From GCMs to river flow: a review of modelling approaches Chong-yu Xu

Progress in Physical Geography 23,2 (1999) pp. 229–249
From GCMs to river flow: a review of
downscaling methods and hydrologic
modelling approaches
Chong-yu Xu
Department of Earth Sciences, Hydrology, Uppsala University, Villavägen 16,
S-75236 Uppsala, Sweden
Abstract: The scientific literature of the past decade contains a large number of reports detailing
the development of downscaling methods and the use of hydrologic models to assess the
potential effects of climate change on a variety of water resource issues. This article reviews the
current state of methodologies for simulating hydrological responses to global climate change.
Emphasis is given to recent advances in climatic downscaling and the problems related to the
practical application of appropriate models in impact studies. Following a discussion of the
advantages and deficiencies of the various approaches, challenges for the future study of the
hydrological impacts of climate change are identified.
Key words: climate change, climate scenario, downscaling, general circulation model, hydrological model.
Global climatic changes caused by increases in the atmospheric concentration of carbon
dioxide and other trace gases may begin to appear within the next few decades. It has
been estimated by many authors (see, e.g., Loaiciga et al., 1996) that if 1990-level
emissions of CO2 to the atmosphere remain unabated, its concentration in the
atmosphere could nearly double by the year 2100 or thereabouts. Assessments of the
consequences of a possible climate change for ‘double CO2’ (2 × CO2) conditions have
become standard practice. Among the most authoritative climate change assessments,
the IPCC reports (Houghton et al., 1990; 1992; 1995) give estimates of temperature
increases in the Northern Hemisphere between 3 °C and 5°C, together with precipitation change by as much as 15% under the assumption of a doubling of atmospheric
One of the most important impacts on society of future climatic changes will be
© Arnold 1999
Downscaling methods and hydrologic modelling
changes in regional water availability. Such hydrologic changes will affect nearly every
aspect of human well-being, from agricultural productivity and energy use to flood
control, municipal and industrial water supply, and fish and wildlife management.
The tremendous importance of water in both society and nature underscores the
necessity of understanding how a change in global climate could affect regional water
Global atmospheric general circulation models (GCMs) have been developed to
simulate the present climate and have been used to predict future climatic change.
While GCMs demonstrate significant skill at the continental and hemispheric spatial
scales and incorporate a large proportion of the complexity of the global system, they
are inherently unable to represent local subgrid-scale features and dynamics (Wigley et
al., 1990; Carter et al., 1994). When considering the impacts of global climate change the
focus is primarily on societal responses to the local and regional consequences of largescale changes. The conflict between GCM performance at regional spatial scales and the
needs of regional-scale impact assessment is largely related to model resolution in such
a way that, while GCM accuracy decreases at increasingly finer spatial scales, the needs
of impacts researchers conversely increase with higher resolution (Hostetler, 1994;
Schulze, 1997).
To circumvent these problems, tools for generating the high-resolution meteorological inputs required for modelling ecohydrological processes are needed (Bass, 1996).
‘Downscaling’ approaches have subsequently emerged as a means of relating largescale atmospheric predictor variables to local- or station-scale meteorological series.
Two broad classes of downscaling approaches exist: dynamic methods, involving the
explicit solving of the process-based physical dynamics of the system (e.g., Giorgi and
Mearns, 1991; Jones et al., 1995); and statistical methods that use identified system relationships derived from observational data (e.g., Wigley et al., 1990; Wilby, 1995;
Hewitson and Crane, 1996).
Hydrologic models provide a framework in which to conceptualize and investigate
the relationship between climate and water resources. The scientific literature of the
past two decades contains a large number of reports dealing with the application of
hydrologic models to the assessment of the potential effects of climate change on a
variety of water resource issues. The use of hydrological models in climate change
studies can range from the evaluation of annual and seasonal streamflow variation
using simple water-balance models (e.g., Arnell, 1992) to the evaluation of variations in
surface and groundwater quantity, quality and timing using complex distributedparameter models that simulate a wide range of water, energy and biogeochemical
processes (e.g., Running and Nemani, 1991).
This article first reviews the current state of methodologies for generating regional
climatic scenarios and assessing hydrological responses to global climate change
(section II). Emphasis is given to recent advances in climatic downscaling. The state of
the art of hydrological modelling and problems related to the practical application of
appropriate models in impact studies are then addressed (section III). Summary and
conclusions are presented in section IV.
