Hydrological impact of the potential future vegetation ...gwang/papers/2008/JGRB08b_Alo.pdfHydrological impact of the potential future vegetation response to ... do not include vegetation

Hydrological impact of the potential future vegetation response to

climate changes projected by 8 GCMs

Clement Aga Alo1 and Guiling Wang1,2

Received 18 September 2007; revised 4 March 2008; accepted 20 March 2008; published 29 July 2008.

[1] This study uses offline simulations with a land surface model to explore how thefuture response of potential vegetation to elevated CO2 and attendant climate changesfeeds back to influence surface hydrological processes. Climate changes are thoseprojected by eight General Circulation Models (GCMs) under the SRESA1B, and thepotential natural vegetation structure corresponding to the Preindustrial control andSRESA1B 2100 climate of the 8 GCMs are simulated by a dynamic global vegetationmodel integrated to equilibrium. For climate change forcing from each GCM, comparisonsare made among three surface hydrology simulations using CLM3.0 driven with differentcombinations of climate forcing, atmospheric CO2 concentration, and potential naturalvegetation. These simulations are designed to separate the effect of structural vegetationfeedback from the combined influence of climate and CO2 changes. With the exception ofthe HadCM scenario, all other GCM scenarios broadly agree on the spatial patterns ofstructural vegetation feedbacks on surface temperature and surface water budget, althoughthe response of soil moisture varies considerably among the GCM scenarios especiallyin the tropics. With the HadCM excluded, averages over the seven GCM scenariosindicate that the CO2-induced warming in winter is stronger than in summer in thenorthern mid and high latitudes, and structural vegetation feedback enhances the winterwarming and reduces the summer warming over a large portion of these regions; theglobal hydrological cycle is expected to accelerate in a warmer future climate, while thestructural vegetation feedback further increases evapotranspiration in a major portion ofthe globe, including parts of South America, tropical Africa, Southeast Asia, andregions north of approximately 45�N, suggesting that vegetation feedback could furtheraccelerate the hydrological cycle. Averaged over the globe, this increase inevapotranspiration due to structural vegetation feedback is equivalent to 78% of that dueto climate and CO2 changes. When changes in vegetation structure are not considered, the7-model average response of soil moisture to climate and CO2 changes is characterized bywetter soil conditions in the northern high latitudes and parts of the midlatitudes.Structural vegetation feedback, however, causes strong midlatitude dryness both in thewinter and in the summer. The impact of vegetation changes corresponding to theHadCM-projected climate changes is markedly different, being either more extreme or in adifferent direction than that corresponding to the other GCMs examined. Our results areconstrained by the lack of consideration for human land use changes and vegetationfeedback to climate, as well as the uncertainty related to the highly disputed physiologicalresponse of ecosystems to elevated CO2. Nevertheless, this study demonstrates thatclimate- and CO2-induced changes in potential vegetation structure substantially influencethe surface hydrological processes, thus emphasizing the importance of includingvegetation feedback in future climate change predictions.

Citation: Alo, C. A., and G. Wang (2008), Hydrological impact of the potential future vegetation response to climate changes

projected by 8 GCMs, J. Geophys. Res., 113, G03011, doi:10.1029/2007JG000598.

1. Introduction

[2] The interactions between atmospheric composition,climate, and vegetation are widely recognized. It is generallyaccepted that radiative effects of rising atmospheric CO2

concentration are leading to an increase in surface temper-atures with hydrological consequences [IPCC, 2007]. Plant

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, G03011, doi:10.1029/2007JG000598, 2008ClickHere

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1Department of Civil and Environmental Engineering, University ofConnecticut, Storrs, Connecticut, USA.

2Center for Environmental Sciences and Engineering, University ofConnecticut, Storrs, Connecticut, USA.

Copyright 2008 by the American Geophysical Union.0148-0227/08/2007JG000598$09.00

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physiological response to CO2 enhancement (e.g., increasein plant water use efficiency resulting from reduced tran-spiration in a CO2-enriched atmosphere) can further influ-ence temperature and hydrological conditions. Both CO2

changes and the resulting temperature and hydrologicalchanges influence vegetation and may lead to significantbiogeographic changes. On the other hand, structuralchanges in vegetation imply modifications in land surfaceproperties such as albedo, surface roughness, rooting depth,stomatal resistance and leaf area index (LAI) [Foley et al.,2000], and these feed back on the hydrological cycle andclimate through the energy andwater fluxes [Arora and Boer,2003], and by impacting carbon storage at the land surface.Past research on biosphere-atmosphere interactions is repletewith indications of such mechanistic links between vegeta-tion and climate including the hydrological cycle [e.g., Xueand Shukla, 1993; Betts et al., 2007, 2004; Levis et al., 1999;Xue et al., 2001; Snyder et al., 2004]. Chase et al. [2001]showed that the climatic impact of historical land coverchanges is comparable to that of past atmospheric CO2

