Introduction
Land-use changes and water resource management efforts have altered
hydrological regimes throughout history , but the
increase in the scale of human interference has led to an intensification in
the effects that our interventions have upon the hydrology of landscapes
around the world, as well as having significant impacts on societal
development, via our co-evolution with water . Indeed the
scale of human intervention that has taken place in meeting the requirements
of a population that has expanded from 200 million to 7 billion over the last
2000 years has required such control that in many locations water now flows
as man dictates, rather than as nature had previously determined
. The pace and scale of change that anthropogenic
activities are bringing to natural systems are such that hydroclimatic shifts
may be brought about in the relatively short term , as
well as leading to a coupling between human and hydrological systems
; this coupling means that both positive and negative
social impacts may be brought about via decisions that impact the
hydrological system. The growing awareness of the impacts humans are having
on a global scale and associated stewardship practices
will, therefore, have impacts beyond the ecological and hydrological spheres.
A number of terms have been coined in order to develop the way in which the
relationship between mankind and nature, and in particular water, are thought
about: “Hydrosociology” , the
“Hydro-social” and “Hydrocosmological”
cycles and “Ecohydrosolidarity” ,
to name a few. The concept of “The Anthropocene”
to describe a new geological epoch in which
we now exist, where mankind represents “a global geological force”
, rivalling the force of nature in the scale of impact on
the earth system , has been in circulation for some time,
and the fact that man and water are linked through a “system of mutual
interaction” has been recognised for many years.
However, due to factors such as the implicit complexity and uncertainty
involved in coupled human and natural systems, the feedbacks and
interrelations between society and water are not commonly modelled when
forecasting and developing policy. The relatively new field of
“Socio-hydrology” , however, seeks to change this by
aiming to understand “the dynamics and co-evolution of coupled human-water
systems”.
This paper seeks to draw together relevant information and concepts
pertaining to the modelling of socio-hydrological systems; it is structured
as dealing with the questions of “why?”, “what?” and “how?”. The
“why?” section deals with why socio-hydrological study would be conducted,
the different contexts in which socio-hydrological models would be applied,
and the possible applications that socio-hydrological models could have; the
“what?” section first looks at the distinguishing features of
socio-hydrology, as well as the characteristics it shares with other
disciplines (and so the lessons that may be learned), before covering
different concepts that need to be understood when developing
socio-hydrological models; the “how?” section critically examines the
application of different modelling techniques to the study of socio-hydrological
systems. This structure is used so that the “why?” and “what?” being
investigated can introduce readers to literature and concepts of importance
to socio-hydrology, and the “how?” section can inform readers of the
specific advantages and disadvantages of using different techniques when
conducting socio-hydrological modelling. This paper is not intended to be a
comprehensive review of all socio-hydrological modelling studies, since there
are at this stage few socio-hydrological models in published literature;
rather, this paper should be seen as an amalgamation of knowledge surrounding
socio-hydrological modelling, such that understanding why and how it could be
undertaken is easily accessible. Recently, there have been two excellent
papers which have reviewed important aspects of socio-hydrology, which are
mentioned here. cover the current state of socio-hydrology
and give an excellent outline of the different research methodologies that
can be used in socio-hydrology (of which modelling is one); the role of the
socio-hydrological researcher is also covered particularly well in this
paper. give an in-depth analysis of: co-evolutionary
processes (particularly in a mathematical sense); the differences between
human and natural systems and the implications of these for modelling; and
the overall socio-hydrological modelling process, common across modelling
techniques and the different modelling archetypes that might be produced
(i.e. stylised versus comprehensive models).
As can be seen in Fig. , the number of articles being
published which relate to socio-hydrological modelling has increased
dramatically over recent years, demonstrating interest in the subject (2015
is not included as this year was not complete at the time of writing, so its
inclusion could cause confusion).
Some background to socio-hydrology
The subject of socio-hydrology, first conceived by ,
seeks to understand the “dynamics and co-evolution of coupled human-water
systems”, including the impacts and dynamics of changing social norms and
values, system behaviours such as tipping points and feedback mechanisms,
some of which may be emergent (unexpected), caused by non-linear interactions
between processes occurring on different spatio-temporal scales. Such
dynamics include “pendulum swings” that have been observed in areas such as
the Murray–Darling Basin, where extensive agricultural development was
followed by a realisation of the impacts this was having and subsequent
implementation of environmental protection policies , the co-evolution of landscapes with irrigation practices and
community dynamics , as well as instances of catastrophe
in which hydrological extremes not been catastrophic in themselves; rather,
social processes that result in vulnerability have made extreme events
catastrophic . There are also cases where social systems have
not interacted with water in the way that was anticipated: examples include
the virtual water efficiency and peak-water paradoxes discussed by
, and yet others where the perception, rather than the
actuality, that people have of a natural system determines the way it is
shaped . Studying these systems requires not only an
interdisciplinary approach, but also an appreciation of two potentially
opposing ontological and epistemological views: the Newtonian view, whereby
reductionism of seemingly complex systems leads to elicitation of fundamental
processes, and the Darwinian view, in which patterns are sought, but
complexity of system processes is maintained . Taking a
dualistic worldview encompassing both of these perspectives, as well as the
manner in which man and water are related , allows for
an appreciation of impacts that actions will have due to physical laws, as
well as other impacts that will be brought about due to adaptations from
either natural or human systems.
In understanding socio-hydrology as a subject, it may be useful to also
briefly understand the history of the terminology within hydrological
thinking, and how this has led to the current understanding. Study of the
hydrological cycle began to “serve particular political ends”
, whereby maximum utility was sought through modification
of the cycle, and was viewed initially as fairly separate from human
interactions: after several decades this led to a focus on water resource
development in the 1970s, language clearly indicative of a utility-based
approach. However, a change in rhetoric occurred in the 1980s, when water
resource management (WRM) became the focus, and from this followed integrated
water resource management (IWRM) and adaptive water management (AWM)
, the shift from “development” to “management”
showing a change in the framing of water, while the concepts of integrated
analysis and adaptivity show a more holistic mindset being taken. The
introduction of the hydrosocial cycle shows another
clear development in thought, which aimed to “avoid the pitfalls of
reductionist…water resource management analysis”
for the purpose of better water management. “A science, but one that is
shaped by economic and policy frameworks” , socio-hydrology
also represents another advancement in hydrological study, which requires
further rethinking of how hydrological science is undertaken.
It is also important to consider how modelling has progressed in the water
sciences, particularly in reference to the inclusion of socio-economic
aspects. Subjects such as integrated assessment modelling consider
socio-economic decisions and impacts alongside biophysical subsystems
(generally in a one-way fashion) and can be applied to water resource
management problems (for more detail, see ).
Hydro-economic modelling includes the capacity to model many aspects of the
human–water system via ascribing economic values to water, which reflect the
need to allocate water as a scarce resource, and which change across space
and time according to the availability and demand (more detail in
). Global water resource models have also seen fascinating
development; initially considering human impacts on global resources as a
boundary condition (considering demand and supply as essentially separate),
they increasingly integrate these two aspects and consider the impacts of
water availability on demand . It
is equally important to remember the points of departure between these
subjects and socio-hydrology, with socio-hydrology focusing particularly on
bi-directional interactions and feedbacks between humans and water, and
involving particularly long timescales considering changing values and norms,
where the previously mentioned disciplines tend either to treat one or the
other system as a boundary condition, or to consider one-way interactions,
and generally focus on slightly shorter timescales.
The importance of socio-hydrology has been recognised since its introduction:
The International Association of Hydrological Sciences (IAHS) has designated
the title of their “Scientific Decade” (2013–2022) as `Panta Rhei
(Everything flows)' , in which the aim `is to reach an
improved interpretation of the processes governing the water cycle by
focusing on their changing dynamics in connection with rapidly changing human
systems' . In the IAHS's assessment of hydrology at
present , it is recognised that current hydrological
models are largely conditioned for analysis of pristine catchments and that
societal interaction is generally included in separately developed models, so
that interactions between the two are not well handled: socio-hydrological
study is posited as a step towards deeper integration that has long been
called for . The recent series of “Debates” papers in
Water Resour. Res. shows a real, continued commitment to the
development of socio-hyrology as a subject; the unified conclusion of these
papers is that the inclusion of the interaction between society and water is
necessary in modelling, though the authors varied in their views on how this
should be conducted, the sphere within which socio-hydrology should operate,
and the value that socio-hydrological models may have. The continued
commitment necessary to the subject is highlighted via the statement that
“if we who have some expertise in hydrologic modelling do not some other
discipline will [include nonhydrologic components in hydrologic models]”
.
Why?
Regarding why socio-hydrology is necessary, continuing on from the recognised
significance of socio-hydrology, understanding of water (perceived or
otherwise), as well as intervention following this understanding, has led to
large changes in landscapes, which have then altered the hydrological
processes that were initially being studied , and as such
the goals of study in hydrology are subject to regular modification and
refinement. The development of socio-hydrology has come from this iterative
process. point out that, as a subject still in its infancy,
socio-hydrology is still learning the questions to ask. However,
sets out the main goals of socio-hydrological study.
Analysis of patterns and dynamics on various spatio-temporal scales
for discernment of underlying features of biophysical and human systems, and
interactions thereof.
Explanation and interpretation of socio-hydrological system responses,
such that possible future system movements may be forecast (current water
management approaches often result in unsustainable management practices due
to current inabilities in prediction).
Furthering the understanding of water in a cultural, social, economic
and political sense, while also accounting for its biophysical
characteristics and recognising its necessity for existence.
It is hoped that the achievement of these goals will lead to more sustainable
water management and may, for example, lead to the ability to distinguish
between human and natural influences on hydrological systems, which has thus
far been difficult . Achievement of these goals will
involve study in several spheres, including in historical, comparative and
process contexts , as well as `across gradients of
climate, socio-economic status, ecological degradation and human management'
. In accomplishing all of this, studies in
socio-hydrology should strive to begin in the correct manner; as
states, “a socio-hydrological world will need a strong
commitment to combined social-hydrological investigations that frame the way
that prediction is undertaken, rather than leaving consideration of social
and economic considerations as concerns to be bolted on to the end of a
hydrological study”.
Socio-hydrology can learn many lessons from other, similarly
interdisciplinary subjects. Ecohydrology is one such subject, whereby the
interaction between ecology and hydrology is explicitly included.
gives a number of the questions that ecohydrology
attempts to answer, which may be very similar to the questions that
socio-hydrology attempts to answer:
“Is there emergence of global properties out of these [eco-hydrological]
dynamics?”
“Does it tend to any equilibrium values?”
“Is there a spontaneous emergence…associated with the temporal
dynamics?”
“Can we reproduce some of the observed…patterns?”
“Is there a hidden order in the space–time evolution which models could help
to uncover?”
“Does the system evolve naturally, for example, without being explicitly directed to do
so?”
Ecohydrology could also necessarily be a constituent part of
socio-hydrological models, since anthropogenic influences such as land cover
change have ecological impacts, which will themselves create feedbacks with
social and hydrological systems.
Another aspect of the question of “why socio-hydrology?” is that, in a
world where the decisions that mankind makes have such influence, those who
make those decisions should be well-informed as to the impacts their
decisions may have. As such, those working in water resources should be
well-versed in socio-hydrological interaction, seeking to be “T-shaped
professionals” (technical skills being vertical, coupled
with “horizontal” integrated resource management skills), and as such
training should certainly reflect this, perhaps learning from the way that
ecohydrology is now trained to hydrologists. Beyond being “T-shaped”,
socio-hydrologists should also seek to collaborate and cooperate with social
scientists and sociologists. Socio-hydrology will require study into subjects
that many with backgrounds in hydrology or engineering will have little
experience in, for instance modelling how social norms change and how these
norms cascade into changing behaviours. Learning from and working with those
who are experts in these subjects is the best way to move the subject
forward.
Regarding why modelling would be conducted in socio-hydrology, there could be
significant demand for socio-hydrological system models in several
circumstances; however, there are three main spheres in which such modelling
could be used :
system understanding
forecasting and prediction
policy and decision-making.
The purpose of this section is to give an idea of why socio-hydrological
modelling may be conducted, as the techniques used should be steered by what
is required of their outputs. This is linked to, though separated from,
current and future applications, since the applications will likely require
study in all three of the mentioned spheres in the solution of complex
problems. In this section, the significance of modelling in each of these
areas will be introduced, the limitations that current techniques have
investigated, and so the developments that socio-hydrological modelling could
bring determined. The three typologies of socio-hydrological study that
present (historical, comparative and process) could all
be used in the different spheres. There are of course significant
difficulties in socio-hydrological modelling, which should not be forgotten,
in particular due to the fact that “characteristics of human variables make
them particularly difficult to handle in models” , as well
as issues brought about by emergence, as models developed on current
understanding may not be able to predict behaviours that have not previously
been observed, or they may indeed predict emergent properties that do not
materialise in real-world systems.
