This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

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1 This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier s archiving and manuscript policies are encouraged to visit:

2 Ecological Modelling 222 (2011) Contents lists available at ScienceDirect Ecological Modelling journa l h o me pa g e: Evaluating the effects of future climate change and elevated CO 2 on the water use efficiency in terrestrial ecosystems of China Qiuan Zhu a,b, Hong Jiang a,c,, Changhui Peng b, Jinxun Liu d, Xiaohua Wei e, Xiuqin Fang f, Shirong Liu g, Guomo Zhou c, Shuquan Yu c a International Institute for Earth System Science, Nanjing University, Hankou Road 22, Nanjing , China b Institute of Environment Sciences, University of Quebec at Montreal, Case Postale 8888, Succursale Centre-Ville, Montreal, QC, Canada H3C 3P8 c International Research Center of Spatial Ecology, Zhejiang Forestry University, Linan , China d Stinger Ghaffarian Technologies (SGT, Inc.), Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, nd St., Sioux Falls, SD 57198, United States 1 e Department of Earth and Environmental Sciences, University of British Columbia (Okanagan), 3333 University Way, Kelowna, BC, Canada V1V 1V7 f State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Hohai University, Nanjing , China g Institute of Forest Ecology and Environment, Chinese Academy of Forestry, Beijing , China a r t i c l e i n f o Article history: Received 22 February 2010 Received in revised form 26 September 2010 Accepted 28 September 2010 Available online 23 October 2010 Keywords: Water use efficiency WUE Climate change Ecosystem level IBIS DGVM China a b s t r a c t Water use efficiency (WUE) is an important variable used in climate change and hydrological studies in relation to how it links ecosystem carbon cycles and hydrological cycles together. However, obtaining reliable WUE results based on site-level flux data remains a great challenge when scaling up to larger regional zones. Biophysical, process-based ecosystem models are powerful tools to study WUE at large spatial and temporal scales. The Integrated BIosphere Simulator (IBIS) was used to evaluate the effects of climate change and elevated CO 2 concentrations on ecosystem-level WUE (defined as the ratio of gross primary production (GPP) to evapotranspiration (ET)) in relation to terrestrial ecosystems in China for Climate scenario data (IPCC SRES A2 and SRES B1) generated from the Third Generation Coupled Global Climate Model (CGCM3) was used in the simulations. Seven simulations were implemented according to the assemblage of different elevated CO 2 concentrations scenarios and different climate change scenarios. Analysis suggests that (1) further elevated CO 2 concentrations will significantly enhance the WUE over China by the end of the twenty-first century, especially in forest areas; (2) effects of climate change on WUE will vary for different geographical regions in China with negative effects occurring primarily in southern regions and positive effects occurring primarily in high latitude and altitude regions (Tibetan Plateau); (3) WUE will maintain the current levels for under the constant climate scenario (i.e. using mean climate condition of and CO 2 concentrations of the 2008 level); and (4) WUE will decrease with the increase of water resource restriction (expressed as evaporation ratio) among different ecosystems Elsevier B.V. All rights reserved. 1. Introduction Water use efficiency (WUE), defined as the ratio of carbon assimilation to water loss, is used to describe the trade-off between water loss and carbon gain during plant photosynthesis carbon assimilation processes (Baldocchi, 1994; Yu et al., 2004). WUE is an important variable in climate change and hydrological studies Corresponding author at: International Institute for Earth System Science, Nanjing University, Hankou Road 22, Nanjing , China. Tel.: ; fax: addresses: jianghong china@hotmail.com, jianghong@nju.edu.cn (H. Jiang). 1 Work performed under USGS contract 08HQCN0005. since it reflects the coupling relationship between carbon and water cycles (Yu et al., 2008). Predicting how WUE would respond to environmental changes and increasing atmospheric CO 2 levels in relation to different plant types is very important (Xu and Hsiao, 2004). WUE is expected to increase with rising CO 2 concentrations, although these increases vary among studies (Eamus, 1991; Gorissen et al., 1995; Jackson et al., 1994; Riedo et al., 1997; Rosenberg, 1981; Wullschleger et al., 2002; Xu and Hsiao, 2004). Experimental results based on isotope measuring techniques also indicate that plants can increase in WUE as CO 2 levels rise (Peñuelas et al., 2008; Saurer et al., 2004). Studies on WUE have recently been carried out on an ecosystem level (Kuglitsch et al., 2008), and understanding WUE at this level is important when attempting to predict climate change influence on energy and water budgets (Lafleur and Humphreys, /$ see front matter 2010 Elsevier B.V. All rights reserved. doi: /j.ecolmodel

3 Q. Zhu et al. / Ecological Modelling 222 (2011) ). In recent years, the eddy covariance technique, a powerful tool in which to measure CO 2 and water exchange between the ecosystem and the atmosphere, has been widely used to evaluate WUE at an ecosystem level (Baldocchi, 1994; Baldocchi et al., 2001; Jassal et al., 2009; Kuglitsch et al., 2008; Lafleur and Humphreys, 2008; Law et al., 2002; Ponton et al., 2006; Scanlon and Albertson, 2004). However, different ecosystems that possess different geographical distributions are expected to undergo different WUE behavior due to inherent physiological variations in leaf gas exchange characteristics and differences in environmental conditions (Farquhar et al., 1989; Ponton et al., 2006). It is important to understand the effects of climate change on carbon and energy budgets of ecosystems through comparative studies in different ecosystems (Ponton et al., 2006). Although experimental studies suggest that elevated CO 2 concentrations improve plant WUE behavior overall, these experiments are typically conducted locally on short-term timeframes and, therefore, are difficult to apply when scaling up to long-term timeframes as well as to differing ecosystem levels (Feng, 1999). Scaling up locally observed data collected by means of the eddy covariance technique to ecosystem, regional, and continental levels remains a challenge and meanwhile observations on large-scale are rare (Beer et al., 2007). Biophysical