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Summary Background Increasing human demand for water and changes in water availability due to climate change threatens water security worldwide. Additionally, exploitation of water resources induces stress on freshwater environments, leading to biodiversity loss and reduced ecosystem services. We aimed to conduct a spatially detailed assessment of global human water stress for low to high environmental flow (EF) protection. Methods In this modelling study, we used the LISFLOOD model to generate daily natural flows without anthropogenic water use for 1980–2018. On the basis of these flows, we selected three EF methods (EF with high ecological protection [EFPROT], EF with minimum flow requirements [EFMIN], and variable monthly flow [EFVMF]) to calculate monthly EFs. We assessed monthly consumptive water use for industry, agricultural crops, livestock, municipalities, and energy production for 2010. We then estimated the corresponding number of people under water stress per month on a global and national level using a spatially detailed population database for 2010. Findings We estimate that 3·2 billion (EFPROT), 2·4 billion (EFVMF), and 2·2 billion (EFMIN) people lived under water stress for at least 1 month per year, corresponding to 46%, 35%, and 32% of the world's population in 2010, respectively. Around 80% of people living under water stress lived in Asia; in particular, India, Pakistan, and northeast China. Compared with EFMIN, imposing EFPROT globally would have put between 710 million (March) to 1 billion (June) additional people under water stress on a monthly basis, whereas this would have been 72 million (August) to 218 million (April) additional people if EFVMF were imposed. Interpretation Ensuring high ecological protection would put nearly half of the world's population (3·2 billion people) under water stress for at least 1 month per year. Policy makers and water managers have to make an important trade-off when allocating limited water resources between direct human needs and the environment. A better understanding of local ecosystem needs is crucial to alleviating current and future human water stress, while sustaining healthy ecosystems. Funding None.

Water quality monitoring is important for the management of freshwater resources in river basins. Allocation of monitoring stations is the first step in the design of a water quality network. For this task, planning objectives are identified and a Multi-Objective Artificial Bee Colony-based optimization algorithm is designed and implemented in a Geographic Information System framework. Specifically, the number of stations is minimized in a range of values at the same time that the detection of lower compliance areas, the affected population and the relative importance of the river stretches are maximized. The estimation of pollutant parameters such as Biochemical Oxygen Demand, Faecal Coliform Bacteria or Total Dissolved Solids is performed by using the WorldQual model. This allows to objectively allocate monitoring stations to rivers where no real measurements are available, and thus it is especially relevant to allocate water quality stations for the first time. This approach has been tested on the Great Fish River basin (South Africa), finding networks improving the values of the objective functions between 22.22% and 47.83% with respect to the ones of the current network. Moreover, the solution analysis provides insightful and valuable information to the decision maker.

Continental-scale water quality modeling is a new scientific approach concerned with computing the level of water pollution for several river basins at once. Uncertainties in these models, and in models of smaller scale, arise especially from the specification of model parameters. To identify and analyze these uncertainties we perform a global sensitivity and uncertainty analysis using Latin Hypercube Sampling on the WorldQual water quality model. The focus of the analysis is the river pathogen model of WorldQual as applied to rivers in Africa. This is the first uncertainty and sensitivity analysis performed on a continental-scale pathogen river pollution model. The median output uncertainty of the model (coefficient of variation, based on log-transformed data), assuming plausible estimates of 42 parameter uncertainties, was 10.7%; 90% of grid cells had output uncertainties below 23%. The parameters making the largest contribution to this uncertainty (in order of importance) are the pathogen waste loading per capita, the in-stream settling velocity of pathogens, the percentage of population in a river basin connected to a sewer system, and the raw effluent concentration from the manufacturing sector. Over the continental study area, model output uncertainty and the most sensitive parameters were found to have a highly irregular spatial pattern. This finding suggests that model performance is a strong function of local and regional conditions and that reducing the uncertainty of a single parameter may not lead to large improvements in model performance over the entire continent. A more efficient approach would be to improve model performance region-by-region and improve the estimation of specific parameters known to have a large influence on model uncertainty in those regions. The analysis showed that only four parameters dominate output uncertainty over 93% of the study area, implying that model performance can be substantially improved by reducing the uncertainty of a small number of parameters.

