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The climate of High Mountain Asia (HMA) has changed in recent decades. While the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are inconsistent, with substantial temporal and spatial variation. Climate warming will have enormous consequences for hydroclimatic extremes. For the higher altitudes of the HMA, which are a significant source of water for the large rivers in Asia, often trends are calculated using a limited number of in situ observations mainly observed in valleys. This study explores the changes in mean, extreme, and compound-extreme climate variables and their seasonality along the full altitudinal range in HMA using daily ERA5 reanalysis data (1979–2018). Our results show that winter warming and summer wetting dominate the interior part of HMA. The results indicate a coherent significant increasing trend in the occurrence of heatwaves across all regions in HMA. The number of days with heavy precipitation shows more significant trends in southern and eastern basins than in other areas of HMA. The dry period occurrence shows a distinct demarcation between lower- and higher-altitude regions and is increasing for most basins. Although precipitation and temperature show variable tendencies, their compound occurrence is coherent in the monsoon-dominated basins. These changes in indicators of climatic extremes may imply substantial increases in the future occurrence of hazards such as floods, landslides, and droughts, which in turn impact economic production and infrastructure.

The original version of this Article contained errors. The fourth sentence of the first paragraph in the section titled “Halving the proportionof untreated wastewater (SDG6.3)” incorrectly read “The largest expansions required before 2030 are in China (40 million m3), the USA (16 million m3) and India (15 million m3), with these three countries alone accounting for ~45% of the required expansions.” The correct version states ‘billion m3 yr-1’ in place of ‘million m3’. In addition, the first sentence of the caption for Figure 2 incorrectly read “a Expansions in wastewater treatment capacity (106 m3) required by 2030 to achieve SDG6.3 for the top 30 countries”. The correct version states “(109 m3 yr-1)” in place of “(106 m3)”. These errors have been corrected in both the PDF and HTML versions of the Article.

Funding Information: This open-access publication was funded by the University of Freiburg. Funding Information: Financial support. The project is supported by the Wassernetzwerk Baden-Württemberg (Water Research Network of the State Baden-Württemberg), which is funded by the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (Ministry of Science, Research and the Arts of the State Baden-Württemberg) (grant no. AZ. 7532.21/2.1.6) and Maa-ja vesitekniikan tuki ry foundation. Doris E. Wendt acknowledges her support as part of the NERC-funded Groundwater Drought Initiative (NE/R004994/1). Lucy J. Barker was supported by the Natural Environment Research Council (NE/R016429/1) as part of the UK-SCAPE programme delivering National Capability. The contributions of Mirek Trnka, Lenka Bartošová, and Jaak Jaagus have been supported by SustES – Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797). Publisher Copyright: © 2022 Veit Blauhut et al. Drought events and their impacts vary spatially and temporally due to diverse pedo-climatic and hydrologic conditions, as well as variations in exposure and vulnerability, such as demographics and response actions. While hazard severity and frequency of past drought events have been studied in detail, little is known about the effect of drought management strategies on the actual impacts and how the hazard is perceived by relevant stakeholders. In a continental study, we characterised and assessed the impacts and the perceptions of two recent drought events (2018 and 2019) in Europe and examined the relationship between management strategies and drought perception, hazard, and impact. The study was based on a pan-European survey involving national representatives from 28 countries and relevant stakeholders responding to a standard questionnaire. The survey focused on collecting information on stakeholders' perceptions of drought, impacts on water resources and beyond, water availability, and current drought management strategies on national and regional scales. The survey results were compared with the actual drought hazard information registered by the European Drought Observatory (EDO) for 2018 and 2019. The results highlighted high diversity in drought perception across different countries and in values of the implemented drought management strategies to alleviate impacts by increasing national and sub-national awareness and resilience. The study identifies an urgent need to further reduce drought impacts by constructing and implementing a European macro-level drought governance approach, such as a directive, which would strengthen national drought management and mitigate damage to human and natural assets.

Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use (IWU) by crop. The goal of this study is to estimate crop-specific IWU from surface water withdrawals (SWW), total groundwater withdrawals (GWW), and nonrenewable groundwater depletion (GWD). To do this, we employ the PCR-GLOBWB 2 global hydrology model to partition irrigation information from the U.S. Geological Survey Water Use Database to specific crops across the Continental United States (CONUS). We incorporate high-resolution input data on agricultural production and climate within the CONUS to obtain crop-specific irrigation estimates for SWW, GWW, and GWD for 20 crops and crop groups from 2008 to 2020 at county spatial resolution. Over the study period, SWW decreased by 20%, while both GWW and GWD increased by 3%. On average, animal feed (alfalfa/hay) uses the most irrigation water across all water sources: 33 from SWW, 13 from GWW, and 10 km3/yr from GWD. Produce used less SWW (43%), but more GWW (57%), and GWD (27%) over the study time-period. The largest changes in IWU for each water source between the years 2008 and 2020 are: rice (SWW decreased by 71%), sugar beets (GWW increased by 232%), and rapeseed (GWD increased by 405%). These results present the first national-scale assessment of irrigation by crop, water source, and year. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).

Hydrological forecasts are important for operational water management and near-future planning, even more so in light of the increased occurrences of extreme events such as floods and droughts. Having a forecasting framework, which is flexible in terms of input forcings and forecasting locations (local, regional, or national) that can deliver this information in fast and computational efficient manner, is critical. In this study, the suitability of a hybrid forecasting framework, combining data-driven approaches and seasonal (re)forecasting information from dynamical models, to predict hydrological variables was explored. Target variables include discharge and surface water levels for various stations at a national scale, with the Netherlands as the focus. Five different machine learning (ML) models, ranging from simple to more complex and trained on historical observations of discharge, precipitation, evaporation, and seawater levels, were run with seasonal (re)forecast data, including the European Flood Awareness System (EFAS) and ECMWF seasonal forecast system (SEAS5), of these driver variables in a hindcast setting. The results were evaluated using the evaluation metrics, i.e. anomaly correlation coefficient (ACC), continuous ranked probability (skill) score (CRPS and CRPSS), and Brier skill score (BSS), in comparison to a climatological reference hindcast. Aggregating the results of all stations and ML models revealed that the hindcasting framework outperformed the climatological reference forecasts by roughly 60 % for discharge predictions (80 % for surface water level predictions). Skilful prediction for the first lead month, independently of the initialization month, can be made for discharge. The skill extends up to 2–3 months for spring months due to snowmelt dynamic captured in the training phase of the model. Surface water level hindcasts showed similar skill and skilful lead times. While the different ML models showed differences in performance during a testing and training phase using historical observations, running the ML framework in a hindcast setting showed only minor differences between the models, which is attributed to the uncertainty in seasonal forecasts. However, despite being trained on historical observations, the hybrid framework used in this study shows similar skilful predictions to previous large-scale forecasting systems. With our study, we show that a hybrid framework is able to bring location-specific skilful seasonal forecast information with global seasonal forecast inputs. At the same time, our hybrid approach is flexible and fast, and as such, a hybrid framework could be adapted to make it even more interesting to water managers and their needs, for instance, as part of a fast model-predictive control framework.

Dikes often have a long history of reinforcement, with each reinforcement adding new material resulting in a heterogeneous dike. As data on the dike internal heterogeneity is sparse, it is generally overlooked in the stability assessment of dikes. We present an object-based and process-based model simulating dike construction history on archeological dike cross, yielding similar patterns of heterogeneity as observed in real dikes, and apply it in a dike safety assessment. Model predictions improve when being based on more accurate statistics of dike buildup, or when being conditioned to ground truth data. When incorporated in a dike stability assessment, multiple model runs can be coupled to hydrological simulations and dike slope stability calculations, resulting in a probabilistic stability assessment considering internal dike heterogeneity. While high-resolution observations are still sparse, good model accuracies can be reached by combining regional information on dike buildup with local point observations and this model provides a parsimonious basis to include information of internal dike heterogeneity in safety assessments.

