Incorporation of a Dynamic Root Distribution into CLM4.5:Evaluation of Carbon and Water Fluxes over the Amazon

Yuanyuan WANG,ZhenghuiXIE,and Binghao JIAState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 00029

2University of Chinese Academy of Sciences,Beijing 100049

Incorporation of a Dynamic Root Distribution into CLM4.5:Evaluation of Carbon and Water Fluxes over the Amazon

Yuanyuan WANG1，2,ZhenghuiXIE?1,and Binghao JIA?1
1State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029

2University of Chinese Academy of Sciences,Beijing 100049

(Received 21 October 2015;revised 5 April 2016;accepted 4 May 2016)

Roots are responsible for the uptake of water and nutrients by plants and have the plasticity to dynamically respond to diff erent environmental conditions.However,most land surface models currently prescribe rooting pro files as a function only of vegetation type,with no consideration of the surroundings.In this study,a dynamic rooting scheme,which describes root growth as a compromise between water and nitrogen availability,was incorporated into CLM4.5 with carbon–nitrogen (CN)interactions(CLM4.5-CN)to investigate the e ff ects of a dynamic root distribution on eco-hydrological modeling.Two paired numerical simulations were conducted for the Tapajos National Forest km83(BRSa3)site and the Amazon,one using CLM4.5-CN without the dynamic rooting scheme and the other including the proposed scheme.Simulations for the BRSa3 site showed that inclusion of the dynamic rooting scheme increased the amplitudes and peak values of diurnal gross primary production(GPP)and latent heat fl ux(LE)for the dry season,and improved the carbon(C)and water cycle modeling by reducing the RMSE of GPP by 0.4 g C m-2d-1,net ecosystem exchange by 1.96 g C m-2d-1,LE by 5.0 W m-2,and soil moisture by 0.03 m3m-3,at the seasonal scale,compared with eddy fl ux measurements,while having little impact during the wet season.For the Amazon,regional analysis also revealed that vegetation responses(including GPP and LE)to seasonal drought and the severe drought of 2005 were better captured with the dynamic rooting scheme incorporated.

CLM4.5,dynamic root distribution,carbon cycle,water cycle,Amazon

1. Introduction

Roots are the primary pathway for the uptake of water and nutrients by plants and play an important role in terrestrial carbon(C)and water cycling(Nepstadet al.,1994;Jackson et al.,1997;Dickinson et al.,1998;Barlage and Zeng, 2004;Zheng and Wang,2007).They connect the soil environment to the atmosphere throughwater and energy fl ux exchanges between the vegetation canopy and the atmosphere (Feddes et al.,2001).Root vertical distribution,one of the most important properties of roots,is an essential component of many eco-hydrological models(Laiand Katul,2000)and land surface models(LSMs)(Zeng et al.,1998;Feddes et al., 2001;El Maayar and Sonnentag,2009);it mainly controls the extent of root water uptake among soil layers,and therefore soil water stress.The soil water stress further in fl uences transpiration,C assimilation,and subsequently other C and water fl uxes(Bonan,1996;Zeng et al.,2002;Ivanov et al., 2008).Thus,a realistic representation of root distribution is very important for hydrological,ecological and climate modeling(Zheng and Wang,2007;Jing et al.,2014).

