Having recognized the limitations in spatial representativeness and/or temporal coverage of (i) current ground ET_a observations and (ii) land surface model‐ and remote sensing‐based ET_a estimates due to uncertainties in soil and vegetation parameters, a calibration‐free nonlinear complementary relationship (CR) model is employed with inputs of air and dew‐point temperature, wind speed, and net radiation to estimate 0.1°, monthly ET_a over China during 1982–2012. The modeled ET_a rates were first validated against 13 eddy‐covariance measurements, producing Nash‐Sutcliffe efficiency values in the range of 0.72–0.94. On the basin scale, the modeled ET_a values yielded a relative bias of 6%, and a Nash‐Sutcliffe efficiency value of 0.80 in comparison with water‐balance‐derived evapotranspiration rates across 10 major river basins in China, indicating the CR‐simulated ET_a rates reliable over China. Further evaluations suggest that the CR‐based ET_a product is more accurate than seven other mainstream ET_a products. The 31‐year mean annual ET_a value decreases from the southeast to the northwest in China, resulting in a country average of 406 ± 15 mm/year. The country‐representative annual ET_a rates slightly decreased with a rate of −0.5 mm/year (p = 0.86) during 1982–2012. Annual ET_a increased significantly over most parts of western and northeastern China but decreased significantly in many regions of the North China Plain as well as in the eastern and southern coastal regions. The present CR‐based method, with its calibration‐free nature and minimal data requirement, could help future calibrations/verifications of the more complex and more data‐intensive land surface model‐ and remote sensing‐based models.

While large‐scale terrestrial evapotranspiration (ET) information is essential for our understanding of the Earth's water and energy cycles, substantial differences exist in current global ET products due partly to uncertainties in soil‐ and vegetation‐related parameters and/or precipitation forcing. Here a calibration‐free complementary relationship (CR) model, driven purely by routine meteorological forcing (air and dew‐point temperature, wind speed, and net radiation), mainly from ERA5, was employed to estimate global ET rates during 1982–2016. Modeled ET agrees favorably with (a) monthly eddy‐covariance measurements of 129 global FLUXNET sites, and; (b) water‐balance‐derived ET of 52 basins at the multiyear mean and annual scales. Additional evaluations demonstrate that the CR is very competitive, in comparison with other 12 widely used global ET products. The 35‐years mean global land ET rate from the CR is 500 ± 6 mm yr⁻¹ (72.3 ± 0.9 × 10³ km³ yr⁻¹) with more than 70% of the land area exhibiting increasing annual ET rates over the study period. Globally, CR ET significantly increased at a rate of 0.31 mm yr⁻¹ during 1982–2016, suggesting a 2.2% increase in global land ET over last 35 years. Model inter‐comparisons indicate that global annual CR ET values and their trend are close to the median of not only the 12 ET products chosen but also that of 20 CMIP6 models. Since this calibration‐free CR model requires no precipitation (except in sea‐shore deserts for a subsequent ET correction), vegetation or soil data, it could be incorporated into complex hydrological and/or climate models, thereby facilitating large‐scale hydrological and climate simulations.

Accurate simulation of energy, water, and carbon fluxes exchanging between the land surface and the atmosphere is beneficial for improving terrestrial ecohydrological and climate predictions. We systematically assessed the Noah land surface model (LSM) with mutiparameterization options (Noah‐MP) in simulating these fluxes and associated variations in terrestrial water storage (TWS) and snow cover fraction (SCF) against various reference products over 18 United States Geological Survey two‐digital hydrological unit code regions of the continental United States (CONUS). In general, Noah‐MP captures better the observed seasonal and interregional variability of net radiation, SCF, and runoff than other variables. With a dynamic vegetation model, it overestimates gross primary productivity by 40% and evapotranspiration (ET) by 22% over the whole CONUS domain; however, with a prescribed climatology of leaf area index, it greatly improves ET simulation with relative bias dropping to 4%. It accurately simulates regional TWS dynamics in most regions except those with large lakes or severely affected by irrigation and/or impoundments. Incorporating the lake water storage variations into the modeled TWS variations largely reduces the TWS simulation bias more obviously over the Great Lakes with model efficiency increasing from 0.18 to 0.76. Noah‐MP simulates runoff well in most regions except an obvious overestimation (underestimation) in the Rio Grande and Lower Colorado (New England). Compared with North American Land Data Assimilation System Phase 2 (NLDAS‐2) LSMs, Noah‐MP shows a better ability to simulate runoff and a comparable skill in simulating R_n but a worse skill in simulating ET over most regions. This study suggests that future model developments should focus on improving the representations of vegetation dynamics, lake water storage dynamics, and human activities including irrigation and impoundments.

Accurate high-resolution actual evapotranspiration (ET) and gross primary production (GPP) information is essential for understanding the large-scale water and carbon dynamics. However, substantial uncertainties exist in the current ET and GPP datasets in China because of insufficient local ground measurements used for model constraint. This study utilizes a water–carbon coupled model, Penman–Monteith–Leuning Version 2 (PML-V2), to estimate 500 m ET and GPP at a daily scale. The parameters of PML-V2(China) were well calibrated against observations of 26 eddy covariance flux towers across nine plant functional types in China, indicated by a Nash–Sutcliffe efficiency (NSE) of 0.75 and a root mean square error (RMSE) of 0.69 mm d⁻¹ for daily ET, respectively, and a NSE of 0.82 and a RMSE of 1.71 g C m⁻² d⁻¹ for daily GPP. The model estimates get a small Bias of 6.28 % and a high NSE of 0.82 against water-balance annual ET estimates across 10 major river basins in China. Further evaluations suggest that the newly developed product is better than other typical products (MOD16A2, SEBAL, GLEAM, MOD17A2H, VPM, and EC-LUE) in estimating both ET and GPP. Moreover, PML-V2(China) accurately monitors the intra-annual variations in ET and GPP in the croplands with a dual-cropping system. The new data showed that, during 2001–2018, the annual GPP and water use efficiency experienced a significant (p<0.001) increase (8.99 g C m⁻² yr⁻² and 0.02 g C mm⁻¹ H₂O yr⁻¹, respectively), but annual ET showed a non-significant (p>0.05) increase (0.43 mm yr⁻²). This indicates that vegetation in China exhibits a huge potential for carbon sequestration with little cost in water resources. The PML-V2(China) product provides a great opportunity for academic communities and various agencies for scientific studies and applications, freely available at https://doi.org/10.11888/Terre.tpdc.272389 (Zhang and He, 2022).

Global land water underpins livelihoods, socioeconomic development, and ecosystems. It remains unclear how water availability has changed in recent decades. Using an ensemble of observations, we quantified global land water availability over the past two decades. We show that the Southern Hemisphere has dominated the declining trend in global water availability from 2001 to 2020. The significant decrease occurs mainly in South America, southwestern Africa, and northwestern Australia. In the Northern Hemisphere, the complex regional increasing and decreasing trends cancel each other, resulting in a negligible hemispheric trend. The variability and trend in water availability in the Southern Hemisphere are largely driven by precipitation associated with climate modes, particularly the El Niño-Southern Oscillation. This study highlights their dominant role in controlling global water availability.