System understanding
“Perhaps a way to combat environmental problems is to understand the
interrelations between ourselves and nature” .
Understanding the mechanisms behind system behaviour can lead to a more
complete picture of how a system will respond to perturbations, and so guide
action to derive the best outcomes. For example, understanding the mechanisms
that bring about droughts, which can have exceptionally severe impacts, can
allow for better preparation as well as mitigative actions
. Creating models to investigate system behaviour can lead
to understanding in many areas; for example, give the
examples of socio-ecological models leading to understanding of how
individual actions create system-level behaviours, as well as how
system-level influences can change individual behaviours.
IWRM has been the method used to investigate human–water interactions in
recent years, but the isolation in which social and hydrological systems are
generally treated in this framework leads to limitations in assimilating
“the more informative co-evolving dynamics and interactions over long
periods” that are present. This isolation has also led
to the understanding of mechanisms behind human–water feedback loops
currently being poor, and so integration has become a priority
.
If models of the coupled human–water system could be developed, this could
give great insight into the interactions that occur, the most important
processes, parameters and patterns, and therefore how systems might be
controlled . Historical, comparative and process-based
studies would all be useful in this regard, as understanding how systems have
evolved (or indeed co-evolved ) through time, comparing
how different locations have responded to change and investigating the
linkages between different parameters are all valuable in the creation of
overall system understanding. Improved system understanding would also lead
to an improvement in the ability for interpretation of long-term impacts of
events that have occurred . It is important to note
that, while this study focuses on modelling, system understanding cannot be
brought about solely through modelling, and other, more qualitative studies
are of value, particularly in the case of historical investigations (e.g.
).
Understanding socio-hydrology
Within the goal of system understanding, there should also be a sub-goal of
understanding socio-hydrology, and indeed meta-understanding within this. As
a subject in which relevance and applicability are gained from the
understanding that it generates, but one which is currently in its infancy,
there is space for the evaluation of what knowledge exists in
socio-hydrology. While the end-goal for socio-hydrology may be to provide
better predictions of system behaviour (though this may not be viewed as the
goal by all) via better understanding of fundamental human–water processes,
this should be informed by an understanding of how well we really understand
these processes.
Insights into data
Another sub-goal of system understanding, which will develop alongside
understanding, is gaining insight into the data that are required to
investigate and describe these systems. When socio-hydrological models are
developed, they will require data for their validation; however, these data
will not necessarily be available and will not necessarily be conventional in
their form . As such, new data collection efforts will be
required which use new and potentially unconventional techniques to collect
new and potentially unconventional data. On the other side of this coin, the
nature of data that are collected will surely influence models that are developed
within socio-hydrology, and indeed theories on socio-hydrological processes.
This brings forth the iterative data–theory–model development process, in
which these aspects of knowledge interact to move each other forward
. The role of data in socio-hydrology is discussed further in
Sect. .
Forecasting and prediction
Once a system is understood, it may be possible to use models to predict what
will happen in the future. Predictive and forecasting models estimate future
values of parameters based on the current state of a system and its known (or
rather supposed) behaviours. Such models generally require the use of past
data in calibration and validation. Being able to forecast future outcomes in
socio-hydrological systems would be of great value, as it would aid in
developing foresight as to the long-term implications of current decisions,
as well as allowing a view to what adaptive actions may be necessary in the
future. state that “Better scenarios of future human
water demand could lead to more skilful projection for the 21st century”,
which could be facilitated by “comprehensive future socio-economic and land
use projections that are consistent with each other”, as well as the
inclusion of human water use and reservoirs, which now have “substantial
impacts on global hydrology and water resources”, as well as “modelling of
interacting processes such as human-nature interactions and feedback”;
socio-hydrological modelling may be able to contribute in all of these areas.
An example area of study in prediction/forecasting is resilience: prediction
of regime transitions is very important in this sphere , and
while IWRM does explore the relationship between people and water, it does so
in a largely scenario-based fashion, which leaves its predictive capacity for
co-evolution behind that of socio-hydrology , and so in
study of such areas a co-evolutionary approach may be more appropriate.
However, there are significant issues in the usage of models for prediction,
including the accumulation of enough data for calibration
. Issues of uncertainty are very important when
models are used for forecasting and prediction, as the act of predicting the
future will always involve uncertainty. This is a particular issue when
social, economic and political systems are included, as they are far more
difficult to predict than physically based systems. The necessity of
including changing norms and values in socio-hydrology exacerbates this
uncertainty, since the timescale and manner in which societies change their
norms are highly unpredictable and often surprising. also
state that “to make predictions in a changing environment, one in which the
system structure may no longer be invariant or in which the system might
exhibit previously unobserved behaviour due to the exceedance of new
thresholds, past observations can no longer serve as a sufficient guide to
the future”. However, it must surely be that guidance for the future must
necessarily be based on past observations, and as such it could be that
interpretations of results based on the past should change.
Policy and decision-making
Decision-making and policy formation are ultimately where model outputs can
be put into practice to make a real difference. Models may be used to
differentiate between policy alternatives, or optimise management strategies,
as well as to frame policy issues, and can be very useful in all of these
cases. However, there are real problems in modelling and implementing policy
in areas such as in the management of water resources : it
is commonly stated that planning involves “wicked” problems, plagued by
issues of problem formulation, innumerable potential solutions, issue
uniqueness and the difficulties involved in testing of solutions (it being
very difficult to accurately test policies without implementing them, and
then where solutions are implemented, extricating the impact that a
particular policy has had is difficult, given the number of variables
typically involved in policy problems) . Models necessarily
incorporate the perceptions of developers, which can certainly vary, and so
models developed to investigate the same issue can also be very different,
and suggest varying solutions . Appropriate timescales
should be used in modelling efforts, as unless policy horizons are very
short, neglecting slow dynamics in socio-ecological systems has been said to
produce inadequate results . There are also the issues of
policies having time lags before impacts (this is compounded by discounting
the value of future benefits), uncertainty in their long-term impacts at time
of uptake, root causes of problems being obscured by complex dynamics and the
fact that large-scale, top-down policy solutions tend not to produce the best
results due to the tendency of water systems to be “resistant to fundamental
change” . While the difficulties in managing complex
systems (such as human–water systems) are clear, they can, however, be good
to manage, as multiple drivers mean that there are multiple targets for
policy efforts that may make at least a small difference
.
Past water resource policy has been built around optimisation efforts, which
have been criticised for having “a very tenuous meaning for complex
human-water systems decision making” , since they assume
“perfect problem formulations, perfect information and evaluation models
that fully capture all states/consequences of the future” ,
meaning that they result in the usage of “optimal” policies that are not
necessarily optimal for many of the possible future system states. Another
tension in finding optimal or pareto-optimal solutions in complex systems
exists where optimising for a given criterion yields solutions which, via the
multiple feedbacks that exist, can impact the rest of the system in very
different ways (impacts on the rest of the system may go unnoticed if a
single criterion is focused on). Techniques such as
multi-criteria/multi-objective methods attempt
to improve upon this, producing pareto-efficient outcomes, but still rarely
account explicitly for human–water feedbacks.
Good evidence is required for the formation of good policy
, and so providing this evidence to influence, and
improve policy and best management practices should be an aim of
socio-hydrology , in particular socio-hydrological
modelling. Changes in land use are brought about by socio-economic drivers,
including policy, but these changes in land use can have knock-on effects
that can impact upon hydrology , and so land
productivity, water availability and livelihoods to such an extent that
policy may be altered in the future. Socio-hydrology should at least attempt
to take account of these future policy decisions, and the interface between
science and policy to improve long-term predictive capacity
. There is a call for a shift in the way that water
resources are managed, towards an ecosystem-based approach, which will
require a “better understanding of the dynamics and links between water
resource management actions, ecological side-effects, and associated
long-term ramifications for sustainability” . SES analysis
has already been used in furthering perceptions on the best governance
structures, and has found that polycentric governance can lead to increased
robustness , and it may well be that socio-hydrology
leads to a similar view of SHSs.
In order for outputs from policy-making models to be relevant they must be
useable by stakeholders and decision-makers, not only experts
. Participatory modelling encourages this through the
involvement of stakeholders in model formulation, and often improves
“buy-in” of stakeholders, and helps in their making sensible decisions
, as well as an increase in uptake in policy
. This technique could be well used in socio-hydrological
modelling. take the scope of socio-hydrology further,
suggesting a need to include a “knowledge exchange”
component in socio-hydrological study, whereby the communication of results
to policy makers and their subsequent decision-making mechanisms are included
to fully encompass socio-hydrological interactions. However,
points out that the prediction of future policy decisions
will be one of the most challenging aspects of socio-hydrology.
Current and future applications
This section follows from the areas of demand for socio-hydrological to give
a few examples (not an exhaustive list) of potential, non-location-specific
examples of how socio-hydrological modelling could be used. These
applications will incorporate system understanding, forecasting and
prediction and policy formation, and where these spheres of study are
involved they will be highlighted. SES models have been applied to fisheries,
rangelands, wildlife management, bioeconomics, ecological economics,
resilience and complex systems , and have resulted in
great steps forward. Application of socio-hydrological modelling in the
following areas could too result in progress in understanding, forecasting,
decision-making and the much-needed modernisation of governance structures
in different scenarios. This section should provide
insight as to the situations where socio-hydrological modelling may be used
in the future, and so guide the discussion of suitable modelling structures.
Understanding system resilience and vulnerability
Resilience can be defined as the ability of a system to persist in a given
state subject to perturbations , and so this
“determines the persistence of relationships within a system” and can be
used to measure the “ability of these systems to absorb changes of state
variables, driving variables, and parameters” . Reduced
resilience can lead to regime shift, “a relatively sharp change in dynamic
state of a system” , which can certainly have negative
social consequences. SES literature has studied resilience in a great number
of ways, and has found it is often the case that natural events do not cause
catastrophe on their own; rather, catastrophe is caused by the interactions
between extreme natural events and a vulnerable social system
. Design principles to develop resilience have been developed
in many spheres (for instance, design principles for management institutions
seeking resilience; ), though in a general sense
terms four clusters of factors which can build resilience:
learning to live with change and uncertainty;
nurturing various types of ecological, social and political diversity;
increasing the range of knowledge for learning and problem solving; and
creating opportunities for self-organisation.
Exposure to natural events can lead to emergent resilience consequences in
some cases, as in the case where a policy regime may be altered to increase
resilience due to the occurrence of a catastrophe, for example London after
1953 , or Vietnamese agriculture , where
the same event could perhaps have caused a loss in resilience were a
different social structure in place .
In all systems, the ability to adapt to circumstances is critical in creating
resilience (though resilience can also breed adaptivity );
in the sphere of water resources, the adaptive capacity that a society has
towards hydrological extremes determines its vulnerability to extremes to a
great extent, and so management of water resources in the context of
vulnerability reduction should involve an assessment of hydrological risk
coupled with societal vulnerability . An example scenario
where socio-hydrological modelling may be used is in determining
resilience/vulnerability to drought, the importance of which is highlighted
by in their discussion of recognising the
anthropogenic facets of drought; sometimes minor droughts can lead to major
crop losses, whereas major droughts can sometimes result in minimal
consequences, which would indicate differing socio-economic vulnerabilities
between cases which “may either counteract or amplify the climate signal”
. Studies such as that carried out by ,
which uses a hydrological model to predict drought severity and frequency
coupled with a socio-economic model to determine vulnerable areas, and
, which looks at the stresses in different basins over time
caused by hydrological and anthropogenic issues, have already integrated
socio-economic and hydrological data to perform vulnerability assessments.
Socio-hydrological modelling could make an impact in investigating how the
hydrological and socio-economic systems interact (the mentioned studies
involve integration of disciplines, though not feedbacks between systems) to
cause long-term impacts, and so determine vulnerabilities over the longer
term. The most appropriate form of governance in socio-hydrological systems
could also be investigated further, as differing governance strategies lead
to differing resilience characteristics :
has investigated community-based irrigation systems
(Acequias) and found that they produce great system resilience to drought,
due to the “complex self-maintaining interactions between culture and
nature” and “hydrologic and human system connections”. There is also a
question of scale in resilience questions surrounding water resources, which
socio-hydrology could be used to investigate: individual resilience may be
developed through individuals' use of measures of self-interest (for example
digging wells in the case of drought vulnerability), though this may
cumulatively result in a long-term decrease in vulnerability
.
An area that socio-hydrological modelling would be able to contribute in is
determining dynamics that are likely to occur in systems: this is highly
relevant to resilience study, as system dynamics and characteristics that
socio-hydrological models may highlight, such as regime shift, tipping
points, bistable states and feedback loops, all feature in resilience
science. The long-term view that socio-hydrology should take will be useful
in this, as it is often long-term changes in slow drivers that drive systems
towards tipping points . Modelling of systems also helps to
determine indicators of vulnerability that can be monitored in real
situations. Areas where desertification has/may take place would be ideal
case-studies, since desertification may be viewed as “a transition between
stable states in a bistable ecosystem” , where feedbacks
between natural and social systems bring about abrupt changes.