Assessment of long-term human exposure to spatiotemporally highly variable air pollution requires accounting for human space–time activity. Individual exposure and space–time track data are not available over large populations and for long periods and a modelling approach is required. However, activity-based exposure models face here challenges in setting up the model and overly-large computations. Aiming for long-term and large-population simulations, we propose an activity model which integrates statistical and agent-based modelling by treating mobility-related variables as random variables. Probability distributions for these variables are estimated or derived from mobility datasets containing observed activities. On top of the activity model, we implemented an exposure model. A case-study of exposure assessment was developed using hourly air pollution maps. The activity model can potentially integrate any mobility data and is thus applicable when limited time activity data is available at the individual level.

Climate change has a large influence on the occurrence of extreme hydrological events. However, reliable estimates of future extreme event probabilities, especially when needed locally, require very long time series with hydrological models, which is often not possible due to computational constraints. In this study we take advantage of two recent developments that allow for more detailed and local estimates of future hydrological extremes. New large climate ensembles (LE) now provide more insight on the occurrence of hydrological extremes as they offer order of magnitude more realizations of future weather. At the same time recent developments in Machine Learning (ML) in hydrology create great opportunities to study current and upcoming problems in a new way, including and combining large amounts of data. In this study, we combined LE together with a local, observation based ML model framework with the goal to see if and how these aspects can be combined and to simulate, assess and produce estimates of hydrological extremes under different warming levels for local scales. For this, first a new post-processing approach was developed that allowed us to use LE simulation data for local applications. The simulation results of discharge extreme events under different warming levels were assessed in terms of frequency, duration and intensity and number of events at national, regional and local scales. Clear seasonal cycles with increased low flow frequency were observed for summer and autumn months as well as increased high flow periods for early spring. For both extreme events, the 3C warmer climate scenario showed the highest percentages. Regional differences were seen in terms of shifts and range. These trends were further refined into location specific results. The shifts and trends observed between the different scenarios were due to a change in climate variability. In this study we show that by combining the wealth of information from LE and the speed and local relevance of ML models we can advance the state-of-the-art when it comes to modeling hydrological extremes under different climate change scenarios for national, regional and local scale assessments providing relevant information for water management in terms of long term planning.

Climate change has caused aridification which can alter habitat vegetation, soil and precipitation profiles potentially affecting resident species. Vegetation and soil profiles are important for subterranean mole-rats as increasing aridity causes soils to become harder and geophytes less evenly distributed, and the inter-geophyte distance increases. Mole-rats obtain all water and dietary requirements from geophytes, and thus digging in harder soils may amplify stressors (hyperthermia, dehydration- or exercise-induced damage). This study assessed the oxidative status of the wild common mole-rat along an aridity gradient (arid, semi-arid and mesic). Kidney and liver oxidative markers, including total oxidant status (TOS), total antioxidant capacity (TAC), oxidative stress index (OSI), malondialdehyde (MDA) and superoxide dismutase (SOD) were measured. Liver oxidative status did not demonstrate any significance with the degree of the aridity gradient. Aridity affected the TAC and OSI of the kidney, with individuals in the most arid habitats possessing the highest TAC. The evolution of increased group size to promote survival in African mole-rats in arid habitats may have resulted in the additional benefit of reduced oxidative stress in the kidneys. The SOD activity of the kidneys was higher than that of the liver with lower oxidative damage, suggesting this species pre-emptively protects its kidneys as these are important for water balance and retention.

In the past decade, drones have become available and affordable for civil applications, including mapping and monitoring the Earth with geophysical sensors. In 2017 and 2019, the feasibility of executing frequency domain electromagnetic (FDEM) surveys using an off-the-shelf drone was investigated at Deltares Institute. This paper reports firstly the preparatory tests executed to determine the optimal instrumental configuration, flight path, data processing and inversion schemes and secondly the three field validation tests executed to demonstrate the feasibility of the drone-borne electromagnetic survey in real-scale applications. At several test sites, the optimal configuration of the drone and electromagnetic instruments, such as the mounting device and distance of the electromagnetic (EM) sensor with respect to the drone, the flight altitude, the coil separation and frequency of the EM source, efficiency and safety, and the assemblage of instrument and drone data were investigated. This has resulted in a robust method to acquire accurate and repeatable in-phase, quadrature and apparent resistivity data, and a workflow for data correction, processing and inversion scheme was developed. During those tests, three EM instruments were tested. The drone-borne electromagnetic (DR-EM) system has the ability and efficacy to fly over inaccessible areas and surface water. Compared to helicopter-borne electromagnetic surveys, the spatial resolution is much higher, which allows very detailed 3D mapping of subsurface targets, and the survey costs are relatively low. Repeated drone-borne electromagnetic (DR-EM) surveys allow low-cost monitoring of local changes in water saturation and salinity.

It is difficult to quantify the amount of groundwater inflow on the Tibetan Plateau (TP), yet it can be critically important for sustaining lake water balance. Here we show that most endorheic lakes on the western TP exhibited considerable water level increase during the ice-covered period, which contrasts with lakes in other regions of the TP. An analysis of lake water balance attributes this water surplus to significant groundwater inflow, which is estimated to be about 59%–66% of total inflow into lakes. The groundwater inflow occurred after the 2000s, which is consistent with the rapid lake expansion and significant increase in precipitation. We suggest that the groundwater inflow is mainly related to large-scale active faults in the limestone bedrock and sufficient meltwater from high elevations. Our results imply that groundwater may be deeply involved in the water cycle and modify the seasonal and inter-annual lake variations on the western TP.

The quality of global water resources is increasingly strained by socio-economic developments and climate change, threatening both human livelihoods and ecosystem health. With inadequately managed wastewater being a key driver of deterioration, Sustainable Development Goal (SDG) 6.3 was established to halve the proportion of untreated wastewater discharged to the environment by 2030. Yet, the impact of achieving SDG6.3 on global ambient water quality is unknown. Addressing this knowledge gap, we develop a high-resolution surface water quality model for salinity as indicated by total dissolved solids, organic pollution as indicated by biological oxygen demand and pathogen pollution as indicated by fecal coliform. Our model includes a novel spatially-explicit approach to incorporate wastewater treatment practices, a key determinant of in-stream pollution. We show that achieving SDG6.3 reduces water pollution, but is still insufficient to improve ambient water quality to below key concentration thresholds in several world regions. Particularly in the developing world, reductions in pollutant loadings are locally effective but transmission of pollution from upstream areas still leads to water quality issues downstream. Our results highlight the need to go beyond the SDG-target for wastewater treatment in order to achieve the overarching goal of clean water for all.

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.

