BHS Eleventh National Symposium, Hydrology for a Changing World, Dundee 2012© British Hydrological Society Modelling the hydrological impacts of rural land use change: current state of
Trang 1BHS Eleventh National Symposium, Hydrology for a Changing World, Dundee 2012
© British Hydrological Society
Modelling the hydrological impacts of rural land use change:
current state of the science and future challenges
Neil McIntyre1*, Caroline Ballard1, Michael Bruen2, Nataliya Bulygina1,
Alexander Gelfan7, Tim Hess8, Denis Hughes9, Bethanna Jackson10, Thomas Kjeldsen11, Ralf Merz12, Jong-Sook Park3, Enda O’Connell6,
1 Imperial College London; 2 University College Dublin; 3 Swansea University; 4 The James Hutton Institute, Aberdeen; 5 Karlsruhe Institute of Technology;
6 Newcastle University; 7 Russian Academy of Sciences; 8 Cranfield University; 9 Rhodes University; 10 University of Victoria Wellington;
11 Centre for Ecology and Hydrology; 12Helmholtz Centre for Environmental Research; 1 3Paris University; 14 University of Bologna;
15 Pennsylvania State University; 16 University of Saskatchewan
*Email: n.mcintyre@ic.ac.uk
Abstract
The potential links between rural land use and water-related hazards are well recognised, and there
is increasing interest in managing the landscape to assist with flood risk mitigation and supply
of good quality water However, our ability to quantify the impact of rural land use management
on the hydrological cycle is limited and we are not yet able to provide consistently reliable
evidence to support water-related planning and policy decisions Numerous projects in the UK
and internationally have been attempting to produce new and better prediction methods and tools
over the last few years A two-day international workshop, held at Imperial College London in
June 2011, brought together 20 experts in hydrological impacts modelling with the objectives of
sharing project outputs, knowledge and ideas on the topic of modelling the impacts of land use
management change on hydrology This paper describes the main outputs from that workshop with
the aim of summarising, for selected aspects of this field, the state of the science and priorities
for future research The paper is structured into the following sections (1) Upscaling, including
maximising the role of process knowledge using physics-based models and metamodelling
approaches, and evaluation of how non-linear process dynamics can be maintained over scales
(2) Model regionalisation and paired catchment analysis as a complement or potential alternative
to physics-based modelling (3) Tools for quantifying and illustrating the integrating effect of
the channel network and associated spatial sensitivities (4) Interactions between hydrological
services and other aspects of ecosystem service provision and hence the need for holistic models
The paper concludes with a list of research challenges, a summary of current initiatives that are
addressing these challenges, and ideas for how they might be addressed in the future
Introduction
Background
Catchment management requires capacity for exploring
hydrological impacts of rural land use scenarios For example,
the interactions between land use and flooding, and land use
and drought are of considerable practical interest Sediments,
hydrochemistry and hydroecology are strongly linked to
land use management, both directly and via the hydrological
response, and integrated management would therefore require
these links to be quantified (Wheater and Evans, 2009)
Seeking evidence of the hydrological effects of
rural land use change has been difficult: where signals of
change may be expected, they tend to be obscured by other
sources of variability and data uncertainty (Beven et al.,
2008) For example, evidence-based methods of flood and
low flow estimation (Kjeldsen, 2007; Holmes et al., 2005)
cannot explicitly allow for rural land use changes Where
inter-comparison of small catchments has revealed land use
signals (Buytaert et al., 2007; McIntyre and Marshall, 2010),
these cannot be safely extrapolated to different catchments and larger scales For larger catchments, seeking evidence of effects is even more of a challenge because of the important role of the channel network in dispersion of flow and
chemistry signals (O’Donnell et al., 2011; Pattison and Lane,
2012)
The lack of unambiguous evidence about the effects of land use presents a fundamental problem for the hydrological modellers, who rely on observed evidence to formulate, calibrate and validate models The wide perception that land use has a considerable role to play in solving our water management problems provides motivation for increasing our efforts to address this problem In the past few years, numerous research groups internationally have been conceiving, evolving, discussing and testing new approaches to doing so Some of the groups at the forefront of this research (represented by the authors of this paper) were invited to a workshop in London in summer 2011 sponsored
Trang 2by the UK Flood Risk Management Research Consortium