Socio-hydrology may be able to forecast indicators of possible regime shifts,
utilising SES techniques such as identification of critical slowing down
(CSD) , a slowing of returning to “normal” after a
perturbation which can point to a loss of system resilience, as well as
changes in variance, skewness and autocorrelation, which may all be signs of
altered system resilience , to determine the most effective
methods of combating this problem.
In studying many aspects of resilience, historical socio-hydrology may be
used to examine past instances where vulnerability/resilience has occurred
unexpectedly and comparative studies could be conducted to determine how
different catchments in similar situations have become either vulnerable or
resilient; combinations of these studies could lead to understanding of why
different social structure, governance regimes, or policy frameworks result
in certain levels of resilience. Modelling of system dynamics for the
purposes of system understanding, prediction and policy development are all
clearly of relevance when applied to this topic, since in these the coupling
is key in determination of the capacity for coping with change
.
Understanding risk in socio-hydrological systems
Risk is a hugely important area of hydrological study in the wider context:
assessing the likelihood and possible consequences of floods and droughts
constitutes an area of great importance, and models to determine
flood/drought risk help to determine policy regarding large infrastructure
decisions, as well as inform insurance markets on the pricing of risk.
However, the relationship between humans and hydrological risk is by no means
a simple one, due to the differing perceptions of risk as well as the social
and cultural links that humans have with water , and so
providing adequate evidence for those who require it is a great challenge.
The way in which risk is perceived determines the actions that people take
towards it, and this can create potentially unexpected effects. One such
impact is known as the “levee effect” , whereby areas
protected by levees are perceived as being immune from flooding (though in
extreme events floods exceed levees, and the impacts can be catastrophic when
they do), and so are often heavily developed, leading people to demand
further flood protection and creating a positive feedback cycle. Flood
insurance is also not required in the USA if property is “protected” by
levees designed to protect against 100-year events , leading
to exposure of residents to extreme events. Socio-hydrological thinking is
slowly being applied to flood risk management, as is seen in work such as
that of , which recognises that “A flood loss event is the
outcome of complex interactions along the flood risk chain, from the
flood-triggering rainfall event through the processes in the catchment and
river system, the behaviour of flood defences, the spatial patterns of
inundation processes, the superposition of inundation areas with exposure and
flood damaging mechanisms”, and that determining flood risk involves “not
only the flood hazard, e.g. discharge and inundation extent, but also the
vulnerability and adaptive capacity of the flood-prone regions.”
Socio-hydrology could, however, further investigate the link between human
perceptions of risk, the actions they take, the hydrological implications
that this has, and therefore the impact this has on future risk to determine
emergent risk in socio-hydrological systems.
The impact that humans have on drought is another area where socio-hydrology
could be used; work on the impact that human water use has upon drought has
been done (e.g. ), where it was found that human impacts
“increased drought deficit volumes up to 100 % compared to pristine
conditions”, and suggested that “human influences should be included in
projections of future drought characteristics, considering their large impact
on the changing drought conditions”. Socio-hydrology could perhaps take this
further and investigate the interaction between humans and drought,
determining different responses to past drought and assessing how these
responses may influence the probability of future issues and changes in
resilience of social systems.
Transboundary water management
Across the world, 276 river basins straddle international boundaries
; the issue of transboundary water management is a clear
case where social and hydrological systems interact to create a diverse range
of impacts that have great social consequences, but which are very hard to
predict. These issues draw together wholly socially constructed boundaries
with wholly natural hydrological systems when analysed. The social
implications of transboundary water management have been studied and shown to
lead to varying international power structures (e.g.
“hydro-hegemony” ), as well as incidences of both
cooperation and conflict (in various guises) dependent
on circumstance. The virtual water trade also highlights
an important issue of transboundary water management: the import and export
of goods almost always involves some “virtual water” transfer since those
goods will have required water in their production. This alters the spatial
scale appropriate for transboundary water management , and
investigating policy issues related to this would be very interesting from a
socio-hydrological perspective .
Socio-hydrological modelling could be used to predict the implications that
transboundary policies may have for hydrological systems, and so social
impacts for all those involved. However, the prediction of future
transboundary issues is highly uncertain and subject to a great many factors
removed entirely from the hydrological systems that they may impact, and so
presents a significant challenge.
Land-use management
The final example situation where socio-hydrological modelling may be
applicable is in land-use management. Changes in land use can clearly have
wide-ranging impacts on land productivity, livelihoods, health, hydrology,
and ecosystem services, which all interact to create changes in perception,
which can feed back to result in actions being taken that impact on land
management. posits the idea of further integrating
agricultural and water management: “Given the simultaneously human and
non-human complexion of land-water systems it is perhaps not surprising that
collaboration across the social and natural sciences is regarded as a
necessary, and underpinning, facet of integrated land-water policy”.
Modelling in socio-hydrology may contribute in this sphere through the
development of models which explore the feedbacks mentioned above, and which
can determine the long-term impacts of interaction between human and natural
systems in this context.
What?
The question of “what?” in this paper can be viewed in several different
ways: What are the characteristics of socio-hydrological systems? What is to
be modelled? What are the issues that
socio-hydrological systems will present to modellers?
Socio-hydrology and other subjects
The question of what is different and new about socio-hydrology, and indeed
what is not, is useful to investigate in order to then determine how
knowledge of modelling in other, related subjects can or cannot be
transferred and used in socio-hydrology. Here, the subject of socio-ecology
(as a similar synthesis subject) is introduced, before the similarities and
differences between socio-hydrology and other subjects are summarised.
Socio-ecology
The study of socio-ecological systems (SESs) and coupled human and natural
systems (CHANS), involves many aspects similar to that of socio-hydrology:
feedbacks , non-linear dynamics ,
co-evolution , adaptation ,
resilience , vulnerability , issues of
complexity , governance , policy
and modelling are all
involved in thinking around, and analysis of, SESs. As such, there is much
that socio-hydrology can learn from this fairly established
discipline, and so in this paper a proportion of the literature presented
comes from the field of socio-ecology due to its relevance. Learning from the
approaches taken in socio-ecological studies would be prudent for future
socio-hydrologists, and so much can be learnt from the manner in which
characteristics such as feedback loops, thresholds, time-lags, emergence and
heterogeneity, many of which are included in a great number of
socio-ecological studies are dealt with. Many key concepts
are also applicable to both subject areas, including the organisational,
temporal and spatial (potentially boundary-crossing) coupling of systems
bringing about behaviour “not belonging to either human or natural systems
separately, but emerging from the interactions between them”
, and the required nesting of systems on various
spatio-temporal scales within one another.
Socio-hydrology may, in some ways, be thought of as a sub-discipline of
socio-ecology ; indeed, some studies that have been carried
out under the banner of socio-ecology could perhaps be termed
socio-hydrological studies (e.g. ), and term rivers “complicated
socio-ecological systems that provide resources for a range of water needs”.
There are however, important differences between socio-ecology and
socio-hydrology which should be kept in mind when transferring thinking
between the two disciplines, for example infrastructure developments such as
dams introduce system intervention on a scale rarely seen outside this sphere
, and the speed at which some hydrological processes
occur at means that processes on vastly different temporal scales must be
accounted for . There are also unique challenges in
hydrological data collection; for example, impracticably long timescales are
often being required to capture hydrological extremes and regime changes
. Water also flows and is recycled via the hydrological
cycle, and so the way that it is modelled is very different to subjects
modelled in socio-ecology.
In a study comparable to this, though related to socio-ecological systems,
gives research issues in socio-ecological modelling;
these issues are also likely to be pertinent in socio-hydrological modelling:
Implications of complex social and ecological structure for the
management of SESs
The need to address the uncertainty of ecological and social dynamics in decision making
The role of coevolutionary processes for the management of SESs
Understanding the macroscale effects of microscale drivers of human
behaviour.
Along with studying similarly defined systems and the usage of similar
techniques, socio-ecology has suffered problems that could also potentially
afflict socio-hydrology. For example, different contributors have often
approached problems posed in socio-ecological systems with a bias towards
their own field of study, and prior to great efforts to ensure good
disciplinary integration social scientists may have “neglected environmental
context” and ecologists “focused on pristine environments
in which humans are external” . Even after a coherent SES
framework was introduced , some perceived it to be “lacking
on the ecological side” , and as such missing certain
“ecological rules”. Since socio-hydrology has largely emerged via scholars
with water resources backgrounds, inclusion of knowledge from the social
sciences, and collaboration with those in this field, should therefore be
high on the agenda of those working in socio-hydrology to avoid similar
issues. Another issue that both socio-ecologists and socio-hydrologists face
is the tension between simplicity and complexity: the complexity inherent in
both types of coupled system renders the development of universal solutions
to issues almost impossible, whereas decision-makers prefer solutions to be
simple , and while the inclusion of complexities and
interrelations in models is necessary, including a great deal of complexity
can result in opacity for those not involved in model development, leading to
a variety of issues. The complexity, feedbacks, uncertainties, and presence
of natural variabilities in socio-ecological systems also introduce issues in
learning from systems due to the obfuscation of system signals
, and similar issues will also be prevalent in
socio-hydrological systems.
Similarities between socio-hydrology and other subjects
Complex systems and co-evolution: studies in socio-ecology and
eco-hydrology have had complex and co-evolutionary systems techniques applied
to them, and so socio-hydrology may learn from this. While this is one of the
ways in which socio-hydrology is similar to socio-ecology and eco-hydrology,
it is also one of the ways in which socio-hydrology separates itself from
IWRM. The specific aspects of complex/co-evolutionary dynamics that may be
learnt from include the following.
Non-linear dynamics: socio-hydrology will involve investigating
non-linear dynamics, possibly including regime shift, tipping points and time
lags, all of which have been investigated in socio-ecology.
Feedbacks: the two-way interactions between humans and water will
bring about feedbacks between the two, which have important consequences.
Discerning impacts and causations in systems with feedbacks, and learning to
manage such systems have been covered in socio-ecology and eco-hydrology.
Uncertainties: while some aspects of the uncertainty present in
socio-hydrology are not found in other subjects (see Unique Aspects of
Socio-hydrology), some aspects are common with socio-ecology and
eco-hydrology. In particular, propogative uncertainties present due to
feedbacks and interactions, and the nature of uncertainties brought about by
the inclusion of social systems are shared.
Inter-scale analysis: both socio-ecology and eco-hydrology involve
processes which occur on different spatio-temporal scales, so methods for
this integration can be found in these subjects.
Incorporation of trans-/inter-disciplinary processes: socio-ecological
models have needed to incorporate social and ecological processes, and so
while the particular methods used to incorporate social and hydrological
processes may be different, lessons may certainly be learnt in integrating
social and biophysical processes.
Disciplinary bias: researchers in socio-ecology generally came from
either ecology or the social sciences, and so studies could occasionally be
biased towards either of these. Critiquing and correcting these biases is
something that socio-hydrologists can certainly learn from.
Unique aspects of socio-hydrology
Nature of water combined with nature of social system: while
socio-ecology has incorporated social and ecological systems, and
eco-hydrology has incorporated hydrological and ecological systems, the
integration of hydrological and social systems brings a unique challenge.
Nature of water: water is a unique subject to model in many ways.
It obeys physical rules, but has cultural and religious significance beyond
most other parts of the physical world. It flows, is recycled via the water
cycle, and is required for a multitude of human and natural functions.
Hydrological events of interest are also often extremes.
Nature of social system: aspects of social systems, such as
decision-making mechanisms and organisational structures, require models to
deal with more than biophysical processes.
Particular human–water interactions: there will be particular
processes which occur on the interface between humans and people which
are neither wholly social nor wholly physical processes. These will
require special attention when being modelled, and will necessitate the use
of new forms of data.
The role of changing norms: one of the focuses of socio-hydrological
study is the impact of changing social values. Norms change on long
timescales and are highly unpredictable, and so will present great
difficulties in modelling.
Scale: socio-hydrological systems will involve inter-scale modelling,
but the breadth of spatial and temporal scales necessary for modelling will
present unique problems.
Uncertainties: socio-hydrological systems will involve uncertainties
beyond those dealt with in socio-ecology and traditional water sciences. The
level of unknown (and indeed unknown unknown) is great, and brings about
particular challenges (see later section on uncertainty)
Concepts
Another aspect to the question of “what?” in this paper is the topic of
what concepts are involved when developing socio-hydrological models. These
concepts underpin the theory behind socio-hydrology, and as such modelling of
SHSs; only when they are properly understood is it possible to develop
useful, applicable models. The following sections detail different concepts
applicable to socio-hydrological modelling.
