A global examination of the response of ecosystem water-use efficiency to drought based on MODIS data

Science of the Total Environment 601–602 (2017) 1097–1107

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

A global examination of the response of ecosystem water-use efficiency to drought based on MODIS data Ling Huang, Bin He ⁎, Le Han, Junjie Liu, Haiyan Wang, Ziyue Chen State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The response of WUE to drought was examined at global scale relying on MODIS products. • An opposite response of WUE to drought was observed between humid and arid ecosystems. • Apparent legacy effects of drought on WUE were observed. • WUE sensitively response to abrupt changes in hydrological climate condition.

a r t i c l e

i n f o

Article history: Received 8 November 2016 Received in revised form 18 April 2017 Accepted 10 May 2017 Available online xxxx Editor: Wei Ouyang Keywords: Drought Ecosystem water-use efficiency Response Hydro-climatic conditions

a b s t r a c t Ecosystem water-use efficiency (WUE) plays an important role in carbon and water cycles. Currently, the response of WUE to drought disturbance remains controversial. Based on the global ecosystem gross primary productivity (GPP) product and the evapotranspiration product (ET), both of which were retrieved from the moderate resolution imaging spectroradiometer (MODIS), as well as the drought index, this study comprehensively examined the relationship between ecosystem WUE (WUE = GPP/ET) and drought at the global scale. The response of WUE to drought showed large differences in various regions and biomes. WUE for arid ecosystems typically showed a negative response to drought, whereas WUE for humid ecosystems showed both positive and negative response to drought. Legacy effects of drought on ecosystem WUE were observed. Furthermore, ecosystems showed a sensitive response to abrupt changes in hydrological climatic conditions. The transition from wet to dry years should increase ecosystem WUE, and the opposite change in WUE should occur when an ecosystem experiences a transition from dry to wet years. This indicates the resilience of ecosystems to drought disturbance. Knowledge from this study should provide an in-depth understanding of ecosystem strategies for coping with drought. © 2017 Elsevier B.V. All rights reserved.

1. Introduction

⁎ Corresponding author at: College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. E-mail address: [email protected] (B. He).

http://dx.doi.org/10.1016/j.scitotenv.2017.05.084 0048-9697/© 2017 Elsevier B.V. All rights reserved.

Drought has significant impacts on the terrestrial ecosystem carbon balance by constraining vegetation growth, causing plant mortality, and triggering wildfire, disease, and biotic disturbance (Ciais et al., 2005; Hicke et al., 2012; Tang et al., 2014; Westerling et al., 2006; Yang et al.,

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2016). Drought is projected to increase both in intensity and frequency in the context of climate change (Trenberth et al., 2013; Trenberth et al., 2014) despite with great uncertainties. However, the potential response of ecosystems to this drying trend remains very unclear. Ecosystem water-use efficiency (WUE), defined as the ratio of carbon gain to water consumption, is an important indicator of the coupling of carbon and water cycles (Ponce-Campos et al., 2013; Yang et al., 2016), which links biological and physical processes over the land surface (Yang et al., 2016). Hence, WUE is a key variable for understanding the response of ecosystem productivity to climate change. Increasing drought is associated with decreased precipitation, and reductions or increases in evapotranspiration have significantly disturbed the global water balance (Teuling et al., 2013) and changed the water availability conditions of ecosystems (Huntington, 2006). In recent studies, the variation of WUE has been employed to determine the ecosystem resilience to drought disturbances (Ponce-Campos et al., 2013; Yang et al., 2016; Zhang et al., 2014). In-situ observations have suggested that ecosystem biomes can cope with water deficiency conditions by enhancing their WUE (PonceCampos et al., 2013). However, this theory has been challenged by some regional and global investigations (Liu et al., 2015; Xue et al., 2015; Yang et al., 2016). For example, a recent global examination of the relationship between WUE and drought reported a large divergent response of WUE to drought among different ecosystems (Yang et al., 2016). This suggests an urgent need for more in-depth investigations of WUE-drought relationships. Furthermore, majority previous investigations are based on long-term drought-WUE relationships; few of them have been focused on impacts of abrupt change of dry-wet events on WUE variations, which are crucial for understanding ecosystem processes under drought disturbances. In light of previous studies (Liu et al., 2015; Xue et al., 2015), several indicators had been defined to measure WUE due to the different measures of carbon gain and water loss. Net primary productivity (NPP) is the most extensively used indicator of ecosystem carbon uptake (Zhang et al., 2014). As an alternative, gross primary productivity (GPP) (Liu et al., 2015; Tang et al., 2014), aboveground NPP (ANPP), net ecosystem productivity (NEP) (Vanloocke et al., 2012) etc. were also used. Theoretically speaking, the transpiration is the true water loss consumed by plant photosynthesis. Because the difficulty in partitioning of soil and canopy evaporation and plant transpiration from evapotranspiration (ET) observations (Lawrence et al., 2007), precipitation (Zhang et al., 2014) or ET (Ponce-Campos et al., 2013) are usually used to indicator water loss used by ecosystems. Among various definitions of WUE, GPP/ET is one of the most popular used indicator (Huang et al., 2015; Reichstein et al., 2007). In this study, to make our results can be comparable to some previous global WUE investigations (Huang et al., 2015; Tang et al., 2014; Yang et al., 2016), we used WUE defined as the ratio of GPP to ET. Global and regional features of ecosystem WUE have been reported by previous studies relying on observations from eddy covariance (EC) sites, products retrieved from remote sensing and simulations of process-oriented ecosystem models(Huang et al., 2015; Tang et al., 2014; Xue et al., 2015; Yang et al., 2016). Theoretically, EC measurements have the highest accuracy but are confined to the site scale (Beer et al., 2009; Brümmer et al., 2012), thereby hindering comparisons among biomes. Modeling approaches can provide long-term and broad-scale WUE but are associated with errors due to uncertainties in the model itself. By comparison, remote sensing measurements provide a trade-off solution between EC observations and model simulations for WUE investigation at the global scale (Gang et al., 2016; Liu et al., 2015; Xue et al., 2015). For example, Tang et al. (2014) comprehensively compared ecosystem WUE values estimated from MODIS and EC measurements at the site and global scale and demonstrated that the two methods provided similar and comparable WUE results. In this study, the relationships between ecosystem WUE and drought were examined based on MODIS global GPP and ET products

