1. Introduction
The quantity of water available for runoff (Q) and changing the amount of moisture stored in catchments is the difference between precipitation (P) and evaporation (E). Runoff from river basins is substantial in humid regions where P exceeds E, but there is little or no runoff in arid regions where E ≈ P. Between these extremes, runoff is often the small residual between P and E and subtle changes in either can strongly affect water yields. Global warming associated with rising atmospheric CO2 concentrations is expected to substantially modify the global hydrological cycle (Huntington 2006; Milly et al. 2005) and thus change the balance between P, E, and Q by differing amounts in various regions across the globe. Evaporation from land surfaces is fundamentally determined by the availability of water and energy, and understanding the contributions of trends and changing patterns in water and energy supply to changing evaporation is an important issue for earth system science. Suggested reasons for variations in E and Q include changes in precipitation (Zhang et al. 2007), the impact of global brightening/dimming on available energy (Roderick and Farquhar 2002; Wild et al. 2008, 2005), the coupled changes in photosynthesis and surface conductance due to enhanced greenhouse gas concentrations (Gedney et al. 2006), decreases in soil moisture content (Jung et al. 2010), and changes in land use or land cover (Piao et al. 2007). To identify possible causes for changes in E, Jung et al. (2010) used the model-tree ensemble (MTE) algorithm of Jung et al. (2009) to calculate monthly evaporation rates (EMTE) for the global land surface from 1982 to 2008. The MTE is a machine-learning algorithm trained using evaporation measurements from the global Flux Network (FLUXNET) database, gridded global meteorological data, and remotely sensed fraction of absorbed photosynthetically active radiation. According to this algorithm, global average EMTE increased by 0.71 ± 0.1 mm yr−2 from 1982 to 1997, but with a slight decreasing trend in EMTE in the following decade. An ensemble of outputs from nine independent models gave similar results, and Jung et al. (2010) attributed the reduction in EMTE in the past decade to declining soil water availability (i.e., precipitation), particularly across Africa and Australia where microwave remote sensing–based soil moisture data showed negative trends. This paper complements the work of Jung et al. (2010) by comparing four different approaches to estimating global and regional trends in evaporation from 1983 to 2006: 1) using the water balances of large, unregulated catchments
Section 2 provides a brief summary of methods used for the evaporation calculations, while section 3 documents the data sources used in the analysis. Results are presented in section 4, followed by the discussion in section 5 and conclusions in section 6.
2. Modeling and estimation approaches
We first clarify our notation before introducing the estimation approaches used. Variables EMTE, EPML, P, and Q represent monthly or annual values, while
a. Catchment water balances

b. “Budyko-curve” hydrometeorological model

c. Penman–Monteith combination equation
Term 1 on the right is used to estimate evaporation from the soil by multiplying the equilibrium evaporation rate at the soil surface, ɛAs/(1 + ɛ), by a coefficient f that varies from f = 1 when the soil surface is wet to f = 0 when it is dry. In this paper we follow Zhang et al. (2010) in calculating the temporal variation of f as a function of precipitation and equilibrium evaporation rates for one month before and after the current 1-month time step.
Four of the five parameters in Eqs. (4) and (5) [called the Penman-Monteith-Leuning (PML) model] were assigned constant values (kQ = kA = 0.6, Q50 = 30 W m−2, and D50 = 0.7 kPa; Leuning et al. 2008). Following Zhang et al. (2010), the magnitude of the fifth parameter gsx was estimated separately for each 0.5° land surface pixel used in our analysis by adjusting gsx to force agreement between the 23-yr averages of
We used the above equations to estimate
3. Data and methods
To calculate monthly EPML we used monthly meteorological fields of daytime average air temperature and humidity to calculate Da and Ga, while Ac, As, and Qh were calculated using average incoming solar radiation, combined with remotely sensed estimates of Lai and surface albedo.
Global data fields of vapor pressure and temperature [time series (TS) 3.0] at 0.5° resolution came from the Climate Research Unit (New et al. 2000). Leaf area index and land cover type data at ~8-km resolution were obtained from Boston University (Ganguly et al. 2008a,b). Two precipitation datasets were examined—one from the Global Precipitation Climatology Project (GPCP, version 2; Adler et al. 2003) at 2.5° resolution, the other from the Global Precipitation Climatology Centre (GPCC, version 4; Rudolf and Schneider 2004) at 0.5° resolution. There was little difference between the two datasets at a common resolution of 2.5° (not shown), so the 0.5° GPCC dataset was used in the subsequent analyses.