process-based ecosystem models are powerful tools to extrapolate current information and expand knowledge into larger scales both in terms of space and time, as well as to explore feedbacks between environmental variables and plant physiology (Cramer et al., 2001; Foley et al., 1996; Simioni et al., 2009). The process-based terrestrial biosphere model IBIS (the Integrated BIosphere Simulator) (Foley et al., 1996; Kucharik et al., 2000) was used to investigate the spatiotemporal patterns of WUE under different climate change and CO 2 concentrations scenarios in different ecosystems throughout China. The spatiotemporal distributions of water and heat are diverse and uneven throughout China. The terrestrial ecosystem productivity and water resource balance under such conditions is essential information for the decision making processes of policy makers. Since WUE is a key factor linking plant productivity and water resources, the carbon budgets and water consumption, and the carbon cycles and the water cycles, it is vital to investigate the spatiotemporal patterns of WUE as well as the mechanisms of these patterns under diverse current and future climatic conditions. The primary objectives of this study were: (1) to evaluate the response of WUE of the terrestrial ecosystems in China to future climate change and elevated CO 2 concentrations; (2) to study changes in spatial patterns of WUE by the end of twenty-first century on a national scale; (3) to explore the potential impacts of water resource conditions on WUE for regions that experience different climatic and geographical variation. 2. Methods and data 2.1. Model description IBIS was designed to integrate a variety of terrestrial ecosystem phenomena within a single, physically consistent modeling framework. It represents land surface processes, canopy physiology, vegetation phenology, long-term vegetation dynamics, and carbon cycling (Coe et al., 2002; Foley et al., 1996; Kucharik et al., 2000). These processes are organized to operate at different time steps, ranging from 60 min to 1 year (Kucharik et al., 2000). The hydrological module is constructed based on a land-surfacetransfer scheme (LSX) (Thompson and Pollard, 1995a,b). Two canopy layers, three snow layers, and six soil layers are presented in each grid unit. The total amount of land surface evapotranspiration (ET) is treated as the sum of the three water vapor fluxes: evaporation from the soil surface (including ice and snow), evaporation of water intercepted by the vegetation canopy, and canopy transpiration. Rates of transpiration depend on canopy conductance and are calculated independently for each plant type within the canopy (Foley et al., 1996; Kucharik et al., 2000). To account for evaporation from intercepted rain, the model describes the interception and cascade of precipitation (both rain and snow) through the canopy layer (Foley et al., 1996; Kucharik et al., 2000). Evaporation rates are calculated by means of standard mass transfer equations relating the surface temperature, the vapor pressure deficit, and conductance (Campbell and Norman, 1998; Twine et al., 2004). IBIS assesses natural vegetation by applying plant functional types (PFT) instead of plant species. Parameters of the different PFT used were primarily adapted from Foley et al. (1996) and Kucharik et al. (2000) and are listed in brief in Appendix A. For each PFT, IBIS adopts a mechanistic treatment of canopy photosynthesis based on Farquhar et al. (1980) and Farquhar and Sharkey (1982), and a semi-mechanistic model of stomatal conductance (Ball et al., 1986; Foley et al., 1996; Kucharik et al., 2000) to quantify gross primary productivity (GPP), which is calculated at the end of each year. IBIS has adopted a dynamic vegetation mechanism. It uses a simplified relationship between accumulated growing degree days and budburst to describe vegetation phenology (Kucharik et al., 2006). Moreover, it simulates changes in vegetation structure on an annual time step through PFT competition for light and water (Kucharik et al., 2006). Each PFT is characterized in terms of biomass and the Leaf Area Index (LAI). The geographic distribution of each PFT is determined using a simple set of climatic constraints that determine cold tolerance limitations, growing degree day requirements, and minimum chilling requirements in which changes resulting from climate change are permitted (Foley et al., 1996; Kucharik et al., 2006) Study region and data A ( 10 km) resolution land mask map of China was used in the IBIS simulations that excluded water bodies and the northwestern salty crusts for which no soil information exists. A hole-filled version of the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) dataset (Jarvis et al., 2006) for China was re-sampled to a resolution (Fig. 1) and used for atmospheric pressure calculation. The 1:1,000,000 resolution China soil map provided the fraction of sand, clay, and silt for each soil layer as well as each cell. The 1:4,000,000 resolution China vegetation map (Hou et al., 1979) (including more than 50 vegetation cover types) was reclassified into 15 vegetation types for usage with the IBIS simulation. Croplands were classified into grassland or shrubland since agriculture was not specifically taken into account in the current simulation. To embed current land use and land cover conditions into the simulation, a land cover map of China from the year 2000 was used to construct a vegetation cover fraction layer for each grid cell. Two projected future climate datasets under the IPCC SRES scenarios (A2 and B1) developed for the Third Assessment Report (Nakicenovic and Swart, 2000) and used for the Fourth Assessment Report (AR4) of the IPCC (IPCC, 2007) were selected from the third version of the Canadian Centre for Climate Modeling and Analysis (CCCMA) Coupled Global Climate Model (CGCM3.1). The A2 scenario describes a very heterogeneous world of high population growth, and the B1 scenario describes a convergent world with the same global population. The outputs of CGCM3.1 version T63 have a spatial resolution of about Monthly climate change data based on these two scenarios were used to construct the driving climate datasets for the model that include precipitation, air temperature, relative humidity, and the cloudiness fraction. Relative humidity was not included in the output of CGCM3.1. It was