(FRMRC, www.floodrisk.org.uk) to review progress and
current capability, exchange ideas, and discuss future
priorities This paper presents the outcomes of that workshop
General modelling approaches and their applicability
The discussions at any workshop of this nature inevitably
include debate on the relative merits of different fundamental
approaches: top-down (metric, or data-based) and
bottom-up (mechanistic, or physics-based) modelling, or some
combination of the two (conceptual modelling) Some
introduction to these types of models and the surrounding
issues is worthwhile
The essential characteristic of top-down models is
that they are based primarily on observations and seek to
characterise system response from those data In principle,
such models are limited to the range of observed data,
and effects such as land use change cannot be represented
with any confidence unless well-identified in the data
As discussed above, such signals are rarely present (e.g
Beven et al., 2008) Signals in hydrological extremes are
especially difficult to detect, and data-based approaches such
as standard flood frequency analysis are unlikely to detect
land use influences (He et al., 2011) Intercomparison of
small catchments has led in some cases to land use signals
being identified from data — most notably the experiments
conducted by the US soil conservation service in establishing
the Curve Number method — but interpreting these data,
applying them to new contexts, and upscaling to greater
scales leads to results with high uncertainty (Bulygina et al.,
2011) This is not to say that attempting to do so is worthless,
as discussed further below
In contrast to empirical modelling,
‘bottom-up’ or ‘physics-based’ modelling, uses models explicitly
based on the best available understanding of the physical
hydrological processes (Wheater et al., 1993;, O’Connell
and Todini, 1996) They first became feasible in the 1970s
when computing power became sufficient to solve the
relevant coupled partial differential equations These models
are characterised by parameters that have a direct physical
significance; a theoretical advantage is that if the physical
parameters can be determined a priori, the effects of land
use change can be explicitly represented In practice, the
underlying physics has been (necessarily) derived from
small-scale, mainly laboratory-based, observations Hence
the processes and parameter values may not apply under
conditions and at scales of interest and, in general, calibration
of scale-adjusted values is necessary The problem of
limitations of observed data sets and non-identifiability
of parameter sets then arises, leading to large uncertainty
Another practical problem is that to resolve the small-scale
non-linear processes with satisfactory numerical errors over
a whole catchment may require many days or weeks of
simulation on a personal computer Again, such problems do
not necessarily overcome the attractions of this approach, but
some thought is required about how to manage them
However, the most commonly applied hydrological
models are the ‘conceptual’ class These models abandon the
aspiration to either entirely specify the model using data, or
entirely specify the model using prior knowledge Instead,
prior knowledge is used to define the key components of
the system, typically a series of storages and equations to
describe the fluxes between storages, while calibration is
used to define the parameter values Problems of parameter
and non-identifiability have led to a general preference for
parsimonious conceptual modelling and by extensive use
of Monte Carlo methods to estimate parameter uncertainty
These models do not solve the land use impacts prediction
problem — from one point of view they make it more
complex, introducing some of the limitations of physics-based modelling (prior specification and non-identifiability) and
of data-based modelling (lack of sufficient signals of land use change in the data) The focus on uncertainty estimation
by many conceptual modellers has also raised the complex question of characterising and modelling errors and how that may affect predictions (Mantovan and Todini, 2006)
The choice between approaches in the context of modelling land use impacts not only depends on the scientific basis and potential errors associated with the methods, but also on practical criteria First, many land use interventions are made over areas that are remote from the much larger catchment scale at which flow predictions are needed This implies the need for a spatially distributed model Second, it
is often the case that the hydrological model needs to support prediction of sediment yields or water quality modelling, which will dictate the modelled hydrological variables And thirdly, there may be data and computational limits to the degree of resolution of the model as previously discussed It may be that approaches should be integrated together, or each used for a different component of the catchment, or none used
at all, instead reverting to qualitative methods
