Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2019 Mar 15;7(3):266–282. doi: 10.1029/2018EF001066

A Global Assessment of Terrestrial Evapotranspiration Increase Due to Surface Water Area Change

Shengan Zhan 1, Chunqiao Song 2,✉, Jida Wang 3, Yongwei Sheng 1, Jiping Quan 1,4,5
PMCID: PMC6487836  PMID: 31069243

Abstract

Surface water, which is changing constantly, is a crucial component in the global water cycle, as it greatly affects the water flux between the land and the atmosphere through evaporation. However, the influences of changing surface water area on the global water budget have largely been neglected. Here we estimate an extra water flux of 30.38 ± 15.51 km3/year omitted in global evaporation calculation caused by a net increase of global surface water area between periods 1984–1999 and 2000–2015. Our estimate is at a similar magnitude to the recent average annual change in global evapotranspiration assuming a stationary surface water area. It is also comparable to the estimated trends in various components of the hydrological cycle such as precipitation, discharge, groundwater depletion, and glacier melting. Our findings suggest that the omission of surface water area changes may cause considerable biases in global evaporation estimation, so an improved understanding of water area dynamics and its atmospheric coupling is crucial to reduce the uncertainty in the estimation of future global water budgets.

Keywords: evapotranspiration, global, surface water, water cycle, evaporation

Key Points

  • The increase of global permanent surface water area between 1984–1999 and 2000–2015 has caused 30.38 ± 15.51 km3/year increase in evaporation

  • The magnitude of this change is comparable to that of annual global evapotranspiration change assuming stationary surface water areas

  • Surface water dynamics can lead to considerable changes in evaporation and should not be neglected in future global water budget studies

1. Introduction

Water comprises a critical component of global/regional hydrological and biogeochemical cycles and is an essential resource for all organisms including humans (Lehner & Döll, 2004; Palmer et al., 2015; Sheng et al., 2016). Both the stocks and flow of water in hydrological cycle components are strongly altered by changing climate and human activities (Song et al., 2014; Wang, Liang, et al., 2014; Wang, Sheng, & Tong, 2014; Wang, Xiao, et al., 2014). In return, they impact the climate system and the available water resources for human society (Oki & Kanae, 2006; Wada et al., 2013). In the past several decades, climate change has intensified the hydrological cycle, with significant implications for ecosystem services and feedback to regional and global climate. Several lines of evidence have been found to support the warming‐induced intensification of the water cycle (Huntington, 2006; Jung et al., 2010). Evaporation (E) and evapotranspiration (ET), as a linking mechanism between the land surface and the atmosphere (Mueller et al., 2013), are essential to the water cycle and excellent indicators of the intensity of water cycle. Specifically, about 60% of annual land precipitation is evaporated to the atmosphere, and the figure may exceed 95% in arid climates (Jung et al., 2010; Oki & Kanae, 2006). Knowledge on the temporal changes of ET is crucial for accurately estimating global or regional water budgets and better understanding hydrological interactions between the land and the atmosphere.

Evapotranspiration is a function of solar radiation, temperature, wind speed, humidity, atmospheric pressure, and the surrounding environment. To address ET changes and patterns in the hydrological cycle, several alternative global ET data sets have been produced in recent years, with different spatial and temporal resolutions. These data sets include satellite‐based estimates, land surface models driven with observation‐based forcings, reanalysis data products, estimates based on empirical upscaling of point observations, and atmospheric water balance estimates. Previous studies (Jiménez et al., 2011; Miralles et al., 2011; Mu et al., 2011; Mueller et al., 2011; Weiß & Menzel, 2008) have assessed the spatial patterns of multiyear means and seasonal variations of these data sets and revealed the agreements and differences among these data sets. Studies on temporal variations of ET based on global data sets were conducted by Wang et al. (2010) and Jung et al. (2010) over the periods from 1982 to 2002 and 1982 to 2008, respectively. They both found a tendency of increasing ET for the years 1982 to the late 1990s, which indicates a possible intensification of the hydrological cycle. Jung et al. (2010) further revealed a decline in the period 1998–2008 with a decrease in moisture availability, confirming the cessation of the increasing trend. Another study based on satellite retrievals also confirms the suspension of increasing trend in global land ET after 2000 (Yao et al., 2012).

However, it is important to note that the estimates in these global ET data sets were derived for relatively stationary land areas. They assume that water body areas are constant throughout their study periods. Furthermore, studies of open‐water evaporation from surface hydrologic systems, which is considerably different from land ET, were conducted only on some of the large water bodies or hot spot lakes, for example, Caspian Sea and the Great Lakes, as part of some data sets (Jensen, 2010; Renssen et al., 2007). Most lakes, ponds, and wetlands are merely considered as fixed points for the hydrologic processes in many parts of the world (Fisher et al., 2008; Zhang et al., 2010). However, a recent study (Pekel et al., 2016) used three million Landsat satellite images to quantify changes in global surface water between 1984 and 2015. They found permanent surface water has shrunk by about 90,000 km2 in the past 32 years, while newly emergent permanent water bodies almost doubled the total lost water area. All continents show a net increase in permanent water, except Oceania. Quantification of the spatiotemporal variations of evaporation caused by the conversion between land and water has long been neglected at the global scale. Given this inventory of global conversion between land and water, it is both feasible and vital to understand and quantify evaporation‐induced hydrological flux changes over lakes, reservoirs, and wetlands across various sizes.

The goal of this research is to explore the global water budget estimate biases induced by the omission of evaporation over land‐water conversion zones. Such a worldwide quantification is useful for several purposes. In the most recent Intergovernmental Panel on Climate Change (IPCC) sea level budgets (Church et al., 2013), changes in terrestrial water storage driven by the climate have been assumed to be too small to be included. However, recent advances in gravity satellite measurement enabled a quantification of deceleration of sea level rise by 0.71 ± 0.20 mm/year during 2002–2014, contributed by water stored in land due to climate variability (Reager et al., 2016; Wada et al., 2017). This assessment is essential for reducing the uncertainty in terrestrial water budget estimates. Furthermore, the consideration of such evaporation‐induced water flux changes can efficiently improve uncertainty estimates on regional or basin‐scale hydrologic modeling (Melesse et al., 2009). This study is informative for agricultural and water management communities to estimate future water demands, to make decisions on irrigation regulation and reservoir operations where ET is a crucial factor.

2. Data and Methods

Surface water change over the 1984–2015 period is obtained from the Global Surface Water Database (Pekel et al., 2016; https://global‐surface‐water.appspot.com/download). Specifically, we used the occurrence change intensity data set in the database. We did not use the monthly water data set, which is also available in the Global Surface Water Database, due to the extensive data gaps in Landsat coverage and the noise in the data set. Therefore, our analysis focuses on the changes in permanent water bodies using the occurrence change intensity data set between two time periods: 1984–1999 (P1) and 2000–2015 (P2). Surface water occurrence for a certain month (e.g., July) in a period (e.g., P1) is calculated as the ratio between the number of times a pixel is detected as water in July from 1984 to 1999 and the total number of valid observations in July from 1984 to 1999. The difference in occurrence is calculated for individual pairs of months between the periods of P1 and P2. Occurrence change intensity is the difference in occurrence averaged across all pairs of months in a year and standardized to −1 and 1. In this study, new surface water (NSW) is defined as water pixels with an occurrence change intensity value greater than 0.9, and lost surface water (LSW) is defined as water pixels with a value less than −0.9. Changes between −0.9 and 0.9 are considered as seasonal fluctuations and are not included in the study.

We leveraged various existing data sets to estimate evapotranspiration (ET) and potential ET (PET) during 1984–2015. The analysis focuses only on the regions that experienced conversion between land and water in order to emphasize the contribution of this particular change to terrestrial evaporation. ET/PET changes over permanent land and water areas are not considered. It is assumed that land evaporation can be estimated using ET products and water evaporation can be estimated using PET products. While PET does not equal lake evaporation in some circumstances as lake evaporation is dependent on lake depth, our focus is on the land‐water boundary (i.e., where land‐water conversion happens) where the water depth is relatively limited. Moreover, our estimation is based on a multiannual timescale (each 15‐year period) where lake evaporation changes caused by seasonal variation in the lakes' heat storage are negligible (McMahon et al., 2013; Morton, 1983; Sacks et al., 1994). The change in evaporation caused by the conversion between land and water can thus be quantified as the product of the difference between ET and PET and the changed area.

Specifically, we used five ET products including the monthly global observation‐driven Penman‐Monteith‐Leuning evapotranspiration from the Commonwealth Scientific and Industrial Research Organization (https://data.csiro.au/dap/landingpage?pid=csiro:17375), the Global Land Data Assimilation System Noah Land Surface Model (GLDAS NOAH, https://disc.sci.gsfc.nasa.gov/datasets?keywords=GLDAS), the Global Land Evaporation Amsterdam Model (GLEAM, http://gleam.eu), Global Evapotranspiration (Global ET, http://files.ntsg.umt.edu/data/ET_global_monthly), and Moderate Resolution Imaging Spectroradiometer Global Evapotranspiration Project (MOD16, http://files.ntsg.umt.edu/data/NTSG_Products/MOD16), and three PET products from GLDAS NOAH, GLEAM, and MOD16. The products are chosen based on their temporal coverage and spatial resolution. The spatial resolution has to be finer than the area of drainage basins, which are the spatial units of our analysis. Table 1 describes the characteristics of the different products.

Table 1.

General Information of ET and PET Products Used in This Study

Product ET coverage PET coverage Spatial resolution Methodology Source
CSIRO 1984–2012 N/A 0.5 degree Penman‐Monteith Leuning model Zhang et al., 2016
GLDAS NOAH 1984–2010 1984–2010 0.25 degree Land surface model Rodell et al., 2004
GLEAM 1984–2014 1984–2014 0.25 degree Priestley‐Taylor Martens et al., 2017
Global ET 1984–2013 N/A 8 km Penman‐Montieth Zhang et al., 2010
MOD16 2000–2014 2000–2014 1 km Penman‐Monteith Mu et al., 2013
Open in a new tab

Note. CSIRO = Commonwealth Scientific and Industrial Research Organization; GLDAS NOAH = Global Land Data Assimilation System Noah Land Surface Model; GLEAM = Global Land Evaporation Amsterdam Model; GLOBAL ET = Global Evapotranspiration; MOD16 = Moderate Resolution Imaging Spectroradiometer Global Evapotranspiration Project.

