The Role of Citizen Science in UK Freshwater Monitoring

Abstract

Currently in its pilot phase, Defra’s Natural Capital and Ecosystem Assessment (NCEA) aims in the long term to deliver high quality national and local evidence to assess the state and condition of biodiversity, ecosystems, and natural capital assets across terrestrial, freshwater and marine environments. Ultimately, this evidence will guide the UK Government in the delivery of environmental policy targets, such as those outlined in the 25 Year Environment Plan (25 YEP) [1]. The pilot phase, running across 2020/21, has a number of components focused on six key themes: data analysis and modelling, Earth observation and mapping, local data, field data (terrestrial), field data (water) and citizen science. This report focuses on the complementary roles of citizen science and Environment Agency (EA) monitoring, and comprises part of the field data (water) pilot. The freshwater monitoring context presents a unique opportunity for practical piloting of citizen science as part of the NCEA because it dovetails with ongoing developments in collaborative and partnership working that underpin EA strategy.

Full text

An analysis of the current state and a roadmap for
the future

This report was written by Earthwatch Europe’s (EWE) Freshwater Research Programme Team.
Principal Author
EWE: Isabel Bishop
Thanks to
EWE:
Josephine Head
Caroline Shepherd
Samantha Hayes
Steven Loiselle
Kesella Scott-Somme
Clara Stevenson
Toos van Noordwijk
Environment Agency:
Arron Watson
Riverfly Partnership:
Ben Fitch
Steve Brooks
Bill Brierley
UK Centre for Ecology and Hydrology (UKCEH):
Matthew Fry
Additional thanks go to all of the citizen science data providers and users who attended the expert stakeholder
workshop, and the citizen scientists who contributed to the pilots.
Suggested citation
Bishop, I. J et. al. 2021. The Role of Citizen Science in UK Freshwater Monitoring. Earthwatch Europe, Oxford, UK.
Images
John Hunt and Doug Silverstone
Earthwatch is a global NGO that brings people, science and nature together to build a world in which we live within
our means and in balance with nature. We focus on the environmental challenges where we can have the greatest
impact: reducing pollution of our water bodies; enabling sustainable agricultural land management and creating
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collective action. We therefore take a collaborative approach, working with the public, government, business,
educators and scientists to create knowledge, inspire action and drive change. Our approach is grounded in evidence
and delivered through a combination of engaging and immersive experiences, citizen science and research.
Earthwatch is an Independent Research Organisation, and citizen science is a key feature of almost all of our
environmental research projects. We believe it is a vital part of our mission to create knowledge and empower people
to make positive changes for the environment.
If you are interested in exploring opportunities to collaborate, please contact us at water@earthwatch.org.uk

Executive Summary ............................................................................................................................................................ 5
Context, aims, and objectives ......................................................................................................................................... 5
Methodology and key findings ........................................................................................................................................ 5
Key recommendations .................................................................................................................................................... 6
Chapter 1: The business case for reviewing our approach to freshwater monitoring .................................................. 9
Summary .......................................................................................................................................................................... 9
Ambitions of the 25 Year Environment Plan and the need for data and engagement ................................................ 9
Natural Capital and Ecosystem Assessment and Sentinel ......................................................................................... 10
Aims and objectives of this report ................................................................................................................................ 11
Approach and guide to this report ................................................................................................................................ 11
Chapter 2: Benefits of citizen science for the 25 YEP ................................................................................................... 14
Summary ........................................................................................................................................................................ 14
Potential for environmental impact from citizen science through data and engagement ........................................ 14
Recent innovation in citizen science and citizen science approaches ...................................................................... 19
Specific links to the outcome indicator framework of the 25 YEP ............................................................................. 20
Links to freshwater statutory goals of the 25 YEP and beyond .................................................................................. 21
Chapter 3: The current state of citizen science in UK freshwater monitoring............................................................. 24
Summary ........................................................................................................................................................................ 24
Characteristics of UK freshwater citizen science ........................................................................................................ 24
Availability of relevant citizen science data ................................................................................................................. 25
Links between citizen science projects ........................................................................................................................ 28
Chapter 4: The mass participation census approach: Assessing the state of the water environment on a large
geographic scale ................................................................................................................................................. 30
Summary ........................................................................................................................................................................ 30
Mass participation censuses: An introduction ............................................................................................................ 31
Case study one: The Thames WaterBlitz ..................................................................................................................... 31
Opportunities for future development .......................................................................................................................... 38
Chapter 5: Targeted monitoring: Driving local action via place-based citizen science ......................................... 42
Summary ........................................................................................................................................................................ 42
Interest groups and place-based community action ................................................................................................... 42
Case study two: Early warning of drought on the River Lark ....................................................................................... 43
Case study three: Reducing pollution on the River Evenlode ...................................................................................... 46
Opportunities for future development .......................................................................................................................... 49
Chapter 6: Opportunities and challenges ........................................................................................................................ 53

Summary ........................................................................................................................................................................ 53
Opportunities for scaling up UK freshwater citizen science ....................................................................................... 53
Challenges ...................................................................................................................................................................... 54
Chapter 7: A roadmap for scaling up ............................................................................................................................... 59
Summary ........................................................................................................................................................................ 59
Co-creating a way forward ............................................................................................................................................ 59
Workstream 1: National governance and coordination ............................................................................................... 60
Workstream 2: Data standards and tools..................................................................................................................... 61
Workstream 3: Best practice guidelines ....................................................................................................................... 62
Workstream 4: Data collection drives ........................................................................................................................... 63
Workstream 5: Feedback and communication mechanisms ..................................................................................... 63
Workstream 6: Capacity building .................................................................................................................................. 64
Workstream 7: Engaging the wider public .................................................................................................................... 65
Concluding Remarks ......................................................................................................................................................... 66
References ......................................................................................................................................................................... 67

5
Currently in its pilot phase, Defra’s Natural Capital and
Ecosystem Assessment (NCEA) aims in the long term
to deliver high quality national and local evidence to
assess the state and condition of biodiversity,
ecosystems, and natural capital assets across
terrestrial, freshwater and marine environments.
Ultimately, this evidence will guide the UK Government
in the delivery of environmental policy targets, such as
those outlined in the 25 Year Environment Plan (25
YEP) [1]. The pilot phase, running across 2020/21, has
a number of components focused on six key themes:
data analysis and modelling, Earth observation and
mapping, local data, field data (terrestrial), field data
(water) and citizen science. This report focuses on the
complementary roles of citizen science and
Environment Agency (EA) monitoring, and comprises
part of the field data (water) pilot. The freshwater
monitoring context presents a unique opportunity for
practical piloting of citizen science as part of the
NCEA because it dovetails with ongoing
developments in collaborative and partnership
working that underpin EA strategy.
Following a review of freshwater monitoring, the EA
are developing a new national surveillance monitoring
network that aims to understand long-term pressures
and trends for the purposes of both the Natural Capital
and Ecosystems Assessment and wider EA work. This
will sit alongside a tool called ‘Sentinel’, which brings
together the national surveillance monitoring data
with other sources of evidence. This report focuses on
the role that citizen science might play in Sentinel, and
asks the following key questions:
1. How can citizen science play a role in
monitoring the natural environment?
2. How can citizen science data be used to
complement freshwater statutory monitoring?
3. How can existing citizen science initiatives be
scaled up to provide a national freshwater
monitoring network?
4. What are the risks and challenges that must be
considered when incorporating citizen science
data within Sentinel?
This report details the results of a three stage
research process undertaken between September
2020 and February 2021, which included: a detailed,
literature-based assessment of the current state of
water-related citizen science in the UK, active data
collection pilots to trial integration of citizen science
and EA data, and an expert stakeholder workshop
focused on co-designing a roadmap for scaling up.
Section 1 of this report presents the results of the
literature review to explore the context within which
the citizen science elements of Sentinel currently sit.
In Chapter 1, the business case for reviewing our
approach to freshwater monitoring is presented.
Citizen science is increasingly championed for use in
government-led environmental monitoring, including
by the European Commission [2] and the United
Nations Environment Programme (UNEP) [3]. This is
because citizen science has the capacity to collect
large, high-quality datasets, at a scale that would be
hard for professionals to generate alone, whilst
simultaneously fulfilling other policy requirements
such as engaging the public and contributing to
improving public mental health and wellbeing. Chapter
2 explores how these benefits of citizen science can
be applied in the specific context of the 25 YEP.
Through a combination of data gathering and
engagement, citizen science can contribute to a)
monitoring progress towards and b) achieving the
goals and targets through six key pathways: data for
environmental management, evidence for policy,
community action, social network championing,
political advocacy, and behaviour change [4]. Chapter
3 reviews the current state of freshwater citizen
science in the UK, and shows that a large number of
existing citizen science projects are already
monitoring freshwater natural capital locally,
regionally and nationally. In particular, there is
potential for citizen science data to contribute to
monitoring and action on pollution loads (25 YEP
outcome indicator B1), pollution incidents (indicator
B2), the state of the water environment (indicator B3)
and sustainable abstraction criteria (indicator B5) [5].
However, much citizen science activity in the water
sector is driven from the ‘bottom up’. In the absence

6
of national coordination, the full potential of citizen
science as a complement to regulatory monitoring
has yet to be fully recognised.
Section 2 reports the results of the active pilots, which
tested the practicalities of integrating citizen science
and EA data in order to identify opportunities for future
work. Chapter 4 explores the use of an environmental
census event, the Thames ‘WaterBlitz’, to collect lots
of citizen science data over a large geographic area.
This approach increased spatial granularity of data on
the state of the water environment (25 YEP indicator
B3) across large spatial scales, allowing: refinement
of existing water quality assessments beyond existing
catchment management boundaries, assessment of
multiple environmental indicators, and aggregation of
data at resolutions suitable for natural capital
assessments (e.g. 1km2 grid squares). Additionally, it
engaged a large number of people in social action for
the environment, and provided access to local
knowledge held by volunteers. Chapter 5 builds on the
concept of using citizen science to access local
knowledge by discussing the results from two place-
based, volunteer-led ongoing monitoring projects.
These projects demonstrated how citizen science can
be used to deliver positive environmental action via
the impact pathways outlined in Chapter 2. On the
River Lark, data provided by citizen scientists allowed
the EA to detect the impact and onset of drought
conditions months earlier than these would normally
be recognised, enabling appropriate follow-up actions
according to drought plans. On the River Evenlode,
citizen scientists detected unexpectedly low
concentrations of phosphate upstream of EA
monitoring locations. Having attributed the higher
downstream concentrations to sewage discharges,
they are now working with Thames Water and the EA
to improve water quality. The advantages of linking
local projects like these together via Sentinel are that
it will be possible to identify where certain pressures
and drivers are repeated across the country, under
what conditions they are most likely to occur, and
which local management actions are most effective.
Section 3 discusses the potential for scaling up
freshwater citizen science to form part of a national
monitoring network, and is based on the outcomes of
an expert stakeholder workshop during which the
findings of the literature review and active pilots were
discussed. Chapter 6 focuses on key risks and
challenges identified by the expert stakeholders. The
first major challenge is data management. Existing
citizen science projects have individual data
management systems with minimal interoperability.
Secondly, as citizen science is inherently dependent
on volunteers, end user experience must be
considered, as without adequate training, feedback,
local ownership, and evidence of positive change,
motivation will decline over time. Thirdly, there are a
number of important ethical and legal considerations,
on treating people fairly, avoiding discrimination and
managing health and safety risks and liabilities.
Additionally, citizen science can be subject to
personal biases and inconsistent sampling, so the
scientific approach taken must account for these
differences to statutory monitoring. Finally, significant
challenges related to long-term financing pose a risk
to the sustainability of citizen science initiatives.
Collectively, these challenges can lead to
inefficiencies, sporadic data collection, and loss of
motivation and interest from volunteers. Chapter 7
directly tackles the most pressing of these challenges
by presenting a co-created, seven-step roadmap that
aims to make investment in citizen science as part of
Sentinel and/or the NCEA targeted and efficient,
ensuring the best return possible.
Based on the research findings of this study,
recommendations aimed at scaling up freshwater
citizen science to become part of a national
monitoring network can be grouped into seven
workstreams. These collectively form the co-created
roadmap outlined in Chapter 7:
1. Establishing national coordination through a
Governance Board and National Coordinator
role.
2. Co-creating data management systems,
interoperability standards and tools.
3. Developing best practice guidelines and
standards for scientific methods.
4. Coordinating data collection drives through an
annual national census and targeted local
ongoing monitoring.
5. Developing feedback and communication
mechanisms to build trust and communication
between data providers and data users.

7
6. Capacity building and training for both data
providers and data users.
7. Engaging the wider public to engage new
audiences and disseminate results and impact.
Overall, it is clear that there are benefits in utilising
citizen science as a tool to monitor the natural
environment, and a coordinated national approach is
needed. While there are challenges to consider, there
is clear enthusiasm and optimism from the freshwater
citizen science community in working together with
the Environment Agency and others to co-create a
sustainable, long-term solution.

8

9
The UK Government’s 25 Year Environment Plan (25
YEP) sets out a comprehensive and long-term
approach to protecting and enhancing our natural
resources, with a focus on cleaner air and water,
thriving plant and animal life, and a cleaner, greener
country [1]. The framework was developed using the
concept of natural capital, defined as the ‘elements of
the natural environment which provide valuable goods
and services to people, such as clean air, clean water,
food and recreation’. The framework focuses on three
outcomes and goals grouped together under: 1)
reducing pressures on natural capital (e.g. pollution or
plant disease), 2) improving the state of natural capital
assets (including air, water, land and seas), and 3)
increasing the benefits that we get from those assets.
In order to deliver the goals and targets set out in the
25 YEP, measuring progress is essential. This means
that we need to understand the quantity and quality of
natural capital, where it is vested, and how it changes
through time. This assessment process is guided by
the 25 YEP indicator framework, developed around 10
broad environmental themes that are subdivided into
16 ‘headlines’ and 66 ‘indicators’ that can be used to
assess how the value of any natural asset might be
changing, and enable the government to measure
progress against the Plan’s 10 goals [5].
Fresh water is recognised as a key natural asset in the
UK, and freshwater habitats support a multitude of
ecosystem services including water supply, flood
regulation, recreation, nutrient cycling, and
maintenance of water quality. Several 25 YEP
indicators for measuring fresh water are already
relatively well defined and monitored by the
Environment Agency (e.g. indicators B1-B3), but
regular collection of such data at a national scale is
• Freshwater monitoring is crucial for delivery of the UK Government’s 25 Year Environment Plan (25
YEP). As such, it is a key theme within Defra’s Natural Capital and Ecosystem Assessment (NCEA)
pilots.
• Citizen science has the capacity to collect large, high-quality datasets that relate to several of the 25
YEP freshwater outcome indicators. It can also help to connect more people with the environment,
bringing multiple benefits including improvements to public health and wellbeing.
• The freshwater monitoring context presents a unique opportunity for practical piloting of citizen
science as part of the NCEA because it dovetails with the Environment Agency’s (EA) development of
a new monitoring tool called ‘Sentinel’.
• Sentinel aims to bring together EA data from a fixed national surveillance monitoring network with
other sources of evidence in order to detect and understand long term pressures and trends in the
freshwater environment. The evidence from multiple sources will collectively paint a more nuanced
national picture of our water environment than the national surveillance monitoring could alone.
• This report outlines the potential of citizen science to contribute to freshwater monitoring, with a focus
on Sentinel.