over the period of 2000 to 2014. The drought condition was indicated by a popular drought index, the standardized precipitation evapotranspiration index (SPEI) (Vicente-Serrano et al., 2010). This water balance based drought index has been proved to have good performance in capturing drought impact on agriculture and ecosystem (Vicente-Serrano et al., 2012). Our goal was to identify differences in the response of WUE to drought among different biomes and regions. More importantly, impacts of abrupt shifts of hydro-climatic conditions will also be comprehensively evaluated.

2. Materials and methods 2.1. Data 2.1.1. GPP and ET data Annual GPP (Running et al., 2004; Zhao et al., 2005) and ET (Mu et al., 2007; Mu et al., 2011) data with 1-km resolution from 2000 to 2014 were both retrieved from MODIS. These data were produced by the Numerical Terradynamic Simulation Group and could be freely obtained (http://www.ntsg.umt.edu). The MODIS GPP product (MOD17A3) was developed based on a light-use efficiency model (Heinsch et al., 2003). Its accuracy and reliability have been validated by many studies (Cohen et al., 2006; Heinsch et al., 2006; Turner et al., 2006; Xue et al., 2015) and are considered to be comparable to station observations in many regions and biomes (Cohen et al., 2006; Zhao et al., 2005). MOD17A3 has been widely used in studies of regional or global ecosystem carbon cycles (Wolf et al., 2016; Zscheischler et al., 2014). The global MODIS ET product (MOD16A3) was estimated based on the Penman-Monteith model, which uses meteorological reanalysis data and vegetation property dynamics (e.g., land cover, leaf area index, and albedo) retrieved from MODIS as input variables (Mu et al., 2011; Mu et al., 2013). Validations of this product using station flux tower data in the USA (Mu et al., 2013; Velpuri et al., 2013), Asia (Kim et al., 2012), and other regions have indicated a reasonable accuracy.

2.1.2. SPEI, PDSI and AI data The SPEI developed by Vicente-Serrano et al. (2010) is defined based on the principle of water balance, which involves the precipitation (P) and potential evapotranspiration (PET). Compared to other two popular drought indices, the standardized precipitation index (SPI) (Guttman, 1998; Mckee et al., 1993) and Palmer drought severity index (PDSI) (Alley, 1984; Guttman, 1998; Heddinghaus and Sahol, 1991; Karl, 1937), the SPEI combines the flexible time-scale of SPI and the sensitivity of PDSI to changes in evaporative demand (Vicente-Serrano et al., 2012). SPEI has shown high performance in agricultural, ecological and hydrological applications (Vicente-Serrano et al., 2012). Annual global SPEI data with a spatial resolution of 0.5°covering the period of 2000 to 2014 were obtained from the SPEIbase v.2.4 product (http:// digital.csic.es/handle/10261/128892) developed by Beguería et al. (2010). As a supplement analysis, PDSI data with a spatial resolution of 2.5°covering the period of 2000 to 2014 collected from Aiguo Dai (Dai, 2011a; Dai, 2011b) was also used in this study (http://www.cgd.ucar. edu/cas/catalog/climind/pdsi.html). The PDSI was defined based on theory of water balance and usually was used to indicate of soil moisture conditions. The aridity index (AI) was also calculated to indicate global climate divisions. The AI was defined as the ratio of P to PET (Middleton and Thomas, 1997). The gridded monthly P and PET data (spatial resolution, 0.5°) from 1985 to 2014 used here were obtained from the Climate Research Unit (CRU time series TS 3.23) (Harris et al., 2013). Global continents were divided into humid, sub-humid, semi-arid and arid climate zones according the AI classifications (Gao and Giorgi, 2008). Fig. S1 shows global patterns of multi-year mean P, PET and AI during 1985 to 2014.