A practical requirement, common across all hydrological modelling tasks, is the need for the model and its supporting documentation to be simple enough to use within reasonable constraints of expertise, data, human and computer resources, and to provide the outputs needed to solve the problem at hand Also common to all models is the
good practice modelling procedure (Wagener et al., 2004),
including conceptual design, calibration, testing and review Apart from these general requirements, the rural land use problem places particular requirements on the hydrological model and the way its parameters are estimated:
(1) Extrapolation As introduced previously, the problem
suggests a modelling approach that does not rely entirely
on observed evidence because it is required to make predictions of responses under conditions never yet observed: some ability to integrate expert knowledge and theory is required
(2) Scale The land use management changes of interest
are likely to be at different scales from the hydrological responses of interest, for example flood risk management may involve interventions at the scale of ~100 m while often it is the catchment scale flow response that is
important (Jackson et al., 2008) This suggests a
multi-scale, distributed approach
(3) Uncertainty This is common issue in hydrological
studies, but the difficulty of extrapolating to future land use scenarios, and the potential influence that a wrong prediction may have on planning and policy, provide a particular motivation for modellers to report uncertainty in predictions
(4) Integrated analysis Managing the links between land
use and water management cannot be restricted to plain hydrology — models ideally would have the capacity
to also consider the roles of (at least) sediments and hydrochemistry
The workshop
Considerable advances in our land use impacts modelling techniques have been made over the past few years both nationally and internationally under numerous research projects The workshop was initiated and sponsored by the FRMRC in the recognition that there would be considerable benefit in sharing outcomes and ideas from these projects The workshop was two days, 12–14 June 2011 Some of the main outputs in terms of understanding the problems and tools and
Trang 3ideas for solving them are covered below For convenience
this is broken down into four overlapping sub-topics: (1)
Upscaling to maximise the use of process knowledge and
physics-based models (2) Model regionalisation and paired
catchment analysis as a complement or potential alternative
to physics-based modelling (3) Tools for quantifying and
illustrating the integrating effect of the channel network
and associated spatial sensitivities (4) Interactions between
hydrological services and other aspects of ecosystem service
provision and hence the need for holistic models
Upscaling to maximise the use of process knowledge and
physics-based models
A theoretically attractive approach to modelling land
use impacts is physics-based modelling This involves
discretising the catchment into many elemental units (e.g
Ewen et al., 2006; Park et al., 2009; Gelfan, 2010) and
integrating the elemental responses into a catchment scale
response, explicitly accounting for both non-linearity and
heterogeneity In design at least, this approach allows physical
changes associated with land use scenarios to be represented
as perturbations in model parameters values (Hashemi et
al., 2000; Kuchment and Gelfan, 2002; Park and Cluckie,
2009) However, as noted above, the inherent problems with
physics-based models means that data requirements are
high, results can be highly uncertain, and catchment scale
modelling expensive For example, in the recent work at
Pontbren in Wales, UK, to resolve the physics acceptably, it
was deemed necessary to use a 1 cm vertical discretisation
resulting in (personal computer) run-times of several hours
to cover a 1-month simulation period However, the required
grid size depends on the non-linearity of the specific case
and the accuracy criteria used: for example, applying the
Topkapi model over larger scales, Martina and Todini (2008)
concluded that 10-1000 m grid sizes can be sufficient
The need to lower the expense but preserve the
capability of physics-based models leads to the idea of
metamodelling (Ballard, 2011) Here, metamodelling is
taken to mean the substitution of a physics-based model
by a conceptual model that both maintains the same basic
hydrological principles as the physics-based model and also,
closely replicates its flood responses under a range of relevant
climate and land use scenarios Previous applications of this
general idea in hydrology are few, the closest being the UP
framework of Ewen (1997) and the emulation framework
of Young (2010) The method includes uncertainty analysis
which allows uncertainties associated with data, models
and the upscaling procedure to be propagated through
to predictions After several years of research under the
FRMRC, this upscaling method was considered to have
