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Global water models are widely used for policy-making and in scientific studies, but substantial inter-model differences highlight the need for additional evaluation. Here we evaluate global water models by assessing so-called functional relationships between system forcing and response variables. The more widely used comparisons between observed and simulated fluxes provide insight into model behavior for the representative area of an observation, and can therefore potentially improve the model for that area. Functional relationships, by contrast, aim to capture how system forcing and response variables co-vary across large scales, and thus offer the potential for model improvement over large areas. Using 30-year annual averages from 8 global water models, we quantify such functional relationships by calculating correlations between key forcing variables (precipitation, net radiation) and water fluxes (actual evapotranspiration, groundwater recharge, total runoff). We find strong disagreement for groundwater recharge, some disagreement for total runoff, and the best agreement for evapotranspiration. Observation- and theory-derived functional relationships show varying agreements with models, indicating where model representations and our process understanding are particularly uncertain. Overall, our results suggest that model improvement is most important for the representation of energy balance processes, recharge processes, and generally for model behavior in dry and cold regions. We argue that advancing our ability to simulate global hydrology requires a better perceptual understanding of the global water cycle. To evaluate if our models match that understanding, we should explore alternative evaluation strategies, such as the use of functional relationships.

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

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., 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. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

Blue-Green Infrastructure (BGI, including swales, green roofs, and wetlands) plays an important role in reducing vulnerability to climate change risks such as flooding, heat stress, and water shortages, while enhancing urban environments and quality of life for citizens. Understanding the perceptions that professional stakeholders have of BGI is fundamental in addressing barriers to implementation. A novel application of the Implicit Association Test (IAT) is developed to investigate and compare implicit (unconscious) perceptions of blue-green and grey infrastructure with explicit (conscious) attitudes. This is the first time an IAT about BGI has focused on professional stakeholders. Blue-green and grey infrastructure are perceived positively by the sample population. Overall, respondents implicitly and explicitly prefer BGI to grey infrastructure, and regard it as safer, tidier, more attractive, useful, valuable, and necessary. The individual positive explicit perceptions of grey infrastructure, nonetheless, suggest that integrated blue-green and grey systems may be preferable for professional stakeholders to incorporate into water management and climate change adaptation strategies.

Process-based impact models are frequently used for a range of applications and are valuable for simulating fundamental processes in a changing world. Model Intercomparison Projects like the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, www.isimip.org) act as an umbrella for various sectors (e.g. water, agriculture, health) and numerous modelling teams that are following a common modeling protocol that enables model intercomparison and (cross-) sectoral multi-model impact assessments. However, such assessments require reliable model outputs which can be checked from two perspectives.First, a quality control (QC) check ensures that simulated files follow the standards defined in the modelling protocol and includes plausibility checks. For example, structural inconsistencies and correct metadata entries can be assessed, but also in cases where the range of a specific variable exceeds plausibility limits (e.g. negative precipitation values), such a tool can facilitate error checking which is very helpful especially in the case of high data volume simulation outputs (e.g., errors stemming from an erroneous unit conversion).Second, a quality assessment (QA) tool compares model output to observation data or benchmark models. This is particularly important for model development and improvement as it can highlight benefits and limitations of models for e.g., specific model configurations, but it also informs the identification of models that are best suited for specific regions and research questions.Within the EU COST-Action “Process-based models for climate impact attribution across sectors“ (PROCLIAS), the aim is to establish a QC/QA workflow for the ISIMIP models. A QC tool is already developed and in operation which checks the data format and, exemplarily for the global water sector, each variable for plausibility ranges. An operational QA tool does not yet exist within PROCLIAS and ISIMIP but some experiences have been gained with existing evaluation frameworks such as ILAMB and the ESMValTool.This presentation provides experiences gained with the QC tool and the application of ISIMIP data to existing QA frameworks and outlines the next milestones. It is planned to extend the plausibility ranges to all ISIMIP sectors by a survey within the modelling teams. For the QA tool, specific developments are required to integrate sector-specific evaluation methods (e.g., basin outlines into ILAMB). To use ESMValTool, the model output data needs to be restructured to a CF-compliant format. With the ISIMIP global water sector as a pilot sector, experiences are gained that will then be transferred to other sectors. This activity also calls for an exchange of ideas and experiences from other modeling communities.

Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated simulations of hydrological droughts from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed hydrological droughts of at least one month duration, over 31 years (1971–2001). Hydrological drought events were identified from runoff-deficits and the Standardised Runoff Index (SRI). In all catchments, the CHMs performed relatively better than the GHMs, for simulating monthly runoff-deficits. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means, have limited abilities to accurately simulate drought events in all eight catchments, in terms of their occurrence and magnitude. Overall, there are opportunities to improve both CHMs and GHMs for better characterisation of hydrological droughts.

The hydrological characteristics of a river, including the magnitude and timing of high and low flows, are important determinants of its ecological functioning. Climate change will alter these characteristics, triggering ecological changes in river ecosystems. This study assesses risks of ecological change in 321 major river basins across the globe due to global warming relative to pre‐industrial conditions of 1.0, 1.5, 2.0 and 3.0°C. Risks associated with climate‐driven changes to high and low flows, relative to baseline (1980–2010; 0.6°C warming), are investigated using simulations from nine global hydrological models forced with climate projections from five global climate models, resulting in an ensemble of 14,445 baseline‐scenario members for each warming scenario (9 × 5 × 321). At the global‐scale, the likelihood of high risks of significant ecological change in both high and low flows increase with global warming: across all basins there is a medium‐high risk of change in high (low) flows in 21.4% (22.4%) of ensemble members for 1.0°C warming, increasing to 61.5% (63.2%) for 3.0°C. Risks are particularly pronounced for low flows at 3.0°C for many rivers in South America, southern Africa, Australia, southern Europe and central and eastern USA. Results suggest that boreal regions are least likely to see significant ecological change due to modified river flows but this may be partly the result of the exclusion of processes such as permafrost dynamics from most global hydrological models. The study highlights the ecological fragility and spatial heterogeneity of the risks that unmitigated climate change poses to global river ecosystems.

Droughts are anticipated to intensify in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought indices, makes it difficult to compare drought assessments. This issue is widely known, but its relative importance has never been quantitatively evaluated in comparison to other sources of uncertainty. Here, encompassing three drought categories (meteorological, agricultural, and hydrological droughts) with four temporal scales of interest, we evaluated changes in the drought frequency using multi-model and multi-scenario simulations to identify areas where the definition issue could result in pronounced uncertainties and to what extent. We investigated the disagreement in the signs of changes between drought definitions and decomposed the variance into four main factors: drought definitions, greenhouse gas concentration scenarios, global climate models, and global water models, as well as their interactions. The results show that models were the primary sources of variance over 82% of the global land area. On the other hand, the drought definition was the dominant source of variance in the remaining 17%, especially in parts of northern high-latitudes. Our results highlight specific regions where differences in drought definitions result in a large spread among projections, including areas showing opposite signs of significant changes. At a global scale, 7% of the variance resulted independently from the definition issue, and that value increased to 44% when 1st and 2nd order interactions were considered. The quantitative results suggest that by clarifying hydrological processes or sectors of interest, one could avoid these uncertainties in drought assessments to obtain a clearer picture of future drought change.

Human actions and climate change have drastically altered river flows across the world, resulting in adverse effects on riverine ecosystems. Environmental flows (EFs) have emerged as a prominent tool for safeguarding riverine ecosystems. However, at the global scale, the assessment of EFs is associated with significant uncertainty. Here, we present a novel method to determine EFs by Environmental Flow Envelopes (EFE), which is an envelope of variability bounded by discharge limits within which riverine ecosystems are not seriously compromised. The EFE is defined globally in approximately 4,400 sub–basins at monthly time resolution, considering also the methodological uncertainties related with global EF studies. In addition to a lower bound of discharge, the EFE introduces an upper bound of discharge, identifying areas where streamflow has increased substantially. Further, instead of only showing whether EFs are violated, as commonly done, we quantify, for the first time, the frequency, severity, and trends of EFE violations, which can be considered as potential threats to riverine ecosystems. We use pre–industrial (1801–1860) quasi-natural discharge and a suite of hydrological EFR methods and global hydrological models to estimate EFE, applying data from the ISIMIP 2b ensemble. We then compare the EFEs to recent past (1976–2005) discharge to assess the violations of the EFE. We found that the EFE violations most commonly manifest themselves by insufficient streamflow during the low flow season, with less violations during intermediate flow season, and only few violations during high flow season. These violations are widespread: discharge in half of the sub–basins of the world has violated the EFE during more than 5 % of the months between 1976 and 2005. The trends in EFE violations have mainly been increasing during the past decades and will likely remain problematic with projected increases in anthropogenic water use and hydroclimatic changes. Indications of excessive streamflow through EFE upper bound violations are relatively scarce and spatially distributed, although signs of increasing trends can be identified and potentially attributed to climate change. While the EFE provides a quick and globally robust way of determining environmental flow allocations at the sub–basin scale, local fine–tuning is necessary for practical applications and further research on the coupling between quantitative discharge and riverine ecosystem responses is required.

Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated runoff simulations from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed monthly runoff and hydrological droughts over 31 years (1971–2001). Observed and simulated hydrological drought events were identified using the Standardised Runoff Index (SRI) and were classified based on intensity. Our results show that for all eight catchments, CHMs out-performed GHMs in monthly runoff estimation showing a better representation of observed runoff than GHMs. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means present limited ability to accurately simulate severe drought events in all eight catchments, in terms of their timing and intensity. By analysing the monthly runoff time-series for several extreme droughts over the historical period, we identify room for improvement in the models so that extreme droughts may ultimately be better represented by both CHMs and GHMs.

Droughts are anticipated to intensify or become more frequent in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought definition, makes it difficult to compare drought projections and hampers overviewing future changes in drought. This issue is widely known and underscored in recent reports of the Intergovernmental Panel on Climate Change, but the relative importance of the issue and its spatial distribution have never been quantitatively evaluated compared to other sources of uncertainty.Here, using a multi-scenario and multi-model dataset with combinations of three climate change scenarios, four global climate models and seven global water models, we evaluated changes in the frequency of three categories of drought (meteorological, agricultural, and hydrological droughts) by a consistent standardized approach with four different temporal scales of accumulation periods to show how differences among the drought definitions could result in critical uncertainties. For simplicity, this study focuses on one drought index per drought category. Firstly we investigated the disagreement in the sign of changes between definitions, and then we decomposed the overall uncertainty to estimate the relative importance of each source of uncertainty. By a multifactorial ANOVA, uncertainty was decomposed into four main factors, namely drought definitions, climate change scenarios, global climate models and global water impact models, and their interactions.Our results highlight specific regions where the sign of change disagrees between drought definitions. Importantly, changes in drought frequency in such regions tended to be statistically insignificant with low ensemble member agreement. Drought definition attributed to18% of the main factor uncertainty at the global scale, and the definition was the dominant uncertainty source over 11% of the global land area. The contribution of difference in the drought category showed a higher contribution to overall uncertainty than the difference in scales. The contribution of scenario uncertainty was the least among the main factors in general, though it is a dominant factor in the far-future in a couple of hotspot regions such as the Mediterranean region. Overall, model uncertainties were the primary source of uncertainty, and the definition issue was less important over large areas. However, definition uncertainty was the primal uncertainty source with significant changes in particular regions, such as parts of high-latitude areas in the northern hemisphere. One needs to pay attention to these regions in overviewing future drought change. Nonetheless, what this study quantified is the relative importance of uncertainty stemming from drought definition that should be avoidable or reducible if one treats drought specifically. Our results indicate that we can reduce uncertainty in drought projections to some extent and get a clearer picture by clarifying hydrological processes or sectors of interest.

Blue-Green infrastructure (BGI) is recognised internationally as an approach for managing urban water challenges while enhancing society and the environment through the provision of multiple co-benefits. This research employed an online survey to investigate the perceptions of BGI held by professional stakeholders in four cities with established BGI programs: Newcastle (UK), Ningbo (China), Portland (Oregon USA), and Rotterdam (Netherlands) (64 respondents). The results show that challenges associated with having too much water (e.g., pluvial and fluvial flood risk, water quality deterioration) are driving urban water management agendas. Perceptions of governance drivers for BGI implementation, BGI leaders, and strategies for improving BGI uptake, are markedly different in the four cities reflecting the varied local, regional and national responsibilities for BGI implementation. In addition to managing urban water, BGI is universally valued for its positive impact on residents’ quality of life, however, a transformative change in policy and practice towards truly multifunctional infrastructure is needed to optimise the delivery of multiple BGI benefits to address each city’s priorities and strategic objectives. Changes needed to improve BGI uptake, e.g., increasing the awareness of policy-makers to multifunctional BGI, has international relevance for other cities on their journeys to sustainable blue-green futures.

Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.

Summarization: The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. Presented on: Earth's Future

Water resources management over long term has faced a great challenge due to the increasing demands on water from a growing number of population and a huge variance of water usage in different time and place. Therefore, a new time series model based on Recurrent Neural Network (RNN), has been proposed and developed in this study for intelligent prediction of future water level in different reservoirs. We have carried out experiments on reservoirs in Ningbo, China, and the results have shown that our proposed model is more efficient on intelligent prediction of water level in reservoirs.

Heat uptake is a key variable for understanding the Earth system response to greenhouse gas forcing. Despite the importance of this heat budget, heat uptake by inland waters has so far not been quantified. Here we use a unique combination of global‐scale lake models, global hydrological models and Earth system models to quantify global heat uptake by natural lakes, reservoirs, and rivers. The total net heat uptake by inland waters amounts to 2.6 ± 3.2 ×1020 J over the period 1900–2020, corresponding to 3.6% of the energy stored on land. The overall uptake is dominated by natural lakes (111.7%), followed by reservoir warming (2.3%). Rivers contribute negatively (‐14%) due to a decreasing water volume. The thermal energy of water stored in artificial reservoirs exceeds inland water heat uptake by a factor ∼10.4. This first quantification underlines that the heat uptake by inland waters is relatively small, but non‐negligible., Geophysical Research Letters, 47 (12), ISSN:0094-8276, ISSN:1944-8007

Hydrological models have been developed in response to the need to understand the complex water cycle of the Earth and to assess its interaction with historical and future climate scenarios. In the global water sector of the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b), six land surface models (LSMs), eight hydrological models (GHMs), and one dynamic vegetation model (DGVM) are contributing with transient simulations spanning from 1660 to 2300. The model simulations follow a common protocol and are driven by common bias adjusted climate model outputs combined with plausible socio-economic scenarios and representative concentration pathways. The main goal of this study is to highlight similarities and differences among these models in simulating the vertical water balance. The main similarity of these models consists in the water cycle simulation, even if the models have been developed for different purposes such as energy cycle (LSMs), water cycle (GHMs), or vegetation cycle (DGVM) simulation. In particular, we address the following research question: 1) what equations are used to compute water storages and water fluxes; 2) how different are the equations among the models; 3) how the equations were adjusted; 4) how many parameters are used by the models; 5) how often the parameters are used; 6) how similar or different are the parameters among the models. To this end, we apply a standard writing style of the water storages and water fluxes included in the models, to easily identify the similarities and differences among them. Most of the models include in their structure the canopy, soil, and snow storages, and almost half of them include the groundwater storage. Furthermore, we find that: 1) a model needs a very good documentation, this would help to easily identify and understand the equations in the code; 2) some modelers teams use common approaches resulting in similar equations of water storages or water fluxes, but different models structures still lead to different models results; 3) collaboration and communication among the modelers are necessary, on the one hand, for the realization of the models standard writing style, and on the other hand, for a better understanding of the models themselves, especially their strengths, limitations and results. Overall, our results (i) help to better explain the different models results and to attribute these to the differences in simulating specific processes; (ii) contribute to the remarkable efforts in creating a common protocol and a common input datasets for well-defined simulations; (iii) foster a better understanding of how the models work and finding new ways of improvement and development.

