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The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) is a method developed to enhance hydrologic model predictions while accounting for different sources of uncertainties involved in various layers of model simulations. While the effectiveness of this data assimilation in forecasting streamflow have been proven in previous studies, its potential to improve flood forecasting during extreme events remains unexplored. This study aims to demonstrate this potential by employing HEAVEN to assimilate streamflow data from USGS stations into a conceptual hydrologic model to enhance its capability to forecast hurricane-induced floods across multiple locations within three watersheds in the Southeastern United States. The SAC-SMA hydrologic model is driven by two variables: precipitation and Potential Evapotranspiration (PET), collected from phase 2 of the North American Land Data Assimilation System (NLDAS-2) and MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data, respectively. We have validated the probabilistic streamflow predictions during five instances of hurricane-induced flooding across three regions. The results show that this data assimilation approach significantly improves hydrologic model's ability to forecast extreme river flows. By accounting for different sources of uncertainty in model predictions—in particular model structural uncertainty in addition to model parameter uncertainty, and atmospheric forcing data uncertainty, the HEAVEN emerges as a powerful tool for enhancing flood prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate prediction and assessment of extreme flood events are crucial for effective disaster preparedness, response, and mitigation strategies. One crucial factor influencing the intensity and magnitude of extreme flood events is precipitation. Precipitation patterns, particularly during intense weather phenomena such as hurricanes, can play a significant role in triggering widespread flooding over densely populated areas. Traditional flood prediction models typically rely on single-source precipitation data, which may not adequately capture the inherent variability and uncertainty associated with extreme events due to certain limitations in the precipitation generation framework, availability, or both spatial and temporal resolutions. Moreover, in coastal regions, the complex interaction between local precipitation, river flows, and coastal processes (i.e., storm tide) can result in compound flooding and amplify the overall impact and complexity of flooding patterns. This study presents an implementation of the global copula-embedded Bayesian model averaging (BMA) (Global Cop-BMA) framework for improving the accuracy and reliability of extreme flood modeling. The proposed framework integrates a collection of precipitation products with different spatiotemporal resolutions to account for uncertainty in forcing data for hydrodynamic modeling and generating probabilistic flood inundation maps. The methodology is evaluated with respect to Hurricane Harvey, which was a catastrophic weather event characterized by intense precipitation and compound flooding processes over the city of Houston in the state of Texas in 2017. The results show a significant improvement in predictive accuracy compared to those based on a single precipitation product (e.g., the Nash–Sutcliffe efficiency (NSE) performance of a single quantitative precipitation estimation (QPE) is in the range of 0.695 to 0.846, while the Cop-BMA yields an NSE of 0.858), demonstrating the merits of the Global Cop-BMA approach. Furthermore, this research extends its impact by generating probabilistic flood extension maps that account not only for the primary influence of precipitation as a flood driver but also for the intricate nature of compound flooding processes in coastal environments. [ABSTRACT FROM AUTHOR]

The amount (soil moisture) and state (freeze-thaw) of the water in soil plays a pivotal role in global water, energy, and carbon cycles. The water content of the top few [...]

Hydrology plays a crucial role in understanding Earth's intricate water system and addressing water‐related problems, including against the backdrop of ongoing climate change. A retrospective review of the evolution of hydrology up to the current state of research is of great importance for understanding this role. While there have been some quantitative reviews of large numbers of hydrological publications, there still remains a lack of overarching hydrological research assessment, particularly with the focus on hydrological basins as fundamental spatial‐geographic units of hydrological analysis. Large language models, represented by OpenAI's ChatGPT, have demonstrated powerful textual understanding capabilities, making it possible to extract such overarching and basin information from hydrological publications. Here, we considered publications related to hydrology from Web of Science spanning January 1980 to October 2023, and parsed the information from this extensive body of literature by integrating a large language model and geocoding. These techniques enable quantitative analysis of research characteristics across different spatio‐temporal scales, focusing on hotspot topics, collaboration networks, and various basins worldwide. Our study revealed an increase in hydrological research since the 1990 s, with shifts in research priorities from groundwater and nutrients to climate change and ecohydrology. Some basins in North America and Europe have consistently been hotspots for hydrological research. Since the 2010s, there has been a noteworthy increase in interest toward basins in China and South Asia, but attention to many regions with frequent extreme rainfall remains insufficient. Geographical patterns show different preferred research topics for different basins, but climate change has emerged as the most prominent topic across all regions in the last decade. In conclusion, our study provides an effective approach to quantitative analysis of research trends, offering a fresh view on the evolution of hydrology as a research field, its focus on various hydrological basins around the world, and the emergence of overarching and basin‐specific hot topics over time. Key Points: We explore the literature review capability of a large language model combined with geocoding over the whole field of hydrological researchWe quantify the evolution of hydrological research, hotspot topics, collaboration networks, and basins in focus, worldwide over timeHydrological research publication exhibits major and accelerating growth with WRR emerging as the top influential journal over time [ABSTRACT FROM AUTHOR]