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We introduce a set of global high-resolution (0.05°) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the “true” long-term P using a Budyko curve, which is an empirical equation relating long-term P, Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-art P climatologies [the “WorldClim version 2” database (WorldClim V2); Climatologies at High Resolution for the Earth’s Land Surface Areas, version 1.2 (CHELSA V1.2 ); and Climate Hazards Group Precipitation Climatology, version 1 (CHPclim V1)], after which we used random-forest regression to produce global gap-free bias correction maps for the P climatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors on the basis of gauge catch efficiencies. We found that all three climatologies systematically underestimate P over parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. In addition, all climatologies underestimate P at latitudes >60°N, likely because of gauge undercatch. Exceptionally high long-term correction factors (>1.5) were obtained for all three P climatologies in Alaska, High Mountain Asia, and Chile—regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely used P datasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimate P over Chile, the Himalayas, and along the Pacific coast of North America. Mean P for the global land surface based on the bias-corrected WorldClim V2 is 862 mm yr−1 (a 9.4% increase over the original WorldClim V2). The annual and monthly bias-corrected P climatologies have been released as the Precipitation Bias Correction (PBCOR) dataset, which is available online (http://www.gloh2o.org/pbcor/).

Endorheic (hydrologically landlocked) basins spatially concur with arid/semi-arid climates. Given limited precipitation but high potential evaporation, their water storage is vulnerable to subtle flux perturbations, which are exacerbated by global warming and human activities. Increasing regional evidence suggests a probably recent net decline in endorheic water storage, but this remains unquantified at a global scale. By integrating satellite observations and hydrological modelling, we reveal that during 2002–2016 the global endorheic system experienced a widespread water loss of about 106.3 Gt yr−1, attributed to comparable losses in surface water, soil moisture and groundwater. This decadal decline, disparate from water storage fluctuations in exorheic basins, appears less sensitive to El Niño–Southern Oscillation-driven climate variability, which implies a possible response to longer-term climate conditions and human water management. In the mass-conserved hydrosphere, such an endorheic water loss not only exacerbates local water stress, but also imposes excess water on exorheic basins, leading to a potential sea level rise that matches the contribution of nearly half of the land glacier retreat (excluding Greenland and Antarctica). Given these dual ramifications, we suggest the necessity for long-term monitoring of water storage variation in the global endorheic system and the inclusion of its net contribution to future sea level budgeting.

The complementary principle is an important methodology for estimating actual evaporation by using routinely observed meteorological variables. This review summaries its 56-year development, focusing on how related studies have shifted from adopting a symmetric linear complementary relationship (CR) to employing generalized nonlinear functions. The original CR denotes that the actual evaporation (E) and “apparent” potential evaporation (Epa) depart from the potential evaporation (Ep0) complementarily when the land surface dries from a completely wet environment with constant available energy. The CR was then extended to an asymmetric linear relationship, and the linear nature was retained through properly formulating Epa and/or Ep0. Recently, the linear CR was generalized to a sigmoid function and a polynomial function. The sigmoid function does not involve the formulations of Epa and Ep0 but uses the Penman (1948) potential evaporation and its radiation component as inputs, whereas the polynomial function inherits Ep0 and Epa as inputs and requires proper formulations for application. The generalized complementary principle has a more rigorous physical base and offers a great potential in advancing evaporation estimation. Future studies may cover several topics, including the boundary conditions in wet environments, the parameterization and application over different regions of the world, and integration with other approaches for further development.

Since the 1950s, Europe has undergone large shifts in climate and land cover. Previous assessments of past and future changes in evapotranspiration or streamflow have either focussed on land use/cover or climate contributions or on individual catchments under specific climate conditions, but not on all aspects at larger scales. Here, we aim to understand how decadal changes in climate (e.g. precipitation, temperature) and land use (e.g. deforestation/afforestation, urbanization) have impacted the amount and distribution of water resource availability (both evapotranspiration and streamflow) across Europe since the 1950s. To this end, we simulate the distribution of average evapotranspiration and streamflow at high resolution (1 km2) by combining (a) a steady-state Budyko model for water balance partitioning constrained by long-term (lysimeter) observations across different land use types, (b) a novel decadal high-resolution historical land use reconstruction, and (c) gridded observations of key meteorological variables. The continental-scale patterns in the simulations agree well with coarser-scale observation-based estimates of evapotranspiration and also with observed changes in streamflow from small basins across Europe. We find that strong shifts in the continental-scale patterns of evapotranspiration and streamflow have occurred between the period around 1960 and 2010. In much of central-western Europe, our results show an increase in evapotranspiration of the order of 5 %–15 % between 1955–1965 and 2005–2015, whereas much of the Scandinavian peninsula shows increases exceeding 15 %. The Iberian Peninsula and other parts of the Mediterranean show a decrease of the order of 5 %–15 %. A similar north–south gradient was found for changes in streamflow, although changes in central-western Europe were generally small. Strong decreases and increases exceeding 45 % were found in parts of the Iberian and Scandinavian peninsulas, respectively. In Sweden, for example, increased precipitation is a larger driver than large-scale reforestation and afforestation, leading to increases in both streamflow and evapotranspiration. In most of the Mediterranean, decreased precipitation combines with increased forest cover and potential evapotranspiration to reduce streamflow. In spite of considerable local- and regional-scale complexity, the response of net actual evapotranspiration to changes in land use, precipitation, and potential evaporation is remarkably uniform across Europe, increasing by ∼ 35–60 km3 yr−1, equivalent to the discharge of a large river. For streamflow, effects of changes in precipitation (∼ 95 km3 yr−1) dominate land use and potential evapotranspiration contributions (∼ 45–60 km3 yr−1). Locally, increased forest cover, forest stand age, and urbanization have led to significant decreases and increases in available streamflow, even in catchments that are considered to be near-natural.