Human–water system representations
People interact with water in complex ways which extend between the physical,
social, cultural and spiritual . How the human–water
system is perceived is a vital component of socio-hydrological modelling,
since this perception will feed into the system conceptualisation
, which will then feed into the model, and as such its
outputs. In the past, linear, one-way relationships have often been used,
which observations have suggested “give a misleading representation of how
social-ecological systems work” . This unidirectional
approach may have been more appropriate in the past when anthropogenic
influences were smaller, but since the interactions between hydrology and
society have changed recently (as has been described previously), “new
connections and, in particular, more significant feedbacks which need to be
understood, assessed, modelled and predicted by adopting an interdisciplinary
approach” , and so the view of systems in models should
appreciate this. Views and knowledge of the human–water system have changed
over time, and these changes themselves have had a great impact on the
systems due to the changes in areas of study and policy that perception and
knowledge can bring about .
The concept of the hydrosocial cycle has been a step forward in the way that
the relationship between humans and water is thought about, as it
incorporates both “material and sociocultural relations to water”
. This links well with the view of , who
pictured society as a “heterogeneous set of evolving structures that are
continuously reworked by human action, leading to cyclic change of these
structures and their emergent properties” .
Socio-hydrology uses this hydrosocial representation, and also incorporates
human influences on hydrology, whereby “aquatic features are shaped by
intertwining human and non-human interaction” to form a bi-directional view
of the human–water system . Technology could also
be included in these representations, as was the case in a study by
, where irrigation was considered in both social and
technical terms.
Socio-hydrological human–water system representations should be considered
in a case-specific manner, due to the fact that the relationship is very
different in different climates. To give an extreme example, the way in which
humans and water interact is atypical in a location such as Abu Dhabi, where
water is scarce, desalination and water recycling provide much of the
freshwater, and as such energy plays a key role . In
this case, energy should certainly be included in socio-hydrological problem
formulations since it plays such a key role in the relationship
.
Figure shows an example of a conceptualised
socio-hydrological system , which gives insight into the
view that the author has of the system. It shows the linkage perceived
between the social and hydrological systems, and the “order” in which the
author feels interactions occur. In this system conceptualisation it is
perceived that there are two feedback loops which interact to form system
behaviour. One is a reinforcing loop, whereby increases in land productivity
lead to economic gain, increased population, a higher demand for water and as
such changes in management decisions, likely to be intensification of land
use (and vice versa); the other loop is termed the “sensitivity loop”
, whereby land intensification may impact upon ecosystem
services, which, when the climate and socio-economic and political systems
are taken into account may increase sensitivity to environmentally
detrimental effects, and cause behavioural change. This second loop acts
against the former and forms dynamic system behaviour. Others may have
different views on the system, for example there may be more (or less)
complexity involved in the system, as well as different interconnections
between variables, and this would lead to a different conceptual diagram.
©, reproduced
with permission under the CC Attribution License 3.0. A conceptual
representation of a socio-hydrological system .
When forming a system representation, the topics of complex and
co-evolutionary systems should be kept in mind so that these concepts may be
applied where appropriate. These concepts are introduced in the following
sections.
Complex systems
Complex systems have been studied in many spheres, from economics
, physics, biology, engineering, mathematics, computer
science, and indeed in inter-/trans-disciplinary studies involving these
areas of study , or other systems involving interconnected
entities within heterogeneous systems . By way of a definition
of complex systems, give their view on the necessary and
sufficient conditions for a system to be considered complex.
An “ensemble of many elements”: there must be different elements
within the system in order for interactions to occur, and patterns to emerge.
“Interactions”: elements within a system must be able to exchange or
communicate.
“Disorder”: the distinguishing feature between simple and
complex systems is the apparent disorder created by interactions between
elements.
“Robust order”: elements must interact in the same way in order for patterns to
develop.
“Memory”: robust order leads to memory within a system.
Complex systems representations rely on mechanistic relationships between
variables, meaning that the dynamic relationships between different system
components do not change over time , as opposed to
evolutionary relationships, whereby responses between components change over
time due to natural selection .
investigates the interactions between humans and their landscapes, and
determines that emergent behaviours in these systems are due to the “induced
coupling” between them, and so should be modelled and managed using
complex-systems-appropriate techniques. Resilience has also been studied with
regard to complex systems, and the interactions in complex systems have been
said to lead to resilience . Complex systems are an
excellent framework within which to study socio-hydrological systems, since
they allow for the discernment of the origin of complex behaviours, such as
cross-scale interactions, non-linearity and emergence ,
due to their structure being decomposable and formed of subsystems that may
themselves be analysed.
Co-evolutionary systems
A related, though subtly different view of the human–water relationship is
that of a co-evolutionary system. provide an excellent
analysis of the application of the co-evolutionary framework to
socio-hydrology, and so for an in-depth view of how to model co-evolutionary
systems, the reader is directed here. In this paper an outline of what
co-evolutionary systems are is given, before analysing whether this is
applicable to socio-hydrology and reviewing applications of the
co-evolutionary framework in human–water circumstances.
The strict meaning of a co-evolutionary system is occasionally “diluted”
in discussions of CHANS and socio-hydrology, though a
looser usage of the term is certainly of relevance. In a strict application
of the term co-evolutionary, two or more evolutionary systems are linked such
that the evolution of each system influences that of the other
; an evolutionary system is one in which entities exists,
include responses that may vary with time (as opposed to mechanistic systems,
in which responses are time-invariant), involving the mechanisms of
“variation, inheritance and selection” .
give a guide in identification of co-evolutionary
systems.
Identify evolutionary (sub)systems and entities.
Provide a characterisation of variation in each system.
Identify mechanisms that generate, winnow and provide continuity for variation in each
system.
Describe one or more potential sequences of reciprocal change that result in an evolutionary change in one or more
systems.
Identify possible reciprocal interactions between systems.
Identify effects of reciprocal interactions.
Whether or not the biophysical, hydrological system is viewed as evolutionary
in nature determines whether socio-hydrological dynamics may be termed
co-evolutionary, since state that “Linking an
evolutionary system to a non-evolutionary system does not produce
co-evolutionary dynamics. It produces simple evolutionary dynamics coupled to
a mechanistic environment”, which would imply that socio-hydrological
systems are not co-evolutionary in nature, perhaps rather being complex
systems, or systems of “cultural ecodynamics” .
allows for a looser definition of a
co-evolutionary relationship, whereby two systems interact and impact one
another such that they impact one another's developmental trajectory.
gives paddy rice
agriculture as an example of a co-evolutionary system: in this example,
changes in agricultural practice (investment in irrigation systems for
example) led to higher land productivity and to societal development; the
usage of paddy-based techniques then required the development of social
constructs (water-management institutions and property rights) to sustain
such farming methods, which served to socially perpetuate paddy farming and
to alter ecosystems further in ways that made the gap between land
productivity between farming techniques greater, and so led to yet greater
societal and ecosystem change. Western monoculture may also be viewed in the
same light, with social systems such as insurance markets, government bodies
and agro-technological and agrochemical industries developed to be perfectly
suited to current agriculture , but these constructs
having been borne out of requirements by monocultures previously, and also
serving to perpetuate monoculture and make its usage more attractive. The
crucial difference between the two views is that do not
consider biophysical systems, such as hydrological or agricultural systems,
evolutionary in their nature , since the biophysical
mechanisms behind interactions in these systems are governed by Newtonian,
rather than Darwinian, mechanisms.
Even if the strict definition of a co-evolutionary system does not apply to
socio-hydrology, the co-evolutionary framework may be used as an
epistemological tool , a way to develop understanding, and
so the subtle difference between complex and co-evolutionary systems should
be kept in mind when developing socio-hydrological models, if for no other
reason than it may remind developers that non-stationary responses may exist
(whether this implies co-evolution or not), largely in terms of social
response to hydrological change. The usage of a co-evolutionary framework
also allows the usage of the teleological principle (i.e. an end outcome has
a finite cause), which allows, for example, for policy implications to be
drawn .
There are already examples where a co-evolutionary perspective has been taken
on an issue that may be termed socio-hydrological/-ecological; these examples
and how useful the co-evolutionary analogy is are examined here.
uses a co-evolutionary perspective to look at how water
resources have been developed in the past: Athens in Greece is used as an
example, where expansions in water supply led to increases in demands, which
required further expansion. However, this cycle is not seen as predetermined
and unstoppable; rather, it is dependent on environmental conditions,
governance regimes, technology and geo-politics, all of which are impacted
by, and evolve with, the changes in water supply and demand, as well as each
other. The relationship between the biophysical environment and technology is
particularly interesting: the environment is non-stationary as water supply
expands, as innovation and policy, driven by necessity to overcome
environmental constraints, result in environmental changes, both expected and
unforeseen, which then result in socioeconomic changes and new environmental
challenges to be solved. The evolutionary perspective used in looking at
innovation overcoming temporary environmental constraints, but also creating
new issues in the future, is very useful in understanding how human–water
systems develop. A study by takes a
co-evolutionary approach to climate change impact assessment and determines
that using indicators of sustainability in a bi-directional manner (both as
inputs to and outputs from climate scenarios) is possible, and that a
co-evolutionary view of the human–climate system, involving adaptation as
well as mitigation measures, results in a “more sophisticated and dynamic
account of the potential feedbacks” . The dynamics that
are implied using co-evolutionary frameworks are also interesting, as shown
in studies by , whereby the co-evolution of humans and water
in a river basin system brings about long stable periods of system
equilibrium, punctuated by shifts due to internal or external factors, which
indicates a “resonance rather than a cause-effect relationship”
between the systems.
The usage of a co-evolutionary framework could be beneficial in governance
and modelling of socio-hydrological systems, and the previously mentioned
IAHS paper states that the co-evolution of humans and
water “needs to be recognized and modelled with a suitable approach, in
order to predict their reaction to change”. The co-evolution of societal
norms with environmental state may be particularly interesting in this
respect. The “lock-in” that is created by technological and policy changes
in co-evolutionary systems, which can limit reversibility of decisions in
terms of how resources are allocated , also means that
improving the predictive approach taken should be a matter of priority,
decisions taken now may result in co-evolutionary pathways being taken that
cannot be altered later . The implication of a potential
lack of knowledge of long-term path dependencies for current policy decisions
should be that, rather than seeking optimal policies in the short term,
current decisions should be made that allow development in the long term and
maintain the potential for system evolution in many directions
.
Complex adaptive systems
In understanding the concept of sustainability, explains
that the dynamic behaviour seen in natural systems, “is distinct from
(simple or complex) dynamic or (merely) evolutionary change”, and is instead
a complex mixture of mechanistic and evolutionary behaviours. However, as was
previously explained, the strict use of the term “co-evolutionary” is
perhaps not applicable in socio-ecological systems, and so perhaps a better
term to be used would be “complex adaptive systems” .
Complex adaptive systems are a subset of complex systems in which systems or
system components exhibit adaptivity (not necessarily all elements or
subsystems); gives a good introduction. The important
distinction between complex systems and complex adaptive systems is
that, in complex systems, if a system reaches a previously seen state, this
indicates a cycle, and so the system will return to this state at another
point. Due to the adaptivity and time-variant responses, this is not the case
in complex adaptive systems.
The complex adaptive systems paradigm has already been used in a
socio-hydrological context, being used to investigate Balinese water temples
that are used in irrigation .
Policy implications of complex adaptive systems have also been investigated
by and , and are summarised as the
following.
Nonlinearity – should be included in models such that surprises
are not so surprising. Time-variant responses also mean that adaptive,
changing management practices should be used, as opposed to stationary
practices.
Scale issues – processes occur on different spatial scales and
timescales, and so analysis of policy impacts should be conducted on
appropriate, and if possible on multiple, scales.
Heterogeneity – heterogeneity in complex systems results in the
application of homogeneous policies often being sub-optimal.
Risk and uncertainty – Knightian (irreducible) uncertainty
exists in complex adaptive systems.
Emergence – surprising results should not be seen as
surprising, due to the complex, changing responses within systems.
Nested hierarchies – impacts of decisions can be seen
on multiple system levels due to the hierarchies within complex adaptive
systems.
As can be seen, these policy issues are very similar to those mentioned in
previous sections relating to management of socio-hydrological and
socio-ecological systems, which is not surprising.
Ultimately, in the modelling of socio-hydrological systems, it is not
necessary to state whether the system is being treated as a complex system, a
co-evolutionary system or a complex adaptive system; rather, it is the
implications that the lens through which the system is seen has, via the
representation of the system in model equations, that are most important.
There are clearly dynamics that both do and do not vary in time in
socio-hydrological systems, and so these should all be treated appropriately.
Perhaps the most important outcome of the human–water system representation
should be a mindset to be applied in socio-hydrological modelling, whereby
mechanistic system components are used in harmony with evolutionary and
adaptive components to best represent the system.