As a consequence of a lack of appropriate global root datasets owingtothedifficultyofmeasuringentirerootdistributions throughout the soil pro file(Jing et al.,2014;Warren et al.,2015),the description of root distributions in LSMs is often simplified or ignored(Zeng et al.,2002;Warren et al.,2015).In most LSMs,root distribution is treated as a static component,and three rooting parameterizations are widely used.The first is a one-parameter asymptotic root equation,proposed by Jackson et al.(1996),which describes root distribution decreasing exponentially with depth.It has been used in NCAR’s LSM(Bonan,1996)and the Simple Biosphere Model(Baker et al.,2008).The second is a twoparameter asymptotic root distribution decreasing exponentially with depth(Zeng,2001),which is used in NCAR’s CLM(Oleson et al.,2010,2013).And the third is a logistic dose-response curve root pro file proposed by Schenk and Jackson(2002),which has two shape parameters that describe the soil depth above which 50%and 95%of the root mass occurs.This parameterization is employed in the Conjunctive Surface–Subsurface Process Model(Yuan andLiang,2011)and Mechanistic Multilayer Canopy–Soil–Root System Model(Drewry et al.,2010;Le et al.,2012).All parameters in these three root distribution schemes depend only on vegetationtypes,with root distributions spatially and temporallyinvariant.However,substantial diff erencesin root distributions are apparent even for the same type of vegetation,as determined from measuring root pro files in diff erent irrigation and fertilization experiments(Weaver,1926;Liet al.,1998;Fan et al.,2012).Furthermore,it has been demonstrated that plants tend to allocate C to enhance the acquisition of a limited resource(Hutchings and de Kroon,1994), and thus tend to grow more roots in zones where soil moisture is more freely available,especially when su ff ering from water de ficit(Coelho and Or,1999;Collins and Bras,2007; Sivandran and Bras,2013),and where more nutrients can be acquired(McMurtrie et al.,2012).These aspects imply that rootsystems havethe plasticityto dynamicallyrespondto environmental conditions,such as water and nutrient availability(Schenk and Jackson,2002;Hodge,2004;Schenk,2008; Smithwick et al.,2014;El Masriet al.,2015),indicating that the three rooting schemes mentioned above are insufficient in their representation of the actual root distribution,and thus need to be improved.

In this study,a dynamic root distribution scheme that describes root growth as a compromise between water and nitrogen(N)availability,was implementedin CLM4.5(Oleson et al.,2013).The respective impacts on terrestrial C and water cycles were evaluated over the Amazon.The evaluation focused on the model prognostic skill with respect to gross primary production(GPP),net ecosystem exchange(NEE), latent heat fl ux(LE)and soil water content(SWC).Section 2 describes the model development,study area,experimental design and data used.Results are givenin section 3,followed by conclusions and discussion in section 4.

2. Methods

2.1. Model development

2.1.1.CLM4.5

CLM4.5,a state-of-the-art LSM,is the latest version of the CLM family of models and the land component of CESM1.2(Oleson et al.,2013).It succeeds CLM4,with updatestothephotosynthesis,soilbiogeochemistry, firedynamics,cold region hydrology,lake model,and biogenic volatile organic compounds model(Liet al.,2013).The spatial heterogeneity of the land surface is represented in CLM as a nested sub-grid hierarchy,and vegetation is classified into 16 plant functional types(PFTs)according to diff erent photosynthesis parameters and optical properties(leaf and stem refl ectanceandtransmittancein visibleand near-infraredwavebands).The soil columns have 15 vertical layers,but hydrologycalculationsare onlymadeforthe top10layers.CLM4.5 also has an option to run with an interactive C–N(CN)cycle(denoted as CLM4.5-CN),which is fully prognostic with respect to all C and N state variables in vegetation,litter and soil organicmatter.When the CN biogeochemistrymoduleis active,N limitation on photosynthesis is prognostic and leaf area,stem area indices and vegetation heights are all determined prognostically by the model(Lawrence et al.,2011). A detaileddescriptionof its biogeophysicalandbiogeochemical parameterizationsandnumericalimplementationis given in Oleson et al.(2013).

A root distribution function determines the fraction of roots in each soil layer.CLM4.5 uses the root distribution equation of Zeng(2001):

where zh，i(m)is the depth from the soil surface to the interface between layer iand i+1,and raand rbare two PFT-dependent root parameters.

2.1.2.Dynamic rooting scheme and its implementation

Atpresent,althoughtherootCpooldoesvarytemporally, due to the static rooting scheme there is no net change to the root fraction within each soil layer.To represent actual root growth in CLM4.5 dynamically,we adopted a dynamic rootingscheme proposedby Hatzis(2010),which allows the total new root C gain at each time step to dynamically allocate to each soil layer accordingto the surroundingenvironment,i.e. a compromise between soil water and soil mineral N,as expressed by Eq.(2):

where ΔCfr(units:g C m-2s-1)is the newly assimilated C allocated to roots,Δzi(units:m)is the soil layer thickness,ni(units:g N m-3)is soil mineral N content,and wiis the plant wilting factorof layer i.βtis the soil water stress due to water de ficiency,depending on wiand root fraction(ri),expressed as:

whereψiisthesoilwatermatricpotential(units:mm),andψcand ψo(hù)are the soil water potential(units:mm)when stomata are fully closed or fully open,respectively.θsat，iand θice，iare the saturated volumetric water and ice content,respectively (units:m3m-3).The function βtranges from 0 to 1,with larger values indicating higher water availability.The root distribution after the new dynamic allocation is then updated, based on the root C(Cfr，i;units:g C m-2)of layer iand the total root C