4 2416 Q. Zhu et al. / Ecological Modelling 222 (2011) Fig. 1. Digital elevation model (DEM) map (Jarvis et al., 2006) and geographical delineation of climate zones in China: (1) Marginal Tropical (MT); (2) Southern Subtropical (SS); (3) Middle Subtropical (MS); (4) Northern Subtropical (NS); (5) Wet Warm Temperate (WWT); (6) Dry Warm Temperate (DWT); (7) Wet Middle Temperate (WMT); (8) Dry Middle Temperate (DMT); (9) Cold Temperate (CT); (10) Plateau Temperate (PT); (11) Plateau Frigid (PF). calculated based on specific humidity, air temperature, and air pressure that could be extracted from the output of CGCM3.1. Averaged observed meteorological data (precipitation, air temperature, relative humidity, and the cloudiness fraction) taken from the period 1950 to 2006 were used as the baseline climate condition. The number of wet days per month, diurnal temperature range, and mean monthly wind speed were derived from daily records. The CGCM3 data and meteorological observations (historical data) were interpolated to a grid using the program package ANUSPLIN (Hutchinson, 1984; Hutchinson and Gessler, 1994). To adjust climate model bias, anomalies were constructed with the additive departures for temperature and the multiplicative departures for precipitation, relative humidity, and the cloudiness fraction during relative to the projected mean climate data. Anomalies were then applied to the mean historical meteorological data during to produce a time series of future climatic conditions during the timeframe. This strategy preserved interannual variability of the projected climatic conditions while simultaneously forcing their spatial patterns to agree with recent observations (Cramer et al., 2001; Ju et al., 2007). Compared to the average, the mean annual temperature of China in 2099 will increase by nearly 5 in scenario A2 and by about 2.5 in scenario B1. Mean annual precipitation will increase by about 21.8% in scenario A2 and 9.2% in scenario B1 at the conclusion of the twenty-first century ( ) (Fig. 2a and b). CO 2 concentrations during the simulation period were composed of three parts. Historical observed CO 2 concentrations were used for the period , and the atmospheric CO 2 concentrations were derived by Keeling et al. (2005) from in situ air measurements at Mauna Loa Observatory, Hawaii. CO 2 concentrations before 1958 were adopted from the IS92a Global CO 2 Concentration Yearly Dataset that was derived using a spline fit of Mauna Loa and ice core data (Enting et al., 1994). Scenarios A2 and B1 CO 2 concentrations were taken from the Third Assessment Report of the IPCC based on the Bern-CC carbon cycle model (IPCC, 2001) (Fig. 2c). By 2100, the CO 2 concentrations will increase to 836 ppmv under scenario A2 and to 540 ppmv under scenario B1 (IPCC, 2001). The constant CO 2 concentrations scenario assumed that CO 2 concentrations will remain at the same level as 2008 (385 ppmv) for the period (Fig. 2c) Simulations and model validation Seven comparable simulations were conducted under different climate change and CO 2 concentrations scenarios (Table 1). Impacts of climate change alone, CO 2 concentrations increasing alone, and a combination of these two factors on ecosystem water use efficiency were evaluated for terrestrial ecosystems of China. Simulations were run with projected climate data, soil texture, and initial vegetation conditions. A 200-year simulation for a spin-up process run Fig. 2. (a and b) The anomaly of mean annual precipitation/temperature relative to in China for the period under scenarios SRES A2 and SRES B1. Thin lines represent original values and thick lines represent 10-year running average values for precipitation or temperature; (c) CO 2 concentrations in the study under scenarios of constant CO 2 concentrations (CCO 2), SRES A2, and SRES B1 for

5 Q. Zhu et al. / Ecological Modelling 222 (2011) Table 1 List of simulation scenarios performed; NOCC: applying the mean climatic data from 1950 to 2006; 385 ppmv: 2008 CO 2 concentrations. Scenarios Climatic data CO 2 concentrations ( ) NOCC & CCO 2 NOCC 385 ppmv NOCC & DCO 2(A2) NOCC SRES A2 ( ppmv) NOCC & DCO 2(B1) NOCC SRES B1 ( ppmv) A2 & CCO 2 SRES A2 (CGCM3) 385 ppmv A2 & DCO 2 SRES A2 (CGCM3) SRES A2 ( ppmv) B1 & CCO 2 SRES B1 (CGCM3) 385 ppmv B1 & DCO 2 SRES B1 (CGCM3) SRES B1 ( ppmv) with averaged historical meteorological data was also included. During the spin-up period, the soil carbon pools were allowed to nearly reach equilibrium in Simulation results from 1950 to 2099 were then analyzed. IBIS has been widely tested at different spatial scales and for different ecosystems in the investigation of carbon and water cycling (Foley et al., 1996; Kucharik et al., 2006, 2000). In China, hydrological processes of the IBIS model were validated with observed runoff (up to 39 years of observed data for 85 hydrological gauges) (Zhu et al., 2010b), soil moisture data (up to 40 soil moisture stations monthly observed data taken between 1981 and 1999) (Zhu et al., 2009), and soil temperature data (up to 650 soil temperature stations monthly observed data taken between 1955 and 2000) (Zhu et al., 2010a). For the carbon exchange process, net primary productivity and biomass were validated with forestry inventory data while gross primary productivity was validated with flux site data. Simulated net primary productivity, biomass, soil respiration, and net ecosystem exchanges were compared to data from literature. Validation and comparison showed reasonable agreement and selected graphs are listed in Appendix B (Zhu, 2009) WUE calculation WUE retains different definitions depending on the temporal and spatial scales of the processes and the system aggregation to which it refers (Steduto and Albrizio, 2005). Studies on WUE have been conducted on leaf, canopy, and ecosystem levels by different communities (including biologists, physiologists, agriculture scientists, geoscientists, and ecologists) during the past few decades in which all retained with different definitions (Kuglitsch et al., 2008). In this study, WUE was calculated as the ratio of GPP to ET, a definition widely adopted to estimate ecosystem level WUE (Beer et al., 2009; Chapin et al., 2002; Hu et al., 2008; Kuglitsch et al., 2008; Law et al., 2002; Ponton et al., 2006; Yu et al., 2008). Most of the analysis was based on the 11 climate zones (Ju et al., 2007; Liu, 2000) into which China has been divided (Fig. 1). 