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Various transboundary river basins are facing increased pressure on water resources in near future. However, little is known ab out the future drivers globally, namely, changes in natural local runoff and natural inflows from upstream parts of a basin, as well as local and upstream water consumption. Here we use an ensemble of four global hydrological models forced by five global climate models and the latest greenhouse‐gas concentration (RCP) and socioeconomic pathway (SSP) scenarios to assess the impact of these drivers on transboundary water stress in the past and future. Our results show that population under water stress is expected to increase by 50% under a low population growth and emissions scenario (SSP1‐RCP2.6) and double under a high population growth and emission scenario (SSP3‐RCP6.0), compared to the year 2010. As changes in water availability have a smaller effect when water is not yet scarce, changes in water stress globally are dominated by local water consumption—managing local demand is thus necessary in order to avoid future stress. Focusing then on the role of upstream changes, we identified upstream availability (i.e., less natural runoff or increased water consumption) as the dominant driver of changes in net water availability in most downstream areas. Moreover, an increased number of people will be living in areas dependent on upstream originating water in 2050. International water treaties and management will therefore have an increasingly crucial role in these hot spot regions to ensure fair management of transboundary water resources. Key Points: Water stress will mostly intensify in areas already under stress in all future scenarios for most transboundary river basinsChanges in water stress are dominated by local water consumption—managing local demand is thus a key strategy to alleviate future stressLess natural runoff or increased water consumption in upstream areas is the dominant driver of changes in downstream net water availability [ABSTRACT FROM AUTHOR]

Hydrological data and information on the availability of water are essential to support water allocation decisions in irrigated agriculture, especially under increasingly water scarce conditions. However, in many agricultural regions hydrological information is scarce, leading to sub-optimal water allocation decisions and crop yield reduction. Here we assess the benefit of using surface water availability estimates derived from an ensemble of global hydrological models for establishing the potential area that can be irrigated; where the size is determined based on agreed water supply reliability targets. Risky outcomes of the annual agricultural production due to the true occurrence of water scarcity are generated for both the reference and established irrigation areas, resulting in a Relative Utility Value (RUV) that expresses the utility of the information used. Results show that using an ensemble of global hydrological models provides more robust estimates of the planned area compared to using any of the single global models that constitutes the ensemble. A comparison of the information content in the ensemble shows that an ensemble with a period of record of 15 years has an information content equivalent to a single model of 30 years. [ABSTRACT FROM AUTHOR]

Precipitation and surface runoff vary strongly in space and time across Morocco. The country's water management is primarily governed at the basin level, following a decentralized approach. However, in some cases, water is shared between basins, which increases the complexity and the potential for conflicts. The current study, conducted at Oum Er Rbia (OER) basin and Moroccan country scales, evaluates the use of novel Earth Observation (EO) products (surface soil moisture and evapotranspiration) combined with global water balance model (PCR-GLOBWB) for basin level surface runoff and country level drought assessment. At the basin level, OER River discharges considerable surface water amounts from the Middle Atlas Mountains to large reservoirs, providing water for various sectors, predominantly irrigated agriculture. The EO based PCR-GLOBWB model yielded satisfactory monthly surface runoff results validated through two OER streamflow gauges. Spatially distributed quarterly annual surface runoff matched well with the simulations achieved through more detailed Soil and Water Assessment Tool (SWAT) modeling. EO data and PCR-GLOBWB model were subsequently used to investigate country scale drought occurrence using various drought indicators (Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSMI)). The study concludes on a foremost local to regional nature of droughts. Consistent assessment of water stress situations, from the basin to the country scale, suggest the good potential of novel EO products and global models to support demand driven water management, especially in data scarce areas. [ABSTRACT FROM AUTHOR]