Municipal water systems provide crucial services for human well-being, and will undergo a major transformation this century following global technological, socioeconomic and environmental changes. Future demand scenarios integrating these drivers over multi-decadal planning horizons are needed to develop effective adaptation strategies. This paper presents a new long-term scenario modeling framework that projects future daily municipal water demand at a 1/8° global spatial resolution. The methodology incorporates improved representations of important demand drivers such as urbanization and climate change. The framework is applied across multiple future socioeconomic and climate scenarios to explore municipal water demand uncertainties over the 21st century. The scenario analysis reveals that achieving a low-carbon development pathway can potentially reduce global municipal water demands in 2060 by 2-4%, although the timing and scale of impacts vary significantly with geographic location. Future global municipal water demand scenarios generated for coupled RCP-SSP pathways.Integration of climate and socioeconomic drivers to downscale long-term scenarios to 1/8°.Climate change impacts to annual global demand in 2060s ranges from 2 to 4%.Mapped climate change impacts to peak daily demand in 2060s range from 0 to 12%.

Information on fecal coliform (FC) concentrations in European rivers is scarce. The objective of this study was to identify hotspots of water pollution in Europe and provide information on the different FC sources and their contributions to the loads that lead to concentrations in rivers. Model simulations were carried-out with the large-scale water quality model WorldQual to assess the calculated loads regarding to its associated sources and to further estimate the related in-stream concentration. For the year 1995, model results indicated that FC loadings were higher in central Europe with 500 to above 2000 1010?cfu?km-2?a-1 than in northern and eastern Europe where loadings ranged between 0 and 200 1010?cfu?km-2?a-1. Major sources of FC loadings are domestic sewage, followed by scattered settlements (private treatment), urban surface runoff and manure application. Concentrations showed similar regional patterns as loadings, with high concentrations in central Europe and low concentrations in northern and eastern Europe. We model fecal coliform loadings and in-stream concentrations in European rivers.The domestic sector is the main contributor to fecal coliform loadings.The regional patterns of loadings and in-stream concentrations are similar.Hotspots of fecal coliform in-stream concentrations are located in central Europe.

A B S T R A C T Urban growth is increasing the demand for freshwater resources, yet surprisingly the water sources of the world’s large cities have never been globally assessed, hampering efforts to assess the distribution and causes of urban water stress. We conducted the first global survey of the large cities’ water sources, and show that previous global hydrologic models that ignored urban water infrastructure significantly overestimated urban water stress. Large cities obtain 78 � 3% of their water from surface sources, some of which are far away: cumulatively, large cities moved 504 billion liters a day (184 km 3 yr � 1 ) a distance of 27,000 � 3800 km, and the upstream contributing area of urban water sources is 41% of the global land surface. Despite this infrastructure, one in four cities, containing $4.8 � 0.7 trillion in economic activity, remain water stressed due to geographical and financial limitations. The strategic management of these cities’ water sources is therefore important for the future of the global economy.

To enhance global water use assessment, the WaterGAP3 model was improved for back-calculating domestic, manufacturing and thermoelectric water uses until 1950 for 177 countries. Model simulations were carried-out on a national scale to estimate water withdrawals and consumption as well as cooling water required for industrial processes and electricity production. Additionally, the amount of treated and untreated wastewater as generated by the domestic and manufacturing sectors was modeled. In the view of data availability, model simulations are based on key socio-economic driving forces and thermal electricity production. Technological change rates were derived from statistical records in order to consider developments in water use efficiency, which turned out to have a crucial role in water use dynamics. Simulated domestic and industrial water uses increased from ca. 300 km 3 in 1950 to 1345 km 3 in 2010, 12% of which were consumed and 88% of which were discharged back into freshwater bodies. The amount of domestic and manufacturing wastewater increased considerably over the last decade, but only half of it was untreated. The downscaling of the untreated wastewater volume to river basin scale indicates a matter of concern in East and Southeast Asia, Northern Africa, and Eastern and Southern Europe. In order to reach the Millennium Development Goals, securing water supply and the reduction of untreated wastewater discharges should be amongst the priority actions to be undertaken. Population growth and increased prosperity have led to increasing water demands. However, societal and political transformation processes as well as policy regulations resulting in new water-saving technologies and improvements counteract this development by slowing down and even reducing global domestic and industrial water uses.

Irrigated cropland plays a crucial role for agricultural production but has also been identified as a major user of global freshwater resources. The amount of irrigation water needed depends on climate conditions and socio-economic factors (e.g. amount of crop production and technology). In this study we analyze the development of irrigated area and the corresponding net irrigation water requirements for pan-Europe using a spatially explicit land-use model (LandSHIFT) together with a hydrology and water-use model (WaterGAP3). Four simulation experiments for the assessment of irrigation water requirements were conducted: (1) irrigated area for the base year 2000 under climate normal conditions, (2) irrigated area for 2000 with two climate scenarios for 2050, (3) two scenarios for changing irrigated area until 2050 under climate normal conditions and (4) the two irrigated area scenarios in combination with the climate scenarios. Results show that under baseline conditions yearly irrigation water requirements sum up to 53 billion m 3 . The expansion of irrigated area strongly depends on the combination of socio-economic drivers and climate change. Generally, the two analyzed climate scenarios lead to an expansion of irrigated area which we explain by negative effects of increasing air temperatures on crop growth conditions. Analysing the irrigated area scenarios and climate scenarios separately, we find that the effect of land-use change on net irrigation water requirements is larger than the climate effect. Combined analysis of socio-economic and climate drivers reveal that the expansion of irrigated area is not directly correlated to the changes of irrigation water requirements as the adaptation of sowing dates to the changing climatic conditions might help to overcome seasonal water deficits. The study underlines the importance of considering both the change of irrigated area and agricultural management as well as hydrology aspects in regional water use analysis and shows that simulation models can serve as tools to provide this information to the development of regional water management strategies.