increase combined with aerosols. However, most climatepredictions, including those upon which the 4th AssessmentReport of the Intergovernmental Panel on Climate Change(IPCC AR4) is based, do not include vegetation response toclimate changes. How atmospheric CO2, climate, and vege-tation will interact to shape future climatic and biosphereconditions is therefore largely unknown.[3] Greenhouse gas-induced changes in future climate as

predicted by eight General Circulation Models (GCMs)contributing to the IPCC AR4 have been shown to substan-tially alter the simulated global distribution of potentialnatural vegetation [Alo and Wang, 2008]. While modeledhydrological effects may have contributed to induce thesevegetation changes, modified vegetation characteristics mayhave also contributed to the simulated changes in hydro-logical conditions under future climate. This leads to aquestion which this paper seeks to address: Does thestructural vegetation response to climate change consider-ably modify the hydrological impact of climate change?This is an important question because of potential implica-tions for climate predictions using models (e.g., IPCC AR4models) that do not include vegetation dynamics. Feddemaet al. [2005] determined that the inclusion of effects ofprojected changes in land cover (due to climate and humanland use changes) for the IPCC Special Report on Emis-sions Scenarios (SRES) A2 and B2 made an importantdifference in regional climates simulated by the Departmentof Energy Parallel Climate Model (DOE-PCM). In thepresent study, though, human land use changes and plantmigration processes, effects that may limit ecosystemresponses [Neilson et al., 2005; Higgins and Harte, 2006]and associated hydrological impacts, are not incorporated.While at present modeling the physiological response ofecosystems to CO2 enrichment remains a challenge, paststudies [e.g., Leipprand and Gerten, 2006; Betts et al., 2007]have highlighted the importance of including CO2 physio-logical effects in assessing hydrological impacts of CO2-induced climate change. The complexity of responsesamong plot level manipulative case-study CO2 experimentsand the difficulty in translating results from these experi-ments to the ecosystem scale [Norby and Luo, 2004;Walther et al., 2002] pose the challenge in accurately

parameterizing responses to CO2 in models. In this studywe include CO2 physiological effects based on currentmodeling capabilities, while recognizing the reliance ofmodeled effects on our model’s accuracy in its parameter-ization of responses to CO2. Furthermore, we recognize thata climate modeling framework with full representation ofvegetation dynamics is ideally suited to examining thefeedbacks of climate-induced changes in vegetation on thehydrological cycle and climate. However, given the modeldependence of climate predictions, and before the commonuse of fully coupled models in climate prediction, using aland surface model (offline) to explore the surface hydro-logical impacts of long-term potential natural vegetationchanges (as simulated by a dynamic vegetation model) dueto climate changes (as predicted by GCMs in the IPCCAR4) provides a computationally less expensive means toevaluate the first-order effects of vegetation feedback. Suchan analysis, as in this paper, will demonstrate the role ofvegetation in surface hydrology under climate change in thefuture and will also provide a foundation that shouldfacilitate the understanding of effects of vegetation feedbackin fully coupled simulations.[4] In this study, by driving a state-of-the-science land

surface model with climate changes predicted by eightdifferent GCMs and with simulated equilibrium naturalpotential vegetation changes consistent with each GCM’sclimate prediction, we assess the hydrological impact ofpotential natural vegetation changes. Here, we focus on thesensitivities of variables related to surface hydrologicalconditions, such as surface temperature, evapotranspiration,soil moisture, and runoff, to simulated natural vegetationchanges for the future. For a qualitative comparison ofrelative importance, we separate the contribution of struc-tural vegetation changes to simulated hydrological changesfrom that of the elevated atmospheric CO2 concentration(physiological effect) and CO2-induced climate changecombined. The next section describes the methods used,followed by results in section 3. We discuss the results andsummarize the main findings of the study in section 4.

2. Methods

2.1. Land Surface Model

[5] We assess the surface hydrological impact of climate-induced vegetation structural changes using the NationalCenter for Atmospheric Research (NCAR) CommunityLand Model 3.0 (CLM3.0 [Oleson et al., 2004]). Asdetailed by Oleson et al. [2004], the model describes thestate of the land surface and simulates flux exchanges ofenergy, water, momentum, and CO2 between the landsurface and the atmosphere. CLM3.0 has one vegetationlayer, ten unevenly spaced soil layers, and up to five snowlayers depending on snow depth. Vegetation is representedby a combination of different plant functional types (PFTs)to account for differences in plant physiognomy, leaf shapeand longevity, and photosynthetic pathway. While CLM3.0with observed vegetation considers 16 PFTs, only 10 PFTsare simulated by the CLM-DGVM. Since vegetation in thisstudy is derived from CLM-DGVM simulations [Alo andWang, 2008], only 10 PFTs, namely, needleleaf evergreentemperate trees, needleleaf evergreen boreal trees, broadleafevergreen tropical trees, broadleaf evergreen temperate

