Showing papers on "Water cycle published in 2021"

ERA5-Land: a state-of-the-art global reanalysis dataset for land applications

Abstract: . Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly ( Munoz-Sabater , 2019 a ) and monthly ( Munoz-Sabater , 2019 b ) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30 , respectively.

Global terrestrial water storage and drought severity under climate change

Abstract: Terrestrial water storage (TWS) modulates the hydrological cycle and is a key determinant of water availability and an indicator of drought. While historical TWS variations have been increasingly studied, future changes in TWS and the linkages to droughts remain unexamined. Here, using ensemble hydrological simulations, we show that climate change could reduce TWS in many regions, especially those in the Southern Hemisphere. Strong inter-ensemble agreement indicates high confidence in the projected changes that are driven primarily by climate forcing rather than land and water management activities. Declines in TWS translate to increases in future droughts. By the late twenty-first century, the global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976–2005 to 7% and 8%, respectively. Our findings highlight the importance of climate change mitigation to avoid adverse TWS impacts and increased droughts, and the need for improved water resource management and adaptation.

Human alteration of global surface water storage variability.

Abstract: Knowing the extent of human influence on the global hydrological cycle is essential for the sustainability of freshwater resources on Earth1,2. However, a lack of water level observations for the world’s ponds, lakes and reservoirs has limited the quantification of human-managed (reservoir) changes in surface water storage compared to its natural variability3. The global storage variability in surface water bodies and the extent to which it is altered by humans therefore remain unknown. Here we show that 57 per cent of the Earth’s seasonal surface water storage variability occurs in human-managed reservoirs. Using measurements from NASA’s ICESat-2 satellite laser altimeter, which was launched in late 2018, we assemble an extensive global water level dataset that quantifies water level variability for 227,386 water bodies from October 2018 to July 2020. We find that seasonal variability in human-managed reservoirs averages 0.86 metres, whereas natural water bodies vary by only 0.22 metres. Natural variability in surface water storage is greatest in tropical basins, whereas human-managed variability is greatest in the Middle East, southern Africa and the western USA. Strong regional patterns are also found, with human influence driving 67 per cent of surface water storage variability south of 45 degrees north and nearly 100 per cent in certain arid and semi-arid regions. As economic development, population growth and climate change continue to pressure global water resources4, our approach provides a useful baseline from which ICESat-2 and future satellite missions will be able to track human modifications to the global hydrologic cycle. Data from the ICESat-2 satellite quantifying the variability of water levels in natural and human-managed water bodies show that a disproportionate majority of global water storage variability occurs in human-managed reservoirs.

Increasing precipitation variability on daily-to-multiyear time scales in a warmer world.

Abstract: The hydrological cycle intensifies under global warming with precipitation increases. How the increased precipitation varies temporally at a given location has vital implications for regional climates and ecosystem services. On the basis of ensemble climate model projections under a high-emission scenario, here, we show that approximately two-thirds of land on Earth will face a "wetter and more variable" hydroclimate on daily to multiyear time scales. This means wider swings between wet and dry extremes. Such an amplification of precipitation variability is particularly prominent over climatologically wet regions, with percentage increases in variability more than twice those in mean precipitation. Thermodynamic effects, linked to increased moisture availability, increase precipitation variability uniformly everywhere. It is the dynamic effects (negative) linked to weakened circulation variability that make precipitation variability changes strongly region dependent. The increase in precipitation variability poses an additional challenge to the climate resilience of infrastructures and human society.

No projected global drylands expansion under greenhouse warming

Abstract: Drylands, comprising land regions characterized by water-limited, sparse vegetation, have commonly been projected to expand globally under climate warming. Such projections, however, rely on an atmospheric proxy for drylands, the aridity index, which has recently been shown to yield qualitatively incorrect projections of various components of the terrestrial water cycle. Here, we use an alternative index of drylands, based directly on relevant ecohydrological variables, and compare projections of both indices in Coupled Model Intercomparison Project Phase 5 climate models as well as Dynamic Global Vegetation Models. The aridity index overestimates simulated ecohydrological index changes. This divergence reflects different index sensitivities to hydroclimate change and opposite responses to the physiological effect on vegetation of increasing atmospheric CO2. Atmospheric aridity is thus not an accurate proxy of the future extent of drylands. Despite greater uncertainties than in atmospheric projections, climate model ecohydrological projections indicate no global drylands expansion under greenhouse warming, contrary to previous claims based on atmospheric aridity. Model projections of future drylands distribution using a proxy based on atmospheric aridity show expansion under climate change, but may not be an accurate representation. An alternative index based on ecohydrological variables such as water limitation shows no global expansion of drylands.