3. Results 3.1. Variances of WUE in climate zones Zonal statistics were applied to calculate mean WUE for each climate zone. The annual anomaly of WUE relative to the mean value for the period for each climate zone is shown in Fig. 3. WUE time series patterns for simulations in which climate changes does not occur (NOCC & CCO 2, NOCC & DCO 2 (A2), NOCC & DCO 2 (B1)) were consistent with CO 2 concentrations patterns for each scenario in each climate zone (Fig. 3). Simulated WUE largely remained at the stable 2008 level throughout the period under the NOCC & CCO 2 scenario (Table 2 and Fig. 3). Under the NOCC & DCO 2 (A2) and NOCC & DCO 2 (B1) scenarios, simulated WUE progressively increased. Furthermore, the increasing rate of WUE under the former scenario was higher than under the latter scenario. These differences increased by a factor of two for the period in all climate zones (Table 2). Significant differences in WUE exhibited under these two conditions of CO 2 concentrations were also clearly exhibited in the combined simulations (A2 & DCO 2, B1 & DCO 2 ) (Table 2 and Fig. 3). In climate zones for simulations under climate change alone scenarios (A2 & CCO 2 and B1 & CCO 2 ), simulated WUE time series were below the constant simulation (NOCC & CCO 2 ) level and showed a decreasing trend (Fig. 3). Declining rates of WUE under the A2 & CCO 2 scenario (a reduction of approximately 3% in climate zone 05 9% in climate zone 01) was higher than under the B1 & CCO 2 scenario (a reduction of approximately 1.1% in climate zone % in climate zone 01) (Table 2 and Fig. 3). Simulated WUE showed slightly increasing trends in comparison to similar quantities in climate zones (Table 2 and Fig. 3). Comparing the results under elevated CO 2 concentrations with climate change (A2 & DCO 2 and B1 & DCO 2 ) or without climate change (NOCC & DCO 2 (A2), NOCC & DCO 2 (B1)), simulated WUE temporal patterns showed considerable differences throughout the different climate zones. Specifically, in climate zones 01 05, simulated WUE under scenarios without climate change is higher than that under scenarios with climate change (Table 2 and Fig. 3). In cli- Table 2 The variation of multiyear mean WUE during the period (the V column) compared to the multiyear mean WUE during the period (the WUE column) for 11 climate zones. Scenarios 01 MT 02 SS 03 MS 04 NS 05 WWT 06 DWT WUE V WUE V WUE V WUE V WUE V WUE V NOCC & DCO 2(A2) A2 & DCO NOCC & DCO 2(B1) B1 & DCO NOCC & CCO A2 & CCO B1 & CCO Scenarios 07 WMT 08 DMT 09 CT 10 PT 11 PF WUE V WUE V WUE V WUE V WUE V NOCC & DCO 2(A2) A2 & DCO NOCC & DCO 2(B1) B1 & DCO NOCC & CCO A2 & CCO B1 & CCO

6 2418 Q. Zhu et al. / Ecological Modelling 222 (2011) Fig. 3. Variations of anomaly of WUE relative to mean WUE of in each climate zone under different scenarios for Thin white lines and thick lines represent the original and 10-year running average of WUE anomaly values, respectively, for the climate change scenarios (A2 & CCO 2, A2 & DCO 2, B1 & CCO 2, and B1 & DCO 2).

7 Q. Zhu et al. / Ecological Modelling 222 (2011) Fig. 4. Scatterplots of the relationship between annual anomaly values of WUE and temperature or precipitation in each climate zone during

8 2420 Q. Zhu et al. / Ecological Modelling 222 (2011) Table 3 Parameters of linear regression for annual anomalies (relative to ) between WUE/GPP/ET and temperature/precipitation for each climate zone during under the climate change only scenarios (A2 & CCO 2 and B1 & CCO 2). T: the sign of slope, +, positive trend,, negative trend. Regions Scenarios Temperature vs. WUE Precipitation vs. WUE Temperature vs. GPP Temperature vs. ET Precipitation vs. GPP Precipitation vs. ET T r p T r p T r p T r p T r p T r p 01 MT A2 & CCO < < < < <0.05 B1 & CCO < < < < SS A2 & CCO < < < B1 & CCO < < < < MS A2 & CCO < < < < <0.05 B1 & CCO < < < < < NS A2 & CCO < < < < < < B1 & CCO < < < < < WWT A2 & CCO < < < < < < B1 & CCO < < < < < DWT A2 & CCO < < < < B1 & CCO < < < < WMT A2 & CCO < < < < < < B1 & CCO < < < < < DMT A2 & CCO < < < < < B1 & CCO < < < < CT A2 & CCO < < < < < <0.001 B1 & CCO < < < < PT A2 & CCO < < < < < < B1 & CCO < < < < < < PF A2 & CCO < < < < < B1 & CCO < < < < < mate zones 07 and 09 11, simulated WUE temporal patterns were reversed. In climate zone 06, the pattern is quite similar with or without the occurrence of climate change (Table 2 and Fig. 3). By the end of twenty-first century ( ), simulated WUE obtained the maximum and minimum values within the Southern Subtropical (SS) zone (climate zone 02) and the Dry Warm Temperate (DWT) zone (climate zone 06), respectively, for all simulation scenarios (Table 2). The Marginal Tropical (MT) zone (climate zone 01) and Middle Subtropical (MS) zone (climate zone 03) showed a high level of WUE, while the Wet Warm Temperate (WWT) zone (climate zone 05), the Dry Middle Temperate (DMT) zone (climate zone 08), and the Plateau Frigid (PF) zone (climate zone 11) showed a low level of WUE (Table 2) Effects of temperature, precipitation, and CO 2 concentrations on WUE Analysis was carried out between the annual anomaly of WUE and annual anomaly of precipitation or temperature on the simulated future period ( ) (Table 3 and Fig. 4). Correlations between GPP or ET and precipitation or temperature are provided in Table 3. The anomaly was calculated relative to the mean value of the period Simulated results under climate change only scenarios (A2 & CCO 2 and B1 & CCO 2 ) were introduced in this part to exclude the effects of increasing CO 2 concentrations. In regard to the relationship between annual WUE and temperature anomalies, significant negative correlations were shown in climate zones except for the WWT zone (climate zone 05) under the B1 & CCO 2 scenario (Table 3). In regard to climate zones 06 11, significant positive correlations were found except in climate zone 08 (A2 & CCO 2 and B1 & CCO 2 ), climate zone 06 (A2 & CCO 2 ), and climate zone 09 (B1 & CCO 2 ). For the relationship between annual of WUE and precipitation anomalies, significant positive correlations were exhibited in climate zone 08, 09, and 10, and significant negative correlations were exhibited in climate zone 05 (Table 3). Correlations between annual WUE and temperature anomalies as well as annual WUE and precipitation anomalies presented the same trends in climate zones 05 and and opposite trends in climate zones 01, 02, and 06 (Table 3 and Fig. 4). While considering the effects of increasing CO 2 concentrations on WUE, only simulations under elevated CO 2 concentrations in which climate change was not a factor were considered (NOCC & DCO 2 (A2) and NOCC & DCO 2 (B1)). High correlations between WUE and CO 2 concentrations were found (Fig. 5) WUE spatial pattern changes at the end of the twenty-first century To evaluate the spatial pattern