The planetary boundaries framework defines a safe operating space for humanity based on the intrinsic biophysical processes that regulate the stability of the Earth system. Here, we revise and update the planetary boundary framework, with a focus on the underpinning biophysical science, based on targeted input from expert research communities and on more general scientific advances over the past 5 years. Several of the boundaries now have a two-tier approach, reflecting the importance of cross-scale interactions and the regional-level heterogeneity of the processes that underpin the boundaries. Two core boundaries—climate change and biosphere integrity—have been identified, each of which has the potential on its own to drive the Earth system into a new state should they be substantially and persistently transgressed.

More than half of the solar energy absorbed by land surfaces is currently used to evaporate water(1). Climate change is expected to intensify the hydrological cycle(2) and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land-a key diagnostic criterion of the effects of climate change and variability-remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network(3), meteorological and remote-sensing observations, and a machine-learning algorithm(4). In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface-models. Our results suggest that global annual evapotranspiration increased on average by 7.1 +/- 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Nino event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science

Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2°C above present (approximately 2.7°C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m 3 per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2°C, whereas indicators of very severe impacts increase unabated beyond 2°C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.F reshwater is one of the most vital natural resources of the planet. The quantities that humans need for drinking and sanitation are relatively small, and the fact that these basic needs are not satisfied for many people today is primarily a matter of access to, and quality of, available water resources (1). Much larger quantities of water are required for many other purposes, most importantly irrigated agriculture, but also for industrial use, in particular for hydropower and the cooling of thermoelectric power plants (2, 3). These activities critically depend on a sufficient amount of freshwater that can be withdrawn from rivers, lakes, and groundwater aquifers. Whereas scarcity of freshwater resources already constrains development and societal well-being in many countries (4, 5), the expected growth of global population over the coming decades, together with growing economic prosperity, will increase water demand and thus aggravate these problems (6-8).Climate change poses an additional threat to water security because changes in precipitation and other climatic variables may lead to significant changes in water supply in many regions (6-11). The effect of climate change on water resources is, however, uncertain for a number of reasons. Climate model projections, although rather ...

Humans directly change the dynamics of the water cycle through dams constructed for water storage, and through water withdrawals for industrial, agricultural, or domestic purposes. Climate change is expected to additionally affect water supply and demand. Here, analyses of climate change and direct human impacts on the terrestrial water cycle are presented and compared using a multimodel approach. Seven global hydrological models have been forced with multiple climate projections, and with and without taking into account impacts of human interventions such as dams and water withdrawals on the hydrological cycle. Model results are analyzed for different levels of global warming, allowing for analyses in line with temperature targets for climate change mitigation. The results indicate that direct human impacts on the water cycle in some regions, e.g., parts of Asia and in the western United States, are of the same order of magnitude, or even exceed impacts to be expected for moderate levels of global warming (+2 K). Despite some spread in model projections, irrigation water consumption is generally projected to increase with higher global mean temperatures. Irrigation water scarcity is particularly large in parts of southern and eastern Asia, and is expected to become even larger in the future. ISI-MIP | WaterMIPT errestrial water fluxes are affected by both climate and direct human interventions, e.g., dam operations and water withdrawals. Climate change is expected to alter the water cycle and will subsequently impact water availability and demand. Several hydrologic modeling studies have focused on climate change impacts on discharge in large river basins or global terrestrial areas under naturalized conditions using a single hydrologic model forced with multiple climate projections (1, 2). Recently, hydrological projections from eight global hydrological models (GHMs) were compared (3). In many areas, there was a large spread in projected runoff changes within the climate-hydrology modeling chain. However, at high latitudes there was a clear increase in runoff, whereas some midlatitude regions showed a robust signal of reduced runoff. The study also concluded that the choice of GHM adds to the uncertainty for hydrological change caused by the choice of atmosphere-ocean general circulation models (hereafter called GCMs) (3). Expected runoff increases in the north and decreases in parts of the middle latitudes have been found also when analyzing runoff from 23 GCMs (4).These studies focused on the naturalized hydrological cycle, i.e., the effects of direct human interventions were not taken into account. However, in many river basins humans substantially alter the hydrological cycle by constructing dams and through water withdrawals. Reservoir operations alter the timing of discharge, although mean annual discharge does not necessarily change much. A study with the water balance model (WBM) showed that the impact of human disturbances, i.e., dams and water consumption, in some river basins is equal to or greater...

Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.58 spatial resolution for the global land areas for a 15-yr period (1985-99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr 21 (from 60 000 to 85 000 km 3 yr 21 ), and simulated runoff ranges from 290 to 457 mm yr 21 (from 42 000 to 66 000 km 3 yr 21 ). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degreeday approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

[1] This paper presents a quantitative estimation of the impact of reservoirs on discharge and irrigation water supply during the 20th century at global, continental, and river basin scale. Compared to a natural situation the combined effect of reservoir operation and irrigation extractions decreased mean annual discharge to oceans and significantly changed the timing of this discharge. For example, in Europe, May discharge decreased by 10%, while in February it increased by 8%. At the end of the 20th century, reservoir operations and irrigation extractions decreased annual global discharge by about 2.1% (930 km 3 yr À1 ). Simulation results show that reservoirs contribute significantly to irrigation water supply in many regions. Basins that rely heavily on reservoir water are the Colorado and Columbia River basins in the United States and several large basins in India, China, and central Asia (e.g., in the Krishna and Huang He basins, reservoirs more than doubled surface water supply). Continents gaining the most are North America, Africa, and Asia, where reservoirs supplied 57, 22, and 360 km 3 yr À1 respectively between 1981 -2000, which is in all cases 40% more than the availability in the situation without reservoirs. Globally, the irrigation water supply from reservoirs increased from around 18 km 3 yr À1 (adding 5% to surface water supply) at the beginning of the 20th century to 460 km 3 yr À1 (adding almost 40% to surface water supply) at the end of the 20th century. The analysis is performed using a newly developed and validated reservoir operation scheme within a global-scale hydrology and vegetation model (LPJmL).

Abstract. Global agricultural production is heavily sustained by irrigation, but irrigation system efficiencies are often surprisingly low. However, our knowledge of irrigation efficiencies is mostly confined to rough indicative estimates for countries or regions that do not account for spatiotemporal heterogeneity due to climate and other biophysical dependencies. To allow for refined estimates of global agricultural water use, and of water saving and water productivity potentials constrained by biophysical processes and also non-trivial downstream effects, we incorporated a process-based representation of the three major irrigation systems (surface, sprinkler, and drip) into a bio- and agrosphere model, LPJmL. Based on this enhanced model we provide a gridded world map of irrigation efficiencies that are calculated in direct linkage to differences in system types, crop types, climatic and hydrologic conditions, and overall crop management. We find pronounced regional patterns in beneficial irrigation efficiency (a refined irrigation efficiency indicator accounting for crop-productive water consumption only), due to differences in these features, with the lowest values (< 30 %) in south Asia and sub-Saharan Africa and the highest values (> 60 %) in Europe and North America. We arrive at an estimate of global irrigation water withdrawal of 2469 km3 (2004–2009 average); irrigation water consumption is calculated to be 1257 km3, of which 608 km3 are non-beneficially consumed, i.e., lost through evaporation, interception, and conveyance. Replacing surface systems by sprinkler or drip systems could, on average across the world's river basins, reduce the non-beneficial consumption at river basin level by 54 and 76 %, respectively, while maintaining the current level of crop yields. Accordingly, crop water productivity would increase by 9 and 15 %, respectively, and by much more in specific regions such as in the Indus basin. This study significantly advances the global quantification of irrigation systems while providing a framework for assessing potential future transitions in these systems. In this paper, presented opportunities associated with irrigation improvements are significant and suggest that they should be considered an important means on the way to sustainable food security.

Future climate model scenarios depend crucially on the models' adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use stateof-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Météorologiques Coupled GCM, version 3 (CNRM-CM3), and the atmospheric component of the L'Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) coupled model (called LMDZ-4)-were bias corrected. After the validation of the bias-corrected data, the original and the biascorrected GCM data were used to force two global hydrology models (GHMs): 1) the hydrological model of the Max Planck Institute for Meteorology (MPI-HM) consisting of the simplified land surface (SL) scheme and the hydrological discharge (HD) model, and 2) the dynamic global vegetation model called LPJmL. The impact of the bias correction on the projected simulated hydrological changes is analyzed, and the simulation results of the two GHMs are compared. Here, the projected changes in 2071-2100 are considered relative to 1961-90. It is shown for both GHMs that the usage of bias-corrected GCM data leads to an improved simulation of river runoff for most catchments. But it is also found that the bias correction has an impact on the climate change signal for specific locations and months, thereby identifying another level of uncertainty in the modeling chain from the GCM to the simulated changes calculated by the GHMs. This uncertainty may be of the same order of magnitude as uncertainty related to the choice of the GCM or GHM. Note that this uncertainty is primarily attached to the GCM and only becomes obvious by applying the statistical bias correction methodology.