Chong-yu Xu
Methodologies for assessing hydrological responses to global climate change
The use of direct GCM-derived hydrological output
Global atmospheric GCMs have been used directly to simulate streamflow under
present climate and to predict the impact of future climatic change in macroscale
watersheds. For the present climate, Manabe (1969) reported what appears to be the
first numerical parameterization of the hydrologic cycle within a GCM. This was a
simple bucket model assuming that, if precipitation exceeded evaporation, then the soil
‘bucket’ would fill up. If the soil reaches saturation, then runoff occurs. As long as the
soil was at or near saturation the actual evaporation rate would be equal to the potential
evaporation rate (Manabe’s bucket model did not consider vegetation). Otherwise, the
actual evaporation would be proportional to the potential rate. Russell and Miller
(1990) used the atmospheric model of Hansen et al. (1983) to calculate the mean annual
river runoff from a 5-year GCM simulation and compared the results with observed
annual runoff given by Milliman and Meade (1983). They found generally better
agreement for river basins with mean annual runoff between 200 and 600 km3/yr–1 and
poor agreement for large basins with small total runoff. Miller and Russell (1992) used
the same model to calculate the annual river runoff for 33 of the world’s major rivers
for the present climate and for a doubled CO2 climate. They showed that there are large
errors in GCM-inferred estimates of mean annual runoff. Discrepancies attributed to,
among other things, the poor model precipitation fields, the model’s parameterizations
of soil moisture and evapotranspiration are also too simplistic. Kuhl and Miller (1992)
extended the work of Russell and Miller (1990) to examine the extent to which the
atmospheric model simulates the seasonal river runoff of world rivers with an annual
discharge greater than 100 km3/yr–1 or drainage areas greater than 5 × 105 km2. The
limitations of the model in simulating seasonal river runoffs were discussed in their
The analysis of GCM-predicted runoff showed that a simplistic representation of the
hydrologic cycle within a global model of general atmospheric circulation leads to poor
hydrologic predictive skill (e.g., Kuhl and Miller, 1992; Miller and Russell, 1992). The
problem, from a hydrologic point of view, is that most GCMs contains no lateral
transfer of water within the land phase. Such models carry out a vertical water distribution at each grid point at each time interval using precipitation, evapotranspiration
and groundwater storages. However, any ‘water excess’ or overflow is simply
discarded and plays no further role in the model computations. This means that, even
if the GCMs were able to simulate water excess correctly, they would still be operating
with an incomplete hydrological cycle (Kite et al., 1994).
Coupling GCMs and macroscale hydrologic models
It is clear that the present version of the GCM does not give a good estimation of the
hydrologic responses of climatic changes. There is a need to couple hydrological models
with GCMs. Few case studies have been carried out on the world’s largest river basins.
Liston et al. (1994) used a two-linear reservoir-routing model with daily precipitation
and potential evaporation from several GCMs to simulate flows in the Mississippi River
basin. The routing model used grid boxes 2 ° latitude by 2.5 ° longitude. Kite et al. (1994)
Downscaling methods and hydrologic modelling
combined a hydrological model (the SLURP model) with a GCM for a macroscale
watershed. A water balance was carried out at 12-hour time intervals for a 10-year
period using the Canadian Climate Centre GCM II data set for grid points within and
surrounding the 1.6 × 106 km2 Mackenzie River basin in northeastern Canada. The
water surpluses from each relevant grid point were accumulated to provide a simulated
hydrograph at the outlet of the river. Nash and Gleick (1993) studied the hydrological
impact of climate change on the Colorado River basin in the western USA. Several
GCMs were used to simulate climate over the Colorado River for double CO2
conditions. Precipitation and temperature predictions from both the GCMs as well as
from hypothetical scenarios were then input to the National Weather Service River
Forecasting and Simulation (NWSRFS) hydrologic model to simulate the river basin
hydrologic cycle, including runoff. By linking atmospheric, hydrologic and river
simulation models Nash and Gleick (1993) were able to assess the potential impacts of
greenhouse warming in the Colorado River basin.
The results of these studies showed that coupling the hydrological model with the
GCM produces a better representation of the recorded flow regime than GCM
predictions of runoff for very large river basins. However, GCMs cannot ‘see’ smallerscale river basins because of their coarse grid resolution. Subgrid-scale hydrologic
models and nesting schemes are needed to resolve the large-scale GCM predictions and
to predict smaller-scale hydrologic phenomena (Hostetler and Giorgi, 1993).
Downscaled GCM climate output for use in hydrological models
Given the limitations of GCM grid-point predictions for regional climate change impact
studies, an alternative option to using direct GCM-derived hydrological output is to
downscale the GCM’s climate output for use in hydrological models. Two categories of
climatic downscaling, namely, dynamic approaches (in which physical dynamics are
solved explicitly) and empirical (the so-called ‘statistical downscaling’) exist, and are
discussed in the following sections.
a The use of dynamic downscaling approaches: Dynamic downscaling has been
attempted with three approaches (Rummukainen, 1997):
1) Running a regional-scale limited-area model with the coarse GCM data as geographical or spectral boundary conditions (also known as ‘one-way nesting’).