of changing WUE under the different scenarios for China as a whole, differences in simulated WUE, climate conditions (precipitation, temperature), GPP, and ET were calculated between the mean values at the end period ( ) and the start period ( ) of twenty-first century (Figs. 6 and 7). Under the scenario with constant CO 2 concentrations in which no climate change occurred (NOCC & CCO 2 ), simulated WUE decreased slightly in the central eastern and northwestern regions of China and increased slightly in most other regions (Fig. 6g). No obvious variation in simulated WUE was detected in zonal statistics based on latitude belt under this scenario (Fig. 8). Under scenarios with elevated CO 2 concentrations in which no climate change occurred (NOCC & DCO 2 (A2) and NOCC & DCO 2 (B1)), simulated WUE exhibited increasing trends in most regions (Fig. 6a and d). Simulated WUE would be considerably more enhanced under the NOCC & DCO 2 (A2) scenario than under the NOCC & DCO 2 (B1) scenario in regions within northern, northeastern, southeastern, and southwestern China and the eastern and southern edge of the Tibetan Plateau. The remaining regions exhibited similar WUE trends. The highest simulated WUE values were detected in the southeastern region of China and most regions of northeastern China under both scenarios.

9 Q. Zhu et al. / Ecological Modelling 222 (2011) Fig. 5. Relationship between annual WUE anomaly values and CO 2 concentrations during for each climate zone (the climate zones codes were marked beside the curves). Under simulations in which climate change alone was a factor (A2 & CCO 2 and B1 & CCO 2 ), the effects of climate change were similar in terms of geographical distribution (Fig. 6b and e). Simulated WUE exhibited no obvious variation in the central region of China (from lat 30 N to lat 42 N) (Fig. 8) and increased slightly only in the northernmost region of China and the Tibetan Plateau (Fig. 6b and e). A similar increasing pattern was detected in northern China (from lat 42 N to lat 53 N) (Fig. 8). A slight decrease was detected in most of the remaining regions. Simulated WUE decreased much considerably under the A2 & CCO 2 scenario than under the B1 & CCO 2 scenario, especially in southern China (from lat 18 N to lat 30 N) (Fig. 6b and e), which was also detected in the zonal statistics based on latitude belt (Fig. 8). Under the combined simulations (A2 & DCO 2 and B1 & DCO 2 ), simulated WUE would be considerably enhanced in most regions of the country except for small areas in the southern region of Xinjiang Province in northwestern China. The highest simulated WUE was detected in southeastern regions of Northeastern China for both scenarios (Fig. 6c and f). When comparing the geographical distribution of simulated WUE between scenarios NOCC & DCO 2 (A2) and A2 & DCO 2 (Fig. 6a and c) or between scenarios NOCC & DCO 2 (B1) and B1 & DCO 2 Fig. 6. Spatial pattern maps for differences in multiyear mean WUE between the end period ( ) and the start period ( ) of the twenty-first century under different simulation scenarios.

10 2422 Q. Zhu et al. / Ecological Modelling 222 (2011) Fig. 7. Spatial pattern maps for differences in multiyear mean precipitation (a) and temperature (b) for the SRES A2 and B1 scenarios, for multiyear mean simulated GPP (c), and ET (d) under climate change only scenarios (A2 & CCO 2, B1 & CCO 2), between the end period ( ) and the start period of the twenty-first century ( ). (Fig. 6d and f), climate change produced evident negative effects on WUE in southeastern and southwestern China and positive effects in northeastern China. In terms of the zonal statistics based on latitude belt, positive impacts in northern China (from lat 48 N to lat 53 N) and negative impacts in southern China (from lat 18 N to lat 29 N) are also provided in Fig. 8. Simulated WUE remained virtually at the same level from lat 29 N to lat 48 N while exhibiting considerable differences in southern and northernmost China (Fig. 8). Under these four scenarios, simulated WUE would be considerably more enhanced in southeastern, southwestern, and northeastern China, where forests are mainly located, than in the remaining areas (Fig. 6a, c, d, and f). Moreover, WUE would be considerably more enhanced under the A2 CO 2 concentrations scenarios (A2 & DCO 2 and NOCC & DCO 2 (A2)) than under the B1 CO 2 concentrations scenarios (B1 & DCO 2 and NOCC & DCO 2 (B1)) (Fig. 8). Fig. 7 provides changes in precipitation, temperature, GPP, and ET for the end of the twenty-first century under climate change alone scenarios (A2 & CCO 2 and B1 & CCO 2 ). Decreasing precipitation rates occurred in a small area of southern China under the SRES A2 scenario and in the western regions of the Tibetan Plateau

11 Q. Zhu et al. / Ecological Modelling 222 (2011) Fig. 8. Differences in multiyear mean WUE for each latitude belt (1 latitude degree) between the end period ( ) and the beginning period ( ) of the twenty-first century under different simulation scenarios (marked beside the curves). under the SRES B1 scenario. Increasing precipitation appeared in the central region and southwestern region of China and was more significant under the SRES A2 scenario than under the SRES B1 scenario (Fig. 7a). An overall increasing trend of temperature was exhibited throughout the country and was especially significant in the northernmost and western regions of China. Temperature in the SRES A2 scenario would be twice as high as in the SRES B1 scenario (Fig. 7b). Moreover, GPP would be enhanced in most regions of China except for certain regions in southern China under the A2 & CCO 2 scenario and those in northwestern China under the B1 & CCO 2 scenario (Fig. 7c). ET presented increasing trends for most regions except for certain regions near the western border of the country. ET would be at a higher level under the A2 & CCO 2 scenario than under the B1 & CCO 2 scenario (Fig. 7d) Relationship between WUE and the evaporation ratio The evaporation ratio (the ratio of annual evapotranspiration to precipitation) can be described as a function of the aridity index (the ratio of potential evaporation to precipitation) first introduced by Budyko (Arora, 2002). Evaporation ratios were calculated for each climate zone during each year throughout the period Scatter graphs were used between simulated WUE and evaporation ratios to show spatial patterns of simulated WUE under a diverse range of water resource conditions (Fig. 9). Only scenarios in which climate change was a factor were considered (A2 & CCO 2, A2 & DCO 2, B1 & CCO 2, and B1 & DCO 2 ) in this section. Evaporation ratios in climate zones 06, 08, 09, and 11 were higher than 1.0 during certain years under all four scenarios, especially in the DWT zone (climate zone 06), indicating that aridity conditions must have occurred in these regions for the years in question. Simulated WUE behaved at different levels according to the evaporation ratio of the different climate zones (Fig. 9). Significant negative correlations between the evaporation ratio and simulated WUE were detected and are provided in Fig. 9. Among the different ecosystems under investigation, low level WUE activity was detected in regions where water resources are limited while high level WUE activity was detected in regions where water resources are abundant. The lowest WUE activity occurred in the DWT zone (climate zone 06) and the highest in the tropical and subtropical zones. The DWT zone, which includes the largest desert area in China, is located within a region that exhibits the poorest water resource conditions, while the tropical and subtropical zones experience the richest water resource conditions. 4. Discussion 4.1. Accuracy assessment of simulated WUE Table 4 summarizes the simulated WUE of the major vegetation types with those reported in the literature during since most reported data is accessible for this period. Table 4 indicates that most results of other studies were in the range of simulated WUE (Hu et al., 2008; Kuglitsch et al., 2008; Law et al., 2002; Mahrt and Vickers, 2002; Wever et al., 2002; Yu et al., 2008). Especially, the simulated WUE matched well with the results yielded in Chinese ecosystems (Hu et al., 2008; Yu et al., 2008). The results reported by Beer et al. (2009) and Ponton et al. (2006) were higher than results obtained in this as well as other studies. One reason for this is that data from rainy days in their studies were excluded from analysis to reduce the contribution of the evaporation component (Beer et al., 2009; Ponton et al., 2006). Moreover, several potential points of uncertainty should be considered when comparing WUE simulated by ecosystem models with that generated from flux data on an ecosystem scale. First, differences in spatial scale exist between a flux site and a model simulation grid. Data collected from flux observation equipment setup within a typical ecosystem can only represent conditions at a relatively small spatial scale. The modeled variables (GPP, ET, etc.) in this study are the mean values of one grid with an approximate area of km 2. Second, differences in a vegetation cover fraction exist between a flux observation site area and a modeling grid, which is an important uncertainty factor. The evaporation proportion of ET will be higher at low vegetation cover fraction conditions because more water is lost from bare soil by evaporation. For sites with sparse vegetation, evaporation from the soil surface accounts for a high proportion total ecosystem ET (Hu et al., 2008; Lauenroth and Bradford, 2006). Third, differences in LAI exist between the flux observation site area and the modeling grid. Canopy closure at high LAI reduces the amount of radiation reaching the ground and reduces soil evaporation (Beer et al., 2009). Understory and soil ET tend to be inversely proportional to LAI (Law et al., 2002). Hu et al. (2008) suggested that the proportion of evaporation within ET is primary controlled by LAI, and a good correlation will be gen-

12 2424 Q. Zhu et al. / Ecological Modelling 222 (2011) Fig. 9. Scatterplots between annual WUE and the evaporation ratio (ET/P) during for all 11 climate zones under climate change scenarios with or without elevated CO 2 concentrations (A2 & CCO 2, A2 & DCO 2, B1 & CCO 2, and B1 & DCO 2). erated between LAI and ecosystem WUE. Finally, WUE exhibits a strong time scale dependency (Kuglitsch et al., 2008). In this study, annual WUE was calculated as the ratio of annual GPP and annual evapotranspiration. Overall, water loss and carbon gain processes are complex at the ecosystem level (Yu et al., 2008). It is therefore difficult to specify a WUE value to each vegetation type (Kuglitsch et al., 2008) Effects of future CO 2 enrichment on WUE in China Simulated WUE consistently increased under elevated CO 2 concentrations scenarios (NOCC & DCO 2 (A2), NOCC & DCO 2 (B1), A2 & DCO 2, B1 & DCO 2 )). The major reason for this is that enriched CO 2 concentrations will reduce leaf stomatal conductance (Li et al., 2010). Furthermore, elevated CO 2 concentrations increase the efficiency of plant water use by reducing transpiration rates (Farquhar, 1997). Plants need to open the stomata as much as possible for CO 2 uptaking, which risks wasting much water (Jassal et al., 2009). A principle has been proposed that plants control stomata to optimally satisfy the trade-off between the amount of carbon assimilated and the amount of water transpired (Cowan and Farquhar, 1977). The intrinsic link between carbon and water flux through stomatal conductance is a factor at an ecosystem level and on an annual timescale (Beer et al., 2009). Under conditions of elevated CO 2, although stomatal conductance is reduced, carbon assimilation can be maintained at original levels. At the same time, stomatal conductance limits water loss by transpiration more so than CO 2 assimilation (Farquhar and Sharkey, 1982). Water flux will therefore be considerably reduced and will result in decreasing ET. Certain field experiments also suggest that ET will decrease at an elevated CO 2 concentrations condition (Li et al., 2010; Polley et al., 2008). Since WUE is expressed as the ratio of GPP to ET, it will undoubtedly increase with enriched ambient CO 2 concentrations Effects of future climate change and CO 2 concentrations on WUE in China Effects of future climate change and CO 2 concentrations on WUE will occur at different levels in different geographical locations (Figs. 3 and 6). Climate change will exert negative impacts on WUE in tropical, subtropical, and WWT zones (climate zones 01 05), have no significant effect on the WUE in the DWT zone (climate zone 06), and have a positive effects on WUE in climate zones (Fig. 3). In the tropical and subtropical climate zones (climate zones 01 04), climate change will have a much greater negative impact on WUE under the A2 climate change scenario than under the B1 climate change scenario. The considerable difference in temperature between these two scenarios is one of the primary reasons for this condition since temperature is higher under the A2 scenario than under the B1 scenario. Meanwhile, significant negative correlations between simulated WUE and temperature have been shown in these regions, while no significant trends in the relationship between simulated WUE and precipitation have been detected under most conditions (Fig. 4 and Table 3). Results suggest that the negative effects of climate change on WUE in these regions are

