From GCMs to river flow: a review of downscaling methods and ...

From GCMs to river flow: a review ofdownscaling methods and hydrologicmodelling approachesChong-yu XuDepartment 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 detailingthe development of downscaling methods and the use of hydrologic models to assess thepotential effects of climate change on a variety of water resource issues. This article reviews thecurrent 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 thepractical application of appropriate models in impact studies. Following a discussion of theadvantages and deficiencies of the various approaches, challenges for the future study of thehydrological impacts of climate change are identified.

Key words: climate change, climate scenario, downscaling, general circulation model, hydro-logical model.

I Introduction

Global climatic changes caused by increases in the atmospheric concentration of carbondioxide and other trace gases may begin to appear within the next few decades. It hasbeen estimated by many authors (see, e.g., Loaiciga et al., 1996) that if 1990-levelemissions of CO2 to the atmosphere remain unabated, its concentration in theatmosphere could nearly double by the year 2100 or thereabouts. Assessments of theconsequences of a possible climate change for ‘double CO2’ (2 × CO2) conditions havebecome standard practice. Among the most authoritative climate change assessments,the IPCC reports (Houghton et al., 1990; 1992; 1995) give estimates of temperatureincreases in the Northern Hemisphere between 3°C and 5°C, together with precipita-tion change by as much as 15% under the assumption of a doubling of atmosphericCO2.

One of the most important impacts on society of future climatic changes will be

Progress in Physical Geography 23,2 (1999) pp. 229–249

© Arnold 1999 0309–1333(99)PP212RA

230 Downscaling methods and hydrologic modelling

changes in regional water availability. Such hydrologic changes will affect nearly everyaspect of human well-being, from agricultural productivity and energy use to floodcontrol, municipal and industrial water supply, and fish and wildlife management.The tremendous importance of water in both society and nature underscores thenecessity of understanding how a change in global climate could affect regional watersupplies.

Global atmospheric general circulation models (GCMs) have been developed tosimulate the present climate and have been used to predict future climatic change.While GCMs demonstrate significant skill at the continental and hemispheric spatialscales and incorporate a large proportion of the complexity of the global system, theyare inherently unable to represent local subgrid-scale features and dynamics (Wigley etal., 1990; Carter et al., 1994). When considering the impacts of global climate change thefocus is primarily on societal responses to the local and regional consequences of large-scale changes. The conflict between GCM performance at regional spatial scales and theneeds of regional-scale impact assessment is largely related to model resolution in sucha way that, while GCM accuracy decreases at increasingly finer spatial scales, the needsof impacts researchers conversely increase with higher resolution (Hostetler, 1994;Schulze, 1997).

To circumvent these problems, tools for generating the high-resolution meteorologi-cal inputs required for modelling ecohydrological processes are needed (Bass, 1996).‘Downscaling’ approaches have subsequently emerged as a means of relating large-scale atmospheric predictor variables to local- or station-scale meteorological series.Two broad classes of downscaling approaches exist: dynamic methods, involving theexplicit solving of the process-based physical dynamics of the system (e.g., Giorgi andMearns, 1991; Jones et al., 1995); and statistical methods that use identified system rela-tionships 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 investigatethe relationship between climate and water resources. The scientific literature of thepast two decades contains a large number of reports dealing with the application ofhydrologic models to the assessment of the potential effects of climate change on avariety of water resource issues. The use of hydrological models in climate changestudies can range from the evaluation of annual and seasonal streamflow variationusing simple water-balance models (e.g., Arnell, 1992) to the evaluation of variations insurface and groundwater quantity, quality and timing using complex distributed-parameter models that simulate a wide range of water, energy and biogeochemicalprocesses (e.g., Running and Nemani, 1991).

This article first reviews the current state of methodologies for generating regionalclimatic scenarios and assessing hydrological responses to global climate change(section II). Emphasis is given to recent advances in climatic downscaling. The state ofthe art of hydrological modelling and problems related to the practical application ofappropriate models in impact studies are then addressed (section III). Summary andconclusions are presented in section IV.