The Ganges-Brahmaputra-Meghna (GBM) delta is one of the world's largest deltas. It is currently experiencing high rates of relative sea-level rise of about 5 mm/year, reflecting anthropogenic climate change and land subsidence. This is expected to accelerate further through the 21st Century, so there are concerns that the GBM delta will be progressively submerged. In this context, a core question is: can sedimentation on the delta surface maintain its elevation relative to sea level? This research seeks to answer this question by applying a two-dimensional flow and morphological model which is capable of handling dynamic interactions between the river and floodplain systems and simulating floodplain sedimentation under different flow-sediment regimes and anthropogenic interventions. We find that across a range of flood frequencies and adaptation scenarios (including the natural polder-free state), the retained volume of sediment varies between 22% and 50% of the corresponding sediment input. This translates to average rates of sedimentation on the delta surface of 5.5 mm/yr to 7.5 mm/yr. Hence, under present conditions, sedimentation associated with quasi-natural conditions can exceed current rates of relative sea-level rise and potentially create new land mass. These findings highlight that encouraging quasi-natural conditions through the widespread application of active sediment management measures has the potential to promote more sustainable outcomes for the GBM delta. Practical measures to promote include tidal river management, and appropriate combinations of cross-dams, bandal-like structures, and dredging.

Countries have implemented control programmes (CPs) for cattle diseases such as bovine viral diarrhoea virus (BVDV) that are tailored to each country-specific situation. Practical methods are needed to assess the output of these CPs in terms of the confidence of freedom from infection that is achieved. As part of the STOC free project, a Bayesian Hidden Markov model was developed, called STOC free model, to estimate the probability of infection at herd-level. In the current study, the STOC free model was applied to BVDV field data in four study regions, from CPs based on ear notch samples. The aim of this study was to estimate the probability of herd-level freedom from BVDV in regions that are not (yet) free. We additionally evaluated the sensitivity of the parameter estimates and predicted probabilities of freedom to the prior distributions for the different model parameters. First, default priors were used in the model to enable comparison of model outputs between study regions. Thereafter, country-specific priors based on expert opinion or historical data were used in the model, to study the influence of the priors on the results and to obtain country-specific estimates. The STOC free model calculates a posterior value for the model parameters (e.g. herd-level test sensitivity and specificity, probability of introduction of infection) and a predicted probability of infection. The probability of freedom from infection was computed as one minus the probability of infection. For dairy herds that were considered free from infection within their own CP, the predicted probabilities of freedom were very high for all study regions ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The priors did have more influence on two of the model parameters, herd-level sensitivity and the probability of remaining infected, due to the low prevalence and incidence of BVDV in the study regions. The advantage of STOC free model compared to scenario tree modelling, the reference method, is that actual data from the CP can be used and estimates are easily updated when new data becomes available. European Food Safety Authority (EFSA) Dutch Ministry of Agriculture, Nature and Food Quality

Global warming-induced melting and thawing of the cryosphere are severely altering the volume and timing of water supplied from High Mountain Asia, adversely affecting downstream food and energy systems that are relied on by billions of people. The construction of more reservoirs designed to regulate streamflow and produce hydropower is a critical part of strategies for adapting to these changes. However, these projects are vulnerable to a complex set of interacting processes that are destabilizing landscapes throughout the region. Ranging in severity and the pace of change, these processes include glacial retreat and detachments, permafrost thaw and associated landslides, rock–ice avalanches, debris flows and outburst floods from glacial lakes and landslide-dammed lakes. The result is large amounts of sediment being mobilized that can fill up reservoirs, cause dam failure and degrade power turbines. Here we recommend forward-looking design and maintenance measures and sustainable sediment management solutions that can help transition towards climate change-resilient dams and reservoirs in High Mountain Asia, in large part based on improved monitoring and prediction of compound and cascading hazards.

Global satellite data quantify changes in sediment flux in 414 rivers

There is growing evidence that climate change impacts ecosystems and socio-economic activities in freshwater environments. Consistent global data of projected streamflow and water temperature are key to global impact assessments, but such a dataset is currently lacking. Here we present FutureStreams, the first global dataset of projected future streamflow and water temperature for multiple climate scenarios (up to 2099) gridded at a 5 arcminute spatial resolution (~10 km at the equator), including recent past data (1976–2005) for comparison. We generated the data using global hydrological and water temperature models (PCR-GLOBWB, DynWat) forced with climate data from five general circulation models. We included four representative concentration pathways to cover multiple future greenhouse gas emission trajectories and associated changes in climate. Our dataset includes weekly streamflow and water temperature for each year as well as a set of derived indicators that are particularly relevant from an ecological perspective. FutureStreams provides a crucial starting point for large-scale assessments of the implications of changes in streamflow and water temperature for society and freshwater ecosystems.

Irrigated agriculture in South Asia depends on meltwater, monsoon rains and groundwater. Climate change alters the hydrology and causes shifts in the timing, composition and magnitude of these sources of water supply. Simultaneously, socio-economic growth increases water demand. Here we use a high-resolution cryosphere–hydrology–crop model forced with an ensemble of climate and socio-economic projections to assess how the sources of irrigation water supply may shift during the twenty-first century. We find increases in the importance of meltwater and groundwater for irrigated agriculture. An earlier melt peak increases meltwater withdrawal at the onset of the cropping season in May and June in the Indus, whereas increasing peak irrigation water demand during July and August aggravates non-renewable groundwater pumping in the Indus and Ganges despite runoff increases. Increasing inter-annual variability in rainfall runoff increases the need for meltwater and groundwater to complement rainfall runoff during future dry years.

Disturbance-dependent grasslands, often associated with hydromorphological and fire dynamics, are threatened, especially in subtropical climates. In the Nepalese and Indian Terai Arc Landscape at the foot of the Himalayas, natural and cultural grasslands serve a viable role for greater one-horned rhinoceros (Rhinoceros unicornis) and for grazers that form prey of the Royal Bengal tiger (Panthera tigris). The grasslands are vulnerable to encroachment of forest. We aimed to establish the effects of environmental drivers, in particular river discharge, river channel dynamics, precipitation and forest fires, on the spatio-temporal dynamics of these grasslands. The study area is the floodplain of the eastern branch of the Karnali River and adjacent western part of Bardia National Park. We created annual time series (1993–2019) of land cover with the use of field data, remotely sensed LANDSAT imagery and a supervised classification model. Additionally, we analysed the pattern of grassland patches and aerial photographs of 1964. Between 1964 and 2019, grassland patches decreased in abundance and size due to encroachment of forest. Outside the floodplain, conversion of grassland to bare substrate coincides with extreme precipitation events. Within the floodplain, conversion of grassland to bare substrate correlates with the magnitude of the annual peak discharge of the bifurcated Karnali River. Since 2009, however, this correlation is absent due to a shift of the main discharge channel to the western branch of the Karnali River. Consequently, alluvial tall grasslands (Saccharum spontaneum dominant) have vastly expanded between 2009 and 2019. Because the hydromorphological processes in the floodplain have become more static, other sources of disturbances – local flooding of ephemeral streams, anthropogenic maintenance, grazing and fires – are more paramount to prevent encroachment of grasslands. Altogether, our findings underscore that a change in the environmental drivers impact the surface area and heterogeneity of grassland patches in the landscape, which can lead to cascading effects for the grassland-dependent megafauna.