13 Q. Zhu et al. / Ecological Modelling 222 (2011) Table 4 Comparison of simulated WUE for major vegetation types between 2000 and 2006 with those reported in literature. Mean WUE for each vegetation type with the WUE value range in parentheses was shown for this study. Source WUE (gc/kg H 2O) Vegetation type in literature Tropical/subtropical evergreen forest This study 1.88 ( ) Yu et al. (2008) 2.53 Conifer plantation forest Yu et al. (2008) 1.88 Evergreen broad-leaved forest Temperate evergreen broadleaf forest This study 1.82 ( ) Kuglitsch et al. (2008) 0.9 Mediterranean broad-leaved evergreen Beer et al. (2009) 3.16 Evergreen broad-leaved forest Temperate deciduous forest This study 1.51 ( ) Law et al. (2002) 0.87 Deciduous broadleaf forests Kuglitsch et al. (2008) 0.93 Mediterranean broad-leaved deciduous Kuglitsch et al. (2008) 1.36 Temperate broad-leaved deciduous Yu et al. (2008) 2.57 Deciduous forest Beer et al. (2009) 4.14 Deciduous broad-leaved forest Mixed forest This study 1.52 ( ) Kuglitsch et al. (2008) 1.23 Temperate mixed forests Beer et al. (2009) 4.54 Mixed forest Boreal evergreen forest This study 1.11 ( ) Law et al. (2002) 0.65 Evergreen conifer forests Kuglitsch et al. (2008) 0.79 Boreal evergreen conifers Mahrt and Vickers (2002) 1.04 Young Jack pine Beer et al. (2009) 3.40 Evergreen needle-leaved forest Ponton et al. (2006) 5.40 Douglas fir Grassland/steppe This study 0.90 ( ) Hu et al. (2008) 0.41 Alpine meadow-steppe Hu et al. (2008) 0.71 Alpine swamp, meadow Hu et al. (2008) 0.80 Temperate steppe Hu et al. (2008) 1.26 Alpine shrub-grass, meadow Law et al. (2002) 0.93 Grassland Ponton et al. (2006) 1.73 Grassland Wever et al. (2002) 1.90 Temperate grassland Beer et al. (2009) 2.93 Grassland Tundra This study 0.71 ( ) Law et al. (2002) 0.41 Tundra Boreal deciduous forest This study 1.21 ( ) Dense shrubland This study 1.05 ( ) Open shrubland This study 0.76 ( ) primary due to increasing temperature. The relationship between GPP and precipitation is stronger than it is between GPP and temperature in the Marginal Tropical (MT) and South Subtropical (SS) regions (climate zones 01 and 02) while the reverse is true for the Middle Subtropical (MS) and Northern Subtropical (NS) regions (climate zone 03 and 04) (Table 3). The relationship between ET and temperature is stronger compared to ET and precipitation in these zones (climate zones 01 and 02), indicating that water quantity is not the primary limiting factor of ET. A negative relationship was detected between GPP and temperature in climate zone 01 (A2 & CCO 2 and B1 & CCO 2 ) and climate zone 02 (A2 & CCO 2 ). The increase in temperature greatly enhanced ET over GPP, although GPP is promoted by an increase in precipitation (climate zones 01 04) or an increase in temperature (climate zones 03 04) (Table 3). With abundant precipitation, an increase in temperature will enhance ET significantly, which forces a negative relationship between simulated WUE and temperature. By studying the seasonal WUE variance, Yu et al. (2008) pointed out that in two subtropical forests, the increasing rate of ET is much greater compare to GPP under high temperature, strong radiation, and low humidity conditions during summer, leading to a reduction in WUE. In the Wet Warm Temperate (WWT) zone (climate zone 05), the primary factor that produces a negative effect on simulated WUE is increased precipitation because significant negative correlations are shown between simulated WUE and precipitation for both the A2 and B1 scenarios (Fig. 4 and Table 3). Although a significant positive relationships have been shown between ET and both temperature and precipitation, precipitation has a stronger relationship to ET compared to temperature (Table 3). ET will increase far greater than carbon sequestration since an increase in precipitation will satisfy the water consumption by enhanced ET that is driven by a continuously increasing temperature. In the Dry Warm Temperate (DWT) zone (climate zone 06), WUE levels under the elevated CO 2 concentrations scenario will be the same as under the constant CO 2 concentrations scenario regardless whether climate changes occurs or not (Fig. 3), indicating no evident effects exist for climate change on WUE (Table 3). This zone is dominated by desert and is the most arid region in China. A significant positive relationship between GPP/ET and temperature/precipitation (with the exception of ET and temperature under the B1 & CCO 2 scenario) is shown in Table 3. Precipitation has a more significant relationship with GPP or ET compared to temperature, indicating that precipitation has a stronger effect on GPP and ET in this region. On one hand, climate change with increased precipitation which optimizes the water resource con-

14 2426 Q. Zhu et al. / Ecological Modelling 222 (2011) dition will increase the productivity of plants in this region. On the other hand, ET will also be enhanced with increased temperature and precipitation. No effects of climate change on WUE will occur as a result since both plant productivity and ET will be improved simultaneously by climate change. Under elevated CO 2 concentrations, enhanced plant production can be offset by a decrease in water use by way of reduced stomatal conductance that occurs within a desert ecosystem (Nowak et al., 2004). In climate zones 07 11, both precipitation and temperature are the primary operative drivers for WUE in the Plateau Temperate (PT) zone (climate zone 10). Precipitation is the primary operative driver for the Dry Middle Temperate (DMT) zone (climate zone 08) and Cold Temperate (CT) zone (climate zone 09) while temperature is the primary operative driver in the Wet Middle Temperate (WMT) zone (climate zone 07) and the Plateau Frigid (PF) zone (climate zone 11) (Table 3). Although GPP and ET will be significantly enhanced with increased precipitation and temperature in most situations (Table 3), the promotion of plant productivity will outweigh ET, producing positive impacts on WUE expressed as the ratio of GPP and ET (Fig. 3). Climate change effects were also indicated within the simulated WUE