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2.1.3. Global land cover data Global biome types were determined according the MODIS global land cover product (MCD12C1) (Fig. S2). We classified global biomes into 12 types as follows: Evergreen needleleaf forest (ENF), Evergreen broadleaf forest (EBF), Deciduous needleleaf forest (DNF), Deciduous broadleaf forest (DBF), Mixed forest (MF), Shrublands in the Southern Hemisphere (Shrublands_S), Shrublands in the Northern Hemisphere (Shrublands_N), Savannas, Grasslands, Croplands, Woody savannas (WS) and Cropland/natural vegetation mosaic(C/N). The GPP, ET, PDSI and land cover datasets were aggregated to a 0.5° resolution to match the spatial resolution of the SPEI dataset. 2.2. Methodology 2.2.1. Trend analysis The Mann-Kendall trend analysis (McLeod, 2005) was used to determine trend of WUE and drought over the period of 2000–2014. This nonparametric method does not require normality of the data series, and is widely used in trend test of climate data and vegetation data (Lanzante, 1997; Martínez and Gilabert, 2009). The significance level was assessed at P b 0.05. 2.2.2. Correlation analysis The relationships between ecosystem WUE and SPEI and PDSI over the period 2000–2014 were examined using non-parametric Spearman correlation. This method is usually employed when variables are not normally distributed or the length of data time series is limited (Isobe et al., 1986). The significance level was assessed at P b 0.05. To investigate the legacy effect of drought on WUE, correlations of WUE with current-year drought and previous-year drought were calculated. In this study, linear regression models were built between WUE and current-year SPEI (Model 1: WUEcurrent = a0 + a1 × SPEIcurrent) and between WUE and two-year SPEI (Model 2: WUEcurrent = b0 + b1 × SPEIcurrent + b2 × SPEIprevious). The latter model was considered to include impacts on WUE both from current-year drought and previous-year drought. Here, 12-month SPEI in December was used to indicate the average dry/wet condition of a year. The independent influence of previous-year drought was determined by the difference in the R2 of the two models. Akaike Information Criterion (AIC) analysis (Akaike, 1998) was employed here to further evaluate whether adding the previous-year influence improved the performance of the regression model between WUE and SPEI, where a lower AIC values suggests a better performance of the regression model (Goetz et al., 2007). Here, if the AIC of regression model built by SPEIcurrent and SPEIprevious (Model 2) was reduced by N3.0 (Tu, 2011; Tu and Xia, 2008) compared to the AIC of the regression model built by SPEIcurrent (Model 1), we think that the Model 2 was greatly improved over the Model 1 (Yang et al., 2016).

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were defined as a dry year followed by a wet year or wet year followed by a dry year. Here, 12-month SPEI were used to indicate the dry/wet condition of a year. Those shifts from dry/wet year to normal year were excluded from this analysis. Total six types of dry-wet transitions were defined based on the classification of SPEI (Wang et al., 2014), as shown in Table 1. 3. Results 3.1. Global trends of WUE and drought The global pattern of mean GPP during 2000 to 2014 shows high consistency with that of ET (Fig. S3). High GPP corresponds to high ET, and vice versa. Highest values of GPP and ET observed in trophic areas, while lowest values located in very dry or cold areas. During 2000 to 2014, the global mean annual WUE was 1.7 gC/kg·H2O and showed great spatial variations (Fig. 1a). A large WUE ranging from 2.5 gC/kg·H2O to 5.1 gC/kg·H2O was observed in dry ecosystems in Australia, the northern and southern regions of Africa, and the central region of South America. A low WUE b0.5 gC/kg·H2O was mainly located in the Northern Hemisphere, such as ecosystems at high latitudes. At the biome level (Fig. 1b), DBF and Shrublands_S showed the largest WUE, and Grassland and Shrublands_N had the lowest WUE. According the climate divisions based on the AI index (Fig. 1c), arid ecosystems had the highest WUE (2.4 gC/kg·H2O), followed by semi-arid and humid ecosystems, which showed the comparable WUE values (1.6 gC/kg·H2O). The lowest WUE (1.6 gC/kg·H2O) was observed for semi-humid ecosystems. The observed differences in WUE among biomes and ecosystems have been well documented by previous studies (Tang et al., 2014; Xue et al., 2015; Yang et al., 2016). This pattern is determined by heterogeneities in both environmental conditions and plant physiological characteristics. Xue et al. (2015) comprehensively investigated drivers of the spatial pattern of WUE, and suggested that elevation, latitude, plant morphology, and climate conditions all contribute the spatial differences of WUE. Over the past 15 years, approximately 14% of global vegetated areas presented significant positive trends in WUE, and these areas were mainly located in central Africa, the north of Northern America, the Indian peninsula, and the east of Asia. Simultaneously, significant decreases in WUE accounting 9% of vegetated areas were observed in central of South America, Sahel region of Africa, western Asia and Australian continent (Fig. 2a). The spatial distributions of SPEI trends are shown in Fig. 2b. N6% of global vegetated areas were becoming more wet, in contrast to 4% of areas that showed drying trends. A general consistency of drying (wetting) trends with decreasing (increasing) WUE trends were observed over continents except for southeastern Canada, indicating coupling between two variables. 3.2. Global patterns of the relationships between WUE and SPEI and PDSI

