Stakeholders Model Needs Meeting, March 7th 2012, Oxford
Notes and Minutes of the Meeting
Attended by
Louise Webb Louise.Webb@environment-agency.gov.uk
Alistair Maltby alistair@theriverstrust.org
Michelle Walker michelle@theriverstrust.org
Wharfe, Jim jim.wharfe@environment-agency.gov.uk
Peter Daldorph (representing UKWIR/Industry) Peter.Daldorph@atkinsglobal.com
Osborn, Daniel dano@nerc.ac.uk
Hallard, Mark Mark.Hallard@sepa.org.uk
Willows, Robert robert.willows@environment-agency.gov.uk
McGonigle, Daniel (FFG-EKB) (daniel.mcgonigle@DEFRA.GSI.GOV.UK)
Spadavecchia, Luke (FFG-EKB) Luke.Spadavecchia@defra.gsi.gov.uk
Paul Whitehead paul.whitehead@ouce.ox.ac.uk
Jonathan.WestlakeJonathan.Westlake@thameswater.co.uk
Ian Bernard ian.bernard@britishwater.co.uk
Kerry Thomas kerry.thomas@earth.ox.ac.uk
Skates, James (Sustainable Futures – Wales Gov) James.Skates@wales.gsi.gov.uk
Jill Crossman jill.crossman@ouce.ox.ac.uk
Franklin, Christopher J. cfr@nerc.ac.uk
Russ Money (Natural England) Russ.Money@naturalengland.org.uk
Murray Gardener NERC KE murd@nerc.ac.uk
Neil Runnalls NERC Water KE Programme nrr@ceh.ac.uk
1 Objective of the Meeting
This meeting was held to provide a forum for discussing the modelling needs of stakeholders in the area of
macronutrients (Nitrogen, Phosphors and Carbon) and their interactions and links to Ecology. This need
has arisen because of the large number of policy issues (see appendix 1) that require analysis with the
increasing demand for an understanding of nutrient interactions and their impacts on ecology. The
questions addressed in the meeting were as follows:


What are the modelling needs of DEFRA , EA and other Stakeholders to answer current and future
policy and management questions
Can we identify the needs and the gaps in modelling, methodologies and specific tools
Can we create a project to fill the gaps and fund such a project
NERC are funding several programmes that can feed into such a modelling framework, these being
Macronutrient Cycles (MNC), Changing Water Cycles (CWC), Biodiversity and Ecosystem Service
Sustainability (BESS) and Environmental Virtual Observatory (EVO).
2 Views of the Stakeholders
DEFRA (Dan McGonigle and Luke Spadavecchia)
Existing water quality and air quality models (numerical representations of processes) tend to focus on
individual pollutants at specific spatial scales. They have often suffered from a lack of testing against data,
due to a scarcity of observational data. Furthermore our current modelling resource is somewhat atomised,
with access limited by institutional boundaries. Where models are shared, version control may be an issue,
but also the lack of common datasets to parameterise and run them means that results are often difficult to
compare.
The need for integration of models has never been stronger with moves to integrate policies and policy
levers (advice, incentives etc) for different pollutants (see Natural Environment White Paper), the
development of localised land management and catchment management approaches covering multiple
issues (see Water White Paper), and a government agenda to increase transparency in decision making
and developing a less prescriptive approach to farming and land management policy (see MacDonald Task
Force). In parallel, the water industry is increasingly adopting catchment approaches in place of ‘end of
pipe’ solutions to improve the quality of abstracted water. There is a need for open and transparent tools to
translate data to useful outputs for stakeholders.
Government, agencies and industry would benefit from better access to the existing UK numerical
modelling resource for macronutrients. This would entail the following:
1. Testing models against data to assess which models should be used and where.
2. Bringing together existing and emerging models and running them on common data-sets through a
coherent, open source framework.
This would allow ensemble approaches in assessing
interactions between pollutants, effects over increasing spatial and temporal scales and predicting
nutrient losses to water and air in different scenarios.
3. Developing tailored decision-support software for end-users (e.g. Environment Agency, water
companies etc).
Several large scale research initiatives have emerged or are currently emerging which are developing high
resolution datasets that could be used to support the first two of these activities. These include the NERC
Macronutrient Cycles programme, the Greenhouse Gas Research Platform, the Demonstration Test
Catchments, CEH catchment studies, National Ecosystem assessment and others.
Developing such an integrated modelling resource would provide a user interface for knowledge emerging
from basic research funded by research councils. If an stakeholder modelling framework could be
developed, it would provide a vehicle to accelerate knowledge transfer from researchers working on
fundamental environmental processes to the users of that knowledge. Figure 1 shows a potential structure
for a stakeholder modelling framework and further ideas and details of the DEFRA approach are given in
appendix 1.
Figure 1 A Proposed Modelling Strategy to Meet the needs of Stakeholders
Environment Agency (Jim Wharfe, Robert Willows and Louise Webb)
The EA has a strong need for process based models that can account for the key factors that control water
flows, water chemistry and ecology in rivers, lakes, wetlands and catchments. This is so that policies and
mitigation measures can be tested and evaluated. The main aim is to develop a modelling system that