geographical map (Fig. 6). In the northernmost and western regions of China, WUE was enhanced under climate change alone scenarios (A2 & CCO 2 and B1 & CCO 2 ) (Fig. 6b and e), exhibiting similar patterns as temperature changes (Fig. 7b). This indicates that temperature plays an important role in WUE changes. Although GPP and ET also show increasing trends in these regions, increased temperature enhances GPP considerably more than ET. WUE will be promoted as a consequence. In the central and northern regions of China, the increase in precipitation is highest (Fig. 7a) while WUE levels at the end period of twenty-first century are lower than in the beginning period for these regions (Fig. 6b and e). The major reason for this is that ET will be considerably more enhanced compared to GPP, although optimized heat and water conditions will promote the plant productivity nevertheless (Fig. 7c). In southern China, GPP will undergo a decreasing pattern under the A2 & CCO 2 scenario (Fig. 7c). One of the reasons for this is that plant productivity will be negatively influenced by the double increased temperature and the decreased precipitation under the A2 & CCO 2 scenario (Fig. 7a and b). WUE in this area is decreased greatly under the A2 & CCO 2 scenario due to a decrease in GPP and an increase in ET (Fig. 6b and e). In the cold regions located within high latitude or throughout the Tibet Plateau, heat and water are both limited resources for plant productivity. With increased precipitation and temperature, the limitation will be eliminated to a certain extent, which may stimulate more carbon fixation than water loss through evapotranspiration. Jansson et al. (2008) studied WUE in a Norway Spruce ecosystem located within a high latitude area and proposed that WUE will be improved in the future climate scenarios regardless of elevated CO 2 concentrations. They suggested that in terms of future climate change scenarios, an increase in WUE within the northern regions is related to the combined effect of air and soil temperature that will extend the growing season. On the whole, climate change will weaken WUE in southern China and enhance WUE in the northernmost regions of the country (high latitude areas) as well as the Tibetan Plateau (high altitude areas). Under all elevated CO 2 concentrations simulations, differences in simulated WUE between scenarios A2 and B1 are primarily produced by different levels of CO 2 concentrations. It is noteworthy that different vegetation types reacting in different ways to climate change and elevated CO 2 concentrations may exist within a single climate zone. It could also make considerable contribution to different spatial patterns of WUE Interactions between WUE and evaporation ratio The relationship between WUE and the evaporation ratio indicates that WUE will react in diverse patterns among the different ecosystems mostly because the available water resources among the diverse ecosystems are different throughout different geographical regions. The ecosystem with a better water resource condition will get a higher WUE. At the leaf level, studies conducted for intrinsic water use efficiency indicates that WUE will increase in water-stressed plants (Hessini et al., 2008). At individual ecosystem level, slight water stress will enhance WUE (Yu et al., 2008) and extreme water stress will reduce WUE (Reichstein et al., 2002). For each climate zone, the WUE activity appears conservative across a range of evaporation ratios (Fig. 9) for the following reasons: the studies on the effects of water stress on WUE were based on soil water content, while the evaporation ratio in this study may not appropriate in describing the variance of soil water conditions; the climate zone reflects plant distribution to a certain extent since one constraint is the climatic condition for the geographical distribution of PFT, but different plant types that coexist in the same climate zone will create a higher complexity in WUE changes than homogeneous plant type or ecosystem structures; WUE as well as evaporation ratio are presented on an annual scale, which could conceal seasonal or daily variation. In this study, the negative correlation between WUE and the evaporation ratio is actually generated to focus on the spatial characteristics of the effects of water resource conditions on WUE among different ecosystems throughout China Limitations and future investigation As a linkage between carbon and hydrological cycles, WUE is an important variable possessing complex interactive processes among biological, meteorological, and environmental factors besides those discussed in this study (CO 2 concentrations, precipitation, and temperature). It will exhibit different patterns at different spatial or temporal scales. Many studies have also been conducted to investigate the interaction between WUE and the vapor pressure deficit (Baldocchi, 1994; Ponton et al., 2006; Scanlon and Albertson, 2004), the relative plant available water holding capacity of the soil and maximum LAI (Beer et al., 2007), the crown ratio of trees (Schwalm and Ek, 2004), stand structure and age (Jassal et al., 2009), soil moisture (Moore and Field, 2006), water stress, and water availability (Grunzweig et al., 2003). First, further research must be undertaken to investigate the relationship between WUE and the multiple environmental factors that influence it. Second, although climate zone reflects plant distribution, it would be instructive to investigate WUE variation for specific PFT or vegetation types on large scales based on ecosystem modeling. Finally, since several studies based on eddy covariance data were conducted to investigate WUE variance of crops in China under climate change conditions (Mo et al., 2009; Tong et al., 2009; Zhang and Liu, 2005; Zhao et al., 2007), the incorporation of an agricultural ecosystem would further improve model performance. In 2008, for example, cultivated field area throughout China was approximately 12%. Water and nutrient conditions of agricultural area are inherently different from natural ecosystems due to human activity (irrigation, fertilization, etc.), and the nutrient condition has significant effects on plant WUE (Field and Mooney, 1983; Lajtha and Whitford, 1989; Yu et al., 2004). 5. Conclusion A process-based terrestrial biosphere model (IBIS) was used to evaluate the response of WUE within terrestrial ecosystems in China to elevated CO 2 concentrations and climate change in the twenty-first century under different scenarios. Results and anal-