2.2.3. Identification of shifts in hydro-climatic conditions To explore the impact of change of hydro-climatic conditions on WUE, major dry-wet shifts were identified. The abrupt dry-wet shifts

Table 1 Classifications of abrupt dry-wet transitions. Types Abbreviations Definition

The area ratio accounting for total vegetated lands

1 2 3 4 5 6

0.25% 0.23% 3.45% 3.30% 8.32% 8.14%

ED-EW EW-ED SD-SW SW-SD MD-MW MW-MD

SPEI≤−2 to SPEI≥2 SPEI≥2 to SPEI≤−2 −2bSPEI≤−1.5 to 1.5≤SPEIb2 1.5≤SPEIb2 to −2bSPEI≤−1.5 −1.5bSPEI≤−1 to 1≤SPEIb1.5 1≤SPEIb1.5 to −1.5bSPEI≤−1

Type 1: Extreme dry to extreme wet; Type 2: Extreme wet to extreme dry; Type 3: Severe dry to Severe wet; Type 4: Severe wet to Severe dry; Type 5: Moderate dry to moderate wet; Type 6: Moderate wet to moderate dry.

The correlations between annual WUE and SPEI and PDSI during 2000 to 2014 showed similar patterns over global vegetated areas (Fig. 3a and b), both of which had large spatial heterogeneity. WUE negatively responded to SPEI over 63% of vegetated lands, of which 21% were dominated by significant relationships (P b 0.1). These areas were observed in Australia, the southern region of Africa, and high northern latitudes. These areas are mainly featured with arid or cold climate. In light of previous study (Liu et al., 2015; Vicente-Serrano et al., 2013), plants in arid regions have high adaptation and resilience to water deficits owing to a series of conservative water-use strategies. These strategies can help them reduce water loss and maintain vegetation growth. Plant growth in cold region is mainly constrained by temperature and solar radiation. The promoted carbon take due to enhanced solar radiation during drought condition may be responsible for the drought associated WUE increase (Liu et al., 2015). WUE positively responded to SPEI in the central region of Asia and North America,

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Fig. 1. (a) Global patterns of ecosystem mean WUE and its differences among (b) biome types and (c) climate conditions. Desert and ice areas are excluded and not discussed in the study.

eastern Europe, and the northern region of South America, accounting for approximately 37% of vegetated lands. This pattern is similar to that reported in certain global and regional studies and suggests a divergence response of WUE to drought among different ecosystems (Liu et al., 2015; Yang et al., 2016). Yang et al. (2016) suggested a contrasting

response of ecosystem WUE to drought between arid and semi-arid/ sub-humid ecosystems. Here, this difference was not apparent (Fig. 3c), and should be attributed to the different data sources used for WUE calculation. We observed a negative response of WUE to drought for arid ecosystems (e.g., Shrublands_S and Savanna); while for the

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Fig. 2. Global patterns of trends in WUE and drought index from 2000 to 2014. The percentages in the legend represent the area ratio of each classification to global.

humid ecosystems, both positive (DNF, DBF and MF) and negative (ENF and EBF) WUE-SPEI relationships were identified. 3.3. Legacy effects of drought on ecosystem WUE Apparent drought legacy effects have been reported in previous studies (Liu et al., 2015; Yang et al., 2016; Zhang et al., 2014). In this study, this effect was also detected. Fig. 4a shows the relationships between WUE and the previous year SPEI. These results show a pattern similar to that in Fig. 3a despite the lower correlation between WUE and previous year SPEI compared to the current year SPEI. This finding indicates that for the majority of vegetated areas, WUE responds to current and previous drought in same direction, as indicated by the similar pattern in the two figures. However, for areas noted using red circles in Fig. 4a, WUE showed a positive response to previous-year drought but negative response to current-year drought. This negative-to-positive shift in the WUE-SPEI relationship could be further verified through analysis at the biome scale (Fig. 4b). Apparent shifts were observed for Savanna, Shrublands in the North Hemisphere and EBF. Similar shift in relationships between current-year and previous-year and WUE has also been reported by Yang et al. (2016). The above analysis suggests prominent legacy effects of drought on ecosystem WUE. Fig. S5 and Fig. S6a shows the difference in R2 of the regression models constructed using WUE and SPEIcurrent and SPEIprevious and using WUE and SPEIcurrent, which reflects the contributions of