II Methodologies for assessing hydrological responses to global climate change

1 The use of direct GCM-derived hydrological output

Global atmospheric GCMs have been used directly to simulate streamflow underpresent climate and to predict the impact of future climatic change in macroscalewatersheds. For the present climate, Manabe (1969) reported what appears to be thefirst numerical parameterization of the hydrologic cycle within a GCM. This was asimple 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 thesoil was at or near saturation the actual evaporation rate would be equal to the potentialevaporation rate (Manabe’s bucket model did not consider vegetation). Otherwise, theactual 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 annualriver runoff from a 5-year GCM simulation and compared the results with observedannual runoff given by Milliman and Meade (1983). They found generally betteragreement for river basins with mean annual runoff between 200 and 600 km3/yr–1 andpoor agreement for large basins with small total runoff. Miller and Russell (1992) usedthe same model to calculate the annual river runoff for 33 of the world’s major riversfor the present climate and for a doubled CO2 climate. They showed that there are largeerrors in GCM-inferred estimates of mean annual runoff. Discrepancies attributed to,among other things, the poor model precipitation fields, the model’s parameterizationsof 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 theatmospheric model simulates the seasonal river runoff of world rivers with an annualdischarge greater than 100 km3/yr–1 or drainage areas greater than 5 × 105 km2. Thelimitations of the model in simulating seasonal river runoffs were discussed in theirstudy.

The analysis of GCM-predicted runoff showed that a simplistic representation of thehydrologic cycle within a global model of general atmospheric circulation leads to poorhydrologic predictive skill (e.g., Kuhl and Miller, 1992; Miller and Russell, 1992). Theproblem, from a hydrologic point of view, is that most GCMs contains no lateraltransfer of water within the land phase. Such models carry out a vertical water distrib-ution at each grid point at each time interval using precipitation, evapotranspirationand groundwater storages. However, any ‘water excess’ or overflow is simplydiscarded and plays no further role in the model computations. This means that, evenif the GCMs were able to simulate water excess correctly, they would still be operatingwith an incomplete hydrological cycle (Kite et al., 1994).

2 Coupling GCMs and macroscale hydrologic models

It is clear that the present version of the GCM does not give a good estimation of thehydrologic responses of climatic changes. There is a need to couple hydrological modelswith 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 precipitationand potential evaporation from several GCMs to simulate flows in the Mississippi Riverbasin. The routing model used grid boxes 2 ° latitude by 2.5 ° longitude. Kite et al. (1994)

Chong-yu Xu 231


combined a hydrological model (the SLURP model) with a GCM for a macroscalewatershed. A water balance was carried out at 12-hour time intervals for a 10-yearperiod using the Canadian Climate Centre GCM II data set for grid points within andsurrounding the 1.6 × 106 km2 Mackenzie River basin in northeastern Canada. Thewater surpluses from each relevant grid point were accumulated to provide a simulatedhydrograph at the outlet of the river. Nash and Gleick (1993) studied the hydrologicalimpact of climate change on the Colorado River basin in the western USA. SeveralGCMs were used to simulate climate over the Colorado River for double CO2conditions. Precipitation and temperature predictions from both the GCMs as well asfrom hypothetical scenarios were then input to the National Weather Service RiverForecasting and Simulation (NWSRFS) hydrologic model to simulate the river basinhydrologic cycle, including runoff. By linking atmospheric, hydrologic and riversimulation models Nash and Gleick (1993) were able to assess the potential impacts ofgreenhouse warming in the Colorado River basin.

The results of these studies showed that coupling the hydrological model with theGCM produces a better representation of the recorded flow regime than GCMpredictions of runoff for very large river basins. However, GCMs cannot ‘see’ smaller-scale river basins because of their coarse grid resolution. Subgrid-scale hydrologicmodels and nesting schemes are needed to resolve the large-scale GCM predictions andto predict smaller-scale hydrologic phenomena (Hostetler and Giorgi, 1993).

3 Downscaled GCM climate output for use in hydrological models

Given the limitations of GCM grid-point predictions for regional climate change impactstudies, an alternative option to using direct GCM-derived hydrological output is todownscale the GCM’s climate output for use in hydrological models. Two categories ofclimatic downscaling, namely, dynamic approaches (in which physical dynamics aresolved explicitly) and empirical (the so-called ‘statistical downscaling’) exist, and arediscussed in the following sections.

a The use of dynamic downscaling approaches: Dynamic downscaling has beenattempted with three approaches (Rummukainen, 1997):

1) Running a regional-scale limited-area model with the coarse GCM data as geo-graphical or spectral boundary conditions (also known as ‘one-way nesting’).

