAP are based upon digital terrain analysis.
SCIMAP couples the a/Tan(beta) concept that provides a soil wetness propensity index (a =
uphill contributing area, and beta is the local slope angle: which underpins the TOPMODEL
approach) and a Network Index (a surface flow connectivity index explained further below) to
provide a risk map for the connectivity of surface flows that can be linked to diffuse pollutants
(Reaney et al., in press; Lane et al., 2010). Within SCIMAP, routing of overland flow is
accomplished by assigning a travel time value to each cell. Travel time is calculated by
accumulating distance from the outlet to the source (by tracing back along the direction of
steepest slope) and dividing by the flow velocity. Second, connectivity is handled using the
concept of a network saturation value. The network saturation value of a source cell is the
lowest saturation value on the flow path to the channel. When the network saturation value
reaches a threshold, the source cell is connected and overland flow can occur. Each cell
therefore has three attributes: local saturation value, network saturation value and travel
time. These attributes are the basis for a new definition of hydrologically similar zones.
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Potential to consider as microbial modelling platform?: MODERATE to HIGH as a riskbased tool, but clearly WEAK for process based model. The project team need to
engage with end-users to determine appropriate needs for FIO modelling. Some
discussion surrounding the linkage of the SCIMAP model to the PEDAL model (for P)
has been taking place between Lancaster and Durham and this represents a
potentially useful development if SCIMAP could be extended to FIOs.
The
discussions are based upon the possibility that PEDAL can be used to identify
absolute diffuse pollution risk “hotspots” at the headwater catchment scale and
SCIMAP then used to map the relative risk of different areas within a given catchment.
PEDAL2: [Note potential conflict of interest as four members of the project team are
involved in the PEDAL2 project – needs to be borne in mind]: The PEDAL2 project is a
continuation of Defra funded research into a modelling platform to predict the delivery of P to
watercourses using a decision-tree approach. The platform makes use of national coverage
data held within GIS at the 1km2 scale. This combines with a ‘field toolkit’ of measurements
and qualitative observations.
The scale of operation of PEDAL is the headwater catchment scale and it is being developed
to predict delivery of P and FIO to headwaters draining small catchments. There would need
to be a linkage with other modelling platforms to enable subsequent routing and in-stream
processing of E. coli loading through stream networks of any larger catchment, or a targeted
campaign of measurement of instream effects. Furthermore, PEDAL is specifically designed
to account for the diffuse signal of P and FIO contributions to surface waters, it therefore
does not consider point source inputs nor in-stream processing or direct deposition to
streams by livestock, although some of these factors are implicitly included in the delivery
coefficients calculated as they cannot always be separated.
The PEDAL2 modelling platform differs considerably from applications such as SWAT and
HSPF (see later descriptions) largely because the PEDAL philosophy is to use fewer model
parameters to constrain uncertainty in model output. The ultimate aim of the PEDAL
approach is to attempt to identify whether mitigation measures aimed at the control of
sources, mobilisation or delivery (or a combination of these) of pollutants is likely to be more
effective. Quantifying the actual amount of pollutants such as FIOs and P delivered to
waterbodies is challenging owing to the variability of factors that affect their mobility and
transport. We have increasing scientific knowledge of these factors at smaller experimental
scales but we still do not have reliable general rules that can predict delivery of pollutants at
larger scales such as catchments and river basins that are often used operationally by
regulatory bodies. The PEDAL project has provided scale-dependent estimates of P
delivery from headwater catchments to water courses using simple physical drivers of P
transport (i.e. rainfall, hydrological soil classification and connectivity to local watercourse).
This has been done using a decision tree approach. The approach was calibrated to
delivery coefficients observed at project catchments (i.e. using a measure of annual P loss
divided by the DESPRAL measure of mobilisation: PE0106). The delivery coefficient
estimates produced were expressed such that predictions reflected the associated
uncertainty, rather than a single number. These initial delivery coefficient ranges were
modified by structured catchment visual assessments (VAs) that identified whether land
management, land boundaries and/or land use, for example, would increase or decrease the
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likely magnitude of P delivery: this was quantified into a catchment VA score. The modelling
structure proposed for the FIO component of PEDAL2 focuses on identifying physical FIO
loss by estimating delivery of FIOs to headwater streams. PEDAL2 facilitates this by
estimating FIO delivery as a ratio of the observed FIO loss to FIO sources using a similar
fuzzy decision tree approach to PEDAL. However, there are few existing studies on the
magnitude and dynamics of catchment FIO sources (c.f.(Coffey, Cummins et al. 2010;
Coffey, Cummins et al. 2010) for SWAT) or field FIO survival studies. The FIO model is
currently being developed in a similar manner to the P model with an expert workshop for
evaluation of model structure, development of an FIO VA and a farmer questionnaire and
identification of fuzzy rules for predicting the effects of mitigation measures on FIO sources
and delivery coefficients. Ideally, the focus of a predictive model would be to determine FIO
fluxes at hydrological event-scale as these timescales are the key requirement of the policy
community (Kay, Edwards et al. 2007). However, this is a very difficult task given the limited
high resolution data that exists from previous studies (to date few studies have measured
FIO dynamics through events to facilitate model evaluation: (Kay, Edwards et al. 2007)).
Given the storm-event focus of PEDAL, the project team will obtain delivery coefficients at
hydrological event scale, which is a temporal disaggregation of export coefficients.
Once delivery coefficients have been determined for monitored sites the model will be used
to provide fuzzy estimates of sources and delivery from national scale data on land use,
farming practices and catchment physical characteristics. As with the P model, the VA and
farmer questionnaire can be used to refine these estimates at locations of interest. The new
PEDAL model structure has been calibrated on existing P data because at current time the
project does not have a full year’s data yet from the new catchments. A GUI is to be
developed in accordance with EA/Defra wishes as agreed at the June 2010 steering group
meeting.
The model code will be provided to Defra and the EA. All output from the project will be
jointly owned by DEFRA and the research consortium and will be made available to potential
users provided they receive sufficient training in the use and potential limitations of the
models developed, the field methodologies and the modelling results. This is comparable
with say TOPMODEL whereby it is free for academic use, but not free for consultancy. So
for PEDAL, project team and / or Defra can decide who can use it.
Potential to consider as microbial modelling platform?: WEAK in considering
applicability to process-based modelling at catchment scale; MODERATE TO HIGH
(given already considering FIOs) for a sub-component of a larger FIO modelling
platform but like SCIMAP the package is not useable as a stand-alone. It could help
inform catchment modelling aspects but does not account for point sources or instream impacts. Lack of accounting for point sources does not rule out the PEDAL
model as a contender platform but instead highlights the need to clearly define the
purpose of any FIO model to determine appropriate development needs. Issues such
as those raised have already been brought to the fore at a recent PEDAL FIO expert
workshop which was conducted, in part, to highlight future development needs for
improvement and uptake of the PEDAL2 model.
MHtracking Modelling @ Newcastle University: In a 2007 review paper O’ Connell et al.
(2007) refer to the tracking of ‘impact information’ through a catchment via a modelling
approach. The underlying concept is to track ‘packets’ of water from source areas to a site of
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impact (using a pixel-based approach) via routing through the stream network. The model
has been developed primarily for flood prediction (see FRMRC2 project) but clearly the
approach offers potential for application to pollution prediction and tracking.
The general philosophy for the distributed Mass and Head tracking (MHtracking) models