10
extremely challenging. This is in part because fresh
water is traditionally managed on a catchment scale,
meaning that existing monitoring systems are not
designed to assess natural capital assets across
large/national geographic areas. In addition, the
spatial coverage of the Environment Agency’s
monitoring programme has been reduced across
England over the past two decades. In particular,
reduced capacity means that headwater streams and
small ponds are not routinely monitored and are
therefore in danger of being overlooked in natural
capital assessments [6].
Alongside data to monitor progress, delivery of the 25
YEP also calls for increased public engagement with
the environment in order to increase the benefits that
we gain from natural assets. Water has always been a
crucial part of the landscape and of human interaction
with nature, and this natural human connection to
water makes it a popular environment for public
engagement [7]. Accessing ‘blue infrastructure’ is also
strongly associated with positive benefits to physical
and mental health [8]. There are therefore ample
opportunities for initiatives that promote engagement
with freshwater ecosystems, and to use fresh water as
a means to achieve the 25 YEP action ‘connect more
people with the environment to improve health and
wellbeing’.
Citizen science (i.e. the involvement of non-scientists
in scientific research) is widely recognised for its
ability to collect valuable scientific data while
simultaneously engaging the public. There is
increasing interest in harnessing the many and varied
scientific, social, economic, environmental, and
political benefits of citizen science by using it within
official national environmental monitoring schemes.
The European Commission recently published a staff
working document outlining best practices for the use
of citizen science in environmental monitoring [2], and
the United Nations Environment Programme (UNEP)
referenced the potential of citizen science for
delivering the Sustainable Development Goals in their
most recent Global Environment Outlook report [3].
Against this backdrop, citizen science is well placed to
meet both the data and engagement needs of the 25
YEP.
In 2020, Defra launched the pilot phase of the Natural
Capital and Ecosystem Assessment (NCEA), which
aims in the long term to deliver high quality national
and local evidence to assess the state and condition
of biodiversity, ecosystems, and natural capital assets
across terrestrial, freshwater and marine
environments to support the 25 YEP. The pilot is cut
across six key themes: data analysis and modelling,
Earth observation and mapping, local data, field data
(terrestrial), field data (water) and citizen science. In
reality, citizen science overlaps with many of these
themes. Here, we focus on the complementary roles
of citizen science and EA monitoring to provide field
data in the freshwater environment. The freshwater
monitoring context presents a unique opportunity for
practical piloting of citizen science as part of the
NCEA because it dovetails with ongoing
developments in collaborative and partnership
working that underpin EA strategy.
In 2014, the EA conducted its Strategic Monitoring
Review (SMR) with the aim of making better use of
available evidence to manage water. This represented
a shift to a new way of delivering and commissioning
freshwater monitoring, while still encompassing much
of the work completed by the EA through the 1990s
[9]. This new way of working will therefore underpin all
future freshwater monitoring, including (but not
limited to) providing data for the 25 YEP indicators.
The new EA monitoring programme aims to gain
greater value from investing in information collection
and use. It uses a collaborative approach that will
enable the EA to obtain reliable information from a
range of sources to improve the water environment for
everyone. This vision of monitoring and working
together provides better understanding of where
problems exist in different catchment areas (regions)
of England and, by working with catchment partners
and stakeholders, aims to solve these problems.
The EA’s new approach incorporates two linked
monitoring programmes. The first is a locally
managed, time-bound programme that allows
different catchments to structure their specific
monitoring needs. The second is the national
surveillance monitoring network, which is a relatively
fixed long-term national network aiming to understand
long-term pressures and trends. In 2018, the EA ran

11
two prototype monitoring programmes across five
catchments in England. An important lesson has been
that working with partners to share data is key. As a
result, the EA’s Strategic Evidence, Design and
Assessment Team are now designing ‘Sentinel’, a tool
that brings together the national surveillance
monitoring data with other sources of evidence which
will collectively paint a more nuanced national picture
of our water environment than the national
surveillance monitoring could alone. Citizen science is
a key piece of the Sentinel evidence ‘jigsaw’.
This report aims to provide a comprehensive
assessment of the role and opportunities provided by
citizen science to contribute to environmental
monitoring, with a focus on the freshwater
environment as a proven example of its potential. The
report identifies examples of citizen science
approaches that are aligned with, and complementary
to, both 25 YEP indicators and EA monitoring
objectives. It provides worked examples of how
citizen science data can be combined with EA data,
focusing on water quality, and outlines potential
opportunities for further integration of citizen science
and EA data at the national scale. Through these
worked examples, relationships between two citizen
science projects (FreshWater Watch (FWW) and the
Angler’s Riverfly Monitoring Initiative (ARMI)) are
established, and opportunities and challenges for
extending this to additional projects are discussed.
Finally, the report provides a roadmap for expanding
water quality citizen science from a local to national
scale network, highlights citizen science opportunities
for other indicators/natural capital metrics, and
explores how to effectively integrate different forms
of citizen science from different organisations.
This report has been compiled following a three stage
process. Firstly, a detailed assessment of the current
state of water-related citizen science in the UK and
review of previous work on integrating citizen science
for environmental monitoring was undertaken.
Secondly, a number of active pilots were run to explore
the practical opportunities and challenges for
integrating citizen science and EA data. Finally, an
expert stakeholder workshop was held to identify key
challenges associated with citizen science monitoring
on a national scale and to co-design a roadmap for
expanding the citizen science element of Sentinel.
In order to assess the current state of water-related
citizen science in the UK, a strategic literature review
of both published and grey literature was carried out.
Relevant literature and project descriptions were
abstracted from searches of Web of Science and
Google Scholar (scientific literature) and Google,
Twitter, Zooniverse, SciStarter, and the EU Citizen
Science platform (grey literature). A consistent
combination of keywords were used in all searches,
and included terms related to citizen science (‘citizen
science’, ‘community based monitoring’, ‘volunteer’,
‘crowd science’, ‘civic science’) and terms related to
water (‘aquatic’, ‘stream’, ‘river’, ‘pond’, ‘lake’, ‘wetland’,
‘water’). This resulted in a database of ~500 citizen
science projects related to water across the globe,
which was then filtered to include only those projects
active in the UK. This research formed the basis of the
remainder of Section 1. Chapter 2 covers the potential
held within citizen science for delivering the 25 YEP,
and Chapter 3 explores the current state of freshwater
citizen science in the UK in more detail.
Additionally, a number of key texts related to the
integration of statutory and citizen science data were
identified:
● 2015 CaBA monitoring framework [10]
● 2017 CDUG workshop outputs on
collaborative monitoring [11]
● 2020 UKEOF governments and citizen science
workshop summary [12]
● 2020 Catchment Monitoring Cooperative
proposal and stakeholder consultation [13]
● 2020 European Commission staff working
document: Best practices for citizen science
monitoring [2]
These key texts all contained appraisals of the
opportunities and challenges related to the use of
citizen science data for statutory purposes. We
collated these opportunities and challenges and sub-
divided them into themes that formed the basis of the
expert stakeholder workshop (see below).

12
Between September 2020 and February 2021, we ran
a number of pilot activities designed to test the
practicalities of integrating EA and citizen science
data and to explore potential outputs. These are
described in Section 2 of this report. Firstly, we ran an
environmental census event called a ‘WaterBlitz’ in
September 2020 (Chapter 4). This mass participation
approach aimed to engage large numbers of people to
collect as much data as possible over a large
geographic area (the Thames River Basin District) in a
short space of time. This allowed us to explore how
citizen science might be applied at scale. Secondly,
we analysed existing and new data from ongoing
localised citizen science activities on the River Lark
(Suffolk) and River Evenlode (Oxfordshire) (Chapter
5). These projects enabled us to evaluate how the
large numbers of locally-driven activities like these
that already exist in the UK might contribute to a
national monitoring network.
For all pilot activities, we focused on two existing
citizen science initiatives: FreshWater Watch (FWW)
and the Angler’s Riverfly Monitoring Initiative (ARMI)
(Table 1.1). These two initiatives represent a subset of
the citizen science initiatives that are: already
operating at a national level, collecting data that can
be directly related to EA parameters and to 25 YEP
outcome indicators, and have an appropriate quality
assurance programme. They both operate a linked
local-national approach, whereby all citizen science
activity in the country uses the same methodology and
infrastructure but is coordinated through small
‘groups’ or ‘hubs’ around specific local environments
or issues. This setup means that they could be applied
in both of our case study approaches (environmental
census and localised monitoring), and we could also
draw additional data from their wider project
databases. This combined data (FWW, ARMI, and EA)
was made available to the citizen scientists via an
online data portal designed in collaboration with UK
Centre for Ecology and Hydrology (UKCEH) [14].
Table 1.1: Descriptions of FreshWater Watch and the Angler’s Riverfly Monitoring Initiative - the two key citizen
science initiatives involved in our active pilots.

13
Section 3 of this report discusses the potential for expanding citizen science monitoring to a national network as
part of Sentinel. To ensure the needs of as many stakeholders as possible were considered in our recommendations,
we convened an online workshop on 12th January 2021 for a group of 26 experts. These experts collectively
represented a wide range of different stakeholders, including data providers, data users (both governmental and non-
governmental), and technical infrastructure providers (Fig. 1.2). The workshop followed established processes for
co-design [20]. During the workshop, participants reviewed the opportunities and challenges we identified during our
literature review, prioritised them, and identified any additional ones that had not yet been considered. These
opportunities and challenges are discussed in Chapter 6. The second portion of the workshop was solutions-focused
and encouraged participants to consider their ideal solutions to the challenges raised. The outputs of the workshop
were recorded online, and were used to inform the roadmap for scaling up, outlined in Chapter 7.
Figure 1.2: Expert stakeholder workshop participant organisations

14
As outlined in Chapter 1, the Environment Agency’s
(EA) new monitoring programme seeks to take a more
collaborative approach, and aims to work with a range
of catchment partners and stakeholders to obtain
reliable information from a range of sources to solve
existing problems and improve the water environment
for everyone. One such collaborative opportunity is the
integration of existing citizen science data with EA
monitoring data as part of the Sentinel toolkit. Water
quality data generated by citizen scientists (defined as
the involvement of non-scientists in scientific
research) is already widely available and regularly
collected by many catchment partners and other
stakeholders (see Chapter 3).
Citizen science has been shown to bring multiple
benefits to the management of freshwater habitats
[21]. Firstly, given the application of appropriate
methods to ensure accuracy and reduce bias, citizen
scientists are capable of producing new, high-quality
datasets that would be difficult for professionals to
generate alone [22]. There is a clear opportunity for the
EA to complement existing monitoring programmes
with citizen-generated data, and to collectively paint a
more nuanced national picture of our water
environment than EA data could alone [23]. This data
would greatly improve capacity to monitor natural
capital on local, regional, and national scales.
• Citizen science as a source of data has multiple benefits including: provision of high-quality datasets
that would be difficult for professionals to generate alone, opportunities for public engagement and
education, opportunities to make better use of local knowledge, increased support for decision-making
processes, improvements to health and wellbeing as citizen scientists are encouraged to spend more
time in natural spaces, and increased public action to improve the environment.
• There are six key pathways through which citizen science can create the types of positive environmental
change that the 25 YEP aims to achieve. These are: data for environmental management, evidence for
policy, community action, social network championing, political advocacy, and behaviour change.
• Participation and engagement are key to achieving impact through these pathways. Barriers include:
difficulties designing projects that appeal to volunteers with different motivations, inadequate use of
data and associated lack of volunteer acknowledgement, limited data accessibility, and neglect of
privacy issues.
• Recent innovations in citizen science have sought to address concerns about data quality, management,
control, and access. With good data quality control, assurance and management practices, as well as
appropriate recognition of the biases held within all datasets, these concerns should not be a barrier to
using citizen science data.
• There are numerous opportunities to use citizen science to collect data that support the 25 YEP outcome
indicator framework across multiple themes and headlines, including those related to fresh water.
Integrating this data with data from other datastreams via Sentinel can facilitate a thorough and robust
baseline survey of freshwater natural capital assets.
• Citizen science data can also contribute to other freshwater statutory functions by filling data gaps,
increasing certainty of predictive models, and informing decision making.

15
Secondly, citizen science projects can have wider
impacts on the participants as well as the systems
they operate in, with impacts of citizen science
affecting the environment, society, the economy,
science, and governance [24]. Among other things,
citizen science provides opportunities for public
engagement and education, making better use of local
knowledge, and increasing support for decision-
making processes [21, 25]. This means that citizen
science fulfils a unique role in environmental
monitoring; it not only provides complementary data
to enhance understanding of environmental trends,
but can simultaneously help to drive positive
environmental changes [4]. In summary, citizen
science can not only help to monitor progress towards
the 25 YEP, but can also actively improve natural
capital by delivering impacts across the natural capital
framework (Fig. 2.1).
Figure 2.1: The potential role of citizen science in (a) providing data to monitor natural capital, and (b) delivering
impacts to drive the natural capital framework. Adapted from Natural England’s Natural Capital Framework
schematic [26].

16
One of the goals within the 25 YEP is ‘enhancing
beauty, heritage and engagement with the natural
environment’. To achieve this goal, targets have been
set to a) encourage people to spend more time in
natural spaces to benefit their health and wellbeing,
and b) focus on increasing action to improve the
environment from all sectors of society. Citizen
science contributes to both of these targets. Citizen
science, by its nature, encourages people to engage
with and interact with the natural world (see case
study 1 in Chapter 4). This engagement in itself
increases the value of ecosystem assets by enabling
people to access and benefit from the ecosystem
services they provide. Beyond the wellbeing benefits
associated with engagement, participation in citizen
science activities can also motivate people to take
further actions to protect and enhance ecosystem
services, such as becoming involved in management
interventions or changing their behaviour to reduce
pressures on the environment [4]. As such, citizen
science has the potential not only to generate data, but
also to act as a catalyst for public involvement in the
delivery of many of the 25 YEP goals.
According to the Natural Capital Framework, impacts
on the environment occur when changes are made to
resource management and practices that directly
influence ecosystem assets and ecosystem services.
This includes changes in institutional practice (i.e.
activities of organisations, businesses, and
governments), collective practice (i.e. the actions of a
group of people, such as a local community), or
individual practice. Well-designed citizen science
projects can drive these changes by engaging and
motivating participants to adopt more sustainable
behaviour, and by fostering collaboration between
scientists, businesses, governments, and civic society
[4].
There are six key pathways through which citizen
science can create positive environmental change,
described in Box 2.2. In practice, however, few
projects deliver all of these benefits at scale at the
same time. Different citizen science project types
operate on different spatial scales, attract participants
with different motivations and skill levels, and can be
associated with different impact pathways. In order to
maximise the range of positive environmental
changes that can be achieved via citizen science, each
of these different project types should play a role.
In all pathways, sufficient participation by citizen
scientists is central to maximising impact - both in
terms of quantity of data collected, and the number of
participants engaged. A critical mass of data is
required to produce scientific results, while large-
scale engagement increases the potential for
community action, social networks and behaviour
change. In theory, a well-developed citizen science
project can tick all the boxes for positive
environmental change at scale. However, in reality,
there are challenges to consider that can restrict the
scale of impact.
Firstly, participants’ motivations vary, and the same
approach is unlikely to appeal to all audiences. For
example, while some citizens tend to prioritize fun and
interest when considering participation, others will be
motivated by data use that brings about real change,
or recognition for their involvement in a scientific
project [27]. On the flip side, there are also barriers to
uptake, with key factors being inadequate data use,
not feeling acknowledged, limited data accessibility
and neglect of privacy issues [28].
Designing engaging projects that provide adequate
communication of results alongside a well-defined
data policy are important steps to enhance the impact
of citizen science activities. Maximising impact is best
achieved by ensuring that motivations of project
participants are well understood, and aligned with the
intended project objectives and data requirements.
Participant types can be broadly split into four
categories: Captive Learning Groups, Place-Based
Community Action, Interest Groups, and Mass
Participation (Fig. 2.3). Engagement of these
participant groups requires quite different
approaches, and is linked to different objectives,
outcomes and impacts. Freshwater-focused citizen
science projects in the UK that are relevant to Sentinel
exist across all of these project types, with some
incorporating multiple project types and impact
pathways. Table 2.4 illustrates how all of these impact
pathways and project types feature across the
different groups taking part in the citizen science
project FreshWater Watch.