considerable merits for land use change impacts analysis
However, uncertainty was high, especially where the
physics-based models were developed without supporting small-scale
measurements In the Hodder catchment case study, the errors
in predicted changes in peak flows due to the metamodelling
step had magnitudes between 0 and 20% at small scale and 0
cases these errors were larger than the predicted change — the
degree of error should ideally be assessed for each individual
case
The metamodelling approach requires that the
conceptual model retains as much of the relevant hydrological
processes as possible Martina et al (2011), using the
Topkapi model (Todini and Ciarapica, 2001), illustrate the
strong hysteresis that can exist in the soil moisture-saturated
area relationship for both hill-slopes and large catchments,
and the importance of saturation excess processes after the
end of rainfall They also illustrated that typical conceptual
models, which have unique and lumped relationships between soil wetness and runoff, will produce inaccurate runoff predictions; and then showed how such hysteresis may be included to produce more satisfactory results Such attention
to knowledge of physical processes is arguably essential to development of metamodelling (and conceptual modelling more generally), to allow for the challenge of prediction beyond the range of calibration data
Another innovation towards managing the computational requirements of physics-based models, while including Monte Carlo-based uncertainty analysis for the purpose of flood risk assessments, is the specific censoring procedure of Gelfan (2010) This approach uses dynamic-stochastic modelling (Kuchment and Gelfan, 1991), which couples a deterministic physics-based model with a stochastic weather generator A very large number of samples of weather inputs is generated, but these are censored so that only those that have the potential to pose a flood risk are run through the model Such censoring/screening of Monte Carlo samples may have valuable potential for managing land use impacts uncertainties as well as climate variability
Model regionalisation and paired catchment analysis
Model regionalisation is a general term used to describe the process of generalising an empirical or conceptual model over
a whole region (or country, or even continent), rather than just
a particular catchment This is relevant because if a model can be successfully generalised over space, capturing relevant spatial signals in land use, then there is some basis for generalising the model over future land use scenarios (Merz
and Bloschl, 2009; Oudin et al.; 2010; Buytaert and Beven,
2011; Wagener and Montanari, 2011) Various problems arise however: the well-known difficulty of, and uncertainty
in, generalising conceptual model parameters over space
(McIntyre et al., 2005, Oudin et al., 2010); signals of rural
land use are weak or non-existent within most regionalisation studies (Merz and Bloschl, 2009, 2011) or do not cover all scenarios of interest (McIntyre and Marshall, 2010); and there
is an incompatibility of scale between regionalisation studies
scales at which land use change is implemented, for example field scale Nevertheless, where the physics-based upscaling route is considered too expensive and/or uncertain due to lack of the necessary data, regionalisation may be the best applicable source of information Several groups have taken this view and proposed approaches which attempt to address
the three main problems listed above (Yadav et al., 2007; Merz and Bloschl, 2009; Bulygina et al., 2011; Singh et al.,
2011)
The method of Bulygina et al (2011) encompasses
ideas from all these groups They condition a conceptual field-scale model using regionalised values of flow response indices: the baseflow index from the HOST soils classification
system (Boorman et al., 1995) and the Curve Number from
the USDA soil classification system (USDA, 1986) The indices are used to condition parameters of the model using Bayes’ equations, giving posterior parameter distributions for a given land use scenario The posterior likelihood of
a sampled parameter set is proportional to the consistency
of the simulated hydrological response with the response indices predicted for the same catchment by the HOST/ USDA systems, taking into account, not just the expected value of the index, but also the probability distribution of the index derived from these sources Trading space for time, the posterior distribution of parameter values is propagated
to uncertainty in predictions under land use scenarios In validation tests over different land uses, the regionalisation gave hydrograph outputs broadly consistent with observed
differences (Bulygina et al., 2009, 2011) although with
Trang 4high uncertainty A major limitation of this approach is the
incomplete and uncertain information about field-scale land
use management effects contained in the USDA and HOST
databases, and it was recommended that new informative
indices are sought, and carefully selected information from
