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Deep learning (DL) models have been used for rapid assessments of environmental phenomena like mapping compound flood hazards from cyclones. However, predicting compound flood dynamics (e.g., flood extent and inundation depth over time) is often done with physically-based models because they capture physical drivers, nonlinear interactions, and hysteresis in system behavior. Here, we show that a customized DL model can efficiently learn spatiotemporal dependencies of multiple flood events in Galveston, TX. The proposed model combines the spatial feature extraction of CNN, temporal regression of LSTM, and a novel cluster-based temporal attention approach to assimilate multimodal inputs; thus, accurately replicating compound flood dynamics of physically-based models. The DL model achieves satisfactory flood timing (±1 h), critical success index above 60 %, RMSE below 0.10 m, and nearly perfect error bias of 1. These results demonstrate the model's potential to assist in flood preparation and response efforts in vulnerable coastal regions. 

abstract: Farmers have time and again adopted new methods or technologies. However, recent increases in global temperatures and occurrences of extreme weather events, call for an urgency to address and reduce the risks associated with climate change. Irrigation is a key adaptation that reduces crop heat stress and enhances agricultural production. Alabama is considered water-rich but lately has experienced increased rainfall variability and temperature extremes. Various state-wide initiatives to increase irrigation have been implemented, but adoption remains limited. Existing studies have explored factors influencing irrigation uptake, but none have engaged in a state-level assessment of its adoption potential. In this study, we provide spatially explicit estimates of the potential to implement irrigation practices across the state. Moreover, we derive an irrigation adoption index map for Alabama to identify areas where implementation is more or less likely based on a multi-criteria analysis. The results highlight a large potential for expansion in areas that have high shares of existing irrigation. Such an analysis can enable targeted mobilization of resources towards areas where uptake is currently low but feasible through increased adaptation efforts. Additionally, these estimates can be further used to evaluate future water demands or conduct other regional analyses. 

Compound floods may happen in low-lying estuarine environments when sea level above normal tide co-occurs with high river flow. Thus, comprehensive flood risk assessments for estuaries should not only account for the individual hazard arising from each environmental variable in isolation, but also for the case of bivariate hazard. Characterization of the dependence structure of the two flood drivers becomes then crucial, especially under climatic variability and change that may affect their relationship. In this article, we demonstrate our search for evidence of non-stationarity in the dependence between river discharge and storm surge along the East and Gulf coasts of the United States, driven by large-scale climate variability, particularly El-Niño Southern Oscillation and North Atlantic Oscillation (NAO). Leveraging prolonged overlapping observational records and copula theory, we recover parameters of both stationary and dynamic copulas using state-of-the-art Markov Chain Monte Carlo methods. Physics-informed copulas are developed by modeling the magnitude of dependence as a linear function of large-scale climate indices, i.e., Oceanic Niño Index or NAO index. After model comparison via suitable Bayesian metrics, we find no strong indication of such non-stationarity for most estuaries included in our analysis. However, when non-stationarity due to these climate modes cannot be neglected, this work highlights the importance of appropriately characterizing bivariate hazard under non-stationarity assumption. As an example, we find that during a strong El-Niño year, Galveston Bay, TX, is much more likely to experience a coincidence of abnormal sea level and elevated river stage. 

Tropical cyclones can rapidly intensify under favorable oceanic and atmospheric conditions. This phenomenon is complex and difficult to predict, making it a serious challenge for coastal communities. A key contributing factor to the intensification process is the presence of prolonged high sea surface temperatures, also known as marine heatwaves. However, the extent to which marine heatwaves contribute to the potential of rapid intensification events is not yet fully explored. Here, we conduct a probabilistic analysis to assess how the likelihood of rapid intensification changes during marine heatwaves in the Gulf of Mexico and northwestern Caribbean Sea. Approximately 70% of hurricanes that formed between 1950 and 2022 were influenced by marine heatwaves. Notably, rapid intensification is, on average, 50% more likely during marine heatwaves. As marine heatwaves are on the increase due to climate change, our findings indicate that more frequent rapid intensification events can be expected in the warming climate. 

Abstract Drought, a potent natural climatic phenomenon, significantly challenges hydropower systems, bearing adverse consequences for economies, societies, and the environment. This study delves into the profound impact of drought on hydropower generation (HG) in the United States, revealing a robust correlation between hydrologic drought and hydroelectricity generation. Our analysis of the period from 2003 to 2020 for the Contiguous United States (CONUS) indicates that drought events led to a considerable decline in hydroelectricity generation, amounting to approximately 300 million MWh, and resulting in an estimated loss of $28 billion to the sector. Moreover, our findings highlight the adverse environmental effect of drought-induced HG reductions, which are often compensated by increased reliance on natural gas usage, which led to substantial emissions of carbon dioxide (CO₂), sulfur dioxide (SO₂), and nitrogen oxide (NO_X), totaling 161 700 kilotons, 1199 tons, and 181 977 tons, respectively. In addition to these findings, we assess the state-level vulnerability of hydropower to drought, identifying Washington and California as the most vulnerable states, while Nevada exhibits the least vulnerability. Overall, this study enhances understanding of the multifaceted effects of drought on hydropower, which can assist in informing policies and practices related to drought management and energy production. 

Abstract Analyzing flood events has been the focus of numerous studies across local, regional, and global scales, aiming to understand their magnitude, drivers, and spatiotemporal distributions. Traditionally, flood hazards are defined by analyzing the likelihood of flood drivers exceeding their respective thresholds. This approach relies on events around gauge locations with accessible records. The availability of reanalysis and satellite data sets now allows us to leverage data from multiple flood reporting agencies to examine various flood event types, including compound and non‐compound events, and their drivers. We analyzed three decades of flood events in the US Gulf Coast states, where compound flood events are common. We found that rainfall is the predominant driver, contributing to over 45% of reported floods classified as compound events. Fluvial and pluvial floods are more frequent and severe during tropical seasons, and especially during the Fall compared with other calendar seasons.