Mean ET and PET for each product are calculated for the two periods according to the surface water occurrence change data set. All data sets are then resampled to the same spatial resolution (i.e., 8 km or 5 arc minutes) and clipped to the same spatial extent, which includes all land surface area except for Greenland and Antarctica. We then averaged the data sets by basins to reduce the influence of spatial resampling and data gaps in large lakes. The basins were primarily derived from the World Wildlife Fund HydroBASINS data set (http://hydrosheds.org/page/hydrobasins). For mainland Oceania and High Mountain Asia area, the coarse division was substituted with the drainage basin data from the National Catchment and Stream Environment Database (http://data.gov.au/dataset/national‐catchment‐boundaries‐v‐1‐1‐4) and the Global Drainage Basin Database (http://www.cger.nies.go.jp/db/gdbd/gdbd_index_e.html), respectively. We merged neighboring basins that are smaller than 200 km2 to ensure an adequate number of cells of ET/PET data within a basin. A few large basins (e.g., Caspian Sea basin) were also manually divided into smaller basins for finer assessment. The basin‐averaged ET estimations are used to calculate land evapotranspiration, and basin‐averaged PET estimations are used to calculate water body evaporation. While the spatial heterogeneity of ET and PET within a basin is not considered, this method fills the data gaps in large water bodies (such as the Great Lakes, the Caspian Sea, the Aral Sea, and Lake Victoria), which are masked by most products, and we included such heterogeneities in the uncertainty assessment.

The ET products agree reasonably well with each other (Figure 1), so we simply averaged the ET estimates for each basin and calculated a standard error from the five products used. However, the PET estimates have large discrepancies among different products (Figure 2). GLDAS NOAH estimates are consistently much higher than GLEAM estimates especially in wetter regions. MOD16 estimates lie in between the other two but are closer to GLDAS NOAH estimates especially in drier regions. We compared the PET products to the results by Weiß and Menzel (2008), which compared the performance of four potential evapotranspiration equations globally and found that GLDAS NOAH and MOD16 overestimated while GLEAM underestimated PET in most regions. We also compiled field‐measured or modeled evaporation estimates for over 130 lakes worldwide that are reported in the literature (see Table S1 in the supporting information). The distribution of those lakes is shown in Figure 3. Thirteen lakes have independent field‐measured and modeled evaporations (see Table S2). The correlation between field‐measured and modeled evaporations for those lakes is over 0.9 (Figure 4). We found that evaporations that are measured only, modeled only, and both combined all have similar correlations with GLDAS NOAH PET (Figures 5 and 6a). Thus, we used all available measurements regardless of method and temporal coverage to assess the quality of the PET products. When multiple measurements for a lake are available, the average for the lake was used. We compared the products to the reported measurements and also found that GLDAS NOAH and MOD16 tend to overestimate PET and GLEAM tends to underestimate (Figure 6). However, it was found that the correlation between GLEAM PET and reported lake surface ET measurements was rather poor. MOD16 has large data gaps in dry and poorly vegetated regions such as Northern Africa, the Arabian Peninsula, and the Tibetan Plateau even after aggregating the estimates by basins. Moreover, MOD16 only covers the period from 2000 to 2014 and was not as well correlated with reported data as GLDAS NOAH. Therefore, we used MOD16 for ET estimates but not for PET estimates. Since the ET products agree well with each other, including MOD16 only in P2 does not affect the results much. GLDAS NOAH is the only product used for PET estimates.

Figure 1.

Figure 1

Open in a new tab

Comparison of the five evapotranspiration products in different continental regions. Color lines illustrate the product means for each of the continents, and the shade illustrates the product range. AF = Africa; AS = Asia; AU = Australia; EU = Europe; NA = North America; SA = South America.

Figure 2.

Figure 2

Open in a new tab

Comparison of the three potential evapotranspiration products in different continental regions. Color lines illustrate the product means for each of the continents, and the shade illustrates the product range. AF = Africa; AS = Asia; AU = Australia; EU = Europe; NA = North America; SA = South America.

Figure 3.

Figure 3

Open in a new tab

Distribution of lakes and basins with field‐measured or modeled evaporations.

Figure 4.

Figure 4

Open in a new tab

Correlation between independent field‐measured and modeled lake evaporations. The orange line represents one‐to‐one ratio.

Figure 5.

Figure 5

Open in a new tab

Comparison of the Global Land Data Assimilation System Noah Land Surface Model (GLDAS NOAH) product against measured (a) and modeled (b) evaporation reported in the literature. The orange line represents one‐to‐one ratio.

Figure 6.

Figure 6

Open in a new tab

Comparison of potential evapotranspiration products against reported (measured or modeled) lake evapotranspiration measurements. (a) Global Land Data Assimilation System Noah Land Surface Model (GLDAS NOAH) versus reported. (b) Global Land Evaporation Amsterdam Model (GLEAM) versus reported. (c) Moderate Resolution Imaging Spectroradiometer Global Evapotranspiration Project (MOD16) versus reported. The orange line represents one‐to‐one ratio.

Next, we used a least median of squares regression (Rousseeuw, 1984), which is insensitive to outliers, between GLDAS NOAH PET and the reported lake ET to correct for the biases in GLDAS NOAH estimates (Figure 7). The PET of basins that have lakes with reported measurements was replaced with those reported measurements instead of using the bias‐corrected GLDAS NOAH estimates. While these basins only account for 3% of the total number of basins and 18% of the total land surface area, they represent about 24% and 49% of total NSW and LSW worldwide. Thus, we believe that bias correcting the GLDAS NOAH estimates and replacing the PET of selected basins with reported measurements substantially reduces the uncertainty of our evaporation estimates. To further account for the uncertainty induced by various time coverages in our collected lake evaporations, we calculated the root mean square error (RMSE) in the PET bias correction (483.37 mm; Figure 7). This RMSE was combined with the standard errors of ET products for each basin (as previously described) to propagate the error bounds of our estimated terrestrial ET change rates.

Figure 7.

Figure 7

Open in a new tab

Bias‐corrected Global Land Data Assimilation System Noah Land Surface Model (GLDAS NOAH) potential evapotranspiration versus reported lake evapotranspiration from published literature. RMSE = root mean square error.

Finally, we aggregated surface water occurrence change area by basins to obtain the total areas of NSW and LSW during 1984–2015 within each basin. The volume of evaporative change caused by NSW and LSW (∆V) is the difference between averaged PET and ET during the two 15‐year periods (P1 and P2) multiplied by the areas of NSW and LSW, respectively. Thus, the volume of evaporative change over the two periods induced by surface water changes within each basin is calculated as follows:

∆V=PETP2−ETp1×NSW−PETP1−ETp2×LSW.#1

PETP2 − ETp1 represents the changes in ET when land is converted to water in P2 while PETP1 − ETp2 represents such change when water is converted to land in P2. NSW and LSW are the total area of new and LSW in a basin, respectively. The equation is applied to each basin, and a positive value indicates a net loss of water to the atmosphere through evaporation.

3. Results

3.1. Global Surface Water Changes and Associated Increase in Evaporation

Between 1984–1999 and 2000–2015, the Earth's surface gained 183,807.2 km2 and lost 88,366.8 km2 of surface water area with a net gain of 95,440.4 km2. The spatial distribution of net surface water area changes by basin is shown in Figure 8. All continents had a positive net change of surface water area. The gain is mostly due to the filling of reservoirs and partly due to climate change while the loss is due to drought and human withdrawal (Pekel et al., 2016). For a detailed discussion of the spatial distribution of NSW and LSW and the cause of changes, the reader is referred to Pekel et al. (2016).

Figure 8.

Figure 8

Open in a new tab

Net surface water area change by basin from 1984 to 2015.

Associated with the global increase in surface water area is the universal net increase in evaporation across all continents. Figure 9 shows evaporation changes caused by the conversion between land and water aggregated by basin. Globally, evaporation increased by 30.38 ± 15.51 km3/year between 1984–1999 and 2000–2015 (Table 2). If we assume constant ET and PET throughout the period (i.e., using average ET/PET from 1984 to 2015 as opposed to calculating average ET/PET for P1 and P2), the estimate of increased evaporation is 27.86 ± 15.51 km3/year. This suggests that most of the ET change (~92%) was caused by the increase in surface water area while changes in ET/PET over the periods play a minor role (less than 3 km3/year or ~8%).

Figure 9.

Figure 9

Open in a new tab

Evaporation changes induced by surface water area change by basin (km3/year).

Table 2.

Surface Water Changes and Associated Evaporation Changes by Continent

Continent Total NSW (km2) Total LSW (km2) Net change (km2) Evaporation change rate (km3/year) Change caused by land‐water conversion (km3/year)
Africa 12,652.54 3,913.23 8,739.31 12.43 ± 0.92 12.61 ± 0.92
Asia 68,289.75 39,982.94 28,306.81 1.64 ± 13.02 1.03 ± 13.02
Australia 6,477.81 2,749.99 3,727.82 0.80 ± 0.65 0.27 ± 0.65
Europe 29,026.10 7,606.81 21,419.29 12.88 ± 7.44 12.53 ± 7.44
North America 39,292.48 18,729.65 20,562.83 2.04 ± 2.10 1.37 ± 2.10
South America 28,068.53 15,384.23 12,684.30 0.59 ± 3.14 0.06 ± 3.14
Total 183,807.21 88,366.85 95,440.36 30.38 ± 15.51 27.86 ± 15.51
Open in a new tab

Figure 10 shows the difference between PET and ET by basin. Different evaporation changes given a constant net surface area change can be caused by the difference between a region's PET and ET. If a region's PET is much higher than its ET, a greater proportion of water will be evaporated when land is inundated by water. Similarly, a greater reduction of ET will be observed when water area is converted into land in the same region. Whereas if a region's ET and PET are similar, not much change in evaporation can be observed during land‐water conversion. This explains why the net increase in surface area in Africa is only about 40% of that in Europe but they had a similar increase in ET. It can also explain the different ET change between Europe and North America, where both continents have similar increase in surface water area.

Figure 10.

Figure 10

Open in a new tab

The difference between potential evapotranspiration (PET) and evapotranspiration by basin from 1984 to 2015.

3.2. Spatial Distribution of Surface Water Change‐Induced Evaporation Change Across the Globe

While all continents experienced evaporation increases, spatial heterogeneity exists within continents (Figure 9). Most regions have an increased evaporation, which is consistent with the net gain of surface water area globally. The increase in evaporation can be largely explained by the construction of dams, coastal flooding, permafrost dynamics (both increase and decrease), and the change of large individual water bodies such the Caspian Sea, while decreases in evaporation are largely confined to local regions that are mostly affected by droughts and especially the desiccation of the Aral Sea.

