Global Land Evaporation Amsterdam Model Research Articles

Study on the Spatiotemporal Evolution of Evapotranspiration and the Integration of Multi-Source Data in the Water Source Area of the Middle Route of the South-to-North Water Transfer Project

This study takes the Danjiangkou reservoir basin, which is the water source area of the South-to-North Water Diversion Project, one of the largest water diversion projects in the world, as the research area. Three different types of evapotranspiration (ET) datasets are adopted, including the Global Land Evaporation Amsterdam Model (GLEAM), European Centre for Medium-Range Weather Forecasts ERA5—Land Component (ERA5Land), and Complementary Relationship (CR) datasets. These datasets are analyzed for spatiotemporal evolution and data fusion using Mann–Kendall analysis, Sen’s Slope analysis, and Extended Triple Collocation (ETC). The aim is to improve the accuracy of evapotranspiration estimation in the watershed of the water source area. The results show the following: (1) All three sets of evapotranspiration data indicate an increasing trend in the watershed, with rates of 0.78 mm/year, 0.14 mm/year, and 2.56 mm/year, respectively. Additionally, the seasonal variation in evapotranspiration is significant, with the rate of change being summer > spring > autumn > winter. (2) The data fusion results indicate that ERA5Land performs best in the water source area watershed, with the smallest root mean square error (RMSE) value. In the fused data, ERA5Land’s evapotranspiration data account for the largest proportion at 59.93%, GLEAM ET data account for 39.96%, and CR’s evapotranspiration data account for the smallest proportion at only 0.11%. (3) The spatial distribution shows that the fused data fully exploits the advantages of different evapotranspiration data, inherits the advantages of ERA5Land and GLEAM ET products, and achieves effective fusion of multi-source data, thereby forming a more accurate dataset. These research findings provide scientific references for the construction of digital twin watersheds, intelligent water resource allocation, and effective responses to climate change in the water source area of the South-to-North Water Diversion Project.

GLEAM4: global land evaporation and soil moisture dataset at 0.1° resolution from 1980 to near present

Terrestrial evaporation plays a crucial role in modulating climate and water resources. Here, we present a continuous, daily dataset covering 1980–2023 with a 0.1°spatial resolution, produced using the fourth generation of the Global Land Evaporation Amsterdam Model (GLEAM). GLEAM4 embraces developments in hybrid modelling, learning evaporative stress from eddy-covariance and sapflow data. It features improved representation of key factors such as interception, atmospheric water demand, soil moisture, and plant access to groundwater. Estimates are inter-compared with existing global evaporation products and validated against in situ measurements, including data from 473 eddy-covariance sites, showing a median correlation of 0.73, root-mean-square error of 0.95 mm d−1, and Kling–Gupta efficiency of 0.49. Global land evaporation is estimated at 68.5 × 103 km3 yr−1, with 62% attributed to transpiration. Beyond actual evaporation and its components (transpiration, interception loss, soil evaporation, etc.), the dataset also provides soil moisture, potential evaporation, sensible heat flux, and evaporative stress, facilitating a wide range of hydrological, climatic, and ecological studies.

Exploring the contribution of vegetation and climate factors to changes in terrestrial evapotranspiration in China.

Exploring the contribution of vegetation and climate factors to changes in terrestrial evapotranspiration in China.

Can CMIP6 Models Accurately Reproduce Terrestrial Evapotranspiration Across China?

