Search

Water scarcity threatens people in various regions, and has predominantly been studied from a water quantity perspective only. Here we show that global water scarcity is driven by both water quantity and water quality issues, and quantify expansions in clean water technologies (i.e. desalination and treated wastewater reuse) to 'reduce the number of people suffering from water scarcity' as urgently required by UN's Sustainable Development Goal 6. Including water quality (i.e. water temperature, salinity, organic pollution and nutrients) contributes to an increase in percentage of world's population currently suffering from severe water scarcity from an annual average of 30% (22%–35% monthly range; water quantity only) to 40% (31%–46%; both water quantity and quality). Water quality impacts are in particular high in severe water scarcity regions, such as in eastern China and India. In these regions, excessive sectoral water withdrawals do not only contribute to water scarcity from a water quantity perspective, but polluted return flows degrade water quality, exacerbating water scarcity. We show that expanding desalination (from 2.9 to 13.6 billion m3 month−1) and treated wastewater uses (from 1.6 to 4.0 billion m3 month−1) can strongly reduce water scarcity levels and the number of people affected, especially in Asia, although the side effects (e.g. brine, energy demand, economic costs) must be considered. The presented results have potential for follow-up integrated analyses accounting for technical and economic constraints of expanding desalination and treated wastewater reuse across the world.

Water scarcity threatens people in various regions, and has predominantly been studied from a water quantity perspective only. Here we show that global water scarcity is driven by both water quantity and water quality issues, and quantify expansions in clean water technologies (i.e. desalination and treated wastewater reuse) to ‘reduce the number of people suffering from water scarcity’ as urgently required by UN’s Sustainable Development Goal 6. Including water quality (i.e. water temperature, salinity, organic pollution and nutrients) contributes to an increase in percentage of world’s population currently suffering from severe water scarcity from an annual average of 30% (22%–35% monthly range; water quantity only) to 40% (31%–46%; both water quantity and quality). Water quality impacts are in particular high in severe water scarcity regions, such as in eastern China and India. In these regions, excessive sectoral water withdrawals do not only contribute to water scarcity from a water quantity perspective, but polluted return flows degrade water quality, exacerbating water scarcity. We show that expanding desalination (from 2.9 to 13.6 billion m3 month−1) and treated wastewater uses (from 1.6 to 4.0 billion m3 month−1) can strongly reduce water scarcity levels and the number of people affected, especially in Asia, although the side effects (e.g. brine, energy demand, economic costs) must be considered. The presented results have potential for follow-up integrated analyses accounting for technical and economic constraints of expanding desalination and treated wastewater reuse across the world.

Questions related to historical and future water resources and scarcity have been addressed by several macroscale hydrological models. One of these models is the Variable Infiltration Capacity (VIC) model. However, further model developments were needed to holistically assess anthropogenic impacts on global water resources using VIC. Our study developed VIC-WUR, which extends the VIC model using (1) integrated routing, (2) surface and groundwater use for various sectors (irrigation, domestic, industrial, energy, and livestock), (3) environmental flow requirements for both surface and groundwater systems, and (4) dam operation. Global gridded datasets on sectoral demands were developed separately and used as an input for the VIC-WUR model. Simulated national water withdrawals were in line with reported Food and Agriculture Organization (FAO) national annual withdrawals (adjusted R2 > 0.8), both per sector and per source. However, trends in time for domestic and industrial water withdrawal were mixed compared with previous studies. Gravity Recovery and Climate Experiment (GRACE) monthly terrestrial water storage anomalies were well represented (global mean root-mean-squared error, RMSE, values of 1.9 and 3.5 mm for annual and interannual anomalies respectively), whereas groundwater depletion trends were overestimated. The implemented anthropogenic impact modules increased simulated streamflow performance for 370 of the 462 anthropogenically impacted Global Runoff Data Centre (GRDC) monitoring stations, mostly due to the effects of reservoir operation. An assessment of environmental flow requirements indicates that global water withdrawals have to be severely limited (by 39 %) to protect aquatic ecosystems, especially with respect to groundwater withdrawals. VIC-WUR has potential for studying the impacts of climate change and anthropogenic developments on current and future water resources and sector-specific water scarcity. The additions presented here make the VIC mo

This paper uses hindcasts (1981-2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mai