The impoundment of water by dams is largely responsible for the increased ET in South America, Africa, and South Asia. According to the Global Reservoir and Dam database (Lehner et al., 2011), South American countries have built 73 dams since 1984 with a total impounded surface water area of 15,748 km2. The largest contributor is Brazil with 42 dams and 9,638 km2 of impounded water followed by Venezuela with nine dams and 4,047 km2 of impounded water. African countries have built 121 dams with 3,879 km2 of impounded water. South and Southeast Asian countries of India, Thailand, Vietnam, Myanmar, Laos, and Sri Lanka combined have built 63 dams with 3,013 km2 of impounded water. India is by far the largest contributor in the region with 39 new dams and 1,370 km2 of impounded water. Note that Global Reservoir and Dam does not include many newly constructed small reservoirs; therefore, the areas here are likely underestimated.

Various coastal regions have seen increases in evaporation. This is partly due to coastal flooding of seawater caused by sea level rise, storm surges, and the retainment of sediment by upstream dams (Jongman et al., 2012; Mentaschi et al., 2018). This can be seen clearly in Louisiana, Florida, and along the coasts of south Asia and China. It is also caused by the expansion of aquaculture (Naylor et al., 2000) especially in major river deltas such as the Nile, the Yangtze, and the Pearl River deltas.

Both increases and decreases of evaporation are observed in the pan‐Arctic region. Lakes expand in the ice‐rich permafrost through thermomechanical erosion of the surrounding soil and drain through bank overflow or into the subsurface aquifer when the permafrost underlying the lakes thaws completely (Frohn et al., 2005; Plug & West, 2009; Yoshikawa & Hinzman, 2003). An increase in the abundance and area of lakes in the continuous permafrost regions and a decrease in the discontinuous, isolated, and sporadic permafrost regions have been observed in Siberia (Smith et al., 2005). Thus, the evaporation changes observed across the pan‐Arctic are likely the combined result of lake expansion and drainage caused by permafrost dynamics.

Certain local regions have revealed interesting patterns of increasing evaporation as well. For example, the inner Tibetan Plateau has experienced a drastic increase in evaporation as the region gained about 8,000 km2 of NSW and lost less than 500 km2 of LSW with a net gain of over 7,500 km2. The regional evaporation increased by 4.91 ± 0.41 km3/year. This is because the majority of endorheic lakes have experienced expansion due to the wetter climate and increasing glacier meltwater supply (Lutz et al., 2014). Studies based on satellite altimetry and gravimetry measurements also confirmed increasing lake water levels and storages due to more precipitation and more concentrated localized meltwater since the late 1990s (Song et al., 2013, 2014; Yao et al., 2018; Zhang et al., 2017). The rate of warming in the Tibetan Plateau is over 0.4 °C/decade from 1961–2001, which is about double the global rate (Hansen et al., 2010; Xu et al., 2008). The conversion from extremely dry land to water results in significant water loss by evaporation.

The Canadian Prairies have experienced increased evaporation of about 2.5 km3/year. This region is known for wetlands occupying the potholes created by the late Pleistocene glaciation (Johnson et al., 2005). These wetlands provide important ecosystem services and are highly sensitive to climate change and land use change (Conly & Van Der Kamp, 2001). Studies have reported increased precipitation since the 1960s and a higher Palmer Drought Severity Index (higher means wetter) in the 1990s than in the 1980s (Coles et al., 2017; Millett et al., 2009). This may have contributed to the expansion of wetlands and thus increased water body evaporation in the region.

The Caspian Sea has caused the largest increase in evaporation among all basins. The Caspian Sea basin gained over 15,000 km2 and lost 1,800 km2 of surface water with a net gain of 13,000 km2. As a result, the evaporation in the Caspian Sea basin increased by 10.59 ± 8.87 km3/year. Almost all gained surface water comes from the Kara‐Bogaz‐Gol Bay, which is connected to the Caspian Sea through a narrow strait. Water in the bay completely depends on the inflow through the strait. A dam was built on the strait in 1980 in order to reduce the outflow from the Caspian Sea and stabilize its decline, and the bay dried up almost entirely by 1984 (Kouraev et al., 2011). After the dam was removed in 1992, the bay restored to a relatively stable extent since 1997.

Despite the overall increasing evaporation, certain regions show clustered patterns of decreased evaporation. Droughts are responsible for the decrease of evaporation in the western United States (MacDonald, 2010) and southeastern Australia (van Dijk et al., 2013). California, which was struck by two droughts from 2007 to 2009 and 2012 to 2016 (Xiao et al., 2017), lost over 1,000 km2 of surface water. The Great Salt Lake and Lake Mead lost over 1,800 and 200 km2 of surface area, respectively. The western United States combined had about 5.5 km3/year of decrease in evaporation. Southeast Australia experienced the worst drought on record from 2001 to 2009 (van Dijk et al., 2013). This is referred to as the “Millennium Drought” (van Dijk et al., 2013). The region lost over 1,000 km2 of surface water during 2000–2015 compared to 1984–1999 and as a result the region had a 1 km3/year of decrease in evaporation.

The Aral Sea has caused the largest decrease in evaporation among all basins. The Aral Sea basin gained 2,400 km2 and lost over 26,000 km2 of surface water with a net loss of over 23,000 km2. As a result, the basin‐wide evaporation decreased by 23.18 ± 12.80 km3/year. Micklin et al. (2014) reported a roughly 30,000‐km2 loss of surface area of the Aral Sea from 1981 to 2010, and Shi et al. (2014) reported a roughly 35,000‐km2 loss from 1981 to 2013. These numbers suggest that the Aral Sea is likely the single cause for LSW in the basin. The NSW is likely the terminal lakes formed in the deserts due to the drainage of irrigated water (Micklin et al., 2014). The decline of the Aral Sea was mostly due to the diversion of water from the Amu Darya and the Syr Darya for agricultural irrigation (Micklin, 1988; Micklin et al., 2014; Wang et al., 2018). Note that the increased agricultural activities in the Aral Sea basin may have offset some of the evaporation decreases in the basin.

4. Discussions and Conclusions

4.1. Uncertainties of Estimating the Evaporated Water Budgets

According to Pekel et al. (2016), the user's and producer's accuracy of permanent water from different Landsat sensors are both above 98%. However, optical imagery‐derived water body data sets underestimate water extent under vegetation cover especially in the rainforests. This effect is not accounted for in the study, which adds some unquantified uncertainties in the changes of water body areas in those regions. Occurrence change intensity cannot be calculated for areas that do not have pairs of months in the two periods. The conversion between land and water body happens at the land‐water boundary where the water is shallow. Therefore, the influence of lake depth and thus lake heat storage on evaporation in the transition area can be neglected. There also exists a scale mismatch between the ET/PET products and the land‐water conversion zones, which is inherent in the data sets. Using a basin‐averaged ET/PET to calculate the evaporation changes in small land‐water conversion zones within the basins may lead to some uncertainties in the estimates.

The largest source of uncertainty comes from PET estimates. Unlike ET products, different sources of PET data provide drastically different PET estimates. As discussed earlier, we searched the literature for field measurements of lake surface ET and used them as reference to correct and replace the GLDAS NOAH product. The lakes we collected cover all continents and a wide range of climate conditions. While a few field measurements themselves have noticeable uncertainties, they are much more consistent with each other than the global products. We performed a simple test for the sensitivity of our estimates of evaporation changes to PET estimates in the Aral Sea and the Caspian Sea basins, which are by far the largest contribution of a single basin, using the largest and smallest reported PET from different literature sources. Evaporation from the Aral Sea basin ranged from −25.18 to −20.75 km3/year while the Caspian Sea basin ranged from 9.52 to 11.48 km3/year. These numbers serve as the upper and lower boundaries of the ET estimates as affected by PET products, and the range of the boundaries are less than 20% of their means. Our uncertainty estimates in Table 2 include both the standard errors of ET products and the RMSE of the bias correction for PET products, which can be quite large for continents like Asia and South America. Nevertheless, we provide the first of such global assessment given the limitations in the available data sets.

4.2. Implications to the Future Water Cycle Studies

Jung et al. (2010) estimated a 7.1 mm/year per decade of increased ET during 1982–1997 and a 7.9 mm/year per decade of decrease during 1998–2008. This is equivalent to annual ET increases and decreases of 92.95 and 103.43 km3, respectively. On average, these estimates render a net increasing rate of 1.1 mm/year per decade (14.40 km3/year) in ET during 1982–2008. This rate is based on a constant extent of water bodies. Our calculations reveal a net annual evaporation of 30.38 ± 15.51 km3 upon the changed surface water body areas over the three decades, which has been omitted in conventional estimates. This suggests that a considerable amount of ET change can be caused by surface water dynamics. Most such studies on ET changes are therefore missing a considerable contributor of ET changes by assuming a constant water surface throughout their study period.

In comparison, global precipitation has been increasing at about 1 mm/year per decade (13 km3/year) since 1901 (Smith et al., 2012), and global discharge has been increasing at about 3 mm/year per decade (40 km3/year; Syed et al., 2010). In other components of the water cycle, ice caps and glaciers globally are losing 290 km3/year of equivalent water volume (Gardner et al., 2013); the ice sheets in Greenland and Antarctica are retreating at 142 and 71 km3/year, respectively (Shepherd et al., 2012); the global groundwater is being depleted at an annual rate of 204 km3/year (Wada et al., 2012). These comparisons highlight the importance of understanding long‐term land and water transformation, as they may cause a considerable amount of evaporated water flux. Also, changes in sea level rise caused by changes in surface water bodies may not be as inconsequential as the IPCC report has assumed.

Previous studies related to trends in global evapotranspiration have inappropriately omitted this considerable source of evaporation change by assuming constant land and water areas throughout their study periods. This study shows that the noticeable changes of surface water area observed in recent decades may have considerably altered terrestrial surface evaporation rates and led to obvious changes in the surface water budget. Our results are expected to lower the uncertainties in closing the water budgets on both the global and regional scales. This is essential for assessing future water availability and providing water management authorities with insight into future management practices under ongoing climate changes.