ABSTRACTTerrestrial evapotranspiration (ET) plays a fundamental role in the climate system. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides a valuable framework for assessing global climate model performance, but gaps remain in evaluating its ET estimates, particularly in China. To fill this gap, we employed the Global Land Evaporation Amsterdam Model (GLEAM) and the water balance ET method to validate the CMIP6 ET outputs from 1980 to 2014 at both national and river basin scales. Key findings include: (1) GLEAM ET performs comparably to the water balance method, making it reliable for validating CMIP6 ET outputs. From 1980 to 2014, the annual mean ET in GLEAM for China ranges from 355 to 411 mm/year. In contrast, most CMIP6 models overestimate ET, with the multi‐model ensemble (MME) mean ranging from 524 to 542 mm/year, showing considerable variation among models. Spatially, the MME overestimates ET across over 90% of China. Bayesian model averaging (BMA) results align closely with reference data, with overestimation concentrated in southwest China. (2) At the national scale, CMIP6 trends range from −0.36 to 0.58 mm/year2, which contrasts sharply with the GLEAM trend of 1.27 mm/year2. At the basin scale, most models overestimate annual ET compared to GLEAM, with discrepancies particularly evident in the major river basins. The smallest difference in ET trend simulation occurs in the Northwest River basin, where model distributions are more concentrated, while the largest discrepancies appear in the Pearl River basin, where model performance is more scattered. Furthermore, signal‐to‐noise ratio (SNR) analysis reveals high ensemble consistency in regions such as the Haihe, Yellow, Yangtze, Pearl and Songliao River basins, indicating more reliable model performance in these areas. This study contributes to enhancing the reliability and accuracy of climate projections, which is essential for informed decision‐making and policy formulation in atmospheric science.

An Evaluation of Evapotranspiration Products over the Tibetan Plateau

Abstract In situ observations from 13 sites located over the Tibetan Plateau (TP) are used to evaluate evapotranspiration (ET) products, including remote sensing–based, land surface–modeled, and reanalysis products. It is found that the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the simulations by the Community Land Model–dynamic global vegetation model [biogeochemical dynamic vegetation (CLM–BGCDV)] are the top-ranked products measured by the percentage bias, root-mean-square error, and correlation coefficient against in situ observations. The evaluated data are then used to examine the consistency in spatial and temporal variability of summer ET and its controlling factors on the TP and the three-river source region (TRSR). All products show consistently that precipitation in the central semiarid part of TP is the dominant factor influencing summer ET, while air temperature plays a certain role in the southeastern and eastern TP. Uncertainties exist in the western TP, possibly due to the lack of observations or gaps in the satellite data. Some suggestions for improving ET product development based on models and satellite retrievals, particularly for the cold and complex surface of the TP, are also given. Significance Statement Evapotranspiration (ET) is a crucial water cycle component, but accurately assessing it remains difficult. In situ measurements are scarce, while other products have large uncertainties, such as satellite remote sensing retrievals, land surface model simulations, and reanalyses. This is particularly a problem for the Tibetan Plateau (TP). With some considerable effort, ET fluxes from 13 sites and reasonable length in the TP have become available. These in situ data are used to evaluate gridded ET products. Based on an overall score, they are ranked as the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the model simulation by the Community Land Model–dynamic global vegetation model [biogeochemical dynamic vegetation (CLM–BGCDV)]. Then, we used these top-ranked products to detect the spatial and trend consistencies and the environmental impacts to further discuss the product conformance. It was shown that all products show precipitation dominated the summer ET variation in the semiarid region in the central TP, while air temperature had more impact on the relatively humid region in the east and southeast of the TP. Uncertainties exist in the west TP possibly due to the lack of observations in the assimilation product or missing gaps in the satellite product. Specifically, we conclude that dynamic vegetation could play an increasing role under the background of climate warming, which should be considered in the ET model–based product generation. The suggestions for ET generation based on models and satellite retrievals, particularly for the special cold and complex surface of the TP, are also provided.

On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?