Labour productivity declines in hot conditions. The frequency and intensities of extreme heat events is projected to increase substantially with climate change across the world, which causes not only severe impacts on health and well-being but could also lead to adverse impacts on the economy in particular in developing countries. Wet bulb globe temperature (WBGT) is a commonly used metric that combines temperature and humidity to estimate the occurrence of heat stress in occupational health. Although the links between heat stress and economic effects are well established, there are substantial differences between existing impact models of labour productivity. Here we present results of future changes in labour productivity based on a comprehensive intercomparison of labour productivity models across indoor and outdoor working environments, locations and countries. Under the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we applied projections from multiple bias corrected global climate models to multiple labour productivity impact models and consider different socioeconomic futures. In addition to models used in existing literature, we use a newly developed model based on empirical exposure-response functions estimated from three- hundred surveys (56 million observations) from 89 countries, that allows for projections at the sub-national level. Based on our model intercomparison results, we can provide robust and spatially explicit projections for changes in labour productivity across the globe. At the same time, our approach allows us to assess and compare existing models of labour productivity estimates, therefore covering multiple dimensions of uncertainty.

Heat uptake is a key variable for understanding Earth system response to greenhouse gas forcing. Recent assessments highlighted that most of the excess energy is stored in the oceans, whereas the land, atmosphere and ice melt take up smaller amounts. However, despite the importance of this heat budget, heat uptake by inland waters has so far not been quantified. Here we use a unique combination of global-scale lake models, global hydrological models and Earth system models to, for the first time, quantify global heat uptake by lakes, reservoirs and rivers over the industrial period (1900-2020). We use a total of 16 different simulations of global-scale lake models and global hydrological models driven by the same bias-corrected climate forcing from four different global climate models, conducted within the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). The model output is combined with reservoir and lake data from the Global Reservoir and Dam (GRanD) database and HydroLAKES. Total inland water heat uptake in the industrial period amounts to 2.8 ± 4.3x1020 J by the end of the period, with the largest uptake realised after 1990. The overall uptake is dominated by warming of natural lakes (2.9 ± 2.0x1020 J, the multi-model mean and standard deviation; 103% of total inland water heat uptake), followed by reservoir warming (5.9 ± 2.7x1018 J; 2.1%). The multi-model mean heat uptake by rivers contributes negatively to the total heat uptake (-0.15 ± 4.3x1020 J; -5.3%), but encompasses a large uncertainty originating from the river storage term, simulated by the global hydrological models. The global picture of positive heat uptake by natural lakes is confirmed at the regional scale in the major lake regions by all global-scale lake model and global climate model combinations. The heat uptake by inland waters makes up ~3.2% of continental heat uptake reported in the IPCC AR5 (2013). The rapid increase in dam construction and resulting reservoir expansion in the second half of the 20th century causes a heat redistribution from ocean to land by storing extra water on land. Remarkably, this heat redistribution exceeds the anthropogenic heat uptake by inland waters by a factor of ~ 9.6, adding up to 27 ± 2.1x1020 J. Our results overall underline the importance of inland waters for buffering atmospheric warming through enhanced anthropogenic greenhouse gas concentrations.

Water scarcity has become a major constraint to socio-economic development and a threat to livelihood in increasing parts of the world. Since the late 1980s, water scarcity research has attracted much political and public attention. We here review a variety of indicators that have been developed to capture different characteristics of water scarcity. Population, water availability and water use are the key elements of these indicators. Most of the progress made in the last few decades has been on the quantification of water availability and use by applying spatially explicit models. However, challenges remain on appropriate incorporation of green water (soil moisture), water quality, environmental flow requirements, globalization and virtual water trade in water scarcity assessment. Meanwhile, inter- and intra- annual variability of water availability and use also calls for assessing the temporal dimension of water scarcity. It requires concerted efforts of hydrologists, economists, social scientists, and environmental scientists to develop integrated approaches to capture the multi-faceted nature of water scarcity.

To improve the understanding of trends in extreme flows related to flood events at the global scale, historical and future changes of annual maximum streamflow are investigated, using a comprehensive streamflow archive and six global hydrological models. The models' capacity to characterise trends in annual maximum streamflow is evaluated across 3,666 river gauge locations over the period from 1971 to 2005, focusing on four aspects of trends over continental and global scale: (i) mean, (ii) standard deviation, (iii) percentage of locations showing significant trends and (iv) spatial pattern. Compared to observed trends, simulated trends driven by observed climate forcing generally have a higher mean, lower spread, and a similar percentage of locations showing significant trends. Models show a moderate capacity to simulate spatial patterns of historical trends, with approximately only 12–25 % of the spatial variance of observed trends across all gauge stations accounted for by the simulations. Interestingly, there are significant differences between trends simulated by GHMs forced with historical climate and forced by bias corrected climate model output during the historical period, suggesting the important role of the stochastic natural (decadal, inter-annual) climate variability. Significant differences were found in simulated flood trend results when averaged only at gauged locations compared to when averaging across all simulated grid cells, highlighting the potential for bias toward well-observed regions in the state-of-understanding of changes in floods. Future climate projections (simulated under RCP2.6 and RCP6.0 greenhouse gas concentration scenario) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or decrease) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of high-risk locations under RCP6.0 greenhouse gas concentration scenario by more than an order of magnitude (0.9 % compared to 11.7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change.