Hydrological cycle and water resources in a changing world: A review

Abstract: Water is the fundamental natural resource that supports life, ecosystems and human society. Thus studying the water cycle is important for sustainable development. In the context of global climate change, a better understanding of the water cycle is needed. This study summarises current research and highlights future directions of water science from four perspectives: (i) the water cycle; (ii) hydrologic processes; (iii) coupled natural-social water systems; and (iv) integrated watershed management. Emphasis should be placed on understanding the joint impacts of climate change and human activities on hydrological processes and water resources across temporal and spatial scales. Understanding the interactions between land and atmosphere are keys to addressing this issue. Furthermore systematic approaches should be developed for large basin studies. Areas for focused research include: variations of cryosphere hydrological processes in upper alpine zones; and human activities on the water cycle and relevant biogeochemical processes in middle-lower reaches. Because the water cycle is naturally coupled with social characteristics across multiple scales, multi-process and multi-scale models are needed. Hydrological studies should use this new paradigm as part of water-food-energy frontier research. This will help to promote interdisciplinary study across natural and social sciences in accordance with the United Nation's sustainable development goals.

Assessment of CMIP6 performance and projected temperature and precipitation changes over South America

Abstract: We evaluate the performance of a large ensemble of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over South America for a recent past reference period and examine their projections of twenty-first century precipitation and temperature changes. The future changes are computed for two time slices (2040–2059 and 2080–2099) relative to the reference period (1995–2014) under four Shared Socioeconomic Pathways (SSPs, SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5). The CMIP6 GCMs successfully capture the main climate characteristics across South America. However, they exhibit varying skill in the spatiotemporal distribution of precipitation and temperature at the sub-regional scale, particularly over high latitudes and altitudes. Future precipitation exhibits a decrease over the east of the northern Andes in tropical South America and the southern Andes in Chile and Amazonia, and an increase over southeastern South America and the northern Andes—a result generally consistent with earlier CMIP (3 and 5) projections. However, most of these changes remain within the range of variability of the reference period. In contrast, temperature increases are robust in terms of magnitude even under the SSP1–2.6. Future changes mostly progress monotonically from the weakest to the strongest forcing scenario, and from the mid-century to late-century projection period. There is an increase in the seasonality of the intra-annual precipitation distribution, as the wetter part of the year contributes relatively more to the annual total. Furthermore, an increasingly heavy-tailed precipitation distribution and a rightward shifted temperature distribution provide strong indications of a more intense hydrological cycle as greenhouse gas emissions increase. The relative distance of an individual GCM from the ensemble mean does not substantially vary across different scenarios. We found no clear systematic linkage between model spread about the mean in the reference period and the magnitude of simulated sub-regional climate change in the future period. Overall, these results could be useful for regional climate change impact assessments across South America.

DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

Abstract: We present results from an ensemble of eight climate models, each of which has carried out simulations of the early Eocene climate optimum (EECO, g1/4 50 million years ago). These simulations have been carried out in the framework of the Deep-Time Model Intercomparison Project (DeepMIP; http://www.deepmip.org, last access: 10 January 2021); thus, all models have been configured with the same paleogeographic and vegetation boundary conditions. The results indicate that these non-CO2 boundary conditions contribute between 3 and 5 g C to Eocene warmth. Compared with results from previous studies, the DeepMIP simulations generally show a reduced spread of the global mean surface temperature response across the ensemble for a given atmospheric CO2 concentration as well as an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with the preindustrial period mostly arises from decreases in emissivity due to the elevated CO2 concentration (and associated water vapour and long-wave cloud feedbacks), whereas the reduction in the Eocene in terms of the meridional temperature gradient is primarily due to emissivity and albedo changes owing to the non-CO2 boundary conditions (i.e. the removal of the Antarctic ice sheet and changes in vegetation). Three of the models (the Community Earth System Model, CESM; the Geophysical Fluid Dynamics Laboratory, GFDL, model; and the Norwegian Earth System Model, NorESM) show results that are consistent with the proxies in terms of the global mean temperature, meridional SST gradient, and CO2, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale, the models lack skill. In particular, the modelled anomalies are substantially lower than those indicated by the proxies in the southwest Pacific; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxies or models in this region. Our aim is that the documentation of the large-scale features and model-data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability, and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols.