developed at Newcastle is that a complete understanding of the water flows and water
quality downstream in a river catchment is possible only if high-quality space-time modelling
is used that allows hydraulic head and the various masses of water, solute, sediment and ice
to be tracked all the way through the catchment, starting from source locations. Head and
the various masses move at different space-time-varying speeds and the accurate prediction
of impacts downstream depends on getting these speeds approximately correct. Even in the
simplest possible simulations where a secondary mass such as solute is carried freely with
water the time-varying impact on the water quality downstream depends to a large degree
on the relative speeds of the hydraulic head and the bulk water velocity.
This approach has been a long time in development, starting in the mid-1990s using the
SHETRAN physically-based spatially distributed modelling system (e.g. using solute species
as a label to track sediment from different source locations, (Ewen, Parkin et al. 2000)). It
has evolved considerably over the years, partly in response to an improved understanding of
the fundamentals of tracking but also an understanding of the limitation of field data, such as
the inadequacy of DEMs as a basis for the prediction of the downstream impact of hot-spot
(point, or limited area diffuse) pollution. Fine-scale grid nesting, where fine grids are nested
within coarse grids to allow attention to focus on important locations in the catchment, is
possible in a new (unpublished) version of SHETRAN that was developed and used for the
nuclear industry, but even this is not completely satisfactory when working with very small
hot spots.
One positive consequence of the evolution has been the adoption of a modular modelling
approach, to create a suite of MHtracking models, so that different models are used to meet
the various demands for modelling source areas, runoff generation and channel networks.
There are four classes of models: (1) the classic integrated approach, e.g. using SHETRAN;
(2) adjoint models for studying the propagation of mathematical derivatives, to give insight
into the sensitivity of downstream impacts to upstream causes (this has been applied to
various models (e.g. (Ewen, O'Donnell et al. 2010)); (3) mass tracking over the top of
distributed water flow model, including tracking particles and packets (e.g. (O'Donnell 2008;
Geris, Ewen et al. 2010)) ; and (4) tracking in which the fundamental model is a tracking
model (e.g. the types of approaches used in Ewen (2000) and (1996) where water mass is
treated as particles and water packets).
The MH tracking approaches allow the various dispersion effects to be represented explicitly
and their consequences to be analysed. These include hydrodynamic and geomorphologic
dispersion of head and the dispersion of the various masses, including dispersion associated
with instream variations in water flow velocity (i.e. variations across cross-sections, around
the bulk mean velocity), settling velocities and mass-mass interactions.
The various MHtracking models are distributed and use either (or a combination of): regular
grids, nested grids, dendritic networks, or free-form polygon grids (e.g. customised grids that
can accommodate linear features such as sheep pathways, moorland grips, and roads).
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They work with fixed time steps of appropriate size (e.g. 5 minutes or 2 hours) and typically
simulate several years.
Fundamentally, the MHmodels are physics based and use data that fully define the various
structures and media for the region being modelled. The demand for data depends on the
scope and ambition of the study. The basic data are time series for rainfall and potential
evaporation. Most of the models require a DEM, to be used as a starting point for the
network and grid creation. The other requirements depend on the model and problem, but
can include, for example, survey data for hot spots, survey data for channel geometry and
vegetation, and national data sets for land use, soils, etc. One of the main purposes in
developing the MHtracking models is to study the impact of changes in land
use/management and climate. Models within MHtracking can represent transport in surface
and subsurface waters, adsorption to sediment with in-stream deposition and remobilisation,
and solute decay. Both diffuse and hot-spot sources can be represented.
Some of the MHtracking models have custom GIS capabilities for pre-processing raster data
sets to enable rapid model set-up, and model outputs in ESRI ARC/INFO grid format. It is a
goal to make some of the MHtracking models open-source. The codes primarily use
FORTRAN 1995/2003. Outreach has started with SHETRAN, which is available as an
executable free from www.ceg.ncl.ac.uk/shetran. Figure 1 shows typical examples of the
MHTracking approach.
Mass tracking: water source depth map for a flood
event in Jan. 2005 in the Upper Eden (69km2)
created by tracking water packets in a network
model.
Head tracking: sensitivity of flood peak to changes in
surface roughness for a flood in Feb. 2005 in the
Dunsop catchment (26 km2) created using an adjoint
version of SHETRAN.
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Combined head and mass tracking: a customised
polygon grid for analysing hot-spot effects
associated with sheep stocking on a small
headwater tributary (2.5 km2) of the Hodder
catchment. The grid was refined based on field
surveys of the natural triangular-shaped severe soil
compaction zones that develop at river-crossing
points. All the coloured elements are part of the
grid, including the linear blue elements that
comprise the drainage network and the thin orange
elements that comprise the sheep tracks.
Figure 1: Typical examples of distributed Mass and Head tracking (MHtracking): provided by
Greg O’Donnell
Potential to consider as microbial modelling platform?: MODERATE TO HIGH as the
modelling approach scores positively in terms of the key criteria listed in earlier
sections of this report. The hydrological modelling forms a key component of the
model and its ability to operate at high resolution time scales is important for FIO
response to storm events. The ability to account for flow via different pathways and
in-stream sedimentation and resuspension is of benefit for transferability to account
for FIO dynamics in catchments, as is the ability to account for particle adsorption
and contaminant decay.
MIKE SHE: MIKE SHE (Refsgaard, Storm et al. 1995) is a watershed-scale physically
based, spatially distributed model for water flow and sediment transport. This modelling
platform emerged in the 1980’s and has been developed and extended by DHI Water &
Environment ever since.
MIKE SHE uses MIKE 11 to simulate channel flow. Flow and transport processes are
represented by either finite difference representations of partial differential equations or by
derived empirical equations. The following principal submodels are involved:
1. Evapotranspiration: Penman-Monteith formalism
2. Erosion: Detachment equations for raindrop and overland flow
3. Overland and Channel Flow: Saint-Venant equations of continuity and
momentum
4. Overland Flow Sediment (and WQ parameter) Transport: 2D total sediment
load conservation equation
5. Unsaturated Flow: Richards equation
6. Saturated Flow: Darcy's law and the mass conservation of 2D laminar flow
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7. Channel Sediment Transport 1D mass conservation equation.
This model can analyze effects of land use and climate changes upon in-stream water
quality, with consideration of groundwater interactions, and is able to simulate both event
driven episodes alongside longer-term continuous hydrology and water quality responses in
catchments. A grid network represents spatial distributions of the model parameters, inputs,
and results with vertical layers for each grid.
Lumped parameter conceptual models (e.g. SWAT, INCA, HSPF) are an attractive
alternative because they are easier to operate and require less data. However, critics of
lumped conceptual models will argue that their predictive capability with respect to assessing
the impacts of changing management practices is questionable due to the semi empirical
nature of the process descriptions (Hansen, Refsgaard et al. 2007). The MIKE SHE
modeling system is proprietary software, owned and distributed by DHI. The software must
be purchased from DHI. It is worth noting that in an comprehensive review of modelling
platforms Merritt et al. (2003) state that the predictive capability of MIKE-11 (part of the DHI
Mike model suite) is undermined by several factors. The most striking referred to by these
authors is that Mike-11 opts to use one-dimensional equations to represent threedimensional processes and as a consequence major simplifications of critical interactions
are made, or worse still ignored completely.
Potential to consider as microbial modelling platform?: WEAK, based on the fact that
it is overly complex and can be overparameterised for simpler applications such as
predicting catchment outlet discharge. In many cases some of the parameters are