2) Performing global-scale experiments with high-resolution AGCMs (atmosphere
GCMs), with coarse GCM data as initial (and partially also as boundary) conditions.
3) Using a variable-resolution global model (with the highest resolution over the area
of interest).
The goal of dynamic downscaling (i.e., to extract the local-scale information from the
large-scale GCM data) is achieved by developing and using limited-area models
(LAMs) or regional climate models (RCMs).
RCMs have recently been developed that can attain horizontal resolution in the
order of tens of kilometres or less over selected areas of interest. They have been
applied with relative success to numerous regions (e.g., Giorgi and Bates, 1989;
Giorgi, 1990; Giorgi and Mearns, 1991; Hostetler and Giorgi, 1992; 1993; Giorgi et al.,
Chong-yu Xu
1990; 1993; 1994; Jones et al., 1995; Jenkins and Barron, 1997).
Compared with GCMs the resolution of these RCMs is much closer to that of
landscape-scale hydrologic models (LSHMs) and makes coupling of RCMs and LSHMs
potentially suitable for evaluating the effects of hydrologic systems. Hostetler and
Giorgi (1993) linked an RCM with an LSHM to study the effects of climate on
hydrologic systems in Steamboat Creek, Oregon. Their RCM has a 3000 × 3000 km2
domain centred over the Great Basin of the western USA. The streamflow model used
is a lumped parameter model that requires monthly average values of solar radiation
and precipitation as input. The results of the simulations indicated that it may be
feasible to use output data from RCM simulations directly as input to hydrologic
models (or other energy balance models) in climate change research aimed at, for
example, assessing potential changes in various components of the basin hydrologic
budget under increased levels of atmospheric CO2.
Another nested model approach was developed by Leavesley et al. (1992) to disaggregate large-scale model output for application in mountainous regions. Output
from a coupled GCM–mesoscale atmospheric model is used as input to an
orographic–precipitation model. The orographic–precipitation model output, at a
resolution of 2.5, 5 or 10 km, is used as input to a distributed-parameter hydrologic
model. The nested approach permits the simulation of smaller-scale atmospheric
processes and can reflect changes in precipitation frequency, magnitude and duration
consistent with the GCM response.
While nested modelling is likely to be the most informative approach to driving
regional information for 20–50 km horizontal grid spacing and 100–1000 m vertical
resolution, there are several acknowledged limitations of the approach. RCMs still
require considerable computing resources and are as expensive to run as a global GCM.
Dynamical downscaling operates on some (high-resolution) grid-point scales and thus
the results will be in the form of spatial averages. These models still cannot meet the
needs of spatially explicit models of ecosystems or hydrological systems, and there will
remain the need to downscale the results from such models to individual sites or
localities for impact studies (DoE, 1996).
b The use of statistical downscaling methods: A second, less computationally
demanding approach is statistical downscaling. In this approach, regional-scale
atmospheric predictor variables (such as area averages of precipitation or temperature)
and circulation characteristics (such as mean sea-level pressure or vorticity) are related
to station-scale meteorological series (Kim et al., 1984; Karl et al., 1990; Wigley et al.,
1990; Hay et al., 1991; 1992; ). The statistics involved can be simple or extensive, but the
final relationships are typically arrived at with some form of regression analysis.
Statistical downscaling methods can be classified according to the techniques used
(Wilby and Wigley, 1997) or according to the chosen predictor variables
(Rummukainen, 1997). In Wilby and Wigley’s study, statistical downscaling techniques
are described using three categories, namely: regression methods (e.g., Kim et al., 1984;
Wigley et al., 1990; von Storch et al., 1993); weather pattern-based approaches (e.g.,
Lamb, 1972; Hay et al., 1991; Bardossy and Plate, 1992; Wilby, 1995); and stochastic
weather generators (e.g., Richardson, 1981; Wilks, 1992; Gregory et al., 1993; Katz, 1996).
In Rummukainen’s (1997) study, statistical downscaling methods are classified as
Downscaling methods and hydrologic modelling
1) Downscaling with surface variables. This involves the establishment of empirical
statistical relationships between large-scale averages of surface variables and localscale surface variables (e.g., Kim et al., 1984; Wilks, 1989). To develop the relationships, large-scale averages constructed from local time series are used. In
application, the same local-scale surface variables will be the predictands.
2) The perfect prog(nosis) (PP) method (e.g., Zorita et al., 1992). This involves the
development of statistical relationships between large-scale free tropospheric
variables and local surface variables. In this method, both the free atmospheric data
and the surface data are from observations.