17
Box 2.2: Pathways to environmental impact through citizen science, as described by van Noordwijk ei al (2020) [4]

18
Figure 2.3: The four types of citizen science participation in data collection [4].

19
Table 2.4: Impact pathways in the FreshWater Watch citizen science initiative
Alongside challenges in public participation,
assessing impact from citizen science projects is also
hindered by the fact that many impacts occur after
data collection has taken place, yet project funding
rarely extends beyond this point. Therefore, ensuring
that dedicated funding streams are allocated to
support impact evaluation in the longer term is crucial
to maximise impact. Furthermore, there exist several
challenges to measuring the impact of citizen science
programmes. Understanding the contribution of
citizen science to environmental change and
measuring and evaluating impact requires targeted
tools and shared evaluation frameworks. To date,
such tools have been lacking, although significant
progress is being made - for example within
Earthwatch’s MICS (Measuring Impact of Citizen
Science) project, which is developing metrics and
instruments to evaluate citizen-science impacts on
the environment and society [29].
Over the past decade there has been significant
growth in both the use and the development of citizen
science methods and approaches. Today there is an
active international community of practitioners,
agreed standards and principles for citizen science,
and strategies to ensure data quality are in place.
Furthermore, a growing body of publications clearly
shows that diverse types of citizen‐science projects

20
can produce data with accuracy equal to, or
surpassing that of, professionals [22]. Increasingly,
citizen science data is being used in tandem with data
from professional environmental monitoring,
automated and semi-automated sensors (in some
cases designed and operated by citizen scientists),
and remote sensing data [30]. Earthwatch has been at
the forefront of this growth, working with
communities, businesses and academia to drive
forward the science of citizen science.
However, to ensure that citizen science research
results in valuable data, certain conditions must first
be met and particular areas of concern accounted for.
The first condition concerns data quality. Data quality
in citizen science has become a crowded and
contested landscape in recent years, as various
citizen science projects and their stakeholders often
claim and seek different levels of data quality. While
many citizen science projects do produce high-quality
data, others are limited by poor data practices, lack of
accuracy, absence of standardised sampling
protocols, poor spatial or temporal representation,
and insufficient sample size [31]. However, these
issues are not unique to citizen science, and are also
observed across the wider scientific community [32].
Other challenges are more specific to citizen science.
Among them are data collection protocols not being
followed by participants - typically occurring where the
protocol is complex and the citizen scientist more
interested in the experience of participating than in
how the data will be used. Similarly, problems can
arise where there is a mismatch in data quality
expectations between the producers and consumers
of citizen science data. Striking a balance between
citizen capabilities and data quality needs of
researchers is key to success.
There can also be inherent biases to citizen science
data. For example, often participants are often
allowed to choose sampling locations for themselves,
leading to oversampling of popular areas and data
gaps in other areas. Similarly, sampling may not occur
equally over time as volunteer participation may be
weather dependent. Inter-observer reliability amongst
untrained participants is also a commonly cited issue,
but such problems occur with equal frequency among
trained scientists and are by no means unique to
citizen science [22]. In some cases, combining
datasets with different relative biases can actually
increase the availability of data, as long as the biases
are acknowledged and accounted for. For example, in
England, citizen scientist biases towards small
waterbodies mean that, when combined with EA water
quality monitoring data, FreshWater Watch data
provides complementary information about
previously unrecorded waterbodies [33].
Whether science is citizen or otherwise led, assessing
data quality is a prerequisite to data use. Key
questions that citizen science projects need to
address include: are task procedures and data entry
systematic? Are volunteer equipment and collection
effort standardized and calibrated? Does the project
capture relevant metadata? Does the project assess
data quality by appropriate comparison with
professionals, data replicates, and data controls? Are
good data management practices used? If these
questions are well addressed when developing citizen
science projects, there is no reason that citizen
generated data should not be of high quality.
To deliver on the goals of the 25 YEP, an
understanding of the current state of the environment
is required, and this understanding needs to be
routinely refreshed. In its 6th Annual Report, the
Natural Capital Committee (NCC) called for the
government to commission a comprehensive national
environmental census on the presence and status of
natural capital in England, in order to inform the 25
YEP indicator framework and provide a baseline from
which to measure progress against the Plan’s 10
goals [34]. The 25 YEP includes an assessment
framework that has been developed around 16
headlines that are subdivided into 66 indicators [5].
Earthwatch has assessed five of these headlines (3, 7,
8, 10 and 11) and found that they include at least 15
indicators that are well suited to citizen science
involvement in data collection (see Table 2.5). Further
examination of the remaining headlines will
undoubtedly highlight more indicators of relevance to
citizen science.

21
Table 2.5: Links between the 25 YEP headlines, framework indicators, and citizen science monitoring.
Undertaking a national environmental census will
require significant manpower and resources,
significantly more than is currently spent on
monitoring of certain natural capital indicators (e.g.
soil monitoring [42]). Citizen science offers a
promising and inexpensive opportunity to collect
robust baseline data at a national scale. Indeed, the
government has already indicated an interest in
involving citizens in such a census [43]. However, the
NCC stress that citizen science should not be the only
component of such a census, and that baseline
surveys must also incorporate the extensive
monitoring that is already carried out by government
agencies, local authorities, research centres,
academia, and NGOs [34].
A further benefit of using a citizen science approach
to obtain baseline data on natural capital is that in
addition to providing data points, the census can
incorporate guidance and encouragement for
participants to take positive action for increasing
natural capital in their locality, feeding into Headline
11 (“people enjoying and caring about the natural
environment”). Such an approach ensures that
participants are part of the change required and
actively contributing to meeting the government
targets outlined in the 25 YEP.
Within the 25 YEP outcome indicator framework, there
are seven indicators relating directly to fresh water.
Many of these draw on existing EA monitoring of
pollution loads (indicator B1), pollution incidents
(indicator B2), and the state of the water environment
(indicator B3). The NCC states that citizen science
alone is insufficient for gathering the baseline data
needed in any environment, including fresh water.

22
There is therefore a need for mechanisms that
integrate citizen science with other monitoring
techniques. The EA’s Sentinel tool aims to work with
diverse partners to share data and bring together
national surveillance monitoring data with other
complementary sources of evidence in order to build
up a more comprehensive national picture of our
water environment. Sentinel is therefore perfectly
placed to address the concerns of the NCC about
making best use of existing data streams, and to start
to deliver some of the baseline data required. There
are already a wide range of citizen science projects
that could contribute data to Sentinel (see Chapter 3),
and there is clear potential for data from citizen
science projects to supplement EA monitoring data,
both within existing operations and in the broader
context of the 25 YEP. Detailed examples are
discussed in Section 2 of this report.
The initial development of Sentinel focuses on three
types of data: water quality, ecology, and hydrology.
Within this remit lie a broad range of parameters that
comprise the routine monitoring carried out by the EA
(Box 2.6). While some of these (e.g. priority
substances) are not currently monitored by citizen
scientists, the vast majority of them are monitored to
some degree (see Chapter 3). Some, such as nitrates
and phosphates, are easily measured by non-expert
volunteers. Others, such as invertebrates, require a
more in-depth volunteer expertise (e.g. invertebrate
taxonomy). Citizen science data can therefore be used
to supplement and complement EA monitoring,
building on existing knowledge and enhancing our
understanding of the freshwater environment.
Notwithstanding the direct relevance of citizen
science data to the valuation of natural capital assets,
the integration of citizen science data into
Sentinel offers opportunities within the EA’s existing
operational work. In October 2019, a working group
within the Greater Manchester, Merseyside and
Cheshire (GMMC) Area office conducted an
exploratory study to quantify the potential utility of
citizen science data, with a specific focus on data
from two citizen science projects: FreshWater Watch
and the Angler’s Riverfly Monitoring Initiative (ARMI)
(Box 2.7). The working group identified several
specific opportunities for the use of citizen science
data, including increasing the spatial and temporal
granularity of EA monitoring networks, increasing the
certainty of predictive models, and informing decision
making. They also acknowledged the additional
benefits of citizen science, in particular for building
relationships between the public and the EA.
Box 2.6 - Parameters routinely monitored by the EA
Physico-chemical water quality
(e.g. ammonia, dissolved oxygen, pH, nitrate, phosphate, temperature)
Water chemistry
(e.g. priority chemical substances, specific pollutants)
Biological quality elements
(as defined within the Water Framework Directive)
Ecology
(e.g. fish, macrophytes, invertebrates, phytobenthos)
Hydrology
(e.g. hydrological regime, hydromorphology)

23
Box 2.7: An EA Area Team perspective: The potential for citizen science data use in the GMMC area office [44]
The GMMC working group identified the following opportunities for using citizen science data within their
current work:
• Filling gaps in data, both spatially and temporally
Analysis of GMMC Water Framework Directive cycle 2 data revealed that the number of data points per
waterbody is variable across parameters, and there is scope for data gaps to be filled using citizen science.
The Land and Water team, for example, identified opportunities for FreshWater Watch to collect water
quality data to help support annual schemes of action such as locating input problems from septic tanks.
• Increasing certainty and ‘ground-truthing’ of predictive models
GMMC data shows that the availability of monitoring data impacts the confidence with which the quality of
any given waterbody can be stated. This has an impact on decision-making, since basing large financial
decisions for conservation and restoration requires high certainty. To make such decisions, data is
imperative. With the reduction in monitoring programmes, fewer sites translates to a poorer spatial
resolution. In such cases, predictive modelling is often used to boost certainty and reduce monitoring
costs. The GMMC monitoring team suggested that citizen science data could increase the accuracy and
spatial granularity of these predictions by providing on-the-ground data to support or refine the model
outputs, allowing staff to be more quickly directed to problem locations.
• Informing decision-making
Often, decision-making (for example in response to a decline in water quality at a monitoring site) requires
the collection of additional information before an appropriate response can be determined. Currently, this
process is managed via time-consuming targeted investigations by EA teams via desktop study and
collection of additional data. GMMC teams acknowledged that making citizen science data available to
support specific management decisions would allow them to make better use of resources and to better
target their investigations. For example, the Land and Water team suggested that both FreshWater Watch
and ARMI could be used to detect and respond to persistent agricultural pollution that current seasonal
monitoring cannot capture.

24
Volunteer data collection for freshwater monitoring
has a long history in the UK; citizen ‘Riverkeepers’ have
been assigned by anglers to safeguard rivers in
England since the Middle Ages [45]. In the 20th
century, citizen science across all domains, including
fresh water, boomed as a result of a variety of social
and technological advances including rapid growth of
higher education, increased leisure time, widespread
internet connectivity, and the advent of smartphones
[46]. This increased popularity led to the development
of a number of freshwater-focused citizen science
projects in the UK, such as FreshWater Watch and the
Angler’s Riverfly Monitoring Initiative (ARMI). Many of
these initiatives were developed by professional
scientists hoping to involve the public in their
research.
Alongside this researcher-led development of citizen
science, a large number of community-driven
freshwater citizen science projects have emerged in
the UK in recent years. This has been aided by the
introduction of the Catchment Based Approach to
managing the water environment in 2013 [47]. This
approach promotes local collaboration and
transparent decision-making, and has led to the
establishment of formal, multi-stakeholder Catchment
Partnerships. Many of these Catchment Partnerships
have sought to collect their own data to support their
involvement in local Catchment Planning processes.
As a result, several have adopted citizen science
approaches both to collect data and to foster local
collaborations with the wider public. There is
consequently a large ‘bottom-up’ influence on the UK
freshwater citizen science landscape.
• The use of citizen science is not new, but has been gaining traction and respectability within the scientific
and environmental management communities.
• Development of freshwater citizen science projects in the UK is characterised by a mix of a) initiatives led
by scientific organisations hoping to involve the public in their research, and b) initiatives led by ‘bottom-
up’ community-focused organisations. The Catchment Based Approach (CaBA) is a key driver of the latter.
• Citizen science projects in the UK can be split into national and local initiatives. The true number,
particularly of local initiatives, is probably vastly underestimated due to lack of online visibility. However,
these local initiatives should not be discounted because they provide opportunities for creating positive
environmental change and for creating innovative approaches to citizen science monitoring.
• This mix of initiatives has resulted in a disjointed citizen science landscape, with a wide variety of different
projects designed with individual goals in mind. There is therefore a clear need for sector-wide
collaboration.
• The proposed ‘Catchment Monitoring Cooperative’, led by the Rivers Trusts, aims to foster collaboration
and cohesion between the disparate citizen science projects that fall under the CaBA ‘umbrella’. If funded,
this initiative has the potential to aid the development of Sentinel by creating pathways for local initiatives
to contribute data. However, care should be taken both by Sentinel and by the Catchment Monitoring
Cooperative not to exclude citizen science initiatives that do not fall within the CaBA remit.

25
Perhaps because of the size of this ‘bottom-up’ sector,
UK freshwater citizen science is currently fairly
disjointed. In the US, a methods manual on volunteer
stream monitoring was published by the United States
Environmental Protection Agency (USEPA) in 1997,
and is still used to guide volunteer monitoring
methods including watershed and habitat
assessments, macroinvertebrate sampling, and water
quality monitoring [48]. Closer to home, UK volunteer
biodiversity records are to some extent co-ordinated
by the Association of Local Environmental Records
Centres (ALERC) [49]. There are currently some
initiatives that aim to bring together different
freshwater-focused citizen science projects in the UK
(see page 28, but, to date, national coordination of
projects remains limited.
Another consequence of the combination of
researcher-led and community-driven citizen science
projects has been the development of a variety of
different project types, each serving its own purpose.
ARMI, for example, was developed as an ‘interest
group’ project to target anglers. FreshWater Watch
was initially developed as a ‘captive learning’ project
but the method is now applied across all project types
[4]. Many of the local, community-driven projects were
set up as ‘place-based community action’ projects.
This means that UK freshwater citizen science already
attracts a wide variety of different audiences with
different motivations. With strong mechanisms for
public participation in collaborative management of
water resources already in place via the Catchment
Based Approach, the potential for widespread public
engagement and positive environmental action
through citizen science is huge. The current
fragmented landscape under-utilises this potential. It
is therefore important to understand where, how, and
by whom citizen science data is currently being
collected, and to ensure that any attempts at
integrating this data via Sentinel are inclusive of the
diversity of freshwater citizen science currently
present in the UK.
There are many UK citizen science schemes that
monitor freshwater environments. These can be
grouped into a) national/global initiatives, and b) local
initiatives. Many citizen science projects run over a set
timescale, either to fulfil a specific research goal or
because of a loss of funding or volunteers. One
example is the OPAL project, which was run by
Imperial College London to collect ecosystem health
data for lakes and ponds, and to test the reliability of
citizen science data collection. Between 2007 and
2019 it engaged more than 1 million people in citizen
science activity, but the project has now been
terminated due to lack of funds [50].
Our literature review of scientific and grey literature
identified close to 500 freshwater-related citizen
science projects operating globally, however, it is likely
that the true number is much higher than this. It is well
known that localised, place-based citizen science
initiatives are less likely to be represented in scientific
literature than larger projects [51]. Other reasons that
projects may have been excluded from our review
include lack of online presence, absence of the term
‘citizen science’ or related terms, and lack of clarity
about whether volunteer activity includes data
collection or research.
Within the theme of water, there are seven indicators
outlined in the 25 YEP indicator framework. Several of
these draw on existing EA monitoring data, including
serious pollution incidents to water (B2), state of the
water environment (B3), and water bodies achieving
sustainable abstraction criteria (B5). Much of the EA
data that is relevant to these indicators can be
supplemented with data from existing national citizen
science initiatives. Indeed, a limited number of citizen
science projects are already connected with the EA
through the Riverfly Partnership. The Riverfly
Partnership runs its own citizen science project
(ARMI) as well as acting as an umbrella for relevant
national citizen science projects through its ‘Riverfly
Plus’ initiative. These initiatives, described in Figure
3.1, can be directly related to EA operations and have
been proven to collect robust, reliable data, with
appropriate quality control and quality assurance
measures in place. Many of them operate a ‘hub’
approach, whereby local project coordinators run
‘hubs’ or ‘groups’ of volunteers that all work together
within a single river catchment system or local area.
This allows them to gain the benefits associated with
localised action while still retaining national
coordination. Two of these national initiatives -
FreshWater Watch and ARMI - were used in the active
pilots presented in Section 2 of this report.

26
In addition to Riverfly Plus, a number of existing
national citizen science initiatives collect information
that is complementary to EA monitoring but cannot be
directly related to it and are not currently being widely
used by the EA (also listed in Figure 3.1). For example,
the UK Centre for Ecology & Hydrology (UKCEH)
‘Bloomin’ Algae’ project encourages volunteers to
record the locations of algal blooms that may occur as
a result of poor physico-chemical water quality.
Figure 3.1: National citizen science initiatives
The full extent of relevant locally-driven citizen
science initiatives is more difficult to assess due to
their limited presence in national and international
literature and their reduced online presence. Many are
run either through Catchment Partnership hosts (e.g.
the Rivers Trusts) or community volunteer groups (e.g.
Groundworks). However, the importance of these
local projects should not be underestimated.
Effective influencing using citizen science is
dependent on audiences relating to the material being
communicated, and stories which are of personal
relevance to the public, particularly where they invoke
a connection to a local issue, are more likely to gain
traction [52]. There are a large number of impact
pathways associated with place-based community
action projects for this reason (see Chapter 2).
Examples of relevant local initiatives are shown in Box
3.2.