2) Performing global-scale experiments with high-resolution AGCMs (atmosphereGCMs), 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 areaof interest).

The goal of dynamic downscaling (i.e., to extract the local-scale information from thelarge-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 theorder of tens of kilometres or less over selected areas of interest. They have beenapplied 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.,

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 LSHMspotentially suitable for evaluating the effects of hydrologic systems. Hostetler andGiorgi (1993) linked an RCM with an LSHM to study the effects of climate onhydrologic 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 usedis a lumped parameter model that requires monthly average values of solar radiationand precipitation as input. The results of the simulations indicated that it may befeasible to use output data from RCM simulations directly as input to hydrologicmodels (or other energy balance models) in climate change research aimed at, forexample, assessing potential changes in various components of the basin hydrologicbudget under increased levels of atmospheric CO2.

Another nested model approach was developed by Leavesley et al. (1992) to disag-gregate large-scale model output for application in mountainous regions. Outputfrom a coupled GCM–mesoscale atmospheric model is used as input to anorographic–precipitation model. The orographic–precipitation model output, at aresolution of 2.5, 5 or 10 km, is used as input to a distributed-parameter hydrologicmodel. The nested approach permits the simulation of smaller-scale atmosphericprocesses and can reflect changes in precipitation frequency, magnitude and durationconsistent with the GCM response.

While nested modelling is likely to be the most informative approach to drivingregional information for 20–50 km horizontal grid spacing and 100–1000 m verticalresolution, there are several acknowledged limitations of the approach. RCMs stillrequire considerable computing resources and are as expensive to run as a global GCM.Dynamical downscaling operates on some (high-resolution) grid-point scales and thusthe results will be in the form of spatial averages. These models still cannot meet theneeds of spatially explicit models of ecosystems or hydrological systems, and there willremain the need to downscale the results from such models to individual sites orlocalities for impact studies (DoE, 1996).

b The use of statistical downscaling methods: A second, less computationallydemanding approach is statistical downscaling. In this approach, regional-scaleatmospheric predictor variables (such as area averages of precipitation or temperature)and circulation characteristics (such as mean sea-level pressure or vorticity) are relatedto 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 thefinal 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 techniquesare 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 stochasticweather generators (e.g., Richardson, 1981; Wilks, 1992; Gregory et al., 1993; Katz, 1996).In Rummukainen’s (1997) study, statistical downscaling methods are classified asfollows:

Chong-yu Xu 233


1) Downscaling with surface variables. This involves the establishment of empiricalstatistical relationships between large-scale averages of surface variables and local-scale surface variables (e.g., Kim et al., 1984; Wilks, 1989). To develop the relation-ships, large-scale averages constructed from local time series are used. Inapplication, 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 thedevelopment of statistical relationships between large-scale free troposphericvariables and local surface variables. In this method, both the free atmospheric dataand the surface data are from observations.

3) The model output statistics (MOS) method (e.g., Karl et al., 1990). This is similar to thePP method, except that the free atmospheric variables, which are used to develop thestatistical relationships, are taken from GCM output.

In reality, many downscaling approaches embrace the attributes of more than one ofthese methods and therefore tend to be hybrid in nature.

The general limitations, theory and practice of downscaling are well described in theliterature (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 inTable 1.

4 Using hypothetical scenarios as input to hydrological models

Ideally, the climate simulations from the GCMs could be used directly to drivehydrologic models, which in turn could be used to evaluate the hydrologic and waterresource effects of climate change. The issue is complicated, as discussed above, by theincompatibility of space (and, to a lesser extent, time) scales between hydrologicprocesses and GCMs. The former must account for variations at the small-to-mediumcatchment scale (e.g., tens of kilometres) and must later operate at scales of hundreds tothousands of kilometres and upwards. More importantly, the climate models –including the better parameterized ones (GCMs) – give different values of climatevariable changes and so do not provide a single reliable estimate that could beadvanced as a deterministic forecast for hydrological planning. Accordingly, methodsof simple alteration of the present conditions are widely used by hydrologists. Varioushypothetical 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 twosteps:

1) Estimate average annual changes in precipitation and temperature using eitherGCM 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, bymultiplying 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 constantdistributions of climatic changes, and multiplied historical precipitation records by

constant factors and adjusted historical temperatures by constant increments.Sanderson and Smith (1990) used GISS (Goddard Institute of Space Studies) scenariosof predicted monthly changes in temperature and precipitation.