will allow the impact of farming and other land uses on a range of environmental and policy relevant
endpoints to be assessed (eg chemical and ecological quality measures, ecosystem service benefits.
enjoys academic, DEFRA support
will have the confidence of key stakeholders.
It will need to

quantify the multiple impacts of farming at a range of scales
o eg this particular farm or local area contributes this amount of harm or benefit to this local river
or this area of catchment.

quantify the benefits of policies and land management practices to reduce those impacts
o eg this type of farm or practice contributes this amount of harm or benefit to our rivers when
integrated across whole one or more RBD or England.

harm to be assessed in a variety of ways eg exposure represented as a distribution of concentrations
(eg seasonally), in terms of load exported by a particular pathway (in-stream, bed load, groundwater
flow) integrated over periods (days, seasons, years)

quantify the contribution farming makes to these impacts in comparison to other sources eg point
sources

make use of the best available science and data, including National datasets (including remote
sensing, farm census, distributed monitoring), data from research case studies.

exploit or develop existing model formulations where possible

incorporate local observations where this can improve model prediction

allow extrapolation of knowledge to data poor catchments, and allow upscaling and integration

allow risks to a range statutory duties (eg WFD) and developing policy objectives

provide a basis for evidence based policy analysis, evaluation to achieve multiple objectives