Space and time in socio-hydrological modelling
In several previous sections, the issues of scale that socio-ecological and
socio-hydrological systems can face were presented and their significance
stressed. As such, a section looking at space and time in socio-hydrology is
warranted. Hydrology involves “feedbacks that operate at multiple
spatiotemporal scales” , and when coupled with human
activities, which are also complex on spatial and temporal scales
, this picture becomes yet more complicated, though these
cross-scale interactions are the “essence of the human-water relationship”
. As a method of enquiry, modelling allows for investigations
to be conducted on spatiotemporal scales that are not feasible using other
methods, such as experiments and observations (though the advent of global
satellite observations is changing the role that observations have and the
relationship between observations and modelling to one of modelling
downscaling observations and converting raw observations into actionable
information) (see Fig. ), and so is a
useful tool in investigating socio-hydrology. However, ensuring the correct
scale for modelling and policy implementation is of great importance, as both
of these factors can have great impacts on the end results
.
In terms of space, the interactions that occur between natural and
constructed scales are superimposed with interactions occurring between
local, regional and global spatial scales. Basins and watersheds are seen as
“natural” scales for analysis, since these are the
spatial units in which water flows (though there are of course watersheds of
different scales and watersheds within basins, and so watershed-scale
analysis does not answer the question of spatial scale on its own); however,
these often do not match with the scales on which human activities occur, and
indeed human intervention has, in some cases, rendered the meaning of a
“basin” less relevant due to water transfers . The
importance of regional and global scales has been recognised, with
stating that “the meso-scale focus on river basins
will no longer suffice”. Another issue of spatial scale is that of the
extents at which issues are created and experienced
: some issues, for instance point-source pollution, are
created locally and experienced more widely, whereas issues of climate are
created globally, but problems are experienced more locally in the form of
droughts and floods. This dissonance between cause and effect can only be
combated with policy on the correct scale. Creating models involves scale
decisions, often involving trade-offs between practicalities of computing
power and coarseness of representation , which can impact
the quality of model output. The previous points all indicate there being no
single spatial scale appropriate for socio-hydrological analysis; instead,
each problem should be considered individually, with the relevant processes
and their scales identified and modelling scales determined accordingly. This
could result in potentially heterogeneous spatial scales within a model.
Temporal and spatial scales at which different
research approaches are appropriate (adapted with permission from
, ©, used under the CC
Attribution License 3.0).
The interactions between slow and fast processes create the temporal dynamics
seen in socio-ecological systems ; slow, often unnoticed,
processes can be driven which lead to regime shift on a much shorter
timescale , and in modelling efforts these slow processes
must be incorporated with faster processes. Different locations will evolve
in a socio-hydrological sense at different paces, due to hydrogeological
and social factors, and so socio-hydrological models
should be developed with this in mind. Also, different policy options are
appropriate on different timescales, with efforts such as rationing and
source-switching appropriate in the short term, as opposed to infrastructure
decisions and water rights changes being more appropriate in the long term
. All of these factors mean that a variety of
timescales, and interactions between these, should be included in models, and
analyses on different timescales should not be seen as incompatible
.
Data
One of the cornerstones of study in hydrological sciences is data. However,
there are significant problems in obtaining the data required in a
socio-hydrological sense. Some of the issues present in this area are the
following.
Timescales: an issue in accruing data for long-term hydrological
studies is that “detailed hydrologic data has a finite history”
. Good data from historical case studies are difficult to
obtain, and so shorter-term studies sometimes have to suffice. The focus on
long-term analysis that socio-hydrology takes exacerbates this problem,
particularly since historical case studies are of great use during the
system-understanding phase that the subject is currently in.
Availability: where data are widely available, it may
be possible for minimal analysis to be carried out, and for data-centric
studies to be carried out , but when the boundaries of the
system of interest are expanded to include the social side of the system,
data requirements naturally increase, and modellers are exposed to data
scarcity in multiple disciplines . Hydrological modelling
often suffers from data unavailability , but
significant work has been carried out in recent years on prediction
in ungauged basins to reduce this, and so
perhaps the potential multi-disciplinary data scarcity issues in
socio-hydrology could borrow and adapt some techniques. Papers discussing
solutions for a lack of data in a socio-hydrological context are also already
appearing . Data scarcity can heavily influence the
modelling technique used : lumped conceptual models tend to
have “more modest…data requirements” , whereas
distributed, physically based models tend to have “large data and computer
requirements” . A smaller amount of data may be
necessary in some socio-hydrological studies, since the collection of a
significant quantity of extra data (when compared to hydrological studies)
also incurs an extra cost, both in terms of financial cost and time .
Interdisciplinary integration: the integration of
different data types from different fields is complex ;
socio-hydrology will have to cope with this, since some aspects of
socio-hydrological study are necessarily quantitative and some qualitative.
Since the subject of socio-hydrology has come largely from those with a
hydrology background, integrating qualitative data sources with more
quantitative sources that hydrologists are commonly more comfortable with
could pose some issues . However, the necessary
interdisciplinary nature of socio-hydrology also means that communication
between model developers from different subject areas should be enhanced
, so that everyone may gain.
New data: in order to capture some of the complex
socio-hydrological interactions, socio-hydrology should seek to go beyond
merely summing together hydrological and social data, and instead investigate
the use of new, different data types. Saying that this should be done is
easy, but carrying it out in practice may be much more difficult, since the
nature of these data and how they would be collected are presently unknown.
To this end, point out that the use of stylised
models can help to guide researchers towards the data that are needed,
setting off an iterative process of model–data–theory development. With
regard to unconventional data, have propounded the use of
proxy data in socio-hydrology where data do not exist, and
have investigated the potential for an unconventional
data source for socio-hydrology: historical maps.
Complexity
The expansion of system boundaries to include both social and hydrological
systems introduces more complexity than when each system is considered
separately. The increased complexity of the system leads to a greater degree
of emergence present in the system, though this does not necessarily mean
more complex behaviours . The level of complexity required
in a model of a more complex system will probably itself be more complex
(though not necessarily, as said, “the art of modelling
is to incorporate the essential details, and no more”) than that of a
simpler system, since model quality should be judged by the ability to match
the emergent properties of the behaviour a system .
introduces the different types of complexity:
Algorithmic complexity: this may be split into two varieties of
complexity. One is the computational effort required to solve a problem, and
the other is complexity of the simplest algorithm capable of reproducing
system behaviour.
While the first side of algorithmic complexity is important in
socio-hydrological modelling, since mathematical problems should be kept as
simple as is practicable, the second facet of algorithmic complexity is most
applicable to socio-hydrological modelling, as modellers should be seeking to
develop the simplest possible models that can replicate the behaviour of
socio-hydrological systems.
Deterministic complexity: the notion that every outcome has a root
cause that may be determined, however detached they may seemingly be, is at
the heart of deterministic complexity. Feedbacks, sensitivities to changes in
parameters and tipping points are all part of deterministic complexity.
The study of complex systems using mechanistic equations implies that
there are deterministic relationships within a system; since
socio-hydrological modelling will use such techniques, deterministic
complexity is of interest. Using deterministic principles, modellers may seek
to determine the overall impacts that alterations to a system may have.
Aggregate complexity: this is concerned with the interactions within
a system causing overall system changes. The relationships within a system
lead to the emergent behaviours that are of such interest, and determining
the strengths of various correlations and how different interactions lead to
system level behaviours gives an idea of the aggregate complexity of a
system.
Aggregate complexity is of great interest to modellers of socio-hydrological
systems. Determining how macro-scale impacts are created via interactions
between system variables is a central challenge in the subject, and so
determining the aggregate complexity of socio-hydrological systems may be an
interesting area of study.
The increased complexity of the system, and the previously mentioned issues
of possible data scarcity from multiple disciplines, could lead to issues.
Including more complexity in models does not necessarily make them more
accurate, particularly in the case of uncertain or poor resolution input data
; this should be kept in mind when developing
socio-hydrological models, and in some cases simple models may outperform
more complex models. Keeping in mind the various forms of complexity when
developing models, socio-hydrologists should have an idea of how models
should be developed and what they may be capable of telling us.
Model resolution
As well as being structured in different ways, there are different ways in
which models can be used to obtain results via different resolutions. Methods
include analytical resolution, Monte Carlo simulations, scenario-based
techniques and optimisation . Analytical resolutions,
while they give a very good analysis of systems in which they are applied,
will generally be inapplicable in socio-hydrological applications, due to the
lack of certain mathematical formulations and deterministic relationships
between variables which are required for analytical solutions. Monte
Carlo analyses involve running a model multiple times using various
input parameters and initial conditions. This is a good method for
investigating the impacts that uncertainties can have (an important aspect in
socio-hydrology), though the large number of model runs required can lead to
large computational requirements. Optimisation techniques are useful when
decisions are to be made; using computer programs to determine the “best”
decision can aid in policy-making, however, optimisation techniques should be
used with care: the impacts that uncertainties can have, as well as issues of
subjectivity and model imperfections can (and have) lead to sub-optimal
decisions being made. Techniques such as multi-objective optimisation
seek to make more clear the trade-offs involved in
determining “optimal” strategies.
Uncertainty
Uncertainty is an issue to be kept at the forefront of a modeller's mind
before a modelling technique is chosen, while models are being developed and
once they produce results. There are implications that uncertainty has in all
modelling applications, and so it is important to cope appropriately with
them, as well as to communicate their existence . Some of
the modelling techniques, for instance Bayesian networks, deal with
uncertainty in an explicit fashion, while other techniques may require
sensitivity analyses or scenario-based methods to deal with uncertainty. In
any case, the method by which uncertainty is dealt with is an important
consideration in determining an appropriate modelling technique.
Uncertainty in socio-hydrology could certainly be the subject of a paper on
its own, and so while this paper outlines some of the aspects of uncertainty
which have particular significance for modelling, some aspects are not
covered in full detail. For more detailed coverage of uncertainty in a
socio-hydrological context, the reader is directed towards
and .
Uncertainty in hydrological models
Hydrological models on their own are subject to great uncertainties, which
arise for an array of reasons and from different places, including external
sources (for instance uncertainties in precipitation or human agency,
internal sources (model structure and parameterisation), as well as data
issues and problem uniqueness . In the current changing
world, many of the assumptions on which hydrological models have been built,
for instance non-stationarity , have been challenged, and
new uncertainties are arising . However, the extensive
investigations into dealing with uncertainty (particularly the recent focus
on prediction in ungauged basins ) can only be of
benefit to studies which widen system boundaries. The trade-offs between
model complexity and “empirical risk” in modelling,
ways to deal with large numbers of parameters and limited data
, as well as statistical techniques to cope with
uncertainties have all been well investigated, and knowledge
from these areas can certainly be applied to future studies.
Uncertainty in coupled socio-hydrological models
Interactive and compound uncertainties are an issue in many subjects, and
indeed already in water science (particularly the policy domain). Techniques
already exist in water resource management for taking action under such
uncertainties, for instance the method used by , whereby
upper and lower bounds are found for an objective function that is to be
minimised/maximised to help identify the “best” decision, and to identify
those that may suffer due to various uncertainties. This approach extends
that taken in sensitivity analyses, and is a step forward, since sensitivity
analyses usually examine “the effects of changes in a single parameter...
assuming no changes in all other parameters” , which can
fail to detect the impact of combined uncertainties in systems with a great
deal of interconnections and feedbacks. The amplifications that feedback
loops can induce in dynamic systems mean that the impact of uncertainties,
particularly initial condition uncertainties, can be great .
There are aspects to socio-hydrology which induce issues regarding
uncertainties which are beyond mere propagation of deterministic uncertainty.
The nature of the hydrological input brings about “aleatory” uncertainty
, in which random variability brings uncertainty;
this variability can be coped with in modelling to a certain extent by using
probabilistic or stochastic methods; however, some of the effects that it
brings about, for instance surprise , have much more serious
implications. The random nature of the times at which extreme hydrological
events occur, and the often event-based response that humans take, means that
very different trajectories can be predicted in socio-hydrological systems,
depending on when events occur. argue that surprise should
be accounted for more fully in flood risk assessment, and that thorough
analyses should be carried out in which the possibility of surprise and the
vulnerability of a system to surprising events are accounted for.
Examples of studies that include some
aspect of modelling human–water interaction.