Supporting information

Supporting Information S1

Click here for additional data file. (119.2KB, docx)

Acknowledgments

This study was supported in part by the National Key R & D Program of China (Grants 2018YFD0900804 and 2018YFD1100101) and the Thousand Young Talents Program in China to C. Song and in part by the U.S. Geological Survey (USGS) Landsat Science Team Program Grant G12PC00071, the National Aeronautics and Space Administration (NASA) Terrestrial Hydrology Program under Grant NNX08AE51G, and Surface Water and Ocean Topography (SWOT) Program under Grant NNX16AH85G to Y. Sheng. The authors appreciate two anonymous reviewers and editor for their constructive comments that greatly helped to improve the study. We thank Austin Madson for editing the language of this paper. We also thank the Global Surface Water Database, the Commonwealth Scientific and Industrial Research Organization, the National Aeronautics Space and Administration Goddard Earth Sciences Data Information Services Center, Global Land Evaporation Amsterdam Model, the Numerical Terradynamic Simulation Group at University of Montana, the World Wildlife Fund, the National Catchment and Stream Environment Database, and the Global Drainage Basin Database for making the data sets used in this study publicly available. The source of the data sets can be found in section 2.

Zhan, S. , Song, C. , Wang, J. , Sheng, Y. , & Quan, J. (2019). A global assessment of terrestrial evapotranspiration increase due to surface water area change. Earth's Future, 7, 266–282. 10.1029/2018EF001066