27
There is also huge potential for new innovations in
citizen science methods to be developed within local
initiatives. Although some local projects form hubs
linked to national initiatives such as FreshWater
Watch or ARMI, others develop and use their own
methods. These include Westcountry CSI [53], which
has developed its own water quality monitoring
method based on FreshWater Watch, or the Don
Catchment Rivers Trust, which uses its own
invertebrate sampling method [54]. Some projects are
developed by one organisation and are then adapted
and upscaled by others, such as Outfall Safari, which
was originally developed by ZSL for use by volunteers
in the River Crane [55].
Box 3.2: Examples of local initiatives
Zoological Society of London (ZSL)
ZSL operate a variety of both short-term and ongoing citizen science projects in the London area:
● Smelt conservation project: Gathering information on endangered smelt populations
● Conserving the European eel: Collecting data on elver migrations
● Tracking the spread of Invasive Species in the Thames: using eDNA and surveys to track key invasive
species
Rivers Trusts and Catchment Partnerships
Not all Catchment Partnerships offer citizen science opportunities, but many offer local citizen science
projects or activities via their catchment hosts, such as the Rivers Trusts. Some connect with other
projects, such as ARMI, MoRPH and FreshWater Watch to offer citizen science opportunities. Others use
their own methods for monitoring water quality or collecting invertebrate data using citizen scientists.
There are a few projects which do species specific surveys, such as water voles, invasive species or
white claw crayfish.
Some examples of local Rivers Trust citizen science projects are:
● Westcountry Rivers Trust: Westcountry CSI is a water quality monitoring project. The method is based on
FreshWater Watch but has been adapted to meet specific local needs.
● Yorkshire Dales Rivers Trust: iWharfe is a project where volunteers collect water samples for lab analysis.
● Tyne Rivers Trust: MyTyne is an app for people to record wildlife sightings.
● Mersey Rivers Trust: River guardians include water quality sampling, kick sampling, water vole surveys
and outfall safaris.
Groundwork
Groundwork is a federation of charities mobilising practical community action on poverty and the
environment in the UK. They run a number of citizen science projects, including:
● Love My River: A citizen science project which monitors water quality using walkover surveys.
● Healthy Rivers: Delivers accredited training for volunteers to monitor and restore rivers.
● Junior River Wardens: Involves children in water quality monitoring.
● Rediscovering the River Colne: Water quality monitoring associated with restoration of the River Colne.

28
With the exception of the Riverfly Plus umbrella,
connection between citizen science projects in the UK
is variable. There are, for example, 63 Rivers Trusts
across the UK, many of which run their own projects
with citizen science elements. This has led to a
number of challenges faced by organisations wishing
to collect data using citizen science, most of which are
associated with choosing a citizen science
methodology, making best use of the data, and
reduced funding because of competition from a large
number of similar projects [11].
In response to these challenges, the Catchment Based
Approach (CaBA - an initiative that supports and
promotes Catchment Partnership working) has
recently developed a proposal for a national
‘Catchment Monitoring Cooperative’ [13]. This £5m
proposal aims to bring together, coordinate, and grow
existing citizen science projects in the UK for the
purposes of Catchment Partnership working. It
focuses on increasing cohesion between the local
initiatives run by individual Catchment Partnerships,
and collecting data that is suitable for use in
catchment management planning. If funded, the
Catchment Monitoring Cooperative could therefore
support the coordination of a national network of local
initiatives needed to incorporate citizen science within
Sentinel, and could fit within the roadmap presented in
Chapter 7. In addition to this bottom-up cooperative
focused on the needs of local Catchment
Partnerships, top-down national coordination is also
needed, to ensure that the data produced by projects
can be integrated in a way that contributes to a
national understanding of the state of the water
environment, and that projects which fall outside of
Catchment Partnership working (e.g. Groundwork) are
also included.

29

30
• Mass participation censuses are a type of citizen science project that is particularly well-suited to the
aims of Sentinel and the Natural Capital and Ecosystems Assessment (NCEA) because they typically
produce lots of data over large geographic scales. They are characterised by having a simple call to
action that appeals to a broad audience, and they are typically associated with ‘evidence for policy’,
‘behaviour change’, and ‘social network influencing’ impact pathways.
• The Thames WaterBlitz is an example of a mass participation census that monitors freshwater natural
capital assets. It can contribute valuable data to the following 25 YEP indicators: B2 - Serious pollution
incidents to water; B3 - State of the water environment; G4 - Engagement with the natural environment;
G5 - People engaged in social action for the environment; and G6 - Environmental attitudes and
behaviours.
• In September 2020, 966 FreshWater Watch and 59 Angler’s Riverfly Monitoring Initiative (ARMI)
measurements were made by citizen scientists during the tenth Thames WaterBlitz. They provided
spatially resolute data across the Thames River Basin District, providing a ‘broad brush’ snapshot of
the state of the water environment across the entire region.
• Volunteers also provided additional qualitative information about ecosystem services which they
personally derive from the freshwater environment. Some highlighted issues of local concern related
to the state of the environment.
• The data and information collected is spatially resolute enough to be aggregated at different spatial
scales (e.g. 1km2 grid squares, waterbodies, operational catchments). This means it can be analysed
in multiple different ways to answer different questions, and can be used to address problems at a
variety of spatial scales.
• There are numerous opportunities associated with integrating data from mass participation events
like the Thames WaterBlitz into Sentinel, including:
○ Improving our understanding of the state of the water environment.
○ Monitoring the environment as a system.
○ Assessing natural capital.
○ Increasing public engagement with the environment.

31
Chapter 2 outlines six pathways through which
citizen science can achieve positive environmental
impact. Collectively, changes to institutional
practices, collective practices, and individual
practices that arise from all of these impact
pathways have the potential to drive significant
progress towards many of the goals of the UK
Government's 25 Year Environment Plan (25 YEP).
The Environment Agency’s (EA) Sentinel tool aims to
bring different streams of data and information
together to understand long-term pressures and
trends associated with freshwater natural capital,
and it will be used primarily to inform strategic
decision making at the institutional level. In this
context, the ‘evidence for policy’ impact pathway is
particularly relevant.
There are several different types of citizen science
projects that are particularly well-suited to this
impact pathway, including place-based community
action, interest group research, and mass
participation censuses. Since a key aim of Sentinel is
to identify trends and pressures on a national scale,
the latter of these three approaches is perhaps the
most appropriate for producing data and engaging
citizen scientists at the required volume and with the
correct geographic scope. Place-based community
action and interest group research projects are
usually (but not always) more localised and place-
based. This can bring additional benefits, particularly
with relation to creating on-the-ground environmental
action, and these are explored further in Chapter 5. In
this chapter, we present the results from a mass
participation census (The Thames WaterBlitz), and
discuss how the census approach might be used
within Sentinel based on our findings.
Mass participation censuses are developed to appeal
to a diverse audience and to have the lowest barriers
to participation of any citizen science approach.
Tasks are simple, widely advertised, have clear
societal relevance, and require little commitment in
terms of time from participants [4]. Most are
conducted over a short time frame such as a
weekend or period of several days, and often they are
advertised as a fun activity, although many
individuals are motivated to participate through a
desire to help science and the environment. Mass
participation projects can be conducted over large
geographic areas and have the potential to collect a
significant amount of data in a short time frame,
something that would be challenging or impossible
with traditional research approaches. Importantly,
mass participation censuses can increase public
awareness of specific issues at scale. This means
that they are particularly associated with three
impact pathways: evidence for policy, behaviour
change, and social network championing. However,
ensuring sufficient uptake and impact of mass
participation involves significant public outreach and
communication, and must be accompanied by a
strong message and clear instructions for
participants.
.
The ‘Thames WaterBlitz’ citizen science initiative aims to collect as much data as possible within a small temporal
window and across a large geographic area (the Thames River Basin District (RBD); total area 16,200 km2). It is a
bi-annual event that has been running since 2014 using the FreshWater Watch method to produce a highly resolute
‘snapshot’ of the state of the water environment in time, following the concept of an ‘environmental census’. In
order to encourage participation and foster scientific curiosity, volunteers self-select sites that are of interest to
them and report the geolocation of their measurements. A WaterBlitz is a positive engagement tool that meets all
the criteria of a mass participation census. It can contribute valuable data to the following 25 YEP indicators: B2 -
Serious pollution incidents to water, B3 - State of the water environment, G4 - Engagement with the natural
environment, G5 - People engaged in social action for the environment, and G6 - Environmental attitudes and
behaviours.
The tenth Thames WaterBlitz was held on 25th - 28th September 2020, with a specific focus on exploring how the
WaterBlitz approach might be used to collect data en masse and at a relatively large geographic scale for Sentinel.

32
Unlike previous events, significant effort was placed on recruiting volunteers strategically across the entire region,
for example by using geographically-targeted social media advertising. In addition to water quality monitoring, the
September 2020 WaterBlitz provided an opportunity to engage volunteers from other national citizen science
initiatives to collect additional data. This was the first coordinated effort to collect different types of data on rivers
using different volunteers within a single, centrally coordinated citizen science event. In addition to the 966
FreshWater Watch water quality measurements that were collected during the WaterBlitz, 59 ARMI measurements
of biological quality (macroinvertebrates) were made. This allowed direct comparison of physico-chemical and
biological data from citizen scientists. Additional information about volunteer demographics and motivations was
obtained via participant questionnaires both during the event registration process and post-event.
Nitrates were high across the Thames River Basin District (RBD), with 79% of all measurements recording NO3-N
concentrations > 2.0 mg/L. For phosphates, 27.4% of all measurements indicated high concentrations (PO4-P > 0.1
mg/L). By mapping the median result per 1km2 grid square across the entire Thames RBD, it is possible to identify
spatial variations in water quality. These spatial variations are also aggregated to give a snapshot of water quality
by waterbody, operational catchment, or river basin district, allowing cross-waterbody comparisons to be made.
Example results for phosphates are shown in Fig. 4.1. Full results for nitrates and invertebrates are available on
request, or can be accessed via the FreshWater Watch and ARMI websites.

33
Fig 4.1: Median phosphate concentrations measured during the September 2020 Thames WaterBlitz, aggregated
by 1km2 grid square (top), waterbody (middle), and operational catchment (bottom).
Using the 1km2 grid square approach, it is possible to co-locate FreshWater Watch and ARMI measurements made
during the WaterBlitz. Relationships between macroinvertebrate species abundance and community composition
are well established [56], meaning co-located samples can be used to see whether inter-relationships between

34
different environmental indicators can be detected by citizen scientists. In the 2020 WaterBlitz, benthic
macroinvertebrates were found in higher numbers where phosphates were < 0.1 mg/L. Sites with lower phosphates
also had higher species diversity than sites where phosphates were high. Mayflies, stoneflies, cased caddis, and
blue-winged olives were particularly associated with lower phosphates. However, when these observations were
reduced to a single ARMI score, there was no significant correlation between phosphate concentrations and ARMI
score (Spearman’s Rho = -0.22, p = 0.34).
Comparing the results of the 2020 WaterBlitz to previous WaterBlitz events makes it possible to determine how the
state of the water environment has changed across the Thames RBD through time. This was achieved by a)
identifying overall water quality trends for the entire RBD, and b) where grid squares have been surveyed repeatedly,
identifying specific grid squares that have deviated from the expected trend, either occasionally or persistently (Fig.
4.2). For the purposes of this analysis we calculated the median (+/- 1.5 times the interquartile range to allow for
expected variation) to give a crude overall water quality trend for the entire RBD, but this could be better achieved
via spatial modelling using data from the national surveillance monitoring network. NB: Currently this analysis can
only be completed with FreshWater Watch data because ARMI volunteers did not take part in the WaterBlitz before
September 2020.
Figure 4.2: Example phosphate trends from two 1km2 grid squares compared to overall trend for the entire
Thames RBD.

35
We also examined the results of the WaterBlitz on a more local level. Existing EA Water Framework Directive
assessments are already used to assess the health of individual waterbodies, but, because EA monitoring is
managed per waterbody, there are often only a few monitoring locations per waterbody. This means that variations
in water quality within individual waterbodies are missed. Because of its spatial resolution, WaterBlitz data can be
used to examine intra-waterbody patterns. An example is shown in Fig. 4.3. Further information about localised
water quality trends can be extracted from notes left by citizen scientists in association with their measurement.
All WaterBlitz participants are asked why they are interested in the WaterBlitz during the event registration process,
and volunteers are also given the opportunity to attach freeform notes to their data submission. In 2020, 61
volunteers indicated intent to investigate specific local concerns, including illegal waste dumping from houseboats,
sewage pollution, glyphosate spraying on allotments, and the impacts of urban development on chalk streams. Ten
volunteers reported specific observations in their notes, including presence of invasive species, cattle accessing
the river, and the apparent nutrient filtration effects of a disused watercress bed. Although these comments are not
included in the numeric data, they represent local knowledge that could be used to help explain observed patterns
in the data.
Figure 4.3: Spatial variation in phosphate concentrations measured in the Lower Roding (Loughton to Thames)
waterbody during the September 2020 Thames WaterBlitz.

36
A total of 927 volunteers took part in the September 2020 WaterBlitz. The majority of these (879) took FreshWater
Watch measurements, with the remainder either conducting ARMI monitoring or taking part in both projects
simultaneously. This represents an approximate total of 350 volunteer-hours spent engaged in social action for the
environment (25 YEP outcome indicator G5). Like the environmental data, participation data can also be aggregated
to give a value for the number of participants per km2 grid square, waterbody, operational catchment, or river basin
district.
Based on data collected during the post-WaterBlitz survey, 31% of respondents had no prior involvement in
environmental volunteering. A variety of age ranges were represented, and participant locales varied from rural to
urban (Fig 4.4). This means that the 2020 WaterBlitz actively contributed to Headline 11 of the 25 YEP to increase
a) engagement with, and b) social action to improve, the natural environment from all sectors of society.
Figure 4.4: Breakdown of WaterBlitz participant backgrounds
When asked about their motivations for taking part in the WaterBlitz prior to the event, a number of respondents
mentioned specific ecosystem services that they were hoping to experience or protect as a result of the event. Of
600 respondents, 14% were interested in monitoring water resources linked to recreational activities, 6% were
interested in the educational experience provided by the activity, and 1% acknowledged the health and wellbeing
benefits they personally associate with clean and healthy water.

37
In the post-WaterBlitz survey, respondents were asked questions that were specifically designed to assess the
benefits gained from engaging with nature during the WaterBlitz. Firstly, they were asked to consider their
WaterBlitz experience and then to rate the extent to which they agreed with the statement, ‘spending time in nature
can improve health and wellbeing’. The average rating was 88/100. They were also asked to rate the extent to which
they were likely to spend more time visiting local waterbodies in the future, and the average rating here was 70/100.
Finally, we asked respondents to share the most interesting thing they learned during the WaterBlitz. Responses to
this question were linked to improved understanding of science, connections with nature in the volunteer’s local
area, and linking personal experiences to the wider regional/national context (Figure 4.5). Together, these
responses suggest that participation in the WaterBlitz improved volunteer conceptualisation of the intrinsic value
of nature and the ecosystem services it provides.
Figure 4.5: Participant feedback on their experience of the WaterBlitz.