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trees, broadleaf deciduous tropical trees, broadleaf decidu-ous temperate trees, broadleaf deciduous boreal trees, C3

arctic grasses, C3 nonarctic grasses and C4 grasses, areincluded. The model is driven with atmospheric forcing(precipitation, specific humidity, solar radiation, tempera-ture, and wind speed) and land surface boundary conditions(fraction of grid cell covered by glaciers, lakes, wetlands,and vegetation, fractional coverage of PFTs and their LAIand stem area index (SAI) values over vegetated land, andsoil properties).[6] In an evaluation of the performance of CLM3.0 when

coupled with the Community Atmosphere Model, Dickinsonet al. [2006] suggested that better treatments of canopyinterception, soil water storage, runoff and transpirationcould improve a prominent dry bias they identified in theAmazon. Bonan and Levis [2006] noted that reducingcanopy-intercepted precipitation improved the simulatedglobal vegetation distribution and NPP in their offlineCLM-DGVM simulations. The version of CLM3.0 usedin this study includes an improved canopy hydrologyscheme that reduces canopy interception by accountingfor impacts of precipitation subgrid variability (see Wangand Wang [2007] and Wang et al. [2007] for details).

2.2. Atmospheric Forcing and Surface Data

[7] Atmospheric forcing data used in this study areidentical to those used by Alo and Wang [2008]. Theforcings were based on climatological monthly meansderived from preindustrial control simulations (PICNTRL)and SRESA1B stabilization experiments of eight GCMsthat participated in IPCC AR4. In PICNTRL, atmosphericCO2 concentration is held constant at 275 ppm; in SRE-SA1B, CO2 concentration follows the SRESA1B scenariofrom 2000 to 2100 and stabilizes at 720 ppm beyond 2100[Nakicenovic and Swart, 2000]. The monthly data werelinearly interpolated to daily resolution first; the hourlyvalues were then derived from the daily data using aweather generator [Foley et al., 1996]. The eight GCMsthat provided the atmospheric forcing are: US NationalCenter for Atmospheric Research CCSM3 (CCSM hereaf-ter), US Geophysical Fluid Dynamics Laboratory GFDL-CM2.1 (GFDL hereafter), US Goddard Institute for SpaceStudies GISS-ER (GISS hereafter), UK Hadley Center forClimate Prediction and Research UKMO-HadCM3 (HadCMhereafter), Germany Max Planck Institute for MeteorologyECHAM5/MPI-OM (ECHAM hereafter), Japan Center forClimate System Research’s MIROC3.2 (MIROC hereafter),Canadian Centre for Climate Modeling and AnalysisCCCma-CGCM3.1/T47 (CGCM hereafter), and ChinaInstitute of Atmospheric Physics FGOALS-g1.0 (FGOALShereafter). The SRESA1B forcing we use is from theperiod 2071–2100.[8] Soils, lakes, wetlands, and glaciers data are supplied

with the publicly available CLM3.0 distribution (http://www.cgd.ucar.edu/tss/clm/distribution/clm3.0/). Potentialnatural vegetation structure and distribution is obtainedfrom our prior 200-year simulations by CLM-DGVMdriven with the PICNTRL and SRESA1B climates fromthe various GCMs [Alo and Wang, 2008]. These are used toprescribe the vegetation in our surface hydrological simu-lations with CLM3.0, which is done through initializingCLM-DGVM with initial files previously written by CLM-

DGVM at the end of each 200-year simulation and runningCLM-DGVM but disabling the yearly update of vegetationstructure. Although the structure update at the yearlytimescale is disabled, the phenology module is still active.Essentially, vegetation structure and distribution are pre-scribed, but the seasonal course of LAI is predicted by thephenology scheme.[9] Vegetation structural changes at the PFT level include

changes in fractional coverage, height, number of individ-uals, LAI and SAI. A detailed description of the vegetationstructural changes is found elsewhere [Alo and Wang,2008]. The prior CLM-DGVM simulations with the stan-dard LAI parameterization produced LAI values (>12)much higher than observed in portions of tropical rainforests under some of the climate forcing scenarios. Thisresulted in very large increases in LAI (DLAI � 6) in partsof the Amazon and Central Africa [Alo and Wang, 2008].Given that the focus of the present paper is on hydrologicalprocesses, and LAI is a key driver of the processes, weconstrain the LAI of each vegetation type not to exceed 10in the CLM3.0 simulations in this study so as to improve therealism of simulated LAI changes. Figure 1 shows thespatial distribution of grid-average LAI changes fromPICNTRL to SRESA1B corresponding to the eight GCMswe used. Except for the HadCM scenario (which showsstrong LAI decreases in South America and Africa), wide-spread LAI increases are seen across the globe for all otherGCM scenarios. In contrast with the other GCMs, the GISSscenario yields no LAI changes in a large portion of thenorthern high latitudes. It is important to note that whileLAI increases across the globe and across GCM scenarios,decreases in fractional coverage of evergreen trees in favorof deciduous trees are widespread in the tropics due to waterstress [Alo and Wang, 2008]. In the high latitudes, highertemperature and longer growing season generally favorexpansion of needleleaf evergreen trees and/or deciduoustrees at the expense of grasses. There are observationalstudies that showed widespread expansion of shrubs in theArctic over the past 50 years, apparently in response to awarming climate [Sturm et al., 2001; Tape et al., 2006].

