Flooding leads to disastrous impacts on human society and activities worldwide, including damage to physical assets and interruptions to daily activities. However, evaluation for such impacts remains challenging, particularly beyond inundation zones, due to the difficulties in monitoring human activities on a global scale. Nighttime light (NTL) remote sensing data provides a unique perspective for human activities on a large scale, reflecting variations in light intensity caused by flood impact. Here we show the possibility of using a high-quality NTL dataset to assess flood impact on human society and activities. Indices providing impact severity and duration were generated with NTL as proxies for flood impact on pixel scale. Results show the consistency of NTL-derived and reported impact duration for five selected cases, which confirms the reliability of NTL flood impact. A large portion (> 96%) of NTL-based affected areas did not overlap with the satellite-based inundation area for 99 cases in 2013, indicating the unique value of NTL in assessing flood impact beyond inundation. The NTL flood impact indices were mapped at 15 arc-second spatial resolution for 876 events on a global scale from 2013 to 2021. Then, administrative-level characteristics of NTL flood impact were compared at a global scale. It was found that lower developed regions exhibit higher vulnerability and challenge in recovery, and are more likely to experience extremely serious and long-lasting impacts compared to higher developed areasverall, using NTL data, in addition to conventional inundation-based methods, offers an innovative perspective on flood impact evaluation.

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 the implementation of a benchmark system designed to facilitate the assessment of river models and enables comparisons against established benchmarks. The benchmark system incorporates satellite remote sensing data, including water surface elevation and inundation extent information, with necessary preprocessing. Consequently, this evaluation system encompasses a larger geographical area compared to 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 the incorporation of 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 as well as facilitate intercomparisons among different models. The source codes are accessiable from https://doi.org/10.5281/zenodo.10903211.