38
The state of the water environment is currently
monitored by the EA for various purposes, including
the requirements of the EU Water Framework
Directive (WFD). Such assessments are made at the
individual waterbody level. Currently there is
monitoring of physico-chemical water quality in every
catchment on a monthly basis, and benthic
macroinvertebrates bi-annually. The resulting data is
aggregated to indicate the percentage of
waterbodies in the UK that are meeting ‘good
ecological status’. The state of the water
environment is also one of the 25 YEP’s headline
indicators (B3). While the information collected by
the EA provides a good understanding of the overall
quality of each individual waterbody and how it
changes throughout the year, there are very few
occasions when EA data is collected from different
waterbodies within a limited temporal window to
allow comparison of conditions in similar
meteorological conditions. This makes spatial
comparisons between waterbodies difficult using EA
data alone. There is therefore a need for initiatives
that ‘join up’ data on the state of the water
environment across regional and national scales,
using different data streams. This should be partially
achieved via the new national surveillance monitoring
network. This network, however, is limited by the
number of monitoring sites it includes. It is designed
to provide a broad national understanding of long-
term trends and environmental changes and
therefore covers only a few hundred strategic sites
across the country.
Unlike EA monitoring data, WaterBlitz data is
collected as part of an ‘environmental census’ over a
very short time span. While EA data shows seasonal
and climatic variations that occur through the year at
specific sites, WaterBlitz data can be used to directly
compare water quality across large areas at single
touch-points in time. Collecting such spatially
resolute data from multiple sites simultaneously, at
regional or national spatial scales, would be difficult
for the EA to achieve alone. As such, citizen science
data collected in this fashion can aid comparisons of
the state of the water environment across different
geographic locations, with the citizen science data
acting as an anchor-point in time. However, the
limitations of citizen science data must also be
considered when making such comparisons.
Although citizen science data can be accurate given
appropriate quality assurance and quality control
procedures, it may not be as precise as EA data. The
environmental census approach should therefore be
considered as a mechanism for obtaining a ‘broad-
brush understanding’ of spatial variations in water
quality at a snapshot in time.
Sentinel presents an opportunity to use this spatially-
resolute citizen science data to refine the EA’s
understanding of the state of the water environment.
Existing EA WFD data already provide information
about the ecological status of each waterbody. The
national surveillance monitoring network will extend
this understanding to national and regional scales by
modelling the state of the water environment based
on spatial and temporal links between monitoring
locations. Using the WaterBlitz data, we were able to
compare WaterBlitz results from individual 1km2 grid
squares to an estimate of the ‘expected’ regional
water quality trend derived by averaging all results.
Instead of using this crude calculation, ‘expected’
results for any point on the river network could be
ascertained via the national surveillance monitoring
network, and citizen science results could then be
compared to this model. In theory, the citizen science
data should corroborate the model, increasing
certainty in model outputs. In practice, it is possible
that some citizen science measurements will not
agree with the national surveillance monitoring
network. Indeed, on a waterbody scale, the WaterBlitz
results highlight that citizen science can detect
variations in water quality that are not currently
captured by EA monitoring. This may be because
citizen scientists tend to select small bodies of water
such as low order streams or backwaters [23, 33], or
because the citizen science data has captured a
specific event such as a pollution incident [57]. In
such cases, this otherwise unavailable information
can be used to refine ecosystem assessments at a
local level.
Where localised variations in water quality are
detected via a WaterBlitz, it is important that further
investigations are carried out to verify the data within
the local context. Again, citizen science can provide a

39
valuable source of information to guide these
investigations. During the September 2020
WaterBlitz, valuable local knowledge was shared by
participants both within the event registration form,
and attached to data submissions. This information
shows the potential for citizen science to move
Sentinel beyond a system that simply detects trends
in water quality and towards a system that can start
to assess why changes are occurring. As such, the
WaterBlitz approach could be used to strategically
direct more localised, targeted monitoring. This could
lead to local actions being taken to protect or restore
freshwater assets (see Chapter 5). The results of
these local investigations and actions could also be
fed into Sentinel, anchored by regular ongoing
participation in national WaterBlitzes, meaning that it
will be possible to identify where seemingly localised
patterns of ecosystem change are being repeated
across the country (Fig 4.6).
Fig 4.6: Links between local environmental observations and regional/national trends observed by citizen
scientists.
Moving beyond water quality, combining different
types of citizen science data within Sentinel may help
to further refine the understanding of slower, long-
term trends in the environment. The September 2020
WaterBlitz demonstrates that coordination between
different citizen science projects to collect data of
different types is logistically possible (although many
challenges remain, particularly with regards to data
integration - see Section 3). Using 1km2 grid squares,
we were able to co-locate water quality
measurements taken by FreshWater Watch
volunteers with benthic macroinvertebrate
measurements taken by ARMI volunteers.
Strategically combining multiple spatially-resolute
datasets and analysing them for concurrent changes
over time and space has the potential to provide a
more granular understanding of the state of the water
environment. Water quality, for example, is highly
variable in time, whereas invertebrates take longer to
respond to ecological changes. Where multiple data
types all show changes in space or time, this can
indicate that changes are impacting multiple
elements of the ecosystem. However, the fact that
ARMI scores and FreshWater Watch water quality

40
measurements did not correlate when considered
over the large geographic area surveyed in the
WaterBlitz reflects the fact that there are multiple
drivers of ecosystem response, and the interplay
between these drivers also varies through space and
time. Invertebrates, for example, might respond to
trophic gradients, primary producers, and silt rather
than directly to water quality [56]. It is therefore
important that ecosystem assessments using citizen
science data move beyond simplistic correlations
between variables and instead draw on a systemic
understanding of the environment.
Using citizen science data, there is ample opportunity
to collate data that supports a systems modelling
approach. Chapter 3 describes the huge variety of
water-related citizen science projects that already
exist in the UK. Beyond the freshwater environment,
citizen science projects also collect data that is
relevant to drivers and pressures that act on
freshwater systems. Examples include: LandSense,
an EU citizen observatory which maps land use
change [58], the numerous biodiversity monitoring
schemes run by the Biological Records Centre [49],
and Earthwatchers, which invites citizen scientists to
take part in a range of national environmental
monitoring initiatives such as plastic waste mapping,
household carbon footprinting and tree tracking [59].
These projects collectively monitor a wide variety of
relevant variables, and, like ARMI, many could take
part in a coordinated WaterBlitz or broader
environmental census. Care must be taken to ensure
that data collected during environmental censuses
retains its spatial comparability. This means that
projects taking part will need to be able to operate on
a regional or national scale, covering, with as little
spatial bias as possible, the geographic area targeted
by the census. Consideration will also need to be
given to the timing of events. Some variables, such as
water quality, are highly variable in time and therefore
must be captured within a short time frame. Others,
such as plant biodiversity, may be reliably surveyed
over a longer period of time. Despite these
challenges, bringing multiple citizen science projects
together for regular coordinated environmental
census events would extend current practice towards
a more systems-based approach.
One of the challenges associated with measuring
environmental assets as natural capital in a way that
includes the whole environment system is that
different environmental components operate on a
multitude of different spatial scales. Cross-
landscape links between different components are
often multifaceted and complex, and aren’t always
accounted for in the way we monitor the environment
[60]. Water quality, for example, is usually measured
on a waterbody or catchment scale, because this is
the logical functional spatial unit for this variable. By
contrast, biodiversity is often measured on a habitat
scale, and social systems are measured on district,
town, or county scales. Some citizen science
datasets, including the WaterBlitz data, include
variables that are relevant to multiple different
elements of natural capital, such as water quality
data and social engagement data. A common
approach to resolving this challenge is to aggregate
data in spatially distinct grids [23, 61]. This is difficult
to achieve with small numbers of monitoring
locations spread over large areas, but is commonly
applied to large citizen science datasets with a high
spatial resolution [62]. The WaterBlitz data is
therefore well-suited to this approach.
By aggregating data to grid squares rather than
waterbodies or catchments, it is possible to use the
WaterBlitz data to attribute combined measures of
natural capital to each grid square. Natural capital
from different types of ecosystems within each grid
square can be considered separately by considering
WaterBlitz data from each waterbody type (ponds,
rivers, streams, lakes, and wetlands) individually. This
data can also be supplemented with data from other
sources to paint a fuller picture of natural capital, and
future extensions to the FreshWater Watch method
(such as inclusion of questions about human use of
the surveyed waterbody) could provide information
tailored to national capital assessments. Examples
are shown in Table 4.7. While this approach risks
reducing the information that can be extracted from
each individual datapoint, it does provide a
standardised framework that allows natural capital
measurements from rivers to be compared to
measurements from other ecosystem types. Once
spatial inter-dependencies between grid squares are
considered, this should easily allow full ecosystem
assessments to be made. In such a scenario,

41
environmental census/WaterBlitz data would
increase the number of grid squares for which data
was available, as well as provide a mechanism for
‘pinning’ professional data from different catchments
together where natural capital assessments cross
catchment thresholds. A significant amount of work
remains to be done to develop the statistical and
spatial models that would underpin this approach.
Test cases based on freshwater biodiversity data are
already underway [63]. It is clear that the
environmental census approach can provide
quantities of data that can be aggregated in different
spatial units, from grid squares to catchments, and
that the potential applications of this data stretch
beyond regulatory catchment-scale assessments of
the state of the water environment.
Table 4.7: Example of WaterBlitz data providing information tailored to Natural Capital Assessments.
Beyond the data, the September 2020 WaterBlitz
provided additional social and economic benefits at
a time of uncertainty linked to the COVID pandemic.
Because the WaterBlitz is an event that can be carried
out by individuals or household groups in isolation,
and in their local area, it proved to be a safe way to
encourage large numbers of people to get outdoors
and to engage with their local freshwater
environments. This is important not just because it
provided an opportunity for two-way transfers of data
and knowledge between the EA and the public, but it
also enabled data to be collected at a time when
professional monitoring was under pressure from
additional workplace regulations. Inclusion of citizen
science and other third party data in Sentinel can
provide resilience in environmental monitoring,
allowing continuation of data collection from some
sources at times of crisis. While these benefits are
less immediately measurable and tangible than those
outlined above, opportunities to maximise public
engagement through Sentinel should be maximised.
This is discussed further in Section 3.

42
While mass participation censuses are ideal for
collecting evidence for policy across large spatial
scales (see chapter 4), WaterBlitzes and similar
initiatives are currently relatively rare in the UK. Far
more common are citizen science projects that are
run on a local scale, led by a community leader. In
some cases, these projects are ‘groups’ or ‘hubs’ for
national citizen science initiatives like FreshWater
Watch and ARMI. In other cases, such as the
numerous projects run by Rivers Trusts, projects are
wholly confined to a small geographic area. Some
projects sit within the ‘interest group research’
• Interest group research projects and place-based community action projects encourage citizen
scientists to act as stewards for their local environment and are already powerful vehicles for positive
environmental change, as demonstrated by the case studies presented in this chapter.
• Case study two concerns an interest group monitoring benthic invertebrates on the River Lark as part
of the Angler’s Riverfly Monitoring Initiative (ARMI). Data collected by this group allowed the EA to detect
the impact and onset of drought conditions months earlier than they would otherwise, enabling timely
and appropriate follow-up actions according to drought plans.
• Case study three concerns a place-based community action group monitoring phosphate on the River
Evenlode as part of the FreshWater Watch initiative. By combining their data from upstream of sewage
treatment works with EA data from downstream, citizen scientists were able to identify acute and
chronic pollution events. Through the Evenlode Catchment Partnership, this data was used to support
Thames Water in identifying specific sources of phosphates in the catchment and to appropriately
target phosphate capping measures.
• Integrating data from local, ongoing citizen science monitoring projects into Sentinel provides a
pathway from using citizen science for monitoring and public engagement, towards active public
involvement in the delivery of the goals and targets outlined in the 25 YEP.
• Specific opportunities for Sentinel linked to interest group and place-based community action group
projects include:
○ Linking local knowledge and action to a national monitoring network, thereby improving our
understanding of trends, pressures, and drivers that are active in multiple locations across the
country.
○ Creating a national early warning system for environmental changes linked to climate change and to
other slow or chronic drivers, such as long-term, low-level pollution.
○ Delivering positive environmental change through collaborative working with the concerned public.

43
project type, while others could be classified as
‘place-based community action’ (see Chapter 2).
Together, these projects enable volunteers to
translate their data into on-the-ground action via a
number of different impact pathways. They therefore
have an important role to play in Sentinel because
they provide a pathway from monitoring and public
engagement towards active public involvement in the
delivery of the goals and targets outlined in the 25
YEP.
Interest group research projects target volunteers
who are already skilled or interested in a specific
topic. Many ARMI hubs located near fisheries could
be classed as interest group projects because they
target anglers who are already interested in
invertebrates and river health. Interest group
participants are likely to stay engaged in a project
over a long period of time, to collect repeat
measurements, and to commit more time to learning
complex monitoring protocols. Groups usually
consist of a small number of committed individuals
and the pool of potential participants for any one
group can be limited, but there are opportunities for
individual groups to network with like-minded
volunteers through nationally-joined-up initiatives like
ARMI. Interest group research projects are
particularly suited to creating impact through the
‘data for environmental management’, ‘evidence for
policy’, and ‘political advocacy’ pathways.
Place-based community action projects are generally
focused on tackling an environmental concern in a
specific geographic area, with participation from the
local community. Participants join the project
through an attachment to ‘their’ location or potential
benefits to their personal life. Volunteers may or may
not have a pre-existing interest in scientific research,
but are often prepared to invest significant time in the
project because of the benefits it brings to them
and/or their local community. These projects require
the support of local stakeholders and are often co-
designed or wholly created by the volunteers
themselves. Because place-based issues are often
highly emotive, this project type has an extremely
high potential for creating positive environmental
change through all of the impact pathways outlined
in Chapter 2.
A common theme across interest group research and
place-based community action projects is that
volunteers can remain engaged over long time
periods, often taking repeated measurements. This
means that they can be considered stewards of their
local environment, and many have acquired high
levels of knowledge and expertise on their own
specific patch. As such, these volunteers are not only
accessing ecosystem services provided by the
freshwater environment by taking part in citizen
science, they are also potentially protecting and
improving them. Within the FreshWater Watch
initiative, there are 18 active groups in the UK. All of
these groups provide volunteers with access to
cultural provisioning ecosystem services by allowing
volunteers to spend time in nature and connect with
their local environments (and often each other). Six
groups run citizen science activities in combination
with other volunteer opportunities such as habitat
restoration, and two work in partnership with farmers
to improve land management practices. As such,
FreshWater Watch volunteers are active in
maximising the ecosystem services provided by their
local natural assets.
Recognising that these ongoing citizen science
projects are already powerful vehicles for change at
a local scale, this chapter explores how the data,
knowledge, and action outcomes from interest group
research projects and place-based community action
projects can be integrated into Sentinel. As a basis,
and building on case study one (Chapter 4), we
present and discuss the outcomes from two further
case studies that are led by local community
groups/hubs linked to the national ARMI and
FreshWater Watch initiatives. Case study two
focuses on an interest group research project in
Suffolk that uses ARMI, and case study three
concerns a place-based community action project in
Oxfordshire using FreshWater Watch.
Members of the ARMI citizen science project, coordinated by the Riverfly Partnership, monitor benthic
macroinvertebrate communities at regular intervals at predetermined sites across the UK. The programme was

44
officially launched in 2007, receives annual funding from the EA, which has in the past been levied from anglers’
rod license sales, and is an integral part of the national fisheries improvement effort. ARMI can be conducted at
greater spatial and temporal (typically monthly) frequencies than the EA is able to monitor, as volunteers and local
Catchment Partnerships have different priorities and constraints. Since the establishment of ARMI, other
methodologies have been developed, often in conjunction with EA staff, to monitor the river environment more
holistically using citizen science. These include the Urban Riverfly Method which has been adapted to monitor
urban impacted rivers, and the Extended Riverfly Monitoring Scheme which helps indicate impacts of several
stressors including flow and drought. The data collected using this suite of methodologies can contribute data to
the following 25 YEP outcome indicators: B2 - Serious pollution incidents to water, B3 - State of the water
environment, B5 - Water bodies achieving sustainable abstraction criteria, D4 - Relative abundance and/or distribution
of widespread species, G4 - Engagement with the natural environment, G5 - People engaged in social action for the
environment, and G6 - Environmental attitudes and behaviours.
Riverfly Partnership monitoring programmes throughout England have highlighted how positive collaborative work
between ARMI volunteers and EA officers can extract new information about the environment, using combined
datasets supplemented with the local knowledge held by volunteers [19]. This new information can then be used
to inform local EA responses, leading to positive action for the environment through the ‘data for environmental
management’ impact pathway. These actions also help to strengthen relationships between the EA and volunteers.
One example of this working relationship is from the River Lark in Suffolk between Ian Hawkins, East Anglia Riverfly
Hub Coordinator, and the South East Brampton office Environment Agency.
In 2019, the ARMI volunteers took 55 macroinvertebrate samples from nine locations along the River Lark. Their
data showed a clear declining trend in ARMI score appearing from mid-July (example shown in Fig. 5.1). According
to ARMI standard protocol, every ARMI monitoring site has an associated ‘trigger level’ set by the EA. This trigger
level gives the volunteer an indication of the expected ARMI score for their site. The Riverfly Partnership trains ARMI
volunteers to confirm trigger level breaches via repeat sampling, and then to report them to the EA so that EA
officers can further investigate. The trigger level in the River Lark was repeatedly breached during the summer of
2019. This led to an investigation by the EA officer at the end of July.
Figure 5.1: Decline in ARMI score (blue) compared to site ‘trigger level’ (red) at a site on the River Lark in summer
2019.
During an investigation of this type, the EA area ecology contact attends the river site to conduct a 1-minute benthic
invertebrate kick sample in the river, sort the sample on the river bank, and calculate a score. This scoring uses EA

45
standardised ecological assessment indices, which can be compared directly to the ARMI volunteer results but
also produce more detailed data. Two of these EA scoring systems were applied in the River Lark investigation.
The first calculates impacts of flow on benthic macroinvertebrate species (Lotic Invertebrate Flow Equation: LIFE);
the second shows responses of sediment sensitive macroinvertebrate species (Proportion of Sediment Sensitive
Invertebrates: PSI). The results of the EA investigation suggested that both scores were being negatively impacted
and that the macroinvertebrate communities were negatively responding to the environment (Fig. 5.2). The
investigation concluded that drought was having an impact on the ecology of the river in multiple locations across
the catchment, including in connected waterbodies, such as the River Little Ouse and the River Tiffey.
The EA have six macroinvertebrate monitoring locations on the River Lark, but data is only collected from these
sites twice a year in March and October. By being able to access ARMI information, it was possible to detect the
impact and onset of drought conditions months earlier than the EA would have normally recognised, and to take
appropriate follow-up actions according to drought plans.
LIFE index: historic data from 2009 to incident PSI index: historic data from 2009 to incident
Figure 5.2: Results of EA ecological assessments made during investigation on the River Lark compared to
previously collected monitoring data. Left: LIFE index. Right: PSI index.
The use of ARMI data by the EA is common in response to localised pollution events, but the investigation on the
River Lark represented a more novel use of citizen science data to respond to a climate-related incident that was
occurring on a catchment scale. Current EA data allows waterbodies to be broadly classified according to whether
their hydrological regime is capable of supporting good ecological status or not. However, it can be particularly
difficult to link specific events detected in EA river flow data to ecological responses when the timing of EA
scheduled ecological monitoring does not correspond with the event in question. The use of citizen science data
as presented here therefore has potentially far-reaching implications for the ability of the EA to detect and respond
to impacts of climate change on flow at both the catchment scale and on a national basis. This is particularly
relevant for the 25 YEP outcome indicators that are responsive to climate change, such as indicator B5 - Water
bodies achieving sustainable abstraction criteria.