provide a basis for spatially explicit targeting of measures on the basis of the evidence. Measures to
include advice, incentives, regulation, etc
In order to do this the model will need to be able to simulate the movement of 3-4 principal sediment
fractions, and associated substances though a spatially distributed, dynamic, process-based, hydrological
model. Substances to be included are the principal chemical forms of N, P, org C, FIO’s. Further details of
the EA requirements are given in Appendix 2.
Water Industry (Peter Daldorph and Johnathan Westlake)
The water industry has a strong requirement for models so that they can assess issues such as the impacts
of effluents on rivers, catchment management and economics and keep in step with the EA and DEFRA in
terms of meeting EU Directives and UK Government Policy. Peter Daldorph gave an overview of UKWIR
initiatives on developing a new UK wide database and modelling platform, which at the moment has built in
the SIMCAT model. This is a lumped, semi process based model which generates steady state of average
water quality but can also provide statistical assessments of discharge impacts on water quality. It does rely
on an export coefficient type model for delivery from the land, which is of limited value of projecting ahead
to assess climate and land use change, since to assumes the export coefficients are fixed. However, it
does form a national platform that can be built on. In many ways, this reflects the trend across Northern
Europe where countries such as Sweden, Finland and Norway are setting up national models for hydrology
and water quality and running them for all catchments, thus generating both spatial and temporal
responses.
Other chemicals beyond nutrients were of concern to the water industry such as the DOC (plus water
colour) and POPs (Persistent Organic Pollutants) as these could create problems for supply and had big
cost implications for treatment. Also, effect of climate change on cyanobacteria and their toxin breakdown
products, were an issue, again from a water supply perspective.
Getting models correct was a key issue for Thames Water, as the costs of chemical treatment, carbon
offsetting, power, and sludge disposal were very high and sound environmental and economic decisions
have to be made. Also, a better understanding and modelling of the links between flow, water quality and
ecology was needed to ensure that sustainable solutions were correctly evaluated. Also, issues of concern
are also not restricted to just freshwaters. It is very important to consider estuary systems and the loads of
material entering from river systems, as well as the point sources direct into the tideway. Thus models need
to cover the estuary systems as well.
Natural England (Russ Money)
Natural England has not made a great use of models in their work but can see that these tools will be very
useful for understanding nutrient sources and pathways. The impacts of changing nutrient levels on SSSIs
were important. Estuary protection is a keen issue with concerns over the fluxes of nutrients entering and
impacting ecosystems in estuaries.
British Water (Ian Bernard)
British Water coordinates the activities of the suppliers to the water industry and issues that affect hardware
or software supply would be of interest. So septic tanks are one area where there is concern about the
effectiveness of these and it is known that septic tanks do contribute to nutrient loads in rural catchments.
The industry would be interested in helping to evaluate any monitoring or modelling programmes.
Welsh Government (James Skates)
Wales is moving to a single integrated environmental body in the near future. This will enable better
planning and policy assessment and modelling would be a key feature of this process. Thus the Welsh
Government would be very keen to cooperate in any joint programme of work in the modelling area.
SEPA (Mark Hallard)
The WFD is a main driver for SEPA and knowledge of how nutrient mitigation measures will improve river
water quality and ecology is key issue. UKTAG are about to release some new recommendations for
instream standards and these may affect policy decisions. Models will help in assessing policy but must be
able to demonstrate their accuracy such that they can be used with confidence. The models must be future
proof so they can be used for climate change assessment. Good visualisation of model outputs is required
so they can be evaluated quickly and compared with environmental standards and objectives.
Environmental Sustainability KTN and TSB (Technology Strategy Board)- (Kerry Thomas)
The TSB is keen to support wealth creation for the UK and will support opportunities for new technology.
This could be new processes, new monitoring technology, new software developments, etc. A new water
innovation fund has just been announced and this could contribute to a modelling programme provided it is
strongly industry based and led. The ESKTN would be keen to assist with any new modelling platform.
LWEC (Dan Osborn)
A key function of LWEC is that Stakeholders should seek to collaborate and collectively support common
programmes
APPENDIX 1
DEFRA---Developing a national modelling framework for nutrient sources and impact to support end
user requirements
1. Rationale
Existing water quality and air quality models (numerical representations of processes) tend to focus on
individual pollutants at specific spatial scales. They have often suffered from a lack of testing against data,
due to a scarcity of observational data. Furthermore our current modelling resource is somewhat atomised,
with access limited by institutional boundaries. Where models are shared, version control may be an issue,
but also the lack of common datasets to parameterise and run them means that results are often difficult to
compare.
The need for integration of models has never been stronger with moves to integrate policies and policy
levers (advice, incentives etc) for different pollutants (see Natural Environment White Paper), the
development of localised land management and catchment management approaches covering multiple
issues (see Water White Paper), and a government agenda to increase transparency in decision making
and developing a less prescriptive approach to farming and land management policy (see MacDonald Task
Force). In parallel, the water industry is increasingly adopting catchment approaches in place of ‘end of
pipe’ solutions to improve the quality of abstracted water. There is a need for open and transparent tools to
translate data to useful outputs for stakeholders.
Government, agencies and industry would benefit from better access to the existing UK numerical
modelling resource for macronutrients. This would entail the following:
4. Testing models against data to assess which models should be used and where.
5. Bringing together existing and emerging models and running them on common data-sets through a
coherent, open source framework.
This would allow ensemble approaches in assessing
interactions between pollutants, effects over increasing spatial and temporal scales and predicting
nutrient losses to water and air in different scenarios.
6. Developing tailored decision-support software for end-users (e.g. Environment Agency, water
companies etc).
Several large scale research initiatives have emerged or are currently emerging which are developing high
resolution datasets that could be used to support the first two of these activities. These include the NERC
Macronutrient Cycles programme, the Greenhouse Gas Research Platform, the Demonstration Test
Catchments, CEH catchment studies, National Ecosystem assessment and others.
Developing such an integrated modelling resource would provide a user interface for knowledge emerging
from basic research funded by research councils. If an open-source framework could be developed, it
would provide a vehicle to accelerate knowledge transfer from researchers working on fundamental
environmental processes to the users of that knowledge.
2. Scope
The proposed modelling framework would cover macronutrient losses to the environment from farming,
land management and other rural sources, and their subsequent impacts on ecology, people and
ecosystem services, including:
 Water quality and impacts of surface and groundwater pollution:

o
Nutrients and sediment in water and impacts on ecology
o
Faecal indicator organisms/ pathogens and their fate
Air quality and its impacts:
o

Ammonia emissions
Greenhouse gas emissions from agriculture and land use:
o
Nitrous Oxide
o
Methane
It would address the interests of the following end users of scientific knowledge:
 National policy makers (e.g. at Defra, Scottish Government, Welsh Government),

Regulators (e.g. Environment Agency, OFWAT)