In this study, we examine the spatial and temporal characteristics of water stress in China for the historical (1971–2010) and the future (2021–2050) periods using a multimodel simulation approach. Three water stress indices (WSIs), that is, the ratios of water withdrawals to locally generated runoff (WSIR), to natural streamflow (WSIQ), and to natural streamflow minus upstream consumptive water withdrawals (WSIC), are used for the assessment. At the basin level, WSIR estimates generally match the reported data and indicate severe water stress in most northern basins. At the grid cell level, the WSIs show distinct spatial patterns of water stress wherein WSIR (WSIQ) estimates higher (lower) water stress compared to WSIC. Based on the WSIC estimates, 368 million people (nearly one third of the total population) are affected by severe water stress annually during the historical period, while WSIR and WSIQ suggest 595 and 340 million, respectively. Future projections of WSIC indicate that more than 600 million people (43% of the total) might be affected by severe water stress, and half of China's land area would be exposed to stress. The found aggravating water stress conditions could be partly attributed to the elevated future water withdrawals. This study emphasizes the necessity of considering explicit upstream and downstream relations with respect to both water availability and water use in water stress assessment and calls for more attention to increasing levels of water stress in China in the coming decades. Plain Language Summary: Severe water stress in China has been widely reported, but its time evolution and spatial patterns are rarely assessed. We examine the spatial and temporal change patterns of water stress in China by using multimodel simulations and three different water stress indices (WSIs). Results suggest that different WSIs imply distinct spatial patterns of water stress over China. The WSIs indicate that water stress conditions in northern China are quite distinct from that in southern China. During the past decades (1971–2010), severe water stress is found in northern areas while little is found in southern areas. In the future (2021–2050), however, water‐stressed areas might expand in southern China and water stress levels might aggravate in urban areas, putting considerably more people exposed to severe water stress. This assessment provides useful information for regional water planning/management within the context of future climate change and socioeconomic development in China. Key Points: Water stress estimates can be much different with the consideration of upstream water withdrawal and consumptionSevere water stress conditions are found in northern China in the pastWater‐stressed areas significantly expand over southern China in the future [ABSTRACT FROM AUTHOR]

Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global reanalysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global reanalysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resources Reanalysis (WRR) dataset developed in the European Union's Seventh Framework Programme (EU-FP7) eartH2Observe (E2O) project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results show that, while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25° resolution global model, when compared to the lower-resolution 0.5° models, thus underlining the added value of increased-resolution global models. The skill of the global hydrological models (GHMs) in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though this could be improved if further details on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local-scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global reanalysis data for identifying impacting flood events in data-sparse regions. [ABSTRACT FROM AUTHOR]

Despite increases in the quantity, quality and coverage of meteorological data, there remain regions which due to low capacity, understanding or access are unable to use this data effectively. Southern Africa is such region that is not only data sparse, but suffers acute adverse effects from climatology such as floods and droughts. Novel global datasets have the potential to provide hydro-meteorological data of sufficient quality to these areas. However these datasets are not yet extensively validated and their suitability to capture the regional climatic variability are not well understood. This research critically assesses the potential for flood event identification using the Water Resources Reanalysis (WRR) dataset developed in the European Union's Seventh Framework Programme eartH\superscript{2}Observe project. The study area is the data sparse and flood vulnerable Limpopo River Basin. The discharge outputs of several global hydrological models as well as land surface models are compared against two benchmarks; discharge observations and flood event reporting. This study demonstrates the applicability of global datasets in this data sparse environment in order to capture the magnitude of impacting flood events. Since global models such as those included in the WRR dataset are also used by large-scale flood forecasting systems, this research gives an insight in the information that could be used for local-scale flood risk applications. [ABSTRACT FROM AUTHOR]

Severe water stress in China has been widely reported but its time-evolution and spatial patterns are rarely assessed. In this study, we illustrate the spatial and temporal characteristics of water stress in China for the historical (1971-2010) and the future (2021-2050) periods using multimodel simulations. Three water stress indices (WSIs), i.e., the ratios of water withdrawal to locally generated runoff (WSIR), to natural streamflow (WSIQ), and to natural streamflow minus upstream consumptive water withdrawal (WSIC), are used for the assessment. At the basin level, WSIR estimates generally match the reported data and indicate severe water stress in most northern basins during the historical period. At grid cell level, the WSIs show distinct spatial patterns wherein WSIR (WSIQ) overestimate (underestimate) water stress compared to WSIC. Based on the WSIC estimates, 365 million people (one-third of the total population) was under severe water stress every year during the historical period, while WSIR and WSIQ suggest 600 and 335 million people, respectively. It indicates that different estimations of water availability can affect water stress assessment greatly. The future projections of WSIC suggest that ~500 million people (one-third of the total) will be affected by severe water stress every year. Future severe water stress will expand to nearly 40% of the total areas of China because of stiffened competitive water uses, especially in the southern basins with abundant water availability and historically little water stress. This study emphasizes the necessity of considering explicit upstream and downstream relations with respect to both water availability and water use in water stress assessment and calls more attention to the increasing water stress in China in the coming decades. [ABSTRACT FROM AUTHOR]