previous-year drought to the regression model. To evaluate whether contributions from previous drought improved the performance of the regression model between WUE and SPEI, Akaike Information Criterion (AIC) analysis on the aforementioned two models was performed, as shown in Fig. S6b. According to this evaluation, areas with apparent legacy impacts of drought on WUE were identified.

3.4. Changes in WUE with shifts in hydro-climatic conditions To further investigate how WUE responds to changing hydro-climatic conditions, the changes in WUE under abrupt dry-wet shifts were analyzed. Over the past 15 years, approximately 0.25% (0.23%), 3.45% (3.30%), and 8.32% (8.14%) of vegetated lands experienced a simultaneous extreme dry (wet) to extreme wet (dry) transition, severe dry (wet) to severe wet (dry) transition, moderate dry (wet) to moderate wet (dry) transition, respectively. Due to the rarity of extreme drywet transition events, we excluded them from further investigation. WUE variations during abrupt dry-wet transitions are shown in Fig. 5. Generally, decreases in ecosystem WUE were observed when an ecosystem experienced a transition from a dry year to a wet year, whereas WUE increased when an ecosystem experienced an opposite transition. This inverse responses of WUE to dry-wet transition compared to wet-dry transition were also observed at the biome level except for DNF (Fig. 6).

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Fig. 3. Spearman correlation between WUE and a) SPEI and b) PDSI for 2000–2014; c) correlations at the biome level. Significances of Spearman correlations are shown in Fig. S4.

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Fig. 4. (a) Correlation between WUE and previous-year SPEI; Comparison with Fig. 3a, for areas noted using red circles, WUE showed a positive response to previous-year drought but negative response to current-year drought. Significances of Spearman correlations are shown in Fig. S4. (b) comparisons of correlations between WUE and current-year drought and previous-year drought. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4. Discussion Multiyear global mean WUE and its spatial pattern estimated in this study were consistent with previous studies using the same MODIS data (Tang et al., 2014; Xue et al., 2015). The results were also comparable to another global examination of WUE based on a data-driven MTE model, although certain differences in the spatial distribution were identified (Yang et al., 2016). In this study, the response of WUE to drought showed large differences between regions and biomes. The most significant negative relationships were found for arid ecosystems, e.g., Shrublands in the Southern Hemisphere. Both positive and negative relationships were observed for humid ecosystems (e.g., DNF and ENF). This finding is consistent with Yang et al.’s (2016) investigation; despite they reported an opposite response of WUE to drought between arid and semi-arid/subhumid ecosystems. Liu et al. (2015) investigated changes of WUE during four dry years in China, and suggested that the response of WUE to drought varied with drought severity, the timing of drought happened and the biomes types. Generally, the diverse response of WUE to drought can be attributed to the different sensitivities of ecosystem GPP and ET to drought (Liu et al., 2015; Yang et al., 2016). In light of Vicente-Serrano et al.’s (2013) study, vegetation growth in arid ecosystem has a rapid reaction to drought disturbance owing to long-term

adaptation to water deficiency conditions and maintained GPP loss (Vicente-Serrano et al., 2013). Therefore, the decrease in GPP is less than the decrease in ET, causing an increase in WUE (GPP/ET) under drought condition. In contrast, plants in humid ecosystems have poor adaptability to drought, causing a prompt response of vegetation growth to drought. But the enhanced solar radiation associated with drought should increases the ET due to soil evaporation (Liu et al., 2015). The constrained increase of GPP and enhanced ET causes a decrease in WUE under drought disturbance (Liu et al., 2015). This explanation appears to be inappropriate for very humid rainforests, as a longterm EC observational study in a tropical rainforest suggested that the response of WUE is mainly determined by the sensitivity of GPP to drought (Tan et al., 2015). This may explain the divergent responses of WUE to drought among humid ecosystems. The apparent positive responses of WUE to drought for semi-arid and sub-humid ecosystems reported by Yang et al. (2016) are failed to observe in our study. The potential reasons are still needed more in-depth investigations. This study also observed obvious legacy effects of drought on ecosystem WUE, as reported in a previous study. A study by Yang et al. (2016) suggested an opposite sign with respect to impacts on WUE between previous-year drought and current-year drought. This phenomenon was not common in our investigation. We found that previous-year drought and current-year drought impacts on WUE in same direction.