2.3. Experimental Design

[10] Three simulations were performed with CLM3.0 forclimate forcing and vegetation pertaining to each of theeight GCMs (Table 1). In simulation PIC, CLM3.0 wasforced with the PICNTRL climate, a fixed atmospheric CO2

concentration of 275 ppm, and a prescribed potential naturalvegetation state consistent with the PICNTRL CO2 concen-tration and climate. In simulation A1B, the model wasforced with the SRESA1B 2100 climate, a fixed CO2

concentration of 720 ppm, and a prescribed potential naturalvegetation state consistent with the SRESA1B 2100 CO2

concentration and climate. Simulation A1B_PICVEG wasforced with the SRESA1B 2100 climate, a fixed CO2

concentration of 720ppm, but a prescribed potential naturalvegetation state consistent with the PICNTRL CO2 concen-tration and climate. Thus a total of twenty-four simulationswere performed. In all simulations, CLM3.0 was run for 10years at the T42 spatial resolution (approximately 2.8� by2.8�) and a time step of 30 minutes. We discarded the first 5years for land surface spin-up, and present results based onaverages of the last 5 years of the simulations. This


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Figure 1. Simulated changes in LAI of potential natural vegetation (LAI under SRESA1B climateminus LAI under PICNTRL climate) (in m2/m2) for the eight GCM scenarios. Note that tropical rainforest LAI increases under some GCM scenarios shown here are less pronounced than those shown byAlo and Wang [2008]. Here we constrain the LAI of each vegetation type not to exceed 10 in simulationsfor both the PICNTRL and SRESA1B climates whereas in the work of Alo and Wang [2008], thestandard CLM3.0 LAI parameterization is used.


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experimental design allows us to isolate the impact ofvegetation structural changes from the combined impactof climate change and CO2 physiological effects. Differ-ences between A1B_PICVEG and PIC reveal the combinedimpact of climate change and physiological response onsurface hydrological processes, whereas differences betweenA1B and A1B_PICVEG depict the impact of structuralvegetation feedback.

3. Results

3.1. Surface Temperature

[11] With the exception of HadCM, surface temperatureresponse to the combination of elevated CO2 and climatechange for the different GCM scenarios shows a consistentspatial pattern. The left column of Figure 2a shows thechanges in mean surface temperature for the winter(December–January–February (DJF)) and summer (June–July–August (JJA)) seasons due to the combined impactof CO2-induced climate change and vegetation physiolog-ical response, with the magnitude of temperature changesranging from 2�C to more than 6�C, depending on thelocation and season. Here, the changes are averaged overseven of the eight GCM scenarios (HadCM excluded) sinceintermodel differences are small for the seven. Althoughatmospheric CO2 concentration, and therefore the radiativeeffect of CO2 increase, is fairly spatially uniform, warmingis noticeably stronger in the northern high latitudes in DJFboth relative to other regions in the same season and relativeto other seasons over the same region. The additional DJFwarming in the high latitudes partly results from the effectof the lower surface albedo due to decreased snow coverunder global warming.[12] This seasonal and spatial contrast is further enhanced

when the impact of vegetation structural changes is consid-ered (Figure 2a, right column). In DJF, vegetation feedbackwarms the snow covered high latitudes. The strong increasesof LAI in these regions (Figure 1) increase masking ofsnow, thus lowering albedo and causing the warming [Bettset al., 2000]. Some weak cooling impact is produced bystructural vegetation impact over portions of western andcentral United States, and over portions of eastern andcentral Asia, possibly as a result of increased evapotranspi-ration due to higher LAI. In JJA, increased evapotranspira-tion resulting from LAI increases leads to strong cooling inlarge portions of the middle and high latitudes. In northernRussia and Alaska, shifts from grass dominance under thePICNTRL climate to needleleaf evergreen tree dominanceunder future climate generally underlie the LAI changes[see Alo and Wang, 2008]. In northern Russia, the coolingimpact of increased evapotranspiration due to LAI increaseand the competing warming effect of increased net radiation

due to the lower albedo of needleleaf evergreen trees thangrassland seem to offset each other, leading to negligibleimpact of structural vegetation feedback on surface temper-ature in a large portion of this region in JJA. The albedoeffect, hence the warming impact of vegetation feedback,dominates in portions of Alaska in JJA. Compared with thecombined climate change and CO2 physiological effects,vegetation structural changes have a much smaller impacton surface temperature in the middle and high latitudes,amplifying the warming by �6% (+0.4�C, on average, overthe region indicated by boxes in Figure 2) in DJF andreducing the warming by �8% (�0.3�C) in JJA. Noconsiderable effects are found in most of the tropics.[13] The character of temperature response in the

HadCM-driven simulation is notably different than in sim-ulations driven by the other GCM predictions. In DJF, largerLAI increases simulated under the HadCM scenario lead tostronger additional warming (+0.7�C over boxed regionshown in Figure 2, compared to an average of +0.4�C forthe other GCMs) in the high latitudes. In JJA, however, thewarming effect of lower albedo offsets the cooling impact ofhigher evapotranspiration in most of the mid and highlatitudes. In contrast to the other GCM scenarios, largedecreases in LAI (associated with collapse of tropicalforests) in South America and Africa under the HadCMscenario results in widespread amplification of warming inthese regions, both in DJF and in JJA (Figure 2b, rightcolumn).

