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. 2016 Jan 11:6:19124.
doi: 10.1038/srep19124.

Multi-decadal trends in global terrestrial evapotranspiration and its components

Yongqiang Zhang  1 , Jorge L Peña-Arancibia  1 , Tim R McVicar  1   2 , Francis H S Chiew  1 , Jai Vaze  1 , Changming Liu  3 , Xingjie Lu  4 , Hongxing Zheng  1 , Yingping Wang  4 , Yi Y Liu  2 , Diego G Miralles  5   6 , Ming Pan  7
Affiliations

Multi-decadal trends in global terrestrial evapotranspiration and its components

Yongqiang Zhang et al. Sci Rep. .

Abstract

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981-2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.

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Figures

Figure 1
Figure 1. Validation of the PML-ET model.
(a) Comparison of the estimated mean annual ET (1981–2012, mm year−1) to catchment ET (P–Q) observations in 643 catchments (red dots in the global map). (b) Comparison of estimated annual ET trend (mm year−2) to catchment annual ET (P–Q) for 46 large catchments (>10, 000 km2) with less than 3-year missing data. (c) Comparison of the estimated ET (mm month−1) to the measured ET at 95 flux sites (red dots in the global map). (d) Annual anomalies of the estimated ET, ET components, and catchment observed ET (P–Q), all aggregated from catchments (number of catchments per year provided using grey bars) with annual Q observations. (e) Annual anomalies of the estimated ET, ET components, and the observed microwave soil moisture, all aggregated between 40 °N and 40 °S (area covered by the microwave soil moisture data). Error bars for ET and its components are s.d. obtained from the two PML simulations. Error bars for P–Q are s.d. obtained from the two precipitation datasets. R2 in Fig. 1(a–d) is obtained from comparing measured ET and estimated ET. R2 in Fig. 1(e) is obtained from comparing soil moisture and estimated Es. The maps were generated using a commercial software MATLAB.
Figure 2
Figure 2. Global maps of climatology (1981–2012).
(a) aridity index (the ratio of mean annual precipitation to mean annual potential ET). (b) mean annual ET. (c) the percentage of Et to ET. (d) the percentage of Es to ET. The maps were generated using MATLAB.
Figure 3
Figure 3. Annual global anomalies (mm year−1) in ET.
Outputs from 39 CMIP5 models span from 1982 to 2005; outputs from 9 land surface models are from 1982 to 2008; outputs from other models (i.e., MTE, GLEAM and PML) are from 1982 to 2011. Dash lines show linear trends for the median of nine land surface models (yellow), MTE (blue), GLEAM (cyan) and PML (black), respectively.
Figure 4
Figure 4. Mean global land surface and continental averages (1981–2012) of various statistics for P, ET, Et, Es and Ei.
(a) Mean annual values (mm year−1). (b) Variance or covariance of annual values (mm2 year−2). (c) Trend (mm year−2). Error bars are s.d. obtained from PML simulations 1 and 2. The symbol ** indicates significance level 1–α = 99% (p < 0.01); the symbol * indicates significance level 1–α = 95% (p < 0.05); the symbol ‘n’ is not significant (p > 0.05). In each figure sub-part for each geographic area the numbers presented are ordered equivalently to the sub-part legend. Note that scaling is applied to the Var (P) on part (b).
Figure 5
Figure 5. Global maps of trend and correlation (1981–2012).
(a) ET trend (mm year−2). (b) Et trend (mm year−2). (c) Es trend (mm year−2). (d) LAI trend (m2 m−2 year−1). (e) correlation between annual P and annual ET (for land grid cells where p < 0.01, else they are white). (f) P trend (mm year−2). Trends in ET, Et, and Es are obtained from the average of the two PML simulations. Trends in LAI are obtained from the AVHRR based LAI product, and P trends are averaged from the two P products (i.e., PGF and WFDEI). The maps were generated using MATLAB.
Figure 6
Figure 6. Global maps of trend difference.
(a) ET (mm year−2). (b) Et (mm year−2). (c) Es (mm year−2). (d) Ei (mm year−2). Using the PML model, the trend difference is calculated between the average estimates using the observed LAI time series (experiments 1 and 2) minus the average estimates using detrended LAI time series (experiments 3 and 4); details of these experiments are provided in the Methods section. The maps were generated using MATLAB.
Figure 7
Figure 7. Summary of variance and trend in global ET components and LAI obtained from CMIP5 and PML models for 1981–2005.
(a) variance in ET components (mm year−2). (b) variance in LAI (m4 m−4). (c) trend in ET components (mm year−2). (d) trend in LAI (m2 m−2 year−1). The global variance and trend in the CMIP5 and PML models are obtained using area–weighted average over all land gird cells. All eight CMIP5 models with archived ET, Et and Es outputs are used. Five of the eight models also archived LAI outputs. The model details are summarised in Table S1. Green dots are variance or trend estimated by PML using observed LAI time series (Fig. 7a,c), and are variance and trend in LAI time series (Fig. 7b,d); black dots are variance or trend estimated by PML using detrended LAI time series (Fig. 7a,c), and are variance and trend in detrended LAI time series (Fig. 7b,d). Red crosses are the variance or trend estimated by each CMIP5 model. The bottom, middle and top of each box are the 25th, 50th, and 75th percentiles, respectively, and the bottom and top whiskers represent the minimum and maximum values.
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