References

References

  1. Church, J. A. , Clark, P. U. , Cazenave, A. , Gregory, J. M. , Jevrejeva, S. , Levermann, A. , Merrifield, M. A. , Milne, G. A. , Nerem, R. S. , Nunn, P. D. , Payne, A. J. , Pfeffer, W. T. , Stammer, D. , & Unnikrishnan, A. S. (2013). Sea level change In Stocker T. F. et al. (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY: Cambridge University Press. [Google Scholar]
  2. Coles, A. E. , McConkey, B. G. , & McDonnell, J. J. (2017). Climate change impacts on hillslope runoff on the northern Great Plains, 1962–2013. Journal of Hydrology, 550, 538–548. [Google Scholar]
  3. Conly, F. M. , & Van Der Kamp, G. (2001). Monitoring the hydrology of Canadian prairie wetlands to detect the effects of climate change and land use changes. Environmental Monitoring and Assessment, 67(1), 195–215. [DOI] [PubMed] [Google Scholar]
  4. Fisher, J. B. , Tu, K. , & Baldocchi, D. D. (2008). Global estimates of the land‐atmosphere water flux based on monthly AVHRR and ISLSCP‐II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3), 901–919. [Google Scholar]
  5. Frohn, R. C. , Hinkel, K. M. , & Eisner, W. R. (2005). Satellite remote sensing classification of thaw lakes and drained thaw lake basins on the North Slope of Alaska. Remote Sensing of Environment, 97(1), 116–126. [Google Scholar]
  6. Gardner, A. S. , Moholdt, G. , Cogley, J. G. , Wouters, B. , Arendt, A. A. , Wahr, J. , et al. (2013). A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science, 340(6134), 852–857. [DOI] [PubMed] [Google Scholar]
  7. Hansen, J. , Ruedy, R. , Sato, M. , & Lo, K. (2010). Global surface temperature change. Reviews of Geophysics, 48, RG4004 10.1029/2010RG000345 [DOI] [Google Scholar]
  8. Huntington, T. G. (2006). Evidence for intensification of the global water cycle: Review and synthesis. Journal of Hydrology, 319(1), 83–95. [Google Scholar]
  9. Jensen, M. E. (2010). Estimating evaporation from water surfaces, paper presented at CSU/ARS Evapotranspiration Workshop, Fort Collins, CO.
  10. Jiménez, C. , Prigent, C. , Mueller, B. , Seneviratne, S. I. , McCabe, M. F. , Wood, E. F. , Rossow, W. B. , Balsamo, G. , Betts, A. K. , Dirmeyer, P. A. , Fisher, J. B. , Jung, M. , Kanamitsu, M. , Reichle, R. H. , Reichstein, M. , Rodell, M. , Sheffield, J. , Tu, K. , & Wang, K. (2011). Global intercomparison of 12 land surface heat flux estimates. Journal of Geophysical Research, 116, D02102 10.1029/2010JD014545 [DOI] [Google Scholar]
  11. Johnson, W. C. , Millett, B. V. , Gilmanov, T. , Voldseth, R. A. , Guntenspergen, G. R. , & Naugle, D. E. (2005). Vulnerability of Northern Prairie Wetlands to Climate Change. BioScience, 55(10), 863. [Google Scholar]
  12. Jongman, B. , Ward, P. J. , & Aerts, J. C. J. H. (2012). Global exposure to river and coastal flooding: Long term trends and changes. Global Environmental Change, 22(4), 823–835. [Google Scholar]
  13. Jung, M. , Reichstein, M. , Ciais, P. , Seneviratne, S. I. , Sheffield, J. , Goulden, M. L. , Bonan, G. , Cescatti, A. , Chen, J. , & De Jeu, R. (2010). Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318), 951–954. [DOI] [PubMed] [Google Scholar]
  14. Kouraev, A. V. , Crétaux, J.‐F. , Lebedev, S. A. , Kostianoy, A. G. , Ginzburg, A. I. , Sheremet, N. A. , Mamedov, R. , Zakharova, E. , Roblou, L. , Lyard, F. , Calmant, S. , & Bergé‐Nguyen, M. (2011). Satellite Altimetry Applications in the Caspian Sea In Vignudelli S., Kostianoy A. G., Cipollini P., & Benveniste J. (Eds.), Coastal altimetry (pp. 331–366). Berlin Heidelberg: Springer. [Google Scholar]
  15. Lehner, B. , & Döll, P. (2004). Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296(1), 1–22. [Google Scholar]
  16. Lehner, B. , Liermann, C. R. , Revenga, C. , Vörösmarty, C. , Fekete, B. , Crouzet, P. , Döll, P. , Endejan, M. , Frenken, K. , Magome, J. , Nilsson, C. , Robertson, J. C. , Rödel, R. , Sindorf, N. , & Wisser, D. (2011). High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management. Frontiers in Ecology and the Environment, 9(9), 494–502. [Google Scholar]
  17. Lutz, A. F. , Immerzeel, W. W. , Shrestha, A. B. , & Bierkens, M. F. P. (2014). Consistent increase in High Asia's runoff due to increasing glacier melt and precipitation. Nature Climate Change, 4(7), 587–592. [Google Scholar]
  18. MacDonald, G. M. (2010). Water, climate change, and sustainability in the southwest. Proceedings of the National Academy of Sciences, 107(50), 21256–21262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Martens, B. , Miralles, D. G. , Lievens, H. , van der Schalie, R. , de Jeu, R. A. M. , Fernández‐Prieto, D. , Beck, H. E. , Dorigo, W. A. , & Verhoest, N. E. C. (2017). GLEAM v3: satellite‐based land evaporation and root‐zone soil moisture. Geoscientific Model Development, 10(5), 1903–1925. [Google Scholar]
  20. McMahon, T. A. , Peel, M. C. , Lowe, L. , Srikanthan, R. , & McVicar, T. R. (2013). Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences, 17(4), 1331–1363. [Google Scholar]
  21. Melesse, A. M. , Abtew, W. , & Dessalegne, T. (2009). Evaporation estimation of rift valley lakes: comparison of models. Sensors (Basel, Switzerland), 9(12), 9603–9615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Mentaschi, L. , Vousdoukas, M. I. , Pekel, J.‐F. , Voukouvalas, E. , & Feyen, L. (2018). Global long‐term observations of coastal erosion and accretion. Scientific Reports, 8(1), 12,876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Micklin, P. , Aladin, N. V. , & Plotnikov, I. (Eds.). (2014). The Aral Sea. Berlin, Heidelberg: Springer. [Google Scholar]
  24. Micklin, P. P. (1988). Disiccation of the Aral Sea: A water management disaster in the Soviet Union. Science, 241(4870), 1170–1176. [DOI] [PubMed] [Google Scholar]
  25. Millett, B. , Johnson, W. C. , & Guntenspergen, G. (2009). Climate trends of the North American prairie pothole region 1906–2000. Climatic Change, 93(1–2), 243–267. [Google Scholar]
  26. Miralles, D. G. , DeJeu, R. A. M. , Gash, J. H. , Holmes, T. R. H. , & Dolman, A. J. (2011). Magnitude and variability of land evaporation and its components at the global scale. Hydrology and Earth System Sciences, 15(3), 967–981. [Google Scholar]
  27. Morton, F. I. (1983). Operational estimates of lake evaporation. Journal of Hydrology, 66(1), 77–100. [Google Scholar]
  28. Mu, Q. , Zhao, M. , & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800. [Google Scholar]
  29. Mu, Q. , Zhao, M. , & Running, S. W. (2013). MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3). Algorithm Theoretical Basis Document, Collection, 5.
  30. Mueller, B. , Hirschi, M. , Jimenez, C. , Ciais, P. , Dirmeyer, P. A. , Dolman, A. J. , Fisher, J. B. , Jung, M. , Ludwig, F. , Maignan, F. , Miralles, D. G. , McCabe, M. F. , Reichstein, M. , Sheffield, J. , Wang, K. , Wood, E. F. , Zhang, Y. , & Seneviratne, S. I. (2013). Benchmark products for land evapotranspiration: LandFlux‐EVAL multi‐data set synthesis. Hydrology and Earth System Sciences, 17(10), 3707–3720. [Google Scholar]
  31. Mueller, B. , Seneviratne, S. , Jimenez, C. , Corti, T. , Hirschi, M. , Balsamo, G. , Ciais, P. , Dirmeyer, P. , Fisher, J. , & Guo, Z. (2011). Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations. Geophysical Research Letters, 38, L06402 10.1029/2010GL046230 [DOI] [Google Scholar]
  32. Naylor, R. L. , Goldburg, R. J. , Primavera, J. H. , Kautsky, N. , Beveridge, M. C. , Clay, J. , Folke, C. , Lubchenco, J. , Mooney, H. , & Troell, M. (2000). Effect of aquaculture on world fish supplies. Nature, 405(6790), 1017–1024. [DOI] [PubMed] [Google Scholar]
  33. Oki, T. , & Kanae, S. (2006). Global hydrological cycles and world water resources. Science, 313(5790), 1068–1072. [DOI] [PubMed] [Google Scholar]
  34. Palmer, S. C. , Kutser, T. , & Hunter, P. D. (2015). Remote sensing of inland waters: Challenges, progress and future directions. Remote Sensing of Environment, 157, 1–8. [Google Scholar]
  35. Pekel, J.‐F. , Cottam, A. , Gorelick, N. , & Belward, A. S. (2016). High‐resolution mapping of global surface water and its long‐term changes. Nature, 540(7633), 418–422. [DOI] [PubMed] [Google Scholar]
  36. Plug, L. J. , & West, J. J. (2009). Thaw lake expansion in a two‐dimensional coupled model of heat transfer, thaw subsidence, and mass movement. Journal of Geophysical Research, 114, F01002 10.1029/2006JF000740 [DOI] [Google Scholar]
  37. Reager, J. T. , Gardner, A. S. , Famiglietti, J. S. , Wiese, D. N. , Eicker, A. , & Lo, M.‐H. (2016). A decade of sea level rise slowed by climate‐driven hydrology. Science, 351(6274), 699–703. [DOI] [PubMed] [Google Scholar]
  38. Renssen, H. , Lougheed, B. C. , Aerts, J. C. J. H. , de Moel, H. , Ward, P. J. , & Kwadijk, J. C. J. (2007). Simulating long‐term Caspian Sea level changes: The impact of Holocene and future climate conditions. Earth and Planetary Science Letters, 261(3), 685–693. [Google Scholar]
  39. Rodell, M. , Houser, P. R. , Jambor, U. , Gottschalck, J. , Mitchell, K. , Meng, C.‐J. , Arsenault, K. , Cosgrove, B. , Radakovich, J. , Bosilovich, M. , Entin, J. K. , Walker, J. P. , Lohmann, D. , & Toll, D. (2004). The Global Land Data Assimilation System. Bulletin of the American Meteorological Society, 85(3), 381–394. [Google Scholar]
  40. Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871–880. [Google Scholar]
  41. Sacks, L. A. , Lee, T. M. , & Radell, M. J. (1994). Comparison of energy‐budget evaporation losses from two morphometrically different Florida seepage lakes. Journal of Hydrology, 156(1), 311–334. [Google Scholar]
  42. Sheng, Y. , Song, C. , Wang, J. , Lyons, E. A. , Knox, B. R. , Cox, J. S. , & Gao, F. (2016). Representative lake water extent mapping at continental scales using multi‐temporal Landsat‐8 imagery. Remote Sensing of Environment, 185, 129–141. [Google Scholar]
  43. Shepherd, A. , Ivins, E. R. , Geruo, A. , Barletta, V. R. , Bentley, M. J. , Bettadpur, S. , Briggs, K. H. , Bromwich, D. H. , Forsberg, R. , Galin, N. , Horwath, M. , Jacobs, S. , Joughin, I. , King, M. A. , Lenaerts, J. T. M. , Li, J. , Ligtenberg, S. R. M. , Luckman, A. , Luthcke, S. B. , McMillan, M. , Meister, R. , Milne, G. , Mouginot, J. , Muir, A. , Nicolas, J. P. , Paden, J. , Payne, A. J. , Pritchard, H. , Rignot, E. , Rott, H. , Sørensen, L. S. , Scambos, T. A. , Scheuchl, B. , Schrama, E. J. O. , Smith, B. , Sundal, A. V. , van Angelen, J. H. , van de Berg, W. J. , van den Broeke, M. R. , Vaughan, D. G. , Velicogna, I. , Wahr, J. , Whitehouse, P. L. , Wingham, D. J. , Yi, D. , Young, D. , & Zwally, H. J. (2012). A reconciled estimate of ice‐sheet mass balance. Science, 338(6111), 1183–1189. [DOI] [PubMed] [Google Scholar]
  44. Shi, W. , Wang, M. , & Guo, W. (2014). Long‐term hydrological changes of the Aral Sea observed by satellites. Journal of Geophysical Research: Oceans, 119, 3313–3326. 10.1002/2014JC009988 [DOI] [Google Scholar]
  45. Smith, L. C. , Sheng, Y. , MacDonald, G. M. , & Hinzman, L. D. (2005). Disappearing Arctic Lakes. Science, 308(5727), 1429–1429. [DOI] [PubMed] [Google Scholar]
  46. Smith, T. M. , Arkin, P. A. , Ren, L. , & Shen, S. S. P. (2012). Improved reconstruction of global precipitation since 1900. Journal of Atmospheric and Oceanic Technology, 29(10), 1505–1517. [Google Scholar]
  47. Song, C. , Huang, B. , & Ke, L. (2013). Modeling and analysis of lake water storage changes on the Tibetan Plateau using multi‐mission satellite data. Remote Sensing of Environment, 135, 25–35. [Google Scholar]
  48. Song, C. , Huang, B. , Richards, K. , Ke, L. , & Phan, V. H. (2014). Accelerated lake expansion on the Tibetan Plateau in the 2000s: Induced by glacial melting or other processes? Water Resources Research, 50, 3170–3186. 10.1002/2013WR014724 [DOI] [Google Scholar]
  49. Syed, T. H. , Famiglietti, J. S. , Chambers, D. P. , Willis, J. K. , & Hilburn, K. (2010). Satellite‐based global‐ocean mass balance estimates of interannual variability and emerging trends in continental freshwater discharge. Proceedings of the National Academy of Sciences, 107(42), 17916–17921. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. van Dijk, A. I. J. M. , Beck, H. E. , Crosbie, R. S. , de Jeu, R. A. M. , Liu, Y. Y. , Podger, G. M. , Timbal, B. , & Viney, N. R. (2013). The millennium drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resources Research, 49, 1040–1057. 10.1002/wrcr.20123 [DOI] [Google Scholar]
  51. Wada, Y. , Reager, J. T. , Chao, B. F. , Wang, J. , Lo, M.‐H. , Song, C. , Li, Y. , & Gardner, A. S. (2017). Recent changes in land water storage and its contribution to sea level variations. Surveys in Geophysics, 38(1), 131–152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Wada, Y. , van Beek, L. P. , Sperna Weiland, F. C. , Chao, B. F. , Wu, Y. H. , & Bierkens, M. F. (2012). Past and future contribution of global groundwater depletion to sea‐level rise. Geophysical Research Letters, 39, L09402 10.1029/2012GL051230 [DOI] [Google Scholar]
  53. Wada, Y. , van Beek, L. P. H. , Wanders, N. , & Bierkens, M. F. P. (2013). Human water consumption intensifies hydrological drought worldwide. Environmental Research Letters, 8(3), 034036. [Google Scholar]
  54. Wang, J. , Sheng, Y. , & Tong, T. S. D. (2014). Monitoring decadal lake dynamics across the Yangtze Basin downstream of Three Gorges Dam. Remote Sensing of Environment, 152, 251–269. [Google Scholar]
  55. Wang, J. , Song, C. , Reager, J. T. , Yao, F. , Famiglietti, J. S. , Sheng, Y. , MacDonald, G. M. , Brun, F. , Schmied, H. M. , Marston, R. A. , & Wada, Y. (2018). Recent global decline in endorheic basin water storages. Nature Geoscience, 11(12), 926. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Wang, K. , Dickinson, R. E. , Wild, M. , & Liang, S. (2010). Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development & Yoshihide Wada. Journal Geophysical Research, 115, D20113 10.1029/2010JD013847 [DOI] [Google Scholar]
  57. Wang, L. , Liang, T. , Zhong, B. , Li, K. , Zhang, Q. , & Zhang, C. (2014). Study on nitrogen dynamics at the sediment‐water interface of Dongting Lake, China. Aquatic Geochemistry, 20(5), 501–517. [Google Scholar]
  58. Wang, W. , Xiao, W. , Cao, C. , Gao, Z. , Hu, Z. , Liu, S. , Shen, S. , Wang, L. , Xiao, Q. , & Xu, J. (2014). Temporal and spatial variations in radiation and energy balance across a large freshwater lake in China. Journal of Hydrodynamics, 511, 811–824. [Google Scholar]
  59. Weiß, M. , & Menzel, L. (2008). A global comparison of four potential evapotranspiration equations and their relevance to stream flow modelling in semi‐arid environments. Advances in Geosciences, 18, 15–23. [Google Scholar]
  60. Xiao, M. , Koppa, A. , Mekonnen, Z. , Pagán, B. R. , Zhan, S. , Cao, Q. , Aierken, A. , Lee, H. , & Lettenmaier, D. P. (2017). How much groundwater did California's Central Valley lose during the 2012–2016 drought? Geophysical Research Letters, 44, 4872–4879. 10.1002/2017GL073333 [DOI] [Google Scholar]
  61. Xu, Z. X. , Gong, T. L. , & Li, J. Y. (2008). Decadal trend of climate in the Tibetan Plateau—Regional temperature and precipitation. Hydrological Processes, 22(16), 3056–3065. [Google Scholar]
  62. Yao, F. , Wang, J. , Yang, K. , Wang, C. , Walter, B. A. , & Crétaux, J.‐F. (2018). Lake storage variation on the endorheic Tibetan Plateau and its attribution to climate change since the new millennium. Environmental Research Letters, 13(6), 064011. [Google Scholar]
  63. Yao, Y. , Liang, S. , Qin, Q. , Wang, K. , Liu, S. , & Zhao, S. (2012). Satellite detection of increases in global land surface evapotranspiration during 1984–2007. International Journal of Digital Earth, 5(4), 299–318. [Google Scholar]
  64. Yoshikawa, K. , & Hinzman, L. D. (2003). Shrinking thermokarst ponds and groundwater dynamics in discontinuous permafrost near council, Alaska. Permafrost and Periglacial Processes, 14(2), 151–160. [Google Scholar]
  65. Zhang, G. , Yao, T. , Shum, C. K. , Yi, S. , Yang, K. , Xie, H. , Feng, W. , Bolch, T. , Wang, L. , Behrangi, A. , Zhang, H. , Wang, W. , Xiang, Y. , & Yu, J. (2017). Lake volume and groundwater storage variations in Tibetan Plateau's endorheic basin. Geophysical Research Letters, 44, 5550–5560. 10.1002/2017GL073773 [DOI] [Google Scholar]
  66. Zhang, K. , Kimball, J. S. , Nemani, R. R. , & Running, S. W. (2010). A continuous satellite‐derived global record of land surface evapotranspiration from 1983 to 2006. Water Resources Research, 46, W09522 10.1029/2009WR008800 [DOI] [Google Scholar]
  67. Zhang, Y. , Pena‐Arancibia, J. , McVicar, T. , Chiew, F. , Vaze, J. , Zheng, H. , & Wang, Y. (2016). Monthly global observation‐driven Penman‐Monteith‐Leuning (PML) evapotranspiration and components. v2. CSIRO. Data Collection.