To incorporate this scheme into CLM4.5, the total N (TN) data from the Global Soil Dataset for Earth System Mod-eling,developed by the Land–Atmosphere Interaction Research Group at Beijing Normal University,were used to replace the vertical soil mineral N content,as the vertically resolved soil mineral N is not predicted in CLM4.5.The TN data have a resolution of 30 arc-seconds,with the vertical variation captured by eight layers to a depth of 2.3 m(i.e.0–0.045,0.045–0.091,0.091–0.166,0.166–0.289,0.289–0.493, 0.493–0.829,0.829–1.383 and 1.383–2.296 m),consistent with the vertical layers of CLM4.5(Shangguan et al.,2014). Here,we up-scaled the TN data from 30 arc-seconds to 0.5°by means of an area-weightedaverage andused linear regressions(Hatzis,2010)to estimate TN values for the residual two layers.

The dynamic rooting scheme influences the ecohydrological modeling in CLM4.5 in multiple ways (Fig. 1). First,the varying root distribution has a direct impact on βt, as in Eq.(3).On the one hand,βtin fl uences photosynthesis by multiplying it by the maximum catalytic capacity of the Rubisco enzyme(Vcmax).On the other hand,βtfurther in fl uences plant transpiration through stomatal conductance, as stomatal conductanceis linearly related to βtin the model. Second,the varyingrootfractionin fl uencesthe calculationof the e ff ective root fraction,which a ff ects the water extracted from each layer,and therefore the SWC.In addition,the soil N plays an importantpart,it not onlyin fl uences root fraction, as Eq.(2)shows,but also controls the amount of N that can be absorbed by plants,and thus limits photosynthesis.

2.2. Study area

The Amazon region shown with a black border in Fig.2 (Zeng et al.,2008;Marthews et al.,2014),which contains about 50%of the world’s tropical forests,is crucial to global hydrological and C cycles,and changes in its biophysical state can exert a strong in fl uence on global climate(Baker et al.,2008).It is mainly covered by tropical broadleaf evergreen tree(BET Tr),tropical broadleaf deciduous tree(BDT Tr),C3grass(C3NA)and C4grass(C4)(Fig.2a),according to MODIS land cover data in CLM4.5(Lawrence and Chase, 2007).The driving climatic forcing of energy,water and C cycles in the Amazon is the spatial and temporal distribution of precipitation(Ichiiet al.,2007).The dry seasons are usually de fined as months with less than 100 mm precipitation (Baker et al.,2008).Mean monthly precipitationin the Amazon(Fig.2b)is 185.35 mm month-1,with a range of 29.14–372.64 mm month-1,based on CRU–NCEP reanalysis data (CRUNCEP)from1982–2010(Viovy,2011).Thedryseason length increases from the northwestern to southeastern Amazon,along with a transition from evergreen broadleaf forest to deciduous broadleaf forest and C4grass(Fig.2c).

The Large Scale Biosphere–Atmosphere Experiment (LBA)in the Amazon(Avissar et al.,2002)monitored water, energy and C exchange between ecosystems and the atmosphere.BRSa3(3.02°S,54.97°W)is a typical site of LBA, located within the Tapajos National Forest,Par′a,Brazil(Fig. 2b),covered by BET Tr.During the study period of 2001–2003,the mean annual air temperature and solar radiation were 25.9°C and 188.7 W m-2,respectively.The mean annual total precipitation was 1658 mm,with less rainfall during the dry season of July–December(Fig.3).The seasonal variation of monthly air temperature was quite small(＜2°C) and the solar radiation of the dry season was slightly higher than that of the wet season.At BRSa3,an eddy covariance system was installed to measure the fl uxes of carbon dioxide,LE and all meteorological variables required for running CLM4.5.