Long-term drying of Mars by sequestration of ocean-scale volumes of water in the crust

Abstract: Geological evidence shows that ancient Mars had large volumes of liquid water. Models of past hydrogen escape to space, calibrated with observations of the current escape rate, cannot explain the present-day deuterium-to-hydrogen isotope ratio (D/H). We simulated volcanic degassing, atmospheric escape, and crustal hydration on Mars, incorporating observational constraints from spacecraft, rovers, and meteorites. We found that ancient water volumes equivalent to a 100 to 1500 meter global layer are simultaneously compatible with the geological evidence, loss rate estimates, and D/H measurements. In our model, the volume of water participating in the hydrological cycle decreased by 40 to 95% over the Noachian period (~3.7 billion to 4.1 billion years ago), reaching present-day values by ~3.0 billion years ago. Between 30 and 99% of martian water was sequestered through crustal hydration, demonstrating that irreversible chemical weathering can increase the aridity of terrestrial planets.

New climate models reveal faster and larger increases in Arctic precipitation than previously projected.

Abstract: As the Arctic continues to warm faster than the rest of the planet, evidence mounts that the region is experiencing unprecedented environmental change. The hydrological cycle is projected to intensify throughout the twenty-first century, with increased evaporation from expanding open water areas and more precipitation. The latest projections from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) point to more rapid Arctic warming and sea-ice loss by the year 2100 than in previous projections, and consequently, larger and faster changes in the hydrological cycle. Arctic precipitation (rainfall) increases more rapidly in CMIP6 than in CMIP5 due to greater global warming and poleward moisture transport, greater Arctic amplification and sea-ice loss and increased sensitivity of precipitation to Arctic warming. The transition from a snow- to rain-dominated Arctic in the summer and autumn is projected to occur decades earlier and at a lower level of global warming, potentially under 1.5 °C, with profound climatic, ecosystem and socio-economic impacts.

Revisiting future extreme precipitation trends in the Mediterranean

Abstract: Global warming is anticipated to intensify the hydrological cycle. However, this is neither expected to be globally uniform nor is the relationship between temperature increase and rainfall intensities expected to be linear. The objective of this study is to assess changes in annual rainfall extremes, total annual precipitation, and their relationship in the larger Mediterranean region. We use an up-to-date ensemble of 33 regional climate simulations from the EURO-CORDEX initiative at 0.11° resolution. We analyse the significance of trends for 1951–2000 and 2001–2100 under a ‘business-as-usual’ pathway (RCP8.5). Our future projections indicate a strong north/south Mediterranean gradient, with significant, decreasing trends in the magnitude of daily precipitation extremes in the south and the Maghreb region (up to −10 mm/decade) and less profound, increasing trends in the north. Despite the contrasting future trends, the 50-year daily precipitation extremes are projected to strongly increase (up to 100%) throughout the region. The 100-year extremes, derived with traditional extreme value approaches from the 1951–2000 simulations, underestimate the magnitude of these extreme events in the 2001–2100 projections by 30% for the drier areas of the Mediterranean (200–500 mm average annual rainfall) and by up to 20–30% for the wetter parts of the region. These 100-year extremes can occur at any time in any Mediterranean location. The contribution of the wettest day per year to the annual total precipitation is expected to increase (5–30%) throughout the region. The projected increase in extremes and the strong reductions in mean annual precipitation in the drier, southern and eastern Mediterranean will amplify the challenges for water resource management.

Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan

Abstract: Reanalysis precipitation products (RPPs) are frequently used for studying the water cycle changes from short to long-term scale globally. In the current study, ERA-5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japanese 55-year Reanalysis (JRA-55), the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and the Climate Forecast System version 2 (CFS-2) precipitation products were evaluated with the rain-gauge data as a reference during 1981–2019 over Pakistan. The performance was assessed using statistical error metrics on daily, monthly, and annual timescales. The reanalysis precipitation products (RPPs) captured the precipitation intensities and the extreme precipitation events (75th to 99th percentile) across climatic classes. On a daily scale, the ERA-5 follows rain-gauges very closely (RC: 0.67, R: 0.81, RMSE: 1.69 mm), consistently capturing the precipitation intensities (light to violent) and extreme precipitation events (95th percentile), followed by CFS-2. The MERRA-2 captured precipitation intensity but did not detect extreme precipitation events in some regions. The JRA-55 produced good results in the central area while overestimated the precipitation in the northern and southern parts of the study area. On a monthly time scale, ERA-5 performed well as compared to the rest of RPPs, with regression coefficient values of 0.91, correlation coefficient (0.96), and a lower value of RMSE (11.09 mm), followed by JRA-55, MERRA-2, and CFS-2. All the RPPs performed better in winter, pre-monsoon, and post-monsoon seasons with slight deviations/differences, but in monsoon season, the ERA-5 and JRA-55 (MERRA-2, CFS-2) overestimated (underestimated) precipitation mean. The findings can help the researchers select reliable datasets for bias correction of the projections and real-time application in flood, drought estimation, and prediction.

Emerging satellite observations for diurnal cycling of ecosystem processes.

Abstract: Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.

LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe

Abstract: . Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH-CE (LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, LamaH for short; the geographical extension “-CE” is omitted in the text and the dataset) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border of Austria–Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of about 170 000 km2 in nine countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 gauged catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with a daily and hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with a high temporal resolution. The runoff time series are classified by over 20 attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets. In contrast, however, LamaH does not only consider independent basins, covering the full upstream area. Intermediate catchments are covered as well, which allows together with novel attributes the considering of the hydrological network and river topology in applications. We not only describe the basic datasets used and methodology of data preparation but also focus on possible limitations and uncertainties. LamaH contains additionally results of a conceptual hydrological baseline model for checking plausibility of the inputs as well as benchmarking. Potential applications of LamaH are outlined as well, since it is intended to serve as a uniform data basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).

Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present

Abstract: Challenges exist for assessing the impacts of climate and climate change on the hydrological cycle on local and regional scales, and in turn on water resources, food, energy, and natural hazards. Potential evapotranspiration (PET) represents atmospheric demand for water, which is required at high spatial and temporal resolutions to compute actual evapotranspiration and thus close the water balance near the land surface for many such applications, but there are currently no available high-resolution datasets of PET. Here we develop an hourly PET dataset (hPET) for the global land surface at 0.1° spatial resolution, based on output from the recently developed ERA5-Land reanalysis dataset, over the period 1981 to present. We show how hPET compares to other available global PET datasets, over common spatiotemporal resolutions and time frames, with respect to spatial patterns of climatology and seasonal variations for selected humid and arid locations across the globe. We provide the data for users to employ for multiple applications to explore diurnal and seasonal variations in evaporative demand for water.

Evapotranspiration in the Amazon: spatial patterns, seasonality, and recent trends in observations, reanalysis, and climate models