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

Synoptic climatology has a long history of research where weather data are aggregated and composited to gain a better understanding of atmospheric effects on non-atmospheric variables. This has resulted in an applied scientific discipline that yields methods and tools designed for applications across disciplinary boundaries. The spatial synoptic classification (SSC) is an example of such a tool that helps researcher bridge methodological gaps between disciplines, especially those studying weather effects on human health. The SSC has been applied in several multi-discipline projects, and it appears that there is ample opportunity for growth into new topical areas. Likewise, there is opportunity for the SSC network to be expanded across the globe, especially into mid-latitude locations in the southern hemisphere. There is some question of the utility of the SSC in tropical locations, but such decisions must be based on the actual weather data from individual locations. Despite all of the strengths and potential uses of the SSC, there are some research problems, some locations, and some datasets for which it is not suitable. Nevertheless, the success of the SSC as a cross-disciplinary method is noteworthy because it has become a catalyst for collaboration.

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.

Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. Here, a thermal sharpening technique is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed Super-Resolution Thermal Sharpener (SRTS) does not rely on any surface index, ensuring the independence of the derived LST to be as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: the thermal sharpening algorithm (TsHARP), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM). The privilege of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, even without requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable.

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.

Extreme weather causes substantial adverse socio-economic impacts by damaging and disrupting the infrastructure services that underpin modern society. Globally, $2.5tn a year is spent on infrastructure which is typically designed to last decades, over which period projected changes in the climate will modify infrastructure performance. A systems approach has been developed to assess risks across all infrastructure sectors to guide national policy making and adaptation investment. The method analyses diverse evidence of climate risks and adaptation actions, to assess the urgency and extent of adaptation required. Application to the UK shows that despite recent adaptation efforts, risks to infrastructure outweigh opportunities. Flooding is the greatest risk to all infrastructure sectors: even if the Paris Agreement to limit global warming to 2°C is achieved, the number of users reliant on electricity infrastructure at risk of flooding would double, while a 4°C rise could triple UK flood damage. Other risks are significant, for example 5% and 20% of river catchments would be unable to meet water demand with 2°C and 4°C global warming respectively. Increased interdependence between infrastructure systems, especially from energy and information and communication technology (ICT), are amplifying risks, but adaptation action is limited by lack of clear responsibilities. A programme to build national capability is urgently required to improve infrastructure risk assessment. This article is part of the theme issue ‘Advances in risk assessment for climate change adaptation policy’.

Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and anessential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties thatare impeding a precise attribution of human-induced climate change. Here, we aim at comparing arange of independent global monthly land ET estimates with historical model simulations from theglobal water, agriculture, and biomes sectors participating in the second phase of the Inter-SectoralImpact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use theEartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensembleproduct (LandFlux-EVAL), and an updated collection of recently published datasets thatalgorithmically derive ET from observations or observations-based estimates (diagnostic datasets). Acluster analysis is applied in order to identify spatio-temporal differences among all datasets and tothus identify factors that dominate overall uncertainties. The clustering is controlled by several factorsincluding the model choice, the meteorological forcing used to drive the assessed models, the datacategory (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates,reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers inthe models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we findthat themodel choicemostly dominates (24%–40%of variance explained), except for spatio-temporalpatterns of total ET, where the forcing explains the largest fraction of the variance (29%). The mostdominant clusters of datasets are further compared with individual diagnostic and reanalysis-basedestimates to assess their representation of selected heat waves and droughts in the Great Plains,Central Europe and western Russia. Although most of the ET estimates capture these extreme events,the generally large spread among the entire ensemble indicates substantial uncertainties.