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Fig. 5. Changes in WUE under a) transition from severe dry year to severe wet year (SD-SW); b) transition from severe wet year to severe dry year (SW-SD); c) transition from moderate dry year to moderate wet year (MD-MW); and d) transition from moderate wet year to moderate dry year (MW-MD).

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Fig. 6. Changes in WUE among biomes under dry-wet shifts. a) Changes in WUE under shift from severe dry year to severe wet year (blue), and vice versa (red); b) Changes in WUE under shift from moderate dry year to moderate wet year (green), and vice versa (orange). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

This indicates that the legacy effects from previous-year drought should greatly enhance the impact on ecosystem WUE of the current year for two consecutive year droughts. The decreased WUE from a dry year to wet year and the increased WUE from a wet year to dry year suggest a high sensitivity of ecosystems to abrupt changes in hydro-climatic conditions, which is also indicative of the resilience of ecosystems to disturbance. This WUEindicated ecosystem resilience is consistent with previous findings (Huxman et al., 2004; Ponce-Campos et al., 2013; Zhang et al., 2014). Based on long-term in-situ measurements in the United States, Puerto Rico and Australia, Ponce-Campos et al. (2013) found a common ecosystem WUE across biomes and capacity of ecosystems to regulate their WUE to adapt to changing hydro-climatic conditions. Zhang et al. (2014) studied the rainfall use efficiency (RUE) relying on MODIS products and also observed a similar phenomenon associated with dry and wet conditions; they attributed this phenomenon to the legacy effect of precipitation as well as the resilience of biomes. Compared to

previous studies, our study examined this ecosystem function at broader scale and further confirmed the role of ecosystem WUE in coping with external environment disturbances. Although the results of this study confirmed certain previous findings, such as the divergent response of WUE to drought, the memory effect of drought on WUE, and the sensitivity of WUE to hydro-climatic shifts, there were some differences. These divergences between studies could be due to differences in the data, methods, periods, and study region. Converging on an understanding of the response of ecosystem WUE to drought is crucial for forecasting ecosystem dynamics in future climate scenarios. It should be noted that this study was carried out using the global MODIS dataset (MOD16A3, MOD17A3) during a short-term data (2000–2014). This is a main deficiency which would introduce some uncertainties of our results. This highlights the need for continued field observations, improvements in the accuracy of remote sensing and upgrades in the performance of models.

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5. Conclusions In this study, the response of ecosystem WUE to drought was examined using MODIS GPP and ET products and the SPEI index over the period of 2000 to 2014. The multiyear global mean WUE was 1.7 gC/kg·H2O, and a high WUE was mainly observed in arid ecosystems, e.g., Shrublands in the Southern Hemisphere. Divergent responses of WUE to drought disturbance among different biomes were identified. Generally, the WUE of arid or cold ecosystems showed a negative response to drought, whereas in humid ecosystems, both positive and negative relationships between WUE and drought were found. At the biome level, the majority of biomes were characterized as having negative a WUE-drought relationship, and positive relationships were observed for DNF and DBF. Apparent legacy effects of drought on ecosystem WUE were identified and quantified. In addition, WUE was found to be sensitive to shifts in hydro-climatic conditions. A shift from dry to wet years is predicted to cause a decrease in ecosystem WUE, whereas an opposite shift should promote WUE. Findings of this study are somewhat different from previous reports; therefore, more abundant observations and model simulations are still needed to consolidate these divergences. In addition, future investigations should pay more attention on the response processes of ET and GPP to drought disturbance which are crucial for an in-depth understanding the WUEdrought relationships. Acknowledgments This work was financially supported by the National Natural Science Foundation of China (grant 41671083) and the National Key Scientific Research and Development Program of China (grants 2015CB953600 and 2016YFC0401307). Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2017.05.084. References Akaike, H., 1998. Information theory and an extension of the maximum likelihood principle. Selected Papers of Hirotugu Akaike. Springer, pp. 199–213. Alley, W.M., 1984. The Palmer drought severity index: limitations and assumptions. J. Appl. Meteorol. 23, 1100–1109. Beer, C., Ciais, P., Reichstein, M., Baldocchi, D., Law, B., Papale, D., et al., 2009. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23. Beguería, S., Vicente Serrano, S.M., Angulo-Martínez, M., 2010. A Multiscalar Global Drought Dataset: The Speibase: A New Gridded Product for the Analysis of Drought Variability and Impacts. Brümmer, C., Black, T.A., Jassal, R.S., Grant, N.J., Spittlehouse, D.L., Chen, B., et al., 2012. How climate and vegetation type influence evapotranspiration and water use efficiency in Canadian forest, peatland and grassland ecosystems. Agric. For. Meteorol. 153, 14–30. Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogée, J., Allard, V., et al., 2005. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533. Cohen, W.B., Maiersperger, T.K., Turner, D.P., Ritts, W.D., Pflugmacher, D., Kennedy, R.E., et al., 2006. MODIS Land Cover and Lai Collection 4 Product Quality Across Nine States in the Western Hemisphere. Dai, A., 2011a. Characteristics and trends in various forms of the Palmer drought severity index during 1900–2008. J. Geophys. Res.-Atmos. 116. Dai, A., 2011b. Drought under global warming: a review. Wiley Interdiscip. Rev. Clim. Chang. 2, 45–65. Gang, C., Wang, Z., Chen, Y., Yang, Y., Li, J., Cheng, J., et al., 2016. Drought-induced dynamics of carbon and water use efficiency of global grasslands from 2000 to 2011. Ecol. Indic. 67, 788–797. Gao, X., Giorgi, F., 2008. Increased aridity in the Mediterranean region under greenhouse gas forcing estimated from high resolution simulations with a regional climate model. Glob. Planet. Chang. 62, 195–209. Goetz, S., Steinberg, D., Dubayah, R., Blair, B., 2007. Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA. Remote Sens. Environ. 108, 254–263. Guttman, N.B., 1998. Comparing the palmer drought index and the standardized precipitation index 1. JAWRA J. Am. Water Resour. Assoc. 34, 113–121.