3.2. Surface Hydrologic Responses

3.2.1. Evapotranspiration[14] The three components of annual evapotranspiration

respond to vegetation structural changes differently (Figure3).As expected, the spatial distribution of changes in intercep-tion loss (evaporation of canopy-intercepted water) con-forms to the spatial distribution of LAI changes, withwidespread increases across the globe under nearly all theGCM scenarios. The main exception is found in theHadCM-driven simulation where over South America andCentral Africa, the collapse of tropical forests leads tostrong decreases in LAI and therefore strong reductionsin interception loss. Moderate to minor decreases in inter-ception loss, coinciding with decreases in LAI, in parts ofWest Africa, Southern Africa, northern South Americaand southwestern North America are also seen in simula-tions for some models (e.g., HadCM, GFDL, CGCM, andFGOALS).[15] Canopy transpiration increases in some portions of

the middle and high latitudes in response to vegetationchanges, although the increases appear to be on a quitesmall scale and differ between scenarios, relating predom-inately to increases in LAI. In most places in the middle andhigh latitudes, with no or slight increase in precipitation(Figure 4), increased canopy interception promoted byhigher LAI leaves less water available to infiltrate into thesoil, leading to reduced soil water availability. This acts tomaintain transpiration in those locations at levels close tothe preindustrial rates despite the increases in LAI under thefuture climate. Thus no noticeable changes in canopytranspiration are seen in many portions of the middle andhigh latitudes across all the GCM scenarios. Generally,except for the HadCM scenario, decreases in transpiration

Table 1. Climate, Atmospheric CO2 Concentration, and Potential

Natural Vegetation Scenarios Used for the Simulations With

CLM3.0

Simulation Climate CO2 Potential Vegetation

PIC Preindustrial 275 ppm PreindustrialAIB SRESA1B 2100 720 ppm SRESA1B 2100A1B_PICVEG SRESA1B 2100 720 ppm Preindustrial


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Figure 2. Spatial distribution of surface temperature (2-m air temperature) changes (in �C) due toclimate and CO2 changes combined (left column), and due to vegetation structural changes alone (rightcolumn), (a) averaged for all the GCMs excluding HadCM, and (b) for HadCM, during December toFebruary (top row) and during June to August (bottom row). Boxes indicate the region used for averagingin order to provide quantitative estimates of changes.


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Figure 3. Changes in components of annual evapotranspiration between the preindustrial and futurestates (in mm) due to vegetation structural changes alone (i.e., A1B-A1B_PICVEG) for the eight differentGCM scenarios: evaporation of canopy-intercepted water (first column), canopy transpiration (secondcolumn), and ground evaporation (third column).


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in response to vegetation structural changes are widespreadin the tropics for all GCM scenarios. This is attributable tosoil water stress that causes widespread decline of evergreentrees in favor of deciduous trees [Alo and Wang, 2008], with

the latter not transpiring in the leaf-off season. In contrast,under the HadCM scenario, the change from evergreen treedominance to grass dominance in most of the tropics [Aloand Wang, 2008] leads to an increase in canopy transpira-

Figure 4. Projected changes (SRESA1B minus PICNTRL) in annual precipitation (in mm) by the eightGCMs.


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Figure 5. Spatial distribution of changes in annual evapotranspiration between preindustrial and futurestates (in mm) due to climate and CO2 changes combined (left column), and due to vegetation structuralchanges alone (right column) for the eight different GCM scenarios.


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tion despite the decreases in LAI in these areas. There is lesscompetition for radiation per unit leaf area for grasses thanfor evergreen trees and more water reaches the ground sincegrasses intercept less, resulting in increased transpiration.[16] For each of the GCM scenarios, the spatial distribu-

tion of ground evaporation response to changes in vegeta-tion structure is consistent with LAI changes, and consistentwith changes in interception loss for that matter. As LAIincreases (decreases), more (less) ground area is shaded, soground evaporation decreases (increases).[17] Since over vegetated land, interception is the first of