Seasonal snow contributes significantly to the annual runoff in the Himalaya and both timing and volume are important for downstream users. In polar regions, meltwater refreezing within snowpacks has been well-studied. While the conditions in the Himalaya are considered favorable for refreezing, little is known about refreezing in this region, hindering a complete understanding of seasonal snowmelt dynamics. In this study, we simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment in the Nepalese Himalaya covering a 5-year period. Thereby, we aim to improve our understanding about how refreezing varies in space and time and to provide a framework for future snow modeling studies. The first part of this study focuses extensively on developing meteorological forcing data, which were derived from an unique elaborate network of meteorological stations and high-resolution meteorological simulations. The snow model was validated against in-situ snow observations and snow cover satellite data. In the second part of this study, we analyze the spatial and temporal refreezing patterns, and attempt to identify possible driving factors. The results show that the annual average refreezing amounts to 122 mm (21% of the total melt). We found that the magnitude of refreezing varies strongly in space depending on elevation and aspect. In addition, there is a strong seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. We also found a substantial intra-annual variability, which mainly results from fluctuations of snowfall, highlighting the importance of using multi-year time series in refreezing assessments. Daily refreezing simulations decreased by 84% (to an average 19 mm year-1) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture diurnal melt-refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to future changes in air temperature, as a temperature increase of 2°C leads to a refreezing decrease of 35%. We conclude that including refreezing with a sub-daily temporal resolution is highly relevant for understanding snow dynamics in the current and future climate of the Himalaya.

Tropical forests modify the conditions they depend on through feedbacks at different spatial scales. These feedbacks shape the hysteresis (history-dependence) of tropical forests, thus controlling their resilience to deforestation and response to climate change. Here, we determine the emergent hysteresis from local-scale tipping points and regional-scale forest-rainfall feedbacks across the tropics under the recent climate and a severe climate-change scenario. By integrating remote sensing, a global hydrological model, and detailed atmospheric moisture tracking simulations, we find that forest-rainfall feedback expands the geographic range of possible forest distributions, especially in the Amazon. The Amazon forest could partially recover from complete deforestation, but may lose that resilience later this century. The Congo forest currently lacks resilience, but is predicted to gain it under climate change, whereas forests in Australasia are resilient under both current and future climates. Our results show how tropical forests shape their own distributions and create the climatic conditions that enable them., Tropical rainforests partly create their own climatic conditions by promoting precipitation, therefore rainforest losses may trigger dramatic shifts. Here the authors combine remote sensing, hydrological modelling, and atmospheric moisture tracking simulations to assess forest-rainfall feedbacks in three major tropical rainforest regions on Earth and simulate potential changes under a severe climate change scenario.

Debris-covered glaciers account for almost one-fifth of the total glacier ice volume in High Mountain Asia; however, their contribution to the total glacier melt remains uncertain, and the drivers controlling this melt are still largely unknown. Debris influences the properties (e.g. albedo, thermal conductivity, roughness) of the glacier surface and thus the surface energy balance and glacier melt. In this study we have used sensitivity tests to assess the effect of surface properties of debris on the spatial distribution of micrometeorological variables such as wind fields, moisture and temperature. Subsequently we investigated how those surface properties drive the turbulent fluxes and eventually the conductive heat flux of a debris-covered glacier. We simulated a debris-covered glacier (Lirung Glacier, Nepal) at a 1 m resolution with the MicroHH model, with boundary conditions retrieved from an automatic weather station (temperature, wind and specific humidity) and unmanned aerial vehicle flights (digital elevation map and surface temperature). The model was validated using eddy covariance data. A sensitivity analysis was then performed to provide insight into how heterogeneous surface variables control the glacier microclimate. Additionally, we show that ice cliffs are local melt hot spots and that turbulent fluxes and local heat advection amplify spatial heterogeneity on the surface. The high spatial variability of small-scale meteorological variables suggests that point-based station observations cannot be simply extrapolated to an entire glacier. These outcomes should be considered in future studies for a better estimation of glacier melt in High Mountain Asia.

Freshwater aquifers in low elevation coastal zones are known to be threatened by saltwater intrusion (SWI). As these areas host a significant share of the world's population, an excellent understanding of this phenomenon is required to effectively manage the availability of freshwater. SWI is a dynamic process, therefore saline groundwater distributions can change quickly over time – particularly in stressed areas with anthropogenic drivers. To model these changes, regional 3D variable-density groundwater (3D-VDG) flow and coupled salt transport models are often used to estimate the current (and future distributions) of saline groundwater. Unfortunately, parameterising 3D-VDG models is a challenging task with many uncertainties. Generally, uncertainty is reduced through the addition of observational data – such as Airborne Electromagnetic (AEM) surveys or ground-based information – that offer information about parameters such as salinity and hydraulic head. Recent research has shown the ability of AEM surveys to provide accurate 3D groundwater salinity models across regional scales, as well as highlighting the potential for good survey repeatability. To this end we investigated the novel approach of using repeat AEM surveys (flown over the same area at different points in time) and 3D-VDG models to jointly improve the parameterisation of 3D-VDG models - while simultaneously providing a detailed 3D map of groundwater salinity distributions. Using detailed 3D synthetic models, the results of this study quantitatively highlight the usefulness of this approach, while offering practical information on implementation and further research.

Nutrient pollution is a widespread problem in rivers in China. Managing nutrient pollution requires better knowledge of in-stream processes governing the surface water quality. As current nutrient models for China mainly focus on river export of nutrients to seas, in-stream surface water quality and their contributing sources and processes are, therefore not well understood. This requires accounting for combined effects of nutrient inputs to rivers from produced waste, biochemistry of different forms of nutrients and their transport by river network. Moreover, improvements can be made in evaluating the model performance of large-scale nutrient models based on water quality measurements in China (using the surface water quality classes from 1 to 6). The objective of this study is to quantify the spatial variation in in-stream water quality for nutrients, and associated sources, for water quality classes in China. Our new Model to Assess River Inputs of Nutrients to seAs (MARINA 3.0) for in-stream water quality distinguishes different nutrient forms including dissolved inorganic (DIN, DIP) and organic (DON, DOP) nitrogen and phosphorus and was applied for the year 2012. Our model simulations compare reasonably well with measurements across 155 river sections. Results show that between 12% and 66% of the streams are highly polluted (exceeding water quality class 3) and depending on nutrient form. Diffuse sources dominate in 76% of the streams for DIN. Point sources such as direct discharges of animal manure dominate in 46%–59% of the streams for DON, DIP and DOP. The dominant sources vary considerably between rivers and nutrient forms. This indicates the need account for nutrient forms in models and national monitoring programs. Our model results could support effective management to reduce nutrient pollution in China.

Future irrigated agriculture will be strongly affected by climate change and agricultural management. However, the extent that agricultural management adaptation can counterbalance negative climate-change impacts and achieve sustainable agricultural production remains poorly quantified. Such quantification is especially important for the Indus basin, as irrigated agriculture is essential for its food security and will be highly affected by increasing temperatures and changing water availability. Our study quantified these effects for several climate-change mitigation scenarios and agricultural management-adaptation strategies using the state-of-the-art VIC-WOFOST hydrology–crop model. Our results show that by the 2030s, management adaptation through improved nutrient availability and constrained irrigation will be sufficient to achieve sustainable and increased agricultural production. However, by the 2080s agricultural productivity will strongly depend on worldwide climate-change mitigation efforts. Especially under limited climate-change mitigation, management adaptation will be insufficient to compensate the severe production losses due to heat stress. Our study clearly indicates the limits to management adaptation in the Indus basin, and only further adaptation or strong worldwide climate-change mitigation will secure the Indus’ food productivity.