Supporting Information References

  1. Abtew, W. (2001). Evaporation estimation for Lake Okeechobee in south Florida. Journal of Irrigation and Drainage Engineering, 127(3), 140–147. [Google Scholar]
  2. Abtew, W. , & Melesse, A. (2013). Lake Evaporation In Evaporation and evapotranspiration: Measurements and estimations (pp. 109–132). Netherlands, Dordrecht: Springer. [Google Scholar]
  3. Abtew, W. , Obeysekera, J. , & Iricanin, N. (2011). Pan evaporation and potential evapotranspiration trends in South Florida. Hydrological Processes, 25(6), 958–969. [Google Scholar]
  4. Abtew, W. , Obeysekera, J. , Irizarry‐Ortiz, M. , Lyons, D. , & Reardon, A. (2003). Evapotranspiration estimation for south Florida. Paper presented at World Water & Environmental Resources Congress 2003.
  5. Adame, M. F. , Alcocer, J. , & Escobar, E. (2008). Size‐fractionated phytoplankton biomass and its implications for the dynamics of an oligotrophic tropical lake. Freshwater Biology, 53(1), 22–31. [Google Scholar]
  6. Alcocer, J. , & Filonov, A. E. (2007). A note on the effects of an individual large rainfall event on saline Lake Alchichica, Mexico. Environmental Geology, 53(4), 777–783. [Google Scholar]
  7. Alemayehu, T. , Ayenew, T. , & Kebede, S. (2006). Hydrogeochemical and lake level changes in the Ethiopian Rift. Journal of Hydrodynamics, 316(1), 290–300. [Google Scholar]
  8. Alemayehu, T. , McCartney, M. , & Kebede, S. (2010). The water resource implications of planned development in the Lake Tana catchment, Ethiopia. Ecohydrology & Hydrobiology, 10(2), 211–221. [Google Scholar]
  9. Al‐Farraj, A. (2008). Desert pavement development on the lake shorelines of Lake Eyre (South), South Australia. Geomorphology, 100(1), 154–163. [Google Scholar]
  10. Ali, H. , Madramootoo, C. , & Abdel Gwad, S. (2001). Evaporation model of Lake Qaroun as influenced by lake salinity. Irrigation and Drainage, 50(1), 9–17. [Google Scholar]
  11. Alvarez, V. M. , González‐Real, M. , Baille, A. , Valero, J. M. , & Elvira, B. G. (2008). Regional assessment of evaporation from agricultural irrigation reservoirs in a semiarid climate. Agricultural Water Management, 95(9), 1056–1066. [Google Scholar]
  12. Andersen, M. , & Jobson, H. (1982). Comparison of techniques for estimating annual lake evaporation using climatological data. Water Resources Research, 18, 630–636. 10.1029/WR018i003p00630 [DOI] [Google Scholar]
  13. Antonopoulos, V. Z. , Gianniou, S. K. , & Antonopoulos, A. V. (2016). Artificial neural networks and empirical equations to estimate daily evaporation: application to lake Vegoritis, Greece. Hydrological Sciences Journal, 61(14), 2590–2599. [Google Scholar]
  14. Ayenew, T. (2002). Recent changes in the level of Lake Abiyata, central main Ethiopian Rift. Hydrological Sciences Journal, 47(3), 493–503. [Google Scholar]
  15. Bastawesy, M. A. , Khalaf, F. I. , & Arafat, S. M. (2008). The use of remote sensing and GIS for the estimation of water loss from Tushka lakes, southwestern desert, Egypt. Journal of African Earth Sciences, 52(3), 73–80. [Google Scholar]
  16. Bek, M. , Lowndes, I. , & Hargreaves, D. (2010). The application of a validated hydrodynamic model to improve the water management of an Egyptian shallow water coastal lake. International Congress on Environmental Modelling and Software, 466.
  17. Benduhn, F. , & Renard, P. (2004). A dynamic model of the Aral Sea water and salt balance. Journal of Marine Systems, 47(1), 35–50. [Google Scholar]
  18. Bennington, V. , McKinley, G. A. , Kimura, N. , & Wu, C. H. (2010). General circulation of Lake Superior: Mean, variability, and trends from 1979 to 2006. Journal of Geophysical Research, 115, C12015 10.1029/2010JC006261 [DOI] [Google Scholar]
  19. Berry, W. , & Stichling, W. (1954). Evaporation from lakes and reservoirs in the northern plains region of North America. IUGG General Assembly at Rome, IASH, 38, 121–134. [Google Scholar]
  20. Blanken, P. D. , Rouse, W. R. , Culf, A. D. , Spence, C. , Boudreau, L. D. , Jasper, J. N. , Kochtubajda, B. , Schertzer, W. M. , Marsh, P. , & Verseghy, D. (2000). Eddy covariance measurements of evaporation from Great Slave lake, Northwest Territories, Canada. Water Resources Research, 36, 1069–1077. 10.1029/1999WR900338 [DOI] [Google Scholar]
  21. Bonnet, M.‐P. , Barroux, G. , Martinez, J.‐M. , Seyler, F. , Moreira‐Turcq, P. , Cochonneau, G. , Melack, J. , Boaventura, G. , Maurice‐Bourgoin, L. , & León, J. (2008). Floodplain hydrology in an Amazon floodplain lake (Lago Grande de Curuaí). Journal of Hydrodynamics, 349(1), 18–30. [Google Scholar]
  22. Bowman, D. , Korjenkov, A. , & Porat, N. (2004). Late‐Pleistocene seismites from Lake Issyk‐Kul, the Tien Shan range, Kyrghyzstan. Sedimentary Geology, 163(3), 211–228. [Google Scholar]
  23. Bureau, U. W. (1959). Evaporation maps for the United States. TechnicalPaper, 37, 13. [Google Scholar]
  24. Chen, F. , Huang, X. , Zhang, J. , Holmes, J. , & Chen, J. (2006). Humid little ice age in arid central Asia documented by Bosten Lake, Xinjiang, China. Science in China Series D: Earth Sciences, 49(12), 1280–1290. [Google Scholar]
  25. Chen, M. , Rowland, J. C. , Wilson, C. J. , Altmann, G. L. , & Brumby, S. P. (2014). Temporal and spatial pattern of thermokarst lake area changes at Yukon Flats, Alaska. Hydrological Processes, 28(3), 837–852. [Google Scholar]
  26. Cyrus, D. , Vivier, L. , & Jerling, H. (2010). Effect of hypersaline and low lake conditions on ecological functioning of St Lucia estuarine system, South Africa: An overview 2002–2008. Estuarine, Coastal and Shelf Science, 86(4), 535–542. [Google Scholar]
  27. Delclaux, F. , & Coudrain, A. (2005). Optimal evaporation models for simulation of large lake levels: Application to Lake Titicaca, South America. paper presented at Geophys. Res. Abstr.
  28. Delclaux, F. , Coudrain, A. , & Condom, T. (2007). Evaporation estimation on Lake Titicaca: A synthesis review and modelling. Hydrological Processes, 21(13), 1664–1677. [Google Scholar]
  29. Deus, D. , & Gloaguen, R. (2013). Remote sensing analysis of lake dynamics in semi‐arid regions: Implication for water resource management. Lake Manyara, East African Rift, Northern Tanzania. Water, 5(2), 698–727. [Google Scholar]
  30. Duan, Y. , Wu, Y. , Cao, X. , Zhao, Y. , & Ma, L. (2014). Hydrogen isotope ratios of individual n‐alkanes in plants from Gannan Gahai Lake (China) and surrounding area. Organic Geochemistry, 77, 96–105. [Google Scholar]
  31. Elsawwaf, M. , & Willems, P. (2012). Analysis of the climate variability on Lake Nasser evaporation based on the Bowen ratio energy budget method. Journal of Environmental Biology, 33, 475–485. [PubMed] [Google Scholar]
  32. Elsawwaf, M. , Willems, P. , & Feyen, J. (2010). Assessment of the sensitivity and prediction uncertainty of evaporation models applied to Nasser Lake, Egypt. Journal of Hydrodynamics, 395(1), 10–22. [Google Scholar]
  33. Erol Keskin, M. , & Terzi, Ö. (2006). Evaporation estimation models for lake Eğirdir, Turkey. Hydrological Processes, 20(11), 2381–2391. [Google Scholar]
  34. Fedorov, A. , Gavriliev, P. , Konstantinov, P. , Hiyama, T. , Iijima, Y. , & Iwahana, G. (2014). Estimating the water balance of a thermokarst lake in the middle of the Lena River basin, eastern Siberia. Ecohydrology, 7(2), 188–196. [Google Scholar]
  35. Finch, J. (2001). A comparison between measured and modelled open water evaporation from a reservoir in south‐east England. Hydrological Processes, 15(14), 2771–2778. [Google Scholar]
  36. Fu, G. , Liu, C. , Chen, S. , & Hong, J. (2004). Investigating the conversion coefficients for free water surface evaporation of different evaporation pans. Hydrological Processes, 18(12), 2247–2262. [Google Scholar]
  37. Gebeyehu, E. (2017). Impact of climate change on Lake Chamo Water Balance, Ethiopia. International Journal of Water Resources and Environmental Engineering, 9(4), 86–95. [Google Scholar]
  38. Gebreegziabher, Y . (2005). Assessment of the water balance of Lake Awassa catchment, Ethiopia (Master's thesis). Retrieved from https://www.itc.nl/library/papers_2005/msc/wrem/yemane.pdf
  39. Gianniou, S. K. , & Antonopoulos, V. Z. (2007). Evaporation and energy budget in Lake Vegoritis, Greece. Journal of Hydrodynamics, 345(3), 212–223. [Google Scholar]
  40. Gibson, J. (2002). Short‐term evaporation and water budget comparisons in shallow Arctic lakes using non‐steady isotope mass balance. Journal of Hydrodynamics, 264(1), 242–261. [Google Scholar]
  41. Gibson, J. , Prowse, T. , & Peters, D. (2006). Hydroclimatic controls on water balance and water level variability in Great Slave Lake. Hydrological Processes, 20(19), 4155–4172. [Google Scholar]
  42. Goel, A. (2009). ANN based modeling for prediction of evaporation in reservoirs (Research Note). International Journal of Engineering, Transactions A: Basics, 22(4), 351. [Google Scholar]
  43. Guo, H. , Hu, Q. , & Jiang, T. (2008). Annual and seasonal streamflow responses to climate and land‐cover changes in the Poyang Lake basin, China. Journal of Hydrodynamics, 355(1), 106–122. [Google Scholar]