simply not available. A clear criticism of MIKE-SHE is that it is extremely intensive in
terms of computational requirements and that this can inhibit its uptake and
application for large catchments where its use simply becomes too inefficient. As
noted by Jakeman et al. (2006) - Model structures with too many parameters are still
endemic. A compromise between physically-based complex models and black box
approaches could be represented better by lumped parameter models. MIKE-SHE is
not freely available and data requirements can be prohibitive in terms of costs.
SWAT: SWAT is a basin scale, physically-based, continuous time distributed parameter
model capable of operating on a daily time-step. The model code and structure originated
from the USDA-ARS with the intention of providing long-term yield predictions. It can
accommodate a large number of parameters but of course not all parameters are common to
all catchments. It is a direct descendent of the Simulator for Water Resources in Rural
Basins (SWRRB) model. The SWAT architecture has undergone continued evolution and
extension since the 90’s (Gassman, Reyes et al. 2007). SWAT does not adopt a massbalance equation approach for routing water through catchment systems and instead opts to
maintain water balance by accounting daily or sub-daily water budgets and uses an
empirical procedure to route the water through channels. Reference is also made to the
Runoff Curve Number method to determine runoff volumes.
The model structure allows for basins to be subdivided into watersheds which are then
broken down into unique hydrological response units (HRU’s). Each sub-basin should
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accommodate between 1 and 10 HRUs. Implicit to the concept of HRUs is the assumption
that no interaction occurs between HRUs in any given sub-basin. Load exports from each
HRU are calculated separately and then summed for each sub-basin. SWAT will only allow
for spatial relationships to be developed at the sub-basin level. A common procedure within
SWAT is to simulate the land phase of the hydrological cycle and thus determine pollutant
loadings to the main drainage channel, with a subsequent modelling of the movement and
transformations occurring within the channel during passage through the subbasin. Within
SWAT bacteria are added to HRU’s at a rate specified by the model user (Chin, SakuraLemessy et al. 2009). This input rate can vary over time and is determined as the product of
the bacteria content of manure and manure loading rate which can be constant or vary on a
day-to-day basis. SWAT partitions bacteria into either soluble or sorbed phases using a
linear isotherm approach. SWAT also requires that any subsurface FIO contributions to
receiving waters are regarded as zero which is a considerable limitation given, for example,
the reported potential for preferential flow transport of bacteria (Chin, Sakura-Lemessy et al.
2009). A preliminary assessment of the SWAT model at a more coarse timestep (monthly)
was undertaken with regard to Cryptosporidium modelling (Coffey, Cummins et al. 2010). It
is important to stress the absence of observed water quality monitoring data and need for
extensive input data for Cryptosporidium in this catchment study and so the reported study
was only considered as an initial step in model development with no calibration or validation
(Coffey, pers comm.). Others have explored the development of an hourly timestep within
the SWAT framework, including the Enhanced Soil & Water Assessment Tool (ESWAT)
(Debele, Srinivasan et al. 2009) and more recently sub-hourly time-steps for rainfall-runoff
modelling within SWAT (Jeong, Kannan et al. 2010). A detailed breakdown of SWAT
parameter needs and a summary of applications are presented in Excel spreadsheet 2.
SWAT is able to accommodate changes in management practice. The model structure is
such that the following can be simulated: (i) terracing; (ii) tile drainage; (iii) contouring; (iv)
filter strip provision; (v) strip cropping; (vi) fire; (vii) grassed waterways and (viii) change of
specified plant type. Modelling long term water quality impact of BMPs using SWAT is
reported in Bracmort (2006) and Tuppard et al. (2010). Land use change can also be
included as a management change. Critically, as noted by Borah et al. (2007), SWAT is not
capable of detailed, single event flood routing because it operates on a daily time-step. A
sub-daily time-step is required for high detail event modelling (e.g. HSPF, AGNPS,
ANSWERS, DWSM). However, this means that it is better suited to predicting long-term
effects of hydrological fluctuations and management practices.
The concept and need for a microbial submodel within SWAT was first raised by Sadeghi
and Arnold (2002). Consequently, Swat2005, a later version of the model, has several
significant enhancements; the most significant with respect to this review is the inclusion of
bacteria transport routines (Arnold and Fohrer 2005). However, Benham et al (2006)
comment that SWAT, as well as HSPF, have limited flexibility and options; the model does
not have much capacity to account for bacterial life cycles or simulate bacterial
concentrations in extreme conditions. More recently, Kim et al (2010) report on SWAT2005
and the bacteria transport routine within which bacteria die-off is the only in-stream process.
Kim et al. (2010) presented details relating to the development of the partial model of
sediment associated bacterial transport to evaluate the significant of the bedload store of E.
coli, and subsequent release. Likewise, Parajuli et al. (2009) provide details of refinements
to SWAT 2005 in their source characterisation of faecal bacteria and sensitivity analysis of
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SWAT2005. Specifically, they assessed the sensitivity of the following user-defined
parameters: (i) bacteria soil partition coefficient in surface runoff; temperature adjustment
factor; less persistent bacteria die-off in solution phase; less persistent bacteria die-off in
sorbed phase; and bacteria partition coefficient in manure, and one input parameter, - faecal
coliform bacteria concentration in manure. Their use of the model allowed for spatial faecal
inputs relating to confined animals, grazing, failing septic tanks and wildlife. The bacteria soil
partition coefficient in surface runoff had the greatest sensitivity and the authors cautioned
users to select locally relevant data for this parameter.
Coffey et al. (2010) demonstrate the development of a pathogen model using SWAT and the
microbial submodel. As per SWAT requirements input data regarding agricultural practice,
demographics and hydrological parameters was used. Importantly, the Coffey paper
identified key areas where future research is needed in helping define input data – a key
requirement was found to be the initial concentration of E. coli in human / animal waste.
Sensitivity analysis of the bacteria subroutine provides a key insight to the important
parameters needed to parameterise the model for the river Fergus Catchment. The role of
temperature on cell die-off and the bacterial partition coefficient were both found to be most
sensitive parameters whereas initial concentration of E. coli in faecal material of both human
and animal origin was determined to be the most significant realtime variable. Coffey et al.
(2010) propose that the ‘ideal catchment model’ should be capable of simulating four specific
factor categories: (i) land use factors; (ii) climate factors; (iii) topographical factors; and (iv)
hydrological factors. Data requirements are listed in the accompanying spreadsheet. [are
these less sensitive that hydrology though – probably, because mobilisation is likely to be
more sensitive. Sensitivity analysis on microbial submodel alone is not so important – double
check]. Other recent developments include the coupling of SWAT with MARS-2D for
microbial predictions in downstream estuaries (Bougeard, Le Saux et al. 2010).
Potential to consider as microbial modelling platform?: MODERATE - HIGH, given
microbial submodels are already in development, freely available and able to operate
at a range of timesteps, including a daily (it is an averaged daily value) and hourly
timestep. The SWAT platform has evolved considerably probably because of its
public domain status which has allowed numerous modifications and refinements (e.g
additional water quality parameters, finer resolution timesteps etc.). The option to
use a monthly time-step seems inappropriate because it appeared to bring about
unexpected (and underestimated) predictions of Cryptosporidium in the Coffey study
but clearly finer resolution time-step options are available. Reference to spreadsheet
1 indicates that a high number of the ‘key indicators’ are satisfied for taking this
modelling platform forward.
HSPF: HSPF is a conceptual, lumped parameter model that was first publicly released in
1980 (Johanson, Imhoff et al. 1980). It was developed by the USEPA and simulates for
extended periods of time the hydrology and associated water quality response to land
surfaces and in-stream processing. It uses continuous rainfall and other meteorological
records to compute streamflow hydrographs and pollutographs and can therefore produce a