3) The model output statistics (MOS) method (e.g., Karl et al., 1990). This is similar to the
PP method, except that the free atmospheric variables, which are used to develop the
statistical relationships, are taken from GCM output.
In reality, many downscaling approaches embrace the attributes of more than one of
these methods and therefore tend to be hybrid in nature.
The general limitations, theory and practice of downscaling are well described in the
literature (e.g., Grotch and MacCracken, 1991; von Storch et al., 1993; Wilby and Wigley,
1997). A summary with respect to their assumptions, uses and limitations is given in
Table 1.
Using hypothetical scenarios as input to hydrological models
Ideally, the climate simulations from the GCMs could be used directly to drive
hydrologic models, which in turn could be used to evaluate the hydrologic and water
resource effects of climate change. The issue is complicated, as discussed above, by the
incompatibility of space (and, to a lesser extent, time) scales between hydrologic
processes and GCMs. The former must account for variations at the small-to-medium
catchment scale (e.g., tens of kilometres) and must later operate at scales of hundreds to
thousands of kilometres and upwards. More importantly, the climate models –
including the better parameterized ones (GCMs) – give different values of climate
variable changes and so do not provide a single reliable estimate that could be
advanced as a deterministic forecast for hydrological planning. Accordingly, methods
of simple alteration of the present conditions are widely used by hydrologists. Various
hypothetical climate change scenarios have been adopted and climate predictions for
‘double CO2’ conditions have become standard (e.g., Loaiciga et al., 1996).
In the simple alteration method, the generation of climate scenarios consists of two
1) Estimate average annual changes in precipitation and temperature using either
GCM results or historical measurements of change, or personal estimates (typically,
∆T = +1, +2 and +4 °C and ∆P = 0, ±10%, ±20%).
2) Adjust the historic temperature series by adding ∆T and, for precipitation, by
multiplying the values by (1 + ∆P/100).
In practice, these annual changes were distributed during the year by various methods.
For example, Nemec and Schaake (1982) and Ng and Marsalek (1992) assumed constant
distributions of climatic changes, and multiplied historical precipitation records by
Chong-yu Xu
constant factors and adjusted historical temperatures by constant increments.
Sanderson and Smith (1990) used GISS (Goddard Institute of Space Studies) scenarios
of predicted monthly changes in temperature and precipitation.
The general procedure for estimating the impacts of hypothetical climate change on
hydrological behaviour has the following stages:
1) Determine the parameters of a hydrological model in the study catchment using
current climatic inputs and observed river flows for model validation.
2) Perturb the historical time series of climatic data according to some climate change
3) Simulate the hydrological characteristics of the catchment under the perturbed
climate using the calibrated hydrological model.
4) Compare the model simulations of the current and possible future hydrological
There are a great number of studies that use such altered time series to assess possible
effects of climate change. The distinguishing characteristics of some studies are
summarized in Table 2.
At this juncture it is necessary to make clear that the climate change scenarios used
in the above studies should not necessarily be seen as the most likely future climates in
the region: they are primarily designed to show the sensitivity to change within a
reasonable interval.
Hydrologic modelling methodology
The potential applicability of hydrological models to climatic change impact studies
Research hydrologists develop simulation models that describe, in mathematical terms,
the detail involved in the dynamic and nonlinear transformation of precipitation into
streamflow via processes such as interception, infiltration, soil-water redistribution,
evaporation, transpiration, snowmelt, surface, subsurface and groundwater flows.
Operational hydrologists then use these models to seek solutions to water resource
problems, such as flood protection, frequency and duration of extreme hydrological
events (floods, droughts), irrigation, spillway design, hydropower evaluation or water
supply design, under different climatic conditions at one location or at different
locations. Following Schulze (1997), the advantages of such models in climate change
impact studies include the following:
1) Models tested for different climatic/physiographic conditions, as well as models
structured for use at various spatial scales and dominant process representations, are
readily available.
2) GCM-derived climate perturbations (at different levels of downscaling) can be used
as model input.
3) A variety of responses to climate changes scenarios can hence be modelled.
4) The models can convert climate change output to relevant water resource variables
related, for example, to reservoir operation, irrigation demand, drinking water
supply, etc.