46
The Wild Oxfordshire Evenlode Catchment Champions (ECC) project was established by a community group in
Oxfordshire who had observed increases in turbidity in their local river and were concerned about acute and chronic
pollution from sewage treatment works (STWs) and combined storm overflows (CSOs). It was funded between
April 2019 and April 2020 by Thames Water as part of their Smarter Water Catchments programme, and has been
extended as part of the Sentinel pilot. Volunteers in the project chose strategic points, with supervision from
Earthwatch Europe, to investigate the impacts of a number of STWs and CSOs on phosphate concentrations in the
river using FreshWater Watch. On five sites on the Littlestock Brook (a tributary of the Evenlode), one volunteer
combined the FreshWater Watch methodology with ARMI monitoring to see how the changes in water quality
reflected in the FreshWater Watch tests affected invertebrate populations and diversity. Volunteers have made 268
water quality measurements at 21 sites across the catchments of the Rivers Evenlode, Glyme, and Dorne in
Oxfordshire. The data they have collected can contribute to the following 25 YEP outcome indicators: B2 - Serious
pollution incidents to water, B3 - State of the water environment, G4 - Engagement with the natural environment, G5 -
People engaged in social action for the environment, and G6 - Environmental attitudes and behaviours. The citizen
science activity has also led the local community to become advocates for changes to sewage treatment
processes, affecting institutional changes on a local level as well as influencing national environmental policy.
At two locations, volunteers identified significant differences in average phosphate concentrations between
upstream and downstream locations (Fig. 5.3). At the first, near the town of Moreton-in-Marsh, the STW in question
was located on a small tributary of the main River Evenlode, and volunteers monitored upstream and downstream
of the STW at monthly intervals using FreshWater Watch. The second location concerned another tributary of the
main channel near the village of Shipton, called Littlestock Brook. Here, volunteers took bi-monthly FreshWater
Watch measurements of two small streams upstream of the STW, and one location on the Littlestock Brook
downstream of the STW. They also used FreshWater Watch to monitor the main River Evenlode, upstream and
downstream of the point at which the Littlestock Brook joins the river. An ARMI monitor additionally sampled
benthic macroinvertebrates bi-monthly upstream and downstream of the STW on Littlestock Brook.
At both Moreton-in-Marsh and Littlestock Brook, the EA monitors phosphates downstream of the STWs on a
monthly basis. At Littlestock Brook, citizen science measurements of phosphate conditions were comparable to
EA measurements. At Moreton-in-Marsh, phosphates detected at the EA monitoring site were frequently above the
maximum detection limit of the FreshWater Watch kits. At both locations, both the EA and citizen scientists
detected variations in phosphate concentrations throughout the year at locations downstream of STWs (Fig. 5.4).
At the downstream Littlestock Brook volunteer site, one incident of elevated phosphates coincided with a recorded
ARMI ‘trigger level’ breach on 24th July 2020, which was followed up with an EA investigation. By contrast,
phosphate concentrations were relatively consistent throughout the year at upstream locations monitored by
citizen scientists.

47
Figure 5.3: Phosphate concentrations measured by citizen scientists and the EA upstream (yellow) and
downstream (purple) of STWs and associated CSOs in the River Evenlode catchment.

48
Figure 5.4: Variations in phosphates through time measured by citizen scientists and the EA, upstream (yellow)
and downstream (purple) of STWs in the River Evenlode catchment.
The majority of waterbodies within the Evenlode operational catchment have been classified as ‘poor’ for
phosphate concentrations based on EA data [64]. Volunteer data from small streams located upstream of STWs in
the Evenlode catchment reveals localised trends that are not reflected in EA data collected further downstream.
When combined with EA observations, these upstream observations can be used to help explain why the EA are
observing poor phosphate conditions in this catchment. Differences both in median phosphate concentrations and
temporal variations in phosphates between citizen science sites upstream and downstream of STWs suggest that
phosphates are much lower in small streams upstream of STWs. This suggests that phosphate discharges from
STWs are having both acute and chronic impacts on phosphate concentrations in the river. While this pollution may
not always fall within the definition of a ‘pollution incident’ requiring enforcement action, it is clearly having an
impact on the state of the water environment.
Based on their findings, ECC volunteers have been working with the Evenlode Catchment Partnership and Thames
Water to explore potential solutions to phosphate discharges from the STWs. This includes identifying
opportunities to introduce phosphate stripping at the Milton STWs on Littlestock Brook, with the potential to extend
to other STWs on the Evenlode. They are using their data alongside EA data to quantitatively demonstrate their
concerns about the impact of phosphate discharges from the works on the stream water quality and biota. Through

49
this activity, they have also identified other groups across the country who have identified similar problems using
citizen science, such as iWharfe, and Windrush Against Sewage Pollution. Together these groups have been
instrumental in supporting the development of new government policies, including securing the commitment of a
joint government-industry group to set a long term goal to eliminate harm from storm overflows linked to STWs. In
this way, citizen science data has led to collaborative, solutions-focussed dialogue between industry, government,
and the concerned public.
While the national surveillance monitoring network is
designed to assess how the state of the water
environment is changing at a national scale, it is, by
necessity, rigid in its design and does not offer
spatially and temporally targeted monitoring. This
means that it may not be able to detect localised
patterns that can help to explain why a particular
change is occurring. Chapter 4 demonstrates that
citizen science can reveal pockets of local knowledge
which can be used to explain spatial patterns in data
collected, either by the national surveillance
monitoring network or during an environmental
census event. The two case studies presented in this
chapter show that place-based, localised citizen
science activities undertaken by committed
volunteers on a long-term basis can provide detailed
insight into the pressures and drivers that are active
on the waterbodies they monitor and steward. The
advantage of linking these local projects to Sentinel
is that it will be possible to identify where certain
pressures and drivers are repeated across the
country, and under what conditions they are most
likely to occur. This potential is well illustrated by the
case studies presented here, which already
contribute data to the national citizen science
monitoring initiatives they belong to. However, there
are many other local projects that do not sit within a
national umbrella initiative, but that are all recording
similar patterns across the country. A large number
of place-based citizen science projects, for example,
monitor water quality in association with STWs and
CSOs. As shown in Chapter 4, these initiatives could
easily be ‘anchored’ into a national network by
ensuring overlap between the local monitoring and
regional or national assessments of the state of the
environment. A WaterBlitz, for example, could be
supplemented with ongoing local monitoring in key
target areas, with WaterBlitz data used as an anchor-
point in time that exists in all projects and allows for
cross-comparison to other local monitoring
initiatives.
The key to linking local knowledge into our national
understanding of the water environment is
partnership working. In both case studies presented
in this chapter, the local knowledge provided by
citizen scientists has only been recognised because
of collaborations between the citizen scientists and
local EA staff (the River Lark), or between citizen
scientists and the local catchment partnership (the
River Evenlode). Partnership working with citizen
scientists is already embedded within EA practice via
the ARMI initiative, which has an agreement with the
EA that each Area EA office should be in contact with
their local ARMI groups. Historically there have been
some challenges associated with this approach,
mainly as a result of the early evolution of the
EA/ARMI partnership working relationship. Since this
relationship between EA staff and ARMI groups is not
included within the job descriptions of the local EA
staff, there have been problems identified by both EA
staff and volunteers. Volunteers have commented on
the lack of engagement by EA staff in a few parts of
England. There has also been some hesitancy from
some EA staff about working with volunteers
because of the quality of the data. These issues have
been diminishing as the roles and responsibilities,
processes, and trust between volunteers and EA staff
continues to develop. This situation will hopefully
steadily improve by continuing the close dialogue
between EA ecology officers and citizen scientists.
By integrating locally-derived citizen science data
with a national EA monitoring network, Sentinel offers
a formal mechanism to continue to resolve some of
the existing issues surrounding partnership working
that the Riverfly Partnership have already started to
address.

50
The Environment Agency’s monitoring programme
has been reduced in coverage, spatially, across
England over the past two decades and, as a
consequence, there has been a reduction in the
amount of data collected across all EA areas. This
means that, while the EA maintains a strong
monitoring network for assessing the state of the
water environment at the waterbody scale, their
ability to identify where there are specific localised
problems has been reduced. Examples of these types
of problems include organic pollution and localised
ecological responses to drought. Ongoing monitoring
by local citizen science groups acting as ‘stewards’ of
their natural environment can supplement EA
monitoring by providing data during months when the
EA do not monitor (such as the ARMI monitors on the
River Lark), or by monitoring smaller streams and
waterbodies that do not fall under existing EA
schemes (such as the FreshWater Watch volunteers
on the Evenlode). These citizen scientists are often
the first to notice variation in the data, or when results
are not as expected. This means that they are well-
placed to identify acute events which require an
immediate regulatory response, and mechanisms for
this are already well-established. In cases of acute
pollution where citizen science monitoring is regular,
data that was recorded before an incident can also be
used to aid investigation.
Moving beyond detection of acute pollution events,
citizen science may also play a valuable role in
detecting long-term changes. Annual drought and
flooding events are predicted to increase in the future
due to climate change, and this could have impacts
on water quality and numerous other variables
commonly measured by citizen scientists [65]. In
some cases, these impacts may be cumulative over
time and could therefore present as chronic or
repeated changes in conditions. This means a more
extensive network for monitoring weather-responsive
conditions is needed, and outcome indicators within
the 25 YEP need to be adaptive to change. The River
Lark case study demonstrates that ARMI volunteers
can provide an early warning of drought conditions,
while the River Evenlode case study shows that
volunteers can detect chronically elevated phosphate
concentrations that persist through time, but are not
considered a pollution ‘event’. In both cases, ongoing
local monitoring and ‘stewardship’ of waterbodies
detected subtle, site-specific changes that were
reflective of a more widespread problem. If scaled up,
a network of environmental ‘stewards’ could create
an early warning system for widespread ecological
changes. This information would enable early
adaptation of outcome indicators in the context of
climate change, as well as facilitating appropriate
local responses to specific actionable problems such
as drought or pollution.
Citizen science provides opportunities to both
increase the engagement of the public with their
environment, as well as provide new information on
the degree of engagement of their fellow citizens. A
key objective of most citizen science programmes is
to increase participant engagement in the
environment and in the environmental issues on
which the project is focused [66]. This is often
supported by a significant learning and capacity
building element [67]. The overall result of
participation in citizen science works along two
fronts: increasing the engagement of people with
their environment and, secondly, increasing their
knowledge of the environment and related literacy
[68] and skills [69]. This increased engagement has
the potential to influence personal behaviour.
Volunteers on the River Evenlode, for example,
became actively involved in a number of
environmental activities linked to their citizen science
activity through their local catchment partnership.
The most impactful of these was their advocacy
work, which triggered plans for phosphate stripping
on local sewage treatment works and contributed to
a positive national campaign to reduce sewage
pollution in rivers. However, volunteers were also
engaged in other activities within the catchment,
such as riparian tree planting, farmer experiments to
capture soil runoff, and engaging local school and
scout groups with river-related activities.
While it should be noted that increased
environmental knowledge is not always associated
with pro‐environmental attitudes [70], studies
indicate that the participatory nature of citizen
science favours community participation in

51
management [71] and local environmental action [72,
73]. The potential for citizen science to promote
environmental engagement can be measured by the
number of persons who participate, and systematic
studies of the benefits accrued during participation.
Measuring impact is the focus of a number of
national and EU studies [74], and clearly depends on
the citizen science programme and the experience of
the programme promoters. In order to maximise the
potential for positive environmental impact,
volunteers need to be managed and engaged with the
project. Consistent sampling at one site over many
years is not always appealing to volunteers and the
benefits of the project and of regular monitoring need
to be clearly explained. Expectations also need to be
managed, since volunteers will likely want to see
improvements in their local area reflected in the data
they are collecting, and without these they may lose
interest.
Sentinel offers the opportunity to place regulatory
value on citizen science data by providing a
mechanism through which the data can help deliver
the goals of the 25 YEP. In order to maximise the
benefits gained from citizen science, attention should
also be given to ensuring volunteers remain engaged
and motivated, and providing clear pathways from
data to action. This means that the future
development of Sentinel will need to take a holistic
approach to citizen science, considering not only the
mechanisms for collating, integrating, and using data,
but also the needs, experiences, and motivations of
citizen scientists and project coordinators.
Consideration should also be given to the fact that
citizen science is just one of a number of different
available monitoring techniques. It should be used
only where there are clear advantages to engaging
with the public, and, ideally, where these benefits are
anticipated and maximised. The impact pathways
outlined in Chapter 2 can provide a useful framework
for identifying a) where and how citizen science
would be a beneficial approach, and b) which citizen
science projects are most likely to deliver positive
environmental impacts. If these impact pathways can
be recognised and facilitated through Sentinel, there
is potential for citizen science to enable the public to
make significant contributions towards the goals of
the 25 YEP.

52

53
Section 1 of this report highlighted the huge
potential for existing UK citizen science activities in
freshwater environments to contribute to existing EA
operations, to the Natural Capital and Ecosystems
Assessment (NCEA), and to delivering the goals and
targets outlined in the 25 Year Environment Plan.
However, the current citizen science landscape is
disjointed and under-utilises this potential. Section 2
presented results from active pilots designed to test
how the EA might gain value from incorporating
citizen science into a combined evidence base via
Sentinel. These pilots demonstrate that both
regional/national scale mass participation censuses
and more localised, targeted, and ongoing
• Integrating and scaling up freshwater citizen science initiatives bring a number of opportunities and
benefits that go beyond data collection and provide opportunities for a more connected and engaged
society, alongside benefits to the citizen scientists themselves. There is already an appetite among
UK stakeholders to scale freshwater citizen science in order to realise its full potential.
• For citizen science to fully contribute to the goals and ambitions of Sentinel, the NCEA, and the 25 YEP,
this scaling up process needs to be considered in the specific context of a joined-up, national network
of data, evidence, knowledge, and action. This means that a coordinated approach is required, with
participation from a wide variety of stakeholders. The pilots presented in Chapter 2 are a first step in
this process.
• Before further upscaling can be considered, it is important to understand the barriers that are likely to
be faced. Previous studies combined with input from expert stakeholders reveal a number of
challenges that are likely to emerge as the citizen science elements of Sentinel develop. These
challenges fall into five key themes:
○ Data management: Different citizen science projects designed for different purposes hold data in
separate databases. They use their own data management systems and formats. Methods for
data integration will need to be established.
○ End user experiences: There will be multiple end users of the Sentinel system, including Defra, the
EA, Catchment Partnerships, and citizen scientists themselves. Their diverse needs will all need
to be catered for.
○ Ethical and legal considerations: There are legal and moral obligations towards citizen scientists,
data providers, and the general public that must be respected.
○ Scientific approaches: The Sentinel tool will require data that is capable of answering multiple
complex environmental questions at a variety of spatial scales. There may be mismatches
between the data needs of Sentinel and the requirements and capacities of data providers.
○ Coordination, sustainability, and funding: Many citizen science projects and project leaders are
under-resourced. There is a risk that contributing to Sentinel may place extra burdens on them.