2.3. Experimental design and data

Fig.1.Conceptual diagram of the impacts of a dynamic root distribution on eco-hydrological modeling in CLM4.5.

Fig.2.(a)The dominant PFTs in the Amazon[bare soil(Bare);temperate needleleaf evergreen tree(NEM Tr);boreal needleleaf evergreen tree(NEB Tr);boreal needleleaf deciduous tree(NDB Tr);tropical broadleaf evergreen tree(BET Tr);temperate broadleaf evergreen tree(BEM Tr);tropical broadleaf deciduous tree(BDT Tr);temperate broadleaf deciduous tree(BDM Tr);boreal broadleaf deciduous tree(BDB Tr);temperate broadleaf evergreen shrub(BE Sh);temperate broadleaf deciduous shrub(BDM Sh);boreal broadleaf deciduous shrub(BDB Sh);C3 arctic grass(C3 AR);C3 grass(C3 NA);C4 grass(C4);and Crop].(b)Average monthly(1982–2010)precipitation(units:mm month-1)over the Amazon according to CRUNCEP,and the location of Tapajos National Forest km8(BRSa3).(c)Number of dry months per year,de fined as monthly precipitation less than 100 mm(the two black boxes represent the two study areas analyzed in section 3.2,denoted as R1 and R2,respectively). The border of the Amazon is shown as a black line.

Two pairs of experimentswere conductedto study the effects of dynamic root distribution on eco-hydrological modeling:one for the BRSa3 site and the other for the Amazon region.For each pair of experiments,two offline simulations were conducted,both with CLM4.5-CN:simulations using the default model(control run,“CTL”)and the model with dynamic root distribution(new run,“NEW”).For establishing the C and N pools and fl uxes(Castillo et al.,2012; Hudiburg et al.,2013),the 1200-year spun-up results were used as initial conditions for both site-level and regional simulations(e.g.the soil C pool of the BRSa3 site was initialized from 0 to about 5.89 kg C m-2).The two simulations of each pair of experiments shared the same initial conditions,thus eliminating changes other than those from dynamic root distribution(Yan and Dickinson,2014).

For this study,half-hourly,daily and monthly gapfilled observations at the BRSa3 site were downloaded from FLUXNET(www. fl uxdata.org).For site-level simulations, the meteorological data,including wind speed,2-m air temperature,specific humidity,air pressure,incident solar radiation and precipitation,measured at 30-min intervals at the BRSa3 site during 2001–03,were used to force the offline simulations.Observed GPP,NEE,LE and SWC(mean of SWC measured at 10 and 20 cm),corresponding with the study period,were used to assess the models’abilities.

For the regional case,CRUNCEP was used as the atmospheric forcing.This is a 110-year(1901–2010)observationbased atmospheric forcing dataset,which is a combination of two existing datasets:the CRU TS3.2 0.5°×0.5°monthly data covering the period 1901–2002,and the NCEP reanalysis 2.5°×2.5°six-hourly data from 1948 to 2010(Viovy, 2011).The dataset comprises six-hourly data on precipitation,solar radiation,air temperature,pressure,humidity and wind.We utilized CRUNCEP for 1901–81 in the spun-up simulation and results for 1982–2010 at a 0.5°×0.5°resolution.Since evaluating GPP and LE from LSMs at regional scales is hindered by a lack of extensive observations,two products were used as reference for benchmarking our comparisons in the Amazon region:the global GPP(monthly,0.5°×0.5°)and LE(monthly,0.5°×0.5°),up-scaled from FLUXNET observations using the machine learning technique,and model tree ensembles(MTE)data for 1982–2010 (Jung et al.,2009,2011).

Fig.3.Average monthly precipitation(PR;units:mm month-1;bars),shortwave downward radiation(SWDR;units:W m-2;solid line with asterisks)and air temperature (TA;units:°C;solid line with circles)at the BRSa3 site according to observations from 2001–03(grey area indicates the dry season).