a sequence of hydrological processes that happen after therain falls, the response of total evapotranspiration to vege-tation structural changes is dominated by the response ofcanopy interception loss (Figure 5, right column). Thereforethe overall signal is an increase of evapotranspiration asa result of vegetation structural changes, with the onlyexception in the HadCM scenario where LAI decreasesand vegetation degradation together cause decreases inevapotranspiration in most of the tropics. The magnitudeof the evapotranspiration changes is, in general, muchsmaller than that of interception loss changes, as changesin interception loss are partially offset by changes in canopytranspiration and ground evaporation. It is likely that thedominance of canopy interception in determining thechanges in evapotranspiration relates in part to a knowndeficiency in the partitioning of evapotranspiration byCLM3.0 [Hack et al., 2006; Lawrence et al., 2007].Lawrence et al. [2007] found the partitioning of globalevapotranspiration to be 13% transpiration, 44% groundevaporation, and 43% interception loss, in a defaultCLM3.0 simulation driven by observed climate. The 7-modelaverage of our PIC simulations shows the partitioning tobe 27% transpiration, 40% ground evaporation, and 33%interception loss, at the global scale. Although groundevaporation remains the dominant component instead oftranspiration as suggested by available observations andother model-based estimates (e.g., 48% transpiration, 36%ground evaporation, and 16% interception loss in Dirmeyeret al. [2006]), our simulated canopy interception loss ratio inthe 7-model average at the local and regional scale validatesquite well with site-scale observations in correspondingregions (Table 2) [Wang et al., 2007].[18] The differences between GCM scenarios in the pat-

tern of evapotranspiration response to the CO2-inducedclimate and physiological changes (Figure 5, left column)reflect primarily the differences in precipitation and temper-ature changes projected by the GCMs used. In large portionsof the middle and/or high latitudes, strong warming accom-panied by increased precipitation promotes higher evapo-

transpiration under all GCM scenarios except GFDL (whichpredicts strong decrease of precipitation over midlatitudes)and GISS (which predicts very weak warming). In these twomodels over large portions of the northern middle and highlatitudes, reduction in transpiration due to water stress orCO2-induced stomatal closure dominates or offsets theaccelerating effect of warming on evapotranspiration.In the case of the GFDL model, another factor contributingto the decrease of evapotranspiration is the runoff increasedue to higher rain intensity. In the wet tropics where waterstress rarely occurs (e.g., tropical South America and CentralAfrica), no matter how precipitation changes, evapotranspi-ration increases in all the GCM scenarios, as temperature isthe primary influencing factor. In the relatively arid portionsof tropical and subtropical regions where evapotranspirationis water limited, the spatial pattern of evapotranspirationchanges due to the combination of climate change and theCO2 physiological effect for the different GCM scenariossimply follow the spatial patterns of precipitation changes.Substantial areas of evapotranspiration decrease are foundunder most GCM scenarios over southwestern North Amer-ica, North Africa, South Africa, and Australia. Over thesearid regions, the magnitude of evapotranspiration decreasereflects that of precipitation decrease, since precipitationequals evapotranspiration with no runoff term in the waterbalance equation.3.2.2. Runoff[19] The left column of Figure 6 shows the combined

impact of climate change and CO2 physiological effects ontotal annual runoff (surface runoff plus subsurface drainage)for the eight GCM scenarios. Increased amount and/orintensity of precipitation cause increases in runoff in largeportions of the northern middle and high latitudes under allscenarios, although increases are weaker and less extensiveunder the GISS and HadCM scenarios. Runoff decreasesresulting from reduced precipitation are widespread in thetropics under most GCM scenarios. Over arid regions (e.g.,southwestern North America, North Africa, southern Africa,and Australia), precipitation is much lower than potentialevaporation, leading to zero runoff. In such regions, runoffremains zero in case of precipitation decrease or even slightprecipitation increase.[20] In response to changes in vegetation distribution,

runoff decreases in many portions of the globe across theGCM scenarios (Figure 6, right column). The decreases inrunoff are due to increases in LAI, which enhance intercep-tion of water by canopy, thereby reducing the amount ofprecipitation reaching the ground. Conversely, the strongdecrease of LAI in parts of South America and CentralAfrica under the HadCM scenario causes runoff to increase.

Table 2. Comparison of Simulated Annual Interception Loss Ratio in the 7-Model Average of the PIC Experiments With Published

Observations

Observations CLM3.0 (Simulation PIC)

LocationInterceptionLoss Ratio, % Source Region Averaged

InterceptionLoss Ratio, %

38�320N, 121�460W 15 Xiao et al. [2000] 38�N–43�N, 115�W–120�W (W U.S.) 1254�60N, 10�150E 10.9 Hormann et al. [1996] 49�N–54�N, 8�E–13�E (W. Europe) 131�330S, 37�80E 10.7 Jackson [2000] 3�S–2�N, 34�E–39�E (E. Africa) 25.6�4�20S, 79�50W 36.4 Fleischbein et al. [2005] 2�S–7�S, 74�W–79�W (NW S. America) 3231�520S, 141�290E 1.1 Dunkerley and Booth [1999] 28�S–33�S, 139�E–144�E (SE Australia) 2.7


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Figure 6. Spatial distribution of changes in annual runoff between preindustrial and future states (inmm) due to climate and CO2 changes combined (left column), and due to vegetation structural changesalone (right column), for the eight different GCM scenarios.