54
monitoring initiatives have important and
complementary roles to play. Mass participation
censuses provide consistent and comparable data
that show the holistic state of freshwater
environments. Local monitoring adds depth to this
picture, and allows for more detailed investigation
into drivers, pressures, impacts, and responses. By
combining local and national monitoring, local
knowledge and national interpretation, effective
action can be taken - at a national level through
policy intervention, and at a local level through
targeted action.
There is already a large appetite from citizen science
project coordinators and other stakeholders to scale
UK freshwater citizen science in a coordinated
fashion in order to maximise its contribution to
catchment management planning processes, as
demonstrated by the proposed CaBA Catchment
Monitoring Cooperative [13]. These stakeholders
recognise a number of key opportunities associated
with increasing freshwater citizen science activity
across the country. Firstly, citizen science provides
more data; a holistic view of the national state of
freshwater and detailed local data. This provides the
opportunity for multiple data users, including the
Environment Agency, Defra, water utilities and
Catchment Partnerships to make better-informed
decisions and actions. These could include policy
changes, local restoration plans, business and
consumer behaviour change. Secondly, citizen
science creates a unique opportunity to engage wide
sectors of society with fresh water. Different
projects and different models (mass participation
events, place-based community action projects, and
interest group research) will suit the differing
interests and motivations of the population.
Engaging this wider audience will also help to drive
community and individual behaviour change,
through a heightened connection and understanding
of freshwater environments. Finally, increased
participation in citizen science from wider sections
of society, and increased time in nature, will create
opportunities to improve social connectedness and
mental wellbeing. By providing opportunities for
training, education and skill-sharing, citizen
scientists will be given an opportunity to develop
their skills, providing social, economic, and individual
and community health and wellbeing benefits.
For citizen science to fully contribute to the goals
and ambitions of Sentinel, the NCEA, and the 25 YEP,
this scaling up process needs to be considered in the
specific context of a joined-up, national network of
data, evidence, knowledge, and action that can be
used to understand long-term pressures and trends
in freshwater environments at national, regional, and
local scales. This will require a coordinated effort
from stakeholders that moves beyond using citizen
science for local or catchment-scale objectives
towards considering the roles and contributions of
individual projects to national-scale environmental
protection efforts. Although some skeleton national
frameworks, such as Riverfly Plus, already exist (see
Chapter 3), they currently lack the resources and
investment required to scale up UK citizen science
activity to meet the needs of Sentinel. There is
therefore a need to better understand the steps
required to expand the contributions of citizen
science to Sentinel beyond the pilots presented in
this report.
Before scaling up can be considered, it is important
to understand the barriers that are likely to be faced.
The challenges associated with integrating citizen
science and regulatory data have been considered
previously by a variety of organisations, including
Earthwatch [33], CaBA [11], the UK Environmental
Observation Framework (UKEOF) [12], and the
European Commission [2]. A thematic analysis of
these previous works allowed us to split these
challenges into the following key themes:
● Data management
● End user experiences
● Ethical and legal considerations
● Scientific approaches
● Coordination, sustainability, and funding
We also recognised that additional challenges may
exist within the specific contexts of UK freshwater
citizen science and Sentinel. Many of these emerged
during the course of this research as a result of the
active pilots and the expert stakeholder workshop
(see approach and guide to this report, Chapter 1).
As a result, we have been able to compile the
following information about the ‘known unknowns’
that must be tackled as the citizen science element
of Sentinel develops.

55
All citizen science projects are underpinned by their
data management systems, which include the data
model, the tools and infrastructure that are used by
volunteers to collect and submit data (e.g. web
platforms, apps, paper data forms), the database in
which the data are stored, automated and manual
quality assurance and quality control procedures,
and metadata [75]. Many citizen science projects
keep multiple related datasets, for example datasets
describing the variables measured by volunteers and
datasets containing information about the
volunteers themselves. Some citizen science
projects choose to develop their data systems from
scratch (e.g. FreshWater Watch). Recently, it has
become increasingly common for citizen science
projects to build their data systems with the help of
an ‘out of the box’ solution provider such as ESRI,
Cartographer, or Spotteron. Regardless, because
most citizen science projects in the UK have
developed independently of one another, Sentinel
will require data to be accessed from many different
databases, all of which have different features and
have been designed for different purposes. While the
individual systems used to collect data must remain
fully functional for citizen scientists and project
coordinators, systems used to view, aggregate, and
analyse data will additionally need to be used by
Defra, the EA, and other environmental stakeholders
such as catchment partnerships. The interface
between data collection systems and data
aggregation systems will therefore need to be
standardised to avoid constant and resource-
intensive pre-processing of data.
Currently, very few citizen science projects in the UK
are fully interoperable in this manner, and the
different variables that might link them up are
unknown. There are already standard file formats
that allow basic sharing of datasets (e.g. GeoJSON,
WFS), and some standard metadata formats for
specifying commonly used variables (e.g.
coordinate systems, spatial extents). However, there
are no systems in place to allow important
information about data to be accessed once data is
aggregated. There are mismatches in technical
definitions, methodologies, measurement accuracy
and precision, and quality assurance and quality
control between datasets. Not all citizen science
data is numeric, and qualitative and semi-
quantitative data may not be easily standardised for
cross-dataset comparison. Furthermore, some
datasets carry specific conditions, such as licensing
terms, requirements for data attribution, chains of
custody, and requirements for retrospective editing
of data entries. To allow datasets to fully
interoperate with one another, with Defra data
holdings, and with third party data, additional
metadata on the structure and content of each
dataset is required.
There is already a good conceptual understanding of
the types of metadata that should be considered as
part of a common ontology for citizen science
interoperability [76]. Compilation of ARMI and
FreshWater Watch datasets for the pilots presented
in Section 2 demonstrated the need for a common
approach to metadata. In the absence of shared
metadata, close, time-intensive collaboration was
required between FreshWater Watch and the Riverfly
Partnership in order to understand and interpret the
results. Expert stakeholders suggested that there is
currently limited capacity among project
coordinators to understand and conform to complex
metadata guidelines. Any new metadata guidelines
must therefore be kept as easy as possible. An
additional challenge lies in the long-term security of
data; with many citizen science projects existing for
relatively short time periods, project coordinators
will need to be encouraged to consider digital
archiving of data. Some project coordinators may
not have the financial resources, skills, or incentive
to do this.
Data will need to remain free and open access, whilst
simultaneously protecting individual users’
anonymity, especially in regards to their location.
The exact location of collected data is intrinsically
essential to the interpretation of the dataset,
therefore it will be important to enable
anonymisation of citizen scientist names and
personal information linked to each datapoint, in
adherence to the General Data Protection Regulation
(GDPR).
Data must be easy to visualise and comprehendible,
to allow end users to interpret trends and outcomes.
The expectations, needs and requirements of end
users will naturally vary between organisations, with
many participating in multiple related projects.

56
Crossover of the data and interaction of projects will
need to be taken into account, as there may be an
inherent risk of data duplication across multiple
platforms.
As part of the pilots described in Section 2, a
publicly-accessible data visualisation tool was
created [14]. While this is an important step in
providing access to data, further work needs to be
done to fully understand user requirements and
expectations from such a platform. In many cases,
end users may prefer to feed data into their own
systems to facilitate feedback to volunteers. Many
Rivers Trusts, for example, use ESRI StoryMaps for
this purpose. In order to maximise the potential
benefits of citizen science, provisions will need to be
made to enable data and information to be
accessible to all end users and participants.
Comprehensive training and support will be required
by the end user in order to facilitate successful
navigation and utilisation of any centralised data
platform or visualisation tools, with language and
terminology consistent across different regions.
Tools will need to be comprehendible and flexible
enough over the long-term to accommodate any
handover in management due to funding changes.
Collaboration will also need to go beyond
quantitative data and include additional qualitative
information, such as anecdotal evidence.
Challenging barriers often exist between end users
and citizen scientists regarding the reliability and
accuracy of the collected data. End users may not
feel able to fully trust the quality of citizen science
collected data, while the citizen scientists
themselves may be untrusting of organisations to be
transparent and to take the full data into account
when undertaking their analysis. It will be vital that a
clear explanation is given to both sides about the
usefulness and statistical significance of the data
with associated pathways, and how these can feed
directly into policy-making to enact real change.
Coordinators have a duty of care that must be upheld
for their involvement with citizen scientists [78]. The
resources, time and financial investment of
volunteer coordination need to be acknowledged,
understood and sustained over the long-term. There
exists a potential liability on the part of coordinators
who are instructing citizen scientists to undertake
outdoor monitoring activities on their behalf, as
many of these activities pose an inherent risk to
personal safety and/or wellbeing. It will be
necessary to undertake a comprehensive risk
assessment of all associated activities, prior to any
expectation of data being contributed voluntarily.
Citizen scientists should not be considered as
merely a free source of data and should not be taken
advantage of in this regard, for example by asking
them to contribute data more frequently than they
feel able to. They must be treated fairly and
respectfully, with clear procedures in place to
protect their personal data and anonymity at all
times, as well as being given the option to opt-out of
the project if they choose to withdraw their
involvement at any stage. Currently no official legal
protections exist for organisations taking part in
collaborative monitoring of this type.
Diversity and inclusivity pose a big challenge for the
environmental sector, with very few positions in
environmental science representing people from
ethnic minority and LGBTQ+ backgrounds [77].
Citizen scientists from these minority groups should
not be discriminated against, and proactive effort
should be undertaken to ensure that outreach and
recruitment reaches as many people as possible.
Expectations between citizen scientists and the end
users are naturally likely to differ, with each party
holding different motivations for the data collection.
For example, a participant is likely to favour data
collection from a specific location that is close to
their heart, such as their local stream, as this directly
affects them and their wellbeing. Meanwhile,
scientists are trained to look at the bigger picture
and take whole ecosystems and catchments into
account when analysing data, so are unlikely to be
able to reach conclusions on the health of a single
stream unless a parameter measurement is
significantly above a set threshold. These differing
expectations and motivations can cause friction and
mistrust between the two sides, with each
approaching the data from their own bias.
The quality and robustness of citizen science data
are naturally influenced by the specific needs and
interest of the volunteers, often leading to one-off

57
measurements being contributed from locations
that are local and easily accessible to specific
individuals, rather than from locations that are in the
greatest need of monitoring by expert scientists [79].
There may be a lack of interest or reluctance from
volunteers in expending extra effort and time to
conduct more frequent monitoring from a specific
location not of their choosing, or venturing further
away from their habitation to a location that is less
accessible for them. Short-term data is less reliable
than longer-term repeated measurements from a
monitoring perspective and can impact scalability,
so the quality and potential limitations of the dataset
should be made clear to end users from the outset.
Weather variation can also have a strong influence
on the regularity or accuracy of data collection, with
many citizen scientists feeling more inclined to go
outside and take measurements on warm sunny
days as opposed to cold rainy ones. These personal
biases are difficult to mitigate against and should be
taken into account when considering the influence
on spatial or temporal randomness of the collected
data. Some of the specific key identified challenges
and mitigating factors are outlined below:
● Volunteer participation – Inconsistent
sampling can lead to gaps in datasets. Keeping
volunteers engaged, and monitoring
participation so that a strict monitoring
schedule is adhered to is essential. This will
include managing volunteer expectations, to
ensure they don’t lose interest, and having a
recruitment plan to be able to source
replacements if necessary.
● Consistent funding – Inconsistent funding can
also result in gaps to the dataset, either
because funding is not sufficient to enable a
strong volunteer engagement and participation
support, or because funding does not enable
any continuation of the project.
● User error – Most citizen science initiatives
have a strong focus on volunteer training,
however, user error is still a risk. This can be
minimised by ensuring training refreshers are
given when new volunteers join.
● Technological issues – Primarily due to a lack
of mapping of smaller waterbodies, checking
the location of smaller waterbodies to ensure
that there are no issues with location is difficult.
Location checking falls upon local project leads
who have the necessary local knowledge, and is
therefore difficult to manage during mass
participation census events that cover larger
geographic areas.
● Quality control – The responsibility for data
quality assurance and quality control lies with
citizen science project coordinators. Without
specific knowledge about a particular method,
it can be difficult for data users to understand
the weight of evidence that should be placed on
any particular datapoint given the method of
data collection used.
More recent thinking within the citizen science realm
emphasizes the need for co-design and close
collaboration with the citizen scientists at all stages.
It can be an issue ensuring that the monitoring
processes and data remain scientifically sound
whilst still supporting the needs of the local
community.
Part of the public appeal of the bi-annual, mass
participation WaterBlitz events (Chapter 4) is the
freedom for citizen scientists to monitor at locations
that interest them personally. This can be incredibly
beneficial because it can highlight issues of public
concern that may not be captured in a more
structured sampling strategy. In the 2020 WaterBlitz,
for example, ten individuals reported concerns about
illegal dumping of waste by houseboats in an area
where the EA do not regularly monitor. However, this
unstructured approach has implications for an
integrated monitoring programme that need to be
considered, specifically around biases that might be
included within the data [33, 79]. These biases will
need to be quantified and accounted for as data is
analysed. Alternatively, additional direction could be
provided to citizen scientists to guide their selection
of sampling locations. This guidance could include
location suggestions based on modelled locations
of high uncertainty, or specific areas of interest
based on local conditions.
Citizen science projects are often under-resourced
and under-funded. Even established national
projects like FreshWater Watch and the Riverfly

58
Partnership require ongoing funding support. Many
of the costs associated with citizen science are not
immediately obvious. For example, to equip each
volunteer with a FreshWater Watch test kit for the
Thames WaterBlitz (Chapter 4) costs approximately
£5. However, in order to realise the full suite of
benefits that can be gained from the event, funding
also needs to be found to support event promotion,
volunteer recruitment and coordination, provision of
data collection infrastructure (e.g. web platform and
app), scientific analysis of data, interpretation of
scientific results and feedback to volunteers and
other stakeholders, and to support volunteers
wishing to take further actions. This means that the
cost of regional mass participation environmental
censuses like the Thames WaterBlitz can run into
tens of thousands of pounds, with prices increasing
further as numbers of volunteers and geographic
scope expands.
There is a risk that contributing data to Sentinel
could place additional burdens on already stretched
resources, particularly if projects are asked to adopt
new approaches too (e.g. improving quality control
and increasing volunteer retention and
engagement). It is important that investment is
provided to enable the co-creation and development
of the frameworks underpinning Sentinel, with
consistent methodology and training. Additionally,
citizen science projects typically receive short-term,
time-bound funding, which leads to data collection
ending when funding runs out. Long-term,
sustainable financing is needed beyond
environmental funding streams (e.g. access to
funding for social health and wellbeing).
In addition to funding concerns, end user experience
is key to sustaining citizen science projects.
Training, feedback and communication are
essential, as is ongoing recruitment to replace
existing volunteers who move away from the
projects. See the ‘End user experiences’ section
above for more detail.
There is limited existing coordination of governance
between different citizen science projects, with no
single overview of all co-existing activities or sharing
of issues and best practice between projects.
Approaches to data collection vary between
different groups and there is currently no consistent
shared standard methodology. There is also a
current lack of guidance on how to effectively
organise citizen science projects to complement
statutory monitoring. This could create tension
between citizen scientists and Sentinel, for example
if any resulting data is used to support policy
changes that have negative impacts on individuals
or companies. Finally, Sentinel will need to be
flexible and adapt throughout project life cycles, and
be prepared to tailor existing methodologies to suit
new citizen science projects, as and when they are
created.

59
By working with stakeholders to anticipate potential
barriers to scaling up the citizen science elements of
Sentinel early in the process (Chapter 6), it will be
much easier to ensure that the Sentinel tool is
designed to make the best possible use of the huge
diversity of citizen science projects that are
associated with fresh water in the UK. Increasingly,
though, it is recognised that ‘shared environmental
information systems’ which integrate and make data
available from multiple sources are most effective
when they are co-designed [27]. This means that all
stakeholders should be involved in the design and
use of the system, including citizens, decision and
policy makers, data aggregators, and scientists. In
cases where co-design has not been employed,
initiatives have struggled to engage core
stakeholders.
• The success of ‘shared environmental information systems’ like Sentinel is frequently dependent on
the involvement of all stakeholders in its creation and use. Co-creation should therefore be considered
at every stage in Sentinel’s development.
• Through a workshop, expert stakeholders were given the opportunity to identify a set of actions that
could be taken to ensure the scaling up of UK freshwater citizen science meets the needs of Sentinel.
These actions have been translated into a roadmap comprising seven ‘workstreams’.
• Local expertise and delivery must be incorporated alongside national coordination of plans,
procedures and training.
• Existing data standards should be collaboratively amended and extended to meet user needs for
citizen science data.
• A Technical Advisory Group is needed to develop standards and best practice guidelines for scientific
methods.
• A consistent supply of citizen science data and activity could be achieved through a regular national
mass-participation census. This could be supplemented by a) regular ongoing monitoring that is
already being undertaken by citizen scientists, and b) targeted, commissioned citizen science activity
that responds to specific needs identified via the Sentinel system.
• Multi-directional feedback is needed between data users and data providers in order to maintain
volunteer engagement and to maximise local knowledge.
• Training and capacity development will be needed for both data users and data providers.
• Communications should focus on engaging a wider and more diverse audience, and sharing successes
and impacts.
• While the roadmap presented here aims to make investment in citizen science efficient and targeted
to ensure the best return possible, it is important to recognise that sustainable, long-term financing is
required if citizen science is to make an effective contribution to the EA, Sentinel, and the NCEA, and
to achieve the goals and ambitions of the 25 YEP.