Seasonal predictions of river flow can be exploited among others to optimise hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to Europe. This paper presents the development of the system and the evaluation of its skill. The variable infiltration capacity (VIC) hydrological model is forced with bias-corrected output of ECMWF's seasonal forecast system 4. For the assessment of skill, we analysed hindcasts (1981-2010) against a reference run, in which VIC was forced by gridded meteorological observations. The reference run was also used to generate initial hydrological conditions for the hindcasts. The skill in run-off and discharge hindcasts is analysed with monthly temporal resolution, up to 7 months of lead time, for the entire annual cycle. Using the reference run output as pseudo-observations and taking the correlation coefficient as metric, hot spots of significant theoretical skill in discharge and run-off were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, northern Germany, Romania and Bulgaria (mainly from November to January), western France (from December to May) and the eastern side of Great Britain (January to April). Generally, the skill decreases with increasing lead time, except in spring in regions with snow-rich winters. In some areas some skill persists even at the longest lead times (7 months). Theoretical skill was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is generally substantially less than theoretical skill. This effect is stronger for small basins than for large basins. Qualitatively, the use of different skill metrics (correlation coefficient; relative operating characteristics, ROC, area; an

Future changes in runoff can have important implications for water resources and flooding. In this study, runoff projections from ISI-MIP (Inter-sectoral Impact Model Intercomparison Project) simulations forced with HadGEM2-ES bias-corrected climate data under the Representative Concentration Pathway 8.5 have been analysed for differences between impact models. Projections of change from a baseline period (1981–2010) to the future (2070–2099) from 12 impacts models which contributed to the hydrological and biomes sectors of ISI-MIP were studied. The biome models differed from the hydrological models by the inclusion of CO2 impacts and most also included a dynamic vegetation distribution. The biome and hydrological models agreed on the sign of runoff change for most regions of the world. However, in West Africa, the hydrological models projected drying, and the biome models a moistening. The biome models tended to produce larger increases and smaller decreases in regionally averaged runoff than the hydrological models, although there is large inter-model spread. The timing of runoff change was similar, but there were differences in magnitude, particularly at peak runoff. The impact of vegetation distribution change was much smaller than the projected change over time, while elevated CO2 had an effect as large as the magnitude of change over time projected by some models in some regions. The effect of CO2 on runoff was not consistent across the models, with two models showing increases and two decreases. There was also more spread in projections from the runs with elevated CO2 than with constant CO2. The biome models which gave increased runoff from elevated CO2 were also those which differed most from the hydrological models. Spatially, regions with most difference between model types tended to be projected to have most effect from elevated CO2, and seasonal differences were also similar, so elevated CO2 can partly explain the differences between hydrological and bio

Projections of future changes in runoff can have important implications for water resources and flooding. In this study, runoff projections from ISI-MIP (Inter-sectoral Impact Model Intercomparison Project) simulations forced with HadGEM2-ES bias-corrected climate data under the Representative Concentration Pathway 8.5 have been analysed. Projections of change from the baseline period (1981–2010) to the future (2070–2099) from a number of different ecosystems and hydrological models were studied. The differences between projections from the two types of model were looked at globally and regionally. Typically, across different regions the ecosystem models tended to project larger increases and smaller decreases in runoff than the hydrological models. However, the differences varied both regionally and seasonally. Sensitivity experiments were also used to investigate the contributions of varying CO2 and allowing vegetation distribution to evolve on projected changes in runoff. In two out of four models which had data available from CO2 sensitivity experiments, allowing CO2 to vary was found to increase runoff more than keeping CO2 constant, while in two models runoff decreased. This suggests more uncertainty in runoff responses to elevated CO2 than previously considered. As CO2 effects on evapotranspiration via stomatal conductance and leaf-area index are more commonly included in ecosystems models than in hydrological models, this may partially explain some of the difference between model types. Keeping the vegetation distribution static in JULES runs had much less effect on runoff projections than varying CO2, but this may be more pronounced if looked at over a longer timescale as vegetation changes may take longer to reach a new state.

The Water Model Intercomparison Project (WaterMIP) aims to compare a variety of models of the terrestrial hydrological cycle, and to produce multi-model ensemble estimates of the state of the world’s water resources for the 20th and 21st centuries. WaterMIP is a joint activity between the EU Water and Global Change (WATCH) FP6 project and the Global Water System Project (GWSP).