Abstract: . Water recycled through transpiring forests influences the spatial distribution of precipitation in the Amazon and has been shown to play a role in the initiation of the wet season. However, due to the challenges and costs associated with measuring evapotranspiration (ET) directly and high uncertainty in remote-sensing ET retrievals, the spatial and temporal patterns in Amazon ET remain poorly understood. In this study, we estimated ET over the Amazon and 10 sub-basins using a catchment-balance approach, whereby ET is calculated directly as the balance between precipitation, runoff, and change in groundwater storage. We compared our results with ET from remote-sensing datasets, reanalysis, models from Phase 5 and Phase 6 of the Coupled Model Intercomparison Projects (CMIP5 and CMIP6 respectively), and in situ flux tower measurements to provide a comprehensive overview of current understanding. Catchment-balance analysis revealed a gradient in ET from east to west/southwest across the Amazon Basin, a strong seasonal cycle in basin-mean ET primarily controlled by net incoming radiation, and no trend in ET over the past 2 decades. This approach has a degree of uncertainty, due to errors in each of the terms of the water budget; therefore, we conducted an error analysis to identify the range of likely values. Satellite datasets, reanalysis, and climate models all tended to overestimate the magnitude of ET relative to catchment-balance estimates, underestimate seasonal and interannual variability, and show conflicting positive and negative trends. Only two out of six satellite and model datasets analysed reproduced spatial and seasonal variation in Amazon ET, and captured the same controls on ET as indicated by catchment-balance analysis. CMIP5 and CMIP6 ET was inconsistent with catchment-balance estimates over all scales analysed. Overall, the discrepancies between data products and models revealed by our analysis demonstrate a need for more ground-based ET measurements in the Amazon as well as a need to substantially improve model representation of this fundamental component of the Amazon hydrological cycle.

Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5

Abstract: Precipitation is a key component of the hydrological cycle and one of the most important variables in weather and climate studies. Accurate and reliable precipitation data are crucial for determining climate trends and variability. In this study, eleven different precipitation datasets are compared, six reanalysis and five observational datasets, including the reanalysis datasets ERA5 and WFDE5 from the ECMWF family, to quantify the differences between the widely used precipitation datasets and to identify their particular strengths and shortcomings. The comparisons are focused on the common time period 1983 through 2016 and on monthly, seasonal, and inter-annual times scales in regions representing different precipitation regimes, i.e., the Tropics, the Pacific Inter Tropical Convergence Zone (ITCZ), Central Europe, and the South Asian Monsoon region. For the analysis, satellite-gauge precipitation data from the Global Precipitation Climatology Project (GPCP-SG) are used as a reference. The comparison shows that ERA5 and ERA5-Land are a clear improvement over ERA-Interim and show in most cases smaller biases than the other reanalysis datasets (e.g., around 13% high bias in the Tropics compared to 17% for MERRA-2 and 36% for JRA-55). ERA5 agrees well with observations for Central Europe and the South Asian Monsoon region but underestimates very low precipitation rates in the Tropics. In particular, the tropical ocean remains challenging for reanalyses with three out of four products overestimating precipitation rates over the Atlantic and Indian Ocean.

Towards Hybrid Modeling of the Global Hydrological Cycle

Abstract: . Progress in machine learning in conjunction with the increasing availability of relevant Earth observation data streams may help to overcome uncertainties of global hydrological models due to the complexity of the processes, diversity, and heterogeneity of the land surface and subsurface, as well as scale-dependency of processes and parameters. In this study, we exemplify a hybrid approach to global hydrological modeling that exploits the data-adaptiveness of machine learning for representing uncertain processes within a model structure based on physical principles like mass conservation. Our H2M model simulates the dynamics of snow, soil moisture, and groundwater pools globally at 1o spatial resolution and daily time step where simulated water fluxes depend on an embedded recurrent neural network. We trained the model simultaneously against observational products of terrestrial water storage variations (TWS), runoff, evapotranspiration, and snow water equivalent with a multi-task learning approach. We find that H2M is capable of reproducing the key patterns of global water cycle components with model performances being at least on par with four state-of-the-art global hydrological models. The neural network learned hydrological responses of evapotranspiration and runoff generation to antecedent soil moisture state that are qualitatively consistent with our understanding and theory. Simulated contributions of groundwater, soil moisture, and snowpack variability to TWS variations are plausible and within the large range of traditional GHMs. H2M indicates a somewhat stronger role of soil moisture for TWS variations in transitional and tropical regions compared to GHMs. Overall, we present a proof of concept for global hybrid hydrological modeling in providing a new, complementary, and data-driven perspective on global water cycle variations. With further increasing Earth observations, hybrid modeling has a large potential to advance our capability to monitor and understand the Earth system by facilitating a data-adaptive yet physically consistent, joint interpretation of heterogeneous data streams.