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Aridity is a complex concept that ideally requires a comprehensive assessment of hydroclimatological and hydroecological variables to fully understand anticipated changes. A widely used (offline) impact model to assess projected changes in aridity is the aridity index (AI) (defined as the ratio of potential evaporation to precipitation), summarizing the aridity concept into a single number. Based on the AI, it was shown that aridity will generally increase under conditions of increased CO2 and associated global warming. However, assessing the same climate model output directly suggests a more nuanced response of aridity to global warming, raising the question if the AI provides a good representation of the complex nature of anticipated aridity changes. By systematically comparing projections of the AI against projections for various hydroclimatological and ecohydrological variables, we show that the AI generally provides a rather poor proxy for projected aridity conditions. Direct climate model output is shown to contradict signals of increasing aridity obtained from the AI in at least half of the global land area with robust change. We further show that part of this discrepancy can be related to the parameterization of potential evaporation. Especially the most commonly used potential evaporation model likely leads to an overestimation of future aridity due to incorrect assumptions under increasing atmospheric CO2. Our results show that AI-based approaches do not correctly communicate changes projected by the fully coupled climate models. The solution is to directly analyse the model outputs rather than use a separate offline impact model. We thus urge for a direct and joint assessment of climate model output when assessing future aridity changes rather than using simple index-based impact models that use climate model output as input and are potentially subject to significant biases.

Water scarcity is rapidly increasing in many regions. In a novel, multi-model assessment, we examine how human interventions (HI: land use and land cover change, man-made reservoirs and human water use) affected monthly river water availability and water scarcity over the period 1971 - 2010. Here we show that HI drastically change the critical dimensions of water scarcity, aggravating water scarcity for 8.8%(7.4 - 16.5 %) ) of the global population but alleviating it for another 8.3 % (6.4 -15.8 %). Positive impacts of HI mostly occur upstream, whereas HI aggravate water scarcity downstream; HI cause water scarcity to travel downstream. Attribution of water scarcity changes to HI components is complex and varies among the hydrological models. Seasonal variation in impacts and dominant HI components is also substantial. A thorough consideration of the spatially and temporally varying interactions among HI components and of uncertainties is therefore crucial for the success of water scarcity adaptation by HI.

Mountains are the water towers of the world, supplying a substantial part of both natural and anthropogenic water demands 1 , 2 . They are highly sensitive and prone to climate change 3 , 4 , yet their importance and vulnerability have not been quantified at the global scale. Here we present a global water tower index (WTI), which ranks all water towers in terms of their water-supplying role and the downstream dependence of ecosystems and society. For each water tower, we assess its vulnerability related to water stress, governance, hydropolitical tension and future climatic and socio-economic changes. We conclude that the most important (highest WTI) water towers are also among the most vulnerable, and that climatic and socio-economic changes will affect them profoundly. This could negatively impact 1.9 billion people living in (0.3 billion) or directly downstream of (1.6 billion) mountainous areas. Immediate action is required to safeguard the future of the world’s most important and vulnerable water towers. The worldwide distribution and water supply of water towers (snowy or glacierized mountain ranges) is indexed, showing that the most important water towers are also the most vulnerable to socio-economic and climate-change stresses, with huge potential negative impacts on populations downstream.

To sustain growing food demand and increasing standard of living, global water withdrawal and consumptive water use have been increasing rapidly. To analyze the human perturbation on water resources consistently over large scales, a number of macro-scale hydrological models (MHMs) have been developed in recent decades. However, few models consider the interaction between terrestrial water fluxes, and human activities and associated water use, and even fewer models distinguish water use from surface water and groundwater resources. Here, we couple a global water demand model with a global hydrological model and dynamically simulate daily water withdrawal and consumptive water use over the period 1979–2010, using two re-analysis products: ERA-Interim and MERRA. We explicitly take into account the mutual feedback between supply and demand, and implement a newly developed water allocation scheme to distinguish surface water and groundwater use. Moreover, we include a new irrigation scheme, which works dynamically with a daily surface and soil water balance, and incorporate the newly available extensive Global Reservoir and Dams data set (GRanD). Simulated surface water and groundwater withdrawals generally show good agreement with reported national and subnational statistics. The results show a consistent increase in both surface water and groundwater use worldwide, with a more rapid increase in groundwater use since the 1990s. Human impacts on terrestrial water storage (TWS) signals are evident, altering the seasonal and interannual variability. This alteration is particularly large over heavily regulated basins such as the Colorado and the Columbia, and over the major irrigated basins such as the Mississippi, the Indus, and the Ganges. Including human water use and associated reservoir operations generally improves the correlation of simulated TWS anomalies with those of the GRACE observations.