2.4. Mathematical Indices for Model’s Performance

To evaluate the agreement between model simulations and observations,four indices were used:agreement index (d)(Liet al.,2012),correlation coefficient(R),mean bias error(MBE)and root mean square error(RMSE),de fined as follows:

where xsimis model simulation either from CTL or NEW, xobsis the corresponding observation,are the mean of xsimand xobs,respectively.For d,a value of 1 indicates a perfect match and 0 indicates no agreement at all. RMSE provides an estimate of the absolute bias in the model simulation and the smaller the value of RMSE,the better the agreement between the simulation and observation is.

3. Results

For optimal evaluation of the e ff ects of a dynamic root distribution on eco-hydrological modeling,the diurnal cycles of βt,GPP,NEE,LE and SWC(mean of the top 20 cm) for the wet(April)and dry(October)seasons at the BRSa3 site are presented in Fig.4,together with their corresponding climate variables(precipitation,solar radiation and temperature).GPP and LE in from CTL and NEW showed the same diurnal cycle as observed,with a peak value at noon(Figs. 4e,g,m and o),which was mainly driven by solar radiation(Figs.4b and j).Furthermore,the two simulations did not diff er from one another regardingGPP and LE during the wet season,which had sufficient rainfall(Fig.4a)for no soil water stress(βt=1;Fig.4d),and agreed well with observation.However,during the dry season,with little precipitation (Fig.4i)and thus severe water stress(βt＜0.8;Fig.4l),CTL obviously underestimated daytime GPP(～40%at noon;Fig. 4m)and LE(typically＞20%around noon;Fig.4o).By incorporating the dynamic rooting scheme in NEW,more root C was allocated into deeper soil layers(Fig.5).Compared with theobservedrootdistributiondata(Jacksonet al.,1996), the dynamic root scheme realistically captured the observed root pro file,better than the static root distribution,with the largest fraction of roots in deep layers,and thus more water could be taken up by roots.This further reduced the soil water stress(Fig.4l),and so the amplitudes and peak values of GPP(Fig.4m)and LE(Fig.4o)for the dry season increased. That said,part of the underestimation still remained,indicating that other mechanisms apart from the dynamic rooting scheme still need to be considered.

NEE is an expression of net C exchange between ecosystem and atmosphere,with positive values indicating efflux into the atmosphere and negative values indicating uptake by the biosphere,calculated as per Eq.(10):

Fig.4.Diurnal(a)precipitation(PR;units:mm h-1),(b)shortwave downward radiation(SWDR;units:W m-2),(c)air temperature (TA;units:°C),(d)βt,(e)GPP(units:g C m-2h-1),(f)NEE(units:g C m-2h-1),(g)LE(units:W m-2)and(h)SWC(mean of 0–20 cm units:m3m-3)for wet(April)months at the BRSa3 site,aggregated over 2001–03.Panels(i–p)are the same as panels(a–h) but for the dry(October)season.

where GR is the growth respiration,MR is the maintenance respiration,HR is the heterotrophicrespiration,AR is the autotrophic respiration(AR=GR+MR),and ER is the total ecosystem respiration(ER=AR+HR).For the wet season, both the two runs captured the amplitudes and peak value of observed NEE well,with the biosphere acting as a C source in the morning and evening,but a C sink at noon(Fig.4f). However,for the dry season,CTL greatly underestimated the peak value of C uptake at noon(Fig.4n),due to the severe water stress.However,during the dry season,GR,MR and HR all increased due to the increase in photosynthesis,which then led to higher ER(not shown).Because GPP increased more than ER,the NEE values(negative)became smaller, and thus NEW improved the simulation of NEE,with more C uptake at noon,closer to that observed.

For the limited SWC observation,just the mean value of SWC fromthe top layers(0–20cm)of the two runs was compared with observation(Figs.4h and p).SWC showed little diurnalvariationandwas underestimatedboth forthe dry and wet seasons—more severely for the dry season.The underestimation of SWC for the top layers in the dry season was slightlyreducedinNEW(Fig.4p),becausethedynamicrooting scheme allowed the roots to absorb water from the deep soil(Fig.5).However,despite improvementdue to the incorporation of a dynamic root distribution,significant biases in SWC simulations remained.

Fig.5.Mean root pro file over the 3-year(2001–03)simulations of the two runs.