Changes in vegetation greenness have altered the regional terrestrial water cycle, yet their influence on drought remains unclear. To quantify the impact of vegetation greenness change on drought across global vegetation zones, this study conducted two simulations with and without linear trends in the Leaf Area Index (LAI), based on the Standardized Precipitation‐Evapotranspiration Index (SPEI) combined with the calibrated Shuttleworth‐Wallace potential evapotranspiration (PET) equation. Results revealed vegetation greening affected 71% of areas, and over 55% of areas experienced increases in PET, decreases in SPEI (indicating drying), and intensified drought conditions. The linear trends in LAI increased potential transpiration but decreased potential soil evaporation in most regions. The changes in drought are determined by the combined effects of these changes in potential transpiration and soil evaporation. This study highlights the critical role of vegetation greenness change in influencing drought. Plain Language Summary Vegetation is the most ever‐changing land surface component, influencing nearly all aspects of the terrestrial water cycle, including evapotranspiration, by controlling the partitioning of energy and water. Given its crucial role and evident changes in vegetation greenness (usually reflected by the Leaf Area Index (LAI)), we hypothesize that vegetation greenness change can also affect drought by regulating atmospheric water demand (i.e., potential evapotranspiration (PET)). To test this, we conducted simulations using a drought index combined with a new PET equation, comparing scenarios with and without linear trends in LAI across global vegetation zones. The results showed that vegetation greenness changes significantly influenced PET and drought, with over half of the regions experiencing increased PET, drying, and intensified drought. We also found that increased vegetation greenness enhanced potential transpiration while reducing potential evaporation, and vice versa, with their combined effects driving changes in PET, dryness/wetness, and drought severity. This study highlights the vital role of vegetation greenness change in shaping variations in dryness‐wetness and drought through influencing PET. Key Points Impacts of vegetation greenness change on potential evapotranspiration (PET) and drought were evaluated across global vegetation zones Vegetation greenness changes increased PET and drought over more than 55% of areas The combined variations in potential transpiration and soil evaporation determined the changes in PET and drought

Potential evaporation (Ep) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of Ep exist, which reflect a diverse range of methods to calculate it. A comparison of the performance of these methods against field observations in different global ecosystems is urgently needed. In this study, potential evaporation was defined as the rate of terrestrial evaporation (or evapotranspiration) that the actual ecosystem would attain if it were to evaporate at maximal rate for the given atmospheric conditions. We use eddy-covariance measurements from the FLUXNET2015 database, covering 11 different biomes, to parameterise and inter-compare the most widely used Ep methods and to uncover their relative performance. For each of the 107 sites, we isolate days for which ecosystems can be considered unstressed, based on both an energy balance and a soil water content approach. Evaporation measurements during these days are used as reference to calibrate and validate the different methods to estimate Ep. Our results indicate that a simple radiation-driven method, calibrated per biome, consistently performs best against in situ measurements (mean correlation of 0.93; unbiased RMSE of 0.56 mm day−1; and bias of −0.02 mm day−1). A Priestley and Taylor method, calibrated per biome, performed just slightly worse, yet substantially and consistently better than more complex Penman-based, Penman–Monteith-based or temperature-driven approaches. We show that the poor performance of Penman–Monteith-based approaches largely relates to the fact that the unstressed stomatal conductance cannot be assumed to be constant in time at the ecosystem scale. On the contrary, the biome-specific parameters required by simpler radiation-driven methods are relatively constant in time and per biome type. This makes these methods a robust way to estimate Ep and a suitable tool to investigate the impact of water use and demand, drought severity and biome productivity.