  44. Haginoya, S. , Fujii, H. , Kuwagata, T. , Xu, J. , Ishigooka, Y. , Kang, S. , & Zhang, Y. (2009). Air‐lake interaction features found in heat and water exchanges over Nam Co on the Tibetan Plateau. Sola, 5, 172–175. [Google Scholar]
  45. Helfer, F. , Lemckert, C. , & Zhang, H. (2012). Impacts of climate change on temperature and evaporation from a large reservoir in Australia. Journal of Hydrodynamics, 475, 365–378. [Google Scholar]
  46. Hostetler, S. (2009). Use of models and observations to assess trends in the 1950–2005 water balance and climate of Upper Klamath Lake, Oregon. Water Resources Research, 45, W12409 10.1029/2008WR007295 [DOI] [Google Scholar]
  47. Jakimavičius, D. , Kriaučiūnienė, J. , Gailiušis, B. , & Šarauskienė, D. (2013). Assessment of uncertainty in estimating the evaporation from the Curonian Lagoon. Baltica, 26(2), 177–186. [Google Scholar]
  48. Jin, Z. , You, C.‐F. , Wang, Y. , & Shi, Y. (2010). Hydrological and solute budgets of Lake Qinghai, the largest lake on the Tibetan Plateau. Quaternary International, 218(1), 151–156. [Google Scholar]
  49. Kebede, S. , Travi, Y. , Alemayehu, T. , & Marc, V. (2006). Water balance of Lake Tana and its sensitivity to fluctuations in rainfall, Blue Nile basin, Ethiopia. Journal of Hydrodynamics, 316(1), 233–247. [Google Scholar]
  50. Lamon, E. , & Stow, C. (2010). Lake superior water level fluctuation and climatic factors: A dynamic linear model analysis. Journal of Great Lakes Research, 36(1), 172–178. [Google Scholar]
  51. Lensky, N. , Dvorkin, Y. , Lyakhovsky, V. , Gertman, I. , & Gavrieli, I. (2005). Water, salt, and energy balances of the Dead Sea. Water Resources Research, 41, W12418 10.1029/2005WR004084 [DOI] [Google Scholar]
  52. Lenters, J. D. , Kratz, T. K. , & Bowser, C. J. (2005). Effects of climate variability on lake evaporation: Results from a long‐term energy budget study of Sparkling Lake, northern Wisconsin (USA). Journal of Hydrodynamics, 308(1), 168–195. [Google Scholar]
  53. Li, B. , Zhang, J. , Yu, Z. , Liang, Z. , Chen, L. , & Acharya, K. (2017). Climate change driven water budget dynamics of a Tibetan inland lake. Global and Planetary Change, 150, 70–80. [Google Scholar]
  54. Li, X.‐Y. , Xu, H.‐Y. , Sun, Y.‐L. , Zhang, D.‐S. , & Yang, Z.‐P. (2007). Lake‐level change and water balance analysis at Lake Qinghai, west China during recent decades. Water Resources Management, 21(9), 1505. [Google Scholar]
  55. Liu, J. , Wang, S. , Yu, S. , Yang, D. , & Zhang, L. (2009). Climate warming and growth of high‐elevation inland lakes on the Tibetan Plateau. Global and Planetary Change, 67(3), 209–217. [Google Scholar]
  56. Liu, X. , Yu, J. , Wang, P. , Zhang, Y. , & Du, C. (2016). Lake evaporation in a hyper‐arid environment, Northwest of China—Measurement and estimation. Water, 8(11), 527. [Google Scholar]
  57. Loáiciga, H. A. (2002). Reservoir design and operation with variable lake hydrology. Journal of Water Resources Planning and Management, 128(6), 399–405. [Google Scholar]
  58. Lyons, R. P. , Kroll, C. N. , & Scholz, C. A. (2011). An energy‐balance hydrologic model for the Lake Malawi Rift basin, East Africa. Global and Planetary Change, 75(1), 83–97. [Google Scholar]
  59. Ma, L. , Lowenstein, T. K. , & Russell, J. M. (2011). A brine evolution model and mineralogy of chemical sediments in a volcanic crater, Lake Kitagata, Uganda. Aquatic Geochemistry, 17(2), 129–140. [Google Scholar]
  60. Ma, M. , Wang, X. , Veroustraete, F. , & Dong, L. (2007). Change in area of Ebinur Lake during the 1998–2005 period. International Journal of Remote Sensing, 28(24), 5523–5533. [Google Scholar]
  61. Ma, N. , Szilagyi, J. , Niu, G.‐Y. , Zhang, Y. , Zhang, T. , Wang, B. , & Wu, Y. (2016). Evaporation variability of Nam Co Lake in the Tibetan Plateau and its role in recent rapid lake expansion. Journal of Hydrodynamics, 537, 27–35. [Google Scholar]
  62. Mahowald, N. M. , Bryant, R. G. , del Corral, J. , & Steinberger, L. (2003). Ephemeral lakes and desert dust sources. Geophysical Research Letters, 30(2), 1074 10.1029/2002GL016041 [DOI] [Google Scholar]
  63. Mažeika, J. , Taminskas, J. , Paškauskas, R. , Bodoyan, A. , Baghdassaryan, H. , Tozalakyan, P. , Davtyan, V. , Grillot, J.‐C. , & Travi, Y. (2006). Ecohydrological evolution in the catchment of Lake Drūkšiai, Lithuania, under anthropogenic pressure. Ekologija, 4, 40–50. [Google Scholar]
  64. Mohammed, I. N. , & Tarboton, D. G. (2012). An examination of the sensitivity of the Great Salt Lake to changes in inputs. Water Resources Research, 48, W11511 10.1029/2012WR011908 [DOI] [Google Scholar]
  65. Momii, K. , & Ito, Y. (2008). Heat budget estimates for lake Ikeda, Japan. Journal of Hydrodynamics, 361(3), 362–370. [Google Scholar]
  66. Morrill, C. (2004). The influence of Asian summer monsoon variability on the water balance of a Tibetan lake. Journal of Paleolimnology, 32(3), 273–286. [Google Scholar]
  67. Morton, F. I. (1979). Climatological estimates of lake evaporation. Water Resources Research, 15, 64–76. 10.1029/WR015i001p00064 [DOI] [Google Scholar]
  68. Morton, F. I. (1986). Practical estimates of lake evaporation. Journal of Climate and Applied Meteorology, 25(3), 371–387. [Google Scholar]
  69. Ngatcha, B. N. (2009). Water resources protection in the Lake Chad Basin in the changing environment. European water, 25(26), 3–12. [Google Scholar]
  70. Nicholls, J. F. , & Toumi, R. (2014). On the lake effects of the Caspian Sea. Quarterly Journal of the Royal Meteorological Society, 140(681), 1399–1408. [Google Scholar]
  71. Nicholson, S. E. , & Yin, X. (2001). Rainfall conditions in equatorial East Africa during the nineteenth century as inferred from the record of Lake Victoria. Climatic Change, 48(2), 387–398. [Google Scholar]
  72. Nicholson, S. E. , Yin, X. , & Ba, M. B. (2000). On the feasibility of using a lake water balance model to infer rainfall: an example from Lake Victoria. Hydrological Sciences Journal, 45(1), 75–95. [Google Scholar]
  73. Nováky, B. (2008). Climate change impact on water balance of Lake Balaton. Water Science and Technology, 58(9), 1865–1869. [DOI] [PubMed] [Google Scholar]
  74. Piper, B. , Plinston, D. , & Sutcliffe, J. (1986). The water balance of Lake Victoria. Hydrological Sciences Journal, 31(1), 25–37. [Google Scholar]
  75. Raini, J. A. (2009). Impact of land use changes on water resources and biodiversity of Lake Nakuru catchment basin, Kenya. African Journal of Ecology, 47(s1), 39–45. [Google Scholar]
  76. Redmond, K. T. (2007). Evaporation and the hydrologic budget of Crater Lake, Oregon. Long‐term Limnological Research and Monitoring at Crater Lake, Oregon, 29–46.
  77. Rientjes, T. , Perera, B. , Haile, A. , Reggiani, P. , & Muthuwatta, L. (2011). Regionalisation for lake level simulation‐the case of Lake Tana in the Upper Blue Nile, Ethiopia. Hydrology and Earth System Sciences, 15(4), 1167. [Google Scholar]
  78. Rimmer, A. , Givati, A. , Samuels, R. , & Alpert, P. (2011). Using ensemble of climate models to evaluate future water and solutes budgets in Lake Kinneret, Israel. Journal of Hydrodynamics, 410(3), 248–259. [Google Scholar]
  79. Rimmer, A. , Samuels, R. , & Lechinsky, Y. (2009). A comprehensive study across methods and time scales to estimate surface fluxes from Lake Kinneret, Israel. Journal of Hydrodynamics, 379(1), 181–192. [Google Scholar]
  80. Rouse, W. , Blyth, E. , Crawford, R. , Gyakum, J. , Janowicz, J. , Kochtubajda, B. , Leighton, H. , Marsh, P. , Martz, L. , & Pietroniro, A. (2003). Energy and water cycles in a high‐latitude, north‐flowing river system: Summary of results from the Mackenzie GEWEX Study—Phase 1. Bulletin of the American Meteorological Society, 84(1), 73–87. [Google Scholar]
  81. Sahoo, G. , Schladow, S. , Reuter, J. , Coats, R. , Dettinger, M. , Riverson, J. , Wolfe, B. , & Costa‐Cabral, M. (2013). The response of Lake Tahoe to climate change. Climatic Change, 116(1), 71–95. [Google Scholar]
  82. Salazar, L. , & Poveda, G. (2006). Validation of diverse evapotranspiration estimation methods using the long‐term water balance in the Amazon River Basin. Proceesings of 8 ICSHMO, 815–820.
  83. Sellinger, C. E. , Stow, C. A. , Lamon, E. C. , & Qian, S. S. (2007). Recent water level declines in the Lake Michigan‐Huron System. Environmental Science & Technology, 42(2), 367–373. [DOI] [PubMed] [Google Scholar]
  84. Setegn, S. G. , Chowdary, V. , Mal, B. , Yohannes, F. , & Kono, Y. (2011). Water balance study and irrigation strategies for sustainable management of a tropical Ethiopian lake: A case study of Lake Alemaya. Water Resources Management, 25(9), 2081–2107. [Google Scholar]
  85. Shafir, H. , & Alpert, P. (2011). Regional and local climatic effects on the Dead‐Sea evaporation. Climatic Change, 105(3–4), 455–468. [Google Scholar]
  86. Shanahan, T. M. , Overpeck, J. T. , Sharp, W. , Scholz, C. A. , & Arko, J. A. (2007). Simulating the response of a closed‐basin lake to recent climate changes in tropical West Africa (Lake Bosumtwi, Ghana). Hydrological Processes, 21(13), 1678–1691. [Google Scholar]