The general procedure for estimating the impacts of hypothetical climate change onhydrological behaviour has the following stages:

1) Determine the parameters of a hydrological model in the study catchment usingcurrent climatic inputs and observed river flows for model validation.

2) Perturb the historical time series of climatic data according to some climate changescenarios.

3) Simulate the hydrological characteristics of the catchment under the perturbedclimate using the calibrated hydrological model.

4) Compare the model simulations of the current and possible future hydrologicalcharacteristics.

There are a great number of studies that use such altered time series to assess possibleeffects of climate change. The distinguishing characteristics of some studies aresummarized in Table 2.

At this juncture it is necessary to make clear that the climate change scenarios usedin the above studies should not necessarily be seen as the most likely future climates inthe region: they are primarily designed to show the sensitivity to change within areasonable interval.

III Hydrologic modelling methodology

1 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 intostreamflow 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 resourceproblems, such as flood protection, frequency and duration of extreme hydrologicalevents (floods, droughts), irrigation, spillway design, hydropower evaluation or watersupply design, under different climatic conditions at one location or at differentlocations. Following Schulze (1997), the advantages of such models in climate changeimpact studies include the following:

1) Models tested for different climatic/physiographic conditions, as well as modelsstructured for use at various spatial scales and dominant process representations, arereadily available.

2) GCM-derived climate perturbations (at different levels of downscaling) can be usedas 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 watersupply, etc.

Chong-yu Xu 235


Tab

le 1

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of

syno

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tmos

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daily

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con

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s th

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cal p

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into

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Find

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prob

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occu

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wea

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type

, usi

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to th

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at a

poi

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y nu

mbe

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daily

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nera

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., M

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al.,

1995

)

Chong-yu Xu 237

Exam

ples

of

1)La

rge-

scal

e pr

edic

tors

and

loca

l-sc

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of s

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ions

pred

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nds

are

the

sam

e va

riab

les

syno

ptic

wea

ther

pat

tern

s:ge

nera

tors

for

the

pres

ent

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s, 1

989;

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wn

etal

., 19

95;

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b, 1

972;

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eta

l., 1

991;

clim

ate

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hard

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1;C

arbo

ne a

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ram

ante

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5; P

eric

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Lette

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rge-

scal

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edic

tors

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l-sc

ale

1993

; Bar

doss

y et

al.,

1994

;19

87; Z

ucch

ini a

nd G

utto

rp,

pred

icta

nds

are

diffe

rent

var

iabl

es19

95; W

ilby,

199

5; 1

997

1991

)(W

igle

y et

al.,

1990

; Zor

ita e

tal.,

2)R

elat

es lo

ng-t

erm

tem

pera

ture

2)U

se o

f sto

chas

tic w

eath

er19

92; v

on S

torc

h et

al.,

1993

; Kaa

s,flu

ctua

tions

to s

ynop

tic w

eath

erge

nera

tors

for

clim

ate

chan

ge19

93; 1

994;

Joha

nnes

son

etal

.,pa

ttern

s (S

owde

n an

d Pa

rker

,an

d im

pact

s st

udie

s (W

ilks,

1995

; Con

way

eta

l., 1

996;

1981

; Mos

es e

tal.,

198

7)19

92; G

rego

ry e

tal.,

199

3;B

rand

sma

and

Bui

shan

d, 1

997)

3)R

elat

es p

reci

pita

tion

chem

istr

yK

atz,

199

6; M

earn

s et

al.,

3)U

se o

f art

ifici

al n

eura

l net

wor

kto

syn

optic

wea

ther

pat

tern

s19

96; S

emen

ov a

nd B

arro

w,

(AN

N) a

ppro

ache

s (H

ewits

on a

nd(D

avie

s et

al.,

1986

; Wilb

y,19

97)

Cra

ne, 1

992;

199

6)19

89)

4)R

elat

es r

iver

flow

to s

ynop

ticw

eath

er p

atte

rns

(Duc

kste

inet

al.,

1993

; Wilb

y, 1

993)


Table 2 Comparison of the characteristics of selected studies that have usedhypothetical scenarios as input to hydrological models