time history of water quantity and quality at any given point in a catchment. HSPF simulates
interception soil moisture, surface runoff, interflow, baseflow among others. The model
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contains hundreds of process algorithms developed from theory, laboratory experimentation
and empirical relations from instrumented catchment studies. HSPF is able to accommodate
a timestep of <1 day and therefore is well suited to the simulation of specific events. In the
study of Chin et al (2009) which compared output of SWAT and HSPF it was noted that
HSPF provided higher performance in terms of predicting daily flows, a direct effect of the
higher resolution time-step. That said, Borah and Bera (2007) acknowledge that HSPF is
not adequate for simulation of intense single events, especially in large catchment areas
because it is not able to reliably represent single-event flood waves. Interestingly, for the
prediction of bacteria concentrations at a catchment outlet, Chin et al (2009) found that
SWAT outperformed HSPF suggesting that the bacteria fate and transport processes used
within SWAT provide a better representation of catchment activity (though how transferable
such a finding is outside of the study catchment used here is uncertain). For the daily
timestep FIO concentrations are averaged to give the daily output. The hydrological process
equations within HSPF are therefore fundamentally different from those used within SWAT.
However, as with ANSWERS and ANSWERS-continuous this modelling platform uses a
simple storage-based (non-linear) reservoir equation to determine flow routing within the
catchment.
Essential data requirements for HSPF include:
1. Meteorological records of precipitation and estimates of potential evapotranspiration
2. Water quality simulation needs tillage practices, point source inputs, application info
For microbial prediction it is FIO source loads which are essential input parameters and
accurate estimation of these loads is extremely important for good model performance using
HSPF (Zeckoski, Mostaghimi et al. 2003). In fact, both daily and hourly faecal coliform loads
from direct deposition to streams and from deposition on various land use types are required
by the HSPF platform. Direct deposition data is required in a time-series format and land
application via a tabular format. Zeckoski et al (2003) have developed a FIO calculator to
provide input data for HSPF that is written in visual basic and outputs using Excel
spreadsheets. This calculator component requires information on livestock numbers, stream
access and land use.
The processes describing FIO fate and transfer are fundamentally different in HSPF from
those in SWAT leading some to suggest that a reduction in model structural uncertainty
could be achieved by using multi-model approaches (Chin, Sakura-Lemessy et al. 2009). In
HSPF the bacteria loading rate to land is specified directly by the model user and can take
the form of a constant or be varied monthly. HSPF allows for transport of FIOs either via
direct entrainment within overland flow or by association with sediment. HSPF, in contrast to
SWAT, allows for subsurface contributions of FIOs to streamflow via throughflow and
interflow and this can vary on a month by month basis within HSPF. HSPF has provided a
major application to US catchment studies via the Chesapeake Bay Basin model (Donigian,
Bicknell et al. 1986) and has been incorporated (along with SWAT) into BASINS (better
assessment science integrating point and non-point sources) programme with a key
requirement of developing TMDL’s nationwide.
A recent MSc thesis by Russo (2007) describes an in-stream water quality model that
incorporates a consideration of bacteria-sediment association and explores this relationship
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and its effects on microbial fate and transport within an impaired stream. Russo (2007)
therefore modified the HSPF model to allow for modelling terms relating to microbial
partitioning to stream sediments and also accommodated particle settling rates and critical
shear stress coefficients relevant to the particles themselves. HSPF is able to incorporate
microbial die-off via 1st order decay equations that can be corrected for temperature and
which can vary for freely suspended cells, cells associated with suspended sediment and
cells associated with bed-sediment. In the Russo (2007) study, rates of sediment resuspension and deposition are expressed as a mass flux (kg m-2day-1). Bacteria-sediment
association is determined using linear-reversible adsorption isotherms. The author was
limited to this function by HSPF and it is noted that the literature generally assumes an
irreversible attachment to particle but that this is not possible within the structure of the
model code.
Russo (2007) details the ‘in-stream’ model parameters that are needed for the microbial
association with sediment modelling components of HSPF. These need to be estimated via
field-based studies, or from values reported in the literature. They include: particle diameter,
particle density, settling velocity, critical shear stress for deposition, critical shear stress for
re-suspension, erodibility factor, FIO die-off rates (unattached, attached to suspended
sediment, attached to bed sediment) and partition coefficients. The sensitivity analysis
conducted by Russo provides important information with regard to the in stream microbial
component of HSPF. Most literature values for partition coefficients (Kd) are for bacteria in
groundwater (in the range of 10-4-10-6 L mg-1). It is believed that water column coefficients
are much larger (Mahler, Personne et al. 2000), but little research has been done in this area
so the partition coefficient was varied over several orders of magnitude. Russo also
identified the lack of information relating to the survival of FIOs associated with suspended
sediment and so values were tested in the sensitivity analysis that ranges from 0.2-0.8 day-1.
None of the changes in parameter values resulted in greater than 10% change in the mean
or 95th percentile FIO concentration leading Russo to propose that microbial concentration
was influenced greatest by inputs to the watercourse and advective flow.
In Russo’s (2007) concluding remarks he identifies a number of recommendations for HSPF
which it is important to acknowledge within this review should this modelling platform be
taken forward. While the HSPF model incorporates algorithms to allow for microbial
partitioning, re-suspension and deposition within the in-stream environment the assumption
of a vertically homogeneous streambed was questioned. This is static within HSPF and
Russo believes that it could lead to a significant underestimation of sediment re-suspension.
The other important recommendation relates to better parameterisation linked to sediment
settling velocities and critical shear stresses at local field-sites that represent typical
watershed characteristics in order to improve model performance.
Others have provided an analysis of the HSPF water quality parameter uncertainty in
predicting peak in-stream faecal coliform concentrations (Paul, Haan et al. 2004). A key
finding of this study was that small errors in parameterising the maximum storage of FIO’s
for a given land-use class can result in large errors in predicted FIO counts, highlighting this
as a key focus for increased input precision. LaWare et al (2006) modelled FIO
contamination of the Rio Grande using HSPF and found that the modelling approach was
limited by sparse flow and FIO data. More recently, Jia and Culver (2008) applied a
generalized likelihood uncertainty estimation (GLUE) approach to a Hydrological Simulation
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Program-Fortran (HSPF) model used for the simulation of hydrology and transport of faecal
coliform bacteria in a small creek. The study found that among 50,000 randomly generated
parameter sets, a total of 381 were found to be acceptable. The accepted parameter sets
can enable various HSPF flow and faecal coliform simulations to closely match the observed
data given the observation errors.
Potential to consider as microbial modelling platform?: MODERATE – HIGH given
sub-daily timestep and current microbial developments linked to the model structure
(see spreadsheet 1). Limitations exist because spatial and temporal variability in
direct deposition inputs will be high but tools are available to input such data based
on livestock numbers and distributions. Clearly, a time-series of direct deposition
inputs to a stream will accommodate high uncertainty but the model makes an
attempt to account for such inputs that are considered important contributors to
diffuse microbial pollution. A large number of parameters need to be calibrated within
this model which does raise concerns over parameter identifiability and their
meaningfulness within the model.
AGNPS (including annAGNPS): The Agricultural Non Point Source model (Young, Onstad
et al. 1989) was developed by the USDA-ARS. It is an empirically-based, lumped parameter,
event driven model, meaning that its predictive capability is restricted to outputs from single