Figures 6a–e show the mean daily βt,GPP,NEE,LE and SWC(0–20 cm),respectively,averaged for 2001–03, and the diff erences in GPP,NEE,LE and SWC between the two runs were all significant at the 95%con fidence level according to the Student’st-test.Decreases in GPP and LE for July–December(Figs.6b and d)due to dryness(βt＜1; Fig.6a)were found in CTL,which were much lower than observed,possibly caused by the model’s excessive sensitivity to drought(Baker et al.,2008).However,NEW,with its dynamic rooting scheme,improved the simulation for GPP and LE during the dry season,which were closer to their corresponding observations,by reducing the underestimation of GPP and LE by higher βt(lower soil water stress),resulting in lower MBE(Figs.7b and j)and RMSE(Figs.7c and k). For NEE,CTL simulated positive values during the dry season,indicating the biosphere acted as a C source,contrary to observation(Fig.6c).When a dynamic root distribution was considered,the biosphere was altered to a C sink or the magnitude of C emissions was reduced for July–December, which was closer to observations.This reduced the MBE from 1.25 to 0.40 g C m-2d-1(Fig.7f)and the RMSE from 3.91 to 1.95 g C m-2d-1(Fig.7g).For the mean SWC of the top 0–20 cm,both runs gave large underestimations.However,NEW reduced the underestimation for July–December, with the RMSE lowered from 0.18 to 0.15 m3m-3,as the dynamic root distribution allowed roots to absorb more water from deeper soil layers(Fig.6e).Overall,GPP,NEE,LE and SWC were better estimated using the new model,with lower MBE and RMSE and higherRandd,especially during dry months.

Fig.6.Diff erence among the simulated mean daily values of(a)βt,(b)GPP,(c)NEE,(d)LE and(e)SWC(mean of 0–20 cm)at the BRSa3 site averaged from 2001 to 2003(grey areas indicate the dry season).

Fig.7.Comparison between the results of CTL and NEW at the BRSa3 site for(a–d)GPP,(e–h)NEE,(i–l)LE,and(m–p)SWC(mean of 0–20 cm)compared with corresponding observations for wet months,dry months and the whole year.The four indices used are de fined as Eq.(6–9)in section 2.4.

To further evaluate how a dynamic root distribution affectsthe responseofterrestrialC andwater cycles toseasonal droughts in the Amazon,two study regions(denoted R1 and R2),dominated by BET Tr and C4grass,respectively,were selected for analysis(Fig.2c).The mean monthly precipitation for R1 and R2 was 180.48 and 136.35 mm month-1, respectively.Figure 8 shows the annual cycle of simulated and observed GPP and LE averaged over the two study areas across 1982–2010,together with βt.For R1,the dry season lasted four months:June–September.Both GPP and LE simulated byCTL showedobviousreductionsdueto the decreasing βt(Fig.8a)during the dry season,with large negative biasescomparedtoobservation(Figs.8bandc).Incontrast,the monthly variations of GPP and LE for NEW became smaller than those of CTL,with the RMSE reduced from 39.52 to 29.87 g C m-2month-1for GPP,and from 18.80 to 17.65 W m-2for LE.During the dry season,the mean GPP and LE increased from 195.95 to 211.62 g C m-2month-1,and from 91.47to 98.83W m-2,respectively–closerto the corresponding MTE observations.In R2,both simulated and observed GPP and LE were lower than that of R1 due to the diff erence of parameters for photosynthesis and transpiration between the two vegetation types(Figs.8b and e).In this region the dry season was May–September,with βtobviously decreasing from 1 to 0.6.During this period,both the two simulations showed significant decreases in GPP and LE,similar to observation,but too steep in CTL.In contrast,NEW showed similar improvements in GPP and LE in R2 as R1 (Figs.8e and f),with the mean GPP increasing from 128.84 to 146.93 g C m-2month-1,and LE from 78.0 to 87.69 W m-2,during June to September.Furthermore,the RMSE reduced from 65.70 to 54.42 g C m-2month-1for GPP,and from 22.0 to 19.62 W m-2for LE,compared to observations. To summarize,the plant response to seasonal drought was better captured with a dynamic root distribution considered, though some divergence still remained.