60
In order to design a comprehensive system that
considers the motivations, incentives, and barriers to
participation from different perspectives, we used
established co-design methodologies [27] to
develop a roadmap for scaling up the citizen science
elements of Sentinel from the pilots presented in
Section 2 to an inclusive, national network. This
roadmap consists of seven related ‘workstreams’
(described in the remainder of this chapter), which
work together to progress according to the timeline
shown in Fig. 7.1.
It is important to recognise that delivering the
roadmap will require further investment. One
analysis estimated that terrestrial biodiversity
surveillance in the UK by volunteers could be valued
at £20 million, for a government investment of £7
million [80]. As such, a holistic view of government
investment should be considered that links to
different ambitions including the 25 YEP Green
Recovery Plan [81], Levelling Up Fund [82], and the
UK’s Wellbeing programme [83]. Furthermore,
resources and support will need to be provided at
every level and over the long term, rather than be
project-specific or time-bound. Multi-sector funding
models should also be investigated, exploring new
approaches such as Social Bridging Finance [84].
This roadmap aims to make sure that investment in
citizen science as part of the NCEA and beyond is
targeted and efficient, ensuring the best return
possible
.
Figure 7.1: Overview of the timeline for incorporating citizen science data within Sentinel
National coordination of citizen science will be
essential to provide consistency, efficiency,
governance and best practice sharing. Coordination
should be hosted by a single organisation and
modelled on successful existing systems, such as
the Biological Records Centre, taking heed of
lessons learned from them where applicable.
Despite being hosted by a single organisation for
the sake of efficiency, national coordination must
remain collaborative and the coordinator must be
considered a convener first and foremost. It must
consider local needs, interests and innovation,
ensuring the system works both at the national scale
and at the hyper-local level, both for existing
stakeholders and to attract new innovative projects.
National coordination should therefore be held by an
organisation adept at facilitating partnership
working, and should be supported by a multi-
stakeholder board (see Fig. 7.2).

61
The national coordinator should be responsible for
the following tasks within the proposed roadmap:
● Convening the governance board (see below)
and keeping general strategic oversight of the
entire work programme.
● Leading the board to establish relevant sub-
committees, including the ethics, diversity and
inclusion (EDI) sub-committee, data
management sub-committee, and Technical
Advisory Group.
● Coordinating national census data drives.
● Tracking and reporting the impact of citizen
science.
● Coordinating communications.
● Hosting Annual General Meetings.
In order to ensure ongoing co-design and a true
partnership approach, a funded governance board
should be hosted by the national coordinator, with
representatives from the EA, Defra, the Rivers Trusts,
Catchment Monitoring Cooperative (if funded),
citizen science project leads, and community groups
engaged in data collection. This board could be
modelled on the existing Riverfly Partnership and
Riverfly Plus initiatives (see Chapter 3), allowing
inclusion of multiple different citizen science
models. The board should develop a transparent
governance process and charter that citizen science
projects wishing to become part of the national
network and/or to contribute data to Sentinel can
sign up to. Where government funding is made
available to support specific citizen science activity
for the purposes of Sentinel or other national
statutory monitoring (for example, for data drives
described below), the board should work with Defra
and the EA to oversee the distribution of funds to
citizen science projects. Standards should also be
mutually agreed for health and safety, insurance, and
ethics. A separate, but linked, sub-committee for
equality, diversity and inclusion (EDI) should oversee
goal setting, strategy, best practice and reporting of
EDI performance.
Figure 7.2: Overview of key roles
To effectively manage data, standards will need to
be agreed for data interoperability. These standards
should be agreed collaboratively by a data
management sub-committee of experts and key
stakeholders (including the citizen science
community, the Environment Agency, Defra, and
data experts such as Cartographer and UKCEH).
Any solutions devised by this sub-committee must
incorporate two elements:
a) A set of standards for defining interoperability of
projects.
b) Sufficient support and resources to encourage
uptake of these standards by project coordinators.
It is not necessary to start from scratch in creating a
set of interoperability standards. Instead, elements
of existing standards should be extended to meet
user needs for citizen science data, which could be
readily identified via a detailed stakeholder survey. A
suitable aim would be to create guidelines that meet
80% of the use cases identified. A linked data
approach would allow data to be aligned with Defra
data holdings. The standards should then be tested
thoroughly with lots of data, and involving as many

62
partners as possible. In order to encourage uptake, it
is important that any data format and metadata
guidelines are designed and agreed upon by the
citizen science community, however it is also
recognised that specialist expertise from both within
and outside of the EA/Defra will be required to guide
the process. One approach might be for the sub-
committee to deliver strawman implementations
and to solicit feedback from the wider citizen
science community.
To support community uptake, a set of open source
reference software systems that demonstrate ways
of conforming to standards could be implemented.
The open source format would allow anyone to
submit suggestions and change requests, fostering
ongoing collaboration. Associated tools could
include MS Excel add-ins, and tools for managing
and visualising data. These would form building
blocks to allow stakeholders to develop their own
compatible systems to meet their own individual
data aggregation needs. Ideally, financial incentives
should be offered to companies (from large to start-
ups) to build and support these tools, and data
centres should be involved to ensure that the tools
and formats are supported. Additionally, support will
need to be provided to citizen science project
coordinators in the form of capacity building (linking
with the ‘training’ element of this roadmap). This
might include developing a community vocabulary
for all of the metadata terms required, supporting the
community to manage these terms, and funding
community liaison to help groups to use tools and
populate data. Community liaison could be
supported by a role within the EA/Defra to assist with
specific troubleshooting and to foster positive
relationships between the EA and data providers.
To ensure that citizen science data is transparent
and available to a wide range of data users, a user-
friendly, centralised, online hub could be established,
building on the data portal built by UKCEH to support
the pilots presented in Section 2 of this report [14].
This will also serve to provide mechanisms for
feedback to volunteers and wider public
engagement. Data can be extracted from and shared
with existing citizen science platforms via APIs in
line with the standards defined above and FAIR data
principles.
A Technical Advisory Group, formed of experts in
scientific sampling and data, should be responsible
for developing standards and best practice
guidelines for scientific methods and approaches.
This would cover appropriate citizen science
methodologies, sampling strategies, quality
assurance and quality control measures specifically
linked to citizen science, and appropriate techniques
for volunteer training. The necessity for these
standards, as well as a proposed approach, form a
large part of the Catchment Monitoring Cooperative
proposal so will not be covered here [13]. Should the
Catchment Monitoring Cooperative receive funding,
it will be a significant driver for the creation of
scientific best practice guidelines and will help to
ensure that the citizen science data used within
Sentinel is of high quality.
Consideration will need to be given to whether all
citizen science projects wishing to contribute data to
Sentinel must conform to best practice guidelines.
Adherence to guidelines should not be onerous, nor
should they act as a disincentive for data sharing.
One approach might be to accept data from all
sources on the condition that associated metadata
contains sufficient information about scientific
approach, but to make adherence to best practice
guidelines a prerequisite for government funded
data drives.
The Technical Advice Group should also create a
toolbox of accredited resources (such as the CaBA
kite-marked methods proposed as part of the
Catchment Monitoring Cooperative) to help to bridge
the gap between citizen science data and statutory
data, and to make it easier for stakeholders to
understand which citizen science methods are the
best choice for contributing to statutory monitoring
and informing Catchment Management Plans. This
toolbox should make use of existing national
standard methods such as ARMI, Modular River
Survey, and rapid appraisal methods. Tools should
offer simple, scalable, analytical outputs for all
users, and be made publicly accessible, for example
via a centralised online citizen science hub.

63
Data collection drives are vital for two reasons:
1. They ensure that data is continually available.
2. They allow practical testing and iterative
development of outputs of all other
workstreams.
Based on expanding the pilots presented in Section
2, we propose a two-pronged approach to data
collection. Firstly, an annual national census, to
understand the overall picture of freshwater health
and give a comparison between geographic regions.
This would focus on a limited number of core
indicators of freshwater health, measured
consistently across the country. To ensure the
census is scientifically credible and consistent, the
annual census should be coordinated at a national
scale, use nationally available methods such as
FreshWater Watch and ARMI, and be centrally
funded (e.g. by the EA). To maximise efficiency and
harness local knowledge, the monitoring activity and
volunteer coordination should be assisted locally by
the relevant Catchment Partnership host, in
accordance with best practice guidelines on
monitoring locations and data density requirements.
Data from these annual events should be analysed
locally and nationally in order to provide feedback to
volunteers (such as through collaborative data
hackathons), and combined with EA data to identify
local trends and to establish where more in-depth
ongoing monitoring is required. There may also be
an opportunity to use existing data and modelled
outputs to direct volunteers towards locations of
particular interest, for example where models
suggest high levels of uncertainty [63].
Secondly, regular, localised, targeted monitoring
should be commissioned where additional data is
required and where the benefits of using citizen
science are clear. Examples might be where there
are clear pathways between citizen science
monitoring and 25 YEP outcomes, such as where
volunteers are monitoring localised effects of river
restoration activities. The impact pathways
framework described in Chapter 2 could be used to
inform decisions about when citizen science is most
appropriate [4]. This monitoring should be led at
local level by partners who have agreed to the
charter mentioned in Workstream 1, and should be
ongoing throughout the year. This allows a variety of
data to be collected, dependent on locally-specific
issues identified in the national census, EA
requirements, and local needs. For example, one
area may need targeted FreshWater Watch
monitoring, while another needs in-depth Riverfly
Monitoring. Not all data will need to be collated in
every region.
Commissioned monitoring as described above
should not be the only mechanism through which
citizen science projects can contribute data to
Sentinel, and should only be used where specific
requirements for citizen science data or activity are
identified. Existing ongoing projects that are
conducting regular or sporadic monitoring should be
routinely encouraged and incentivised to share data
with Sentinel, and newly developed projects should
also be supported to do so. Many of these projects
will continue to be designed around their own aims
and objectives, but they may also wish to adapt their
strategies to accommodate the needs of Sentinel
and the EA in order to boost their own impacts. One
mechanism for achieving this could be a digital map
showing existing monitoring locations and where
additional monitoring is needed. In such cases, the
longevity of the citizen science contribution should
be considered; ongoing monitoring that has not been
specifically commissioned will require a sustainable
finance model, funded by a variety of data users and
investors.
A comprehensive multi-directional communication
system is essential to sustaining citizen science
projects over a long term (Fig. 7.3). The approach
must allow for communication between data users
and data providers (citizen scientists) to empower
both groups and build trust in how the data is
collected and used. Extensive research has shown
that citizen scientists need to receive feedback to
sustain their motivation to continue to volunteer -
this should focus on the local and national impacts
linked to their data [85]. For example, many of the
citizen scientists involved in the River Evenlode case
study described in Chapter 5 are motivated to

64
continue monitoring because of the feedback they
receive on the actions taken to reduce phosphate
pollution in the catchment that have arisen from their
data. Feedback needs to be candid about the
realities of the situation, including which actions are
possible and which are not, and explaining how data
is used in practice, all via a transparent and open
process. The system should also empower citizen
scientists to input into the development of project
design, in order to harness local knowledge and
innovation.
Data users can also benefit from receiving more
than just numeric data from citizen scientists;
facilitating communication enables citizen
scientists to share qualitative information that builds
a much richer picture to inform local decision
making.
Figure 7.3: Feedback flows between data providers and users
An online citizen science hub (outlined in
Workstream 2) can be used to facilitate this
communication and feedback, transparently
demonstrating where data has been used and what
the resulting impacts have been, in a visually
interesting style. This online hub would allow people
to develop, advertise and manage their projects, as
well as sharing feedback. This will require all
stakeholders, including data users such as the EA,
Catchment Partnerships, and the water industry, to
be willing to provide the necessary resources to keep
the information held on this platform up to date.
Of course, feedback must also go beyond a digital
platform, and will need to take multiple forms (e.g. in
person liaison between data users and providers,
and at Catchment Partnership forums). This is
particularly important in the context of EDI,
recognising that not all stakeholders find online
resources easy to access or intuitive to use.
It will be important to have mechanisms in place for
capacity development of both data providers and
data users (Fig. 7.4). Training should be coordinated
centrally by the national coordinator and governance
board, to ensure that it covers the standards and
best practices they set.
Training for data providers could be achieved by
disseminating information about Sentinel to citizen
science leaders, such as project coordinators. This
training could include “Train the Trainer” content, to
enable citizen science leaders to include information
about Sentinel when they train citizen scientists
within their projects/initiatives. This coordinated
approach to capacity building for citizen science
leaders should filter down to allow for differing levels
of commitment from volunteers, enabling a broader
range of people to participate in citizen science, and
provide clear mechanisms for progression.
Mechanisms for facilitating citizen scientist

65
progression across projects have been considered
extensively within the Catchment Monitoring
Cooperative proposal [13].
Training and support on the use of the citizen
science elements of the Sentinel tool should also be
provided to data users (e.g. local developers,
national strategic decision makers, Catchment
Partnerships). This training should allow data users
to effectively and accurately use and interpret citizen
science data for natural capital assessments and
decision making. It should also enable strategic
decision makers to effectively communicate with
citizen scientists, including recognising and
gathering local knowledge held by citizen scientists
that might inform their decision making. This
training should be based on a standard set of
principles, but tailored to the specific audience.
.
Figure 7.4: Overview of approach to training and capacity development
The scaling up of citizen science requires expansion
of existing activities to new audiences. A
communication strategy should be devised to reach
the general public audience, and to encourage them
to take part in citizen science activity that interests
and motivates them. Involvement of
communications specialists with expertise in
engaging diverse audiences would add significant
value to the future development of Sentinel. A
suitable communication strategy should focus on
the following five goals:
1. Engaging new audiences - Lots of the public
are engaged with citizen science, but the
majority are not; communications should be
focused on engaging new groups of society
with citizen science.
2. Engaging new citizen science projects -
New projects should be encouraged to
develop, to harness innovation and meet
specific data requirements.
3. Supporting volunteer recruitment -
Recruitment of volunteers should be led
locally, but would benefit from support from
a national campaign.
4. Dissemination of results - Sharing results
and impact will be key to maintaining citizen
scientist motivation.
5. Celebrating successes - Positive
environmental stories are known to
motivate further participation, and may be
an effective way to engage new audiences

66
Regulatory monitoring of UK freshwaters has a long
and prestigious history and is currently undergoing
an exciting period of development. The natural
capital approach of the UK Government’s 25 Year
Environment Plan is an ambitious world-first, and
requires a joined-up, long-term approach to
environmental monitoring and management that
considers the whole ecosystem and its connections
to human health and prosperity. This approach must
be accompanied by a recognition that existing
statutory monitoring has been developed for
specific purposes, but it cannot be everywhere and
cannot measure everything. It is constrained by
funding, and therefore is targeted to meet regulatory
requirements and not more.
Citizen science has been proven as an effective
contribution to local and national scale monitoring,
in particular in the area of biodiversity monitoring.
The State of Nature reports, for example, rely heavily
on citizen science data and have done for many
years [86]. There are increasing amounts of water
quality monitoring data from citizen schemes,
particularly linked to the growth of Catchment
Partnership groups and increased public interest in
their local environment. Thanks to recent advances
in citizen science, the quality of this data is
increasing.
Whilst citizen science data often originates from
attempts to understand local issues, it has potential
for adding to regulatory data at the national scale. In
particular, citizen science monitoring of rivers and
streams that are not covered by the regulator can
help to improve our understanding of river
ecosystem health at higher spatial and temporal
resolutions than would otherwise be possible.
Statistical analysis can enable this data to be
merged with regulatory data to give a better picture
of the overall state of river health. It can also be used
to understand the uncertainties in the monitoring
data from regulatory systems, and thus to iteratively
improve regulatory monitoring. It can even be used
to target some of those areas of uncertainty, and to
identify the causes of large scale changes identified
by the regulatory network.
The huge variety of citizen science schemes in the
UK are not only able to provide data, but are also
associated with a range of benefits including
physical and mental health impacts, increased
public awareness of local environment, and public
engagement with - and action to improve - local
issues. Within the context of the 25 YEP, there is a
huge opportunity for citizen science to protect and
improve our natural freshwater assets by creating an
increased sense of public stewardship.
To maximise these benefits there is a need for
coordination of citizen science monitoring, not by
dictat, but through incentivisation, data sharing,
collaboration, and partnership working. This will
involve: ensuring data is suitably described so that
statistical analyses can be undertaken robustly,
making data accessible to all stakeholders,
transparently communicating how data is being
used to inform national strategic decision-making,
and encouraging a virtuous circle of increasing
engagement, participation, useful data provision,
and public action. If all of these elements come
together and are supported by sustainable, long-
term financing, citizen science has the potential to
radically transform our approach to statutory
freshwater monitoring and management.

67
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