rainfall events of ‘storm’ duration. It is best known for its prediction of runoff along with P, N
and chemical oxygen demand. Its application generates a single value for a number of
output variables that include: runoff volume; peak flow; sediment yield; and average
concentration of nutrients. However, AGNPS has since undergone revision and
amendments that have brought about annAGNPS (Bingner and Theurer 2001) which is
essentially an annualised version of the original model that now facilitates continuous
simulation of hydrology and associated water quality parameters. Similar to SWAT, the
AGNPS does not adopt a mass-balance equation approach for routing water through
catchment systems but opts to maintain water balance by accounting daily or sub daily water
budgets and the Runoff Curve Number method is consulted to determine runoff volumes.
Potential to consider as microbial modelling platform?: LOW, the output is limited and
lacking any dynamic, time varying discharge information. A further lack of any
subsurface component adds additional weight to ranking this modelling platform as
less of a contender for UK application for microbial submodel development.
ANSWERS (including ANSWERS-continuous): The Areal Non point Source Watershed
Environmental Response Simulation model is a deterministic model and was originally
developed to predict response of flow and associated contaminants to single hydrological
events, however, (as per AGNPS) there is an extension of the ANSWERS platform to allow
for continuous simulation of hydrological response and water quality parameters and this is
known as ANSWERS-continuous. ANSWERS utilises the distributed parameter concept in
order to model processes such as runoff, infiltration, subsurface drainage and erosion that
vary spatially across catchments. The key components of ANSWERS are a hydrological
Page 23 of 37
model and an upland erosion response model. The watershed is divided into gridded
elements and each elements hydrological response is computed mathematically as a
function of time, by an explicit backward difference solution of a continuity equation This can
be solved when combined with a stage-discharge relationship. There are 6 general types of
data inputs required by ANSWERS. These are (i) simulation requirements (measurement
units); (ii) rainfall information; (iii) soils information; (iv) land use and surface information; (v)
channel description; and (vi) individual element information (e.g. location, topography,
BMPs, land use). ANSWERScontinuous has long temporal capability for predictions but can
function on a daily time-step for dry days and at a temporal resolution of 30 seconds for wet
days.
Potential to consider as microbial modelling platform?: WEAK, not yet developed for
microbial pollution and not a commonly used UK platform
DWSM: The Dynamic Watershed Simulation Model (Borah, Bera et al. 2001) is designed to
simulate surface and subsurface runoff distributed across the catchment area and also
predicts the propagation of flood waves along with erosion of sediment from upland soils and
stream bed sources and the subsequent transfer of those sediments and a number of
associated agricultural chemicals during single events. Similar to some of the other
modelling platforms discussed it also the Runoff Curve Number method to compute rainfallexcess rates. Given its application to simulate single events its application may prove useful.
It is important to note that although a storm-event duration model the DWSM has
interchangeable, compatible, or complementary features to link with SWAT (Borah, Arnold et
al. 2007).
The DWSM calculates rainfall excess and infiltration using timeframes of minutes via runoff
curve number equations. A surface water routing algorithm is computed for overland planes
and channel segments and is based on kinematic wave approximations. Subsurface routing
is also accounted for and enhanced if tile drains are present but in contrast to SWAT, DWSM
lumps lateral subsurface, tile and groundwater flows into subsurface flows.
Potential to consider as microbial modelling platform?: A real advantage of DWSM is
that it provides a balance between computationally intensive modelling platforms
such as MIKE-SHE and relatively simple lumped models such as AGNPS. It could be
interesting to consider DWSM as a complementary component to SWAT given its
event focus but there could be accessibility issues?
SIMCAT: SIMCAT is an EA software tool that has been developed further by WRc and has
been used by the Agency for over 20 years. It represents a ‘data matching’ model but does
not attempt to represent chemical or biological processes in specific detail, but instead
adopts ‘decay’ or ‘reaeration’ rates within its calculations to match measured data. Typical
applications include predicting the outcomes of pollution control policies, impacts of
mitigation measures for reducing diffuse pollution, effects of temporal and spatial change of
polluting inputs. Its use is also seen as instrumental for helping to improve monitoring
strategies. SIMCAT is therefore able to account for water quality benefits of PoMs. SIMCAT
Page 24 of 37
is one of the most commonly used models by the EA but features rarely in published peerreviewed literature (Jamieson and Fedra 1996), probably because of its limited use for
regulation outside of the UK and its stochastic component as well as a lack of commercial
exposure (Cox 2003). SIMCAT is not able to model time-series data (for example, daily
means of loads).
SIMCAT is a mathematical river water quality model of a 1D, steady state, stochastic,
deterministic nature. Being a stochastic-deterministic model based on input data from routine
data it can simulate a statistical distribution of discharge and associated water quality data
for multiple effluent inputs along a watercourse. The statistical distributions contribute to a
monte-carlo (MC) simulation approach. SIMCAT uses this MC approach to mix discharges
and diffuse inputs with stream water and then routes the flow in the stream down through the
catchment while applying water quality transformation processes to parameters en route.
The model uses a ‘continually stirred tank reactors in series’ (CSTRS) approach meaning
that perfect mixing is assumed within each element of the model. It is able to account for
diffuse and point source inputs and describes in-stream decay of pollutants. Essentially, any
load not accounted for by a point source discharge is attributed to ‘other sources’ and what
those other sources are defined to be is dependent on the characteristics of the catchment
under study (Cox 2003).The following water quality parameters are represented within
SIMCAT models: flow, BOD, DO, Ammonia, Phosphate, TON. For each of these parameters
their mean quality attributed to diffuse flow is estimated and decay rate included for nonconservative parameters. Such diffuse run-off can be user defined or alternatively can be
added by the model as an autocalibration parameter. Pollutants can be represented via
three states: conservative, non-conservative and dissolved oxygen with reaeration and BOD
decay interaction.
SIMCAT operates by dividing watercourses into user-defined SIMCAT reaches with input
data defined as statistical distributions. In addition a number of ‘features’ are assigned to
appropriate reaches (for example inputs, abstractions, diffuse pollution inputs etc). The
model is capable of accounting for 600 reaches and up to 1400 ‘features’. A mass-balance is
performed at the top of each reach using basic additions of flow and load values and an
empirical velocity-flow relationship is used to determine the water flow rate (which is also
essential for identifying residence time for solute concentrations within the reach linked to
1st-order decay rates). SIMCAT models for catchments can be developed using an approach
devised by WRc called ‘Pre-SIMCAT Investigation (PSI)’. This approach uses statistical tools
commonly used by the EA (Aardvark and Test Data Facility) carried out for all available data
provided by the EA for defined time periods (e.g. a 5 year monitoring duration). The final
stage of PSI is referred to as ‘testing of assorted data’ (TOAD) and represents a programme
to calculate the summary statistics for SIMCAT input. Diffuse pollutant loads can be added
as input derived from the output of other models such as PSYCHIC (see (Crabtree, Kelly et
al. 2009)). Users basically need the mean, standard deviation and number of samples for the
quality of rivers and discharges in the catchment of interest and estimates of the mean river
flow.
It is therefore able to simulate the flow and water quality at any given point in a stream reach
of a river catchment based on the statistics of observed data. Results are produced as
statistical comparisons with specific river water quality models. Thus, SIMCAT considers
errors linked to sampling of data rather than errors associated with calibration of more
Page 25 of 37
deterministic water quality process representations. The model therefore recognises that
uncertainties in model predictions stem by and large from limitations in calibration and
pollution load data rather than the assumptions implicit to model equations (McIntyre 2004).
