Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented.

Abstract. This paper outlines a new strategy to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale and 0.25 degree spatial resolution. Central to this methodology is the use of the Priestley and Taylor (PT) evaporation model. The minimalistic PT equation combines a small number of inputs, the majority of which can be detected from space. This reduces the number of variables that need to be modelled. Key distinguishing features of the approach are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the detailed estimation of rainfall interception loss. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R = 0.80, N = 43) and present a low average bias (−5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R = 0.83, still lower than the R = 0.90 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented.

Vegetation change plays a critical role in the Earth's carbon (C) budget and its associated radiative forcing in response to anthropogenic and natural climate change 1-4 . Existing global estimates of aboveground biomass carbon (ABC) based on field survey data provide brief snapshots that are mainly limited to forest ecosystems 5-8 . Here we use an entirely new remote sensing approach to derive global ABC estimates for both forest and non-forest biomes during the past two decades from satellite passive microwave observations. We estimate a global average ABC of 362 PgC over the period 1998-2002, of which 65% is in forests and 17% in savannahs. Over the period 1993-2012, an estimated −0.07 PgC yr −1 ABC was lost globally, mostly resulting from the loss of tropical forests (−0.26 PgC yr −1 ) and net gains in mixed forests over boreal and temperate regions (+0.13 PgC yr −1 ) and tropical savannahs and shrublands (+0.05 PgC yr −1 ). Interannual ABC patterns are greatly influenced by the strong response of water-limited ecosystems to rainfall variability, particularly savannahs. From 2003 onwards, forest in Russia and China expanded and tropical deforestation declined. Increased ABC associated with wetter conditions in the savannahs of northern Australia and southern Africa reversed global ABC loss, leading to an overall gain, consistent with trends in the global carbon sink reported in recent studies 1,3,9 .Over the past two decades, the terrestrial biosphere has acted as a sink for atmospheric CO 2 , removing on average approximately 2.5 petagrams of carbon per year (PgC yr −1 ): equivalent to 25% of fossil fuel emissions 1-4 . Additional emissions from land-use change reduce the global net land sink to approximately 1.5 PgC yr −1 , with forests playing a dominant role 5 . Monitoring C stock changes over time can be used to determine which ecosystems and processes drive changes in C fluxes and to develop strategies for climate change mitigation. The existing global ABC estimates based on field survey data, such as the recent global synthesis by Pan et al. 5 , provide snapshots in time and are limited to forest ecosystems only. Estimating ABC using satellite remote sensing can provide a more consistent methodology and global coverage 6 . Although offering high spatial resolution, current remote sensing products have limited temporal frequency and record length at the global scale 6-8 .Here, we derive global ABC estimates for all vegetation types for the past two decades using an entirely new approach that uses satellite-based passive microwave data rather than the optical or radar observations used previously. The intensity of natural microwave radiation from the Earth is a function of its temperature, soil moisture and the shielding effect of water in aboveground vegetation biomass, including canopy and woody components 10-12 . The biomass signal is captured in the vegetation optical depth (VOD; refs 13,14). A distinct advantage of passive microwavederived VOD is that it remains sensitive to biomass variations at a ...

Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include: (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 35-year data set spanning the period 1980–2014 (v3.0a, based on satellite-observed soil moisture, vegetation optical depth and snow water equivalents, reanalysis air temperature and radiation, and a multi-source precipitation product), and two fully satellite-based data sets. The latter two share most of their forcing, except for the vegetation optical depth and soil moisture products, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative data sets) for the first data set (v3.0b, spanning the period 2003–2015) and observations from the Soil Moisture and Ocean Salinity satellite in the second data set (v3.0c, spanning the period 2011–2015). These three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 64 eddy-covariance towers and 2338 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in case of the v3.0a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the two fully satellite-based data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar as the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements between 0.78 and 0.80 for the three different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at http://GLEAM.eu and may be used for large-scale hydrological applications, climate studies and research on land-atmosphere feedbacks.

Key Points Drivers and impacts of Australia's record drought were analyzed Impacts accumulated and propagated through the water cycle at different rates Future droughts may not be managed better than past ones.