Reference
Approach
Case studied
Reason for modelling
ABM
Irrigation system, Senegal River Valley
Determining suitability
of modelling approach to application
ABM
Water management, northern Thailand
Analysis of policy approaches
ABM
Prediction of farmer responses to policy options
Understanding behavioural processes
ABM
Amu Darya River basin, Central Asia
Determining origins of system resilience
CCM
Herault (France) and Ebro (Spain) catchments
Understanding supply–demand dynamics
CCM
Worldwide, areas of cereal production
Predicting areas of future vulnerability
SD
Pastoral drylands, Kalahari, Botswana
Predicting areas of future vulnerability
SD
Murrumbidgee Catchment, Australia
System understanding
SD
Murrumbidgee Catchment, Australia
System understanding
SD
Water quality of Dianchi Lake, Yunnan Province, China
Decision support
SD
Tarim River basin, Western China
System understanding
SD
Acequia irrigation systems, New Mexico, USA
System understanding; stakeholder participation;
prediction of future scenarios
SD
Human–flood interactions, fictional catchment
System understanding
SD
Human–flood interactions, fictional catchment
System understanding
SD
Reservoir operation policies
System understanding
GT
Multi-operator reservoir systems (no specific case)
Policy
BN
Nyando Papyrus Wetlands, Kenya
System understanding;
evaluation of policy options
Other
Water supply and demand, Chennai, India
System understanding; analysis of possible
alternative historical trajectories
Other
Decreasing flows in the Arkavathy River, South India
Policy; focusing future research efforts
Other
Social, ecological and hydrological dynamics
System understanding
of the Lake Naivasha basin, Kenya
ABM: agent-based modelling; CCM: coupled
component modelling; SD: system dynamics; GT: game theory; BN: Bayesian
network; POM: pattern-oriented modelling.
Another aspect of uncertainty that socio-hydrology needs to consider is that
which term epistemic uncertainty. At present,
understanding of the nature of human–water system dynamics is relatively
poor, and this lack of knowledge means that significant uncertainty exists
around whether representations of these dynamics are correct.
characterise epistemic uncertainty as arising from
three sources: known unknowns, unknown unknowns and wrong assumptions. These
three sources of uncertainty lead to the present approach to modelling,
whereby we model based on assumed system behaviour, being called into
question. This epistemic uncertainty is related to the issue of Knightian
uncertainty: the inherent indeterminacy of the system (“that which cannot be
known” – ). In cases of epistemic and Knightian
uncertainty, the use of adaptive management techniques
is an effective way of acting in a practical sense, but does
not
necessarily provide a solution to unknown unknowns. Modelling is a key part
of the reduction of epistemic uncertainty: call
for the iterative process of “new observations, empirical studies and
conceptual modelling” to increase knowledge regarding human–water systems,
in order to reduce these uncertainties.
How?
The final component of this paper covers the “how” of socio-hydrological
modelling. give an excellent overview of how the
overall modelling process should be carried out in socio-hydrology, which the
reader is highly encouraged to read. This paper focuses on the different
specific techniques available to modellers, the background to these
techniques, how they would be developed, applied and used in socio-hydrology,
as well as the difficulties that might be faced. The above “what?” and
“why?” sections will be utilised to aid in these discussions.
Table shows some examples of modelling studies
which involve some element of human–water interaction, including details of
the technique that is used, the case studied and the reason for modelling.
While some of the studies included would be deemed socio-hydrological in
nature, many of them would not be, but are present as the inclusion of some
aspect of human–water interaction that they exhibit may be useful to future
socio-hydrological modellers.
said that “modelling is thinking made public”, and so
models may be used to demonstrate the knowledge currently held in a
community. even state that socio-hydrological models at
present may be thought of as hypotheses (rather than predictive tools), and
so reinforce this view. With the current feeling in socio-hydrological
circles being that the integration of the social and economic interactions
with water is a vital component of study, this integration should be seen and
should be included centrally in models in such a way that demonstrates the
importance of these interactions to modellers . This should
mean integration of the two disciplines in a holistic sense, including
integrating the issues faced across hydrological, social and economic
spheres, the integration of different processes from the different areas of
study, integration of different levels of scale (hydrological processes will
operate on a different scale to social and economic processes), as well as
the integration of different stakeholders across the different disciplines
.
There are numerous ways to classify models, and so before each individual
modelling technique is detailed, the more general classifications will be
detailed.
Model classifications
Data-based vs. physics-based vs. conceptual
The distinction between these different types of model is fairly clear:
physics-based models use mathematical representations of physical processes
to determine system response, data-based models seek to reproduce system
behaviour utilising available data (there also
exist hybrid models using a combination of these two approaches), and
conceptual models are based on a modeller's conceptual view of a system. The
common criticisms of the two approaches are that physics-based model results
are not always supported by the available data and are
limited due to the homogenous nature of equations in a heterogeneous world
, while metric models can represent processes that have no
physical relevance .
Bottom-up vs. top-down
There is a similar distinction between bottom-up and top-down models as
between metric and physically based. Bottom-up modelling techniques involve
the representation of processes (not necessarily physical) to develop system
behaviour, whereas top-down approaches look at system outcomes and try to
look for correlations to determine system behaviours. Top-down approaches
have been criticised for their inability to determine base-level processes
within a system, and so their inability to model the impact of implementing
policies and technologies . Bottom-up methods, while
the message they present does not need to be “disentangled”
, require a great deal of knowledge regarding specific
processes and sites, which in social circumstances in particular can be very
challenging and specific in both a spatial and temporal
sense. More detail on bottom-up and top-down modelling approaches will be
given in the sections on agent-based modelling and system dynamics modelling,
since these are the archetypal bottom-up and top-down approaches
respectively.
Distributed vs. lumped
The final distinction that is drawn here is that of distributed and lumped
models. Distributed models include provisions for spatial, as well as
temporal, heterogeneity, while lumped models concentrate study at discrete
spatial points, where dynamics vary only in time. The advantages of
distributed models are clear, particularly in a hydrological context where
spatial heterogeneity is of such importance; however, the drawbacks of
high-resolution data requirements, with high potential for uncertainty, and
larger computational requirements mean that lumped
models can be an attractive choice.
Approaches
gives an excellent, critical overview of which
modelling approaches may be used in modelling socio-ecological systems. As
socio-hydrology is closely linked to socio-ecology, these modelling
approaches are largely the same. The modelling techniques that will be
discussed here are
agent-based modelling (ABM)
system dynamics (SD),
pattern-oriented modelling (POM),
Bayesian networks (BN),
coupled-component modelling (CCM),
scenario-based modelling, and
heuristic/knowledge-based modelling.
While it is acknowledged that the modelling techniques detailed in this
review are established, traditional techniques, this should certainly not be
taken as implying that modellers in socio-hydrology should only use
traditional techniques. As has been said, this review is not intended to be a
review of socio-hydrological modelling thus far, but rather a review of
current knowledge designed to guide future socio-hydrological modelling
efforts. New or hybrid modelling techniques are likely to emerge to tackle
the specific problems that socio-hydrology poses, but any new techniques are
very likely to be based around existing methods. As such, these modelling
processes for these approaches are detailed, with a critical view on their
application in socio-hydrology taken.
In the discussions that follow, the factors that would affect the choice of
modelling approach will also be used. These are
model purpose
data availability (quantity, quality and whether it is quantitative or
qualitative),
treatment of space,
treatment of time,
treatment of system entities,
uncertainty, and
model resolution.
Now that these pre-discussions have been included, a section on the
importance of model conceptualisation is included, before each modelling
approach is focused on.
The importance of model conceptualisation
The previously mentioned statement of modelling being “thinking made
public” highlights the significance of the process
behind model development for the distribution of knowledge. The conceptual
basis on which a model is built defines the vision that a developer has of a
system (“framing the problem” – ), and is
therefore both a vital step in model development and a way that understanding
can be shared. Conceptualisations often involve “pictures”, whether these
be mental or physical pictures, and these pictures can be an excellent point
of access for those who wish to understand a system, but who do not wish to
delve into the potentially more quantitative or involved aspects. In some
cases, a conceptual modelling study can also be an important first step
towards the creation of a later quantified model (e.g. ).
There are certain facets of socio-hydrology that should be captured in all
SHS models, and so frameworks for socio-hydrological models should underlie
conceptualisations. Two frameworks for socio-hydrological models that have
been developed thus far are those of and
. The framework of highlights some key
facets of the human side of the system that are important to capture:
“Political agenda and economic development
Governance: laws and institutions
Technology and engineering
Land and resource use
Societal response”.
present a framework for the whole system, which is
composed of
catchment hydrology,
population dynamics,
economics,
ecosystem services,
societal sensitivity, and
behavioural response.
Both of these frameworks give a view of the key parts of socio-hydrological
systems: the second gives a good base for modelling the entirety of the
system, and has a very abstracted point of view of the societal dynamics,
whereas the former takes a more detailed look at the societal constructs that
lead to a particular response. Depending on the level of detail that is
sought, either or both of these frameworks could be used as a basis for a
socio-hydrological conceptualisation.
Agent-based modelling (ABM)
Having its origins in object-oriented programming, game theory and cognitive
psychology , ABM is a bottom-up approach to the modelling of a
system, in which the focus is on the behaviour and decision-making of
individual “agents” within a system . These agents may
be individuals, groups of individuals, or institutions, but are defined by
the attributes of being autonomous and self-contained, the presence of a
state and the existence of interactions with other agents and/or the
environment in which an agent exists . Decision rules are
determined for agents (these may be homogeneous or heterogeneous), which
determine the interactions and feedbacks that occur between agents (often
agents on different organisational levels ), as well as
between agents and the environment. ABMs are almost necessarily coupled in a
socio-ecological sense (though they are often not necessarily termed as
such), given that they use the decision-making processes of those within a
society to determine the actions that they will take, and as such their
impacts upon the environment and associated feedbacks, though they might not
fully look at impacts that society has upon the environment, and rather look
at human reactions to environmental changes.
Agent-based models themselves come in many forms, for example:
Microeconomic: agent rules are prescribed to optimise a given variable,
for instance profit, and make rational (or bounded rational) choices with
regards to this (e.g. ).
Evolutionary: agent decision-making processes change over time as agents
“learn” (e.g.. ) and test strategies (e.g.
).
Heuristic/experience-based: agents' rules are determined either through
via either experience, or the examination of data (e.g. ).
Scenario-based: various environmental scenarios are investigated to see
the impact upon behaviours, or different scenarios of societal behaviours are
investigated to see impacts upon the environment (e.g.
).
The development of an ABM involves a fairly set method, the general steps of
which are the following.
Problem definition
Determination of relevant system agents
Description of the environment in which agents exist
Elicitation of agent decision-making process and behaviours
Determination of the interactions between agents
Determination of the interactions between agents and the environment
Development of computational algorithms to represent agents,
environment, decision-making processes, behaviours and interactions
Model validation and calibration.
The results from ABMs will generally be spatially explicit representations of
system evolution over time, and so lend themselves well to integration with
GIS software .
ABMs may be used in socio-hydrological modelling in two contexts: firstly,
the discovery of emergent behaviour in a system, and
secondly determining the macro-scale consequences that arise from
interactions between many individual heterogeneous agents and the
environment. ABM may be used for a number of different reasons: in the
context of system understanding, the elicitation of emergent behaviours and
outcomes leads to an understanding of the system, and in particular
decision-making mechanisms where they can represent important phenomena that
may be difficult to represent mathematically . ABMs are
also very applicable in the area of policy-making, as the outcomes of
different policy options may be compared when the impact of agent behaviours
are accounted for; for instance, suggest that ABMs may
be more useful in determining appropriate flood investments than current
cost-benefit analysis (CBA) methods. In the area of resilience, the
importance of human behaviours in creating adaptive capacity of
socio-ecological systems has meant that ABMs have been
used to look at the differing levels of resilience in
different governance regimes . The usage of ABM can be
particularly strong in participatory modelling , where
agents may be interviewed to determine their strategies, and then included in
subsequent modelling stages. While ABM is seen by many as a technique with a
wide range of uses, others are less sure of its powers ,
particularly in predictive power at small scales , along with
the difficulties that can be present in validation and verification of
decision-making mechanisms . One study that has been carried
out in the specific area of socio-hydrology which incorporates agent-based
aspects is that of . In this historical study, social
and hydrological change in Chennai, India was
investigated to determine the vulnerability of those within the city to water
supply issues. The model was successfully able to incorporate different
temporal scales, and was able to identify the possibility for vulnerability
of water supplies on both a macro- and micro-scale level; the adaptive
decisions of agents that the model was able to account for played a big part
in this success. This work has been carried on via another study
in which alternative trajectories are investigated to
examine how the system might now be different had different decisions been
made in the past.
Procedure for building SFD using CLD (from
).
Step
Purpose
Key variable recognition
Identify main drivers
Stock identification
Identify system resources (stocks) associated with the main drivers
Flow module development
Provide rates of change and represent processes governing each stock
Qualitative analysis
Identify (i) additional main drivers that may have been overlooked;
(ii) causal relationships that require further analysing by specific methods;
(iii) controllable variables and their controllers;
(iv) systemic impact of changes to controllable variables;
(v) system's vulnerability to changes in uncontrollable variables.