  87. Shao, Z. , Meng, X. , Zhu, D. , Zheng, D. , Qiao, Z. , Yang, C. , Yu, J. , Meng, Q. , & Lü, R. (2008). Characteristics of the change of major lakes on the Qinghai‐Tibet Plateau in the last 25 years. Frontiers of Earth Science in China, 2(3), 364–377. [Google Scholar]
  88. Shilo, E. , Ziv, B. , Shamir, E. , & Rimmer, A. (2015). Evaporation from Lake Kinneret, Israel, during hot summer days. Journal of Hydrodynamics, 528, 264–275. [Google Scholar]
  89. Sima, S. , Ahmadalipour, A. , & Tajrishy, M. (2013). Mapping surface temperature in a hyper‐saline lake and investigating the effect of temperature distribution on the lake evaporation. Remote Sensing of Environment, 136, 374–385. [Google Scholar]
  90. Small, E. E. , Giorgi, F. , Sloan, L. C. , & Hostetler, S. (2001). The effects of desiccation and climatic change on the hydrology of the Aral Sea. Journal of Climate, 14(3), 300–322. [Google Scholar]
  91. Spence, C. , Blanken, P. , Lenters, J. , & Hedstrom, N. (2013). The importance of spring and autumn atmospheric conditions for the evaporation regime of Lake Superior. Journal of Hydrometeorology, 14(5), 1647–1658. [Google Scholar]
  92. Sugita, M. , Ikura, H. , Miyano, A. , Yamamoto, K. , & Zhongwang, W. (2014). Evaporation from Lake Kasumigaura: Annual totals and variability in time and space. Hydrological Research Letters, 8(3), 103–107. [Google Scholar]
  93. Sumner, D. , & Belaineh, G. (2005). Evaporation, precipitation, and associated salinity changes at a humid, subtropical estuary. Estuaries and Coasts, 28(6), 844–855. [Google Scholar]
  94. Swenson, S. , & Wahr, J. (2009). Monitoring the water balance of Lake Victoria, East Africa, from space. Journal of Hydrodynamics, 370(1), 163–176. [Google Scholar]
  95. Tasumi, M. (2005). A review of evaporation research on Japanese lakes. In Impacts of Global Climate Change, edited, pp. 1–10.
  96. Tian, L. , Liu, Z. , Gong, T. , Yin, C. , Yu, W. , & Yao, T. (2008). Isotopic variation in the lake water balance at the Yamdruktso basin, southern Tibetan Plateau. Hydrological Processes, 22(17), 3386–3392. [Google Scholar]
  97. Troin, M. , Vallet‐Coulomb, C. , Sylvestre, F. , & Piovano, E. (2010). Hydrological modelling of a closed lake (Laguna Mar Chiquita, Argentina) in the context of 20th century climatic changes. Journal of Hydrodynamics, 393(3), 233–244. [Google Scholar]
  98. Ufoegbune, G. , Yusuf H., Eruola A., and Awomeso J. (2011). Estimation of water balance of Oyan Lake in the North West Region of Abeokuta, Nigeria.
  99. Vallet‐Coulomb, C. , Legesse, D. , Gasse, F. , Travi, Y. , & Chernet, T. (2001). Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). Journal of Hydrodynamics, 245(1), 1–18. [Google Scholar]
  100. Van Afferden, M. , & Hansen, A. M. (2004). Forecast of lake volume and salt concentration in Lake Chapala, Mexico. Aquatic Sciences‐Research Across Boundaries, 66(3), 257–265. [Google Scholar]
  101. van de Giesen, N. , Andreini, M. , van Edig, A. , & Vlek, P. (2001). Competition for water resources of the Volta basin. IAHS Publication, 199–206. [Google Scholar]
  102. Verburg, P. , & Hecky, R. E. (2003). Wind patterns, evaporation, and related physical variables in Lake Tanganyika, East Africa. Journal of Great Lakes Research, 29, 48–61. [Google Scholar]
  103. Vining, K. C. (2003). Estimation of Monthly Evaporation from Lake Ashtabula in North Dakota, Orwell Lake in Minnesota, and Lake Traverse in Minnesota and South Dakota, 1931–‐2001. Report No. 2003–4282. United States Geological Survey.
  104. Vyruchalkina, T. Y. (2004). Lake Baikal and the Angara River before and after the construction of reservoirs. Water Resources, 31(5), 483–489. [Google Scholar]
  105. Wale, A. , Rientjes, T. , Gieske, A. , & Getachew, H. (2009). Ungauged catchment contributions to Lake Tana's water balance. Hydrological Processes, 23(26), 3682–3693. [Google Scholar]
  106. Watras, C. , Read, J. , Holman, K. , Liu, Z. , Song, Y. Y. , Watras, A. , Morgan, S. , & Stanley, E. (2014). Decadal oscillation of lakes and aquifers in the upper Great Lakes region of North America: Hydroclimatic implications. Geophysical Research Letters, 41, 456–462. 10.1002/2013GL058679 [DOI] [Google Scholar]
  107. Weisse, R. , & Oestreicher, R. (2001). Reconstruction of potential evaporation for water balance studies. Climate Research, 16(2), 123–131. [Google Scholar]
  108. Wen, R. , Xiao, J. , Chang, Z. , Zhai, D. , Xu, Q. , Li, Y. , & Itoh, S. (2010). Holocene precipitation and temperature variations in the East Asian monsoonal margin from pollen data from Hulun Lake in northeastern Inner Mongolia, China. Boreas, 39(2), 262–272. [Google Scholar]
  109. Westenburg, C. L. , DeMeo, G. A. , & Tanko, D. J. (2006). Evaporation from Lake Mead, Arizona and Nevada, 1997‐99. Scientific Investigations Report 2006–5252. United States Geological Survey.
  110. Winter, T. C. , Buso, D. C. , Rosenberry, D. O. , Likens, G. E. , Sturrock, A. Jr. , & Mau, D. P. (2003). Evaporation determined by the energy‐budget method for Mirror Lake, New Hampshire. Limnology and Oceanography, 48(3), 995–1009. [Google Scholar]
  111. Wurbs, R. A. , & Ayala, R. A. (2014). Reservoir evaporation in Texas, USA. Journal of Hydrodynamics, 510, 1–9. [Google Scholar]
  112. Xue, J. , & Zhong, W. (2011). Holocene climate variation denoted by Barkol Lake sediments in northeastern Xinjiang and its possible linkage to the high and low latitude climates. Science China Earth Sciences, 54(4), 603–614. [Google Scholar]
  113. Yang, H. , Shen, Z. , Zhu, S. , & Wang, W. (2008). Heavy metals in wetland plants and soil of Lake Taihu, China. Environmental Toxicology and Chemistry, 27(1), 38–42. [DOI] [PubMed] [Google Scholar]
  114. Yao, H.‐X. (2009). Long‐Term study of lake evaporation and evaluation of seven estimation methods: results from Dickie Lake, South‐Central Ontario, Canada. Journal of Environmental Protection, 1(01), 1. [Google Scholar]
  115. Ye, Q. , Yao, T. , Chen, F. , Kang, S. , Zhang, X. , & Wang, Y. (2008). Response of glacier and lake covariations to climate change in Mapam Yumco basin on Tibetan Plateau during 1974–2003. Journal of China University of Geosciences, 19(2), 135–145. [Google Scholar]
  116. Yihdego, Y. , Webb, J. , & Leahy, P. (2016). Modelling water and salt balances in a deep, groundwater‐throughflow lake—Lake Purrumbete, southeastern Australia. Hydrological Sciences Journal, 61(1), 186–199. [Google Scholar]
  117. Yin, X. , Nicholson, S. E. , & Ba, M. B. (2000). On the diurnal cycle of cloudiness over Lake Victoria and its influence on evaporation from the lake. Hydrological Sciences Journal, 45(3), 407–424. [Google Scholar]
  118. Yu, S. , Liu, J. , Xu, J. , & Wang, H. (2011). Evaporation and energy balance estimates over a large inland lake in the Tibet‐Himalaya. Environment and Earth Science, 64(4), 1169–1176. [Google Scholar]
  119. Zacharias, I. , Dimitriou, E. , & Koussouris, T. (2003). Estimating groundwater discharge into a lake through underwater springs by using GIS technologies. Environmental Geology, 44(7), 843–851. [Google Scholar]
  120. Zhang, B. , Wu, Y. , Zhu, L. , Wang, J. , Li, J. , & Chen, D. (2011). Estimation and trend detection of water storage at Nam Co Lake, central Tibetan Plateau. Journal of Hydrodynamics, 405(1), 161–170. [Google Scholar]
  121. Zhang, Q. , Li, L. , Wang, Y. G. , Werner, A. D. , Xin, P. , Jiang, T. , & Barry, D. A. (2012). Has the Three‐Gorges Dam made the Poyang Lake wetlands wetter and drier? Geophysical Research Letters, 39, L20402 10.1029/2012GL053431 [DOI] [Google Scholar]
  122. Zhao, C. , Yu, Z. , Zhao, Y. , & Ito, E. (2009). Possible orographic and solar controls of Late Holocene centennial‐scale moisture oscillations in the northeastern Tibetan Plateau. Geophysical Research Letters, 36, L21705 10.1029/2009GL040951 [DOI] [Google Scholar]
  123. Zhou, J. , Liu, Y. , Guo, H. , & He, D. (2014). Combining the SWAT model with sequential uncertainty fitting algorithm for streamflow prediction and uncertainty analysis for the Lake Dianchi Basin, China. Hydrological Processes, 28(3), 521–533. [Google Scholar]
  124. Zhou, J. , Wang, L. , Zhang, Y. , Guo, Y. , & He, D. (2016). Spatiotemporal variations of actual evapotranspiration over the Lake Selin Co and surrounding small lakes (Tibetan Plateau) during 2003–2012. Science China Earth Sciences, 59(12), 2441–2453. [Google Scholar]
  125. Zhou, J. , Wang, L. , Zhang, Y. , Guo, Y. , Li, X. , & Liu, W. (2015). Exploring the water storage changes in the largest lake (Selin Co) over the Tibetan Plateau during 2003–2012 from a basin‐wide hydrological modeling. Water Resources Research, 51, 8060–8086. 10.1002/2014WR015846 [DOI] [Google Scholar]
  126. Zuo, Q. , Dou, M. , Chen, X. , & Zhou, K. (2006). Physically‐based model for studying the salinization of Bosten Lake in China. Hydrological Sciences Journal, 51(3), 432–449. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information S1

Click here for additional data file. (119.2KB, docx)

Articles from Earth's Future are provided here courtesy of Wiley

RESOURCES