Accurate quantification of global land evapotranspiration is necessary for understanding variability in the global water cycle, which is expected to intensify under climate change1–3. Current global evapotranspiration products are derived from a variety of sources, including models4,5, remote sensing6,7 and in situ observations8–10. However, existing approaches contain extensive uncertainties; for example, relating to model structure or the upscaling of observations to a global level11. As a result, variability and trends in global evapotranspiration remain unclear12. Here we show that global land evapotranspiration increased by 10 ± 2 per cent between 2003 and 2019, and that land precipitation is increasingly partitioned into evapotranspiration rather than runoff. Our results are based on an independent water-balance ensemble time series of global land evapotranspiration and the corresponding uncertainty distribution, using data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) satellites13. Variability in global land evapotranspiration is positively correlated with El Nino–Southern Oscillation. The main driver of the trend, however, is increasing land temperature. Our findings provide an observational constraint on global land evapotranspiration, and are consistent with the hypothesis that global evapotranspiration should increase in a warming climate. Using a global mass-balance approach to calculate evapotranspiration, it is shown that global land evapotranspiration increased by 10% between 2003 and 2019, driven mainly by warming land temperatures.

A 10 per cent increase in global land evapotranspiration from 2003 to 2019.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2020GL087285

Quantifying the Central European Droughts in 2018 and 2019 With GRACE Follow-On

Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

Rapidly declining surface and terrestrial water resources in Central Asia driven by socio-economic and climatic changes

Sub-regional groundwater storage recovery in North China Plain after the South-to-North water diversion project

/pdf/extending-the-global-mass-change-data-record-grace-follow-on-35cmy02xau.pdf

Extending the Global Mass Change Data Record: GRACE Follow-On Instrument and Science Data Performance

Gridded terrestrial water storage (TWS) variations observed by GRACE or GRACE-FO typically show a spatial correlation structure that is both anisotropic (direction-dependent) and non-homogeneous (latitude-dependent). We introduce a new correlation model to represent this structure. This correlation model allows GRACE and GRACE-FO data users to get realistic correlations of the TWS grids without the need to derive them from the formal spherical harmonic uncertainties. Further, we found that the modelled correlations fit the spatial structure of uncertainties to a greater extent in a simulation environment. The model is based on a direction-dependent Bessel function of the first kind which allows to model the longer correlation lengths in the longitudinal direction via a shape parameter, and also to account for residual GRACE striping errors that might remain after spatial filtering. The global scale and shape parameters vary with latitude by means of even Legendre polynomials. The correlation between two points transformed to covariance by scaling with the standard deviations of each point. The covariance model is valid on the sphere which is empirically verified with a Monte-Carlo approach. The covariance model is subsequently applied to 5 years of simulated GRACE-FO data which allow for immediate validation with true uncertainties from the differences between the input mass signal and the recovered gravity fields. Four different realisations of the point standard deviations were tested: two based on the formal errors provided with the simulated Stokes coefficients, and two based on empirical standard deviations, where the first is spatially variant and temporally invariant, and the second spatially invariant and temporally variant. These four different covariance models are applied to compute TWS time series uncertainties for both the fifty largest discharge basins and regular grid cells over the continents. These four models are compared with the true uncertainties available in the simulations. The two empirically-based covariance models provide more realistic TWS uncertainties than the ones based on the formal errors. Especially, the empirically-based covariance models are better in reflecting the spatial pattern of the uncertainties of the simulated GRACE-FO data including their latitude dependence. However, these modelled uncertainties are in general too large. But with only one global scaling factor, a statistical test confirms the equivalence between the empirically-based covariance model with temporally variable point standard deviations and the true uncertainties. Thus at the end, this covariance model represents the closest fit in the simulation environment. The simulated GRACE-FO data are assumed to be very realistic which is why we recommend the new covariance model to be further investigated for the characterisation of real GRACE and GRACE-FO terrestrial water storage data.

/pdf/modelling-spatial-covariances-for-terrestrial-water-storage-1nyoimkayt.pdf

Modelling spatial covariances for terrestrial water storage variations verified with synthetic GRACE-FO data

e2.motion - Earth System Mass Transport Mission (Square) - Concept for a Next Generation Gravity Field Mission - Final Report of Project "Satellite Gravimetry of the Next Generation (NGGM-D)"

M. Murböck

Papers

Extending the Global Mass Change Data Record: GRACE Follow-On Instrument and Science Data Performance

Modelling spatial covariances for terrestrial water storage variations verified with synthetic GRACE-FO data

e2.motion - Earth System Mass Transport Mission (Square) - Concept for a Next Generation Gravity Field Mission - Final Report of Project "Satellite Gravimetry of the Next Generation (NGGM-D)"