1) Identify a large-scale predictor G,
which controls the local parameter L
2) Find a statistical relationship
between L and G
3) Validate the relationship with
independent data
4) If the relationship is confirmed,
G can be derived from GCM
experiments to estimate L
1) Simple, less computationally
2) Application is limited to the sites,
variables and seasons when the
local climate is related well to the
conditions in the free troposphere
3) Very long observation series needed
Advantages and
Regression method
1) Possible to generate
daily sequences of precipitation
at a point for any number of
days based on limited historic
data sets
2) Weather classification schemes
are somehow parochial and/or
subjective. There is a need for
more general classification
systems (Wilby, 1994)
1) Classify atmospheric
circulation pattern into limited
number of classes
2) Simulate weather types through
the use of stochastic models
3) Link the likelihood of rainfall
occurring to weather type, using
conditional probabilities
4) Simulate rainfall and/or other
hydrometeorological processes
using weather types
Circulation-based method
Table 1 Comparison of statistical downscaling approaches
Common assumptions
1) Local-scale parameters are a function of synoptic forcing.
2) The GCM used to derive downscaled relationships is valid at the scale used.
3) The relationship derived remains valid under greenhouse forcing.
1) Able to generate realistic,
statistical characteristics of
daily rainfall series
2) Relies on GCM predicted
changes in mean precipitation,
which are known to be
unreliable – a problem that is
of primary importance to
hydrology and that is yet to be
resolved (e.g., Mearns et al.,
1) Use observed daily rainfall
data to determine the
probability of the state of a day
2) Use exponential (or other)
distribution to fit and estimate
the amount of rainfall on a wet
3) Condition other weather
variables (temperature,
radiation, etc.) on the wet/dry
status of the day
Weather generator method
Downscaling methods and hydrologic modelling
Examples of
1) Large-scale predictors and local-scale 1) Relates regional rainfall to
predictands are the same variables
synoptic weather patterns:
Lamb, 1972; Hay et al., 1991;
(Wilks, 1989; Brown et al., 1995;
Carbone and Bramante, 1995; Perica
Bardossy and Plate, 1992;
and Foufoula-Georgiou, 1996)
Hughes et al., 1993; Jones et al.,
2) Large-scale predictors and local-scale
1993; Bardossy et al., 1994;
predictands are different variables
1995; Wilby, 1995; 1997
2) Relates long-term temperature
(Wigley et al., 1990; Zorita et al.,
fluctuations to synoptic weather
1992; von Storch et al., 1993; Kaas,
patterns (Sowden and Parker,
1993; 1994; Johannesson et al.,
1981; Moses et al., 1987)
1995; Conway et al., 1996;
Brandsma and Buishand, 1997)
3) Relates precipitation chemistry
3) Use of artificial neural network
to synoptic weather patterns
(ANN) approaches (Hewitson and
(Davies et al., 1986; Wilby,
Crane, 1992; 1996)
4) Relates river flow to synoptic
weather patterns (Duckstein
et al., 1993; Wilby, 1993)
1) Use of stochastic weather
generators for the present
climate (Richardson, 1981;
Foufoula-Georgiou and
Lettenmaier, 1987; Smith,
1987; Zucchini and Guttorp,
2) Use of stochastic weather
generators for climate change
and impacts studies (Wilks,
1992; Gregory et al., 1993;
Katz, 1996; Mearns et al.,
1996; Semenov and Barrow,
Chong-yu Xu
Downscaling methods and hydrologic modelling
Table 2 Comparison of the characteristics of selected studies that have used
hypothetical scenarios as input to hydrological models
Study region
Climatic scenarios
Hydrological models
Nemec and Schaake
Two basins in the
USA; one basin
in Kenya
Combination of
∆P = 0, ±10%, ±25%,
∆E = ±4%, +12%
uniformly distributed
to daily input data
Sacramento model
Ng and Marsalek
One small basin
in Newfoundland,
Combination of
∆P = ±5%, ±10%, ±20%
∆T = 1, +2, +3, +4 °C
uniformly distributed
to hourly input data
HSPF model (the
Hydrologic Simulation
Program – FORTRAN)
Bultot et al. (1988)
Three catchments
in Belgium
Range of changes:
IRMB model (Royal
∆P = –2.7 mm, ~+10.5 mm Meteorological
Institute of Belgium)
∆T = +2.3 ~ +3.4 °C
unevenly distributed to
daily input data
McCabe and Ayers
Delaware, USA
Combination of
∆P = ±10%, ±20%
∆T = +2, +4 °C
plus three GCMestimated scenarios
to 2 × Co2 levels
Thornthwaite monthly
water-balance model
McCabe et al. (1990)
Different scenarios
based on GCMestimated changes of
temperature and
precipitation to
2 × CO2 levels
Thornthwaite moisture
Schaake and Liu
52 catchments in
the southeastern
∆P = +10%
∆E = +10%
Simple monthly waterbalance model
Panagoulia (1991)
Acheloos River
in central
Combination of
∆P = 0, ±10%, ±20%
∆T = +1, +2, +4 °C
uniformly distributed
to monthly input data
US NWS (US National
Weather Service
snowmelt and soil
moisture accounting
Arnell (1992)
15 catchments in
the UK
Annual changes in
precipitation and
evaporation are
unevenly distributed
during the year
Thornthwaite monthly
water-balance model
Braun et al. (1994)
Romanche River,