To sustain growing food demand and increasing standard of living, global water use increased by nearly 6 times during the last 100 years, and continues to grow. As water demands get closer and closer to the water availability in many regions, each drop of water becomes increasingly valuable and water must be managed more efficiently and intensively. However, soaring water use worsens water scarcity conditions already prevalent in semi-arid and arid regions, increasing uncertainty for sustainable food production and economic development. Planning for future development and investments requires that we prepare water projections for the future. However, estimations are complicated because the future of the world's waters will be influenced by a combination of environmental, social, economic, and political factors, and there is only limited knowledge and data available about freshwater resources and how they are being used. The Water Futures and Solutions (WFaS) initiative coordinates its work with other ongoing scenario efforts for the sake of establishing a consistent set of new global water scenarios based on the shared socio-economic pathways (SSPs) and the representative concentration pathways (RCPs). The WFaS \"fast-track\" assessment uses three global water models, namely H08, PCR-GLOBWB, and WaterGAP. This study assesses the state of the art for estimating and projecting water use regionally and globally in a consistent manner. It provides an overview of different approaches, the uncertainty, strengths and weaknesses of the various estimation methods, types of management and policy decisions for which the current estimation methods are useful. We also discuss additional information most needed to be able to improve water use estimates and be able to assess a greater range of management options across the water–energy–climate nexus.

Climate change impacts on water availability and hydrological extremes are major concerns as regards the Sustainable Development Goals. Impacts on hydrology are normally investigated as part of a modelling chain, in which climate projections from multiple climate models are used as inputs to multiple impact models, under different greenhouse gas emissions scenarios, which result in different amounts of global temperature rise. While the goal is generally to investigate the relevance of changes in climate for the water cycle, water resources or hydrological extremes, it is often the case that variations in other components of the model chain obscure the effect of climate scenario variation. This is particularly important when assessing the impacts of relatively lower magnitudes of global warming, such as those associated with the aspirational goals of the Paris Agreement. In our study, we use ANOVA (analyses of variance) to allocate and quantify the main sources of uncertainty in the hydrological impact modelling chain. In turn we determine the statistical significance of different sources of uncertainty. We achieve this by using a set of five climate models and up to 13 hydrological models, for nine large scale river basins across the globe, under four emissions scenarios. The impact variable we consider in our analysis is daily river discharge. We analyze overall water availability and flow regime, including seasonality, high flows and low flows. Scaling effects are investigated by separately looking at discharge generated by global and regional hydrological models respectively. Finally, we compare our results with other recently published studies. We find that small differences in global temperature rise associated with some emissions scenarios have mostly significant impacts on river discharge-however, climate model related uncertainty is so large that it obscures the sensitivity of the hydrological system.

Growing population and water demand have increased pressure on water resources in various parts of the globe, including many transboundary river basins. While the impacts of upstream water use on downstream water availability have been analysed in many of these international river basins, this has not been systematically done at the global scale using coherent and comparable datasets. In this study, we aim to assess the change in downstream water stress due to upstream water use in the world's transboundary river basins. Water stress was first calculated considering only local water use of each sub-basin based on country-basin mesh, then compared with the situation when upstream water use was subtracted from downstream water availability. We found that water stress was generally already high when considering only local water use, affecting 0.95-1.44 billion people or 33%-51% of the population in transboundary river basins. After accounting for upstream water use, stress level increased by at least 1 percentage-point for 30-65 sub-basins, affecting 0.29-1.13 billion people. Altogether 288 out of 298 middle-stream and downstream sub-basin areas experienced some change in stress level. Further, we assessed whether there is a link between increased water stress due to upstream water use and the number of conflictive and cooperative events in the transboundary river basins, as captured by two prominent databases. No direct relationship was found. This supports the argument that conflicts and cooperation events originate from a combination of different drivers, among which upstream-induced water stress may play a role. Our findings contribute to better understanding of upstream-downstream dynamics in water stress to help address water allocation problems.

Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of mean, high- and low-flows. The analysis is performed for 471 gauging stations across the globe for the period 1971-2010. We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. The inclusion of HIP leads to a significant decrease in the bias of the long-term mean monthly discharge in 36%-73% of the studied catchments, and an improvement in the modeled hydrological variability in 31%-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in the simulated high-flows, it can lead to either increases or decreases in the low-flows. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties. Even with the inclusion of HIP, we find that the model performance is still not optimal. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. The large variation in performance between GHMs, regions and performance indicators, calls for a careful selection of GHMs, model components and evaluation metrics in future model applications.

During the past decades, human water use has more than doubled, yet available freshwater resources are finite. As a result, water scarcity has been prevalent in various regions of the world. Here, we present the first global assessment of past development of water stress considering not only climate variability but also growing water demand, desalinated water use and non-renewable groundwater abstraction over the period 1960–2001 at a spatial resolution of 0.5°. Agricultural water demand is estimated based on past extents of irrigated areas and livestock densities. We approximate past economic development based on GDP, energy and household consumption and electricity production, which are subsequently used together with population numbers to estimate industrial and domestic water demand. Climate variability is expressed by simulated blue water availability defined by freshwater in rivers, lakes, wetlands and reservoirs by means of the global hydrological model PCR-GLOBWB. We thus define blue water stress by comparing blue water availability with corresponding net total blue water demand by means of the commonly used, Water Scarcity Index. The results show a drastic increase in the global population living under water-stressed conditions (i.e. moderate to high water stress) due to growing water demand, primarily for irrigation, which has more than doubled from 1708/818 to 3708/1832 km3 yr−1 (gross/net) over the period 1960–2000. We estimate that 800 million people or 27% of the global population were living under water-stressed conditions for 1960. This number is eventually increased to 2.6 billion or 43% for 2000. Our results indicate that increased water demand is a decisive factor for heightened water stress in various regions such as India and North China, enhancing the intensity of water stress up to 200%, while climate variability is often a main determinant of extreme events. However, our results also suggest that in several emerging and developing economies (e.g. India, Turkey, Romania and Cuba) some of past extreme events were anthropogenically driven due to increased water demand rather than being climate-induced.

Relativistic Runaway Electrons Avalanches (RREAs) development depends on the applied electric field and the environment's air density. This dependency controls the RREA exponential growth length scale. The RREA development affects the bremsstrahlung excess occurring due to the passage of charged particles through the thundercloud's electric fields, the gamma‐ray glow. We used Monte Carlo simulations to develop an empirical model showing the RREA behavior in a realistic atmospheric density profile. The new formulation shows how the density variation modulates the electron population under electric field strengths near the RREA electric field threshold. The model limits the initial RREA altitude range as a function of the electric field strength. The new model is valid between ∼0.6 and ∼18 km, covering the relevant heights to investigate the generation of ground‐detected gamma‐ray glows. Plain Language Summary Thunderclouds are energy sources for trespassing charged particles from cosmic rays. This extra energy gain may induce electron avalanches, known as Relativistic Runaway Electron Avalanches (RREAs), resulting in an enhanced gamma‐ray flux via bremsstrahlung, the gamma‐ray glow. Recent studies relate this enhancement to electric field strengths close to the RREA requirement. The atmospheric density variations affect avalanche development by modifying the RREA requirement, resulting in isolated avalanches by imposing limits to the avalanche's initial altitude. We show how RREAs develop in a realistic atmospheric density profile. We present a modification on the characteristic avalanche length under this condition. The initial avalanche altitude is crucial because it completely modifies the density profile trespassed by a downward electron shower. Finally, we discuss the consequences of isolated RREAs for high‐energy emissions and show that the electric field strength constrains the possible initial altitudes for the gamma‐ray glow. Key Points A new empirical model quantifies how electron avalanches vanish because of atmospheric density variations with ∼10% accuracy The model limits the initial altitude of electron avalanche development for electric field strengths near the avalanche threshold We narrow the possible gamma‐ray glow source height range with the new model which is valid through ∼0.6–18 km