the metamodelling procedure described previously is also
being introduced (Bulygina et al., 2012)
In using regionalisation for land use impacts
analysis, errors are also introduced in translating evidence of
regional variations into speculations of future time variations
Bulygina et al (2011) and Holman et al (2011) discuss
this in the context of how to apply the US Curve Number
system to UK impacts studies and Buytaert and Beven (2009)
propose including an additional term in the Bayes’ equation to
formally include this evident translation error
The problem of course changes depending on the
availability of data to support regionalisation In cases where
hundreds of well-gauged catchments are available to support
empirical regionalisation (McIntyre et al., 2005; Merz and
Bloschl, 2009; Oudin et al., 2011; Sawicz et al., 2011) the
role of expert knowledge may be relatively small However,
in more typical cases, the specification of the model and its
parameter uncertainty may rely heavily on expert knowledge
(Kapangaziwiri et al., 2009) These authors describe the
challenge in South Africa of predicting under environmental
change, where the collection and formalisation of expert
knowledge is crucial; and they describe a procedure for
comparing and combining this knowledge with regionalised
indices
Quantifying and illustrating the integrating effect of the
channel network
Runoff from the land surface travels to sites of interest via
open-channel flow in networks of ditches, streams and river
channels The impact of a land use change on the shape of
a hydrograph can depend strongly on the properties of the
channel network, and hence the accurate representation
of channel routing processes can be critical to the impacts
assessment
The aim when estimating impact at a downstream
site is to estimate the outcome when the effects of the
runoff impact hydrographs from the various subcatchments
or pixels interact and accumulate as their runoff moves
through the network This can be approached by running
a catchment hydrological model, with an explicit channel
routing model, before and after suitable adjustments are
made to the parameters describing land use at selected
pixel(s) However, achieving a sufficiently accurate model
is a demanding requirement because it is known that even
models that are calibrated accurately against observed
discharge can have false sensitivities and give inaccurate
estimates for impact (Ewen et al., 2006) Such perturbation
analysis can also be computationally demanding if we require
extensive explorations of sensitivity, for example if we want
to explore spatial variability of sensitivity, which may involve
perturbations of land use at hundreds or thousands of pixels
A new method of ‘information tracking’ (O’Donnell
et al., 2011) helps solve these problems To explain this
Hodder catchment, where there are 2634 pixels (average
an 8-parameter conceptual model In total there are 21 072
(i.e 2634 × 8) parameters that can change as a result of a
change in land use If 21 072 simulations are run in each of
which only one parameter is perturbed, this gives the basic
data required to calculate the sensitivity to a change in each
parameter This is, in effect, what is done in information
tracking, except that reverse algorithmic differentiation
(Griewank, 2000) is used to calculate the 21, 072 sensitivities
in an extremely accurate and efficient way The resulting pixel-scale sensitivities are illustrated on maps These can be used to answer questions such as: Which areas of the Hodder are most sensitive to change? What is the importance of soil type on land use impacts? And how does impact change with the rainfall pattern? The main assumption behind this method
is ‘spatial linearity’ whereby it is assumed that the total downstream impact is the sum of the impacts from individual pixels When tested on the Hodder, the error introduced
by this assumption was small for winter storms and larger for summer storms due to increased non-linearity Further exploration of this method is recommended
Interactions between hydrological services and other aspects
of ecosystem service provision
The preceding discussion highlights some of the difficulties
in demonstrating the benefits of various land use practices for managing water hazards This may make it difficult
to justify expenditure on changes in land use because the cost effectiveness cannot be quantified In practice, land management activities may have multiple effects above and beyond those that are being targeted, through modifications
to sediment or pollutant transport, ecological responses and biodiversity Some of these other responses may be easier to quantify and the multiple benefits that the land use changes deliver may become easier to justify on economic grounds This calls for implementation of an ecosystem services approach that takes a holistic overview of all of the processes occurring and their various roles in providing services
(Millenium Ecosystem Assessment, 2005; Morris et al., 2005; Jackson et al., 2012).