Background: Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to “adaptation uncertainty” (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. Objectives: This study had three aims: a) Compare the range in projected impacts that arises from using different adaptation modeling methods; b) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c) recommend modeling method(s) to use in future impact assessments. Methods: We estimated impacts for 2070–2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. Results: The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Conclusions: Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634

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., Water scarcity threatens a growing number of global catchments. Here, the authors examine how human interventions (HI) affected water scarcity between 1971 and 2010 and find that HI caused increases in the average duration and occurrence of water scarcity for 32% and 34% of the global population, respectively.

We present one of the first climate change impact assessments on river runoff that utilises an ensemble of global hydrological models (Glob-HMs) and an ensemble of catchment-scale hydrological models (Cat-HMs), across multiple catchments: the upper Amazon, Darling, Ganges, Lena, upper Mississippi, upper Niger, Rhine and Tagus. Relative changes in simulated mean annual runoff (MAR) and four indicators of high and low extreme flows are compared between the two ensembles. The ensemble median values of changes in runoff with three different scenarios of global-mean warming (1, 2 and 3 °C above pre-industrial levels) are generally similar between the two ensembles, although the ensemble spread is often larger for the Glob-HM ensemble. In addition the ensemble spread is normally larger than the difference between the two ensemble medians. Whilst we find compelling evidence for projected runoff changes for the Rhine (decrease), Tagus (decrease) and Lena (increase) with global warming, the sign and magnitude of change for the other catchments is unclear. Our model results highlight that for these three catchments in particular, global climate change mitigation, which limits global-mean temperature rise to below 2 °C above preindustrial levels, could avoid some of the hydrological hazards that could be seen with higher magnitudes of global warming. © 2016 The Author(s) BMBF/01LS1201A Beijing Natural Science Foundation National Natural Science Foundation of China National Science and Technology Major Project Norwegian Research council Islamic Development Bank

Climate change due to anthropogenic greenhouse gas emissions is expected to increase the frequency and intensity of precipitation events, which is likely to affect the probability of flooding into the future. In this paper we use river flow simulations from nine global hydrology and land surface models to explore uncertainties in the potential impacts of climate change on flood hazard at global scale. As an indicator of flood hazard we looked at changes in the 30-y return level of 5-d average peak flows under representative concentration pathway RCP8.5 at the end of this century. Not everywhere does climate change result in an increase in flood hazard: decreases in the magnitude and frequency of the 30-y return level of river flow occur at roughly one-third (20–45%) of the global land grid points, particularly in areas where the hydrograph is dominated by the snowmelt flood peak in spring. In most model experiments, however, an increase in flooding frequency was found in more than half of the grid points. The current 30-y flood peak is projected to occur in more than 1 in 5 y across 5–30% of land grid points. The large-scale patterns of change are remarkably consistent among impact models and even the driving climate models, but at local scale and in individual river basins there can be disagreement even on the sign of change, indicating large modeling uncertainty which needs to be taken into account in local adaptation studies.

We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.

This paper presents a global scale assessment of the impact of climate change on water scarcity. Patterns of climate change from 21 Global Climate Models (GCMs) under four SRES scenarios are applied to a global hydrological model to estimate water resources across 1339 watersheds. The Water Crowding Index (WCI) and the Water Stress Index (WSI) are used to calculate exposure to increases and decreases in global water scarcity due to climate change. 1.6 (WCI) and 2.4 (WSI) billion people are estimated to be currently living within watersheds exposed to water scarcity. Using the WCI, by 2050 under the A1B scenario, 0.5 to 3.1 billion people are exposed to an increase in water scarcity due to climate change (range across 21 GCMs). This represents a higher upper-estimate than previous assessments because scenarios are constructed from a wider range of GCMs. A substantial proportion of the uncertainty in the global-scale effect of climate change on water scarcity is due to uncertainty in the estimates for South Asia and East Asia. Sensitivity to the WCI and WSI thresholds that define water scarcity can be comparable to the sensitivity to climate change pattern. More of the world will see an increase in exposure to water scarcity than a decrease due to climate change but this is not consistent across all climate change patterns. Additionally, investigation of the effects of a set of prescribed global mean temperature change scenarios show rapid increases in water scarcity due to climate change across many regions of the globe, up to 2 °C, followed by stabilisation to 4 °C.