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[21] Based on the water balance equation, it is easy tounderstand that Figure 6 (left column) is largely the mirrorimage of Figure 5 (left column) with an opposite sign.3.2.3. Soil Moisture[22] The response of soil moisture to climate changes and

to vegetation structural changes during different seasonsis examined. Differences among different seasons, evenbetween DJF and JJA, are surprisingly negligible. Figure 7shows the results in DJF as an example. The first column ofFigure 7 shows the combined impact of climate change, CO2

physiological effect and structural vegetation feedback onsoil moisture content for DJF. The middle column depicts theimpact of climate and physiological vegetation feedback onsoil moisture, while the third column isolates the role ofstructural vegetation feedback alone. Without consideringchanges in vegetation structure, climate change and CO2

lead to wetter soils in large portions of the northern highlatitudes and little changes in the middle latitudes under allthe GCM scenarios. Structural vegetation changes, however,substantially modify this, resulting in drier soils in largeportions of the midlatitudes in future climate. The dryingsignal from structural vegetation feedback results primarilyfrom increased evapotranspiration due to increases in LAI. Inthe tropics however, under most scenarios, decreased pre-cipitation and/or increased evapotranspiration due to thecombined climate and CO2 physiological effects lead todrier conditions (Figure 7, middle column), and the impactof structural vegetation feedback is mostly small. Theexception is found in the HadCM scenario, where structuralvegetation changes in the tropics (from forest to grassland)have a wetting impact. Overall, predicted soil moisturechanges are dominated by the combined impact of climatechanges and CO2 physiological effect in the tropics, andby the impact of vegetation structural changes in themidlatitudes.

3.3. Overall Model Behavior in Surface HydrologicResponse

[23] The simulated impacts of vegetation structuralchanges on evapotranspiration and runoff are broadly con-sistent in seven of the eight GCM scenarios (Figures 4 and5), although their impact on soil moisture shows littleconsistency between the different GCMs during both DJFand JJA especially in the tropics (Figure 6). Figure 8presents the 7-model average changes to demonstrate theoverall/average model behavior in hydrological response.The HadCM-driven simulation was the one excluded in theaveraging as it shows particularly extreme or oppositeresponse in the tropics.[24] As a consequence of CO2-induced climate and phys-

iological changes, canopy interception loss increases inportions of the tropics (especially in the rain forest regions)but shows little discernable changes in middle and highlatitudes. Structural vegetation feedback increases canopyinterception loss across the globe. Transpiration decreases insome tropical and subtropical areas, notably southwesternNorth America, northern South America, West Africa, andsouthern Africa, as a result of climate change and CO2

physiological effects. Vegetation structural changes enhancetranspiration in portions of the midlatitudes and reduce it inmost of the tropics. Ground evaporation is accelerated inlarge parts of the tropics and midlatitudes due to combined

climate change and CO2 physiological effects. Vegetationstructural changes, however, produce widespread decreasesin ground evaporation across the globe, with some excep-tion in West Africa. The combined climate and CO2

physiological effect on total evapotranspiration is anincrease in the wet tropical regions and most of themidlatitudes, and a decrease in the subtropics, with negli-gible effects in the high latitudes. Vegetation structuralchanges substantially increase total evapotranspiration overa major portion of the globe, including parts of SouthAmerica, tropical Africa, Southeast Asia, and in the north-ern latitudes north of approximately 45�N. Averaged overthe globe, the increase in evapotranspiration due to struc-tural vegetation feedback is of a magnitude comparable tothat due to climate and CO2 changes (equivalent to 78% ofthe increase due to climate and CO2 changes). This suggeststhat vegetation feedback could further accelerate the atmo-spheric branch of the global water cycling under futureclimate change.[25] Runoff increases in northern high latitudes and

decreases elsewhere due to climate change and CO2 phys-iological effects. In contrast to its impact on evapotranspi-ration, structural vegetation feedback reduces runoff acrossthe globe. Because of the lack of vegetation feedback to theatmospheric climate (therefore to precipitation), the impactof vegetation structural change on runoff and that onevapotranspiration are similar in magnitude but oppositein direction.[26] Soil moisture also generally increases in the northern

mid and high latitudes, but decreases in most of the tropics,due to the combined climate change and CO2 physiologicaleffect. Vegetation feedback enhances wetness in the highlatitudes but leads to strong midlatitude dryness.

4. Discussion and Conclusions

[27] The use of a land surface model, climate changepredictions by eight GCMs, and simulated potential naturalvegetation conditions consistent with the climate predic-tions, enabled us to answer the question raised in theintroduction: Does the structural vegetation response toclimate change considerably modify the influence of climatechange on surface hydrological processes? Generally, suchvegetation feedback increases temperatures in parts of thenorthern middle and high latitudes during the DJF seasonand reduces temperatures in these regions during the JJAseason, but the impact is rather small in comparison with thecombined climate change and CO2 physiological effect. Itsimpact on the hydrological processes is much stronger thanon the thermal state of the climate. Structural vegetationfeedback substantially enhances evapotranspiration andreduces runoff in large areas across the globe, suggestingthat structural vegetation feedback could further acceleratethe atmospheric branch of the hydrological cycle. All GCMscenarios show a widespread soil drying signal over largeareas in the northern middle latitudes and a wetting signal inhigh latitudes in response to changes in vegetation structure.In the tropics, the impact is uncertain due to inconsistenciesamong the GCM scenarios. In many regions, the impact ofthe structural vegetation feedback is at a magnitude com-parable to or even larger than the impact of the combinedclimate change and CO2 physiological effect.