Reference Study region Climatic scenarios Hydrological models

Nemec and Schaake Two basins in the Combination of Sacramento model(1982) USA; one basin ∆P = 0, ±10%, ±25%,

in Kenya ∆E = ±4%, +12%uniformly distributedto daily input data

Ng and Marsalek One small basin Combination of HSPF model (the(1992) in Newfoundland, ∆P = ±5%, ±10%, ±20% Hydrologic Simulation

Canada ∆T = 1, +2, +3, +4°C Program – FORTRAN)uniformly distributedto hourly input data

Bultot et al. (1988) Three catchments Range of changes: IRMB model (Royalin Belgium ∆P = –2.7 mm, ~+10.5 mm Meteorological

∆T = +2.3 ~ +3.4°C Institute of Belgium)unevenly distributed todaily input data

McCabe and Ayers Delaware, USA Combination of Thornthwaite monthly(1989) ∆P = ±10%, ±20% water-balance model

∆T = +2, +4°Cplus three GCM-estimated scenariosto 2 × Co2 levels

McCabe et al. (1990) USA Different scenarios Thornthwaite moisturebased on GCM- indexestimated changes oftemperature andprecipitation to2 × CO2 levels

Schaake and Liu 52 catchments in ∆P = +10% Simple monthly water-(1989) the southeastern ∆E = +10% balance model

USA

Panagoulia (1991) Acheloos River Combination of US NWS (US Nationalin central ∆P = 0, ±10%, ±20% Weather ServiceGreece ∆T = +1, +2, +4°C snowmelt and soil

uniformly distributed moisture accountingto monthly input data model)

Arnell (1992) 15 catchments in Annual changes in Thornthwaite monthlythe UK precipitation and water-balance model

evaporation areunevenly distributedduring the year

Braun et al. (1994) Romanche River, ∆T = +2, plus changes HBVFrance in radiation

2 The practical application of hydrological models to climatic change impact studies

The scientific literature over the past two decades contains a large number of reportsdealing with the application of hydrologic models to the assessment of the potentialeffects of climate change on a variety of water resource issues. These investigations canrange from the evaluation of annual and seasonal streamflow variation using simplewater-balance models to the evaluation of variations in surface and groundwaterquantity, quality and timing using complex distributed-parameter models that simulatea wide range of water, energy and biogeochemical processes. Based on the level ofcomplexity, these models can be grouped into four categories (Leavesley, 1994):

1) Empirical models (annual base).2) Water-balance models (monthly base).3) Conceptual lumped-parameter models (daily base).4) 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-balancemodels were found useful for identifying the hydrologic consequences of changes intemperature, precipitation and other climatic variables (Alley, 1984; Gleick, 1986; Xuand Singh, 1998). Arnell (1992) used a three-parameter water-balance model developedby Thornthwaite and Mather (1955) in 15 basins in the UK to estimate changes in themonthly river flow regimes and to investigate the factors controlling the effects ofclimate change on river flow regimes in a humid temperate climate. Gleick (1987a)

Chong-yu Xu 239

Table 2 continued

Reference Study region Climatic scenarios Hydrological models

Vehviläinen and 19 catchments Within the range: HBVLohvansuu (1991) approx. evenly ∆P = +11 m ~ +30 mm/

distributed in monthFinland ∆T = +2.2 ~ +6°C

∆E = +1 mm ~ +27 mm/monthdepending on the regionand season

Xu and Halldin 11 catchments in Combination of NOPEX monthly(1997) central Sweden ∆P = 0, ±10%, ±20% water-balance

∆T = +1, +2, +4°C modeluniformly distributedto monthly input

Lettenmaier and Four catchments Scenarios to 2 × CO2 Sacramento modelGan (1990) in the USA levels simulated by

three GCMs are used


developed and applied a monthly water-balance model to simulate and evaluatechanges in runoff and soil moisture under 18 assumed climate scenarios. A monthlywater-balance model that also accounts for snow process was developed and appliedby Mimikou et al. (1991) for evaluating regional hydrologic effects of climate change inthe central mountainous region of Greece. Schaake and Liu (1989) developed andapplied a nonlinear monthly water-balance model for the evaluation of regionalchanges in annual runoff associated with assumed changes in climate. Recently, Xu andHalldin (1997) and Xu (1998a) used a monthly water-balance model developed by Xu etal. (1996) to study the effects of climate change on river flow and snow cover in 11 and25 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 MoistureAccounting Model (Burnash et al., 1973). This model has been used by many researchersin 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 andGleick, 1991). Panagoulia (1992) used the same model to assess the effects of climatechange on a basin in central Greece.