Three global radiation products were used to calculate
Catchment water balances were calculated for 197 unregulated catchments over the hydrological year, defined as October–September to minimize effects of snowfall on yearly water balances (Dai et al. 2009). Selected catchments have an area >500 km2 and missing daily streamflow data are less than 5% of the total. Streamflow data were from several sources: 1) 55 monthly series from the 925 gauges of Dai et al. (2009), 2) 53 daily series from the Global Runoff Data Centre streamflow database (http://www.bafg.de/GRDC/EN/Home/homepage__node.html), and 3) 88 daily series from Australia. Catchment boundaries were respectively delineated by 1) the Simulated Topological Network (STP-30p; Vorosmarty et al. 2000), 2) the HYDRO1k digital elevation model (DEM; Peel et al. 2010), and 3) the Australian GEODATA 9-s digital elevation model (Hutchinson 2002). Regulated catchments were identified from the 1) International Commission of Large Dams (Vorosmarty et al. 2003), 2) Meridian World Data (http://www.meridianworlddata.com/), and 3) National Land and Water Resources Audit of Australia (http://www.nlwra.gov.au/). It is noted that even in unregulated catchments, there may be changes in streamflow because the land is experiencing change draws of water for irrigation, flood control engineering, land use change, wetlands loss, etc. Such changes do not affect our analysis provided precipitation, evaporation, and runoff occur within the same catchment.
Trends in
4. Results
Before examining trends in evaporation rates predicted by the methods described above, we first compare mean annual evaporation for 1983–2006 for the global land surface for “wet” pixels, where the aridity index
Comparison of mean annual evaporation rates E for the global land surface, wet pixels (where the aridity index AI ≤ 1.5), and dry pixels (where AI > 1.5) for the period 1983–2006. Volume of water evaporated annually is also shown. Here
Spatial pattern of wet pixels (aridity index; AI ≤ 1.5) and dry pixels (AI > 1.5) across global land surface. Boundaries for the 197 unregulated catchments are shown in black.
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
Time series of anomalies in annual EMTE and EPML relative to their respective means are shown in Fig. 2 for the global land surface and for wet and dry pixels. We see that interannual variation in EPML is greater than for EMTE in all three panels and that the variation in EPML is greatest for wet pixels. There is a statistically significant increasing trend in the evaporation anomalies for all three classes (p < 0.01), but the slope of 1.088 mm yr−1 in the global trend for annual EPML is almost double that of 0.528 mm yr−1 for EMTE. Trends predicted using annual EPML are also double those from EMTE for wet and dry catchments.
Time series of annual evaporation EPML and EMTE for (top) the global land surface, (middle) wet catchments, and (bottom) dry catchments.
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
Patterns of 23-yr average evaporation rates
Spatial patterns in (a)
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
While there is good agreement between the global patterns in
Correlation coefficient matrix between trends (1983–2006) in
Figures 4a–d show global maps of average trends in
Global map of trends in (a)
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
Global maps of trends in the key drivers of evaporation—precipitation
Global map of trends (1983–2006) in (a) precipitation,
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
Values of
Trends (1983–2006) in available energy from (a) the ISCCP radiation products (Zhang et al. 2004), (b) the SRB (Gupta et al. 2006), and (c) the NCEP–NCAR reanalysis data (Kalnay et al. 1996). Boundaries for the 197 unregulated catchments are shown in black.
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
Time series of annual A and EPML, both aggregated from grids of the 110 wet catchments. Values after colons are the mean trend slopes. The offset in A between the ISCCP and SRB datasets does not appear in trends in EPML because EPML is constrained by the Fu (1981) hydrometeorological model using each radiation dataset independently.
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
The results presented in Figs. 1–7 are for pixels across the global land surface. In Figs. 8 and 9, trend analysis is conducted for 110 unregulated wet and 87 dry catchments for which we calculated
Comparison of trends in
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
Comparison of trends in
Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-012.1
Trends in
Regressions for trends in
The results shown in Figs. 8 and 9 may be biased because they were obtained from 5-yr moving averages, which can increase the serial correlation in the data. However, recalculation of the trends in
The values of
5. Discussion
Our analysis has shown that decadal trends in evaporation calculated using water balances,
We note that remotely sensed radiation is a key input to the two structurally different diagnostic models (EPML and EFu) and thus the lack of correlation between trends in these quantities and
Model structural limitations as well as errors in input data may be responsible for the discrepancies in trends in
6. Conclusions
Improvements are needed in global datasets of precipitation, runoff, radiation, and meteorological forcing before we can be confident in model estimates of the magnitude and sign of trends in evaporation from land surfaces. Effective combination of precipitation and soil moisture information with satellite radiation and vegetation data will undoubtedly improve estimation of trends in global E by hydrological models in the future.
Acknowledgments
Aiguo Dai provided monthly streamflow data for the 925 global river basins, and the Global Runoff Data Centre, Koblen, Germany provided daily streamflow data for 107 gauges. We thank Dr. Chris Smith, Dr. Michael Roderick, and two anonymous reviewers for their helpful comments.
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