∆T = +2, plus changes
in radiation
Chong-yu Xu
Table 2 continued
Study region
Climatic scenarios
Hydrological models
Vehviläinen and
Lohvansuu (1991)
19 catchments
approx. evenly
distributed in
Within the range:
∆P = +11 m ~ +30 mm/
∆T = +2.2 ~ +6 °C
∆E = +1 mm ~ +27 mm/
depending on the region
and season
Xu and Halldin
11 catchments in
central Sweden
Combination of
∆P = 0, ±10%, ±20%
∆T = +1, +2, +4 °C
uniformly distributed
to monthly input
NOPEX monthly
Lettenmaier and
Gan (1990)
Four catchments
in the USA
Scenarios to 2 × CO2
levels simulated by
three GCMs are used
Sacramento model
The practical application of hydrological models to climatic change impact studies
The scientific literature over the past two decades contains a large number of reports
dealing with the application of hydrologic models to the assessment of the potential
effects of climate change on a variety of water resource issues. These investigations can
range from the evaluation of annual and seasonal streamflow variation using simple
water-balance models to the evaluation of variations in surface and groundwater
quantity, quality and timing using complex distributed-parameter models that simulate
a wide range of water, energy and biogeochemical processes. Based on the level of
complexity, these models can be grouped into four categories (Leavesley, 1994):
Empirical models (annual base).
Water-balance models (monthly base).
Conceptual lumped-parameter models (daily base).
Process-based distributed-parameter models (hourly or finer base).
The choice of a model for a particular case study depends on many factors (Gleick,
1986), with the purpose of the study and model availability being the dominant ones
(Ng and Marsalek, 1992). Examples of such applications follow.
For assessing water resource management on a regional scale, monthly water-balance
models were found useful for identifying the hydrologic consequences of changes in
temperature, precipitation and other climatic variables (Alley, 1984; Gleick, 1986; Xu
and Singh, 1998). Arnell (1992) used a three-parameter water-balance model developed
by Thornthwaite and Mather (1955) in 15 basins in the UK to estimate changes in the
monthly river flow regimes and to investigate the factors controlling the effects of
climate change on river flow regimes in a humid temperate climate. Gleick (1987a)
Downscaling methods and hydrologic modelling
developed and applied a monthly water-balance model to simulate and evaluate
changes in runoff and soil moisture under 18 assumed climate scenarios. A monthly
water-balance model that also accounts for snow process was developed and applied
by Mimikou et al. (1991) for evaluating regional hydrologic effects of climate change in
the central mountainous region of Greece. Schaake and Liu (1989) developed and
applied a nonlinear monthly water-balance model for the evaluation of regional
changes in annual runoff associated with assumed changes in climate. Recently, Xu and
Halldin (1997) and Xu (1998a) used a monthly water-balance model developed by Xu et
al. (1996) to study the effects of climate change on river flow and snow cover in 11 and
25 catchments in central Sweden, respectively.
For detailed assessments of surface flow, conceptual lumped-parameter models are used.
One of the more frequently used models in this group is the Sacramento Soil Moisture
Accounting Model (Burnash et al., 1973). This model has been used by many researchers
in the USA when studying the impact of climate change (e.g., Nemec and Schaake, 1982;
Gleick, 1987b; Cooley, 1990; Lettenmaier and Gan, 1990; Schaake, 1990; Nash and
Gleick, 1991). Panagoulia (1992) used the same model to assess the effects of climate
change on a basin in central Greece.
The HBV (Hydrologiska Bryång Vattenbalansavdelning) model (Bergström, 1976) is
widely used in Nordic countries as a tool to assess the climate change effects.
Vehviläinen and Lohvansuu (1991) applied the model to evaluate the effects of climate
changes on river flow and snowpack in Finland. Climate change impacts on runoff and
hydropower in the Nordic countries have been studied by using the HBV model (e.g.,
Saelthun et al., 1994; Erichsen and Saelthun, 1995; Saelthun, 1995). Other examples of
using the HBV model in climate change studies are available from the literature. (e.g.,
Bergström et al., 1997; Bergström, 1998)
Several other models that have a similar structure to the above-mentioned two
models but that have different process conceptualizations, have been used to assess the
effect of climate change on many regions of the globe (see Leavesley, 1994). Among
others, the Institute of Royal Meteorology, Belgium, model (Bultot and Dupriez, 1976)
has been applied to basins in Belgium (Bultot et al., 1988) and Switzerland (Bultot et al.,
1992). The HYDROLOG model (Porter and McMahon, 1971) was applied to two basins
in South Australia (Nathan et al., 1988). The Hydrologic Simulation Program –
FORTRAN (HSPF) model (USEPA, 1984) has been applied to a basin in Newfoundland,
Canada (Ng and Marsalek, 1992).