The HBV (Hydrologiska Bryång Vattenbalansavdelning) model (Bergström, 1976) iswidely 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 climatechanges on river flow and snowpack in Finland. Climate change impacts on runoff andhydropower 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 ofusing 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 twomodels but that have different process conceptualizations, have been used to assess theeffect of climate change on many regions of the globe (see Leavesley, 1994). Amongothers, 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 basinsin 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, process-based 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 inthe Arhus region of Denmark. Running and Nemani (1991) used a process-levelecosystem 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 includeStockton and Boggess (1979) and Revelle and Waggoner (1983) in the USA, and Arnelland Reynard (1989) in the UK.

3 Modelling approaches: discussion

Gleick (1986) reviewed various modelling approaches for evaluating the regionalhydrologic impacts of global climatic changes and concluded that monthly water-balance models appear to offer significant advantages over other methods in terms ofaccuracy, flexibility and ease of use. One of the limitations of monthly water-balancemodels is their inability to account adequately for possible changes in individual stormrunoff characteristics at the time steps to which they are applied.

Compared with monthly water-balance models, conceptual lumped-parametermodels enable a more detailed assessment of the magnitude and timing of processresponse to climate change. However, these capabilities are accompanied by an increasein the number of process parameters, and in the amount and types of data, needed asinput to run the simulations.

The applicability of process-based distributed-parameter models to the simulation ofspatial 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 qualityof basin and climate data at the spatial and temporal resolution needed to estimatemodel parameters and to validate model results at this level of detail.

Since empirical models reflect only the relations between input and output for theclimate and basin conditions during the time period in which they were developed, theextension of these relations to climate or basin conditions different from those used forthe development of the function is questionable (Leavesley, 1994).

4 Validation of hydrologic models in climate change studies

Hydrologic models (either for forecasting or simulation) were designed for stationaryconditions. However, in climate change studies, the hydrologic models employed willbe applied to changing conditions. Special validation (testing) methods, therefore, haveto be performed. Klemes (1986) discussed most of the types of validation tests in currentuse. He considered the general problem of validating catchment hydrological modelsand proposed a testing framework. The proposed scheme is termed hierarchicalbecause the modelling tasks are ordered according to their increasing complexity; thedemands of the test also increase in the same direction. The general hierarchical schemeproposed by Klemes (1986) is summarized in Table 3.

Simple split-sample testing involves dividing the available measured time-series datafor the test catchment into two sets, each of which should be used in turn for calibrationand validation and the results from both arrangements compared. The model isconsidered 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 isassumed to be geographically transferable within a region hydrologically and climati-cally homogeneous. If the goal is to simulate streamflow for an ungauged basin C, thenthe model to be used should be tested on two gauged basins, A and B. The modelshould be calibrated on basin A and validated on basin B and vice versa. Only if bothproxy-basin tests are acceptable should one consider the model as geographically trans-ferable. The differential split-sample test (for climatic transferability) applies whentesting hydrologic models under conditions different from those used to calibrate them.

Chong-yu Xu 241


The basic idea behind this test is to split the record into different climatic regimes andto demonstrate that the model has general validity in predicting the values of theoutput variables for different climatic conditions. For example, if it is intended that themodel should simulate streamflow for a wet climate scenario, then it should becalibrated on a dry set of the historic record and validated on a wet set. The mostinvolved model test is the proxy-basin differential split-sample test for geographic,land-use and climatic transferability. Such a broad transferability is probably theultimate objective of most hydrologic models. The test aims, for example, to assesswhether a model calibrated to a dry climate on basin A can simulate streamflow for awet climate on basin B, and vice versa.