Summary This paper introduces an approach to route discharge with a variable river flow velocity based on the Manning–Strickler formula within large scale hydrological models. The approach has been developed for the global scale hydrological model WaterGAP and model results have been analyzed focusing on Europe. The goal was to find a method that is simple enough to derive the required parameters from globally available data while being sophisticated enough to deliver realistic flow velocity estimates for a large variety of environmental conditions. The river bed roughness (Manning’s n ) is approximated in a spatially explicit way based on topography, the location of urban population, and river sinuosity. The hydraulic radius is estimated from actual river discharge, and river bed slope is derived by combining a high resolution DEM, a 5 arc min drainage direction map, and river sinuosity. The modeled river flow velocity has been validated against data of US gauging stations. The representation of lateral transport has clearly been improved compared to the constant flow velocity applied in older model versions. The effect of incorporating variable flow velocities as compared to a constant flow velocity is largest on flood discharge, which generally increases in large rivers. The impact on monthly discharge hydrographs is marginal only. WaterGAP has been driven by three climate change projections for the 2050s to assess climate change impacts on flow velocity, and on the residence time of water in the European river system. Results indicate a decrease in residence times for Northern Europe and an increase for parts of the Mediterranean.

Summary Bankfull flow is a relevant parameter in the field of large scale modelling especially for the analysis of environmental flows and flood related hydrological processes. In our case, bankfull flow data were required within the SCENES project in order to analyse ecological important inundation events at selected grid cells of a European raster. In practise, the determination of bankfull flow is a complex task even on local scale. Subsequent to a literature survey of bankfull flow studies, this paper describes a method which can be applied to estimate bankfull flow on a global or continental grid cell raster. The method is based on the partial duration series approach taking into account a 40-years time series of daily discharge data modelled by the global water model WaterGAP. An increasing threshold censoring procedure, a declustering scheme and the generalised Pareto distribution are applied. Modelled bankfull flow values are then validated by different efficiency criteria against bankfull flows observed at gauging stations in Europe. Thereby, the impact of (i) the applied distribution function, (ii) the threshold setting in the partial duration series, (iii) the climate input data and (iv) applying the annual maxima series are evaluated and compared to the proposed approach. The results show that bankfull flow can be reasonably estimated with a high model efficiency (E1 = 0.71) and weighted correlation (ωr2 = 0.90) as well as a systematic overestimation of 22.8%. Finally it turned out that in our study focusing on hydrological extremes, the appliance of the daily climate input data is a basic requirement. While the choice of the distribution function had no significant impact on the final results, the threshold setting in the partial duration series was crucial.

During the last decades the Aral Sea basin has suffered an enormous depletion of water resources within its lakes and rivers with consequences for society, economy, and nature. Within this model study, Global Change impacts on the Amu Darya and Syr Darya rivers, as well as on the Aral Sea itself, are being analysed for the period 1958–2002. In a first step, a multi-annual data base on crop specific irrigated areas has been set-up, which has then been integrated in the hydrology and water use model WaterGAP3. As a second step, anthropogenic water abstractions have been calculated, which were then assimilated in the simulation of river runoff of the Amu Darya and Syr Darya. The last step includes the simulation of the water balance of the Aral Sea, by taking into account modelled river inflow. Within WaterGAP3, the water use module has been switched on and off to separate the impacts of Climate and Global Change (i.e. water abstractions). Irrigation water abstractions are very well represented by WaterGAP3 and lie within the range of reported values. Modelled river discharge also shows a good fit to observed data, whereas phases are in sync but volumes are slightly overestimated. Simulated volumes of the Aral Sea itself are well reflected by the model, though results for the period 1990–2002 are too high. In this study, the Climate Change impacts are much smaller (14%) than the water use impacts (86%) on the shrinkage of the Aral Sea. Finally, an outlook on potential scenario model studies is given, which could analyse the different strategies of mitigation and adaptation of Global Change in the Aral Sea basin.