A GIS-version of SIMCAT has been developed (see South West River Basin District
application in Ireland) allowing for population of a specific national SIMCAT database using
export coefficients based on published values. The limitations here are that such national
datasets are held by a number of different institutions which creates problems linked to IPR
and licensing issues. The SIMCAT approach relies on the use of existing routine monitoring
data but has been designed to minimise the limitations associated with such data by
producing results with identified confidence levels for comparison with water quality
standards. Given that data availability often proves to be the dominant limiting factor this can
help explain why stochastic models can be popular by providing the necessary statistical
output but by functioning on relatively little data in the first instance because they do not
attempt to capture all processes within their model structure other than 1st order decay rates
for example (Cox 2003).
McIntyre (2004) highlights limitations associated with SIMCAT and suggests that the
simplicity of the model structure is insufficient for simulation of dynamic events because the
effects of model structural error are likely to be magnified at such times. McIntyre also
reiterates that SIMCAT does not model catchment runoff, groundwater or urban sewerage
systems; instead their effects are represented as point or distributed sources to the river.
Furthermore, SIMCAT offers no choice for model structure meaning that the user is
restricted to fixed and relatively simple formulations and the effects of sediment and
sediment interactions are not explicitly considered within the model (Cox 2003) which is a
significant drawback if considering SIMCAT for a microbial parameter application. Given that
SIMCAT assumes that the condition of the stream does not vary with time (ie steady state)
this can be considered as a serious limitation for the model (ie no allowance for temporal
variability), but conversely it does allow for rapid application with relatively few data (Cox
2003).
Potential to consider as microbial modelling platform?: WEAK, Lack of existing
monitoring data may hamper development here and timestep implications may prove
a disadvantage. It should be noted that considerable data analysis is needed in order
to derive statistics appropriate for inclusion into the model. The input of annual
statistics doesn’t allow for an account of storm based responses and while this can
be improved (slightly in terms of output) through the incorporation of monthly
statistics this equates to a significant increase in data requirements and still does not
necessarily give the event-level information sought for a predictive model of microbial
pollution. Furthermore, in its current shape and form SIMCAT is only appropriate for
modelling parameters that do not rely on sediment interactions within the water
column. It is unlikely that SIMCAT would produce tight E. coli predictions in many
situations since the processing capability only allows for the inclusion of a 1st order
decay.
eWATER (E2 / EG / Toolkit): The E2 model is a ‘whole of the catchment’ model used in
Australia. It forms a catchment modelling framework within the Cooperative Research Centre
for Catchment Hydrology (CRCCH) Catchment Modelling Toolkit. The E2 system has now
Page 26 of 37
been replaced by ‘Source Catchments’ but its architecture remains within the new platform.
The modelling approach, detailed within a technical report (Haydon 2008), divides the area
into subcatchments which are then divided further into functional units (FUs). Functional
Units are similar in concept to Hydrological Response Units (HRU’s) used in SWAT. They
are areas considered to function in a similar manner. Each FU can be associated with
component models that represent critical catchment processes such as: (i) runoff generation
[rainfall-runoff model]; (ii) constituent (ie contaminant) generation model; and (iii) filtering
model (for example, management interventions, 1st-order decay etc). The river system is
then modelled using a system of links connected by nodes.
The EG model is the mass-balance pathogen model component which has been proposed
by Haydon and Deletic (Haydon and Deletic 2006; Haydon and Deletic 2007; Haydon and
Deletic 2009). It is a lumped conceptual model intended to integrate with a lumped
conceptual hydrologic model. This pathogen component considers surface and subsurface
microbial transport by means of wash-off and loss equations. It is in fact a relatively simple
model with only four model coefficients (two of which are loss equations and the other two
are transport equations). The EG model links with an existing hydrologic model called
SimHyd that predicts flow. SimHyd accommodates three stores (interception, soil moisture
and groundwater stores). To run the model the user needs access to information on rainfall,
potential evapotranspiration, estimates of pathogen deposition rates for the catchment, and
finally the catchment area. Faecal contributions are assumed to be uniformly dispersed
across the landscape, with the actual load dictated by the land use type. Information related
to manure spreading is also needed. Interestingly, potential evaporation is used as a driver
of bacterial die-off because of its incorporation of the severity of UV, dessication and
temperature on microbial numbers (though of course it is worth bearing in mind this model
was developed in Australia where this may be more applicable). Calibration of the model
requires streamflow data and storm and base flow microbial data.
The pathogen transport simulation takes places via a six-step process. First pathogen
numbers are determined as a function of land use. Next a proportion are lost from the store
via physical and microbiological factors. Then a proportion are washed from the store by
surface flow. A proportion will be transported into subsurface via infiltration and a further
fraction will be lost to the subsurface due to microbiological and physical factors (and septic
inputs can be added as a point source contribution too). Finally, pathogens from the
subsurface store are washed out of this store by both interflow and overflows from the soil
moisture store and baseflow. The model timestep is variable and can function at daily and
hourly intervals. The EG model has been incorporated into a special version of the eWater
catchment model E2 (Toolkit, 2005). Future plans for the EG / E2 model are to link it with a
stream network model that has in-stream routing facility.
Potential to consider as microbial modelling platform?: This is actually quite an
interesting platform – it is not as complex as some of the other models and is freely
available for the first year after which the fee is still relatively modest ($900 pa). It
competes with SWAT and HSPF but lacks the instream processing component. While
the authors of the model suggest that the key factor that limits the use of this model
in catchments is the unavailability of storm event data it should be noted that CREH
have a growing collection of such data for UK catchments and have summarised the
high and low flow FIO outputs/concentrations of different land use assemblages. The
Page 27 of 37
timestep options are particularly favourable for catchment modelling of microbial
parameters. With in-stream processing not accounted for within the model this does
represent a limitation.
PAMIMO-C: Scottish-led work has developed a catchment scale distributed model of
pathogen / indicator fate and transport termed PAMIMO-C. This model simulates sources,
sinks and delivery processes to watercourses of E. coli and subsequent fate within an
integrated river channel network. Similar to other models previously discussed, Spatial data
considered by PAMIMO-C include land topography, soil type, farm stores, soil drainage
characteristics, animal grazing location and applied organic waste location. Temporal
dynamics are considered through the inclusion of E. coli die-off processes, and therefore via
time-based predictions of E. coli in soils and water networks.
Specific details on this model are difficult to find. The Vinten et al (2004) paper states that
the model is described in detail elsewhere – in Lewis et al (2003). However, this is in fact a
proceedings abstract from an IWA conference and no further information is provided. A short
paragraph description is available in the SAC/SEPA Proceedings of 2004.
Potential to consider as microbial modelling platform: WEAK, lack of transparency
and published information does not appear to be extensive.
WATFLOOD: WATFLOOD is a flood forecast hydrologically distributed model that was
developed by Kouwen (1988) to operate at the catchment scale. SPL is the underlying
hydrological modelling component of WATFLOOD and radar data is used to generate hourly
precipitation input files. Full details are provided in Kouwen and Mousavi, (2002). The
WATFLOOD executable is freely downloadable via the worldwide web. Key to the
functioning of WATFLOOD is the use of grouped representations known as ‘grouped
response units’ with process parameters linked to land cover. Adaptations to WATFLOOD