Agent-based modelling may be particularly well placed to investigate the role
of changing norms and values in socio-hydrology; by considering the
decision-making processes of individual agents, there is an ability to
determine the implications of slow changes in these decision-making
processes. This does not, however, diminish the difficulty involved in
determining how to represent these changing norms.
Game theory
“Game theory asks what moves or choices or allocations are consistent with
(are optimal given) other agents' moves or choices or allocations in a
strategic situation.” , and so is potentially very
applicable to agent-based modelling in determining the decisions that agents
make . For a great deal of time, game theory has been
used to determine outcomes in socio-ecological systems (for example the
tragedy of the commons – ), and game theory has been
used extensively in water resource management problems , so
there is the potential that game theory could be extended to problems in a
socio-hydrological setting. However, the uncertainties that will be dealt
with in socio-hydrology (which have been discussed earlier) would be beyond
those that are currently considered in game theory, and so special attention
would need to be paid to this area were game theory to be applied.
System dynamics (SD)
System dynamics (and the linked technique of system analysis
) takes a very much top-down view of a system; rather than
focusing on the individual processes that lead to overall system behaviours,
system dynamics looks at the way a system converts inputs to outputs and uses
this as a way to determine overall system behaviour. In system dynamics,
describing the way a system “works” is the goal rather than determining the
“nature of the system” by examining the system components
and the physical laws that connect them. System dynamics can, therefore,
avoid the potentially misleading analysis of the interactions and scaling up
of small-scale processes (potentially misleading due to the complexity
present in small-scale interactions not scaling up) .
Macro-scale outcomes such as non-linearities, emergence, cross-scale
interactions and surprise can all be investigated well using system dynamics
, and its high-level system outlook allows for holism in
system comprehension .
An important facet of the system dynamics approach is the development
procedure: a clear and helpful framework that is integral in the development
of a successful model, and also provides an important part of the learning
experience. As with other modelling techniques, this begins with a system
conceptualisation, which, in this case, involves the development of a causal
loop diagram (CLD). A CLD (see examples in Figs.
and ) is a qualitative, pictorial view of the components of a
system and the linkages between them. This allows for a model developer to
visualise the potential feedbacks and interconnections that may lead to
system-level behaviours from a qualitative perspective,
without needing to delve into the quantitative identification of the
significance of the different interconnections. Depending on how a modeller
wishes to represent a system, different levels of complexity may be included
in a CLD (this complexity may then later be revisited during the more
quantitative model development phases), and CLDs (and indeed SD models) of
different complexity may be useful in different circumstances. The
differences in complexity between Figs. and
show very different levels of complexity that modellers may choose to use
(particularly since Fig. is only a CLD for one of four linked
subsystems). Once a CLD has been devised, the next stage in model development
is to turn the CLD into a stocks and flows diagram (SFD). This process is
detailed in Table , and essentially involves a qualitative
process of determining the accumulation and transfer of “stocks” (the
variables, or proxy variables used to measure the various resources and
drivers) in and around a system. Figure shows the SFD
developed from a CLD. SFD formulation lends itself better to subsequent
development into a full quantitative model, though is still qualitative in
nature and fairly simple to develop, requiring little or no computer
simulation (a good thing, as says, “extensive computer
simulations should be performed only after a clear picture…has been
established”). Once a SFD has been developed, this then leads into the
development of a full quantitative model, which will help “better understand
the magnitude and directionality of the different variables within each
subsystem and the overall impacts that the interactions
between variables have. Turning the SFD into a quantitative model essentially
involves the application of mathematical computations in the form of
differential/difference equations to each of the interactions highlighted in
the SFD. As with other modelling techniques, this quantitative model should
go through full validation and calibration steps before it is used.
©, reproduced under the
CC Attribution License 3.0. An example of a complex CLD (this is
approximately one quarter of the complete diagram).
©, reproduced
with permission under the CC Attribution License 3.0. An example of a simple
CLD from .
An example of a stocks and flows diagram (SFD)
developed from a causal loop diagram (CLD).
The application of a top-down modelling strategy, such as system dynamics,
carries with it certain advantages. The impact that individual system
processes and interactions thereof may be identified, as the root causes of
feedbacks, time-lags and other non-linear effects can be traced. This trait
makes system dynamics modelling particularly good in system understanding
applications. The usefulness of SD in learning circumstances is increased by
the different levels on which system understanding can be generated: the
different stages of model development, varying from entirely qualitative and
visual to entirely quantitative, allow for those with different levels of
understanding and inclination to garner insight at their own level, and
during different stages of model development. As such, system dynamics is an
excellent tool for use in participatory modelling circumstances. SD
techniques also give a fairly good level of control over model complexity to
the developer, since the level at which subsystems and interactions are
defined by the model developer. There are clear outcomes that emerge in many
socio-ecological and socio-hydrological systems, but the inherent complexity
and levels of interaction of small-scale processes “prohibits accurate
mechanistic modelling” , and so viewing (and modelling)
the system from a level at which complexity is appreciated but not
overwhelming allows for modelling and analyses. Another advantage that
follows from this point is that system dynamics may be used in situations
where the physical basis for a relationship is either unknown or difficult to
represent, since correlative relationships may be used as a basis for
modelling . The nature of SD models also makes it easy to
integrate the important aspect of spatio-temporal scale
integration, and the data-based typology of system dynamics means that the
“opportunity” presented by big data can be harnessed
in water resource management.
There are, of course, reasons why system dynamics would not be chosen as a
modelling technique. The first of these is the fundamental issue that all
models that view systems from a top-down perspective, inferring system
characteristics from behaviours, can only produce deterministic results
. Great care must also be taken with the level of complexity
included in a system dynamics model, since very simplistic relationships
between variables will fail to capture the complexity that is present
, while the inclusion of too much complexity is easy,
and can result in relationships that do not occur in the real world
. In systems of evolution and co-evolution, using SD
techniques may also be difficult, as the “very nature of systems may change
over time” , and so time invariant equations may not
properly model long-term dynamics. This is of particular importance in
socio-hydrology, where changing (and so time invariant) social norms and
values play a particularly important role. As such, for application in
socio-hydrology, the use of time-variant equations in SD models may be
useful.
Key advantages and disadvantages of top-down and
bottom-up modelling techniques.
Advantages
Disadvantages
Top-down
– Incomplete knowledge of system and/or processes acceptable
– Difficult to determine underlying processes
– Complexity determined more by modeller
– Correlations in data may be coincidental,
rather than due to underlying processes
Bottom-up
– Processes properly represented (where they are understood)
– Large amount of system knowledge required
– Causal link between process and outcome discernable
– Model complexity determined in part
by process complexities
Of all of the modelling techniques detailed in this review, system dynamics
has perhaps seen the most explicit usage in socio-hydrology thus far. This is
perhaps due to the usefulness of SD in developing system understanding (the
stage that socio-hydrology would currently be characterised as being at), and
the ease with which disciplines may be integrated. Models thus far have
generally been fairly simple, involving five or so system components, using
proxy measures for high-level system “parameters”. Examples include the
work of in which there are five system parameters
with a total of seven difference equations governing the behaviour of a
fictional system investigating the coupled dynamics of flood control
infrastructure, development and population in a flood-prone area. The
parameters used are proxies for the subsystems of the economy, politics,
hydrology, technology and societal sensitivity. The usage of a fairly simple
model has allowed for further work using this model, in which the impact of
changing parameters which represent the risk-taking attitude of a society,
its collective memory and trust in risk-reduction strategies are
investigated, alongside developments in which a stochastic hydrological input
was used , and a study in which control theory was used
to investigate optimality in this context, and in which the stochastic
elements of the model were replaced with periodic deterministic functions
. The model was further developed, this time simplified in
structure, by ; here, the core dynamics were focused
on, and the number of parameters and variables reduced. This step of
simplification is surely good in system dynamics models, isolating the core
features and relationships which produce system-level outcomes, while
reducing the risks of overparameterisation and excessive model complexity.
The structure of the modelling framework allowed for the development of a
fairly simple model that could show complex interactions between society and
hydrology, producing emergent outcomes, and leading to development in thought
around the subject. Another example of a system dynamics approach being taken
in socio-hydrological study is the work of , where the
co-evolution of human and water systems in the Murrumbidgee Basin (part of
the Murray–Darling Basin) was investigated in a qualitative sense to form a
system conceptualisation; this was then followed by work by
in which this conceptualised system view was turned
into a quantitative model, formed from coupled differential equations capable
of modelling past system behaviour. In this case, a slightly different set of
variables are investigated (reservoir storage, irrigated area, human
population, ecosystem health and environmental awareness), which provide
indicators of the economic and political systems in a more indirect (e.g. the
irrigated area giving an idea of economic agricultural production) but
directly measurable way. Again, this fairly simple mathematical model was
able to replicate the complex, emergent behaviours seen in the system,
particularly the “pendulum swing” between behaviours of environmental
exploitation and restoration. Studies investigating the Tarim Basin, Western
China, have followed a similar development process, with a conceptual model
developed first to examine the system from a qualitative,
historical perspective, before a quantitative approach ,
including proxy variables for hydrological, ecological, economic and social
sub-systems, is taken to develop further understanding of how and why
specific co-evolutionary dynamics have occurred; the focus in this study was
on system learning, and so a simple model was developed to facilitate easy
understanding. The final socio-hydrological study that explicitly takes a
system dynamics approach looks at the dynamics of lake systems
; this study involves a slightly more complex SD model, but
is an excellent example of the development path through conceptualisation,
CLD formation, conversion to an SFD and subsequent quantitative analysis. The
five feedback loops that exist within the model, and their significance in
terms of system behaviour, are well explained. Again, similar (though a
slightly higher number of) variables are used in the model, including
population, economics, water demand, discharge, pollutant load and water
quality. As is clear from the choice of variables, the hydrological system is
viewed in more detail in this study, and the aspects of community sensitivity
and behavioural responses are not included explicitly.
As is clear from the studies highlighted, system dynamics has been well
applied to socio-hydrological studies. The ease with which SD facilitates
system learning, the ability for relatively simple models to (re)produce
emergent phenomena seen in socio-hydrological systems, and the clear model
development process have led to this being a common choice of modelling
framework in early socio-hydrological system study. The highlighted studies
make clear the aspects of integrated socio-hydrological systems that should
be included in all such studies (i.e. some inclusion of hydrological systems,
impacts on livelihoods and societal responses), but also the importance of
tailoring models to show in more detail those aspects that are pertinent to a
particular case study.
Pattern-oriented modelling (POM)
The previously described techniques of agent-based modelling and system
dynamics are archetypal examples of bottom-up and top-down modelling
frameworks respectively. The advantages and disadvantages of these approaches
have been detailed earlier, but are summed up in Table .
Overcoming these deficiencies is key in furthering the pursuit of accurate,
useful modelling. One way of attempting to overcome the difficulties posed by
top-down and bottom-up strategies is to attempt to “meet in the middle”
(something that has been called for a long while; ), and
this is where POM sits. Pattern-oriented models are essentially process-based
(and so bottom-up) models where system results are matched to observed
patterns of behaviour in the model calibration/validation stage
. The use of patterns in calibration, as opposed to exact
magnitudes of output parameters, makes validation simpler
, since maximum use may be found for data that are
available, and the often impracticable collection of data regarding all
output parameters becomes less necessary. Also, imperfect knowledge of
base-level processes may be overcome through emergent pattern identification
. The use of POM would allow for a simpler process-based
model, with few parameters, overcoming the problems associated with the
complexity in bottom-up models, whereby overparameterisation may lead to the
tendency for models to be able to fit data despite potentially incorrect
processes and structure, as well as reducing model uncertainty, while also
being defined by processes, rather than data, and so overcoming the
criticisms commonly levelled at top-down approaches. There are, of course,
drawbacks to the use of POM: a model being able to fit patterns does not
necessarily mean that the mechanisms included in the model are correct, and
the data required for model validation may be quite different to those which
are commonly required at present, and so using POM may require a different
approach to data collection . Also, pattern-oriented
models may still be significantly more complex than system dynamics models,
due to the modelling of base-level processes. The very fact that they are
pattern-oriented also leaves difficulties in dealing with surprise, a very
important aspect of socio-hydrology.
The model development process in POM is the following
.
Identification of processes and development of a process-based model
Model parameterisation
Aggregation of relevant data and identification of patterns
Comparison of observed patterns and those predicted by the model
Comparison of model results with other predictions (key model
outputs may need to be validated against as well as patterns)
Necessary cyclical repetition of previous steps
Pattern-oriented models would be well applied in socio-hydrological
situations. The various emergent characteristics and patterns that are
created in coupled socio-ecological and socio-hydrological systems lend
themselves perfectly to the integrated use of processes and patterns,
particularly since there are sub-systems and processes which are well
understood and the dynamics of which can be well modelled, but also those
system components which are less well understood. In less well understood
system sections, underlying processes may be uncovered by using the patterns
which define the system . POM has already found applications
in socio-ecological investigations into land-use change , though it has potential uses in many other areas.