Abstract. The miombo woodland is the largest dry woodland formation in sub-Saharan Africa, covering an estimated area of 2.7–3.6 million km2. Compared to other global ecosystems, the miombo woodland demonstrates unique interactions between plant phenology and climate. For instance, it experiences an increase in the leaf area index (LAI) during the dry season. However, due to limited surface exchange observations in the miombo region, there is a lack of information regarding the effect of these properties on miombo woodland evaporation. It is crucial to have a better understanding of miombo evaporation for accurate hydrological and climate modelling in this region. Currently, the only available regional evaporation estimates are based on satellite data. However, the accuracy of these estimates is questionable due to the scarcity of field estimates with which to compare. Therefore, this study aims to compare the temporal dynamics and magnitudes of six satellite-based evaporation estimates – the Topography-driven Flux Exchange (FLEX-Topo) model, Global Land Evaporation Amsterdam Model (GLEAM), Moderate-Resolution Imaging Spectrometer (MODIS) MOD16 product, operational Simplified Surface Energy Balance (SSEBop) model, Thornthwaite–Mather climatic Water Balance (TerraClimate) dataset, and Water Productivity through Open access of Remotely sensed derived data (WaPOR) – during different phenophases in the miombo woodland of the Luangwa Basin, a representative river basin in southern Africa. The goal of this comparison is to determine if the temporal dynamics and magnitudes of the satellite-based evaporation estimates align with the documented feedback between miombo woodland and climate. In the absence of basin-scale field observations, actual evaporation estimates based on the multi-annual water balance (Ewb) are used for comparison. The results show significant discrepancies among the satellite-based evaporation estimates during the dormant and green-up and mid-green-up phenophases. These phenophases involve substantial changes in miombo species' canopy phenology, including the co-occurrence of leaf fall and leaf flush, as well as access to deeper moisture stocks to support leaf flush in preparation for the rainy season. The satellite-based evaporation estimates show the highest agreement during the senescence phenophase, which corresponds to the period of high temperature, high soil moisture, high leaf chlorophyll content, and highest LAI (i.e. late rainy season into the cool-dry season). In comparison to basin-scale actual evaporation, all six satellite-based evaporation estimates appear to underestimate evaporation. Satellite-based evaporation estimates do not accurately represent evaporation in this data-sparse region, which has a phenology and seasonality that significantly differ from the typical case in data-rich ground-truth locations. This may also be true for other locations with limited data coverage. Based on this study, it is crucial to conduct field-based observations of evaporation during different miombo species phenophases to improve satellite-based evaporation estimates in miombo woodlands.

Toolkit for assessing water accounting in data-scarce river basins using global databases

Toolkit for assessing water accounting in data-scarce river basins using global databases

Different Vegetation Covers Leading to the Uncertainty and Consistency of ET Estimation: A Case Study Assessment with Extended Triple Collocation

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas.

Optimal combinations of global evapotranspiration and terrestrial water storage products for catchment water balance

ABSTRACT During the last two decades, a great number of global products about evapotranspiration ( E ) and terrestrial water storage ( S ) have been released. This has led to numerous combinations for describing catchment water balance, and hence determining an appropriate combination has become an important issue. The main objective of this study is to evaluate various global products of E and S at the catchment scale and determine the most appropriate combination, taking two Korean catchments as examples. For E , we evaluated global evapotranspiration (Global ET), Global Land Evaporation Amsterdam Model (GLEAM), and Penman–Monteith–Leuning (PML). For S , six individual datasets of Gravity Recovery and Climate Experiment (GRACE) were evaluated, with Global Land Data Assimilation System (GLDAS) products used for comparison. We found that the performance is sensitive to the choice of E product while various S products displayed minimal differences in results. Based on four evaluation criteria, the combination of PML and GRACE-SH-GFZ is suggested as the most suitable pair. For the period between 2003 and 2013, these data exhibit noteworthy trends of increasing E , offset by decreasing S . Warming climate is suspected to be behind these trends. Our research presents an approach that allows for the estimation of monthly streamflow exclusively with global data products, which is an advancement in hydrological analysis and particularly useful for regions that lack in-situ data networks. This approach provides a new perspective in the application of global datasets for the assessment of water balance and could significantly improve predictions in ungauged basins.

A comprehensive evaluation of hydrological processes in a second‐generation dynamic vegetation model

AbstractThe global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere–hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund‐Potsdam‐Jena General Ecosystem Simulator (LPJ‐GUESS), using a broad range of the latest available global observation‐based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ‐GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ‐GUESS accurately captures both inter‐ and intra‐annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ‐GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML‐V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ‐GUESS aligns with ESA‐CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use.

Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

AbstractDue to limited direct measurements, regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Although the inhomogeneity of the observed climate data has been widely reported, its impact on the calculated ET has not been adequately quantified. This study aimed to calculate ET using the modified Penman‐Monteith (MPM) model with raw and homogenized meteorological data. Additionally, we compared the calculated ET with those estimates from variable methods (water balance, satellite‐based, and reanalysis) in China and its six major river basins from 1982 to 2020. During the overlapping period of 1997–2018, ET calculated from raw input data decreased slightly at −0.39 mm yr−2 (p = 0.64) in China, whereas homogenized ET showed a significant increasing trend of 0.93 mm yr−2 (p = 0.02), with a better agreement with water balance ET (1.93 mm yr−2, p = 0). Global Land Evaporation Amsterdam Model (GLEAM) and Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2) could reproduce the increasing trends with 2.08 mm yr−2 (p = 0) and 2.59 mm yr−2 (p = 0). The intercomparison of input variables (solar radiation, relative humidity, wind speed, precipitation, and air temperature) among ET products revealed substantial differences, which can account for the discrepancies in ET estimates. Homogenized ET, GLEAM and MERRA2 exhibited significant increasing trends in China and most river basins from 1982 to 2020. Our findings underscore the importance of utilizing homogenized input data for more accurate ET estimation.

The influence of variations in actual evapotranspiration on drought in China's Southeast River basin

Revealing changes in actual evapotranspiration is essential to understanding regional extreme hydrological events (e.g., droughts). This study utilized the Global Land Evaporation Amsterdam Model (GLEAM) to analyse the spatial and temporal characteristics of actual evapotranspiration over 40 years in the Southeast River basin of China. The relationship between changes in actual evapotranspiration and the drought index was quantified. The results indicated a significant increase in actual evapotranspiration in the Southeast River basin from 1981 to 2020 (2.51 mm/year, p < 0.01). The actual evapotranspiration components were dominated by vegetation transpiration (73.45%) and canopy interception (18.26%). The actual evapotranspiration was closely related to the normalised difference vegetation index (r = 0.78, p < 0.01), and vegetation changes could explain 10.66% of the increase of actual evapotranspiration in the Southeast River basin since 2000. Meanwhile, actual evapotranspiration and standardised precipitation evapotranspiration index (SPEI) showed a highly significant negative spatial correlation, with a Moran's I index of − 0.513. The rise in actual evapotranspiration is an important trigger factor for seasonal droughts in the region. Therefore, these results help deepen the understanding of hydro-climatic process changes in the southeastern coastal region of China.

Global high-resolution drought indices for 1981–2022

Abstract. Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency, and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (&gt;50 km). To fill this gap, we developed four high-resolution (5 km) gridded drought records based on the standardized precipitation evaporation index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8), and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and hourly Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS_GLEAM, CHIRPS_hPET, MSWEP_GLEAM, and MSWEP_hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and can be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA; https://doi.org/10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023).

Geospatial dataset for hydrologic analyses in India (GHI): a quality-controlled dataset on river gauges, catchment boundaries and hydrometeorological time series