Operational staff involved in land management or catchment management including:
o
government agencies (e.g. Environment Agency, Natural England)
o
NGOs involved in delivering advice to land managers (e.g. Rivers Trusts)
o
Commercial consultants
o
Water industry
o
Agricultural industry
o
Local catchment management initiatives.
Figure 1: The proposed modelling framework would address the central layer in the diagram above:
allowing combinations of existing models to be run on common datasets to underpin decision support tools
which would be developed separately.
3. Required capability for a macronutrient modelling framework:
The framework will develop ways of integrating the current suite of macronutrient models and test them
against emerging datasets from the Macronutrient Cycles programme, the Demonstration Test Catchments
and other research initiatives. It will provide a framework into which future models can be added as they are
developed. It will also underpin user-focused decision support tools. This is shown diagrammatically in
figure 1 above.
The development of the framework should support decision making in the following areas:
1. Predicting sources and impacts of across scales: developing approaches and tools
(incorporating appropriate uncertainty frameworks) to combine multiple models to:
a. Predict the sources and impacts of nutrients entering the environment in poorly monitored
areas. This could include development of a catchment or landscape typology (i.e.
understanding which modelling approaches to use in a catchment of a particular type) to
transfer understanding gained from well studied areas to catchments with far less monitoring
infrastructure.
b. Apportion pollutant sources between different sectors (e.g. fertiliser use vs ammonia
deposition vs septic tanks as sources of nitrate).
c. Scale-up observations at the field/ sub-catchment scale to understand nutrient losses and
impacts at (1) catchment and (2) national scales.
d. Predicting ecological response to macronutrients in the aquatic environment and their
impact on ecosystem services.
2. Predicting the effectiveness of measures in delivering reductions in multiple pollutants and
greenhouse gasses at different scales.
a. Predict the timescales needed to deliver reductions in emissions under different mitigation
scenarios.
b. Disaggregate the cost-effectiveness of individual measures from catchment outlet
monitoring data.
Furthermore, the frame work should help:
3. Communicate modelling capabilities and outputs:
a. Better cataloguing of model metadata (including details of the primary purpose of the model,
known limitations and caveats for use) to help decision makers select the right tool for the
job.
b. Provide the underpinning science to support development of decision support/synthesis
tools to translate complex multi-model output into information which is targeted to key
stakeholder groups (operational staff, policy makers, farmers etc.)
c. Develop approaches that integrate numerical data with alternative ‘soft’ information sources
including local knowledge etc. This includes investigating ways to improve the way in which
users interact with models.
4. Improve confidence in predictions through testing and parameterisation of models: using
data-sets collected in the Macronutrient Cycles programme and DTC catchments to:
a. Parameterise and run models,
b. Ground-truth and test existing numerical models, setting realistic confidence intervals on our
estimates.
Analysis of the discrepancy between models and outputs should challenge our conceptual models
of catchment function, and improve both our understanding and model representations over time. A
risk-based evidence approach should be encouraged, using the distribution of multiple model
outputs as a basis of decision making.
4. Key principles:
A modelling framework should:

Embrace open-source development and code-sharing to increase transparency and scrutiny of
underlying assumptions
o
To allow researchers to use models and add to them as appropriate through iterative
developments, and
o
To allow wider scrutiny by the research community.
o
To allow end user organisations to add to them and develop bespoke decision support frontends

Build on existing models and systems of integration where available (e.g. EA open source
modelling, NERC pilot Environmental Virtual Observatory, developments in cloud computing,
‘models as web services’ approaches, etc)

Take account of the wider data-sharing agenda (e.g.

Have a web-enabled user interface/interfaces to allow free access. This would increase access and
understanding of existing models and how they work
5. Previous work to inform model integration
Several pieces of work have made recommendations that support this proposed approach:

Defra Scientific Advisory Council Modelling Sub Group report (http://sac.defra.gov.uk/subgroups/modelling-sub-group/)

Defra project IF0111: Setting the priorities for future work on nutrient decision support systems
(http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=0
&ProjectID=14515)

Defra project ES0204: Agricultural decision support tools for prediction and management of nutrient
input
and
loss:
NIL
IMPACT
http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&ProjectID=134
92&FromSearch=Y&Publisher=1&SearchText=es0204&SortString=ProjectCode&SortOrder=Asc&P
aging=10#Description

Defra Demonstration Test Catchments Modelling Strategy
APPENDIX 3
Environment Agency--Catchment Modelling Requirements (draft)
Broad objectives
The main aim is to develop a modelling system that



will allow the impact of farming and other land uses on a range of environmental and policy relevant
endpoints to be assessed (eg chemical and ecological quality measures, ecosystem service benefits.
enjoys academic, DEFRA support
will have the confidence of key stakeholders.
It will need to

quantify the multiple impacts of farming at a range of scales
o eg this particular farm or local area contributes this amount of harm or benefit to this local river
or this area of catchment.

quantify the benefits of policies and land management practices to reduce those impacts
o eg this type of farm or practice contributes this amount of harm or benefit to our rivers when
integrated across whole one or more RBD or England.