Fig.8.Annual cycle of simulated βt,GPP and LE,compared with their corresponding observations(MTE GPP and LE),averaged over the two study areas in the Amazon across 1982–2010:(a–c)for R1 and(d–f)for R2(shaded areas indicate the dry season).

In 2005,the Amazon experienced a severe drought—the worst for over a century(Saleska et al.,2007;Chen et al., 2009).Amazon rainfall reductions were the most extensive for July–September 2005 when the subtropical North Atlantic SST was at its highest(Zeng et al.,2008).Based on the 29-year climatology for 1982–2010 from CRUNCEP, the drought in 2005 was captured(Fig.9a)and the blackboxed region with the largest negative precipitation anomaly (≤-50 mm month-1)was analyzed(hereafter R3).Figure 9b shows that the mean rainfall of R3 from July to September in 2005 was the lowest during the 29 years,at just 41.4 mm month-1.Note that the 2005 rainfall anomaly based on CRUNCEP for1982–2010wassimilartothatfor1901–2010, butfortemporalconsistencyonlytheformeris shownandanalyzed.Figures 9c–e show the annual cycle of simulated and observed GPP and LE averaged over R3 for 2005 and averaged across 1982–2010,together with βt.During the 2005 drought,the simulated GPP and LE decreased in R3(Figs. 9d and e),substantially lower than the observed multi-year average,but more rapidly in CTL than in NEW,especially in July–September,as a result of the decreasing βt,indicative of more severe soil water stress(Fig.9c).However,NEW mitigated the underestimation of GPP and LE in July–September during the 2005 drought by increasing the soil water availability,with the RMSE reduced from 30.3 to 23.1 g C m-2month-1for GPP and from 16.9 to 14.3 W m-2for LE.In general,the vegetation response to the severe 2005 drought was better captured with a dynamic rooting scheme incorporated.

4. Conclusions and discussion

In this study,a dynamic rooting scheme that describes root growth as a compromise between water and N availability in the subsurface,was incorporated in CLM4.5-CN and its e ff ects on C(GPP and NEE)and water cycle(LE and SWC)modeling were evaluated over the Amazon.At the BRSa3 site,the two simulations diff ered little in their results duringthe wet season.However,duringthe dryseason(July–December),CTL underestimated GPP,LE and SWC,possibly as a result of the model’s excessive sensitivity to drought. However,with the new rooting strategy,more root C was allocated into deeper soil layers and more water was able to be absorbed by the roots.This further reduced the soil water stress,and thus improved the C and water cycle modeling by reducing the RMSE in GPP by 0.4 g C m-2d-1,NEE by 1.96 g C m-2d-1,LE by 5.0 W m-2,and SWC by 0.03 m3m-3,compared with observations.Additionally,NEW was able to overcome part of the underestimation,indicating that a dynamic root distribution is not the only mechanism that needs to be considered.For the Amazon region,the defaultmodel showed obvious reductions in simulated GPP and LE due to the decreasing βtduring the dry season in both R1 and R2,with large negative biases.The C and water simulations were improved in NEW,with the RMSE for GPP reduced from 39.52 to 29.87 g C m-2month-1in R1,and from 65.70 to 54.42 g C m-2month-1in R2;and for LE,from 18.80 to 17.65 W m-2in R1,and from 22.0 to 19.62 W m-2in R2.In the severe 2005 drought,the region with the largest negative precipitationanomaly(R3)showedobviousdecreasesinGPP andLE–substantiallylower thanthe observedmulti-yearaverage.The soil water availability during this period was able to be increased in NEW,and thus mitigated the underestimation of GPP and LE,with the RMSE reduced from 30.3 to 23.1 g C m-2month-1for GPP,and from 16.9 to 14.3 W m-2for LE.In general,the vegetation response(including GPP and LE)to seasonal droughtand the severe 2005 droughtwas better captured when a dynamic root distribution was incorporated,although some divergence still remained.

Fig.9.(a)Monthly precipitation(PR)anomaly(units:mm month-1)for July–September 2005,based on the 29-year climatology from 1982–2010 calculated from CRUNCEP(the black box represents the study region analyzed in section 3.3,denoted as R3).(b)Time series of monthly mean PR(units:mm month-1)for July–September averaged over R3 from 1982–2010.(c–e)Annual cycle of simulated βt,GPP and LE averaged over R3 for 2005 and averaged across 1982–2010,compared with their corresponding observations(MTE GPP and LE).The border of the Amazon is shown as a black line.