Examples of land use activities that might have multiple benefits include temporary flood storage zones (Environment Agency, 2012) As well as attenuating a flood peak through slowing the movement of water through the catchment, flood storage ponds often provide suitable conditions for deposition of sediment and associated particulate pollutants, such as phosphorus The wetland environment created can also be valuable for decreasing nitrate concentrations through enhanced de-nitrification Finally, wetland environments often bring biodiversity benefits and can be visually appealing Set against this, consideration of potential negative factors also needs to
be made For example, enhancement of the de-nitrification process will generate increased greenhouse gas production, and if sediments are allowed to accumulate in the storage pond, then they may be remobilised, causing enhanced pollutant transport at different times Many other land management activities such as riparian management, tree planting, minimum tillage and drain blocking will also affect more than one aspect of the water environment, pointing to the need for consideration of multiple responses
Another issue is that many land use interventions (such as those that might be implemented by a land owner) are typically quite small, meaning that on their own they would be ineffective at achieving any measurable changes
in catchment responses However, where multiple activities are undertaken, their combined effectiveness could become
valuable (Balana et al., 2012) This calls for the need of
a catchment approach to address land use and a holistic overview to identify synergistic activities or potential conflicts Furthermore, it highlights the need for buy-in from land managers if land use management techniques are to become effective in mitigating against environmental hazards
Consideration of all of these aspects clearly makes the modelling challenge much more complex, but ultimately
is more likely to deliver sustainable management practices that are both efficient and cost-effective in their outcomes
Trang 5The problem of modelling the water-related impacts of rural
land use change is closely related to that of prediction in
ungauged catchments, which have been extensively studied
over the last decade and more But in some respects the land
use problem is more difficult It usually necessitates the
ability to upscale local (e.g field scale) land use changes; it
usually requires consideration of non-stationary of vegetation,
soils and geomorphology; water managers, under public
and political pressure, want modellers to produce
near-immediate and definitive answers while, of course, not
getting it wrong; and the importance of integrated land use
management requires models of hydrology-driven variables
such as nutrients and sediments The June 2011 workshop at
Imperial College London brought together representatives of
international research groups taking a lead in addressing these
problems
The need to upscale the impacts of small-scale
change and make best use of process knowledge means that
there is significant interest in physics-based modelling The
workshop discussed methods of addressing the problem using
physics-based models within data and computational cost
constraints (Jackson et al., 2008; Gelfan, 2010), including
the estimation of errors in upscaling procedures (Martina and
Todini, 2008; Ballard, 2011) There is clearly much work
to do on providing guidance for upscaling and trade-offs
between errors and computational expense
The workshop also covered the provision of tools
and data to support spatially distributed models The need
for sufficient quality and resolution of rainfall data is clear,
and work on remote sensing (Zhu et al 2008) and stochastic
modelling (Gelfan, 2010) has improved its provision To
understand and communicate the spatial aspect of land use
change impacts, the workshop discussed tools that assess how
catchment scale impacts of a given land use change will vary
with the location of that change (O’Donnell et al., 2011)
Looking at more empirical and conceptual modelling
methods, the workshop explored concepts for classifying
catchments and dynamic signatures, to help us identify and
categorise changes and to predict possible change (Wagener
et al., 2007; Sawicz et al., 2011) The need for spatially
distributed models also requires us to use all possible sources
of information about these signatures for model identification,
including exploring merging different information types
(Kapangaziwiri et al., 2009; Bulygina et al., 2012) There is
also continued interest in managing errors and uncertainty
(Buytaert and Beven, 2009; Bulygina et al., 2011)
Clearly many of the problems and proposed
solutions discussed at the workshop are not new, and lessons
learnt in the past should not be forgotten Nevertheless, it
was clear that the problem of land use impacts modelling
is a relatively young and dynamic research field, and there
are substantial challenges yet to be met in all the sub-topics
discussed Perhaps the greatest challenge, encompassing
many theoretical and practical hurdles, will be moving on
from pure hydrology to provide multi-constituent models
needed to support a more integrated approach to land use
impacts management
Acknowledgements
The workshop was supported by the Flood Risk Management
Research Consortium Phase 2 (www.floosdrisk.org.uk),
Engineering and Physical Sciences Research Council grant
EP/F020511/1 A wide variety of funders supported the
research presented at the workshop
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