A significant increase in surface water salinization in low-lying deltas is expected globally due to saline groundwater exfiltration driven by rising sea levels and decreasing freshwater availability. Sustaining fresh water-dependent agriculture in such areas will entail an increased demand for fresh water flushing. Unfortunately, the flushing of surface water is not operationally optimised and results in excessive use of scarce freshwater. To meet the increased demand for flushing, while minimizing the need for diverted freshwater, new operational designs are required. This paper presents a novel network model based approach that uses De Saint Venant (SV) and Advection Dispersion (AD) equations to optimize multiple objectives on water level and salinity control using a Nonlinear Model Predictive Control (NMPC). The resulting NMPC problem is solved with a receding horizon implementation, where the nonlinear program (NLP) at each iteration is solved using state-of-the-art large scale interior point solver (IPOPT). We evaluate the performance of the proposed approach and compare it to the traditional fixed flushing for a representative Dutch polder. Firstly, the approach is shown to be capable of controlling the water level and salinity level in the polder. Secondly, the results highlight that the network of canals, which were originally made for drainage, could not be made sufficiently fresh with current intake capacity. A simple design approach was used to identify appropriate new capacities for two of the gates that allow optimal flushing to guarantee the required water level and salinity constraints.

This global spatially explicit (0.5 by 0.5 degree) analysis presents the nitrogen (N) and phosphorus (P) inputs, processing and biogeochemical retention and delivery to surface waters and river export to coastal seas according to the five shared socioeconomic pathways (SSP). Four systems are considered: (i) human system; (ii) agriculture; (iii) aquaculture; (iv) nature. Exploring the changes during 1980–2015 and 2015–2050 according to the SSPs shows that the natural nutrient sources have been declining in the past decades and will continue to decline in all SSPs in future decades due to massive land transformations, while agriculture, human sewage and aquaculture are becoming increasingly dominant (globally up to 80% of nutrient delivery). More efforts than those employed in any of the SSPs are needed to slow down the global nutrient cycles. One of the drivers of the proliferation of harmful algal blooms is the tendency towards increasing N:P ratios in global freshwaters and export to the global coastal seas; this is the result of increasing N:P in inputs in food production, more efficient biogeochemical retention of P than of N in river basins, and groundwater N legacies, which seems to be most pronounced in a united world that strives after sustainability. The diverging strategies to achieve UN Sustainable Development Goals 14 (life below water), 2 (zero hunger) and 6 (clean water and sanitation) therefore require a balanced management system for both N and P in all systems, that accounts for future nutrient legacies.

Deltas worldwide are at risk of elevation loss and drowning due to relative sea-level rise. Management strategies to restore or enhance sedimentation on delta plains, Sedimentation-Enhancing Strategies (hereafter SES), are now being pursued in many deltas but there has been limited cross-disciplinary and cross-delta review. Here we compare 21 existing and planned SES, synthesizing their physical characteristics, funding, governance arrangements, stakeholder engagement, process of implementation, environmental impact, land use change, and potential for upscaling. Strategies exist at various scales, from ~0.05 km2 - 500 km2. 79% of strategies are capable of outpacing high rates of sea-level rise. Cheaper strategies are limited to short term impacts and small spatial scales, while more expensive strategies can have longer lifetimes. Most strategies create wetlands and flood water storage. Some create opportunities for agriculture, aquaculture, housing, or recreational land use. Combinations of SES will likely be the most effective and sustainable method for maintaining elevation in river deltas.

This study simulates carbon dioxide (CO2) sequestration in 300 major world river basins (about 70% of global surface area) through carbonates dissolution and silicate hydrolysis. For each river basin, the daily timescale impacts under the RCP 2.6 and RCP 8.5 climate scenarios were assessed relative to a historical baseline (1969–1999) using a cascade of models accounting for the hydrological evolution under climate change scenarios. Here we show that the global temporal evolution of the CO2 uptake presents a general increase in the annual amount of CO2 consumed from 0.247 ± 0.045 Pg C year−1 to 0.261 and 0.273 ± 0.054 Pg C year−1, respectively for RCP 2.6 and RCP 8.5. Despite showing a general increase in the global daily carbon sequestration, both climate scenarios show a decrease between June and August. Such projected changes have been mapped and evaluated against changes in hydrology, identifying hot spots and moments for the annual and seasonal periods.

In the past decade, several efforts have been made to project armed conflict risk into the future. This study broadens current approaches by presenting a first-of-its-kind application of machine learning (ML) methods to project sub-national armed conflict risk over the African continent along three Shared Socioeconomic Pathway (SSP) scenarios and three Representative Concentration Pathways towards 2050. Results of the open-source ML framework CoPro are consistent with the underlying socioeconomic storylines of the SSPs, and the resulting out-of-sample armed conflict projections obtained with Random Forest classifiers agree with the patterns observed in comparable studies. In SSP1-RCP2.6, conflict risk is low in most regions although the Horn of Africa and parts of East Africa continue to be conflict-prone. Conflict risk increases in the more adverse SSP3-RCP6.0 scenario, especially in Central Africa and large parts of Western Africa. We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited compared to main conflict predictors but results suggest that changing climatic conditions may both increase and decrease conflict risk, depending on the location: in Northern Africa and large parts of Eastern Africa climate change increases projected conflict risk whereas for areas in the West and northern part of the Sahel shifting climatic conditions may reduce conflict risk. With our study being at the forefront of ML applications for conflict risk projections, we identify various challenges for this arising scientific field. A major concern is the limited selection of relevant quantified indicators for the SSPs at present. Nevertheless, ML models such as the one presented here are a viable and scalable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security.

East of the Continental Divide in the cold interior of Western Canada, the Mackenzie and Nelson River basins have some of the world's most extreme and variable climates, and the warming climate is changing the landscape, vegetation, cryosphere, and hydrology. Available data consist of streamflow records from a large number (395) of natural (unmanaged) gauged basins, where flow may be perennial or temporary, collected either year-round or during only the warm season, for a different series of years between 1910 and 2012. An annual warm-season time window where observations were available across all stations was used to classify (1) streamflow regime and (2) seasonal trend patterns. Streamflow trends were compared to changes in satellite Normalized Difference Indices. Clustering using dynamic time warping, which overcomes differences in streamflow timing due to latitude or elevation, identified 12 regime types. Streamflow regime types exhibit a strong connection to location; there is a strong distinction between mountains and plains and associated with ecozones. Clustering of seasonal trends resulted in six trend patterns that also follow a distinct spatial organization. The trend patterns include one with decreasing streamflow, four with different patterns of increasing streamflow, and one without structure. The spatial patterns of trends in mean, minimum, and maximum of Normalized Difference Indices of water and snow (NDWI and NDSI) were similar to each other but different from Normalized Difference Index of vegetation (NDVI) trends. Regime types, trend patterns, and satellite indices trends each showed spatially coherent patterns separating the Canadian Rockies and other mountain ranges in the west from the poorly defined drainage basins in the east and north. Three specific areas of change were identified: (i) in the mountains and cold taiga-covered subarctic, streamflow and greenness were increasing while wetness and snowcover were decreasing, (ii) in the forested Boreal Plains, particularly in the mountainous west, streamflows and greenness were decreasing but wetness and snowcover were not changing, and (iii) in the semi-arid to sub-humid agricultural Prairies, three patterns of increasing streamflow and an increase in the wetness index were observed. The largest changes in streamflow occurred in the eastern Canadian Prairies.

With up to 15% of the world’s population being protected by dikes from flooding, climate-change-induced river levels may dramatically increase the flood risk of these societies. Reliable assessments of dike stability will become increasingly important, but groundwater flow through dikes is often oversimplified due to limited understanding of the important process parameters. To improve the understanding of these parameters, we performed a global sensitivity analysis on a comprehensive hydro-stability model. The sensitivity analysis encompassed fifteen parameters related to geometry, drainage conditions and material properties. The following three sensitivity settings were selected to characterize model behavior: parameter prioritization, trend identification and interaction qualification. The first two showed that dike stability is mostly dependent on the dike slope, followed by the type of subsurface material. Interaction quantification indicated a very prominent interaction between the dike and subsurface material, as it influences both groundwater conditions and dike stability directly. Despite our relatively simple model setup, a database containing the results of the extensive Monte Carlo analysis succeeded in finding most of the unsafe sections identified by the official inspection results. This supports the applicability of our results and demonstrates that both geometry and subsurface parameters affect the groundwater conditions and dike stability.