Abstract. Streamflow gauging stations not only track the pulse of rivers but also act as common reference points for hydrologic and other environmental analyses. As such, streamflow data and metadata on gauging stations – Geographic Information System (GIS) data on station locations, their upstream catchment boundaries and river flow networks – are critical for analyses. However, for India's river basins, the availability of such data is limited; when available, data are not in an analysis-ready format and can have substantial errors. Studies often use available information from India's water agencies as is, without checking its validity. This study addresses the above limitations by building a new dataset using existing metadata (from the Central Water Commission, CWC, and the Water Resources Information System, WRIS) and checking it against publicly available information from global data sources (e.g., World Wildlife Fund, Multi-Error-Removed Improved-Terrain Hydro and Copernicus) and online maps (e.g., Google Maps). The quality control process categorizes existing metadata based on their consistency with these sources; also, existing metadata are supplemented with additional information where needed. The new dataset developed here is called the “Geospatial dataset for Hydrologic analyses in India” (GHI) and uses Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS) data as the underlying template. GHI has both geospatial and time series information. In this initial version of GHI, the spatial domain includes only the river basins of Peninsular India where daily streamflow data are publicly available. Following the quality control process, the CWC's 645 stations in Peninsular India were categorized into three groups: Group 1 (reliable metadata and adequate daily streamflow data; 213 stations), Group 2 (reliable metadata and inadequate or no daily streamflow data; 259 stations) and Group 3 (missing or unreliable metadata; 173 stations). For each of the 472 stations falling into groups 1 and 2, catchment-specific annual and monthly time series spanning 71 water years (1950–2020) of the following were compiled: observed precipitation from the Indian Meteorological Department (IMD); observed streamflow from WRIS; estimated precipitation, evapotranspiration (ET) and streamflow from ERA5-Land; and ET from the Global Land Evaporation Amsterdam Model (GLEAM). A preliminary analysis of catchment-scale time series of data indicates that, while the compiled data appear reasonable over most of the study domain, spurious runoff–precipitation ratios were observed in the hilly coastal regions of Western India. This adds to yet another data-related obstacle faced by the hydrologic community. In order to quantify historical changes and reconcile them with anticipated future changes, the community needs robust and reliable hydrographic and hydrometeorological datasets as well as unrestricted access to such datasets. The goal of this study is to highlight the limitations of existing datasets and pave the way for a community-led effort towards building the needed datasets. GHI serves as a placeholder until such datasets become available. Potential improvements to GHI are discussed. GHI is publicly available at https://doi.org/10.5281/zenodo.7563599 (Goteti, 2023).

Exploring the contribution of environmental factors to evapotranspiration dynamics in the Three-River-Source region, China

Exploring the contribution of environmental factors to evapotranspiration dynamics in the Three-River-Source region, China

Spatial patterns and recent temporal trends in global transpiration modelled using eco-evolutionary optimality

Spatial patterns and recent temporal trends in global transpiration modelled using eco-evolutionary optimality

Evaluation of flash drought under the impact of heat wave events in southwestern Germany

Evaluation of flash drought under the impact of heat wave events in southwestern Germany

Incorporating Plant Access to Groundwater in Existing Global, Satellite‐Based Evaporation Estimates

AbstractGroundwater is an important water source for evaporation, especially during dry conditions. Despite this recognition, plant access to groundwater is often neglected in global evaporation models. This study proposes a new, conceptual approach to incorporate plant access to groundwater in existing global evaporation models, and analyses the groundwater contribution to evaporation globally. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used. The new GLEAM‐Hydro model relies on the linear reservoir assumption for modeling groundwater flow, and introduces a transpiration partitioning approach to estimate groundwater contributions. Model estimates are validated globally against field observations of evaporation, soil moisture, discharge and groundwater level for the time period 2015–2021, and compared to a regional groundwater model. Representing groundwater access influences evaporation in 22% of the continental surface. Globally averaged, evaporation increases by 2.5 mm year−1 (0.5% of terrestrial evaporation), but locally, evaporation can increase up to 245.2 mm year−1 (149.7%). The groundwater contribution to transpiration is highest for tall vegetation under dry conditions due to more frequent groundwater access. The temporal dynamics of the simulated evaporation improve across 75% of the stations where groundwater is a relevant water source. The skill of the model for variables such as soil moisture and runoff remains similar to GLEAM v3. The proposed approach enables a more realistic process representation of evaporation under water‐limited conditions in satellite‐data driven models such as GLEAM, and sets the ground to assimilate satellite gravimetry data in the future.

Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor

Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor

Contribution of climate change and vegetation restoration to interannual variability of evapotranspiration in the agro-pastoral ecotone in northern China

Contribution of climate change and vegetation restoration to interannual variability of evapotranspiration in the agro-pastoral ecotone in northern China