For the simulation of spatial patterns of hydrologic response within a basin, processbased distributed-parameter models are needed (Beven, 1989; Bathurst and O’Connell,
1992). Thomsen (1990) proposed the use of the European Hydrological System (SHE)
model in the assessment of climate variability and change impacts on groundwater in
the Arhus region of Denmark. Running and Nemani (1991) used a process-level
ecosystem model to examine climate change-induced forest response for a 1540 km2
region in northwestern Montana using a grid resolution of 1 × 1 km2.
For estimating changes in the average annual runoff for different climate change
scenarios, simple empirical and regression models have been used. Examples include
Stockton and Boggess (1979) and Revelle and Waggoner (1983) in the USA, and Arnell
and Reynard (1989) in the UK.
Chong-yu Xu
Modelling approaches: discussion
Gleick (1986) reviewed various modelling approaches for evaluating the regional
hydrologic impacts of global climatic changes and concluded that monthly waterbalance models appear to offer significant advantages over other methods in terms of
accuracy, flexibility and ease of use. One of the limitations of monthly water-balance
models is their inability to account adequately for possible changes in individual storm
runoff characteristics at the time steps to which they are applied.
Compared with monthly water-balance models, conceptual lumped-parameter
models enable a more detailed assessment of the magnitude and timing of process
response to climate change. However, these capabilities are accompanied by an increase
in the number of process parameters, and in the amount and types of data, needed as
input to run the simulations.
The applicability of process-based distributed-parameter models to the simulation of
spatial patterns of hydrologic response within a basin has been recognized (Beven,
1989; Bathurst and O’Connell, 1992), but few applications have been presented to date.
The major limitations to the application of these models are the availability and quality
of basin and climate data at the spatial and temporal resolution needed to estimate
model parameters and to validate model results at this level of detail.
Since empirical models reflect only the relations between input and output for the
climate and basin conditions during the time period in which they were developed, the
extension of these relations to climate or basin conditions different from those used for
the development of the function is questionable (Leavesley, 1994).
Validation of hydrologic models in climate change studies
Hydrologic models (either for forecasting or simulation) were designed for stationary
conditions. However, in climate change studies, the hydrologic models employed will
be applied to changing conditions. Special validation (testing) methods, therefore, have
to be performed. Klemes (1986) discussed most of the types of validation tests in current
use. He considered the general problem of validating catchment hydrological models
and proposed a testing framework. The proposed scheme is termed hierarchical
because the modelling tasks are ordered according to their increasing complexity; the
demands of the test also increase in the same direction. The general hierarchical scheme
proposed by Klemes (1986) is summarized in Table 3.
Simple split-sample testing involves dividing the available measured time-series data
for the test catchment into two sets, each of which should be used in turn for calibration
and validation and the results from both arrangements compared. The model is
considered acceptable only if the model validation results, in both cases, are acceptable.
The proxy-basin test for geographic transferability is required for any model that is
assumed to be geographically transferable within a region hydrologically and climatically homogeneous. If the goal is to simulate streamflow for an ungauged basin C, then
the model to be used should be tested on two gauged basins, A and B. The model
should be calibrated on basin A and validated on basin B and vice versa. Only if both
proxy-basin tests are acceptable should one consider the model as geographically transferable. The differential split-sample test (for climatic transferability) applies when
testing hydrologic models under conditions different from those used to calibrate them.
Downscaling methods and hydrologic modelling
Table 3 Hierarchical scheme for the operational testing of hydrologic simulation
Stationary conditions
Transient conditions
Basin A
Basin B
Basin A
Basin B
Basin A
Split-sample test
Proxy-basin test
Differential split-sample
differential splitsample test
Basin B
Proxy-basin test
Split-sample test
Proxy-basin differential
split-sample test
split-sample test
The basic idea behind this test is to split the record into different climatic regimes and
to demonstrate that the model has general validity in predicting the values of the
output variables for different climatic conditions. For example, if it is intended that the
model should simulate streamflow for a wet climate scenario, then it should be
calibrated on a dry set of the historic record and validated on a wet set. The most
involved model test is the proxy-basin differential split-sample test for geographic,
land-use and climatic transferability. Such a broad transferability is probably the
ultimate objective of most hydrologic models. The test aims, for example, to assess
whether a model calibrated to a dry climate on basin A can simulate streamflow for a
wet climate on basin B, and vice versa.