Future flood and drought risks have been predicted to transition from moderate to high levels at global warmings of 1.5 °C and 2.0 °C above pre-industrial levels, respectively. However, these results were obtained by approximating the equilibrium climate using transient simulations with steadily warming. This approach was recently criticised due to the warmer global land temperature and higher mean precipitation intensities of the transient climate in comparison with the equilibrium climate. Therefore, it is unclear whether floods and droughts projected under a transient climate can be systematically substituted for those occurring in an equilibrated climate. Here, by employing a large ensemble of global hydrological models (HMs) forced by global climate models, we assess the validity of estimating flood and drought characteristics under equilibrium climates from transient simulations. Differences in flood characteristics under transient and equilibrium climates could be largely ascribed to natural variability, indicating that the floods derived from a transient climate reasonably approximate the floods expected in an equally warm, equilibrated climate. By contrast, significant differences in drought intensity between transient and equilibrium climates were detected over a larger global land area than expected from natural variability. Despite the large differences among HMs in representing the low streamflow regime, we found that the drought intensities occurring under a transient climate may not validly represent the intensities in an equally warm equilibrated climate for approximately 6.7% of the global land area. Environment Research and Technology Development Fund Ministry of Education, Culture, Sports and Technology/Japan Society for the Promotion of Science National Science Foundation

Integrated adaptation strategies are needed to achieve the highly interlinked Sustainable Development Goals (SDGs) for water, food- and energy security in the Indus basin. However, detailed quantitative scenarios for the plausible dimensions of future resource security requirements under socio-economic development are lacking. Here we define three quantitative and spatially downscaled scenarios for future water, food and energy requirements in the Indus basin and we assess the implications of socio-economic development for the integrated resource security challenge. High-resolution gridded scenarios for resource security requirements are developed by combining three regionalised and spatialised Shared Socioeconomic Pathways (SSPs) with quantitative regional water, food and energy security thresholds. The results demonstrate that by 2080 basin level water- and energy security requirements are likely to at least double and potentially triple compared to the current situation. Food requirements could increase only marginally and double at most. Migration and urbanisation additionally drive the growing requirements to spatially converge around the largest cities of the basin. This demonstrates that socio-economic development increases the complexity of the water-food-energy security challenge by increasing its magnitude and spatial concentration. Future research and policymaking should anticipate for this heterogeneous growth of resource security challenges when developing adaptation strategies.

Recent droughts in Europe have shown that national water systems are facing increasing challenges when dealing with drought impacts. Especially the Netherlands has seen an increasing need to adapt their water management to improve preparedness for future drought events. Ideally, the necessary information needed for operational water management decisions should be readily available ahead of time and/or computed flexibly and efficiently to ensure sufficient time to evaluate the various management actions. In this study, we show that in addition to physically based hydrological models, the upcoming and promising trend of incorporating machine learning (ML) in hydrology can provide the basis for future efforts in supporting national operational water management by providing the needed information efficiently and with the required accuracy. As a precursor for their use in a forecasting system, we assessed the ability of five different data driven methods to simulate hydrological variables at a national-scale. We developed a unified workflow where we use limited information on hydro-meteorological variables and general water management policies to simulate historic timeseries of discharge, groundwater levels, surface water levels and surface water temperatures. We find that all ML methods, ranging from very simple to more complex ones, showed a generally good performance for stations and target levels which are closely linked to the input data and location (e.g. stations along main river network). For downstream stations and small rivers, the Random Forest method outperforms the other methods both for discharge and surface water levels. For surface water temperature no location dependency was observed and for groundwater levels, all methods were performing comparable with most stations ranging in nRMSE 0.2-0.3. Generally, the best performances were reached by the more advanced Random Forest and LSTM methods, which was also seen when simulating high and low flow events. High flow events were slightly better captured than low flow events but overall simulating extreme events based on a simple input data set remains challenging. Specific training sets, including event related information and additional input variables, could like improve future assessments. Including the feature importance of the methods allowed us to detect how and where water management influence played an important role. The addition of information on water management in the ML routines increases overall performance, although limited. We conclude that ML and other data driven approaches have potential in predicting different hydrological variables. We were able to capture and incorporate water management aspects in our analysis, creating a base for future experiments where scenario analysis might reveal ML based mitigation strategies. The combination of limited input data requirement and short computation times makes this new framework suitable for forecasting purposes.

Chinese lakes, including ponds and reservoirs, are increasingly threatened by algal blooms. Yet, each lake is unique, leading to large inter-lake variation in lake vulnerability to algal blooms. Here, we aim to assess the effects of unique lake characteristics on lake vulnerability to algal blooms. To this end, we built a novel and comprehensive database of lake morphometric, climate and sediment characteristics of 19,536 Chinese lakes, including ponds and reservoirs (>0.1 km2). We assessed lake characteristics for nine stratification classes and show that lakes, including ponds and reservoirs, in eastern China typically have a warm stratification class (Tavg>4 °C) and are slightly deeper than those in western China. Model results for representative lakes suggest that the most vulnerable lakes to algal blooms are in eastern China where pollution levels are also highest. Our characterization provides an important baseline to inform policymakers in what regions lakes are potentially most vulnerable to algal blooms.

Rising temperatures, rapid urbanization and soaring demand for natural resources threaten deltas worldwide and make them vulnerable to rising seas, subsidence, droughts, floods, and salt intrusion. However, climate change projections in deltas often address climate-driven stressors in isolation and disregard parallel anthropogenic processes, leading to insufficient socio-political drive. Here, using a combination of process-based numerical models that integrate both climatic and anthropogenic environmental stressors, we project salt intrusion within the Mekong mega-Delta, in the next three decades. We assess the relative effects of various drivers and show that anthropogenic forces such as groundwater extraction-induced subsidence and riverbed level incisions due to sediment starvation can increase the salinity-affected areas by 10–27% compared to the present-day situation, while future sea level rise adds another 6–19% increase. These projections provide crucial input for adaptation policy development in the Mekong Delta and the methodology inspires future systemic studies of environmental changes in other deltas. Human activities, such as groundwater extraction and sediment starvation, are projected to add to climatic factors like sea level rise to exacerbate saline water intrusion into the Mekong Delta, Vietnam over the next 30 years, according to process-based model simulations.