have allowed the model to evolve to accommodate water quality predictions and most
recently microbial assessments of water quality predictions. A PhD thesis (Leon 1999)
provides details on the integration of a water quality component to the original WATFLOOD
model framework and led to the development of a distributed water quality model for diffuse
pollution modelling in agricultural catchments (Leon, Soulis et al. 2001). This allowed for
assessment of runoff, sediment and soluble nutrient concentrations each calculated
separately for each landcover class and then weighted by area and routed downstream. The
approach integrated AGNPS with WATFLOOD to assess the results from water quality
model component.
The underlying hydrologic model was augmented with a microbial component by Dorner et
al. (2006). The pathogen transport model was an original model developed within the PhD
thesis of Dorner (2004). With the development of a pathogen bolt-on there was a need to
account for microbial transport which was accounted for via three approaches (i) overland
flow; (ii) tile drainage; and (iii) in stream routing. The microbial component required a series
of inputs which included an estimated environmental loading of pathogens and generalised
1st order inactivation constants of microbes in different environmental matrices. For each
pathogen and each season a 1st order lumped parameter was calculated to represent all
processes attributed to inactivation. Dorner et al (2006) needed to derive key assumptions
Page 28 of 37
linked to the environmental loading of pathogens throughout modelled catchments and
proposed that pathogens would be randomly distributed across the landscape but
associated with particular land classes and application periods. Five land use classification
are used (forest, agricultural, urban, wetland and water) though only agricultural land is
assumed to accommodate a pathogen supply at the land surface. The subsequent
detachment of microbes in resulting overland flow was assumed to be analogous to soil
particle detachment and so a relatively simple process-based Hartley model was used to
represent erosion and sediment transport from individual storm events rather than adopting
the USLE which accounts for average annual erosion losses. A cautionary note is that no
experimental data exists to validate the Hartley model for microbial transport (Dorner,
Anderson et al. 2006). The microbial model links to WATFLOOD and uses a mass balance
approach to account for flows that are able to transport pathogens through the soil. Point
source inputs are defined by the user. The channel routing and sedimentation phases of the
model are based on continuity, using a mixing cell approach very similar to the represented
transport behaviour of sediment within WATFLOOD. The sedimentation of pathogens is then
assumed to occur once a floc has amassed of mean size 9.1 microns and an approximate
proportion of microbes that form flocs has to be estimated by the model user. The authors do
note that not all of the key sources and processes have been included in this version of the
model and that model evolution is inevitable but a key challenge is overcoming the scarcity
of available pathogen data. This microbial component was developed further by Wu et al.
(2009) who developed additional functionality of the ‘pathogen’ model specific for generic E.
coli modelling by accounting for sediment resuspension within stream beds.
Potential to consider as microbial modelling platform?: WATFLOOD is freely available
but the microbial model was developed separately and could present issues linked to
the availability of access for further development. The sub-daily timestep is attractive
for FIOs. Overall, Moderate.
FERGUSON Pathogen Budget Models: A number of papers have been published in
Australia on a pathogen budget approach (Ferguson, Croke et al. 2005; Ferguson, Croke et
al. 2007) outlining the development of catchment scale models. Input data include: (i)
existing water quality; (ii) rainfall; (iii) hydrological and geographical information systems; and
(iv) land use data (divided into 13 land use classes). This final input data provides
subcatchment information relating to population, fraction of people using on-site systems,
and number of animals of each species present. The Model itself is structured into 5
components, namely: a hydrological, land budget, on-site sewage system, sewage treatment
plant and in-stream transport modules. The land budget module incorporates microbial
loading, deposition, inactivation and movement. The mathematical model uses the
Integrated Catchment Management System (ICMS) software that is available for download
via CSIRO. A sensitivity analysis of the model identified that livestock stocking density, their
access to streams and the rate of direct faecal deposition and manure mobilisation rates in
wet weather to be most important. Ferguson et al (2005) outlines a number of model
assumptions for both dry and wet weather scenarios. They include, for example, 90% of E.
coli being bound to sediment, no decay of microbes that enter the river network from sewage
treatment plants before reaching the catchment outlet and that during wet weather in-stream
processes are unimportant. As a result, in stream processing is ignored during wet weather
simulations with the surface load contribution vastly exceeding the microbial water quality
Page 29 of 37
signal produced from resuspended material. These assumptions are not considered
appropriate in the development of any model routine but they did serve as a starting position
for the Ferguson work.
US – Beach models (e.g. Ohio NOWCAST; Virtual Beach V2): The NOWCAST model
used in Ohio is technically not a catchment scale model. Instead this approach provides a
daily ‘nowcast’ of recreational water quality conditions via a mathematical model. A statistical
approach (multiple linear regression) is used to predict water quality based on a number of
informing parameters that include: log turbidity and wave height, radar rainfall during the
previous 24 hours from six 4-km grids surrounding the beach, and day of the year. More
detailed discussion of the modelling approach is provided in reports published by the USGS
(Francy and Darner 2006; Francy, Darner et al. 2006). The NOWCAST model is basically
made up of a series of beach specific models and the Ohio NOWCAST has been in
operation since 2006. The model output for 2007 is derived using data collected during
2000-2006 and provides a probability that the single sample bathing water standard for E.
coli would be exceeded (Francy 2009). Other published studies exploring wavelet analysis in
linking bacteria concentrations to explanatory variables also incorporate beach water quality
at the Ohio NOWCAST study sites (Ge and Frick 2009). Virtual Beach also uses a Multiple
Linear Regression approach (Frick, Ge et al. 2008) but is not considered further in this
review.
Others?: Clearly other modelling platforms are available including, for example QUAL2E - a
freely available model, but it isn’t appropriate to consider all platforms particularly those
(such as Qual2E) that are not widely used in the UK because of their steady state nature.
Others include SEDMOD (Fraser, Barten et al. 1998), a GIS based subcatchment scale
model that provides an index of pathogen loading to streams through the characterisation of
5 key transport drivers (flow-path hydraulic roughness, gradient, slope shape, stream
proximity and normalised soil moisture index). Other more marginal modelling platforms do
exist but the content of this review has focussed on the most prominent platforms suitable for
consideration for UK extension and development.
FINAL REMARKS
A commonly recurring query with regard to process based models with vast numbers of
parameters is whether such modelling platforms should actually be used if there is not
sufficient empirical data to derive the parametric relationships. Cost, flexibility, adaptability
and ease of understanding are key model selection criteria that would tend to favour more
basic or lumped parameter models. While this draft review and accompanying Excel
spreadsheets go some way in identifying the parameters needed for FIO modelling and
allows for inference of priority gaps to inform models better, the development of a process
based catchment model will need iterative development through a series of experimental
and calibration phases.
Page 30 of 37
In addition, the shortlisting of model platforms does depend, in part, on the question being
asked of the model predictions. Engaging with the relevant stakeholders is now needed in
order to understand their needs and to draw on their technical input but this needs to be
balanced with the complexity involved in taking each of the different modelling platforms
forward. Of course, in reality there is no ideal and perhaps what we should be aiming for is a
modular modelling system (MMS) where the tool chosen matches the question being asked
and
then
that
way
a
choice
is
unnecessary
(see
http://www.sahra.arizona.edu/unesco/shortcourses/chapters/GeorgeLeavesly_L8.pdf).
Page 31 of 37
Table 1: Summary of modelling platforms considered in this report
PSYCHIC
Davison et al
2008
PB
M