harm to be assessed in a variety of ways eg exposure represented as a distribution of concentrations
(eg seasonally), in terms of load exported by a particular pathway (in-stream, bed load, groundwater
flow) integrated over periods (days, seasons, years)

quantify the contribution farming makes to these impacts in comparison to other sources eg point
sources

make use of the best available science and data, including National datasets (incl remote sensing, farm
census, distributed monitoring), data from research case studies.

exploit or develop existing model formulations where possible

incorporate local observations where this can improve model prediction

allow extrapolation of knowledge to data poor catchments, and allow upscaliing and integration

allow risks to a range statutory duties (eg WFD) and developing policy objectives

provide a basis for evidence based policy analysis, evaluation to achieve multiple objectives

provide a basis for spatially explicit targeting of measures on the basis of the evidence. Measures to
include advice, incentives, regulation, etc
In order to do this the model will need to be able to simulate the movement of 3-4 principal sediment
fractions, and associated substances though a spatially distributed, dynamic, process-based, hydrological
model.
Substances to include principal chemical forms of N, P, org C, FIO’s.
Model to allow other substances (eg pharmaceuticals, metals) to be represented through generic properties
and processes (e.g. conservative, first/second order decay, volatalisation, daughter substances,
partitioning, to/from bed and suspended sediment, org fraction, etc.)
To allow the contribution from principal sectors to
 the mass of substances in defined compartments (soil water, river reach or reaches (water,
sediment fraction x, or in combination), groundwater aquifer, fine sediment)
 the mass flux between compartments over time,
 the time-variation (distribution, time series) in concentration at a point or averaged over a spatial
unit.
Source inventory
To include a spatially and temporally explicit representation of the present-day loading of each substance,
or methods to do so.
Using data that is or can be made available to the DEFRA family.
Principal sectors and subsectors to be defined in such away as to allow management actions and policy
options to be explored
Eg Water company discharges : size, type,
Eg Farming : Arable: Crop rotation x, y, z)
(Note the above applies to source inputs that can in principle be controlled spatially. Other sources to be
considered will include natural and non-natural land uses, incl. types of urban, density of households or
population, forestry, atmospheric deposition).
Where there is evidence of significant within year (eg seasonal) variation in source loadings, then this
variation in input should be adequately described.
To explicitly document the evidence and main sources underpinning each process description and
parameterisations, including uncertainties on parameter estimates
Processes
Incorporates important processes affecting transport, exchange, transformation through a catchment.
1) Model(s) to be partially or fully spatially distributed
2) To run on a continuous (daily or sub-daily) temporal resolution
3) Suitable for the simulation of catchment hydrological response to variations in rainfall and
temperature (stationary and non-stationary time series generated from appropriate meteorological
data).
4) To explicitly (but simply) represent spatial variation in surface and groundwater hydrological
pathways
5) Surface water pathway should consider including the influence of surface vegetation, surface
storage, infiltration, overland flow, through flow, percolation to groundwater
6) Hence the model should demonstrate acceptable level of skill representing both high and low flow
components of hydrographs
7) Integrated simulation of hydrological and geochemical processes affecting, in particular, nutrients
(N, P), FIO’s, org C)
8) Software should be modular and open source and/or use commercially available software (eg GIS,
database)
9) For ecological impacts, should provide flexibility such that a broad variety of empirical relationships
(models) between exposure and probable impact on receptors can be incorporated.
Hydrological features
To be considered
Weather generation
Rainfall
Solar radiation
Temperature
Interception
Surface depression storage
Infiltration
Evapotranspiration
Aquifer Recharge
Lateral flowthrough
Channel flow
Sediment erosion, re-suspension and deposition (or at least prediction of in channel sediment
characteristics and volumes)
Solute travel time (reach residence time) (eg aggregated dead zone (flow dependent) or empirical
canonical)
Geochemical features
Nitrogen cycle
Ammonification
Nitrification
De-nitrification
Plant uptake and release (decay)
Terrstrial - Wetlands, grasslands (various) broad leaf/coniferous, moorlands, agricultural
crops?)
Aquatic – suspended algae
Epiphytic algae
Submerged macrophytes
Marginal vegetation
Oxygen
In channel (in stream) DO balance and BOD
Surface O2 exchange (eg Oxygen entrainment and aeration due to channel features (weirs, etc)
Phosphorus
Partitioning between dissolved and suspended particle, soil and particle/sediment bound phases
Chemical trasformations in dissolved and particulate phases, influence of sediment type and redox?)
Plant uptake and release (decay) (see above)
General pollutants
Conservative or first order decay
Coefficient based partitioning based on general properties
Robert Willows, January 2012