However,onlyincludinga dynamicrootdistributionis insufficient to improve the simulations to match observations, especiallyforSWC.Totest the sensitivityofSWC to soil texture,we replaced the soil type using observational data from Liet al.(2012)and Yan and Dickinson(2014)at the BRSa3 site,where the soil type is mainly clay latosol(80%clay, 18%sand and 2%silt),into CLM4.5 instead of the IGBP data(35%clay,45%sand and 20%silt).Thus,the water content at saturation(i.e.porosity)varied from 0.30 to 0.36 m3m-3,and the saturated hydraulicconductivityvariedfrom 0.021 to 0.019 mm s-1.The simulation from observational soil typesagreedbetterwithground-basedSWC observations than that from the original IGBP data.The mean SWC of the top 0–20 cm increased from 0.34 to 0.42 m3m-3for April, and from 0.20 to 0.30 m3m-3for October(Figs.10a and b). This suggests that soil texture is a critical factor for hydraulic properties,and observational soil type can reduce the biases of SWC simulations in CLM4.5.

Fig.10.(a,b)Sensitivity of SWC todiff erent soil textures,and sensitivity of(c,d)GPP and(e,f)LE to diff erent stomatal parameters and root pro files(obs,observation;org,the run with the original model;new,the run with the dynamic rooting scheme;1,the run with observed soil texture;2,the run with new stomatal parameters;3, the run with the observed root pro file).

The soil potential values(mm)when stomata are fully closed(ψc)or fully open(ψo(hù))in CLM4.5,which are PFT-dependent,are from White et al.(2000).However,Verhoef and Egea(2014)found that the ψcand ψo(hù)values are not always realistic.In CLM4.5,ψcand ψo(hù)values of tropical broadleaf evergreen tree(the dominant PFT at the BRSa3 site)are-255 000 mm and-66 000 mm,respectively.To test the sensitivity of GPP and LE to diff erent ψcand ψo(hù)values,we used another set of values(-127500 mm for ψcand -33000 mm for ψo(hù))in the simulations.The results showed that the diff erent ψcand ψo(hù)values caused large diff erences for the GPP and LE simulations(Figs.10c–f).

Toseeifadditionalimprovementscouldbemadebyusing the observed root distribution data,another experiment(denoted as“3”)was conducted for the BRSa3 site,in which the observed root distribution data were used to force CLM4.5. The results showed that the two runs(i.e.the new run and the run with observed root data)did not show large diff er-ences in GPP and LE during both the wet and dry seasons (Figs.10c–f).This suggests that,in addition to the dynamic rootingscheme,manyotherroot-relatedmechanisms,including deep root systems up to 18 m(Canadell et al.,1996), hydraulic redistribution(Ryel et al.,2002)and preferential root water uptake(Laiand Katul,2000),also contribute to dryseason water uptakeandconsequentlydroughtresponses, and should therefore be further examined in modeling studies.Previous studies(Tomasella et al.,2008;Miguez-Macho and Fan,2012)suggest that groundwater in the Amazon can reduce wet season soil drainage and lead to larger soil water stores before the dry season arrives.This is one of the reasons for the observed absence of dry season water stress. In addition,more field observationsand experimentswill improve our understanding of how to represent root activities in plant physiological and ecological aspects(Yan and Dickinson,2014).This paper presents only preliminary comparisons in the Amazon,and more analysis on the e ff ects of a dynamic root distribution on eco-hydrological and climate modeling at the global scale is needed in the future.

Acknowledgements.This study was supported by the National Natural Science Foundation of China(Grant Nos.41305066 and 41575096).The CRUNCEP and MTE data were downloaded freely from the NCAR(http://www.cesm.ucar.edu/)and Max Plank Institute for Biogeochemistry(https://www.bgc-jena.mpg.de/)websites,respectively.We also thank FLUXNET for providing valuable data for the BRSa3 site(http://amerifl ux.lbl.gov/).

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?Corresponding author:Binghao JIA

Email:bhjia@mail.iap.ac.cn