Water availability per capita is among the most fundamental water-scarcity indicators used extensively in global grid-based water resources assessments. Recently, it has extended to include the economic aspect, a proxy of the capability for water management which we applied globally under socioeconomic-climate scenarios using gridded population and economic conditions. We found that population and economic projection choices significantly influence the global water scarcity assessment, particularly the assumption of urban concentrated and dispersed population. Using multiple socioeconomic-climate scenarios, global climate models, and two gridded population datasets, capturing extremities, we show that the water-scarce population ranges from 0.32–665 million in the future. Uncertainties in the socioeconomic-climate scenarios and global climate models are 6.58–489 million and 0.03–248 million, respectively. The population distribution has a similar impact, with an uncertainty of 169.1–338 million. These results highlight the importance of the subregional distribution of socioeconomic factors for future global environment prediction.

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Continental-scale river hydrodynamic modeling is essential for understanding the global hydrological cycle and supporting flood monitoring, as well as for water resource management pertaining to water security and natural hazards (Siqueira et al., 2018). Hydrodynamic modeling processes depend on topographic data to replicate characteristics and processes within a landscape (Callow et al., 2007;Jarihani et al., 2015). Data from a digital elevation model (DEM) representing topography comprises one of the critical data sets required in many types of studies, such as lake water storage changes, river routing, and flood inundation modeling (Hawker, Bates,

Abstract. Satellite altimetry data are useful for monitoring water surface dynamics, evaluating and calibrating hydrodynamic models, and enhancing river-related variables through optimization or assimilation approaches. However, comparing simulated water surface elevations (WSEs) using satellite altimetry data is challenging due to the difficulty of correctly matching the representative locations of satellite altimetry virtual stations (VSs) to the discrete river grids used in hydrodynamic models. In this study, we introduce an automated altimetry mapping procedure (AltiMaP) that allocates VS locations listed in the HydroWeb database to the Multi-Error Removed Improved Terrain Hydrography (MERIT Hydro) river network. Each VS was flagged according to the land cover of the initial pixel allocation, with 10, 20, 30, and 40 representing river channel, land with the nearest single-channel river, land with the nearest multi-channel river, and ocean pixels, respectively. Then, each VS was assigned to the nearest MERIT Hydro river reach according to geometric distance. Among the approximately 12,000 allocated VSs, most were categorized as flag 10 (71.7 %). Flags 10 and 20 were mainly located in upstream and midstream reaches, whereas flags 30 and 40 were mainly located downstream. Approximately 0.8 % of VSs showed bias, with considerable elevation differences (≥|15|m) between the mean observed WSE and MERIT digital elevation model. These biased VSs were predominantly observed in narrow rivers at high altitudes. Following VS allocation using AltiMaP, the median root mean squared error of simulated WSEs compared to satellite altimetry was 7.86 m. The error rate was much lower (10.6 %) than that obtained using a traditional approach, partly due to bias reduction. Thus, allocating VSs to a river network using the proposed AltiMaP framework improved our comparison of WSEs simulated by the global hydrodynamic model to those obtained by satellite altimetry. The AltiMaP source code (https://doi.org/10.5281/zenodo.7597310) (Revel et al., 2023a) and data (https://doi.org/10.4211/hs.632e550deaea46b080bdae986fd19156) (Revel et al., 2022) are freely accessible online and we anticipate that they will be beneficial to the international hydrological community.

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