Original Author
Year
Model type
Timestep
Continuous?
Spatial scale
Field - catchment
Soil characterisitcs

Met inputs

Hydrological subroutine 
Bacterial submodel?
Point source input?

Land management / cover 
Nutrient submodel

In-stream component?
Account for Mitigation 
GIS output?

Freely available?
Key:
PB
RB
GB
M
Process based
Risk-based
Grey box
Monthly
D
SD
SE
V
MC
INCA
Whitehead
1998
PB
D

SWAT
Arnold
1998
PB
D

HSPF
Johanson
1980
PB
SD

SCIMAP
Lane
Catchment
Catchment
Catchment






























ANSWERS
Beasley
1980
PB
SE
PEDAL2
MHTracking
Beven/Heathwaite O'Donnell
GB
V
Catchment
Catchment









RB
PB
V (SD)

MIKE SHE
AGNPS
Refsgaard & Storm
Young et al
1995
1987
PB
PB
V (SD)
V (SE or M)

DWSM
Borah
2001
PB
SE
SIMCAT
EG / E2
Anglian Water
1970's
Stochastic PB
M (need to check)
SD or D

PAMIMO-C WATFLOOD
Lewis & DuncanKouwen
2003
1988
PB
PB
SD

Headwater
Catchment
Plot to Catchment Catchment
Catchment
Catchment
Catchment
















?








?



Daily
Sub daily
Single event
Variable
Monte carlo
Page 32 of 37










in part


Catchment
Catchment
















catchment
Pathogen
budget
Ferguson
2004
PB
D












in part






in part

ICMS software is available
Acknowledgements: We are grateful to Enda O’ Connell, Greg O’Donnell and John Ewen
for providing details of the MHTracking modelling approach.
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