Debris-covered glaciers, especially in high-mountain Asia, have received increased attention in recent years. So far, few field-based observations of distributed mass loss exist and both the properties of the debris layer as well as the atmospheric drivers of melt below debris remain poorly understood. Using multi-year observations of on-glacier atmospheric data, debris properties and spatial surface elevation changes from repeat flights with an unmanned aerial vehicle (UAV), we quantify the necessary variables to compute melt for the Lirung Glacier in the Himalaya. By applying an energy balance model we reproduce observed mass loss during one monsoon season in 2013. We show that melt is especially sensitive to thermal conductivity and thickness of debris. Our observations show that previously used values in literature for the thermal conductivity through debris are valid but variability in space on a single glacier remains high. We also present a simple melt model, which is calibrated based on the results of energy balance model, that is only dependent on air temperature and debris thickness and is therefore applicable for larger scale studies. This simple melt model reproduces melt under thin debris (

Freshwater salinisation is a growing problem, yet cross-regional assessments of freshwater salinity status and the impact of agricultural and other sectoral uses are lacking. Here, we assess inland freshwater salinity patterns and evaluate its interactions with irrigation water use, across seven regional river basins (401 river sub-basins) around the world, using long-term (1980–2010) salinity observations. While a limited number of sub-basins show persistent salinity problems, many sub-basins temporarily exceeded safe irrigation water-use thresholds and 57% experience increasing salinisation trends. We further investigate the role of agricultural activities as drivers of salinisation and find common contributions of irrigation-specific activities (irrigation water withdrawals, return flows and irrigated area) in sub-basins of high salinity levels and increasing salinisation trends, compared to regions without salinity issues. Our results stress the need for considering these irrigation-specific drivers when developing management strategies and as a key human component in water quality modelling and assessment., Freshwater salinisation is a growing water quality problem, but impacts and drivers across regional to global scales have been lacking. A new assessment of inter-regional freshwater salinisation demonstrates the importance of irrigation as a driver of salinisation.

Increasing human demands for water, energy, food and materials, are expected to accentuate resource supply challenges over the coming decades. Experience suggests that long-term strategies for a single sector could yield both trade-offs and synergies for other sectors. Thus, long-term transition pathways for linked resource systems should be informed using nexus approaches. Global integrated assessment models can represent the synergies and trade-offs inherent in the exploitation of water, energy and land (WEL) resources, including the impacts of international trade and climate policies. In this study, we review the current state-of-the-science in global integrated assessment modeling with an emphasis on how models have incorporated integrated WEL solutions. A large-scale assessment of the relevant literature was performed using online databases and structured keyword search queries. The results point to the following main opportunities for future research and model development: (1) improving the temporal and spatial resolution of economic models for the energy and water sectors; (2) balancing energy and land requirements across sectors; (3) integrated representation of the role of distribution infrastructure in alleviating resource challenges; (4) modeling of solution impacts on downstream environmental quality; (5) improved representation of the implementation challenges stemming from regional financial and institutional capacity; (6) enabling dynamic multi-sectoral vulnerability and adaptation needs assessment; and (7) the development of fully-coupled assessment frameworks based on consistent, scalable, and regionally-transferable platforms. Improved database management and computational power are needed to address many of these modeling challenges at a global-scale. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Recent studies determined the residence time of moisture in the atmosphere to be 8?10 days but with large spatial and temporal differences. An unexplained daily cycle in the probability density function (PDF) of the residence time of land evaporation was observed, which was not present for oceanic evaporation. Moreover, the PDF of atmospheric residence time of oceanic evaporation was found to be a monotonically decreasing, while for land evaporation, this function had increasing probabilities during the first few days and monotonically decreasing probabilities thereafter. This research determines the causes of (I) the daily cycle in this PDF and (II) the shape of the atmospheric residence time PDF. The strong daily cycle in the residence time PDF using ERA-Interim is attributed to the fact that the evaporation and precipitation have the same diurnal cycle in ERA-Interim. Therefore, evaporation entering the atmosphere has the highest probability of returning to the land surface as precipitation at the same moment during the day but possibly a number of days later. Interestingly, this diurnal cycle was almost absent in the simulations forced with GLDAS surface fluxes. Therefore, we conclude that the previously found daily cycle in the atmospheric residence time PDF is due to differences in the diurnal cycles of precipitation in the underlying data sets. Transpiration causes the increasing probabilities during the first days, while bare soil evaporation and canopy interception typically have a monotonically decreasing PDF. Therefore, we conclude that transpiration causes the largest differences in atmospheric residence time between ocean and land evaporation.

Spatio-temporal variation of hydrological processes that have a strong lagged autocorrelation (memory), such as soil moisture, snow accumulation and the antecedent hydro-climatic conditions, significantly impact the peaks of flood waves. Ignoring these memory processes leads to biased estimates of floods and high river levels that are sensitive to the occurrence of these compounding hydro-meteorological processes. Here, we investigate the role of memory in hydrological and meteorological systems at different temporal scales for the Rhine basin. We simulate the hydrological regime of the Rhine river basin using a distributed hydrological model (SPHY) forced with 1950&ndash, 2000 atmospheric conditions from an ensemble simulation with a high resolution (0.11&deg, /12 km) regional climate model (RACMO2). The findings show that meltwater from antecedent anomalous snowfall results in a time shift of the discharge peak. Soil moisture modulates the rainfall-runoff relationship and generates a strong runoff response at high soil moisture levels and buffers the generation of runoff peaks at low levels. Additionally, our results show that meteorological autocorrelation (manifesting itself by the occurrence of clustered precipitation events) has a strong impact on the magnitude of peak discharge. Removing meteorological autocorrelation at time scales longer than five days reduces peak discharge by 80% relative to the reference climate. At time scales longer than 30 days this meteorological autocorrelation loses its significant role in generating high discharge levels.

The temperature of river water plays a crucial role in many physical, chemical, and aquatic ecological processes. Despite the importance of having detailed information on this environmental variable at locally relevant scales (≤50 km), high-resolution simulations of water temperature on a large scale are currently lacking. We have developed the dynamical 1-D water energy routing model (DynWat), that solves both the energy and water balance, to simulate river temperatures for the period 1960–2014 at a nominal 10-km and 50-km resolution. The DynWat model accounts for surface water abstraction, reservoirs, riverine flooding, and formation of ice, enabling a realistic representation of the water temperature. We present a novel 10-km water temperature data set at the global scale for all major rivers, lakes, and reservoirs. Validated results against 358 stations worldwide indicate a decrease in the simulated root-mean-square error (0.2 °C) and bias (0.7 °C), going from 50- to 10-km simulations. We find an average global increase in water temperature of 0.16 °C per decade between 1960 and 2014, with more rapid warming toward 2014. Results show increasing trends for the annual daily maxima in the Northern Hemisphere (0.62 °C per decade) and the annual daily minima in the Southern Hemisphere (0.45 °C per decade) for 1960–2014. The high-resolution modeling framework not only improves the model performance, it also positively impacts the relevance of the simulations for regional-scale studies and impact assessments in a region without observations. The resulting global water temperature data set could help to improve the accuracy of decision-support systems that depend on water temperature estimates.

The glacier- and snow-fed river basins of the Hindu Kush Himalaya (HKH) mountains provide water to 1.9 billion people in Asia. The signs of climate change in the HKH mountains are clear, with increased warming and accelerated melting of snow and glaciers. This threatens the water, food, energy and livelihood security for many in Asia. The links between mountains and plains and the differential impacts of climate change on societies upstream and downstream need to be better established to improve adaptation measures. This chapter sheds light on climate change impacts on the cryosphere and mountains, the impact on river systems and the social consequences of such changes in mountains, hills and plains. In high mountains and hills, the impact of climate change is clear, as seen in changes in agropastoral systems and the increasing occurrence of floods and droughts, with losses and damages already high. Moving downstream, the climate change signal is harder to separate from other environmental and management factors. This chapter outlines how climate change in the mountains will impact various sectors in the hills and plains, such as hydropower, irrigation, cities, industries and the environment. It discusses how climate change will potentially lead to increased disasters and out-migration of people. The chapter concludes by highlighting necessary actions, such as the need to reduce emissions globally, build regional cooperation between HKH countries, increase technical and financial support for adaptation, and more robust and interdisciplinary science to address changing policy needs.