Bayesian networks (BN)
Often, relationships between variables are stochastic, rather than
deterministic, i.e. a given input does not always give the same output and
instead there is a distribution of possible outputs. In such situations,
Bayesian networks are well applied. The advantages of using Bayesian networks
come directly from the modelling approach: uncertainties are directly and
explicitly accounted for since all inputs and outputs are stochastic
, and the use of Bayes' theorem means that
probability distributions of output variables may be “updated” as new
knowledge and data become available . Using Bayes' theorem
also allows the use of prior knowledge, since distributions of output
parameters are required to be specified prior to model start-up (to then be
changed and updated), and these prior distributions may be informed by the
literature . The fact that there are relationships (albeit
stochastic rather than deterministic) between variables also means that
direct causal links between variables may be established
. The drawbacks in using BNs are the difficulties present
in modelling dynamic systems, since BNs tend to be set up as “acyclic”
(though object-oriented and dynamic
Bayesian networks , which can model dynamic feedbacks,
are being developed and becoming more prevalent), and in the potential
statistical complexities present. A Bayesian network may be seen as a
stochastic version of a system dynamics model, and so many of the criticisms
of SD models may also be applicable to BNs; in particular, the fact that BNs
are largely based around data-defined relationships (as opposed to physically
determined or process-based relationships) between variables means that BNs
can only yield deterministic (albeit stochastically deterministic) results
that arise from data.
The model development process for a Bayesian network follows the following
basic outline.
The model is conceptualised, with variables represented as “nodes”
in the network and causal linkages between variables determined
“Parent” and “child” nodes are related with a conditional probability
distribution determining how a “child” node changes in relation to parent
nodes
Data are collected and fed into the model.
These new data cause output probability distributions to be updated.
As new data and knowledge are accumulated, the network can be
continually updated, and so the previous two points may be carried out
cyclically.
Many uncertain relationships exist within hydrology and sociology, and indeed
in the linkages between the two, so perhaps the use of stochastic
relationships and the BN framework would be an appropriate technique in
socio-hydrological studies. However adept BNs are at dealing with aleatory
uncertainties, they still cannot include information about what we do not
know we do not know, and so the issues of dealing with epistemic uncertainty
and surprise are still prevalent. has applied an acyclic
BN to a wetlands scenario to determine how wetlands may be impacted by both
natural and anthropogenic factors in an ecosystem functionality sense and how
change in wetlands ecosystems may impact upon livelihoods; however, this
model could not account for potentially significant dynamic feedbacks. The
development of dynamic Bayesian networks in a socio-hydrological context
should be a research priority in this area; the development of such models
would be of value in contexts of system understanding, policy development and
forecasting, due to the vital role that uncertainties play in all of these
areas.
Coupled component modelling (CCM)
Coupled component models take specialised, disciplinary models for each part
of a system and integrate them to form a model for the whole system.
describe how this may be “loose”, involving the
external coupling of models, or much more “tight”, involving the integrated
use of inputs and outputs. CCM therefore offers a flexibility of levels of
integration (this is of course dependent on the degree to which models are
compatible), and can be a very efficient method of model development, since
it takes knowledge from models that already exist, and will already have some
degree of validity in the system that they are modelling. The flexibility
also extends into the fact that different modelling techniques may be
integrated, and so those techniques that suit specific disciplines may be
utilised. CCM can also be an excellent catalyst for interdisciplinary
communication; models that experts from different disciplines have developed
may be integrated, necessitating communication between modellers and leading
to development in understanding of modelling in different disciplines.
However, there are of course drawbacks to using CCM; the models used may not
be built for integration , which may lead to
difficulties and necessitate significant recoding. There may also be aspects
of models that cannot be fully integrated, which could potentially lead to
feedbacks being lost. Different treatments of space and time could
potentially create difficulties in integration (though this could also be a
positive, since aspects that do not require computationally intensive models
may be coupled with those that do and result in savings). Uncertainties could
also be an issue when coupling models directly: models will have been
developed such that the outputs they generate have acceptable levels of
uncertainty, though when integrated these uncertainties may snowball. When
considering applications in socio-hydrology, the use of CCM raises other
points. Using previously developed models means coupling together previously
developed knowledge, which does have the capacity to generate new insights
into coupled systems, but does not perhaps give the view of a totally
integrated system. Some of the most important things in socio-hydrology occur
at the interface between society and water, and so using models developed to
explore each of these aspects separately may limit the capacity to learn
about strictly socio-hydrological processes. New and unconventional data
types, which will be important in socio-hydrology, will also struggle to be
incorporated using coupled disciplinary models. The use of CCM could,
however, be a good way to foster interdisciplinary communication between
those in hydrology and those in the social sciences, and may be a way to
improve transdisciplinary learning (a very important part of
socio-hydrology).
Models have certainly been coupled between hydrology and other disciplines
(for example economics e.g. ), and indeed different
aspects of hydrology have been integrated using CCM . In
socio-hydrology specifically, incorporates a multi-agent
simulation model with a physical groundwater model to try to understand
declining water table levels.
Scenario-based modelling
While perhaps not a “modelling technique” per se, and rather a method of
resolution that can be applied, the usage of scenarios in analysis has
important implications for modelling that warrant discussion. Scenario-based
approaches fall into two main categories, those which investigate different
policy implementation scenarios, and those which use scenarios of different
initial conditions (within this, initial conditions could be for instance
different socio-economic behavioural patterns, or future system states). This
means that the impact that policies may have can be analysed from two angles;
that of assuming knowledge of system behaviour and comparing decisions that
may be made, as well as admitting lack of system knowledge and analysing how
different system behaviour may impact the results that decisions have (indeed
these may also be mixed). There are several issues that socio-hydrological
modelling studies may encounter that will lead to scenario-based techniques
being applicable. Firstly, long-term modelling of systems that will involve a
large amount of uncertainty, particularly in terms of socio-economic
development, is difficult due to the snowballing of uncertainties; as such,
using likely scenarios of future development may be a more prudent starting
point for modelling studies that go a long way into the future. In a similar
way, scenarios that look at the occurrence of different surprising events
would be useful in socio-hydrology. Even if uncertainties are deemed
acceptable, the computational effort required to conduct integrated modelling
studies far into the future may make such studies infeasible, and so the use
of scenarios as future initial conditions may be necessary. Particularly in a
policy context, policies are generally discrete options, and so the first use
of scenario-based approaches mentioned (comparing options) certainly makes
sense. Studies conducted on the subject of climate change tend to use a
scenario-based approach for socio-economic development, and CHANS studies
also sometimes use scenario-based approaches (e.g. ).
The usage of scenarios has been said to have improved recently
, with more scenarios generally being used, and
appropriate interpretation of the relative probabilities of different
scenarios occurring being investigated. While the use of a scenario-based
approach for analysing policy alternatives involves very few compromises, the
use of scenarios as initial conditions for modelling future system states can
involve compromise in that the “dynamic interactions” between social and
hydrological systems will be lost in the intervening period
between model development and the time at which the model is analysing.
Heuristic/knowledge-based modelling
Heuristic modelling involves collecting knowledge of a system and using logic
or rules to infer outcomes . The process of model
development here is quite clear, with an establishment of the system
boundaries and processes, and simply gathering knowledge of system behaviour
to determine outcomes. As with scenario-based modelling and coupled component
modelling, the use of heurism in models allows the use of different modelling
techniques within the tag of “heurism”, for example
have used ABMs encoded with a great deal of
heuristic knowledge. The advantage of heuristic modelling is in the heurism:
experience and knowledge of systems is a valuable source of information, and
if system processes are understood well enough that logic may be used to
determine outcomes, then this is an excellent method. However, where system
knowledge is incomplete, or imperfect in any way (as in socio-hydrology at
present), then the usefulness of experience-based techniques falls down.
Heuristic modelling is also not generally all that useful in system learning
applications, though in cases where disciplinary models are integrated, new
heurism may be generated in the interplay between subjects.
have identified that some current socio-hydrological models
(that of ) may have “heuristic value”
, as opposed to practical, applicable value, in that some
conceptualised models of socio-hydrological systems tend to assume
relationships between variables, rather than define them via data. This gives
a different value to the term heuristic, and implies the development of
models of different structures via heuristic means. The challenge in taking
this approach “is to avoid biasing the model to predict the social behaviour
that we think should happen” .
Conclusions
This paper has reviewed the literature surrounding the modelling of
socio-hydrological systems, including concepts that underpin all such models
(for example conceptualisation, data and complexity) and modelling techniques
that have and/or could been applied in socio-hydrological study. It shows
that there is a breadth of issues to consider when undertaking model-based
study in socio-hydrology, and also a wide range of techniques and approaches
that may be used. Essentially, however, in socio-hydrological modelling,
there is a decision to be made between top-down and bottom-up modelling,
which represents a choice between representing individual system processes
(including the behaviours and decisions of people in this case) and viewing
the system as a whole; both of these approaches have advantages and
disadvantages, and the task of the modeller is to maximise the advantages and
minimise the disadvantages. There are significant challenges in representing,
modelling and analysing coupled human–water systems, though the importance
of the interactions that now occur between humans and water means that these
challenges should be the focus of significant research efforts. With regards
to future research that could be conducted following the work that has been
reviewed here, without resorting to the platitudes of improving predictions,
reducing and managing uncertainties, increasing interdisciplinary integration
and improving data, there are several examples of areas in which research
would be of benefit. Some of these topics are common to other subjects;
however, there are specific aspects that are of particular importance in
socio-hydrology.
Conceptual models of stylised socio-hydrological systems,
for example systems of inter-basin water transfer, drought or agricultural
water use: the strength that socio-hydrology should bring is a greater
understanding of how human–water interaction affects overall system
behaviour. A great deal of understanding can be generated through conceptual
studies of generalised systems, and so modelling of archetypal systems would
be of benefit. The challenge here is to move beyond models developed to mimic
behaviour that we expect, towards those capable of giving insight.
Determining the appropriate complexity for models of highly
interconnected socio-hydrological systems: the broadening of system
boundaries brings issues regarding model complexity and trade-offs between
deterministic uncertainty and uncertainty propagation. Quantifying these
trade-offs in socio-hydrological circumstances, and so determining the
appropriate level of abstraction for modelling would allow for more effective
modelling efforts.
Gathering data in socio-hydrological studies: as an
interdisciplinary subject, data in socio-hydrological study will come from a
variety of sources. While methods for collection of hydrological data are
well established, the social data that will be required, and indeed the new,
unconventional data that may be required to describe socio-hydrological
processes, may pose issues in availability and collection. The challenge here
is to maximise the utility of what is available and to develop models in an
iterative fashion, allowing early stage, conceptual models to guide data
collection, and adapting models to suit what data are available.
Determining methods for calibration and validation in
socio-hydrology: calibration and validation are issues in almost all
modelling areas. However, as a new subject, there is no
calibration/validation protocol for socio-hydrological modelling, and with
the aforementioned issues with social science data, conducting formal
calibration and validation may be difficult. As such, the development of
guidelines regarding what constitutes “validation” in socio-hydrology would
be worthy of investigation.
Discussion of emergence in socio-hydrological systems,
particularly emergence of more abstract properties, such as risk,
vulnerability and resilience: the stochastic nature of hydrological drivers
and the unpredictability of human responses renders any definite statement
regarding system behaviour largely anecdotal (though often anecdotes of
merit), and so acknowledging this stochasticity in analysis and discussion,
using properties of more abstract meaning to describe the system may be
useful in socio-hydrology.
More in-depth socio-hydrological modelling studies
across social, economic and hydrological gradients: while conceptual
modelling can build understanding to a point, case-based models can often
give a greater insight into specific system behaviours. Applying
socio-hydrological models to a range of cases will help build understanding
in this way, particularly if these cases are similar, but differentiated in
some way (e.g. responses to drought across a range of levels of economic
development). The challenge (and opportunity) that this presents is
understanding the dynamics which are general across cases, those which vary
across gradients and those which are place-specific.
Determining how best to present and use findings from
socio-hydrological studies in policy applications: the way that
socio-hydrological understanding will likely be applied in the real world is
via policy decisions. As such, understanding the best way to communicate
findings in socio-hydrology is vital. The challenge here is to communicate
the differences between the outcomes predicted by traditional analyses and
socio-hydrological studies regarding the way that policy decisions may impact
the system in the long term, while acknowledging the limitations in both
approaches.
The unifying feature of these future research topics is the development of
understanding regarding socio-hydrological systems. The most important way in
which socio-hydrology differs from other water management subjects is in
understanding the system as a whole, as opposed to focusing on problem
solving. As such, the research priorities at this stage are focused on
different ways of improving and communicating understanding.