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Figure 7. Spatial distribution of soil moisture (soil water depth) changes between preindustrial andfuture states (in mm) due to a combination of changes in climate and CO2, and vegetation changes inresponse to climate and CO2 changes (first column), due to climate and CO2 changes combined (middlecolumn), and due to vegetation structural changes alone (third column), during December to February forthe eight different GCM scenarios.


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Figure 8. Spatial distribution of changes in individual components of annual evapotranspiration and theannual surface water budget between preindustrial and future states due to a combination of climate andCO2 changes (left column), and due to vegetation structural changes alone (right column), averaged overall the different GCM scenarios excluding HadCM.


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[28] This study utilized recent multimodel climate pre-dictions to reinforce previous modeling evidence [e.g.,Bonan et al., 1992; Levis et al., 2000; Feddema et al.,2005] that changes in vegetation cover, either in response toclimate change and elevated CO2 or due to human land usechanges, exert an important influence on the hydrologicresponse to climate change and elevated CO2. For example,without considering structural vegetation feedback, ourresults show that climate change and the physiologicaleffect of CO2 has little impacts on soil moisture in northernmiddle latitudes, while inclusion of vegetation responseleads to a strong drying signal. Also, climate change andCO2 physiological effects together reduce runoff andincrease evapotranspiration in the tropics while includingstructural vegetation feedback can double the runoff reduc-tions (and evapotranspiration increases) in most of thetropics.[29] Wang [2005] analyzed the predicted impact of the

greenhouse warming on soil moisture changes by fifteenGCMs (including seven of the GCMs applied in this study)based on data from the IPCC data archive. She found a lowlevel of consistency among the GCMs in predicting soilmoisture changes whereas predicted precipitation changeswere quite consistent among the GCMs. It was suggestedthat differences in simulated soil moisture changes amongthe GCMs are largely due to differences in land surfaceschemes. Using one land surface model driven with climatechange predictions from different GCMs in the presentstudy allowed us to explore a possible range of alterationof surface hydrological impacts of climate change bychanges in natural vegetation distribution. Our results maybe limited by the particular model used in this study. Forinstance, Wang [2005] also found that soil moisture changesshow strong seasonal dependency in some GCMs and weakseasonal dependency in others. CCSM, which uses CLM3.0as its land component, was among the models that demon-strated very weak seasonality in soil moisture changes. Inthis study too, we see very little seasonality in the simulatedsoil moisture changes.[30] The physiological response of plants to elevated CO2

as observed in short-term controlled field or greenhouseexperiments may differ in real-world ecosystems as atmo-spheric CO2 increases into the future due to possiblenutrient limitations on photosynthesis and acclimatizationof plant physiological processes (e.g., photosynthesis) toelevated CO2 and temperature [Norby and Luo, 2004;Rustad, 2006]. Moreover, the scaling of the leaf-levelexperiments to the canopy and ecosystem scale remains achallenge [Luo et al., 2004; Norby and Luo, 2004]. Thus therepresentation of CO2 effects on photosynthesis and stoma-tal conductance in CLM-DGVM is a potential source ofuncertainty in the simulated vegetation structural changesand, consequently, the simulated surface hydrologicalimpacts. Dispersal capabilities of plant species and humanland use changes, effects not included in the present study,may limit plant migration under future climate change[Neilson et al., 2005; Higgins and Harte, 2006]. This addssome uncertainty in the modeled changes in vegetationcoverage and related hydrological impacts. Furthermore,the effects modeled here, both the response of vegetationcover to CO2-induced climate change and its impact onhydrological processes, are simulated using offline land

surface and vegetation models driven with prescribed climateforcings. The feedback from vegetation on climate forcing istherefore missing. Nonetheless, this multi-GCM based anal-ysis provides a first-order estimate of the magnitude anddirection of possible surface hydrological responses toclimate- and CO2-induced vegetation changes, and demon-strates that structural vegetation feedback could substantiallyinfluence the response of hydrological conditions to theCO2 and climate changes at regional to global scales.

[31] Acknowledgments. We would like to acknowledge the interna-tional modeling groups for providing their data for analysis, the Program forClimate Model Diagnosis and Intercomparison (PCMDI) for collecting andarchiving the model data, the JSC/CLIVER Working group on CoupledModeling (WGCM) and their Coupled Model Intercomparison Project(CMIP) and Climate Simulation Panel for organizing the model dataanalysis activity, and the IPCC WG1 TSU for technical support. The IPCCData Archive at Lawrence Livermore National Laboratory is supported bythe Office of Science, U.S. Department of Energy. We are also grateful tothe anonymous reviewers whose comments have helped to improve thepresentation.

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��C. A. Alo and G. Wang, Department of Civil and Environmental

Engineering, University of Connecticut, 261 Glenbrook Road, Storrs, CT06269-2037, USA. ([email protected]; [email protected])


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