Harris, I., Jones, P.D., Osborn, T.J., et al., 2014. Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. Int. J. Climatol 34, 623–642. Heddinghaus, T.R., Sahol, P., 1991. A review of the Palmer Drought Severity Index and where do we go from here. Preprints. Seventh Conference on Applied Climatology (Dallas, TX. Proc. conf. on Applied Climatol American Meteorological Society). Heinsch, F., Reeves, M., Votava, P., Kang, S., Milesi, C., Zhao, M., et al., 2003. In: MODIS Land Team (Ed.), User's Guide GPP and NPP (MOD17A2/A3) Products NASA MODIS Land Algorithm Sioux Falls, SD. Heinsch, F.A., Zhao, M., Running, S.W., Kimball, J.S., Nemani, R.R., Davis, K.J., et al., 2006. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Trans. Geosci. Remote Sens. 7, 1908–1925. Hicke, J.A., Allen, C.D., Desai, A.R., Dietze, M.C., Hall, R.J., Kashian, D.M., et al., 2012. Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Glob. Chang. Biol. 18, 7–34. Huang, M., Piao, S., Sun, Y., Ciais, P., Cheng, L., Mao, J., et al., 2015. Change In Terrestrial Ecosystem Water-use Efficiency over the Last Three Decades. Huntington, T.G., 2006. Evidence for intensification of the global water cycle: review and synthesis. J. Hydrol. 319, 83–95. Huxman, T.E., Smith, M.D., Fay, P.A., Knapp, A.K., Shaw, M.R., Loik, M.E., et al., 2004. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654. Isobe, T., Feigelson, E., Nelson, P.I., 1986. Statistical methods for astronomical data with upper limits II-Correlation and regression. Astrophys. J. 306, 490–507. Karl, T.R., 1937. The sensitivity of the Palmer drought severity index and Palmer's Z-index to their calibration coefficients including potential evapotranspiration. J. Appl. Meteorol. 25, 77–86. Kim, H.W., Hwang, K., Mu, Q., Lee, S.O., Choi, M., 2012. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia. KSCE J. Civ. Eng. 16, 229–238. Lanzante, J.R., 1997. Lag relationships involving tropical sea surface temperatures. Oceanogr. Lit. Rev. 44, 436. Lawrence, D.M., Thornton, P.E., Oleson, K.W., Bonan, G.B., 2007. The partitioning of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a GCM: impacts on land atmosphere interaction. J. Hydrometeorol. 8, 862. Liu, Y., Xiao, J., Ju, W., Zhou, Y., Wang, S., Wu, X., 2015. Water use efficiency of China's terrestrial ecosystems and responses to drought. Sci. Rep. 5. Martínez, B., Gilabert, M.A., 2009. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens. Environ. 113, 1823–1842. Mckee, T.B., Doesken, N.J., Kleist, J., 1993. The Relationship of Drought Frequency and Duration to Time Scales. McLeod, A.I., 2005. Kendall rank correlation and Mann-Kendall trend test. R Package Kendall. Middleton, N., Thomas, D., 1997. World Atlas of Desertification. Arnold Hodder Headline, PLC. Mu, Q., Heinsch, F.A., Zhao, M., Running, S.W., 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111, 519–536. Mu, Q., Zhao, M., Running, S.W., 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800. Mu, Q., Zhao, M., Running, S.W., 2013. MODIS global terrestrial evapotranspiration (ET) product (NASA MOD16A2/A3). Algorithm Theoretical Basis Document, Collection. 5. Ponce-Campos, G.E., Moran, M.S., Huete, A., Zhang, Y., Bresloff, C., Huxman, T.E., et al., 2013. Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature 494, 349–352. Reichstein, M., Ciais, P., Papale, D., Valentini, R., Running, S., Viovy, N., et al., 2007. Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: a joint flux tower, remote sensing and modelling analysis. Glob. Chang. Biol. 13, 634–651. Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M., Hashimoto, H., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560. Tan, Z.H., Zhang, Y.P., Deng, X.B., Song, Q.H., Liu, W.J., Deng, Y., et al., 2015. Interannual and seasonal variability of water use efficiency in a tropical rainforest: results from a 9 year eddy flux time series. J. Geophys. Res.-Atmos. 120, 464–479. Tang, X., Li, H., Desai, A.R., Nagy, Z., Luo, J., Kolb, T.E., et al., 2014. How is water-use efficiency of terrestrial ecosystems distributed and changing on Earth? Sci. Rep. 4, 7483. Teuling, A.J., Van Loon, A.F., Seneviratne, S.I., Lehner, I., Aubinet, M., Heinesch, B., et al., 2013. Evapotranspiration amplifies European summer drought. Geophys. Res. Lett. 40, 2071–2075. Trenberth, K.E., Dai, A., van der Schrier, G., Jones, P.D., Barichivich, J., Briffa, K.R., et al., 2013. Global warming and changes in drought. Nat. Clim. Chang. 4, 17–22. Trenberth, K.E., Dai, A., van der Schrier, G., Jones, P.D., Barichivich, J., Briffa, K.R., et al., 2014. Global warming and changes in drought. Nat. Clim. Chang. 4, 17–22. Tu, J., 2011. Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. Appl. Geogr. 31, 376–392. Tu, J., Xia, Z.-G., 2008. Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation. Sci. Total Environ. 407, 358–378. Turner, D.P., Ritts, W.D., Cohen, W.B., Gower, S.T., Running, S.W., Zhao, M., et al., 2006. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sens. Environ. 102, 282–292. Vanloocke, A., Twine, T.E., Zeri, M., Bernacchi, C.J., 2012. A regional comparison of water use efficiency for miscanthus, switchgrass and maize. Agric. For. Meteorol. 164, 82–95. Velpuri, N.M., Senay, G.B., Singh, R.K., Bohms, S., Verdin, J.P., 2013. A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: using point and gridded FLUXNET and water balance ET. Remote Sens. Environ. 139, 35–49.