Change in Land Water Storage (LWS) is one of the main components driving sea-level rise over the twenty-first century. LWS alteration results from both human activities and climate change. Up to now, all components to sea-level change are usually quantified upon a certain climate change scenario except land water changes. Here, we propose to improve this by analyzing the contribution of LWS to regional sea-level change by considering five Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models forced by three different Representative Concentration Pathway (RCP) greenhouse gas emission scenarios. For this analysis, we used LWS output of the global hydrological and water resources model, PCR-GLOBWB 2, in order to project regional sea-level patterns. Projections of ensemble means indicate a range of LWS-driven sea-level rise with larger differences in projections among climate models than between scenarios. Our results suggest that LWS change will contribute around 10% to the projected global mean sea-level rise by the end of twenty-first century. Contribution of LWS to regional sea-level rise is projected to be considerably larger than the global mean over several regions, up to 60% higher than global average of LWS-driven sea-level rise, including the Pacific islands, the south coast of Africa and the west coast of Australia.

Continually improving and affordable wastewater management provides opportunities for both pollution reduction and clean water supply augmentation, while simultaneously promoting sustainable development and supporting the transition to a circular economy. This study aims to provide the first comprehensive and consistent global outlook on the state of domestic and manufacturing wastewater production, collection, treatment and reuse. We use a data-driven approach, collating, cross-examining and standardising country-level wastewater data from online data resources. Where unavailable, data are estimated using multiple linear regression. Country-level wastewater data are subsequently downscaled and validated at 5 arcmin (∼10 km) resolution. This study estimates global wastewater production at 359.4×109 m3 yr−1, of which 63 % (225.6×109 m3 yr−1) is collected and 52 % (188.1×109 m3 yr−1) is treated. By extension, we estimate that 48 % of global wastewater production is released to the environment untreated, which is substantially lower than previous estimates of ∼80 %. An estimated 40.7×109 m3 yr−1 of treated wastewater is intentionally reused. Substantial differences in per capita wastewater production, collection and treatment are observed across different geographic regions and by level of economic development. For example, just over 16 % of the global population in high-income countries produces 41 % of global wastewater. Treated-wastewater reuse is particularly substantial in the Middle East and North Africa (15 %) and western Europe (16 %), while comprising just 5.8 % and 5.7 % of the global population, respectively. Our database serves as a reference for understanding the global wastewater status and for identifying hotspots where untreated wastewater is released to the environment, which are found particularly in South and Southeast Asia. Importantly, our results also serve as a baseline for evaluating progress towards many policy goals that are both directly and indirectly connected to wastewater management. Our spatially explicit results available at 5 arcmin resolution are well suited for supporting more detailed hydrological analyses such as water quality modelling and large-scale water resource assessments and can be accessed at https://doi.org/10.1594/PANGAEA.918731 (Jones et al., 2020).

Natural disasters in High Mountain Asia (HMA) are largely induced by precipitation and temperatures extremes. Precipitation extremes will change due to global warming, but these low frequency events are difficult to analyse using (short) observed time series. In this study, we analysed large 2000 year ensembles of present day climate and of a 2 and 3°C warmer world produced with the EC‐Earth model. We performed a regional assessment of climate indicators related to temperature and precipitation (positive degree days, accumulated precipitation, [pre‐ and post‐] monsoon precipitation), their sensitivity to temperature change and the change in return periods of extreme temperature and precipitation in a 2 and 3°C warmer climate. In general, the 2°C warmer world shows a homogeneous response of changes in climate indicators and return periods, while distinct differences between regions are present in a 3°C warmer world and changes no longer follow a general trend. This non‐linear effect can indicate the presence of a tipping point in the climate system. The most affected regions are located in monsoon‐dominated regions, where precipitation amounts, positive degree days, extreme temperature, extreme precipitation and compound events are projected to increase the most. Largest changes in climate indicators are found in East Himalaya, followed by the Hindu Kush and West and Central Himalaya regions. Western regions will experience drier summers and wetter winters, while monsoon dominated regions drier winters and wetter summers and northern regions a wetter climate year round. We also found that precipitation increases in HMA in a 3°C warmer world are substantially larger (13%) compared to the global average (5.9%). Additionally, the increase in weather extremes will exacerbate natural hazards with large possible impacts for mountain communities. The results of this study could provide important guidance for formulating climate change adaptation strategies in HMA.

In hydrological modelling, traditionally one calibration was performed over a certain calibration period before the model is used to study the hydrological system. This implies that a constant model structure and parameterization are assumed. However, if the catchment system is subject to changes that are not incorporated in the model, the parameter values found in a calibration period may not be optimal for other periods, which is called systemic change. The aim of this study was to identify systemic change and its possible causes with the PCR-GLOBWB hydrological model in the Rhine-Meuse basin, by performing a brute-force calibration for multiple periods for five calibration locations between 1901-2010. Systemic change was studied for the main model components, by selecting a key parameter from each component (minimum soil depth fraction, saturated hydraulic conductivity, groundwater recession coefficient, degree day factor, Manning's n). These parameters were calibrated for 10-year rolling periods between 1901-2010. The results showed that at the downstream locations, the changes in optimal parameter values were small, while at the upstream locations, the optimal values of most parameters changed considerably over the different rolling calibration periods, signifying systemic change. Especially the degree day factor showed large variations, varying over time between 0.5 and 2.5 times its default value at Basel and Maxau (upstream and middle part of the Rhine basin). Based on correlation analysis, it was found that climate change as well as changes in land use and river structure are possible causes of changes in optimal parameter values through time.

Currently, irrigation withdrawals are resulting in groundwater exploitation and unmet ecosystem water requirements. However, to achieve worldwide food security, there is a need to focus on sustainable intensification of crop production. This requires a more sustainable use of water for irrigated croplands. Our presentation focuses on quantifying attainable wheat, maize, rice and soybean production on currently irrigated cropland under sustainable water use. Attainable production accounts for increases in nutrient application, while limiting irrigation withdrawals to renewable water availability and without compromising river ecosystem water requirements.Attainable crop production was quantified using a newly developed two-way coupling between the VIC hydrological model (Droppers et al., 2020) and the WOFOST crop model (Wit et al., 2019). This VIC-WOFOST model framework comprehensively simulates biophysical processes related to water availability and crop growth under water and nutrient limitations. Our results indicate that worldwide crop nitrogen uptake should increase by 20%, to achieve production gap closure. However, worldwide irrigation withdrawals should decrease by more than a third in order to ensure sustainable water use. Under these constraints, decreases in attainable irrigated yields of 5% are expected (14% decrease due to water constraints, 9% increase due to increased nutrient availability). Moreover, achievable irrigated crop production in the extensively irrigated croplands of north-eastern China, Pakistan and north-western India would be reduced by up to a third.In addition we explored the impact of atmospheric CO2 enrichment on worldwide attainable irrigated production using VIC-WOFOST. Increased atmospheric CO2 concentration increases crop assimilation and decreases crop transpiration. Initial results show that these effects may offset the unsustainable water withdrawals and increase attainable irrigated yields.References:Droppers, B., Franssen, W. H., Van Vliet, M. T., Nijssen, B., & Ludwig, F. (2020): Simulating human impacts on global water resources using VIC-5. Geoscientific Model Development, 13(10), 5029-5052, https://doi.org/10.5194/gmd-13-5029-2020de Wit, A., Boogaard, H., Fumagalli, D., Janssen, S., Knapen, R., van Kraalingen, D., ... & van Diepen, K. (2019): 25 years of the WOFOST cropping systems model. Agricultural Systems, 168, 154-167, https://doi.org/10.1016/j.agsy.2018.06.018.