The differential split-sample test and the proxy-basin differential split-sample test areclearly needed when testing hydrologic models embedded in GCMs. Changes inclimate 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 baseddistributed-parameter models was developed by Beven and Binley (1992). Thisprovides a methodology for evaluating the uncertainty limits of the model for futureevents for which observed data are not available. Ewen and Parkin (1996) proposed anew method (a ‘blind’ approach) for testing the ability of SHETRAN (a physicallybased, distributed flow and transport modelling system based on the SHE model) topredict the effects of changes in land use and climate. The results of the first applicationof the method are reported in Parkin et al. (1996). The central feature of the method isthat it involves making predictions for a test catchment as if it were a hypotheticalcatchment. The modeller is, therefore, not allowed sight of the output data for the testcatchment (i.e., the method involves ‘blind’ testing) and, as a result, cannot calibrate themodel for the test catchment.

IV 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 hydrologistsconcerning regional hydrologic variability. GCMs were not designed for climate changeimpact studies in hydrology. There were originally developed to predict the average,

Table 3 Hierarchical scheme for the operational testing of hydrologic simulationmodels

Stationary conditions Transient conditions

Basin A Basin B Basin A Basin B

Basin A Split-sample test Proxy-basin test Differential split-sample Proxy-basintest differential split-

sample test

Basin B Proxy-basin test Split-sample test Proxy-basin differential Differentialsplit-sample test split-sample test

synoptic-scale, general-circulation patterns of the atmosphere. Their direct representa-tions of hydrological quantities are thus still generally highly simplified large-scaleaverages with little spatial reliability or relevance to specific regions. In this regard ithas been noted that GCM skill decreases with increasing resolution, and that alternativemethodologies need to be adopted to circumvent this difficulty.

The hydrologic literature now abounds with regional-scale hydrologic simulationsunder greenhouse scenarios. Such scenarios are either GCM-simulated or hypothetical.One of the most widely used methods of scenario generation has been to estimateaverage annual changes in precipitation and temperature for a region using one or moreGCM and then applying these estimates to adjust the historic time series of precipita-tion and temperature. In the simplest procedure, the adjustment is made by multiplyingthe historic precipitation by a percentage change and adding an absolute change to thehistoric temperature. Hypothetical scenarios using personal estimates or historic mea-surements 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 variabilityproblem, a number of methods have been developed to disaggregate (downscale) theGCM output for direct application to hydrologic models.

Current downscaling research, in spite of some shortcomings as discussed above, isstarting to provide hydrologically useful regional algorithms. Recent higher-resolutionregional climate models provide better agreement with observations on synoptic andregional scales and on monthly, seasonal and interannual timescales. Statisticaldownscaling approaches linking GCMs to meteorologic and hydrologic modelsresolved at finer scales have been developed and implemented. This is the state-of-the-art approach to bridge the gap between the coarse-resolution GCMs and hydroclimaticmodelling at the river-basin scale.

The challenges for future studies of the hydrological impacts of climate changeinclude, among others, the following:

1) Improved methodologies to develop climate change scenarios. Scenarios mustprovide the spatial and temporal resolution required by assessment models and theymust incorporate the simulated changes in both the mean and variability of theclimate variables.

2) There remain considerable opportunities for the development and comparison ofexisting downscaling methods. Given the range of downscaling techniques and thefact that each approach has its own advantages and shortcomings, there exists nouniversal method which works for all situations. In fact, all downscaling methodsare still at the stage of development and testing. It is recommended, in particular,that rigorous testing and comparison of statistical downscaling approaches shouldbe undertaken with LAMs.

3) Rigorous model validation procedures must be applied to the selected anddeveloped models before they are used to simulate future climate change impacts.In particular, the model’s climatic transferability must be checked to demonstratethat the model has general validity for predicting the values of the output variablesfor different climatic conditions.

4) A major limitation in performing downscaling and in using hydrological modelapproaches is the lack of sufficient data at spatial and temporal scales. Detailed datasets in a variety of climatic and physiographic regions collected at a range of spatial

Chong-yu Xu 243


and temporal scales are critical to improving our understanding of hydrologicprocesses and in testing and validating the downscaling techniques and hydrologi-cal models that are being developed.

Acknowledgements

This article is abstracted from the author’s annual report to SWECLIM (SwedishRegional Climate Modelling Programme). The author has also received researchfunding from NFR (Swedish Natural Science Research Council). The valuable supportand advice offered by Dr Wilby (National Center for Atmospheric Research, USA) andProfessor Bardossy (Stuttgart University) are acknowledged gratefully. I am alsograteful to the referees who provided helpful suggestions that led to an improvedmanuscript.

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