L. Huang et al. / Science of the Total Environment 601–602 (2017) 1097–1107 Vicente-Serrano, S.M., Beguería, S., López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718. Vicente-Serrano, S.M., Beguería, S., Lorenzo-Lacruz, J., Camarero, J.J., López-Moreno, J.I., Azorin-Molina, C., et al., 2012. Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interact. 16, 1–27. Vicente-Serrano, S.M., Gouveia, C., Camarero, J.J., Beguería, S., Trigo, R., López-Moreno, J.I., et al., 2013. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. 110, 52–57. Wang, Q., Wu, J., Lei, T., He, B., Wu, Z., Liu, M., et al., 2014. Temporal-spatial characteristics of severe drought events and their impact on agriculture on a global scale. Quat. Int. 349, 10–21. Westerling, A.L., Hidalgo, H.G., Cayan, D.R., Swetnam, T.W., 2006. Warming and earlier spring increase western US forest wildfire activity. Science 313, 940–943. Wolf, S., Keenan, T.F., Fisher, J.B., Baldocchi, D.D., Desai, A.R., Richardson, A.D., et al., 2016. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl. Acad. Sci. (201519620).

1107

Xue, B.-L., Guo, Q., Otto, A., Xiao, J., Tao, S., Li, L., 2015. Global patterns, trends, and drivers of water use efficiency from 2000 to 2013. Ecosphere 6 (art174). Yang, Y., Guan, H., Batelaan, O., McVicar, T.R., Long, D., Piao, S., et al., 2016. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci. Rep. 6. Zhang, X., Moran, M.S., Zhao, X., Liu, S., Zhou, T., Ponce-Campos, G.E., et al., 2014. Impact of prolonged drought on rainfall use efficiency using MODIS data across China in the early 21st century. Remote Sens. Environ. 150, 188–197. Zhao, M., Heinsch, F.A., Nemani, R.R., Running, S.W., 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176. Zscheischler, J., Mahecha, M.D., von Buttlar, J., Harmeling, S., Jung, M., Rammig, A., et al., 2014. A few extreme events dominate global interannual variability in gross primary production. Environ. Res. Lett. 9, 035001.