Understanding the influence of vegetation on water storage and flux in the upper soil is crucial in assessing the consequences of climate and land use change. We sampled the upper 20 cm of podzolic soils at 5 cm intervals in four sites differing in their vegetation (Scots Pine (Pinus sylvestris) and heather (Calluna sp. and Erica Sp)) and aspect. The sites were located within the Bruntland Burn long-term experimental catchment in the Scottish Highlands, a low energy, wet environment. Sampling took place on 11 occasions between September 2015 and September 2016 to capture seasonal variability in isotope dynamics. The pore waters of soil samples were analyzed for their isotopic composition (δ2H and δ18O) with the direct-equilibration method. Our results show that the soil waters in the top soil are, despite the low potential evaporation rates in such northern latitudes, kinetically fractionated compared to the precipitation input throughout the year. This fractionation signal decreases within the upper 15 cm resulting in the top 5 cm being isotopically differentiated to the soil at 15–20 cm soil depth. There are significant differences in the fractionation signal between soils beneath heather and soils beneath Scots pine, with the latter being more pronounced. But again, this difference diminishes within the upper 15 cm of soil. The enrichment in heavy isotopes in the topsoil follows a seasonal hysteresis pattern, indicating a lag time between the fractionation signal in the soil and the increase/decrease of soil evaporation in spring/autumn. Based on the kinetic enrichment of the soil water isotopes, we estimated the soil evaporation losses to be about 5 and 10 % of the infiltrating water for soils beneath heather and Scots pine, respectively. The high sampling frequency in time (monthly) and depth (5 cm intervals) revealed high temporal and spatial variability of the isotopic composition of soil waters, which can be critical, when using stable isotopes as tracers to assess plant water uptake patterns within the critical zone or applying them to calibrate tracer-aided hydrological models either at the plot to the catchment scale.

Irrigation is the main driver of crop production in many agricultural regions across the world. The estimation of irrigation water has the potential to enhance our comprehension of the Earth system, thus providing crucial data for food production. In this study, we have created a unique operational system for estimating irrigation water using data from satellite soil moisture, reanalysis precipitation, and potential evaporation. As a proof of concept, we implemented the method at a high resolution (1 km) during the period of 2015–2023 over the area south of the Kakhovka Dam in Ukraine, which collapsed on 6 June 2023. The selected study area enabled us to showcase that our operational system is able to track the effect of the pandemic and conflict on the irrigation water supply. Significant decreases of 63 % and 44 % in irrigation water compared to the mean irrigation water between 2015 and 2023 have been identified as being linked to the collapse of the dam and, potentially, to the COVID-19 pandemic, respectively.

State-of-the-art evaporation models usually assume net radiation (Rn) and surface temperature (Ts; or near-surface air temperature) to be independent forcings on evaporation. However, Rn depends directly on Ts via outgoing longwave radiation, and this creates a physical coupling between Rn and Ts that extends to evaporation. In this study, we test a maximum evaporation theory originally developed for the global ocean over saturated land surfaces, which explicitly acknowledges the interactions between radiation, Ts, and evaporation. Similar to the ocean surface, we find that a maximum evaporation (LEmax) emerges over saturated land that represents a generic trade-off between a lower Rn and a higher evaporation fraction as Ts increases. Compared with flux site observations at the daily scale, we show that LEmax corresponds well to observed evaporation under non-water-limited conditions and that the Ts value at which LEmax occurs also corresponds with the observed Ts. Our results suggest that saturated land surfaces behave essentially the same as ocean surfaces at timescales longer than a day and further imply that the maximum evaporation concept is a natural attribute of saturated land surfaces, which can be the basis of a new approach to estimating evaporation.

Bias correction is an essential technique to correct climate model outputs for local or site-specific climate change impact studies. Most commonly used bias correction methods operate on a single variable, which ignores dependency among multiple variables. The misrepresentation of multivariable dependence may result in biased assessment of climate change impacts. To solve this problem, a new multivariate bias correction method referred to as two-stage quantile mapping (TSQM) is proposed by combining a single-variable bias correction method with a distribution-free shuffle approach. Specifically, a quantile mapping method is used to correct the marginal distribution of single variable and then a distribution-free shuffle approach to introduce proper multivariable correlations. The proposed method is compared with the other four state-of-the-art multivariate bias correction methods for correcting monthly precipitation, and maximum and minimum temperatures simulated by global climate models. The results show that the TSQM method is capable of both bias correcting univariate statistics and inducing proper inter-variable rank correlations. Especially, it outperforms all the other four methods in reproducing inter-variable rank correlations and in simulating mean temperature and potential evaporation for wet and dry months of the validation period. Overall, without complex algorithm and iterations, TSQM is fast, simple and easy to implement, and is proved a competitive bias correction technique to be widely applied in climate change impact studies.