The differential split-sample test and the proxy-basin differential split-sample test are
clearly needed when testing hydrologic models embedded in GCMs. Changes in
climate justify using these complex model-testing methodologies. In a recent study, Xu
(1998b) discusses the importance of the differential split-sample test in some detail.
A procedure for the calibration and uncertainty estimation of physically based
distributed-parameter models was developed by Beven and Binley (1992). This
provides a methodology for evaluating the uncertainty limits of the model for future
events for which observed data are not available. Ewen and Parkin (1996) proposed a
new method (a ‘blind’ approach) for testing the ability of SHETRAN (a physically
based, distributed flow and transport modelling system based on the SHE model) to
predict the effects of changes in land use and climate. The results of the first application
of the method are reported in Parkin et al. (1996). The central feature of the method is
that it involves making predictions for a test catchment as if it were a hypothetical
catchment. The modeller is, therefore, not allowed sight of the output data for the test
catchment (i.e., the method involves ‘blind’ testing) and, as a result, cannot calibrate the
model for the test catchment.
Summary and conclusions
GCMs (the only available tool for the detailed modelling of future climate evolution)
are not well suited for answering questions of primary interest to hydrologists
concerning regional hydrologic variability. GCMs were not designed for climate change
impact studies in hydrology. There were originally developed to predict the average,
Chong-yu Xu
synoptic-scale, general-circulation patterns of the atmosphere. Their direct representations of hydrological quantities are thus still generally highly simplified large-scale
averages with little spatial reliability or relevance to specific regions. In this regard it
has been noted that GCM skill decreases with increasing resolution, and that alternative
methodologies need to be adopted to circumvent this difficulty.
The hydrologic literature now abounds with regional-scale hydrologic simulations
under greenhouse scenarios. Such scenarios are either GCM-simulated or hypothetical.
One of the most widely used methods of scenario generation has been to estimate
average annual changes in precipitation and temperature for a region using one or more
GCM and then applying these estimates to adjust the historic time series of precipitation and temperature. In the simplest procedure, the adjustment is made by multiplying
the historic precipitation by a percentage change and adding an absolute change to the
historic temperature. Hypothetical scenarios using personal estimates or historic measurements of change instead of GCM results were also generated using this procedure.
This procedure does not provide for a change in the variance. To address the variability
problem, a number of methods have been developed to disaggregate (downscale) the
GCM output for direct application to hydrologic models.
Current downscaling research, in spite of some shortcomings as discussed above, is
starting to provide hydrologically useful regional algorithms. Recent higher-resolution
regional climate models provide better agreement with observations on synoptic and
regional scales and on monthly, seasonal and interannual timescales. Statistical
downscaling approaches linking GCMs to meteorologic and hydrologic models
resolved at finer scales have been developed and implemented. This is the state-of-theart approach to bridge the gap between the coarse-resolution GCMs and hydroclimatic
modelling at the river-basin scale.
The challenges for future studies of the hydrological impacts of climate change
include, among others, the following:
1) Improved methodologies to develop climate change scenarios. Scenarios must
provide the spatial and temporal resolution required by assessment models and they
must incorporate the simulated changes in both the mean and variability of the
climate variables.
2) There remain considerable opportunities for the development and comparison of
existing downscaling methods. Given the range of downscaling techniques and the
fact that each approach has its own advantages and shortcomings, there exists no
universal method which works for all situations. In fact, all downscaling methods
are still at the stage of development and testing. It is recommended, in particular,
that rigorous testing and comparison of statistical downscaling approaches should
be undertaken with LAMs.
3) Rigorous model validation procedures must be applied to the selected and
developed models before they are used to simulate future climate change impacts.
In particular, the model’s climatic transferability must be checked to demonstrate
that the model has general validity for predicting the values of the output variables
for different climatic conditions.
4) A major limitation in performing downscaling and in using hydrological model
approaches is the lack of sufficient data at spatial and temporal scales. Detailed data
sets in a variety of climatic and physiographic regions collected at a range of spatial
Downscaling methods and hydrologic modelling
and temporal scales are critical to improving our understanding of hydrologic
processes and in testing and validating the downscaling techniques and hydrological models that are being developed.
This article is abstracted from the author’s annual report to SWECLIM (Swedish
Regional Climate Modelling Programme). The author has also received research
funding from NFR (Swedish Natural Science Research Council). The valuable support
and advice offered by Dr Wilby (National Center for Atmospheric Research, USA) and
Professor Bardossy (Stuttgart University) are acknowledged gratefully. I am also
grateful to the referees who provided helpful suggestions that led to an improved
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