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Bangladesh is highly vulnerable to flood hazards, and its flood risk is projected to increase with global warming. In addition to climate change, internal climate variation, such as the El Niño–Southern Oscillation (ENSO), influences flooding in many rivers worldwide. However, the impact of internal climate variability on flooding in Bangladesh remains unclear due to the limited observations. Here, we assess the impacts of ENSO and climate change on flood occurrence in Bangladesh using a large-ensemble climate simulation dataset and a global river model (CaMa-Flood). After separating 6000 years of simulation (100-member ensemble river simulations for 1950–2010) into El Niño, La Niña, and neutral years, we calculated the extent to which each ENSO stage increased flood occurrence probability relative to the neutral state using the fraction of attributable risk method. In addition, we estimated the impact of historical climate change on past flood occurrence through a comparison of simulations with and without historical global warming. Under the no-global-warming climate, La Niña increased the occurrence probability of a 10 year return period flood at Hardinge Bridge on the Ganges River by 38% compared to neutral years. The influence of La Niña or El Niño state on flood occurrence probability in the Brahmaputra River at Bahadurabad station is negligible. Historical global warming increased the occurrence of flooding in the Ganges River, the Brahmaputra River, and their confluence by 59%, 44%, and 55%, respectively. The impact of ENSO on flood occurrence probability decreased in the historical simulation, likely due to the conflation of ENSO and climate change signals, and no significant correlation between ENSO and flood occurrence was detected when only small-ensemble simulations were used. These findings suggest that the use of large-ensemble climate simulation datasets is essential for precise attribution of the impacts of internal climate variability on flooding in Bangladesh.

Global River Models (GRMs), which simulate river flow and flood processes, have rapidly developed in recent decades. However, these advancements necessitate meaningful and standardized quality assessments and comparisons against a suitable set of observational variables using appropriate metrics, a requirement currently lacking within GRM communities. This study proposes implementing a benchmark system designed to facilitate the assessment of river models and enable comparisons against established benchmarks. The benchmark system incorporates satellite remote sensing data complementing in situ data, including water surface elevation and inundation extent information, with necessary preprocessing. Consequently, this evaluation system encompasses a larger geographical area than traditional methods relying solely on in‐situ river discharge measurements for GRMs. A set of evaluation and comparison metrics has been developed, including a quantile‐based comparison metric that allows for a comprehensive analysis of multiple simulation outputs. The test application of this benchmark system to a global river model (CaMa‐Flood), utilizing diverse runoff inputs, illustrates that incorporating bias‐corrected runoff data leads to improved model performance across various observational variables and performance metrics. The current iteration of the benchmark system is suitable for global‐scale assessments and can effectively evaluate the impact of model development and facilitate intercomparisons among different models. The source codes are accessible from https://doi.org/10.5281/zenodo.10903210. Plain Language Summary River models, which help us understand river flows and flood propagation, have improved significantly over the years. However, there lacks an agreed‐upon way to check whether these models are doing well by comparing them to real‐world data. This study suggests a system to test and compare river models more effectively. This new system uses both satellite images and ground measurements to get a complete picture of the model abilities to simulate different flow characteristics. To see how well the system works, this study tried this system on a specific global river model, CaMa‐Flood, with varying data types. Results found that using corrected data makes the model predictions better match what we see in the real world across various tests and measurements. This testing system is ready to be used worldwide and can help see how model changes improve their predictions. It also makes it easier to compare different models to see which ones work best. Key Points We developed a benchmark framework for global river models, ensuring quick, and comprehensive performance analysis Remote sensing data for water surface elevation and inundation extent helps address the lack of extensive in situ discharge observations The benchmark model is highly adaptable, allowing for evaluation of model development and intercomparison across multiple models

Large‐scale hydrodynamic models are vital for flood risk assessment and understanding the global water cycle; however, their results can include uncertainties related to spatial resolution. Few studies have evaluated hydrodynamic models across a range of spatial resolutions, with most focusing on a few variables (e.g., discharge) and often neglecting performance at ungauged sites or the role of parameter optimization. We addressed these limitations by comparing Catchment‐based Macro‐scale Floodplain (CaMa‐Flood) model simulations in the Amazon River basin at different spatial resolutions, using the higher resolution as a benchmark in each comparison. We found good inter‐resolution performance in simulating discharge and water depth, with coefficients of determination exceeding 0.88 in >80% of locations. The normalized Nash–Sutcliffe efficiencies for discharge and water depth were greater than 0.83 and 0.68, respectively, in more than 75% of locations, suggesting that most locations had consistent hydrodynamics. We detected large discrepancies in discharge between simulations at ∼2.5% of locations due to limited representation of bifurcation flow, floodplain conveyance, and backwater at river confluences in the model. Water depth also differed significantly at ∼3% of locations, mainly at headwaters, due to width bottleneck sections. Flood extent patterns differed minimally between simulations around the main stream and large sub‐streams, whereas improvements in the downscaling method are required for small sub‐streams. Our results demonstrate the need to improve the representation of bifurcation channels and floodplain parameterization for specific locations, although the general river hydrodynamics patterns were well‐captured by computationally efficient moderate‐resolution (i.e., 6 arcmin) CaMa‐Flood simulations. Plain Language Summary Large‐scale models that predict how water moves are crucial for assessing flood risks and understanding the global water cycle. However, these models can have uncertainties related to their spatial resolution. Few studies have evaluated these models at different levels of detail but usually focused on a few variables or ignored areas without data and parameter adjustments. To address these gaps, we compared the Catchment‐based Macro‐scale Floodplain (CaMa‐Flood) model's simulations of the Amazon River at different resolutions, using the highest level of detail as a reference. The model simulated flow and depth at lower resolutions, achieving a strong agreement with higher resolutions at 80%–90% of locations. Most locations showed consistent results for flow and depth. Still, there were significant differences in a small percentage of areas due to the model's limited ability to represent complex flow patterns and certain channel features such as bifurcation flow, backwater effect, floodplain conveyance, and bottleneck channels, across various resolutions. Flood extent patterns were generally similar between simulations, but improvements are needed for smaller streams. Our findings highlight the need to enhance the model's representation by improving baseline river network data, although overall, the model's moderate‐resolution simulations effectively captured general river hydrodynamics. Key Points Sub‐grid parameterization makes moderate‐resolution (6 arcmin) simulation results almost similar to those at higher resolution Causes of differences in simulated hydrodynamics in a few locations are attributed to the characteristics of river models' physics Better sub‐grid topography treatment (bifurcation, river confluence, channel width) would potentially enhance low‐resolution simulations

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.

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 square error of simulated WSEs compared to satellite altimetry was 7.86 m. The error rate was improved meaningfully (10.6 %) compared to 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.